arXiv subjects

Han Wang

Publications and source records attributed to Han Wang.

At least 487 records · Page 27Linked to original sources

arXiv2019

Robust Convergence Analysis of Three-Operator Splitting

Operator splitting methods solve composite optimization problems by breaking them into smaller sub-problems that can be solved sequentially or in parallel. In this paper, we propose a unified framework for certifying both linear and sublinear convergence rates for three-operator splitting (TOS) method under a variety of assumptions about the objective function. By viewing the algorithm as a dynamical system with feedback uncertainty (the oracle model), we leverage robust control theory to analyze the worst-case performance of the algorithm using matrix inequalities. We then show how these matrix inequalities can be used to verify sublinear/linear convergence of the TOS algorithm and guide the search for selecting the parameters of the algorithm (both symbolically and numerically) for optimal worst-case performance. We illustrate our results numerically by solving an input-constrained optimal control problem.

math.OC↗

arXiv2019

Hardy type inequalities on closed manifolds via Ricci curvature

The paper is devoted to Hardy type inequalities on closed manifolds. By means of various weighted Ricci curvatures, we establish several sharp Hardy type inequalities on closed weighted Riemannian manifolds. Our results complement in several aspects those obtained recently in the noncompact Riemannian setting.

math.DG↗

arXiv2019

VideoDP: A Universal Platform for Video Analytics with Differential Privacy

Massive amounts of video data are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis, pedestrian behavior analysis, video surveillance). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps) without quantifying and bounding the privacy leakage in the sanitization. In this paper, to the best of our knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee. Different from traditional noise-injection based differentially private mechanisms, given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the utility-driven video while ensuring differential privacy. Finally, we conduct experiments on real videos, and the experimental results demonstrate that our VideoDP effectively functions video analytics with good utility.

cs.CR↗

arXiv2019

Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics

Simulating warm dense matter that undergoes a wide range of temperatures and densities is challenging. Predictive theoretical models, such as quantum-mechanics-based first-principles molecular dynamics (FPMD), require a huge amount of computational resources. Herein, we propose a deep learning based scheme, called electron temperature dependent deep potential molecular dynamics (TDDPMD), for efficiently simulating warm dense matter with the accuracy of FPMD. The TDDPMD simulation is several orders of magnitudes faster than FPMD, and, unlike FPMD, its efficiency is not affected by the electron temperature. We apply the TDDPMD scheme to beryllium (Be) in a wide range of temperatures (0.4 to 2500 eV) and densities (3.50 to 8.25 g/cm$^3$). Our results demonstrate that the TDDPMD method not only accurately reproduces the structural properties of Be along the principal Hugoniot curve at the FPMD level, but also yields even more reliable diffusion coefficients than typical FPMD simulations due to its ability to simulate larger systems with longer time.

physics.comp-ph↗

arXiv2019

MIN: Co-Governing Multi-Identifier Network Architecture and its Prototype on Operator's Network

IP protocol is the core of TCP/IP network layer. However, since IP address and its Domain Name are allocated and managed by a single agency, there are risks of centralization. The semantic overload of IP address also reduces its scalability and mobility, which further hinders the security. This paper proposes a co-governing Multi-Identifier Network (MIN) architecture that constructs a network layer with parallel coexistence of multiple identifiers, including identity, content, geographic information, and IP address. On the management plane, we develop an efficient management system using consortium blockchain with voting consensus, so the network can simultaneously manage and support by hundreds or thousands of nodes with high throughput. On the data plane, we propose an algorithm merging hash table and prefix tree (HTP) for FIB, which avoids the false-negative error and can inter-translate different identifiers with tens of billions of entries. Further, we propose a scheme to transport IP packets using CCN as a tunnel for supporting progressive deployment. We deployed the prototype of MIN to the largest operators' network in Mainland China, Hongkong and Macao, and demonstrated that the network can register identifier under co-governing consensus algorithm, support VoD service very well.

cs.NI↗

arXiv2019

Deterministic facilitated excitation of the weakly-driven atom in heteronuclear Rydberg atom pairs beyond antiblockade

Due to the intrinsic strong blockaded interaction shifting the energy level of Rydberg state, the steady Rydberg probability may be substantially restrained to a low level, especially for atoms suffering from weak drivings. We report an exotic excitation facilitation using strong blockaded energy shifting the double excitation state, leading to the weakly-driven atom deterministically excited by a significantly improved fraction even at far off-resonant regimes. This phenomenon is attributed to the effective induction of a reduced detuning to the weakly-driven atom by optimizing parameters of an auxiliary strongly-driven atom, arising a modified large probability preserved for a broadening range of detunings. The influence from breaking blockade condition on the excitation probability is also discussed. In contrast to previous approaches, our excitation facilitation mechanism does not rely on antiblockade effect to the compensation of Rydberg energy, serving as a fresh way to a deterministic single-atom excitation of the target atom in a pair of heteronuclear Rydberg atoms, where a strong interatomic interaction is required.

physics.atom-ph↗

arXiv2019

The Prototype of Decentralized Multilateral Co-Governing Post-IP Internet Architecture and Its Testing on Operator Networks

The Internet has become the most important infrastructure of modern society, while the existing IP network is unable to provide high-quality service. The unilateralism IP network is unable to satisfy the Co-managing and Co-governing demands to Cyberspace for most Nations in the world as well. Facing this challenge, we propose a novel Decentralized Multilateral Co-Governing Post-IP Internet architecture. To verify its effectiveness, we develop the prototype on the operator's networks including China Mainland, Hong Kong, and Macao. The experiments and testing results show that this architecture is feasible for co-existing of Content-Centric Networking and IP network, and it might become a Chinese Solution to the world.

cs.NI↗

arXiv2019

Isotope Effects in Liquid Water via Deep Potential Molecular Dynamics

A comprehensive microscopic understanding of ambient liquid water is a major challenge for $ab$ $initio$ simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive sampling of configuration space. Due to the presence of light atoms (e.g., H or D), nuclear quantum fluctuations lead to observable changes in the structural properties of liquid water (e.g., isotope effects), and therefore provide yet another challenge for $ab$ $initio$ approaches. In this work, we demonstrate that the combination of dispersion-inclusive hybrid density functional theory (DFT), the Feynman discretized path-integral (PI) approach, and machine learning (ML) constitutes a versatile $ab$ $initio$ based framework that enables extensive sampling of both thermal and nuclear quantum fluctuations on a quite accurate underlying PES. In particular, we employ the recently developed deep potential molecular dynamics (DPMD) model---a neural-network representation of the $ab$ $initio$ PES---in conjunction with a PI approach based on the generalized Langevin equation (PIGLET) to investigate how isotope effects influence the structural properties of ambient liquid H$_2$O and D$_2$O. Through a detailed analysis of the interference differential cross sections as well as several radial and angular distribution functions, we demonstrate that this approach can furnish a semi-quantitative prediction of these subtle isotope effects.

physics.chem-ph↗

arXiv2019

Linear Dichroism Conversion in Quasi One-Dimensional Perovskite Chalcogenide

Anisotropic photonic materials with linear dichroism are crucial components in many sensing, imaging and communication applications. Such materials play an important role as polarizers, filters and wave-plates in photonic devices and circuits. Conventional crystalline materials with optical anisotropy typically show unidirectional linear dichroism over a broad wavelength range. The linear dichroism conversion phenomenon has not been observed in crystalline materials. Here, we report the investigation of the unique linear dichroism conversion phenomenon in quasi-one-dimensional (quasi-1D) hexagonal perovskite chalcogenide BaTiS3. The material shows record level of optical anisotropy within the visible wavelength range. In contrast to conventional anisotropic optical materials, the linear dichroism polarity in BaTiS3 makes an orthogonal change at an optical wavelength corresponding to the photon energy of 1.78 eV. First principle calculations reveal that this anomalous linear dichroism conversion behavior originates from different selection rules of the optical transitions from the parallel bands in the BaTiS3 material. Wavelength dependent polarized Raman spectroscopy further confirms this phenomenon. Such material with linear dichroism conversion property can facilitate new ability to control and sense the energy and polarization of light, and lead to novel photonic devices such as polarization-wavelength selective detectors and lasers for multispectral imaging, sensing and optical communication applications.

cond-mat.mtrl-sci↗

arXiv2019

Adiabatic and high-fidelity quantum gates with hybrid Rydberg-Rydberg interactions

Rydberg blockaded gate is a fundamental ingredient for scalable quantum computation with neutral Rydberg atoms. However the fidelity of such a gate is intrinsically limited by a blockade error coming from a Rydberg level shift that forbids its extensive use. Based on a dark-state adiabatic passage, we develop a novel protocol for realizing a two-atom blockade-error-free quantum gate in a hybrid system with simultaneous van der Waals (vdWsI) and resonant dipole-dipole interactions (DDI). The basic idea relies on converting the roles of two interactions, which is, the DDI serves as one time-dependent tunable pulse and the vdWsI acts as a negligible middle level shift as long as the adiabatic condition is preserved. We adopt an optimized super-Gaussian optical pulse with $kπ$ ($k\gg 1$) area accompanied by a smooth tuning for the DDI, composing a circular stimulated Raman adiabatic passage, which can robustly ensure a faster operation time $\sim 80ns$ as well as a highly-efficient gate fidelity $\sim0.9996$. This theoretical protocol offers a flexible treatment for hybrid interactions in complex Rydberg systems, enabling on-demand design of new types of effective Rydberg quantum gate devices.

physics.atom-ph↗

arXiv2019

Machine Learning Approach for Device-Circuit Co-Optimization of Stochastic-Memristive-Device-Based Boltzmann Machine

A Boltzmann machine whose effective "temperature" can be dynamically "cooled" provides a stochastic neural network realization of simulated annealing, which is an important metaheuristic for solving combinatorial or global optimization problems with broad applications in machine intelligence and operations research. However, the hardware realization of the Boltzmann stochastic element with "cooling" capability has never been achieved within an individual semiconductor device. Here we demonstrate a new memristive device concept based on two-dimensional material heterostructures that enables this critical stochastic element in a Boltzmann machine. The dynamic cooling effect in simulated annealing can be emulated in this multi-terminal memristive device through electrostatic bias with sigmoidal thresholding distributions. We also show that a machine-learning-based method is efficient for device-circuit co-design of the Boltzmann machine based on the stochastic memristor devices in simulated annealing. The experimental demonstrations of the tunable stochastic memristors combined with the machine-learning-based device-circuit co-optimization approach for stochastic-memristor-based neural-network circuits chart a pathway for the efficient hardware realization of stochastic neural networks with applications in a broad range of electronics and computing disciplines.

cs.ET↗

arXiv2019

Machine learning-guided synthesis of advanced inorganic materials

Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods leads to high uncertainty, numerous trials and exorbitant cost. Recently, machine learning (ML) has demonstrated tremendous potential for material research. Here, we report the application of ML to optimize and accelerate material synthesis process in two representative multi-variable systems. A classification ML model on chemical vapor deposition-grown MoS2 is established, capable of optimizing the synthesis conditions to achieve higher success rate. While a regression model is constructed on the hydrothermal-synthesized carbon quantum dots, to enhance the process-related properties such as the photoluminescence quantum yield. Progressive adaptive model is further developed, aiming to involve ML at the beginning stage of new material synthesis. Optimization of the experimental outcome with minimized number of trials can be achieved with the effective feedback loops. This work serves as proof of concept revealing the feasibility and remarkable capability of ML to facilitate the synthesis of inorganic materials, and opens up a new window for accelerating material development.

cond-mat.mtrl-sci↗

arXiv2019

English Out-of-Vocabulary Lexical Evaluation Task

Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge. The OOV words are words that only appear in test samples. The goal of tasks is to provide solutions for OOV lexical classification and prediction. The tasks require annotators to conclude the attributes of the OOV words based on their related contexts. Then, we utilize unsupervised word embedding methods such as Word2Vec and Word2GM to perform the baseline experiments on the categorical classification task and OOV words attribute prediction tasks.

cs.CL↗

arXiv2019

Deep learning inter-atomic potential model for accurate irradiation damage simulations

We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy model. The resulting DP-ZBL model can not only provide overall good performance on the predictions of near-equilibrium material properties but also capture the right physics when atoms are extremely close to each other, an event that frequently happens in computational simulations of irradiation damage events. We applied this scheme to the simulation of the irradiation damage processes in the face-centered-cubic aluminium system, and found better descriptions in terms of the defect formation energy, evolution of collision cascades, displacement threshold energy, and residual point defects, than the widely-adopted ZBL modified embedded atom method potentials and its variants. Our work provides a reliable and feasible scheme to accurately simulate the irradiation damage processes and opens up new opportunities to solve the predicament of lacking accurate potentials for enormous newly-discovered materials in the irradiation effect field.

physics.comp-ph↗

arXiv2019

Homogeneous and Mixed Energy Communities Discovery with Spatial-Temporal Net Energy

Smart grid has integrated an increasing number of distributed energy resources to improve the efficiency and flexibility of power generation and consumption as well as the resilience of the power grid. The energy consumers on the power grid (e.g., households) equipped with the distributed energy resources can be considered as "microgrids" that both generate and consume electricity. In this paper, we study the energy community discovery problems which identify multiple kinds of energy communities for the microgrids to facilitate energy management (e.g., power supply adjustment, load balancing, energy sharing) on the grid, such as homogeneous energy communities (HECs), mixed energy communities (MECs), and self-sufficient energy communities (SECs). Specifically, we present efficient algorithms to discover such communities of microgrids by taking into account not only their geo-locations but also their net energy over any period. Finally, we experimentally validate the performance of the algorithms using both synthetic and real datasets.

eess.SY↗

arXiv2019

GMC: Grid Based Motion Clustering in Dynamic Environment

Conventional SLAM algorithms takes a strong assumption of scene motionlessness, which limits the application in real environments. This paper tries to tackle the challenging visual SLAM issue of moving objects in dynamic environments. We present GMC, grid-based motion clustering approach, a lightweight dynamic object filtering method that is free from high-power and expensive processors. GMC encapsulates motion consistency as the statistical likelihood of detected key points within a certain region. Using this method can we provide real-time and robust correspondence algorithm that can differentiate dynamic objects with static backgrounds. We evaluate our system in public TUM dataset. To compare with the state-of-the-art methods, our system can provide more accurate results by detecting dynamic objects.

cs.RO↗

arXiv2019

Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation

An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.

physics.comp-ph↗

arXiv2019

Partitioned Paxos via the Network Data Plane

Consensus protocols are the foundation for building fault-tolerant, distributed systems, and services. They are also widely acknowledged as performance bottlenecks. Several recent systems have proposed accelerating these protocols using the network data plane. But, while network-accelerated consensus shows great promise, current systems suffer from an important limitation: they assume that the network hardware also accelerates the application itself. Consequently, they provide a specialized replicated service, rather than providing a general-purpose high-performance consensus that fits any off-the-shelf application. To address this problem, this paper proposes Partitioned Paxos, a novel approach to network-accelerated consensus. The key insight behind Partitioned Paxos is to separate the two aspects of Paxos, agreement, and execution, and optimize them separately. First, Partitioned Paxos uses the network forwarding plane to accelerate agreement. Then, it uses state partitioning and parallelization to accelerate execution at the replicas. Our experiments show that using this combination of data plane acceleration and parallelization, Partitioned Paxos is able to provide at least x3 latency improvement and x11 throughput improvement for a replicated instance of a RocksDB key-value store.

cs.DC↗