pwc-archive/papers-with-abstracts huggingface dataset:
Att-Adapter: A Robust and Precise Domain-Specific Multi-Attributes T2I Diffusion Adapter via Conditional Variational Autoencoder
Authors: Wonwoong Cho, Yan-Ying Chen, Matthew Klenk, David I. Inouye, Yanxia Zhang
Date: 2025-03-15 00:00:00
Tasks: Attribute, Disentanglement

Text-to-Image (T2I) Diffusion Models have achieved remarkable performance in generating high quality images. However, enabling precise control of continuous attributes, especially multiple attributes simultaneously, in a new domain (e.g., numeric values like eye openness or car width) with text-only guidance remains a significant challenge. To address this, we introduce the Attribute (Att) Adapter, a novel plug-and-play module designed to enable fine-grained, multi-attributes control in pretrained diffusion models. Our approach learns a single control adapter from a set of sample images that can be unpaired and contain multiple visual attributes. The Att-Adapter leverages the decoupled cross attention module to naturally harmonize the multiple domain attributes with text conditioning. We further introduce Conditional Variational Autoencoder (CVAE) to the Att-Adapter to mitigate overfitting, matching the diverse nature of the visual world. Evaluations on two public datasets show that Att-Adapter outperforms all LoRA-based baselines in controlling continuous attributes. Additionally, our method enables a broader control range and also improves disentanglement across multiple attributes, surpassing StyleGAN-based techniques. Notably, Att-Adapter is flexible, requiring no paired synthetic data for training, and is easily scalable to multiple attributes within a single model.
Decomposition of one-layer neural networks via the infinite sum of reproducing kernel Banach spaces
Authors: Seungcheol Shin, Myungjoo Kang
Date: 2024-08-09 00:00:00

In this paper, we define the sum of RKBSs using the characterization theorem of RKBSs and show that the sum of RKBSs is compatible with the direct sum of feature spaces. Moreover, we decompose the integral RKBS into the sum of $p$-norm RKBSs. Finally, we provide applications for the structural understanding of the integral RKBS class.
Improving Complex Reasoning with Dynamic Prompt Corruption: A soft prompt Optimization Approach
Authors: Sinan Fan, Liang Xie, Chen Shen, Ge Teng, Xiaosong Yuan, Xiaofeng Zhang, Chenxi Huang, Wenxiao Wang, Xiaofei He, Jieping Ye
Date: 2025-03-17 00:00:00
Tasks: GSM8K, Math

Prompt-tuning (PT) for large language models (LLMs) can facilitate the performance on various conventional NLP tasks with significantly fewer trainable parameters. However, our investigation reveals that PT provides limited improvement and may even degrade the primitive performance of LLMs on complex reasoning tasks. Such a phenomenon suggests that soft prompts can positively impact certain instances while negatively affecting others, particularly during the later phases of reasoning. To address these challenges, We first identify an information accumulation within the soft prompts. Through detailed analysis, we demonstrate that this phenomenon is often accompanied by erroneous information flow patterns in the deeper layers of the model, which ultimately lead to incorrect reasoning outcomes. we propose a novel method called Dynamic Prompt Corruption (DPC) to take better advantage of soft prompts in complex reasoning tasks, which dynamically adjusts the influence of soft prompts based on their impact on the reasoning process. Specifically, DPC consists of two stages: Dynamic Trigger and Dynamic Corruption. First, Dynamic Trigger measures the impact of soft prompts, identifying whether beneficial or detrimental. Then, Dynamic Corruption mitigates the negative effects of soft prompts by selectively masking key tokens that interfere with the reasoning process. We validate the proposed approach through extensive experiments on various LLMs and reasoning tasks, including GSM8K, MATH, and AQuA. Experimental results demonstrate that DPC can consistently enhance the performance of PT, achieving 4%-8% accuracy gains compared to vanilla prompt tuning, highlighting the effectiveness of our approach and its potential to enhance complex reasoning in LLMs.
Lie Detector: Unified Backdoor Detection via Cross-Examination Framework
Authors: Xuan Wang, Siyuan Liang, Dongping Liao, Han Fang, Aishan Liu, Xiaochun Cao, Yu-liang Lu, Ee-Chien Chang, Xitong Gao
Date: 2025-03-21 00:00:00

Institutions with limited data and computing resources often outsource model training to third-party providers in a semi-honest setting, assuming adherence to prescribed training protocols with pre-defined learning paradigm (e.g., supervised or semi-supervised learning). However, this practice can introduce severe security risks, as adversaries may poison the training data to embed backdoors into the resulting model. Existing detection approaches predominantly rely on statistical analyses, which often fail to maintain universally accurate detection accuracy across different learning paradigms. To address this challenge, we propose a unified backdoor detection framework in the semi-honest setting that exploits cross-examination of model inconsistencies between two independent service providers. Specifically, we integrate central kernel alignment to enable robust feature similarity measurements across different model architectures and learning paradigms, thereby facilitating precise recovery and identification of backdoor triggers. We further introduce backdoor fine-tuning sensitivity analysis to distinguish backdoor triggers from adversarial perturbations, substantially reducing false positives. Extensive experiments demonstrate that our method achieves superior detection performance, improving accuracy by 5.4%, 1.6%, and 11.9% over SoTA baselines across supervised, semi-supervised, and autoregressive learning tasks, respectively. Notably, it is the first to effectively detect backdoors in multimodal large language models, further highlighting its broad applicability and advancing secure deep learning.
ExMAG: Learning of Maximally Ancestral Graphs
Authors: Petr Ryšavý, Pavel Rytíř, Xiaoyu He, Georgios Korpas, Jakub Mareček
Date: 2025-03-11 00:00:00

As one transitions from statistical to causal learning, one is seeking the most appropriate causal model. Dynamic Bayesian networks are a popular model, where a weighted directed acyclic graph represents the causal relationships. Stochastic processes are represented by its vertices, and weighted oriented edges suggest the strength of the causal relationships. When there are confounders, one would like to utilize both oriented edges (when the direction of causality is clear) and edges that are not oriented (when there is a confounder or not a relationship), yielding mixed graphs. A little-studied extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs with consideration of confounders. We propose a score-based learning algorithm for learning maximally ancestral graphs. A mixed-integer quadratic program is formulated, and an algorithmic approach is proposed, in which the pre-generation of exponentially many constraints is avoided by generating only violated constraints in the so-called branch-and-cut (``lazy constraint'') method. Comparing the novel approach to the state-of-the-art, we show that the proposed approach turns out to produce more accurate results when applied to small and medium-sized synthetic instances containing up to 25 variables.
Personalized Denoising Implicit Feedback for Robust Recommender System
Authors: Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, HuaWei Shen, Xueqi Cheng
Date: 2025-02-01 00:00:00
Tasks: Denoising, Recommendation Systems

While implicit feedback is foundational to modern recommender systems, factors such as human error, uncertainty, and ambiguity in user behavior inevitably introduce significant noise into this feedback, adversely affecting the accuracy and robustness of recommendations. To address this issue, existing methods typically aim to reduce the training weight of noisy feedback or discard it entirely, based on the observation that noisy interactions often exhibit higher losses in the overall loss distribution. However, we identify two key issues: (1) there is a significant overlap between normal and noisy interactions in the overall loss distribution, and (2) this overlap becomes even more pronounced when transitioning from pointwise loss functions (e.g., BCE loss) to pairwise loss functions (e.g., BPR loss). This overlap leads traditional methods to misclassify noisy interactions as normal, and vice versa. To tackle these challenges, we further investigate the loss overlap and find that for a given user, there is a clear distinction between normal and noisy interactions in the user's personal loss distribution. Based on this insight, we propose a resampling strategy to Denoise using the user's Personal Loss distribution, named PLD, which reduces the probability of noisy interactions being optimized. Specifically, during each optimization iteration, we create a candidate item pool for each user and resample the items from this pool based on the user's personal loss distribution, prioritizing normal interactions. Additionally, we conduct a theoretical analysis to validate PLD's effectiveness and suggest ways to further enhance its performance. Extensive experiments conducted on three datasets with varying noise ratios demonstrate PLD's efficacy and robustness.
GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition
Authors: Anthony Yazdani, Ihor Stepanov, Douglas Teodoro
Date: 2025-04-01 00:00:00
Tasks: Computational Efficiency, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Synthetic Data Generation, Zero-Shot Learning

Biomedical named entity recognition (NER) presents unique challenges due to specialized vocabularies, the sheer volume of entities, and the continuous emergence of novel entities. Traditional NER models, constrained by fixed taxonomies and human annotations, struggle to generalize beyond predefined entity types. To address these issues, we introduce GLiNER-BioMed, a domain-adapted suite of Generalist and Lightweight Model for NER (GLiNER) models specifically tailored for biomedicine. In contrast to conventional approaches, GLiNER uses natural language labels to infer arbitrary entity types, enabling zero-shot recognition. Our approach first distills the annotation capabilities of large language models (LLMs) into a smaller, more efficient model, enabling the generation of high-coverage synthetic biomedical NER data. We subsequently train two GLiNER architectures, uni- and bi-encoder, at multiple scales to balance computational efficiency and recognition performance. Experiments on several biomedical datasets demonstrate that GLiNER-BioMed outperforms the state-of-the-art in both zero- and few-shot scenarios, achieving 5.96% improvement in F1-score over the strongest baseline (p-value < 0.001). Ablation studies highlight the effectiveness of our synthetic data generation strategy and emphasize the complementary benefits of synthetic biomedical pre-training combined with fine-tuning on general-domain annotations. All datasets, models, and training pipelines are publicly available at https://github.com/ds4dh/GLiNER-biomed.
LGIN: Defining an Approximately Powerful Hyperbolic GNN
Authors: Srinitish Srinivasan, Omkumar CU
Date: 2025-03-31 00:00:00
Tasks: Graph Learning, Graph Neural Network, Graph Representation Learning, Representation Learning

While graph neural networks (GNNs) operating in hyperbolic spaces have shown promise for modeling hierarchical and complex relational data, a critical limitation often overlooked is their potentially limited discriminative power compared to their Euclidean counterparts or fundamental graph isomorphism tests like the Weisfeiler-Lehman (WL) hierarchy. Existing hyperbolic aggregation schemes, while curvature-aware, may not sufficiently capture the intricate structural differences required to robustly distinguish non-isomorphic graphs owing to non-injective aggregation functions. To address this expressiveness gap in hyperbolic graph learning, we introduce the Lorentzian Graph Isomorphic Network (LGIN), a novel GNN designed to achieve enhanced discriminative capabilities within the Lorentzian model of hyperbolic space. LGIN proposes a new update rule that effectively combines local neighborhood information with a richer representation of graph structure designed to preserve the Lorentzian metric tensor. This represents a significant step towards building more expressive GNNs in non-Euclidean geometries, overcoming a common bottleneck in current hyperbolic methods. We conduct extensive evaluations across nine diverse benchmark datasets, including molecular and protein structures. LGIN consistently outperforms or matches state-of-the-art hyperbolic and Euclidean GNNs, showcasing its practical efficacy and validating its superior ability to capture complex graph structures and distinguish between different graphs. To the best of our knowledge, LGIN is the first work to study the framework behind a powerful GNN on the hyperbolic space. The code for our paper can be found at https://github.com/Deceptrax123/LGIN
Eyelid’s Intrinsic Motion-aware Feature Learning for Real-time Eyeblink Detection in the Wild
Authors: Wenzheng Zeng, Yang Xiao, Guilei Hu, Zhiguo Cao, Sicheng Wei, Zhiwen Fang, Joey Tianyi Zhou, Junsong Yuan
Date: 2023-08-03 00:00:00
Proceeding: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023 8
Tasks: Attribute, Descriptive, Eyeblink detection, Knowledge Distillation, Self-Knowledge Distillation

Real-time eyeblink detection in the wild is a recently emerged challenging task that suffers from dramatic variations in face attribute, pose, illumination, camera view and distance, etc. One key issue is to well characterize eyelid’s intrinsic motion (i.e., approaching and departure between upper and lower eyelid) robustly, under unconstrained conditions. Towards this, a novel eyelid’s intrinsic motion-aware feature learning approach is proposed. Our proposition lies in 3 folds. First, the feature extractor is led to focus on informative eye region adaptively via introducing visual attention in a coarse-to-fine way, to guarantee robustness and fine-grained descriptive ability jointly. Then, 2 constraints are proposed to make feature learning be aware of eyelid’s intrinsic motion. Particularly, one concerns the fact that the inter-frame feature divergence within eyeblink processes should be greater than non-eyeblink ones to better reveal eyelid’s intrinsic motion. The other constraint minimizes the inter-frame feature divergence of non-eyeblink samples, to suppress motion clues due to head or camera movement, illumination change, etc. Meanwhile, concerning the high ambiguity between eyeblink and non-eyeblink samples, soft sample labels are acquired via self-knowledge distillation to conduct feature learning with finer supervision than the hard ones. The experiments verify that, our proposition is significantly superior to the state-of-the-art ones, and with real-time running efficiency. It is also of strong generalization capacity towards constrained conditions.
Training Frozen Feature Pyramid DINOv2 for Eyelid Measurements with Infinite Encoding and Orthogonal Regularization
Authors: Chun-Hung Chen
Date: 2025-04-01 00:00:00
Tasks: Self-Supervised Learning

Accurate measurement of eyelid parameters such as Margin Reflex Distances (MRD1, MRD2) and Levator Function (LF) is critical in oculoplastic diagnostics but remains limited by manual, inconsistent methods. This study evaluates deep learning models: SE-ResNet, EfficientNet, and the vision transformer-based DINOv2 for automating these measurements using smartphone-acquired images. We assess performance across frozen and fine-tuned settings, using MSE, MAE, and R2 metrics. DINOv2, pretrained through self-supervised learning, demonstrates superior scalability and robustness, especially under frozen conditions ideal for mobile deployment. Lightweight regressors such as MLP and Deep Ensemble offer high precision with minimal computational overhead. To address class imbalance and improve generalization, we integrate focal loss, orthogonal regularization, and binary encoding strategies. Our results show that DINOv2 combined with these enhancements delivers consistent, accurate predictions across all tasks, making it a strong candidate for real-world, mobile-friendly clinical applications. This work highlights the potential of foundation models in advancing AI-powered ophthalmic care.
Leveraging LLMs to explain DRL decisions for transparent 6G network slicing
Authors: Mazene Ameur, Bouziane Brik, and Adlen Ksentini
Date: 2024-07-10 00:00:00
Proceeding: IEEE 10th International Conference on Network Softwarization (NetSoft) 2024 7
Tasks: Decision Making, Deep Reinforcement Learning, Prompt Engineering, reinforcement-learning, Reinforcement Learning

The emergence of 6G networks heralds a transformative era in network slicing, facilitating tailored service delivery and optimal resource utilization. Despite its promise, network slice optimization heavily relies on Deep Reinforcement Learning (DRL) models, often criticized for their black-box decision-making processes. This paper introduces a novel Composable eXplainable Reinforcement Learning (XRL) framework customized for distributed systems like 6G Network Slicing. The proposed framework leverages Large Language Models (LLMs) and Prompt Engineering techniques to elucidate DRL algorithms’ decision-making mechanisms, with a specific emphasis on user
profiles. The latter transforms the inherently opaque nature of DRL into an interpretable textual format accessible not only to
eXplainable AI (XAI) experts but also to diverse network slice provider stakeholders, engineers, leaders, and beyond. Experimental results underscore the efficacy of the proposed Composable XRL framework, showcasing substantial improvements in transparency and comprehensibility of DRL decisions within the context of 6G network slicing.
CellVTA: Enhancing Vision Foundation Models for Accurate Cell Segmentation and Classification
Authors: Yang Yang, Xijie Xu, Yixun Zhou, Jie Zheng
Date: 2025-04-01 00:00:00
Tasks: Cell Segmentation, Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation

Cell instance segmentation is a fundamental task in digital pathology with broad clinical applications. Recently, vision foundation models, which are predominantly based on Vision Transformers (ViTs), have achieved remarkable success in pathology image analysis. However, their improvements in cell instance segmentation remain limited. A key challenge arises from the tokenization process in ViTs, which substantially reduces the spatial resolution of input images, leading to suboptimal segmentation quality, especially for small and densely packed cells. To address this problem, we propose CellVTA (Cell Vision Transformer with Adapter), a novel method that improves the performance of vision foundation models for cell instance segmentation by incorporating a CNN-based adapter module. This adapter extracts high-resolution spatial information from input images and injects it into the ViT through a cross-attention mechanism. Our method preserves the core architecture of ViT, ensuring seamless integration with pretrained foundation models. Extensive experiments show that CellVTA achieves 0.538 mPQ on the CoNIC dataset and 0.506 mPQ on the PanNuke dataset, which significantly outperforms the state-of-the-art cell segmentation methods. Ablation studies confirm the superiority of our approach over other fine-tuning strategies, including decoder-only fine-tuning and full fine-tuning. Our code and models are publicly available at https://github.com/JieZheng-ShanghaiTech/CellVTA.
Predicting mine water inrush accidents based on water level anomalies of borehole groups using long short-term memory and isolation forest
Authors: Huichao Yin
Date: 2023-01-01 00:00:00
Proceeding: Journal of Hydrology 2023 1
Tasks: Prediction

Water level variation of explorational boreholes in mining sites is one of the most direct representations of water inrush risk. Despite recent efforts on mine water inrush accident prediction based on various types of observation data including water level of boreholes using a wide range of machine learning models, the accuracy and timeliness of the prediction for major accidents are still unsatisfactory. In this study, a novel prediction method based on the analysis of water level variation anomalies in borehole groups is proposed using the coupled model of long short-time memory (LSTM) and Isolation forest (IForest). Multi-variate LSTM algorithm is firstly used to model the correlated time sequence water level data in the borehole group, followed by the full extraction of the data variation feature based on the combination of variables including the prediction error being used as inputs of the IForest algorithm. As a result, anomalous variations of the data are detected as possible precursors of water inrush accident. Finally, a set of water inrush risk level grading rule is established based on the number of simultaneous anomalous data instances being detected within a borehole group. The results obtained for the water inrush accident in Xingdong coal mine demonstrate extraordinary timeliness and high accuracy as high water-inrush risk warning is issued for more than 2 days earlier than the time of actual sighting of the accident with very few false alarms being caused throughout the period of the testing dataset. This study provides an innovative solution to the low prediction accuracy and slow responding speed during major water inrush accidents of existing methods. The major parameters of the proposed coupled model of LSTM + IForest can also be easily calibrated based on traditional empirical methods for the best performance, allowing the method to be widely applied in various mining conditions.
Flow Matching on Lie Groups
Authors: Finn M. Sherry, Bart M. N. Smets
Date: 2025-04-01 00:00:00

Flow Matching (FM) is a recent generative modelling technique: we aim to learn how to sample from distribution $\mathfrak{X}_1$ by flowing samples from some distribution $\mathfrak{X}_0$ that is easy to sample from. The key trick is that this flow field can be trained while conditioning on the end point in $\mathfrak{X}_1$: given an end point, simply move along a straight line segment to the end point (Lipman et al. 2022). However, straight line segments are only well-defined on Euclidean space. Consequently, Chen and Lipman (2023) generalised the method to FM on Riemannian manifolds, replacing line segments with geodesics or their spectral approximations. We take an alternative point of view: we generalise to FM on Lie groups by instead substituting exponential curves for line segments. This leads to a simple, intrinsic, and fast implementation for many matrix Lie groups, since the required Lie group operations (products, inverses, exponentials, logarithms) are simply given by the corresponding matrix operations. FM on Lie groups could then be used for generative modelling with data consisting of sets of features (in $\mathbb{R}^n$) and poses (in some Lie group), e.g. the latent codes of Equivariant Neural Fields (Wessels et al. 2025).