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, […]

Ver mais

arXiv:2602.21276v1 Announce Type: cross Abstract: Neural networks are trained by optimizing multi-dimensional sets of fitting parameters on non-convex loss landscapes. Low-loss regions of the landscapes correspond to the parameter sets that perform well on the training data. A key issue in machine learning is the performance of trained neural networks on previously unseen test data. Here, we investigate neural network training by stochastic gradient descent (SGD) – a non-convex global optimization algorithm which relies only on the gradient […]

Ver mais

This tutorial shows you how to build a real-time medical transcription system that captures patient conversations, separates speakers, and automatically generates SOAP notes. You’ll create a streaming application that processes audio as it happens, giving healthcare providers instant transcription with proper clinical documentation format. You’ll use AssemblyAI’s Python SDK for real-time speech-to-text with multichannel speaker separation, combined with OpenAI’s GPT-4 API to transform raw transcripts into structured clinical notes. The implementation includes microphone audio capture, streaming transcription processing, […]

Ver mais

The Problem When building frontends, I didn’t want to hardcode JSON data everywhere. I wanted to build against actual API endpoints and practice the integration work, but spinning up a real backend for every prototype felt like overkill. After doing this dance too many times – writing throwaway Express servers or mocking fetch calls with static JSON – I realized other developers probably face this same friction. The typical workflow looked like this: 1. Start building a React […]

Ver mais

Scaling cooperative multi-agent reinforcement learning (MARL) is fundamentally limited by cross-agent noise: when agents share a common reward, the actions of all $N$ agents jointly determine each agent’s learning signal, so cross-agent noise grows with $N$. In the policy gradient setting, per-agent gradient estimate variance scales as $Θ(N)$, yielding sample complexity $mathcal{O}(N/ε)$. We observe that many domains — cloud computing, transportation, power systems — have differentiable analytical models that prescribe efficient system states. In this work, we propose […]

Ver mais

These thirty-four powerful essays, based on Taoist and Buddhist thought, constitute a guide to what the author calls “non-volitional living”—the ancient understanding that our efforts to grasp our true nature are futile. Wei Wu Wei explains these venerable spiritual traditions in the context of modern experience, using wit and considerable precision to convey their profound insight into the very nature of existence.

Ver mais

Hello! My friend and I developed 3 models for predicting a win in a Tetr.io match based on playstyle and gameplay. We used this dataset: https://www.kaggle.com/datasets/n3koasakura/tetr-io-top-players-replays, and we had 7 million rows to work with. Some interesting findings for someone who is about only a month into playing Tetr.io (i copypasted from my notebook): • ⁠The amount of garbage received in a match is the most dominant contributor to losing. Receiving a large amount of garbage tends to […]

Ver mais

arXiv:2603.02356v1 Announce Type: new Abstract: We explore the question of how to learn an optimal search strategy within the example of a parking problem where parking opportunities arrive according to an unknown inhomogeneous Poisson process. The optimal policy is a threshold-type stopping rule characterized by an indifference position. We propose an algorithm that learns this threshold by estimating the integrated jump intensity rather than the intensity function itself. We show that our algorithm achieves a logarithmic regret growth, […]

Ver mais

arXiv:2601.02492v1 Announce Type: new Abstract: We introduce variational spectral learning (VSL), a machine learning framework for solving partial differential equations (PDEs) that operates directly in the coefficient space of spectral expansions. VSL offers a principled bridge between variational PDE theory, spectral discretization, and contemporary machine learning practice. The core idea is to recast a given PDE [ mathcal{L}u = f quad text{in} quad Q=Omegatimes(0,T), ] together with boundary and initial conditions, into differentiable space–time energies built from strong-form […]

Ver mais