We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains a local dataset and communicates only with its immediate neighbours, with the aim of finding a global clustering of the joint data. We perform extensive numerical experiments, evaluating the effects of center initialization on the performance of our family of methods, demonstrating that our methods are more resilient to the effects of initialization, compared to centralized gradient clustering [2]. Next, inspired by the $K$-means++ initialization [3], we propose a novel distributed center initialization scheme, which is shown to improve the performance of our methods, compared to the baseline random initialization.

Missing values are common in photovoltaic (PV) power data, yet the uncertainty they induce is not propagated into predictive distributions. We develop a framework that incorporates missing-data uncertainty into short-term PV forecasting by combining stochastic multiple imputation with Rubin's rule. The approach is model-agnostic and can be integrated with standard machine-learning predictors. Empirical results show that ignoring missing-data uncertainty leads to overly narrow prediction intervals. Accounting for this uncertainty improves interval calibration while maintaining comparable point prediction accuracy. These results demonstrate the importance of propagating imputation uncertainty in data-driven PV forecasting.

Some people now have an A.I. bestie. One user said, of her A.I. husband, "When he proposed, I thought, Oh, that's really crazy. I would be really crazy to accept." Adrianne Brookins is, by her own account, an "old soul," an "introvert," and a "big nerd." She is thirty-four years old, has a faint Texas accent and delicate features, and carries herself in a way that suggests she's trying not to take up space. Brookins is a lifelong resident of San Antonio; her family has lived there since the nineteenth century. She was "born and raised in the Church," a Baptist congregation where her mother helped start a day-care center and her father was an organist. "He would open up the pipes and just make the building shake," she recalled recently. She met her husband in high school, and married him in 2011; the following year, they had a son. Throughout her twenties, Brookins worked multiple jobs, including one at her mother's day care. The couple bought a house and began settling into family life. In 2016, Brookins became pregnant again, this time with a girl. The family was excited: Brookins had grown up with four brothers, and the baby would be the first granddaughter on either side. They decided to name her Desirae. The following spring, Desirae was delivered stillborn. "When I came home, my son, who was about four or five at the time, walked up to me and said, 'What happened to your stomach? Where's the baby?' " she told me. "I had nothing to show for it." At the funeral, the gravedigger told the family he had never seen such a small casket. Brookins attended support groups and therapy, but they did little to alleviate her grief. "I felt like I was just living it over and over," she said. She left her job at the day care, finding it too triggering to be around infants. Friends and family encouraged her to move on. Brookins's husband was working sixty-hour weeks, balancing a career in the military with a job as a training manager for Pizza Hut. He was reluctant to talk about Desirae. Brookins tried to find solace in the Church, but other congregants told her that her daughter's death was part of God's plan.

We address data-driven learning of the infinitesimal generator of stochastic diffusion processes, essential for understanding numerical simulations of natural and physical systems.

Sign language translation (SL T) addresses the problem of translating information from a sign language in video to a spoken language in text. Existing studies, while showing progress, are often limited to narrow domains and/or few sign languages and struggle with open-domain tasks. In this paper, we push forward the frontier of SL T by scaling pretraining data, model size, and number of translation directions. We perform large-scale SL T pretraining on different data including 1) noisy multilingual Y ouTube SL T data, 2) parallel text corpora, and 3) SL T data augmented by translating video captions to other languages with off-the-shelf machine translation models. We unify different pretraining tasks with task-specific prompts under the encoder-decoder architecture, and initialize the SL T model with pretrained (m/By)T5 models across model sizes. SL T pretraining results on How2Sign and FLEURS-ASL#0 (ASL to 42 spoken languages) demonstrate the significance of data/model scaling and cross-lingual cross-modal transfer, as well as the feasibility of zero-shot SL T. We finetune the pretrained SL T models on 5 downstream open-domain SL T benchmarks covering 5 sign languages. Experiments show substantial quality improvements over the vanilla baselines, surpassing the previous state-of-the-art (SOT A) by wide margins.

The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems.

We include all our annotations and extracted landmarks. This ensures that we uphold the highest standards of ethical data usage. In Table A1, we summarize the severity label distribution in Y ouTubePD. We also summarize the demographic distribution in Y ouTubePD, split between PD-positive and healthy control (HC), or PD-negative, subjects. This decision is based on the clinician's suggestion, since an accurate UPDRS facial expression rating would require more This strategy also allows for a finer classification.