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Motivated by experiments employing picosecond-long, kilojoule laser pulses, we examined x-ray emission in a finite-length underdense plasma irradiated by such a pulse using two-dimensional particle-in-cell simulations. We found that, in addition to the expected forward emission, the plasma also efficiently emits in the backward direction. Our simulations reveal that the backward emission occurs when the laser exits the plasma. The longitudinal plasma electric field generated by the laser at the density down-ramp turns around some of the laser-accelerated electrons and re-accelerates them in the backward direction. As the electrons collide with the laser, they emit hard x rays. The energy conversion efficiency is comparable to that for the forward emission, but the effective source size is smaller. We show that the picosecond laser duration is required for achieving a spatial overlap between the laser and the backward energetic electrons. At peak laser intensity of 1.4×1020 W/cm2, backward-emitted photons (energies above 100 keV and 10° divergence angle) account for 2×10−5 of the incident laser energy. This conversion efficiency is three times higher than that for similarly selected forward-emitted photons. The source size of the backward photons (5 μm) is three times smaller than the source size of the forward photons. 

The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks via an ordinary differential equation (ODE) model, which characterizes spatial-temporal brain dynamics. Our framework is validated on several clinical phenotype prediction tasks using two independent publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods. 

Navigating safely and independently presents considerable challenges for people who are blind or have low vision (BLV), as it re- quires a comprehensive understanding of their neighborhood environments. Our user study reveals that understanding sidewalk materials and objects on the sidewalks plays a crucial role in navigation tasks. This paper presents a pioneering study in the field of navigational aids for BLV individuals. We investigate the feasibility of using auditory data, specifically the sounds produced by cane tips against various sidewalk materials, to achieve material identification. Our approach utilizes ma- chine learning and deep learning techniques to classify sidewalk materials solely based on audio cues, marking a significant step towards empowering BLV individuals with greater autonomy in their navigation. This study contributes in two major ways: Firstly, a lightweight and practical method is developed for volunteers or BLV individuals to autonomously collect auditory data of sidewalk materials using a microphone-equipped white cane. This innovative approach transforms routine cane usage into an effective data-collection tool. Secondly, a deep learning-based classifier algorithm is designed that leverages a dual architecture to enhance audio feature extraction. This includes a pre-trained Convolutional Neural Network (CNN) for regional feature extraction from two-dimensional Mel-spectrograms and a booster module for global feature enrichment. Experimental results indicate that the optimal model achieves an accuracy of 80.96% using audio data only, which can effectively recognize sidewalk materials. 

Navigating safely and independently presents considerable challenges for people who are blind or have low vision (BLV), as it re- quires a comprehensive understanding of their neighborhood environments. Our user study reveals that understanding sidewalk materials and objects on the sidewalks plays a crucial role in navigation tasks. This paper presents a pioneering study in the field of navigational aids for BLV individuals. We investigate the feasibility of using auditory data, specifically the sounds produced by cane tips against various sidewalk materials, to achieve material identification. Our approach utilizes ma- chine learning and deep learning techniques to classify sidewalk materials solely based on audio cues, marking a significant step towards empowering BLV individuals with greater autonomy in their navigation. This study contributes in two major ways: Firstly, a lightweight and practical method is developed for volunteers or BLV individuals to autonomously collect auditory data of sidewalk materials using a microphone-equipped white cane. This innovative approach transforms routine cane usage into an effective data-collection tool. Secondly, a deep learning-based classifier algorithm is designed that leverages a dual architecture to enhance audio feature extraction. This includes a pre-trained Convolutional Neural Network (CNN) for regional feature extraction from two-dimensional Mel-spectrograms and a booster module for global feature enrichment. 

Although various navigation apps are available, people who are blind or have low vision (PVIB) still face challenges to locate store entrances due to missing geospatial information in existing map services. Previously, we have developed a crowdsourcing platform to collect storefront accessibility and localization data to address the above challenges. In this paper, we have significantly improved the efficiency of data collection and user engagement in our new AI-enabled Smart DoorFront platform by designing and developing multiple important features, including a gamified credit ranking system, a volunteer contribution estimator, an AI-based pre-labeling function, and an image gallery feature. For achieving these, we integrate a specially designed deep learning model called MultiCLU into the Smart DoorFront. We also introduce an online machine learning mechanism to iteratively train the MultiCLU model, by using newly labeled storefront accessibility objects and their locations in images. Our new DoorFront platform not only significantly improves the efficiency of storefront accessibility data collection, but optimizes user experience. We have conducted interviews with six adults who are blind to better understand their daily travel challenges and their feedback indicated that the storefront accessibility data collected via the DoorFront platform would be very beneficial for them. 

Conformance tests are critical for finding security weaknesses in carrier network systems. However, building a conformance test procedure from specifications is challenging, as indicated by the slow progress made by the 3GPP, particularly in developing security-related tests, even with a large amount of resources already committed. A unique challenge in building the procedure is that a testing system often cannot directly invoke the condition event in a security requirement or directly observe the occurrence of the operation expected to be triggered by the event. Addressing this issue requires an event chain to be found, which once initiated leads to a chain reaction so the testing system can either indirectly triggers the target event or indirectly observe the occurrence of the expected event. To find a solution to this problem and make progress towards a fully automated conformance test generation, we developed a new approach called Contester , which utilizes natural language processing and machine learning to build an event dependency graph from a 3GPP specification, and further perform automated reasoning on the graph to discover the event chains for a given security requirement. Such event chains are further converted by Contester into a conformance testing procedure, which is then executed by a testing system to evaluate the compliance of user equipment (UE) with the security requirement. Our evaluation shows that given 22 security requirements from the LTE NAS specifications, Contester successfully generated over a hundred test procedures in just 25 minutes. After running these procedures on 22 popular UEs including iPhone 13, Pixel 5a and IoT devices, our approach uncovered 197 security requirement violations, with 190 never reported before, rendering these devices to serious security risks such as MITM, fake base station and reply attacks. 

The modeling of the interaction between brain structure and function using deep learning techniques has yielded remarkable success in identifying potential biomarkers for different clinical phenotypes and brain diseases. However, most existing studies focus on one-way mapping, either projecting brain function to brain structure or inversely. This type of unidirectional mapping approach is limited by the fact that it treats the mapping as a one-way task and neglects the intrinsic unity between these two modalities. Moreover, when dealing with the same biological brain, mapping from structure to function and from function to structure yields dissimilar outcomes, highlighting the likelihood of bias in one-way mapping. To address this issue, we propose a novel bidirectional mapping model, named Bidirectional Mapping with Contrastive Learning (BMCL), to reduce the bias between these two unidirectional mappings via ROI-level contrastive learning. We evaluate our framework on clinical phenotype and neurodegenerative disease predictions using two publicly available datasets (HCP and OASIS). Our results demonstrate the superiority of BMCL compared to several state-of-the-art methods. 

The storefront accessibility can substantially impact the way people who are blind or visually impaired (BVI) travel in urban environments. Entrance localization is one of the biggest challenges to the BVI people. In addition, improperly designed staircases and obstructive store decorations can create considerable mobility challenges for BVI people, making it more difficult for them to navigate their community hence reducing their desire to travel. Unfortunately, there are few approaches to acquiring this information in advance through computational tools or services. In this paper, we propose a solution to collect large- scale accessibility data of New York City (NYC) storefronts using a crowdsourcing approach on Google Street View (GSV) panoramas. We develop a web-based crowdsourcing application, DoorFront, which enables volunteers not only to remotely label storefront accessibility data on GSV images, but also to validate the labeling result to ensure high data quality. In order to study the usability and user experience of our application, an informal beta-test is conducted and a user experience survey is designed for testing volunteers. The user feedback is very positive and indicates the high potential and usability of the proposed application.