With feedbackfromthesevolunteers,wedevelopedalightweight,smallandunobtrusive prototype equipped with a tiny stereo camera and an embedded system on a single board computer to capture the samples from 10 different locations. We describe instrument development, datacollection, preprocessing, annotation, and experiments conducted.

As part of a trend loosely called'sovereign AI', governments around the world are developing their own AI technologies As part of a trend loosely called'sovereign AI', governments around the world are developing their own AI technologies Governments are spending billions on their own'sovereign' AI technologies - is it a big waste of money? The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. In Malaysia, ILMUchat, built by a local construction conglomerate, boasts that it "knows which Georgetown you're referring to" - that is, the capital of Penang and not the private university in the US. Meanwhile, Switzerland's Apertus, unveiled in September, understands when to use the Swiss German "ss" and not the German-language character "ß".

Capturing nonlinear relationships without sacrificing interpretability remains a persistent challenge in regression modeling. We introduce SplitWise, a novel framework that enhances stepwise regression. It adaptively transforms numeric predictors into threshold-based binary features using shallow decision trees, but only when such transformations improve model fit, as assessed by the Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC). This approach preserves the transparency of linear models while flexibly capturing nonlinear effects. Implemented as a user-friendly R package, SplitWise is evaluated on both synthetic and real-world datasets. The results show that it consistently produces more parsimonious and generalizable models than traditional stepwise and penalized regression techniques.

Recent advances in 4D radar-inertial odometry have demonstrated promising potential for autonomous lo calization in adverse conditions. However, effective handling of sparse and noisy radar measurements remains a critical challenge. In this paper, we propose a radar-inertial odometry with a spatial weighting method that adapts to unevenly distributed points and a novel point-description histogram for challenging point registration. To make full use of the Doppler velocity from different spatial sections, we propose a weighting calculation model. To enhance the point cloud registration performance under challenging scenarios, we con struct a novel point histogram descriptor that combines local geometric features and radar cross-section (RCS) features. We have also conducted extensive experiments on both public and self-constructed datasets. The results demonstrate the precision and robustness of the proposed VGC-RIO.

Kulim, Malaysia – When tech giant AT&S decided a few years ago that it needed to ramp up production to keep pace with the artificial intelligence (AI) boom, it did not look to its largest manufacturing facilities in China. The Austrian firm's plants in Chongqing and Shanghai – opened in 2022 and 2016, respectively – employ some 9,000 workers between them, churning out high-end components used in everything from consumer electronics to cars. But AT&S was at the same time coming to grips with the risks of concentrating production in one country. Like many tech firms grappling with the disruption of the COVID-19 pandemic and the trade war salvoes between the United States and China, AT&S decided it needed to diversify its supply chains. Malaysia quickly emerged at the top of the company's list of potential locations for its next plant.

Automating a production line with robotic arms is a complex, demanding task that requires not only substantial resources but also a deep understanding of the automated processes and available technologies and tools. Expert integrators must consider factors such as placement, payload, and robot reach requirements to determine the feasibility of automation. Ideally, such considerations are based on a detailed digital simulation developed before any hardware is deployed. However, this process is often time-consuming and challenging. To simplify these processes, we introduce a much simpler method for the feasibility analysis of robotic arms' reachability, designed for non-experts. We implement this method through a mobile, sensing-based prototype tool. The two-step experimental evaluation included the expert user study results, which helped us identify the difficulty levels of various deployment scenarios and refine the initial prototype. The results of the subsequent quantitative study with 22 non-expert participants utilizing both scenarios indicate that users could complete both simple and complex feasibility analyses in under ten minutes, exhibiting similar cognitive loads and high engagement. Overall, the results suggest that the tool was well-received and rated as highly usable, thereby showing a new path for changing the ease of feasibility analysis for automation.

This paper explores the design strategies for hybrid pole- or trunk-climbing robots, focusing on methods to inform design decisions and assess metrics such as adaptability and performance. A wheeled-grasping hybrid robot with modular, tendon-driven grasping arms and a wheeled drive system mounted on a turret was developed to climb columns of varying diameters. Here, the key innovation is the underactuated arms that can be adjusted to different column sizes by adding or removing modular linkages, though the robot also features capabilities like self-locking (the ability of the robot to stay on the column by friction without power), autonomous grasping, and rotation around the column axis. Mathematical models describe conditions for self-locking and vertical climbing. Experimental results demonstrate the robot's efficacy in climbing and self-locking, validating the proposed models and highlighting the potential for fully automated solutions in industrial applications. This work provides a comprehensive framework for evaluating and designing hybrid climbing robots, contributing to advancements in autonomous robotics for environments where climbing tall structures is critical.

Social event detection (SED) is a task focused on identifying specific real-world events and has broad applications across various domains. It is integral to many mobile applications with social features, including major platforms like Twitter, Weibo, and Facebook. By enabling the analysis of social events, SED provides valuable insights for businesses to understand consumer preferences and supports public services in handling emergencies and disaster management. Due to the hierarchical structure of event detection data, traditional approaches in Euclidean space often fall short in capturing the complexity of such relationships. While existing methods in both Euclidean and hyperbolic spaces have shown promising results, they tend to overlook multi-order relationships between events. To address these limitations, this paper introduces a novel framework, Multi-Order Hyperbolic Graph Convolution with Aggregated Attention (MOHGCAA), designed to enhance the performance of SED. Experimental results demonstrate significant improvements under both supervised and unsupervised settings. To further validate the effectiveness and robustness of the proposed framework, we conducted extensive evaluations across multiple datasets, confirming its superiority in tackling common challenges in social event detection.

Breast cancer detection based on pre-trained convolution neural network (CNN) has gained much interest among other conventional computer-based systems. In the past few years, CNN technology has been the most promising way to find cancer in mammogram scans. In this paper, the effect of layer freezing in a pre-trained CNN is investigated for breast cancer detection by classifying mammogram images as benign or malignant. Different VGG19 scenarios have been examined based on the number of convolution layer blocks that have been frozen. There are a total of six scenarios in this study. The primary benefits of this research are twofold: it improves the model's ability to detect breast cancer cases and it reduces the training time of VGG19 by freezing certain layers.To evaluate the performance of these scenarios, 1693 microbiological images of benign and malignant breast cancers were utilized. According to the reported results, the best recognition rate was obtained from a frozen first block of VGG19 with a sensitivity of 95.64 %, while the training of the entire VGG19 yielded 94.48%.

This letter provides what is, to the best of our knowledge, a first study on the applicability of ultra-low-resolution thermal cameras for providing rotational odometry measurements to navigational devices such as rovers and drones. Our use of an ultra-low-resolution thermal camera instead of other modalities such as an RGB camera is motivated by its robustness to lighting conditions, while being one order of magnitude less cost-expensive compared to higher-resolution thermal cameras. After setting up a custom data acquisition system and acquiring thermal camera data together with its associated rotational speed label, we train a small 4-layer Convolutional Neural Network (CNN) for regressing the rotational speed from the thermal data. Experiments and ablation studies are conducted for determining the impact of thermal camera resolution and the number of successive frames on the CNN estimation precision. Finally, our novel dataset for the study of low-resolution thermal odometry is openly released with the hope of benefiting future research.