arXiv subjects

Tao Hu

Publications and source records attributed to Tao Hu.

At least 145 records · Page 8Linked to original sources

arXiv2021

Spatiotemporal Differences of COVID-19 Infection among Healthcare Workers and Patients in China from January to March 2020

Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

physics.soc-ph↗

arXiv2020

The replacement system of the JUNO liquid scintillator pilot experiment at Daya Bay

The Jiangmen Underground Neutrino Observatory (JUNO), a multi-purpose neutrino experiment, will use 20 kt liquid scintillator (LS). To achieve the physics goal of determining the neutrino mass ordering, 3$\%$ energy resolution at 1 MeV is required. This puts strict requirements on the LS light yield and the transparency. Four LS purification steps have been designed and mid-scale plants have been built at Daya Bay. To examine the performance of the purified LS and find the optimized LS composition, the purified LS was injected to the antineutrino detector 1 in the experimental hall 1 (EH1-AD1) of the Daya Bay neutrino experiment. To pump out the original gadolinium loaded LS and fill the new LS, a LS replacement system has been built in EH1 in 2017. By replacing the Gd-LS with purified water, then replacing the water with purified LS, the replacement system successfully achieved the designed goal. Subsequently, the fluorescence and the wavelength shifter were added to higher concentrations via the replacement system. The data taken at various LS compositions helped JUNO determine the final LS cocktail. Details of the design, the construction, and the operation of the replacement system are reported in this paper.

physics.ins-det↗

arXiv2020

Tunable Magnetism and Insulator-Metal Transition in Bilayer Perovskites

Two-dimensional (2D) transition-metal oxide perovskites greatly expand the field of available 2D multifunctional material systems. Here, based on density functional theory calculations, we predicted the presence of ferromagnetism orders accompanying with an insulator-metal phase transition in bilayer $KNbO_{3}$ and $KTaO_{3}$ by applying strain engineering and/or external electric field. Our results will contribute to the applications of few-layer transition metal oxide perovskites in the emerging spintronics and straintronics.

cond-mat.mtrl-sci↗

arXiv2020

Dynamics for droplet-based electricity generators

The finding of droplet-based electricity generator (DEG), based on the moving boundary of electrical double layer, has triggered great research enthusiasm, and a breakthrough in instantaneous electric power density was achieved recently. However, the dynamic mechanism for such droplet-based electricity generators remains elusive, impeding optimization of the DEG for practical applications. Through comprehensive experiments, we developed a dynamic model of surface charge density that can explain the underlying mechanism for the DEGs. The spreading droplet in touch with the top electrode can be equivalently regarded as an additional part of the top plate of the DEG capacitor, and the change of droplet area causes the change of surface charge density of the plates, driving electrons to migrate between the two plates. The insight of the dynamic mechanism paves a way for optimal design and practical applications of DEGs

cond-mat.mtrl-sci↗

arXiv2020

Observation of Two-dimensional Superconductivity in an Ultrathin Iron-Arsenic Superconductor

Two-dimensional (2D) superconductors supply important platforms for exploring new quantum physics and high-$T_c$ superconductivity. The intrinsic superconducting properties in the 2D iron-arsenic superconductors are still unknown owing to the difficulties in the preparation of ultrathin samples. Here we report the fabrication and physical investigations of the high quality single-crystalline ultrathin films of the iron-arsenic superconductor KCa$_2$Fe$_4$As$_4$F$_2$. For the sample with the thickness of 2.6$\sim$5 nm (1$\sim$2 unit cells), a sharp superconducting transition at around 30 K (onset point) is observed. Compare with the bulk material, the ultrathin sample reveals a relatively lower $T_c$, wider transition width, and broader flux liquid region under the in-plane field. Moreover, the angle dependent upper critical field follows the Tinkham model, demonstrating the two-dimensional superconductivity in ultrathin KCa$_2$Fe$_4$As$_4$F$_2$.

cond-mat.supr-con↗

arXiv2020

Taking the pulse of COVID-19: A spatiotemporal perspective

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.

physics.soc-ph↗

arXiv2020

Spatiotemporal Patterns of COVID-19 Impact on Human Activities and Environment in China Using Nighttime Light and Air Quality Data

In order to analyze the impact of COVID-19 on people's lives, activities and the natural environment, this paper investigates the spatial and temporal characteristics of Night Time Light (NTL) radiance and Air Quality Index (AQI) before and during the pandemic in mainland China. Our results show that the monthly average NTL brightness is much lower during the quarantine period than before. This study categorizes NTL into three classes: residential area, transportation and public facilities and commercial centers, with NTL radiance ranges of 5-20, 20-40 and greater than 40 nW/(cm*cm*sr), respectively. We found that the Number Of Pixels (NOP) with NTL detection increased in the residential area and decreased in the commercial centers for most of the provinces after the shutdown, while transportation and public facilities generally stayed the same. More specifically, we examined these factors in Wuhan, where the first confirmed cases were reported, and where the earliest quarantine measures were taken. Observations and analysis of pixels associated with commercial centers were observed to have lower NTL radiance values, indicating a dimming behavior, while residential area pixels recorded increased levels of brightness, after the beginning of the lockdown. The study also discovered a significant decreasing trend in the daily average AQI for the whole country, with cleaner air in most provinces during February and March, compared to January 2020. In conclusion, the outbreak and spread of COVID-19 has had a crucial impact on people's daily lives and activity ranges through the increased implementation of lockdown and quarantine policies. On the other hand, the air quality of China has improved with the reduction of non-essential industries and motor vehicle usage.

physics.soc-ph↗

arXiv2020

Hierarchical Modes Exploring in Generative Adversarial Networks

In conditional Generative Adversarial Networks (cGANs), when two different initial noises are concatenated with the same conditional information, the distance between their outputs is relatively smaller, which makes minor modes likely to collapse into large modes. To prevent this happen, we proposed a hierarchical mode exploring method to alleviate mode collapse in cGANs by introducing a diversity measurement into the objective function as the regularization term. We also introduced the Expected Ratios of Expansion (ERE) into the regularization term, by minimizing the sum of differences between the real change of distance and ERE, we can control the diversity of generated images w.r.t specific-level features. We validated the proposed algorithm on four conditional image synthesis tasks including categorical generation, paired and un-paired image translation and text-to-image generation. Both qualitative and quantitative results show that the proposed method is effective in alleviating the mode collapse problem in cGANs, and can control the diversity of output images w.r.t specific-level features.

cs.CV↗

arXiv2020

Multi-object Tracking via End-to-end Tracklet Searching and Ranking

Recent works in multiple object tracking use sequence model to calculate the similarity score between the detections and the previous tracklets. However, the forced exposure to ground-truth in the training stage leads to the training-inference discrepancy problem, i.e., exposure bias, where association error could accumulate in the inference and make the trajectories drift. In this paper, we propose a novel method for optimizing tracklet consistency, which directly takes the prediction errors into account by introducing an online, end-to-end tracklet search training process. Notably, our methods directly optimize the whole tracklet score instead of pairwise affinity. With sequence model as appearance encoders of tracklet, our tracker achieves remarkable performance gain from conventional tracklet association baseline. Our methods have also achieved state-of-the-art in MOT15~17 challenge benchmarks using public detection and online settings.

cs.CV↗

arXiv2019

Learning to Generate Dense Point Clouds with Textures on Multiple Categories

3D reconstruction from images is a core problem in computer vision. With recent advances in deep learning, it has become possible to recover plausible 3D shapes even from single RGB images for the first time. However, obtaining detailed geometry and texture for objects with arbitrary topology remains challenging. In this paper, we propose a novel approach for reconstructing point clouds from RGB images. Unlike other methods, we can recover dense point clouds with hundreds of thousands of points, and we also include RGB textures. In addition, we train our model on multiple categories which leads to superior generalization to unseen categories compared to previous techniques. We achieve this using a two-stage approach, where we first infer an object coordinate map from the input RGB image, and then obtain the final point cloud using a reprojection and completion step. We show results on standard benchmarks that demonstrate the advantages of our technique. Code is available at https://github.com/TaoHuUMD/3D-Reconstruction.

cs.CV↗

arXiv2019

3D Shape Completion with Multi-view Consistent Inference

3D shape completion is important to enable machines to perceive the complete geometry of objects from partial observations. To address this problem, view-based methods have been presented. These methods represent shapes as multiple depth images, which can be back-projected to yield corresponding 3D point clouds, and they perform shape completion by learning to complete each depth image using neural networks. While view-based methods lead to state-of-the-art results, they currently do not enforce geometric consistency among the completed views during the inference stage. To resolve this issue, we propose a multi-view consistent inference technique for 3D shape completion, which we express as an energy minimization problem including a data term and a regularization term. We formulate the regularization term as a consistency loss that encourages geometric consistency among multiple views, while the data term guarantees that the optimized views do not drift away too much from a learned shape descriptor. Experimental results demonstrate that our method completes shapes more accurately than previous techniques.

cs.CV↗

arXiv2019

Strong Pauli paramagnetic effect in the upper critical field of KCa$_2$Fe$_4$As$_4$F$_2$

Recently, 12442 system of Fe-based superconductors has attracted considerable attention owing to its unique double-FeAs-layer structure. A steep increase in the in-plane upper critical field with cooling has been observed near the superconducting transition temperature, $T_c$, in KCa$_2$Fe$_4$As$_4$F$_2$ single crystals. Herein, we report a high-field investigation on upper critical field of this material over a wide temperature range, and both out-of-plane ($H\|c$, $H_{c2}^{c}$) and in-plane ($H\|ab$, $H_{c2}^{ab}$) directions have been measured. A sublinear temperature-dependent behavior is observed for the out-of-plane $H_{c2}^{c}$, whereas strong convex curvature with cooling is observed for the in-plane $H_{c2}^{ab}$. Such behaviors could not be described by the conventional Werthamer--Helfand--Hohenberg (WHH) model. The data analysis based on the WHH model by considering the spin aspects reveals a large Maki parameter $α=9$, indicating that the in-plane upper critical field is affected by a very strong Pauli paramagnetic effect.

cond-mat.supr-con↗

arXiv2019

A Radio Signal Modulation Recognition Algorithm Based on Residual Networks and Attention Mechanisms

To solve the problem of inaccurate recognition of types of communication signal modulation, a RNN neural network recognition algorithm combining residual block network with attention mechanism is proposed. In this method, 10 kinds of communication signals with Gaussian white noise are generated from standard data sets, such as MASK, MPSK, MFSK, OFDM, 16QAM, AM and FM. Based on the original RNN neural network, residual block network is added to solve the problem of gradient disappearance caused by deep network layers. Attention mechanism is added to the network to accelerate the gradient descent. In the experiment, 16QAM, 2FSK and 4FSK are used as actual samples, IQ data frames of signals are used as input, and the RNN neural network combined with residual block network and attention mechanism is trained. The final recognition results show that the average recognition rate of real-time signals is over 93%. The network has high robustness and good use value.

eess.SP↗

arXiv2019

Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion

We propose a novel approach for 3D shape completion by synthesizing multi-view depth maps. While previous work for shape completion relies on volumetric representations, meshes, or point clouds, we propose to use multi-view depth maps from a set of fixed viewing angles as our shape representation. This allows us to be free of the limitations of memory for volumetric representations and point clouds by casting shape completion into an image-to-image translation problem. Specifically, we render depth maps of the incomplete shape from a fixed set of viewpoints, and perform depth map completion in each view. Different from image-to-image translation network that completes each view separately, our novel network, multi-view completion net (MVCN), leverages information from all views of a 3D shape to help the completion of each single view. This enables MVCN to leverage more information from different depth views to achieve high accuracy in single depth view completion and keep the consistency among the completed depth images in different views. Benefited by the multi-view representation and the novel network structure, MVCN significantly improves the accuracy of 3D shape completion in large-scale benchmarks compared to the state of the art.

cs.CV↗

arXiv2019

Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network

More powerful feature representations derived from deep neural networks benefit visual tracking algorithms widely. However, the lack of exploitation on temporal information prevents tracking algorithms from adapting to appearances changing or resisting to drift. This paper proposes a correlation filter based tracking method which aggregates historical features in a spatial-aligned and scale-aware paradigm. The features of historical frames are sampled and aggregated to search frame according to a pixel-level alignment module based on deformable convolutions. In addition, we also use a feature pyramid structure to handle motion estimation at different scales, and address the different demands on feature granularity between tracking losses and deformation offset learning. By this design, the tracker, named as Spatial-Aware Temporal Aggregation network (SATA), is able to assemble appearances and motion contexts of various scales in a time period, resulting in better performance compared to a single static image. Our tracker achieves leading performance in OTB2013, OTB2015, VOT2015, VOT2016 and LaSOT, and operates at a real-time speed of 26 FPS, which indicates our method is effective and practical. Our code will be made publicly available at \href{https://github.com/ecart18/SATA}{https://github.com/ecart18/SATA}.

cs.CV↗

arXiv2019

VITAL: A Visual Interpretation on Text with Adversarial Learning for Image Labeling

In this paper, we propose a novel way to interpret text information by extracting visual feature presentation from multiple high-resolution and photo-realistic synthetic images generated by Text-to-image Generative Adversarial Network (GAN) to improve the performance of image labeling. Firstly, we design a stacked Generative Multi-Adversarial Network (GMAN), StackGMAN++, a modified version of the current state-of-the-art Text-to-image GAN, StackGAN++, to generate multiple synthetic images with various prior noises conditioned on a text. And then we extract deep visual features from the generated synthetic images to explore the underlying visual concepts for text. Finally, we combine image-level visual feature, text-level feature and visual features based on synthetic images together to predict labels for images. We conduct experiments on two benchmark datasets and the experimental results clearly demonstrate the efficacy of our proposed approach.

cs.CV↗

arXiv2019

Two-Gap Superconductivity in CaFe_{0.88}Co_{0.12}AsF Revealed by Temperature Dependence of the Lower Critical Field H_{c1}^c(T)

Gap symmetry and structure are crucial issues in understanding the superconducting mechanism of unconventional superconductors. Here we report an in-depth investigation on the out-of-plane lower critical field $H_{c1}^{c}$ of fluorine-based 1111 system superconductor CaFe$_{0.88}$Co$_{0.12}$AsF with $T_c$ = 21 K. A pronounced two-gap feature is revealed by the kink in the temperature dependent $H_{c1}^c(T)$ curve. The magnitudes of the two gaps are determined to be $Δ_1$ = 0.86 meV and $Δ_2$ = 4.48 meV, which account for 74% and 26% of the total superfluid density respectively. Our results suggest that the local antiferromagnetic exchange pairing picture is favored compared to the Fermi surface nesting scenario.

cond-mat.supr-con↗

arXiv2019

Note on Thompson sampling for large decision problems

There is increasing interest in using streaming data to inform decision making across a wide range of application domains including mobile health, food safety, security, and resource management. A decision support system formalizes online decision making as a map from up-to-date information to a recommended decision. Online estimation of an optimal decision strategy from streaming data requires simultaneous estimation of components of the underlying system dynamics as well as the optimal decision strategy given these dynamics; thus, there is an inherent trade-off between choosing decisions that lead to improved estimates and choosing decisions that appear to be optimal based on current estimates. Thompson (1933) was among the first to formalize this trade-off in the context of choosing between two treatments for a stream of patients; he proposed a simple heuristic wherein a treatment is selected randomly at each time point with selection probability proportional to the posterior probability that it is optimal. We consider a variant of Thompson sampling that is simple to implement and can be applied to large and complex decision problems. We show that the proposed Thompson sampling estimator is consistent for the optimal decision support system and provide rates of convergence and finite sample error bounds. The proposed algorithm is illustrated using an agent-based model of the spread of influenza on a network and management of mallard populations in the United States.

stat.ME↗