arXiv subjects

Jia Liu

Publications and source records attributed to Jia Liu.

At least 343 records · Page 19Linked to original sources

arXiv2020

Efficient Parallel Simulation of Blood Flows in Abdominal Aorta

It is known that the maximum diameter for the rupture-risk assessment of the abdominal aortic aneurysm is a generally good method, but not sufficient. Alternative features obtained with computational modeling may provide additional useful criteria. Though computational approaches are noninvasive, they are often time-consuming because of the high computational complexity. In this paper, we present a highly parallel algorithm for the numerical simulation of unsteady blood flows in the patient-specific abdominal aorta. We model the blood flow with the unsteady incompressible Navier-Stokes equations, and solve the discretized system with a highly scalable domain decomposition method. With this approach, the complete flow field can be obtained in less than an hour, instead of days with older methods. We show experimentally that the proposed method offers accurate solutions of the pressure, the velocity and the wall shear stress, and the parallel efficiency is higher than 70% on a parallel computer with more than 1,000 processor cores.

cs.CE↗

arXiv2020

Toward Low-Cost and Stable Blockchain Networks

Envisioned to be the future of secured distributed systems, blockchain networks have received increasing attention from both the industry and academia in recent years. However, blockchain mining processes demand high hardware costs and consume a vast amount of energy (studies have shown that the amount of energy consumed in Bitcoin mining is almost the same as the electricity used in Ireland). To address the high mining cost problem of blockchain networks, in this paper, we propose a blockchain mining resources allocation algorithm to reduce the mining cost in PoW-based (proof-of-work-based) blockchain networks. We first propose an analytical queueing model for general blockchain networks. In our queueing model, transactions arrive randomly to the queue and are served in a batch manner with unknown service rate probability distribution and agnostic to any priority mechanism. Then, we leverage the Lyapunov optimization techniques to propose a dynamic mining resources allocation algorithm (DMRA), which is parameterized by a tuning parameter $K>0$. We show that our algorithm achieves an $[O(1/K), O(K)]$ cost-optimality-gap-vs-delay tradeoff. Our simulation results also demonstrate the effectiveness of DMRA in reducing mining costs.

cs.CR↗

arXiv2020

Detecting non-Abelian statistics of topological states on a chain of superconducting circuits

In view of the fundamental importance and many promising potential applications, non-Abelian statistics of topologically protected states have attracted much attention recently. However, due to the operational difficulties in solid-state materials, experimental realization of non-Abelian statistics is lacking. The superconducting quantum circuit system is scalable and controllable, and thus is a promising platform for quantum simulation. Here we propose a scheme to demonstrate non-Abelian statistics of topologically protected zero-energy edge modes on a chain of superconducting circuits. Specifically, we can realize topological phase transition by varying the hopping strength and magnetic field in the chain, and the realized non-Abelian operation can be used in topological quantum computation. Considering the advantages of the superconducting quantum circuits, our protocol may shed light on quantum computation via topologically protected states.

quant-ph↗

arXiv2020

Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization

We propose a new \textit{randomized Bregman (block) coordinate descent} (RBCD) method for minimizing a composite problem, where the objective function could be either convex or nonconvex, and the smooth part are freed from the global Lipschitz-continuous (partial) gradient assumption. Under the notion of relative smoothness based on the Bregman distance, we prove that every limit point of the generated sequence is a stationary point. Further, we show that the iteration complexity of the proposed method is $O(n\varepsilon^{-2})$ to achieve $ε$-stationary point, where $n$ is the number of blocks of coordinates. If the objective is assumed to be convex, the iteration complexity is improved to $O(nε^{-1} )$. If, in addition, the objective is strongly convex (relative to the reference function), the global linear convergence rate is recovered. We also present the accelerated version of the RBCD method, which attains an $O(n\varepsilon^{-1/γ} )$ iteration complexity for the convex case, where the scalar $γ\in [1,2]$ is determined by the \textit{generalized translation variant} of the Bregman distance. Convergence analysis without assuming the global Lipschitz-continuous (partial) gradient sets our results apart from the existing works in the composite problems.

math.OC↗

arXiv2019

Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems

In the applications of signal processing and data analytics, there is a wide class of non-convex problems whose objective function is freed from the common global Lipschitz continuous gradient assumption (e.g., the nonnegative matrix factorization (NMF) problem). Recently, this type of problem with some certain special structures has been solved by Bregman proximal gradient (BPG). This inspires us to propose a new Block-wise two-references Bregman proximal gradient (B2B) method, which adopts two reference functions so that a closed-form solution in the Bregman projection is obtained. Based on the relative smoothness, we prove the global convergence of the proposed algorithms for various block selection rules. In particular, we establish the global convergence rate of $O(\frac{\sqrt{s}}{\sqrt{k}})$ for the greedy and randomized block updating rule for B2B, which is $O(\sqrt{s})$ times faster than the cyclic variant, i.e., $O(\frac{s}{\sqrt{k}} )$, where $s$ is the number of blocks, and $k$ is the number of iterations. Multiple numerical results are provided to illustrate the superiority of the proposed B2B compared to the state-of-the-art works in solving NMF problems.

math.OC↗

arXiv2019

Co-Interacting Dark Matter

We propose a novel mechanism with two component dark matter models. The subdominant dark matter can thermalize the dominant one in galaxies, and leads to core density profiles. Using ultralight dark photons and $\mathrm{GeV}$-$\mathrm{TeV}$ Dirac fermions as an example, we couple the two dark matter candidates with a $U(1)$ interaction. This mechanism differs from self-interacting dark matter, due to three effects: (1) higher occupation numbers, (2) forward-backward scattering cancellation, and (3) the multiple scatterings required for the heavy dark matter. Furthermore, the Bullet Cluster bound is evaded due to the reduced Bose enhancement factor. Unlike the fuzzy dark matter solution to the small structure problems which have tension with Lyman-$α$, the ultralight dark photons with mass $\gtrsim 10^{-21}$ eV can have a core profile through interactions with $ψ$ and are not constrained by other astrophysical observations.

hep-ph↗

arXiv2019

Detecting Cyberattacks in Industrial Control Systems Using Online Learning Algorithms

Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive maneuvers launched by "anonymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusion detection systems that serve as the first line of defense by monitoring and reporting potentially malicious activities. Classical machine-learning-based intrusion detection methods usually generate prediction models by learning modest-sized training samples all at once. Such approach is not always applicable to industrial control systems, as industrial control systems must process continuous control commands with limited computational resources in a nonstop way. To satisfy such requirements, we propose using online learning to learn prediction models from the controlling data stream. We introduce several state-of-the-art online learning algorithms categorically, and illustrate their efficacies on two typically used testbeds---power system and gas pipeline. Further, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance problem that is pervasive in industrial intrusion detection systems. Our experimental results indicate that the proposed algorithm can achieve an overall improvement in the detection rate of cyberattacks in industrial control systems.

cs.LG↗

arXiv2019

Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors

Network-distributed optimization has attracted significant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (DGD) algorithm is communication-inefficient for large-scale and high-dimensional network-distributed optimization problems. To address this challenge, many compressed DGD-based algorithms have been proposed. However, most of the existing works have high complexity and assume compressors with bounded noise power. To overcome these limitations, in this paper, we propose a new differential-coded compressed DGD (DC-DGD) algorithm. The key features of DC-DGD include: i) DC-DGD works with general SNR-constrained compressors, relaxing the bounded noise power assumption; ii) The differential-coded design entails the same convergence rate as the original DGD algorithm; and iii) DC-DGD has the same low-complexity structure as the original DGD due to a {\em self-noise-reduction effect}. Moreover, the above features inspire us to develop a hybrid compression scheme that offers a systematic mechanism to minimize the communication cost. Finally, we conduct extensive experiments to verify the efficacy of the proposed DC-DGD and hybrid compressor.

cs.DC↗

arXiv2019

Data Analytics for Fog Computing by Distributed Online Learning with Asynchronous Update

Fog computing extends the cloud computing paradigm by allocating substantial portions of computations and services towards the edge of a network, and is, therefore, particularly suitable for large-scale, geo-distributed, and data-intensive applications. As the popularity of fog applications increases, there is a demand for the development of smart data analytic tools, which can process massive data streams in an efficient manner. To satisfy such requirements, we propose a system in which data streams generated from distributed sources are digested almost locally, whereas a relatively small amount of distilled information is converged to a center. The center extracts knowledge from the collected information, and shares it across all subordinates to boost their performances. Upon the proposed system, we devise a distributed machine learning algorithm using the online learning approach, which is well known for its high efficiency and innate ability to cope with streaming data. An asynchronous update strategy with rigorous theoretical support is applied to enhance the system robustness. Experimental results demonstrate that the proposed method is comparable with a model trained over a centralized platform in terms of the classification accuracy, whereas the efficiency and scalability of the overall system are improved.

eess.SP↗

arXiv2019

Habitat: A Platform for Embodied AI Research

We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast -- when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-to-end development of embodied AI algorithms -- defining tasks (e.g., navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents. These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or 'merely' impractical. Specifically, in the context of point-goal navigation: (1) we revisit the comparison between learning and SLAM approaches from two recent works and find evidence for the opposite conclusion -- that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and (2) we conduct the first cross-dataset generalization experiments {train, test} x {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI.

cs.CV↗

arXiv2019

Combinatorial Sleeping Bandits with Fairness Constraints

The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters. The goal of the player here is to maximize the cumulative reward in the face of uncertainty. However, the basic MAB model neglects several important factors of the system in many real-world applications, where multiple arms can be simultaneously played and an arm could sometimes be "sleeping". Besides, ensuring fairness is also a key design concern in practice. To that end, we propose a new Combinatorial Sleeping MAB model with Fairness constraints, called CSMAB-F, aiming to address the aforementioned crucial modeling issues. The objective is now to maximize the reward while satisfying the fairness requirement of a minimum selection fraction for each individual arm. To tackle this new problem, we extend an online learning algorithm, UCB, to deal with a critical tradeoff between exploitation and exploration and employ the virtual queue technique to properly handle the fairness constraints. By carefully integrating these two techniques, we develop a new algorithm, called Learning with Fairness Guarantee (LFG), for the CSMAB-F problem. Further, we rigorously prove that not only LFG is feasibility-optimal, but it also has a time-average regret upper bounded by $\frac{N}{2η}+\frac{β_1\sqrt{mNT\log{T}}+β_2 N}{T}$, where N is the total number of arms, m is the maximum number of arms that can be simultaneously played, T is the time horizon, $β_1$ and $β_2$ are constants, and $η$ is a design parameter that we can tune. Finally, we perform extensive simulations to corroborate the effectiveness of the proposed algorithm. Interestingly, the simulation results reveal an important tradeoff between the regret and the speed of convergence to a point satisfying the fairness constraints.

cs.LG↗

arXiv2019

FDDWNet: A Lightweight Convolutional Neural Network for Real-time Sementic Segmentation

This paper introduces a lightweight convolutional neural network, called FDDWNet, for real-time accurate semantic segmentation. In contrast to recent advances of lightweight networks that prefer to utilize shallow structure, FDDWNet makes an effort to design more deeper network architecture, while maintains faster inference speed and higher segmentation accuracy. Our network uses factorized dilated depth-wise separable convolutions (FDDWC) to learn feature representations from different scale receptive fields with fewer model parameters. Additionally, FDDWNet has multiple branches of skipped connections to gather context cues from intermediate convolution layers. The experiments show that FDDWNet only has 0.8M model size, while achieves 60 FPS running speed on a single RTX 2080Ti GPU with a 1024x512 input image. The comprehensive experiments demonstrate that our model achieves state-of-the-art results in terms of available speed and accuracy trade-off on CityScapes and CamVid datasets.

cs.CV↗

arXiv2019

Hierarchical Feature-Aware Tracking

In this paper, we propose a hierarchical feature-aware tracking framework for efficient visual tracking. Recent years, ensembled trackers which combine multiple component trackers have achieved impressive performance. In ensembled trackers, the decision of results is usually a post-event process, i.e., tracking result for each tracker is first obtained and then the suitable one is selected according to result ensemble. In this paper, we propose a pre-event method. We construct an expert pool with each expert being one set of features. For each frame, several experts are first selected in the pool according to their past performance and then they are used to predict the object. The selection rate of each expert in the pool is then updated and tracking result is obtained according to result ensemble. We propose a novel pre-known expert-adaptive selection strategy. Since the process is more efficient, more experts can be constructed by fusing more types of features which leads to more robustness. Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated. Experiments on several public available datasets demonstrate the superiority of the proposed method and its state-of-the-art performance among ensembled trackers.

cs.CV↗

arXiv2019

The void halo mass function: a promising probe of neutrino mass

Cosmic voids, the underdense regions in the universe, are particularly sensitive to diffuse density components such as cosmic neutrinos. This sensitivity is enhanced by the match between void sizes and the free-streaming scale of massive neutrinos. Using the massive neutrino simulations \texttt{MassiveNuS}, we investigate the effect of neutrino mass on dark matter halos as a function of environment. We find that the halo mass function depends strongly on neutrino mass and that this dependence is more pronounced in voids than in high-density environments. An observational program that measured the characteristic mass of the most massive halos in voids should be able to place novel constraints on the sum of the masses of neutrinos $\sum m_ν$. The neutrino mass effect in the simulations is quite strong: In a 512$^3$ $h^{-3}$ Mpc$^3$ survey, the mean mass of the 1000 most massive halos in the void interiors is $(4.82 \pm 0.11) \times 10^{12} h^{-1}M_{\odot}$ for $\sum m_ν= 0.6$ eV and $(8.21 \pm 0.13) \times 10^{12} h^{-1}M_{\odot}$ for $\sum m_ν= 0.1$ eV. Subaru (SuMIRe), Euclid and WFIRST will have both spectroscopic and weak lensing surveys. Covering volumes at least 50 times larger than our simulations, they should be sensitive probes of neutrino mass through void substructure.

astro-ph.CO↗

arXiv2019

Weak Lensing Minima and Peaks: Cosmological Constraints and the Impact of Baryons

We present a novel statistic to extract cosmological information in weak lensing data: the lensing minima. We also investigate the effect of baryons on the cosmological constraints from peak and minimum counts. Using the \texttt{MassiveNuS} simulations, we find that lensing minima are sensitive to non-Gaussian cosmological information and are complementary to the lensing power spectrum and peak counts. For an LSST-like survey, we obtain $95\%$ credible intervals from a combination of lensing minima and peaks that are significantly stronger than from the power spectrum alone, by $44\%$, $11\%$, and $63\%$ for the neutrino mass sum $\sum m_ν$, matter density $Ω_m$, and amplitude of fluctuation $A_s$, respectively. We explore the effect of baryonic processes on lensing minima and peaks using the hydrodynamical simulations \texttt{BAHAMAS} and \texttt{Osato15}. We find that ignoring baryonic effects would lead to strong ($\approx 4 σ$) biases in inferences from peak counts, but negligible ($\approx 0.5 σ$) for minimum counts, suggesting lensing minima are a potentially more robust tool against baryonic effects. Finally, we demonstrate that the biases can in principle be mitigated without significantly degrading cosmological constraints when we model and marginalize the baryonic effects.

astro-ph.CO↗

arXiv2019

An Improved Test of the Binary Black Hole Hypothesis for Quasars with Double-peaked Broad Balmer Lines

Velocity offsets in the broad Balmer lines of quasars and their temporal variations serve as indirect evidence for bound supermassive black hole binaries (SBHBs) at sub-parsec separations. In this work, we test the SBHB hypothesis for 14 quasars with double-peaked broad emission lines using their long-term (14--41 years) radial velocity curves. We improve on previous work by (a) using elliptical instead of circular orbits for the SBHBs, (b) adopting a statistical model for radial velocity jitter, (c) employing a Markov Chain Monte Carlo method to explore the orbital parameter space efficiently and build posterior distributions of physical parameters and (d) incorporating new observations. We determine empirically that jitter comprises approximately Gaussian distributed fluctuations about the smooth radial velocity curves that are larger than the measurement errors by factors of order a few. We initially treat jitter by enlarging the effective error bars and then verify this approach via a variety of Gaussian process models for it. We find lower mass limits for the hypothesized SBHBs in the range $10^8$--$10^{11}\;M_{\odot}$. For seven objects the SBHB scenario appears unlikely based on goodness-of-fit tests. For two additional objects the minimum SBHB masses are unreasonably large ($>10^{10}\;M_{\odot}$), strongly disfavoring the SBHB scenario. Using constraints on the orbital inclination angle (which requires some assumptions) makes the minimum masses of four more objects unreasonably large. We also cite physical and observational arguments against the SBHB hypothesis for nine objects. We conclude that the SBHB explanation is not the favoured explanation of double-peaked broad emission lines.

astro-ph.GA↗

arXiv2019

Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach

In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who could send arbitrary information to the parameter server to disrupt the training process. Toward this end, we propose a new Lipschitz-inspired coordinate-wise median approach (LICM-SGD) to mitigate Byzantine attacks. We show that our LICM-SGD algorithm can resist up to half of the workers being Byzantine attackers, while still converging almost surely to a stationary region in non-convex settings. Also, our LICM-SGD method does not require any information about the number of attackers and the Lipschitz constant, which makes it attractive for practical implementations. Moreover, our LICM-SGD method enjoys the optimal $O(md)$ computational time-complexity in the sense that the time-complexity is the same as that of the standard SGD under no attacks. We conduct extensive experiments to show that our LICM-SGD algorithm consistently outperforms existing methods in training multi-class logistic regression and convolutional neural networks with MNIST and CIFAR-10 datasets. In our experiments, LICM-SGD also achieves a much faster running time thanks to its low computational time-complexity.

cs.LG↗

arXiv2019

Compressed Distributed Gradient Descent: Communication-Efficient Consensus over Networks

Network consensus optimization has received increasing attention in recent years and has found important applications in many scientific and engineering fields. To solve network consensus optimization problems, one of the most well-known approaches is the distributed gradient descent method (DGD). However, in networks with slow communication rates, DGD's performance is unsatisfactory for solving high-dimensional network consensus problems due to the communication bottleneck. This motivates us to design a communication-efficient DGD-type algorithm based on compressed information exchanges. Our contributions in this paper are three-fold: i) We develop a communication-efficient algorithm called amplified-differential compression DGD (ADC-DGD) and show that it converges under {\em any} unbiased compression operator; ii) We rigorously prove the convergence performances of ADC-DGD and show that they match with those of DGD without compression; iii) We reveal an interesting phase transition phenomenon in the convergence speed of ADC-DGD. Collectively, our findings advance the state-of-the-art of network consensus optimization theory.

cs.DC↗