arXiv:2601.21856v1 Announce Type: cross Abstract: Ultrasound (US) interpretation is hampered by multiplicative speckle, acquisition blur from the point-spread function (PSF), and scanner- and operator-dependent artifacts. Supervised enhancement methods assume access to clean targets or known degradations; conditions rarely met in practice. We present a blind, self-supervised enhancement framework that jointly deconvolves and denoises B-mode images using a Swin Convolutional U-Net trained with a emph{physics-guided} degradation model. From each training frame, we extract rotated/cropped patches and synthesize inputs by […]

Ver mais

Because I write about LLMs (and maybe because of my em dash text replacement code) a lot of people assume that the writing on my blog is partially or fully created by those LLMs. My current policy on this is that if text expresses opinions or has “I” pronouns attached to it then it’s written by me. I don’t let LLMs speak for me in this way. I’ll let an LLM update code documentation or even write a […]

Ver mais

Here’s a problem: say you’re ingesting roughly 4 million Reddit comments per day, and you need to detect the presence of 10,000 specific keywords in near real time. The naive answer is obvious – loop through each comment and keyword. Done. for comment in reddit_comments: for keyword in keywords: if keyword in comment: # alert user It’s simple. Also visibly slow. Even if you ignore the math, it’s (easily) doing billions of string comparisons. The complexity is quadratic, […]

Ver mais

Allegro is a machine learning interatomic potential (MLIP) model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. For this reason we apply multi-objective hyperparameter optimization to the two objectives. Additionally, we experiment with modified architectures by making variants of Allegro some by adding strictly classical multi-layer perceptron (MLP) layers and some by adding quantum-classical hybrid layers. We compare the […]

Ver mais

Deep learning has non-convex loss landscape and its optimization dynamics is hard to analyze or control. Nevertheless, the dynamics can be empirically convex-like across various tasks, models, optimizers, hyperparameters, etc. In this work, we examine the applicability of convexity and Lipschitz continuity in deep learning, in order to precisely control the loss dynamics via the learning rate schedules. We illustrate that deep learning quickly becomes weakly convex after a short period of training, and the loss is predicable […]

Ver mais

arXiv:2602.15961v1 Announce Type: new Abstract: The rapid expansion of renewable energy, particularly wind and solar power, has made reliable forecasting critical for power system operations. While recent deep learning models have achieved strong average accuracy, the increasing frequency and intensity of climate-driven extreme weather events pose severe threats to grid stability and operational security. Consequently, developing robust forecasting models that can withstand volatile conditions has become a paramount challenge. In this paper, we present R$^2$Energy, a large-scale benchmark […]

Ver mais

arXiv:2601.20036v1 Announce Type: new Abstract: Let $S$ be a set of $n$ points in the plane. We present several different algorithms for finding a pair of points in $S$ such that any disk that contains that pair must contain at least $cn$ points of $S$, for some constant $c>0$. The first is a randomized algorithm that finds a pair in $O(nlog n)$ expected time for points in general position, and $c = 1/2-sqrt{(1+2alpha)/12}$, for any $0<alpha<1$. The second […]

Ver mais

The rapid adoption of the Internet of Medical Things (IoMT) is transforming healthcare by enabling seamless connectivity among medical devices, systems, and services. However, it also introduces serious cybersecurity and patient safety concerns as attackers increasingly exploit new methods and emerging vulnerabilities to infiltrate IoMT networks. This paper proposes a novel Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for detecting a wide range of cyberattacks targeting IoMT networks. The TM is a rule-based and interpretable machine learning (ML) […]

Ver mais

arXiv:2601.16412v1 Announce Type: new Abstract: The study of textit{regret minimization in fixed-price bilateral trade} has received considerable attention in recent research. Previous works [CCC+24a, CCC+24b, AFF24, BCCF24, CJLZ25, LCM25a, GDFS25] have acquired a thorough understanding of the problem, except for determining the tight regret bound for GBB semi-feedback fixed-price mechanisms under adversarial values. In this paper, we resolve this open question by devising an $widetilde{O}(T^{2 / 3})$-regret mechanism, matching the $Omega(T^{2 / 3})$ lower bound from [CJLZ25] up […]

Ver mais