We developed a new method PROTES for black-box optimization, which is based on the probabilistic sampling from a probability density function given in the low-parametric tensor train format. We tested it on complex multidimensional arrays and discretized multivariable functions taken, among others, from real-world applications, including unconstrained binary optimization and optimal control problems, for which the possible number of elements is up to 21000. In numerical experiments, both on analytic model functions and on complex problems, PROTES outperforms popular discrete optimization methods (Particle Swarm Optimization, Covariance Matrix Adaptation, Differential Evolution, and others).

What does the data tell us about immigration in Wales? Like many countries, Wales sees a steady flow of people arriving and leaving for other countries each year. The difference between those arriving and those leaving is known as net migration. Focusing on people moving from abroad, latest estimates say Wales' population - which was 3.2 million in June 2024 - had increased by about 23,000 over the previous year as a result of net international migration. A recent YouGov poll found a quarter of people surveyed in Wales believed that immigration, alongside the economy, should be among the issues prioritised by the Welsh government, even though immigration is controlled by the UK government.

Image caption, Will Pep Guardiola or Mikel Arteta be lifting the Premier League trophy next month? With five games to go, Manchester City and Arsenal are only separated on goals scored at the top of the Premier League table. It's a new league now, says Gunners boss Mikel Arteta, whose side had been top of the table for 209 days until Wednesday. Manchester City's 2-1 win over Arsenal on Sunday boosted their hopes - and a 1-0 victory at Burnley on Wednesday sent them top. Who is going to win the title now?

Andon Market in San Francisco represents a vision, however flawed, of a future when more sophisticated AI agents take over work traditionally done by humans. In San Francisco's upscale Cow Hollow district, the introduction of a boutique selling coffee table games, tote bags and other household items would be pretty unremarkable. However, Andon Market has one key differentiator: It's run by AI. At this store, an artificial intelligence agent named Luna effectively acts as the chief executive officer of the operation. It decides what products to offer and how much to charge for them.

We focus on the task of learning a single index model σ(w x) with respect to the isotropic Gaussian distribution in d dimensions. Prior work has shown that the sample complexity of learning w is governed by the information exponent k of the link function σ, which is defined as the index of the first nonzero Hermite coefficient of σ.

The supplementary materials are organized as follows. In Appendix B, we discuss the motivations for our UniHOI. In Appendix C, we provide an in-depth explanation of differences between our UniHOI and previous CLIP-based methods. In Appendix D, we examine the effects of VL foundation models of different scales. In Appendix E, we provide an detailed explanation of the training and hyperparameter setting.

Human-object interaction (HOI) detection aims to comprehend the intricate relationships between humans and objects, predicting < human,action,object >triplets, and serving as the foundation for numerous computer vision tasks. The complexity and diversity of human-object interactions in the real world, however, pose significant challenges for both annotation and recognition, particularly in recognizing interactions within an open world context. This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs). The proposed method is dubbed as UniHOI. We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning. Our design includes an HOPrompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image. Furthermore, we utilize a LLM (i.e.

We present a novel actor-critic algorithm for an environment with delayed feedback, which addresses the state-space explosion problem of conventional approaches. Conventional approaches use an augmented state constructed from the last observed state and actions executed since visiting the last observed state Using the augmented state space, the correct Markov decision process for delayed environments can be constructed; however, this causes the state space to explode as the number of delayed timesteps increases, leading to slow convergence. Our proposed algorithm, called Belief-Projection-Based Q-learning (BPQL), addresses the state-space explosion problem by evaluating the values of the critic for which the input state size is equal to the original state-space size rather than that of the augmented one. We compare BPQL to traditional approaches in continuous control tasks and demonstrate that it significantly outperforms other algorithms in terms of asymptotic performance and sample efficiency. We also show that BPQL solves long-delayed environments, which conventional approaches are unable to do.