arXiv subjects

Jie Wang

Publications and source records attributed to Jie Wang.

At least 505 records · Page 28Linked to original sources

arXiv2021

XMUSPEECH System for VoxCeleb Speaker Recognition Challenge 2021

This paper describes the XMUSPEECH speaker recognition and diarisation systems for the VoxCeleb Speaker Recognition Challenge 2021. For track 2, we evaluate two systems including ResNet34-SE and ECAPA-TDNN. For track 4, an important part of our system is VAD module which greatly improves the performance. Our best submission on the track 4 obtained on the evaluation set DER 5.54% and JER 27.11%, while the performance on the development set is DER 2.92% and JER 20.84%.

eess.AS↗

arXiv2021

Hartree-Fock study of the moiré Hubbard model for twisted bilayer transition metal dichalcogenides

Twisted bilayer transition metal dichalcogenides have emerged as important model systems for the investigation of correlated electron physics because their interaction strength, carrier concentration, band structure, and inversion symmetry breaking are controllable by device fabrication, twist angle, and most importantly, gate voltage, which can be varied in situ. The low energy physics of some of these materials has been shown to be described by a "moiré Hubbard model" generalized from the usual Hubbard model by the addition of strong, tunable spin orbit coupling and inversion symmetry breaking. In this work, we use a Hartree-Fock approximation to reach a comprehensive understanding of the moiré Hubbard model on the mean field level. We determine the magnetic and metal-insulator phase diagrams, and assess the effects of spin orbit coupling, inversion symmetry breaking, and the tunable van Hove singularity. We also consider the spin and orbital effects of applied magnetic fields. This work provides guidance for experiments and sets the stage for beyond mean-field calculations.

cond-mat.str-el↗

arXiv2021

Dynamic behavior of polar nanoregions in re-entrant relaxor 0.6Bi(Mg1/2Ti1/2)O3-0.4PbTiO3

The existence of polar nanoregions is the most important characteristic of ferroelectric relaxors, however, the size determination and dynamic of PNRs remains uncertain. We reveal a re-entrant relaxor behavior and ferroelectric-paraelectric transition coexists in complex perovskite oxide 0.6Bi(Mg1/2Ti1/2)O3-0.4PbTiO3. Two dielectric anomalies (i) the low-temperature re-entrant relaxor transition and (ii) the high-temperature diffuse phase transition (DPT) were described by the phenomenological statistical model. The sizes of the two kinds of polar nanoregions (PNRs) corresponding to two ferroelectric states were obtained. The dynamic of PNRs were analyzed using isothermal electrical modulus, which shows three critical temperatures associated with the diffuse phase transition, the formation and freezing of PNRs, respectively. The temperature evolution of the PNRs evolution depends on the stoichiometry of bismuth. The results provide new insights into the dynamic behavior of PNRs and the modification way of re-entrant relaxor behavior.

cond-mat.mtrl-sci↗

arXiv2021

Adversarially learning disentangled speech representations for robust multi-factor voice conversion

Factorizing speech as disentangled speech representations is vital to achieve highly controllable style transfer in voice conversion (VC). Conventional speech representation learning methods in VC only factorize speech as speaker and content, lacking controllability on other prosody-related factors. State-of-the-art speech representation learning methods for more speechfactors are using primary disentangle algorithms such as random resampling and ad-hoc bottleneck layer size adjustment,which however is hard to ensure robust speech representationdisentanglement. To increase the robustness of highly controllable style transfer on multiple factors in VC, we propose a disentangled speech representation learning framework based on adversarial learning. Four speech representations characterizing content, timbre, rhythm and pitch are extracted, and further disentangled by an adversarial Mask-And-Predict (MAP)network inspired by BERT. The adversarial network is used tominimize the correlations between the speech representations,by randomly masking and predicting one of the representationsfrom the others. Experimental results show that the proposedframework significantly improves the robustness of VC on multiple factors by increasing the speech quality MOS from 2.79 to3.30 and decreasing the MCD from 3.89 to 3.58.

eess.AS↗

arXiv2021

Indra: a Public Computationally Accessible Suite of Cosmological $N$-body Simulations

Indra is a suite of large-volume cosmological $N$-body simulations with the goal of providing excellent statistics of the large-scale features of the distribution of dark matter. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, with 1024$^3$ dark matter particles in a box of length 1 Gpc/h, 64 snapshots of particle data and halo catalogs, and 505 time steps of the Fourier modes of the density field, amounting to almost a petabyte of data. All of the Indra data are immediately available for analysis via the SciServer science platform, which provides interactive and batch computing modes, personal data storage, and other hosted data sets such as the Millennium simulations and many astronomical surveys. We present the Indra simulations, describe the data products and how to access them, and measure ensemble averages, variances, and covariances of the matter power spectrum, the matter correlation function, and the halo mass function to demonstrate the types of computations that Indra enables. We hope that Indra will be both a resource for large-scale structure research and a demonstration of how to make very large datasets public and computationally accessible.

astro-ph.CO↗

arXiv2021

Dynamics of Polar Skyrmion Bubbles under Electric Fields

Room-temperature polar skyrmion bubbles that are recently found in oxide superlattice, have received enormous interests for their potential applications in nanoelectronics due to the nanometer size, emergent chirality, and negative capacitance. For practical applications, the ability to controllably manipulate them by using external stimuli is prerequisite. Here, we study the dynamics of individual polar skyrmion bubbles at the nanoscale by using in situ biasing in a scanning transmission electron microscope. The reversible electric field-driven phase transition between topological and trivial polar states are demonstrated. We create, erase and monitor the shrinkage and expansion of individual polar skyrmions. We find that their transition behaviors are substantially different from that of magnetic analogue. The underlying mechanism is discussed by combing with the phase-field simulations. The controllable manipulation of nanoscale polar skyrmions allows us to tune the dielectric permittivity at atomic scale and detailed knowledge of their phase transition behaviors provides fundamentals for their applications in nanoelectronics.

cond-mat.mes-hall↗

arXiv2021

A 60-GHz Radar Sensor for Micron-Scale Motion Detection

A compact, continuous-wave, mmWave radar sensor is developed for non-contact detection of micron-scale motions. This board-integrated radar system consists of a pair of mmWave transmitter and receiver, two series-fed microstrip patch arrays, an IF subsystem, and a microcontroller. Working at 60-GHz frequency, this super-heterodyne, digital-IF radar sensor exhibits an agile sensitivity and a robust anti-noise performance. Assisted by a gradient-descent DC offset estimation and a phase demodulation algorithm based on extended differential and cross-multiply, a 45-μm pendulum swing can be experimentally detected with a 20.6-dB SNR, verifying its strong ability in Doppler micro-motion detections.

eess.SY↗

arXiv2021

Variational Representations and Neural Network Estimation of Rényi Divergences

We derive a new variational formula for the Rényi family of divergences, $R_α(Q\|P)$, between probability measures $Q$ and $P$. Our result generalizes the classical Donsker-Varadhan variational formula for the Kullback-Leibler divergence. We further show that this Rényi variational formula holds over a range of function spaces; this leads to a formula for the optimizer under very weak assumptions and is also key in our development of a consistency theory for Rényi divergence estimators. By applying this theory to neural-network estimators, we show that if a neural network family satisfies one of several strengthened versions of the universal approximation property then the corresponding Rényi divergence estimator is consistent. In contrast to density-estimator based methods, our estimators involve only expectations under $Q$ and $P$ and hence are more effective in high dimensional systems. We illustrate this via several numerical examples of neural network estimation in systems of up to 5000 dimensions.

stat.ML↗

arXiv2021

Technical Report of Team GraphMIRAcles in the WikiKG90M-LSC Track of OGB-LSC @ KDD Cup 2021

Link prediction in large-scale knowledge graphs has gained increasing attention recently. The OGB-LSC team presented OGB Large-Scale Challenge (OGB-LSC), a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph machine learning. In this paper, we introduce the solution of our team GraphMIRAcles in the WikiKG90M-LSC track of OGB-LSC @ KDD Cup 2021. In the WikiKG90M-LSC track, the goal is to automatically predict missing links in WikiKG90M, a large scale knowledge graph extracted from Wikidata. To address this challenge, we propose a framework that integrates three components -- a basic model ComplEx-CMRC, a rule miner AMIE 3, and an inference model to predict missing links. Experiments demonstrate that our solution achieves an MRR of 0.9707 on the test dataset. Moreover, as the knowledge distillation in the inference model uses test tail candidates -- which are unavailable in practice -- we conduct ablation studies on knowledge distillation. Experiments demonstrate that our model without knowledge distillation achieves an MRR of 0.9533 on the full validation dataset.

cs.CL↗

arXiv2021

Tailoring Dzyaloshinskii-Moriya interaction in a transition metal dichalcogenide by dual-intercalation

Dzyaloshinskii-Moriya interaction (DMI) is vital to form various chiral spin textures, novel behaviors of magnons and permits their potential applications in energy-efficient spintronic devices. Here, we realize a sizable bulk DMI in a transition metal dichalcogenide (TMD) 2H-TaS2 by intercalating Fe atoms, which form the chiral supercells with broken spatial inversion symmetry and also act as the source of magnetic orderings. Using a newly developed protonic gate technology, gate-controlled protons intercalation could further change the carrier density and intensely tune DMI via the Ruderman-Kittel-Kasuya-Yosida mechanism. The resultant giant topological Hall resistivity of 1.4 uohm.cm at -5.2V (about 460% of the zero-bias value) is larger than most of the known magnetic materials. Theoretical analysis indicates that such a large topological Hall effect originates from the two-dimensional Bloch-type chiral spin textures stabilized by DMI, while the large anomalous Hall effect comes from the gapped Dirac nodal lines by spin-orbit interaction. Dual-intercalation in 2HTaS2 provides a model system to reveal the nature of DMI in the large family of TMDs and a promising way of gate tuning of DMI, which further enables an electrical control of the chiral spin textures and related electromagnetic phenomena.

cond-mat.mes-hall↗

arXiv2021

Small-Sample Inferred Adaptive Recoding for Batched Network Coding

Batched network coding is a low-complexity network coding solution to feedbackless multi-hop wireless packet network transmission with packet loss. The data to be transmitted is encoded into batches where each of which consists of a few coded packets. Unlike the traditional forwarding strategy, the intermediate network nodes have to perform recoding, which generates recoded packets by network coding operations restricted within the same batch. Adaptive recoding is a technique to adapt the fluctuation of packet loss by optimizing the number of recoded packets per batch to enhance the throughput. The input rank distribution, which is a piece of information regarding the batches arriving at the node, is required to apply adaptive recoding. However, this distribution is not known in advance in practice as the incoming link's channel condition may change from time to time. On the other hand, to fully utilize the potential of adaptive recoding, we need to have a good estimation of this distribution. In other words, we need to guess this distribution from a few samples so that we can apply adaptive recoding as soon as possible. In this paper, we propose a distributionally robust optimization for adaptive recoding with a small-sample inferred prediction of the input rank distribution. We develop an algorithm to efficiently solve this optimization with the support of theoretical guarantees that our optimization's performance would constitute as a confidence lower bound of the optimal throughput with high probability.

cs.IT↗

arXiv2021

CS-TSSOS: Correlative and term sparsity for large-scale polynomial optimization

This work proposes a new moment-SOS hierarchy, called CS-TSSOS, for solving large-scale sparse polynomial optimization problems. Its novelty is to exploit simultaneously correlative sparsity and term sparsity by combining advantages of two existing frameworks for sparse polynomial optimization. The former is due to Waki et al. while the latter was initially proposed by Wang et al. and later exploited in the TSSOS hierarchy. In doing so we obtain CS-TSSOS -- a two-level hierarchy of semidefinite programming relaxations with (i), the crucial property to involve blocks of SDP matrices and (ii), the guarantee of convergence to the global optimum under certain conditions. We demonstrate its efficiency and scalability on several large-scale instances of the celebrated Max-Cut problem and the important industrial optimal power flow problem, involving up to six thousand variables and tens of thousands of constraints.

math.OC↗

arXiv2021

On Explainability of Graph Neural Networks via Subgraph Explorations

We consider the problem of explaining the predictions of graph neural networks (GNNs), which otherwise are considered as black boxes. Existing methods invariably focus on explaining the importance of graph nodes or edges but ignore the substructures of graphs, which are more intuitive and human-intelligible. In this work, we propose a novel method, known as SubgraphX, to explain GNNs by identifying important subgraphs. Given a trained GNN model and an input graph, our SubgraphX explains its predictions by efficiently exploring different subgraphs with Monte Carlo tree search. To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs. To expedite computations, we propose efficient approximation schemes to compute Shapley values for graph data. Our work represents the first attempt to explain GNNs via identifying subgraphs explicitly and directly. Experimental results show that our SubgraphX achieves significantly improved explanations, while keeping computations at a reasonable level.

cs.LG↗

arXiv2021

Chiral Approximation to Twisted Bilayer Graphene: Exact Intra-Valley Inversion Symmetry, Nodal Structure and Implications for Higher Magic Angles

This paper presents a mathematical and numerical analysis of the flatband wavefunctions occurring in the chiral model of twisted bilayer graphene at the "magic" twist angles. We show that the chiral model possesses an exact intra-valley inversion symmetry. Writing the flatband wavefunction as a product of a lowest Landau level quantum Hall state and a spinor, we show that the components of the spinor are anti-quantum Hall wavefunctions related by the inversion symmetry operation introduced here. We then show numerically that as one moves from the lowest to higher magic angles, the spinor components of the wavefunction exhibit an increasing number of zeros, resembling the changes in the quantum Hall wavefunction as the Landau level index is increased. The wavefunction zeros are characterized by a chirality, with zeros of the same chirality clustering near the center of the moire unit cell, while opposite chirality zeros are pushed to the boundaries of the unit cell. The enhanced phase winding at higher magic angles suggests an increased circulating current. Physical implications for scanning tunneling spectroscopy, orbital magnetization and interaction effects are discussed.

cond-mat.mes-hall↗

arXiv2021

Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems

The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically scheduling vehicles among multiple sites in order to minimize the cost when delivery orders are not known a priori. Although DPDP plays an important role in modern logistics and supply chain management, state-of-the-art DPDP algorithms are still limited on their solution quality and efficiency. In practice, they fail to provide a scalable solution as the numbers of vehicles and sites become large. In this paper, we propose a data-driven approach, Spatial-Temporal Aided Double Deep Graph Network (ST-DDGN), to solve industry-scale DPDP. In our method, the delivery demands are first forecast using spatial-temporal prediction method, which guides the neural network to perceive spatial-temporal distribution of delivery demand when dispatching vehicles. Besides, the relationships of individuals such as vehicles are modelled by establishing a graph-based value function. ST-DDGN incorporates attention-based graph embedding with Double DQN (DDQN). As such, it can make the inference across vehicles more efficiently compared with traditional methods. Our method is entirely data driven and thus adaptive, i.e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically. We have conducted extensive experiments over real-world data to evaluate our solution. The results show that ST-DDGN reduces 11.27% number of the used vehicles and decreases 13.12% total transportation cost on average over the strong baselines, including the heuristic algorithm deployed in our UAT (User Acceptance Test) environment and a variety of vanilla DRL methods. We are due to fully deploy our solution into our online logistics system and it is estimated that millions of USD logistics cost can be saved per year.

cs.AI↗

arXiv2021

Reversible Adversarial Attack based on Reversible Image Transformation

In order to prevent illegal or unauthorized access of image data such as human faces and ensure legitimate users can use authorization-protected data, reversible adversarial attack technique is rise. Reversible adversarial examples (RAE) get both attack capability and reversibility at the same time. However, the existing technique can not meet application requirements because of serious distortion and failure of image recovery when adversarial perturbations get strong. In this paper, we take advantage of Reversible Image Transformation technique to generate RAE and achieve reversible adversarial attack. Experimental results show that proposed RAE generation scheme can ensure imperceptible image distortion and the original image can be reconstructed error-free. What's more, both the attack ability and the image quality are not limited by the perturbation amplitude.

eess.IV↗

arXiv2021

Extending High-Level Synthesis for Task-Parallel Programs

C/C++/OpenCL-based high-level synthesis (HLS) becomes more and more popular for field-programmable gate array (FPGA) accelerators in many application domains in recent years, thanks to its competitive quality of results (QoR) and short development cycles compared with the traditional register-transfer level design approach. Yet, limited by the sequential C semantics, it remains challenging to adopt the same highly productive high-level programming approach in many other application domains, where coarse-grained tasks run in parallel and communicate with each other at a fine-grained level. While current HLS tools do support task-parallel programs, the productivity is greatly limited (1) in the code development cycle due to the poor programmability, (2) in the correctness verification cycle due to restricted software simulation, and (3) in the QoR tuning cycle due to slow code generation. Such limited productivity often defeats the purpose of HLS and hinder programmers from adopting HLS for task-parallel FPGA accelerators. In this paper, we extend the HLS C++ language and present a fully automated framework with programmer-friendly interfaces, unconstrained software simulation, and fast hierarchical code generation to overcome these limitations and demonstrate how task-parallel programs can be productively supported in HLS. Experimental results based on a wide range of real-world task-parallel programs show that, on average, the lines of kernel and host code are reduced by 22% and 51%, respectively, which considerably improves the programmability. The correctness verification and the iterative QoR tuning cycles are both greatly shortened by 3.2x and 6.8x, respectively. Our work is open-source at https://github.com/UCLA-VAST/tapa/.

cs.AR↗

arXiv2021

Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion

Tensor factorization based models have shown great power in knowledge graph completion (KGC). However, their performance usually suffers from the overfitting problem seriously. This motivates various regularizers -- such as the squared Frobenius norm and tensor nuclear norm regularizers -- while the limited applicability significantly limits their practical usage. To address this challenge, we propose a novel regularizer -- namely, DUality-induced RegulArizer (DURA) -- which is not only effective in improving the performance of existing models but widely applicable to various methods. The major novelty of DURA is based on the observation that, for an existing tensor factorization based KGC model (primal), there is often another distance based KGC model (dual) closely associated with it. Experiments show that DURA yields consistent and significant improvements on benchmarks.

cs.LG↗