Trump says he's'not afraid' of Vietnam-style ground combat in Iran Furious US troops erupt at CNN's $20m steak and lobster claims as grim photos expose reality Hollywood's top insider makes VERY catty observation about Kaitlan Collins Pam Bondi is formally subpoenaed by Congress as Trump's Epstein nightmare grows What the Jane Plan did to my body: The unfashionable retro diet's fans say it's life-changing, easy, better than fat jabs - and shifts weight fast. My husband tried a'cure' for his ALS... days later he went blind and couldn't move. The children screamed on video call as he died. Outrage after Pete Hegseth aide ousted for'leaks' lands new top secret intelligence job Everything JFK Jr told friends about his love affair with'sexual dynamo' Madonna... her unprintable pillow talk... and his perverse incest request that she couldn't go through with SARAH VINE: How telling that Meghan's joined the ranks of those peddling wellness and fake lifestyles to the gullible My chilling conversations with the Unabomber and America's worst serial killers when I ran a Supermax prison, revealed in The Crime Desk newsletter Oscars afterparty snitches reveal cringing details of how stars stopped talking to him... a brutal message from Kylie's gloating ex... and her'humiliating' admission to friends Joe Burrow cements his place as the NFL's most eligible bachelor as he is spotted cozying up to Tate McRae and Alix Earle at glitzy Oscars afterparty Dark secret past of husband killer Kouri Richins' Iraq war veteran lover revealed... and their toe-curling sex texts that helped convict her Mystery as Communion bread and wine'miraculously' appear to turn into human tissue and blood READ MORE: Scientists stunned as 500-year-old'miracle' image of Virgin Mary reveals impossible microscopic reflection Catholics believe that during Communion, bread and wine become the body and blood of Jesus Christ, though they continue to appear unchanged to the human eye. But there have been a handful of rare and debated cases in which the sacred elements appeared to take on a far more literal, physical form.

Convolutional Neural Networks(CNNs) are both computation and memory intensive which hindered their deployment in mobile devices.

We find a surprising connection between multitask learning and robustness to neuron failures.

Additionally, some digitizers capture other function-based parameeters, such as the vertical pressure exerted by the pen tip, azimuthal and altitude angles of the pen, and even the pen's in-air trajectory. As a physiological biometric trait, a signature is used in various applications, including access control, commercial transactions, document forgery detection, and the provision of evidence in legal scenarios such as the verification of last wills [9]. In biometrics, where impostors may attempt to forge signatures with varying degrees of skill, robust verification methods are crucial. Since the execution of a signature inherently involves movements of the hand, arm, and forearm, it is hypothesized that these motions may contain kinematic and dynamic unique characteristic of the signer [7]. From a kinematic perspective, this action can be characterized by the arm's angular position, θ(t), and angular velocity, ω(t). Dynamically, these movements are facilitated by force torques, τ(t), applied at the joints. One method used to obtain this valuable biomechanical information involves a physical robot programmed to mimic the act of signing. While a robot's ability to accurately replicate these movements depends on its configuration, working area, and degrees of freedom, it can effectively capture kinematic and dynamic features during the process. However, accessing these robots is costly and cumbersome.

Safety in industrial robotic environments is a hot research topic in the area of human-robot interaction (HRI). Up to now, a robotic arm on an assembly line interacts with other machines away from human workers. Nowadays, robotic arm manufactures are aimed to their robots could increasingly perform tasks collaborating with humans. One of the ways to improve this collaboration is by making the movement of robots more humanlike. This way, it would be easier for a human to foresee the movement of the robot and approach it without fear of contact. The main difference between the movement of a human and of a robotic arm is that the former has a bell-shaped speed profile while the latter has a uniform speed one. To generate this speed profile, the kinematic theory of rapid human movements and its Sigma-Lognormal model has been used. This model is widely used to explain most of the basic phenomena related to the control of human movements. Both human-like and robotic-like movements are transferred to the UR3 robot. In this paper we detail the how the UR3 robot was programmed to produce both kinds of movement. The dissimilarities result between the input motion and output motion to the robot confirm the possibility to develop human-like velocities in the UR3 robot.

Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks. We observe that while these models show promising surface-level crosslingual abilities on machine translation and embedding space analyses, they struggle with deeper crosslingual knowledge transfer, revealing a crosslingual knowledge barrier in both general (MMLU benchmark) and domain-specific (Harry Potter quiz) contexts. We observe that simple inference-time mitigation methods offer only limited improvement. On the other hand, we propose fine-tuning of LLMs on mixed-language data, which effectively reduces these gaps, even when using out-of-domain datasets like WikiText. Our findings suggest the need for explicit optimization to unlock the full crosslingual potential of LLMs.

Autoformalization, the conversion of natural language mathematics into formal languages, offers significant potential for advancing mathematical reasoning. However, existing efforts are limited to formal languages with substantial online corpora and struggle to keep pace with rapidly evolving languages like Lean 4. To bridge this gap, we propose a new benchmark \textbf{Form}alization for \textbf{L}ean~\textbf{4} (\textbf{\name}) designed to evaluate the autoformalization capabilities of large language models (LLMs). This benchmark encompasses a comprehensive assessment of questions, answers, formal statements, and proofs. Additionally, we introduce a \textbf{P}rocess-\textbf{S}upervised \textbf{V}erifier (\textbf{PSV}) model that leverages the precise feedback from Lean 4 compilers to enhance autoformalization. Our experiments demonstrate that the PSV method improves autoformalization, enabling higher accuracy using less filtered training data. Furthermore, when fine-tuned with data containing detailed process information, PSV can leverage the data more effectively, leading to more significant improvements in autoformalization for Lean 4. Our dataset and code are available at \url{https://github.com/rookie-joe/PDA}.

Collaborative robots or cobots interact with humans in a common work environment. In cobots, one under investigated but important issue is related to their movement and how it is perceived by humans. This paper tries to analyze whether humans prefer a robot moving in a human or in a robotic fashion. To this end, the present work lays out what differentiates the movement performed by an industrial robotic arm from that performed by a human one. The main difference lies in the fact that the robotic movement has a trapezoidal speed profile, while for the human arm, the speed profile is bell-shaped and during complex movements, it can be considered as a sum of superimposed bell-shaped movements. Based on the lognormality principle, a procedure was developed for a robotic arm to perform human-like movements. Both speed profiles were implemented in two industrial robots, namely, an ABB IRB 120 and a Universal Robot UR3. Three tests were used to study the subjects' preference when seeing both movements and another analyzed the same when interacting with the robot by touching its ends with their fingers.

Recent advancements in AI have sped up the evolution of versatile robot designs. Chess provides a standardized environment that allows for the evaluation of the influence of robot behaviors on human behavior. This article presents an open-source chess robot for humanrobot interaction (HRI) research, specifically focusing on verbal and non-verbal interactions. OpenChessRobot recognizes chess pieces using computer vision, executes moves, and interacts with the human player using voice and robotic gestures. We detail the software design, provide quantitative evaluations of the robot's efficacy and offer a guide for its reproducibility. Keywords: Artificial Intelligence, Chess, Human-robot Interaction, Open-source, Transfer Learning 1. Introduction Robots are becoming increasingly common across a variety of traditionally human-controlled domains. Examples range from automated mowers that maintain community lawns to robots in assembly lines and agricultural settings. Recent scientific advancements in AI have enabled new opportunities for intelligent sensing, reasoning, and acting by robots. In particular, the rapid development of large language models, such as ChatGPT, and vision-language models, have lowered the barrier of human-to-robot communication by being able to transform text and images into interpretable actions or vice versa. As technology advances, it is likely that robots will attain greater capabilities and will be able to tackle tasks previously within the exclusive realm of human expertise. This ongoing evolution may also lead to closer and more productive interactions between humans and robots. At the same time, integrating different AI-based robotic components remains a challenge, and the human-robot interaction (HRI) field lags in terms of endorsing reproducibility principles (Gunes et al., 2022). Encouraging transparent and reproducible research, therefore, remains an ongoing task. Furthermore, chess has played an important role in advancing the field of AI, starting with Claude Shannon's chess-playing algorithm (Shannon, 1950) to the success of IBM's Deep Blue (Campbell et al., 2002) and DeepMind's self-play learning algorithm (Silver et al., 2018). In this paper, we incorporate modern AI algorithms into the design of a chess-playing robot to be used for studying HRI. HRI research may benefit from a chess-based setup because the game of chess provides a controlled rule-based environment in which the impact of robots on human players can be precisely measured.