This year's Amazon Spring Sale is nearly over, but there's still time to grab a bunch of household, fashion and outdoor gear for record-low prices. Tech isn't a huge focus for this sale, but there are a decent number of devices on sale right now for some of the best prices we've seen all year. The selection may not be as good as that of Amazon Prime Day in July, but it still provides a good opportunity to save on things like headphones, robot vacuums, air purifiers and more. We've collected the best Amazon Spring Sale deals on tech gear here so you don't have to go searching for them. Our top pick for the best budget streaming device can play content at 1080p/60fps and now its remote can also control your TV's power and volume.

This year's Amazon Spring Sale is in full swing, and as promised, a ton of household, fashion and outdoor gear has dropped to record-low prices. Tech isn't a huge focus for this sale, but there are a decent number of devices on sale right now for some of the best prices we've seen all year. The selection may not be as good as that of Amazon Prime Day in July, but it still provides a good opportunity to save on things like headphones, robot vacuums, air purifiers and more. We've collected the best Amazon Spring Sale deals on tech gear here so you don't have to go searching for them. The Spring Sale runs through March 31, so check back here for all of the latest deals as they drop.

The Amazon Spring Sale is here, bringing a slew of discounts on household essentials, fashion, outdoor gear and even furniture. We at Engadget are focused on tech, and while the selection this time around isn't quite as good as that of Amazon Prime Day in July, there are some decent discounts to be had. Some of our favorite robot vacuums, air purifiers, headphones and storage gear are on sale right now for some of the best prices we've seen this year so far. Below, you'll find all of the best tech deals we could find in the Amazon Spring Sale. The shopping event runs through March 31, so be sure to check back for the latest deals as they become available.

This year's Amazon Spring Sale is in full swing, and as promised, a ton of household, fashion and outdoor gear has dropped to record-low prices. Tech isn't a huge focus for this sale, but there are a decent number of devices on sale right now for some of the best prices we've seen all year. The selection may not be as good as that of Amazon Prime Day in July, but it still provides a good opportunity to save on things like headphones, robot vacuums, air purifiers and more. We've collected the best Amazon Spring Sale deals on tech gear here so you don't have to go searching for them. The Spring Sale runs through March 31, so check back here for all of the latest deals as they drop.

The Amazon Spring Sale has arrived, bringing a slew of discounts on household essentials, fashion, outdoor gear and even furniture. We at Engadget are focused on tech, and while the selection this time around isn't quite as good as that of Amazon Prime Day in July, there are some decent discounts to be had. Some of our favorite robot vacuums, air purifiers, headphones and storage gear are on sale right now for some of the best prices we've seen this year so far. Below, you'll find all of the best tech deals we could find in the Amazon Spring Sale. The shopping event runs through March 31, so be sure to check back for the latest deals as they become available.

We use the same η and λ grids for global probing. A combination of (α = 0.25, λ = 0.1, η = 0.001) gives the second lowest alignment loss/highest We observe that η = 0.001 generally gives the best results across Therefore, we exclude α = 1.0 in We used a compute time of approximately 5600 CPU-hours of 2.90GHz Intel Xeon Gold In this section, we outline our anomaly detection experimental setting in more detail. Given a dataset (e.g., CIFAR-10) with C classes, one class (e.g., "airplane") is chosen In contrast to the "one-vs-rest" setting, in LOO we define one class of the In both "one-vs-rest" and LOO AD settings, we evaluate model representations in the following way: We show the pairs of items that change the most in distance in Table B.1. "stethoscope", which are semantically unrelated but perhaps have some slight visual similarity, tend We show the results in Fig. B.1. Table B.1: Distances between pairs of individual items from THINGS, ranked by the relative change in cosine distance from before to after naive alignment (normalized by original distance).

The success rate of catheterization procedures is closely linked to the sensory data provided to the surgeon. Vision-based deep learning models can deliver both tactile and visual information in a sensor-free manner, while also being cost-effective to produce. Given the complexity of these models for devices with limited computational resources, research has focused on force estimation and catheter segmentation separately. However, there is a lack of a comprehensive architecture capable of simultaneously segmenting the catheter from two different angles and estimating the applied forces in 3D. To bridge this gap, this work proposes a novel, lightweight, multi-input, multi-output encoder-decoder-based architecture. It is designed to segment the catheter from two points of view and concurrently measure the applied forces in the x, y, and z directions. This network processes two simultaneous X-Ray images, intended to be fed by a biplane fluoroscopy system, showing a catheter's deflection from different angles. It uses two parallel sub-networks with shared parameters to output two segmentation maps corresponding to the inputs. Additionally, it leverages stereo vision to estimate the applied forces at the catheter's tip in 3D. The architecture features two input channels, two classification heads for segmentation, and a regression head for force estimation through a single end-to-end architecture. The output of all heads was assessed and compared with the literature, demonstrating state-of-the-art performance in both segmentation and force estimation. To the best of the authors' knowledge, this is the first time such a model has been proposed

The increasing complexity of regulatory updates from global authorities presents significant challenges for medical device manufacturers, necessitating agile strategies to sustain compliance and maintain market access. Concurrently, regulatory bodies must effectively monitor manufacturers' responses and develop strategic surveillance plans. This study employs a multi-agent modeling approach, enhanced with Large Language Models (LLMs), to simulate regulatory dynamics and examine the adaptive behaviors of key actors, including regulatory bodies, manufacturers, and competitors. These agents operate within a simulated environment governed by regulatory flow theory, capturing the impacts of regulatory changes on compliance decisions, market adaptation, and innovation strategies. Our findings illuminate the influence of regulatory shifts on industry behaviour and identify strategic opportunities for improving regulatory practices, optimizing compliance, and fostering innovation. By leveraging the integration of multi-agent systems and LLMs, this research provides a novel perspective and offers actionable insights for stakeholders navigating the evolving regulatory landscape of the medical device industry.

In medical-related tasks, soft robots can perform better than conventional robots because of their compliant building materials and the movements they are able perform. However, designing soft robot controllers is not an easy task, due to the non-linear properties of their materials. Since human expertise to design such controllers is yet not sufficiently effective, a formal design process is needed. The present research proposes neuroevolution-based algorithms as the core mechanism to automatically generate controllers for biohybrid actuators that can be used on future medical devices, such as a catheter that will deliver drugs. The controllers generated by methodologies based on Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT (HyperNEAT) are compared against the ones generated by a standard genetic algorithm (SGA). In specific, the metrics considered are the maximum displacement in upward bending movement and the robustness to control different biohybrid actuator morphologies without redesigning the control strategy. Results indicate that the neuroevolution-based algorithms produce better suited controllers than the SGA. In particular, NEAT designed the best controllers, achieving up to 25% higher displacement when compared with SGA-produced specialised controllers trained over a single morphology and 23% when compared with general purpose controllers trained over a set of morphologies.

Artificial intelligence (AI) in medical device software (MDSW) represents a transformative clinical technology, attracting increasing attention within both the medical community and the regulators. In this study, we leverage a data-driven approach to automatically extract and analyze AI-enabled medical devices (AIMD) from the National Medical Products Administration (NMPA) regulatory database. The continued increase in publicly available regulatory data requires scalable methods for analysis. Automation of regulatory information screening is essential to create reproducible insights that can be quickly updated in an ever changing medical device landscape. More than 4 million entries were assessed, identifying 2,174 MDSW registrations, including 531 standalone applications and 1,643 integrated within medical devices, of which 43 were AI-enabled. It was shown that the leading medical specialties utilizing AIMD include respiratory (20.5%), ophthalmology/endocrinology (12.8%), and orthopedics (10.3%). This approach greatly improves the speed of data extracting providing a greater ability to compare and contrast. This study provides the first extensive, data-driven exploration of AIMD in China, showcasing the potential of automated regulatory data analysis in understanding and advancing the landscape of AI in medical technology.