arXiv subjects

Wei Pan

Publications and source records attributed to Wei Pan.

At least 199 records · Page 11Linked to original sources

arXiv2018

Numerically Modelling Stochastic Lie Transport in Fluid Dynamics

We present a numerical investigation of stochastic transport in ideal fluids. According to Holm (Proc Roy Soc, 2015) and Cotter et al. (2017), the principles of transformation theory and multi-time homogenisation, respectively, imply a physically meaningful, data-driven approach for decomposing the fluid transport velocity into its drift and stochastic parts, for a certain class of fluid flows. In the current paper, we develop new methodology to implement this velocity decomposition and then numerically integrate the resulting stochastic partial differential equation using a finite element discretisation for incompressible 2D Euler fluid flows. The new methodology tested here is found to be suitable for coarse graining in this case. Specifically, we perform uncertainty quantification tests of the velocity decomposition of Cotter et al. (2017), by comparing ensembles of coarse-grid realisations of solutions of the resulting stochastic partial differential equation with the "true solutions" of the deterministic fluid partial differential equation, computed on a refined grid. The time discretization used for approximating the solution of the stochastic partial differential equation is shown to be consistent. We include comprehensive numerical tests that confirm the non-Gaussianity of the stream function, velocity and vorticity fields in the case of incompressible 2D Euler fluid flows.

physics.flu-dyn↗

arXiv2018

Enabling Quality Control for Entity Resolution: A Human and Machine Cooperation Framework

Even though many machine algorithms have been proposed for entity resolution, it remains very challenging to find a solution with quality guarantees. In this paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for entity resolution (ER), which divides an ER workload between the machine and the human. HUMO enables a mechanism for quality control that can flexibly enforce both precision and recall levels. We introduce the optimization problem of HUMO, minimizing human cost given a quality requirement, and then present three optimization approaches: a conservative baseline one purely based on the monotonicity assumption of precision, a more aggressive one based on sampling and a hybrid one that can take advantage of the strengths of both previous approaches. Finally, we demonstrate by extensive experiments on real and synthetic datasets that HUMO can achieve high-quality results with reasonable return on investment (ROI) in terms of human cost, and it performs considerably better than the state-of-the-art alternatives in quality control.

cs.DB↗

arXiv2018

π and 4π Josephson Effects Mediated by a Dirac Semimetal

Cd3As2 is a three-dimensional topological Dirac semimetal with connected Fermi-arc surface states. It has been suggested that topological superconductivity can be achieved in the nontrivial surface states of topological materials by utilizing the superconductor proximity effect. Here we report observations of both π and 4π periodic supercurrents in aluminum-Cd3As2-aluminum Josephson junctions. The π period is manifested by both the magnetic-field dependence of the critical supercurrent and the appearance of half-integer Shapiro steps in the a.c. Josephson effect. Our macroscopic theory suggests that the π period arises from interference between the induced bulk superconductivity and the induced Fermi arc surface superconductivity. The 4π period is manifested by the missing first Shapiro steps and is expected for topological superconductivity.

cond-mat.supr-con↗

arXiv2017

Towards Accurate Binary Convolutional Neural Network

We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bitwise operations, leading to much faster test-time inference and lower power consumption. However, previous works on binarizing CNNs usually result in severe prediction accuracy degradation. In this paper, we address this issue with two major innovations: (1) approximating full-precision weights with the linear combination of multiple binary weight bases; (2) employing multiple binary activations to alleviate information loss. The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations.

cs.LG↗

arXiv2017

Strong Photothermoelectric Response and Contact Reactivity of the Dirac Semimetal ZrTe5

The family of three-dimensional topological insulators opens new avenues to discover novel photophysics and to develop novel types of photodetectors. ZrTe5 has been shown to be a Dirac semimetal possessing unique topological electronic and optical properties. Here we present spatially-resolved photocurrent measurements on devices made of nanoplatelets of ZrTe5, demonstrating the photothermoelectric origin of the photoresponse. Due to the high electrical conductivity and good Seebeck coefficient, we obtain noise-equivalent powers as low as 42 pW/Hz1/2 at room temperature for visible light illumination at zero bias. We also show that these devices suffer from significant ambient reactivity such as the formation of a Te-rich surface region driven by Zr oxidation, as well as severe reactions with the metal contacts. This reactivity results in significant stresses in the devices, leading to unusual geometries that are useful for gaining insight into the photocurrent mechanisms. Our results indicate that both the large photothermoelectric response and reactivity must be considered when designing or interpreting photocurrent measurements in these systems.

cond-mat.mes-hall↗

arXiv2017

Mixed Neural Network Approach for Temporal Sleep Stage Classification

This paper proposes a practical approach to addressing limitations posed by use of single active electrodes in applications for sleep stage classification. Electroencephalography (EEG)-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports have explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages in order to make it possible to perform sleep studies at lower cost in the home (rather than only in specialized clinical facilities). However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which can be uncomfortable or difficult for subjects to position. Those that have utilized single EEG channels which contain less sleep information, have showed poor classification performance. We have taken advantage of Rectifier Neural Network for feature detection and Long Short-Term Memory (LSTM) network for sequential data learning to optimize classification performance with single electrode recordings. After exploring alternative electrode placements, we found a comfortable configuration of a single-channel EEG on the forehead and have shown that it can be integrated with additional electrodes for simultaneous recording of the electroocuolgram (EOG). Evaluation of data from 62 people (with 494 hours sleep) demonstrated better performance of our analytical algorithm for automated sleep classification than existing approaches using vertex or occipital electrode placements. Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

q-bio.NC↗

arXiv2017

GraphHP: A Hybrid Platform for Iterative Graph Processing

The Bulk Synchronous Parallel(BSP) computational model has emerged as the dominant distributed framework to build large-scale iterative graph processing systems. While its implementations(e.g., Pregel, Giraph, and Hama) achieve high scalability, frequent synchronization and communication among the workers can cause substantial parallel inefficiency. To help address this critical concern, this paper introduces the GraphHP(Graph Hybrid Processing) platform which inherits the friendly vertex-centric BSP programming interface and optimizes its synchronization and communication overhead. To achieve the goal, we first propose a hybrid execution model which differentiates between the computations within a graph partition and across the partitions, and decouples the computations within a partition from distributed synchronization and communication. By implementing the computations within a partition by pseudo-superstep iteration in memory, the hybrid execution model can effectively reduce synchronization and communication overhead while not requiring heavy scheduling overhead or graph-centric sequential algorithms. We then demonstrate how the hybrid execution model can be easily implemented within the BSP abstraction to preserve its simple programming interface. Finally, we evaluate our implementation of the GraphHP platform on classical BSP applications and show that it performs significantly better than the state-of-the-art BSP implementations. Our GraphHP implementation is based on Hama, but can easily generalize to other BSP platforms.

cs.DC↗

arXiv2017

Computing and Graphing Probability Values of Pearson Distributions: A SAS/IML Macro

Any empirical data can be approximated to one of Pearson distributions using the first four moments of the data (Elderton and Johnson, 1969; Pearson, 1895; Solomon and Stephens, 1978). Thus, Pearson distributions made statistical analysis possible for data with unknown distributions. There are both extant old-fashioned in-print tables (Pearson and Hartley, 1972) and contemporary computer programs (Amos and Daniel, 1971; Bouver and Bargmann, 1974; Bowman and Shenton, 1979; Davis and Stephens, 1983; Pan, 2009) available for obtaining percentage points of Pearson distributions corresponding to certain pre-specifed percentages (or probability values) (e.g., 1.0%, 2.5%, 5.0%, etc.), but they are little useful in statistical analysis because we have to rely on unwieldy second difference interpolation to calculate a probability value of a Pearson distribution corresponding to any given percentage point, such as an observed test statistic in hypothesis testing. Thus, the present study develops a SAS/IML macro program to compute and graph probability values of Pearson distributions for any given percentage point so as to facilitate researchers to conduct statistical analysis on data with unknown distributions.

stat.CO↗

arXiv2017

Quantum-Electron Back Action on Hybridization of Radiative and Evanescent Field Modes

A back action from Dirac electrons in graphene on the hybridization of radiative and evanescent fields is found as an analogy to Newton's third law. Here, the back action appears as a localized polarization field which greatly modifies an incident surface-plasmon-polariton (SPP) field. This yields a high sensitivity to local dielectric environments and provides a scrutiny tool for molecules or proteins selectively bounded with carbons. A scattering matrix is shown with varied frequencies nearby the surface-plasmon (SP) resonance for the increase, decrease and even a full suppression of the polarization field, which enables accurate effective-medium theories to be constructed for Maxwell-equation finite-difference time-domain methods. Moreover, double peaks in the absorption spectra for hybrid SP and graphene-plasmon modes are significant only with a large conductor plasma frequency, but are overshadowed by a round SPP peak at a small plasma frequency as the graphene is placed close to conductor surface. These resonant absorptions facilitate the polariton-only excitations, leading to polariton condensation for a threshold-free laser.

cond-mat.mes-hall↗

arXiv2016

Tunable Photonic Radiofrequency Filter with An Ultra-high Out-Of-Band Rejection

As radiofrequency filtering plays a vital role in electromagnetic devices and systems, recently photonic techniques have been intensively studied to implement radiofrequency filters to harness wide frequency coverage, large instantaneous bandwidth, low frequency-dependent loss, flexible tunability and strong immunity to electromagnetic interference. However, one crucial challenge facing the photonic radiofrequency filter (PRF) is the less impressive out-of-band rejection. Here, to the best of our knowledge, we demonstrate a tunable PRF with a record out-of-band rejection of 80 dB, which is 3 dB higher than the maximum value (~77 dB) reported so far, when incorporating highly selective polarization control and large narrow-band amplification enabled by stimulated Brillouin scattering effect. In particular, this record rejection is arduous to be achieved for a narrow passband (e.g., a few megahertz) and a high finesse in a PRF. Moreover, the proposed PRF is an active one capable of providing negligible insertion loss and even signal gain. Tunable central frequency ranging from 2.1 to 6.1 GHz is also demonstrated. The proposed PRF will provide an ultra-high noise or clutter suppression for harsh electromagnetic scenarios, particularly when room-temperature implementation and remote distribution are needed.

physics.optics↗

arXiv2016

Conceptual Proposal: Frequency Offset Modulation for High-Efficiency Communications

Frequency offset modulation (FOM) is proposed as a new concept to provide both high energy efficiency and high spectral efficiency for communications. In the FOM system, an array of transmitters (TXs) is deployed and only one TX is activated for data transmission at any signaling time instance. The TX index distinguished by a very slight frequency offset among the entire occupied bandwidth is exploited to implicitly convey a bit unit without any power or signal radiation, saving the power and spectral resources. Moreover, the FOM is characterized by removing the stringent requirements on distinguishable spatial channels and perfect priori channel knowledge, while retaining the advantages of no inter-channel interference and no need of inter-antenna synchronization. In addition, a hybrid solution integrating the FOM and the spatial modulation is discussed to further improve the energy efficiency and spectral efficiency. Consequently, the FOM will be an enabling and green solution to support ever-increasing high-capacity data traffic in a variety of interdisciplinary fields.

cs.IT↗

arXiv2016

Human collective intelligence as distributed Bayesian inference

Collective intelligence is believed to underly the remarkable success of human society. The formation of accurate shared beliefs is one of the key components of human collective intelligence. How are accurate shared beliefs formed in groups of fallible individuals? Answering this question requires a multiscale analysis. We must understand both the individual decision mechanisms people use, and the properties and dynamics of those mechanisms in the aggregate. As of yet, mathematical tools for such an approach have been lacking. To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference. Distributed inference occurs through information processing at the individual level, and yields rational belief formation at the group level. We instantiate this framework in a new model of human social decision-making, which we validate using a dataset we collected of over 50,000 users of an online social trading platform where investors mimic each others' trades using real money in foreign exchange and other asset markets. We find that in this setting people use a decision mechanism in which popularity is treated as a prior distribution for which decisions are best to make. This mechanism is boundedly rational at the individual level, but we prove that in the aggregate implements a type of approximate "Thompson sampling"---a well-known and highly effective single-agent Bayesian machine learning algorithm for sequential decision-making. The perspective of distributed Bayesian inference therefore reveals how collective rationality emerges from the boundedly rational decision mechanisms people use.

cs.CY↗

arXiv2016

DropNeuron: Simplifying the Structure of Deep Neural Networks

Deep learning using multi-layer neural networks (NNs) architecture manifests superb power in modern machine learning systems. The trained Deep Neural Networks (DNNs) are typically large. The question we would like to address is whether it is possible to simplify the NN during training process to achieve a reasonable performance within an acceptable computational time. We presented a novel approach of optimising a deep neural network through regularisation of net- work architecture. We proposed regularisers which support a simple mechanism of dropping neurons during a network training process. The method supports the construction of a simpler deep neural networks with compatible performance with its simplified version. As a proof of concept, we evaluate the proposed method with examples including sparse linear regression, deep autoencoder and convolutional neural network. The valuations demonstrate excellent performance. The code for this work can be found in http://www.github.com/panweihit/DropNeuron

cs.CV↗

arXiv2016

Collective, Coherent, and Ultrastrong Coupling of 2D Electrons with Terahertz Cavity Photons

Nonperturbative coupling of light with condensed matter in an optical cavity is expected to reveal a host of coherent many-body phenomena and states. In addition, strong coherent light-matter interaction in a solid-state environment is of great interest to emerging quantum-based technologies. However, creating a system that combines a long electronic coherence time, a large dipole moment, and a high cavity quality ($Q$) factor has been a challenging goal. Here, we report collective ultrastrong light-matter coupling in an ultrahigh-mobility two-dimensional electron gas in a high-$Q$ terahertz photonic-crystal cavity in a quantizing magnetic field, demonstrating a cooperativity of $\sim$360. The splitting of cyclotron resonance (CR) into the lower and upper polariton branches exhibited a $\sqrt{n_\mathrm{e}}$-dependence on the electron density ($n_\mathrm{e}$), a hallmark of collective vacuum Rabi splitting. Furthermore, a small but definite blue shift was observed for the polariton frequencies due to the normally negligible $A^2$ term in the light-matter interaction Hamiltonian. Finally, the high-$Q$ cavity suppressed the superradiant decay of coherent CR, which resulted in an unprecedentedly narrow intrinsic CR linewidth of 5.6 GHz at 2 K. These results open up a variety of new possibilities to combine the traditional disciplines of many-body condensed matter physics and cavity-based quantum optics.

cond-mat.quant-gas↗

arXiv2015

Detection of Majorana Kramers pairs using a quantum point contact

We propose a setup that integrates a quantum point contact (QPC) and a Josephson junction on a quantum spin Hall sample, experimentally realizable in InAs/GaSb quantum wells. The confinement due to both the QPC and the superconductor results in a Kramers pair of Majorana zero-energy bound states when the superconducting phases in the two arms differ by an odd multiple of $π$ across the Josephson junction. We investigate the detection of these Majorana pairs with the integrated QPC, and find a robust switching from normal to Andreev scattering across the edges due to the presence of Majorana Kramers pairs. This transport signature is expected to be exhibited in measurements of differential conductance and/or current cross-correlation at low bias.

cond-mat.mes-hall↗

arXiv2015

Identifying Biochemical Reaction Networks From Heterogeneous Datasets

In this paper, we propose a new method to identify biochemical reaction networks (i.e. both reactions and kinetic parameters) from heterogeneous datasets. Such datasets can contain (a) data from several replicates of an experiment performed on a biological system; (b) data measured from a biochemical network subjected to different experimental conditions, for example, changes/perturbations in biological inductions, temperature, gene knock-out, gene over-expression, etc. Simultaneous integration of various datasets to perform system identification has the potential to avoid non-identifiability issues typically arising when only single datasets are used.

eess.SY↗

arXiv2015

A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems

This technical note considers the identification of nonlinear discrete-time systems with additive process noise but without measurement noise. In particular, we propose a method and its associated algorithm to identify the system nonlinear functional forms and their associated parameters from a limited number of time-series data points. For this, we cast this identification problem as a sparse linear regression problem and take a Bayesian viewpoint to solve it. As such, this approach typically leads to nonconvex optimisations. We propose a convexification procedure relying on an efficient iterative re-weighted $\ell_1$-minimisation algorithm that uses general sparsity inducing priors on the parameters of the system and marginal likelihood maximisation. Using this approach, we also show how convex constraints on the parameters can be easily added to our proposed iterative re-weighted $\ell_1$-minimisation algorithm. In the supplementary material \cite{appendix}, we illustrate the effectiveness of the proposed identification method on two classical systems in biology and physics, namely, a genetic repressilator network and a large scale network of interconnected Kuramoto oscillators.

math.OC↗

arXiv2014

Surface Plasmon Instability Leading to Emission of Radiation

We propose a new energy conversion approach from a dc electric field to a terahertz wave based on hybrid semiconductors by combining two-dimensional (2D) crystalline layers and a thick conducting material with possible applications as a source of coherent radiation. The hybrid nano-structure may consist of a single or pair of sheets of graphene, silicene or a 2D electron gas as would occur at a semiconductor hetero-interface. When an electric current is passed through a layer, we discover that the low-frequency plasmons may become unstable beyond a critical wave vector $q_c$. However, there is no instability for a single driven layer far from the conductor and the instability of an isolated pair of 2D layers occurs only at ultra long wavelengths. To bring in frequency agility for this spontaneous radiation, we manipulate the surface-plasmon induced instability, which leads to the emission of radiation (spiler), to occur at shorter wavelengths by choosing the conductor electron density, layer separation, distances of layers from the conductor surface and the driving-current strength. Applications of terahertz radiation from spiler for chemical analysis, security scanning, medical imaging and telecommunications are expected.

cond-mat.mes-hall↗