pwc-archive/papers-with-abstracts huggingface dataset:
The Fragility of the Reverse Facilitation Effect in the Stroop Task: A Dynamic Neurocognitive Model
Authors: Ahmad Sohrabi, Robert L. West
Date: 2025-03-24 00:00:00

In typical Stroop experiments, participants perform better when the colors and words are congruent compared to when they are incongruent or neutral. Paradoxically, in some experimental conditions, neutral trials are faster than congruent trials. This phenomenon is known as Reverse Facilitation Effect (RFE). However, RFE has not been consistently replicated, leading to the so-called fragile results. There are some models that capture this effect, but they are not parsimonious. Here we employed our previous model of priming effect, without its conflict monitoring module, to demonstrate that RFE, including its fragility, is mainly due to attentional dynamics with limited resources. The simulations of the RFE resulted from weak attentional activation for neutral stimuli (e.g., non-color or nonword strings) that causes less attentional refraction for the target stimuli (e.g., font colors). The refractory period or attenuation of attention usually happens when meaningful stimuli (color name primes) are used along font color targets. This simpler model provides a straightforward understanding of the underlying processes.
A Spectrum-based Filter Design for Periodic Control of Systems with Time Delay
Authors: Can Kutlu Yüksel, Tomáš Vyhlídal, Jaroslav Bušek, Martin Hromčík, Silviu-Iulian Niculescu
Date: 2025-03-25 00:00:00

A fully analytical controller design is proposed to tackle a periodic control problem for stable linear systems with an input delay. Applying the internal model control scheme, the controller design reduces to designing a filter, which is done through the placement of poles and zeros. The zeros are placed to compensate for the harmonics and to achieve the desired degree of properness for the filter. For placing the poles, a quasi-optimal procedure is proposed utilizing the standard LQR method. Given the high-dimensionality of the filter due to targeting a large number of harmonics, the design, as well as controller implementation, is performed over a state-space representation. A thorough experimental case study is included to demonstrate both the practical feasibility and effectiveness of the proposed control design. The experimental validation is performed on a physical system, the goal of which is to reject periodic vibrations acting on a mass-spring-damper setup where the sensor and the actuator are non-collocated.
The low surface brightness dwarf members of nearby groups of galaxies
Authors: E. R. Carrasco, C. Mendes de Oliveira, L. Infante
Date: 2003-03-19 00:00:00

We present the photometric and spectroscopic results of a survey done in a wide-field area of nearby groups and poor clusters. The main goals are identifying and characterizing the properties of low-surface-brightness dwarf galaxies and determining the galaxy luminosity function for Mv>-13. We surveyed areas (typically 0.1--1.0 square degrees) of the groups NGC 6868 (Telescopium), NGC 5846, HCG 42, Hydra I, Dorado and the poor clusters IC 4765 in V and I. In this work we present the results and the analysis of the photometry and spectroscopy done in four groups: HCG 42, NGC 6868, NGC 5846 and IC4765. In these groups, we detected hundreds of new low surface brightness dwarf galaxies. The analysis of their spatial distributions, colors, galaxy luminosity function and other propierties are presented.
Interpretable Generative Models through Post-hoc Concept Bottlenecks
Authors: Akshay Kulkarni, Ge Yan, Chung-En Sun, Tuomas Oikarinen, Tsui-Wei Weng
Date: 2025-03-25 00:00:00
Proceeding: CVPR 2025 1

Concept bottleneck models (CBM) aim to produce inherently interpretable models that rely on human-understandable concepts for their predictions. However, existing approaches to design interpretable generative models based on CBMs are not yet efficient and scalable, as they require expensive generative model training from scratch as well as real images with labor-intensive concept supervision. To address these challenges, we present two novel and low-cost methods to build interpretable generative models through post-hoc techniques and we name our approaches: concept-bottleneck autoencoder (CB-AE) and concept controller (CC). Our proposed approaches enable efficient and scalable training without the need of real data and require only minimal to no concept supervision. Additionally, our methods generalize across modern generative model families including generative adversarial networks and diffusion models. We demonstrate the superior interpretability and steerability of our methods on numerous standard datasets like CelebA, CelebA-HQ, and CUB with large improvements (average ~25%) over the prior work, while being 4-15x faster to train. Finally, a large-scale user study is performed to validate the interpretability and steerability of our methods.
Riemannian Optimization on Relaxed Indicator Matrix Manifold
Authors: Jinghui Yuan, Fangyuan Xie, Feiping Nie, Xuelong Li
Date: 2025-03-26 00:00:00
Tasks: Denoising, global-optimization, Graph Clustering, Image Denoising, Riemannian optimization

The indicator matrix plays an important role in machine learning, but optimizing it is an NP-hard problem. We propose a new relaxation of the indicator matrix and prove that this relaxation forms a manifold, which we call the Relaxed Indicator Matrix Manifold (RIM manifold). Based on Riemannian geometry, we develop a Riemannian toolbox for optimization on the RIM manifold. Specifically, we provide several methods of Retraction, including a fast Retraction method to obtain geodesics. We point out that the RIM manifold is a generalization of the double stochastic manifold, and it is much faster than existing methods on the double stochastic manifold, which has a complexity of \( \mathcal{O}(n^3) \), while RIM manifold optimization is \( \mathcal{O}(n) \) and often yields better results. We conducted extensive experiments, including image denoising, with millions of variables to support our conclusion, and applied the RIM manifold to Ratio Cut, we provide a rigorous convergence proof and achieve clustering results that outperform the state-of-the-art methods. Our Code in \href{https://github.com/Yuan-Jinghui/Riemannian-Optimization-on-Relaxed-Indicator-Matrix-Manifold}{here}.
Statistical Cross-Correlation Absolute Value-Based Blind Spectrum Sensing for MIMO Systems
Authors: Changqing Zhang, Jin Li, Wei Xing Zheng, Fellow, IEEE, and Bingbing Li
Date: 2023-10-15 00:00:00
Proceeding: journal 2023 10

Spectrum sensing can enable unauthorized
users to “opportunistically” access an idle spectrum and
effectively improve the utilization of spectrum resources.
By using the difference between the cross-correlation absolute values of the receiving data in the presence of a signal
and only noise, a blind spectrum sensing (SS) algorithm
based on the absolute value of the statistical cross correlation (test statistic) is proposed for a multi-input–multioutput
system. A theoretical analysis has shown that this method
can effectively employ the correlation between signals and
the independence of noise to construct the test statistic.
The proposed algorithm improves the accuracy of SS as
well as the reliability of the system while enabling signal
detection with fewer samples. According to the statistical
theory, we provide the probability distributions of the test
statistic with and without a signal and then determine the
corresponding detection thresholds with a fixed false alarm
probability. Furthermore, we propose an equivalence theorem of the test statistic and prove its correctness. On this basis, it is theoretically proven that the proposed method
is superior to the classical method based on the absolute value of the covariance. Finally, numerical simulations are
conducted to confirm the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.
Learning-Based Hierarchical Edge Caching for Cloud-Aided Heterogeneous Networks
Authors: Dongyang Li, Haixia Zhang，Dongfeng Yuan，Minggao Zhang
Date: 2022-09-20 00:00:00
Proceeding: IEEE Transactions on Wireless Communications 2022 9
Tasks: Prediction

Edge caching has emerged as a promising technique against latency caused by explosive growth of mobile data traffic through caching popular contents at the edge networks. However, the dynamically changing content popularity nature and limited caching capacity make it challenging to design an effective caching scheme to reduce latency. To solve this, a learning-based hierarchical edge caching (LHEC) scheme is proposed in this work. We first propose a novel deep learning architecture, namely Stacked Autoencoder-Long Short Term Memory Network (SAELSTMNet) to capture both the correlation of the request patterns among different content and the periodicity in time domain to improve the prediction accuracy of the content popularity. Then, to predict the popularity of these newly-added contents, a dynamic content catalog is introduced and a similarity-based content popularity prediction (SCPP) approach is proposed. Based on the content popularity prediction, a hierarchical edge caching optimization problem is formulated to minimize the average content downloading latency. Since the formulated problem is NP-hard and difficult to be solved, a low-complexity algorithm is proposed to obtain the near-optimal solutions. Simulation results show that the proposed content popularity prediction approach outperforms up to 6.36% in terms of the mean absolute error compared with the state-of-the-art methods and the proposed LHEC scheme reduces the average downloading latency at about 5.3%∼7.9% compared with those existing caching schemes.
Hierarchical Cooperative LSTM-Based Spectrum Sensing
Authors: Dimpal Janu, Kuldeep Singh, Sandeep Kumar, Senior Member, and Sandeep Mandia, Member, IEEE
Date: 2023-03-03 00:00:00
Proceeding: journal 2023 3

In this letter, we design a hierarchical cooperative
long short-term memory (LSTM) network-based cooperative
spectrum sensing (CSS) method which utilizes convolutional
neural network (CNN) and LSTM network. The CNN extracts
spatial features from the input covariance matrices (CMs) which
are generated by sensing data of each secondary user (SU)
and the sequence of spatial features corresponding to multiple
sensing periods are fed into secondary user LSTM (SU-LSTM)
so that the PU activity pattern at SU level can be learned. The
cooperative LSTM learns the group-level PU activity pattern
from all SU-level temporal feature representations. The aim of
learning the PU activity pattern at SU-level and group-level is to
improve the detection performance further. To demonstrate the
robustness of the proposed model, the scenario of an imperfect
reporting channel is taken into account. With a sufficient amount
of simulations, the effectiveness of the proposed method is proven
and simulation results demonstrate that the proposed method
outperforms the state-of-the-art in terms of detection probability
and classification accuracy
Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning
Authors: Huajie Tan, Yuheng Ji, Xiaoshuai Hao, Minglan Lin, Pengwei Wang, Zhongyuan Wang, Shanghang Zhang
Date: 2025-03-26 00:00:00
Tasks: Few-Shot Learning, Visual Reasoning

Visual reasoning abilities play a crucial role in understanding complex multimodal data, advancing both domain-specific applications and artificial general intelligence (AGI). Existing methods improve VLM reasoning via Chain-of-Thought (CoT) supervised fine-tuning, using meticulously annotated training data to enhance visual reasoning capabilities. However, this training paradigm may lead to overfitting and cognitive rigidity, restricting the model's ability to transfer visual reasoning skills across domains and limiting its real-world applicability. To address these limitations, we propose Reason-RFT, a novel reinforcement fine-tuning framework that significantly enhances generalization capabilities in visual reasoning tasks. Reason-RFT introduces a two-phase training framework for visual reasoning: (1) Supervised Fine-Tuning (SFT) with curated Chain-of-Thought (CoT) data activates the reasoning potential of Vision-Language Models (VLMs), followed by (2) Group Relative Policy Optimization (GRPO)-based reinforcement learning that generates multiple reasoning-response pairs, significantly enhancing generalization in visual reasoning tasks. To evaluate Reason-RFT's visual reasoning capabilities, we reconstructed a comprehensive dataset spanning visual counting, structure perception, and spatial transformation. Experimental results demonstrate Reasoning-RFT's three key advantages: (1) Performance Enhancement: achieving state-of-the-art results across multiple tasks, outperforming most mainstream open-source and proprietary models; (2) Generalization Superiority: consistently maintaining robust performance across diverse tasks and domains, outperforming alternative training paradigms; (3) Data Efficiency: excelling in few-shot learning scenarios while surpassing full-dataset SFT baselines. Project website: https://tanhuajie.github.io/ReasonRFT
Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videos
Authors: Jiaheng Zhou, Yanfeng Zhou, Wei Fang, Yuxing Tang, Le Lu, Ge Yang
Date: 2025-03-26 00:00:00
Tasks: Diagnostic, Mamba

Ultrasound videos are an important form of clinical imaging data, and deep learning-based automated analysis can improve diagnostic accuracy and clinical efficiency. However, the scarcity of labeled data and the inherent challenges of video analysis have impeded the advancement of related methods. In this work, we introduce E-ViM$^3$, a data-efficient Vision Mamba network that preserves the 3D structure of video data, enhancing long-range dependencies and inductive biases to better model space-time correlations. With our design of Enclosure Global Tokens (EGT), the model captures and aggregates global features more effectively than competing methods. To further improve data efficiency, we employ masked video modeling for self-supervised pre-training, with the proposed Spatial-Temporal Chained (STC) masking strategy designed to adapt to various video scenarios. Experiments demonstrate that E-ViM$^3$ performs as the state-of-the-art in two high-level semantic analysis tasks across four datasets of varying sizes: EchoNet-Dynamic, CAMUS, MICCAI-BUV, and WHBUS. Furthermore, our model achieves competitive performance with limited labels, highlighting its potential impact on real-world clinical applications.
Prompting Vision-Language Model for Nuclei Instance Segmentation and Classification
Authors: Jieru Yao, Guangyu Guo, Dingwen Zhang, Qiang Xie, Longfei Han, Zhaohui Zheng, Junwei Han
Date: 2025-03-27 00:00:00
Proceeding: journal 2025 3
Tasks: Cell Segmentation, Contrastive Learning, Instance Segmentation, Language Modeling, Language Modelling, Nuclei Classification, Nuclei Segmentation and Classfication, Prompt Engineering, Representation Learning, Semantic Segmentation

Nuclei instance segmentation and classification are a fundamental and challenging task in whole slide Imaging (WSI) analysis. Most dense nuclei prediction studies rely heavily on crowd labelled data on high-resolution digital images, leading to a time-consuming and expertise-required paradigm. Recently, Vision-Language Models (VLMs) have been intensively investigated, which learn rich cross-modal correlation from large-scale image-text pairs without tedious annotations. Inspired by this, we build a novel framework, called PromptNu, aiming at infusing abundant nuclei knowledge into the training of the nuclei instance recognition model through vision-language contrastive learning and prompt engineering techniques. Specifically, our approach starts with the creation of multifaceted prompts that integrate comprehensive nuclear knowledge, including visual insights from the GPT-4V model, statistical analyses, and expert insights from the pathology field. Then, we propose a novel prompting methodology that consists of two pivotal vision-language contrastive learning components: the Prompting Nuclei Representation Learning (PNuRL) and the Prompting Nuclei Dense Prediction (PNuDP), which adeptly integrates the expertise embedded in pre-trained VLMs and multifaceted prompts into the feature extraction and prediction process, respectively. Comprehensive experiments on six datasets with extensive WSI scenarios demonstrate the effectiveness of our method for both nuclei instance segmentation and classification tasks.
Adaptive Local Clustering over Attributed Graphs
Authors: Haoran Zheng, Renchi Yang, Jianliang Xu
Date: 2025-03-26 00:00:00
Tasks: Attribute, Clustering, Graph Clustering

Given a graph $G$ and a seed node $v_s$, the objective of local graph clustering (LGC) is to identify a subgraph $C_s \in G$ (a.k.a. local cluster) surrounding $v_s$ in time roughly linear with the size of $C_s$. This approach yields personalized clusters without needing to access the entire graph, which makes it highly suitable for numerous applications involving large graphs. However, most existing solutions merely rely on the topological connectivity between nodes in $G$, rendering them vulnerable to missing or noisy links that are commonly present in real-world graphs. To address this issue, this paper resorts to leveraging the complementary nature of graph topology and node attributes to enhance local clustering quality. To effectively exploit the attribute information, we first formulate the LGC as an estimation of the bidirectional diffusion distribution (BDD), which is specialized for capturing the multi-hop affinity between nodes in the presence of attributes. Furthermore, we propose LACA, an efficient and effective approach for LGC that achieves superb empirical performance on multiple real datasets while maintaining strong locality. The core components of LACA include (i) a fast and theoretically-grounded preprocessing technique for node attributes, (ii) an adaptive algorithm for diffusing any vectors over $G$ with rigorous theoretical guarantees and expedited convergence, and (iii) an effective three-step scheme for BDD approximation. Extensive experiments, comparing 17 competitors on 8 real datasets, show that LACA outperforms all competitors in terms of result quality measured against ground truth local clusters, while also being up to orders of magnitude faster. The code is available at https://github.com/HaoranZ99/alac.
QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions
Authors: Siyin Wang, Wenyi Yu, Xianzhao Chen, Xiaohai Tian, Jun Zhang, Lu Lu, Yu Tsao, Junichi Yamagishi, Yuxuan Wang, Chao Zhang
Date: 2025-03-26 00:00:00

This paper explores a novel perspective to speech quality assessment by leveraging natural language descriptions, offering richer, more nuanced insights than traditional numerical scoring methods. Natural language feedback provides instructive recommendations and detailed evaluations, yet existing datasets lack the comprehensive annotations needed for this approach. To bridge this gap, we introduce QualiSpeech, a comprehensive low-level speech quality assessment dataset encompassing 11 key aspects and detailed natural language comments that include reasoning and contextual insights. Additionally, we propose the QualiSpeech Benchmark to evaluate the low-level speech understanding capabilities of auditory large language models (LLMs). Experimental results demonstrate that finetuned auditory LLMs can reliably generate detailed descriptions of noise and distortion, effectively identifying their types and temporal characteristics. The results further highlight the potential for incorporating reasoning to enhance the accuracy and reliability of quality assessments. The dataset will be released at https://huggingface.co/datasets/tsinghua-ee/QualiSpeech.
sudo rm -rf agentic_security
Authors: Sejin Lee, Jian Kim, Haon Park, Ashkan Yousefpour, Sangyoon Yu, Min Song
Date: 2025-03-26 00:00:00
Tasks: Adversarial Attack, AI and Safety, Real-World Adversarial Attack, Red Teaming, Safety Alignment

Large Language Models (LLMs) are increasingly deployed as computer-use agents, autonomously performing tasks within real desktop or web environments. While this evolution greatly expands practical use cases for humans, it also creates serious security exposures. We present SUDO (Screen-based Universal Detox2Tox Offense), a novel attack framework that systematically bypasses refusal-trained safeguards in commercial computer-use agents, such as Claude for Computer Use. The core mechanism, Detox2Tox, transforms harmful requests (that agents initially reject) into seemingly benign requests via detoxification, secures detailed instructions from advanced vision language models (VLMs), and then reintroduces malicious content via toxification just before execution. Unlike conventional jailbreaks, SUDO iteratively refines its attacks based on a built-in refusal feedback, making it increasingly effective against robust policy filters. In extensive tests spanning 50 real-world tasks and multiple state-of-the-art VLMs, SUDO achieves a stark attack success rate of 24.41% (with no refinement), and up to 41.33% (by its iterative refinement) in Claude for Computer Use. By revealing these vulnerabilities and demonstrating the ease with which they can be exploited in real-world computing environments, this paper highlights an immediate need for robust, context-aware safeguards. WARNING: This paper includes harmful or offensive model outputs
Deep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages
Authors: Gabriel Bo, Justin Gu, Christopher Sun
Date: 2025-03-25 00:00:00
Tasks: Binary Classification, Classification, Deep Learning, Multi-class Classification

We present a foundation modeling framework that leverages deep learning to uncover latent genetic signatures across the hematopoietic hierarchy. Our approach trains a fully connected autoencoder on multipotent progenitor cells, reducing over 20,000 gene features to a 256-dimensional latent space that captures predictive information for both progenitor and downstream differentiated cells such as monocytes and lymphocytes. We validate the quality of these embeddings by training feed-forward, transformer, and graph convolutional architectures for blood disease diagnosis tasks. We also explore zero-shot prediction using a progenitor disease state classification model to classify downstream cell conditions. Our models achieve greater than 95% accuracy for multi-class classification, and in the zero-shot setting, we achieve greater than 0.7 F1-score on the binary classification task. Future work should improve embeddings further to increase robustness on lymphocyte classification specifically.