arXiv:2602.21446v1 Announce Type: new Abstract: Hyperdimensional Computing (HDC) offers a computationally efficient paradigm for neuromorphic learning. Yet, it lacks rigorous uncertainty quantification, leading to open decision boundaries and, consequently, vulnerability to outliers, adversarial perturbations, and out-of-distribution inputs. To address these limitations, we introduce ConformalHDC, a unified framework that combines the statistical guarantees of conformal prediction with the computational efficiency of HDC. For this framework, we propose two complementary variations. First, the set-valued formulation provides finite-sample, distribution-free coverage guarantees. […]

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arXiv:2602.13201v1 Announce Type: new Abstract: In complex Unmanned Aerial Vehicle (UAV) networks, UAVs can establish dynamic and heterogeneous links with one another for various purposes, such as communication coverage, collective sensing, and task collaboration. These interactions give rise to dynamic multiplex UAV networks, where each layer represents a distinct type of interaction among UAVs. Understanding how such links form and evolve is both of theoretical interest and of practical importance for the control and maintenance of networked UAV […]

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This paper presents a distributed AI training system that pools GPU high-bandwidth memory, host DRAM, and SSD into a coordinated parameter-serving hierarchy to support multiterabyte, sparsity-dominated deep models without sharing raw features across machines. The design shards and caches only the working parameters in GPU memory via multi-GPU hash tables, communicates intra-node over NVLink, and performs inter-node synchronization using RDMA-backed collective updates to preserve convergence under data parallelism. A four-stage pipeline overlaps network transfers, SSD I/O, CPU partitioning, […]

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arXiv:2509.21906v2 Announce Type: replace-cross Abstract: Discrete flow models offer a powerful framework for learning distributions over discrete state spaces and have demonstrated superior performance compared to the discrete diffusion models. However, their convergence properties and error analysis remain largely unexplored. In this work, we develop a unified framework grounded in stochastic calculus theory to systematically investigate the theoretical properties of discrete flow models. Specifically, by leveraging a Girsanov-type theorem for the path measures of two continuous-time Markov chains […]

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arXiv:2601.09832v1 Announce Type: new Abstract: Following code style conventions in software projects is essential for maintaining overall code quality. Adhering to these conventions improves maintainability, understandability, and extensibility. Additionally, following best practices during software development enhances performance and reduces the likelihood of errors. This paper analyzes 1,036 popular open-source JAVA projects on GITHUB to study how code style and programming practices are adopted and evolve over time, examining their prevalence and the most common violations. Additionally, we study […]

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I’m working in machine learning- application field. There are very few references which apply machine learning framework in my field of interest. So, even if I have comparison results of our framework with one baseline, I am unable to find more methods that solve the problem I am interested in. I see there is an in-depth comparision analysis provided in the machine learning conference papers. How to manage my analysis work with very few comparison results? I can […]

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Optimism, obsession, self-belief, raw horsepower and personal connections are how things get started. Cohesive teams, the right combination of calmness and urgency, and unreasonable commitment are how things get finished. Long-term orientation is in short supply; try not to worry about what people think in the short term, which will get easier over time. It is easier for a team to do a hard thing that really matters than to do an easy thing that doesn’t really matter; […]

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arXiv:2601.21066v1 Announce Type: new Abstract: Backdoor attacks pose a severe threat to deep learning, yet their impact on object detection remains poorly understood compared to image classification. While attacks have been proposed, we identify critical weaknesses in existing detection-based methods, specifically their reliance on unrealistic assumptions and a lack of physical validation. To bridge this gap, we introduce BadDet+, a penalty-based framework that unifies Region Misclassification Attacks (RMA) and Object Disappearance Attacks (ODA). The core mechanism utilizes a […]

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arXiv:2602.00969v1 Announce Type: new Abstract: Training Large Language Models (LLMs) at ultra-low precision is critically impeded by instability rooted in the conflict between discrete quantization constraints and the intrinsic heavy-tailed spectral nature of linguistic data. By formalizing the connection between Zipfian statistics and random matrix theory, we prove that the power-law decay in the singular value spectra of embeddings is a fundamental requisite for semantic encoding. We derive theoretical bounds showing that uniform quantization introduces a noise floor […]

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