CybAI news

arXiv:2412.01855v1 Announce Type: cross Abstract: Surgical treatment for prostate cancer often involves organ removal, i.e., prostatectomy. Pathology reports on these specimens convey treatment-relevant information. Beyond these reports, the diagnostic process generates extensive and complex information that is difficult to represent in reports, although it is of significant interest to the other medical specialties involved. 3D tissue reconstruction would allow for better spatial visualization, as well as combinations with other imaging modalities. Existing approaches in this area have proven labor-intensive and challenging to integrate into clinical workflows. 3D-SLIVER provides a simplified solution, implemented as an open-source 3DSlicer extension. We outline three…

[Submitted on 21 Feb 2024 (v1), last revised 2 Dec 2024 (this version, v3)] View a PDF of the paper titled Large Language Models for Data Annotation and Synthesis: A Survey, by Zhen Tan and 9 other authors View PDF HTML (experimental) Abstract:Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to automate the complicated process of data…

[Submitted on 19 Mar 2024 (v1), last revised 3 Dec 2024 (this version, v3)] View a PDF of the paper titled A Physics-embedded Deep Learning Framework for Cloth Simulation, by Zhiwei Zhao View PDF Abstract:Delicate cloth simulations have long been desired in computer graphics. Various methods were proposed to improve engaged force interactions, collision handling, and numerical integrations. Deep learning has the potential to achieve fast and real-time simulation, but common neural network structures often demand many parameters to capture cloth dynamics. This paper proposes a physics-embedded learning framework that directly encodes physical features of cloth simulation. The convolutional neural…

[Submitted on 2 Dec 2024] View a PDF of the paper titled Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis, by David Gimeno-G'omez and Catarina Botelho and Anna Pompili and Alberto Abad and Carlos-D. Mart'inez-Hinarejos View PDF HTML (experimental) Abstract:Recent works in pathological speech analysis have increasingly relied on powerful self-supervised speech representations, leading to promising results. However, the complex, black-box nature of these embeddings and the limited research on their interpretability significantly restrict their adoption for clinical diagnosis. To address this gap, we propose a novel, interpretable framework specifically designed to support Parkinson's Disease (PD) diagnosis. Through the…

arXiv:2412.02343v1 Announce Type: new Abstract: In social media, neural network models have been applied to hate speech detection, sentiment analysis, etc., but neural network models are susceptible to adversarial attacks. For instance, in a text classification task, the attacker elaborately introduces perturbations to the original texts that hardly alter the original semantics in order to trick the model into making different predictions. By studying textual adversarial attack methods, the robustness of language models can be evaluated and then improved. Currently, most of the research in this field focuses on English, and there is also a certain amount of research on…