Its march toward automation continues. Amazon has acquired Rivr, a startup focused on autonomous robotics. Rivr is based in Zurich and was valued at $110 million in a funding round from August 2024, which both Amazon and its CEO's Bezos Expeditions participated in. Financial details of the acquisition were not disclosed. Rivr's robots have four legs and wheels that allow it to maneuver on stairs and other potentially uneven surfaces.

'Comatose' Mojtaba Khamenei'is UNAWARE there is a war on and has no idea he is supreme leader', report says - despite regime issuing his'first statement' FBI storms home of Lebanese-born restaurant worker who drove truck filled with explosives into synagogue and opened fire after his'family were killed in airstrike' Trump slammed after lifting oil sanctions on Russia as gas prices skyrocket: 'It's a betrayal' Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Billy Joel's daughter Alexa Ray gives health update amid his battle with rare brain disorder Concerning whispers inside Trump World that Operation Epic Fury is suddenly at risk... and the critical question that will determine how this ends: MARK HALPERIN Meghan Markle masks up to cheer young patients at Los Angeles children's hospital as she agrees deal to sign her latest documentary Beauty queen slams Trump as she's FIRED by White House: 'I stood by you for 20 years... now, I don't even recognize you' Wall Street issues stark warning that Iran oil attacks could wreck Trump's key election promises Truth behind the massacre of 110 school girls in Iran: How shameful episode sparked a deluge of conspiracy theories and lies... as JAKE WALLIS SIMONS explores what really happened Long hair over 45 is ageing and try-hard. I've finally cut mine off. NFL fans left divided as team replace historic logo with'boring' new design as part of franchise rebrand I worked with Carolyn Bessette. This is the'messy' truth about what she was REALLY like in secret. After she met JFK Jr she tried to hide it... but we all knew the nighttime gossip Trump says US is'totally destroying' Iran as he issues chilling threat of more action coming TODAY The 7 types of'hyperarousal' - so, do you get cold sweats or tingling fingers?

Chemistry may not be the'killer app' for quantum computers after all Quantum chemistry calculations that could advance drug development or agriculture have recently emerged as a promising "killer application" of quantum computers, but a new analysis suggests this is unlikely to be the case. Progress in building quantum computers has greatly accelerated in recent years, but it remains an open question what uses are most likely to justify the ongoing investment in this technology. One popular contender is solving problems in quantum chemistry, such as calculating the energy levels of molecules relevant for biomedicine or industry. This requires accounting for the behavior of many quantum particles - electrons in the molecule - simultaneously, so it seems like a good match for computers made from many quantum parts. Quantum computers have finally arrived, but will they ever be useful? However, Xavier Waintal at CEA Grenoble in France and his colleagues have now shown that two leading quantum computing algorithms for this task may actually have, at best, limited use.

From reducing dementia to speeding up recovery after surgery, music is more powerful than you knew. Listening to music can help your brain, research suggests. Breakthroughs, discoveries, and DIY tips sent six days a week. The oldest known musical instruments-- flutes carved from bones --are over 40,000 years old . And humans were likely making music before that, based on fossils showing our ancestors had the ability to sing over 530,000 years ago.

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We study post-calibration uncertainty for trained ensembles of classifiers. Specifically, we consider both aleatoric (label noise) and epistemic (model) uncertainty. Among the most popular and widely used calibration methods in classification are temperature scaling (i.e., pool-then-calibrate) and conformal methods. However, the main shortcoming of these calibration methods is that they do not balance the proportion of aleatoric and epistemic uncertainty. Not balancing these uncertainties can severely misrepresent predictive uncertainty, leading to overconfident predictions in some input regions while being underconfident in others. To address this shortcoming, we present a simple but powerful calibration algorithm Joint Uncertainty Calibration (JUCAL) that jointly calibrates aleatoric and epistemic uncertainty. JUCAL jointly calibrates two constants to weight and scale epistemic and aleatoric uncertainties by optimizing the negative log-likelihood (NLL) on the validation/calibration dataset. JUCAL can be applied to any trained ensemble of classifiers (e.g., transformers, CNNs, or tree-based methods), with minimal computational overhead, without requiring access to the models' internal parameters. We experimentally evaluate JUCAL on various text classification tasks, for ensembles of varying sizes and with different ensembling strategies. Our experiments show that JUCAL significantly outperforms SOTA calibration methods across all considered classification tasks, reducing NLL and predictive set size by up to 15% and 20%, respectively. Interestingly, even applying JUCAL to an ensemble of size 5 can outperform temperature-scaled ensembles of size up to 50 in terms of NLL and predictive set size, resulting in up to 10 times smaller inference costs. Thus, we propose JUCAL as a new go-to method for calibrating ensembles in classification.

The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing methods typically require labeled data from multiple training environments, limiting their applicability when labeled data are scarce. In this work, we study domain generalization in an anti-causal setting, where the outcome causes the observed covariates. Under this structure, environment perturbations that affect the covariates do not propagate to the outcome, which motivates regularizing the model's sensitivity to these perturbations. Crucially, estimating these perturbation directions does not require labels, enabling us to leverage unlabeled data from multiple environments. We propose two methods that penalize the model's sensitivity to variations in the mean and covariance of the covariates across environments, respectively, and prove that these methods have worst-case optimality guarantees under certain classes of environments. Finally, we demonstrate the empirical performance of our approach on a controlled physical system and a physiological signal dataset.

Neural Information Processing Systems http://nips.cc/

The UN estimates 2-5% of global GDP or $0.8 - $2.0 trillion dollars are laundered

Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities.