Qualcomm (United States)

Artificial intelligence-derived macular thinning patterns were associated with central visual field progression in glaucoma and outperformed global thickness metrics in predicting progression across disease severities. To provide spatial patterns of ganglion cell complex thickness and assess their associations with central visual field progression in glaucoma. Macular patterns from the ganglion cell complex were determined using an artificial intelligence algorithm termed archetypal analysis (AA). The diagnostic accuracy of spatial patterns for detecting 10-2 central visual field progression in eyes with at least five 10-2 visual field tests was calculated and compared with the mean global ganglion cell complex thickness. Eyes with progression on either of 2 trend-based methods (significant MD slope less than -0.5dB/y or clustered pointwise linear regression) were classified as "progressors." A total of 4031 macular scans of 1093 eyes (611 patients) were included, with a mean (SD) age of 67.8 (12.7) years. Eleven distinct spatial patterns were identified. While the macular vulnerable zone was preferentially affected in 4 patterns, most of the less vulnerable zones were preserved. The AA models at baseline achieved AUROC [0.73 (95% CI, 0.62-0.84)] and outperformed global ganglion cell complex thickness [0.55 (95% CI, 0.46-0.61), P =0.01] for predicting central VF progression in eyes with early disease at baseline. The AA models AUROC [0.70 (95% CI, 0.59-0.80)] also outperformed ganglion cell complex thickness [0.55 (95% CI, 0.48-0.60), P =0.02] for predicting central VF progression across all severities. Using unsupervised artificial intelligence, characteristic patterns of macular thinning were identified and associated with progression in the central visual field. Spatial macular pattern analysis may enhance individualized care and improve risk stratification for those at risk of central VF damage.

The integration of large-scale foundation models with physical embodiments has led to significant advancements in robotics termed Generative Physical Artificial Intelligence (GPAI). These agentic AI systems autonomously perceive, reason, and act in complex real-world situations. This survey comprehensively analyzes GPAI systems, focusing on their architectural foundations, current applications, and key limitations. We introduce a taxonomy of five distinct approaches: Robot Foundation Models (RFMs) for cross-platform skill transfer; Vision Language Action Models (VLAs) for end-to-end multi-modal perception and control; Large Behavior Models (LBMs) for human-like movement generation; Diffusion Policy Models (DPMs) for diffusion model-based temporally coherent action generation; and World Foundation Models (WFMs) for physics-compliant simulation and data generation. We examine how these approaches complement each other: WFMs generate training data for VLAs and DPMs, RFMs enable cross-platform deployment of learned policies, while LBMs provide motion priors for natural behavior. Through examples across autonomous vehicles, industrial automation, healthcare robotics, and humanoid systems, we identify significant performance improvements and summarize promising research directions in data-efficient learning, sim-to-real transfer, edge-compatible architectures, and safety frameworks. These insights advance embodied AI for IoT-connected environments where intelligent agents interact with networked sensors, actuators, and edge devices.

Digital therapeutics (DTx) for children and adolescents with mental health problems have been developed in the health care industry. Despite reports of side effects from DTx for children and adolescents, there have been no guidelines to address the prevention of DTx overdependence among young users. This study aimed to identify the requirements for guidelines to prevent DTx overdependence in children and adolescents and to develop and evaluate these guidelines. We conducted 2 phases. This study first involved a phase I survey to develop guidelines, including assessments of smartphone usage and mental health conditions. The second phase evaluated the guidelines' effectiveness, reliability, necessity, and satisfaction using a visual analog scale through a randomized controlled trial. Participants-45 children and adolescents aged 9-16 years and 42 caregivers-were randomly assigned to the experimental and control groups. Phase I revealed that blocking mobile applications and notifications (mean 8.5, SD 1.8) and parental monitoring (mean 8.5, SD 2.1) were effective preventive features. Caregivers, children, and adolescents expressed concerns about the side effects and overdependence of DTx and decreased effects due to nonindividualized guidelines in subjective responses to the phase I survey. Based on these insights, personalized guidelines for phase II were developed, in which overall mean visual analog scale scores for guideline evaluation were higher in the experimental group, except for necessity among caregivers (mean 8.5, SD 1.3 versus mean 8.7, SD 1.2). Both caregivers and children and adolescents demonstrated the need for guidelines to prevent overdependence on DTx distinct from smartphone usage. Tailored guidelines may be acceptable for use in real-world therapeutic protocols. Guidelines to prevent overdependence on DTx in children and adolescents and to achieve a balance between their benefits and risks need to be established. Clinical Research Information Service (CRiS) of the Republic of Korea KCT0008893; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=25609.

This paper presents a comprehensive study on low-complexity waveform, modulation and coding (WMC) designs for the 3rd Generation Partnership Project (3GPP) Ambient Internet of Things (A-IoT). A-IoT is a low-cost, low-power IoT system inspired by Ultra High Frequency (UHF) Radio Frequency Identification (RFID) and aims to leverage existing cellular network infrastructure for efficient RF tag management. The paper compares the physical layer (PHY) design challenges and requirements of RFID and A-IoT, particularly focusing on backscatter communications. An overview of the standardization for PHY designs in Release 19 A-IoT is provided, along with detailed schemes of the proposed low-complex WMC designs. The performance of device-to-reader link designs is validated through simulations, demonstrating 8 dB improvements of the proposed baseband waveform with coherent receivers compared to RFID line coding-based solutions with non-coherent receivers when channel coding is adopted.

The drivers of modern operating systems use Direct Memory Access (DMA) to efficiently communicate with peripheral devices. Since the memory accessed by DMA is a shared resource between driver and device, it is a possible source of race conditions. Peripheral devices are also often untrusted, so these race conditions open up a new potential attack vector against a trusted OS kernel. In this paper, we present DMARacer, a dynamic detector called for these DMA-based race conditions in kernel code. DMARacer tracks memory accesses to DMA memory throughout the kernel's lifetime and analyses them for various indicators of race conditions. Additionally, upon detecting a race condition, DMARacer uses taint tracking to trace its impact and identify any potential vulnerabilities it may trigger, such as memory corruption or denial-of-service. We used DMARacer to search the drivers of the Linux kernel for DMA-based errors and find that DMA-based race conditions are a systemic issue in driver code. In total, DMARacer was able to detect 817 problematic memory accesses and 344 vulnerable operations in the scanned Linux kernel drivers.

In this paper, we examine the distribution and convergence properties of the estimation error $W = X - \hat X(Y)$, where $\hat X(Y)$ is the Bayesian estimator of a random variable X from a noisy observation Y = X + σZ where σ is the parameter indicating the strength of noise Z. Using the conditional expectation framework (that is, $\hat X(Y)$ is the conditional mean), we define the normalized error ${{\mathcal{E}}_\sigma } = \frac{W}{\sigma }$ and explore its properties.Specifically, in the first part of the paper, we characterize the probability density function of W and ${{\mathcal{E}}_\sigma }$. Along the way, we also find conditions for the existence of the inverse functions for the conditional expectations. In the second part, we study pointwise (i.e., almost sure) convergence of ${{\mathcal{E}}_\sigma }$ as σ → 0 under various assumptions about the noise and the underlying distributions. Our results extend some of the previous limits of ${{\mathcal{E}}_\sigma }$ as σ → 0 studied under the L<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> convergence, known as the MMSE dimension, to the pointwise case.

Resistive switching, a behavior found in many oxide materials, has the potential to enable emerging computer hardware technologies and architectures. We present resistive switching devices fabricated from epitaxial brownmillerite SrFeO2.5 films with two distinct film orientations, wherein facile oxygen ion diffusion planes are aligned parallel (in-plane) and perpendicular (out-of-plane) with the electrodes. SrFeO2.5 films were grown on (001) oriented Nb:SrTiO3 to enable high-quality interfaces and future integration with Si CMOS technologies. Post-growth vacuum annealing and growth pressure were used to control film orientations, as confirmed by transmission electron microscopy and x-ray diffraction measurements. Films grown with diffusion planes oriented in-plane had oxygen-rich, perovskite-like nanodomains spread throughout the film, and fabricated devices exhibited worse switching consistency and more stochasticity. In contrast, films grown with diffusion planes oriented out-of-plane had a more uniform oxygen-rich perovskite interfacial layer above the bottom electrode, and devices built from this film orientation showed significant statistical improvements in switching voltages and cycling consistency.

Abstract Berms composed of surface gravel and underlying sand and gravel mix (gravel berms) naturally form on beaches and can help mitigate coastal erosion and flooding. Previous studies suggest that subsurface sediments influence gravel berm behavior, although detailed investigations remain limited. Here, we present a novel integrated field approach to quantitatively monitor gravel berm subsurface sediments. From March to October 2023, surface and subsurface sediments were characterized using combined methods including mechanical excavation, image‐based grain size analysis, GNSS, and ground penetrating radar (GPR) surveys, and compared with gravel berm topography at two beaches (South Carlsbad and Torrey Pines) in southern California with contrasting sediment characteristics. South Carlsbad consistently exhibited backshore gravel exposure and limited seasonal sand volume changes, whereas Torrey Pines exhibited varying backshore gravel exposure and more pronounced seasonal sand volume fluctuations. At both beaches, the gravel berms consistently had a surface pure‐gravel (PG) layer overlying a mixed sand‐gravel layer, with the PG layer thickness decreasing seaward. At Torrey Pines, the upper gravel berm profile changed seasonally from spring to late summer as sand gradually accumulated within the berm (resulting in a PG layer thickness decrease) and varied subsurface sediment composition. In contrast, in South Carlsbad, both upper gravel berms and subsurface sediment structures exhibited seasonal consistency, with little change in PG layer thickness. The combined approach enables detailed and repeatable assessment of gravel berm subsurface sediments, offering further insights into the links between internal sediment structure and surface morphology. The present results inform a new conceptual model of seasonal gravel berm evolution.