arXiv subjects

Tao Hu

Publications and source records attributed to Tao Hu.

At least 181 records · Page 10Linked to original sources

arXiv2015

p/${π^+}$ response of single layer THGEM detector in Ar/3%iC$_4$H$_{10}$

In this work, we study the response of single layer Thick GEM (THGEM) detector to p/${π^+}$ at E3 line of Beijing Test Beam Facility. In our experiment, the drift gap of THGEM Detector is 4mm, and the working gas is Ar/3% iso. Result shows at the momentum 500MeV/c to 1000MeV/c, detection efficiency for p is from 93% to 99% in a relatively lower gain($\sim$2000), while the detection efficiency for ${π^+}$ is slightly lower than that for p, which is from 82% to 88%. Meanwhile, simple Geant4 simulations have been done, and results of beam test are almost consistent with it. We preliminarily study the feasibility of THGEM detectors as sampling elements for Digital Hadronic Calorimeter(DHCAL), which may provide related reference for THGEM possibly applied in Circular Electron Positron Collider(CEPC) HCAL.

physics.ins-det↗

arXiv2015

A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data

Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost function these rules are nonlocal and hence biologically implausible. At the same time, biologically plausible local rules have been postulated rather than derived from a principled cost function. Here, to bridge this gap, we derive a biologically plausible network for subspace learning on streaming data by minimizing a principled cost function. In a departure from previous work, where cost was quantified by the representation, or reconstruction, error, we adopt a multidimensional scaling (MDS) cost function for streaming data. The resulting algorithm relies only on biologically plausible Hebbian and anti-Hebbian local learning rules. In a stochastic setting, synaptic weights converge to a stationary state which projects the input data onto the principal subspace. If the data are generated by a nonstationary distribution, the network can track the principal subspace. Thus, our result makes a step towards an algorithmic theory of neural computation.

q-bio.NC↗

arXiv2014

Synthesis, Structural, and Transport Properties of Cr-doped BaTi_2As_2O

The interplay between unconventional superconductivity and the ordering of charge/spin density wave is one of the most vital issues in both condensed matter physics and material science. The Ti-based compound BaTi_2As_2O, which can be seen as the parent phase of superconducting BaTi_2Sb_2O, has a layered structure with a space group P4/mmm, similar to that of cuprate and iron-based superconductors. This material exhibits a charge density wave (CDW) ordering transition revealed by an anomaly at around 200 K in transport measurements. Here, we report the synthesis and systematical study of the physical properties in Cr-doped BaTi_{2-x}Cr_xAs_2O (x = 0 - 0.154), and demonstrate that the transition temperature of the CDW ordering is suppressed gradually by the doped Cr element. The magnetization measurements confirm the evolution of the CDW ordering transition. These observed behaviors are similar to that observed in iron-based superconductors, but no superconductivity emerges down to 2 K. In addition, the first-principles calculations are also carried out for well-understanding the nature of experimental observations.

cond-mat.supr-con↗

arXiv2014

High-T_c superconductivity in ultrathin Bi_2Sr_2CaCu_2O_8+x down to halfunit-cell thickness by protection with graphene

High-T_c superconductors confined to two dimension exhibit novel physical phenomena, such as superconductor-insulator transition. In the Bi_2Sr_2CaCu_2O_8+x (Bi2212) model system, despite extensive studies, the intrinsic superconducting properties at the thinness limit have been difficult to determine. Here we report a method to fabricate high quality single-crystal Bi2212 films down to half-unit-cell thickness in the form of graphene/Bi2212 van der Waals heterostructure, in which sharp superconducting transitions are observed. The heterostructure also exhibits a nonlinear current-voltage characteristic due to the Dirac nature of the graphene band structure. More interestingly, although the critical temperature remains essentially the same with reduced thickness of Bi2212, the slope of the normal state T-linear resistivity varies by a factor of 4-5, and the sheet resistance increases by three orders of magnitude, indicating a surprising decoupling of the normal state resistance and superconductivity. The developed technique is versatile, applicable to investigate other two-dimensional (2D) superconducting materials.

cond-mat.supr-con↗

arXiv2014

Simulation and performance study of the ceramic THGEM

The THGEMs based on ceramic substrate were developed successfully for neutron and single photon detection. The influences on thermal neutron scattering and the internal radioactivity of both ceramic and FR-4 substrates were studied and compared. The ceramic THGEMs are homemade of 200 um hole diameter, 600 um pitch, 200 um thickness, 80 um rim, and 50 mm*50 mm sensitive area. The FR-4 THGEMs with the same geometry were used for the reference. The gas gain, energy resolution and gain stability were measured in different gas mixtures by using the 5.9 keV X-rays. The maximum gain of single layer ceramic THGEM reaches 6*104 and 1.5*104 at Ne+CH4 = 95:5 and Ar+i-C4H10 = 97:3, respectively. The energy resolution is better than 24%. The good gain stability was obtained during more than 100 hour continuous test at Ar+CO2 = 80:20. By using the 239Pu source, the alpha deposited energy spectrum and gain curve of ceramic THGEM were measured.

physics.ins-det↗

arXiv2014

Simulation of background reduction and Compton depression in low-background HPGe spectrometer at a surface laboratory

High-purity germanium detectors are well suited to analysis the radioactivity of samples. In order to reduce the environmental background, low-activity lead and oxygen free copper are installed outside of the probe to shield gammas, outmost is a plastic scintillator to veto the cosmic rays, and an anti-Compton detector can improve the Peak-to-Compton ratio. Using the GEANT4 tools and taking into account a detailed description of the detector, we optimize the sizes of the detectors to reach the design indexes. A group of experimental data from a HPGe spectrometer in using were used to compare with the simulation. As to new HPGe Detector simulation, considering the different thickness of BGO crystals and anti-coincidence efficiency, the simulation results show that the optimal thickness is 5.5cm, and the Peak-to-Compton ratio of 40K is raised to 1000 when the anti-coincidence efficiency is 0.85. As the background simulation, 15 cm oxygen-free copper plus 10 cm lead can reduce the environmental gamma rays to 0.0024 cps/100 cm3 Ge (50keV~2.8MeV), which is about 10-5 of environmental background.

physics.ins-det↗

arXiv2014

Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning

In this paper, we study the generalization performance of regularized multi-task learning (RMTL) in a vector-valued framework, where MTL is considered as a learning process for vector-valued functions. We are mainly concerned with two theoretical questions: 1) under what conditions does RMTL perform better with a smaller task sample size than STL? 2) under what conditions is RMTL generalizable and can guarantee the consistency of each task during simultaneous learning? In particular, we investigate two types of task-group relatedness: the observed discrepancy-dependence measure (ODDM) and the empirical discrepancy-dependence measure (EDDM), both of which detect the dependence between two groups of multiple related tasks (MRTs). We then introduce the Cartesian product-based uniform entropy number (CPUEN) to measure the complexities of vector-valued function classes. By applying the specific deviation and the symmetrization inequalities to the vector-valued framework, we obtain the generalization bound for RMTL, which is the upper bound of the joint probability of the event that there is at least one task with a large empirical discrepancy between the expected and empirical risks. Finally, we present a sufficient condition to guarantee the consistency of each task in the simultaneous learning process, and we discuss how task relatedness affects the generalization performance of RMTL. Our theoretical findings answer the aforementioned two questions.

cs.LG↗

arXiv2014

Phase Diagram and Weak-link Behavior in Nd-doped CaFe_2As_2

The transport properties, phase diagram, and dopant distribution are investigated in systematically Nd doped CaFe_2As_2 single crystals. Coexistence of two superconducting (SC) phases with different critical transition temperature (T_c) is observed. The low-T_c phase emerges as x >= 0.031, and the T_c value increases to its maximum value of about 20 K at x = 0.083, the maximum doping level in our study. As x >= 0.060, the high-T_c phase with a T_c value of about 40 K is observed. The structure transition (STr) from tetragonal to orthorhombic phase vanishes suddenly around x = 0.060, where a new STr from tetragonal to collapsed tetragonal phase begins to turn up. Compared to the low-T_c phase, the end point of SC transition of the high-T_c phase is more sensitive to the magnetic field, showing a characteristic of Josephson weak-link behavior. Possible scenarios about this system are discussed based on our observations. We also find that the non-uniform SC properties cannot be attributed to the heterogeneous Nd distribution on the micro scale, as revealed by the detailed energy dispersive X-ray spectroscopy (EDS) measurements.

cond-mat.supr-con↗

arXiv2014

Fast Genome-Wide QTL Analysis Using Mendel

Pedigree GWAS (Option 29) in the current version of the Mendel software is an optimized subroutine for performing large scale genome-wide QTL analysis. This analysis (a) works for random sample data, pedigree data, or a mix of both, (b) is highly efficient in both run time and memory requirement, (c) accommodates both univariate and multivariate traits, (d) works for autosomal and x-linked loci, (e) correctly deals with missing data in traits, covariates, and genotypes, (f) allows for covariate adjustment and constraints among parameters, (g) uses either theoretical or SNP-based empirical kinship matrix for additive polygenic effects, (h) allows extra variance components such as dominant polygenic effects and household effects, (i) detects and reports outlier individuals and pedigrees, and (j) allows for robust estimation via the $t$-distribution. The current paper assesses these capabilities on the genetics analysis workshop 19 (GAW19) sequencing data. We analyzed simulated and real phenotypes for both family and random sample data sets. For instance, when jointly testing the 8 longitudinally measured systolic blood pressure (SBP) and diastolic blood pressure (DBP) traits, it takes Mendel 78 minutes on a standard laptop computer to read, quality check, and analyze a data set with 849 individuals and 8.3 million SNPs. Genome-wide eQTL analysis of 20,643 expression traits on 641 individuals with 8.3 million SNPs takes 30 hours using 20 parallel runs on a cluster. Mendel is freely available at \url{http://www.genetics.ucla.edu/software}.

stat.AP↗

arXiv2014

A Neuron as a Signal Processing Device

A neuron is a basic physiological and computational unit of the brain. While much is known about the physiological properties of a neuron, its computational role is poorly understood. Here we propose to view a neuron as a signal processing device that represents the incoming streaming data matrix as a sparse vector of synaptic weights scaled by an outgoing sparse activity vector. Formally, a neuron minimizes a cost function comprising a cumulative squared representation error and regularization terms. We derive an online algorithm that minimizes such cost function by alternating between the minimization with respect to activity and with respect to synaptic weights. The steps of this algorithm reproduce well-known physiological properties of a neuron, such as weighted summation and leaky integration of synaptic inputs, as well as an Oja-like, but parameter-free, synaptic learning rule. Our theoretical framework makes several predictions, some of which can be verified by the existing data, others require further experiments. Such framework should allow modeling the function of neuronal circuits without necessarily measuring all the microscopic biophysical parameters, as well as facilitate the design of neuromorphic electronics.

q-bio.NC↗

arXiv2013

Superconductivity induced by U-doping in the SmFeAsO system

Through partial substitution of Sm by U in SmFeAsO, a different member of the family of iron-based superconductors was successfully synthesized. X-ray diffraction measurements show that the lattice constants along the a and c axes are both squeezed through U doping, indicating a successful substitution of U at the Sm site. The parent compound shows a strong resistivity anomaly near 150 K, associated with spin-density-wave instability.U doping suppresses this instability and leads to a transition to the superconducting state at temperatures up to 49 K. Magnetic measurements confirm the bulk superconductivity in this system. For the sample with a doping level of x = 0.2, the external magnetic field suppresses the onset temperature very slowly, indicating a rather high upper critical field. In addition, the Hall effect measurements show that U clearly dopes electrons into the material.

cond-mat.supr-con↗

arXiv2012

Online computation of sparse representations of time varying stimuli using a biologically motivated neural network

Natural stimuli are highly redundant, possessing significant spatial and temporal correlations. While sparse coding has been proposed as an efficient strategy employed by neural systems to encode sensory stimuli, the underlying mechanisms are still not well understood. Most previous approaches model the neural dynamics by the sparse representation dictionary itself and compute the representation coefficients offline. In reality, faced with the challenge of constantly changing stimuli, neurons must compute the sparse representations dynamically in an online fashion. Here, we describe a leaky linearized Bregman iteration (LLBI) algorithm which computes the time varying sparse representations using a biologically motivated network of leaky rectifying neurons. Compared to previous attempt of dynamic sparse coding, LLBI exploits the temporal correlation of stimuli and demonstrate better performance both in representation error and the smoothness of temporal evolution of sparse coefficients.

q-bio.NC↗

arXiv2012

A network of spiking neurons for computing sparse representations in an energy efficient way

Computing sparse redundant representations is an important problem both in applied mathematics and neuroscience. In many applications, this problem must be solved in an energy efficient way. Here, we propose a hybrid distributed algorithm (HDA), which solves this problem on a network of simple nodes communicating via low-bandwidth channels. HDA nodes perform both gradient-descent-like steps on analog internal variables and coordinate-descent-like steps via quantized external variables communicated to each other. Interestingly, such operation is equivalent to a network of integrate-and-fire neurons, suggesting that HDA may serve as a model of neural computation. We show that the numerical performance of HDA is on par with existing algorithms. In the asymptotic regime the representation error of HDA decays with time, t, as 1/t. HDA is stable against time-varying noise, specifically, the representation error decays as 1/sqrt(t) for Gaussian white noise.

cs.NE↗

arXiv2012

Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)

One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits. Synapses onto a neuron can be probed by sequentially stimulating potentially pre-synaptic neurons while monitoring the membrane voltage of the post-synaptic neuron. Reconstructing a large neural circuit using such a "brute force" approach is rather time-consuming and inefficient because the connectivity in neural circuits is sparse. Instead, we propose to measure a post-synaptic neuron's voltage while stimulating sequentially random subsets of multiple potentially pre-synaptic neurons. To reconstruct these synaptic connections from the recorded voltage we apply a decoding algorithm recently developed for compressive sensing. Compared to the brute force approach, our method promises significant time savings that grow with the size of the circuit. We use computer simulations to find optimal stimulation parameters and explore the feasibility of our reconstruction method under realistic experimental conditions including noise and non-linear synaptic integration. Multineuronal stimulation allows reconstructing synaptic connectivity just from the spiking activity of post-synaptic neurons, even when sub-threshold voltage is unavailable. By using calcium indicators, voltage-sensitive dyes, or multi-electrode arrays one could monitor activity of multiple postsynaptic neurons simultaneously, thus mapping their synaptic inputs in parallel, potentially reconstructing a complete neural circuit.

q-bio.NC↗

arXiv2012

Sparse LMS via Online Linearized Bregman Iteration

We propose a version of least-mean-square (LMS) algorithm for sparse system identification. Our algorithm called online linearized Bregman iteration (OLBI) is derived from minimizing the cumulative prediction error squared along with an l1-l2 norm regularizer. By systematically treating the non-differentiable regularizer we arrive at a simple two-step iteration. We demonstrate that OLBI is bias free and compare its operation with existing sparse LMS algorithms by rederiving them in the online convex optimization framework. We perform convergence analysis of OLBI for white input signals and derive theoretical expressions for both the steady state and instantaneous mean square deviations (MSD). We demonstrate numerically that OLBI improves the performance of LMS type algorithms for signals generated from sparse tap weights.

cs.IT↗

arXiv2012

Super-resolution using Sparse Representations over Learned Dictionaries: Reconstruction of Brain Structure using Electron Microscopy

A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.

cs.CV↗

arXiv2011

Experimental study of THGEM detector with mini-rim

The gas gain and energy resolution of single and double THGEM detectors (5{\times}5cm2 effective area) with mini-rims (rim is less than 10μm) were studied. The maximum gain can reach 5{\times}103 and 2{\times}105 for single and double THGEM respectively, while the energy resolution of 5.9 keV X-ray varied from 18% to 28% for both single and double THGEM detectors of different hole sizes and thicknesses.All the experiments were investigated in mixture of noble gases(argon,neon) and small content of other gases(iso-butane,methane) at atmospheric pressure.

physics.ins-det↗

arXiv2011

The fast light of CsI(Na) crystals

The responds of different common alkali halide crystals to alpha-rays and gamma-rays are tested in our research. It is found that only CsI(Na) crystals have significantly different waveforms between alpha and gamma scintillations, while others have not this phenomena. It is suggested that the fast light of CsI(Na) crystals arises from the recombination of free electrons with self-trapped holes of the host crystal CsI. Self-absorption limits the emission of fast light of CsI(Tl) and NaI(Tl) crystals.

physics.ins-det↗