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TexCIL: Text-Guided Continual Learning of Disease with Vision-Language Model

TexCIL: Text-Guided Continual Learning of Disease with Visio...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wentao Zhang Defeng Zhao Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Current intelligent diagnostic systems often catastrophically forget old knowledge when learning new diseases only from the training dataset of the new diseases. Inspired by human learning of visual classes with the effective help of language, we propose a continual learning framework based on a pre-trained visual-language model (VLM) without storing any image of previously learned diseases. In this framework, textual prior knowledge of each new disease can be obtained by utilizing the frozen VLM’s text encoder, and then used to guide the visual learning of the new disease. This framework innovatively utilizes the textual prior knowledge of all previously learned diseases as out-of-distribution (OOD) information to help differentiate currently being-learned diseases from others. Extensive empirical evaluations on both medical and natural image datasets confirm the superiority of the proposed method over existing state-of-the-art methods in continual learning of new visual classes. The source code is available at https://***/OpenMedIA/TexCIL.

关键词： Continuing education Training Visualization source coding Biological system modeling Benchmark testing Bioinformatics Medical diagnostic imaging Diseases

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n-channel multiple descriptions: theory and constructions

n-channel multiple descriptions: theory and constructions

引用

Data Compression Conference (DCC)

作者： R. Puri S.S. Pradhan K. Ramchandran EECS Department University of California Berkeley CA USA EECS Department University of Michigan MI USA

ISBN: (纸本)0769514774

We present new achievable rate regions and code constructions for the symmetric n-channel multiple descriptions (MD) coding problem (Puri et al. (2002)) for n>2. Our approach is inspired by unexplored connections between MD and the problem of distributed source coding (Slepian et al. (1973); Wyner et al. (1976)). For illustrative clarity, we restrict our focus to the important special case relating to (n, k) source-channel erasure codes (Pradhan et al. (2001)). This involves n encodings of a source with the goal of maximizing its reconstruction fidelity with the availability of any k of them, while strictly improving this reconstruction fidelity with the availability of more than k descriptions. We describe the underlying information-theoretic framework, and then formulate practical constructions based on scalar quantizers and linear channel codes for the n=3 case to illustrate our concepts.

关键词： Rate-distortion Decoding Availability source coding Transform coding Error correction codes Data compression Uncertainty Statistical distributions Linear code

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Distributed testing against independence with multiple terminals

Distributed testing against independence with multiple termi...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Wenwen Zhao Lifeng Lai Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA

ISBN: (纸本)9781479980109

Motivated by distributed learning with Big Data sets problems, we study a distributed testing against independence problem with multiple terminals. We connect the problem at hand to a source coding with multiple helpers problem, which is open in general. We fully characterize the rate region of the source coding with multiple helpers problem under a certain Markovian condition. Using this rate region characterization, we obtain a single-letter characterization of the optimal error exponent for the type 2 error probability under the type 1 error probability and communication rates constraints.

关键词： source coding Random variables Markov processes Error probability Decoding Indexes Entropy

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Low complexity sequential lossless coding for piecewise stationary memoryless sources

Low complexity sequential lossless coding for piecewise stat...

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IEEE International Symposium on Information Theory

作者： G.I. Shamir N. Merhav Department EE Technion-Israel Institute of Technology Haifa Israel Department EE University of Notre Dame IN USA

A strongly sequential lossless compression scheme with fixed per-letter computational complexity is presented for memoryless sources with abruptly changing statistics. It achieves redundancy of O(log N/N) when the tra... 详细信息

关键词： Statistics Gas insulated transmission lines Computational complexity source coding Arithmetic Statistical distributions Satellite communication USA Councils Probability distribution

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EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in source Code

EnStack: An Ensemble Stacking Framework of Large Language Mo...

引用

IEEE International Conference on Big Data

作者： Shahriyar Zaman Ridoy Md. Shazzad Hossain Shaon Alfredo Cuzzocrea Mst Shapna Akter Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Computer Science and Engineering Oakland University Rochester MI USA University of Calabria Rende Italy Department of Computer Science Oakland University MI USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Automated detection of software vulnerabilities is critical for enhancing security, yet existing methods often struggle with the complexity and diversity of modern codebases. In this paper, we propose a novel ensemble stacking approach that synergizes multiple pre-trained large language models (LLMs)—CodeBERT, GraphCodeBERT, and UniXcoder—to improve vulnerability detection in source code. Our method uniquely combines the semantic understanding of CodeBERT, the structural code representations of GraphCodeBERT, and the cross-modal capabilities of UniXcoder. By fine-tuning these models on the Draper VDISC dataset and integrating their predictions using meta-classifiers such as Logistic Regression, Support Vector Machines (SVM), Random Forest, and XGBoost, we effectively capture complex code patterns that individual models may miss. The meta-classifiers aggregate the strengths of each model, enhancing overall predictive performance. Our ensemble demonstrates significant performance gains over existing methods, with notable improvements in accuracy, precision, recall, F1-score, and AUC-score. This advancement addresses the challenge of detecting subtle and complex vulnerabilities in diverse programming contexts. The results suggest that our ensemble stacking approach offers a more robust and comprehensive solution for automated vulnerability detection, potentially influencing future AI-driven security practices.

关键词： Support vector machines Logistic regression Codes Accuracy source coding Large language models Stacking Semantics Predictive models Security

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On the Impact of Draft Pull Requests on Accelerating Feedback

On the Impact of Draft Pull Requests on Accelerating Feedbac...

引用

International Conference on Software Maintenance (ICSM)

作者： Firas Harbaoui Mohammed Sayagh Rabe Abdalkareem ETS - Quebec University Montreal QC Omar AL-mukhtar University Al Bayda Libya

ISBN: (数字)9798350395686

ISBN: (纸本)9798350395693

The pull request (PR) mechanism provides a structured way for developers to present their proposed modifications, engage in code review, and address any concerns before the changes are incorporated into the main codebase. As the adoption of PRs has grown over time, a variant known as draft PRs has gained traction, offering developers a novel way to share ideas and get feedback and collaboration on code changes during their development. Despite the benefits that draft PRs offer, there is still a lack of research on their efficiency in reaching the goal for which they are conceived, which is getting early feedback. This research paper aims to fill this gap by exploring how the draft mechanism is used, the impact of the draft mechanism on getting feedback and on the integration of new changes, and the different factors contributing to the responsiveness of comments. We observe that the draft is used by practitioners for complex changes and for different purposes, including the discussion of features to implement, experiments to conduct, and new versions to release. However, the goal of the draft mechanism is not fully reached as practitioners do not receive feedback (i.e., issue or review comments) on their drafts. For that, we leverage explanatory machine learning models to understand the differences between drafts that receive comments from these that did not before becoming ready to review. Our models show a median AUC performance of 0.67 to 0.77 and 0.66 to 0.75 for the issue comments and review comments models, respectively. The interpretation of our models shows that the actions that developers take as events, the description of the draft, the engagement of the author of the draft, and the engagement and responsiveness of the main reviewers play a crucial role in encouraging the reception of comments for draft pull requests.

关键词： Software maintenance Codes Reviews source coding Collaboration Machine learning Complexity theory Software engineering

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Go-Kart Racing Simulator for Reinforcement Learning with Augmented Sim2Real Adaptation

Go-Kart Racing Simulator for Reinforcement Learning with Aug...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Ildar Nurgaliev Andre Kuzminykh Andrey V. Savchenko Ilya Makarov Dataism Laboratory Innopolis University Dataism Laboratory Sber AI Lab AIRI ISP RAS

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Training self-driving cars in real-world scenarios is inefficient due to the possibility of crashes with obstacles and borders. This paper introduces a virtual environment to enhance reinforcement learning training in a virtual Go-Kart racing simulator. The primary objective is to leverage augmented reality to enhance observations inside the simulation, improve policy networks, and make the Value function precise and robust. We develop the wrapper for the CARLA simulator, enabling a cost-effective Sim2Real transition. It is demonstrated that the augmented Sim2Real adaptation successfully integrates simulated training outcomes into real-world scenarios where the real Go-Kart can accomplish six laps in a single-race mode, reaching the maximum speed of 11.5 m/s.

关键词： Training source coding Virtual environments Reinforcement learning Strategic planning Autonomous automobiles Automobiles Artificial intelligence Accidents Testing

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A DCT gain-shape vector quantizer for image coding

A DCT gain-shape vector quantizer for image coding

引用

IEEE International Conference on Communications (ICC)

作者： H.-S. Wu H.-B. Chen Department of Electronics Shanghai Jiaotong University Shanghai China

A discrete cosine-transform (DCT) image-coding technique using the gain-shape vector quantizer is presented. The original image is partitioned into many subblocks, and each subblock undergoes a two-dimensional DCT. The vectors to the quantizer are formed by selecting the same coefficients from th different transform subblocks. The normalized AC and DC vectors with mean removal can be modeled as standard distributions. Various level gain and shape codebooks are designed based on these models. The gain component is quantized by a scalar Lloyd's quantizer (1982). The bit allocation for the vector quantizer is designed to minimize distortion. The technique has been applied to a real image to evaluate its performance.< >

关键词： Discrete cosine transforms Image coding Bit rate Vector quantization Karhunen-Loeve transforms Probability distribution Shape control Shape measurement Discrete transforms source coding

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RoRankNet: Modeling Ordinal Progression in Alzheimer’s Disease with Plackett-Luce Ranking and Hard Instance Learning

RoRankNet: Modeling Ordinal Progression in Alzheimer’s Dise...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Minghang Yu Linlin Gao Dan Wu Jianhua Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Biomedical Engineering and Instrument Science Zhejiang University Zhejiang China Department of Radiology The First Affiliated Hospital of Xiamen University Xiamen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder characterized by progression from normal control (NC) to mild cognitive impairment (MCI), and ultimately to AD. These stages reflect ordinal deterioration of specific brain tissues. Timely and accurately identifying these stages is crucial for effective treatment. While existing multi-classification methods using structural magnetic resonance imaging show promise, they often overlook the inherently ordinal progressive relations. Addressing this, our study introduces RoRankNet, a robust ranking network designed to model the ordinal progression of AD. Specifically, we propose a distance-based Plackett-Luce ranking module to effectively represent the distance relations of these stages, enhancing the alignment between extracted features and disease progression. Additionally, we present a hard instance rectification module to identify and correct challenging instances, enhancing classification robustness. Our extensive experiments demonstrate RoRankNet’s superiority in classifying NC, MCI, and AD. Notably, our approach not only preserves the ordinal nature in feature space but also excels in localizing discriminative biomarkers essential for accurate stage prediction in clinical practice. source codes will be publicly available soon.

关键词： Accuracy source coding Magnetic resonance imaging Biomarkers Feature extraction Brain modeling Robustness Alzheimer's disease Bioinformatics

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Neural Compress-and-Forward for the Relay Channel

Neural Compress-and-Forward for the Relay Channel

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Ezgi Özyılkan Fabrizio Carpi Siddharth Garg Elza Erkip Department of Electrical and Computer Engineering New York University Brooklyn NY

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

The relay channel, consisting of a source-destination pair and a relay, is a fundamental component of cooperative communications. While the capacity of a general relay channel remains unknown, various relaying strategies, including compress-and-forward (CF), have been proposed. For CF, given the correlated signals at the relay and destination, distributed compression techniques, such as Wyner–Ziv coding, can be harnessed to utilize the relay-to-destination link more efficiently. In light of the recent advancements in neural network-based distributed compression, we revisit the relay channel problem, where we integrate a learned one-shot Wyner–Ziv compressor into a primitive relay channel with a finite-capacity and orthogonal (or out-of-band) relay-to-destination link. The resulting neural CF scheme demonstrates that our task-oriented compressor recovers binning of the quantized indices at the relay, mimicking the optimal asymptotic CF strategy, although no structure exploiting the knowledge of source statistics was imposed into the design. We show that the proposed neural CF scheme, employing finite order modulation, operates closely to the capacity of a primitive relay channel that assumes a Gaussian codebook. Our learned compressor provides the first proof-of-concept work toward a practical neural CF relaying scheme.

关键词： Visualization source coding Cooperative communication Conferences Half-duplex system Full-duplex system Decoding Indexes Relays Demodulation

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