A5100341008- Galaxies: Formation, Evolution, Phenomena
- Astronomy and Astrophysical Research
- Adaptive optics and wavefront sensing
- Gamma-ray bursts and supernovae
- Stellar, planetary, and galactic studies
- Anomaly Detection Techniques and Applications
- Face and Expression Recognition
- Advanced Vision and Imaging
- Astrophysics and Star Formation Studies
- AI-based Problem Solving and Planning
- Cosmology and Gravitation Theories
- Intelligent Tutoring Systems and Adaptive Learning
- Advanced Fluorescence Microscopy Techniques
- Machine Learning and Data Classification
- Titanium Alloys Microstructure and Properties
- Topic Modeling
- CCD and CMOS Imaging Sensors
- Text and Document Classification Technologies
- Image Processing Techniques and Applications
- Advanced Welding Techniques Analysis
- Quantum Information and Cryptography
- Domain Adaptation and Few-Shot Learning
- Welding Techniques and Residual Stresses
- Advanced Image Fusion Techniques
- Market Dynamics and Volatility
University of Chicago
2015-2025
National Astronomical Observatories
2012-2024
Chinese Academy of Sciences
2015-2024
University of Chinese Academy of Sciences
2014-2024
Aerospace Information Research Institute
2024
National Space Science Center
2023-2024
Shanghai Jiao Tong University
2010-2024
Dalian University of Technology
2019-2024
Central South University
2022-2024
Tianjin Research Institute of Electric Science (China)
2024
It is a common spoof to use photograph fool face recognition algorithm. In light of differences in optical flow fields generated by movements two-dimensional planes and three-dimensional objects, we proposed new liveness detection method for recognition. Under the assumption that test region plane, can obtain reference field from actual data. Then degree between two be used distinguish photograph. Empirical study shows approach both feasible effective.
GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden "facts" about city dynamics human behaviors. In this paper, we aim discover anomalous driving patterns from taxi's GPS traces, targeting applications like automatically detecting taxi frauds or road network change in modern cites. To achieve objective, firstly group all trajectories crossing same source destination cell-pair represent each trajectory a sequence of...
Hyperspectral image (HSI) change detection is a technique for detecting the changes between multitemporal HSIs of same scene. Many existing methods have achieved good results, but there still exist problems as follows: 1) mixed pixels in HSI due to low spatial resolution hyperspectral sensor and other external interference 2) many deep learning-based networks cannot make full use correlation difference information bitemporal images. These are not conducive further improving accuracy...
Abstract The manipulation of electronic polymers’ solid-state properties through processing is crucial in electronics and energy research. Yet, efficiently polymer solutions into thin films with specific remains a formidable challenge. We introduce Polybot, an artificial intelligence (AI) driven automated material laboratory designed to autonomously explore pathways for achieving high-conductivity, low-defect polymers films. Leveraging importance-guided Bayesian optimization, Polybot...
Trajectories obtained from Global Position System (GPS)-enabled taxis grant us an opportunity not only to extract meaningful statistics, dynamics, and behaviors about certain urban road users but also monitor adverse and/or malicious events. In this paper, we focus on the problem of detecting anomalous routes by comparing latter against time-dependent historically "normal" routes. We propose online method that is able detect trajectories "on-the-fly" identify which parts trajectory are...
Galaxy-scale strong gravitational lensing is not only a valuable probe of the dark matter distribution massive galaxies, but can also provide cosmological constraints, either by studying population lenses or measuring time delays in lensed quasars. Due to rarity galaxy-scale strongly systems, fast and reliable automated lens finding methods will be essential era large surveys such as LSST, Euclid, WFIRST. To tackle this challenge, we introduce CMU DeepLens, new fully galaxy-galaxy method...
Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders magnitudes beyond known today. Finding these rare objects require picking them out at least tens millions images and deriving scientific results from quantifying efficiency bias any search method. To achieve objectives automated methods must be developed. Because gravitational lenses are reducing false positives particularly important. We present a description an open lens...
Abstract This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is starting point for second data challenge (DC2) carried out by LSST Dark Energy Science Collaboration (LSST DESC). The based on trillion-particle, (4.225 Gpc) 3 box cosmological N -body simulation, Outer Rim run. It covers 440 deg 2 of sky area redshift z = and matches expected number densities from...
Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, more than 6000 commonly used characters can be arranged various layouts with numerous fonts. The signboards street view are a good choice for images since they have different backgrounds, fonts layouts. We organized competition called ICDAR2019-ReCTS, which mainly focuses on signboard. This report presents final results competition. A...
ABSTRACT There are several supervised machine learning methods used for the application of automated morphological classification galaxies; however, there has not yet been a clear comparison these different using imaging data, or an investigation maximizing their effectiveness. We carry out between common galaxy [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Networks] by Dark Energy Survey (DES) data combined with...
The precise measurement of cosmic-ray antinuclei serves as an important means for identifying the nature dark matter and other new astrophysical phenomena, could be used with species to understand production propagation in Galaxy. For instance, low-energy antideuterons would provide a "smoking gun" signature annihilation or decay, essentially free background. Studies recent years have emphasized that models must considered together abundant cosmic antiprotons any potential observation...
Abstract We describe the simulated sky survey underlying second data challenge (DC2) carried out in preparation for analysis of Vera C. Rubin Observatory Legacy Survey Space and Time (LSST) by LSST Dark Energy Science Collaboration (LSST DESC). Significant connections across multiple science domains will be a hallmark LSST; DC2 program represents unique modeling effort that stresses this interconnectivity way has not been attempted before. This encompasses full end-to-end approach: starting...
Machine learning algorithms have been widely applied in mineral prospectivity mapping (MPM). In this study, we implemented ensemble of extreme gradient boosting (XGBoost) and random forest (RF) models to create MPM for magmatic hydrothermal tin polymetallic deposits Xianghualing District, southern Hunan Province, China. Machine-learning often require careful adjustment the parameters model hyperparameters optimal global performance. However, parameter tuning entails tedious calculations...
On the premises that total correlations in a bipartite quantum state are measured by mutual information, and separation of into classical parts satisfies an intuitive dominance relation, we examine to what extent various entropic entanglement measures, such as distillable entanglement, relative entropy squashed cost, formation, can be regarded consistent measures correlations. We illustrate formation often overestimates thus is too big genuine measure This indicates does not quantify...
In real-world classification tasks, it is difficult to collect training samples from all possible categories of the environment. Therefore, when an instance unseen class appears in prediction stage, a robust classifier should be able tell that class, instead classifying any known category. this paper, adopting idea adversarial learning, we propose ASG framework for open-category classification. generates positive and negative seen unsupervised manner via learning strategy. With generated...
In this paper we develop a new unsupervised machine learning technique comprised of feature extractor, convolutional autoencoder (CAE), and clustering algorithm consisting Bayesian Gaussian mixture model (BGM). We apply to visual band space-based simulated imaging data from the Euclid Space Telescope using Strong Gravitational Lenses Finding Challenge. Our promisingly captures variety lensing features such as Einstein rings with different radii, distorted arc structures, etc, without...