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Learning Region Features for Object DetectionMar 19 2018While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely hand-crafted, featured by RoI pooling methods. This work proposes a general viewpoint that unifies existing region feature extraction ... More

Mimicking the In-Camera Color Pipeline for Camera-Aware Object CompositingMar 27 2019We present a method for compositing virtual objects into a photograph such that the object colors appear to have been processed by the photo's camera imaging pipeline. Compositing in such a camera-aware manner is essential for high realism, and it requires ... More

A Gram-Gauss-Newton Method Learning Overparameterized Deep Neural Networks for Regression ProblemsMay 28 2019First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better convergence rate, are rarely used in practice due to the prohibitive computational ... More

A Refined Analysis of LSH for Well-dispersed Data PointsDec 14 2016Near neighbor problems are fundamental in algorithms for high-dimensional Euclidean spaces. While classical approaches suffer from the curse of dimensionality, locality sensitive hashing (LSH) can effectively solve a-approximate r-near neighbor problem, ... More

Convergence of Adversarial Training in Overparametrized NetworksJun 19 2019Neural networks are vulnerable to adversarial examples, i.e. inputs that are imperceptibly perturbed from natural data and yet incorrectly classified by the network. Adversarial training, a heuristic form of robust optimization that alternates between ... More

Pairwise Constraint Propagation on Multi-View DataJan 18 2015This paper presents a graph-based learning approach to pairwise constraint propagation on multi-view data. Although pairwise constraint propagation has been studied extensively, pairwise constraints are usually defined over pairs of data points from a ... More

Adversarially Robust Generalization Just Requires More Unlabeled DataJun 03 2019Neural network robustness has recently been highlighted by the existence of adversarial examples. Many previous works show that the learned networks do not perform well on perturbed test data, and significantly more labeled data is required to achieve ... More

KBGAN: Adversarial Learning for Knowledge Graph EmbeddingsNov 11 2017Apr 16 2018We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples ... More

The Optimal Size of Stochastic Hodgkin-Huxley Neuronal Systems for Maximal Energy Efficiency in Coding of Pulse SignalsAug 17 2013The generation and conduction of action potentials represents a fundamental means of communication in the nervous system, and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in a process of transfer ... More

Broken Dynamic Symmetry and Phase Transition PrecursorFeb 22 2013Symmetry breaking is a central concept of Landau phase transition theory, which, however, only considers time-averaged static symmetry of crystal lattice while neglects dynamic symmetry of lattice vibrations thus fails to explain the ubiquitous transformation ... More

Stable Memory Allocation in the Hippocampus: Fundamental Limits and Neural RealizationDec 14 2016It is believed that hippocampus functions as a memory allocator in brain, the mechanism of which remains unrevealed. In Valiant's neuroidal model, the hippocampus was described as a randomly connected graph, the computation on which maps input to a set ... More

A convergent linearized Lagrange finite element method for the magneto-hydrodynamic equations in 2D nonsmooth and nonconvex domainsFeb 12 2019A new fully discrete linearized $H^1$-conforming Lagrange finite element method is proposed for solving the two-dimensional magneto-hydrodynamics equations based on a magnetic potential formulation. The proposed method yields numerical solutions that ... More

Learning Deep Structure-Preserving Image-Text EmbeddingsNov 19 2015Apr 14 2016This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities. The network is trained using a large margin objective that combines ... More

A convergent linearized Lagrange finite element method for the magneto-hydrodynamic equations in 2D nonsmooth and nonconvex domainsFeb 12 2019Mar 09 2019A new fully discrete linearized $H^1$-conforming Lagrange finite element method is proposed for solving the two-dimensional magneto-hydrodynamics equations based on a magnetic potential formulation. The proposed method yields numerical solutions that ... More

Data-Driven Analysis and Common Proper Orthogonal Decomposition (CPOD)-Based Spatio-Temporal Emulator for Design ExplorationJul 26 2017The present study proposes a data-driven framework trained with high-fidelity simulation results to facilitate decision making for combustor designs. At its core is a surrogate model employing a machine-learning technique called kriging, which is combined ... More

Image classification by visual bag-of-words refinement and reductionJan 18 2015This paper presents a new framework for visual bag-of-words (BOW) refinement and reduction to overcome the drawbacks associated with the visual BOW model which has been widely used for image classification. Although very influential in the literature, ... More

Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning PossibleJun 11 2017Non-interactive Local Differential Privacy (LDP) requires data analysts to collect data from users through noisy channel at once. In this paper, we extend the frontiers of Non-interactive LDP learning and estimation from several aspects. For learning ... More

Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation for prediction of spatiotemporally evolving flow dynamicsFeb 24 2018This interdisciplinary study, which combines machine learning, statistical methodologies, high-fidelity simulations, and flow physics, demonstrates a new process for building an efficient surrogate model for predicting spatiotemporally evolving flow dynamics. ... More

Study on Timing Performance of a Readout Circuit for SiPMJun 07 2018In recent years, SiPM photoelectric devices have drawn much attention in the domain of time-of-flight-based positron emission tomography (TOF-PET). Using them to construct PET detectors with excellent coincidence time resolution (CTR) is always one of ... More

Distributed Bandit Learning: How Much Communication is Needed to Achieve (Near) Optimal RegretApr 12 2019We study the communication complexity of distributed multi-armed bandits (MAB) and distributed linear bandits for regret minimization. We propose communication protocols that achieve near-optimal regret bounds and result in optimal speed-up under mild ... More

Differentially Private Data Releasing for Smooth Queries with Synthetic Database OutputJan 06 2014We consider accurately answering smooth queries while preserving differential privacy. A query is said to be $K$-smooth if it is specified by a function defined on $[-1,1]^d$ whose partial derivatives up to order $K$ are all bounded. We develop an $\epsilon$-differentially ... More

An FPGA Based Fast Linear Discharge Method for Nuclear Pulse DigitizationJun 07 2018Inspired by Wilkinson ADC method, we implement a fast linear discharge method based on FPGA to digitize nuclear pulse signal. In this scheme, we use a constant current source to discharge the charge on capacitor which is integrated by the input current ... More

Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDPJan 27 2019A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient. Recently, Jin et al. \cite{jin2018q} proposed a Q-learning algorithm with UCB exploration policy, and proved it has nearly optimal regret bound for ... More

Distributed Bandit Learning: Near-Optimal Regret with Efficient CommunicationApr 12 2019May 29 2019We study the problem of regret minimization for distributed bandits learning, in which $M$ agents work collaboratively to minimize their total regret under the coordination of a central server. Our goal is to design communication protocols with near-optimal ... More

The Expressive Power of Neural Networks: A View from the WidthSep 08 2017Nov 01 2017The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this paper, we study how width affects the expressiveness of neural networks. Classical ... More

SQL-Rank: A Listwise Approach to Collaborative RankingFeb 28 2018Feb 06 2019In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to previous pointwise and pairwise approaches, which are based on treating either ... More

Improving the Generalization of Adversarial Training with Domain AdaptationOct 01 2018Mar 15 2019By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It ... More

Efficient Private ERM for Smooth ObjectivesMar 29 2017May 24 2017In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves ... More

Disentangled Feature for Weakly Supervised Multi-class Sound Event DetectionMay 24 2019Jun 18 2019We propose a disentangled feature for weakly supervised multiclass sound event detection (SED), which helps ameliorate the performance and the training efficiency of class-wise attention based detection system by the introduction of more class-wise prior ... More

Improving the Generalization of Adversarial Training with Domain AdaptationOct 01 2018Jan 17 2019By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It ... More

Training Deeper Convolutional Networks with Deep SupervisionMay 11 2015One of the most promising ways of improving the performance of deep convolutional neural networks is by increasing the number of convolutional layers. However, adding layers makes training more difficult and computationally expensive. In order to train ... More

Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC ClassesFeb 18 2017In this work we study the quantitative relation between the recursive teaching dimension (RTD) and the VC dimension (VCD) of concept classes of finite sizes. The RTD of a concept class $\mathcal C \subseteq \{0, 1\}^n$, introduced by Zilles et al. (2011), ... More

A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored SearchJun 03 2014Jun 04 2014Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers. There have been several pieces of work in the literature ... More

Equipping Experts/Bandits with Long-term MemoryMay 30 2019We propose the first reduction-based approach to obtaining long-term memory guarantees for online learning in the sense of Bousquet and Warmuth, 2002, by reducing the problem to achieving typical switching regret. Specifically, for the classical expert ... More

Disentangled Feature for Weakly Supervised Multi-class Sound Event DetectionMay 24 2019We propose a disentangled feature for weakly supervised multiclass sound event detection (SED), which helps ameliorate the performance and the training efficiency of class-wise attention based detection system by the introduction of more class-wise prior ... More

Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural NetworksJun 14 2017Aug 29 2017Early detection of pulmonary cancer is the most promising way to enhance a patient's chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired ... More

What you need is a more professional teacherJun 06 2019Jun 18 2019We propose a simple and efficient method to combine semi-supervised learning with weakly-supervised learning for deep neural networks. Designing deep neural networks for weakly-supervised learning is always accompanied by a tradeoff between fine-information ... More

Entanglement dynamics of a two-qubit system coupled individually to Ohmic bathsJun 07 2013Aug 08 2013Developed originally for the Holstein polaron, the Davydov D1 ansatz is an efficient, yet extremely accurate trial state for time-dependent variation of the spin-boson model [J. Chem. Phys. 138, 084111 (2013)]. In this work, the Dirac-Frenkel time-dependent ... More

Disentangled Feature for Weakly Supervised Multi-class Sound Event DetectionMay 24 2019Jun 03 2019We propose a disentangled feature for weakly supervised multiclass sound event detection (SED), which helps ameliorate the performance and the training efficiency of class-wise attention based detection system by the introduction of more class-wise prior ... More

RepPoints: Point Set Representation for Object DetectionApr 25 2019Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization ... More

Adaptive Step Size Strategy for Orthogonality Constrained Line Search MethodsJun 07 2019In this paper, we propose an adaptive step size strategy for a class of line search methods for orthogonality constrained minimization problems, which avoids the classic backtracking procedure. We prove the convergence of the line search methods equipped ... More

Multi-scale Orderless Pooling of Deep Convolutional Activation FeaturesMar 07 2014Sep 08 2014Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable ... More

What you need is a more professional teacherJun 06 2019We propose a simple and efficient method to combine semi-supervised learning with weakly-supervised learning for deep neural networks. Designing deep neural networks for weakly-supervised learning is always accompanied by a tradeoff between fine-information ... More

Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical ViewpointsJul 19 2017Algorithm-dependent generalization error bounds are central to statistical learning theory. A learning algorithm may use a large hypothesis space, but the limited number of iterations controls its model capacity and generalization error. The impacts of ... More

Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event DetectionMay 24 2019Jul 10 2019Sound event detection (SED) is to recognize the presence of sound events in the segment of audio and detect their onset as well as offset. SED can be regarded as a supervised learning task when strong annotations (timestamps) are available during learning. ... More

Quantum phase transitions in the spin-boson model without the counterrotating termsDec 10 2018We study the spin-boson model without the counterrotating terms by a numerically exact method based on variational matrix product states. Surprisingly, the second-order quantum phase transition (QPT) is observed for the sub-Ohmic bath in the rotating-wave ... More

Cross-lingual Knowledge Graph Alignment via Graph Matching Neural NetworkMay 28 2019Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs. In this paper, we introduce the ... More

A Theoretical Analysis of NDCG Type Ranking MeasuresApr 24 2013A central problem in ranking is to design a ranking measure for evaluation of ranking functions. In this paper we study, from a theoretical perspective, the widely used Normalized Discounted Cumulative Gain (NDCG)-type ranking measures. Although there ... More

Weighted Sobolev regularity of the Bergman projection on the Hartogs triangleFeb 15 2015Oct 20 2015We prove a weighted Sobolev estimate of the Bergman projection on the Hartogs triangle, where the weight is some power of the distance to the singularity at the boundary. This method also applies to the $n$-dimensional generalization of the Hartogs triangle. ... More

Weighted Bergman Projection on the Hartogs TriangleOct 22 2014Apr 26 2015We prove the $L^p$ regularity of the weighted Bergman projection on the Hartogs triangle, where the weights are powers of the distance to the singularity at the boundary. The restricted range of $p$ is proved to be sharp. By using a two-weight inequality ... More

FPGA Based Pico-second Time Measurement System for a DIRC-like TOF DetectorJun 07 2018A prototype of DIRC-like Time-of-Flight detector (DTOF), including a pico-second time measurement electronics, is developed and tested preliminarily. The basic structure of DTOF is composed of a fused silica radiator connected to fast micro-channel plate ... More

MedSTS: A Resource for Clinical Semantic Textual SimilarityAug 28 2018The wide adoption of electronic health records (EHRs) has enabled a wide range of applications leveraging EHR data. However, the meaningful use of EHR data largely depends on our ability to efficiently extract and consolidate information embedded in clinical ... More

Learning to Navigate for Fine-grained ClassificationSep 02 2018Fine-grained classification is challenging due to the difficulty of finding discriminative features. Finding those subtle traits that fully characterize the object is not straightforward. To handle this circumstance, we propose a novel self-supervision ... More

Big-Data Clustering: K-Means or K-Indicators?Jun 03 2019The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering. Highly sensitive to initializations, however, K-means encounters a scalability bottleneck ... More

The $L^p$ boundedness of the Bergman projection for a class of bounded Hartogs domainsApr 30 2013Jun 08 2015We generalize the Hartogs triangle to a class of bounded Hartogs domains, and we prove that the corresponding Bergman projections are bounded on $L^p$ if and only if $p$ is in the range $(\frac{2n}{n+1},\frac{2n}{n-1})$.

Energy-efficient population coding constrains network size of a neuronal array systemJul 29 2015Here, we consider the open issue of how the energy efficiency of neural information transmission process in a general neuronal array constrains the network size, and how well this network size ensures the neural information being transmitted reliably ... More

On the Depth of Deep Neural Networks: A Theoretical ViewJun 17 2015Nov 28 2015People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound. In margin bound, ... More

Low Rank Approximation of Binary Matrices: Column Subset Selection and GeneralizationsNov 05 2015Apr 20 2017Low rank matrix approximation is an important tool in machine learning. Given a data matrix, low rank approximation helps to find factors, patterns and provides concise representations for the data. Research on low rank approximation usually focus on ... More

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence ModelAug 23 2018Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose ... More

Gradient Descent Finds Global Minima of Deep Neural NetworksNov 09 2018Feb 04 2019Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network ... More

Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same RepresentationOct 28 2018Nov 28 2018It is widely believed that learning good representations is one of the main reasons for the success of deep neural networks. Although highly intuitive, there is a lack of theory and systematic approach quantitatively characterizing what representations ... More

Fast, Diverse and Accurate Image Captioning Guided By Part-of-SpeechMay 31 2018Apr 11 2019Image captioning is an ambiguous problem, with many suitable captions for an image. To address ambiguity, beam search is the de facto method for sampling multiple captions. However, beam search is computationally expensive and known to produce generic ... More

Dual Learning for Machine TranslationNov 01 2016While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop ... More

Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot LearningOct 19 2018Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by learning a projection function between a feature space and a semantic space ... More

Zero-Shot Fine-Grained Classification by Deep Feature Learning with SemanticsJul 04 2017Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task due to two main issues: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. ... More

FRAGE: Frequency-Agnostic Word RepresentationSep 18 2018Continuous word representation (aka word embedding) is a basic building block in many neural network-based models used in natural language processing tasks. Although it is widely accepted that words with similar semantics should be close to each other ... More

Generalized Second Price Auction with Probabilistic Broad MatchApr 15 2014Generalized Second Price (GSP) auctions are widely used by search engines today to sell their ad slots. Most search engines have supported broad match between queries and bid keywords when executing GSP auctions, however, it has been revealed that GSP ... More

Gradient Descent Finds Global Minima of Deep Neural NetworksNov 09 2018May 28 2019Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network ... More

Can Image-Level Labels Replace Pixel-Level Labels for Image ParsingMar 07 2014Nov 13 2014This paper presents a weakly supervised sparse learning approach to the problem of noisily tagged image parsing, or segmenting all the objects within a noisily tagged image and identifying their categories (i.e. tags). Different from the traditional image ... More

Solutions to the quantum Rabi model with two equivalent qubitsJan 25 2014May 29 2014Using extended coherent states, an analytically exact study has been carried out for the quantum Rabi model with two equivalent qubits. Compact transcendental functions of one variable have been derived leading to exact solutions. The energy spectrum ... More

Cross-lingual Data Transformation and Combination for Text ClassificationJun 23 2019Text classification is a fundamental task for text data mining. In order to train a generalizable model, a large volume of text must be collected. To address data insufficiency, cross-lingual data may occasionally be necessary. Cross-lingual data sources ... More

On Low Rank Approximation of Binary MatricesNov 05 2015We consider the problem of low rank approximation of binary matrices. Here we are given a $d \times n$ binary matrix $A$ and a small integer $k < d$. The goal is to find two binary matrices $U$ and $V$ of sizes $d \times k$ and $k \times n$ respectively, ... More

Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence ModelsMay 19 2015Sep 19 2016The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across ... More

Towards Binary-Valued Gates for Robust LSTM TrainingJun 08 2018Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. It aims to use gates to control information flow (e.g., whether to skip some information or not) in the recurrent computations, although its practical ... More

Randomness in Deconvolutional Networks for Visual RepresentationApr 02 2017Feb 20 2018Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of this network ... More

A Comparison of Word Embeddings for the Biomedical Natural Language ProcessingFeb 01 2018Jul 18 2018Word embeddings have been widely used in biomedical Natural Language Processing (NLP) applications as they provide vector representations of words capturing the semantic properties of words and the linguistic relationship between words. Many biomedical ... More

A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information ExtractionApr 20 2018Objective: To automatically create large labeled training datasets and reduce the efforts of feature engineering for training accurate machine learning models for clinical information extraction. Materials and Methods: We propose a distant supervision ... More

CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data ModelJan 22 2019Background: Widespread adoption of electronic health records (EHRs) has enabled secondary use of EHR data for clinical research and healthcare delivery. Natural language processing (NLP) techniques have shown promise in their capability to extract the ... More

Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of ViewJun 06 2019The Transformer architecture is widely used in natural language processing. Despite its success, the design principle of the Transformer remains elusive. In this paper, we provide a novel perspective towards understanding the architecture: we show that ... More

Invariant algebraic surfaces of the FitzHugh-Nagumo systemJan 04 2017In this paper, we characterize all the irreducible Darboux polynomials and polynomial first integrals of FitzHugh-Nagumo (F-N) system. The method of the weight homogeneous polynomials and the characteristic curves is widely used to give a complete classification ... More

Magnetic miniband and magnetotransport property of a graphene superlatticeMar 30 2010Apr 04 2010The eigen energy and the conductivity of a graphene sheet subject to a one-dimensional cosinusoidal potential and in the presence of a magnetic field are calculated. Such a graphene superlattice presents three distinct magnetic miniband structures as ... More

From wurtzite nanoplatelets to zinc blende nanorods: Simultaneous control of shape and phase in ultrathin ZnS nanocrystalsJul 04 2019Ultrathin semiconductor nanocrystals (NCs) with at least one dimension below their exciton Bohr radius receive a rapidly increasing attention due to their unique physicochemical properties such as strong quantum confinement, large surface-to-volume ratio, ... More

Inaudible Voice CommandsAug 24 2017Voice assistants like Siri enable us to control IoT devices conveniently with voice commands, however, they also provide new attack opportunities for adversaries. Previous papers attack voice assistants with obfuscated voice commands by leveraging the ... More

On the Inequalities of Projected Volumes and the Constructible RegionOct 31 2014Dec 08 2018We study the following geometry problem: given a $2^n-1$ dimensional vector $\pi=\{\pi_S\}_{S\subseteq [n], S\ne \emptyset}$, is there an object $T\subseteq\mathbb{R}^n$ such that $\log(\mathsf{vol}(T_S))= \pi_S$, for all $S\subseteq [n]$, where $T_S$ ... More

Analysis of the Fourier series Dirichlet-to-Neumann boundary condition of the Helmholtz equation and its application to finite element methodsSep 02 2016Feb 11 2019It is well known that the Fourier series Dirichlet-to-Neumann (DtN) boundary condition can be used to solve the Helmholtz equation in unbounded domains. In this work, applying such DtN boundary condition and using the finite element method, we solve and ... More

A solution operator for $\bar\partial$ on the Hartogs triangle and $L^p$ estimatesOct 12 2017Sep 20 2018An integral solution operator for $\bar\partial$ is constructed on product domains that include the punctured bidisc. This operator is shown to satisfy $L^p$ estimates for all $1\leq p <\infty$, though with non-standard -- relative to strongly pseudoconvex ... More

A priori error estimates of the DtN-FEM: fluid-solid interaction problemsSep 02 2016We consider the finite element method solving a fluid-solid interaction (FSI) problem in two dimensions. The original problem is reduced to an equivalent nonlocal boundary value problem through an exact Dirichlet-to-Neumann (DtN) mapping defined on an ... More

Construction of Directed Strongly Regular Graphs as Generalized Cayley GraphsOct 05 2014Dec 23 2014Directed strongly regular graphs were introduced by Duval in 1998 as one of the possible generalization of classical strongly regular graphs to the directed case. Duval also provided several construction methods for directed strongly regular graphs. In ... More

Giant Gating Tunability of Optical Refractive Index in Transition Metal Dichalcogenide MonolayersMay 27 2017We report that the refractive index of transition metal dichacolgenide (TMDC) monolayers, such as MoS2, WS2, and WSe2, can be substantially tuned by > 60% in the imaginary part and > 20% in the real part around exciton resonances using CMOS-compatible ... More

Smoothing Properties of the Friedrichs Operator on $L^p$ spacesSep 01 2017Dec 16 2017We show that the Friedrichs operator exhibits smoothing properties in the $L^p$ scale. In particular we prove that on any smoothly bounded pseudoconvex domain the Friedrichs operator maps $A^2(\Omega)$ to $A^p(\Omega)$ for some $p>2$.

Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four MinutesJul 30 2018Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. Although using larger mini-batch sizes can improve the system scalability by reducing the communication-to-computation ... More

Product domains, Multi-Cauchy transforms, and the $\bar \partial$ equationApr 20 2019Solution operators for the equation $\bar \partial u=f$ are constructed on general product domains in $\mathbb{C}^n$. When the factors are one-dimensional, the operator is a simple integral operator: it involves specific derivatives of $f$ integrated ... More

Weighted Bergman Projections on the Hartogs Triangle: Exponential DecayOct 27 2016We study weighted Bergman projections on the Hartogs triangle in $\mathbb{C}^2$. We show that projections corresponding to exponentially vanishing weights have degenerate $L^p$ mapping properties.

Solutions to the anisotropic quantum Rabi modelFeb 02 2015In this work, the anisotropic quantum Rabi model with different coupling strengths of the rotating-wave and counter-rotating wave terms is studied by using two kinds of extended coherent states (ECS). By the first kind of ECS, we can derive a so-called ... More

Analysis of a mixed finite element method for the quad-curl problemNov 16 2018May 19 2019The quad-curl term is an essential part in the resistive magnetohydrodynamic (MHD) equation and the fourth order inverse electromagnetic scattering problem which are both of great significance in science and engineering. It is desirable to develop efficient ... More

Privacy Risks of Securing Machine Learning Models against Adversarial ExamplesMay 24 2019The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and ... More

Mean Field Approximations to a Queueing System with Threshold-Based Workload Control SchemeFeb 20 2018Jul 17 2018In this paper, motivated by considerations of server utilization and energy consumptions in cloud computing, we investigate a homogeneous queueing system with a threshold-based workload control scheme. In this system, a virtual machine will be turned ... More

Boundary integral equation methods for the elastic and thermoelastic waves in three dimensionsFeb 11 2019In this paper, we consider the boundary integral equation (BIE) method for solving the exterior Neumann boundary value problems of elastic and thermoelastic waves in three dimensions based on the Fredholm integral equations of the first kind. The innovative ... More

Regrets of an Online Alternating Direction Method of Multipliers for Online Composite OptimizationApr 05 2019In this paper, we investigate regrets of an online semi-proximal alternating direction method of multiplier (Online-spADMM) for solving online linearly constrained convex composite optimization problems. Under mild conditions, we establish ${\rm O}(\sqrt{N})$ ... More

The Rate of Convergence of the Augmented Lagrangian Method for a Nonlinear Semidefinite Nuclear Norm Composite Optimization ProblemSep 02 2017We propose two basic assumptions, under which the rate of convergence of the augmented Lagrange method for a class of composite optimization problems is estimated. We analyze the rate of local convergence of the augmented Lagrangian method for a nonlinear ... More