A federal prosecutor alleged that one defendant boasted that his father "had engaged in similar business for the Chinese Communist Party." US authorities allege four people based in Florida, Alabama, and California conspired to illegally ship supercomputers and hundreds of Nvidia GPUs to China as recently as July. The charges, which were unsealed in federal court on Wednesday, are part of a wider government effort to crack down on the smuggling of advanced AI chips to China. Over the past few years, the US has introduced a series of export control rules designed to prevent Chinese organizations from acquiring computer chips that have become popular for developing AI chatbots . The restrictions aim to slow China in what US officials have described as a race to develop powerful AI systems, including surveillance tools and autonomous weapons .

Barbie is one of the most successful children's toys in history, spawning a multimedia franchise that includes merchandise, video games and a live-action film. Since US toy giant Mattel launched the original Barbie in 1959, more than 1 billion of the dolls have been sold worldwide. Certainly, Barbie's looks have been tweaked over the years to reflect changing beauty ideals and societal shifts. But according to a new study, one subtle change to Barbie has gone largely unnoticed – until now. Scientists in Australia have found that Barbies today have flatter feet than they did in past decades.

In molecular structure data, SMILES (Simplified Molecular Input Line Entry System) strings are used to analyze molecular structure design. Numerical feature representation of SMILES strings is a challenging task. This work proposes a kernel-based approach for encoding and analyzing molecular structures from SMILES strings. The proposed approach involves computing a kernel matrix using the Sinkhorn-Knopp algorithm while using kernel principal component analysis (PCA) for dimensionality reduction. The resulting low-dimensional embeddings are then used for classification and regression analysis. The kernel matrix is computed by converting the SMILES strings into molecular structures using the Morgan Fingerprint, which computes a fingerprint for each molecule. The distance matrix is computed using the pairwise kernels function. The Sinkhorn-Knopp algorithm is used to compute the final kernel matrix that satisfies the constraints of a probability distribution. This is achieved by iteratively adjusting the kernel matrix until the marginal distributions of the rows and columns match the desired marginal distributions. We provided a comprehensive empirical analysis of the proposed kernel method to evaluate its goodness with greater depth. The suggested method is assessed for drug subcategory prediction (classification task) and solubility AlogPS ``Aqueous solubility and Octanol/Water partition coefficient" (regression task) using the benchmark SMILES string dataset. The outcomes show the proposed method outperforms several baseline methods in terms of supervised analysis and has potential uses in molecular design and drug discovery. Overall, the suggested method is a promising avenue for kernel methods-based molecular structure analysis and design.

Soft robots are known for their ability to perform tasks with great adaptability, enabled by their distributed, non-uniform stiffness and actuation. Bending is the most fundamental motion for soft robot design, but creating robust, and easy-to-fabricate soft bending joint with tunable properties remains an active problem of research. In this work, we demonstrate an inflatable actuation module for soft robots with a defined bending plane enabled by forced partial wrinkling. This lowers the structural stiffness in the bending direction, with the final stiffness easily designed by the ratio of wrinkled and unwrinkled regions. We present models and experimental characterization showing the stiffness properties of the actuation module, as well as its ability to maintain the kinematic constraint over a large range of loading conditions. We demonstrate the potential for complex actuation in a soft continuum robot and for decoupling actuation force and efficiency from load capacity. The module provides a novel method for embedding intelligent actuation into soft pneumatic robots.

Without finger function, people with C5-7 spinal cord injury (SCI) regularly utilize wrist extension to passively close the fingers and thumb together for grasping. Wearable assistive grasping devices often focus on this familiar wrist-driven technique to provide additional support and amplify grasp force. Despite recent research advances in modernizing these tools, people with SCI often abandon such wearable assistive devices in the long term. We suspect that the wrist constraints imposed by such devices generate undesirable reach and grasp kinematics. Here we show that using continuous robotic motor assistance to give users more adaptability in their wrist posture prior to wrist-driven grasping reduces task difficulty and perceived exertion. Our results demonstrate that more free wrist mobility allows users to select comfortable and natural postures depending on task needs, which improves the versatility of the assistive grasping device for easier use across different hand poses in the arm's workspace. This behavior holds the potential to improve ease of use and desirability of future device designs through new modes of combining both body-power and robotic automation.

The integration of technology into exercise regimens has emerged as a strategy to enhance normal human capabilities and return human motor function after injury or illness by enhancing motor learning and retention. Much research has focused on how active devices, whether confined to a lab or made into a wearable format, can apply forces at set times and conditions to optimize the process of learning. However, the focus on active force production often forces devices to either be confined to simple movements or interventions. As such, in this paper, we investigate how passive device behaviors can contribute to the process of motor learning by themselves. Our approach involves using a wearable resistance (WR) device, which is outfitted with elastic bands, to apply a force field that changes in response to a person's movements while performing exercises. We develop a method to measure the produced forces from the device without impeding the function and we characterize the device's force generation abilities. We then present a study assessing the impact of the WR device on motor learning of proper squat form compared to visual or no feedback. Biometrics such as knee and hip angles were used to monitor and assess subject performance. Our findings indicate that the force fields produced while training with the WR device can improve performance in full-body exercises similarly to a more direct visual feedback mechanism, though the improvement is not consistent across all performance metrics. Through our research, we contribute important insights into the application of passive wearable resistance technology in practical exercise settings.

This study introduces a novel approach, combining substruct counting, $k$-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings. The integrated method generates comprehensive molecular embeddings that enhance discriminative power and information content. Experimental evaluations demonstrate its superiority over traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone, improving chemoinformatics tasks such as drug classification. The proposed method offers a more informative representation of chemical structures, advancing molecular similarity analysis and facilitating applications in molecular design and drug discovery. It presents a promising avenue for molecular structure analysis and design, with significant potential for practical implementation.

The tabulation of commonly used letters, as determined by letter frequency, was later utilized to improve typewriter keyboard arrangement by minimizing hand motion [5]. Statistical characteristics of different letters of the English alphabet was further studied in the context of different sentence structures [6]. The letters'B', 'S', 'M', 'H', 'C' were found to most frequently occur as the initial letters of proper nouns, while'E', 'A', 'R', 'N' were the most frequently used letters when the entire proper noun is considered. For entire text documents, the most commonly used letters were found to be'E', 'T', 'A', 'O', 'N'. Interestingly, 95% of the English vocabulary was found to be represented by 13 letters of the alphabet. Our manuscript expanded upon the statistical study of the English alphabet by evaluating letter frequency in the context of different categories of writings. We analyzed news articles, novels, plays, and scientific articles for letter frequency and distribution. As a result, we determined the information density of the letters of the alphabet. Additionally, we developed a metric called "distance, d" to act as a simple algorithm for recognizing writing category.

Knowing the state of a robot is critical for many problems, such as feedback control. For continuum robots, state estimation is incredibly challenging. First, the motion of a continuum robot involves many kinematic states, including poses, strains, and velocities. Second, all these states are infinite-dimensional due to the robot's flexible property. It has remained unclear whether these infinite-dimensional states are observable at all using existing sensing techniques. Recently, we presented a solution to this challenge. It was a mechanics-based dynamic state estimation algorithm, called a Cosserat theoretic boundary observer, which could recover all the infinite-dimensional robot states by only measuring the velocity twist of the tip. In this work, we generalize the algorithm to incorporate tip pose measurements for more tuning freedom. We also validate this algorithm offline using recorded experimental data of a tendon-driven continuum robot. Specifically, we feed the recorded tension of the tendon and the recorded tip measurements into a numerical solver of the Cosserat rod model based on our continuum robot. It is observed that, even with purposely deviated initialization, the state estimates by our algorithm quickly converge to the recorded ground truth states and closely follow the robot's actual motion.

Robotic technologies are becoming increasingly popular in dentistry due to the high level of precision required in delicate dental procedures. Most dental robots available today are designed for implant surgery, helping dentists to accurately place implants in the desired position and depth. In this paper, we introduce the DentiBot, the first robot specifically designed for dental endodontic treatment. The DentiBot is equipped with a force and torque sensor, as well as a string-based Patient Tracking Module, allowing for real-time monitoring of endodontic file contact and patient movement. We propose a 6-DoF hybrid position/force controller that enables autonomous adjustment of the surgical path and compensation for patient movement, while also providing protection against endodontic file fracture. In addition, a file flexibility model is incorporated to compensate for file bending. Pre-clinical evaluations performed on acrylic root canal models and resin teeth confirm the feasibility of the DentiBot in assisting endodontic treatment.