The exhibition is scheduled to continue through Friday. Rapidus, Japan's government-backed semiconductor developer, is betting on artificial intelligence-enhanced design tools to give it an edge over its competitors, in an effort to speed up its chip-design process and minimize costs. The firm made the announcement during Semicon, a semiconductor-industry event being held in Tokyo from Wednesday to Friday at the Tokyo Big Sight convention center. Rapidus' newly announced suite of offerings, which will be rolled out next year, includes Raads Generator, an AI-assisted design tool modeled on large-scale language models -- AI systems trained on datasets -- and optimized for 2-nanometer chip manufacturing. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Arabic is one of the world's most widely spoken languages with at least 400 million speakers, including 200 million native speakers and 200 million to 250 million non-native speakers. Modern Standard Arabic (MSA) serves as the formal language for government, legal matters and education, and it is widely used in international and religious contexts. Additionally, more than 25 dialects are spoken primarily across the Middle East and North Africa. The date was chosen to mark the day in 1973 on which the UN General Assembly adopted Arabic as one of its six official languages. In the following visual explainer, Al Jazeera lists some of the most common words in today's English language that originated from Arabic or passed through Arabic before reaching English.

Japan's top uniformed military officer, Gen. Hiroaki Uchikura, listens during an interview with The Japan Times at the Defense Ministry in Tokyo on Monday. Imagine hundreds if not thousands of enemy missiles and artificial intelligence-enabled drones speeding toward your country, some at hypersonic speeds, capable of overwhelming your air defenses. But for Japan's top uniformed military officer, reinforcing the country's defense capabilities to counter these complex and diverse threats is not just a concern, it's become one of his top priorities. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

The National Police Agency plans to warn against the obscene use of AI at delinquency-prevention lectures at schools and other events. More than half of cases reported to Japanese police of explicit deepfakes targeting those aged under 18 were created with the involvement of students from the same schools as the victims, National Police Agency data have shown. This is the first time that the NPA has released information on minors who became victims of obscene fake images created using generative artificial intelligence and other technologies. The agency plans to create flyers and warn against such use of AI at delinquency-prevention lectures at schools and other locations. According to the NPA, police were consulted over 79 cases of deepfakes targeting those up to the age of 17 from January to September this year.

Rob Reiner and his wife's cause of death revealed Dan Bongino announces he's QUIT FBI to return to popular talk show The full story of Nick Reiner and these murders is so much more unbearable than everyone thinks. Even Hollywood wouldn't dare write it: MAUREEN CALLAHAN I sneakily looked at my perfect son's phone... What a terrible mistake! US car dealer charged with FRAUD after bankruptcy revealed depths of American's debt crisis Tara Reid speaks out for the first time since THAT video emerged... and tells KATIE HIND why she is convinced she was spiked after watching CCTV Chilling new details of father's death a day before facing justice for leaving his daughter, 2, to die in a hot car Pouty dine-and-dash diva interrupts judge MULTIPLE times as she's hauled to court for bill-skipping spree Karoline Leavitt close-up from Vanity Fair's Susie Wiles interview sparks fury: 'Shameful' Symptoms of deadly'super flu' sweeping the US explained and how to tell it apart from Covid Earthquakes stir fear in America's Heartland as deadly fault zone awakens Scandal rocks Trump's deportation force: DHS insiders say boss Kristi Noem's'lover' made'unethical, immoral' requests to agency leaders Disgraced Michigan coach Sherrone Moore had'long history' of domestic violence against victim of alleged knife attack, lawyer claims'Flowing red blood' surging in Persian Gulf sparks wild claims that God's biblical plagues have returned Evidence is'far too limited' to definitively say artificial intelligence hasn't made the leap, expert claims READ MORE: T here may already be a'slightly conscious' AI out in the world Artificial intelligence ( AI) is already helping to solve problems in finance, research and medicine. But could it be reaching consciousness? Dr Tom McClelland, a philosopher from the University of Cambridge has warned that current evidence is'far too limited' to rule this dystopian possibility out.

Partial Least Squares (PLS) is a widely used method for data integration, designed to extract latent components shared across paired high-dimensional datasets. Despite decades of practical success, a precise theoretical understanding of its behavior in high-dimensional regimes remains limited. In this paper, we study a data integration model in which two high-dimensional data matrices share a low-rank common latent structure while also containing individual-specific components. We analyze the singular vectors of the associated cross-covariance matrix using tools from random matrix theory and derive asymptotic characterizations of the alignment between estimated and true latent directions. These results provide a quantitative explanation of the reconstruction performance of the PLS variant based on Singular Value Decomposition (PLS-SVD) and identify regimes where the method exhibits counter-intuitive or limiting behavior. Building on this analysis, we compare PLS-SVD with principal component analysis applied separately to each dataset and show its asymptotic superiority in detecting the common latent subspace. Overall, our results offer a comprehensive theoretical understanding of high-dimensional PLS-SVD, clarifying both its advantages and fundamental limitations.

Spatial boundaries, such as ecological transitions or climatic regime interfaces, capture steep environmental gradients, and shifts in their structure can signal emerging environmental changes. Quantifying uncertainty in spatial boundary locations and formally testing for temporal shifts remains challenging, especially when boundaries are derived from noisy, gridded environmental data. We present a unified framework that combines heteroskedastic Gaussian process (GP) regression with a scaled Maximum Absolute Difference (MAD) Global Envelope Test (GET) to estimate spatial boundary curves and assess whether they evolve over time. The heteroskedastic GP provides a flexible probabilistic reconstruction of boundary lines, capturing spatially varying mean structure and location specific variability, while the test offers a rigorous hypothesis testing tool for detecting departures from expected boundary behaviors. Simulation studies show that the proposed method achieves the correct size under the null and high power for detecting local boundary shifts. Applying our framework to the Sahel Sahara transition zone, using annual Koppen Trewartha climate classifications from 1960 to 1989, we find no statistically significant decade scale changes in the arid and semi arid or semi arid and non arid interfaces. However, the method successfully identifies localized boundary shifts during the extreme drought years of 1983 and 1984, consistent with climate studies documenting regional anomalies in these interfaces during that period.

In this work, inspired by machine learning techniques, we propose a new Bayesian model for Small Area Estimation (SAE), the Fay-Herriot model with Spectral Clustering (FH-SC). Unlike traditional approaches, clustering in FH-SC is based on spectral clustering algorithms that utilize external covariates, rather than geographical or administrative criteria. A major advantage of the FH-SC model is its flexibility in integrating existing SAE approaches, with or without clustering random effects. To enable benchmarking, we leverage the theoretical framework of posterior projections for constrained Bayesian inference and derive closed form expressions for the new Rao-Blackwell (RB) estimators of the posterior mean under the FH-SC model. Additionally, we introduce a novel measure of uncertainty for the benchmarked estimator, the Conditional Posterior Mean Square Error (CPMSE), which is generalizable to other Bayesian SAE estimators. We conduct model-based and data-based simulation studies to evaluate the frequentist properties of the CPMSE. The proposed methodology is motivated by a real case study involving the estimation of the proportion of households with internet access in the municipalities of Colombia. Finally, we also illustrate the advantages of FH-SC over existing Bayesian and frequentist approaches through our case study.

Near an optimal learning point of a neural network, the learning performance of gradient descent dynamics is dictated by the Hessian matrix of the loss function with respect to the network parameters. We characterize the Hessian eigenspectrum for some classes of teacher-student problems, when the teacher and student networks have matching weights, showing that the smaller eigenvalues of the Hessian determine long-time learning performance. For linear networks, we analytically establish that for large networks the spectrum asymptotically follows a convolution of a scaled chi-square distribution with a scaled Marchenko-Pastur distribution. We numerically analyse the Hessian spectrum for polynomial and other non-linear networks. Furthermore, we show that the rank of the Hessian matrix can be seen as an effective number of parameters for networks using polynomial activation functions. For a generic non-linear activation function, such as the error function, we empirically observe that the Hessian matrix is always full rank.

Autoregressive models (ARMs) currently constitute the dominant paradigm for large language models (LLMs). Energy-based models (EBMs) represent another class of models, which have historically been less prevalent in LLM development, yet naturally characterize the optimal policy in post-training alignment. In this paper, we provide a unified view of these two model classes. Taking the chain rule of probability as a starting point, we establish an explicit bijection between ARMs and EBMs in function space, which we show to correspond to a special case of the soft Bellman equation in maximum entropy reinforcement learning. Building upon this bijection, we derive the equivalence between supervised learning of ARMs and EBMs. Furthermore, we analyze the distillation of EBMs into ARMs by providing theoretical error bounds. Our results provide insights into the ability of ARMs to plan ahead, despite being based on the next-token prediction paradigm.