arXiv:2603.00694v1 Announce Type: new Abstract: Explainability and transparent decision-making are essential for the safe deployment of autonomous driving systems. Scene captioning summarizes environmental conditions and risk factors in natural language, improving transparency, safety, and human–robot interaction. However, most existing approaches target structured urban scenarios; in off-road environments, they are vulnerable to single-modality degradations caused by rain, fog, snow, and darkness, and they lack a unified framework that jointly models structured scene captioning and path planning. To bridge this […]

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arXiv:2506.05059v3 Announce Type: replace-cross Abstract: Deep learning has achieved remarkable success across many domains, but it has also created a growing demand for interpretability in model predictions. Although many explainable machine learning methods have been proposed, post-hoc explanations lack guaranteed fidelity and are sensitive to hyperparameter choices, highlighting the appeal of inherently interpretable models. For example, linear regression provides clear feature effects through its coefficients. However, such models are often outperformed by more complex neural networks (NNs) that […]

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It’s also reasonable for people who entered technology in the last couple of decades because it was good job, or because they enjoyed coding to look at this moment with a real feeling of loss. That feeling of loss though can be hard to understand emotionally for people my age who entered tech because we were addicted to feeling of agency it gave us. The web was objectively awful as a technology, and genuinely amazing, and nobody got […]

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Deep learning has driven remarkable progress in visual recognition, yet state-of-the-art models remain heavily reliant on large-scale labeled datasets and high-performance GPU infrastructure, assumptions that rarely hold in real-world industrial settings where data is scarce and deployment occurs on CPU-based systems. This working paper introduces the Ensemble Spatial-Agnostic Basis Adaptation Network (Ensemble SABA-Net), a lightweight classification framework explicitly designed for low-data and resource-constrained environments. The proposed architecture departs from conventional convolutional and attention-based designs by operating directly on […]

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For most of the internet era, educational technology followed a familiar pattern: digitize the textbook, put worksheets on a screen, maybe add a video. The goal was efficiency. Faster distribution. Easier grading. More content, more quickly. But that model is starting to feel dated. Today’s learners don’t just want information delivered. They expect to interact with it. Remix it. Build on top of it. The question is no longer whether technology belongs in education; it’s whether our education […]

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Image-to-PointCloud (I2P) place recognition is crucial for autonomous systems, facing challenges from modality discrepancies and environmental variations. Existing feature fusion strategies often fall short in complex real-world scenarios. We propose AttnLink, a novel framework that significantly enhances I2P place recognition through a sophisticated attention-guided cross-modal feature fusion mechanism. AttnLink integrates an Adaptive Depth Completion Network to generate dense depth maps and an Attention-Guided Cross-Modal Feature Encoder, utilizing lightweight spatial attention for local features and a context-gating mechanism for […]

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I’m working on an optimization problem with a complex environment. Environment is complex in inner working but has only one action input. The action can be either binary, or discrete, or continuous. If the environment is optimized on binary action the maximum reward will be less than when on discrete or continuous actions. PPO works when action is binary or discrete but not when it’s continuous. The input to the model needs to be a value between 0 […]

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The Non-equilibrium Green’s function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices such as transistors, photo-diodes, or memory cells, in the ballistic limit of transport or in the presence of various scattering sources such as electronphonon, electron-photon, or even electron-electron interactions. The inclusion of all these mechanisms has been first demonstrated in small systems, composed of a few atoms, before being scaled up to larger structures made of thousands of […]

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arXiv:2601.13874v2 Announce Type: replace Abstract: The maximum mean discrepancy (MMD) is a kernel-based nonparametric statistic for two-sample testing, whose inferential accuracy depends critically on variance characterization. Existing work provides various finite-sample estimators of the MMD variance, often differing under the null and alternative hypotheses and across balanced or imbalanced sampling schemes. In this paper, we study the variance of the MMD statistic through its U-statistic representation and Hoeffding decomposition, and establish a unified finite-sample characterization covering different hypotheses […]

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