arXiv subjects

Han Wang

Publications and source records attributed to Han Wang.

At least 397 records · Page 22Linked to original sources

arXiv2022

L-SRR: Local Differential Privacy for Location-Based Services with Staircase Randomized Response

Location-based services (LBS) have been significantly developed and widely deployed in mobile devices. It is also well-known that LBS applications may result in severe privacy concerns by collecting sensitive locations. A strong privacy model ''local differential privacy'' (LDP) has been recently deployed in many different applications (e.g., Google RAPPOR, iOS, and Microsoft Telemetry) but not effective for LBS applications due to the low utility of existing LDP mechanisms. To address such deficiency, we propose the first LDP framework for a variety of location-based services (namely ''L-SRR''), which privately collects and analyzes user locations with high utility. Specifically, we design a novel randomization mechanism ''Staircase Randomized Response'' (SRR) and extend the empirical estimation to significantly boost the utility for SRR in different LBS applications (e.g., traffic density estimation, and k-nearest neighbors). We have conducted extensive experiments on four real LBS datasets by benchmarking with other LDP schemes in practical applications. The experimental results demonstrate that L-SRR significantly outperforms them.

cs.CR↗

arXiv2022

Classical and Machine Learning Interatomic Potentials for BCC Vanadium

BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic deformation behaviour controlled by individual crystal lattice defects. Classical empirical and semi-empirical interatomic potentials have limited capability in modelling defect properties such as the screw dislocation core structures and Peierls barriers in the BCC structure. Machine learning (ML) potentials, trained on DFT-based datasets, have shown some successes in reproducing dislocation core properties. However, in group VB TMs, the most widely-used DFT functionals produce erroneous shear moduli C44 which are undesirably transferred to machine-learning interatomic potentials, leaving current ML approaches unsuitable for this important class of metals and alloys. Here, we develop two interatomic potentials for BCC vanadium (V) based on (i) an extension of the partial electron density and screening parameter in the classical semi-empirical modified embedded-atom method (XMEAM-V) and (ii) a recent hybrid descriptor in the ML Deep Potential framework (DP-HYB-V). We describe distinct features in these two disparate approaches, including their dataset generation, training procedure, weakness and strength in modelling lattice and defect properties in BCC V. Both XMEAM-V and DP-HYB-V reproduce a broad range of defect properties relevant to plastic deformation and fracture. In particular, XMEAM-V reproduces nearly all mechanical and thermodynamic properties at DFT accuracies and with C44 near experimental value. XMEAM-V also naturally exhibits the anomalous slip at 77 K widely observed in group VB and VIB TMs and outperforms all existing, publically available interatomic potentials for V. The XMEAM thus provides a practical path to developing accurate and efficient interatomic potentials for nonmagnetic BCC TMs and possibly multi-principal element TM alloys.

cond-mat.mtrl-sci↗

arXiv2022

PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection

Recent years have witnessed a trend of applying context frames to boost the performance of object detection as video object detection. Existing methods usually aggregate features at one stroke to enhance the feature. These methods, however, usually lack spatial information from neighboring frames and suffer from insufficient feature aggregation. To address the issues, we perform a progressive way to introduce both temporal information and spatial information for an integrated enhancement. The temporal information is introduced by the temporal feature aggregation model (TFAM), by conducting an attention mechanism between the context frames and the target frame (i.e., the frame to be detected). Meanwhile, we employ a Spatial Transition Awareness Model (STAM) to convey the location transition information between each context frame and target frame. Built upon a transformer-based detector DETR, our PTSEFormer also follows an end-to-end fashion to avoid heavy post-processing procedures while achieving 88.1% mAP on the ImageNet VID dataset. Codes are available at https://github.com/Hon-Wong/PTSEFormer.

cs.CV↗

arXiv2022

Multiqubit Toffoli gates and optimal geometry with Rydberg atoms

Due to its potential for implementing a scalable quantum computer, multiqubit Toffoli gate lies in the heart of quantum information processing. In this article, we demonstrate a multiqubit blockade gate with atoms arranged in a three-dimension spheroidal array. The gate performance is greatly improved by the method of optimizing control-qubit distributions on the spherical surface via evolutionary algorithm, which leads to an enhanced asymmetric Rydberg blockade. This spheroidal configuration, not only arises a well preservation for the dipole blockade energy between arbitrary control-target pairs, which keeps the asymmetric blockade error at a very low level; but also manifests an unprecedented robustness to the spatial position variations, leading to a negligible position error. Taking account of intrinsic errors and with typical experimental parameters, we numerically show that a C$_6$NOT Rydberg gate can be created with a fidelity of 0.992 which is only limited by the Rydberg state decays.Our protocol opens up a new platform of higher-dimensional atomic arrays for achieving multiqubit neutral-atom quantum computation.

quant-ph↗

arXiv2022

Robustness of optic-fiber-based weak value amplification against amplitude-type noise

Experiments based on free space platform have demonstrated that the weak value amplification (WVA) technique can provide high sensitivity and precision for optical sensing and metrology. To promote this technique for real-world applications, it is more suitable to implement WVA based on optical fiber platform due to the lower cost, smaller scale and higher stability. In contrast to the free space platform, the birefringence in optical fiber is strong enough to cause polarization cross talk, and the amplitude-type noise must be taken into account. By theoretical analysis and experimental demonstration, we show that the optic-fiber-based WVA is robust in presence of amplitude-type noise. In our experiment, even the angular misalignment on optical axes at the interface reaches 0.08rad, the sensitivity loss can be maintained less than 3dB. Moreover, the main results are valid to a simplified detection scheme that recently proposed, which is more compatible with the future design of optical-fiber based WVA. Our results indicate the feasibility of implementing WVA based on optical fiber, which provide a possible way for designing optical sensors with higher sensitivity and stability in the future.

physics.optics↗

arXiv2022

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications.

physics.chem-ph↗

arXiv2022

Linear Convergence Rate Analysis of Proximal Generalized ADMM for Convex Composite Programming

The proximal generalized alternating direction method of multipliers (p-GADMM) is substantially efficient for solving convex composite programming problems of high-dimensional to moderate accuracy. The global convergence of this method was established by Xiao, Chen & Li [Math. Program. Comput., 2018], but its convergence rate was not given. One may take it for granted that the convergence rate could be proved easily by mimicking the proximal ADMM, but we find the relaxed points will certainly cause many difficulties for theoretical analysis. In this paper, we devote to exploring its convergence behavior and show that the sequence generated by p-GADMM possesses Q-linear convergence rate under some mild conditions. We would like to note that the proximal terms at the subproblems are required to be positive definite, which is very common in most practical implementations although it seems to be a bit strong.

math.OC↗

arXiv2022

DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models

Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning based PES model. This scheme, we call DP Compress, is an efficient post-processing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster, and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online.

physics.comp-ph↗

arXiv2022

Unconventional Charge-density-wave Order in a Dilute d-band Semiconductor

Electron-lattice coupling effects in low dimensional materials give rise to charge density wave (CDW) order and phase transitions. These phenomena are critical ingredients for superconductivity and predominantly occur in metallic model systems such as doped cuprates, transition metal dichalcogenides, and more recently, in Kagome lattice materials. However, CDW in semiconducting systems, specifically at the limit of low carrier concentration region, is uncommon. Here, we combine electrical transport, synchrotron X-ray diffraction and optical spectroscopy to discover CDW order in a quasi-one-dimensional (1D), dilute d-band semiconductor, BaTiS3, which suggests the existence of strong electron-phonon coupling. The CDW state further undergoes an unusual transition featuring a sharp increase in carrier mobility. Our work establishes BaTiS3 as a unique platform to study the CDW physics in the dilute filling limit to explore novel electronic phases.

cond-mat.mtrl-sci↗

arXiv2022

Safe Controlled Invariance for Linear Systems Using Sum-of-Squares Programming

Safety is closely related to set invariance for dynamical systems. However, synthesizing a safe invariant set and at the same time synthesizing the associated safe controller still remains challenging. In this note we introduce a simple invariance-based method for linear systems with safety guarantee. The proposed method uses sum-of-squares programming.

math.OC↗

arXiv2022

Proof of a conjecture involving derangements and roots of unity

Let $n>1$ be an odd integer. For any primitive $n$-th root $ζ$ of unity in the complex field. Via the Engenvector-eigenvalue Identity, we show that $$\sum_{τ\in D(n-1)}\mathrm{sign}(τ)\prod_{j=1}^{n-1}\frac{1+ζ^{j-τ(j)}}{1-ζ^{j-τ(j)}} =(-1)^{\frac{n-1}{2}}\frac{((n-2)!!)^2}{n}, $$ where $D(n-1)$ is the set of all derangements of $1,\ldots,n-1$. This confirms a previous conjecture of Z.-W. Sun. Moreover, for each $δ=0,1$ we determine the value of $\det[x+m_{jk}]_{1\le j,k\le n}$ completely, where $$m_{jk}=\begin{cases}(1+ζ^{j-k})/(1-ζ^{j-k})&\text{if}\ j\not=k,\\δ&\text{if}\ j=k. \end{cases}$$

math.CO↗

arXiv2022

DPOAD: Differentially Private Outsourcing of Anomaly Detection through Iterative Sensitivity Learning

Outsourcing anomaly detection to third-parties can allow data owners to overcome resource constraints (e.g., in lightweight IoT devices), facilitate collaborative analysis (e.g., under distributed or multi-party scenarios), and benefit from lower costs and specialized expertise (e.g., of Managed Security Service Providers). Despite such benefits, a data owner may feel reluctant to outsource anomaly detection without sufficient privacy protection. To that end, most existing privacy solutions would face a novel challenge, i.e., preserving privacy usually requires the difference between data entries to be eliminated or reduced, whereas anomaly detection critically depends on that difference. Such a conflict is recently resolved under a local analysis setting with trusted analysts (where no outsourcing is involved) through moving the focus of differential privacy (DP) guarantee from "all" to only "benign" entries. In this paper, we observe that such an approach is not directly applicable to the outsourcing setting, because data owners do not know which entries are "benign" prior to outsourcing, and hence cannot selectively apply DP on data entries. Therefore, we propose a novel iterative solution for the data owner to gradually "disentangle" the anomalous entries from the benign ones such that the third-party analyst can produce accurate anomaly results with sufficient DP guarantee. We design and implement our Differentially Private Outsourcing of Anomaly Detection (DPOAD) framework, and demonstrate its benefits over baseline Laplace and PainFree mechanisms through experiments with real data from different application domains.

cs.CR↗

arXiv2022

On the Linear Convergence Rate of Generalized ADMM for Convex Composite Programming

Over the fast few years, the numerical success of the generalized alternating direction method of multipliers (GADMM) proposed by Eckstein \& Bertsekas [Math. Prog., 1992] has inspired intensive attention in analyzing its theoretical convergence properties. In this paper, we devote to establishing the linear convergence rate of the semi-proximal GADMM (sPGADMM) for solving linearly constrained convex composite optimization problems. The semi-proximal terms contained in each subproblem possess the abilities of handling with multi-block problems efficiently. We initially present some important inequalities for the sequence generated by the sPGADMM, and then establish the local linear convergence rate under the assumption of calmness. As a by-product, the global convergence property is also discussed.

math.OC↗

arXiv2022

An Empirical Study on How Well Do COVID-19 Information Dashboards Service Users' Information Needs

The ongoing COVID-19 pandemic highlights the importance of dashboards for providing critical real-time information. In order to enable people to obtain information in time and to understand complex statistical data, many developers have designed and implemented public-oriented COVID-19 "information dashboards" during the pandemic. However, development often takes a long time and developers are not clear about many people's information needs, resulting in gaps between information needs and supplies. According to our empirical study and observations with popular developed COVID-19 dashboards, this seriously impedes information acquirement. Our study compares people's needs on Twitter with existing information suppliers. We determine that despite the COVID-19 information that is currently on existing dashboards, people are also interested in the relationship between COVID-19 and other viruses, the origin of COVID-19, vaccine development, fake new about COVID-19, impact on women, impact on school/university, and impact on business. Most of these have not yet been well addressed. We also summarise the visualization and interaction patterns commonly applied in dashboards, finding key patterns between data and visualization as well as visualization and interaction. Our findings can help developers to better optimize their dashboard to meet people's needs and make improvements to future crisis management dashboard development.

cs.HC↗

arXiv2022

RVAE-LAMOL: Residual Variational Autoencoder to Enhance Lifelong Language Learning

Lifelong Language Learning (LLL) aims to train a neural network to learn a stream of NLP tasks while retaining knowledge from previous tasks. However, previous works which followed data-free constraint still suffer from catastrophic forgetting issue, where the model forgets what it just learned from previous tasks. In order to alleviate catastrophic forgetting, we propose the residual variational autoencoder (RVAE) to enhance LAMOL, a recent LLL model, by mapping different tasks into a limited unified semantic space. In this space, previous tasks are easy to be correct to their own distribution by pseudo samples. Furthermore, we propose an identity task to make the model is discriminative to recognize the sample belonging to which task. For training RVAE-LAMOL better, we propose a novel training scheme Alternate Lag Training. In the experiments, we test RVAE-LAMOL on permutations of three datasets from DecaNLP. The experimental results demonstrate that RVAE-LAMOL outperforms naïve LAMOL on all permutations and generates more meaningful pseudo-samples.

cs.CL↗

arXiv2022

No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL

The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous, or time consuming. We propose a new approach to tune hyperparameters from offline logs of data, to fully specify the hyperparameters for an RL agent that learns online in the real world. The approach is conceptually simple: we first learn a model of the environment from the offline data, which we call a calibration model, and then simulate learning in the calibration model to identify promising hyperparameters. We identify several criteria to make this strategy effective, and develop an approach that satisfies these criteria. We empirically investigate the method in a variety of settings to identify when it is effective and when it fails.

cs.LG↗

arXiv2022

Multi-modal Semantic SLAM for Complex Dynamic Environments

Simultaneous Localization and Mapping (SLAM) is one of the most essential techniques in many real-world robotic applications. The assumption of static environments is common in most SLAM algorithms, which however, is not the case for most applications. Recent work on semantic SLAM aims to understand the objects in an environment and distinguish dynamic information from a scene context by performing image-based segmentation. However, the segmentation results are often imperfect or incomplete, which can subsequently reduce the quality of mapping and the accuracy of localization. In this paper, we present a robust multi-modal semantic framework to solve the SLAM problem in complex and highly dynamic environments. We propose to learn a more powerful object feature representation and deploy the mechanism of looking and thinking twice to the backbone network, which leads to a better recognition result to our baseline instance segmentation model. Moreover, both geometric-only clustering and visual semantic information are combined to reduce the effect of segmentation error due to small-scale objects, occlusion and motion blur. Thorough experiments have been conducted to evaluate the performance of the proposed method. The results show that our method can precisely identify dynamic objects under recognition imperfection and motion blur. Moreover, the proposed SLAM framework is able to efficiently build a static dense map at a processing rate of more than 10 Hz, which can be implemented in many practical applications. Both training data and the proposed method is open sourced at https://github.com/wh200720041/MMS_SLAM.

cs.RO↗

arXiv2022

SVR-based Observer Design for Unknown Linear Systems: Complexity and Performance

In this paper we consider estimating the system parameters and designing stable observer for unknown noisy linear time-invariant (LTI) systems. We propose a Support Vector Regression (SVR) based estimator to provide adjustable asymmetric error interval for estimations. This estimator is capable to trade-off bias-variance of the estimation error by tuning parameter $γ> 0$ in the loss function. This method enjoys the same sample complexity of $\mathcal{O}(1/\sqrt{N})$ as the Ordinary Least Square (OLS) based methods but achieves a $\mathcal{O}(1/(γ+1))$ smaller variance. Then, a stable observer gain design procedure based on the estimations is proposed. The observation performance bound based on the estimations is evaluated by the mean square observation error, which is shown to be adjustable by tuning the parameter $γ$, thus achieving higher scalability than the OLS methods. The advantages of the estimation error bias-variance trade-off for observer design are also demonstrated through matrix spectrum and observation performance optimality analysis. Extensive simulation validations are conducted to verify the computed estimation error and performance optimality with different $γ$ and noise settings. The variances of the estimation error and the fluctuations in performance are smaller with a properly-designed parameter $γ$ compared with the OLS methods.

eess.SY↗