Perception Imitation: Towards Synthesis-free Simulator for Autonomous Vehicles

We propose a perception imitation method to simulate results of a certain perception model, and discuss a new heuristic route of autonomous driving simulator without data synthesis. The motivation is that original sensor data is not always necessary for tasks such as planning and control when semantic perception results are ready, so that simulating perception directly is more economic and efficient. In this work, a series of evaluation methods such as matching metric and performance of downstream task are exploited to examine the simulation quality. Experiments show that our method is effective to model the behavior of learning-based perception model, and can be further applied in the proposed simulation route smoothly

SkCoder: A Sketch-based Approach for Automatic Code Generation

Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-based approaches that can copy the content from similar code snippets to obtain better performance. Practically, human developers recognize the content in the similar code that is relevant to their needs, which can be viewed as a code sketch. The sketch is further edited to the desired code. However, existing copy-based approaches ignore the code sketches and tend to repeat the similar code without necessary modifications, which leads to generating wrong results. In this paper, we propose a sketch-based code generation approach named SkCoder to mimic developers' code reuse behavior. Given a natural language requirement, SkCoder retrieves a similar code snippet, extracts relevant parts as a code sketch, and edits the sketch into the desired code. Our motivations are that the extracted sketch provides a well-formed pattern for telling models "how to write". The post-editing further adds requirement-specific details to the sketch and outputs the complete code. We conduct experiments on two public datasets and a new dataset collected by this work. We compare our approach to 20 baselines using 5 widely used metrics. Experimental results show that (1) SkCoder can generate more correct programs, and outperforms the state-of-the-art - CodeT5-base by 30.30%, 35.39%, and 29.62% on three datasets. (2) Our approach is effective to multiple code generation models and improves them by up to 120.1% in Pass@1. (3) We investigate three plausible code sketches and discuss the importance of sketches. (4) We manually evaluate the generated code and prove the superiority of our SkCoder in three aspects.Comment: Accepted by the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023

MCT: A tool for commenting programs by multimedia comments

Program comments have always been the key to understanding code. However, typical text comments can easily become verbose or evasive. Thus sometimes code reviewers find an audio or video code narration quite helpful. In this paper, we present our tool, called MCT (Multimedia Commenting Tool), which is an integrated development environment-based tool that enables programmers to easily explain their code by voice, video and mouse movement in the form of comments. With this tool, programmers can replay the audio or video when they feel like. A demonstration video can be accessed at: http://www.youtube.com/watch?v=tHEHqZme4VEhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000333965800166&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Software EngineeringComputer Science, Theory & MethodsEngineering, Electrical & ElectronicEICPCI-S(ISTP)

Cathode Design for Aqueous Rechargeable Multivalent Ion Batteries: Challenges and Opportunities

With the rapid growth in energy consumption, renewable energy is a promising solution. However, renewable energy (e.g., wind, solar, and tidal) is discontinuous and irregular by nature, which poses new challenges to the new generation of large-scale energy storage devices. Rechargeable batteries using aqueous electrolyte and multivalent ion charge are considered more suitable candidates compared to lithium-ion and lead-acid batteries, owing to their low cost, ease of manufacture, good safety, and environmentally benign characteristics. However, some substantial challenges hinder the development of aqueous rechargeable multivalent ion batteries (AMVIBs), including the narrow stable electrochemical window of water (≈1.23 V), sluggish ion diffusion kinetics, and stability issues of electrode materials. To address these challenges, a range of encouraging strategies has been developed in recent years, in the aspects of electrolyte optimization, material structure engineering and theoretical investigations. To inspire new research directions, this review focuses on the latest advances in cathode materials for aqueous batteries based on the multivalent ions (Zn2+, Mg2+, Ca2+, Al3+), their common challenges, and promising strategies for improvement. In addition, further suggestions for development directions and a comparison of the different AMVIBs are covered

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013–2022), the first ten-year stage of the lifespan CCNP (2013–2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” in the Chinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community (https://ccnp.scidb.cn) at the Science Data Bank

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

SoccerNet 2023 Challenges Results

peer reviewedThe SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet

The Facile Synthesis of Hollow CuS Microspheres Assembled from Nanosheets for Li-Ion Storage and Photocatalytic Applications

Herein, well-defined hollow CuS microspheres assembled from nanosheets were successfully synthesized through a facile solvothermal method. Hollow CuS microspheres have an average diameter of 1.5 μm; moreover, the primary CuS nanosheets have an ultrathin thickness of about 10 nm and are bound by {0001} polar facets. When used as anodes for lithium-ion batteries (LIBs), hollow CuS microspheres exhibit excellent electrochemical properties, including a large discharge capacity (610.1 mAh g−1 at 0.5 C), an excellent rate capability (207.6 and 143.4 mAh g−1 at 1 and 5 C), and a superior cyclic stability (196.3 mAh g−1 at 1 C after 500 cycles). When used as photocatalysts for Rhodamine B (RhB), hollow CuS microspheres can degrade more than 99% of the initial RhB within 21 min. These excellent Li-ion storage properties and photocatalytical performances are attributed to their unique hierarchical hollow structure