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Hao Wu

Publications and source records attributed to Hao Wu.

At least 721 records · Page 40Linked to original sources
arXiv2021

Greenplum: A Hybrid Database for Transactional and Analytical Workloads

Demand for enterprise data warehouse solutions to support real-time Online Transaction Processing (OLTP) queries as well as long-running Online Analytical Processing (OLAP) workloads is growing. Greenplum database is traditionally known as an OLAP data warehouse system with limited ability to process OLTP workloads. In this paper, we augment Greenplum into a hybrid system to serve both OLTP and OLAP workloads. The challenge we address here is to achieve this goal while maintaining the ACID properties with minimal performance overhead. In this effort, we identify the engineering and performance bottlenecks such as the under-performing restrictive locking and the two-phase commit protocol. Next we solve the resource contention issues between transactional and analytical queries. We propose a global deadlock detector to increase the concurrency of query processing. When transactions that update data are guaranteed to reside on exactly one segment we introduce one-phase commit to speed up query processing. Our resource group model introduces the capability to separate OLAP and OLTP workloads into more suitable query processing mode. Our experimental evaluation on the TPC-B and CH-benCHmark benchmarks demonstrates the effectiveness of our approach in boosting the OLTP performance without sacrificing the OLAP performance.

cs.DB↗
arXiv2021

Stability of hybrid pantograph stochastic functional differential equations

In this paper, we study a new type of stochastic functional differential equations which is called hybrid pantograph stochastic functional differential equations. We investigate several moment properties and sample properties of the solutions to the equations by using the method of multiple Lyapunov functions, such as the moment exponential stability, almost sure exponential stability and almost sure polynomial stability, etc.

math.PR↗
arXiv2021

Roadmap of spin-orbit torques

Spin-orbit torque (SOT) is an emerging technology that enables the efficient manipulation of spintronic devices. The initial processes of interest in SOTs involved electric fields, spin-orbit coupling, conduction electron spins and magnetization. More recently interest has grown to include a variety of other processes that include phonons, magnons, or heat. Over the past decade, many materials have been explored to achieve a larger SOT efficiency. Recently, holistic design to maximize the performance of SOT devices has extended material research from a nonmagnetic layer to a magnetic layer. The rapid development of SOT has spurred a variety of SOT-based applications. In this Roadmap paper, we first review the theories of SOTs by introducing the various mechanisms thought to generate or control SOTs, such as the spin Hall effect, the Rashba-Edelstein effect, the orbital Hall effect, thermal gradients, magnons, and strain effects. Then, we discuss the materials that enable these effects, including metals, metallic alloys, topological insulators, two-dimensional materials, and complex oxides. We also discuss the important roles in SOT devices of different types of magnetic layers. Afterward, we discuss device applications utilizing SOTs. We discuss and compare three-terminal and two-terminal SOT-magnetoresistive random-access memories (MRAMs); we mention various schemes to eliminate the need for an external field. We provide technological application considerations for SOT-MRAM and give perspectives on SOT-based neuromorphic devices and circuits. In addition to SOT-MRAM, we present SOT-based spintronic terahertz generators, nano-oscillators, and domain wall and skyrmion racetrack memories. This paper aims to achieve a comprehensive review of SOT theory, materials, and applications, guiding future SOT development in both the academic and industrial sectors.

cond-mat.mes-hall↗
arXiv2021

Collaborative Label Correction via Entropy Thresholding

Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels. We examine this behavior in light of the Shannon entropy of the predictions and demonstrate the low entropy predictions determined by a given threshold are much more reliable as the supervision than the original noisy labels. It also shows the advantage in maintaining more training samples than previous methods. Then, we power this entropy criterion with the Collaborative Label Correction (CLC) framework to further avoid undesired local minimums of the single network. A range of experiments have been conducted on multiple benchmarks with both synthetic and real-world settings. Extensive results indicate that our CLC outperforms several state-of-the-art methods.

cs.LG↗
arXiv2021

Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push

Personalized PageRank (PPR) is a critical measure of the importance of a node t to a source node s in a graph. The Single-Source PPR (SSPPR) query computes the PPR's of all the nodes with respect to s on a directed graph $G$ with $n$ nodes and $m$ edges, and it is an essential operation widely used in graph applications. In this paper, we propose novel algorithms for solving two variants of SSPPR: (i) high-precision queries and (ii) approximate queries. For high-precision queries, Power Iteration (PowItr) and Forward Push (FwdPush) are two fundamental approaches. Given an absolute error threshold $λ$, the only known bound of FwdPush is $O(\frac{m}λ)$, much worse than the $O(m \log \frac{1}λ)$-bound of PowItr. Whether FwdPush can achieve the same running time bound as PowItr does still remains an open question in the research community. We give a positive answer to this question by showing that the running time of a common implementation of FwdPush is actually bounded by $O(m \cdot \log \frac{1}λ)$.Based on this finding, we propose a new algorithm, called Power Iteration with Forward Push (PowerPush), which incorporates the strengths of both PowItr and FwdPush. For approximate queries (with a relative error $ε$), we propose a new algorithm, called SpeedPPR, with overall expected time bounded by $O(n \cdot \log n \cdot \log \frac{1}ε)$ on scale-free graphs. This bound greatly improves the $O(\frac{n \cdot \log n}ε)$ bound of a state-of-the-art algorithm FORA.

cs.DS↗
arXiv2021

Mathematical Modeling of Chemotaxis Guided Amoeboid Cell Swimming

Cells and microorganisms adopt various strategies to migrate in response to different environmental stimuli. To date, many modeling research has focused on the crawling-based Dictyostelium discoideum (Dd) cells migration induced by chemotaxis, yet recent experimental results reveal that even without adhesion or contact to a substrate, Dd cells can still swim to follow chemoattractant signals. In this paper, we develop a modeling framework to investigate the chemotaxis induced amoeboid cell swimming dynamics. A minimal swimming system consists of one deformable Dd amoeboid cell and a dilute suspension of bacteria, and the bacteria produce chemoattractant signals that attract the Dd cell. We use the mathematical amoeba model to generate Dd cell deformation and solve the resulting low Reynolds number flows, and use a moving mesh based finite volume method to solve the reaction-diffusion-convection equation. Using the computational model, we show that chemotaxis guides a swimming Dd cell to follow and catch bacteria, while on the other hand, bacterial rheotaxis may help the bacteria to escape from the predator Dd cell.

q-bio.TO↗
arXiv2021

Spherical spin glass model with external field

We analyze the free energy and the overlaps in the 2-spin spherical Sherrington Kirkpatrick spin glass model with an external field for the purpose of understanding the transition between this model and the one without an external field. We compute the limiting values and fluctuations of the free energy as well as three types of overlaps in the setting where the strength of the external field goes to zero as the dimension of the spin variable grows. In particular, we consider overlaps with the external field, the ground state, and a replica. Our methods involve a contour integral representation of the partition function along with random matrix techniques. We also provide computations for the matching between different scaling regimes. Finally, we discuss the implications of our results for susceptibility and for the geometry of the Gibbs measure. Some of the findings of this paper are confirmed rigorously by Landon and Sosoe in their recent paper which came out independently and simultaneously.

cond-mat.dis-nn↗
arXiv2021

XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning

This paper presents our submitted system to SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning. Our system uses a large pre-trained language model as the encoder and an additional dual multi-head co-attention layer to strengthen the relationship between passages and question-answer pairs, following the current state-of-the-art model DUMA. The main difference is that we stack the passage-question and question-passage attention modules instead of calculating parallelly to simulate re-considering process. We also add a layer normalization module to improve the performance of our model. Furthermore, to incorporate our known knowledge about abstract concepts, we retrieve the definitions of candidate answers from WordNet and feed them to the model as extra inputs. Our system, called WordNet-enhanced DUal Multi-head Co-Attention (WN-DUMA), achieves 86.67% and 89.99% accuracy on the official blind test set of subtask 1 and subtask 2 respectively.

cs.CL↗
arXiv2021

Free-space Realization of Tunable Pin-like Optical Vortex Beams

We demonstrate, both analytically and experimentally, free-space pin-like optical vortex beams (POVBs).Such angular-momentum-carrying beams feature tunable peak intensity and undergo robust anti-diffracting propagation, realized by judiciously modulating both the amplitude and the phase profile of a standard laser beam. Specifically, they are generated by superimposing a radially-symmetric power-law phase on a helical phase structure, which allows the inclusion of an orbital angular momentum term to the POVBs. During propagation in free-space, these POVBs initially exhibit autofocusing dynamics, and subsequently, their amplitude patterns morph into a high-order Bessel-like profile characterized by a hollow-core and an annular main-lobe with a constant or tunable width during propagation. In contrast with numerous previous endeavors on Bessel beams, our work represents the first demonstration of long-distance free-space generation of optical vortex 'pins', with their peak intensity evolution controlled by the impressed amplitude structure. Both the Poynting vectors and the optical radiation forces associated with these beams are also numerically analyzed, revealing novel properties that may be useful for a wide range of applications.

physics.optics↗
arXiv2021

Minimization of ion micromotion with artificial neural network

Minimizing the micromotion of the single trapped ion in a linear Paul trap is a tedious and time-consuming work,but is of great importance in cooling the ion into the motional ground state as well as maintaining long coherence time, which is crucial for quantum information processing and quantum computation. Here we demonstrate that systematic machine learning based on artificial neural networks can quickly and efficiently find optimal voltage settings for the electrodes using rf-photon correlation technique, consequently minimizing the micromotion to the minimum. Our approach achieves a very high level of control for the ion micromotion, and can be extended to other configurations of Paul trap.

physics.atom-ph↗
arXiv2021

A Thermodynamic Core using Voltage-Controlled Spin-Orbit-Torque Magnetic Tunnel Junctions

We present a magnetic implementation of a thermodynamic computing fabric. Magnetic devices within computing cores harness thermodynamics through its voltage-controlled thermal stability; while the evolution of network states is guided by the spin-orbit-torque effect. We theoretically derive the dynamics of the cores and show that the computing fabric can successfully compute ground states of a Boltzmann Machine. Subsequently, we demonstrate the physical realization of these devices based on a CoFeB-MgO magnetic tunnel junction structure. The results of this work pave the path towards the realization of highly efficient, high-performance thermodynamic computing hardware. Finally, this paper will also give a perspective of computing beyond thermodynamic computing.

cs.ET↗
arXiv2021

A Convergent Semi-Proximal Alternating Direction Method of Multipliers for Recovering Internet Traffics from Link Measurements

It is challenging to recover the large-scale internet traffic data purely from the link measurements. With the rapid growth of the problem scale, it will be extremely difficult to sustain the recovery accuracy and the computational cost. In this work, we propose a new Sparsity Low-Rank Recovery (SLRR) model and its Schur Complement Based semi-proximal Alternating Direction Method of Multipliers (SCB-spADMM) solver. Our approach distinguishes itself mainly for the following two aspects. First, we fully exploit the spatial low-rank property and the sparsity of traffic data, which are barely considered in the literature. Our model can be divided into a series of subproblems, which only relate to the traffics in a certain individual time interval. Thus, the model scale is significantly reduced. Second, we establish a globally convergent ADMM-type algorithm inspired by [Li et al., Math. Program., 155(2016)] to solve the SLRR model. In each iteration, all the intermediate variables' optimums can be calculated analytically, which makes the algorithm fast and accurate. Besides, due to the separability of the SLRR model, it is possible to design a parallel algorithm to further reduce computational time. According to the numerical results on the classic datasets Abilene and GEANT, our method achieves the best accuracy with a low computational cost. Moreover, in our newly released large-scale Huawei Origin-Destination (HOD) network traffics, our method perfectly reaches the seconds-level feedback, which meets the essential requirement for practical scenarios.

math.OC↗
arXiv2021

Evidence of Andreev blockade in a double quantum dot coupled to a superconductor

We design and investigate an experimental system capable of entering an electron transport blockade regime in which a spin-triplet localized in the path of current is forbidden from entering a spin-singlet superconductor. To stabilize the triplet a double quantum dot is created electrostatically near a superconducting lead in an InAs nanowire. The dots are filled stochastically with electrons of either spin. The superconducting lead is a molecular beam epitaxy grown Al shell. The shell is etched away over a wire segment to make room for the double dot and the normal metal gold lead. The quantum dot closest to the normal lead exhibits Coulomb diamonds, the dot closest to the superconducting lead exhibits Andreev bound states and an induced gap. The experimental observations compare favorably to a theoretical model of Andreev blockade, named so because the triplet double dot configuration suppresses Andreev reflections. Observed leakage currents can be accounted for by finite temperature. We observe the predicted quadruple level degeneracy points of high current and a periodic conductance pattern controlled by the occupation of the normal dot. Even-odd transport asymmetry is lifted with increased temperature and magnetic field. This blockade phenomenon can be used to study spin structure of superconductors. It may also find utility in quantum computing devices that utilize Andreev or Majorana states.

cond-mat.mes-hall↗
arXiv2021

Coalition Game Based Full-duplex Popular Content Distribution in mmWave Vehicular Networks

The millimeter wave (mmWave) communication has drawn intensive attention with abundant band resources. In this paper, we consider the popular content distribution (PCD) problem in the mmWave vehicular network. In order to offload the communication burden of base stations (BSs), vehicle-to-vehicle (V2V) communication is introduced into the PCD problem to transmit contents between on-board units (OBUs) and improve the transmission efficiency. We propose a full-duplex (FD) cooperative scheme based on coalition formation game, and the utility function is provided based on the maximization of the number of received contents. The contribution of each member in the coalition can be transferable to its individual profit. While maximizing the number of received contents in the fixed time, the cooperative scheme also ensures the individual profit of each OBU in the coalition. We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks. Compared with other existing schemes, the proposed scheme has superior performances on the number of possessed contents and system fairness. Besides, the low complexity of the proposed algorithm is demonstrated by the switch operation number and CPU time.

cs.IT↗
arXiv2020

Global Weak Solutions to the Navier-Stokes-Darcy-Boussinesq System for Thermal Convection in Coupled Free and Porous Media Flows

We study the Navier-Stokes-Darcy-Boussinesq system that models the thermal convection of a fluid overlying a saturated porous medium in a general decomposed domain. In both two and three spatial dimensions, we first prove the existence of global weak solutions to the initial boundary value problem subject to the Lions and Beavers-Joseph-Saffman-Jones interface conditions. The proof is based on a proper time-implicit discretization scheme combined with the Leray-Schauder principle and compactness arguments. Next, we establish a weak-strong uniqueness result such that a weak solution coincides with a strong solution emanating from the same initial data as long as the latter exists.

math.AP↗
arXiv2020

Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance. In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system. Tested on the MOBA game Honor of Kings, our AI agent, called Tencent Solo, can defeat top professional human players in full 1v1 games.

cs.AI↗
arXiv2020

All-Dielectric Nanolaser with Subnanometer Field Confinement

The ability to generate a laser field with ultratight spatial confinement is important for pushing the limit of optical science and technology. Although plasmon lasers can offer sub-diffraction-confined laser fields, it is restricted by a trade-off between optical confinement and loss of free-electron oscillation in metals. We show here an all-dielectric nanolaser that can circumvent the confinement-loss trade-off, and offer a field confinement down to subnanometer level. The ultra-confined lasing field, supported by low-loss oscillation of polarized bound electrons surrounding a 1-nm-width slit in a coupled CdS nanowire pair, is manifested as the dominant peak of a TE0-like lasing mode around 520-nm wavelength, with a field confinement down to 0.23 nm and a peak-to-background ratio of ~32.7 dB. This laser may pave a way towards new regions for nanolasers and light-matter interaction.

physics.optics↗