Russian attacks on Ukraine energy sites'particularly depraved', UK PM Starmer says Russia's attacks on Ukraine's energy sector on Monday night - as temperatures dropped to -20C (-4F) - were barbaric and particularly depraved, UK Prime Minister Sir Keir Starmer has said. He made the comments after speaking to US President Donald Trump hours after Russia hit power plants and critical infrastructure in the capital, Kyiv, and elsewhere. The attacks came at the end of a week-long pause that Trump had asked Russia's President Vladimir Putin to observe as a fierce cold swept Ukraine. Trump said on Tuesday that Putin had kept his word and that he would like him to end the war. Top US envoys are meeting negotiators from Russia and Ukraine in Abu Dhabi on Wednesday and Thursday.

Team Mirai leader Takahiro Anno stumps in Tokyo's Shibuya Ward on Jan. 27, the first day of campaigning for the Lower House election slated for Sunday. A small, 9-month-old party that has only one seat in the Upper House may gain as many seats as decadesold peers such as the Japanese Communist Party (JCP) in Sunday's Lower House election with its unconventional campaign pledges to change politics and the government through digital technology. A weekend poll conducted by the Asahi Shimbun showed Team Mirai could win up to 10 seats under the proportional representation system, more than the JCP's nine seats and Reiwa Shinsengumi's six. The party didn't have any seats in the Lower House before its dissolution. The party's founder and leader is a 35-year-old artificial intelligence engineer behind two AI startups -- Takahiro Anno. He had been working on societal reform through digital transformation when he pivoted from business to politics with the launch of Team Mirai last May.

BERLIN - German technology firm DeepL, known for its artificial intelligence-powered translation software, plans to release a Japanese-language version of its real-time interpretation software by the end of this year, a senior company official has said. The age of machine interpretation has arrived, said Leonardo Doin, head of engineering and research for real-time voice translation service DeepL Voice, in a recent interview. You can just wear an earpiece and ... you can just hear it (foreign-language speech) in your language anytime, Doin said. The interpretation software will integrate DeepL's speech recognition and machine translation technologies, and speech synthesis technology that mimics the tones of the speakers' voices. It will be able to handle multiple languages and speakers, he said, with the software's use in online meetings of multinational companies in mind. DeepL plans to roll out the software on smartphones as well.

Court system on'brink of collapse', former senior judge warns The court system is on the brink of collapse as the backlogs for trials reach unprecedented levels, the head of a major review has said. Sir Brian Leveson, a senior retired judge, warned ministers, the police and others that there could not be a pick and mix response to solving the crisis. Last year, in the first stage of the review, Sir Brian called for the right to a jury trial to be scaled back and many intermediate crimes to be dealt with by a judge alone. His second and final report has recommended 130 efficiency changes, from technical measures to allowing prison vans to use bus lanes to hit court appearance deadlines. Sir Brian's two reports were commissioned by ministers as part of an attempt to reverse the backlogs that had reached record levels before Labour came into power, but have continued to worsen since then.

Police told to reinvestigate man's death after suspected blackmail on Grindr Police have been told to reopen their investigation into the death of Scott Gough, who allegedly took his own life after being targeted by a gang of men on the gay dating app Grindr. A police Professional Standards Department (PSD) report found failures in the investigation into the 56-year-old's death, which happened the day after a group of men turned up at his home demanding his car keys. His partner, Cameron Tewson accused the police of marking their own homework after his complaint of homophobia was not upheld. Hertfordshire Police, the investigating force, said it remains committed to ensuring members of the LGBTQ+ community feel supported when approaching the force. The report into the police's actions comes after a BBC investigation found multiple cases of suspected blackmail involving victims targeted on Grindr in Gough's local area, with at least four connected to the same gang, which remains at large.

The Corporation of London is calling on employers to re-skill female workers not currently in technical roles. The Corporation of London is calling on employers to re-skill female workers not currently in technical roles. 'Mid-career' females also being sidelined by rigid hiring processes, says City of London Corporation Women working in tech and financial services are at greater risk of losing their jobs to increased use of AI and automation than their male peers, according to a report that found experienced females were also being sidelined as a result of "rigid hiring processes". "Mid-career" women - with at least five years' experience - are being overlooked for digital roles in the tech and financial and professional services sectors, where they are traditionally underrepresented, according to the report by the City of London Corporation. The governing body that runs the capital's Square Mile found female applicants were discriminated against by rigid, and sometimes automated, screening of their CVs, which did not take into account career gaps related to caring for children or relatives, or only narrowly considered their professional experience.

Quantitative imaging methods, such as magnetic resonance fingerprinting (MRF), aim to extract interpretable pathology biomarkers by estimating biophysical tissue parameters from signal evolutions. However, the pattern-matching algorithms or neural networks used in such inverse problems often lack principled uncertainty quantification, which limits the trustworthiness and transparency, required for clinical acceptance. Here, we describe a physics-structured variational autoencoder (PS-VAE) designed for rapid extraction of voxelwise multi-parameter posterior distributions. Our approach integrates a differentiable spin physics simulator with self-supervised learning, and provides a full covariance that captures the inter-parameter correlations of the latent biophysical space. The method was validated in a multi-proton pool chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) molecular MRF study, across in-vitro phantoms, tumor-bearing mice, healthy human volunteers, and a subject with glioblastoma. The resulting multi-parametric posteriors are in good agreement with those calculated using a brute-force Bayesian analysis, while providing an orders-of-magnitude acceleration in whole brain quantification. In addition, we demonstrate how monitoring the multi-parameter posterior dynamics across progressively acquired signals provides practical insights for protocol optimization and may facilitate real-time adaptive acquisition.

We introduce a new preference-based framework for conditional treatment effect estimation and policy learning, built on the Conditional Preference-based Treatment Effect (CPTE). CPTE requires only that outcomes be ranked under a preference rule, unlocking flexible modeling of heterogeneous effects with multivariate, ordinal, or preference-driven outcomes. This unifies applications such as conditional probability of necessity and sufficiency, conditional Win Ratio, and Generalized Pairwise Comparisons. Despite the intrinsic non-identifiability of comparison-based estimands, CPTE provides interpretable targets and delivers new identifiability conditions for previous unidentifiable estimands. We present estimation strategies via matching, quantile, and distributional regression, and further design efficient influence-function estimators to correct plug-in bias and maximize policy value. Synthetic and semi-synthetic experiments demonstrate clear performance gains and practical impact.

Adversarial training attains strong empirical robustness to specific adversarial attacks by training on concrete adversarial perturbations, but it produces neural networks that are not amenable to strong robustness certificates through neural network verification. On the other hand, earlier certified training schemes directly train on bounds from network relaxations to obtain models that are certifiably robust, but display sub-par standard performance. Recent work has shown that state-of-the-art trade-offs between certified robustness and standard performance can be obtained through a family of losses combining adversarial outputs and neural network bounds. Nevertheless, differently from empirical robustness, verifiability still comes at a significant cost in standard performance. In this work, we propose to leverage empirically-robust teachers to improve the performance of certifiably-robust models through knowledge distillation. Using a versatile feature-space distillation objective, we show that distillation from adversarially-trained teachers consistently improves on the state-of-the-art in certified training for ReLU networks across a series of robust computer vision benchmarks.

Random Forests (RF) are among the most powerful and widely used predictive models for centralized tabular data, yet few methods exist to adapt them to the federated learning setting. Unlike most federated learning approaches, the piecewise-constant nature of RF prevents exact gradient-based optimization. As a result, existing federated RF implementations rely on unprincipled heuristics: for instance, aggregating decision trees trained independently on clients fails to optimize the global impurity criterion, even under simple distribution shifts. We propose FedForest, a new federated RF algorithm for horizontally partitioned data that naturally accommodates diverse forms of client data heterogeneity, from covariate shift to more complex outcome shift mechanisms. We prove that our splitting procedure, based on aggregating carefully chosen client statistics, closely approximates the split selected by a centralized algorithm. Moreover, FedForest allows splits on client indicators, enabling a non-parametric form of personalization that is absent from prior federated random forest methods. Empirically, we demonstrate that the resulting federated forests closely match centralized performance across heterogeneous benchmarks while remaining communication-efficient.