arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 721 records · Page 40Linked to original sources

arXiv2018

Supercooling-enabled giant and tunable thermal rectification ratio of a phase change thermal diode

Phase change thermal diodes (PCTD) suffer from fairly low thermal rectification ratio, which hampers their widespread utilization as thermal management and control units for cutting-edge technologies, encompassing photovoltaics, thermoelectric modules, batteries and other miniaturized electronic products, etc. It is thus indispensable to explore a high-performance PCTD. Herein, a tunable and scalable PCTD is modeled, theoretically analyzed, fabricated and experimentally executed, accessing an unprecedented giant thermal rectification ratio of 3.0 at ambient temperature. With optimized length ratio and feasible assembly of two phase change terminals, the emerging physical states of thermal media continuously contribute to align the convective-intensified thermal conductive profiles, mandatory for heat flux manipulation within temperature bias of 10~40 °C. The most significant finding discloses the fact that supercooling elongates the overall workable temperature bias range, while manual supercooling release allows to tune the thermal rectification ratio at any temperature bias within 10~33 °C. Integrating the conventional asymmetric thermal transport mechanism with state-specific heat transfer hysteresis helps establish the novel governing mechanism.

physics.app-ph↗

arXiv2018

Generalized Beamspace Modulation Using Multiplexing: A Breakthrough in mmWave MIMO

Spatial multiplexing (SMX) multiple-input multiple-output (MIMO) over the best beamspace was considered as the best solution for millimeter wave (mmWave) communications regarding spectral efficiency (SE), referred as the best beamspace selection (BBS) solution. The equivalent MIMO water-filling (WF-MIMO) channel capacity was treated as an unsurpassed SE upper bound. Recently, researchers have proposed various schemes trying to approach the benchmark and the performance bound. But, are they the real limit of mmWave MIMO systems with reduced radio-frequency (RF) chains? In this paper, we challenge the benchmark and the corresponding bound by proposing a better transmission scheme that achieves higher SE, namely the Generalized Beamspace Modulation using Multiplexing (GBMM). Inspired by the concept of spatial modulation, besides the selected beamspace, the selection operation is used to carry information. We prove that GBMM is superior to BBS in terms of SE and can break through the well known `upper bound'. That is, GBMM renews the upper bound of the SE. We investigate SE-oriented precoder activation probability optimization, fully-digital precoder design, optimal power allocation and hybrid precoder design for GBMM. A gradient ascent algorithm is developed to find the optimal solution, which is applicable in all signal-to-noise-ratio (SNR) regimes. The best solution is derived in the high SNR regime. Additionally, we investigate the hybrid receiver design and deduce the minimum number of receive RF chains configured to gain from GBMM in achievable SE. We propose a coding approach to realize the optimized precoder activation. An extension to mmWave broadband communications is also discussed. Comparisons with the benchmark (i.e., WF-MIMO channel capacity) are made under different system configurations to show the superiority of GBMM.

eess.SP↗

arXiv2018

Semi-analytical calculation of gluon fragmentation into ${^{1}\hspace{-0.6mm}S_{0}^{[1,8]}}$ quarkonia at next-to-leading order

We calculate the NLO corrections for the gluon fragmentation functions to a heavy quark-antiquark pair in ${^{1}\hspace{-0.6mm}S_{0}^{[1]}}$ or ${^{1}\hspace{-0.6mm}S_{0}^{[8]}}$ state within NRQCD factorization. We use integration-by-parts reduction to reduce the original expression to simpler master integrals (MIs), and then set up differential equations for these MIs. After calculating the boundary conditions, MIs can be obtained by solving the differential equations numerically. Our results are expressed in terms of asymptotic expansions at singular points of $z$ (light-cone momentum fraction carried by the quark-antiquark pair), which can not only give FFs results with very high precision at any value of $z$, but also provide fully analytical structure at these singularities. We find that the NLO corrections are significant, with K-factors larger than 2 in most regions. The NLO corrections may have important impact on heavy quarkonia (e.g. $η_c$ and $J/ψ$) production at the LHC.

hep-ph↗

arXiv2018

Multiple topological states in iron-based superconductors

Topological insulators and semimetals as well as unconventional iron-based superconductors have attracted major recent attention in condensed matter physics. Previously, however, little overlap has been identified between these two vibrant fields, even though the principal combination of topological bands and superconductivity promises exotic unprecedented avenues of superconducting states and Majorana bound states (MBSs), the central building block for topological quantum computation. Along with progressing laser-based spin-resolved and angle-resolved photoemission spectroscopy (ARPES) towards high energy and momentum resolution, we have resolved topological insulator (TI) and topological Dirac semimetal (TDS) bands near the Fermi level ($E_{\text{F}}$) in the iron-based superconductors Li(Fe,Co)As and Fe(Te,Se), respectively. The TI and TDS bands can be individually tuned to locate close to $E_{\text{F}}$ by carrier doping, allowing to potentially access a plethora of different superconducting topological states in the same material. Our results reveal the generic coexistence of superconductivity and multiple topological states in iron-based superconductors, rendering these materials a promising platform for high-$T_{\text{c}}$ topological superconductivity.

cond-mat.supr-con↗

arXiv2018

Prediction of Coronary Heart Disease Using Routine Blood Tests

Background --The objective of this study was to examine the association of routine blood test results with coronary heart disease (CHD) risk, to incorporate them into coronary prediction models and to compare the discrimination properties of this approach with other prediction functions. Methods and Results --This work was designed as a retrospective, single-center study of a hospital-based cohort. The 5060 CHD patients (2365 men and 2695 women) were 1 to 97 years old at baseline with 8 years (2009-2017) of medical records, 5051 health check-ups and 5075 cases of other diseases. We developed a two-layer Gradient Boosting Decision Tree(GBDT) model based on routine blood data to predict the risk of coronary heart disease, which could identify 86% of people with coronary heart disease. We built a dataset with 15,000 routine blood tests results. Using this dataset, we trained the two-layer GBDT model to classify healthy status, coronary heart disease and other diseases. As a result of the classification after machine learning, we found that the sensitivity of detecting the health data was approximately 93% for all data, and the sensitivity of detecting CHD was 93% for disease data that included coronary heart disease. On this basis, we further visualized the correlation between routine blood results and related data items, and there was an obvious pattern in health and coronary heart disease in all data presentations, which can be used for clinical reference. Finally, we briefly analyzed the results above from the perspective of pathophysiology. Conclusions --Routine blood data provides more information about CHD than what we already know through the correlation between test results and related data items. A simple coronary disease prediction model was developed using a GBDT algorithm, which will allow physicians to predict CHD risk in patients without overt CHD.

physics.med-ph↗

arXiv2018

A Quantum Many-body Wave Function Inspired Language Modeling Approach

The recently proposed quantum language model (QLM) aimed at a principled approach to modeling term dependency by applying the quantum probability theory. The latest development for a more effective QLM has adopted word embeddings as a kind of global dependency information and integrated the quantum-inspired idea in a neural network architecture. While these quantum-inspired LMs are theoretically more general and also practically effective, they have two major limitations. First, they have not taken into account the interaction among words with multiple meanings, which is common and important in understanding natural language text. Second, the integration of the quantum-inspired LM with the neural network was mainly for effective training of parameters, yet lacking a theoretical foundation accounting for such integration. To address these two issues, in this paper, we propose a Quantum Many-body Wave Function (QMWF) inspired language modeling approach. The QMWF inspired LM can adopt the tensor product to model the aforesaid interaction among words. It also enables us to reveal the inherent necessity of using Convolutional Neural Network (CNN) in QMWF language modeling. Furthermore, our approach delivers a simple algorithm to represent and match text/sentence pairs. Systematic evaluation shows the effectiveness of the proposed QMWF-LM algorithm, in comparison with the state of the art quantum-inspired LMs and a couple of CNN-based methods, on three typical Question Answering (QA) datasets.

cs.CL↗

arXiv2018

Thermal conductivity and electrical resistivity of solid iron at Earth's core conditions from first-principles

We compute the thermal conductivity and electrical resistivity of solid hcp Fe to pressures and temperatures of Earth's core. We find significant contributions from electron-electron scattering, usually neglected at high temperatures in transition metals. Our calculations show a quasi-linear relation between electrical resistivity and temperature for hcp Fe at extreme high pressures. We obtain thermal and electrical conductivities that are consistent with experiments considering reasonable error. The predicted thermal conductivity is reduced from previous estimates that neglect electron-electron scattering. Our estimated thermal conductivity for the outer core is 77$\pm$10 W/m/K, and is consistent with a geodynamo driven by thermal convection.

cond-mat.mtrl-sci↗

arXiv2018

Control of Spin-Exchange Interaction between Alkali-Earth Atoms via Confinement-Induced Resonances in a Quasi 1+0 Dimensional System

A nuclear-spin exchange interaction exists between two ultracold fermionic alkali-earth (like) atoms in the electronic $^{1}{\rm S}_{0}$ state ($g$-state) and $^{3}{\rm P}_{0}$ state ($e$-state), and is an essential ingredient for the quantum simulation of Kondo effect. We study the control of this spin-exchange interaction for two atoms simultaneously confined in a quasi-one-dimensional (quasi-1D) tube, where the $g$-atom is freely moving in the axial direction while the $e$-atom is further localized by an additional axial trap and behaves as a quasi-zero-dimensional (quasi-0D) impurity. In this system, the two atoms experience effective-1D spin-exchange interactions in both even and odd partial wave channels, whose intensities can be controlled by the characteristic lengths of the confinements via the confinement-induced-resonances (CIRs). In current work, we go beyond that pure-1D approximation. We model the transverse and axial confinements by harmonic traps with finite characteristic lengths $a_\perp$ and $a_z$, respectively, and exactly solve the "quasi-1D + quasi-0D" scattering problem between these two atoms. Using the solutions we derive the effective 1D spin-exchange interaction and investigate the locations and widths of the even/odd wave CIRs for our system. It is found that when the ratio $a_z/a_\perp$ is larger, the CIRs can be induced by weaker confinements, which are easier to be realized experimentally. The comparison between our results and the recent experiment shows that the two experimentally observed resonance branches of the spin-exchange effect are due to an even-wave CIR and an odd-wave CIR, respectively. Our results are advantageous for the control and description of either the effective spin-exchange interaction or other types of interactions between ultracold atoms in quasi 1+0 dimensional systems.

cond-mat.quant-gas↗

arXiv2018

Disorder-sensitive node-like small gap in FeSe

We investigate the band structure, nematic state and superconducting gap structure of two selected FeSe single crystals containing different amount of disorder. Transport and angle-resolved photoemission spectroscopy measurements show that the small amount of disorder has little effect to the band structure and the nematic state of FeSe. However, temperature and magnetic field dependencies of specific heat for the two samples are quite different. Wave-vector-dependent gap structure are obtained from the three dimensional field-angle-resolved specific heat measurements. A small gap with two vertical-line nodes or gap minima along the $k_z$ direction is found only in the sample with higher quality. Such symmetry-unprotected nodes or gap minima are found to be smeared out by small amount of disorder, and the gap becomes isotropic in the sample of lower quality. Our study reveals that the reported controversy on the gap structure of FeSe is due to the disorder-sensitive node-like small gap.

cond-mat.supr-con↗

arXiv2018

An Efficient Approach to Learning Chinese Judgment Document Similarity Based on Knowledge Summarization

A previous similar case in common law systems can be used as a reference with respect to the current case such that identical situations can be treated similarly in every case. However, current approaches for judgment document similarity computation failed to capture the core semantics of judgment documents and therefore suffer from lower accuracy and higher computation complexity. In this paper, a knowledge block summarization based machine learning approach is proposed to compute the semantic similarity of Chinese judgment documents. By utilizing domain ontologies for judgment documents, the core semantics of Chinese judgment documents is summarized based on knowledge blocks. Then the WMD algorithm is used to calculate the similarity between knowledge blocks. At last, the related experiments were made to illustrate that our approach is very effective and efficient in achieving higher accuracy and faster computation speed in comparison with the traditional approaches.

cs.AI↗

arXiv2018

AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture

CPU-FPGA heterogeneous architectures are attracting ever-increasing attention in an attempt to advance computational capabilities and energy efficiency in today's datacenters. These architectures provide programmers with the ability to reprogram the FPGAs for flexible acceleration of many workloads. Nonetheless, this advantage is often overshadowed by the poor programmability of FPGAs whose programming is conventionally a RTL design practice. Although recent advances in high-level synthesis (HLS) significantly improve the FPGA programmability, it still leaves programmers facing the challenge of identifying the optimal design configuration in a tremendous design space. This paper aims to address this challenge and pave the path from software programs towards high-quality FPGA accelerators. Specifically, we first propose the composable, parallel and pipeline (CPP) microarchitecture as a template of accelerator designs. Such a well-defined template is able to support efficient accelerator designs for a broad class of computation kernels, and more importantly, drastically reduce the design space. Also, we introduce an analytical model to capture the performance and resource trade-offs among different design configurations of the CPP microarchitecture, which lays the foundation for fast design space exploration. On top of the CPP microarchitecture and its analytical model, we develop the AutoAccel framework to make the entire accelerator generation automated. AutoAccel accepts a software program as an input and performs a series of code transformations based on the result of the analytical-model-based design space exploration to construct the desired CPP microarchitecture. Our experiments show that the AutoAccel-generated accelerators outperform their corresponding software implementations by an average of 72x for a broad class of computation kernels.

cs.DC↗

arXiv2018

Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. In this paper, we present a novel model to address these issues. First, a cross-layer bilinear pooling approach is proposed to capture the inter-layer part feature relations, which results in superior performance compared with other bilinear pooling based approaches. Second, we propose a novel hierarchical bilinear pooling framework to integrate multiple cross-layer bilinear features to enhance their representation capability. Our formulation is intuitive, efficient and achieves state-of-the-art results on the widely used fine-grained recognition datasets.

cs.CV↗

arXiv2018

FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data

Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the "Shared Nationwide Interoperability Roadmap" from the Office of the National Coordinator for Health Information Technology (ONC). First, we analyze the ONC requirements and their implications for blockchain-based systems. Second, we present FHIRChain, which is a blockchain-based architecture designed to meet ONC requirements by encapsulating the HL7 Fast Healthcare Interoperability Resources (FHIR) standard for shared clinical data. Third, we demonstrate a FHIRChain-based decentralized app using digital health identities to authenticate participants in a case study of collaborative decision making for remote cancer care. Fourth, we highlight key lessons learned from our case study.

cs.CY↗

arXiv2018

Incomplete Nested Dissection

We present an asymptotically faster algorithm for solving linear systems in well-structured 3-dimensional truss stiffness matrices. These linear systems arise from linear elasticity problems, and can be viewed as extensions of graph Laplacians into higher dimensions. Faster solvers for the 2-D variants of such systems have been studied using generalizations of tools for solving graph Laplacians [Daitch-Spielman CSC'07, Shklarski-Toledo SIMAX'08]. Given a 3-dimensional truss over $n$ vertices which is formed from a union of $k$ convex structures (tetrahedral meshes) with bounded aspect ratios, whose individual tetrahedrons are also in some sense well-conditioned, our algorithm solves a linear system in the associated stiffness matrix up to accuracy $ε$ in time $O(k^{1/3} n^{5/3} \log (1 / ε))$. This asymptotically improves the running time $O(n^2)$ by Nested Dissection for all $k \ll n$. We also give a result that improves on Nested Dissection even when we allow any aspect ratio for each of the $k$ convex structures (but we still require well-conditioned individual tetrahedrons). In this regime, we improve on Nested Dissection for $k \ll n^{1/44}$. The key idea of our algorithm is to combine nested dissection and support theory. Both of these techniques for solving linear systems are well studied, but usually separately. Our algorithm decomposes a 3-dimensional truss into separate and balanced regions with small boundaries. We then bound the spectrum of each such region separately, and utilize such bounds to obtain improved algorithms by preconditioning with partial states of separator-based Gaussian elimination.

cs.DS↗

arXiv2018

Diffusion Based Network Embedding

In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk methods are fragile when the sampling frequency or the number of node sequences changes. Second, in disequilibrium networks such as highly biases networks, random walk methods often perform poorly due to the lack of global network information. In order to solve the limitations, we propose in this paper a network diffusion based embedding method. To solve the first limitation, our method employs a diffusion driven process to capture both depth information and breadth information. The time dimension is also attached to node sequences that can strengthen information preserving. To solve the second limitation, our method uses the network inference technique based on cascades to capture the global network information. To verify the performance, we conduct experiments on node classification tasks using the learned representations. Results show that compared with random walk based methods, diffusion based models are more robust when samplings under each node is rare. We also conduct experiments on a highly imbalanced network. Results shows that the proposed model are more robust under the biased network structure.

cs.LG↗

arXiv2018

Universal Relations of Ultracold Fermi Gases with Arbitrary Spin-Orbit Coupling

We derive the universal relations for an ultracold two-component Fermi gas with spin-orbit coupling (SOC) $\sum_{α,β=x,y,z}λ_{αβ}σ_αp_β$, where $p_{x,y,z}$ and $σ_{x,y,z}$ are the single-atom momentum and Pauli operators for pseudo spin, respectively, and the SOC intensity $λ_{αβ}$ could take arbitrary value. We consider the system with an s-wave short-range interspecies interaction, and ignore the SOC-induced modification for the value of the scattering length. Using the first-quantized approach developed by S. Tan (Phys. Rev. Lett. \textbf{107}, 145302 (2011)), we obtain the short-range and high-momentum expansions for the one-body real-space correlation function and momentum distribution function, respectively. For our system these functions are $2\times2$ matrix in the pseudo-spin basis. We find that the leading-order ($1/k^{4}$) behavior of the diagonal elements of the momentum distribution function (i.e., $n_{\uparrow\uparrow}({\bf k})$ and $n_{\downarrow\downarrow}({\bf k})$) are not modified by the SOC. However, the SOC can significantly modify the behavior of the \textit{non-diagonal elements} of the momentum distribution function, i.e., $n_{\uparrow\downarrow}({\bf k})$ and $n_{\downarrow\uparrow}({\bf k})$, in the large-$k$ limit. In the absence of the SOC, the leading order of these elements is ${\cal O}(1/k^{6})$. When SOC appears, it can induce a term on the order of $1/k^{5}$. We further derive the adiabatic relation and the energy functional. Our results show the SOC can induce a new term in the energy functional, which simply describe the contribution from the SOC to the total energy. The form of the adiabatic relation for our system is not modified by the SOC.

cond-mat.quant-gas↗

arXiv2018

Flickering of buoyant diffusion flames: a vortex dynamics revisit

Previous studies have found that the flickering of buoyant diffusion flames is associated with the periodic shedding of a toroidal vortex, which is formed under gravity-induced shearing at the flames. Moreover, numerous experimental investigations have reported the scaling relation, $f \propto D^{-1/2}$, where $f$ is the flickering frequency and $D$ is the diameter of the fuel inlet. However, the connection between the toroidal vortex mechanism and the scaling relation has not been clearly understood. The current study theoretically revisits this problem from the perspective of vortex dynamics. The theory incorporates a recent finding in the vortex dynamics community: the detachment of a continuously growing vortex ring is inevitable and can be dictated by a universal constant that is essentially a non-dimensional circulation of the vortex. By calculating the total circulation of the toroidal vortex and applying the vortex ring detachment criterion, we mathematically established the connection between the lifetime of a toroidal vortex and the flickering of a buoyant diffusion flame, resulting in a general flickering frequency formulation, which is validated by comparing with existing experimental data of pool and jet diffusion flames.

physics.flu-dyn↗

arXiv2018

The 2017 AIBIRDS Competition

This paper presents an overview of the sixth AIBIRDS competition, held at the 26th International Joint Conference on Artificial Intelligence. This competition tasked participants with developing an intelligent agent which can play the physics-based puzzle game Angry Birds. This game uses a sophisticated physics engine that requires agents to reason and predict the outcome of actions with only limited environmental information. Agents entered into this competition were required to solve a wide assortment of previously unseen levels within a set time limit. The physical reasoning and planning required to solve these levels are very similar to those of many real-world problems. This year's competition featured some of the best agents developed so far and even included several new AI techniques such as deep reinforcement learning. Within this paper we describe the framework, rules, submitted agents and results for this competition. We also provide some background information on related work and other video game AI competitions, as well as discussing some potential ideas for future AIBIRDS competitions and agent improvements.

cs.AI↗