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Predicting on the Extreme Precipitation in East and Southeast Asia during Summer 2022 from the Antecedent Soil Temperature Anomaly over the Tibetan Plateau

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Journal of Climate 2025年第9期38卷 2109-2128页

作者： Kong, Xianghui Wang, Aihui Xue, Yongkang Wei, Nan Zhang, He Hu, Qin He, Juanxiong Bi, Xunqiang Chen, Yue Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Nanjing China Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information Technology Sichuan Province Chengdu China University of California Los AngelesCA United States School of Atmospheric Sciences Sun Yat-sen University Zhuhai China International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China School of Atmospheric Sciences Chengdu University of Information Technology Sichuan Province Chengdu China Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China National Super Computer Center in Tianjin Tianjin China State Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

It has demonstrated that May land surface temperature (LST) and subsurface temperature (SUBT) anomalies on the Tibetan Plateau (TP) can serve as a predictor of June precipitation in downstream *** summer 2022, there was heavy precipitation (severe drought) in June (August) over southern China, coinciding with high May (July) LST/SUBT anomalies in *** the framework of the Impact of Initialized Land Temperature and Snowpack on Subseasonal to Seasonal Prediction (LS4P), this study explores the causality between 2022 May (July) TP LST/SUBT and June (August) precipitation using an ensemble of land–atmosphere coupled model *** show that model prediction with imposed strongly positive May TP LST/SUBT (i.e., a triple) anomaly could produce 67% (46%) of June precipitation anomalies in South (northeast) China, while sea surface temperature (SST) effects account for 5% (51%).The atmosphere condensation heating and surface sensible heating due to TP LST/SUBT anomaly induce anomalous South Asian high, which moves eastward along with the Rossby ***, the above process enhances upward motion, convective instability, and water vapor transport, eventually be favorable for rainfall formation in South China and northeast *** Southeast Asia, it makes a downward movement, decrease in atmospheric water vapor, and unfavorable to the occurrence of ***, July TP LST/SUBT contributes to the prediction skill for August record-breaking drought in southern *** to SST effects, it is found that TP LST/SUBT effects significantly enhance the prediction skill for downstream region, suggesting that the TP land contributes an important source for subseasonal climate prediction. © 2025 American Meteorological Society.

关键词： Rivers

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MQADet: A Plug-and-Play Paradigm for Enhancing Open-Vocabulary Object Detection via Multimodal Question Answering

arXiv

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arXiv 2025年

作者： Li, Caixiong Zhao, Xiongwei Zhang, Jinhang Zhang, Xing Sun, Qihao Wu, Zhou School of Computer and Information Science Qinghai Institute of Technology China School of Information Science and Technology Harbin Institute of Technology Shen Zhen China The State Key Laboratory of Robotics and System Harbin Institute of Technology China School of Computer Science and Technology Qinghai University China Eryuan Digital Technology Co. Ltd. China

Open-vocabulary detection (OVD) is a challenging task to detect and classify objects from an unrestricted set of categories, including those unseen during training. Existing open-vocabulary detectors are limited by complex visual-textual misalignment and long-tailed category imbalances, leading to suboptimal performance in challenging scenarios. To address these limitations, we introduce MQADet, a universal paradigm for enhancing existing open-vocabulary detectors by leveraging the cross-modal reasoning capabilities of multimodal large language models (MLLMs). MQADet functions as a plug-and-play solution that integrates seamlessly with pre-trained object detectors without substantial additional training costs. Specifically, we design a novel three-stage Multimodal Question Answering (MQA) pipeline to guide the MLLMs to precisely localize complex textual and visual targets while effectively enhancing the focus of existing object detectors on relevant objects. To validate our approach, we present a new benchmark for evaluating our paradigm on four challenging open-vocabulary datasets, employing three state-of-the-art object detectors as baselines. Experimental results demonstrate that our proposed paradigm significantly improves the performance of existing detectors, particularly in unseen complex categories, across diverse and challenging scenarios. To facilitate future research, we will publicly release our code. © 2025, CC BY.

关键词： Object detection

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Ultra-fast, high-power MUTC Photodiodes with bandwidth-efficiency product over 130 GHz × 100%

arXiv

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arXiv 2025年

作者： Li, Linze Long, Tianyu Yang, Xiongwei Zhang, Zhouze Wang, Luyu Wang, Jingyi Wang, Mingxu Lu, Juanjuan Yu, Jianjun Chen, Baile School of Information Science and Technology ShanghaiTech University Shanghai201210 China Fastphide Photonics Shanghai201210 China China State Key Laboratory of ASIC and System Fudan University Shanghai200433 China Key Laboratory for Information Science of Electromagnetic Waves Shanghai Institute for Advanced Communication and Data Science Fudan University Shanghai200433 China School of Information Science and Technology Fudan University Shanghai200433 China

The accelerating demand for wireless communication necessitates wideband, energy-efficient photonic sub-terahertz (sub- THz) sources to enable ultra-fast data transfer. However, as critical components for THz photonic mixing, photodiodes (PDs) face a fundamental trade-off between quantum efficiency and bandwidth, presenting a major obstacle to achieving high-speed performance with high optoelectronic conversion efficiency. Here, we overcome this challenge by demonstrating an InP-based, waveguide-integrated modified unitraveling carrier photodiode (MUTC-PD) with a terahertz bandwidth exceeding 200 GHz and a bandwidth-efficiency product (BEP) surpassing 130 GHz × 100%. Through the integration of a spot-size converter (SSC) to enhance external responsivity, alongside optimized electric field distribution, balanced carrier transport, and minimized parasitic capacitance, the device achieves a 3-dB bandwidth of 206 GHz and an external responsivity of 0.8 A/W, setting a new benchmark for BEP. Packaged with WR-5.1 waveguide output, it delivers radiofrequency (RF) power exceeding -5 dBm across the 127-185 GHz frequency range. As a proof of concept, we achieved a wireless transmission of 54 meters with a single-line rate of up to 120 Gbps, leveraging photonics-aided technology without requiring a low-noise amplifier (LNA). This work establishes a pathway to significantly enhance optical power budgets and reduce energy consumption, presenting a transformative step toward high-bandwidth, high-efficiency sub-THz communication systems and next-generation wireless networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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DEEPCON: Improving Distributed Deep Learning Model Consistency in Edge-Cloud Environments via Distillation

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Qian, Bin Qian, Jiaxu Wen, Zhenyu Wu, Di He, Shibo Chen, Jiming Ranjan, Rajiv Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Zhejiang University of Technology Institute of Cyberspace Security College of Information Engineering Hangzhou China University of St Andrews School of Computer Science United Kingdom Newcastle University United Kingdom

In a typical distributed Deep Learning (DL) based application, models are configured differently to meet the requirements of resource constraints. For instance, a large ResNet56 model is deployed on the cloud server while a small lightweight MobileNet model is more suitable for the end-user device with fewer computation resources. However, the heterogeneity of the model architectures and configurations may bring a systemic problem -models may produce different outputs when given the same input. This inconsistency problem may cause severe system failure of prediction agreement inside the application. Current research has not studied the systemic design for efficiently detecting and reducing the inconsistency among models in distributed DL applications. With the increasing scale of distributed DL applications, the challenges of inconsistency mitigation should consider both algorithm and system design. To this end, we design and implement DEEPCON, an adaptive deployment system across the edge-cloud layer with over-the-air model updates. We implement ASRS sampling for efficiently sampling data to reveal the real data distribution as well as model prediction inconsistency. Then, we implement DMML-Par, an asynchronous parallel training algorithm for quickly updating the models and reducing inconsistency. implements over-the-air updates with a set of APIS to enable seamless inconsistency detection and reduction in such deep learning applications. Our experiment results on both vision and language tasks demonstrate that DMML could improve the model consistency up to 4%, 7%, and 13% at CIFAR10/100 and IMDB datasets without sacrificing the accuracy of individual models. We also show that the ASRS sampling can save 90% network bandwidth of data transmission and that DMML-Par is up to 60% faster compared to simple synchronous parallel training. 2015 IEEE.

关键词： Distributed cloud

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Plasmonic nanolaser with tunable emitting wavelength near 1 µm

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Optics Letters 2025年第10期50卷 3305-3308页

作者： Wang, Yuhua Lu, Shudi Ma, Jingteng Lin, Runkang Wang, Peiling Xu, Yipeng Yue, Shizhong Wang, Zhijie Qu, Shengchun Huang, Le Liu, Kong Dong, Huafeng Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou510006 China Laboratory of Solid-State Optoelectronics Information Technology Beijing Key Laboratory of Low Dimensional Semiconductor Materials and Devices Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China College of Physical Science and Technology Hebei Normal University of Science & Technology Qinhuangdao066004 China

Surface plasmon polaritons (SPPs) can produce a confined electromagnetic field near the metal–dielectric interface, allowing energy to be compressed into a scale smaller than the optical diffraction limit. The characteristic enables the fabrication of sub-wavelength-sized, ultra-low-threshold, ultrafast, specific polarization, room-temperature lasing nanolasers, which is especially meaningful for the lasers emitting long wavelength light. In this work, we developed a Cs0.1MA0.9SnI3 perovskite plasmonic laser based on a metal–insulator–semiconductor (MIS) structure, demonstrating a low threshold of 281 µJ cm−2, a tunable emitting wavelength from 973 to 1023 nm, strong mode confinement at the cutoff height of 90 nm, and a rapid response lifetime of 29.43 ps. Our study represents the first, to the best of our knowledge, theoretical design and experimental production of perovskite plasmonic nanolaser with a tunable emitting wavelength near 1 µm, filling a significant research gap and paving the way for the application of perovskite nanolasers in optoelectronic integration and information technology. © 2025 Optica Publishing Group. All rights are reserved.

关键词： Surface plasmon resonance

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In-situ Monitoring of Maximum and Average Chip Junction Temperature within Multichip IGBT Power Modules Using Kelvin Emitter Voltage

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Yang, Jianxiong Ran, Li Che, Yanbo Du, Mingxing Tianjin University of Technology Tianjin Key Laboratory of Control Theory and Applications in Complicated System Tianjin300384 China Chongqing University State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical Engineering Chongqing400044 China University of Warwick School of Engineering CoventryCV4 7AL United Kingdom Tianjin University Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information Engineering Tianjin300072 China

Parallel configurations of power chips within a multichip IGBT power module (M-IGBT-PM) are employed to meet the required current and power ratings. When the internal chips of an M-IGBT-PM experience uneven temperature distributions and package aging, it becomes challenging to determine the maximum and average temperatures of the chips within the module. This paper investigates the relationship between four transient thermal-sensitive electrical parameters (T-TSEPs) derived from the Kelvin emitter voltage and the chip junction temperature, while establishing relevant mathematical models. It compares the sensitivity and linearity of these four T-TSEPs, and also assesses the influence of external gate resistance, bus voltage, and load current on each of them. A key innovation in this paper is the proposal of a method for monitoring the maximum and average temperatures across the chips within the M-IGBT-PM, which is not influenced by the load current. The experimental results suggest that the temperature derived from the quasi-turn-on delay time is a good approximation of the maximum chip temperature, whereas the temperatures predicted by the other T-TSEPs are nearer to the average temperature. © 2025 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Giant Tunneling Magnetoresistance Based on Spin-Valley-Mismatched Ferromagnetic Metals

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Physical Review Letters 2025年第3期134卷 036302-036302页

作者： Kun Yan Li Cheng Yizhi Hu Junjie Gao Xiaolong Zou Xiaobin Chen School of Science State Key Laboratory on Tunable Laser Technology and Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen 518055 China Shenzhen Geim Graphene Center and Shenzhen Key Laboratory of Advanced Layered Materials for Value-added Applications Institute of Materials Research Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China Collaborative Innovation Center of Extreme Optics Shanxi University Taiyuan 030006 China

Half metals, which are amenable to perfect spin filtering, can be utilized for high-magnetoresistive devices. However, available half metals are very limited. Here, we demonstrate that materials with intrinsic spin-valley-mismatched (SVM) states can be used to block charge transport, resembling half metals and leading to giant tunneling magnetoresistance. As an example, by using first-principles transport calculations, we show that ferromagnetic 1T−VSe2, 1T−VS2, and 2H−VS2 are such spin-valley-mismatched metals, and giant magnetoresistance of more than 99% can be realized in spin-valve van der Waals (vdW) junctions using these metals as electrodes. Owing to the intrinsic mismatch of spin states, the central-layer materials for the vdW junctions can be arbitrary nonmagnetic materials, in principle. Our research provides clear physical insights into the mechanism for high magnetoresistance and opens new avenues for the search and design of high-magnetoresistance devices.

关键词： Van der Waals forces

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THC-DL: Three-Party Homomorphic Computing Delegation Learning for Secure Optimization

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IEEE Internet of Things Journal 2025年

作者： Yang, Ze Tian, Youliang Xiong, Jinbo Niu, Kun Li, Mengqian Zhang, Xinyu Ma, Jianfeng Guizhou University Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology College of Computer Science and Technology Guiyang550025 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Xidian University State Key Laboratory of Integrated Services Network School of Cyber Engineering Xi’an710071 China

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits. © 2014 IEEE.

关键词： Convex optimization

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Defense Against Adversarial Faces at the Source: Strengthened Faces Based on Hidden Disturbances

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Li, Shuangliang Wang, Jinwei Wu, Hao Zhang, Jiawei Cheng, Xin Luo, Xiangyang Ma, Bin School of Software Nanjing Universityof Information Science and Technology Nanjing210044 China College of Cryptology and Cyber Science Nankai University Tianjing300073 China State Key Laboratory of Mathemat-ical Engineering and Advanced Computing Zhengzhou450002 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Cyber Security Qilu University of Technology Shandong Academy of Sciences Shandong250353 China

Face Recognition (FR) systems, while widely used across various sectors, are vulnerable to adversarial attacks, particularly those based on deep neural networks. Despite existing efforts to enhance the robustness of FR models, they still face the risk of secondary adversarial attacks. To address this, we propose a novel approach employing "strengthened face" with preemptive defensive perturbations. Strengthened face ensures original recognition accuracy while safeguarding FR systems against secondary attacks. In the white-box scenario, the strengthened face utilizes gradient-based and optimization-based methods to minimize feature representation differences between face pairs. For the black-box scenario, we propose Shielded Gradient Sign Descent (SGSD) to optimize the gradient update direction of strengthened faces, ensuring the transferability and effectiveness against unknown adversarial attacks. Experimental results demonstrate the efficacy of strengthened faces in defending against adversarial faces without compromising the performance of FR models or face image visual quality. Moreover, SGSD outperforms conventional methods, achieving an average performance improvement of 4% in transferability across different attack intensities. © 2020 IEEE.

关键词： adversarial attacks face recognition facial protection

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