pwc-archive/papers-with-abstracts huggingface dataset:
Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers
Authors: Jared Moore, Declan Grabb, William Agnew, Kevin Klyman, Stevie Chancellor, Desmond C. Ong, Nick Haber
Date: 2025-04-25 00:00:00
Tasks: Large Language Model

Should a large language model (LLM) be used as a therapist? In this paper, we investigate the use of LLMs to *replace* mental health providers, a use case promoted in the tech startup and research space. We conduct a mapping review of therapy guides used by major medical institutions to identify crucial aspects of therapeutic relationships, such as the importance of a therapeutic alliance between therapist and client. We then assess the ability of LLMs to reproduce and adhere to these aspects of therapeutic relationships by conducting several experiments investigating the responses of current LLMs, such as `gpt-4o`. Contrary to best practices in the medical community, LLMs 1) express stigma toward those with mental health conditions and 2) respond inappropriately to certain common (and critical) conditions in naturalistic therapy settings -- e.g., LLMs encourage clients' delusional thinking, likely due to their sycophancy. This occurs even with larger and newer LLMs, indicating that current safety practices may not address these gaps. Furthermore, we note foundational and practical barriers to the adoption of LLMs as therapists, such as that a therapeutic alliance requires human characteristics (e.g., identity and stakes). For these reasons, we conclude that LLMs should not replace therapists, and we discuss alternative roles for LLMs in clinical therapy.
FashionM3: Multimodal, Multitask, and Multiround Fashion Assistant based on Unified Vision-Language Model
Authors: Kaicheng Pang, Xingxing Zou, Waikeung Wong
Date: 2025-04-24 00:00:00
Tasks: Image Generation, Language Modeling, Language Modelling, Virtual Try-on

Fashion styling and personalized recommendations are pivotal in modern retail, contributing substantial economic value in the fashion industry. With the advent of vision-language models (VLM), new opportunities have emerged to enhance retailing through natural language and visual interactions. This work proposes FashionM3, a multimodal, multitask, and multiround fashion assistant, built upon a VLM fine-tuned for fashion-specific tasks. It helps users discover satisfying outfits by offering multiple capabilities including personalized recommendation, alternative suggestion, product image generation, and virtual try-on simulation. Fine-tuned on the novel FashionRec dataset, comprising 331,124 multimodal dialogue samples across basic, personalized, and alternative recommendation tasks, FashionM3 delivers contextually personalized suggestions with iterative refinement through multiround interactions. Quantitative and qualitative evaluations, alongside user studies, demonstrate FashionM3's superior performance in recommendation effectiveness and practical value as a fashion assistant.
ACIL: Active Class Incremental Learning for Image Classification
Authors: Aditya R. Bhattacharya∗, Debanjan Goswami∗, Shayok Chakraborty
Date: 2024-12-31 00:00:00
Proceeding: British Machine Vision Conference (BMVC) 2024 12
Tasks: Active Learning, Classification, class-incremental learning, Class Incremental Learning, Continual Learning, image-classification, Image Classification, Incremental Learning

Continual learning (or class incremental learning) is a realistic learning scenario for computer vision systems, where deep neural networks are trained on episodic data, and the data from previous episodes are generally inaccessible to the model. Existing research in this domain has primarily focused on avoiding catastrophic forgetting, which occurs due to the continuously changing class distributions in each episode and the inaccessibility of the data from previous episodes. However, these methods assume that all the training samples in every episode are annotated; this not only incurs a huge annotation cost, but also results in a wastage of annotation effort, since most of the samples in a given episode will not be accessible to the model in subsequent episodes. Active learning algorithms identify the salient and informative samples from large amounts of unlabeled data and are instrumental in reducing the human annotation effort in inducing a deep neural network. In this paper, we propose ACIL, a novel active learning framework for class incremental learning settings. We exploit a criterion based on uncertainty and diversity to identify the exemplar samples that need to be annotated in each episode, and will be appended to the data in the next episode. Such a framework can drastically reduce annotation cost and can also avoid catastrophic forgetting. Our extensive empirical analyses on several vision datasets corroborate the promise and potential of our framework against relevant baselines.
A simple criterion to design optimal non-pharmaceutical interventions for mitigating epidemic outbreaks
Authors: Marco Tulio Angulo, Fernando Castaños, Rodrigo Moreno-Morton, Jorge X. Velasco-Hernández, Jaime A. Moreno
Date: 2021-04-21 00:00:00
Proceeding: Journal of the Royal Society Interface 2021 4

For mitigating the COVID-19 pandemic, much emphasis is made on implementing non-pharmaceutical interventions to keep the reproduction number below one. However, using that objective ignores that some of these interventions, like bans of public events or lockdowns, must be transitory and as short as possible because of their significant economic and societal costs. Here, we derive a simple and mathematically rigorous criterion for designing optimal transitory non-pharmaceutical interventions for mitigating epidemic outbreaks. We find that reducing the reproduction number below one is sufficient but not necessary. Instead, our criterion prescribes the required reduction in the reproduction number according to the desiredmaximum of disease prevalence and the maximum decrease of disease transmission that the interventions can achieve. We study the implications of our theoretical results for designing non-pharmaceutical interventions in 16 cities and regions during the COVID- 19 pandemic. In particular, we estimate the minimal reduction of each region’s contact rate necessary to control the epidemic optimally. Our results contribute to establishing a rigorous methodology to design optimal non-pharmaceutical intervention policies for mitigating epidemic outbreaks.
Switch-Based Multi-Part Neural Network
Authors: Surajit Majumder, Paritosh Ranjan, Prodip Roy, Bhuban Padhan
Date: 2025-04-25 00:00:00
Tasks: Edge-computing, Federated Learning, Privacy Preserving

This paper introduces decentralized and modular neural network framework designed to enhance the scalability, interpretability, and performance of artificial intelligence (AI) systems. At the heart of this framework is a dynamic switch mechanism that governs the selective activation and training of individual neurons based on input characteristics, allowing neurons to specialize in distinct segments of the data domain. This approach enables neurons to learn from disjoint subsets of data, mimicking biological brain function by promoting task specialization and improving the interpretability of neural network behavior. Furthermore, the paper explores the application of federated learning and decentralized training for real-world AI deployments, particularly in edge computing and distributed environments. By simulating localized training on non-overlapping data subsets, we demonstrate how modular networks can be efficiently trained and evaluated. The proposed framework also addresses scalability, enabling AI systems to handle large datasets and distributed processing while preserving model transparency and interpretability. Finally, we discuss the potential of this approach in advancing the design of scalable, privacy-preserving, and efficient AI systems for diverse applications.
Fine-Tuning Adversarially-Robust Transformers for Single-Image Dehazing
Authors: Vlad Vasilescu, Ana Neacsu, Daniela Faur
Date: 2025-04-24 00:00:00
Tasks: Image Dehazing, object-detection, Object Detection, Single Image Dehazing

Single-image dehazing is an important topic in remote sensing applications, enhancing the quality of acquired images and increasing object detection precision. However, the reliability of such structures has not been sufficiently analyzed, which poses them to the risk of imperceptible perturbations that can significantly hinder their performance. In this work, we show that state-of-the-art image-to-image dehazing transformers are susceptible to adversarial noise, with even 1 pixel change being able to decrease the PSNR by as much as 2.8 dB. Next, we propose two lightweight fine-tuning strategies aimed at increasing the robustness of pre-trained transformers. Our methods results in comparable clean performance, while significantly increasing the protection against adversarial data. We further present their applicability in two remote sensing scenarios, showcasing their robust behavior for out-of-distribution data. The source code for adversarial fine-tuning and attack algorithms can be found at github.com/Vladimirescu/RobustDehazing.
Neural operators struggle to learn complex PDEs in pedestrian mobility: Hughes model case study
Authors: Prajwal Chauhan, Salah Eddine Choutri, Mohamed Ghattassi, Nader Masmoudi, Saif Eddin Jabari
Date: 2025-04-25 00:00:00

This paper investigates the limitations of neural operators in learning solutions for a Hughes model, a first-order hyperbolic conservation law system for crowd dynamics. The model couples a Fokker-Planck equation representing pedestrian density with a Hamilton-Jacobi-type (eikonal) equation. This Hughes model belongs to the class of nonlinear hyperbolic systems that often exhibit complex solution structures, including shocks and discontinuities. In this study, we assess the performance of three state-of-the-art neural operators (Fourier Neural Operator, Wavelet Neural Operator, and Multiwavelet Neural Operator) in various challenging scenarios. Specifically, we consider (1) discontinuous and Gaussian initial conditions and (2) diverse boundary conditions, while also examining the impact of different numerical schemes. Our results show that these neural operators perform well in easy scenarios with fewer discontinuities in the initial condition, yet they struggle in complex scenarios with multiple initial discontinuities and dynamic boundary conditions, even when trained specifically on such complex samples. The predicted solutions often appear smoother, resulting in a reduction in total variation and a loss of important physical features. This smoothing behavior is similar to issues discussed by Daganzo (1995), where models that introduce artificial diffusion were shown to miss essential features such as shock waves in hyperbolic systems. These results suggest that current neural operator architectures may introduce unintended regularization effects that limit their ability to capture transport dynamics governed by discontinuities. They also raise concerns about generalizing these methods to traffic applications where shock preservation is essential.
Ta-Adapter: Enhancing few-shot CLIP with task-aware encoders
Authors: Wenbo Zhang, Yifan Zhang, Yuyang Deng, Wenlong Zhang, Jianfeng Lin， Binqiang Huang, Jinlu Zhang, Wenhao Yu
Date: 2024-04-29 00:00:00
Proceeding: Pattern Recognition 153 (2024) 110559 2024 4
Tasks: Few-Shot Learning, image-classification, Image Classification, Prompt Learning

Contrastive Language-Image Pre-training (CLIP) has shown impressive zero-shot transfer capabilities, but its potential for specific downstream tasks is not fully utilized. To further enhance CLIP’s few-shot capability for specific datasets, some subsequent works have been proposed, such as methods based on lightweight adapters and prompt learning. However, since CLIP is pretrained on a diverse collection of image and text pairs sourced from the internet, it is difficult to sufficiently tune models to specific datasets using only lightweight adaptions. In this paper, we argue that largely modifying the internal representations within CLIP’s encoders can yield better results on downstream datasets. In this work, we introduce Ta-Adapter, a method that equips both the visual and textual encoders of CLIP with task-specific prompts. These prompts are generated using a collaborative prompt learning approach, which allows the encoders to produce representations that are better aligned with specific downstream datasets. Then, we initialize an adapter module using the optimized features generated by the task-aware visual encoder for further feature alignment, and this module can also be further fine-tuned. Our extensive experiments on image classification datasets show that compared to the state-of-theart few-shot methods Tip-Adapter-F and MaPLe, our model exhibits good performance and obtains an average absolute gain of 2.04% and 1.62% on 11 different image recognition datasets, respectively. In conclusion, this work presents a unique and effective approach to unlocking the full potential of CLIP’s few-shot learning capabilities.
RGS-DR: Reflective Gaussian Surfels with Deferred Rendering for Shiny Objects
Authors: Georgios Kouros, Minye Wu, Tinne Tuytelaars
Date: 2025-04-25 00:00:00
Tasks: Inverse Rendering, NeRF

We introduce RGS-DR, a novel inverse rendering method for reconstructing and rendering glossy and reflective objects with support for flexible relighting and scene editing. Unlike existing methods (e.g., NeRF and 3D Gaussian Splatting), which struggle with view-dependent effects, RGS-DR utilizes a 2D Gaussian surfel representation to accurately estimate geometry and surface normals, an essential property for high-quality inverse rendering. Our approach explicitly models geometric and material properties through learnable primitives rasterized into a deferred shading pipeline, effectively reducing rendering artifacts and preserving sharp reflections. By employing a multi-level cube mipmap, RGS-DR accurately approximates environment lighting integrals, facilitating high-quality reconstruction and relighting. A residual pass with spherical-mipmap-based directional encoding further refines the appearance modeling. Experiments demonstrate that RGS-DR achieves high-quality reconstruction and rendering quality for shiny objects, often outperforming reconstruction-exclusive state-of-the-art methods incapable of relighting.
Interpretable dimensionality reduction using weighted linear transformation
Authors: Erik Bergh
Date: 2025-03-26 00:00:00
Proceeding: Adv. Artif. Intell. Mach. Learn. 2025 3
Tasks: Dimensionality Reduction

Dimensionality reduction techniques are fundamental for analyzing and visualizing high-dimensional data. With established methods like t-SNE and PCA presenting a trade-off between representational power and interpretability. This paper introduces a novel approach that bridges this gap by combining the interpretability of linear methods with the expressiveness of non-linear transformations.
The proposed algorithm constructs a non-linear mapping between high-dimensional and low-dimensional spaces through a combination of linear transformations, each weighted by Gaussian functions. This architecture enables complex non-linear transformations while preserving the interpretability advantages of linear methods, as each transformation can be analyzed independently. The resulting model provides both powerful dimensionality reduction and transparent insights into the transformed space.
Techniques for interpreting the learned transformations are presented, including methods for identifying suppressed dimensions and how space is expanded and contracted. These tools enable practitioners to understand how the algorithm preserves and modifies geometric relationships during dimensionality reduction. To ensure the practical utility of this algorithm, the creation of user-friendly
software packages is emphasized, facilitating its adoption in both academia and industry.
Interpretable non-linear dimensionality reduction using gaussian weighted linear transformation
Authors: Erik Bergh
Date: 2025-04-24 00:00:00
Tasks: Dimensionality Reduction

Dimensionality reduction techniques are fundamental for analyzing and visualizing high-dimensional data. With established methods like t-SNE and PCA presenting a trade-off between representational power and interpretability. This paper introduces a novel approach that bridges this gap by combining the interpretability of linear methods with the expressiveness of non-linear transformations. The proposed algorithm constructs a non-linear mapping between high-dimensional and low-dimensional spaces through a combination of linear transformations, each weighted by Gaussian functions. This architecture enables complex non-linear transformations while preserving the interpretability advantages of linear methods, as each transformation can be analyzed independently. The resulting model provides both powerful dimensionality reduction and transparent insights into the transformed space. Techniques for interpreting the learned transformations are presented, including methods for identifying suppressed dimensions and how space is expanded and contracted. These tools enable practitioners to understand how the algorithm preserves and modifies geometric relationships during dimensionality reduction. To ensure the practical utility of this algorithm, the creation of user-friendly software packages is emphasized, facilitating its adoption in both academia and industry.
Seeing Soundscapes: Audio-Visual Generation and Separation from Soundscapes Using Audio-Visual Separator
Authors: Minjae Kang, Martim Brandão
Date: 2025-04-25 00:00:00

Recent audio-visual generative models have made substantial progress in generating images from audio. However, existing approaches focus on generating images from single-class audio and fail to generate images from mixed audio. To address this, we propose an Audio-Visual Generation and Separation model (AV-GAS) for generating images from soundscapes (mixed audio containing multiple classes). Our contribution is threefold: First, we propose a new challenge in the audio-visual generation task, which is to generate an image given a multi-class audio input, and we propose a method that solves this task using an audio-visual separator. Second, we introduce a new audio-visual separation task, which involves generating separate images for each class present in a mixed audio input. Lastly, we propose new evaluation metrics for the audio-visual generation task: Class Representation Score (CRS) and a modified R@K. Our model is trained and evaluated on the VGGSound dataset. We show that our method outperforms the state-of-the-art, achieving 7% higher CRS and 4% higher R@2* in generating plausible images with mixed audio.
self-prompting analogical reasoning for uav object detection
Authors: Nianxin Li, Mao Ye, Lihua Zhou, Song Tang, Yan Gan, Zizhuo Liang, Xiatian Zhu
Date: 2025-04-11 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2025 4
Tasks: graph construction, object-detection, Object Detection, Object Detection In Aerial Images, Small Object Detection

Unmanned Aerial Vehicle Object Detection 
(UAVOD)
presents unique challenges due to varying altitudes, dynamic
backgrounds, and the small size of objects. Traditional de-
tection methods often struggle with these challenges, as
they typically rely on visual features only and fail to ex-
tract the semantic relations between the objects. To address
these limitations,we propose a novel approach named Self-
Prompting Analogical Reasoning (SPAR). Ourmethod uti-
lizes the vision-languagemodel (CLIP) to generate context-
aware prompts based on image features, providing rich se-
mantic information that guides analogical reasoning. SPAR
includes two main modules: self-prompting and analogi-
cal reasoning. Self-prompting module based on learnable
description and CLIP-text encoder generates context-aware
prompt by combining specific image feature; then an object-
ness prompt scoremap is produced by computing the simi-
laritybetweenpixel-level features andcontext-awareprompt.
Withthis scoremap,multi-scale image features are enhanced
and pixel-level features are chosen for graph construction.
While for analogical reasoningmodule, graph nodes consist
of category-level prompt nodes and pixel-level image feature
nodes.Analogical inference is based on graph convolution.
Under the guidance of category-level nodes, different-scale
object features have been enhanced, which helps achieve
more accuratedetectionof challengingobjects.Extensive ex-
periments illustrate that SPARoutperforms traditionalmeth-
ods,offeringamorerobustandaccuratesolutionforUAVOD.
LLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection
Authors: Rajesh Yarra
Date: 2025-04-25 00:00:00
Tasks: Feature Engineering, RAG, Retrieval-augmented Generation, Vulnerability Detection

Despite the transformative impact of Artificial Intelligence (AI) across various sectors, cyber security continues to rely on traditional static and dynamic analysis tools, hampered by high false positive rates and superficial code comprehension. While generative AI offers promising automation capabilities for software development, leveraging Large Language Models (LLMs) for vulnerability detection presents unique challenges. This paper explores the potential and limitations of LLMs in identifying vulnerabilities, acknowledging inherent weaknesses such as hallucinations, limited context length, and knowledge cut-offs. Previous attempts employing machine learning models for vulnerability detection have proven ineffective due to limited real-world applicability, feature engineering challenges, lack of contextual understanding, and the complexities of training models to keep pace with the evolving threat landscape. Therefore, we propose a robust AI-driven approach focused on mitigating these limitations and ensuring the quality and reliability of LLM based vulnerability detection. Through innovative methodologies combining Retrieval-Augmented Generation (RAG) and Mixtureof-Agents (MoA), this research seeks to leverage the strengths of LLMs while addressing their weaknesses, ultimately paving the way for dependable and efficient AI-powered solutions in securing the ever-evolving software landscape.