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关键词： Computer science   Galaxies and clusters   Graphene   Machine learning  

摘要： Learning meaningful representations of images in scientific domains that are robust to variations in centroids and orientations remains an important challenge. Here we introduce centroid- and orientation-aware disentangling autoencoder (CODAE), an encoder-decoder-based neural network that learns meaningful content of objects in a latent space. Specifically, a combination of a translation- and rotation-equivariant encoder, Euler encoding and an image moment loss enables CODAE to extract features invariant to positions and orientations of objects of interest from randomly translated and rotated images. We evaluate this approach on several publicly available scientific datasets, including protein images from life sciences, four-dimensional scanning transmission electron microscopy data from material science and galaxy images from astronomy. The evaluation shows that CODAE learns centroids, orientations and their invariant features and outputs, as well as aligned reconstructions and the exact view reconstructions of the input images with high quality.

关键词： Computers   Quantum computing   Qubit   Hardware   Quantum algorithm   Quantum advantage   Computer science   Approximation algorithms   Encryption   Costs  

摘要： In the children’s story of the Tortoise and the Hare, the speedier Hare is outpaced by a Tortoise with other advantages (diligence). An analogous contest is happening in computing, between a Quantum Tortoise and a Classical Hare. Here, the Classical Hare’s speed advantage is literal—classical computers run faster than quantum ones. Like his namesake, the Quantum Tortoise is slower, but also has an advantage—in this case, the ability to run algorithms that are unavailable to classical computers. When this algorithmic advantage is substantial enough, the Quantum Tortoise can beat the Classical Hare and solve a problem faster. This article analyzes when the Quantum Tortoise will beat the Classical Hare—and when it will not. © 1963-2012 IEEE.

关键词： Augmented reality   Computer science   Medical education   Virtual reality   VR headset  

摘要： Objective: Although the operating theatre offers unique didactic opportunities, it can be perceived as an uncomfortable environment by medical students due to the lack of theatre etiquette, time pressure and parallel work of different disciplines. We investigated whether virtual reality (VR) training can significantly reduce some of these fears and improve surgical education for medical students. Methods: We randomly divided a group of 24 medical students and investigated the effects of a VR application (digitally recreated operating theatre tour) and compared it with currently-in-use written instructions for operational tasks in the operating theatre. The parameters analyzed in this study included timekeeping, orientation, and the error rate. Results: Medical students who received a preparational VR training for tasks in the operating theatre showed a statistically significant lower rate of getting stuck (P < 0.03), time to find the operation room (P < 0.01), correct gowning (P < 0.05) and better identification of surgical equipment (P < 0.01). The use of a digitally reconstructed operating theatre using VR led to a significant improvement in orientation and error rate in the behavior of medical students in the operating theatre. Conclusions: Routine VR use as part of clinical training could be beneficial in future medical education.

关键词： artificial intelligence   actor engagement   AI in services   computer science   GenAI   GPT   Human-Machine engagement   socio-technical systems  

摘要： Artificial intelligence (AI) applications in customer-facing settings are growing rapidly. The general shift toward robot- and AI-powered services prompts a reshaping of customer engagement, bringing machines into engagement conceptualizations. In this paper, we build on service research around engagement and AI, incorporating computer science, and socio-technical systems perspective to conceptualize human-machine engagement (HME), offering a typology and nomological network of antecedents and consequences. Through three empirical studies, we develop a typology of four distinct forms of HME (informative, experimenting, praising, apprehensive), which differ in valence and intensity, underpinned by both emotional (excitement) and cognitive (concern, advocacy) drivers. We offer empirical evidence which reveals how these HME forms lead to different cognitive and personality-related outcomes for other users (perceived value of HME, perceived risk, affinity with HME) and service providers (willingness to implement in services, perceived value of HME). We also reveal how outcomes for service providers vary with the presence and absence of competitor pressure. Our findings broaden the scope of engagement research to include non-human actors and suggest both strategic and tactical guidance to service providers currently using and/or seeking to use generative AI (GenAI) in services alongside an agenda to direct future studies on HME.

关键词： Computer science   ScratchJr   Early elementary education   Computational thinking   Evidence-based curriculum  

摘要： Background: and Context: Computer programming is increasingly being taught to young children worldwide. Quality programming curricula are needed to enable students to explore computer science concepts and develop a positive attitude towards programming. However, few studies have been conducted on the available curricula for young learners. Objective: This study aims to provide an evidence-based, public, and free computer science curriculum for early childhood education by investigating the efficacy of the CAL-ScratchJr curriculum, among second graders. Method: A cluster-randomized controlled trial involving 11 schools and 21 second-grade classrooms was conducted to examine the impact of the 24-lesson curriculum on students' coding proficiency and computational thinking. Findings: The results indicated that the CAL-ScratchJr curriculum intervention had a medium positive effect on second graders' coding skills, but no significant impact on computational thinking. Implications: Results caution against incorporating disconnected abstract computational thinking practices in education. The features of the curriculum were discussed.

关键词： Low anterior resection syndrome   Rectal cancer   Survivorship care   Bowel dysfunction   Peer support  

摘要： IntroductionLow Anterior Resection Syndrome (LARS) describes bowel dysfunction post-restorative proctectomy (RP) and is associated with poor quality of life (QoL). The aim of this study was to assess the impact of an interactive online informational and peer support App on participants' QoL (primary outcome), LARS, and emotional distress (secondary outcomes).MethodsA multicentre, randomized, parallel-group trial was conducted across five Canadian colorectal surgery practices. Adult patients who: (1) underwent RP for rectal cancer and completed all treatment and (2) had major/minor LARS were included. Participants were randomized in a 1:1 ratio to treatment or comparison group, stratified by hospital site and years post-RP. The treatment group had access to the App for a period of 6 months, and the comparison group received a booklet containing the same educational material as the App. The primary outcome was change in global QoL (EORTC-QLQ-C30) from baseline to 6 months post-intervention. Per-protocol and intention to treat analysis were performed, controlling for a priori selected variables (sex, time from end of treatment).ResultsOf the 101 enrolled participants, 10 individuals were lost to follow-up and 91 completed the study. Participants were well-balanced in terms of baseline characteristics, QoL and bowel dysfunction. Among the 45 App users, median log-in per person was 21, with 30 (66.7%) participants meeting criteria for adequate app usage. On intention to treat analysis, there was no statistically significant difference in QoL in the App group. On the per-protocol analysis, where only participants who met adequate App usage criteria were included in the treatment group, the group reported statistically significant improvement in QoL (beta 9.5, 95% CI 4.6,14.6) and LARS (beta - 2.7, 95%CI - 5.1,- 0.2) scores following App *** multicenter randomized controlled trial support that, when adequately used, an interactive online informational and

关键词： Scalability   Runtime   Static analysis   Costs   Backtracking   Source coding   Skeleton   Supercomputers   Delays   Computer science   Performance analysis   root-cause detection   scalability bottleneck   static analysis  

摘要： It is challenging to scale parallel applications to modern supercomputers because of load imbalance, resource contention, and communications between processes. Profiling and tracing are two main performance analysis approaches for detecting these scalability bottlenecks. Profiling is low-cost but lacks detailed dependence for identifying root causes. Tracing records plentiful information but incurs significant overheads. To address these issues, we present ScalAna, which employs static analysis techniques to combine the benefits of profiling and tracing - it enables tracing's analyzability with overhead similar to profiling. ScalAna uses static analysis to capture program structures and data dependence of parallel applications, and leverages lightweight profiling approaches to record performance data during runtime. Then a parallel performance graph is generated with both static and dynamic data. Based on this graph, we design a backtracking detection approach to automatically pinpoint the root causes of scaling issues. We evaluate the efficacy and efficiency of ScalAna using several real applications with up to 704K lines of code and demonstrate that our approach can effectively pinpoint the root causes of scaling loss with an average overhead of 5.65% for up to 16,384 processes. By fixing the root causes detected by our tool, it achieves up to 33.01% performance improvement.

关键词： File systems   Source coding   Object recognition   Computer science  

摘要： the above article [1], there is a correction to introduction section in line 6, write application factor (WAF) should be write amplification factor.

关键词： Three-dimensional displays   Semantics   Autonomous vehicles   Pipelines   Object detection   Semantic segmentation   Feature extraction   Computer science   Cameras   Training   3D semantic occupancy   autonomous driving   temporal association   contrastive learning  

摘要： 3D semantic occupancy has garnered considerable attention due to its abundant structural information encompassing the entire autonomous driving scene. However, existing 3D occupancy prediction methods are typically tailored for single-frame inputs, resulting in unsatisfactory performance and temporal inconsistencies in real-world continuous scenarios. In this paper, we introduce LinkOcc, a sparse-queries approach incorporating an efficient temporal association mechanism for 3D semantic occupancy prediction. LinkOcc is conceptually built on the prevalent DETR-like framework for 2D segmentation, and we further construct the temporal association mechanism on this basis. Specifically, we propose a near-online training strategy that jointly trains with two adjacent frames, which successfully combines the benefits of both online and off-online methods. Moreover, we introduce a temporal association strategy with contrastive learning to discriminate features for cross-frame semantic-level association. Comprehensive experiments demonstrate that LinkOcc not only surpasses the state-of-the-art methods in 3D occupancy prediction, but also guarantees a promising performance on foreground classes.

关键词： large language models   programming education   plagiarism   integrity  

摘要： The integration of large language models (LLMs) into educational settings represents a significant technological breakthrough, offering substantial opportunities alongside profound ethical challenges. Higher education institutions face the widespread use of these tools by students, requiring them to navigate complex decisions regarding their adoption. This includes determining whether to allow the use of LLMs, defining their appropriate scope, and establishing guidelines for their responsible and ethical application. In the context of computer science education, these challenges are particularly acute. On the one hand, the capabilities of LLMs significantly enhance the tools available to developers and software engineers. On the other hand, students' over-reliance on LLMs risks hindering their development of foundational skills. This study examines these challenges and proposes strategies to regulate the use of LLMs while upholding academic integrity. It focuses on the specific impact of LLMs in programming education, where dependence on AI-generated solutions may erode active learning and essential skill acquisition. Through a comprehensive literature review and drawing on teaching experience and guidelines from global institutions, this study contributes to the broader discourse on the integration of these advanced technologies into educational environments. The goal is to enhance learning outcomes while ensuring the development of competent, ethical software professionals.