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Yi Feng, Mingyang Song, Jiaqi Wang, Zhuang Chen, Guanqun Bi, Minlie Huang, Liping Jing, Jian Yu

Children with Autism Spectrum Disorder (ASD) often misunderstand social situations and struggle to participate in daily routines. Social Stories™ are traditionally crafted by psychology experts under strict constraints to address these challenges but are costly and limited in diversity. As Large Language Models (LLMs) advance, there's an opportunity to develop more automated, affordable, and accessible methods to generate Social Stories in real-time with broad coverage. However, adapting LLMs to meet the unique and strict constraints of Social Stories is a challenging issue. To this end, we propose SS-GEN, a Social Story GENeration framework with LLMs. Firstly, we develop a constraint-driven sophisticated strategy named StarSow to hierarchically prompt LLMs to generate Social Stories at scale, followed by rigorous human filtering to build a high-quality dataset. Additionally, we introduce quality assessment criteria to evaluate the effectiveness of these generated stories. Considering that powerful closed-source large models require very complex instructions and expensive API fees, we finally fine-tune smaller language models with our curated high-quality dataset, achieving comparable results at lower costs and with simpler instruction and deployment. This work marks a significant step in leveraging AI to personalize Social Stories cost-effectively for autistic children at scale, which we hope can encourage future research on special groups.

Yiting Dong, Xiang He, Guobin Shen, Dongcheng Zhao, Yang Li, Yi Zeng

Dynamic Vision Sensors (DVS) capture event data with high temporal resolution and low power consumption, presenting a more efficient solution for visual processing in dynamic and real-time scenarios compared to conventional video capture methods. Event data augmentation serves as an essential method for overcoming the limitation of scale and diversity in event datasets. Our comparative experiments demonstrate that the two factors, spatial integrity and temporal continuity, can significantly affect the capacity of event data augmentation, which guarantee the maintenance of the sparsity and high dynamic range characteristics unique to event data. However, existing augmentation methods often neglect the preservation of spatial integrity and temporal continuity. To address this, we developed a novel event data augmentation strategy EventZoom, which employs a temporal progressive strategy, embedding transformed samples into the original samples through progressive scaling and shifting. The scaling process avoids the spatial information loss associated with cropping, while the progressive strategy prevents interruptions or abrupt changes in temporal information. We validated EventZoom across various supervised learning frameworks. The experimental results show that EventZoom consistently outperforms existing event data augmentation methods with SOTA performance. For the first time, we have concurrently employed Semi-supervised and Unsupervised learning to verify feasibility on event augmentation algorithms, demonstrating the applicability and effectiveness of EventZoom as a powerful event-based data augmentation tool in handling real-world scenes with high dynamics and variability environments.

Mateus De Oliveira Oliveira, Wim Van Den Broeck

Functional decomposition is the process of breaking down a function f into a composition f=g(f_1,...,f_k) of simpler functions f_1,...,f_k belonging to some class F. This fundamental notion can be used to model applications arising in a wide variety of contexts, ranging from machine learning to formal language theory. In this work, we study functional decomposition by leveraging on the notion of functional reconfiguration. In this setting, constraints are imposed not only on the factor functions f_1,...,f_k but also on the intermediate functions arising during the composition process. We introduce a symbolic framework to address functional reconfiguration and decomposition problems. In our framework, functions arising during the reconfiguration process are represented symbolically, using ordered binary decision diagrams (OBDDs). The function g used to specify the reconfiguration process is represented by a Boolean circuit C. Finally, the function class F is represented by a second-order finite automaton A. Our main result states that functional reconfiguration, and hence functional decomposition, can be solved in fixed-parameter linear time when parameterized by the width of the input OBDD, by structural parameters associated with the reconfiguration circuit C, and by the size of the second-order finite automaton A.

Zhuang Chen, Yaru Cao, Guanqun Bi, Jincenzi Wu, Jinfeng Zhou, Xiyao Xiao, Si Chen, Hongning Wang, Minlie Huang

Emotional support conversation (ESC) helps reduce people's psychological stress and provide emotional value through interactive dialogues. Due to the high cost of crowdsourcing a large ESC corpus, recent attempts use large language models for dialogue augmentation. However, existing approaches largely overlook the social dynamics inherent in ESC, leading to less effective simulations. In this paper, we introduce SocialSim, a novel framework that simulates ESC by integrating key aspects of social interactions: social disclosure and social awareness. On the seeker side, we facilitate social disclosure by constructing a comprehensive persona bank that captures diverse and authentic help-seeking scenarios. On the supporter side, we enhance social awareness by eliciting cognitive reasoning to generate logical and supportive responses. Building upon SocialSim, we construct SSConv, a large-scale synthetic ESC corpus of which quality can even surpass crowdsourced ESC data. We further train a chatbot on SSConv and demonstrate its state-of-the-art performance in both automatic and human evaluations. We believe SocialSim offers a scalable way to synthesize ESC, making emotional care more accessible and practical.

Yuhong Chen, Ailin Song, Huifeng Yin, Shuai Zhong, Fuhai Chen, Qi Xu, Shiping Wang, Mingkun Xu

The rapid evolution of multimedia technology has revolutionized human perception, paving the way for multi-view learning. However, traditional multi-view learning approaches are tailored for scenarios with fixed data views, falling short of emulating the intricate cognitive procedures of the human brain processing signals sequentially. Our cerebral architecture seamlessly integrates sequential data through intricate feed-forward and feedback mechanisms. In stark contrast, traditional methods struggle to generalize effectively when confronted with data spanning diverse domains, highlighting the need for innovative strategies that can mimic the brain's adaptability and dynamic integration capabilities. In this paper, we propose a bio-neurologically inspired multi-view incremental framework named MVIL aimed at emulating the brain's fine-grained fusion of sequentially arriving views. MVIL lies two fundamental modules: structured Hebbian plasticity and synaptic partition learning. The structured Hebbian plasticity reshapes the structure of weights to express the high correlation between view representations, facilitating a fine-grained fusion of view representations. Moreover, synaptic partition learning is efficient in alleviating drastic changes in weights and also retaining old knowledge by inhibiting partial synapses. These modules bionically play a central role in reinforcing crucial associations between newly acquired information and existing knowledge repositories, thereby enhancing the network's capacity for generalization. Experimental results on six benchmark datasets show MVIL's effectiveness over state-of-the-art methods.

Xuping Chen, Wuzhen Shi

The advancement in multimodal research has increased focus on Emotion Recognition in Conversations (ERC), targeting accurately identifying emotional changes. Methods based on graph convolution can better capture the dynamic changes of emotions and improve the accuracy and robustness of emotion recognition. However, existing methods do not distinguish the interaction patterns of a conversation, which results in limiting their ability to model contextual emotional relationships. In this paper, we propose a Dynamic Interactive Bimodal HyperGraph Convolutional Networks (DIB-HGCN), which creatively constructs two types of sub-hypergraphs, i.e., the monologic sub-hypergraph and the dialogic sub-hypergraph, for modeling emotion relationships of different interaction patterns. The monologic sub-hypergraph is used to explore the contextual consistent emotions during the speaker's monologue interactions, while the dialogic sub-hypergraph focuses on capturing the emotional transfers in the dialogic interactions. Meanwhile, the single window partitioning mechanism fails to accommodate the distinct emotional velocity variations across the two interaction patterns. Therefore, we set up dynamic windows in the monologic interactions to fully utilize the information of sentence nodes with consistent emotions, and we add fragment windows to the dialogic interactions to prevent information interference caused by frequent emotional transfers. The experimental results show that our proposed method outperforms existing methods on two benchmark multimodal ERC datasets.

Qing Chang, Yao-Xiang Ding, Kun Zhou

The task of one-shot face video re-enactment aims at generating target video of faces with the same identity of one source frame and facial deformation of the driving video. To achieve high quality generation, it is essential to precisely disentangle identity-related and identity-independent characteristics, meanwhile build expressive features keeping high-frequency facial details, which still remain unaddressed for existing approaches. To deal with these two challenges, we propose a two-stage generation model based on StyleGAN, whose key novel techniques lie in better disentangling identity and deformation codes in the latent space through an identity-based modeling and manipulating intermediate StyleGAN features at the second stage for augmenting facial details of the generating targets. To further improve identity consistency, a data augmentation method is introduced during training for enhancing the key features affecting identity such as hair and wrinkles. Extensive experimental results demonstrate the superiority of our approach compared to state-of-the-art methods.

Linlin Zong, Wenmin Lin, Jiahui Zhou, Xinyue Liu, Xianchao Zhang, Bo Xu, Shimin Wu

Detecting fake news in short videos is crucial for combating misinformation. Existing methods utilize topic modeling and co-attention mechanism, overlooking the modality heterogeneity and resulting in suboptimal performance. To address this issue, we introduce Text-Guided Fine-grained Counterfactual Inference for Short Video Fake News detection (TGFC-SVFN). TGFC-SVFN leverages modality bias removal and teacher-model-enhanced inter-modal knowledge distillation to integrate the heterogeneous modalities in short videos. Specifically, we use causality-based reasoning prompts guided text as teacher model, which then transfers knowledge to the video and audio student models. Subsequently, a multi-head attention mechanism is employed to fuse information from different modalities. In each module, we utilize fine-grained counterfactual inference based on a diffusion model to eliminate modality bias. Experimental results on publicly available fake short video news datasets demonstrate that our method outperforms state-of-the-art techniques.

Yu Zhu, Bo Lei, Chunfeng Song, Wanli Ouyang, Shan Yu, Tiejun Huang

Elucidating the functional mechanisms of the primary visual cortex (V1) remains a fundamental challenge in systems neuroscience. Current computational models face two critical limitations, namely the challenge of cross-modal integration between partial neural recordings and complex visual stimuli, and the inherent variability in neural characteristics across individuals, including differences in neuron populations and firing patterns. To address these challenges, we present a multi-modal identifiable variational autoencoder (miVAE) that employs a two-level disentanglement strategy to map neural activity and visual stimuli into a unified latent space. This framework enables robust identification of cross-modal correlations through refined latent space modeling. We complement this with a novel score-based attribution analysis that traces latent variables back to their origins in the source data space. Evaluation on a large-scale mouse V1 dataset demonstrates that our method achieves state-of-the-art performance in cross-individual latent representation and alignment, without requiring subject-specific fine-tuning, and exhibits improved performance with increasing data size. Significantly, our attribution algorithm successfully identifies distinct neuronal subpopulations characterized by unique temporal patterns and stimulus discrimination properties, while simultaneously revealing stimulus regions that show specific sensitivity to edge features and luminance variations. This scalable framework offers promising applications not only for advancing V1 research but also for broader investigations in neuroscience.

Jun Zhu, Yifu Li, Zhenchao Tang, Cheng Chang

Spatially Resolved Transcriptomics (SRT) has become an indispensable tool in various fields, including tumor microenvironment identification, neurobiology, and the study of complex tissue architecture. However, the accuracy of these insights is often compromised by noise in spatial transcriptomics data due to technical limitations. While recent advancements in denoising methods have shown some promise, they frequently fall short by neglecting spatial features, overlooking the variability in noise levels among genes, and relying heavily on external histological images for supplementary information. In our study, we propose DUSTED, a Dual-Attention Enhanced Spatial Transcriptomics Denoiser, designed to address these challenges. Built on a graph autoencoder framework, DUSTED utilizes gene channel attention and graph attention mechanisms to simultaneously consider spatial features and noise variability in gene expression data. Additionally, it integrates the negative binomial distribution with or without zero-inflation, ensuring a more accurate fit for gene expression distributions. Benchmark tests using simulated datasets demonstrate that DUSTED outperforms existing methods. Furthermore, in real-world applications with the HOCWTA and DLPFC datasets, DUSTED excels in enhancing the correlation between gene and protein expression, recovering spatial gene expression patterns, and improving clustering results. These improvements underscore its potential impact on advancing our understanding of tumor microenvironments, neural tissue organization, and other biologically significant areas.

Ziyi Zhou, Xiaoming Zhang, Shenghan Tan, Litian Zhang, Chaozhuo Li

The proliferation of fake news on social media platforms has exerted a substantial influence on society, leading to discernible impacts and deleterious consequences. Conventional deep learning methodologies employing small language models (SLMs) suffer from the necessity for extensive supervised training and the challenge of adapting to rapidly evolving circumstances. Large language models (LLMs), despite their robust zero-shot capabilities, have fallen short in effectively identifying fake news due to a lack of pertinent demonstrations and the dynamic nature of knowledge. In this paper, a novel framework Multi-Round Collaboration Detection (MRCD) is proposed to address these aforementioned limitations. The MRCD framework is capable of enjoying the merits from both LLMs and SLMs by integrating their generalization abilities and specialized functionalities, respectively. Our approach features a two-stage retrieval module that selects relevant and up-to-date demonstrations and knowledge, enhancing in-context learning for better detection of emerging news events. We further design a multi-round learning framework to ensure more reliable detection results. Our framework MRCD achieves SOTA results on two real-world datasets Pheme and Twitter16, with accuracy improvements of 7.4% and 12.8% compared to using only SLMs, which effectively addresses the limitations of current models and improves the detection of emergent fake news detection.

Ziqi Zhou, Bowen Li, Yufei Song, Zhifei Yu, Shengshan Hu, Wei Wan, Leo Yu Zhang, Dezhong Yao, Hai Jin

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (eg, NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (eg, background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its any internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs' attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness.

Qihua Zhou, Ruibin Li, Jingcai Guo, Yaodong Huang, Zhenda Xu, Laizhong Cui, Song Guo

Recent years have witnessed the rise of Neural-enhanced Video Streaming (NeVS), which integrates neural restoration models into video codecs for higher compression-restoration performance. Despite its benefit, existing work has not well explored the full potential of NeVS paradigm, due to: (1) post-streaming restoration by decoder while lacking the proactive collaboration of encoder, (2) end-to-end optimization based on conventional rate-distortion theory, which has been verified that low distortion is not always a synonym for high perceptual quality, and (3) coupled design for domain-specific tasks that cannot generalize to various video codecs. Observing these limitations, our objective is not to incrementally present an improved restoration model. Instead, we focus on the encoder-decoder synergy, i.e., the codec, which is non-trivial since it inherently strikes the rate-distortion-perception trade-off of NeVS. Aiming at this target, we propose the Diffusion-enhanced Neural Codec (DeNC), a plug-and-play module for current NeVS paradigm, to significantly reduce the required bitrates while preserving high perceptual quality of restored videos. Our key design is twofold. First, DeNC improves the encoder's compression efficiency by simultaneously reducing the resolution and color bit-depth of frame referencing. Second, DeNC empowers the decoder with perception-oriented restoration capability by making its diffusion-based restoration process aware of the encoder's compression conditions. Real-world evaluations show that DeNC improves compression ratios with nearly an order of magnitude and achieves much higher restoration quality (e.g., 93+ VMAF and 23% higher MOS) over the latest baselines.

Penghai Zhao, Qinghua Xing, Kairan Dou, Jinyu Tian, Ying Tai, Jian Yang, Ming-Ming Cheng, Xiang Li

Predicting the future impact of newly published articles is pivotal for advancing scientific discovery in an era of unprecedented scholarly expansion. This paper introduces a promising approach, leveraging the capabilities of LLMs to predict the future impact of newborn articles solely based on titles and abstracts. Breaking away from traditional methods heavily reliant on external data, we propose fine-tuning the LLM to uncover the intrinsic semantic patterns shared by highly impactful articles from a vast collection of text-score pairs. These semantic features are further utilized to predict the proposed indicator, TNCSIsp, which incorporates favorable normalization properties across value, field, and time. To facilitate parameter-efficient fine-tuning of the LLM, we have also meticulously curated a dataset containing over 12,000 entries, each annotated with titles, abstracts, and their corresponding TNCSIsp values. Experimental results reveal an MAE of 0.216 and an NDCG@20 of 0.901, setting new benchmarks in predicting the impact of newborn articles. Finally, we present a real-world application example for predicting the impact of newborn journal articles to demonstrate its noteworthy practical value. Overall, our findings challenge existing paradigms and propose a shift towards a more content-focused prediction of academic impact, offering new insights for article impact prediction.

Guanhao Zhao, Zhenya Huang, Cheng Cheng, Yan Zhuang, Qingyang Mao, Xin Li, Shijin Wang, Enhong Chen

Cognitive diagnosis, which assesses the learners' competence from learners' interaction logs, plays a vital role in education. It provides a crucial reference for gauging learners' proficiency levels and tailoring future learning activities accordingly. Researchers have proposed numerous cognitive diagnosis models to address this task. Despite their success, these models continue to face the ill-posed problem because of the information loss caused by under-expressive interaction function and incomplete observations. In this paper, we address these challenges by proposing a novel cognitive diagnosis model, DMC-CDM, based on the theoretical premise that cognitive states can be captured with minimal information loss by maximizing the mutual information between observed and potential observations. Specifically, DMC-CDM incorporates a semantic extractor to provide a comprehensive semantic understanding of learners' interaction logs, thereby enhancing current collaborative-based cognitive state representations. It then consolidates multi-perspective observations to capture precise cognitive states by maximizing mutual information between these observations. We conducted extensive experiments on three datasets, and the experimental results demonstrate that our proposed model is both effective and beneficial for downstream applications in education.

Ziyang Zhang, Yang Zhao, Ming-Ching Chang, Changyao Lin, Jie Liu

Deep neural network (DNN) models are increasingly popular in edge video analytic applications. However, the computeintensive nature of DNN models pose challenges for energyefficient inference on resource-constrained edge devices. Most existing solutions focus on optimizing DNN inference latency and accuracy, often overlooking energy efficiency. They also fail to account for the varying complexity of video frames, leading to sub-optimal performance in edge video analytics. In this paper, we propose an EnergyEfficient Early-Exit (E4) framework that enhances DNN inference efficiency for edge video analytics by integrating a novel early-exit mechanism with dynamic voltage and frequency scaling (DVFS) governors. It employs an attentionbased cascade module to analyze video frame diversity and automatically determine optimal DNN exit points. Additionally, E4 features a just-in-time (JIT) profiler that uses coordinate descent search to co-optimize CPU and GPU clock frequencies for each layer before the DNN exit points. Extensive evaluations demonstrate that E4 outperforms current state-of-the-art methods, achieving up to 2.8× speedup and 26% average energy saving while maintaining high accuracy.

Zhenbang Zhang, Hongjia Li, Zhiqiang Xu, Wenjia Meng, Renmin Han

Series Section Electron Microscopy (ssEM) is a crucial technique for visualizing three-dimensional (3D) biological structures, which involves collecting electron microscopy images from a series of biological sections along the z-axis and reconstructing the 3D structure. 3D registration is an essential step in ssEM, designed to eliminate axial misalignment and nonlinear distortions introduced during sample sectioning. A significant challenge in 3D registration is eliminating nonlinear distortions while preserving natural deformations. In this paper, we present a new formulation of the 3D registration problem from a frequency domain perspective and propose a Gaussian filtering-based 3D registration method, which defines 3D registration as a superposition problem of high-frequency and low-frequency components. We extend the concept of a one-dimensional Gaussian filter to three-dimensional image stacks and integrate it with optical flow networks to consolidate the deformation field within the receptive field. Extensive experiments demonstrate that our method can successfully decouple nonlinear distortions and natural deformations in the frequency domain, proving superior to existing methods in rapidly and accurately eliminating nonlinear distortions and restoring biological structures, and has the potential to be extended to large datasets.

Yudong Zhang, Xu Wang, Xuan Yu, Zhaoyang Sun, Kai Wang, Yang Wang

Spatiotemporal forecasting (STF) is pivotal in urban computing, yet data scarcity in developing cities hampers robust model training. Addressing this, recent studies leverage transfer learning to migrate knowledge from data-rich (source) to data-poor (target) cities. This strategy, while effective, faces challenges as pre-trained models risk absorbing noise and harmful information due to data distribution disparities, potentially undermining the accuracy of forecasts for target cities. To address this issue, we propose a one-stage STF framework named Target-Skewed Joint Training (TSJT). Central to TSJT is a novel Target-Skewed Backward training strategy that selectively refines gradients from source city data, preserving only the elements that positively impact the target city. To further enhance the quality of these gradients, we have designed a Node Prompting Module (NPM). TSJT is crafted for seamless integration with existing STF models, endowing them with the capability to efficiently tackle challenges stemming from data scarcity. Experimental results on several real-world datasets from multiple cities substantiate the efficacy of TSJT in the realm of cross-city transfer learning.

Xiongqi Zhang, Junwei Xu, Yang Wang, Dongming Xiang, Wang Lin, Zuohua Ding

Barrier certificate generation is an efficient and powerful technique for formally verifying safety properties of cyber-physical systems. Feed-forward neural networks (FNNs) are commonly used to synthesize barrier certificates, but the fixed activation functions limit their efficiency and scalability. In this paper, we propose a novel method for generating barrier certificates using Fourier Kolmogorov-Arnold Networks (KANs). Specifically, it utilizes Fourier KANs to replace FNNs as the template of barrier certificates. Since Fourier KAN has learnable activation functions and uses trigonometric functions as its basis functions, it can efficiently improve the representation power and is easy to train for neural barrier certificates. Then, it formally verifies the validity of the candidate Fourier KAN barrier certificates using both the Lipschitz method and the Satisfiability Modulo Theories, improving the efficiency and success rate of verification. We implement the tool KAN4BC, and evaluate its performance over a set of benchmarks. The experimental results demonstrate the effectiveness and efficiency of our method.

Xin Zhang, Peiliang Zhang, Jingling Yuan, Lin Li

Materials science text mining (MSTM), involving tasks like property extraction and synthesis action retrieval, is pivotal for advancing research by deriving critical insights from scientific literature. Descriptors, serving as essential task labels, often vary in meaning depending on researchers' usage purposes across different mining tasks. (e.g., 'Material' can refer to both synthesis components and participants in fuel cell experiment). This meaning difference makes it difficult for existing methods, fine-tuned to specific task, to handle the same descriptors in other tasks. To overcome above limitation, we propose MatDuck, a simple and effective approach for Zero-Shot MSTM by evoking material knowledge within Large Language Models (LLMs). Specifically, inspired by the Duck Typing principles in programming languages, we present a ClassDefinition-Style Descriptor generation method that evokes task-specific characteristics to address usage variation. Subsequently, we introduce code-style in-context learning for zero-shot tasks, reframing them into code to leverage LLMs' proficiency in code understanding. Extensive experiments on eight benchmark datasets demonstrate that MatDuck, as a plug-and-play approach, significantly improves the Zero-Shot MSTM performance of LLMs by an average of 11.3% across seven tasks.