Data4UrbanMobility hat an der Organisation des 5. PROFILES Workshop über Dataset Profiling und Search mitgewirkt. Dieser fand am 27. Oktober 2019 während der 18. International Semantic Web Conference in Auckland statt. Daten aus dem Web, insbesondere Linked Data, Wissensgraphen und Markup-Daten sind eine informationsreiche und dynamische Quelle für mobilitätsrelevante und veranstaltungs-orientierte Informationen. Der PROFILES Workshop hat das Ziel, Ansätze zur Analyse, Beschreibung und Entdeckung von Datenquellen in Web-Daten zu bündeln, damit diese in Mobilitätsanwendungen und anderen Domänen genutzt werden können.

• Potassium extrusion by plant cells: evolution from an emergency valve to a driver of long‐distance transport. Hmidi, Dorsaf; Muraya, Florence; Fizames, Cécile; Véry, Anne‐Aliénor; Roelfsema, M. Rob G. (2024). 245(1) 69–87.
• SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing. Guo, An; Zhou, Yuan; Tian, Haoxiang; Fang, Chunrong; Sun, Yunjian; Sun, Weisong; Gao, Xinyu; Luu, Anh Tuan; Liu, Yang; Chen, Zhenyu (2024). abs/2409.08081
• Learning Defendant-aware Label Representation for Multi-Defendant Charge Prediction. Chen, Yu; Zhang, You; Wang, Jin; Xu, Dan; Zhang, Xuejie (2024). 1–8.
• From Static to Dynamic: A Deeper, Faster, and Adaptive Language Modeling Approach. Li, Jiajia; Li, Qiwei; Wang, Ping (2024). 1–8.
• Multi-Object Tracking with Partial-Level Features and Adaptive Threshold Mechanism. Kong, Lingjie; Hu, Xiaopeng; Wang, Fan (2024). 1–8.
• VC-EKF for Graphical Nonlinear Systems. Guo, Simeng; Li, Wenling; Zhang, Bin; Liu, Yang (2024). 645–650.
• Fake Alignment: Are LLMs Really Aligned Well?. Wang, Yixu; Teng, Yan; Huang, Kexin; Lyu, Chengqi; Zhang, Songyang; Zhang, Wenwei; Ma, Xingjun; Jiang, Yu-Gang; Qiao, Yu; Wang, Yingchun K. Duh, H. Gómez-Adorno, S. Bethard (eds.) (2024). 4696–4712.
• Gaining or cutting SLAC: the evolution of plant guard cell signalling pathways. Sussmilch, Frances C.; Maierhofer, Tobias; Herrmann, Johannes; Voss, Lena J.; Lind, Christof; Messerer, Maxim; Müller, Heike M.; Bünner, Maria S.; Ache, Peter; Mayer, Klaus F. X.; Becker, Dirk; Roelfsema, M. Rob G.; Geiger, Dietmar; Schultz, Jörg; Hedrich, Rainer (2024). 244(6) 2295–2310.
• Multi-modal fusion for business process prediction in call center scenarios. Cheng, Long; Du, Li; Liu, Cong; Hu, Yang; Fang, Fang; Ward, Tomás (2024). 108 102362.
• EEA-Net: edge-enhanced assistance network for infrared small target detection. Wang, Chen; Hu, Xiaopeng; Gao, Xiang; Wei, Haoyu; Tao, Jiawei; Wang, Fan (2024). 35(4) 70.
• Lightweight image super-resolution via multi-branch aware CNN and efficient transformer. Gao, Xiang; Wu, Sining; Zhou, Ying; Wu, Xinrong; Wang, Fan; Hu, Xiaopeng (2024). 36(10) 5285–5303.
• Sublinear Distributed Product Checks on Replicated Secret-Shared Data over ℤ2k without Ring Extensions. Li, Yun; Escudero, Daniel; Duan, Yufei; Huang, Zhicong; Hong, Cheng; Zhang, Chao; Song, Yifan (2024). 2024 700.
• LCFormer: linear complexity transformer for efficient image super-resolution. Gao, Xiang; Wu, Sining; Zhou, Ying; Wang, Fan; Hu, Xiaopeng (2024). 30(4) 232.
• Advancing TSN flow scheduling: An efficient framework without flow isolation constraint. Feng, Xingbo; Wang, Yi; Lin, Jiashuo; Li, Weichao; Zhan, Shuangping; Liu, Yan; Zhang, Jin; Wang, Jianping (2024). 252 110688.
• MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast. Hu, Shiyan; Zhao, Kai; Qiu, Xiangfei; Shu, Yang; Hu, Jilin; Yang, Bin; Guo, Chenjuan (2024). abs/2410.15997
• InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding. Liu, Haogeng; You, Quanzeng; Han, Xiaotian; Wang, Yiqi; Zhai, Bohan; Liu, Yongfei; Tao, Yunzhe; Huang, Huaibo; He, Ran; Yang, Hongxia (2024). abs/2403.01487
• RobustTSN: A Framework for Protecting Time-Sensitive Networking against Unexpected Delays. Feng, Xingbo; Wang, Yi; Lin, Jiashuo; Li, Weichao; Zhan, Shuangping; Liu, Yan; Zhang, Jin; Wang, Jianping (2024). 1–6.
• UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation. Sun, Ye; Zhang, Hao; Zhang, Tiehua; Ma, Xingjun; Jiang, Yu-Gang (2024). abs/2410.09909
• Pre-trained Model-based Actionable Warning Identification: A Feasibility Study. Ge, Xiuting; Fang, Chunrong; Zhang, Quanjun; Wu, Daoyuan; Yu, Bowen; Zheng, Qirui; Guo, An; Lin, Shangwei; Zhao, Zhihong; Liu, Yang; Chen, Zhenyu (2024). abs/2403.02716
• ROSE: Register Assisted General Time Series Forecasting with Decomposed Frequency Learning. Wang, Yihang; Qiu, Yuying; Chen, Peng; Zhao, Kai; Shu, Yang; Rao, Zhongwen; Pan, Lujia; Yang, Bin; Guo, Chenjuan (2024). abs/2405.17478
• Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning. Wang, Yiqi; Chen, Wentao; Han, Xiaotian; Lin, Xudong; Zhao, Haiteng; Liu, Yongfei; Zhai, Bohan; Yuan, Jianbo; You, Quanzeng; Yang, Hongxia (2024). abs/2401.06805
• Movable Antenna Empowered Physical Layer Security Without Eve’s CSI: Joint Optimization of Beamforming and Antenna Positions. Feng, Zhiyong; Zhao, Yujia; Yu, Kan; Li, Dong (2024). abs/2405.16062
• CityLight: A Universal Model Towards Real-world City-scale Traffic Signal Control Coordination. Zeng, Jinwei; Yu, Chao; Yang, Xinyi; Ao, Wenxuan; Yuan, Jian; Li, Yong; Wang, Yu; Yang, Huazhong (2024). abs/2406.02126
• InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning. Han, Xiaotian; Jian, Yiren; Hu, Xuefeng; Liu, Haogeng; Wang, Yiqi; Fan, Qihang; Ai, Yuang; Huang, Huaibo; He, Ran; Yang, Zhenheng; You, Quanzeng (2024). abs/2409.12568
• FedL2G: Learning to Guide Local Training in Heterogeneous Federated Learning. Zhang, Jianqing; Liu, Yang; Hua, Yang; Cao, Jian; Yang, Qiang (2024). abs/2410.06490
• HortiBot: An Adaptive Multi-Arm System for Robotic Horticulture of Sweet Peppers. Lenz, Christian; Menon, Rohit U.; Schreiber, Michael; Jacob, Melvin Paul; Behnke, Sven; Bennewitz, Maren (2024). abs/2403.15306
• Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learning. Zhao, Kai; Zhuang, Zhihao; Guo, Chenjuan; Miao, Hao; Cheng, Yunyao; Yang, Bin (2024). abs/2410.16888
• CooTest: An Automated Testing Approach for V2X Communication Systems. Guo, An; Gao, Xinyu; Chen, Zhenyu; Xiao, Yuan; Liu, Jiakai; Ge, Xiuting; Sun, Weisong; Fang, Chunrong (2024). abs/2408.16470
• LightTR: A Lightweight Framework for Federated Trajectory Recovery. Liu, Ziqiao; Miao, Hao; Zhao, Yan; Liu, Chenxi; Zheng, Kai; Li, Huan (2024). abs/2405.03409
• Eliminating Backdoors in Neural Code Models via Trigger Inversion. Sun, Weisong; Chen, Yuchen; Fang, Chunrong; Feng, Yebo; Xiao, Yuan; Guo, An; Zhang, Quanjun; Liu, Yang; Xu, Baowen; Chen, Zhenyu (2024). abs/2408.04683
• Learning Energy-Based Models for 3D Human Pose Estimation. Zhu, Xianglu; Zhang, Zhang; Wang, Wei; Wang, Zilei; Wang, Liang (2024). 1–8.
• Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning. Kreis, Benedikt; Dengler, Nils; de Heuvel, Jorge; Menon, Rohit U.; Perur, Hamsa Datta; Bennewitz, Maren (2024). 698–705.
• Stochastic Online Correlated Selection. Chen, Ziyun; Huang, Zhiyi; Sun, Enze (2024). abs/2408.12524
• Decreased northern hemisphere precipitation from consecutive CO2 doublings is associated with significant AMOC weakening. Zhang, X; Waugh, D W; Mitevski, I; Orbe, C; Polvani, L M (2024). 3(4) 041005.

  Previous studies found many climate properties such as northern hemisphere (NH) surface temperature and precipitation respond non-monotonically when CO2 is increased from 1× to 8×CO2 relative to pre-industrial levels. Here, we explore the robustness of the non-monotonicity in the NH precipitation response in 11 coupled climate models. Eight models show a decrease in NH precipitation under repeated CO2 doubling, indicating that the non-monotonic response is a common but not universal result. Although common, the critical CO2 level where the NH precipitation decrease first occurs differs widely across models, ranging from 2×CO2 to 8×CO2. These models also show a prominent weakening in the Atlantic meridional overturning circulation (AMOC) at the same critical CO2 level, with the AMOC weakening leading the precipitation decrease. The sensitivities of NH precipitation and the AMOC to CO2 doublings are positively correlated, especially when the AMOC weakens beyond 10 Sv. This suggests that the differences in models’ AMOC response can explain their contrasting NH precipitation responses, where models with a large AMOC weakening have decreased NH precipitation. Regionally, this decrease in NH precipitation is the most prominent over the North Atlantic, Europe and the tropical Pacific. Our results suggest that special care must be taken with the use of pattern scaling to inform regional climate decision-making.
• Live Demonstration for Input-Sparsity-Aware RRAM Processing-in-Memory Chip. Wang, Junjie; Liu, Shuang; Pan, Ruicheng; Yan, Shiqin; Liu, Yihe; Liu, Yang (2024). 1–2.
• LightTR: A Lightweight Framework for Federated Trajectory Recovery. Liu, Ziqiao; Miao, Hao; Zhao, Yan; Liu, Chenxi; Zheng, Kai; Li, Huan (2024). 4422–4434.
• Adversarial Prompt Tuning for Vision-Language Models. Zhang, Jiaming; Ma, Xingjun; Wang, Xin; Qiu, Lingyu; Wang, Jiaqi; Jiang, Yu-Gang; Sang, Jitao in Lecture Notes in Computer Science, A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, G. Varol (eds.) (2024). (Vol. 15103) 56–72.
• Designing Social Robots for Learning: HRI Participatory Design Workshop. Baksh, Farnaz; Zorec, Matevz Borjan; Kruusamäe, Karl in Lecture Notes in Computer Science, Y.-P. Cheng, M. Pedaste, E. Bardone, Y.-M. Huang (eds.) (2024). (Vol. 14786) 215–223.
• Non-Local Spatial-Wise and Global Channel-Wise Transformer for Efficient Image Super-Resolution. Gao, Xiang; Wu, Sining; Wang, Fan; Hu, Xiaopeng in Lecture Notes in Computer Science, S. Rudinac, A. Hanjalic, C. C. S. Liem, M. Worring, B. Þór Jónsson, B. Liu, Y. Yamakata (eds.) (2024). (Vol. 14556) 70–85.
• The Emotion Dynamics of Literary Novels. Vishnubhotla, Krishnapriya; Hammond, Adam; Hirst, Graeme; Mohammad, Saif M. (2024).
• Differentiable Neural Architecture Search Based on Efficient Architecture for Lightweight Image Super-Resolution. Sheng, Chunyin; Gao, Xiang; Hu, Xiaopeng; Wang, Fan in Lecture Notes in Computer Science, S. Rudinac, A. Hanjalic, C. C. S. Liem, M. Worring, B. Þór Jónsson, B. Liu, Y. Yamakata (eds.) (2024). (Vol. 14556) 169–183.
• South Atlantic meridional overturning circulation and its respective heat and freshwater transports from sustained observations near 34.5°S. Pita, Ivenis; Goes, Marlos; Volkov, Denis L.; Dong, Shenfu; Schmid, Claudia (2024). 11
• 129I and 236U distribution in the subpolar North Atlantic unravels water mass provenance in AR7W and A25 lines. Leist, Lisa G. T.; Castrillejo, Maxi; Smith, John Norton; Christl, Marcus; Vockenhuber, Christof; Velo, Antón; Lherminier, Pascale; Casacuberta, Núria (2024). 11

  The subpolar North Atlantic (SPNA) is crucial in the global ocean circulation system and one of the few regions where deep convection occurs. The intermediate and deep waters formed in the SPNA have long been investigated, yet their sources and pathways are not fully understood. In this study, we employ a combination of two radionuclide tracers, namely, ¹²⁹I and ²³⁶U, to understand water mass provenance and mixing in the SPNA. The concentrations measured between Portugal and Greenland and across the Labrador Sea in 2020/2021 agreed with previously observed tracer distributions. The highest tracer concentrations were measured in the East Greenland Current (EGC), Denmark Strait Overflow Water (DSOW), and, to a lesser extent, in the eastward-flowing Labrador Sea Water (LSW). In contrast, waters of southern origin such as the North East Antarctic Bottom Water and North East Atlantic Central Water (ENACW) carried comparably smaller amounts of ¹²⁹I. By using a binary mixing model, we estimated that the EGC contains about 29%–32% of the Polar Surface Water outflowing the Fram Strait. DSOW was mainly derived from 20% to 35% Return Atlantic Water and mixed with LSW. The Iceland Scotland Overflow Water (ISOW) evolved into North East Atlantic Deep Water in the Irminger and Labrador seas primarily by mixing with LSW and, to a lesser extent, with DSOW. The ¹²⁹I and ²³⁶U binary mixing approach was less conclusive for LSW, reaching the current limitation of the model. This study suggests potential benefits and limitations of using ¹²⁹I and ²³⁶U to investigate the mixing and provenance of water masses in the SPNA.

• AdapCC: Making Collective Communication in Distributed Machine Learning Adaptive. Zhao, Xiaoyang; Zhang, Zhe; Wu, Chuan (2024). 25–35.
• Domain-Driven LLM Development: Insights into RAG and Fine-Tuning Practices. dos Santos Junior, José Cassio; Hu, Rachel; Song, Richard; Bai, Yun-Fei R. Baeza-Yates, F. Bonchi (eds.) (2024). 6416–6417.
• Profile: Rob Roelfsema. Roelfsema, MRG (2024). 244(3) 767–768.
• Guard cells count the number of unitary cytosolic Ca2+ signals to regulate stomatal dynamics. Huang, Shouguang; Roelfsema, M. Rob G.; Gilliham, Matthew; Hetherington, Alistair M.; Hedrich, Rainer (2024). 34(23) 5409–5416.e2.
• InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model. Liu, Haogeng; You, Quanzeng; Wang, Yiqi; Han, Xiaotian; Zhai, Bohan; Liu, Yongfei; Chen, Wentao; Jian, Yiren; Tao, Yunzhe; Yuan, Jianbo; He, Ran; Yang, Hongxia L.-W. Ku, A. Martins, V. Srikumar (eds.) (2024). 485–492.
• Radikaler Universalismus: jenseits von Identität Boehm, Omri (2023). Ullstein, Berlin.

  Ist der Universalismus heute noch zu retten? Ja, aber wir müssen zurück zu seinem Ursprung: Erst wenn wir den humanistischen Appell der biblischen Propheten und Immanuel Kants wirklich verstehen, können wir Ungerechtigkeit kompromisslos bekämpfen – im Namen des radikalen Universalismus, nicht in dem der Identität. Mit „Radikaler Universalismus“ liefert Omri Boehm mehr als eine Neuinterpretation, er revolutioniert unser grundlegendes Verständnis von dem, was Universalismus eigentlich ist. Dabei beruft er sich auf Kant und seine oft missverstandene Wiederbelebung des ethischen Monotheismus der jüdischen Propheten. Ein kühner Entwurf, der in seiner Furchtlosigkeit einen Ausweg aus der festgefahrenen Identitätsdebatte eröffnet.
• Fake Alignment: Are LLMs Really Aligned Well?. Wang, Yixu; Teng, Yan; Huang, Kexin; Lyu, Chengqi; Zhang, Songyang; Zhang, Wenwei; Ma, Xingjun; Jiang, Yu-Gang; Qiao, Yu; Wang, Yingchun (2023). abs/2311.05915
• Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?. Sun, Weisong; Fang, Chunrong; Miao, Yun; You, Yudu; Yuan, Mengzhe; Chen, Yuchen; Zhang, Quanjun; Guo, An; Chen, Xiang; Liu, Yang; Chen, Zhenyu (2023). abs/2312.00413
• Adversarial Prompt Tuning for Vision-Language Models. Zhang, Jiaming; Ma, Xingjun; Wang, Xin; Qiu, Lingyu; Wang, Jiaqi; Jiang, Yu-Gang; Sang, Jitao (2023). abs/2311.11261
• Multi-branch aware module with channel shuffle pixel-wise attention for lightweight image super-resolution. Gao, Xiang; Xu, Lijuan; Wang, Fan; Hu, Xiaopeng (2023). 29(1) 289–303.
• Graph-based View Motion Planning for Fruit Detection. Zaenker, Tobias; Rückin, Julius; Menon, Rohit U.; Popovic, Marija; Bennewitz, Maren (2023). abs/2303.03048
• Secure Ultra-reliable and Low Latency Communication in NOMA-UAV Networks. Zhao, Xiao; Yu, Kan; Li, Dong; Liu, Xiaowu; Luo, Chuanwen (2023). 127–134.
• Viewpoint Push Planning for Mapping of Unknown Confined Spaces. Dengler, Nils; Pan, Sicong; Kalagaturu, Vamsi; Menon, Rohit U.; Dawood, Murad; Bennewitz, Maren (2023). 1178–1184.
• Graph-Based View Motion Planning for Fruit Detection. Zaenker, Tobias; Rückin, Julius; Menon, Rohit U.; Popovic, Marija; Bennewitz, Maren (2023). 4219–4225.
• An Evaluation of a Hybrid STEM-Robotics Programme for Students with Special Needs in Guyana. DeFreitas, Penelope; Baksh, Farnaz; Zorec, Matevz Borjan; Todd, Joyann; Vanlewin, Obena in Communications in Computer and Information Science, C. Stephanidis, M. Antona, S. Ntoa, G. Salvendy (eds.) (2023). (Vol. 1833) 262–269.
• Cross-Level Network Security Element Fusion Extraction Method Based on Deep Learning. Zhao, Jianping; Wang, Huan; Wang, Yichuan; Li, Hua; Wang, Yan (2023). 247–254.
• Viewpoint Push Planning for Mapping of Unknown Confined Spaces. Dengler, Nils; Pan, Sicong; Kalagaturu, Vamsi; Menon, Rohit U.; Dawood, Murad; Bennewitz, Maren (2023). abs/2303.03126
• DawnIK: Decentralized Collision-Aware Inverse Kinematics Solver for Heterogeneous Multi-Arm Systems. Marangoz, Salih; Menon, Rohit U.; Dengler, Nils; Bennewitz, Maren (2023). 1–8.
• NBV-SC: Next Best View Planning Based on Shape Completion for Fruit Mapping and Reconstruction. Menon, Rohit U.; Zaenker, Tobias; Dengler, Nils; Bennewitz, Maren (2023). 4197–4203.
• Proposal for a Maturity Continuum Model for Open Research Data. Guirlet, Marielle; Bongi, Gaia; Point, Elise; Urvoy, Grégoire; Schneider, René Martin (2022). 17(1) 6.
• Deep learning-based Crop Row Following for Infield Navigation of Agri-Robots. de Silva, Rajitha; Cielniak, Grzegorz; Wang, Gang; Gao, Junfeng (2022). abs/2209.04278
• Fruit Mapping with Shape Completion for Autonomous Crop Monitoring. Marangoz, Salih; Zaenker, Tobias; Menon, Rohit U.; Bennewitz, Maren (2022). 471–476.
• Energy Alignment for Bias Rectification in Class Incremental Learning. Zhao, Bowen; Chen, Chen; Xiao, Xi; Ju, Qi; Xia, Shutao (2022). 3513–3517.
• Viewpoint Planning based on Shape Completion for Fruit Mapping and Reconstruction. Menon, Rohit U.; Zaenker, Tobias; Bennewitz, Maren (2022). abs/2209.15376
• Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection. Wang, Junke; Li, Zhenxin; Zhang, Chao; Chen, Jingjing; Wu, Zuxuan; Davis, Larry S.; Jiang, Yu-Gang (2022). abs/2212.05667
• A Pattern Discovery Approach to Multivariate Time Series Forecasting. Cheng, Yunyao; Guo, Chenjuan; Chen, Kaixuan; Zhao, Kai; Yang, Bin; Xie, Jiandong; Jensen, Christian S.; Huang, Feiteng; Zheng, Kai (2022). abs/2212.10306
• Fruit Mapping with Shape Completion for Autonomous Crop Monitoring. Marangoz, Salih; Zaenker, Tobias; Menon, Rohit U.; Bennewitz, Maren (2022). abs/2203.15489
• Large-scale Machine Learning Cluster Scheduling via Multi-agent Graph Reinforcement Learning. Zhao, Xiaoyang; Wu, Chuan (2021). abs/2112.13354
• Energy Aligning for Biased Models. Zhao, Bowen; Chen, Chen; Ju, Qi; Xia, Shutao (2021). abs/2106.03343
• An Efficiency Optimization Control Method With Fast Dynamic Response For Muti-Phase Interleaved BUCK Converter. Zhang, Peng; Liu, Yang; Zhao, Jin; Zhang, Hanshu; Lv, Qiaoqiang (2021). 1–5.
• Perception, Awareness, Presentation: Using Meditation Techniques to Teach Data Visualisation Literacy - A Case Study. Schneider, René Martin in Communications in Computer and Information Science, S. Kurbanoglu, S. Spiranec, Y. Ünal, J. Boustany, D. Kos (eds.) (2021). (Vol. 1533) 99–108.
• On the Generation of Disassembly Ground Truth and the Evaluation of Disassemblers. Li, Kaiyuan; Woo, Maverick; Jia, Limin (2020). abs/2012.09155
• Privacy-Preserving Text Similarity via Non-Prefix-Free Codes. Külekci, M. Oguzhan; Habib, Ismail; Aghabiglou, Amir in Lecture Notes in Computer Science, G. Amato, C. Gennaro, V. Oria, M. Radovanovic (eds.) (2019). (Vol. 11807) 94–102.
• Deep Semi-Supervised Anomaly Detection. Ruff, Lukas; Vandermeulen, Robert A.; Görnitz, Nico; Binder, Alexander; Müller, Emmanuel; Müller, Klaus-Robert; Kloft, Marius (2019). abs/1906.02694
• Improving Base Station Anonymity using Data Funneling and PID Controlled Fake Packets. Aliyu, Farouq; Umar, Sani; Al-Kharoubi, Talal M.; Baroudi, Uthman (2019). 1–6.
• CAP: Context-aware App Usage Prediction with Heterogeneous Graph Embedding. Chen, Xinlei; Wang, Yu; He, Jiayou; Pan, Shijia; Li, Yong; Zhang, Pei (2019). 3(1) 4:1–4:25.
• Towards New Approach to Enhance Learning Based on Internet of Things and Virtual Reality. Fahim, Mohamed; Jakimi, Abdeslam; Lahcen, El Bermi S. Amzazi, A. E. Afia, M. Essaaidi (eds.) (2018). 54:1–54:5.
• Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. Schlegl, Thomas; Seeböck, Philipp; Waldstein, Sebastian M.; Schmidt-Erfurth, Ursula; Langs, Georg (2017). abs/1703.05921
• Investigating the Relationship between Literary Genres and Emotional Plot Development. Kim, Evgeny; Padó, Sebastian; Klinger, Roman B. Alex, S. Degaetano-Ortlieb, A. Feldman, A. Kazantseva, N. Reiter, S. Szpakowicz (eds.) (2017). 17–26.

  Literary genres are commonly viewed as being defined in terms of content and stylistic features. In this paper, we focus on one particular class of lexical features, namely emotion information, and investigate the hypothesis that emotion-related information correlates with particular genres. Using genre classification as a testbed, we compare a model that computes lexicon-based emotion scores globally for complete stories with a model that tracks emotion arcs through stories on a subset of Project Gutenberg with five genres. Our main findings are: (a), the global emotion model is competitive with a large-vocabulary bag-of-words genre classifier (80\%F1); (b), the emotion arc model shows a lower performance (59 \% F1) but shows complementary behavior to the global model, as indicated by a very good performance of an oracle model (94 \% F1) and an improved performance of an ensemble model (84 \% F1); (c), genres differ in the extent to which stories follow the same emotional arcs, with particularly uniform behavior for anger (mystery) and fear (adventures, romance, humor, science fiction).
• A Unified Approach to Interpreting Model Predictions. Lundberg, Scott M; Lee, Su-In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (eds.) (2017). (Vol. 30)
• A Framework for Estimating Long Term Driver Behavior. Gadepally, Vijay; Krishnamurthy, Ashok K. (2016). abs/1607.03189
• Graph Fourier Transform based on Directed Laplacian. Singh, Rahul; Chakraborty, Abhishek; Manoj, B. S. (2016). abs/1601.03204
• Distributed spectrum trading via dynamic matching with evolving preferences. Wang, Jingyi; Long, Yan; Wang, Jie; Errapotu, Sai Mounika; Guo, Yuanxiong; Pan, Miao (2016). 1–6.
• On spectral analysis of node centralities. Singh, Rahul; Chakraborty, Abhishek; Manoj, B. S. (2016). 1–5.
• Parallel MATLAB Techniques. Krishnamurthy, Ashok K.; Samsi, Siddharth; Gadepally, Vijay (2014). abs/1407.2636
• A Hands-on Education Program on Cyber Physical Systems for High School Students. Gadepally, Vijay; Krishnamurthy, Ashok K.; Özgüner, Ümit (2014). abs/1408.0521
• A Framework for Estimating Driver Decisions Near Intersections. Gadepally, Vijay; Krishnamurthy, Ashok K.; Özgüner, Ümit (2014). 15(2) 637–646.
• Sentiment Analysis and Opinion Mining Liu, Bing (2012). (Vol. 5) 1–167. Morgan & Claypool Publishers \($\lbrace$\)LLC\($\rbrace$\).
• An efficient computational framework for the analysis of whole slide images: Application to follicular lymphoma immunohistochemistry. Samsi, Siddharth; Krishnamurthy, Ashok K.; Gurcan, Metin N. (2012). 3(5) 269–279.
• Comparative Performance Study for Integrated 3G/WLAN Networks Using Mobile IP, SIP, and m-SCTP Protocols. Mahmoud, Ashraf S.; Al-Helali, Abdul-Aziz; Abu-Amara, Marwan H.; Al-Kharoubi, Talal M.; Sheltami, Tarek R. (2010). 1–5.
• Analysis of Handoff Delay Components for Mobile IP-Based 3GPP UMTS/WLAN Interworking Architecture. Al-Helali, Abdul-Aziz; Mahmoud, Ashraf S. Hasan; Al-Kharoubi, Talal M.; Sheltami, Tarek R. (2009). 798–803.
• Packet Scheduling Algorithm with QoS Provision in HSDPA. Wang, Ying-Hong; Huang, Kuo-Feng; Le, Chengyi I. Awan, M. Younas, T. Hara, A. Durresi (eds.) (2009). 616–623.
• Characterization of Vertical Handoff Delay for Mobile IP Based 3G/WLAN Integrated Networks. Al-Helali, Abdul-Aziz; Mahmoud, Ashraf S.; Al-Kharoubi, Talal M.; Sheltami, Tarek R. (2009).
• Experiences from Cyberinfrastructure Development for Multi-user Remote Instrumentation. Calyam, Prasad; Kalash, Abdul; Ludban, Neil; Gopalan, Sowmya; Samsi, Siddharth; Tomko, Karen A.; Hudak, David E.; Krishnamurthy, Ashok K. (2008). 293–300.
• A Novel Dual-Mode User Equipment Design and Enhanced Gateway Selection Algorithm for B3G Networks. Al-Helali, Abdul-Aziz; Mahmoud, Ashraf S. Hasan; Al-Kharoubi, Talal M.; Sheltami, Tarek R. W. chi Feng, Y. Yang, G. Agrawal (eds.) (2008). 162–166.
• Survey of Parallel MATLAB Techniques and Applications to Signal and Image Processing. Krishnamurthy, Ashok K.; Nehrbass, John; Chaves, Juan Carlos; Samsi, Siddharth (2007). 1181–1184.
• Der nonkonformistische Intellektuelle: die Entwicklung der kritischen Theorie zur Frankfurter Schule Demirović, Alex in Suhrkamp-Taschenbuch Wissenschaft (1999). Suhrkamp, Frankfurt am Main.

  Vernunft und Wahrheit sind kein allein der Philosophie vorbehaltenes Thema, sondern stellen im spezifischen Sinn eine politische Praxis dar: Dies nötigt zu einer gesellschaftstheoretischen Erweiterung des Gegenstandsbereichs der politischen Theorie und der Ideengeschichte. Eine solche Überlegung verdankt den Arbeiten Michel Foucaults ebensoviel wie den älteren Vertretern der Kritischen Theorie, die Vernunft als materielles Verhältnis verstanden wissen wollten. Die Kritische Theorie ist in der vorliegenden Untersuchung Gegenstand einer so verstandenen politischen Archäologie theoretischer Praxis.