pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction
Authors: Jingui Ma, Yang Hu, Luyang Tang, Jiayu Yang, Yongqi Zhai, Ronggang Wang
Date: 2025-03-30 00:00:00
Tasks: 3DGS, Novel View Synthesis, Prediction, Video Compression

Recently, 3D Gaussian Spatting (3DGS) has gained widespread attention in Novel View Synthesis (NVS) due to the remarkable real-time rendering performance. However, the substantial cost of storage and transmission of vanilla 3DGS hinders its further application (hundreds of megabytes or even gigabytes for a single scene). Motivated by the achievements of prediction in video compression, we introduce the prediction technique into the anchor-based Gaussian representation to effectively reduce the bit rate. Specifically, we propose a spatial condition-based prediction module to utilize the grid-captured scene information for prediction, with a residual compensation strategy designed to learn the missing fine-grained information. Besides, to further compress the residual, we propose an instance-aware hyper prior, developing a structure-aware and instance-aware entropy model. Extensive experiments demonstrate the effectiveness of our prediction-based compression framework and each technical component. Even compared with SOTA compression method, our framework still achieves a bit rate savings of 24.42 percent. Code is to be released!
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis
Authors: Jiahao Qin, Feng Liu, Lu Zong
Date: 2025-03-25 00:00:00
Tasks: Emotion Recognition, Evolutionary Algorithms, Multimodal Sentiment Analysis, Multi-Task Learning, Sentiment Analysis, Transfer Learning

Multimodal sentiment analysis has emerged as a critical tool for understanding human emotions across diverse communication channels. While existing methods have made significant strides, they often struggle to effectively differentiate and integrate modality-shared and modality-specific information, limiting the performance of multimodal learning. To address this challenge, we propose the Hierarchical Adaptive Expert for Multimodal Sentiment Analysis (HAEMSA), a novel framework that synergistically combines evolutionary optimization, cross-modal knowledge transfer, and multi-task learning. HAEMSA employs a hierarchical structure of adaptive experts to capture both global and local modality representations, enabling more nuanced sentiment analysis. Our approach leverages evolutionary algorithms to dynamically optimize network architectures and modality combinations, adapting to both partial and full modality scenarios. Extensive experiments demonstrate HAEMSA's superior performance across multiple benchmark datasets. On CMU-MOSEI, HAEMSA achieves a 2.6% increase in 7-class accuracy and a 0.059 decrease in MAE compared to the previous best method. For CMU-MOSI, we observe a 6.3% improvement in 7-class accuracy and a 0.058 reduction in MAE. On IEMOCAP, HAEMSA outperforms the state-of-the-art by 2.84% in weighted-F1 score for emotion recognition. These results underscore HAEMSA's effectiveness in capturing complex multimodal interactions and generalizing across different emotional contexts.
Finding Unknown Unknowns using Cyber-Physical System Simulators (Extended Report)
Authors: Semaan Douglas Wehbe, Stanley Bak
Date: 2025-03-28 00:00:00

Simulation-based approaches are among the most practical means to search for safety violations, bugs, and other unexpected events in cyber-physical systems (CPS). Where existing approaches search for simulations violating a formal specification or maximizing a notion of coverage, in this work we propose a new goal for testing: to discover unknown rare behaviors by examining discrete mode sequences. We assume a CPS simulator outputs mode information, and strive to explore the sequences of modes produced by varying the initial state or time-varying uncertainties. We hypothesize that rare mode sequences are often the most interesting to a designer, and we develop two accelerated sampling algorithms that speed up the process of finding such sequences. We evaluate our approach on several benchmarks, ranging from synthetic examples to Simulink diagrams of a CPS, demonstrating in some cases a speedup of over 100x compared with a random sampling strategy.
Optimizing Age of Information in Networks with Large and Small Updates
Authors: Zhuoyi Zhao, Vishrant Tripathi, Igor Kadota
Date: 2025-03-31 00:00:00
Tasks: Scheduling

Modern sensing and monitoring applications typically consist of sources transmitting updates of different sizes, ranging from a few bytes (position, temperature, etc.) to multiple megabytes (images, video frames, LIDAR point scans, etc.). Existing approaches to wireless scheduling for information freshness typically ignore this mix of large and small updates, leading to suboptimal performance. In this paper, we consider a single-hop wireless broadcast network with sources transmitting updates of different sizes to a base station over unreliable links. Some sources send large updates spanning many time slots while others send small updates spanning only a few time slots. Due to medium access constraints, only one source can transmit to the base station at any given time, thus requiring careful design of scheduling policies that takes the sizes of updates into account. First, we derive a lower bound on the achievable Age of Information (AoI) by any transmission scheduling policy. Second, we develop optimal randomized policies that consider both switching and no-switching during the transmission of large updates. Third, we introduce a novel Lyapunov function and associated analysis to propose an AoI-based Max-Weight policy that has provable constant factor optimality guarantees. Finally, we evaluate and compare the performance of our proposed scheduling policies through simulations, which show that our Max-Weight policy achieves near-optimal AoI performance.
CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
Authors: Peiding Wang, Li Zhang, Fang Liu, Lin Shi, Minxiao Li, Bo Shen, An Fu
Date: 2025-03-05 00:00:00
Tasks: Code Generation, Instruction Following

Large Language Models (LLMs) have demonstrated exceptional performance in code generation tasks and have become indispensable programming assistants for developers. However, existing code generation benchmarks primarily assess the functional correctness of code generated by LLMs in single-turn interactions, offering limited insight into their capabilities to generate code that strictly follows users' instructions, especially in multi-turn interaction scenarios. In this paper, we introduce CodeIF-Bench, a benchmark for evaluating LLMs' instruction-following capabilities in interactive code generation. Specifically, CodeIF-Bench incorporates nine types of verifiable instructions aligned with the real-world software development requirements, which can be independently and objectively validated through specified test cases, facilitating the evaluation of instruction-following capability in multi-turn interactions. We evaluate nine prominent LLMs using CodeIF-Bench, and the experimental results reveal a significant disparity between their basic programming capability and instruction-following capability, particularly as task complexity, context length, and the number of dialogue rounds increase.
Physically Ground Commonsense Knowledge for Articulated Object Manipulation with Analytic Concepts
Authors: Jianhua Sun, Jiude Wei, YuXuan Li, Cewu Lu
Date: 2025-03-30 00:00:00
Tasks: Object

We human rely on a wide range of commonsense knowledge to interact with an extensive number and categories of objects in the physical world. Likewise, such commonsense knowledge is also crucial for robots to successfully develop generalized object manipulation skills. While recent advancements in Large Language Models (LLM) have showcased their impressive capabilities in acquiring commonsense knowledge and conducting commonsense reasoning, effectively grounding this semantic-level knowledge produced by LLMs to the physical world to thoroughly guide robots in generalized articulated object manipulation remains a challenge that has not been sufficiently addressed. To this end, we introduce analytic concepts, procedurally defined upon mathematical symbolism that can be directly computed and simulated by machines. By leveraging the analytic concepts as a bridge between the semantic-level knowledge inferred by LLMs and the physical world where real robots operate, we are able to figure out the knowledge of object structure and functionality with physics-informed representations, and then use the physically grounded knowledge to instruct robot control policies for generalized, interpretable and accurate articulated object manipulation. Extensive experiments in both simulation and real-world environments demonstrate the superiority of our approach.
Learning Predictive Visuomotor Coordination
Authors: Wenqi Jia, Bolin Lai, Miao Liu, Danfei Xu, James M. Rehg
Date: 2025-03-30 00:00:00

Understanding and predicting human visuomotor coordination is crucial for applications in robotics, human-computer interaction, and assistive technologies. This work introduces a forecasting-based task for visuomotor modeling, where the goal is to predict head pose, gaze, and upper-body motion from egocentric visual and kinematic observations. We propose a \textit{Visuomotor Coordination Representation} (VCR) that learns structured temporal dependencies across these multimodal signals. We extend a diffusion-based motion modeling framework that integrates egocentric vision and kinematic sequences, enabling temporally coherent and accurate visuomotor predictions. Our approach is evaluated on the large-scale EgoExo4D dataset, demonstrating strong generalization across diverse real-world activities. Our results highlight the importance of multimodal integration in understanding visuomotor coordination, contributing to research in visuomotor learning and human behavior modeling.
Energy-Aware Lane Planning for Connected Electric Vehicles in Urban Traffic: Design and Vehicle-in-the-Loop Validation
Authors: Hansung Kim, Eric Yongkeun Choi, Eunhyek Joa, Hotae Lee, Linda Lim, Scott Moura, Francesco Borrelli
Date: 2025-03-29 00:00:00
Tasks: Motion Planning

Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. This neglects the significant impact of lateral decisions, such as lane changes, on overall energy efficiency, especially in environments with traffic signals and heterogeneous traffic flow. To address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our approach estimates long-term energy costs using a graph-based approximation and solves short-horizon optimal control problems under traffic constraints. Using a data-driven energy model calibrated to an actual battery electric vehicle, we demonstrate with vehicle-in-the-loop experiments that our method reduces motion energy consumption by up to 24 percent compared to a human driver, highlighting the potential of connectivity-enabled planning for sustainable urban autonomy.
Evaluation of Remote Driver Performance in Urban Environment Operational Design Domains
Authors: Ole Hans, Benedikt Walter, Jürgen Adamy
Date: 2025-03-29 00:00:00

Remote driving has emerged as a solution for enabling human intervention in scenarios where Automated Driving Systems (ADS) face challenges, particularly in urban Operational Design Domains (ODDs). This study evaluates the performance of Remote Drivers (RDs) of passenger cars in a representative urban ODD in Las Vegas, focusing on the influence of cumulative driving experience and targeted training approaches. Using performance metrics such as efficiency, braking, acceleration, and steering, the study shows that driving experience can lead to noticeable improvements of RDs and demonstrates how experience up to 600 km correlates with improved vehicle control. In addition, driving efficiency exhibited a positive trend with increasing kilometers, particularly during the first 300 km of experience, which reaches a plateau from 400 km within a range of 0.35 to 0.42 km/min in the defined ODD. The research further compares ODD-specific training methods, where the detailed ODD training approaches attains notable advantages over other training approaches. The findings underscore the importance of tailored ODD training in enhancing RD performance, safety, and scalability for Remote Driving System (RDS) in real-world applications, while identifying opportunities for optimizing training protocols to address both routine and extreme scenarios. The study provides a robust foundation for advancing RDS deployment within urban environments, contributing to the development of scalable and safety-critical remote operation standards.
A Multiple Artificial Potential Functions Approach for Collision Avoidance in UAV Systems
Authors: Oscar F. Archila, Alain Vande Wouwer, Johannes Schiffer
Date: 2025-03-28 00:00:00
Tasks: Collision Avoidance

Collision avoidance is a problem largely studied in robotics, particularly in unmanned aerial vehicle (UAV) applications. Among the main challenges in this area are hardware limitations, the need for rapid response, and the uncertainty associated with obstacle detection. Artificial potential functions (APOFs) are a prominent method to address these challenges. However, existing solutions lack assurances regarding closed-loop stability and may result in chattering effects. Motivated by this, we propose a control method for static obstacle avoidance based on multiple artificial potential functions (MAPOFs). We derive tuning conditions on the control parameters that ensure the stability of the final position. The stability proof is established by analyzing the closed-loop system using tools from hybrid systems theory. Furthermore, we validate the performance of the MAPOF control through simulations, showcasing its effectiveness in avoiding static obstacles.
Exploring In-Context Learning Capabilities of ChatGPT for Pathological Speech Detection
Authors: Mahdi Amiri, Hatef Otroshi Shahreza, Ina Kodrasi
Date: 2025-03-31 00:00:00
Tasks: Diagnostic, In-Context Learning

Automatic pathological speech detection approaches have shown promising results, gaining attention as potential diagnostic tools alongside costly traditional methods. While these approaches can achieve high accuracy, their lack of interpretability limits their applicability in clinical practice. In this paper, we investigate the use of multimodal Large Language Models (LLMs), specifically ChatGPT-4o, for automatic pathological speech detection in a few-shot in-context learning setting. Experimental results show that this approach not only delivers promising performance but also provides explanations for its decisions, enhancing model interpretability. To further understand its effectiveness, we conduct an ablation study to analyze the impact of different factors, such as input type and system prompts, on the final results. Our findings highlight the potential of multimodal LLMs for further exploration and advancement in automatic pathological speech detection.
Baseline Systems and Evaluation Metrics for Spatial Semantic Segmentation of Sound Scenes
Authors: Binh Thien Nguyen, Masahiro Yasuda, Daiki Takeuchi, Daisuke Niizumi, Yasunori Ohishi, Noboru Harada
Date: 2025-03-28 00:00:00
Tasks: Audio Tagging, Semantic Segmentation

Immersive communication has made significant advancements, especially with the release of the codec for Immersive Voice and Audio Services. Aiming at its further realization, the DCASE 2025 Challenge has recently introduced a task for spatial semantic segmentation of sound scenes (S5), which focuses on detecting and separating sound events in spatial sound scenes. In this paper, we explore methods for addressing the S5 task. Specifically, we present baseline S5 systems that combine audio tagging (AT) and label-queried source separation (LSS) models. We investigate two LSS approaches based on the ResUNet architecture: a) extracting a single source for each detected event and b) querying multiple sources concurrently. Since each separated source in S5 is identified by its sound event class label, we propose new class-aware metrics to evaluate both the sound sources and labels simultaneously. Experimental results on first-order ambisonics spatial audio demonstrate the effectiveness of the proposed systems and confirm the efficacy of the metrics.
Hierarchical Label Propagation: A Model-Size-Dependent Performance Booster for AudioSet Tagging
Authors: Ludovic Tuncay, Etienne Labbé, Thomas Pellegrini
Date: 2025-03-26 00:00:00
Tasks: Audio Tagging

AudioSet is one of the most used and largest datasets in audio tagging, containing about 2 million audio samples that are manually labeled with 527 event categories organized into an ontology. However, the annotations contain inconsistencies, particularly where categories that should be labeled as positive according to the ontology are frequently mislabeled as negative. To address this issue, we apply Hierarchical Label Propagation (HLP), which propagates labels up the ontology hierarchy, resulting in a mean increase in positive labels per audio clip from 1.98 to 2.39 and affecting 109 out of the 527 classes. Our results demonstrate that HLP provides performance benefits across various model architectures, including convolutional neural networks (PANN's CNN6 and ConvNeXT) and transformers (PaSST), with smaller models showing more improvements. Finally, on FSD50K, another widely used dataset, models trained on AudioSet with HLP consistently outperformed those trained without HLP. Our source code will be made available on GitHub.
Dual Audio-Centric Modality Coupling for Talking Head Generation
Authors: Ao Fu, Ziqi Ni, Yi Zhou
Date: 2025-03-26 00:00:00
Tasks: NeRF, Talking Head Generation, text-to-speech, Text to Speech

The generation of audio-driven talking head videos is a key challenge in computer vision and graphics, with applications in virtual avatars and digital media. Traditional approaches often struggle with capturing the complex interaction between audio and facial dynamics, leading to lip synchronization and visual quality issues. In this paper, we propose a novel NeRF-based framework, Dual Audio-Centric Modality Coupling (DAMC), which effectively integrates content and dynamic features from audio inputs. By leveraging a dual encoder structure, DAMC captures semantic content through the Content-Aware Encoder and ensures precise visual synchronization through the Dynamic-Sync Encoder. These features are fused using a Cross-Synchronized Fusion Module (CSFM), enhancing content representation and lip synchronization. Extensive experiments show that our method outperforms existing state-of-the-art approaches in key metrics such as lip synchronization accuracy and image quality, demonstrating robust generalization across various audio inputs, including synthetic speech from text-to-speech (TTS) systems. Our results provide a promising solution for high-quality, audio-driven talking head generation and present a scalable approach for creating realistic talking heads.
Chirp Localization via Fine-Tuned Transformer Model: A Proof-of-Concept Study
Authors: Nooshin Bahador, Milad Lankarany
Date: 2025-03-24 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Spectrograms are pivotal in time-frequency signal analysis, widely used in audio processing and computational neuroscience. Chirp-like patterns in electroencephalogram (EEG) spectrograms (marked by linear or exponential frequency sweep) are key biomarkers for seizure dynamics, but automated tools for their detection, localization, and feature extraction are lacking. This study bridges this gap by fine-tuning a Vision Transformer (ViT) model on synthetic spectrograms, augmented with Low-Rank Adaptation (LoRA) to boost adaptability. We generated 100000 synthetic spectrograms with chirp parameters, creating the first large-scale benchmark for chirp localization. These spectrograms mimic neural chirps using linear or exponential frequency sweep, Gaussian noise, and smoothing. A ViT model, adapted for regression, predicted chirp parameters. LoRA fine-tuned the attention layers, enabling efficient updates to the pre-trained backbone. Training used MSE loss and the AdamW optimizer, with a learning rate scheduler and early stopping to curb overfitting. Only three features were targeted: Chirp Start Time (Onset Time), Chirp Start Frequency (Onset Frequency), and Chirp End Frequency (Offset Frequency). Performance was evaluated via Pearson correlation between predicted and actual labels. Results showed strong alignment: 0.9841 correlation for chirp start time, with stable inference times (137 to 140s) and minimal bias in error distributions. This approach offers a tool for chirp analysis in EEG time-frequency representation, filling a critical methodological void.
Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets
Authors: Zijun Jia, Jinsong Yu, Hongyu Long, Diyin Tang
Date: 2025-03-24 00:00:00
Tasks: Conformal Prediction, Emotion Recognition, Prediction, Speech Emotion Recognition, speech-recognition, Speech Recognition, Uncertainty Quantification

Road rage, often triggered by emotional suppression and sudden outbursts, significantly threatens road safety by causing collisions and aggressive behavior. Speech emotion recognition technologies can mitigate this risk by identifying negative emotions early and issuing timely alerts. However, current SER methods, such as those based on hidden markov models and Long short-term memory networks, primarily handle one-dimensional signals, frequently experience overfitting, and lack calibration, limiting their safety-critical effectiveness. We propose a novel risk-controlled prediction framework providing statistically rigorous guarantees on prediction accuracy. This approach employs a calibration set to define a binary loss function indicating whether the true label is included in the prediction set. Using a data-driven threshold $\beta$, we optimize a joint loss function to maintain an expected test loss bounded by a user-specified risk level $\alpha$. Evaluations across six baseline models and two benchmark datasets demonstrate our framework consistently achieves a minimum coverage of $1 - \alpha$, effectively controlling marginal error rates despite varying calibration-test split ratios (e.g., 0.1). The robustness and generalizability of the framework are further validated through an extension to small-batch online calibration under a local exchangeability assumption. We construct a non-negative test martingale to maintain prediction validity even in dynamic and non-exchangeable environments. Cross-dataset tests confirm our method's ability to uphold reliable statistical guarantees in realistic, evolving data scenarios.