pwc-archive/papers-with-abstracts huggingface dataset:
Feature Relevancy, Necessity and Usefulness: Complexity and Algorithms
Authors: Tomás Capdevielle, Santiago Cifuentes
Date: 2025-05-06 00:00:00

Given a classification model and a prediction for some input, there are heuristic strategies for ranking features according to their importance in regard to the prediction. One common approach to this task is rooted in propositional logic and the notion of \textit{sufficient reason}. Through this concept, the categories of relevant and necessary features were proposed in order to identify the crucial aspects of the input. This paper improves the existing techniques and algorithms for deciding which are the relevant and/or necessary features, showing in particular that necessity can be detected efficiently in complex models such as neural networks. We also generalize the notion of relevancy and study associated problems. Moreover, we present a new global notion (i.e. that intends to explain whether a feature is important for the behavior of the model in general, not depending on a particular input) of \textit{usefulness} and prove that it is related to relevancy and necessity. Furthermore, we develop efficient algorithms for detecting it in decision trees and other more complex models, and experiment on three datasets to analyze its practical utility.
SpecWav-Attack: Leveraging Spectrogram Resizing and Wav2Vec 2.0 for Attacking Anonymized Speech
Authors: Yuqi Li, Yuanzhong Zheng, Zhongtian Guo, Yaoxuan Wang, Jianjun Yin, Haojun Fei
Date: 2025-01-10 00:00:00

This paper presents SpecWav-Attack, an adversarial model for detecting speakers in anonymized speech. It leverages Wav2Vec2 for feature extraction and incorporates spectrogram resizing and incremental training for improved performance. Evaluated on librispeech-dev and librispeech-test, SpecWav-Attack outperforms conventional attacks, revealing vulnerabilities in anonymized speech systems and emphasizing the need for stronger defenses, benchmarked against the ICASSP 2025 Attacker Challenge.
Toward Evaluative Thinking: Meta Policy Optimization with Evolving Reward Models
Authors: Zae Myung Kim, Chanwoo Park, Vipul Raheja, Suin Kim, Dongyeop Kang
Date: 2025-04-28 00:00:00
Tasks: Mathematical Reasoning, Meta-Learning, Prompt Engineering, Question Answering

Reward-based alignment methods for large language models (LLMs) face two key limitations: vulnerability to reward hacking, where models exploit flaws in the reward signal; and reliance on brittle, labor-intensive prompt engineering when LLMs are used as reward models. We introduce Meta Policy Optimization (MPO), a framework that addresses these challenges by integrating a meta-reward model that dynamically refines the reward model's prompt throughout training. In MPO, the meta-reward model monitors the evolving training context and continuously adjusts the reward model's prompt to maintain high alignment, providing an adaptive reward signal that resists exploitation by the policy. This meta-learning approach promotes a more stable policy optimization, and greatly reduces the need for manual reward prompt design. It yields performance on par with or better than models guided by extensively hand-crafted reward prompts. Furthermore, we show that MPO maintains its effectiveness across diverse tasks, from essay writing to mathematical reasoning, without requiring specialized reward designs. Beyond standard RLAIF, MPO's meta-learning formulation is readily extensible to higher-level alignment frameworks. Overall, this method addresses theoretical and practical challenges in reward-based RL alignment for LLMs, paving the way for more robust and adaptable alignment strategies. The code and data can be accessed at: https://github.com/minnesotanlp/mpo
AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and Care
Authors: Md Asaduzzaman Jabin, Hanqi Jiang, Yiwei Li, Patrick Kaggwa, Eugene Douglass, Juliet N. Sekandi, Tianming Liu
Date: 2025-05-01 00:00:00
Tasks: Language Modeling, Language Modelling, parameter-efficient fine-tuning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Chronic diseases, including diabetes, hypertension, asthma, HIV-AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to avert disease progression, manage symptoms, and decrease mortality rates. Adherence is frequently undermined by factors including patient behavior, caregiver support, elevated medical costs, and insufficient healthcare infrastructure. We propose AdCare-VLM, a specialized Video-LLaVA-based multimodal large vision language model (LVLM) aimed at visual question answering (VQA) concerning medication adherence through patient videos. We employ a private dataset comprising 806 custom-annotated tuberculosis (TB) medication monitoring videos, which have been labeled by clinical experts, to fine-tune the model for adherence pattern detection. We present LLM-TB-VQA, a detailed medical adherence VQA dataset that encompasses positive, negative, and ambiguous adherence cases. Our method identifies correlations between visual features, such as the clear visibility of the patient's face, medication, water intake, and the act of ingestion, and their associated medical concepts in captions. This facilitates the integration of aligned visual-linguistic representations and improves multimodal interactions. Experimental results indicate that our method surpasses parameter-efficient fine-tuning (PEFT) enabled VLM models, such as LLaVA-V1.5 and Chat-UniVi, with absolute improvements ranging from 3.1% to 3.54% across pre-trained, regular, and low-rank adaptation (LoRA) configurations. Comprehensive ablation studies and attention map visualizations substantiate our approach, enhancing interpretability.
Reasoning on a Budget: Miniaturizing DeepSeek R1 with SFT-GRPO Alignment for Instruction-Tuned LLMs
Authors: Esmaeil Narimissa
Date: 2025-05-16 00:00:00
Proceeding: techrxiv 2025 5
Tasks: Deep Reinforcement Learning, Mathematical Reasoning, parameter-efficient fine-tuning, reinforcement-learning, Reinforcement Learning

Large language models (LLMs) excel at general-purpose generation but often struggle with structured reasoning tasks. Recent methods like DeepSeek-R1 have shown that reinforcement learning with rule-based rewards can significantly enhance reasoning capabilities. However, reproducing such pipelines remains computationally intensive and inaccessible to most researchers. In this work, we present a modular, low-cost replication of the DeepSeek-R1 training methodology using Qwen2.5-0.5B-Instruct (a compact instruction-tuned LLM) optimized via a two-stage pipeline: Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO). SFT aligns the base model to reasoning-structured prompts using LoRA-based parameter-efficient fine-tuning. GRPO then refines this policy using a critic-free reinforcement learning algorithm guided by five composable reward functions, including accuracy, reasoning presence, and formatting compliance. The entire training process was executed for under $100 USD on AWS SageMaker, demonstrating that high-impact reasoning alignment is achievable without large-scale compute. Quantitative metrics confirm strong convergence, high reward stability, and consistent output structure. This study contributes a scalable and reproducible template for aligning compact LLMs to reasoning-intensive tasks under constrained computational budgets.
SemanticSugarBeets: A Multi-Task Framework and Dataset for Inspecting Harvest and Storage Characteristics of Sugar Beets
Authors: Gerardus Croonen, Andreas Trondl, Julia Simon, Daniel Steininger
Date: 2025-04-23 00:00:00
Tasks: Segmentation, Semantic Segmentation

While sugar beets are stored prior to processing, they lose sugar due to factors such as microorganisms present in adherent soil and excess vegetation. Their automated visual inspection promises to aide in quality assurance and thereby increase efficiency throughout the processing chain of sugar production. In this work, we present a novel high-quality annotated dataset and two-stage method for the detection, semantic segmentation and mass estimation of post-harvest and post-storage sugar beets in monocular RGB images. We conduct extensive ablation experiments for the detection of sugar beets and their fine-grained semantic segmentation regarding damages, rot, soil adhesion and excess vegetation. For these tasks, we evaluate multiple image sizes, model architectures and encoders, as well as the influence of environmental conditions. Our experiments show an mAP50-95 of 98.8 for sugar-beet detection and an mIoU of 64.0 for the best-performing segmentation model.
ViSpeak: Visual Instruction Feedback in Streaming Videos
Authors: Shenghao Fu, Qize Yang, Yuan-Ming Li, Yi-Xing Peng, Kun-Yu Lin, Xihan Wei, Jian-Fang Hu, Xiaohua Xie, Wei-Shi Zheng
Date: 2025-03-17 00:00:00
Tasks: Streaming video understanding, Video Understanding

Recent advances in Large Multi-modal Models (LMMs) are primarily focused on offline video understanding. Instead, streaming video understanding poses great challenges to recent models due to its time-sensitive, omni-modal and interactive characteristics. In this work, we aim to extend the streaming video understanding from a new perspective and propose a novel task named Visual Instruction Feedback in which models should be aware of visual contents and learn to extract instructions from them. For example, when users wave their hands to agents, agents should recognize the gesture and start conversations with welcome information. Thus, following instructions in visual modality greatly enhances user-agent interactions. To facilitate research, we define seven key subtasks highly relevant to visual modality and collect the ViSpeak-Instruct dataset for training and the ViSpeak-Bench for evaluation. Further, we propose the ViSpeak model, which is a SOTA streaming video understanding LMM with GPT-4o-level performance on various streaming video understanding benchmarks. After finetuning on our ViSpeak-Instruct dataset, ViSpeak is equipped with basic visual instruction feedback ability, serving as a solid baseline for future research.
An Empirical Study on Prompt Compression for Large Language Models
Authors: Zheng Zhang, Jinyi Li, Yihuai Lan, Xiang Wang, Hao Wang
Date: 2025-04-24 00:00:00
Tasks: Articles, Math, Prompt Engineering, Visual Question Answering (VQA)

Prompt engineering enables Large Language Models (LLMs) to perform a variety of tasks. However, lengthy prompts significantly increase computational complexity and economic costs. To address this issue, we study six prompt compression methods for LLMs, aiming to reduce prompt length while maintaining LLM response quality. In this paper, we present a comprehensive analysis covering aspects such as generation performance, model hallucinations, efficacy in multimodal tasks, word omission analysis, and more. We evaluate these methods across 13 datasets, including news, scientific articles, commonsense QA, math QA, long-context QA, and VQA datasets. Our experiments reveal that prompt compression has a greater impact on LLM performance in long contexts compared to short ones. In the Longbench evaluation, moderate compression even enhances LLM performance. Our code and data is available at https://github.com/3DAgentWorld/Toolkit-for-Prompt-Compression.
Efficient LLMs with AMP: Attention Heads and MLP Pruning
Authors: Leandro Giusti Mugnaini, Bruno Lopes Yamamoto, Lucas Lauton de Alcantara, Victor Zacarias, Edson Bollis, Lucas Pellicer, Anna Helena Reali Costa, Artur Jordao
Date: 2025-04-29 00:00:00

Deep learning drives a new wave in computing systems and triggers the automation of increasingly complex problems. In particular, Large Language Models (LLMs) have significantly advanced cognitive tasks, often matching or even surpassing human-level performance. However, their extensive parameters result in high computational costs and slow inference, posing challenges for deployment in resource-limited settings. Among the strategies to overcome the aforementioned challenges, pruning emerges as a successful mechanism since it reduces model size while maintaining predictive ability. In this paper, we introduce AMP: Attention Heads and MLP Pruning, a novel structured pruning method that efficiently compresses LLMs by removing less critical structures within Multi-Head Attention (MHA) and Multilayer Perceptron (MLP). By projecting the input data onto weights, AMP assesses structural importance and overcomes the limitations of existing techniques, which often fall short in flexibility or efficiency. In particular, AMP surpasses the current state-of-the-art on commonsense reasoning tasks by up to 1.49 percentage points, achieving a 30% pruning ratio with minimal impact on zero-shot task performance. Moreover, AMP also improves inference speeds, making it well-suited for deployment in resource-constrained environments. We confirm the flexibility of AMP on different families of LLMs, including LLaMA and Phi.
Param$Δ$ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost
Authors: Sheng Cao, Mingrui Wu, Karthik Prasad, Yuandong Tian, Zechun Liu
Date: 2025-04-23 00:00:00
Tasks: Instruction Following, Language Modeling, Language Modelling, Large Language Model

The post-training phase of large language models is essential for enhancing capabilities such as instruction-following, reasoning, and alignment with human preferences. However, it demands extensive high-quality data and poses risks like overfitting, alongside significant computational costs due to repeated post-training and evaluation after each base model update. This paper introduces $Param\Delta$, a novel method that streamlines post-training by transferring knowledge from an existing post-trained model to a newly updated base model with ZERO additional training. By computing the difference between post-trained model weights ($\Theta_\text{post}$) and base model weights ($\Theta_\text{base}$), and adding this to the updated base model ($\Theta'_\text{base}$), we define $Param\Delta$ Model as: $\Theta_{\text{Param}\Delta} = \Theta_\text{post} - \Theta_\text{base} + \Theta'_\text{base}$. This approach surprisingly equips the new base model with post-trained capabilities, achieving performance comparable to direct post-training. We did analysis on LLama3, Llama3.1, Qwen, and DeepSeek-distilled models. Results indicate $Param\Delta$ Model effectively replicates traditional post-training. For example, the $Param\Delta$ Model obtained from 70B Llama3-inst, Llama3-base, Llama3.1-base models attains approximately 95\% of Llama3.1-inst model's performance on average. $Param\Delta$ brings a new perspective on how to fully leverage models in the open-weight community, where checkpoints for base and instruct models are readily available and frequently updated, by providing a cost-free framework to accelerate the iterative cycle of model development.
RDDGN: minimizing the total resistance distance and diameter using graph neural networks
Authors: Muhammad Shoaib Sardar, Muhammad Shahid Iqbal, Shou-Jun Xu
Date: 2025-03-17 00:00:00
Proceeding: Computational and Applied Mathematics 2025 3

Effective resistance in a weighted graph is analogous to electrical resistance in a resistor network, with conductance determined by edge weights. This concept has broad applications beyond electrical network research, including Markov chain structures, analysis of complex systems, and continuous-time averaging networks. Resistance distance provides critical insights into the performance of interconnection networks in fluid or wave communication systems. The total resistance distance in a network, determined by summing the distances between every pair of nodes, reflects the network’s overall connectivity and efficiency. The resistance diameter of a graph, defined as the maximum resistance distance between any nodes, serves as an indicator of the network’s robustness. Minimizing the network’s resistance diameter and total resistance distance enhances network speed, reliability, and data flow efficiency between nodes. In this article, we employ graph neural networks to achieve these minimization objectives. A deep Learning-based approach for constructing graph convolutional networks (GCNs) was developed to test and improve connection and flow efficiency within networks. Our proposed method consists of four steps: first, compute the Laplacian and resistance matrix; second, train the graph convolutional network; third, perform optimal edge selection; and fourth, visualize the metrics. We consider various classes of networks, such as Path Network, Cycle Network, Hypercube Network, and Lollipop Network. The results, presented both numerically and graphically, demonstrate the effectiveness of our proposed solution. Graph theory and advanced computational methods were applied to optimize the structural features of these network types, showcasing the potential of GCNs in enhancing network architecture.
Time-Series Graph Network for Sea Surface Temperature Prediction
Authors: YongjiaoSuna, XinYaoa, ∗, XinBib, XuechunHuanga, XiangguoZhaoc, BaiyouQiaoa
Date: 2021-06-07 00:00:00
Proceeding: Big Data Research 2021 6
Tasks: Graph Learning, Graph Neural Network, Prediction, Time Series

Sea surface temperature (SST) is an important indicator for balancing surface energy and measuring sea heat. Various effects caused by the sea temperature field significantly affect human activities to a large extent. It is important to predict the sea surface temperature efficiently and accurately. Existing SST prediction methods regard each sensor as an independent individual, without considering the structure or topology of the sensor network and ignoring the connectivity of the sensor network. To address this problem, this study investigates the SST prediction problem from the perspective of graph learning. We propose a time-series graph network (TSGN) that can jointly capture graph-based spatial correlation and temporal dynamics. TSGN uses a long short-term memory network to aggregate the features of time series data and establishes a graph neural network model to complete the SST prediction task. Experiments using SST data from the Pacific Northwest from 2001 to 2005 show that this method is more efficient and accurate when dealing with data containing time-series information and is superior to the existing SST prediction methods
A Unified Loss for Handling Inter-Class and Intra-Class Imbalance in Medical Image Segmentation
Authors: Xu, Feilong and Yang, Feiyang and Li, Xiongfei and Zhang, Xiaoli
Date: 2025-04-11 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2025 4
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

In utilizing deep learning techniques for medical image segmentation, two types of imbalance issues are observed: inter-class imbalance between majority and minority classes and intra-class imbalance between easy and hard samples. However, existing loss functions typically confuse these issues, leading to enhancements that cater to only one aspect. Moreover, loss functions optimized for specific tasks often exhibit limited generalizability. To address these issues, we propose Inter-class and Intra-class Balance loss, as well as a unified loss termed Balance loss. The Inter-class Balance loss controls the extent of hard sample mining for majority class samples by considering the frequency of minority classes present in each input image. This approach requires no manual adjustment weights and adapts automatically to different datasets. The Intra-class Balance loss enhances the network's ability to learn from hard samples by performing mining on hard samples within each class. We evaluate our loss functions on five segmentation tasks with varying degrees of class imbalance. The experimental results show that our proposed Balance loss enhances segmentation performance compared with the current loss functions and exhibits superior robustness.
PHemoNet: A Multimodal Network for Physiological Signals
Authors: Eleonora Lopez, Aurelio Uncini, Danilo Comminiello
Date: 2024-09-13 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Emotion Recognition, Multimodal Deep Learning, Multimodal Emotion Recognition

Emotion recognition is essential across numerous fields, including medical applications and brain-computer interface (BCI). Emotional responses include behavioral reactions, such as tone of voice and body movement, and changes in physiological signals, such as the electroencephalogram (EEG). The latter are involuntary, thus they provide a reliable input for identifying emotions, in contrast to the former which individuals can consciously control. These signals reveal true emotional states without intentional alteration, thus increasing the accuracy of emotion recognition models. However, multimodal deep learning methods from physiological signals have not been significantly investigated. In this paper, we introduce PHemoNet, a fully hypercomplex network for multimodal emotion recognition from physiological signals. In detail, the architecture comprises modality-specific encoders and a fusion module. Both encoders and fusion modules are defined in the hypercomplex domain through parameterized hypercomplex multiplications (PHMs) that can capture latent relations between the different dimensions of each modality and between the modalities themselves. The proposed method outperforms current state-of-the-art models on the MAHNOB-HCI dataset in classifying valence and arousal using electroencephalograms (EEGs) and peripheral physiological signals. The code for this work is available at https://github.com/ispamm/MHyEEG.
AI Opportunistic Coronary Calcium Screening at Veterans Affairs Hospitals
Authors: Raffi Hagopian, Timothy Strebel, Simon Bernatz, Gregory A. Myers, Erik Offerman, Eric Zuniga, Cy Y. Kim, Angie T. Ng, James A. Iwaz, Leonard Nürnberg, Sunny P. Singh, Evan P. Carey, Michael J. Kim, R. Spencer Schaefer, Jeannie Yu, Amilcare Gentili, Hugo J.W.L. Aerts
Date: 2025-05-16 00:00:00
Proceeding: NEJM AI 2025 5
Tasks: Computed Tomography (CT)

Background
Coronary artery calcium (CAC) is highly predictive of cardiovascular events. Although millions of chest computed tomography (CT) scans are performed annually in the United States, CAC is not routinely quantified from scans done for noncardiac purposes.

Methods
We developed a deep learning algorithm, AI-CAC, using 446 expert segmentations to automatically quantify CAC on noncontrast, nongated CT scans. Our study differs from prior works by utilizing imaging data from 98 medical centers across the Veterans Affairs national health care system, capturing extensive heterogeneity in imaging protocols, scanners, and patients. AI-CAC performance on nongated scans was compared against clinical standard electrocardiogram (ECG)-gated CAC scoring in 795 patients with paired gated scans within 1 year of their nongated scan. In addition, the model was tested on 8052 low-dose CTs (LDCTs) to simulate opportunistic CAC screening.

Results
Nongated AI-CAC differentiated zero versus nonzero and less than 100 versus 100 or greater Agatston scores with accuracies of 89.4% (F1 0.93) and 87.3% (F1 0.89), respectively. Nongated AI-CAC was predictive of 10-year all-cause mortality (CAC 0 vs. >400 group: 25.4% vs. 60.2%, Cox hazard ratio 3.49; P<0.005), and composite first-time stroke, myocardial infarction, or death (CAC 0 vs. >400 group: 33.5% vs. 63.8%, Cox hazard ratio 3.00; P<0.005). In the LDCT dataset, 3091 out of 8052 (38.4%) individuals had AI-CAC scores >400. Four cardiologists qualitatively reviewed a random sample of the >400 AI-CAC LDCT patients and verified that 527 of the 531 (99.2%) would benefit from lipid-lowering therapy.

Conclusions
This nongated CT CAC algorithm was developed across a national health care system and shows strong performance in evaluation against paired gated CT scans. The model code and weights are available at https://github.com/Raffi-Hagopian/AI-CAC/. (Funded by the Veterans Affairs health care system.)