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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Amashi Niwarthana Chockalingam Kasi Yi Hao Chan Conghao Wang Jagath C Rajapakse College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Although brain imaging has provided crucial insights into Alzheimer’s disease (AD) progression, its limitations in capturing molecular changes have motivated the need to integrate and interpret transcriptomic data, which can reveal early-stage molecular disruptions. By interpreting both structural and gene expression patterns, there is potential to gain a more comprehensive understanding of the disease and identify interactions that drive AD pathogenesis. This study introduces a novel deep learning framework that integrates structural magnetic resonance imaging (sMRI) and gene expression (GE) data for AD prediction. The model extracts features from both data modalities and employs a novel approach to quantify cross-modal interactions. Our multimodal architecture achieves 83.6% accuracy in AD prediction, demonstrating its effectiveness in combining imaging and transcriptomic data. We present a technique for interpreting interactions between specific brain regions and gene expression patterns, providing insights into their joint contribution to AD risk. This approach enables the identification of both established and potential new AD biomarkers, spanning structural brain changes and transcriptomic alterations. By offering a deeper understanding of AD’s molecular and structural aspects, our work advances the field of multimodal biomarker discovery in neurodegenerative diseases and paves the way for more comprehensive early diagnosis strategies.

关键词： Pathology Magnetic resonance imaging Transcriptomics Biomarkers Feature extraction Brain modeling data models Decoding Gene expression Alzheimer's disease

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Generating Apoptosis-Inducing Anticancer Peptides Targeting BCL-xL Using Latent Diffusion Models on Small datasets

Generating Apoptosis-Inducing Anticancer Peptides Targeting ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tiara Natasha Binte Sayuti Wang Conghao Jagath C Rajapakse College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The overexpression of B-cell lymphoma-extra large (BCL-xL), an anti-apoptotic protein, plays a pivotal role in various cancers by inhibiting apoptosis and promoting tumor progression. We introduce Latent Diffusion Model (LDM) specifically designed to generate apoptosis-inducing Anticancer Peptides (ACPs) targeting BCL-xL. By applying a diffusion process to high-dimensional embeddings generated by ESM-2, a state-of-the-art Protein Language Model (PLM), our approach successfully generates peptides with optimal physicochemical properties, even when trained on a relatively small dataset. In comparison to Variational Autoencoder (VAE) and Wasserstein Autoencoder (WAE), our architecture not only mitigates the limitations associated with small datasets but also produces peptides that closely align with desired physiochemical properties. Evaluation against key metrics—molecular weight, isoelectric point, net charge at pH 7, grand average of hydropathy, instability index, and anticancer peptide score,demonstrates that our model consistently outperforms both VAE and WAE, offering a promising strategy for the development of novel anticancer therapies.

关键词： Proteins Peptides Computational modeling Autoencoders Medical treatment Signal processing Diffusion models Structural engineering Speech processing Tumors

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Uncertainty-Aware Explainable Federated Learning

arXiv

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arXiv 2025年

作者： Zhang, Yanci Yu, Han College of Computing and Data Science Nanyang Technological University Singapore

Federated Learning (FL) is a collaborative machine learning paradigm for enhancing data privacy preservation. Its privacy-preserving nature complicates the explanation of the decision-making processes and the evaluation of the reliability of the generated explanations. In this paper, we propose the Uncertainty-aware eXplainable Federated Learning (UncertainXFL) to address these challenges. It generates explanations for decision-making processes under FL settings and provides information regarding the uncertainty of these explanations. UncertainXFL is the first framework to explicitly offer uncertainty evaluation for explanations within the FL context. Explanatory information is initially generated by the FL clients and then aggregated by the server in a comprehensive and conflict-free manner during FL training. The quality of the explanations, including the uncertainty score and tested validity, guides the FL training process by prioritizing clients with the most reliable explanations through higher weights during model aggregation. Extensive experimental evaluation results demonstrate that UncertainXFL achieves superior model accuracy and explanation accuracy, surpassing the current state-of-the-art model that does not incorporate uncertainty information by 2.71% and 1.77%, respectively. By integrating and quantifying uncertainty in the data into the explanation process, UncertainXFL not only clearly presents the explanation alongside its uncertainty, but also leverages this uncertainty to guide the FL training process, thereby enhancing the robustness and reliability of the resulting models. © 2025, CC BY.

关键词： Federated learning

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SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learning

arXiv

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arXiv 2025年

作者： Qiu, Luyi Till, Tristan Guo, Xiaobao Kong, Adams Wai-Kin College of Computing and Data Science Nanyang Technological University Singapore

Scribble annotations significantly reduce the cost and labor required for dense labeling in large medical datasets with complex anatomical structures. However, current scribble-supervised learning methods are limited in their ability to effectively propagate sparse annotation labels to dense segmentation masks and accurately segment object boundaries. To address these issues, we propose a Progressive Collaborative Learning framework that leverages novel algorithms and the Med-SAM foundation model to enhance information quality during training. (1) We enrich ground truth scribble segmentation labels through a new algorithm, propagating scribbles to estimate object boundaries. (2) We enhance feature representation by optimizing Med-SAM-guided training through the fusion of feature embeddings from Med-SAM and our proposed Sparse Mamba network. This enriched representation also facilitates the fine-tuning of the Med-SAM decoder with enriched scribbles. (3) For inference, we introduce a Sparse Mamba network, which is highly capable of capturing local and global dependencies by replacing the traditional sequential patch processing method with a skip-sampling procedure. Experiments on the ACDC, CHAOS, and MSCMRSeg datasets validate the effectiveness of our framework, outperforming nine state-of-the-art methods. Our code is available at ***. © 2025, CC BY.

关键词： Image segmentation

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Utility-Cost Ratio Maximization in Quantum Networks for Secure Quantum Key Distribution

Utility-Cost Ratio Maximization in Quantum Networks for Secu...

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Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Liangxin Qian Jun Zhao College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

In this paper, we study the utility-cost ratio (UCR) maximization problem in quantum networks, with a particular focus on secure quantum key distribution (QKD). We propose the Quantum Utility-Cost Optimization (QUCO) algorithm to address the challenge of balancing network utility with operational and infrastructure costs. The utility of the network is measured based on the secret key fraction, a key performance metric in QKD, while the costs considered include operational and maintenance expenses, transceiver deployment, auxiliary equipment, and wavelength channel usage. The QUCO algorithm transforms the original non-convex problem into a convex optimization through a series of transformations, enabling efficient resource allocation across quantum network routes. Our simulation results, conducted on a realistic quantum network topology, demonstrate the algorithm’s ability to improve the utility-cost ratio while maintaining high network performance. The QUCO algorithm strikes a balance between minimizing costs and maintaining high end-to-end entanglement fidelity, which is effective for optimizing UCR in quantum networks.

关键词： Costs Network topology Wavelength measurement Transforms Transceivers Quantum key distribution Topology Quantum networks Resource management Optimization

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DAGGER BEHIND SMILE: FOOL LLMS WITH A HAPPY ENDING STORY

arXiv

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arXiv 2025年

作者： Song, Xurui Xie, Zhixin Huai, Shuo Kong, Jiayi Luo, Jun College of Computing and Data Science Nanyang Technological University Singapore

The wide adoption of Large Language Models (LLMs) has attracted significant attention from jailbreak attacks, where adversarial prompts crafted through optimization or manual design exploit LLMs to generate malicious contents. However, optimization-based attacks have limited efficiency and transferability, while existing manual designs are either easily detectable or demand intricate interactions with LLMs. In this paper, we first point out a novel perspective for jailbreak attacks: LLMs are more responsive to positive prompts. Based on this, we deploy Happy Ending Attack (HEA) to wrap up a malicious request in a scenario template involving a positive prompt formed mainly via a happy ending, it thus fools LLMs into jailbreaking either immediately or at a follow-up malicious request. This has made HEA both efficient and effective, as it requires only up to two turns to fully jailbreak LLMs. Extensive experiments show that our HEA can successfully jailbreak on state-of-the-art LLMs, including GPT-4o, Llama3-70b, Gemini-pro, and achieves 88.79% attack success rate on average. We also provide quantitative explanations for the success of HEA. WARNING: This paper contains offensive and harmful content generated by LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： Computer simulation languages

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A Bias-Free Revenue-Maximizing Bidding Strategy for data Consumers in Auction-based Federated Learning 33

A Bias-Free Revenue-Maximizing Bidding Strategy for Data Con...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Tang, Xiaoli Yu, Han Li, Zengxiang Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore ENN Group Beijing China Department of Electrical and Computer Engineering The University of British Columbia Canada

ISBN: (纸本)9781956792041

Auction-based Federated Learning (AFL) is a burgeoning research area. However, existing bidding strategies for AFL data consumers (DCs) primarily focus on maximizing expected accumulated utility, disregarding the more complex goal of revenue maximization. They also only consider winning bids, leading to biased estimates by overlooking information from losing bids. To address these issues, we propose a Bias-free Revenue-maximizing Federated bidding strategy for DCs in AFL (BR-FEDBIDDER). Our theoretical exploration of the relationships between Return on Investment (ROI), bid costs, and utility, and their impact on overall revenue underscores the complexity of maximizing revenue solely by prioritizing ROI enhancement. Leveraging these insights, BR-FEDBIDDER optimizes bid costs with any given ROI constraint. In addition, we incorporate an auxiliary task of winning probability estimation into the framework to achieve bias-free learning by leveraging bid records from historical bid requests, including both winning and losing ones. Extensive experiments on six widely used benchmark datasets show that BR-FEDBIDDER outperforms eight state-of-the-art methods, surpassing the best-performing baseline by 5.66%, 6.08% and 2.44% in terms of the total revenue, ROI, and test accuracy of the resulting FL models, respectively. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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An Encoder-Agnostic Weakly Supervised Method For Describing Textures

An Encoder-Agnostic Weakly Supervised Method For Describing ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Shangbo Mao Deepu Rajan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recent advances in Large Language Models (LLMs) have enabled the semantic description of textures in natural language, aiming to capture them in richer detail. However, most methods are confined to either depending on supervised training with pairs of images and manually annotated visual attributes that most texture datasets lack or using Vision-Language Models (VLMs) such as CLIP. In this paper, we develop an encoder-agnostic Weakly supervised Texture Description Generator (WTDG) that employs a novel Scaled Ranked Kullback-Leibler divergence (SR-KL) loss between image and text modalities. Within the SR-KL loss formulation, we leverage category information, which is always available as ground-truths for all benchmark texture recognition datasets. We further extend our proposed WTDG to assist in texture recognition by using its generated texture descriptions. Thus, we develop a multimodal framework, called $T e x^2$ , which is adept at simultaneous generation of texture description and recognition. Our approach exhibits promising performance in describing and recognizing textures on benchmark datasets.

关键词： Training Visualization Computer vision Accuracy Large language models Semantics Natural languages Benchmark testing Generators

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Harnessing the Power of Vocal Signals in COVID-19 Detection Utilizing Machine Learning

Harnessing the Power of Vocal Signals in COVID-19 Detection ...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Mann, Aleesa Jadhav, Ajinkya P. Matovu, Richard Gupta, Vibhuti Meharry Medical College School of Applied Computational Sciences Department of Computer Science and Data Science NashvilleTN United States Gannon University Department of Computing and Information Science EriePA United States

ISBN: (纸本)9798350362480

The global COVID-19 pandemic has strained health-care systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1-score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible. © 2024 IEEE.

关键词： COVID-19

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MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts 41

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Exp...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Jianan Cao, Zhiguang Wu, Yaoxin Song, Wen Ma, Yining Zhang, Jie Xu, Chi College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology Netherlands Institute of Marine Science and Technology Shandong University China Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much ***, most neural solvers are only structured and trained independently on a specific problem, making them less generic and *** this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants ***, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in *** further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational ***, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark *** further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution *** source code is available at: https://***/RoyalSkye/Routing-MVMoE. Copyright 2024 by the author(s)

关键词： Vehicle routing

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