arXiv subjects

Wei Li

Publications and source records attributed to Wei Li.

At least 1,207 records · Page 67Linked to original sources

arXiv2019

Coulomb gap induced by electronic correlation and enlarged superconducting gap in laterally confined Pb islands grown on SrTiO3

We report high-resolution scanning tunneling microscopy (STM) study of nano-sized Pb islands grown on SrTiO3, where three distinct types of gaps with different energy scales are revealed. At low temperature, an enlarged superconducting gap (Δs) emerges while there is no enhancement in superconducting transition temperature (Tc), giving rise to a larger BCS ratio 2Δs/kBTc ~ 6.22. The strong coupling here may originate from the electron-phonon coupling on the metal-oxide interface. As the superconducting gap is suppressed under applied magnetic field or at elevated temperature, Coulomb gap and pseudogap appear, respectively. The Coulomb gap is sensitive to the lateral size of Pb islands, indicating that quantum size effect is able to influence electronic correlation, which is usually ignored in low-dimensional superconductivity. Our experimental results shall shed important light on the interplay between quantum size effect and correlations in nano-sized superconductors.

cond-mat.supr-con↗

arXiv2019

Visualization of Local Conductance in MoS2/WSe2 Heterostructure Transistors

The vertical stacking of van der Waals (vdW) materials introduces a new degree of freedom to the research of two-dimensional (2D) systems. The interlayer coupling strongly influences the band structure of the heterostructures, resulting in novel properties that can be utilized for electronic and optoelectronic applications. Based on microwave microscopy studies, we report quantitative electrical imaging on gated molybdenum disulfide (MoS2)/tungsten diselenide (WSe2) heterostructure devices, which exhibit an intriguing anti-ambipolar effect in the transfer characteristics. Interestingly, in the region with significant source-drain current, electrons in the n-type MoS2 and holes in the p-type WSe2 segments are nearly balanced, whereas the heterostructure area is depleted of mobile charges. The configuration is analogous to the p-i-n diode, where the injected carriers dominate in the recombination current. The spatial evolution of local conductance can be ascribed to the lateral band bending and formation of depletion regions along the line of MoS2-heterostructure-WSe2. Our work vividly demonstrates the microscopic origin of novel transport behaviors, which is important for the vibrant field of vdW heterojunction research.

physics.app-ph↗

arXiv2019

ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. ORSIm detector adopts a novel spatial-frequency channel feature (SFCF) by jointly considering the rotation-invariant channel features constructed in frequency domain and the original spatial channel features (e.g., color channel, gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely-scaled channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne datasets are performed to demonstrate the superiority and effectiveness in comparison with previous state-of-the-art methods.

cs.CV↗

arXiv2019

The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation

With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity. Despite very promising progress on image and short sequence generation, symbolic music generation remains a challenging problem since the structure of compositions are usually complicated. In this study, we attempt to solve the melody generation problem constrained by the given chord progression. This music meta-creation problem can also be incorporated into a plan recognition system with user inputs and predictive structural outputs. In particular, we explore the effect of explicit architectural encoding of musical structure via comparing two sequential generative models: LSTM (a type of RNN) and WaveNet (dilated temporal-CNN). As far as we know, this is the first study of applying WaveNet to symbolic music generation, as well as the first systematic comparison between temporal-CNN and RNN for music generation. We conduct a survey for evaluation in our generations and implemented Variable Markov Oracle in music pattern discovery. Experimental results show that to encode structure more explicitly using a stack of dilated convolution layers improved the performance significantly, and a global encoding of underlying chord progression into the generation procedure gains even more.

cs.SD↗

arXiv2019

Random Subgroups of Rationals

This paper introduces and studies a notion of \emph{algorithmic randomness} for subgroups of rationals. Given a randomly generated additive subgroup $(G,+)$ of rationals, two main questions are addressed: first, what are the model-theoretic and recursion-theoretic properties of $(G,+)$; second, what learnability properties can one extract from $G$ and its subclass of finitely generated subgroups? For the first question, it is shown that the theory of $(G,+)$ coincides with that of the additive group of integers and is therefore decidable; furthermore, while the word problem for $G$ with respect to any generating sequence for $G$ is not even semi-decidable, one can build a generating sequence $β$ such that the word problem for $G$ with respect to $β$ is co-recursively enumerable (assuming that the set of generators of $G$ is limit-recursive). In regard to the second question, it is proven that there is a generating sequence $β$ for $G$ such that every non-trivial finitely generated subgroup of $G$ is recursively enumerable and the class of all such subgroups of $G$ is behaviourally correctly learnable, that is, every non-trivial finitely generated subgroup can be semantically identified in the limit (again assuming that the set of generators of $G$ is limit-recursive). On the other hand, the class of non-trivial finitely generated subgroups of $G$ cannot be syntactically identified in the limit with respect to any generating sequence for $G$. The present work thus contributes to a recent line of research studying algorithmically random infinite structures and uncovers an interesting connection between the arithmetical complexity of the set of generators of a randomly generated subgroup of rationals and the learnability of its finitely generated subgroups.

cs.LO↗

arXiv2019

Lepton-flavor violation and two loop electroweak corrections to $(g-2)_μ$ in the B-L symmetry SSM

Charged lepton flavor violating processes are forbidden in the standard model (SM), hence the observation of charged lepton flavor transitions would represent a clear signal of new physics beyond the standard Model. In this work, we investigate some lepton flavor violating processes in the minimal supersymmetric extension of the SM with local $B-L$ gauge symmetry (B-LSSM). And including the corrections from some two loop diagrams to the anomalous dipole moments (MDM) of muon, we discuss the corresponding constraint on the relevant parameter space of the model. Considering the constraints from updated experimental data, the numerical results show that, new contributions in the B-LSSM enhance the MSSM predictions on the rates of $l_j-l_i$ transitions about one order of magnitude, and also enhance the MSSM prediction on the muon MDM. In addition, two loop electroweak corrections can make important contributions to the muon MDM in the B-LSSM.

hep-ph↗

arXiv2018

Elimination of unknowns for systems of algebraic differential-difference equations

We establish effective elimination theorems for differential-difference equations. Specifically, we find a computable function $B(r,s)$ of the natural number parameters $r$ and $s$ so that for any system of algebraic differential-difference equations in the variables $\mathbf{x} = x_1, \ldots, x_q$ and $\mathbf{y} = y_1, \ldots, y_r$ each of which has order and degree in $\mathbf{y}$ bounded by $s$ over a differential-difference field, there is a non-trivial consequence of this system involving just the $\mathbf{x}$ variables if and only if such a consequence may be constructed algebraically by applying no more than $B(r,s)$ iterations of the basic difference and derivation operators to the equations in the system. We relate this finiteness theorem to the problem of finding solutions to such systems of differential-difference equations in rings of functions showing that a system of differential-difference equations over $\mathbb{C}$ is algebraically consistent if and only if it has solutions in a certain ring of germs of meromorphic functions.

math.AC↗

arXiv2018

DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems

Deep Neural Networks (DNNs) have been widely applied in many autonomous systems such as autonomous driving. Recently, DNN testing has been intensively studied to automatically generate adversarial examples, which inject small-magnitude perturbations into inputs to test DNNs under extreme situations. While existing testing techniques prove to be effective, they mostly focus on generating digital adversarial perturbations (particularly for autonomous driving), e.g., changing image pixels, which may never happen in physical world. There is a critical missing piece in the literature on autonomous driving testing: understanding and exploiting both digital and physical adversarial perturbation generation for impacting steering decisions. In this paper, we present DeepBillboard, a systematic physical-world testing approach targeting at a common and practical driving scenario: drive-by billboards. DeepBillboard is capable of generating a robust and resilient printable adversarial billboard, which works under dynamic changing driving conditions including viewing angle, distance, and lighting. The objective is to maximize the possibility, degree, and duration of the steering-angle errors of an autonomous vehicle driving by the generated adversarial billboard. We have extensively evaluated the efficacy and robustness of DeepBillboard through conducting both digital and physical-world experiments. Results show that DeepBillboard is effective for various steering models and scenes. Furthermore, DeepBillboard is sufficiently robust and resilient for generating physical-world adversarial billboard tests for real-world driving under various weather conditions. To the best of our knowledge, this is the first study demonstrating the possibility of generating realistic and continuous physical-world tests for practical autonomous driving systems.

cs.CV↗

arXiv2018

Non-Topological Majorana Zero Modes in Inhomogeneous Spin Ladders

We show that the coupling of homogeneous Heisenberg spin-1/2 ladders in different phases leads to the formation of interfacial zero energy Majorana bound states. Unlike Majorana bound states at the interfaces of topological quantum wires, these states are void of topological protection and generally susceptible to local perturbations of the host spin system. However, a key message of our work is that in practice they show a high degree of resilience over wide parameter ranges which may make them interesting candidates for applications.

cond-mat.str-el↗

arXiv2018

The enhanced X-ray Timing and Polarimetry mission - eXTP

In this paper we present the enhanced X-ray Timing and Polarimetry mission - eXTP. eXTP is a space science mission designed to study fundamental physics under extreme conditions of density, gravity and magnetism. The mission aims at determining the equation of state of matter at supra-nuclear density, measuring effects of QED, and understanding the dynamics of matter in strong-field gravity. In addition to investigating fundamental physics, eXTP will be a very powerful observatory for astrophysics that will provide observations of unprecedented quality on a variety of galactic and extragalactic objects. In particular, its wide field monitoring capabilities will be highly instrumental to detect the electro-magnetic counterparts of gravitational wave sources. The paper provides a detailed description of: (1) the technological and technical aspects, and the expected performance of the instruments of the scientific payload; (2) the elements and functions of the mission, from the spacecraft to the ground segment.

astro-ph.IM↗

arXiv2018

Applied Federated Learning: Improving Google Keyboard Query Suggestions

Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access to the underlying user data. We describe our observations in federated training, compare metrics to live deployments, and present resulting quality increases. In whole, we demonstrate how federated learning can be applied end-to-end to both improve user experiences and enhance user privacy.

cs.LG↗

arXiv2018

Quantum mechanics basis of quality control in hard metals

Non-destructive and reliable quality control methods are a key aspect to designing, developing and manufacturing new materials for industrial applications and new technologies. The measurement of the magnetic saturation is one of such methods and it is conventionally employed in the cemented carbides industry. We present a general quantum mechanics based relation between the magnetic saturation and the components of the binder phase of cemented carbides, which can be directly employed as a quality control. To illustrate our results, we calculate the magnetic saturation of a binder phase, 85Ni15Fe binary alloy, using ab-initio methods and compare the theoretical predictions to the magnetic saturation measurements. We also analyse interface and segregation effects on the magnetic saturation by studying the electronic structure of the binder phase. The excellent agreement between calculations and measurements demonstrates the applicability of our method to any binder phase. Since the magnetic saturation is employed to ensure the quality of cemented carbides, the present method allows us to explore new materials for alternative binder phases efficiently

cond-mat.mtrl-sci↗

arXiv2018

Temporal Cloak without Synchronization

Considered to only exist in the fairy tales in the past, invisibility cloaks have been successively converted into reality no matter in the spatial domain or temporal domain. Inspired by the spatial cloaking, time gaps are utilized to hide temporal events. However, a sophisticated synchronization for cloaking is indispensable in these cloaking techniques, therefore leaving inconvenience for the realization of the temporal cloak. Here, by exploiting temporal Talbot effect, we propose a brand new scheme and concept to achieve a temporal cloak without any synchronization for cloaking process, under which the intensity-modulated event is directly turned into invisibility in intensity through temporal averaging effect induced by the Talbot effect. We successfully realize temporal cloak for periodic and pseudo-random signals respectively. We also find that the higher order temporal Talbot effect is beneficial to cloaking performance. Moreover, it is feasible to recover the data from the intensity-cloaked waveform. This method and its concept render a distinct perspective for temporal cloak, extend temporal cloak to the pulsed-wave and promote the development of confidential communication.

physics.optics↗

arXiv2018

Remark on Remnant and Residue Entropy with GUP

In this article, close to the Planck scale, we discuss on the remnant and residue entropy from a Rutz-Schwarzschild black hole in the frame of Finsler geometry. Employing the corrected Hamilton-Jacobi equation, the tunneling radiation of a scalar particle is presented, and the revised tunneling temperature and revised entropy are also found. Taking into account generalized uncertainty principle (GUP), we analyze the remnant stability and residue entropy based on thermodynamic phase transition. In addition, the effects of the Finsler perturbation parameter, GUP parameter and angular momentum parameter on remnant and residual entropy are also discussed.

gr-qc↗

arXiv2018

Learning to discover and localize visual objects with open vocabulary

To alleviate the cost of obtaining accurate bounding boxes for training today's state-of-the-art object detection models, recent weakly supervised detection work has proposed techniques to learn from image-level labels. However, requiring discrete image-level labels is both restrictive and suboptimal. Real-world "supervision" usually consists of more unstructured text, such as captions. In this work we learn association maps between images and captions. We then use a novel objectness criterion to rank the resulting candidate boxes, such that high-ranking boxes have strong gradients along all edges. Thus, we can detect objects beyond a fixed object category vocabulary, if those objects are frequent and distinctive enough. We show that our objectness criterion improves the proposed bounding boxes in relation to prior weakly supervised detection methods. Further, we show encouraging results on object detection from image-level captions only.

cs.CV↗

arXiv2018

Coexistence of Intrinsic Superconductivity and Topological Insulator State in Monoclinic Phase WS2

Recently, intriguing phenomena of superconductivity, type-II Weyl semimetal or quantum spin Hall states were discovered in metastable 1T'-type VIB-group transition metal dichalcogenides (TMDs). Here, we report that monoclinic phase WS2 was discovered and synthesized in our experiments. The intrinsic superconducting transition was observed in monoclinic WS2 with a transition temperature Tc of 8.8 K which is the highest among previously reported TMDs without any fine-tuning process. Intersteing, topological insulator state, defined by topological invariant Z2, was also discovered with a single Dirac cone on the surface, which is different from all topological states reported in TMDs. Further, the electronic structure was found to have a strong anisotropy by Shubnikov-de Haas oscillations and first-principles calculations. Our findings reveal that monoclinic WS2 might be a new topological superconductivity candidate with a strong anisotropy.

cond-mat.supr-con↗

arXiv2018

Controllable coupled-resonator-induced transparency in a dual-recycled Michelson interferometer

We theoretically and experimentally study the effect of the coupled-resonator-induced transparency in the Michelson interferometer with the dual-recycled configuration, which is equivalent to the adjustable coupled resonator. The coupling strength, corresponding to the splitting of the reflection spectrum and associated with the width of the coupled-resonator-induced transparent window,can be controlled by adjusting the arm lengths, i.e., the relative phase of the interference arms on the 50/50 beam splitter. Thus, this tunability of the coupling strength can be very fast, and the absorptive and dispersive properties can be effectively controlled. This work provides a new system to coherently control and storage optical field.

physics.optics↗

arXiv2018

Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification

Learning rate scheduler has been a critical issue in the deep neural network training. Several schedulers and methods have been proposed, including step decay scheduler, adaptive method, cosine scheduler and cyclical scheduler. This paper proposes a new scheduling method, named hyperbolic-tangent decay (HTD). We run experiments on several benchmarks such as: ResNet, Wide ResNet and DenseNet for CIFAR-10 and CIFAR-100 datasets, LSTM for PAMAP2 dataset, ResNet on ImageNet and Fashion-MNIST datasets. In our experiments, HTD outperforms step decay and cosine scheduler in nearly all cases, while requiring less hyperparameters than step decay, and more flexible than cosine scheduler. Code is available at https://github.com/BIGBALLON/HTD.

cs.CV↗