arXiv subjects

Ting Zhang

Publications and source records attributed to Ting Zhang.

At least 199 records · Page 11Linked to original sources

arXiv2021

Field-effect chirality devices with Dirac semimetal

Charge-based field-effect transistors (FETs) greatly suffer from unavoidable carrier scattering and heat dissipation. In analogy to valley degree of freedom in semiconductors, chiral anomaly current in Weyl/Dirac semimetals is theoretically predicted to be nearly non-dissipative over long distances, but still lacks experimental ways to efficiently control its transport. Here we demonstrate field-effect chirality devices with Dirac semimetal PtSe2, in which its Fermi level is close to the Dirac point in conduction band owing to intrinsic defects. The chiral anomaly is further corroborated with nonlocal valley transport measurement, which can also be effectively modulated by external fields, showing robust nonlocal valley transport with micrometer diffusion length. Similar to charge-based FETs, the chiral conductivity in PtSe2 devices can be modulated by electrostatic gating with an ON/OFF ratio more than 103. We also demonstrate basic logic functions in the devices with electric and magnetic fields as input signals.

physics.app-ph↗

arXiv2021

Local well-posedness and finite time blowup for fourth-order Schrödinger equation with complex coefficient

We consider the fourth-order Schrödinger equation $$ i\partial_tu+Δ^2 u+μΔu+λ|u|^αu=0, $$ where $α>0,μ=\pm1$ or $0$ and $λ\in\mathbb{C}$. Firstly, we prove local well-posedness in $H^4\left(\R^N\right)$ in both $H^4$ subcritical and critical case: $α>0$, $(N-8)α\leq8$. Then, for any given compact set $K\subset\mathbb{R}^N$, we construct $H^4(\R^N)$ solutions that are defined on $(-T, 0)$ for some $T>0$, and blow up exactly on $K$ at $t=0$.

math.AP↗

arXiv2021

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation

Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation. In particular, we take one step further and exploit the feature distances from prototypes that provide richer information than mere prototypes. Specifically, we use it to estimate the likelihood of pseudo labels to facilitate online correction in the course of training. Meanwhile, we align the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space. Moreover, we find that distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance. Our method shows tremendous performance advantage over state-of-the-art methods. We will make the code publicly available.

cs.CV↗

arXiv2021

Simulation of an imaging system for internal contamination of lungs using MPA-MURA coded aperture collimator

The nuclides inhaled during nuclear accidents usually cause internal contamination of the lungs with low activity. Although a parallel-hole imaging system, which is widely used in medical gamma cameras, has a high resolution and good image quality, owing to its extremely low detection efficiency, it remains difficult to obtain images of inhaled lung contamination. In this study, the Monte Carlo method was used to study the internal lung contamination imaging using the MPA-MURA coded-aperture collimator. The imaging system consisted of an adult male lung model, with a mosaicked, pattern-centered, and anti-symmetric MURA coded-aperture collimator model and a CsI(Tl) detector model. The MLEM decoding algorithm was used to reconstruct the internal contamination image, and the complementary imaging method was used to reduce the number of artifacts. The full width at half maximum of the I-131 point source image reconstructed by the mosaicked, pattern-centered, and anti-symmetric Modified uniformly redundant array (MPA-MURA) coded-aperture imaging reached 2.51 mm, and the signal-to-noise ratio of the simplified respiratory tract source (I-131) image reconstructed through MPA-MURA coded-aperture imaging was 3.98 dB. Although the spatial resolution of MPA-MURA coded aperture imaging is not as good as that of parallel-hole imaging, the detection efficiency of PMA-MURA coded-aperture imaging is two orders of magnitude higher than that of parallel hole collimator imaging. Considering the low activity level of internal lung contamination caused by nuclear accidents, PMA-MURA coded-aperture imaging has significant potential for the development of lung contamination imaging.

physics.med-ph↗

arXiv2020

Asymptotic stability of Landau solutions to Navier-Stokes system under $L^p$-perturbations

In this paper, we show that Landau solutions to the Navier-Stokes system are asymptotically stable under $L^3$-perturbations. We give the local well-posedness of solutions to the perturbed system with initial data in $L_σ^3$ space and the global well-posedness with small initial data in $L_σ^3$ space, together with a study of the $L^q$ decay for all $q>3.$ Moreover, we have also studied the local well-posedness, global well-posedness and stability in $L^p$ spaces for $3<p<\infty$.

math.AP↗

arXiv2020

Probabilistic K-means Clustering via Nonlinear Programming

K-means is a classical clustering algorithm with wide applications. However, soft K-means, or fuzzy c-means at m=1, remains unsolved since 1981. To address this challenging open problem, we propose a novel clustering model, i.e. Probabilistic K-Means (PKM), which is also a nonlinear programming model constrained on linear equalities and linear inequalities. In theory, we can solve the model by active gradient projection, while inefficiently. Thus, we further propose maximum-step active gradient projection and fast maximum-step active gradient projection to solve it more efficiently. By experiments, we evaluate the performance of PKM and how well the proposed methods solve it in five aspects: initialization robustness, clustering performance, descending stability, iteration number, and convergence speed.

cs.LG↗

arXiv2020

THCluster: herb supplements categorization for precision traditional Chinese medicine

There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.

q-bio.QM↗

arXiv2020

Peierls-type metal-insulator transition in carbon nanostructures

We report the observation of Peierls-type metal-insulator transition in carbon nanostructures formed by chemical vapor deposition inside the pore network of the ZSM-5 zeolite. The Raman spectrum of this nanocarbon@ZSM-5 indicates a clear signature of the radial breathing mode (RBM) for (3,0) carbon nanotubes that can constitute the carbon network segments. Electrical transport measurements on multiple few-micron-sized nanocarbon@ZSM-5 crystals showed metallic temperature of resistance dependence down to 30 K, at which point the resistance exhibited a sharp upturn that is accompanied by the opening of a quasigap at the Fermi level as indicated by the differential resistance measurements. Further Hall measurements have yielded both the sign of the charge carrier and its density. The latter demonstrated excellent consistency with the quasigap data. We employed first-principles calculations to verify that there can indeed be softening of the phonon modes in the (3,0) carbon nanotubes.

cond-mat.mes-hall↗

arXiv2020

Critical regularity criteria for Navier-Stokes equations in terms of one directional derivative of the velocity

In this paper, we consider the 3D Navier-Stokes equations in the whole space. We investigate some new inequalities and \textit{a priori} estimates to provide the critical regularity criteria in terms of one directional derivative of the velocity field, namely $\partial_3 \mathbf{u} \in L^p((0,T); L^q(\mathbb{R}^3)), ~\frac{2}{p} + \frac{3}{q} = 2, ~\frac{3}{2}<q\leq 6$. Moreover, we extend the range of $q$ while the solution is axisymmetric, i.e. the axisymmetric solution $\mathbf{m}{u}$ is regular in $(0,T]$, if $ \partial_3 u^3 \in L^p((0,T); L^q(\mathbb{R}^3)), ~\frac{2}{p} + \frac{3}{q} = 2, ~\frac{3}{2}<q< \infty$.

math.AP↗

arXiv2020

Hole spin in tunable Ge hut wire double quantum dot

Holes in germanium (Ge) exhibit strong spin-orbit interaction, which can be exploited for fast and all-electrical manipulation of spin states. Here, we report transport experiments in a tunable Ge hut wire hole double quantum dot. We observe the signatures of Pauli spin blockade (PSB) with a large singlet-triplet energy splitting of ~1.1 meV and extract the g factor. By analyzing the the PSB leakage current, we obtain a spin-orbit length l_so of ~ 40-100 nm. Furthermore, we demonstrate the electric dipole spin resonance. These results lay a solid foundation for implementing high quality tunable hole spin-orbit qubits.

cond-mat.mes-hall↗

arXiv2020

Face X-ray for More General Face Forgery Detection

In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We observe that most existing face manipulation methods share a common step: blending the altered face into an existing background image. For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms. Face X-ray is general in the sense that it only assumes the existence of a blending step and does not rely on any knowledge of the artifacts associated with a specific face manipulation technique. Indeed, the algorithm for computing face X-ray can be trained without fake images generated by any of the state-of-the-art face manipulation methods. Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection or deepfake detection algorithms experience a significant performance drop.

cs.CV↗

arXiv2019

Two-dimensional ferroelastic semiconductors in Nb2SiTe4 and Nb2GeTe4 with promising electronic properties

Two-dimensional crystals with coupling of ferroelasticity and attractive electronic properties offer unprecedent opportunities for achieving long-sought controllable devices. But so far, the reported proposals are mainly based on hypothetical structures. Here, using first-principles calculations, we identify single-layer Nb2ATe4 (A = Si, Ge), which could be exfoliated from their layered bulks, are promising candidates. Single-layer Nb2ATe4 are found to be dynamically, thermally and chemically stable. They possess excellent ferroelasticity with high reversible ferroelastic strain and moderate ferroelastic transition energy barrier, beneficial for practical applications. Meanwhile, they harbor outstanding anisotropic electronic properties, including anisotropic carrier mobility and optical properties. More importantly, the anisotropic properties of single-layer Nb2ATe4 can be efficiently controlled through ferroelastic switching. These appealing properties combined with the experimental feasibility render single-layer Nb2ATe4 extraordinary platforms for realizing controllable devices.

cond-mat.mtrl-sci↗

arXiv2019

Factor Analysis for High-Dimensional Time Series with Change Point

We consider change-point latent factor models for high-dimensional time series, where a structural break may exist in the underlying factor structure. In particular, we propose consistent estimators for factor loading spaces before and after the change point, and the problem of estimating the change-point location is also considered. Compared with existing results on change-point factor analysis of high-dimensional time series, a distinguished feature of the current paper is that our results allow strong cross-sectional dependence in the noise process. To accommodate the unknown degree of cross-sectional dependence strength, we propose to use self-normalization to pivotalize the change-point test statistic. Numerical experiments including a Monte Carlo simulation study and a real data application are presented to illustrate the proposed methods.

stat.ME↗

arXiv2019

Hierarchical Reinforcement Learning for Multi-agent MOBA Game

Real Time Strategy (RTS) games require macro strategies as well as micro strategies to obtain satisfactory performance since it has large state space, action space, and hidden information. This paper presents a novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games. The novelty of this work are: (1) proposing a hierarchical framework, where agents execute macro strategies by imitation learning and carry out micromanipulations through reinforcement learning, (2) developing a simple self-learning method to get better sample efficiency for training, and (3) designing a dense reward function for multi-agent cooperation in the absence of game engine or Application Programming Interface (API). Finally, various experiments have been performed to validate the superior performance of the proposed method over other state-of-the-art reinforcement learning algorithms. Agent successfully learns to combat and defeat bronze-level built-in AI with 100% win rate, and experiments show that our method can create a competitive multi-agent for a kind of mobile MOBA game {\it King of Glory} in 5v5 mode.

cs.LG↗

arXiv2019

An electrically tunable terahertz plasmonic device based on shape memory alloys and liquid metals

An electrically tunable terahertz (THz) plasmonic device is designed and fabricated using liquid metals (eutectic gallium indium EGaIn) and shape memory alloy wires (Flexinol). The liquid metal is injected into the voids of a poly(dimethyl) siloxane (PDMS) microfluidic mold forming a periodic array of subwavelength apertures, while the wires are inserted into the elastomer below the metal plane. When a DC voltage is applied to the wires, they contract via Joule heating, reducing the aperture periodicity and blue-shifting the transmission resonances of the device. When the voltage is removed, the wires cool and elongate back to their original length, allowing the transmission spectrum to return to its original state. The magnitude of this change depends upon the applied voltage. The device is shown to thermally cycle between the relaxed state and the fully contracted state reproducibly over at least 500 thermal cycles. The asymmetric geometry of the device and the contraction process yield transmission properties that are unexpected: two closely spaced resonances, where both resonances correspond to the same scattering indices, and an increase in the transmission amplitude of the lowest order resonance upon contraction. We use numerical simulations to understand these features.

physics.app-ph↗

arXiv2019

Multiresolution Graph Attention Networks for Relevance Matching

A large number of deep learning models have been proposed for the text matching problem, which is at the core of various typical natural language processing (NLP) tasks. However, existing deep models are mainly designed for the semantic matching between a pair of short texts, such as paraphrase identification and question answering, and do not perform well on the task of relevance matching between short-long text pairs. This is partially due to the fact that the essential characteristics of short-long text matching have not been well considered in these deep models. More specifically, these methods fail to handle extreme length discrepancy between text pieces and neither can they fully characterize the underlying structural information in long text documents. In this paper, we are especially interested in relevance matching between a piece of short text and a long document, which is critical to problems like query-document matching in information retrieval and web searching. To extract the structural information of documents, an undirected graph is constructed, with each vertex representing a keyword and the weight of an edge indicating the degree of interaction between keywords. Based on the keyword graph, we further propose a Multiresolution Graph Attention Network to learn multi-layered representations of vertices through a Graph Convolutional Network (GCN), and then match the short text snippet with the graphical representation of the document with the attention mechanisms applied over each layer of the GCN. Experimental results on two datasets demonstrate that our graph approach outperforms other state-of-the-art deep matching models.

cs.CL↗

arXiv2019

Supervised Quantization for Similarity Search

In this paper, we address the problem of searching for semantically similar images from a large database. We present a compact coding approach, supervised quantization. Our approach simultaneously learns feature selection that linearly transforms the database points into a low-dimensional discriminative subspace, and quantizes the data points in the transformed space. The optimization criterion is that the quantized points not only approximate the transformed points accurately, but also are semantically separable: the points belonging to a class lie in a cluster that is not overlapped with other clusters corresponding to other classes, which is formulated as a classification problem. The experiments on several standard datasets show the superiority of our approach over the state-of-the art supervised hashing and unsupervised quantization algorithms.

cs.CV↗

arXiv2019

Collaborative Quantization for Cross-Modal Similarity Search

Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e.g., using an image to search for texts or using a text to search for images. This paper presents a compact coding solution for efficient search, with a focus on the quantization approach which has already shown the superior performance over the hashing solutions in the single-modal similarity search. We propose a cross-modal quantization approach, which is among the early attempts to introduce quantization into cross-modal search. The major contribution lies in jointly learning the quantizers for both modalities through aligning the quantized representations for each pair of image and text belonging to a document. In addition, our approach simultaneously learns the common space for both modalities in which quantization is conducted to enable efficient and effective search using the Euclidean distance computed in the common space with fast distance table lookup. Experimental results compared with several competitive algorithms over three benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance.

cs.CV↗