pwc-archive/papers-with-abstracts huggingface dataset:
VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge Injection
Authors: Hao Cheng, Zhiwei Zhao, Yichao He, Zhenzhen Hu, Jia Li, Meng Wang, Richang Hong
Date: 2025-05-05 00:00:00
Tasks: Contrastive Learning, Dynamic Facial Expression Recognition, Emotion Recognition, Representation Learning, Self-Supervised Learning, Video Emotion Recognition

Audiovisual emotion recognition (AVER) aims to infer human emotions from nonverbal visual-audio (VA) cues, offering modality-complementary and language-agnostic advantages. However, AVER remains challenging due to the inherent ambiguity of emotional expressions, cross-modal expressive disparities, and the scarcity of reliably annotated data. Recent self-supervised AVER approaches have introduced strong multimodal representations, yet they predominantly rely on modality-specific encoders and coarse content-level alignment, limiting fine-grained emotional semantic modeling. To address these issues, we propose VAEmo, an efficient two-stage framework for emotion-centric joint VA representation learning with external knowledge injection. In Stage 1, a unified and lightweight representation network is pre-trained on large-scale speaker-centric VA corpora via masked reconstruction and contrastive objectives, mitigating the modality gap and learning expressive, complementary representations without emotion labels. In Stage 2, multimodal large language models automatically generate detailed affective descriptions according to our well-designed chain-of-thought prompting for only a small subset of VA samples; these rich textual semantics are then injected by aligning their corresponding embeddings with VA representations through dual-path contrastive learning, further bridging the emotion gap. Extensive experiments on multiple downstream AVER benchmarks show that VAEmo achieves state-of-the-art performance with a compact design, highlighting the benefit of unified cross-modal encoding and emotion-aware semantic guidance for efficient, generalizable VA emotion representations.
FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization
Authors: Hongze Li, Zesheng Zhou, Zhenbiao Cao, Xinhui Li, Wei Chen, Xiaojin Zhang
Date: 2025-05-05 00:00:00
Tasks: Domain Generalization, Knowledge Distillation

Traditional domain generalization approaches predominantly focus on leveraging target domain-aware features while overlooking the critical role of source domain-specific characteristics, particularly in federated settings with inherent data isolation. To address this gap, we propose the Federated Source Domain Awareness Framework (FedSDAF), the first method to systematically exploit source domain-aware features for enhanced federated domain generalization (FedDG). The FedSDAF framework consists of two synergistic components: the Domain-Invariant Adapter, which preserves critical domain-invariant features, and the Domain-Aware Adapter, which extracts and integrates source domain-specific knowledge using a Multihead Self-Attention mechanism (MHSA). Furthermore, we introduce a bidirectional knowledge distillation mechanism that fosters knowledge sharing among clients while safeguarding privacy. Our approach represents the first systematic exploitation of source domain-aware features, resulting in significant advancements in model generalization capability.Extensive experiments on four standard benchmarks (OfficeHome, PACS, VLCS, and DomainNet) show that our method consistently surpasses state-of-the-art federated domain generalization approaches, with accuracy gains of 5.2-13.8%. The source code is available at https://github.com/pizzareapers/FedSDAF.
Inverse Modeling of Dielectric Response in Time Domain using Physics-Informed Neural Networks
Authors: Emir Esenov, Olof Hjortstam, Yuriy Serdyuk, Thomas Hammarström, Christian Häger
Date: 2025-04-28 00:00:00

Dielectric response (DR) of insulating materials is key input information for designing electrical insulation systems and defining safe operating conditions of various HV devices. In dielectric materials, different polarization and conduction processes occur at different time scales, making it challenging to physically interpret raw measured data. To analyze DR measurement results, equivalent circuit models (ECMs) are commonly used, reducing the complexity of the physical system to a number of circuit elements that capture the dominant response. This paper examines the use of physics-informed neural networks (PINNs) for inverse modeling of DR in time domain using parallel RC circuits. To assess their performance, we test PINNs on synthetic data generated from analytical solutions of corresponding ECMs, incorporating Gaussian noise to simulate measurement errors. Our results show that PINNs are highly effective at solving well-conditioned inverse problems, accurately estimating up to five unknown RC parameters with minimal requirements on neural network size, training duration, and hyperparameter tuning. Furthermore, we extend the ECMs to incorporate temperature dependence and demonstrate that PINNs can accurately recover embedded, nonlinear temperature functions from noisy DR data sampled at different temperatures. This case study in modeling DR in time domain presents a solution with wide-ranging potential applications in disciplines relying on ECMs, utilizing the latest technology in machine learning for scientific computation.
AdSight: Scalable and Accurate Quantification of User Attention in Multi-Slot Sponsored Search
Authors: Mario Villaizán-Vallelado, Matteo Salvatori, Kayhan Latifzadeh, Antonio Penta, Luis A. Leiva, Ioannis Arapakis
Date: 2025-04-30 00:00:00
Tasks: Decoder

Modern Search Engine Results Pages (SERPs) present complex layouts where multiple elements compete for visibility. Attention modelling is crucial for optimising web design and computational advertising, whereas attention metrics can inform ad placement and revenue strategies. We introduce AdSight, a method leveraging mouse cursor trajectories to quantify in a scalable and accurate manner user attention in multi-slot environments like SERPs. AdSight uses a novel Transformer-based sequence-to-sequence architecture where the encoder processes cursor trajectory embeddings, and the decoder incorporates slot-specific features, enabling robust attention prediction across various SERP layouts. We evaluate our approach on two Machine Learning tasks: (1) regression, to predict fixation times and counts; and (2) classification, to determine some slot types were noticed. Our findings demonstrate the model's ability to predict attention with unprecedented precision, offering actionable insights for researchers and practitioners.
Uncovering Population PK Covariates from VAE-Generated Latent Spaces
Authors: Diego Perazzolo, Chiara Castellani, Enrico Grisan
Date: 2025-05-05 00:00:00
Tasks: feature selection, regression

Population pharmacokinetic (PopPK) modelling is a fundamental tool for understanding drug behaviour across diverse patient populations and enabling personalized dosing strategies to improve therapeutic outcomes. A key challenge in PopPK analysis lies in identifying and modelling covariates that influence drug absorption, as these relationships are often complex and nonlinear. Traditional methods may fail to capture hidden patterns within the data. In this study, we propose a data-driven, model-free framework that integrates Variational Autoencoders (VAEs) deep learning model and LASSO regression to uncover key covariates from simulated tacrolimus pharmacokinetic (PK) profiles. The VAE compresses high-dimensional PK signals into a structured latent space, achieving accurate reconstruction with a mean absolute percentage error (MAPE) of 2.26%. LASSO regression is then applied to map patient-specific covariates to the latent space, enabling sparse feature selection through L1 regularization. This approach consistently identifies clinically relevant covariates for tacrolimus including SNP, age, albumin, and hemoglobin which are retained across the tested regularization strength levels, while effectively discarding non-informative features. The proposed VAE-LASSO methodology offers a scalable, interpretable, and fully data-driven solution for covariate selection, with promising applications in drug development and precision pharmacotherapy.
Robotic Visual Instruction
Authors: Yanbang Li, Ziyang Gong, Haoyang Li, Xiaoqi Huang, Haolan Kang, Guangping Bai, Xianzheng Ma
Date: 2025-05-01 00:00:00
Proceeding: CVPR 2025 1

Recently, natural language has been the primary medium for human-robot interaction. However, its inherent lack of spatial precision introduces challenges for robotic task definition such as ambiguity and verbosity. Moreover, in some public settings where quiet is required, such as libraries or hospitals, verbal communication with robots is inappropriate. To address these limitations, we introduce the Robotic Visual Instruction (RoVI), a novel paradigm to guide robotic tasks through an object-centric, hand-drawn symbolic representation. RoVI effectively encodes spatial-temporal information into human-interpretable visual instructions through 2D sketches, utilizing arrows, circles, colors, and numbers to direct 3D robotic manipulation. To enable robots to understand RoVI better and generate precise actions based on RoVI, we present Visual Instruction Embodied Workflow (VIEW), a pipeline formulated for RoVI-conditioned policies. This approach leverages Vision-Language Models (VLMs) to interpret RoVI inputs, decode spatial and temporal constraints from 2D pixel space via keypoint extraction, and then transform them into executable 3D action sequences. We additionally curate a specialized dataset of 15K instances to fine-tune small VLMs for edge deployment,enabling them to effectively learn RoVI capabilities. Our approach is rigorously validated across 11 novel tasks in both real and simulated environments, demonstrating significant generalization capability. Notably, VIEW achieves an 87.5% success rate in real-world scenarios involving unseen tasks that feature multi-step actions, with disturbances, and trajectory-following requirements. Project website: https://robotic-visual-instruction.github.io/
Building semi-supervised decision trees with semi-cart algorithm
Authors: Aydin Abedinia, Vahid Seydi
Date: 2024-04-24 00:00:00
Proceeding: International Journal of Machine Learning and Cybernetics 2024 4
Tasks: Classification, regression

Decision trees are a fundamental statistical learning tool for addressing classification and regression problems through a recursive partitioning approach that effectively accommodates numerical and categorical data [1, 2]. The Classification and regression tree (CART) algorithm underlies modern Boosting methodologies such as Gradient boosting machine (GBM), Extreme gradient boosting (XGBoost), and Light gradient boosting machine (LightGBM). However, the standard CART algorithm may require improvement due to its inability to learn from unlabeled data. This study proposes several modifications to incorporate test data into the training phase. Specifically, we introduce a method based on Graph-based semisupervised learning called “Distance-based Weighting,” which calculates and removes irrelevant records from the training
set to accelerate the training process and improve performance. We present Semi-supervised classification and regression tree (Semi-Cart), a new implementation of CART that constructs a decision tree using weighted training data. We evaluated its performance on thirteen datasets from various domains. Our results demonstrate that Semi-Cart outperforms standard CART methods and contributes to statistical learning.
Adaptive Thinking via Mode Policy Optimization for Social Language Agents
Authors: Minzheng Wang, Yongbin Li, Haobo Wang, Xinghua Zhang, Nan Xu, Bingli Wu, Fei Huang, Haiyang Yu, Wenji Mao
Date: 2025-05-04 00:00:00

Effective social intelligence simulation requires language agents to dynamically adjust reasoning depth, a capability notably absent in current studies. Existing methods either lack this kind of reasoning capability or enforce Long Chain-of-Thought reasoning uniformly across all scenarios, resulting in excessive token usage and inflexible social simulation. To address this, we propose an $\textbf{A}$daptive $\textbf{M}$ode $\textbf{L}$earning ($\textbf{AML}$) framework in this paper, aiming to improve the adaptive thinking ability of language agents in dynamic social interactions. To this end, we first identify hierarchical thinking modes ranging from intuitive response to deep deliberation based on the cognitive control theory. We then develop the $\textbf{A}$daptive $\textbf{M}$ode $\textbf{P}$olicy $\textbf{O}$ptimization ($\textbf{AMPO}$) algorithm to optimize the context-aware mode switching and reasoning. Our framework advances existing research in three key aspects: (1) Multi-granular thinking mode design, (2) Context-aware mode switching across social interaction, and (3) Token-efficient reasoning via depth-adaptive processing. Extensive experiments on social intelligence benchmarks verify that AML achieves 15.6% higher task performance than GPT-4o. Notably, our AMPO outperforms GRPO by 7.0% with 32.8% shorter reasoning chains, demonstrating the advantage of adaptive thinking mode selection and optimization mechanism in AMPO over GRPO's fixed-depth solution.
A probabilistic view on Riemannian machine learning models for SPD matrices
Authors: Thibault de Surrel, Florian Yger, Fabien Lotte, Sylvain Chevallier
Date: 2025-05-05 00:00:00
Tasks: Dimensionality Reduction, Outlier Detection

The goal of this paper is to show how different machine learning tools on the Riemannian manifold $\mathcal{P}_d$ of Symmetric Positive Definite (SPD) matrices can be united under a probabilistic framework. For this, we will need several Gaussian distributions defined on $\mathcal{P}_d$. We will show how popular classifiers on $\mathcal{P}_d$ can be reinterpreted as Bayes Classifiers using these Gaussian distributions. These distributions will also be used for outlier detection and dimension reduction. By showing that those distributions are pervasive in the tools used on $\mathcal{P}_d$, we allow for other machine learning tools to be extended to $\mathcal{P}_d$.
Novel Computational Photography for Soft-Focus Effect in Automatic Post Production
Authors: Hao-Yu Tsai, Morris C.-H. Tsai, Scott C.-H. Huang, and Hsiao-Chun Wu
Date: 2025-04-23 00:00:00
Proceeding: IEEE Transactions on Image Processing 2025 4

The well-known soft-focus effect, which relies on either special optical filters or manual post-production techniques, has been intriguing and powerful in photography for quite a while. Nonetheless, how to impose the soft-focus effect automatically simply using sophisticated image-processing (computational photography) algorithms has never been addressed in the literature to the best of our knowledge. In this work, we would like to make the first-ever attempt to design an automatic, optical-filter-free approach to create the appropriate soft-focus effects desired by individual users. Our approach is first to investigate the physical optical filter, namely Kenko Black Mist No. 5, and estimate the corresponding kernel matrix (i.e., the system impulse response matrix) using our proposed novel irradiance-domain kernel-matrix estimation framework. Furthermore, we demonstrate that it is not feasible to find a kernel matrix that precisely characterizes the soft-focus effect by just using a pixel-value-domain image (a regular photo) in post production. To combat the aforementioned problem, we establish a novel pixel-value-to-pseudo-irradiance map such that the pseudo irradiance-domain image can be obtained directly from any pixel-value-domain image. Finally the soft-focus effect can be created from the two-dimensional convolution between the pseudo irradiance-domain image and the estimated kernel. To evaluate our proposed automatic scheme for soft-focus effect, we compare the results from our proposed new scheme and the physical optical filter in terms of the DCT-KLD (Kullback-Leibler divergence of discrete cosine transform) and the conventional PSNR (peak-signal-to-noise ratio). Experiments show that our proposed new scheme can achieve very small DCT-KLDs and very large PSNRs over the ground truth, namely the results from the physical optical filter.
A Synergistic Framework of Nonlinear Acoustic Computing and Reinforcement Learning for Real-World Human-Robot Interaction
Authors: Xiaoliang Chen, Xin Yu, Le Chang, Yunhe Huang, Jiashuai He, Shibo Zhang, Jin Li, Likai Lin, Ziyu Zeng, Xianling Tu, Shuyu Zhang
Date: 2025-05-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, speech-recognition, Speech Recognition

This paper introduces a novel framework integrating nonlinear acoustic computing and reinforcement learning to enhance advanced human-robot interaction under complex noise and reverberation. Leveraging physically informed wave equations (e.g., Westervelt, KZK), the approach captures higher-order phenomena such as harmonic generation and shock formation. By embedding these models in a reinforcement learning-driven control loop, the system adaptively optimizes key parameters (e.g., absorption, beamforming) to mitigate multipath interference and non-stationary noise. Experimental evaluations, covering far-field localization, weak signal detection, and multilingual speech recognition, demonstrate that this hybrid strategy surpasses traditional linear methods and purely data-driven baselines, achieving superior noise suppression, minimal latency, and robust accuracy in demanding real-world scenarios. The proposed system demonstrates broad application prospects in AI hardware, robot, machine audition, artificial audition, and brain-machine interfaces.
Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
Authors: Jiancong Xiao, BoJian Hou, Zhanliang Wang, Ruochen Jin, Qi Long, Weijie J. Su, Li Shen
Date: 2025-05-04 00:00:00

One of the key technologies for the success of Large Language Models (LLMs) is preference alignment. However, a notable side effect of preference alignment is poor calibration: while the pre-trained models are typically well-calibrated, LLMs tend to become poorly calibrated after alignment with human preferences. In this paper, we investigate why preference alignment affects calibration and how to address this issue. For the first question, we observe that the preference collapse issue in alignment undesirably generalizes to the calibration scenario, causing LLMs to exhibit overconfidence and poor calibration. To address this, we demonstrate the importance of fine-tuning with domain-specific knowledge to alleviate the overconfidence issue. To further analyze whether this affects the model's performance, we categorize models into two regimes: calibratable and non-calibratable, defined by bounds of Expected Calibration Error (ECE). In the calibratable regime, we propose a calibration-aware fine-tuning approach to achieve proper calibration without compromising LLMs' performance. However, as models are further fine-tuned for better performance, they enter the non-calibratable regime. For this case, we develop an EM-algorithm-based ECE regularization for the fine-tuning loss to maintain low calibration error. Extensive experiments validate the effectiveness of the proposed methods.
Multi-Class Segmentation of Heterogeneous Areas in Biomedical and Environmental Images Based on the Assessment of Local Edge Density
Authors: A. M. Sinitca, A. I. Lyanova, D. I. Kaplun, P. V. Zelenikhin, R. G. Imaev, A. M. Gafurov, B. M. Usmanov, D. V. Tishin, A. R. Kayumov, M. I. Bogachev
Date: 2023-05-12 00:00:00
Proceeding: ISPRS 2023 5
Tasks: Edge Detection, Image Segmentation, Segmentation, Semantic Segmentation

Imaging techniques employed in biomedical and ecological applications typically require complex equipment and experimental approaches, including sophisticated multispectral cameras, as well as physical markup of samples, altogether limiting their broad availability. Accordingly, computerized methods allowing to obtain similar information from images obtained in visible light spectrum with reasonable accuracy are of considerable interest. Edge detection methods are commonly used to find discriminating curves in image segmentation. Here we follow an alternative route and employ edge detection results as a separate metric characterizing local structural properties of the image. In turn, their characteristics such as density or orientation averaged in a gliding window are used as a virtual channel substituting multispectral imaging and/or physical markup of samples, and the following image segmentation procedures are performed by thresholding. In complex segmentation scenarios, a single fixed threshold often appears sufficient, and thus relevant adaptive multi-threshold algorithms are of interest, with slope difference distribution (SDD) thresholding algorithm representing a prominent example.
ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications
Authors: Tao Zhu, Qi Yu, Xinru Dong, Shiyu Li, Yue Liu, Jinlong Jiang, Lei Shu
Date: 2025-05-04 00:00:00
Tasks: Anomaly Detection In Surveillance Videos, Contrastive Learning, Multiple Instance Learning, Supervised Anomaly Detection, Video Anomaly Detection, Weakly-supervised Anomaly Detection, Weakly-supervised Video Anomaly Detection

Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning. We propose ProDisc-VAD, an efficient framework tackling this via two synergistic components. The Prototype Interaction Layer (PIL) provides controlled normality modeling using a small set of learnable prototypes, establishing a robust baseline without being overwhelmed by dominant normal data. The Pseudo-Instance Discriminative Enhancement (PIDE) loss boosts separability by applying targeted contrastive learning exclusively to the most reliable extreme-scoring instances (highest/lowest scores). ProDisc-VAD achieves strong AUCs (97.98% ShanghaiTech, 87.12% UCF-Crime) using only 0.4M parameters, over 800x fewer than recent ViT-based methods like VadCLIP, demonstrating exceptional efficiency alongside state-of-the-art performance. Code is available at https://github.com/modadundun/ProDisc-VAD.
Detrended Partial-Cross-Correlation Analysis: A New Method for Analyzing Correlations in Complex System
Authors: Naiming Yuan, Zuntao Fu, huan zhang, Lin Piao, Elena Xoplaki, Juerg Luterbacher
Date: 2015-01-30 00:00:00
Proceeding: Scientific Reports 2015 1
Tasks: Time Series Analysis

In this paper, a new method, detrended partial-cross-correlation analysis (DPCCA), is proposed. Based on detrended cross-correlation analysis (DCCA), this method is improved by including partial-correlation technique, which can be applied to quantify the relations of two non-stationary signals (with influences of other signals removed) on different time scales. We illustrate the advantages of this method by performing two numerical tests. Test I shows the advantages of DPCCA in handling non-stationary signals, while Test II reveals the “intrinsic” relations between two considered time series with potential influences of other unconsidered signals removed. To further show the utility of DPCCA in natural complex systems, we provide new evidence on the winter-time Pacific Decadal Oscillation (PDO) and the winter-time Nino3 Sea Surface Temperature Anomaly (Nino3-SSTA) affecting the Summer Rainfall over the middle-lower reaches of the Yangtze River (SRYR). By applying DPCCA, better significant correlations between SRYR and Nino3-SSTA on time scales of 6 ~ 8 years are found over the period 1951 ~ 2012, while significant correlations between SRYR and PDO on time scales of 35 years arise. With these physically explainable results, we have confidence that DPCCA is an useful method in addressing complex systems.