arXiv subjects

Bo Xie

Publications and source records attributed to Bo Xie.

26 records · Page 2Linked to original sources

arXiv2016

Development and test of a real-size MRPC for CBM-TOF

In the CBM (Compressed Baryonic Matter) experiment constructed at the Facility for Anti-proton and Ion Research (Fair) at GSI, Darmstadt, Germany, MRPC(Multi-gap Resistive Plate Chamber) is adopted to construct the large TOF (Time-of-Flight) system to achieve an unprecedented precision of hadron identification, benefiting from its good time resolution, relatively high efficiency and low building price. We have developed a kind of double-ended readout strip MRPC. It uses low resistive glass to keep good performance of time resolution under high-rate condition. The differential double stack structure of 2x4 gas gaps help to reduce the required high voltage to half. There are 24 strips on one counter, and each is 270mm long, 7mm wide and the interval is 3mm. Ground is placed onto the MRPC electrode and feed through is carefully designed to match the 100 Ohm impedance of PADI electronics. The prototype of this strip MRPC has been tested with cosmic ray, a 98% efficiency and 60ps time resolution is gotten. In order to further examine the performance of the detector working under higher particle flux rate, the prototype has been tested in the 2014 October GSI beam time and 2015 February CERN beam time. In both beam times a relatively high rate of 1 kHz/cm2 was obtained. The calibration is done with CBM ROOT. A couple of corrections has been considered in the calibration and analysis process (including time-walk correction, gain correction, strip alignment correction and velocity correction) to access actual counter performances such as efficiency and time resolution. An efficiency of 97% and time resolution of 48ps are obtained. All these results show that the real-size prototype is fully capable of the requirement of the CBM-TOF, and new designs such as self-sealing are modified into the strip counter prototype to obtain even better performance.

physics.ins-det↗

arXiv2016

Communication Efficient Distributed Kernel Principal Component Analysis

Kernel Principal Component Analysis (KPCA) is a key machine learning algorithm for extracting nonlinear features from data. In the presence of a large volume of high dimensional data collected in a distributed fashion, it becomes very costly to communicate all of this data to a single data center and then perform kernel PCA. Can we perform kernel PCA on the entire dataset in a distributed and communication efficient fashion while maintaining provable and strong guarantees in solution quality? In this paper, we give an affirmative answer to the question by developing a communication efficient algorithm to perform kernel PCA in the distributed setting. The algorithm is a clever combination of subspace embedding and adaptive sampling techniques, and we show that the algorithm can take as input an arbitrary configuration of distributed datasets, and compute a set of global kernel principal components with relative error guarantees independent of the dimension of the feature space or the total number of data points. In particular, computing $k$ principal components with relative error $ε$ over $s$ workers has communication cost $\tilde{O}(s ρk/ε+s k^2/ε^3)$ words, where $ρ$ is the average number of nonzero entries in each data point. Furthermore, we experimented the algorithm with large-scale real world datasets and showed that the algorithm produces a high quality kernel PCA solution while using significantly less communication than alternative approaches.

cs.LG↗

arXiv2016

Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients

Nonlinear component analysis such as kernel Principle Component Analysis (KPCA) and kernel Canonical Correlation Analysis (KCCA) are widely used in machine learning, statistics and data analysis, but they can not scale up to big datasets. Recent attempts have employed random feature approximations to convert the problem to the primal form for linear computational complexity. However, to obtain high quality solutions, the number of random features should be the same order of magnitude as the number of data points, making such approach not directly applicable to the regime with millions of data points. We propose a simple, computationally efficient, and memory friendly algorithm based on the "doubly stochastic gradients" to scale up a range of kernel nonlinear component analysis, such as kernel PCA, CCA and SVD. Despite the \emph{non-convex} nature of these problems, our method enjoys theoretical guarantees that it converges at the rate $\tilde{O}(1/t)$ to the global optimum, even for the top $k$ eigen subspace. Unlike many alternatives, our algorithm does not require explicit orthogonalization, which is infeasible on big datasets. We demonstrate the effectiveness and scalability of our algorithm on large scale synthetic and real world datasets.

cs.LG↗

arXiv2015

Scalable Kernel Methods via Doubly Stochastic Gradients

The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems. Or have we simply not tried hard enough for kernel methods? Here we propose an approach that scales up kernel methods using a novel concept called "doubly stochastic functional gradients". Our approach relies on the fact that many kernel methods can be expressed as convex optimization problems, and we solve the problems by making two unbiased stochastic approximations to the functional gradient, one using random training points and another using random functions associated with the kernel, and then descending using this noisy functional gradient. We show that a function produced by this procedure after $t$ iterations converges to the optimal function in the reproducing kernel Hilbert space in rate $O(1/t)$, and achieves a generalization performance of $O(1/\sqrt{t})$. This doubly stochasticity also allows us to avoid keeping the support vectors and to implement the algorithm in a small memory footprint, which is linear in number of iterations and independent of data dimension. Our approach can readily scale kernel methods up to the regimes which are dominated by neural nets. We show that our method can achieve competitive performance to neural nets in datasets such as 8 million handwritten digits from MNIST, 2.3 million energy materials from MolecularSpace, and 1 million photos from ImageNet.

cs.LG↗

arXiv2015

Online aging study of high rate MRPC

With the constant increase of accelerator luminosity, the rate requirements of the MRPC detectors become important. In the same time, aging problem of the detector has to be studied meticulously. An online aging test system is set up in our Lab. The setup of the system is described and the purpose is to study the performance stability during the long time running under high luminosity environment. The high rate MRPC has been irradiated by X-ray for 36 days and accumulated charge density reached 0.1C/cm2. No obvious performance degradation is observed for the detector.

physics.ins-det↗

arXiv2014

LIPS: A Light Intensity Based Positioning System For Indoor Environments

This paper presents LIPS, a Light Intensity based Positioning System for indoor environments. The system uses off-the-shelf LED lamps as signal sources, and uses light sensors as signal receivers. The design is inspired by the observation that a light sensor has deterministic sensitivity to both distance and incident angle of light signal, an under-utilized feature of photodiodes now widely found on mobile devices. We develop a stable and accurate light intensity model to capture the phenomenon, based on which a new positioning principle, Multi-Face Light Positioning (MFLP), is established that uses three collocated sensors to uniquely determine the receiver's position, assuming merely a single source of light. We have implemented a prototype on both dedicated embedded systems and smartphones. Experimental results show average positioning accuracy within 0.4 meters across different environments, with high stability against interferences from obstacles, ambient lights, temperature variation, etc.

cs.NI↗

arXiv2013

Nonparametric Estimation of Multi-View Latent Variable Models

Spectral methods have greatly advanced the estimation of latent variable models, generating a sequence of novel and efficient algorithms with strong theoretical guarantees. However, current spectral algorithms are largely restricted to mixtures of discrete or Gaussian distributions. In this paper, we propose a kernel method for learning multi-view latent variable models, allowing each mixture component to be nonparametric. The key idea of the method is to embed the joint distribution of a multi-view latent variable into a reproducing kernel Hilbert space, and then the latent parameters are recovered using a robust tensor power method. We establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters. Thus, our non-parametric tensor approach to learning latent variable models enjoys good sample and computational efficiencies. Moreover, the non-parametric tensor power method compares favorably to EM algorithm and other existing spectral algorithms in our experiments.

cs.LG↗

arXiv2011

Mechanical Properties of Graphene Papers

Graphene-based papers attract particular interests recently owing to their outstanding properties, the key of which is their layer-by-layer hierarchical structures similar to the biomaterials such as bone, teeth and nacre, combining intralayer strong sp2 bonds and interlayer crosslinks for efficient load transfer. Here we firstly study the mechanical properties of various interlayer and intralayer crosslinks via first-principles calculations and then perform continuum model analysis for the overall mechanical properties of graphene-based papers. We find that there is a characteristic length scale l_{0}, defined as \Sqrt{Dh_{0}/4G}, where D is the stiffness of the graphene sheet, h_{0} and G are the height of interlayer crosslink and shear modulus respectively. When the size of the graphene sheets exceeds 3l_{0}, the tension-shear (TS) chain model that are widely used for nanocomposites fails to predict the overall mechanical properties of the graphene-based papers. Instead we proposed here a deformable tension-shear (DTS) model by considering the elastic deformation of the graphene sheets, also the interlayer and intralayer crosslinks. The DTS is then applied to predict the mechanics of graphene-based paper materials under tensile loading. According to the results we thus obtain, optimal design strategies are provided for designing graphene papers with ultrahigh stiffness, strength and toughness.

cond-mat.mtrl-sci↗