arXiv subjects

Wei Li

Publications and source records attributed to Wei Li.

At least 1,099 records · Page 61Linked to original sources

arXiv2020

A compact analytical approximation for a light sterile neutrino oscillation in matter

The existence of light sterile neutrinos is a long standing question for particle physics. Several experimental ``anomalies'' could be explained by introducing ~eV mass scaled light sterile neutrinos. Many experiments are actively hunting for such light sterile neutrinos through neutrino oscillation. For long baseline experiments, matter effect needs to be treated carefully for precise neutrino oscillation probability calculation. However, it is usually time-consuming or analytical complexity. In this manuscript we adopt the Jacobi-like method to diagonalize the Hermitian Hamiltonian matrix and derive analytically simplified neutrino oscillation probabilities for 3 (active) + 1 (sterile)-neutrino mixing for a constant matter density. These approximations can reach quite high numerical accuracy while keeping its analytical simplicity and fast computing speed. It would be useful for the current and future long baseline neutrino oscillation experiments.

hep-ph↗

arXiv2020

Mass spectra of heavy pseudoscalars using instantaneous Bethe-Salpeter equation with different kernels

We solved the instantaneous Bethe-Salpeter equation for heavy pseudoscalars in different kernels, where the kernels are obtained using linear scalar potential plus one gluon exchange vector potentials in Feynman gauge, Landau gauge, Coulomb gauge and time-component Coulomb gauge. We obtained the mass spectra of heavy pseudoscalars, and compared the results between different kernels, found that using the same parameters we obtain the smallest mass splitting in time-component Coulomb gauge, the similar largest mass splitting in Feynman and Coulomb gauges, middle size splitting in Landau gauge.

hep-ph↗

arXiv2020

Experimental quantum key distribution secure against malicious devices

The fabrication of quantum key distribution (QKD) systems typically involves several parties, thus providing Eve with multiple opportunities to meddle with the devices. As a consequence, conventional hardware and/or software hacking attacks pose natural threats to the security of practical QKD. Fortunately, if the number of corrupted devices is limited, the security can be restored by using redundant apparatuses. Here, we report on the demonstration of a secure QKD setup with optical devices and classical post-processing units possibly controlled by an eavesdropper. We implement a 1.25 GHz chip-based measurement-device-independent QKD system secure against malicious devices on \emph{both} the measurement and the users' sides. The secret key rate reaches 137 bps over a 24 dB channel loss. Our setup, benefiting from high clock rate, miniaturized transmitters and a cost-effective structure, provides a promising solution for widespread applications requiring uncompromising communication security.

quant-ph↗

arXiv2020

Irreducibility of the Fermi Surface for Planar Periodic Graph Operators

We prove that the Fermi surface of a connected doubly periodic self-adjoint discrete graph operator is irreducible at all but finitely many energies provided that the graph (1) can be drawn in the plane without crossing edges (2) has positive coupling coefficients (3) has two vertices per period. If "positive" is relaxed to "complex", the only cases of reducible Fermi surface occur for the graph of the tetrakis square tiling, and these can be explicitly parameterized when the coupling coefficients are real. The irreducibility result applies to weighted graph Laplacians with positive weights.

math-ph↗

arXiv2020

Acoplanarity of QED pairs accompanied by nuclear dissociation in ultra-peripheral heavy ion collisions

This paper investigates the transverse momentum broadening effect for electromagnetic production of dileptons in ultra-peripheral heavy ion collisions accompanied by nuclear dissociation. The electromagnetic dissociation probability of nuclei for different neutron multiplicities is estimated, which could serve as a centrality definition (i.e. impact parameter estimate) in ultra-peripheral collisions. In the framework of lowest-order QED, the acoplanarity of dilepton pairs is calculated for different neutron emission scenarios in ultra-peripheral collisions, indicating significant impact-parameter dependence. The verification of impact-parameter dependence is crucially important to understand the broadening effect observed in hadronic heavy-ion collisions.

hep-ph↗

arXiv2020

High Temporal Resolution Rainfall Runoff Modelling Using Long-Short-Term-Memory (LSTM) Networks

Accurate and efficient models for rainfall runoff (RR) simulations are crucial for flood risk management. Most rainfall models in use today are process-driven; i.e. they solve either simplified empirical formulas or some variation of the St. Venant (shallow water) equations. With the development of machine-learning techniques, we may now be able to emulate rainfall models using, for example, neural networks. In this study, a data-driven RR model using a sequence-to-sequence Long-short-Term-Memory (LSTM) network was constructed. The model was tested for a watershed in Houston, TX, known for severe flood events. The LSTM network's capability in learning long-term dependencies between the input and output of the network allowed modeling RR with high resolution in time (15 minutes). Using 10-years precipitation from 153 rainfall gages and river channel discharge data (more than 5.3 million data points), and by designing several numerical tests the developed model performance in predicting river discharge was tested. The model results were also compared with the output of a process-driven model Gridded Surface Subsurface Hydrologic Analysis (GSSHA). Moreover, physical consistency of the LSTM model was explored. The model results showed that the LSTM model was able to efficiently predict discharge and achieve good model performance. When compared to GSSHA, the data-driven model was more efficient and robust in terms of prediction and calibration. Interestingly, the performance of the LSTM model improved (test Nash-Sutcliffe model efficiency from 0.666 to 0.942) when a selected subset of rainfall gages based on the model performance, were used as input instead of all rainfall gages.

eess.SP↗

arXiv2020

Universal Thermodynamics in the Kitaev Fractional Liquid

In the Kitaev honeycomb model, the quantum spin fractionalizes into itinerant Majorana and gauge flux spontaneously upon cooling, leading to rich experimental ramifications at finite temperature and an upsurge of research interest. In this work, we employ the exponential tensor renormalization group approach to explore the Kitaev model under various perturbations, including the external fields, Heisenberg, and the off-diagonal couplings that are common in the Kitaev materials. Through large-scale manybody calculations, we find a Kitaev fractional liquid at intermediate temperature that is robust against perturbations. The fractional liquid exhibits universal thermodynamic behaviors, including the fractional thermal entropy, metallic specific heat, and an intermediate-temperature Curie law of magnetic susceptibility. The emergent universal susceptibility behavior, with a modified Curie constant, can be ascribed to the strongly fluctuating $\mathbb{Z}_2$ fluxes as well as the extremely short-ranged and bond-directional spin correlations. With this insight, we revisit the susceptibility measurements of Na$_2$IrO$_3$ and $α$-RuCl$_3$, and find evident signatures of finite-temperature fractionalization and ferromagnetic Kitaev couplings. Moreover, the peculiar spin correlation in the fractional liquid corresponds to a stripy structure factor which rotates in the extended Brillouin zone as the spin component changes. Therefore, our findings encourage future experimental exploration of fractional liquid in the Kitaev materials by thermodynamic measurements and spin-resolved structure factor probes.

cond-mat.str-el↗

arXiv2020

Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution

Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a real depth sensor. In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with real-world DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. First, we propose to classify the generation of LR depth maps into two types: non-linear downsampling with noise and interval downsampling, for which DSR models are learned correspondingly. Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively re-exploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss. Extensive experiments on benchmarking datasets demonstrate the superiority of our method over current state-of-the-art DSR methods.

cs.CV↗

arXiv2020

Reducible Fermi surface for multi-layer quantum graphs including stacked graphene

We construct two types of multi-layer quantum graphs (Schrödinger operators on metric graphs) for which the dispersion function of wave vector and energy is proved to be a polynomial in the dispersion function of the single layer. This leads to the reducibility of the algebraic Fermi surface, at any energy, into several components. Each component contributes a set of bands to the spectrum of the graph operator. When the layers are graphene, AA-, AB-, and ABC-stacking are allowed within the same multi-layer structure. Conical singularities (Dirac cones) characteristic of single-layer graphene break when multiple layers are coupled, except for special AA-stacking. One of the tools we introduce is a surgery-type calculus for obtaining the dispersion function for a periodic quantum graph by gluing two graphs together.

math-ph↗

arXiv2020

Can experiment determine the stacking fault energy of metastable alloys?

Stacking fault energy (SFE) plays an important role in deformation mechanisms and mechanical properties of face-centered cubic (fcc) metals and alloys. In metastable fcc alloys, the SFEs determined from density functional theory (DFT) calculations and experimental methods often have opposite signs. Here, we show that the negative SFE by DFT reflects the thermodynamic instability of the fcc phase relative to the hexagonal close-packed one; while the experimentally determined SFEs are restricted to be positive by the models behind the indirect measurements. We argue that the common models underlying the experimental measurements of SFE fail in metastable alloys. In various concentrated solid solutions, we demonstrate that the SFEs obtained by DFT calculations correlate well with the primary deformation mechanisms observed experimentally, showing a better resolution than the experimentally measured SFEs. Furthermore, we believe that the negative SFE is important for understanding the abnormal behaviors of partial dislocations in metastable alloys under deformation. The present work advances the fundamental understanding of SFE and its relation to plastic deformations, and sheds light on future alloy design by physical metallurgy.

cond-mat.mtrl-sci↗

arXiv2020

Leveraging Graph to Improve Abstractive Multi-Document Summarization

Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this paper, we develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents such as similarity graph and discourse graph, to more effectively process multiple input documents and produce abstractive summaries. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries. Furthermore, pre-trained language models can be easily combined with our model, which further improve the summarization performance significantly. Empirical results on the WikiSum and MultiNews dataset show that the proposed architecture brings substantial improvements over several strong baselines.

cs.CL↗

arXiv2020

Improving Accent Conversion with Reference Encoder and End-To-End Text-To-Speech

Accent conversion (AC) transforms a non-native speaker's accent into a native accent while maintaining the speaker's voice timbre. In this paper, we propose approaches to improving accent conversion applicability, as well as quality. First of all, we assume no reference speech is available at the conversion stage, and hence we employ an end-to-end text-to-speech system that is trained on native speech to generate native reference speech. To improve the quality and accent of the converted speech, we introduce reference encoders which make us capable of utilizing multi-source information. This is motivated by acoustic features extracted from native reference and linguistic information, which are complementary to conventional phonetic posteriorgrams (PPGs), so they can be concatenated as features to improve a baseline system based only on PPGs. Moreover, we optimize model architecture using GMM-based attention instead of windowed attention to elevate synthesized performance. Experimental results indicate when the proposed techniques are applied the integrated system significantly raises the scores of acoustic quality (30$\%$ relative increase in mean opinion score) and native accent (68$\%$ relative preference) while retaining the voice identity of the non-native speaker.

cs.CL↗

arXiv2020

High Dimensional Three-Periods Locally Ideal MIP Formulations for the UC Problem

The thermal unit commitment (UC) problem often can be formulated as a mixed integer quadratic programming (MIQP), which is difficult to solve efficiently, especially for large-scale instances. The tighter characteristic reduces the search space, therefore, as a natural conse-quence, significantly reduces the computational burden. In the literature, many tightened formulations for single units with parts of constraints were reported without presenting how they were derived. In this paper, a sys-tematic approach is developed to formulate the tight formulations. The idea is using more new variables in high dimension space to capture all the states for single units within three periods, and then, using these state variables systematic derive three-periods locally ideal expressions for a subset of the constraints in UC. Meanwhile, the linear dependence relations of those new state variables are leveraged to keep the compactness of the obtained formulations. Based on this approach, we propose two tighter models, namely 3P-HD and 3P-HD-Pr. The proposed models and other four state-of-the-art models were tested on 51 instances, including 42 realistic instances and 9 8-unit-based instances, over a scheduling period of 24 h for systems ranging from 10 to 1080 generating units. The simulation results show that our proposed MIQP UC formulations are the tightest and can be solved most efficiently. After using piecewise technique to approxi-mate the quadratic operational cost function, the six UC MIQP formulations can be approximated by six corre-sponding mixed-integer linear programming (MILP) formulations. Our experiments show that the proposed 3P-HD and 3P-HD-Pr MILP formulations also perform the best in terms of tightness and solution times.

math.OC↗

arXiv2020

Inverse design of lightweight broadband reflector for efficient lightsail propulsion

Light can exert forces on objects, promising to propel a meter-scale lightsail to near the speed of light. The key to address many challenges in such an ambition hinges on the nanostructuring of lightsails to tailor their optical scattering properties. In this letter, we present a first exhaustive study of photonic design of lightsails by applying large-scale optimization techniques to a generic geometry based on stacked photonic crystal layers. The optimization is performed by rigorous coupled-wave analysis amended with automatic differentiation methods for adjoint-variable gradient evaluations. Employing these methods the propulsion efficiency of a lightsail that involves a tradeoff between high broadband reflectivity and mass reduction is optimized. Surprisingly, regardless of the material choice, the optimal structures turn out to be simply one-dimensional subwavelength gratings, exhibiting nearly 50% improvement in acceleration distance performance compared to prior studies. Our framework can be extended to address other lightsail challenges such as thermal management and propulsion stability, and applications in integrated photonics such as compact mirrors.

physics.optics↗

arXiv2020

Orthogonal electric control of the out-of-plane field-effect in two-dimensional ferroelectric alpha-In2Se3

Tuning the electric properties of crystalline solids is at the heart of material science and electronics. Generating the electric field-effect via an external voltage is a clean, continuous and systematic method. Here, utilizing the unique electric dipole locking in van der Waals (vdW) ferroelectric alpha-In2Se3, we report a new approach to establish the electric gating effect, where the electrostatic doping in the out-of-plane direction is induced and controlled by an in-plane voltage. With the vertical vdW heterostructure of ultrathin alpha-In2Se3 and MoS2, we validate an in-plane voltage gated coplanar field-effect transistor (CP-FET) with distinguished and retentive on/off ratio. Our results demonstrate unprecedented electric control of ferroelectricity, which paves the way for integrating two-dimensional (2D) ferroelectric into novel nanoelectronic devices with broad applications.

cond-mat.mes-hall↗

arXiv2020

A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e., the time the hypothesis is finalized after the user stops speaking. In this paper, we develop a first-pass Recurrent Neural Network Transducer (RNN-T) model and a second-pass Listen, Attend, Spell (LAS) rescorer that surpasses a conventional model in both quality and latency. On the quality side, we incorporate a large number of utterances across varied domains to increase acoustic diversity and the vocabulary seen by the model. We also train with accented English speech to make the model more robust to different pronunciations. In addition, given the increased amount of training data, we explore a varied learning rate schedule. On the latency front, we explore using the end-of-sentence decision emitted by the RNN-T model to close the microphone, and also introduce various optimizations to improve the speed of LAS rescoring. Overall, we find that RNN-T+LAS offers a better WER and latency tradeoff compared to a conventional model. For example, for the same latency, RNN-T+LAS obtains a 8% relative improvement in WER, while being more than 400-times smaller in model size.

cs.CL↗

arXiv2020

Evidence the ferromagnetic order on CoSb layer of LaCoSb$_2$

The emergence of unconventional superconductivity is generally considered to be related to spin fluctuations. Unveiling the intriguing behaviors of spin fluctuations in parent compounds with layered transition-metal ions may shed light on the search for exotic unconventional superconductors. Here, based on the framework of the first-principles calculations, we theoretically propose that LaCoSb$_2$ is a weak antiferromagnetic layered metal with an in-plane ferromagnetic moment of 0.88 $μ_B$ at the Co sites, as a candidate parent compound of the cobalt-based superconductors. Importantly, this theoretical finding is experimentally supported by our magnetization measurements on polycrystalline samples of LaCo$_{0.78}$Sb$_2$. Following the symmetry analysis, we suggest a possible $p$-wave superconductivity hosted in doped LaCoSb$_2$ emerging at the verge of ferromagnetic spin fluctuations, which implies potential applications in topological quantum computing in future.

cond-mat.supr-con↗

arXiv2020

Demonstration of a Reconfigurable Entangled Radiofrequency-Photonic Sensor Network

Quantum metrology takes advantage of nonclassical resources such as entanglement to achieve a sensitivity level below the standard quantum limit. To date, almost all quantum-metrology demonstrations are restricted to improving the measurement performance at a single sensor, but a plethora of applications require multiple sensors that work jointly to tackle distributed sensing problems. Here, we propose and experimentally demonstrate a reconfigurable sensor network empowered by continuous-variable (CV) multipartite entanglement. Our experiment establishes a connection between the entanglement structure and the achievable quantum advantage in different distributed sensing problems. The demonstrated entangled sensor network is composed of three sensor nodes each equipped with an electro-optic transducer for the detection of radiofrequency (RF) signals. By properly tailoring the CV multipartite entangled states, the entangled sensor network can be reconfigured to maximize the quantum advantage in distributed RF sensing problems such as measuring the angle of arrival of an RF field. The rich physics of CV multipartite entanglement unveiled by our work would open a new avenue for distributed quantum sensing and would lead to applications in ultrasensitive positioning, navigation, and timing.

quant-ph↗