arXiv subjects

Hao Wang

Publications and source records attributed to Hao Wang.

At least 1,531 records · Page 85Linked to original sources

arXiv2020

Polarization Transfer from the Twisted Light to an Atom

When polarized light is absorbed by an atom, the excited atomic system carries information about the initial polarization of light. For the light that carries an orbital angular momentum, or the twisted light, the polarization states are described by eight independent parameters, as opposed to three Stokes parameters for plane waves. We use a parameterization of the spin-density matrix of the twisted light in terms of vector and tensor polarization, in analogy with massive spin-1 particles, and derive formulae that define atom's response to specific polarization components of the twisted light. It is shown that for dipole ($S\to P$) atomic transitions, the atom's polarization is in one-to-one correspondence with polarization of the incident light; this relation is violated, however, for the transitions of higher multipolarity ($S\to D$, $S\to F$, etc.) We pay special attention to contributions of the longitudinal electric field into the matrix elements of atomic transitions.

quant-ph↗

arXiv2020

Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems

Channel state information (CSI) feedback is critical for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. Most conventional algorithms are based on compressive sensing (CS) and are highly dependent on the level of channel sparsity. To address the issue, a recent approach adopts deep learning (DL) to compress CSI into a codeword with low dimensionality, which has shown much better performance than the CS algorithms when feedback link is perfect. In practical scenario, however, there exists various interference and non-linear effect. In this article, we design a DL-based denoise network, called DNNet, to improve the performance of channel feedback. Numerical results show that the DL-based feedback algorithm with the proposed DNNet has superior performance over the existing algorithms, especially at low signal-to-noise ratio (SNR).

eess.SP↗

arXiv2020

Secret Sharing based Secure Regressions with Applications

Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potential gains are highly anticipated if different organizations could somehow collaboratively share their data for technological improvements. On the other hand, data security concerns may arise for both data holders and data providers due to commercial or sociological concerns. To make a balance between technical improvements and security limitations, we implement secure and scalable protocols for multiple data holders to train linear regression and logistic regression models. We build our protocols based on the secret sharing scheme, which is scalable and efficient in applications. Moreover, our proposed paradigm can be generalized to any secure multiparty training scenarios where only matrix summation and matrix multiplications are used. We demonstrate our approach by experiments which shows the scalability and efficiency of our proposed protocols, and finally present its real-world applications.

cs.LG↗

arXiv2020

Black Hole Mass Function of Coalescing Neutron Star-Black Hole Binary Systems: The Prospect of Reconstruction with the Gravitational Wave Observations

The discovery of gravitational waves from compact objects coalescence opens a brand-new window to observe the universe. With more events being detected in the future, statistical examinations would be essential to better understand the underlying astrophysical processes. In this work we investigate the prospect of measuring the mass function of black holes that are merging with the neutron stars. Applying Bayesian parameter estimation for hundreds of simulated neutron star$-$black hole (NSBH) mergers, we find that the parameters for most of the injected events can be well recovered. We also take a Bayesian hierarchical model to reconstruct the population properties of the masses of black holes, in the presence of a low mass gap, both the mass gap and power-law index ($α$) of black hole mass function can be well measured, thus we can reveal where the $α$ is different for binary black hole (BBH) and NSBH systems. In the absence of a low mass gap, the gravitational wave data as well as the electromagnetic data can be used to pin down the nature of the merger event and then measure the mass of these very light black holes. However, as a result of the misclassification of BBH into NSBH, the measurement of $α$ is more challenging and further dedicated efforts are needed.

astro-ph.HE↗

arXiv2020

Privacy with Estimation Guarantees

We study the central problem in data privacy: how to share data with an analyst while providing both privacy and utility guarantees to the user that owns the data. In this setting, we present an estimation-theoretic analysis of the privacy-utility trade-off (PUT). Here, an analyst is allowed to reconstruct (in a mean-squared error sense) certain functions of the data (utility), while other private functions should not be reconstructed with distortion below a certain threshold (privacy). We demonstrate how chi-square information captures the fundamental PUT in this case and provide bounds for the best PUT. We propose a convex program to compute privacy-assuring mappings when the functions to be disclosed and hidden are known a priori and the data distribution is known. We derive lower bounds on the minimum mean-squared error of estimating a target function from the disclosed data and evaluate the robustness of our approach when an empirical distribution is used to compute the privacy-assuring mappings instead of the true data distribution. We illustrate the proposed approach through two numerical experiments.

cs.IT↗

arXiv2020

Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction

Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the V2V/V2I-based short-range prediction, is difficult and error-prone. The multiple timescales inherent to power and thermal systems call for a variable timescale optimization framework with access to short- and long-term vehicle speed preview. To this end, a model predictive controller (MPC) with a multi-range speed preview for integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs) is presented in this paper. The MPC is formulated to manage the power-split between the engine and the battery while enforcing the power and thermal (engine coolant and catalytic converter temperatures) constraints. The MPC exploits prediction and optimization over a shorter receding horizon and longer shrinking horizon. Over the longer shrinking horizon, the vehicle speed estimation is based on the data collected from the connected vehicles traveling on the same route as the ego-vehicle. Simulation results of applying the MPC over real-world urban driving cycles in Ann Arbor, MI are presented to demonstrate the effectiveness and fuel-saving potentials of the proposed iPTM strategy under the uncertainty associated with long-term predictions of the CV's speed.

eess.SY↗

arXiv2020

On the Robustness of Information-Theoretic Privacy Measures and Mechanisms

Consider a data publishing setting for a dataset composed by both private and non-private features. The publisher uses an empirical distribution, estimated from $n$ i.i.d. samples, to design a privacy mechanism which is applied to new fresh samples afterward. In this paper, we study the discrepancy between the privacy-utility guarantees for the empirical distribution, used to design the privacy mechanism, and those for the true distribution, experienced by the privacy mechanism in practice. We first show that, for any privacy mechanism, these discrepancies vanish at speed $O(1/\sqrt{n})$ with high probability. These bounds follow from our main technical results regarding the Lipschitz continuity of the considered information leakage measures. Then we prove that the optimal privacy mechanisms for the empirical distribution approach the corresponding mechanisms for the true distribution as the sample size $n$ increases, thereby establishing the statistical consistency of the optimal privacy mechanisms. Finally, we introduce and study uniform privacy mechanisms which, by construction, provide privacy to all the distributions within a neighborhood of the estimated distribution and, thereby, guarantee privacy for the true distribution with high probability.

cs.IT↗

arXiv2020

Integrated Power and Thermal Management of Connected HEVs via Multi-Horizon MPC

In this paper, a multi-horizon model predictive controller (MH-MPC) is developed for integrated power and thermal management (iPTM) of a power-split hybrid electric vehicle (HEV). The proposed MH-MPC leverages an accurate short-horizon vehicle speed preview and an approximate forecast over a longer shrinking horizon till the end of the driving cycle. This multiple-horizon scheme is developed to cope with fast and slow dynamics associated with power and thermal responses. The main objective of the proposed MH-MPC is to minimize fuel consumption and enforce the power and thermal constraints on the battery state-of-charge and engine coolant temperature, while meeting the driving (traction) and cabin air conditioning (heating) demands. The proposed MH-MPC allows for exploiting the engine coolant as thermal energy storage, providing more flexibility for the HEV energy flow optimization. The simulation results show that the proposed MH-MPC provides near-optimal results in reference to the Dynamic Programming (DP) solution with an affordable computational cost. Moreover, compared with a more conventional MPC strategy, the MH-MPC can leverage the speed previews with different resolutions effectively to achieve the desired performance with satisfactory robustness.

eess.SY↗

arXiv2020

Sparse Optimization for Green Edge AI Inference

With the rapid upsurge of deep learning tasks at the network edge, effective edge artificial intelligence (AI) inference becomes critical to provide low-latency intelligent services for mobile users via leveraging the edge computing capability. In such scenarios, energy efficiency becomes a primary concern. In this paper, we present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference through minimizing the overall power consumption consisting of both computation and transmission power consumption, yielding a mixed combinatorial optimization problem. By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem. To solve this challenging problem, we propose a log-sum function based three-stage approach. By adopting the log-sum function to enhance the group sparsity, a proximal iteratively reweighted algorithm is developed. Furthermore, we establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm. Simulation results will demonstrate the effectiveness of the proposed approach for improving energy efficiency in edge AI inference systems.

cs.IT↗

arXiv2020

Intrinsic Valley Polarization and Anomalous Valley Hall Effect in Single-Layer 2H-FeCl2

Valley, as a new degree of freedom for electrons, has drawn considerable attention due to its significant potential for encoding and storing information. Lifting the energy degeneracy to achieve valley polarization is necessary for realizing valleytronic devices. Here, on the basis of first-principles calculations, we show that single-layer FeCl2 exhibits a large spontaneous valley polarization (~101 meV) arising from the broken time-reversal symmetry and spin-orbital coupling, which can be continuously tuned by varying the direction of magnetic crystalline. By employing the perturbation theory, the underlying physical mechanism is unveiled. Moreover, the coupling between valley degree of freedom and ferromagnetic order could generate a spin- and valley-polarized anomalous Hall current in the presence of the in-plane electric field, facilitating its experimental exploration and practical applications.

cond-mat.mtrl-sci↗

arXiv2020

Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes

We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes. Our system adaptively uses information from complementary sensors such as GNSS, LiDAR, and IMU to achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. Rather than relying only on LiDAR intensity or 3D geometry, we make innovative use of LiDAR intensity and altitude cues to significantly improve localization system accuracy and robustness. Our GNSS RTK module utilizes the help of the multi-sensor fusion framework and achieves a better ambiguity resolution success rate. An error-state Kalman filter is applied to fuse the localization measurements from different sources with novel uncertainty estimation. We validate, in detail, the effectiveness of our approaches, achieving 5-10cm RMS accuracy and outperforming previous state-of-the-art systems. Importantly, our system, while deployed in a large autonomous driving fleet, made our vehicles fully autonomous in crowded city streets despite road construction that occurred from time to time. A dataset including more than 60 km real traffic driving in various urban roads is used to comprehensively test our system.

cs.CV↗

arXiv2020

Towards information-rich, logical text generation with knowledge-enhanced neural models

Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life. However, existing end-to-end neural models suffer from the problem of tending to generate uninformative and generic text because they cannot ground input context with background knowledge. In order to solve this problem, many researchers begin to consider combining external knowledge in text generation systems, namely knowledge-enhanced text generation. The challenges of knowledge enhanced text generation including how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate knowledge into generation process. This survey gives a comprehensive review of knowledge-enhanced text generation systems, summarizes research progress to solving these challenges and proposes some open issues and research directions.

cs.AI↗

arXiv2020

A Study of Geometry in Anisotropic Quantum Hall States by Principal Component Analysis

In the presence of mass anisotropy, anisotropic interaction, or in-plane magnetic field, quantum Hall droplets can exhibit shape deformation and internal geometrical degree of freedom. We characterize the geometry of quantum Hall states by principal component analysis, which is a statistical technique that emphasizes variation in a dataset. We first test the method in an integer quantum Hall droplet with dipole-dipole interaction in disk geometry. In the subsequent application to fractional quantum Hall systems with anisotropic Coulomb interaction in torus geometry, we demonstrate that the principal component analysis can quantify the metric degree of freedom and predict the collapse of a $ν= 1/3$ state. We also calculate the metric response to interaction anisotropy at filling fractions $ν= 1/5$ and $2/5$ and show that the response is roughly the same within the same Jain sequence, but can differ at large anisotropy for different sequences.

cond-mat.str-el↗

arXiv2020

Inexact Sequential Quadratic Optimization with Penalty Parameter Updates Within the QP Solve: Extended Version

This paper focuses on the design of sequential quadratic optimization (commonly known as SQP) methods for solving large-scale nonlinear optimization problems. The most computationally demanding aspect of such an approach is the computation of the search direction during each iteration, for which we consider the use of matrix-free methods. In particular, we develop a method that requires an inexact solve of a single QP subproblem to establish the convergence of the overall SQP method. It is known that SQP methods can be plagued by poor behavior of the global convergence mechanism. To confront this issue, we propose the use of an exact penalty function with a dynamic penalty parameter updating strategy to be employed within the subproblem solver in such a way that the resulting search direction predicts progress toward both feasibility and optimality. We present our parameter updating strategy and prove that, under reasonable assumptions, the strategy does not modify the penalty parameter unnecessarily. We also discuss a matrix-free subproblem solver in which our updating strategy can be incorporated. We close the paper with a discussion of the results of numerical experiments that illustrate the benefits of our proposed techniques.

math.OC↗

arXiv2020

History-dependent percolation on multiplex networks

The structure of interconnected systems and its impact on the system dynamics is a much-studied cross-disciplinary topic. Although various critical phenomena have been found in different models, the study on the connections between different percolation transitions is still lacking. Here we propose a unified framework to study the origins of the discontinuous transitions of the percolation process on interacting networks. The model evolves in generations with the result of the present percolation depending on the previous state and thus is history-dependent. Both theoretical analysis and Monte Carlo simulations reveal that the nature of the transition remains the same at finite generations but exhibits an abrupt change for the infinite generation. We use brain functional correlation and morphological similarity data to show that our model also provides a general method to explore the network structure and can contribute to many practical applications, such as detecting the abnormal structures of human brain networks.

cond-mat.stat-mech↗

arXiv2020

Medusa: Blockchain Powered Log Storage System

Blockchain is one of the most heavily invested technologies in recent years. Due to its tamper-proof and decentralization properties, blockchain has become an ideal utility for data storage that is applicable in many real world industrial scenarios. One important scenario is web log, which is treated as sources of technical significance and commercial revenues in major internet companies. In this paper, we illustrate our design of a web log storage system based on HyperLedger. HyperLedger yields higher throughput and lower latency compared with other blockchain systems. Alongside its efficiency advantages, HyperLeger is a permissioned blockchain, which is an ideal fit for enterprise software design scenario.

cs.CR↗

arXiv2020

Causal Discovery from Incomplete Data: A Deep Learning Approach

As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs. By encoding a series of cause-effect relations between events, causal networks can facilitate the prediction of effects from a given action and analyze their underlying data generation mechanism. However, missing data are ubiquitous in practical scenarios. Directly performing existing casual discovery algorithms on partially observed data may lead to the incorrect inference. To alleviate this issue, we proposed a deep learning framework, dubbed Imputated Causal Learning (ICL), to perform iterative missing data imputation and causal structure discovery. Through extensive simulations on both synthetic and real data, we show that ICL can outperform state-of-the-art methods under different missing data mechanisms.

cs.LG↗

arXiv2020

A Two-Stream Meticulous Processing Network for Retinal Vessel Segmentation

Vessel segmentation in fundus is a key diagnostic capability in ophthalmology, and there are various challenges remained in this essential task. Early approaches indicate that it is often difficult to obtain desirable segmentation performance on thin vessels and boundary areas due to the imbalance of vessel pixels with different thickness levels. In this paper, we propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem. To pay more attention to the thin vessels and boundary areas, we firstly propose an efficient hierarchical model automatically stratifies the ground-truth masks into different thickness levels. Then a novel two-stream adversarial network is introduced to use the stratification results with a balanced loss function and an integration operation to achieve a better performance, especially in thin vessels and boundary areas detecting. Our model is proved to outperform state-of-the-art methods on DRIVE, STARE, and CHASE_DB1 datasets.

cs.CV↗