arXiv subjects

Xin Wang

Publications and source records attributed to Xin Wang.

At least 1,261 records · Page 70Linked to original sources

arXiv2022

Facility Location with Congestion and Priority in Drone-Based Emergency Delivery

Thanks to their fast delivery, reduced traffic restrictions, and low manpower need, drones have been increasingly deployed to deliver time-critical materials, such as medication, blood, and exam kits, in emergency situations. This paper considers a facility location model of using drones as mobile servers in emergency delivery. The model jointly optimizes the location of facilities, the capacity of drones deployed at opened facilities, and the allocation of demands, with an objective of equitable response times among all demand sites. To this end, we employ queues to model the system congestion of drone requests and consider three queuing disciplines: non-priority, static priority, and dynamic priority. For each discipline, we approximate the model as a mixed-integer second-order conic program (MISOCP), which can readily be solved in commercial solvers. We conduct extensive computational experiments to demonstrate the effectiveness and accuracy of our approach. Additionally, we compare the system performance under the three queuing disciplines and various problem parameters, from which we produce operational recommendations to decision makers in emergency delivery.

math.OC↗

arXiv2022

Microscopic theory on magnetic-field-tuned sweet spot of exchange interactions in multielectron quantum-dot systems

The exchange interaction in a singlet-triplet qubit defined by two-electron states in the double-quantum-dot system ("two-electron singlet-triplet qubit") typically varies monotonically with the exchange interaction and thus carries no sweet spot. Here we study a singlet-triplet qubit defined by four-electron states in the double-quantum-dot system ("four-electron singlet-triplet qubit"). We demonstrate, using configuration-interaction calculations, that in the four-electron singlet-triplet qubit the exchange energy as a function of detuning can be non-monotonic, suggesting existence of sweet spots. We further show that the tuning of the sweet spot and the corresponding exchange energy by perpendicular magnetic field can be related to the variation of orbital splitting. Our results suggest that a singlet-triplet qubit with more than two electrons can have advantages in the realization of quantum computing.

cond-mat.mes-hall↗

arXiv2022

Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces

Generative adversary network (GAN) generated high-realistic human faces have been used as profile images for fake social media accounts and are visually challenging to discern from real ones. In this work, we show that GAN-generated faces can be exposed via irregular pupil shapes. This phenomenon is caused by the lack of physiological constraints in the GAN models. We demonstrate that such artifacts exist widely in high-quality GAN-generated faces and further describe an automatic method to extract the pupils from two eyes and analysis their shapes for exposing the GAN-generated faces. Qualitative and quantitative evaluations of our method suggest its simplicity and effectiveness in distinguishing GAN-generated faces.

cs.CV↗

arXiv2022

Counterdiabatic driving for pseudo- and antipseudo- Hermitian systems

In this work, we study the counterdiabatic driving scheme in pseudo- and antipseudo- Hermitian systems. By discussing the adiabatic condition for non-Hermitian system, we show that the adiabatic evolution of state can only be realized in the non-Hermitian system which possesses real energy spectrum. Therefore, the counterdiabatic driving scheme to reproduce an exact evolution of an energy eigenstate needs either real energy spectrum or dropping its parts of dynamic phase and Berry phase. In this sense, we derive the adiabatic conditions and counterdiabatic driving Hamiltonians for the pseudo-Hermitian Hamiltonian which possesses either real or complex energy spectrum and the antipseudo-Hermitian Hamiltonian which possesses either imaginary or complex energy spectrum. We also find the condition to get self-normalized energy eigenstates in pseudo- and antipseudo- Hermitian system and derive the well-defined population of bare states on this energy eigenstate. Our results are illustrated by studying the counterdiabatic driving for a non-Hermitian three level system, and a perfect population transfer with loss or gain is realized.

quant-ph↗

arXiv2022

Hybrid subconvexity bounds for twists of $\rm GL(3)$ $L$-functions

Let $π$ be a $SL(3,\mathbb Z)$ Hecke-Maass cusp form and $χ$ a primitive Dirichlet character of prime power conductor $\mathfrak{q}=p^k$ with $p$ prime. In this paper we will prove the following subconvexity bound $$ L\left(\frac{1}{2}+it,π\times χ\right)\ll_{π,\varepsilon} p^{3/4}\big(\mathfrak{q}(1+|t|)\big)^{3/4-3/40+\varepsilon}, $$ for any $\varepsilon >0$ and $t \in \mathbb{R}$.

math.NT↗

arXiv2022

The VoicePrivacy 2020 Challenge Evaluation Plan

The VoicePrivacy Challenge aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges. In this document, we formulate the voice anonymization task selected for the VoicePrivacy 2020 Challenge and describe the datasets used for system development and evaluation. We also present the attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and report objective evaluation results.

cs.CL↗

arXiv2022

Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces

AI-synthesized faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social impacts. Although progress has been made in developing automatic methods to detect AI-synthesized faces, there is no open platform to study the human performance of AI-synthesized faces detection. In this work, we develop an online platform called Open-eye to study the human performance of AI-synthesized face detection. We describe the design and workflow of the Open-eye in this paper.

cs.CV↗

arXiv2022

Social Distancing Alert with Smartwatches

Social distancing is an efficient public health practice during the COVID-19 pandemic. However, people would violate the social distancing practice unconsciously when they conduct some social activities such as handshaking, hugging, kissing on the face or forehead, etc. In this paper, we present SoDA, a social distancing practice violation alert system based on smartwatches, for preventing COVID-19 virus transmission. SoDA utilizes recordings of accelerometers and gyroscopes to recognize activities that may violate social distancing practice with simple yet effective Vision Transformer models. Extensive experiments over 10 volunteers and 1800+ samples demonstrate that SoDA achieves social activity recognition with the accuracy of 94.7%, 1.8% negative alert, and 2.2% missing alert.

cs.HC↗

arXiv2022

Mask Wearing Status Estimation with Smartwatches

We present MaskReminder, an automatic mask-wearing status estimation system based on smartwatches, to remind users who may be exposed to the COVID-19 virus transmission scenarios, to wear a mask. MaskReminder with the powerful MLP-Mixer deep learning model can effectively learn long-short range information from the inertial measurement unit readings, and can recognize the mask-related hand movements such as wearing a mask, lowering the metal strap of the mask, removing the strap from behind one side of the ears, etc. Extensive experiments on 20 volunteers and 8000+ data samples show that the average recognition accuracy is 89%. Moreover, MaskReminder is capable to remind a user to wear with a success rate of 90% even in the user-independent setting.

cs.HC↗

arXiv2022

Chiral Quantum Network with Giant Atoms

In superconducting quantum circuits (SQCs), chiral routing quantum information is often realized with the ferrite circulators, which are usually bulky, lossy and require strong magnetic fields. To overcome those problems, we propose a novel method to realize chiral quantum networks by exploiting giant atom effects in SQC platforms. By assuming each coupling point being modulated with time, the interaction becomes momentum-dependent, and giant atoms will chirally emit photons due to interference effects. The chiral factor can approach 1, and both the emission direction and rate can be freely tuned by the modulating signals. We demonstrate that a high-fidelity state transfer between remote giant atoms can be realized. Our proposal can be integrated on the superconducting chip easily, and has the potential to work as a tunable toolbox for quantum information processing in future chiral quantum networks.

quant-ph↗

arXiv2022

Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing

Deep learning (DL) techniques are proven effective in many challenging tasks, and become widely-adopted in practice. However, previous work has shown that DL libraries, the basis of building and executing DL models, contain bugs and can cause severe consequences. Unfortunately, existing testing approaches still cannot comprehensively exercise DL libraries. They utilize existing trained models and only detect bugs in model inference phase. In this work we propose Muffin to address these issues. To this end, Muffin applies a specifically-designed model fuzzing approach, which allows it to generate diverse DL models to explore the target library, instead of relying only on existing trained models. Muffin makes differential testing feasible in the model training phase by tailoring a set of metrics to measure the inconsistencies between different DL libraries. In this way, Muffin can best exercise the library code to detect more bugs. To evaluate the effectiveness of Muffin, we conduct experiments on three widely-used DL libraries. The results demonstrate that Muffin can detect 39 new bugs in the latest release versions of popular DL libraries, including Tensorflow, CNTK, and Theano.

cs.SE↗

arXiv2022

CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training

Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various works have been proposed to represent the complex semantics of source code from different views, including plain text, Abstract Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow Graph). However, most of them only consider a single view of source code independently, ignoring the correspondences among different views. In this paper, we propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training, and name our model as CODE-MVP. Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework. Inspired by the type checking in compilation, we also design a fine-grained type inference objective in the pre-training. Experiments on three downstream tasks over five datasets demonstrate the superiority of CODE-MVP when compared with several state-of-the-art baselines. For example, we achieve 2.4/2.3/1.1 gain in terms of MRR/MAP/Accuracy metrics on natural language code retrieval, code similarity, and code defect detection tasks, respectively.

cs.PL↗

arXiv2022

Topological strings and Wilson loops

We propose the refined topological string correspondence to the expectation values of half-BPS Wilson loop operators in 5d $\mathcal{N}=1$ gauge theory partition function on the Omega-deformed background $\mathbb{R}^4_{ε_{1,2}}\times S^1$. We provide the refined topological vertex method and the refined holomorphic anomaly equation method in the topological string theory, from which we have exact computations on the 5d Wilson loops partition functions in both A- and B-models. Finally, with the exact results we have in B-model, we recover the quantum periods of local $\mathbb{P}^1\times\mathbb{P}^1$ model and local $\mathbb{P}^2$ model in the study of quantum geometry and we further give a refined generalization of A-period.

hep-th↗

arXiv2022

Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration

Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods need to iteratively optimize an objective function via a specific deformation model along with meticulous parameter tuning, but which have limited capabilities in registering images with large deformations. While deep learning-based methods can learn the complex mapping from input images to their respective deformation field, it is regression-based and is prone to be stuck at local minima, particularly when large deformations are involved. To this end, we present Stochastic Planner-Actor-Critic (SPAC), a novel reinforcement learning-based framework that performs step-wise registration. The key notion is warping a moving image successively by each time step to finally align to a fixed image. Considering that it is challenging to handle high dimensional continuous action and state spaces in the conventional reinforcement learning (RL) framework, we introduce a new concept `Plan' to the standard Actor-Critic model, which is of low dimension and can facilitate the actor to generate a tractable high dimensional action. The entire framework is based on unsupervised training and operates in an end-to-end manner. We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations. Our empirical results highlight that our work achieves consistent, significant gains and outperforms state-of-the-art methods.

eess.IV↗

arXiv2022

Language-Independent Speaker Anonymization Approach using Self-Supervised Pre-Trained Models

Speaker anonymization aims to protect the privacy of speakers while preserving spoken linguistic information from speech. Current mainstream neural network speaker anonymization systems are complicated, containing an F0 extractor, speaker encoder, automatic speech recognition acoustic model (ASR AM), speech synthesis acoustic model and speech waveform generation model. Moreover, as an ASR AM is language-dependent, trained on English data, it is hard to adapt it into another language. In this paper, we propose a simpler self-supervised learning (SSL)-based method for language-independent speaker anonymization without any explicit language-dependent model, which can be easily used for other languages. Extensive experiments were conducted on the VoicePrivacy Challenge 2020 datasets in English and AISHELL-3 datasets in Mandarin to demonstrate the effectiveness of our proposed SSL-based language-independent speaker anonymization method.

cs.SD↗

arXiv2022

Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards

Generating accurate descriptions for online fashion items is important not only for enhancing customers' shopping experiences, but also for the increase of online sales. Besides the need of correctly presenting the attributes of items, the expressions in an enchanting style could better attract customer interests. The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning. Different from popular work on image captioning, it is hard to identify and describe the rich attributes of fashion items. We seed the description of an item by first identifying its attributes, and introduce attribute-level semantic (ALS) reward and sentence-level semantic (SLS) reward as metrics to improve the quality of text descriptions. We further integrate the training of our model with maximum likelihood estimation (MLE), attribute embedding, and Reinforcement Learning (RL). To facilitate the learning, we build a new FAshion CAptioning Dataset (FACAD), which contains 993K images and 130K corresponding enchanting and diverse descriptions. Experiments on FACAD demonstrate the effectiveness of our model.

cs.CV↗

arXiv2022

Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum

Various neural network models have been proposed to tackle combinatorial optimization problems such as the travelling salesman problem (TSP). Existing learning-based TSP methods adopt a simple setting that the training and testing data are independent and identically distributed. However, the existing literature fails to solve TSP instances when training and testing data have different distributions. Concretely, we find that different training and testing distribution will result in more difficult TSP instances, i.e., the solution obtained by the model has a large gap from the optimal solution. To tackle this problem, in this work, we study learning-based TSP methods when training and testing data have different distributions using adaptive-hardness, i.e., how difficult a TSP instance can be for a solver. This problem is challenging because it is non-trivial to (1) define hardness measurement quantitatively; (2) efficiently and continuously generate sufficiently hard TSP instances upon model training; (3) fully utilize instances with different levels of hardness to learn a more powerful TSP solver. To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances. Then, we propose a hardness-adaptive generator to generate instances with different hardness. We further propose a curriculum learner fully utilizing these instances to train the TSP solver. Experiments show that our hardness-adaptive generator can generate instances ten times harder than the existing methods, and our proposed method achieves significant improvement over state-of-the-art models in terms of the optimality gap.

cs.LG↗

arXiv2022

Sign-switching of superexchange mediated by few electrons under non-uniform magnetic field

Long range interaction between distant spins is an important building block for the realization of large quantum-dot network in which couplings between pairs of spins can be selectively addressed. Recent experiments on coherent logical states oscillation between remote spins facilitated by intermediate electron states has paved the first step for large scale quantum information processing. Reaching this ultimate goal requires extensive studies on the superexchange interaction on different quantum-dot spatial arrangements and electron configurations. Here, we consider a linear triple-quantum-dot with two anti-parallel spins in the outer dots forming the logical states while various number of electrons in the middle dot forming a mediator, which facilitates the superexchange interaction. We show that the superexchange is enhanced when the number of mediating electrons increases. In addition, we show that forming a four-electron triplet in the mediator dot further enhance the superexchange strength. Our work can be a guide to scale up the quantum-dot array with controllable and dense connectivity.

cond-mat.mes-hall↗