Why Shanghai’s medical AI showdown reveals more about system architecture than clinical superiority The headline writes itself: AI defeats human doctors in medical diagnosis, delivering results in under two seconds versus 13 minutes. But beneath this dramatic 390x speed differential lies a far more nuanced story about specialized model architecture, the evolving role of clinical decision support systems, and the critical distinction between diagnostic speed and comprehensive patient care. The Technical Architecture Behind the Victory Shanghai AI Lab’s Gastrointestinal Multimodal […]

Ver mais

Chinese electric vehicles (EVs) have been making a big splash in markets worldwide, with one major target for Chinese-made EVs being the United Arab Emirates (UAE). The UAE is a prime location for Chinese EVs to enter the Gulf market, enabling increased regional credibility while providing a robust market for EV development. The UAE also stands out as an investment opportunity for Chinese state-owned enterprises (SOEs) and other groups from an electric grid perspective, with China taking the […]

Ver mais

Laplace learning is a semi-supervised method, a solution for finding missing labels from a partially labeled dataset utilizing the geometry given by the unlabeled data points. The method minimizes a Dirichlet energy defined on a (discrete) graph constructed from the full dataset. In finite dimensions the asymptotics in the large (unlabeled) data limit are well understood with convergence from the graph setting to a continuum Sobolev semi-norm weighted by the Lebesgue density of the data-generating measure. The lack […]

Ver mais

arXiv:2601.02569v1 Announce Type: new Abstract: Autoregressive large language models (LLMs) are bottlenecked by sequential decoding, where each new token typically requires executing all transformer layers. Existing dynamic-depth and layer-skipping methods reduce this cost, but often rely on auxiliary routing mechanisms or incur accuracy degradation when bypassed layers are left uncompensated. We present textbf{LoRA-Drop}, a plug-and-play inference framework that accelerates decoding by applying a emph{temporal compute schedule} to a fixed subset of intermediate layers: on most decoding steps, selected […]

Ver mais

This study addresses the challenge of accurately forecasting electricity load in Pakistan, focusing on the Faisalabad Electric Supply Company (FESCO). The load forecasting problem in this region is exacerbated by the highly volatile nature of the data and the low baseload, further complicated by external factors such as weather conditions. To tackle this issue, we utilized historical electricity load data from FESCO from 2019 to 2022 and weather data from NASA’s LaRC POWER Project. Our approach involved comprehensive […]

Ver mais

Miami, Florida, January 22nd, 2026/CyberNewsWire/–Halo Security, a leading provider of external attack surface management and penetration testing services, today announced it has successfully achieved SOC 2 Type II compliance following an extensive multi-month audit by Insight Assurance. This certification validates that Halo Security’s security controls are not only properly designed but also operate effectively and consistently over time. “SOC 2 Type II compliance demonstrates our unwavering commitment to protecting customer data through proven, operational security practices,” said Lisa […]

Ver mais

arXiv:2409.14123v3 Announce Type: replace Abstract: Although overparameterized models have achieved remarkable practical success, their theoretical properties, particularly their generalization behavior, remain incompletely understood. The well known double descents phenomenon suggests that the test error curve of neural networks decreases monotonically as model size grows and eventually converges to a non-zero constant. This work aims to explain the theoretical mechanism underlying this tail behavior and study the statistical consistency of deep overparameterized neural networks in many different learning tasks […]

Ver mais

We show that quantum phase estimation (QPE) resolution is a linear rescaling of the spectral gap of a unitary operator, making the correlation between these two quantities athematically guaranteed. Using this observation, we demonstrate that spectral properties alone can reliably predict quantum algorithm performance without executing full circuit simulations. Through analysis of 400 randomly generated two-qubit unitaries and six standard quantum gates, we confirm that the spectral gap and QPEresolution exhibit a perfect Pearson correlation (r = 1.0000, […]

Ver mais