Online reviews represent a major source of information for evaluating customer experience and supporting decision making in the hospitality industry, yet rating prediction from review content remains challenging because review text is often short, noisy, and internally inconsistent. This study presents a deep learning framework for predicting hotel ratings from guest reviews while explicitly addressing data quality before model training. Data reliability is treated as a central modeling concern. The proposed methodology combines review titles, review texts, and […]

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The Taylor Green vortex is a classic benchmark for unsteady incompressible flows, but most existing physics-informed neural network (PINN) studies on this problem report only early time results or omit the pressure field entirely. We present a complete, step by step PINN simulation of the two-dimensional decaying vortex at Reynolds number 100 using the DeepXDE library. A fully connected network with four hidden layers of 128 neurons each is trained on 20,000 collocation points, 2,000 boundary points, and […]

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arXiv:2603.15708v1 Announce Type: new Abstract: Imbalanced data distribution remains a critical challenge in sequential learning, leading models to easily recognize frequent categories while failing to detect minority classes adequately. The Mixture-of-Experts model offers a scalable solution, yet its application is often hindered by parameter inefficiency, poor expert specialization, and difficulty in resolving prediction conflicts. To Master the Minority classes effectively, we propose the Uncertainty-based Multi-Expert fusion network (UME) framework. UME is designed with three core innovations: First, we […]

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arXiv:2603.10053v1 Announce Type: new Abstract: The Pickup and Delivery Problem (PDP) is a fundamental and challenging variant of the Vehicle Routing Problem, characterized by tightly coupled pickup–delivery pairs, precedence constraints, and spatial layouts that often exhibit clustering. Existing deep reinforcement learning (DRL) approaches either model all nodes on a flat graph, relying on implicit learning to enforce constraints, or achieve strong performance through inference-time collaborative search at the cost of substantial latency. In this paper, we propose emph{CAADRL} […]

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arXiv:2603.12446v1 Announce Type: new Abstract: Eavesdropping on voice conversations presents a growing threat to personal privacy and information security. In this paper, we present RadEar, a novel RF backscatter-based system designed to enable covert voice eavesdropping through walls. RadEar consists of two key components: (i) a batteryless RF backscatter tag covertly deployed inside the target space, and (ii) an RF reader located outside the room that performs signal demodulation, voice separation, and denoising. The tag features a compact, […]

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arXiv:2601.15339v1 Announce Type: new Abstract: Code understanding is a foundational capability in software engineering tools and developer workflows. However, most existing systems are designed for English-speaking users interacting via keyboards, which limits accessibility in multilingual and voice-first settings, particularly in regions like India. Voice-based interfaces offer a more inclusive modality, but spoken queries involving code present unique challenges due to the presence of non-standard English usage, domain-specific vocabulary, and custom identifiers such as variable and function names, often […]

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arXiv:2603.24825v1 Announce Type: new Abstract: Patch reviewing is critical for software development, especially in distributed open-source development, which highly depends on voluntary work, such as Linux. This paper studies the past 10 years of patch reviews of the Linux memory management subsystem to characterize the challenges involved in patch reviewing at scale. Our study reveals that the review process is still primarily reliant on human effort despite a wide-range of automatic checking tools. Although kernel developers strive to […]

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Transformer models have achieved remarkable empirical successes, largely due to their in-context learning capabilities. Inspired by this, we explore training an autoregressive transformer for in-context reinforcement learning (ICRL). In this setting, we initially train a transformer on an offline dataset consisting of trajectories collected from various RL tasks, and then fix and use this transformer to create an action policy for new RL tasks. Notably, we consider the setting where the offline dataset contains trajectories sampled from suboptimal […]

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Synchronous federated learning scales poorly due to the straggler effect. Asynchronous algorithms increase the update throughput by processing updates upon arrival, but they introduce two fundamental challenges: gradient staleness, which degrades convergence, and bias toward faster clients under heterogeneous data distributions. Although algorithms such as AsyncSGD and Generalized AsyncSGD mitigate this bias via client-side task queues, most existing analyses neglect the underlying queueing dynamics and lack closed-form characterizations of the update throughput and gradient staleness. To close this […]

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arXiv:2604.01367v1 Announce Type: new Abstract: We study algorithms for approximating the permanent of a random matrix when the entries are slightly biased away from zero. This question is motivated by the goal of understanding the classical complexity of linear optics and emph{boson sampling} (Aaronson and Arkhipov ’11; Eldar and Mehraban ’17). Barvinok’s interpolation method enables efficient approximation of the permanent, provided one can establish a sufficiently large zero-free region for the polynomial $mathrm{per}(zJ + W)$, where $J$ is […]

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