84 research outputs found
Evolution of topological charge through chiral anomaly transport
Built upon the state-of-the-art model a multiphase transport (AMPT), we
develop a new module of chiral anomaly transport (CAT), which can trace the
evolution of the initial topological charge of gauge field created through
sphaleron transition at finite temperature and external magnetic field in heavy
ion collisions. The eventual experimental signals of chiral magnetic
effect(CME) can be measured. The CAT explicitly shows the generation and
evolution of the charge separation, and the signals of CME through the CAT are
quantitatively in agreement with the experimental measurements in Au+Au
collision at , and the centrality dependence of the CME
fraction follows that of the fireball temperature.Comment: 7 pages, 6 figure
Robust tracking with discriminative ranking middle-level patches
The appearance model has been shown to be essential for robust visual tracking since it is the basic criterion to locating targets in video sequences. Though existing tracking-by-detection algorithms have shown to be greatly promising, they still suffer from the drift problem, which is caused by updating appearance models. In this paper, we propose a new appearance model composed of ranking middle-level patches to capture more object distinctiveness than traditional tracking-by-detection models. Targets and backgrounds are represented by both low-level bottom-up features and high-level top-down patches, which can compensate each other. Bottom-up features are defined at the pixel level, and each feature gets its discrimination score through selective feature attention mechanism. In top-down feature extraction, rectangular patches are ranked according to their bottom-up discrimination scores, by which all of them are clustered into irregular patches, named ranking middle-level patches. In addition, at the stage of classifier training, the online random forests algorithm is specially refined to reduce drifting problems. Experiments on challenging public datasets and our test videos demonstrate that our approach can effectively prevent the tracker drifting problem and obtain competitive performance in visual tracking
Knowledge Guided Entity-aware Video Captioning and A Basketball Benchmark
Despite the recent emergence of video captioning models, how to generate the
text description with specific entity names and fine-grained actions is far
from being solved, which however has great applications such as basketball live
text broadcast. In this paper, a new multimodal knowledge graph supported
basketball benchmark for video captioning is proposed. Specifically, we
construct a multimodal basketball game knowledge graph (KG_NBA_2022) to provide
additional knowledge beyond videos. Then, a multimodal basketball game video
captioning (VC_NBA_2022) dataset that contains 9 types of fine-grained shooting
events and 286 players' knowledge (i.e., images and names) is constructed based
on KG_NBA_2022. We develop a knowledge guided entity-aware video captioning
network (KEANet) based on a candidate player list in encoder-decoder form for
basketball live text broadcast. The temporal contextual information in video is
encoded by introducing the bi-directional GRU (Bi-GRU) module. And the
entity-aware module is designed to model the relationships among the players
and highlight the key players. Extensive experiments on multiple sports
benchmarks demonstrate that KEANet effectively leverages extera knowledge and
outperforms advanced video captioning models. The proposed dataset and
corresponding codes will be publicly available soo
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Synergistic effects of neck circumference and metabolic risk factors on insulin resistance: the Cardiometabolic Risk in Chinese (CRC) study
Objectives: Recent studies have associated neck circumference (NC) with insulin resistance (IR). We examined whether such relation was modified by other metabolic risk factors. Methods: The study samples were from a community-based health examination survey in central China. A total of 2588 apparently healthy Chinese men and women were included. Results: Plasma levels of total cholesterol (TC), HDL-C, uric acid (UA) and diastolic blood pressure (DBP) were independently associated with NC after adjusted for age, sex, body mass index (BMI), waist circumference (WC) and hip circumference (HC) (P = 0.009, 0.001, 0.015 and 0.015, respectively). We observed significant interactions of NC with triglyceride (TG) and UA (all the p for interaction = 0.001) in relation to HOMA-IR. It appeared that the associations between NC and HOMA-IR were more evident in those with higher UA or TG level. Conclusions: Our data indicate that in apparently healthy Chinese adults, there were synergistic effects of UA, TG and neck circumference on insulin resistance
GMHL: Generalized Multi-Hop Locks for Privacy-Preserving Payment Channel Networks
Payment channel network (PCN), not only improving the transaction throughput of blockchain but also realizing cross-chain payment, is a very promising solution to blockchain scalability problem. Most existing PCN constructions focus on either atomicity or privacy properties. Moreover, they are built on specific scripting features of the underlying blockchain such as HTLC or are tailored to several signature algorithms like ECDSA and Schnorr. In this work, we devise a Generalized Multi-Hop Locks (GMHL) based on adaptor signature and randomizable puzzle, which supports both atomicity and privacy preserving(unlinkability). We instantiate GMHL with a concrete design that relies on a Guillou-Quisquater-based adaptor signature and a novel designed RSA-based randomizable puzzle. Furthermore, we present a generic PCN construction based on GMHL, and formally prove its security in the universal composability framework. This construction only requires the underlying blockchain to perform signature verification, and thus can be applied to various (non-/Turing-complete) blockchains. Finally, we simulate the proposed GMHL instance and compare with other protocols. The results show that our construction is efficient comparable to other constructions while remaining the good functionalities
Abnormal focal segments in left uncinate fasciculus in adults with obsessive–compulsive disorder
BackgroundAlthough the specific role of the uncinate fasciculus (UF) in emotional processing in patients with obsessive–compulsive disorder (OCD) has been investigated, the exact focal abnormalities in the UF have not been identified. The aim of the current study was to identify focal abnormalities in the white matter (WM) microstructure of the UF and to determine the associations between clinical features and structural neural substrates.MethodsIn total, 71 drug-naïve patients with OCD and 81 age- and sex-matched healthy controls (HCs) were included. Automated fiber quantification (AFQ), a tract-based quantitative approach, was adopted to measure alterations in diffusion parameters, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD), along the trajectory of the UF. Additionally, we utilized partial correlation analyses to explore the relationship between the altered diffusion parameters and clinical characteristics.ResultsOCD patients showed significantly higher FA and lower RD at the level of the temporal and insular portions in the left UF than HCs. In the insular segments of the left UF, increased FA was positively correlated with the Hamilton Anxiety Scale (HAMA) score, while decreased RD was negatively correlated with the duration of illness.ConclusionWe observed specific focal abnormalities in the left UF in adult patients with OCD. Correlations with measures of anxiety and duration of illness underscore the functional importance of the insular portion of left UF disturbance in OCD patients
Single Image Super-Resolution Using Multi-Scale Deep Encoder-Decoder with Phase Congruency Edge Map Guidance
This paper presents an end-to-end multi-scale deep encoder (convolution) and decoder (deconvolution) network for single image super-resolution (SISR) guided by phase congruency (PC) edge map. Our system starts by a single scale symmetrical encoder-decoder structure for SISR, which is extended to a multi-scale model by integrating wavelet multi-resolution analysis into our network. The new multi-scale deep learning system allows the low resolution (LR) input and its PC edge map to be combined so as to precisely predict the multi-scale super-resolved edge details with the guidance of the high-resolution (HR) PC edge map. In this way, the proposed deep model takes both the reconstruction of image pixels’ intensities and the recovery of multi-scale edge details into consideration under the same framework. We evaluate the proposed model on benchmark datasets of different data scenarios, such as Set14 and BSD100 - natural images, Middlebury and New Tsukuba - depth images. The evaluations based on both PSNR and visual perception reveal that the proposed model is superior to the state-of-the-art methods
Isomerization of sp2-hybridized carbon nanomaterials: structural transformation and topological defects of fullerene, carbon nanotube, and graphene
The structural transformation of various carbon nanomaterials, such as fullerene, carbon nanotube (CNT), and graphene, has been extensively studied both experimentally and theoretically. It was broadly recognized that the isomerization of the sp2-hybridized carbon network through the generalized Stone–Wales transformation (GSWT), which is equivalent to a CC bond's in-plane rotation, is the key mechanism facilitating most structural revolutions in carbon materials. The GSWT process also plays a crucial role in the shape change, defect healing and the growth in these carbon materials and may greatly affect their mechanical, chemical, and electronic performances. In this review, we summarize the previous studies on the GSWT and topological defects in the sp2 carbon network as well as the consequent results of sp2-hybridized carbon materials??? isomerization, including structural shrinkage of the giant fullerenes and CNTs at high temperature, plastic deformation of CNTs, coalescence of the fullerenes and carbon peapods, topological defects evolution under high energetic irradiation, and healing of the defects during the chemical vapor deposition growth of CNT and graphene. This review provides a clear picture of the isomerization of the sp2-hybridized carbon materials, from single step process until the large-scale structural transformation, and many examples for the readers to get into the topic deeply step by step. WIREs Comput Mol Sci 2017, 7:e1283. doi: 10.1002/wcms.1283. For further resources related to this article, please visit the WIREs website.clos
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