Data4UrbanMobility nimmt an der Ausstellung „Marktplatz der Möglichkeiten“ bei dem Leibniz Startup & Industry Event “Künstliche Intelligenz” teil. Die Veranstaltung findet am 20.11.2018 im Lichthof der Leibniz Universität Hannover statt und wird vom Forschungszentrum L3S organisiert.

• Fall Advances in the Timing of Molt in Birds in the Southwestern United States. Kittelberger, Kyle D.; Neate-Clegg, Montague H. C.; cSekerciouglu, cCaugan Hakki (2026). 000–000.

  AbstractMolt is a critical event in the annual cycle of birds. Although we know an increasing amount about the impacts of climate change on the timing of other avian events, there has been relatively limited work conducted on changes in molt phenology over time. In this study, we utilized a 13-year bird-banding dataset from southeastern Utah to examine long-term trends in the molt timing of body and flight feathers during both the spring and the fall migratory seasons, accounting for temporal trends in nonmolting birds and how trends may vary between different sexes and ages of birds. We found that there were no significant temporal trends in molt timing in the spring but there were significant trends in the fall, such that birds were advancing the timing of their body and flight feather molt over time. Finally, we highlight the significant influence of climate on molt phenology: El Niño/Southern Oscillation and maximum temperature were both associated with advances in spring body molt, maximum temperature was associated with delays in fall flight feather molt timing, and precipitation was associated with advances in both fall body and flight feather molt timing. This study provides the first examination of long-term trends in the molt phenology of North American birds, showing that over the past decade, birds in the western United States have advanced their feather molt timing in the fall at a rate of roughly one day/year.
• Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria. Dhillon, Maninder Singh; Koellner, Thomas; Asam, Sarah; Bogenreuther, Jakob; Dech, Stefan; Gessner, Ursula; Gruschwitz, Daniel; Annuth, Sylvia Helena; Kraus, Tanja; Rummler, Thomas; Schaefer, Christian; Schönbrodt-Stitt, Sarah; Steffan-Dewenter, Ingolf; Wilde, Martina; Ullmann, Tobias (2025).
• How HR Consultancy Services Can Transform Your Business in 2025 Mandal, Chakrika (C. Mandal, ed.) (2025). 128–132.
• PaCA: Partial Connection Adaptation for Efficient Fine-Tuning. Woo, Sunghyeon; Namkung, Sol; Lee, Sunwoo; Jeong, Inho; Kim, Beomseok; Jeon, Dongsuk (2025).
• Preference-Oriented Supervised Fine-Tuning: Favoring Target Model over Aligned Large Language Models. Fan, Yuchen; Hong, Yuzhong; Wang, Qiushi; Bao, Junwei; Jiang, Hongfei; Song, Yang T. Walsh, J. Shah, Z. Kolter (eds.) (2025). 23859–23867.
• Scaling the Vocabulary of Non-autoregressive Models for Fast Generative Retrieval. Valluri, Ravisri; Mohankumar, Akash Kumar; Dave, Kushal; Singh, Amit; Jiao, Jian; Varma, Manik; Sinha, Gaurav Y. Sun, F. Chierichetti, H. W. Lauw, C. Perlich, W. H. Tok, A. Tomkins (eds.) (2025). 1409–1420.
• 9.4 A 102ns/V 94.3%-Peak-Efficiency Symbol-Power-Tracking Supply Modulator for 5G NR Power Amplifiers. Shang, Minghao; Wang, Baochuang; Chen, Changjin; Jin, Ji; Cheng, Lin (2025). 1–3.
• Fast Interpreter-Based Instruction Set Simulation for Virtual Prototypes. Schlägl, Manfred; Große, Daniel (2025). 1–7.
• Identifying Magnetotail Jet Front Signatures in a 3D + 3V Global Hybrid‐Vlasov Simulation. Pänkäläinen, Lauri; Cozzani, Giulia; Battarbee, Markus; Ganse, Urs; Pfau‐Kempf, Yann; Suni, Jonas; Palmroth, Minna (2025). 130(6)
• Evaluating the Response of the Labrador Sea to Greenland Meltwater Influx With High-Resolution Eddy-Rich NEMO Simulations. Hoshyar, P.; Pennelly, C.; Myers, P. G. (2025). 130(5) e2024JC022102.

  Abstract The Greenland Ice Sheet has undergone substantial mass loss in recent decades primarily due to oceanic and atmospheric warming, contributing to global sea-level rise and enhanced ocean stratification. This study examines the Labrador Sea's response to increased Greenland freshwater influx using a very high-resolution, eddy-rich configuration of the NEMO ocean model. Two 9-year long experiments with identical setups, except one excluding Greenland runoff, suggest that freshwater influx leads to freshwater content increase (by up to 39\%) and intensified stratification (enhanced convective resistance by up to 29\%) in the Labrador Sea, whereas heat content remains mainly unchanged (up to 6\% increase). Strengthened stratification weakens but does not entirely stop deep convection allowing continued water mass formation within the region (maximum mixed layer depth differences of up to 635 m). A sensitivity analysis indicates warmer, saltier surface waters and colder, fresher subsurface layers when Greenland meltwater is excluded likely driven by reduced lateral exchange from the West Greenland Current system and enhanced southward Arctic Water transport across Davis Strait. Offshore freshwater exchange in the Labrador Sea interior is not solely controlled by Greenland meltwater influx but also by air-sea fluxes and deepwater formation. Over longer timescales exceeding a decade, ongoing freshening and intensified stratification could further disrupt essential oceanic processes with potential consequences for deepwater formation and Atlantic Meridional Overturning Circulation. These findings underscore the importance of accurately representing Greenland meltwater and shelf-basin exchanges in models to improve the predictions of Labrador Sea under future warming.
• Weakened Atlantic Meridional Overturning Circulation causes the historical North Atlantic Warming Hole. Li, Kai-Yuan; Liu, Wei (2025). 6(1) 416.

  Most oceans over the globe have experienced surface warming during the past century, but the subpolar Atlantic is quite otherwise. The sea surface temperature cooling trend to the south of Greenland, known as the North Atlantic Warming Hole, has raised debate over whether it is driven by the slowing of the Atlantic Meridional Overturning Circulation. Here we use observations as a benchmark and climate models as a tool to demonstrate that only models simulating a weakened historical Atlantic overturning can broadly reproduce the observed cooling and freshening in the warming hole region. This, in turn, indicates that the realistic Atlantic overturning slowed between 1900 and 2005, at a rate of −1.01 to −2.97thinspaceSv century−1 (1\thinspaceSv\thinspace=\thinspace106\thinspacem3\thinspaces−1), according to a sea-surface-temperature-based fingerprint index estimate. Particularly, the Atlantic overturning slowdown causes an oceanic heat transport divergence across the subpolar North Atlantic, which, while partially offset by enhanced ocean heat uptake, results in cooling over the warming hole region.
• Quantum Monte Carlo study of hydrodynamics in systems with particle-hole symmetry. Reingruber, Adrien; Pongsangangan, Kitinan; Assaad, Fakher; Ulybyshev, Maksim (2025). 134(20) 206303.
• Semantic-Based Integrated Sensing, Computing, Communication, and Control for Goal-Oriented Applications. Li, Qingliang; Chang, Bo; Mei, Weidong; Chen, Zhi (2025). 1–6.
• Optimizing Handovers in Integrated 5G UAV Networks: A Core-RAN Co-location Approach. Jiang, Yue; Liang, Houze; Wang, Xijun; Chen, Xiang; Huang, Xiao (2024). 596–601.
• Task-oriented Age of Information for Remote Monitoring Systems. Gan, Shuying; Wang, Xijun; Xu, Chao; Chen, Xiang (2024). 247–252.
• ABCF: An Adaptive Balanced Multimodal Website Classification Framework. Liu, Zhiyuan; Liu, Wei; Tong, Xinyang; Shen, Qi; Zheng, Qiang; Hu, Hengrui; Liu, Hong; Wang, Xiaojie W. Shen, J.-P. A. Barthès, J. Luo, T. Qiu, X. Zhou, J. Zhang, H. Zhu, K. Peng, T. Xu, N. Chen (eds.) (2024). 79–84.
• 31.8 A 11.7W 9mV/A-Cross-Regulation Single-Inductor Triple-Output Buck Converter Using Unordered Power-Distributive Control for a 2A Load Transient. Wang, Baochuang; Wu, Xiaonan; Cheng, Lin (2024). 510–512.
• Automated Modeling of Influence Diversity with Graph Convolutional Network for Social Recommendation. Bing, Rui; Yuan, Guan; Cai, Zhuo; Li, Bohan; Zhou, Yong in Lecture Notes in Computer Science, W. Zhang, A. K. H. Tung, Z. Zheng, Z. Yang, X. Wang, H. Guo (eds.) (2024). (Vol. 14962) 33–49.
• A 5.6 dB NF Two-Stage 110 - 125 GHz LNA Gain-Boosted by RC-over-Neutralization for Radar Applications in 28 nm CMOS. Lasserre, V.; Koop-Brinkmann, S.; Caruso, M.; Maistro, D. Dal; Volpato, G.; Le, Q. H.; Kämpfe, Thomas; Ziegler, Christian; Stapelfeldt, Finn; Issakov, Vadim (2024). 146–149.
• Fed-NAD: Backdoor Resilient Federated Learning via Neural Attention Distillation. Ma, Hao; Qi, Senmao; Yao, Jiayue; Yuan, Yuan; Zou, Yifei; Yu, Dongxiao (2024). 7–13.
• Word Embeddings Are Steers for Language Models. Han, Chi; Xu, Jialiang; Li, Manling; Fung, Yi; Sun, Chenkai; Jiang, Nan; Abdelzaher, Tarek F.; Ji, Heng L.-W. Ku, A. Martins, V. Srikumar (eds.) (2024). 16410–16430.
• Fed-MS: Fault Tolerant Federated Edge Learning with Multiple Byzantine Servers. Qi, Senmao; Ma, Hao; Zou, Yifei; Yuan, Yuan; Li, Peng; Yu, Dongxiao (2024). 982–992.
• Cloud-RAN Over-the-Air Federated Learning. Ma, Haoming; Yuan, Xiaojun; Ding, Zhi (2024). 4257–4262.
• Fed-MoE: Efficient Federated Learning for Mixture-of-Experts Models via Empirical Pruning. Zou, Yifei; Qi, Senmao; Yuan, Yuan; Wang, Dawei; Shen, Shikun; Wu, Libing; Guo, Shaoyong; Yu, Dongxiao in Lecture Notes in Computer Science, Y. Li, Y. Zhang, J. Xu (eds.) (2024). (Vol. 15502) 128–139.
• RACC: Rapid and Accurate INT-Based Congestion Control in RDMA Network. Zhao, Yanzhe; Wang, Shuo; Han, Siyu; Zhou, Dong; Peng, Guoyu; Huang, Tao (2024). 3811–3816.
• Plag-Llama for IoT-Enabled Data Centers: A Multivariate Time Series Forecasting Approach. Sun, Yu; Cheng, Bo; Li, Jinan; Jiang, Gaoxiang; Zhang, Tianqi; Zhou, Haibo (2024). 4376–4381.
• Diffusion Models for Visual Computing. Mitra, Niloy; Cohen-Or, Daniel; Sung, Minhyuk; Huang, Chun-Hao; Ceylan, Duygu; Guerrero, Paul in SIGGRAPH Courses ’24 (2024). 1–290.
• Was isst Künstliche Intelligenz?. Redecker, Eva von; Herbelot, Aurélie (2024).

  KI verbraucht Unmengen an Energie, verdaut immer mehr Daten. Wird die Maschinenherrschaft des Kapitalismus Mensch und Planeten zerstören?
• Secure Beamforming and Obstacle Avoidance Trajectory Design for UAV-Assisted ISAC. Xu, Xiaolong; Ju, Ying; Tu, Yulong; Liu, Lei; Gong, Yi; Du, Jianbo; Yau, Kok-Lim Alvin W. Shi, D. Ganesan, N. D. Lane (eds.) (2024). 2197–2202.
• 131TOPS/W 8b ACIM Exploiting Weight-Embedded Auto-Accumulation and Supporting Symmetric Quantization Networks. He, Wei; Bai, Puvi; Luo, Hongyang; Jin, Zhenghao; Wu, Han; Zhang, Junyi; Chao, Xingchen; Liu, Haiqi; He, Yajuan; Li, Qiang (2024). 1–2.
• BR-DeFedRL: Byzantine-Robust Decentralized Federated Reinforcement Learning with Fast Convergence and Communication Efficiency. Qiao, Jing; Zhang, Zuyuan; Yue, Sheng; Yuan, Yuan; Cai, Zhipeng; Zhang, Xiao; Ren, Ju; Yu, Dongxiao (2024). 141–150.
• Diagnosis of Early Mild Cognitive Impairment in Type 2 Diabetes Mellitus by Deep Learning of Multimodal Images and Metadata. Han, Kangfu; Yue, Xiaomei; Qiu, Shijun; Yang, Feng; Li, Gang (2024). 1–4.
• Exploring the Best Practices of Query Expansion with Large Language Models. Zhang, Le; Wu, Yihong; Yang, Qian; Nie, Jian-Yun Y. Al-Onaizan, M. Bansal, Y.-N. Chen (eds.) (2024). 1872–1883.
• COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning. Pan, Jing; Wu, Jian; Gaur, Yashesh; Sivasankaran, Sunit; Chen, Zhuo; Liu, Shujie; Li, Jinyu I. Lapidot, S. Gannot (eds.) (2024).
• Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint. Xiong, Wei; Dong, Hanze; Ye, Chenlu; Wang, Ziqi; Zhong, Han; Ji, Heng; Jiang, Nan; Zhang, Tong (2024).
• Global and Adaptive Local Label Correlation for Multi-label Learning with Missing Labels. Jiang, Qingxia; Li, Peipei; Zhang, Yuhong; Hu, Xuegang (2023). 1–8.
• Leveraging Free Labels to Power up Heterophilic Graph Learning in Weakly-Supervised Settings: An Empirical Study. Wu, Xugang; Wu, Huijun; Wang, Ruibo; Li, Duanyu; Zhou, Xu; Lu, Kai in Lecture Notes in Computer Science, D. Koutra, C. Plant, M. G. Rodriguez, E. Baralis, F. Bonchi (eds.) (2023). (Vol. 14171) 140–156.
• MEWL: Few-shot multimodal word learning with referential uncertainty. Jiang, Guangyuan; Xu, Manjie; Xin, Shiji; Liang, Wei; Peng, Yujia; Zhang, Chi; Zhu, Yixin (2023, June).
• What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys. Ge, Yubin; Xiao, Ziang; Diesner, Jana; Ji, Heng; Karahalios, Karrie; Sundaram, Hari C.-R. Huang, Y. Harada, J.-B. Kim, S. Chen, Y.-Y. Hsu, E. Chersoni, P. A, W. H. Zeng, B. Peng, Y. Li, J. Li (eds.) (2023). 113–124.
• Supplementary Material for Men’s Perceptions of Gender Inequality in Software Engineering. Canedo, Edna Dias; Rocha, Larissa; Silva, Geovana Ramos Sousa; dos Santos, Verônica Souza; Mendes, Fabiana Freitas (2023, May).
• NGAME: Negative Mining-aware Mini-batching for Extreme Classification. Dahiya, Kunal; Gupta, Nilesh; Saini, Deepak; Soni, Akshay; Wang, Yajun; Dave, Kushal; Jiao, Jian; K, Gururaj; Dey, Prasenjit; Singh, Amit; Hada, Deepesh; Jain, Vidit; Paliwal, Bhawna; Mittal, Anshul; Mehta, Sonu; Ramjee, Ramachandran; Agarwal, Sumeet; Kar, Purushottam; Varma, Manik T.-S. Chua, H. W. Lauw, L. Si, E. Terzi, P. Tsaparas (eds.) (2023). 258–266.
• Can Smoothing Methods Recognize the Patterns of the Hazard Function in Complex Clinical Scenarios? - A Simulation Study Using Discrete-Time Survival Models. Marano, Giuseppe; Biganzoli, Giacomo; Luconi, Ester; Biganzoli, Elia M.; Boracchi, Patrizia in Lecture Notes in Computer Science, M. Vettoretti, E. Tavazzi, E. Longato, G. Baruzzo, M. Bellato (eds.) (2023). (Vol. 14513) 43–57.
• Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation. Wang, Ziqi; Wu, Yuexin; Liu, Frederick; Liu, Daogao; Hou, Le; Yu, Hongkun; Li, Jing; Ji, Heng (2023).
• Resource-Adaptive Newton’s Method for Distributed Learning. Chen, Shuzhen; Yuan, Yuan; Tao, Youming; Cai, Zhipeng; Yu, Dongxiao in Lecture Notes in Computer Science, W. Wu, G. Tong (eds.) (2023). (Vol. 14422) 335–346.
• SIGGRAPH 2023 Course on Diffusion Models. Meng, Chenlin; Song, Jiaming; Li, Shuang; Zhu, Jun-Yan; Ermon, Stefano; Lin, Tsung-Yi; Lin, Chen-Hsuan; Kreis, Karsten in SIGGRAPH ’23 (2023). 1–113.

  Diffusion models have been successfully used in various applications such as text-to-image generation, 3D assets generation, controllable image editing, video generation, natural language generation, audio synthesis, and motion generation. The rate of progress on diffusion models is astonishing. In the year 2022 alone, diffusion models have been applied to many large-scale text-to-image foundation models, such as DALL-E 2 [Ramesh et al. 2022], Imagen [Saharia et al. 2022], Stable Diffusion [Rombach et al. 2022], and eDiff-I [Balaji et al. 2022]; video generation models such as Imagen Video [Ho et al. 2022] and Make-a-video [Singer et al. 2022]; 3D asset generation models such as Magic3D [Lin et al. 2022] and DreamFusion [Poole et al. 2022]. This course covers the advances in diffusion models over the last few years and will be tailored to the computer graphics community. We will first cover the fundamental machine learning and deep learning techniques relevant to diffusion models. Next, we will present state-of-the-art techniques for the application of diffusion models to high-fidelity image synthesis, controllable image generation, compositional representation learning, and 3D asset generation. Finally, we will conclude with a discussion on the future application of this technology, societal impact and open research problems. After the course, the attendees will learn basic knowledge about diffusion models and how such models can be applied to different applications such as image generation, image editing, and 3D asset generation.
• Rate-Fairness Balancing with DRL in Cell-Free Massive MIMO-NOMA Networks. Pang, Mingliang; Wang, Chaowei; Wang, Wenyuan; Deng, Danhao; Jiang, Fan; Gao, Feifei; Han, Guangjie; Zhang, Zhi; Wang, Weidong (2023). 643–648.
• Efficient Cross Dynamic Task Assignment in Spatial Crowdsourcing. Ren, Tianyue; Zhou, Xu; Li, Kenli; Gao, Yunjun; Zhang, Ji; Li, Keqin (2023). 1420–1432.
• A 150nA IQ, 850mA ILOAD, 90% Efficiency over 10μA to 400mA Loading Range. Wang, Baochuang; Xie, Yiling; Guo, Jianping; Cheng, Lin (2023). 1–2.
• Effective Learning and Online Modulation for Robotic Variable Impedance Skills. Zhai, Xueqian; Ou, Yangmin; Xu, Zhihao; Jiang, Li; Zhou, Xuefeng; Wu, Hongmin (2022). 1–6.
• BaGuaLu: targeting brain scale pretrained models with over 37 million cores. Ma, Zixuan; He, Jiaao; Qiu, Jiezhong; Cao, Huanqi; Wang, Yuanwei; Sun, Zhenbo; Zheng, Liyan; Wang, Haojie; Tang, Shizhi; Zheng, Tianyu; Lin, Junyang; Feng, Guanyu; Huang, Zeqiang; Gao, Jie; Zeng, Aohan; Zhang, Jianwei; Zhong, Runxin; Shi, Tianhui; Liu, Sha; Zheng, Weimin; Tang, Jie; Yang, Hongxia; Liu, Xin; Zhai, Jidong; Chen, Wenguang J. Lee, K. Agrawal, M. F. Spear (eds.) (2022). 192–204.
• Region-Adaptive Deformable Network for Image Quality Assessment. Shi, Shuwei; Bai, Qingyan; Cao, Mingdeng; Xia, Weihao; Wang, Jiahao; Chen, Yifan; Yang, Yujiu (2021). 324–333.
• UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction. Wu, Huanqin; Liu, Wei; Li, Lei; Nie, Dan; Chen, Tao; Zhang, Feng; Wang, Di in Findings of ACL, C. Zong, F. Xia, W. Li, R. Navigli (eds.) (2021). (Vol. ACL/IJCNLP 2021) 825–835.
• GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification. Saini, Deepak; Jain, Arnav Kumar; Dave, Kushal; Jiao, Jian; Singh, Amit; Zhang, Ruofei; Varma, Manik J. Leskovec, M. Grobelnik, M. Najork, J. Tang, L. Zia (eds.) (2021). 3733–3744.
• Kinematic control of wheeled mobile manipulators subject to inherent physical constraints and noise disturbances. Li, Xiaoxiao; Xu, Zhihao; Li, Shuai; Jiang, Kanyang; Zhou, Xuefeng; Jiang, Li (2021). 417–422.
• A Parameter-Free Approach for Lossless Streaming Graph Summarization. Ma, Ziyi; Yang, Jianye; Li, Kenli; Liu, Yuling; Zhou, Xu; Hu, Yikun in Lecture Notes in Computer Science, C. S. Jensen, E.-P. Lim, D.-N. Yang, W.-C. Lee, V. S. Tseng, V. Kalogeraki, J.-W. Huang, C.-Y. Shen (eds.) (2021). (Vol. 12681) 385–393.
• SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels. Dahiya, Kunal; Agarwal, Ananye; Saini, Deepak; K, Gururaj; Jiao, Jian; Singh, Amit; Agarwal, Sumeet; Kar, Purushottam; Varma, Manik in Proceedings of Machine Learning Research, M. Meila, T. Zhang (eds.) (2021). (Vol. 139) 2330–2340.
• Weakly-Supervised Video Moment Retrieval via Semantic Completion Network. Lin, Zhijie; Zhao, Zhou; Zhang, Zhu; Wang, Qi; Liu, Huasheng (2020). 11539–11546.
• CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization. Li, Lei; Xie, Yang; Liu, Wei; Liu, Yinan; Jiang, Yafei; Qi, Siya; Li, Xingyuan M. K. Chandrasekaran, A. de Waard, G. Feigenblat, D. Freitag, T. Ghosal, E. H. Hovy, P. Knoth, D. Konopnicki, P. Mayr, R. M. Patton, M. Shmueli-Scheuer (eds.) (2020). 225–234.
• Performance Analysis and Optimization for MTTKRP of Sparse Tensor on CPU and GPU. Hu, Rong; Yang, Wangdong; Zhou, Xu; Li, Kenli; Li, Keqin (2020). 545–550.
• Maintenance of Range Skyline Query. Li, Xiaoxue; Shi, Yuanquan; Zhou, Xu; Li, Kenli E. Chen, G. Antoniou (eds.) (2020). 615–620.
• Complexity-efficient Fano Decoding of Polarization-adjusted Convolutional (PAC) Codes. Rowshan, Mohammad; Burg, Andreas; Viterbo, Emanuele (2020). 200–204.
• A Two-Stage Clustering Algorithm Based on Improved K-Means and Density Peak Clustering. Xiao, Na; Zhou, Xu; Huang, Xin; Yang, ZhiBang Y. Gao, R. Möller, X. Wu, R. Kotagiri (eds.) (2019). 296–301.
• CIST@CLSciSumm-19: Automatic Scientific Paper Summarization with Citances and Facets. Li, Lei; Zhu, Yingqi; Xie, Yang; Huang, Zuying; Liu, Wei; Li, Xingyuan; Liu, Yinan in CEUR Workshop Proceedings, M. K. Chandrasekaran, P. Mayr (eds.) (2019). (Vol. 2414) 196–207.
• A Novel Clustering Algorithm based on Directional Propagation of Cluster Labels. Xiao, Na; Li, Kenli; Zhou, Xu; Li, Keqin (2019). 1–8.
• TopicInk: Visualizing Disaster-Related Textual Data Using LDA Topic Modeling: Vast Challenge 2019: Honorable Mention for Clear Articulation of Methodology. Guo, Chen; Liu, Xiang; Cai, Evie; Chen, Yingjie Victor; Qian, Zhenyu Cheryl; Li, Rui R. Chang, D. A. Keim, R. Maciejewski (eds.) (2019). 124–125.
• Fast Fault-Tolerant Sampling via Random Walk in Dynamic Networks. Yuan, Yuan; Li, Feng; Yu, Dongxiao; Yu, Jiguo; Wu, Yu; Lv, Weifeng; Cheng, Xiuzhen (2019). 536–544.
• MMH: Multi-Modal Hash for Instant Mobile Video Search. Gao, Wenhui; Liu, Xinchen; Ma, Huadong; Li, Yanan; Liu, Liang (2018). 57–62.
• “L’Aimable Visir” : Pierre David’s Consulship under Hüsrev Mehemed Pasha’s Rule as Governor of Bosnia. Cascavilla, Giuseppe Pio (2018). 41 611–645.
• Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition. Ge, Tao; Dou, Qing; Ji, Heng; Cui, Lei; Chang, Baobao; Sui, Zhifang; Wei, Furu; Zhou, Ming E. Riloff, D. Chiang, J. Hockenmaier, J. Tsujii (eds.) (2018). 2496–2506.
• Seq2RDF: An End-to-end Application for Deriving Triples from Natural Language Text. Liu, Yue; Zhang, Tongtao; Liang, Zhicheng; Ji, Heng; McGuinness, Deborah L. in CEUR Workshop Proceedings, M. van Erp, M. Atre, V. López, K. Srinivas, C. Fortuna (eds.) (2018). (Vol. 2180)
• Resource Allocation Based on Double Auction for Cloud Computing System. Xu, Lei; Wang, Jun; Nallanathan, Arumugam; Li, Yaping J. Chen, L. T. Yang (eds.) (2016). 1538–1543.
• Identifying most preferential skyline product combinations under price promotion. Yang, ZhiBang; Zhou, Xu; Zeng, Yifu; Zeng, Fei; Zhou, Yantao (2016). 1824–1828.
• Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion. Liu, Yue; Ge, Tao; Mathews, Kusum; Ji, Heng; McGuinness, Deborah L. K. B. Cohen, D. Demner-Fushman, S. Ananiadou, J. Tsujii (eds.) (2015). 92–97.
• Parameter estimation for LP regularized image deconvolution. Zhou, Xu; Zhou, Fugen; Bai, Xiangzhi (2015). 4892–4896.
• Uncertain dynamic skyline queries for uncertain databases. Yang, ZhiBang; Yang, Xiaoniu; Zhou, Xu (2015). 1797–1802.
• Parallel algorithm for solving penta-diagonal linear systems. Zhu, Cheng; Li, Kenli; Yang, Wangdong; Zhou, Xu (2015). 2437–2442.
• Language and Domain Independent Entity Linking with Quantified Collective Validation. Wang, Han; Zheng, Jinguang; Ma, Xiaogang; Fox, Peter; Ji, Heng L. Màrquez, C. Callison-Burch, J. Su, D. Pighin, Y. Marton (eds.) (2015). 695–704.
• Embedded system for real-time person detecting in infrared images/videos using super-resolution and Haar-like feature techniques. Ramos, Gilberto Guadalupe Jara; Sánchez-García, Juan Carlos; Ponomariov, Volodymyr (2015). 1–6.
• An EP-Topk Query over Uncertain Data. Yang, ZhiBang; Zhou, Xu (2014). 577–580.
• Enhancing the Security of Parallel Programs via Reducing Scheduling Space. Zhou, Xu; Li, Gen; Lu, Kai; Wang, Shuangxi (2014). 133–138.
• RaceFree: an efficient multi-threading model for determinism. Lu, Kai; Zhou, Xu; Wang, Xiaoping; Zhang, Wenzhe; Li, Gen A. Nicolau, X. Shen, S. P. Amarasinghe, R. W. Vuduc (eds.) (2013). 297–298.
• Relabeling Distantly Supervised Training Data for Temporal Knowledge Base Population. Tamang, Suzanne; Ji, Heng J. Fan, R. Hoffman, A. Kalyanpur, S. Riedel, F. M. Suchanek, P. P. Talukdar (eds.) (2012). 25–30.
• Combining Social Cognitive Theories with Linguistic Features for Multi-genre Sentiment Analysis. Li, Hao; Chen, Yu; Ji, Heng; Muresan, Smaranda; Zheng, Dequan (2012). 127–136.
• Linguistic Resources for Entity Linking Evaluation: from Monolingual to Cross-lingual. Li, Xuansong; Strassel, Stephanie M.; Ji, Heng; Griffitt, Kira; Ellis, Joe N. Calzolari, K. Choukri, T. Declerck, M. U. Dogan, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis (eds.) (2012). 3098–3105.
• Knowledge Base Population: Successful Approaches and Challenges. Ji, Heng; Grishman, Ralph D. Lin, Y. Matsumoto, R. Mihalcea (eds.) (2011). 1148–1158.
• A novel and fast parallel solution for the vertex cover problem on DNA-based supercomputing. Zhou, Xu; Yue, Guangxue; Li, Kenli; Yu, Xiang; Li, Renfa (2010). 190–194.
• A novel solution for the P2P network observer placement problem on DNA-based supercomputing. Yue, Guangxue; Zhou, Xu; Li, Kenli; Li, Renfa; Zhao, Liping; Yu, Xiang (2010). 185–189.
• Automatic Recognition of Logical Relations for English, Chinese and Japanese in the GLARF Framework. Meyers, Adam; Kosaka, Michiko; Xue, Nianwen; Ji, Heng; Sun, Ang; Liao, Shasha; Xu, Wei E. Agirre, L. Màrquez, R. Wicentowski (eds.) (2009). 146–154.
• A parameter-varying lowpass filter with reduced transient response. de Anda, Miguel Ángel Gutiérrez; Dector, Isabelo Meza; Sánchez-García, Juan Carlos (2009). 149–152.
• Transducing Logical Relations from Automatic and Manual GLARF. Meyers, Adam; Kosaka, Michiko; Ji, Heng; Xue, Nianwen; Harper, Mary P.; Sun, Ang; Xu, Wei; Liao, Shasha (2009). 116–120.
• The effects of high quality translations of named entities in cross-language information exploration. Wu, Dan; He, Daqing; Ji, Heng; Grishman, Ralph (2008). 1–8.
• A study of using an out-of-box commercial MT system for query translation in CLIR. Wu, Dan; He, Daqing; Ji, Heng; Grishman, Ralph F. Lazarinis, E. N. Efthimiadis, J. Vilares, J. Tait (eds.) (2008). 71–76.
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• Refining Event Extraction through Cross-Document Inference. Ji, Heng; Grishman, Ralph K. R. McKeown, J. D. Moore, S. Teufel, J. Allan, S. Furui (eds.) (2008). 254–262.
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• Improving Name Tagging by Reference Resolution and Relation Detection. Ji, Heng; Grishman, Ralph K. Knight, H. T. Ng, K. Oflazer (eds.) (2005). 411–418.
• Using Semantic Relations to Refine Coreference Decisions. Ji, Heng; Westbrook, David; Grishman, Ralph (2005). 17–24.
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