Data4UrbanMobility was invited to present the paper  “Crosstown traffic – supervised prediction of impact of planned special events on urban traffic”, which was published in the GeoInformatic Journal, at the  SSTD-Conferenz (16th International Symposium on Spatial and Temporal Databases, http://sstd2019.org/). The paper describes how the impact of public special events on road traffic can be measured and predicted. The full article is available here: Crosstown Traffic – Supervised Prediction of Impact of Planned Special Events on Urban Traffic. Tempelmeier, Nicolas; Dietze, Stefan; Demidova, Elena (2019).
URL: https://link.springer.com/article/10.1007/s10707-019-00366-x

• A novel graph kernel algorithm for improving the effect of text classification. Yang, Fan; Zhu, Tan; Huang, Jing; Huang, Zhilin; Xie, Guoqi (2026). 95 101818.
• Universal constituency treebanking and parsing: A pilot study. Li, Jianling; Zhang, Meishan; Wang, Jianrong; Zhang, Min; Zhang, Yue (2026). 95 101826.
• ASVspoof 5: Design, collection and validation of resources for spoofing, deepfake, and adversarial attack detection using crowdsourced speech. Wang, Xin; Delgado, Héctor; Tak, Hemlata; weon Jung, Jee; jin Shim, Hye; Todisco, Massimiliano; Kukanov, Ivan; Liu, Xuechen; Sahidullah, Md.; Kinnunen, Tomi; Evans, Nicholas W. D.; Lee, Kong Aik; Yamagishi, Junichi; Jeong, Myeonghun; Zhu, Ge; Zang, Yongyi; Zhang, You; Maiti, Soumi; Lux, Florian; Müller, Nicolas; Zhang, Wangyou; Sun, Chengzhe; Hou, Shuwei; Lyu, Siwei; Maguer, Sébastien Le; Gong, Cheng; Guo, Hanjie; Chen, Liping; Singh, Vishwanath Pratap (2026). 95 101825.
• Learning Theory from First Principles Bach, Francis (2025). The MIT Press.
• TDDet: A novel lightweight and efficient tea disease detector. Sun, Yange; Li, Zhihao; Guo, Huaping; Feng, Yan; Tang, Yongqiang; Zhang, Wensheng; Gu, Jingqiu (2025). 237 110481.
• Prompt Inversion Attack Against Collaborative Inference of Large Language Models. Qu, Wenjie; Zhou, Yuguang; Wu, Yongji; Xiao, Tingsong; Yuan, Binhang; Li, Yiming; Zhang, Jiaheng (2025). 1695–1712.
• Alternating Guided Training for Robust Adversarial Defense. Liu, Xinlei; Ma, Chunlai; Chen, Bo; Hu, Tao; Ma, Hailong; Yi, Peng; Jiang, Yiming; Hu, Yuxiang Z. (Mark) Zhang, E. Ricci, Y. Yan, L. Nie, V. Oria, L. Ballan (eds.) (2025). 2008–2012.
• Unveiling the multifunctional use of ochre in the Middle Stone Age: Specialized ochre retouchers from Blombos Cave. Velliky, Elizabeth C.; d’Errico, Francesco; van Niekerk, Karen L.; Henshilwood, Christopher S. (2025). 11(26) eads2797.

  Ochre, an iron-rich pigment, is widely associated with symbolic communication, but its functional applications in the Middle Stone Age (MSA) remain poorly understood. Experimental and ethnographic evidence suggests ochre being useful for hide tanning, hafting adhesives, and skin protection, although direct archeological evidence is scarce. We address this gap by presenting ochre tools from Blombos Cave, South Africa, found in Still Bay to pre–Still Bay layers dated 90 to 70,000 years ago. Seven ochre pieces were deliberately modified into lithic retouchers, showing clear use-wear patterns and evidence of intentional shaping. Targeted experiments confirm that some were used for pressure flaking and were rejuvenated to maintain function. These findings provide direct evidence of ochre being used to retouch lithic artifacts during the MSA, highlighting its role in technological systems of this period. The results emphasize the multifunctionality of ochre and suggest that such curated tools may have held personal, cultural, or technological significance within early modern human communities. Seven ochre artifacts from Blombos Cave show evidence of lithic retouching and pressure flaking, revealing specialized MSA tools.
• Introduction to Cryptographic Definitions - A Step-by-Step Guide for Beginners Guo, Fuchun; Susilo, Willy; Nguyen, Khou; Chen, Xiaofeng; Zhao, Zhen in Undergraduate Topics in Computer Science (2025). 1–221. Springer.
• Intelligent and Efficient Video Moment Localization Liu, Meng; Hu, Yupeng; Guan, Weili; Nie, Liqiang (2025). 1–154. Springer.
• Adaptive Trust Architecture for Secure IoT Communication in 6G. Ahmad, Ijaz; Gimhana, Shakthi; Ahmad, Ijaz; Harjula, Erkki (2025). 7(2) 113–116.
• Distributionally robust optimization methods and applications. Li, Xiang; Liu, Yankui (2025). 182 107121.
• Flexible Job Shop Scheduling Problem using graph neural networks and reinforcement learning. Liu, Xi; Chen, Xin; Chau, Vincent; Musial, Jedrzej; Blazewicz, Jacek (2025). 182 107139.
• Efficient configuration of high-dimensional hyperparameters in deep convolutional neural networks for classification assisted by surrogate models. Zhang, Rui; Zhang, Yuanyuan; Sun, Chaoli; Zhang, Yanjun; Dong, Zehua; Wang, Xiaobing (2025). 95 101940.
• Towards real-time weed detection and segmentation with lightweight CNN models on edge devices. Islam, Md Didarul; Liu, Wenxin; Izere, Pascal; Singh, Puranjit; Yu, Chong; Riggan, Benjamin; Zhang, Kuan; Jhala, Amit J.; Knezevic, Stevan; Ge, Yufeng; Pitla, Santosh K.; Luck, Joe D.; Shi, Yeyin (2025). 237 110600.
• Inference Scaling for Long-Context Retrieval Augmented Generation. Yue, Zhenrui; Zhuang, Honglei; Bai, Aijun; Hui, Kai; Jagerman, Rolf; Zeng, Hansi; Qin, Zhen; Wang, Dong; Wang, Xuanhui; Bendersky, Michael (2025).
• Bi-classifier with neighborhood aggregation for unsupervised domain adaptation. Lü, Shuai; Zhang, Xinyu; Li, Zongze; Li, Jingyao; Kang, Meng (2025). 718 122399.
• MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search. Yang, Zonglin; Liu, Wanhao; Gao, Ben; Liu, Yujie; Li, Wei; Xie, Tong; Bing, Lidong; Ouyang, Wanli; Cambria, Erik; Zhou, Dongzhan (2025). abs/2505.19209
• MEGADance: Mixture-of-Experts Architecture for Genre-Aware 3D Dance Generation. Yang, Kaixing; Tang, Xulong; Peng, Ziqiao; Hu, Yuxuan; He, Jun; Liu, Hongyan (2025). abs/2505.17543
• SRDiffusion: Accelerate Video Diffusion Inference via Sketching-Rendering Cooperation. Cheng, Shenggan; Wei, Yuanxin; Diao, Lansong; Liu, Yong; Chen, Bujiao; Huang, Lianghua; Liu, Yu; Yu, Wenyuan; Du, Jiangsu; Lin, Wei; You, Yang (2025). abs/2505.19151
• MIDB: Multilingual Instruction Data Booster for Enhancing Multilingual Instruction Synthesis. Liu, Yilun; Zhao, Chunguang; Yang, Xinhua; Zeng, Hongyong; Tao, Shimin; Meng, Weibin; He, Minggui; Su, Chang; Yu, Yan; Ma, Hongxia; Zhang, Li; Wei, Daimeng; Yang, Hao (2025). abs/2505.17671
• R1-Compress: Long Chain-of-Thought Compression via Chunk Compression and Search. Wang, Yibo; Shen, Li; Yao, Huanjin; Huang, Tiansheng; Liu, Rui; Tan, Naiqiang; Huang, Jiaxing; Zhang, Kai; Tao, Dacheng (2025). abs/2505.16838
• Does Representation Intervention Really Identify Desired Concepts and Elicit Alignment?. Yang, Hongzheng; Chen, Yongqiang; Qin, Zeyu; Liu, Tongliang; Xiao, Chaowei; Zhang, Kun; Han, Bo (2025). abs/2505.18672
• FlowCut: Rethinking Redundancy via Information Flow for Efficient Vision-Language Models. Tong, Jintao; Jin, Wenwei; Qin, Pengda; Li, Anqi; Zou, Yixiong; Li, Yuhong; Li, Yuhua; Li, Ruixuan (2025). abs/2505.19536
• Wolf Hidden in Sheep’s Conversations: Toward Harmless Data-Based Backdoor Attacks for Jailbreaking Large Language Models. Kong, Jiawei; Fang, Hao; Yang, Xiaochen; Gao, Kuofeng; Chen, Bin; Xia, Shu-Tao; Wang, Yaowei; Zhang, Min (2025). abs/2505.17601
• B-score: Detecting biases in large language models using response history. Vo, An; Taesiri, Mohammad Reza; Kim, Daeyoung; Nguyen, Anh Totti (2025). abs/2505.18545
• SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis. Sun, Shuang; Song, Huatong; Wang, Yuhao; Ren, Ruiyang; Jiang, Jinhao; Zhang, Junjie; Bai, Fei; Deng, Jia; Zhao, Wayne Xin; Liu, Zheng; Fang, Lei; Wang, Zhongyuan; Wen, Ji-Rong (2025). abs/2505.16834
• MPO: Multilingual Safety Alignment via Reward Gap Optimization. Zhao, Weixiang; Hu, Yulin; Deng, Yang; Wu, Tongtong; Zhang, Wenxuan; Guo, Jiahe; Zhang, An; Zhao, Yanyan; Qin, Bing; Chua, Tat-Seng; Liu, Ting (2025). abs/2505.16869
• MTSA: Multi-turn Safety Alignment for LLMs through Multi-round Red-teaming. Guo, Weiyang; Li, Jing; Wang, Wenya; Li, Yu; He, Daojing; Yu, Jun; Zhang, Min (2025). abs/2505.17147
• HyperPianist: Pianist with Linear-Time Prover and Logarithmic Communication Cost. Li, Chongrong; Zhu, Pengfei; Li, Yun; Hong, Cheng; Qu, Wenjie; Zhang, Jiaheng (2025). 3383–3401.
• LLM Meeting Decision Trees on Tabular Data. Ye, Hangting; Li, Jinmeng; Zhao, He; Guo, Dandan; Chang, Yi (2025). abs/2505.17918
• Key components of effective non-pharmacological interventions for childhood obesity: a review considering social determinants of health. Zamanillo-Campos, R; Colom-Rossello, M; Rodríguez-Calero, M A; Martín, M I; Planas, T; Núñez-Jiménez, C; Ramos, M (2025). 184(7) 449–449.

  Childhood obesity, driven by an obesogenic environment, remains a public health priority. To review the effectiveness of non-pharmacological interventions for treating childhood obesity, with a focus on weight reduction and consideration of social determinants of health (SDOH). The review included randomized and quasi-experimental studies, published between August 2016 and December 2021, that evaluated the effectiveness of interventions aimed at reducing body weight in children aged 2 to 14 years with obesity. (PROSPERO ID: CRD42022301191). A total of 59 interventions were identified, showing wide variability in design, duration, components and settings. Most included physical activity (93%) and nutrition (81%), while fewer incorporated psychological support (46%) or digital tools (25%). Despite the diversity of approaches, 56% of the interventions were effective, particularly those implemented in healthcare or school settings, involving physical activity, and delivered by multidisciplinary teams. However, only six demonstrated sustained effects beyond 6 months. Risk of bias was high in over half of the studies, and only a small proportion (22%) reported participants' social characteristics such as family structure, race, or socioeconomic status. Importantly, very few interventions were designed or evaluated with explicit consideration of SDOH. CONCLUSION: Overall, effective interventions tended to target both children and parents, include multiple components-especially physical activity-and be delivered in accessible settings such as schools or primary care. Yet, the generalizability of findings is limited, as most studies involved children from relatively advantaged backgrounds. Future interventions should integrate SDOH in both design and evaluation to ensure equity and long-term effectiveness in diverse populations. WHAT IS KNOWN: • Childhood obesity remains a critical public health issue, heavily influenced by obesogenic environments and social determinants of health (SDOH). • Non-pharmacological interventions, particularly those incorporating physical activity and nutritional guidance, have been commonly used to address childhood obesity, though with varying degrees of success. WHAT IS NEW: • This review highlights that interventions delivered in healthcare or school settings by multidisciplinary teams, and involving both physical activity and parental engagement, show higher effectiveness in reducing childhood obesity. • The study identifies a significant gap in the integration of social determinants of health in both the design and evaluation of interventions, limiting their long-term impact and equity across diverse populations.
• On the O(frac√d. Li, Huan; Dong, Yiming; Lin, Zhouchen (2025).
• Extreme synchronization transitions. Lee, Seungjae; Kuklinski, Lennart J.; Timme, Marc (2025). 16(1)
• Sustainable visions: unsupervised machine learning insights on global development goals. García-Rodríguez, Alberto; Núñez, Matias; Pérez, Miguel Robles; Govezensky, Tzipe; Barrio, Rafael A; Gershenson, Carlos; Kaski, Kimmo K; Tagüeña, Julia (2025). 20(3)
• Eye-to-Eye or Face-to-Face? Face and Head Substitution for Co-Located AR. Kullmann, Peter; Schell, Theresa; Botsch, Mario; Latoschik, Marc Erich (J. Saint-Aubert, ed.) (2025). 6
• ANTS: Abstractive Entity Summarization in Knowledge Graphs. Firmansyah, Asep Fajar; Zahera, Hamada M.; Sherif, Mohamed Ahmed; and Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (2025).
• On the role of AMOC weakening in shaping wintertime Euro-Atlantic atmospheric circulation. Vacca, Andrea Vito; Bellomo, Katinka; Fabiano, Federico; von Hardenberg, Jost (2025). 63(6) 273.

  Climate change simulations project the slowdown of the Atlantic Meridional Overturning Circulation (AMOC) in the 21st century, though the rate and extent of the decline vary across models. In the Euro-Atlantic region, where the AMOC strongly influences sea surface temperature (SST) variability, climate projections also show significant uncertainty in the evolution of large-scale atmospheric circulation. We hypothesise that the decline of the AMOC and the uncertainty in Euro-Atlantic atmospheric circulation are interconnected. To test this, we analyse three coordinated experiments from the CMIP6 archive (SSP2\\($\$\)-\\($\$\)4.5, SSP5\\($\$\)-\\($\$\)8.5 and abrupt-4xCO2) adopting a storyline approach. In particular, we compare groups of models projecting a larger AMOC decline to groups of models projecting a smaller AMOC decline. Our results indicate that a stronger AMOC weakening is associated with an intensification of the North Atlantic storm track and jet stream, as well as an increased frequency of the NAO\\($\$\)+\\($\$\)weather regime. We link these atmospheric changes to the influence of a reduced warming of the subpolar North Atlantic, known as the North Atlantic Warming Hole (NAWH), associated with the AMOC weakening. To further support our findings, we conduct an additional experiment using the EC-Earth3 model, comparing an abrupt-4xCO2 experiment with one where the AMOC is fixed at preindustrial levels. This experiment corroborates the CMIP6 analysis and validates our hypothesis. We conclude that the AMOC plays a critical role in driving atmospheric changes over the Euro-Atlantic region. Improved monitoring and better constraints on future AMOC decline would help reduce uncertainty in predictions of atmospheric circulation and climate impacts over Europe.
• AI Narrative Breakdown. A Critical Assessment of Power and Promise. Rehak, Rainer in FAccT ’25 (2025). 1250–1260.

  This article sets off for an exploration of the still evolving discourse surrounding artificial intelligence (AI) in the wake of the release of ChatGPT. It scrutinizes the pervasive narratives that are shaping the societal engagement with AI, spotlighting key themes such as agency and decision-making, autonomy, truthfulness, knowledge processing, prediction, general purpose, neutrality and objectivity, apolitical optimization, sustainability game-changer, democratization, mass unemployment, and the dualistic portrayal of AI as either a harbinger of societal utopia or dystopia. Those narratives are analysed critically based on insights from critical computer science, critical data and algorithm studies, from STS, data protection theory, as well as from the philosophy of mind and semiotics. To properly analyse the narratives presented, the article first delves into a historical and technical contextualisation of the AI discourse itself. The article then introduces the notion of "Zeitgeist AI" to critique the imprecise and misleading application of the term "AI" across various societal sectors. Then, by discussing common narratives with nuance, the article contextualises and challenges often assumed socio-political implications of AI, uncovering in detail and with examples the inherent political, power infused and value-laden decisions within all AI applications. Concluding with a call for a more grounded engagement with AI, the article carves out acute problems ignored by the narratives discussed and proposes new narratives recognizing AI as a human-directed tool necessarily subject to societal governance.
• Security and Privacy of Current and Emerging IoT Devices and Systems (Dagstuhl Seminar 2431). Crispo, Bruno; Dmitrienko, Alexandra; Tsudik, Gene; Xu, Wenyuan; Sendner, Christoph (2025).
• A 5.76GS/s 180MHz-BW 74.1dB-DR 2x TI Extrapolated CT DSM with Broadband Hybrid-Inputs Current-Mode Adder in 28nm CMOS. Liu, Yuekai; Guo, Meng; Jin, Yichen; Liu, Yan; Guo, Mingqiang; Sin, Sai-Weng; Tan, Zhichao; Qi, Liang (2024). 1–3.
• A 2.8-to-7.2GT/s DDR5 Registering Clock Driver IC with Parallel-Data Timing and Pin-to-Pin Skew Calibration for a Dual In-Line Memory Module. Kim, Joomyoung; Jung, Jaehong; Lim, Kwangmin; Sung, Barosaim; Kim, Jaekwon; Lim, Baekmin; Noh, Tae-Gun; Lee, Jaehoon; Seok, Hyun-Gi; Cho, Youngsea; Kim, Gyusik; Nomiyama, Takahiro; Kang, Sunhye; Jeong, Yeongcheol; Cho, Sanguk; Kim, Gyumin; Oh, Do-Hyun; Kim, Jaenam; Lim, Yong; Kim, Seungjin; Oh, Seunghyun; Lee, Jongwoo (2024). 1–3.
• A 33.6nW Capacitively Coupled Chopper Amplifier with Stacked Input-Boosted Gate-and-Bulk-Driven Inverter Achieving 0.59 NEF and 0.42 PEF in 180nm CMOS. Hu, Ke; Wu, Jiangchao; Wang, Bo; Law, Man Kay (2024). 1–3.
• A Real-Time Optical-Flow-based SLAM FPGA Accelerator with Inter-Frame Similarity Exploitation and Correlation-Guided Mixed-Precision Flow Update. Li, Mengjie; Zhu, Haozhe; Zhang, Yiming; He, Siqi; Liu, Qi; Zeng, Xiaoyang; Chen, Chixiao (2024). 1–3.
• A 28nm 128-kb Exponent- and Mantissa-Computation-InMemory Dual-macro for Floating-point and INT CNNs. Guo, An; Dong, Xueshan; Dong, Fangyuan; Li, Dongqi; Zhang, Yiran; Zhang, Jingmin; Tang, Yuchen; Shi, Yuhui; Tan, Xiao; Liu, Bo; Shan, Weiwei; Cai, Hao; Yang, Jun; Si, Xin (2024). 1–3.
• A 130-to-170 GHz Reconfigurable Heterodyne/Homodyne Transceiver Achieving 20.5-dBm Psat for Emerging Integrated Wireless Access and Backhaul Networks. Wu, Qixiu; Deng, Wei; Guo, Ziyuan; Jia, Haikun; Ma, Taikun; Peng, Qiuyu; Liu, Chang; Chi, Baoyong (2024). 1–3.
• Temporal Dynamics and Success Patterns of Online Petitions: A Time-Series Clustering Approach. Kovács, Máté; Buryakov, Daniil; Gohourou, Didier; Serdült, Uwe (2024). 103–109.
• A 28nm 76.25TOPS/W RRAM/SRAM-Collaborative CIM Fine-Tuning Accelerator with RRAM-Endurance/Latency-Aware Weight Allocation for CNN and Transformer. Mu, Chen; Huang, Zhirui; Jiang, Hao; Liao, Jie; Jiao, Bo; Zhou, Yuliang; Zhu, Haozhe; Yang, Jianguo; Liu, Qi; Chen, Chixiao (2024). 1–3.
• Time Series Diffusion in the Frequency Domain. Crabbé, Jonathan; Huynh, Nicolas; Stanczuk, Jan Pawel; Van Der Schaar, Mihaela in Proceedings of Machine Learning Research, R. Salakhutdinov, Z. Kolter, K. Heller, A. Weller, N. Oliver, J. Scarlett, F. Berkenkamp (eds.) (2024). (Vol. 235) 9407–9438.

  Fourier analysis has been an instrumental tool in the development of signal processing. This leads us to wonder whether this framework could similarly benefit generative modelling. In this paper, we explore this question through the scope of time series diffusion models. More specifically, we analyze whether representing time series in the frequency domain is a useful inductive bias for score-based diffusion models. By starting from the canonical SDE formulation of diffusion in the time domain, we show that a dual diffusion process occurs in the frequency domain with an important nuance: Brownian motions are replaced by what we call mirrored Brownian motions, characterized by mirror symmetries among their components. Building on this insight, we show how to adapt the denoising score matching approach to implement diffusion models in the frequency domain. This results in frequency diffusion models, which we compare to canonical time diffusion models. Our empirical evaluation on real-world datasets, covering various domains like healthcare and finance, shows that frequency diffusion models better capture the training distribution than time diffusion models. We explain this observation by showing that time series from these datasets tend to be more localized in the frequency domain than in the time domain, which makes them easier to model in the former case. All our observations point towards impactful synergies between Fourier analysis and diffusion models.
• Denoising Diffusion Probabilistic Model for Realistic Financial Correlation Matrices. Kubiak, Szymon; Weyde, Tillman; Galkin, Oleksandr; Philps, Daniel; Gopal, Ram (2024). 1–9.
• HyperPianist: Pianist with Linear-Time Prover via Fully Distributed HyperPlonk. Li, Chongrong; Li, Yun; Zhu, Pengfei; Qu, Wenjie; Zhang, Jiaheng (2024). 2024 1273.
• A TFT-Based Flexible PPG Acquisition Circuit in a 3-μm LTPS Process for Packaging-Free Smart Ring Applications. Zhang, Hanbo; Lou, Yuqing; Bo, Yang; Ma, Zhouchen; Wu, Hao; Qi, Liang; Li, Yongfu; Guo, Xiaojun; Wang, Guoxing; Zhao, Jian (2024). 1–3.
• Robust LS-VCE for the Nonlinear Gauss-Helmert Model: Case Studies for Point Cloud Fitting and Geodetic Symmetric Transformation. Wang, Bin; Zhao, Zhisheng; Wang, Shuai; Yu, Jie; Chen, Yu (2024). 62 1–13.
• SANet: A Self-Attention Network for Agricultural Hyperspectral Image Classification. Zhang, Bo; Chen, Yaxiong; Li, Zhiheng; Xiong, Shengwu; Lu, Xiaoqiang (2024). 62 1–15.
• Ant-Search Algorithm for Distributed Knowledge Graphs. Chepizhko, Oleksandr; Forgács, Péter; Schranz, Melanie (2024).
• The Two-Bridge Ant Experiment as an Interactive Netlogo Library Model. Umlauft, Martina; Schranz, Melanie (2024).
• Efficient self-organization of informal public transport networks. Mittal, Kush Mohan; Timme, Marc; Schröder, Malte (2024). 15(1)
• A 28nm 166.9 TOPS/W x Mb/mm2 DRAM-Free QLC Compute-in-ROM Macro Supporting High Task-Level Inference Energy Efficiency for Tiny AI Edge Devices. Cheong, Ling-An; Wang, Chen; Zhou, Mufeng; Liao, Tianyu; Lee, Mingyen; Ke, Yue; Tang, Wenjun; Chen, Yiming; Du, Xirui; Liu, Yongpan; Yang, Huazhong; Li, Xueqing (2024). 1–3.
• A 0.9V CMOS Voltage Reference with 0.41% Untrimmed Accuracy and 44.7 ppm/°C TC from -35°C to 130°C. Chen, Xuanlin; Wu, Jiangchao; Wang, Bo; Law, Man Kay (2024). 1–3.
• A 48MHz Temperature Compensated Crystal Oscillator Utilizing Time-Domain Accuracy Boosting Techniques Achieving ±1.4ppm Frequency Stability from -55°C to 115°C. Yin, Junyang; Deng, Wei; Jia, Haikun; Wang, Minshuo; Chi, Baoyong (2024). 1–3.
• A 32×32 SPAD LiDAR Sensor with In-pixel Spatial-temporal Coincidence and Per-SPAD Pulse Delay for Resolution-lossless Imaging. Chen, Bu; Huang, Zhangcheng; Wang, Jingyi; Shang, Hongyang; Lv, Hankun; Liu, Qi; Liu, Ming (2024). 1–3.
• SDNet: Noise-Robust Bandwidth Extension under Flexible Sampling Rates. Yang, Junkang; Liu, Hongqing; Gan, Lu; Zhou, Yi; Li, Xing; Jia, Jie; Yao, Jinzhuo (2024). 1–6.
• A 64-channel SPAD-based LiDAR sensor with Compact Coincidence Detection Circuit and Inter-frame Correlation-based Nonlinear Partial Histogram Builder. Hu, Jin; Li, Dong; Wang, Xiayu; Ma, Rui; Liu, Yang; Zhu, Zhangming (2024). 1–3.
• A reduced complexity MLSD-based adaptive Duo-PAM4 detector in 28-nm CMOS for 56-Gb/s wireline receiver. Xu, Chaolong; Lv, Fangxu; Lai, Mingche; Luo, Zhang; Qi, Xingyun; Xu, Jiaqing; Wang, Qiang; Xiao, Liquan; Liu, Cewen; Xu, Hua (2024). 1–3.
• HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation. Jing, Linglin; Ding, Yiming; Gao, Yunpeng; Wang, Zhigang; Yan, Xu; Wang, Dong; Schaefer, Gerald; Fang, Hui; Zhao, Bin; Li, Xuelong (2024). 23128–23137.
• Knowledge Generation for Zero-shot Knowledge-based VQA. Cao, Rui; Jiang, Jing Y. Graham, M. Purver (eds.) (2024). 533–549.
• A 13.75-14.75-GHz 32.1-fsRMS Jitter -100.6-dBc Reference Spur -261.4-dB FoM Sub-Sampling PLL Using a KPD-Doubled Isolated Sub-Sampling Phase Detector for Reliable Spur-Jitter-Joint Optimization. Xu, Yiqing; Li, Yixi; Zhang, Zhao; Qi, Nan; Liu, Jian; Wu, Nanjian; Liu, Liyuan (2024). 1–3.
• ANVMP: A 28nm 52.6 μW 1.25pJ/SOP Asynchronous Non- Volatile-Memory-based Computing-In-Memory Neuromorphic Processor for Edge-Al Applications. Zhang, Jilin; Wei, Qiumeng; Huo, Dexuan; Li, Tao; Gao, Bin; Qian, He; Wu, Huaqiang; Tang, Kea-Tiong; Chen, Hong (2024). 1–3.
• A 75.6Gb/S 22-Bit Floating-Point Reconfigurable DSP Accelerator Embedding 2048×256-Point Complex FFT and 26K-Point 1-D Signal Processing. Xu, Xuanzhe; Liu, Xianjun; Wei, Siyuan; Yu, Shuangming; Dou, Runjiang; Yang, Xu; Wang, Zhe; Liu, Jian; Wu, Nanjian; Liu, Liyuan (2024). 1–3.
• A Wireless Battery-Free Cerebral-Oxygen-Monitoring Micro-System Featuring 0.028% Sensing Resolution. Wang, Weixiao; Zhu, Boyu; Xuan, Yangfan; Shen, Yili; Xiao, Qijing; Wang, Zhiyu; Luo, Yuxuan; Pan, Gang; Su, Bin; Zhao, Bo (2024). 1–3.
• RMMIC: A 40 μW Reconfigurable Multi-modal Multi-channel Interface Circuit System for Bio-signals Monitoring. Zhou, Ting; Huang, Jiajie; Wang, Chao; Hao, Yuzhi; Ji, Yuxin; Gu, Zhiwen; Gao, Zhuo; Yang, Feng; Zhang, Yuhang; Zhao, Yang; Zhao, Jian; Liu, Yan; Chen, Mingyi; Wang, Guoxing; Lian, Yong; Li, Yongfu (2024). 1–3.
• A 28-nm 323 TOPS/mm2/b and 2007 TOPS/W/b Ternary Latch Based Sparsity-Aware CIM Macro With Double-Sampling Ternary ADC. Kim, Myoung; Jeong, Hoichang; Kim, Woo-Seok; Jeong, Yesong; Choi, Young-Eun; Park, Junyoung; Ryu, Min Woo; Lee, Yong Kyu; Lee, Kyuho Jason; Kim, Kyung Rok (2024). 1–3.
• A 28nm 118.26TOPS/W Multi-Dimensional Fault-Tolerant Al Processor Enabling Voltage-Frequency Scaling Below Point-of-First-Failure. Wang, Yang; Yang, Xiaolong; Qin, Yubin; Zhao, Zhiren; Guo, Ruiqi; Yue, Zhiheng; Wei, Shaojun; Hu, Yang; Yin, Shouyi (2024). 1–3.
• A 0.04 μJ/ Classification High-Accuracy Energy-Efficient ECG Processor with SNN On-Chip Backpropagation and Adaptive Threshold Encoding. Fan, Haodong; Zhou, Junlu; Guo, Zilong; Chang, Zhiyuan; Yang, Xi; Hu, Zhicheng; Zeng, Jiahao; Lin, Shuisheng; Zhou, Liang; Zhou, Jun; Zhang, Li; Huang, Jinxi; Su, Jianlei; Gao, Wei; Wu, Qiang; Chang, Liang (2024). 1–3.
• A 0.0006-mm2 0.13-pJ/bit 9-21-Gb/s Sampling CDR with Inverter-Based Frequency Multiplier and Embedded 1: 3 DEMUX in 65-nm CMOS. Dong, Zhicheng; Zhao, Xiaoteng; Yang, Zekai; Su, Xianting; Han, Haolin; Bu, Feng; Zhou, Rong; Sun, Depeng; Chen, Yong; Liu, Shubin; Ding, Ruixue; Zhu, Zhangming (2024). 1–3.
• RePreM: Representation Pre-training with Masked Model for Reinforcement Learning. Cai, Yuanying; Zhang, Chuheng; Shen, Wei; Zhang, Xuyun; Ruan, Wenjie; Huang, Longbo B. Williams, Y. Chen, J. Neville (eds.) (2023). 6879–6887.
• Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion. Wu, Han; Yin, Jie; Rajaratnam, Bala; Guo, Jianyuan (2023).
• MATNet: A Combining Multi-Attention and Transformer Network for Hyperspectral Image Classification. Zhang, Bo; Chen, Yaxiong; Rong, Yi; Xiong, Shengwu; Lu, Xiaoqiang (2023). 61 1–15.
• Imitation Learning to Outperform Demonstrators by Directly Extrapolating Demonstrations. Cai, Yuanying; Zhang, Chuheng; Shen, Wei; He, Xiaonan; Zhang, Xuyun; Huang, Longbo M. A. Hasan, L. Xiong (eds.) (2022). 128–137.
• A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS. Shen, Wei; He, Xiaonan; Zhang, Chuheng; Zhang, Xuyun; Xie, Jian M. A. Hasan, L. Xiong (eds.) (2022). 1777–1786.
• Dynamics of ranking. I~niguez, Gerardo; Pineda, Carlos; Gershenson, Carlos; Barabási, Albert-László (2022). 13(1)
• Efficient and Robust Solution to Universal Symmetric Transformation for 3-D Point Sets. Wang, Bin; Yu, Jie; Chen, Yu; Zhao, Zhisheng; Liu, Chao (2022). 60 1–15.
• Experimental Characterization Techniques for Solid-Liquid Slurry Flows in Pipelines: A Review. Silva, Rui C. (2022). 10(3)

  In industrial environments, having instrumentation able to attain fast, accurate, and autonomous measurements is pivotal to understanding the dynamics of liquid and particles during transport. Ideally, these instruments, consisting of either probes or sensors, should be robust, fast, and unintrusive, i.e., not cause interference on the very flows being monitored, and require minimal maintenance. Beyond monitoring, the process knowledge gained through real time inspection allows teams to make informed technical decisions based on particle behavior, i.e., settling of particles causing pipe wear and clustering or blockages that can damage the unit or cause shutdowns, both of which with economical drawbacks. The purpose of this review is to examine experimental measurement techniques used to characterize physical properties and operational parameters of solid-liquid slurry flows, focusing on non-ionizing radiation methods. With this text the intent is not to provide an exhaustive examination of each individual technique but rather an overview on the most pertinent types of instrumentation, which will be presented, in addition to application examples from the literature, while directing the reader for pertinent seminal and review papers for a more in-depth analysis.
• TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets. Cai, Yuanying; Zhang, Chuheng; Zhao, Li; Shen, Wei; Zhang, Xuyun; Song, Lei; Bian, Jiang; Qin, Tao; Liu, Tieyan X. Zhu, S. Ranka, M. T. Thai, T. Washio, X. Wu (eds.) (2022). 21–30.
• Variational multimodal machine translation with underlying semantic alignment. Liu, Xiao; Zhao, Jing; Sun, Shiliang; Liu, Huawen; Yang, Hao (2021). 69 73–80.
• COVID-19 in South Africa: outbreak despite interventions. Schröder, Malte; Bossert, Andreas; Kersting, Moritz; Aeffner, Sebastian; Coetzee, Justin; Timme, Marc; Schlüter, Jan (2021). 11(1)
• cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope. Marti, Gautier; Goubet, Victor; Nielsen, Frank in Lecture Notes in Computer Science, F. Nielsen, F. Barbaresco (eds.) (2021). (Vol. 12829) 613–620.
• Incentive-driven transition to high ride-sharing adoption. Storch, David-Maximilian; Timme, Marc; Schröder, Malte (2021). 12(1)
• Feldmessgeräte: 1. AgriSens FELDTAG „Einsatz von Fernerkundungstechnologien in der Landwirtschaft“. Truckenbrodt, Sina; Ahmadian, Nima; Borg, Erik; Gerighausen, Heike; Böttcher, Falk; Conrad, Christopher; Dahms, Thorsten; Harfenmeister, Katharina; Hüttich, Christian; Dhillon, Maninder Singh; Spengler, Daniel; Thürkow, Detlef; Thiel, Michael (2021).
• CORRGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks. Marti, Gautier (2020). 8459–8463.
• Comparing the performance of crop growth models using synthetic remote sensing data at DEMMIN, Germany. Dhillon, Maninder Singh; Dahms, Thorsten; Kuebert-Flock, Carina; Conrad, Christopher (2019).

  This research has been conducted on a main agricultural production region of the NorthEast Germany over the time frame of 2015. Five widely used crop growth models, namely, WOFOST, Aqua Crop, Ceres Wheat, CropSyst and Light Use Efficiency (LUE), are compared to predict the biomass of winter wheat. To obtain the higher spatial and temporal coverage, this study explains the usage of a fusion model in which both Landsat (30m) and Modis (500m) were combined to generate the synthetic remote sensing time series for the accurate evaluation of vegetation on a daily basis.
• Light Use Efficiency Model to Estimate Biomass and YIELD with and Without Vapour Pressure Deficit Dhillon, Maninder Singh; Dahms, Thorsten; Nill, Leon (2018). University of Würzburg.
• Experience in the implementation of projects in professional environment in a 1st cycle of studies of Civil Engineering. Ribeiro, Diogo; Neto, Teresa; Santos, Ricardo; de Fátima Portela, Maria F. J. García-Peñalvo (ed.) (2018). 93–99.
• Joint Modeling of Users’ Interests and Mobility Patterns for Point-of-Interest Recommendation. Yin, Hongzhi; Cui, Bin; Huang, Zi; Wang, Weiqing; Wu, Xian; Zhou, Xiaofang X. Zhou, A. F. Smeaton, Q. Tian, D. C. A. Bulterman, H. T. Shen, K. Mayer-Patel, S. Yan (eds.) (2015). 819–822.
• Unifying Local and Global Agreement and Disagreement Classification in Online Debates. Yin, Jie; Narang, Nalin; Thomas, Paul; Paris, Cécile A. Balahur, A. Montoyo, P. Martínez-Barco, E. Boldrini (eds.) (2012). 61–69.
• Addressing the out-of-vocabulary problem for large-scale Chinese spoken term detection. Meng, Sha; Shao, Jian; Yu, Roger Peng; Liu, Jia; Seide, Frank (2008). 2146–2149.
• Abrasive Flow Polishing of Micro Bores. Yin, Ling; Ramesh, Kuppuswamy; Wan, Stephen; Liu, Xiang Dong; Huang, Han; Liu, Yu Chan (2004). 19(2) 187–207.

  Micro bore finishing for metal and ceramic materials has been a challenge in the manufacturing industry. Unfortunately, little is understood about how to polish a micro bore and how to assess its inner wall quality because it is difficult to access the micro bore for either polishing or measurement. This article reports on a feasibility study of the abrasive flow polishing of micro bores of 260 ˜ 500-µm diameters and 25 ˜ 50 length/diameter ratios for both metal and ceramic materials. An abrasive flow polishing machine was designed and built with turbulent flow characteristics. Polishing of steel S45C bores of 400- and 500-µm diameters, stainless steel 304 bores of 500-µm diameter, and zirconia bores of 260-µm diameter was conducted. Surface roughness and topography of the polished inner walls of micro bores were characterized using profilometry and optical interferometry from the three-dimensional point of view. Significant reduction in surface roughness of the micro bore inner walls has been made in the polishing processes. The results indicate that it is feasible to apply the abrasive flow polishing technology for metal and ceramic micro bores of diameters of 260 µm or larger and the length/diameter ratios of 25 or higher. It is found that surface roughness of the polished micro bore inner walls decreases with an increase of the abrasive flow passes.
• Slurry systems handbook Abulnaga, Baha E. in McGraw-Hill Handbooks (2002). McGraw-Hill, New York.
• Development of high speed slurry flow finishing of the inner wall of stainless steel capillary: Polishing and gas flow characteristics of various size of capillaries. Kurobe, T.; Yamada, Y.; Yamamoto, K. (2001). 25(2) 100–106.

  A new finishing method for inner wall of capillaries has been developed, in which the polishing is performed by letting the slurry flow through the capillary at high speed. With the increasing number of slurry passes, the surface roughness of as-received capillaries decreases rapidly in the early stage of the low number of slurry passes, and then tends to saturate to a certain level depending on the diameter of the capillaries. It is also found that the decreasing surface roughness of the inner wall results in the increasing conductance of gas flow through the capillary. Accounting for the effect of the capillary diameter D on the differential pressure which is measured in nitrogen gas flow through the capillary at a constant flow rate, an empirical equation is found between the differential pressure P and the surface roughness Ra, logP · D4 = 0.362 + logRa1/6.
• An inverse of the evaluation functional for typed Lambda-calculus. Berger, Ulrich; Schwichtenberg, Helmut R. Vermuri (ed.) (1991). 203–211.

  In any model of typed \($\lambda$\)-calculus conianing some basic arithmetic, a functional p - * (procedure--* expression) will be defined which inverts the evaluation functional for typed X-terms, Combined with the evaluation functional, p-e yields an efficient normalization algorithm. The method is extended to X-calculi with constants and is used to normalize (the X-representations of) natural deduction proofs of (higher order) arithmetic. A consequence of theoretical interest is a strong completeness theorem for \($\beta$\)\($\eta$\)-reduction, generalizing results of Friedman [1] and Statman [31: If two Xterms have the same value in some model containing representations of the primitive recursive functions (of level 1) then they are provably equal in the \($\beta$\)\($\eta$\)- calculus.