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Spatiotemporal characteristics and dynamic prediction of agricultural carbon compensation potential in the middle and lower reaches of the Yellow River Basin

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Environmental science and Pollution Research 2025年第4期32卷 1903-1917页

作者： Luo, Jikang Zhao, Zhen Pang, Jing College of Science Inner Mongolia University of Technology Hohhot015000 China School of Mathematics and Statistics Ningbo University Ningbo315000 China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modelling Inner Mongolia University of Technology Hohhot015000 China

Climate change, driven by carbon emissions, has emerged as a pressing global ecological and environmental challenge. Here, we leverage the panel data of five provinces and above prefecture-level cities in the middle and lower reaches of the Yellow River Basin to estimate the agricultural carbon emissions (CEs), carbon sinks (CSs), carbon compensation rate (CCR), and carbon compensation potential (CCP) from 2001 to 2022 and investigate the spatiotemporal evolution characteristics for this region. We propose an improved GLM-stacking ensemble learning method for CE prediction with limited sample data. The findings indicate the following: (i) From 2001 to 2022, the overall CEs show a trend of "development - decline - stabilization" and reach a peak of 172.54 Mt in 2005. CCR first exceeded the "CCR = 1" in 2008, which also indicates that reducing CEs and increasing CSs are the paths to achieving agricultural carbon neutrality. (ii) Although each province has achieved "net-zero emissions," the CCP of most urban agglomerations is about 0.5 and shows a certain agglomeration trend, indicating significant room for further carbon offset. (iii) The novel GLM-stacking model has higher prediction accuracy when compared to a single model. These findings provide scientific and technological support to realize the provincial dual carbon goals in China. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Carbon capture and utilization

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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Subpart Suppression network for Few-Shot Object Counting

Subpart Suppression Network for Few-Shot Object Counting

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Lanxin Liu, Xinyan Li, Guorong School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computer Science and Technology Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350368741

Few-shot object counting and detection aim to count objects along with their bounding boxes specified by exemplar bounding boxes. Current mainstream methods predict density maps by applying similarity between exemplar and image features to get counting results and detecting peak points from the density map as the positions of objects. However, the sub-parts of objects can also have high similarity to the exemplars, harming the counting and detection performance. To address these issues, we propose a two-stage Subpart Suppression network (SSN), consisting of a Subpart Suppression Density map Predictor (SSDP), which enforces the model focus on whole objects rather than subparts, and a SAM-based Detector and Verifier, which refines the final prediction by a clustering method. Extensive experiments on two popular datasets show an advantage in performance over the state-of-the-art methods and prove the components' effectiveness. © 2025 IEEE.

关键词： Counting Detection Few-Shot Localization

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Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model 31

Enhancing Rumor Detection Methods with Propagation Structure...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Cui, Chaoqun Li, Siyuan Ma, Kunkun Jia, Caiyan School of Computer Science and Technology & Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798891761964

Pretrained Language Models (PLMs) have excelled in various Natural Language Processing tasks, benefiting from large-scale pretraining and self-attention mechanism's ability to capture long-range dependencies. However, their performance on social media application tasks like rumor detection remains suboptimal. We attribute this to mismatches between pretraining corpora and social texts, inadequate handling of unique social symbols, and pretraining tasks ill-suited for modeling user engagements implicit in propagation structures. To address these issues, we propose a continue pretraining strategy called Post Engagement Prediction (PEP) to infuse information from propagation structures into PLMs. PEP makes models to predict root, branch, and parent relations between posts, capturing interactions of stance and sentiment crucial for rumor detection. We also curate and release large-scale Twitter corpus: TwitterCorpus (269GB text), and two unlabeled claim conversation datasets with propagation structures (UTwitter and UWeibo). Utilizing these resources and PEP strategy, we train a Twitter-tailored PLM called SoLM. Extensive experiments demonstrate PEP significantly boosts rumor detection performance across universal and social media PLMs, even in few-shot scenarios. On benchmark datasets, PEP enhances baseline models by 1.0-3.7% accuracy, even enabling it to outperform current state-of-the-art methods on multiple datasets. SoLM alone, without high-level modules, also achieves competitive results, highlighting the strategy's effectiveness in learning discriminative post interaction features. © 2025 Association for Computational Linguistics.

关键词： Tweets

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Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning 31

Towards Real-World Rumor Detection: Anomaly Detection Framew...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Cui, Chaoqun Jia, Caiyan School of Computer Science and Technology & Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798891761964

Current rumor detection methods based on propagation structure learning predominately treat rumor detection as a class-balanced classification task on limited labeled data. However, real-world social media data exhibits an imbalanced distribution with a minority of rumors among massive regular posts. To address the data scarcity and imbalance issues, we construct two large-scale conversation datasets from Weibo and Twitter and analyze the domain distributions. We find obvious differences between rumor and non-rumor distributions, with non-rumors mostly in entertainment domains while rumors concentrate in news, indicating the conformity of rumor detection to an anomaly detection paradigm. Correspondingly, we propose the Anomaly Detection framework with Graph Supervised Contrastive Learning (AD-GSCL). It heuristically treats unlabeled data as non-rumors and adapts graph contrastive learning for rumor detection. Extensive experiments demonstrate AD-GSCL's superiority under class-balanced, imbalanced, and few-shot conditions. Our findings provide valuable insights for real-world rumor detection featuring imbalanced data distributions. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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NeuVSA: A Unified and Efficient Accelerator for Neural Vector Search 31

NeuVSA: A Unified and Efficient Accelerator for Neural Vecto...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Yuan, Ziming Dai, Lei Li, Wen Zhang, Jie Liang, Shengwen Wang, Ying Liu, Cheng Li, Huawei Li, Xiaowei Guo, Jiafeng Wang, Peng Chen, Renhai Zhang, Gong Shanxi University School of Computer and Information Technology China Peking University School of Computer Science China Institute of Computing Technology CAS State Key Lab of Processors China University of Chinese Academy of Sciences China Zhongguancun National Laboratory Beijing China Institute of Computing Technology CAS Key Lab of Network Data Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9798331506476

Neural Vector Search (NVS) has exhibited superior search quality over traditional key-based strategies for information retrieval tasks. An effective NVS architecture requires high recall, low latency, and high throughput to enhance user experience and cost-efficiency. However, implementing NVS on existing neural network accelerators and vector search accelerators is sub-optimal due to the separation between the embedding stage and vector search stage at both algorithm and architecture levels. Fortunately, we unveil that Product Quantization (PQ) opens up an opportunity to break separation. However, existing PQ algorithms and accelerators still focus on either the embedding stage or the vector search stage, rather than both simultaneously. Simply combining existing solutions still follows the beaten track of separation and suffers from insufficient parallelization, frequent data access conflicts, and the absence of scheduling, thus failing to reach optimal recall, latency, and throughput. To this end, we propose a unified and efficient NVS accelerator dubbed NeuVSA based on algorithm and architecture co-design philosophy. Specifically, on the algorithm level, we propose a learned PQ-based unified NVS algorithm that consolidates two separate stages into the same computing and memory access paradigm. It integrates an end-to-end joint training strategy to learn the optimal codebook and index for enhanced recall and reduced PQ complexity, thus achieving smoother acceleration. On the architecture level, we customize a homogeneous NVS accelerator based on the unified NVS algorithm. Each sub-accelerator is optimized to exploit all parallelism exposed by unified NVS, incorporating a structured index assignment strategy and an elastic on-chip buffer to alleviate buffer conflicts for reduced latency. All sub-accelerators are coordinated using a hardware-aware scheduling strategy for boosted throughput. Experimental results show that the joint training strategy improves recall

关键词： network embeddings

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Deduplication and Approximate Analytics for Encrypted IoT data in Fog-Assisted Cloud Storage 24th

Deduplication and Approximate Analytics for Encrypted IoT ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Rongxi Ha, Guanxiong Jia, Chunfu Li, Ruiqi Su, Zhen The College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China The College of Safety Science and Engineering Civil Aviation University of China Tianjin China

ISBN: (纸本)9789819615476

With the advancement of the Internet of Things (IoT) technologies, there has been a rapid increase in the volume of IoT data, leading to escalating costs in storage, transmission, and analytics. The benefits of conventional data deduplication schemes are diminishing when applied to IoT data that is similar but distinct, necessitating the development of new approaches to accommodate these new scenarios. This paper proposes a deduplication and approximate analytics scheme for encrypted IoT data in fog-assisted cloud storage. The scheme is based on Generalized Deduplication (GD), Message Locked Encryption (MLE), Homomorphic Encryption (HE), and ciphertext conversion techniques. We employ GD to divide similar but distinct IoT data into bases and deviations, and perform deduplication on the encrypted base to achieve efficient storage while protecting data privacy. Additionally, we utilize Hybrid Homomorphic Encryption (HHE) techniques to convert the symmetric ciphertext of IoT data into homomorphic ciphertext, facilitating approximate analytics while ensuring privacy protection of IoT data in fog-assisted cloud storage and reducing the computation overhead on IoT devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud storage

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CPSNet: Comprehensive Enhancement Representation for Polyp Segmentation Task

CPSNet: Comprehensive Enhancement Representation for Polyp S...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cai, Jiati Liu, Xiaogang Yang, Hongjie Ding, Yi Zhong, Ting Qin, Zhen Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Department of Gastroenterology and Hepatology University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Accurately segmenting polyp regions in colonoscopy images is crucial for the diagnosis and intervention of colorectal cancer. However, the task of polyp segmentation remains challenging due to the diverse size and shape variations among polyps, their extreme similarity to the background, and frequent rotation of the lens, which further increases the diversity in polyp presentation. To address these challenges effectively, we propose a comprehensive polyp segmentation network (CPSNet). Specifically, we introduce a Comprehensive Spatial Feature Extraction Module (CFEM) that progressively and densely integrates features while forming receptive windows with various shapes. This enables enhanced perception of polyps at manifold sizes and shapes. Additionally, we propose a Fine-grained Region Strengthen Module (FGSM) to supplement uncertain areas around polyps by mitigating background noise interference. In terms of training strategy, we further introduce a Rotation-augmented Constrained Loss (RC Loss), which reinforces consistency constraints on polyp images under multiple rotation angles. Qualitative and quantitative experiments conducted on five public datasets demonstrate both the plug-and-play capability of CFEM as well as the effectiveness and excellence achieved by CPSNet. © 2025 IEEE.

关键词： colonoscopy computer vision polyp segmentation representation enhancement

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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MSPL: Multi-modal Statistical Prompt Learning for New Energy Equipment Defect Recognition

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IEEE Internet of Things Journal 2025年第11期12卷 16438-16450页

作者： Wang, Zhenwei Wang, Pengfei Han, Guangjie Zhou, Chengjie Lin, Qing Zhang, Jianxin Fan, Guangjie Zhang, Qiang Dalian University of Technology School of Computer Science and Technology Dalian116024 China Dalian116024 China Shenyang Huayan Electric Technology Co Ltd Shenyang110180 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China Deputy Director of the Yantai Big Data Center and Senior Engineer Yantai264003 China Dalian Minzu University School of Computer Science and Engineering Dalian116600 China

Monitoring renewable energy devices is crucial for the timely detection of faults and the improvement of system stability, making it a key component of the Internet of Things (IoT) ecosystem. However, existing intelligent algorithms and IoT methods rely on edge devices with limited computational capacity, which requires balancing real-time performance and accuracy. Additionally, most methods require initial training on large-scale data using cloud platforms or high-performance servers, leading to high initial costs. To address these challenges, we propose Multi-modal Statistical Prompt Learning (MSPL) for new energy equipment defect recognition on IoT edge devices. This method enables rapid learning with only a few samples, avoiding the need for large-scale centralized training. Specifically, MSPL uses the pre-trained Contrastive Language-Image Pre-training model as its backbone, leveraging text-based conceptual information to enhance the understanding of visual inputs. A Statistical Query (SQ) module is implemented at the end of the backbone to extract distinctive features from the outputs, integrating these features with soft prompts to customize them for the defect recognition task. Since the learnable parameters are limited to soft prompts added at the end of the backbone, MSPL restricts gradient backpropagation to this point. This improves parameter and memory efficiency, making it more suitable for scenarios with limited computational capacity on IoT edge devices. Experimental results of MSPL on two renewable energy equipment defect datasets and edge devices indicate that it meets real-time processing requirements while maintaining high accuracy, outperforming other methods. © 2025 IEEE.

关键词： Zero-shot learning

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