Data4UrbanMobility is going to be presented at the “Dienstleistungstagung 2019 Service Systems Innovation” in Paderborn. The event takes place at April 8th and 9th 2019. Data4UrbanMobiltiy can be found  in Area 4”: “Individuell und Urban”.

• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. DeepSeek-AI; Guo, Daya; Yang, Dejian; Zhang, Haowei; Song, Junxiao; Zhang, Ruoyu; Xu, Runxin; Zhu, Qihao; Ma, Shirong; Wang, Peiyi; Bi, Xiao; Zhang, Xiaokang; Yu, Xingkai; Wu, Yu; Wu, Z. F.; Gou, Zhibin; Shao, Zhihong; Li, Zhuoshu; Gao, Ziyi; Liu, Aixin; Xue, Bing; Wang, Bingxuan; Wu, Bochao; Feng, Bei; Lu, Chengda; Zhao, Chenggang; Deng, Chengqi; Zhang, Chenyu; Ruan, Chong; Dai, Damai; Chen, Deli; Ji, Dongjie; Li, Erhang; Lin, Fangyun; Dai, Fucong; Luo, Fuli; Hao, Guangbo; Chen, Guanting; Li, Guowei; Zhang, H.; Bao, Han; Xu, Hanwei; Wang, Haocheng; Ding, Honghui; Xin, Huajian; Gao, Huazuo; Qu, Hui; Li, Hui; Guo, Jianzhong; Li, Jiashi; Wang, Jiawei; Chen, Jingchang; Yuan, Jingyang; Qiu, Junjie; Li, Junlong; Cai, J. L.; Ni, Jiaqi; Liang, Jian; Chen, Jin; Dong, Kai; Hu, Kai; Gao, Kaige; Guan, Kang; Huang, Kexin; Yu, Kuai; Wang, Lean; Zhang, Lecong; Zhao, Liang; Wang, Litong; Zhang, Liyue; Xu, Lei; Xia, Leyi; Zhang, Mingchuan; Zhang, Minghua; Tang, Minghui; Li, Meng; Wang, Miaojun; Li, Mingming; Tian, Ning; Huang, Panpan; Zhang, Peng; Wang, Qiancheng; Chen, Qinyu; Du, Qiushi; Ge, Ruiqi; Zhang, Ruisong; Pan, Ruizhe; Wang, Runji; Chen, R. J.; Jin, R. L.; Chen, Ruyi; Lu, Shanghao; Zhou, Shangyan; Chen, Shanhuang; Ye, Shengfeng; Wang, Shiyu; Yu, Shuiping; Zhou, Shunfeng; Pan, Shuting; Li, S. S.; Zhou, Shuang; Wu, Shaoqing; Ye, Shengfeng; Yun, Tao; Pei, Tian; Sun, Tianyu; Wang, T.; Zeng, Wangding; Zhao, Wanjia; Liu, Wen; Liang, Wenfeng; Gao, Wenjun; Yu, Wenqin; Zhang, Wentao; Xiao, W. L.; An, Wei; Liu, Xiaodong; Wang, Xiaohan; Chen, Xiaokang; Nie, Xiaotao; Cheng, Xin; Liu, Xin; Xie, Xin; Liu, Xingchao; Yang, Xinyu; Li, Xinyuan; Su, Xuecheng; Lin, Xuheng; Li, X. Q.; Jin, Xiangyue; Shen, Xiaojin; Chen, Xiaosha; Sun, Xiaowen; Wang, Xiaoxiang; Song, Xinnan; Zhou, Xinyi; Wang, Xianzu; Shan, Xinxia; Li, Y. K.; Wang, Y. Q.; Wei, Y. X.; Zhang, Yang; Xu, Yanhong; Li, Yao; Zhao, Yao; Sun, Yaofeng; Wang, Yaohui; Yu, Yi; Zhang, Yichao; Shi, Yifan; Xiong, Yiliang; He, Ying; Piao, Yishi; Wang, Yisong; Tan, Yixuan; Ma, Yiyang; Liu, Yiyuan; Guo, Yongqiang; Ou, Yuan; Wang, Yuduan; Gong, Yue; Zou, Yuheng; He, Yujia; Xiong, Yunfan; Luo, Yuxiang; You, Yuxiang; Liu, Yuxuan; Zhou, Yuyang; Zhu, Y. X.; Xu, Yanhong; Huang, Yanping; Li, Yaohui; Zheng, Yi; Zhu, Yuchen; Ma, Yunxian; Tang, Ying; Zha, Yukun; Yan, Yuting; Ren, Z. Z.; Ren, Zehui; Sha, Zhangli; Fu, Zhe; Xu, Zhean; Xie, Zhenda; Zhang, Zhengyan; Hao, Zhewen; Ma, Zhicheng; Yan, Zhigang; Wu, Zhiyu; Gu, Zihui; Zhu, Zijia; Liu, Zijun; Li, Zilin; Xie, Ziwei; Song, Ziyang; Pan, Zizheng; Huang, Zhen; Xu, Zhipeng; Zhang, Zhongyu; Zhang, Zhen (2025).

  We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors. However, it encounters challenges such as poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks. To support the research community, we open-source DeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama.
• Minimum Monotone Spanning Trees. Di Giacomo, Emilio; Didimo, Walter; Katsanou, Eleni; Schlipf, Lena; Symvonis, Antonios; Wolff, Alexander in Lecture Notes in Computer Science, R. Královič, V. Kůrková (eds.) (2025). (Vol. 15538) 1–16.
• Enwrapped perylene bisimide enables room temperature polariton lasing and photonic lattices. Horneber, Dominik; Düreth, Johannes; Schembri, Tim; Betzold, Simon; Stolte, Matthias; Höfling, Sven; Würthner, Frank; Klembt, Sebastian (2025). n/a(n/a) 2402617-.

  Abstract Perylene bisimides (PBIs) are organic dyes with photoluminescence quantum yields (PLQY) close to unity in solution and great thermal and photo-chemical stability. These features alongside the tunability of their solid-state packing arrangement via chemical functionalization make this material class an excellent candidate for exciton-polariton lasing at room temperature. Polariton lasing is well understood in III?V semiconductors at cryogenic temperatures, however, the search for emitter materials for robust and versatile room-temperature applications is ongoing. While, e.g., perovskites and several organic materials are identified to support polariton lasing, many of these materials lack tunability and long-term stability under ambient conditions. Here, optical microcavities are fabricated using a strongly enwrapped PBI chromophore with prevailing monomer-like absorption and emission properties in the solid state. Voluminous bay-substituents prevent π-π-stacking induced PLQY-quenching, thereby enabling polariton lasing at room temperature. Additionally, photonic confinement in single hemispheric resonators is demonstrated leading to localized polaritonic modes with discrete energies, as well as optical lattices revealing distinct polaritonic band-structures. Due to the possibility of tunable properties by the precise control of the solid-state packing arrangement of PBI emitters, the results pave the way for polarization-dependent light-matter coupling, including topological photonic effects within oriented crystalline thin-film microcavity structures.
• Multipocket synergy towards high thermoelectric performance in topological semimetal TaAs\($_\mathbf2$\). Hu, Haihua; Feng, Xiaolong; Pan, Yu; Hasse, Vicky; Wang, Honghui; He, Bin; Felser, Claudia (2025). 16(1) 119.

  Charge-carrier compensation in topological semimetals amplifies the Nernst signal and simultaneously degrades the Seebeck coefficient. In this study, we report the simultaneous achievement of both a large Nernst signal and an unsaturating magneto-Seebeck coefficient in a topological nodal-line semimetal TaAs2 single crystal. The unique dual-high transverse and longitudinal thermopowers are attributed to multipocket synergy effects: the combination of a strong phonon-drag effect and the two overlapping highly dispersive conduction and valence bands with electron--hole compensation and high mobility, promising a large Nernst effect; the third Dirac band causes a large magneto-Seebeck effect. High transverse and longitudinal power factors of textasciitilde3100 and \textasciitilde50\thinspace\($\mu$\)W cm−1 K−2, respectively, are achieved, surpassing those of other topological semimetals and mainstream semiconductors. Our study presents a feasible approach for optimizing the longitudinal and transverse thermopowers in topological semimetals simultaneously and demonstrates the potential of TaAs2 for low temperature solid-state cooling.
• Probing magnetism in self-assembled organometallic complexes using Kondo spectroscopy. Huang, Wantong; Greule, Paul; Stark, Máté; van Slageren, Joris; Sürgers, Christoph; Wernsdorfer, Wolfgang; Sangiovanni, Giorgio; Wolf, Christoph; Willke, Philip (2025). 19(1) 1190–1197.
• How do rural industrial sites impact migrant motives and the socio-economic conditions of local and migrant populations?. Abu, Itohan-Osa; Thiel, Michael; Nyamekye, Clement; Asare, Yaw Mensah; Agyapong, Emmanuel; Agbemor, Benjamin; Boateng, Ebenezer N.K.; Ibebuchi, Chibuike; Ullmann, Tobias; Rauh, Jürgen; Taubenböck, Hannes (2025). 27(e02546) 18.

  Understanding the impact of rural industrialization on migration motives and the socio-economic well-being of local and migrant populations is critical for developing policies that aim at improving living standards and ensuring inclusive economic growth. Previous studies have emphasized rural-to-urban migration in Ghana, this study investigates the socio-economic impacts of rural industrial sites on local and migrant populations, drawing on a comprehensive survey conducted across various villages in Ghana, West Africa. We examine the pull effects of rural industrial sites as sources of economic growth and social transformation. Through a detailed questionnaire survey leveraging mobile technology, the research captures responses from 1,102 respondents, comprising both Indigenous people and migrants, to understand the complex socioeconomic dynamics at play. Our findings show that among other factors, migration patterns are principally influenced by socio-economic motives, with significant gender differences in both motivations and outcomes. The logistic regression analysis indicates that men are more likely to migrate for socio-economic reasons compared to women, with an odds ratio (OR) of 1.50, which is statistically significant at a 95 % confidence level. On the other hand, women are more likely to migrate for family reunification. Furthermore, individuals with earnings above the minimum wage prior to migrating were significantly more likely to migrate, with a statistically significant OR of 2.82, suggesting that higher initial earnings might ease the migration process, and it is not always not the poorest people that migrate. Additionally, migrants citing socio-economic reasons for moving to the rural industrial sites are significantly more likely to achieve satisfactory outcomes, with a statistically significant OR of 5.12. Our results show that though migrant experiences may vary, industrial sites in rural communities boost local employment and economic activities. These findings emphasize the critical role of economic success, information access, and demographic factors in shaping migration experiences and outcomes, offering valuable insights for cross-national labour migration and policy development.
• International and European labour law: article-by-article commentary Ales, Edoardo; Bell, Mark; Deinert, Olaf; Robin-Olivier, Sophie (2025). (2. Auflage ) Beck; Nomos; Hart, München; Baden-Baden; Oxford.

  Das internationale und europäische Arbeitsrecht wird immer wichtiger. Grenzüberschreitende Sachverhalte nehmen in einer globalisierten Wirtschaftsumgebung ständig zu, Entsendungen sind an der Tagesordnung, das Zusammenspiel von nationalen, europäischen und internationalen Regeln ist vielfach schwer zu verstehen. Der Einfluss der europäischen Richtlinien auf die nationale Gesetzgebung ist erheblich, die internationalen Regeln wirken zum Teil unmittelbar. Der neue Großkommentar trägt dem durch eine englischsprachige Kommentierung aller relevanten Regelungen auf internationaler und europäischer Ebene Rechnung. Artikel für Artikel werden die wesentlichen Vorschriften systematisch erfasst, im Einzelnen erläutert und damit für die Beratung verständlich gemacht. Ausgehend von den europäischen Vorschriften, werden alle wichtigen internationalen Arbeitsrechtsregeln, vom internationalen Privatrecht bis hin zum Datenschutz, integriert dargestellt. Kommentiert werden auch die relevanten Vorschriften der europäischen Menschenrechtskonvention, der europäischen Sozialcharta und alle relevanten ILO-Vorgaben. Der Schwerpunkt der Darstellung liegt auf den Themen: Grundrechte, Arbeitnehmerfreizügigkeit, Gleichbehandlung, Individualarbeitsrecht, Kollektivarbeitsrecht und Beilegung von Streitigkeiten.
• My Co-worker ChatGPT: Development of an XR Application for Embodied Artificial Intelligence in Work Environments. Krop, Philipp; Obremski, David; Carolus, Astrid; Latoschik, Marc Erich; Wienrich, Carolin (2025).

  With recent developments in spatial computing, work contexts might shift to augmented reality. Embodied AI - virtual conversa- tional agents backed by AI systems - have the potential to enhance these contexts and open up more communication channels than just text. To support knowledge transfer from virtual agent research to the general populace, we developed My CoWorker ChatGPT - an interactive demo where employees can try out various embodied AIs in a virtual office or their own using augmented reality. We use state-of-the-art speech synthesis and body-scanning technology to create believable and trustworthy AI assistants. The demo was shown at multiple events throughout Germany, where it was well received and sparked fruitful conversations about the possibilities of embodied AI in work contexts.
• Sex Differences in Long COVID. Shah, Dimpy P.; Thaweethai, Tanayott; Karlson, Elizabeth W.; Bonilla, Hector; Horne, Benjamin D.; Mullington, Janet M.; Wisnivesky, Juan P.; Hornig, Mady; Shinnick, Daniel J.; Klein, Jonathan D.; Erdmann, Nathaniel B.; Brosnahan, Shari B.; Lee-Iannotti, Joyce K.; Metz, Torri D.; Maughan, Christine; Ofotokun, Ighovwerha; Reeder, Harrison T.; Stiles, Lauren E.; Shaukat, Aasma; Hess, Rachel; Ashktorab, Hassan; Bartram, Logan; Bassett, Ingrid V.; Becker, Jacqueline H.; Brim, Hassan; Charney, Alexander W.; Chopra, Tananshi; Clifton, Rebecca G.; Deeks, Steven G.; Erlandson, Kristine M.; Fierer, Daniel S.; Flaherman, Valerie J.; Fonseca, Vivian; Gander, Jennifer C.; Hodder, Sally L.; Jacoby, Vanessa L.; Kotini-Shah, Pavitra; Krishnan, Jerry A.; Kumar, Andre; Levy, Bruce D.; Lieberman, David; Lin, Jenny J.; Martin, Jeffrey N.; McComsey, Grace A.; Moukabary, Talal; Okumura, Megumi J.; Peluso, Michael J.; Rosen, Clifford J.; Saade, George; Shah, Pankil K.; Sherif, Zaki A.; Taylor, Barbara S.; Tuttle, Katherine R.; Urdaneta, Alfredo E.; Wallick, Julie A.; Wiley, Zanthia; Zhang, David; Horwitz, Leora I.; Foulkes, Andrea S.; Singer, Nora G.; Cribbs, Sushma K.; Francis, Anyssa G.; Hang, Tina P.; Ingram, Ketteria D.; Lainez, Jordi; Wiley, Zanthia; Lewis, Kennedy C.; Ager, Arijan; Atha, Mary; Gray, Natalie; Grimes, Ash; Hewitt, Lauren N.; Huerta, Christopher; Johnson, Brandi; Khalil, Lana; Kleinhenz, Dean; Koumanelis, Alexandra; Kozoman, Rebecca; Lee, Matthew A.; Litvack, Matthew; Panganiban, Bernadine; Rahman, Kazi; Smith, Veronica E.; Stringer, Andre; Tolbert, Maliya; Traenkner, Jessica; Unterberger, Kristen; Wimberly, Erika; Han, Jenny E.; Marconi, Vincent C.; Ofotokun, Ighovwerha; Patzer, Rachel E.; Walker, Tiffany A.; Abraham, Rachael; Aguilar, Franchesca A.; Ahmadi-Izad, Ghazal; Ahmed, Dilshad R.; Alvarez, Alicarmen; Anderson, Blake; Anwar, Faisal; Asencios, Walter D.; Beaty, Casey L.; Bedi, Brahmchetna; Berry, Jasmine A.; Boone, Donchel; Bower, Mary; Bremner, James D.; Brent, Corbin; Brown-Smith, Ke’Ara; Bull, Rachel; Capo, Gustavo; Carl-Igwe, Kelechi; Chitadze, Calista; Chukwumerije, Nachi; Clyburn, Erna; Collins, Shelby; Costello, Julie; Couture, Grace; Craft, Angel; Cui, Xiangqin; del Rio, Carlos; Detelich, Joshua F.; Dixon, Cartia; Dow, Jeanne; Doyle, D’Andrea; Elchommali, Jannah; Elsey, Imani; Franks, Nicole; Gallini, Julia; Gutter, Evan; Harding, Jess; Hernandez, Liliana; Holloway, Carla; Ifejika, Cynthia; Jasarevic, Rijalda; Javia, Vidhi N.; Jeter, Mykayla; Joseph, Yasha; Juarez, Monica; Kirkpatrick, Caitlin M.; Koumanelis, Athena; Krishnan, Shilpa; Leon, Jose D.; Lew, Valerie; Maier, Cheryl L.; Makkaoui, Nour; Maroney, Mara; Martin, Christopher F.; Mbogo, Loice; McCaslin, Atuarra; McIntyre, Jerrod; Moanna, Abeer; Montoya, Miranda; Morales, Elena; Moran, Caitlin A.; Murray, Calista; Nelson, Roslin; Nguyen, Tran; Ojoawo, Bukkie; Osinski, Eileen; Oviedo, Sofia; Paredes-Gaitan, Yolanda; Prude, Michael; Ramakrishnan, Grace; Rebolledo, Paulina A.; Roberts, Marjorie; Robinson, Keysha; Rogers, Chantrice; Rouphael, Nadine G.; Searles, Charles; Segall, Marni; Shah, Anand; Silva, Ruvina; Simpson, Cheryl; Simpson-Derrell, Krystal; Sirajud-Deen, Talib; Stroud, Jacob; Suthar, Mehul S.; Sylber, Cory; Sylvera, Ashley; Teunis, Larissa J.; Thomas, Kodasha M.; Titanji, Kehmia; Toy, Christopher; Truong, Alex; Vaccarino, Viola; Varney, Kris; Vyas, Kartavya; Vyas, Kurt; Walkow, Max; Wesley, Tamara; Winston, Juton R.; Winter, Terra J.; Wongtrakool, Cherry; Gander, Jennifer C.; Neuman, Robert B.; Bush, Patricia A.; Hudgins, Alex F.; Kolailat, Imanii; Martinez, Monica; Thompson, Sierra G.; Agu, Chiagoziem; Pemu, Priscilla; Dandy, Annette; Ibeawuchi, Carmel; Lawrence, Brianna; Morgan-Billingslea, Jan; Ojemakinde, Elizabeth I.; Mullington, Janet M.; Collier, Ai-Ris; Fong, Tamara; Haack, Monika; Hauser, Kristine S.; Maley, Jason H.; Quintana, Yuri; Shaughnessy, Lynn; Stephenson, Kathryn; Thomas, Robert J.; Torres, Robert; Marathe, Jai G.; Duffy, Elizabeth; Hamburg, Naomi; Kobayashi, Misaki .; O’Connor, George T.; Shepherd, Fitzgerald M.; Williams, Charles T.; Zhang, Haihua; Levy, Bruce D.; Aikawa, Masanori; Baden, Lindsey; Baslet, Gaston; Bennett, Lindsey; Bhattacharyya, Shamik; Buring, Julie; Cagnina, Rebecca E.; Chen, Li Qing; Clark, Cheryl R.; Cohen, Pieter; Czeisler, Charles; Estill, Peter; Gay, Elizabeth; Hong, Jessica; Lamas, Daniela; Lay, Sarina; Levy-Carrick, Nomi; Manson, JoAnn; Monafrared, Tobasom; Redline, Susan; Remis, Elijah J.; Schilkrut, Daniel; Sesso, Howard D.; Solomon, Scott; Sparks, Jeffrey A.; Spencer, Lia L.; Systrom, David; Thu, Phyo Phyo Min; Walt, David; Washko, George; Whittelsey, Maureen; Wiener, Rebecca; John, Janice; Ticotsky, Amberly; Bassett, Ingrid V.; Alba, George A.; Aung, Taing N.; Ginns, Leo C.; Haas, Jennifer; Hu, Yanxiang; Juelg, Boris D.; Kanjilal, Diane G.; Kim, Arthur Y.; Klerman, Elizabeth B.; Lewis, Gregory; Ojikutu, Bisola; Perlis, Roy; Rosand, Jonathan; Wallace, Zachary S.; Xerras, Dean; Zionts, Danielle; Ward, Honorine D.; Blazey-Martin, Deborah; Ghamloush, Maher; Jordan, Michael; Kogelman, Laura; Carton, Thomas W.; Miele, Lucio; Brown, Todd; Sutherland, Erica; Fuloria, Jyotsna; Datri, Paula; Hagensee, Michael; Leggio, Cathryn; Perkins, Allen; Trauth, Amber; Trotter, Siobhan; Van Deerlin, Alexander; Weiser, Sharon; Young, Madeline; Erdmann, Nathaniel; Levitan, Emily B.; Tita, Alan T.; Armstrong, Donna; Binkley, Susan E.; Blackwell, Kenneth; Causey, Annalia; Cook, Felice; Domingo, Julio; Donahue, Conner; Eady, Maitlyn; Edberg, Jeffrey; Frazier, Patrick; Garcia-McClaney, Noah; Garner, Melissa; Gray, Brandon; Hall, Wanda; Hart, Cady; Hebson, Camden L.; Hidalgo, Bertha; Holtzapfel, Kaylen; Jinright, Alexis; Judd, Suzanne E.; Kennedy, Teri; Kirkwood, Leigh; Maier, Megan; McCormack, Patricia; Mitchell, Kevin; Montgomery, Aoyjai; Peralta-Carcelen, Myriam; Pilco, Juan P.; Powell, Leigh; Royster, Jacob; Shevin, Rachael; Skipworth, Sidney; Spurgeon, Leah; Steele, Courtney; Ware, Gregory; Wilson, Rosanne; Woodruff, Dana; Young, Brandon; Gillespie, Mark; Daniel, Casey L.; Hansel, Jamie; Wu, Jing; Ashktorab, Hassan; Brim, Hassan; Laiyemo, Adeyinka O.; Sherif, Zaki A.; Durrani, Saima; Nezamloo, Ali; Ngwa, Julius; Njoku, Noelle; Gentil, Monique P.; Mehari, Alem; Solemani, Akbar; Chang, Linda; Thuluvath, Paul; Alemu, Mhret; Anderson, Jordan; Chauhan, Mahak; Cho, Sung; Goodman, Karli; Lanke, Gandi; Lebron, Ralph; Maheshwari, Anurag; Ok, Jina; To, Chau; Shellito, Judd; Greenway, Frank L.; Kirwan, John P.; Rosen, Clifford J.; Arruda, Abigail; Emery, Ivette F.; Roelke, Theresa; Berger, Paul; Hoover, Susan E.; Black, Lora; Tjarks, Brian; Fonseca, Vivian; Gupta, Shaveeta; Longo, Michele; Yang, Mei; Shikuma, Cecilia M.; Chow, Dominic C.; MarGangcuangco, Louis; Castro, Mario; Bengtson, Charles; Howard, Theresa; Koontz, Brandon; Spikes, Leslie A.; Simmons, Christopher; Whiteheart, Sidney W.; Garvy, Beth; Wood, Jeremy P.; Marshall, Gailen D.; Garla, Vishnu; Kuebler, Joy; Nandi, Utsav; Vasey, Andrew; Warren, David E.; Dickinson, John D.; VanWagoner, Timothy M.; Bogie, Amanda; James, Judith A.; Scott, James; Sukhera, Fatima I.; Luciano Roman, Carlos A.; Perez Frontera, Sigrid; Santana Bagur, Jorge; Hodder, Sally L.; Bardes, James M.; Dominique-Villanueva, Daphne; Juskowich, Joy; Reece, Rebecca; Sarwari, Arif; Certa, Marta; Chessier, Erica; Everett, Emily; Kindred, Elijah; Harris, Pastor C.; Sudhindra, Praveen; Olds, Lela; Aponte-Soto, Lisa; Del Rios, Marina; Diaz, Maya Z.; Ibanez, Alejandra L.; Rolon, Cesar; Cranford, Savannah; Brown, Daniel; Dixon, Jennifer; Gale, Lisa; Hammerl, Savannah; Hartwig, Kimberly; Madineni, Abhigna; Swearingen, Peyton; Hendrickson, Monica; Noland, Seth; Terlinde, Tracy; Hobbs, Brianna; Klein, Jonathan D.; Krishnan, Jerry A.; Lin, Janet Y.; Muramatsu, Naoko; Prabhakar, Bellur S.; Prendergast, Heather M.; Vanden Hoek, Terry L.; Adams, Dara; Baker, Aileen; Barbera, Sunni; Basu, Sanjib; Bleasdale, Susan; Boyd, Andrew D.; Breiter, Taylor; Buhimschi, Irina A.; Carrithers, Michael D.; Chalamalla, Rashmika; Chestek, David; Cook, Judith A.; Darbar, Dawood; Dasgupta, Raktima; Davis Blakley, Felicia; DeLisa, Julie A.; Diviak, Kathleen R.; Donlon, Meghan F.; Dworkin, Mark S.; Ellison, Angela; Flanigan, Clarie; Freedman, Michael B.; Gerald, Lynn B.; Giles, Wayne H.; Gordon, Howard S.; Hammad, Bayan; Hasek, Sharon; Hasse, Wendy; Hryniewicka, Martyna; Illendula, Sai D.; Ismail, Nahed; Jain, Akash; Jennette, Kyle J.; Kadubek, Grace; Kent, Denise; Kent, Denise A.; Kim, Keri S.; Kotini-Shah, Pavitra; Large, Lucia; Lash, James .; Lu, Jun; Mahamed, Abeer M.; Malchenko, Sergey; Martinez, Miriam; Mauntel-Medici, Cammeo; McCauley, Mark; Menchaca, Martha; Mermelstein, Robin; Moreno, David J.; Morrissy, Liam; Musick, Hugh; Norwick, Lourdes; Novak, Richard M.; Ortiz, Marilyn; Patel, Khushboo; Perez, Nicolas L.; Pliskin, Neil H.; Pope, Sam; Prasad, Bharati; Predki, Barbara; Quigley, John G.; Ramchandran, Ramaswamy; Ramirez, Ana; Rappe, Sarah; Rehman, Jalees; Rowley, Matthew; Rudraraju, Gowrisree; Rutherfoord, Melissa; Sculley, Jennifer A.; Smith-Mack, Jerisha; Sun, Jun; Tartt, Nancy; Villanueva, Laura; Warfield Kelly, Sara; Yazici, Cemal; Stewart de Ramirez, Sarah A.; Bolliger, Dawn; Boyineni, Jerusha; Chebrolu, Praneeth; Curry, Hannah L.; Donohue, Sarah E.; Edmonds, Sherrie; Kelly, Sara W.; Maholovich, Phoebe; Sader, Samer B.; Thompson, Tiffany; Welter, Hannah; Woolley, Brittany; Hafner, John; Hanson, Keith A.; Hutton, Robert; Charney, Alexander W.; Kovatch, Patricia; Merad, Miriam; Nadkarni, Girish N.; Wisnivesky, Juan P.; Aberg, Judith A.; Ascolillo, Steven; Bagiella, Emilia; Bartram, Logan; Becker, Jacqueline; Beckmann, Noam D.; Bendl, Ashley; Chen, Benjamin K.; Civil, Alyssa; Crawford, Ginger Y.; Dhar, Kaberi; Evo-Ortega, Lorraine; Fierer, Daniel; Gallagher, Emily J.; Garcia-Sastre, Adolfo; Gnjatic, Sacha; Gray, Ian; Guliyeva, Sabina; Harvey-Ingram, Lori; Herrera-Moreno, Julia; Hill, Matthew; Horowitz, Carol R.; Jackson, Rachel; Kastrat, Din; Lala-Trindade, Anu; Lin, Jenny; Macaluso, Nick; Marcon, Kathryn; Meyer, Dara; Morinigo, Janice; Natelson, Benjamin H.; Nussenzweig, Maya; Padua, Tiffani; Putrino, David; Richardson, Lynne; Russo, Scott; Seifert, Alan C.; Serri, Abdullah; Walker, Jordan; Yee, Michell; Bateman, Lucinda; Vernon, Suzanne D.; Deakyne Davies, Sara J.; Gardner, Edward M.; Bryan, Tanner W.; Buck, Kaitlin E.; Hawkins, Kellie L.; Oakes, Judy L.; Horne, Benjamin D.; Knowlton, Kirk; Woller, Scott C.; Aguirre, Bailee; Anderson, Jeff; Bair, Tami; Bosh, Lindsay; Evans, Lorlie; Garrett, Chase; Harris, Dixie; Herrera, Katherine; Iverson, Leslie; Jensen, McKenna M.; Juan, James; Knight, Stacey; Leither, Lindsay; Maestas, Heather; May, Heidi T.; Najarian, Gabriel; Webb, Tiaura; Zubal, Shyanne; Erlandson, Kristine M.; Sokol, Ron J.; Brightman, Marisa; Davis, Debra; Feuerriegel, Elen M.; Fudge, Harrison Z.; Garg, Lohit; Graham, Paige; Higgins, Janine; Johnson, Brandon M.; Jolley, Sarah E.; Lockie, Tim; McCandless, Sean A.; Molden, Joie; Owen, Megan; Pitch, Chloe; Reid, Kayleigh M.; Reusch, Jane E.; Thurman, Brook; Tran, Huong; West, Shelby C.; Wodushek, Thomas; Friedman, Naomi P.; Ludwig, Katelyn R.; Decker, Lauren A.; Raissy, Hengameh; Adolphi, Natalie L.; Archuleta, David A.; Bradfute, Steven B.; Brito, Rebecca; Elifritz, Jamie; Garcia-Soberanez, Noella D.; Gentry, Frederick D.; Harkins, Michelle S.; Martinez, Noah I.; Montoya, Lorenzo A.; Parada, Alisha N.; Quinn, Davin K.; Ramos, Alfredo; Sheehan, Elyce B.; Treacher, Irena S.; Hess, Rachel; Truong, Dongngan T.; Alekhina, Natalya; Barlocker, Jackson; Brown, Jeanette P.; Cortez, Melissa; Donohue, Dagny K; Facelli, Julio C.; Ford, Isaac; Gouripeddi, Ramkiran; Hermansen, Jessica A.; Johnny, Jace D.; Lindsay, Ashton M.; Ling, Leah; Lloyd, Jennifer; Lu, Yue; Medina, Juliemar C.; Morimoto, Sarah Shizuko; Pace, Laura A.; Powell, Jenny M.; Scholand, Mary Beth; Shah, Kevin S.; Sharareh, Nasser; Spivak, Adam M.; Stringham, Caitlyn; Trinity, Joel D.; Velinder, Matt; Weaver, Lisa J.; McComsey, Grace; Adams, Cara; Andrefsky, John; Atieh, Ornina; Baissary, Jhonny; Boldt, Nicholas; Conrad, Ann; D’Anza, Brian; Daher, Ziad; Daher, Jovianne; Dawson, Sarah; DiFrancesco, Kathryn; Durieux, Jared; Foster (Rodgers), Theresa; Gallagher, Michelle; Gupta, Amit; Harrill, Jami; Harris, Paul; Hernandez, Carla; Ialacci, Sarah; Jacono, Frank; Kelly, Jordyn; Kumar, Rohini; Labbato, Danielle; Lesco, Elizabeth; Lim, Joaquin; Pettinato, Kimberly; Rodgers, Michael; Rosolia, Breandan; Roy, Arnab; Scott, Sarah; Smith, Beth; Tejani, Viral; Tribout, Megan; Yendewa, George; Zhang, David; Singer, Nora; Ayache, Mirna; Barnboym, Emma; Brown, Alexis; Chesnick, Hailey; Edminston, Marissa; Greenwood, Carla; Haghiac, Maricela; Kaufman, Elizabeth; Lengu, Ketrin; Lowenthal, Rebecca; Malakooti, Shahdi; Oleson, Christine; Pearman, Ann; Rizea, Allison; Smith, Cheryl; Washington, Maggie; Weinberger, Elisheva; Chen, Peter; Caudill, Antonina; Chopra, Tananshi; Contreras, Fatima; Dahlke, Lea; Gudipudi, Lasya; Jackman, Susan; Modes, Matthew; Muttera, Nicole; Parimon, Tanyalak; Salinas, Nancy; Tadeo, Josie; Torbati, Sam; Heath, James R.; Brennan, Conor; Edmark, Rick; Gutierrez, Vanessa; Hadlock, Jennifer; Li, Sarah; Magis, Andrew T.; McDonald, Connor; Murray, Kim M.; Rowen, Lee; Yuan, Dan; Tuttle, Katherine R.; Alicic, Radica; Baxter, Joni; Emerson, Sarah; Hood, Susan; Kuykendall, Kelli; White, Shane; Wilcox, Lauren E.; Goldman, Jason D.; Algren, Heather A.; Del Alcazar, James; Duven, Alexandria M.; Kaneko, John; Kim, Christina; Manner, Paula; Mason, Carly; Poussier, Rachel; Satira, Richard; Wallick, Julie A.; Chu, Helen Y.; Elias-Warren, Anna; Harteloo, Alex; Logue, Jennifer K.; McCaffrey, Kathryn; Nguyen, Helen; Hasbani, Keren; Nambiar, Anoop M.; Patterson, Thomas F.; Potter, Jennifer S.; Salehi, Marzieh; Sharma, Kumar; Verduzco-Gutierrez, Monica; Anderson, Reed; Arellanes, Azaneth; Barajas, Rose A.; Chauhan, Suneet P.; Clarke, Geoffrey D.; Farner, Cheryl E.; Fischer, Melinda S.; Goldberg, Mark P.; Hastings, Gabrielyd; Heard, Patricia; Hernandez, Jessica; Herrera, Italia; Infante, Edgar; Johnson, Hillary; Jones, Johnnie; Kellogg, Dean L.; Kraig, Ellen; Longoria, Lisa; Okafor, Emeka; Patterson, Jan E.; Pinones, Alexis; Reeves, W. B.; Scholler, Irma; Seshadri, Sudha; Shah, Pankil; Shah, Dimpy P.; Soileau, Bridgette; Solis, Pamela; Stoebner, Carmen; Sullivan, Michael; Taylor, Barbara S.; Tragus, Robin; Tsevat, Joel; Saade, George R.; Sharma, Kavita; Paredes, Claudia C.; Alvarado, Stephanie; Kumar, Andre; Maldonado, Yvonne; Utz, Paul J.; Blish, Catherine A.; Go, Minjoung; Jagannathan, Prasanna; Singh, Upinder; Ahuja, Neera; Blomkalns, Andra L.; Bonilla, Hector; Brotherton, Richard; Clinton, Kimberly; Dingankar, Vaidehi; Geng, Linda N.; Haddad, Francois; Jamero, Christopher; Jee, Kathryn; Jia, Xiaolin K.; Khurana, Naresh; Miglis, Mitchell G.; O’Conor, Ellen; Olszewski, Kelly; Pathak, Divya; Quintero, Orlando; Saperia, Corey; Scott, Jake; Urdaneta, Alfredo E.; Varkey, Mary R.; Reiman, Eric M.; Bime, Christian; Hansen, Lillian; Hughes, Trina; Lieberman, David; Soto, Francisco; Wilson, Cathleen; Zapien, Alyssa; Lee-Iannotti, Joyce K.; Autry, Lynn; Beydoun, Hassan; Borwege, Sabine; Copeland, Jacquelynn; DiLise-Russo, Marjorie; Fadden, Susan; Gomez, Isaias; Grischo, Garrett; Hartley, William; Hillier, Leah; Ismail, Hira; Iusim, Stephanie; James, Michelle; Kala, Mrinalini; Kim, Daniel; Murthy, Ganesh; Unzek, Samuel; Vadovicky, Sheila; Veres, Sharry; Nikolich, Janko Z.; Parthasarathy, Sairam; Ernst, Kacey C.; Esquivel, Denise R.; Harris, David T.; Harris, Stefanie; Hernandez, Michael; Hsu, Harvey; Karnafel, Maria; Knox, Kenneth S.; Koleski, Alison; LaFleur, Bonnie; Lambert, Brenda; LaRue, Sicily; Lutrick, Karen; Merchant, Nirav; Morton, Christopher; Mosier, Jarrod M.; Olorunnisola, Toluwanimi; Peralta, Jeanette; Pilling, William (.; Pogreba-Brown, Kristen; Rischard, Franz P.; Ryan, Lee T.; Smith, Terry; Snyder, Manuel; Subbian, Vignesh; Suhr, Kyle; Velarde, Deanna; Deeks, Steven G.; Kelly, John D.; Martin, Jeffrey N.; Peluso, Michael J.; Anderson, Grace; Anglin, Khamal; Argueta, Urania; Asare, Kofi; Buitrago, Melissa; Chang Song, Celina; Clark, Alexus; Conway, Emily; Del Castillo, Nicole; Deswal, Monika; Durstenfeld, Matthew S.; Eilkhani, Elnaz; Eun, Avery; Fehrman, Emily; Figueroa, Tony; Flores, Diana; Grebe, Halle; Henrich, Timothy J.; Hoh, Rebecca; Hsue, Priscilla; Huang, Beatrice; Ibrahim, Rania; Kerbleski, Marian; Kirtikar, Raushun; Lew, Megan T.; Lombardo, James; Lopez, Monica; Luna, Michael; Marquez, Carina; Munter, Sadie; Ngo, Lynn; Pineda-Ramirez, Jesus; Prather, Aric; Rhoads, Kim; Rodriguez, Antonio; Romero, Justin; Ryder, Dylan; So, Matthew; Somsouk, Ma; Tai, Viva; Tran, Brandon; Uy, Julian; Valdivieso, Daisy; Verma, Deepshika; Williams, Meghann; Zamora, Andhy; Newman, Lisa T.; Abella, Julie; Barnette, Quinn; Bevc, Christine; Beverly, Jennifer; Ceger, Patricia; Croxford, Julie; Enger, Mike; Fain, Katie; Farris, Tonya; Hanlon, Sean; Hines, David; Johnson-Lawrence, Vicki; Jordan, Kevin; Lefebvre, Craig; Linas, Beth; Luukinen, Bryan; Mandal, Meisha; McKoy, Nikki J.; Nance, Susan; Pasquarelli, Demian; Quiner, Claire; Sembajwe, Rita; Shaw, Gwendolyn; Thornburg, Vanessa; Tosco, Kendall; Wright, Hannah; Gross, Rachel S.; Hochman, Judith S.; Horwitz, Leora I.; Katz, Stuart D.; Troxel, Andrea B.; Adler, Lenard; Akinbo, Precious; Almenana, Ramona; Aschalew, Malate; Balick, Lara; Bello, Ola; Bhuiyan, Sultana; Blachman, Nina; Branski, Ryan; Briscoe, Jasmine; Brosnahan, Shari; Bueler, Elliott; Burgos, Yvette; Caplin, Nina; Chaplin, Domonique N.; Chen, Yu; Cheng, Shen; Choe, Peter; Choi, Jess; Chung, Alicia; Church, Richard; Cobos, Stanley; Croft, Nakia; Cruz Irving, Angelique; Del Boccio, Phoebe; Díaz, Iván; Divers, Jasmin; Doshi, Vishal; Dreyer, Benard; Ebel, Samantha; Esquenazi-Karonika, Shari; Faustin, Arline; Febres, Elias; Fine, Jeffrey; Fink, Sandra; Freeland, Catherine; Frontera, Jennifer; Gallagher, Richard; Gonzalez-Duarte, Alejandra; Hasson, Denise; Hill, Sophia; Hossain, Jennifer; Islam, Shahidul; Johnson, Stephen; Kansal, Neha; Kenney, Rachel; Kershner, Tammy; Kewlani, Deepshikha; Kwak, Judy; Lamendola-Essel, Michelle F.; Laury, Sarah; Laynor, Gregory; Lei, Lei; Leon, Terry; Lewczak, Zoe A.; Linton, Janelle; Logan, Max; Malik, Nadia; Mamistvalova, Lia; Mandel, Hannah; Maranga, Gabrielle; Mathews, Patenne D.; Mattoo, Aprajita; Mei, Tony; Mendelsohn, Alan; Mercier, Emmanuelle; Millar Vernetti, Patricio; Miller, Marc; Mitchell, Maika; Moreira, Andre; Mudumbi, Praveen C.; Nahin, Erica; Nair, Nandini; Nekulak, Joseph; Owens, Kellie; Parent, Brendan; Patibandla, Nandan; Petrov, Peter; Postelnicu, Radu; Pratt, Francesca; Randall, Isabelle; Rao, Priyatha; Rapkiewicz, Amy; Rizzo, JohnRoss; Rosas, Johana; Rose, Chelsea; Saint-Jean, Christina; Santacatterina, Michelle; Shah, Binita; Shaukat, Aasma; Simon, Naomi; Simsir, Aylin; Stinson, Miranda; Tang, Wenfei; Tatapudi, Vasishta; Thawani, Sujata; Thomas, Mary; Thorpe, Lorna; Tom, MeeLee; Treiha, Ethan; Truong, Jennifer; Udosen, Mmekom; Valencia, Carlos; Velazquez-Perez, Jessica; Vernetti, Patricio M.; Vidal, Crystal; Viswanathan, Anand; Willerford, Amy; Williams, Natasha; Wong, Crystal; Wood, Marion J.; Wuller, Shannon W.; Yin, Shonna H.; Young, Chloe; Zaretsky, Jonah; Zavlunova, Susanna; Foulkes, Andrea; Karlson, Elizabeth W.; Murphy, Shawn; Ahirwar, Shreya; Ahmed, Shifa; Ainsworth, Layne L.; Atchley-Challenner, Rachel; Avilach, Paul; Balan, Trisha T.; Benik, Nicholas; Benoit, Barbara; Bind, Marie-Abèle C.; Bonaventura, William J.; Boutin, Natalie; Brion, Beverly; Cagan, Andrew; Cai, Tianrun; Cao, Tingyi; Castro, Victor M.; Cerretani, Xander R.; Chan, James G.; Cheng, David; Chibnik, Lori B.; Ciriello, Mark; Costenbader, Karen; Dimitrov, Dimitar S.; Estiri, Hossein; Fayad, Maria; Feldman, Candace H.; Gainer, Vivian; Ghosh, Bhaswati; Gollub, Randy; Guan, Zoe; Harris, Alan; Helmer, Karl; Hendrix, Andrew; Holzbach, Ana; Huang, Weixing; Kaufman, Daniel; Keogh, Diane; Kerr, James D.; Klann, Jeffrey G.; Krishnamoorthy, Aparna; Lasky-Su, Jessica A.; Liao, Katherine P.; MacFadden, Doug; Maram, Anupama; Martel, Megan W.; Mendis, Michael; Metta, Reeta; Monteiro, Jonathan; Morales, Eduardo; Morse, Richard E.; Nazaire, Marc-Danie; Neils, Gregory; Nguyen, Amber N.; Norman, James; Paik, Henry H.; Pant, Deepti; Park, Heekyong; Rabideau, Dustin J.; Reeder, Harrison T.; Rossi-Roh, Kathleen; Santacroce, Leah M.; Schlepphorst, Katherine; Schulte, Carolin; Selvaggi, Caitlin A.; Shinnick, Daniel J.; Simons, William; Simpson, Lynn A.; St. Jean Flanders, Mary L.; Strasser, Zachary; Thakrar, Mansi R.; Thaweethai, Tanayott; Thorn, Madeleine; Trewett, Philip; Van Fleet, Dustin; Wagholikar, Kavishwar B.; Wang, Taowei D.; Wattanasin, Nich; Weber, Griffin; Williams, Michael A.; Zhang, Ren Zhe; Cerda, Marta; Clash, Victor H.; Taylor, Brittany; Zissis, Mike; Akintonwa, Teresa; Blancero, Frank; Brown, Heather-Elizabeth; Carmilani, Megan; Copeland, Debra; Hall, Yvonka; kondo, kevin; Lerma, Lydia; Lindsay, Jacqui; Marti, Heather; Maughan, Christine; Minor, Tony; Vincent, Hyatt; Anderson, Brett; Bardhan, Sujata; Castro-Baucom, Leah; Chisolm, Deena; Corchado, Claudia; Dehority, Walter; Dinwiddie, Gniesha; Goldman Rosas, Lisa; Thomas, Gelise L.; Lopez, Keila; MacDonald Pinkett, Shelly; Regino, Lidia; Vangeepuram, Nita; Walden, Anita; Williams, Neely A.; Burton, Phoebe; Catallozzi, Marina; Dworetzky, Beth; Edwards, Belinda; Ferrer, Robert L.; Pearson, Gail; Rothman, Russell; Wagner, Laura; Wallace, Ann; Aragon, Leyna; Bander, Bryan; Bishof, Karyn; Carignan, Etienne; Castro Baucom, Leah; Clash, Victor; Coombs, Krista; Corchado, Claudia G.; Davis, Hannah; Diggs, Marissa; Dunn, Matthew; Fisher, Liza; Fitzgerald, Megan; Gage Witvliet, Margot; Gustafson, Tyler; Holmes, Verna; Hornig, Mady; Hornig, Maxwell; Jain, Nita; Lam, Julie; Letts, Rebecca; Lewis, Juan; Martinez, Thomas; McCorkell, Lisa; McGrath, Rebecca; Nguyen, Kian; Nichols, Lauren; O’Brien, Lisa; Peddie, Aimee; Perlowski, Alice; Phillips-Lorenzo, Elizabeth; Prentiss, Lisa; Raytselis, Nadia; Rockwell, Megan; Rutter, Jacqueline; Seibert, Elle; Sekar, Anisha; Taylor, Emily; Wilensky, Rochelle; Williams, Melissa; Williams, Neely; Williams-Dawson, Kay; Wylam, Andrew (2025). 8(1) e2455430.
• SynDEc: A Synthetic Data Ecosystem. Karst, Fabian Sven; Li, Mahei Manhai; Leimeister, Jan Marco (2025). 35

  Given the critical role of data availability for growth and innovation in financial services, especially small and mid-sized banks lack the data volumes required to fully leverage AI advancements for enhancing fraud detection, operational efficiency, and risk management. With existing solutions facing challenges in scalability, inconsistent standards, and complex privacy regulations, we introduce a synthetic data sharing ecosystem (SynDEc) using generative AI. Employing design science research in collaboration with two banks, among them UnionBank of the Philippines, we developed and validated a synthetic data sharing ecosystem for financial institutions. The derived design principles highlight synthetic data setup, training configurations, and incentivization. Furthermore, our findings show that smaller banks benefit most from SynDEcs and our solution is viable even with limited participation. Thus, we advance data ecosystem design knowledge, show its viability for financial services, and offer practical guidance for privacy-resilient synthetic data sharing, laying groundwork for future applications of SynDEcs.
• Designing a Governance Model for Blockchain-Based Traceability Systems in Supply Chain Consortia. Greiner, Maximilian; Zeiß, Christian; Neis, Nicolas; Seidenfad, Karl; Lechner, Ulrike; Winkelmann, Axel (2025). 5581–5590.

  Implementing effective governance for blockchain-based traceability systems in supply chain consortia is essential to align IT strategies with broader business goals, ensuring that these systems drive value creation and improve overall supply chain efficiency. Establishing these governance structures helps consortia optimize transparency, compliance, and collaboration, which are critical for managing complex logistics and maintaining stakeholder trust. Utilizing a Design Science Research approach, we aim to develop a governance model that comprises essential principles and requirements for the implementation and value creation of blockchain-based traceability systems in supply chains. Our model provides actionable insights for practitioners looking to leverage blockchain for enhanced traceability and efficiency and offers a structured pathway to navigate complexities for digital transformation in decentralized environments.
• A Comparative Study of Cooperative and Non-Cooperative Wideband Spectrum Sensing in Cognitive Radio Networks for 5g Applications Meghanathan, Natarajan (2025). (Vol. 16) IJCNC.

  The rapid advancements in 5G technologies have created an unprecedented need for efficient spectrum utilization to support increasing data traffic and diverse communication services. In this context, accurate and reliable spectrum sensing is essential. This study explores wideband spectrum sensing strategies, comparing non-cooperative cognitive radio (CR) techniques with cooperative methods across multiple subbands. A novel cooperative wideband spectrum sensing framework was developed, incorporating a K-outof-N fusion rule at the fusion center to make optimal decisions by selecting an appropriate K for a given number of cooperating CRs. This approach addresses noise uncertainty, a common challenge in traditional non-cooperative energy detection methods, particularly in 5G environments under Additive White Gaussian Noise (AWGN) conditions, assumed to be identically and independently distributed (i.i.d.). However, while cooperative sensing significantly improves detection in low signal-to-noise ratio (SNR) scenarios with higher false alarm rates (between 0.5 and 1), our findings reveal that it does not consistently outperform non-cooperative methods at very low false alarm rates (0.01 and 0.1) under poor SNR conditions. These findings highlight the need for further research to enhance cooperative sensing strategies for various operational environments.
• Die sozialpolitische Doktrin der Neuen Rechten - Strategische Vereinnahmung und kalkulierte Provokation. Technical Report (362), Hagedorny, Matheus; Schilk, Felix; Kiess, Johannes (2025).

  Das Working Paper zeigt anhand von zwei Fallbeispielen, wie die sogenannte Neue Rechte linke und sozialpolitische Ansätze rezipiert: Die selektive und rhetorische Übernahme einiger solcher Ideen dient der Provokation, der Reduktion von Angriffsflächen und der scheinbaren Bearbeitung der "sozialen Frage". Trotz Bezugnahmen auf nicht-rechte Ansätze bleibt die Ideologie der Neuen Rechten antiegalitär und auf Ungleichheit fokussiert. Konzepte wie der von Björn Höcke ausgerufene "solidarische Patriotismus" oder die von Recherche Dresden propagierte "nachbarschaftliche Marktwirtschaft" fördern eine exkludierende Solidarität und die Verpflichtung aller auf Dienst, Gehorsam und Leistungsbereitschaft.
• Transcriptome-scale analysis uncovers conserved residues in the hydrophobic core of the bacterial RNA chaperone Hfq required for small regulatory RNA stability. McQuail, Josh; Krepl, Miroslav; Katsuya-Gaviria, Kai; Tabib-Salazar, Aline; Burchell, Lynn; Bischler, Thorsten; Gräfenhan, Tom; Brear, Paul; Šponer, Jiří; Luisi, Ben F; Wigneshweraraj, Sivaramesh (2025). 53(3)

  The RNA chaperone Hfq plays crucial roles in bacterial gene expression and is a major facilitator of small regulatory RNA (sRNA) action. The toroidal architecture of the Hfq hexamer presents three well-characterized surfaces that allow it to bind sRNAs to stabilize them and engage target transcripts. Hfq-interacting sRNAs are categorized into two classes based on the surfaces they use to bind Hfq. By characterizing a systematic alanine mutant library of Hfq to identify amino acid residues that impact survival of Escherichia coli experiencing nitrogen (N) starvation, we corroborated the important role of the three RNA-binding surfaces for Hfq function. We uncovered two, previously uncharacterized, conserved residues, V22 and G34, in the hydrophobic core of Hfq, to have a profound impact on Hfq’s RNA-binding activity in vivo. Transcriptome-scale analysis revealed that V22A and G34A Hfq mutants cause widespread destabilization of both sRNA classes, to the same extent as seen in bacteria devoid of Hfq. However, the alanine substitutions at these residues resulted in only modest alteration in stability and structure of Hfq. We propose that V22 and G34 have impact on Hfq function, especially critical under cellular conditions when there is an increased demand for Hfq, such as N starvation.
• LOS MEJORES CARROS EN COLOMBIA EN ENERO. me (2025).
• Safer interaction with IVAs: The impact of privacy literacy training on competent use of intelligent voice assistants. Markus, A.; Baumann, M.; Pfister, J.; Carolus, A.; Hotho, A.; Wienrich, C. (2025). 8(100372)
• CASTER: Direct species tree inference from whole-genome alignments. Zhang, Chao; Nielsen, Rasmus; Mirarab, Siavash (2025). eadk9688.

  Genomes contain mosaics of discordant evolutionary histories, challenging the accurate inference of the tree of life. While genome-wide data are routinely used for discordance-aware phylogenomic analyses, due to modeling and scalability limitations, the current practice leaves out large chunks of genomes. As more high-quality genomes become available, we urgently need discordance-aware methods to infer the tree directly from a multiple genome alignment. Here, we introduce CASTER, a theoretically justified site-based method that eliminates the need to predefine recombination-free loci. CASTER is scalable to hundreds of mammalian whole genomes. We demonstrate the accuracy and scalability of CASTER in simulations that include recombination and apply CASTER to several biological datasets, showing that its per-site scores can reveal both biological and artefactual patterns of discordance across the genome.
• Recursively Provable Dilators. Aguilera, Juan P.; Freund, Anton; Pakhomov, Fedor (2025). 12 pp.
• Microbial iCLIP2: enhanced mapping of RNA-protein interaction by promoting protein and RNA stability. Stoffel, Nina Kim; Sankaranarayanan, Srimeenakshi; Müntjes, Kira; Körtel, Nadine; Busch, Anke; Zarnack, Kathi; König, Julian; Feldbrügge, Michael (2025). 31(2) 258–272.

  The entire RNA life cycle, spanning from transcription to decay, is intricately regulated by RNA-binding proteins (RBPs). To understand their precise functions, it is crucial to identify direct targets, pinpoint their exact binding sites, and unravel the underlying specificity in vivo. Individual-nucleotide resolution UV cross-linking and immunoprecipitation 2 (iCLIP2) is a state-of-the-art technique that enables the identification of RBP-binding sites at single-nucleotide resolution. However, in the field of microbiology, optimized iCLIP protocols compared to mammalian systems are lacking. Here, we present the first microbial iCLIP2 approach using the multi-RRM domain protein Rrm4 from the fungus Ustilago maydis as an example. Key challenges, such as inherently high RNase and protease activity in fungi, were addressed by improving mechanical cell disruption and lysis buffer composition. Our modifications increased the yield of cross-link events and improved the identification of Rrm4-binding sites. Thereby, we were able to pinpoint that Rrm4 binds the stop codons of nuclear-encoded mRNAs of mitochondrial respiratory complexes I, III, and V-revealing an intimate link between endosomal mRNA transport and mitochondrial physiology. Thus, our study using U. maydis as an example might serve as a blueprint for optimizing iCLIP2 procedures in other microorganisms with high RNase/protease conditions.
• Modeling the Quality of Dialogical Explanations. Alshomary, Milad; Lange, Felix; Booshehri, Meisam; Sengupta, Meghdut; Cimiano, Philipp; Wachsmuth, Henning in 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, N. Xue (eds.) (2024). 11523–11536.

  Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an explainer discusses a concept or phenomenon of interest with an explainee. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee's side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum Explain Like I am Five and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.
• Utilizing Analytic Network Process for Evaluating Event Management Companies: A Case Study in Celebration Activities. Huang, Han-Chen (2024). (506) 291–359.
• BirdSet: A Multi-Task Benchmark for Classification in Avian Bioacoustics. Rauch, Lukas; Schwinger, Raphael; Wirth, Moritz; Heinrich, René; Lange, Jonas; Kahl, Stefan; Sick, Bernhard; Tomforde, Sven; Scholz, Christoph (2024). abs/2403.10380
• ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. Becktepe, Jannis; Dierkes, Julian; Benjamins, Carolin; Mohan, Aditya; Salinas, David; Rajan, Raghu; Hutter, Frank; Hoos, Holger; Lindauer, Marius; Eimer, Theresa (2024).
• Bayesian Optimisation for Protein Sequence Design: Gaussian Processes with Zero-Shot Protein Language Model Prior Mean. Benjamins, Carolin; Surana, Shikha; Bent, Oliver; Lindauer, Marius; Duckworth, Paul (2024).
• Bayesian Optimization for Protein Sequence Design: Back to Simplicity with Gaussian Processes. Benjamins, Carolin; Surana, Shikha; Bent, Oliver; Lindauer, Marius; Duckworth, Paul (2024).
• Structure in Deep Reinforcement Learning: A Survey and Open Problems. Mohan, Aditya; Zhang, Amy; Lindauer, Marius (2024).

  Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural Networks (DNNs) for function approximation, has demonstrated considerable success in numerous applications. However, its practicality in addressing a wide range of real-world scenarios, characterized by diverse and unpredictable dynamics, noisy signals, and large state and action spaces, remains limited. This limitation stems from issues such as poor data efficiency, limited generalization capabilities, a lack of safety guarantees, and the absence of interpretability, among other factors. To overcome these challenges and improve performance across these crucial metrics, one promising avenue is to incorporate additional structural information about the problem into the RL learning process. Various sub-fields of RL have proposed methods for incorporating such inductive biases. We amalgamate these diverse methodologies under a unified framework, shedding light on the role of structure in the learning problem, and classify these methods into distinct patterns of incorporating structure. By leveraging this comprehensive framework, we provide valuable insights into the challenges associated with structured RL and lay the groundwork for a design pattern perspective on RL research. This novel perspective paves the way for future advancements and aids in the development of more effective and efficient RL algorithms that can potentially handle real-world scenarios better.
• Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. Benjamins, Carolin; Cenikj, Gjorgjina; Nikolikj, Ana; Mohan, Aditya; Eftimov, Tome; Lindauer, Marius (2024).

  Dynamic Algorithm Configuration (DAC) addresses the challengeof dynamically setting hyperparameters of an algorithm for a diverse set of instances rather than focusing solely on individualtasks. Agents trained with Deep Reinforcement Learning (RL) offer a pathway to solve such settings. However, the limited generalization performance of these agents has significantly hinderedthe application in DAC. Our hypothesis is that a potential bias inthe training instances limits generalization capabilities. We takea step towards mitigating this by selecting a representative subset of training instances to overcome overrepresentation and thenretraining the agent on this subset to improve its generalizationperformance. For constructing the meta-features for the subset selection, we particularly account for the dynamic nature of the RLagent by computing time series features on trajectories of actionsand rewards generated by the agenttextquoterights interaction with the environment. Through empirical evaluations on the Sigmoid and CMA-ESbenchmarks from the standard benchmark library for DAC, calledDACBench, we discuss the potentials of our selection techniquecompared to training on the entire instance set. Our results highlight the efficacy of instance selection in refining DAC policies fordiverse instance spaces.
• AMLTK: A Modular AutoML Toolkit in Python. Bergman, Edward; Feurer, Matthias; Bahram, Aron; Rezaei, Amir; Purucker, Lennart; Segel, Sarah; Lindauer, Marius; Eggensperger, Katharina (2024). 9(100)
• Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. Deng, Difan; Lindauer, Marius in ArXiv (2024).

  The rapid development of time series forecasting research has brought many deep learning-based modules in this field. However, despite the increasing amount of new forecasting architectures, it is still unclear if we have leveraged the full potential of these existing modules within a properly designed architecture. In this work, we propose a novel hierarchical neural architecture search approach for time series forecasting tasks. With the design of a hierarchical search space, we incorporate many architecture types designed for forecasting tasks and allow for the efficient combination of different forecasting architecture modules. Results on long-term-time-series-forecasting tasks show that our approach can search for lightweight high-performing forecasting architectures across different forecasting tasks.
• Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. Hennig, Leona; Tornede, Tanja; Lindauer, Marius (2024).

  Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep shift neural networks (DSNNs) offer a solution by leveraging shift operations to reduce computational complexity at inference. Following the insights from standard DNNs, we are interested in leveraging the full potential of DSNNs by means of AutoML techniques. We study the impact of hyperparameter optimization (HPO) to maximize DSNN performance while minimizing resource consumption. Since this combines multi-objective (MO) optimization with accuracy and energy consumption as potentially complementary objectives, we propose to combine state-of-the-art multi-fidelity (MF) HPO with multi-objective optimization. Experimental results demonstrate the effectiveness of our approach, resulting in models with over 80% in accuracy and low computational cost. Overall, our method accelerates efficient model development while enabling sustainable AI applications.
• AutoML in Heavily Constrained Applications. Neutatz, Felix; Lindauer, Marius; Abedjan, Ziawasch (2024). 33(4) 957–979.

  Optimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hyperparameters for the given training dataset. Yet, depending on the AutoML systemtextquoterights own second-order meta-configuration, the performance of the AutoML process can vary significantly. Current AutoML systems cannot automatically adapt their own configuration to a specific use case. Further, they cannot compile user-defined application constraints on the effectiveness and efficiency of the pipeline and its generation. In this paper, we propose Caml, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand. The dynamic AutoML strategy of Caml takes user-defined constraints into account and obtains constraint-satisfying pipelines with high predictive performance.
• Position Paper: A Call to Action for a Human-Centered AutoML Paradigm. Lindauer, Marius; Karl, Florian; Klier, Anne; Moosbauer, Julia; Tornede, Alexander; Müller, Andreas; Hutter, Frank; Feurer, Matthias; Bischl, Bernd (2024).
• When to use a metaphor: Metaphors in dialogical explanations with addressees of different expertise. Scharlau, Ingrid; Körber, Miriam; Sengupta, Meghdut; Wachsmuth, Henning (2024). 3 1474924.
• Who Determines What Is Relevant? Humans or AI? Why Not Both?: A spectrum of human–artificial intelligence collaboration in assessing relevance. Faggioli, Guglielmo; Dietz, Laura; Clarke, Charles L.A.; Demartini, Gianluca; Hagen, Matthias; Hauff, Claudia; Kando, Noriko; Kanoulas, Evangelos; Potthast, Martin; Stein, Benno; Wachsmuth, Henning (2024). 67(4) 31–34.
• Analyzing the Use of Metaphors in News Editorials for Political Framing. Sengupta, Meghdut; El Baff, Roxanne; Alshomary, Milad; Wachsmuth, Henning in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024, K. Duh, H. Gomez, S. Bethard (eds.) (2024). 3621–3631.

  Metaphorical language is a pivotal element in the realm of political framing. Existing work from linguistics and the social sciences provides compelling evidence regarding the distinctiveness of conceptual framing for political ideology perspectives. However, the nature and utilization of metaphors and the effect on audiences of different political ideologies within political discourses are hardly explored. To enable research in this direction, in this work we create a dataset, originally based on news editorials and labeled with their persuasive effects on liberals and conservatives and extend it with annotations pertaining to metaphorical usage of language. To that end, first, we identify all single metaphors and composite metaphors. Secondly, we provide annotations of the source and target domains for each metaphor. As a result, our corpus consists of 300 news editorials annotated with spans of texts containing metaphors and the corresponding domains of which these metaphors draw from. Our analysis shows that liberal readers are affected by metaphors, whereas conservatives are resistant to them. Both ideologies are affected differently based on the metaphor source and target category. For example, liberals are affected by metaphors in the Darkness & Light (e.g., death) source domains, where as the source domain of Nature affects conservatives more significantly.
• The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments. Mirzakhmedova, Nailia; Kiesel, Johannes; Alshomary, Milad; Heinrich, Maximilian; Handke, Nicolas; Cai, Xiaoni; Barriere, Valentin; Dastgheib, Doratossadat; Ghahroodi, Omid; SadraeiJavaheri, MohammadAli; Asgari, Ehsaneddin; Kawaletz, Lea; Wachsmuth, Henning; Stein, Benno N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, N. Xue (eds.) (2024). 16121–16134.

  While human values play a crucial role in making arguments persuasive, we currently lack the necessary extensive datasets to develop methods for analyzing the values underlying these arguments on a large scale. To address this gap, we present the Touché23-ValueEval dataset, an expansion of the Webis-ArgValues-22 dataset. We collected and annotated an additional 4780 new arguments, doubling the dataset`s size to 9324 arguments. These arguments were sourced from six diverse sources, covering religious texts, community discussions, free-text arguments, newspaper editorials, and political debates. Each argument is annotated by three crowdworkers for 54 human values, following the methodology established in the original dataset. The Touché23-ValueEval dataset was utilized in the SemEval 2023 Task 4. ValueEval: Identification of Human Values behind Arguments, where an ensemble of transformer models demonstrated state-of-the-art performance. Furthermore, our experiments show that a fine-tuned large language model, Llama-2-7B, achieves comparable results.
• Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness. Spliethöver, Maximilian; Menon, Sai Nikhil; Wachsmuth, Henning in Proceedings of the Annual Meeting of the Association for Computational Linguistics, L.-W. Ku, A. Martins, V. Srikumar (eds.) (2024). 9294–9313.

  Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unfair behavior of the methods, conveying negative bias towards dialect speakers. While previous work has studied dialect-related fairness for aspects like hate speech, other aspects of biased language, such as lewdness, remain fully unexplored. To fill this gap, we investigate performance disparities between dialects in the detection of five aspects of biased language and how to mitigate them. To alleviate bias, we present a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. In our experiments with African-American English dialect, we provide empirical evidence that complementing common learning approaches with dialect modeling improves their fairness. Furthermore, the results suggest that multitask learning achieves state-of-the-art performance and helps to detect properties of biased language more reliably.
• Overview of Touché 2024: Argumentation Systems. Kiesel, Johannes; c Cağrı c Cöltekin; Heinrich, Maximilian; Fröbe, Maik; Alshomary, Milad; De Longueville, Bertrand; Erjavec, Tomav z; Handke, Nicolas; Kopp, Matyáv s; Ljubev si’c, Nikola; Meden, Katja; Mirzhakhmedova, Nailia; Morkeviv cius, Vaidas; Reitis-Münstermann, Theresa; Scharfbillig, Mario; Stefanovitch, Nicolas; Wachsmuth, Henning; Potthast, Martin; Stein, Benno in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), N. Goharian, N. Tonellotto, Y. He, A. Lipani, G. McDonald, C. Macdonald, I. Ounis (eds.) (2024). 466–473.

  Decision-making and opinion-forming are everyday tasks that involve weighing pro and con arguments. The goal of Touché is to foster the development of support-technologies for decision-making and opinion-forming and to improve our understanding of these processes. This fifth edition of the lab features three shared tasks: (1) Human value detection (ValueEval), where participants detect (implicit) references to human values and their attainment in text; (2) Multilingual Ideology and Power Identification in Parliamentary Debates, where participants identify from a speech the political leaning of the speakertextquoterights party and whether it was governing at the time of the speech (new task); and (3) Image retrieval or generation in order to convey the premise of an argument with visually. In this paper, we briefly describe the planned setup for the fifth lab edition at CLEF 2024 and summarize the results of the 2023 edition.
• Towards Modeling and Evaluating Instructional Explanations in Teacher-Student Dialogues. Feldhus, Nils; Anagnostopoulou, Aliki; Wang, Qianli; Alshomary, Milad; Wachsmuth, Henning; Sonntag, Daniel; Möller, Sebastian (2024). 225–230.

  For dialogues in which teachers explain difficult concepts to students, didactics research often debates which teaching strategies lead to the best learning outcome. In this paper, we test if LLMs can reliably annotate such explanation dialogues, s.t. they could assist in lesson planning and tutoring systems. We first create a new annotation scheme of teaching acts aligned with contemporary teaching models and re-annotate a dataset of conversational explanations about communicating scientific understanding in teacher-student settings on five levels of the explaineetextquoterights expertise: ReWIRED contains three layers of acts (Teaching, Explanation, Dialogue) with increased granularity (span-level). We then evaluate language models on the labeling of such acts and find that the broad range and structure of the proposed labels is hard to model for LLMs such as GPT-3.5/-4 via prompting, but a fine-tuned BERT can perform both act classification and span labeling well. Finally, we operationalize a series of quality metrics for instructional explanations in the form of a test suite, finding that they match the five expertise levels well.1
• A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality. Stahl, Maja; Michel, Nadine; Kilsbach, Sebastian; Schmidtke, Julian; Rezat, Sara; Wachsmuth, Henning in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024, K. Duh, H. Gomez, S. Bethard (eds.) (2024). 2661–2674.

  Learning argumentative writing is challenging. Besides writing fundamentals such as syntax and grammar, learners must select and arrange argument components meaningfully to create high-quality essays. To support argumentative writing computationally, one step is to mine the argumentative structure. When combined with automatic essay scoring, interactions of the argumentative structure and quality scores can be exploited for comprehensive writing support. Although studies have shown the usefulness of using information about the argumentative structure for essay scoring, no argument mining corpus with ground-truth essay quality annotations has been published yet. Moreover, none of the existing corpora contain essays written by school students specifically. To fill this research gap, we present a German corpus of 1,320 essays from school students of two age groups. Each essay has been manually annotated for argumentative structure and quality on multiple levels of granularity. We propose baseline approaches to argument mining and essay scoring, and we analyze interactions between both tasks, thereby laying the ground for quality-oriented argumentative writing support.
• Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation. Stahl, Maja; Biermann, Leon; Nehring, Andreas; Wachsmuth, Henning (2024). 283–298.
• Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning. Giovanelli, Joseph; Tornede, Alexander; Tornede, Tanja; Lindauer, Marius in Proceedings of the AAAI Conference on Artificial Intelligence, M. Wooldridge, J. Dy, S. Natarajan (eds.) (2024). 12172–12180.

  Hyperparameter optimization (HPO) is important to leverage the full potential of machine learning (ML). In practice, users are often interested in multi-objective (MO) problems, i.e., optimizing potentially conflicting objectives, like accuracy and energy consumption. To tackle this, the vast majority of MO-ML algorithms return a Pareto front of non-dominated machine learning models to the user. Optimizing the hyperparameters of such algorithms is non-trivial as evaluating a hyperparameter configuration entails evaluating the quality of the resulting Pareto front. In literature, there are known indicators that assess the quality of a Pareto front (e.g., hypervolume, R2) by quantifying different properties (e.g., volume, proximity to a reference point). However, choosing the indicator that leads to the desired Pareto front might be a hard task for a user. In this paper, we propose a human-centered interactive HPO approach tailored towards multi-objective ML leveraging preference learning to extract desiderata from users that guide the optimization. Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator. Concretely, we leverage pairwise comparisons of distinct Pareto fronts to learn such an appropriate quality indicator. Then, we optimize the hyperparameters of the underlying MO-ML algorithm towards this learned indicator using a state-of-the-art HPO approach. In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.
• Towards Enhancing Predictive Representations using Relational Structure in Reinforcement Learning. Mohan, Aditya; Lindauer, Marius (2024).

  While Reinforcement Learning (RL) has demonstrated promising results, its practical application remains limited due to brittleness in complex environments characterized by attributes such as high-dimensional observations, sparse rewards, partial observability, and changing dynamics. To overcome these challenges, we propose enhancing representation learning in RL by incorporating structural inductive biases through Graph Neural Networks (GNNs). Our approach leverages a structured GNN latent model to capture relational structures, thereby improving belief representation end-to-end. We validate our modeltextquoterights benefits through empirical evaluation in selected challenging environments within the Minigrid suite, which offers relational complexity, against a baseline that uses a Multi-Layer Perceptron (MLP) as the latent model. Additionally, we explore the robustness of these representations in continually changing environments by increasing the size and adding decision points in the form of distractors. Through this analysis, we offer initial insights into the advantages of combining relational latent representations using GNNs for end-to-end representation learning in RL and pave the way for future methods of incorporating graph structure for representation learning in RL.
• Argument Quality Assessment in the Age of Instruction-Following Large Language Models. Wachsmuth, Henning; Lapesa, Gabriella; Cabrio, Elena; Lauscher, Anne; Park, Joonsuk; Vecchi, Eva Maria; Villata, Serena; Ziegenbein, Timon (2024).

  The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, writing education, and the like. A critical task in any such application is the assessment of an argumenttextquoterights quality - but it is also particularly challenging. In this position paper, we start from a brief survey of argument quality research, where we identify the diversity of quality notions and the subjectiveness of their perception as the main hurdles towards substantial progress on argument quality assessment. We argue that the capabilities of instruction-following large language models (LLMs) to leverage knowledge across contexts enable a much more reliable assessment. Rather than just fine-tuning LLMs towards leaderboard chasing on assessment tasks, they need to be instructed systematically with argumentation theories and scenarios as well as with ways to solve argument-related problems. We discuss the real-world opportunities and ethical issues emerging thereby.
• LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback. Ziegenbein, Timon; Skitalinska, Gabriella; Bayat Makou, Alireza; Wachsmuth, Henning (2024).

  Ensuring that online discussions are civil and productive is a major challenge for social media platforms. Such platforms usually rely both on users and on automated detection tools to flag inappropriate arguments of other users, which moderators then review. However, this kind of post-hoc moderation is expensive and time-consuming, and moderators are often overwhelmed by the amount and severity of flagged content. Instead, a promising alternative is to prevent negative behavior during content creation. This paper studies how inappropriate language in arguments can be computationally mitigated. We propose a reinforcement learning-based rewriting approach that balances content preservation and appropriateness based on existing classifiers, prompting an instruction-finetuned large language model (LLM) as our initial policy. Unlike related style transfer tasks, rewriting inappropriate arguments allows deleting and adding content permanently. It is therefore tackled on document level rather than sentence level. We evaluate different weighting schemes for the reward function in both absolute and relative human assessment studies. Systematic experiments on non-parallel data provide evidence that our approach can mitigate the inappropriateness of arguments while largely preserving their content. It significantly outperforms competitive baselines, including few-shot learning, prompting, and humans.
• Improving Argument Effectiveness Across Ideologies using Instruction-tuned Large Language Models. El Baff, Roxanne; Khatib, Khalid Al; Alshomary, Milad; Konen, Kai; Stein, Benno; Wachsmuth, Henning Y. Al-Onaizan, M. Bansal, Y.-N. Chen (eds.) (2024). 4604–4622.

  Different political ideologies (e.g., liberal and conservative Americans) hold different worldviews, which leads to opposing stances on different issues (e.g., gun control) and, thereby, fostering societal polarization. Arguments are a means of bringing the perspectives of people with different ideologies closer together, depending on how well they reach their audience. In this paper, we study how to computationally turn ineffective arguments into effective arguments for people with certain ideologies by using instruction-tuned large language models (LLMs), looking closely at style features. For development and evaluation, we collect ineffective arguments per ideology from debate.org, and we generate about 30k, which we rewrite using three LLM methods tailored to our task: zero-shot prompting, few-shot prompting, and LLM steering. Our experiments provide evidence that LLMs naturally improve argument effectiveness for liberals. Our LLM-based and human evaluation show a clear preference towards the rewritten arguments. Code and link to the data are available here: https://github.com/roxanneelbaff/emnlp2024-iesta.
• Objective Argument Summarization in Search. Ziegenbein, Timon; Syed, Shahbaz; Potthast, Martin; Wachsmuth, Henning (2024).

  Decision-making and opinion formation are influenced by arguments from various online sources, including social media, web publishers, and, not least, the search engines used to retrieve them. However, many, if not most, arguments on the web are informal, especially in online discussions or on personal pages. They can be long and unstructured, subjective and emotional, and contain inappropriate language. This makes it difficult to find relevant arguments efficiently. We hypothesize that, on search engine results pages,“objective snippets” of arguments are better suited than the commonly used extractive snippets and develop corresponding methods for two important tasks: snippet generation and neutralization. For each of these tasks, we investigate two approaches based on (1) prompt engineering for large language models (LLMs), and (2) supervised models trained on existing datasets. We find that a supervised summarization model outperforms zero-shot summarization with LLMs for snippet generation. For neutralization, using reinforcement learning to align an LLM with human preferences for suitable arguments leads to the best results. Both tasks are complementary, and their combination leads to the best snippets of arguments according to automatic and human evaluation.
• Reference-guided Style-Consistent Content Transfer. Chen, Wei Fan; Alshomary, Milad; Stahl, Maja; Al Khatib, Khalid; Stein, Benno; Wachsmuth, Henning N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, N. Xue (eds.) (2024). 13754–13768.

  In this paper, we introduce the task of style-consistent content transfer, which concerns modifying a text's content based on a provided reference statement while preserving its original style. We approach the task by employing multi-task learning to ensure that the modified text meets three important conditions: reference faithfulness, style adherence, and coherence. In particular, we train three independent classifiers for each condition. During inference, these classifiers are used to determine the best modified text variant. Our evaluation, conducted on hotel reviews and news articles, compares our approach with sequence-to-sequence and error correction baselines. The results demonstrate that our approach reasonably generates text satisfying all three conditions. In subsequent analyses, we highlight the strengths and limitations of our approach, providing valuable insights for future research directions.
• AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Tornede, Alexander; Deng, Difan; Eimer, Theresa; Giovanelli, Joseph; Mohan, Aditya; Ruhkopf, Tim; Segel, Sarah; Theodorakopoulos, Daphne; Tornede, Tanja; Wachsmuth, Henning; Lindauer, Marius (2024).

  The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language Models (LLMs) have experienced a rapid series of breakthroughs very recently. We envision that the two fields can radically push the boundaries of each other through tight integration. To showcase this vision, we explore the potential of a symbiotic relationship between AutoML and LLMs, shedding light on how they can benefit each other. In particular, we investigate both the opportunities to enhance AutoML approaches with LLMs from different perspectives and the challenges of leveraging AutoML to further improve LLMs. To this end, we survey existing work, and we critically assess risks. We strongly believe that the integration of the two fields has the potential to disrupt both fields, NLP and AutoML. By highlighting conceivable synergies, but also risks, we aim to foster further exploration at the intersection of AutoML and LLMs.
• auto-sktime: Automated Time Series Forecasting. Zöller, Marc; Lindauer, Marius; Huber, Marco (2024).
• Hyperparameter Importance Analysis for Multi-Objective AutoML. Theodorakopoulos, Daphne; Stahl, Frederic; Lindauer, Marius (2024).

  Hyperparameter optimization plays a pivotal role in enhancing the predictive performance and generalization capabilities of ML models. However, in many applications, we do not only care about predictive performance but also about objectives such as inference time, memory, or energy consumption. In such MOO scenarios, determining the importance of hyperparameters poses a significant challenge due to the complex interplay between the conflicting objectives. In this paper, we propose the first method for assessing the importance of hyperparameters in the context of multi-objective hyperparameter optimization. Our approach leverages surrogate-based hyperparameter importance (HPI) measures, i.e. fANOVA and ablation paths, to provide insights into the impact of hyperparameters on the optimization objectives. Specifically, we compute the a-priori scalarization of the objectives and determine the importance of the hyperparameters for different objective tradeoffs. Through extensive empirical evaluations on diverse benchmark datasets with three different objectives paired with accuracy, namely time, demographic parity, and energy consumption, we demonstrate the effectiveness and robustness of our proposed method. Our findings not only offer valuable guidance for hyperparameter tuning in MOO tasks but also contribute to advancing the understanding of HPI in complex optimization scenarios.
• Upper bound on the number of Weyl points born from a nongeneric degeneracy point. Pintér, Gergő; Frank, György; Varjas, Dániel; Pályi, András (2024). 110(24) 245124.
• SECURING DIGITAL IDENTITIES: THE SYNERGY OF INFORMATION TECHNOLOGY SECURITY, TRUST, AND PRIVACY. Selvam, Damodar (D. Selvam, ed.) (2024). 14(5) 14.

  The convergence of information technology (IT) security, trust, and privacy has emerged as a fundamental paradigm in the digital era, especially concerning the management of digital identities. This paper explores the complex interplay among these spheres, explaining how their fusion can bolster the safeguarding of digital identities. The article endeavours to provide a comprehensive understanding of the convergence and its repercussions by examining current trends, technological advancements, and existing impediments. The results underscore efficient tactics and frameworks that enrich the security, reliability, and confidentiality of digital identities, ultimately fostering more resilient digital environments.
• High-field magnetic properties of the alternating ferromagnetic-antiferromagnetic spin-chain compound Cu\($_\mathbf2$\)(OH)\($_\mathbf3$\)Br. Povarov, K. Yu.; Skourskii, Y.; Wosnitza, J.; Graf, D. E.; Zhao, Z.; Zvyagin, S. A. (2024). 110(21) 214421.
• Parametrically long lifetime of superdiffusion in nonintegrable spin chains. McRoberts, Adam J.; Moessner, Roderich (2024). 133(25) 256301.
• A randomized trial of pharmacological ascorbate, gemcitabine, and nab-paclitaxel for metastatic pancreatic cancer. Bodeker, Kellie L.; Smith, Brian J.; Berg, Daniel J.; Chandrasekharan, Chandrikha; Sharif, Saima; Fei, Naomi; Vollstedt, Sandy; Brown, Heather; Chandler, Meghan; Lorack, Amanda; McMichael, Stacy; Wulfekuhle, Jared; Wagner, Brett A.; Buettner, Garry R.; Allen, Bryan G.; Caster, Joseph M.; Dion, Barbara; Kamgar, Mandana; Buatti, John M.; Cullen, Joseph J. (2024). 77 103375.

  Abstract: Background Patients with metastatic pancreatic ductal adenocarcinoma (PDAC) have poor 5-year survival. Pharmacological ascorbate (P-AscH-, high dose, intravenous, vitamin C) has shown promise as an adjunct to chemotherapy. We hypothesized adding P-AscH- to gemcitabine and nab-paclitaxel would increase survival in patients with metastatic PDAC. Methods Patients diagnosed with stage IV pancreatic cancer randomized 1:1 to gemcitabine and nab-paclitaxel only (SOC, control) or to SOC with concomitant P-AscH−, 75 g three times weekly (ASC, investigational). The primary outcome was overall survival with secondary objectives of determining progression-free survival and adverse event incidence. Quality of life and patient reported outcomes for common oncologic symptoms were captured as an exploratory objective. Thirty-six participants were randomized; of this 34 received their assigned study treatment. All analyses were based on data frozen on December 11, 2023. Results Intravenous P-AscH- increased serum ascorbate levels from micromolar to millimolar levels. P-AscH- added to the gemcitabine + nab-paclitaxel (ASC) increased overall survival to 16 months compared to 8.3 months with gemcitabine + nab-paclitaxel (SOC) (HR = 0.46; 90 % CI 0.23, 0.92; p = 0.030). Median progression free survival was 6.2 (ASC) vs. 3.9 months (SOC) (HR = 0.43; 90 % CI 0.20, 0.92; p = 0.029). Adding P-AscH- did not negatively impact quality of life or increase the frequency or severity of adverse events. Conclusions P-AscH− infusions of 75 g three times weekly in patients with metastatic pancreatic cancer prolongs overall and progression free survival without detriment to quality of life or added toxicity Keywords: Pancreatic neoplasms; Ascorbic acid; Controlled clinical trial; Gemcitabine; Nab-paclitaxel
• Pressure-tuning of α-RuCl\($_\mathbf3$\) towards a quantum spin liquid. Stahl, Q.; Ritschel, T.; Garbarino, G.; Cova, F.; Isaeva, A.; Doert, T.; Geck, J. (2024). 15(1) 8142.

  The layered material \($\alpha$\)-RuCl3 is a promising candidate to realize the Kitaev quantum spin liquid (QSL) state. However, at ambient pressure, deviations from the perfect Kitaev geometry prevent the existence of the QSL state at low temperatures. Here we present the discovery of a pressure-induced high-symmetry phase in \($\alpha$\)-RuCl3, which creates close to ideal conditions for the emergence of a QSL. Employing a novel approach based on Bragg and diffuse scattering of synchrotron radiation, we reveal a pressure-induced reorganization of the RuCl3-layers. Most importantly, this reorganization affects the structure of the layers themselves, which acquire a high trigonal symmetry. For this trigonal phase the largest ratio between the Kitaev (K) and the Heisenberg exchange (J) ever encountered is found: K/J = 124. Additionally, we demonstrate that this phase can also be stabilized by a slight biaxial pressure. This not only resolves the conflicting reports of low-temperature structures in the literature, but also facilitates the investigation of the high-symmetry phase and its potential QSL using a range of experimental techniques.
• KINETIC CITIES: VIABILITY OF ADAPTABLE PNP CONTAINER MODULES FOR SMART LIVING. Spencer, Constance (C. Spencer, ed.) (2024). 11(1)

  The US is experiencing a housing crisis, especially for first time buyers, low income and baby boomers, as well as globally in areas hit by natural disasters due to changing weather systems. Contributing factors to this issue include the slow rebound of housing production from the last recession as well as the recent Covid pandemic market rush to the suburbs, which has resulted in climbing prices and low housing availability (Joint Center, 2019, Spencer, 2021). One possible solution for these consumers would be a container house, but until recently this tiny house option meant that the homeowner would most likely be skirting by-laws since it is often illegal to build small dwellings in many municipalities.
• Enhanced Weyl semimetal signature in Co\($_\mathbf3$\)Sn\($_\mathbf2$\)S\($_\mathbf2$\) Kagome ferromagnet by chlorine doping. He, Bin; Yao, Mengyu; Pan, Yu; Arpino, Kathryn E.; Chen, Dong; Serrano-Sanchez, Federico M.; Ju, Sailong; Shi, Ming; Sun, Yan; Felser, Claudia (2024). 5(1) 275.

  Weyl fermions are chiral massless fermions with exotic properties. In the first established magnetic Weyl semimetal, Co3Sn2S2, a giant anomalous Hall effect has been observed, while its Fermi energy remaining 60thinspacemeV from the Weyl points. Shifting the Fermi energy closer to the Weyl points may assist in the identification of Weyl Fermion related transport signatures. Here we show that effective chlorine doping has resulted in a shift of the Fermi energy by 15thinspacemeV towards the Weyl points, which is confirmed by a combination of the systematic angular-resolved photoemission spectroscopy measurements and density function theory calculations. A five-fold reduction in resistivity is observed in the ferromagnetic phase, accompanied by a pronounced magnetoresistance of over 150%. The anomalous Hall conductivity shows a peak of 1680 Scm−1 at 40thinspaceK, which is 30% higher than the undoped sample due to a stronger Weyl point contribution. This work demonstrates the essential role of doping in Co3Sn2S2 for an enhanced Weyl semimetal signature.
• Modelo de riesgo a incendios de pastizal-matorral y el aumento de la temperatura superficial post incendio 2021 en la Zona Metropolitana de la ciudad de Guanajuato. Ayala-Carillo, M; Farfán, M; Lemoine-Rodríguez, R J. Hernández Gutiérrez, A. Espinoza Maya (eds.) (2024). 235–266.
• Appearance of c-axis magnetic moment in odd-parity antiferromagnetic state in CeRh\($_\mathbf2$\)As\($_\mathbf2$\) revealed by \($^\mathbf75$\)As-NMR. Ogata, Shiki; Kitagawa, Shunsaku; Kinjo, Katsuki; Ishida, Kenji; Brando, Manuel; Hassinger, Elena; Geibel, Christoph; Khim, Seunghyun (2024). 110(21) 214509.
• Dive into Time-Series Anomaly Detection: A Decade Review. Boniol, Paul; Liu, Qinghua; Huang, Mingyi; Palpanas, Themis; Paparrizos, John (2024).
• Ternary Ni−Sb−Te nanoparticles by co-reduction in a one-pot microwave-assisted polyol process. Wang, Yiran; Carrillo-Aravena, Eduardo; Roslova, Maria; Doert, Thomas; Ruck, Michael (2024). 27(36) e202400525-.

  Abstract Utilizing the polyol process to produce ternary or multinary phases is a challenge due to the different redox potentials of the components. We succeeded in synthesizing compounds in the composition range Ni4?δ(SbxTe1?x)6 (?0.20≤δ≤0.36, 0.13≤x≤0.3) by heating a basic ethylene glycol solution containing nickel(II) acetate, antimony(III) acetate, and ammonium tellurate(VI) in a microwave at 280?°C for 25?min without adding auxiliary reducing agents. The products consist of thin nano-platelets, which can be transformed into larger crystals by annealing. Single crystal X-ray diffraction revealed a new structure type for the composition Ni4?δ(Sb0.25Te0.75)6 (x=0.25) in the space group P 3? \($\bar3$\) m1 with a=3.897(1)?Å and c=15.701(1)?Å. The triple superstructure of the NiAs type is due to a complex concentration profile of the Ni atoms in the octahedral voids. The trigonal TlCdS2 type is found for lower Sb contents (0≤x≤0.20), while for higher Sb contents (x≥0.28) the hexagonal Ni2SbTe2 type is adopted, which both are ordering variants of the NiAs type. Ni4?δ(Sb0.25Te0.75)6 is paramagnetic and exhibits poor metallic conductivity in the temperature range 6?K≤T≤300?K.
• Observation of circular dichroism induced by electronic chirality. Xiao, Qian; Janson, Oleg; Francoual, Sonia; Qiu, Qingzheng; Li, Qizhi; Zhang, Shilong; Xie, Wu; Bereciartua, Pablo; van den Brink, Jeroen; van Wezel, Jasper; Peng, Yingying (2024). 133(12) 126402.
• Integrated top-gate organic electrochemical transistors: A scalable approach for fast and efficient operation. Solgi, Ali; Weissbach, Anton; Asl Soleimani, Yahya; Yoon, Yeohoon; Krauss, Gert; Meier, Tommy; Tseng, Hsin; Thelakkat, Mukundan; Leo, Karl; Kleemann, Hans (2024). n/a(n/a) 2400656-.

  Abstract Organic electrochemical transistors (OECTs) are gaining attention for their ease of fabrication, flexibility, and biocompatibility, with applications in biosignal sensing, neuromorphic computing, wearable health monitors, environmental monitoring, and bioelectronic interfaces. The interactions between ionic and electronic subcircuits in OECTs raise fundamental questions about the relationship between device design and performance. A major challenge is to meet specific integration, processing, and device performance requirements. While miniaturization of OECTs can improve transconductance and maximum operating frequency, it often compromises cost effectiveness and integratability. This work investigates an OECT architecture that incorporates both a crosslinkable printed aqueous electrolyte and a printed poly(3,4-ethylenedioxythiophene):ploy(4-styrenesulfonate) (PEDOT:PSS) top-gate to achieve efficient gating, higher operating frequencies, and easy integration with low-cost printing techniques. Improved performance is demonstrated in this top-gate OECTs over conventional side-gate structures, achieving sub-millisecond device operation with channel lengths of 100 µm. This configuration shows practical potential for circuit integration, as demonstrated with a complementary inverter using an ambipolar material.
• Multiprobe analysis to separate edge currents from bulk currents in quantum spin Hall insulators and to analyze their temperature dependence. Benlemqwanssa, S.; Krishtopenko, S.S.; Meyer, M.; Benhamou-Bui, B.; Bonnet, L.; Wolf, A.; Bray, C.; Consejo, C.; Ruffenach, S.; Nanot, S.; Rodriguez, J.-B.; Tournié, E.; Hartmann, F.; Höfling, S.; Teppe, F.; Jouault, B. (2024). 22(6) 064059.
• Large topological Hall effect in a chiral antiferromagnet in hopping transport regime. Yi, Changjiang; Peshcherenko, Nikolai; Zhou, Yishui; Samanta, Kartik; Yang, Qun; Roychowdhury, Subhajit; Yanda, Premakumar; Borrmann, Horst; Vergniory, Maia G.; Zhang, Yang; Su, Yixi; Shekhar, Chandra; Felser, Claudia (2024). 6(4) 043295.
• Anisotropic magnetism and spin fluctuations in the triangular-lattice spin-liquid candidate NaYbSe\($_\mathbf2$\): A single-crystal \($^\mathbf23$\)Na and \($^\mathbf23$\)Se NMR study. Luther, S.; Ranjith, K. M.; Doert, Th.; Wosnitza, J.; Kühne, H.; Baenitz, M. (2024). 110(21) 214440.
• Impact of ion exchange on vibrational modes in Rb-doped KTiOPO\($_\mathbf4$\): A Raman spectroscopy study on the interplay between ion exchange and polarization switching. Lee, Cherrie S. J.; Canalias, Carlota; Buschbeck, Robin; Koppitz, Boris; Hempel, Franz; Amber, Zeeshan; Eng, Lukas M.; Rüsing, Michael (2024). 110(21) 214115.
• FaaSt: A Latency-aware Serverless Scheme for Edge–Cloud Environments. Nguyen, TrungKien; Dao, Hiep; Nguyen, Tung; Loh, Frank; Huu Thanh, Nguyen; Hoßfeld, Tobias (2024).
• The Illusory Goal of Automating Ontology Development -- with or without Large Language Models. Neuhaus, Fabian; Hastings, Janna I. Oliveira, P. P. F. Barcelos, R. Calhau, C. M. Fonseca, G. Righetti (eds.) (2024).
• CEAR: Creating a knowledge graph of chemical entities and roles in scientific literature. Langer, Stefan; Neuhaus, Fabian; Nürnberger, Andreas in CEUR Workshop Proceedings (2024).
• Single versus Combination Treatment in Tinnitus: A Randomized, Multicenter Trial. Schoisswohl, Stefan; Basso, Laura; Simoes, Jorge; Engelke, Milena; Langguth, Berthold; Mazurek, Birgit; Lopez-Escamez, Jose Antonio; Kikidis, Dimitrios; Cima, Rilana; Bernal-Robledano, Alberto; Boecking, Benjamin; Bulla, Jan; Cederroth, Christopher; Denys, Sam; Escalera-Balsera, Alba; Gallego-Martinez, Alvaro; Gallus, Silvano; Hidalgo-Lopez, Leire; Jarach, Carlotta; Kader, Hafez; Koller, Michael; Lugo, Alessandra; Marcrum, Steven; Markatos, Nikos; Martin-Lagos, Juan; Martinez-Martinez, Marta; Muller-Locatelli, Nicolas; Neff, Patrick; Niemann, Uli; Perez-Carpena, Patricia; Pryss, Ruediger; Puga, Clara; Robles-Bolivar, Paula; Rose, Matthias; Schecklmann, Martin; Schiele, Tabea; Schleicher, Miro; Schobel, Johannes; Spiliopoulou, Myra; Stark, Sabine; Staudinger, Susanne; Stege, Alexandra; Toedtli, Beat; Trochidis, Ilias; Unnikrishnan, Vishnu; Vassou, Evgenia; Verhaert, Nicolas; Vogel, Carsten; Zachou, Zoi; Schlee, Winfried (2024).
• High pressure induced superconductivity and chirality-neutral Fermi surface in SrS\($_\mathbf2$\). Yao, M.-Y.; Noky, J.; Mu, Q.-G.; Manna, K.; Kumar, N.; Strocov, V. N.; Shekhar, C.; Medvedev, S.; Sun, Y.; Felser, C. (2024). 110(22) 224514.
• Using the diagrammatic image schema language for joint human-machine cognition. Mossakowski, Till; Hedblom, Maria; Neuhaus, Fabian; Arevalo Arboleda, Stephanie; Raake, Alexander; Ilmenau Scientific Colloquium. Technische Universität Ilmenau ; 60 (Ilmenau) : 2023.09.04-08 (K.-U. Sattler, ed.) (2023).
• Embodied Provenance for Immersive Sensemaking. Zhang, Yidan; Ens, Barrett; Satriadi, Kadek Ananta; Yang, Ying; Goodwin, Sarah (2023). 7(ISS)

  Immersive analytics research has explored how embodied data representations and interactions can be used to engage users in sensemaking. Prior research has broadly overlooked the potential of immersive space for supporting analytic provenance, the understanding of sensemaking processes through users’ interaction histories. We propose the concept of embodied provenance, the use of three-dimensional space and embodied interactions in supporting recalling, reproducing, annotating and sharing analysis history in immersive environments. We design a conceptual framework for embodied provenance by highlighting a set of design criteria for analytic provenance drawn from prior work and identifying essential properties for embodied provenance. We develop a prototype system in virtual reality to demonstrate the concept and support the conceptual framework by providing multiple data views and embodied interaction metaphors in a large virtual space. We present a use case scenario of energy consumption analysis and evaluated the system through a qualitative evaluation with 17 participants, which show the system’s potential for assisting analytic provenance using embodiment. Our exploration of embodied provenance through this prototype provides lessons learnt to guide the design of immersive analytic tools for embodied provenance.
• Minimum Rectilinear Polygons for Given Angle Sequences. Evans, William S.; Fleszar, Krzysztof; Kindermann, Philipp; Saeedi, Noushin; Shin, Chan-Su; Wolff, Alexander (2022). 100(101820) 1–39.

  A rectilinear polygon is a simple polygon whose edges are axis-aligned. Walking counterclockwise on the boundary of such a polygon yields a sequence of left turns and right turns. The number of left turns always equals the number of right turns plus four. It is known that any such sequence can be realized by a rectilinear polygon. par In this paper, we consider the problem of finding realizations that minimize the perimeter or the area of the polygon or the area of the bounding box of the polygon. We show that all three problems are NP-hard in general. This answers an open question of Patrignani [CGTA 2001], who showed that it is NP-hard to minimize the area of the bounding box of an orthogonal drawing of a given planar graph. We also show that realizing a polyline within a bounding box of minimum area (or within a fixed given rectangle) is NP-hard. Then we consider the special cases of x-monotone and xy-monotone rectilinear polygons. For these, we can optimize the three objectives efficiently.
• JOB MATCHING USING ARTIFICIAL INTELLIGENCE. Kitichalermkiat, Sukit (S. Kitichalermkiat, ed.) (2022). 11(03) 12.
• RangeShifter 2.0: an extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species’ responses to environmental changes. Bocedi, Greta; Palmer, Stephen C. F.; Malchow, Anne-Kathleen; Zurell, Damaris; Watts, Kevin; Travis, Justin M. J. (2021). 44(10) 1453–1462.

  Process-based models are becoming increasingly used tools for understanding how species are likely to respond to environmental changes and to potential management options. RangeShifter is one such modelling platform, which has been used to address a range of questions including identifying effective reintroduction strategies, understanding patterns of range expansion and assessing population viability of species across complex landscapes. Here we introduce a new version, RangeShifter 2.0, which incorporates important new functionality. It is now possible to simulate dynamics over user-specified, temporally changing landscapes. Additionally, we integrated a new genetic module, notably introducing an explicit genetic modelling architecture, which allows for simulation of neutral and adaptive genetic processes. Furthermore, emigration, transfer and settlement traits can now all evolve, allowing for sophisticated simulation of the evolution of dispersal. We illustrate the potential application of RangeShifter 2.0's new functionality by two examples. The first illustrates the range expansion of a virtual species across a dynamically changing UK landscape. The second demonstrates how the software can be used to explore the concept of evolving connectivity in response to land-use modification, by examining how movement rules come under selection over landscapes of different structure and composition. RangeShifter 2.0 is built using object-oriented C++ providing computationally efficient simulation of complex individual-based, eco-evolutionary models. The code has been redeveloped to enable use across operating systems, including on high performance computing clusters, and the Windows graphical user interface has been enhanced. RangeShifter 2.0 will facilitate the development of in-silico assessments of how species will respond to environmental changes and to potential management options for conserving or controlling them. By making the code available open source, we hope to inspire further collaborations and extensions by the ecological community.
• Operational Intelligence Performance Inferencing Across Business Processes. Kaula, Rajeev (2020). 12(1) 01–21.

  Business process intelligence improves operational efficiency that is essential for achieving business objectives, besides facilitating competitive advantage. As an organization is a collection of business processes, operations in one business process do influence or have relationship with other business processes. Consequently, from an operational intelligence standpoint, insights from one business process may have their genesis or implications in the performance of some other business process. This paper outlines a framework to sequence insights in the form of performance inferencing across multiple business processes. The framework logically sequences insights across business processes in the form of business rules. The paper illustrates the concepts through a prototype that is implemented in Oracle’s PL/SQL language.
• QuantiNemo 2: a Swiss knife to simulate complex demographic and genetic scenarios, forward and backward in time. Neuenschwander, Samuel; Michaud, Frédéric; Goudet, Jérôme (2018). 35(5) 886–888.

  QuantiNemo 2 is a stochastic simulation program for quantitative population genetics. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits and neutral markers in structured populations connected by migration and located in heterogeneous habitats. A specific feature is that it allows to switch between an individual-based full-featured mode and a population-based faster mode. Several demographic, genetic and selective parameters can be fine-tuned in QuantiNemo 2: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography and mating system are the main features.QuantiNemo 2 is a C++ program with a source code available under the GNU General Public License version 3. Executables are provided for Windows, MacOS and Linux platforms, together with a comprehensive manual and tutorials illustrating its flexibility. The executable, manual and tutorial can be found on the website www2.unil.ch/popgen/softwares/quantinemo/, while the source code and user support are given through GitHub: github.com/jgx65/quantinemo.Supplementary data are available at Bioinformatics online.
• HexSim: a modeling environment for ecology and conservation. Schumaker, Nathan H.; Brookes, Allen (2018). 33(2) 197–211.

  Simulation models are increasingly used in both theoretical and applied studies to explore system responses to natural and anthropogenic forcing functions, develop defensible predictions of future conditions, challenge simplifying assumptions that facilitated past research, and to train students in scientific concepts and technology. Researcher's increased use of simulation models has created a demand for new platforms that balance performance, utility, and flexibility.
• Fractional pseudo-Newton method and its use in the solution of a nonlinear system that allows the construction of a hybrid solar receiver. Torres-Hernandez, A. (2017). (506) 291–359.
• CDMetaPOP: an individual-based, eco-evolutionary model for spatially explicit simulation of landscape demogenetics. Landguth, Erin L.; Bearlin, Andrew; Day, Casey C.; Dunham, Jason (2017). 8(1) 4–11.

  Summary Combining landscape demographic and genetics models offers powerful methods for addressing questions for eco-evolutionary applications. Using two illustrative examples, we present Cost–Distance Meta-POPulation, a program to simulate changes in neutral and/or selection-driven genotypes through time as a function of individual-based movement, complex spatial population dynamics, and multiple and changing landscape drivers. Cost–Distance Meta-POPulation provides a novel tool for questions in landscape genetics by incorporating population viability analysis, while linking directly to conservation applications.
• Minimum Rectilinear Polygons for Given Angle Sequences. Evans, William S.; Fleszar, Krzysztof; Kindermann, Philipp; Saeedi, Noushin; Shin, Chan-Su; Wolff, Alexander in Lecture Notes in Computer Science, J. Akiyama, H. Ito, T. Sakai (eds.) (2016). (Vol. 9943) 105–119.
• Probabilistic programming in Python using PyMC3. Salvatier, John; Wiecki, Thomas V.; Fonnesbeck, Christopher (2016). 2(e55)

  Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.
• A solution of the conservation law form of the Serre equations. Zoppou, C.; Roberts, S. G.; Pitt, J. (2016). 57(4) 385–394.

  The nonlinear and weakly dispersive Serre equations contain higher-order dispersive terms. These include mixed spatial and temporal derivative flux terms which are difficult to handle numerically. These terms can be replaced by an alternative combination of equivalent temporal and spatial terms, so that the Serre equations can be written in conservation law form. The water depth and new conserved quantities are evolved using a second-order finite-volume scheme. The remaining primitive variable, the depth-averaged horizontal velocity, is obtained by solving a second-order elliptic equation using simple finite differences. Using an analytical solution and simulating the dam-break problem, the proposed scheme is shown to be accurate, simple to implement and stable for a range of problems, including flows with steep gradients. It is only slightly more computationally expensive than solving the shallow water wave equations.
• Supporting Reasoning with Different Types of Evidence in Intelligence Analysis. Toniolo, Alice; Norman, Timothy J.; Etuk, Anthony; Cerutti, Federico; Ouyang, Robin Wentao; Srivastava, Mani; Oren, Nir; Dropps, Timothy; Allen, John A.; Sullivan, Paul in AAMAS ’15 (2015). 781–789.

  The aim of intelligence analysis is to make sense of information that is often conflicting or incomplete, and to weigh competing hypotheses that may explain a situation. This imposes a high cognitive load on analysts, and there are few automated tools to aid them in their task. In this paper, we present an agent-based tool to help analysts in acquiring, evaluating and interpreting information in collaboration with others. Agents assist analysts in reasoning with different types of evidence to identify what happened and why, what is credible, and how to obtain further evidence. Argumentation schemes lie at the heart of the tool, and sensemaking agents assist analysts in structuring evidence and identifying plausible hypotheses. A crowdsourcing agent is used to reason about structured information explicitly obtained from groups of contributors, and provenance is used to assess the credibility of hypotheses based on the origins of the supporting information.
• A Dictionary of Chemical Engineering Schaschke, Carl (2014). Oxford University Press, Oxford.

  This dictionary provides definitions and explanations for chemical engineering terms in areas including: materials, energy balances, reactions, separations, sustainability, safety, and ethics. Comprehensively cross-referenced and complemented by over 60 line drawings, this dictionary is the most authoritative of its kind. It also covers many pertinent terms from the fields of chemistry, physics, biology, and mathematics.
• Towards Creation of Sustainable Enclaves for Small and Medium-Size Enterprises in Kumasi, Ghana. Paul Amoateng, Patrick B. Cobbinah (2014). Vol:8(No:1) 34.

  Although the importance of small and medium-size enterprises (SMEs) to local development is globally recognized, less attention is given to their design, development and promotion particularly in developing countries. The main focus of this paper is to examine the process of designing, developing and promoting SMEs in developing countries. Results of a study conducted in a SMEs’ enclave in Kumasi (Ghana) are presented and discussed. Results show that although SMEs in developing countries remain a major source of livelihood for many individuals, their potential contribution to local development can be enhanced and sustained through the creation of common geographical enclaves for related SMEs. Findings indicated that the concentration of SMEs involved in wood processing in one location in Kumasi has reduced the cost of production (e.g., transportation), and resulted in marginal increase in sales for many SMEs, despite the widespread challenges of lack of access to credit and low promotion of products.
• SimAdapt: an individual-based genetic model for simulating landscape management impacts on populations. Rebaudo, François; Le Rouzic, Arnaud; Dupas, Stéphane; Silvain, Jean-François; Harry, Myriam; Dangles, Olivier (2013). 4(6) 595–600.

  Summary Simulation models are essential tools in landscape genetics to study how genetic processes are affected by landscape heterogeneity. However, there is still a need to develop different simulation approaches in landscape genetics, so that users may dispose of additional programs to explore further the impact of land-use and land-cover changes on population genetics. We developed a spatially explicit, individual-based, forward-time, landscape-genetic simulation model combined with a landscape cellular automaton to represent evolutionary processes of adaptation and population dynamics in changing landscapes, using the NetLogo environment. This simulation model represents a unique tool for scientists and scholars looking for a practical and pedagogical framework to explore both empirical and theoretical situations.
• cdpop: A spatially explicit cost distance population genetics program. Landguth, Erin L.; Cushman, S. A. (2010). 10(1) 156–161.

  Abstract Spatially explicit simulation of gene flow in complex landscapes is essential to explain observed population responses and provide a foundation for landscape genetics. To address this need, we wrote a spatially explicit, individual-based population genetics model (cdpop). The model implements individual-based population modelling with Mendelian inheritance and k-allele mutation on a resistant landscape. The model simulates changes in population and genotypes through time as functions of individual based movement, reproduction, mortality and dispersal on a continuous cost surface. This model will be a valuable tool for the study of landscape genetics by increasing our understanding about the effects of life history, vagility and differential models of landscape resistance on the genetic structure of populations in complex landscapes.
• Configurations with Few Crossings in Topological Graphs. Knauer, Christian; Schramm, Étienne; Spillner, Andreas; Wolff, Alexander in Lecture Notes in Computer Science, X. Deng, D.-Z. Du (eds.) (2005). (Vol. 3827) 604–613.

  In this paper we study the problem of computing subgraphs of a certain configuration in a given topological graph \($G$\) such that the number of crossings in the subgraph is minimum. The configurations that we consider are spanning trees, \($s$\)--\($t$\) paths, cycles, matchings, and \($\kappa$\)-factors for \($\kappa \in \1,2\$\). We show that it is NP-hard to approximate the minimum number of crossings for these configurations within a factor of \(k^1-\varepsilon\) for any \(\varepsilon > 0\), where \($k$\) is the number of crossings in \($G$\). We then show that the problems are fixed-parameter tractable if we use the number of crossings in the given graph as the parameter. Finally we present a simple but effective heuristic for spanning trees.
• Flow Through Fixed Beds. Yang, Wen-Ching W.-C. Yang (ed.) (2003). (First ) 29–52.
• Control Engineering : An Introductory Course Wilkie, Jacqueline; Johnson, Michael; Katebi, Reza (2002). (First ) Palgrave, Basingstoke, Hampshire.

  Control Engineering "An Introductory Course" is aimed at second or third year courses in Electrical and Mechanical Engineering, and provides for the needs of these courses without being over-burdened with detail. The authors work in one of the foremost centres in Europe for Control Engineering, and bring both teaching and practical consultancy experience to the text, which links theoretical approaches to actual case histories. Including an introduction to the software tools of MATLAB and SIMULINK, this book also includes simulations and examples throughout, and will give a straightforward and no-nonsense introduction to Control Engineering for students, and those wishing to refresh their knowledge.
• A Tutorial for Designing Flexible Geometric Algorithms. Kapoor, Vikas; Kühl, Dietmar; Wolff, Alexander (2002). 33(1) 52–70.

  The implementation of an algorithm is faced with the issues of efficiency, flexibility, and ease-of-use. In this paper we suggest a design concept that greatly increases the flexibility of an implementation without sacrificing ease-of-use and efficiency. par We demonstrate the advantages of our concept through a C++ implementation of a simple rectangle-intersection algorithm, which follows the well-known sweep-line paradigm. We lead the reader, who should be familiar with C++ and its template mechanism, from a naive interface in a step-by-step guide to an interface offering full flexibility. The gain in flexibility can reduce the overall implementation effort by facilitating code reuse. Reusability in turn helps to achieve correctness simply because more users mean more testing. par Though most of the ingredients of our concept have already been suggested elsewhere, to our knowledge this is the first time that they are applied vigorously in a geometric setting. We include a thorough experimental analysis on random and real-world data.
• A Simple Factor-2/3 Approximation Algorithm for Two-Circle Point Labeling. Wolff, Alexander; Thon, Michael; Xu, Yinfeng (2002). 12(4) 269–281.

  Given a set \($P$\) of \($n$\) points in the plane, the two-circle point-labeling problem consists of placing \($2n$\) uniform, non-intersecting, maximum-size open circles such that each point touches exactly two circles. par It is known that this problem is NP-hard to approximate. In this paper we give a simple algorithm that improves the best previously known approximation factor from \($4/(1+\sqrt33) \approx 0.5931$\) to 2/3. The main steps of our algorithm are as follows. We first compute the Voronoi diagram, then label each point emphoptimally within its cell, compute the smallest label diameter over all points and finally shrink all labels to this size. We keep the \($O(n \log n)$\) time and \($O(n)$\) space bounds of the previously best algorithm.
• Footprints: History-Rich Tools for Information Foraging. Wexelblat, Alan; Maes, Pattie in CHI ’99 (1999). 270–277.

  Inspired by Hill and Hollans original work [7], we have been developing a theory of interaction history and building tools to apply this theory to navigation in a complex information space. We have built a series of tools - map, paths, annota- tions and signposts - based on a physical-world navigation metaphor. These tools have been in use for over a year. Our user study involved a controlled browse task and showed that users were able to get the same amount of work done with significantly less effort.
• Fluidization Engineering Kunii Daizō; Levenspiel, Octave (1969). Wiley, New York.