April 2026

arXiv:2604.24771v1 Announce Type: new Abstract: This study develops a dynamic fundamental diagram (FD) framework tailored to mixed traffic environments comprising automated vehicles (AVs) and human-driven vehicles (HDVs). Describing function analysis is employed to derive approximate linear transfer functions for nonlinear HDV car-following models. A sequence-based stochastic dynamic FD is then formulated for mixed platoons, enabling the evaluation of hysteresis in the evolution of flow-density relations across different vehicle sequencing scenarios and AV penetration levels. Monte Carlo simulation results […]

Ver mais

arXiv:2604.24770v1 Announce Type: new Abstract: Despite recent progress in automatic speech recognition (ASR), elderly ASR (EASR) remains challenging due to limited training data and the distinct acoustic and linguistic characteristics of elderly speech. In this work, we address data scarcity in EASR through a data augmentation pipeline that combines large language model (LLM)-based transcript paraphrasing with text-to-speech (TTS) synthesis. Given an elderly speech dataset, the LLM first generates elderly-contextual paraphrases of the original transcripts, and the TTS model […]

Ver mais

arXiv:2604.24768v1 Announce Type: new Abstract: In this chapter, we investigate the bending behavior of a perforated nanobeam subjected to sinusoidal loading using an efficient and computationally robust Physics-Informed Functional Link Constrained Framework with Domain Mapping (DFL-TFC) method. Our aim is to determine the relationship between static bending response and dynamic deflection of a perforated nanobeam for various perforation cases. The static bending is obtained using the FL-TFC with Domain mapped method, whereas dynamic deflection is determined using the […]

Ver mais

arXiv:2604.24767v1 Announce Type: new Abstract: Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costly and inaccessible in low-resource settings. Diagnosis is delayed due to limited skilled experts, whose ability to interpret pathological patterns varies significantly, causing inter- and intra-clinician variability. Therefore, we present a new method for a more accessible diagnostic modality, the digital stethoscope, to detect CHDs. Our method is […]

Ver mais

arXiv:2604.24766v1 Announce Type: new Abstract: With the rise of time-of-use and tiered electricity pricing, energy consumers are encouraged to adopt peak-shifting strategies by automatically controlling high-power appliances. These help lower energy costs while enhancing the power grid’s stability. To support such energy management with high resilience and responsiveness, reliable short-term load forecasting (STLF) plays a critical role. STLF predicts electricity consumption over time horizons ranging from minutes to days, using historical data, temporal patterns, and contextual factors. Traditional […]

Ver mais

Why don’t LLMs use explicit vector-based reasoning instead of language-based chain-of-thought? What would happen if they did? Most LLM reasoning we see is expressed through language: step-by-step text, explanations, chain-of-thought style outputs, etc. But internally, models already operate on high-dimensional vectors. So my question is: Why don’t we have models that reason more explicitly in latent/vector space instead of producing intermediate reasoning in natural language? Would vector-based reasoning be faster, more compressed, and better for intuition-like tasks? Or […]

Ver mais

What if a language model had never heard of the internet, smartphones, or even World War II? That’s not a hypothetical — it’s exactly what a team of researchers led by Nick Levine, David Duvenaud, and Alec Radford has built. They call it talkie, and it may be the most historically disciplined large language model ever released to the public. Talkie is a 13-billion parameter open-weight language model trained exclusively on pre-1931 English text. The project is developed […]

Ver mais

In this tutorial, we build an embodied simulation vision agent that learns to perceive, plan, predict, and replan directly from pixel observations. We create a fully NumPy-rendered grid world in which the agent observes RGB frames rather than symbolic state variables, enabling us to simulate a simplified Vision-Language-Action-style pipeline. We train a lightweight world model that encodes visual input into a latent representation, predicts future states conditioned on actions and goals, and reconstructs the next frame. Using model […]

Ver mais