arXiv subjects

Zhen Li

Publications and source records attributed to Zhen Li.

486 records · Page 27Linked to original sources

arXiv2016

Protonation enhancement by dichloromethane doping in low-pressure photoionization

Doping has been used to enhance the ionization efficiency of analytes in atmospheric pressure photoionization, which is based on charge exchange. Compounds with excellent ionization efficiencies are usually chosen as dopants. In this paper, we report a new phenomenon observed in low-pressure photoionization: Protonation enhancement by dichloromethane (CH2Cl2) doping. CH2Cl2 is not a common dopant due to its high ionization energy (11.33 eV). The low-pressure photoionization source was built using a krypton VUV lamp that emits photons with energies of 10.0 and 10.6 eV and was operated at 500-1000 Pa. Protonation of water, methanol, ethanol, and acetaldehyde was respectively enhanced by 481.7 +/- 122.4, 197.8 +/- 18.8, 87.3 +/- 7.8, and 93.5 +/- 35.5 times after doping 291 ppmv CH2Cl2, meanwhile CH2Cl2 almost does not generate noticeable ions itself. This phenomenon has not been documented in the literature. A new protonation process involving in ion-pair and H-bond formations was proposed to expound the phenomenon. The observed phenomenon opens a new prospect for the improvement of the detection efficiency of VUV photoionization.

physics.chem-ph↗

arXiv2016

LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling

Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. It generates pixelwise and fine-grained label maps from simultaneously sensed photometric (RGB) and depth channels. This paper addresses this problem by i) developing a novel Long Short-Term Memorized Context Fusion (LSTM-CF) Model that captures and fuses contextual information from multiple channels of photometric and depth data, and ii) incorporating this model into deep convolutional neural networks (CNNs) for end-to-end training. Specifically, contexts in photometric and depth channels are, respectively, captured by stacking several convolutional layers and a long short-term memory layer; the memory layer encodes both short-range and long-range spatial dependencies in an image along the vertical direction. Another long short-term memorized fusion layer is set up to integrate the contexts along the vertical direction from different channels, and perform bi-directional propagation of the fused vertical contexts along the horizontal direction to obtain true 2D global contexts. At last, the fused contextual representation is concatenated with the convolutional features extracted from the photometric channels in order to improve the accuracy of fine-scale semantic labeling. Our proposed model has set a new state of the art, i.e., 48.1% and 49.4% average class accuracy over 37 categories (2.2% and 5.4% improvement) on the large-scale SUNRGBD dataset and the NYUDv2dataset, respectively.

cs.CV↗

arXiv2016

Charge Neutral MoS2 Field Effect Transistors Through Oxygen Plasma Treatment

Lithographically fabricated MoS2 field effect transistors suffer from several critical imperfections, including low sub-threshold swings, large turn-on gate voltages (VT), and wide device-to-device variability. The large magnitude and variability of VT stems from unclean interfaces, trapped charges in the underlying substrate, and sulfur vacancies created during the mechanical exfoliation process. In this study, we demonstrate a simple and reliable oxygen plasma treatment, which mitigates the effects of unintentional doping created by surface defect cites, such as S vacancies, and surface contamination. This plasma treatment restores charge neutrality to the MoS2 and shifts the threshold turn-on voltage towards 0V. Out of the 8 devices measured, all exhibit a shift of the FET turn-on voltage from an average of -18.7V to -0.9V. The oxygen plasma treatment passivates these defects, which reduces surface scattering, causing increased mobility and improved subthreshold swing. For as-prepared devices with low mobilities (~0.01cm2/V.s), we observe up to a 190-fold increase in mobility after exposure to the oxygen plasma. Perhaps the most important aspect of this oxygen plasma treatment is that it reduces the device-to-device variability, which is a crucial factor in realizing any practical application of these devices.

cond-mat.mes-hall↗

arXiv2016

Systematic parameter inference in stochastic mesoscopic modeling

We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are sparse. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.

physics.comp-ph↗

arXiv2016

Towards Better Analysis of Deep Convolutional Neural Networks

Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this formulation, a hybrid visualization is developed to disclose the multiple facets of each neuron and the interactions between them. In particular, we introduce a hierarchical rectangle packing algorithm and a matrix reordering algorithm to show the derived features of a neuron cluster. We also propose a biclustering-based edge bundling method to reduce visual clutter caused by a large number of connections between neurons. We evaluated our method on a set of CNNs and the results are generally favorable.

cs.CV↗

arXiv2016

Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks

Protein secondary structure prediction is an important problem in bioinformatics. Inspired by the recent successes of deep neural networks, in this paper, we propose an end-to-end deep network that predicts protein secondary structures from integrated local and global contextual features. Our deep architecture leverages convolutional neural networks with different kernel sizes to extract multiscale local contextual features. In addition, considering long-range dependencies existing in amino acid sequences, we set up a bidirectional neural network consisting of gated recurrent unit to capture global contextual features. Furthermore, multi-task learning is utilized to predict secondary structure labels and amino-acid solvent accessibility simultaneously. Our proposed deep network demonstrates its effectiveness by achieving state-of-the-art performance, i.e., 69.7% Q8 accuracy on the public benchmark CB513, 76.9% Q8 accuracy on CASP10 and 73.1% Q8 accuracy on CASP11. Our model and results are publicly available.

q-bio.BM↗

arXiv2016

A convergent point integral method for isotropic elliptic equations on point cloud

In this paper, we propose a numerical method to solve isotropic elliptic equations on point cloud by generalizing the point integral method. The idea of the point integral method is to approximate the differential operators by integral operators and discretize the corresponding integral equation on point cloud. The key step is to get the integral approximation. In this paper, with proper kernel function, we get an integral approximation for the elliptic operators with isotropic coefficients. Moreover, the integral approximation has been proved to keep the coercivity of the original elliptic operator. The convergence of the point integral method is also proved.

math.NA↗

arXiv2016

Keyboard Surface Interaction: Making the keyboard into a pointing device

Pointing devices that reside on the keyboard can reduce the overall time needed to perform mixed pointing and typing tasks, since the hand of the user does not have to reach for the pointing device. However, previous implementations of this kind of device have a higher movement time compared to the mouse and trackpad due to large error rate, low speed and spatial resolution. In this paper we introduce Keyboard Surface Interaction (KSI), an interaction approach that turns the surface of a keyboard into an interaction surface and allows users to rest their hands on the keyboard at all times to minimize fatigue. We developed a proof-of-concept implementation, Fingers, which we optimized over a series of studies. Finally, we evaluated Fingers against the mouse and trackpad in a user study with 25 participants on a Fitts law test style, mixed typing and pointing task. Results showed that for users with more exposure to KSI, our KSI device had better performance (reduced movement and homing time) and reduced discomfort compared to the trackpad. When compared to the mouse, KSI had reduced homing time and reduced discomfort, but increased movement time. This interaction approach is not only a new way to capitalize on the space on top of the keyboard, but also a call to innovate and think beyond the touchscreen, touchpad, and mouse as our main pointing devices. The results of our studies serve as a specification for future KSI devices.

cs.HC↗

arXiv2015

Blockout: Dynamic Model Selection for Hierarchical Deep Networks

Most deep architectures for image classification--even those that are trained to classify a large number of diverse categories--learn shared image representations with a single model. Intuitively, however, categories that are more similar should share more information than those that are very different. While hierarchical deep networks address this problem by learning separate features for subsets of related categories, current implementations require simplified models using fixed architectures specified via heuristic clustering methods. Instead, we propose Blockout, a method for regularization and model selection that simultaneously learns both the model architecture and parameters. A generalization of Dropout, our approach gives a novel parametrization of hierarchical architectures that allows for structure learning via back-propagation. To demonstrate its utility, we evaluate Blockout on the CIFAR and ImageNet datasets, demonstrating improved classification accuracy, better regularization performance, faster training, and the clear emergence of hierarchical network structures.

cs.CV↗

arXiv2015

Recognition of Brain Waves of Left and Right Hand Movement Imagery with Portable Electroencephalographs

With the development of the modern society, mind control applied to both the recovery of disabled individuals and auxiliary control of normal people has obtained great attention in numerous researches. In our research, we attempt to recognize the brain waves of left and right hand movement imagery with portable electroencephalographs. Considering the inconvenience of wearing traditional multiple-electrode electroencephalographs, we choose Muse to collect data which is a portable headband launched lately with a number of useful functions and channels and it is much easier for the public to use. Additionally, previous researches generally focused on discrimination of EEG of left and right hand movement imagery by using data from C3 and C4 electrodes which locate on the top of the head. However, we choose the gamma wave channels of F7 and F8 and obtain data when subjects imagine their left or right hand to move with their eyeballs rotated in the corresponding direction. With the help of the Common Space Pattern algorithm to extract features of brain waves between left and right hand movement imagery, we make use of the Support Vector Machine to classify different brain waves. Traditionally, the accuracy rate of classification was approximately 90% using the EEG data from C3 and C4 electrode poles; however, the accuracy rate reaches 95.1% by using the gamma wave data from F7 and F8 in our experiment. Finally, we design a plane program in Python where a plane can be controlled to go left or right when users imagine their left or right hand to move. 8 subjects are tested and all of them can control the plane flexibly which reveals that our model can be applied to control hardware which is useful for disabled individuals and normal people.

cs.HC↗

arXiv2015

Prediction of Brain States of Concentration and Relaxation in Real Time with Portable Electroencephalographs

In our research, we attempt to help people recognize their brain state of concentration or relaxation more conveniently and in real time. Considering the inconvenience of wearing traditional multiple electrode electroencephalographs, we choose Muse to collect data which is a portable headband launched lately with a number of useful functions and channels and it is much easier for the public to use. Besides, traditional online analysis did not focus on the synchronism between users and computers and the time delay problem did exist. To solve the problem, by building the Analytic Hierarchy Model, we choose the two gamma wave channels of F7 and F8 as the data source instead of using both beta and alpha channels traditionally.Using the Common Space Pattern algorithm and the Support Vector Machine model, the channels we choose have a higher recognition accuracy rate and smaller amount of data to be dealt with by the computer than the traditional ones. Furthermore, we make use of the Feedforward Neural Network Model to predict subjects'brain states in half a second. Finally, we design a plane program in Python where a plane can be controlled to go up or down when users concentrate or relax. The SVM model and the Feedforward Neural Network model have both been tested by 12 subjects and they give an evaluation ranging from 1 to 10 points. The former gets 7.58 points while the latter gets 8.83, which proves that the time delay problem is improved once more.

cs.HC↗

arXiv2015

Point Integral Method for Solving Poisson-type Equations on Manifolds from Point Clouds with Convergence Guarantees

Partial differential equations (PDE) on manifolds arise in many areas, including mathematics and many applied fields. Among all kinds of PDEs, the Poisson-type equations including the standard Poisson equation and the related eigenproblem of the Laplace-Beltrami operator are of the most important. Due to the complicated geometrical structure of the manifold, it is difficult to get efficient numerical method to solve PDE on manifold. In the paper, we propose a method called point integral method (PIM) to solve the Poisson-type equations from point clouds with convergence guarantees. In PIM, the key idea is to derive the integral equations which approximates the Poisson-type equations and contains no derivatives but only the values of the unknown function. The latter makes the integral equation easy to be approximated from point cloud. In the paper, we explain the derivation of the integral equations, describe the point integral method and its implementation, and present the numerical experiments to demonstrate the convergence of PIM.

math.NA↗

arXiv2015

Mesoscale modeling of phase transition dynamics of thermoresponsive polymers

We present a non-isothermal mesoscopic model for investigation of the phase transition dynamics of thermoresponsive polymers. Since this model conserves energy in the simulations, it is able to correctly capture not only the transient behavior of polymer precipitation from solvent, but also the energy variation associated with the phase transition process. Simulations provide dynamic details of the thermally induced phase transition and confirm two different mechanisms dominating the phase transition dynamics. A shift of endothermic peak with concentration is observed and the underlying mechanism is explored.

physics.chem-ph↗

arXiv2015

Multiscale Universal Interface: A Concurrent Framework for Coupling Heterogeneous Solvers

Concurrently coupled numerical simulations using heterogeneous solvers are powerful tools for modeling multiscale phenomena. However, major modifications to existing codes are often required to enable such simulations, posing significant difficulties in practice. In this paper we present a C++ library, i.e. the Multiscale Universal Interface (MUI), which is capable of facilitating the coupling effort for a wide range of multiscale simulations. The library adopts a header-only form with minimal external dependency and hence can be easily dropped into existing codes. A data sampler concept is introduced, combined with a hybrid dynamic/static typing mechanism, to create an easily customizable framework for solver-independent data interpretation. The library integrates MPI MPMD support and an asynchronous communication protocol to handle inter-solver information exchange irrespective of the solvers' own MPI awareness. Template metaprogramming is heavily employed to simultaneously improve runtime performance and code flexibility. We validated the library by solving three different multiscale problems, which also serve to demonstrate the flexibility of the framework in handling heterogeneous models and solvers. In the first example, a Couette flow was simulated using two concurrently coupled Smoothed Particle Hydrodynamics (SPH) simulations of different spatial resolutions. In the second example, we coupled the deterministic SPH method with the stochastic Dissipative Particle Dynamics (DPD) method to study the effect of surface grafting on the hydrodynamics properties on the surface. In the third example, we consider conjugate heat transfer between a solid domain and a fluid domain by coupling the particle-based energy-conserving DPD (eDPD) method with the Finite Element Method (FEM).

physics.comp-ph↗

arXiv2013

A new metric for routing in military wireless network

Wireless Ad-hoc network is generally employed in military and emergencies due to its flexibility and easy-to-use. It's suitable for military wireless network that has the characteristics of mobility and works effectively under severe environment and electromagnetic interfering conditions. However, military network cannot benefit from existing routing protocol directly; there exists quite many features which are only typical for military network. This paper presents a new metric for routing, which is employed in A* algorithm. The goal of the metric is to choose a route of less distance and less transmission delay between a source and a destination. Our metric is a function of the distance between the ends and the bandwidth over the link. Moreover, we take frequency selection into account since a node can work on multi-frequencies. This paper proposed the new metric, and experimented it based on A* algorithm. The simulation results show that this metric can find the optimal route which has less transmission delay compared to the shortest path routing.

cs.NI↗

arXiv2012

Simultaneous SNP identification in association studies with missing data

Association testing aims to discover the underlying relationship between genotypes (usually Single Nucleotide Polymorphisms, or SNPs) and phenotypes (attributes, or traits). The typically large data sets used in association testing often contain missing values. Standard statistical methods either impute the missing values using relatively simple assumptions, or delete them, or both, which can generate biased results. Here we describe the Bayesian hierarchical model BAMD (Bayesian Association with Missing Data). BAMD is a Gibbs sampler, in which missing values are multiply imputed based upon all of the available information in the data set. We estimate the parameters and prove that updating one SNP at each iteration preserves the ergodic property of the Markov chain, and at the same time improves computational speed. We also implement a model selection option in BAMD, which enables potential detection of SNP interactions. Simulations show that unbiased estimates of SNP effects are recovered with missing genotype data. Also, we validate associations between SNPs and a carbon isotope discrimination phenotype that were previously reported using a family based method, and discover an additional SNP associated with the trait. BAMD is available as an R-package from http://cran.r-project.org/package=BAMD

stat.AP↗

arXiv2011

Special electronic structures and quantum conduction of B/P co-doping carbon nanotubes under electric field using the first principle

Boron (B)/phosphorus (P) doped single wall carbon nanotubes (B-PSWNTs) are studied by using the First- Principle method based on density function theory (DFT). Mayer bond order, band structure, electrons density and density of states are calculated. It concludes that the B-PSWNTs have special band structure which is quite different from BN nanotubes, and that metallic carbon nanotubes will be converted to semiconductor due to boron/phosphorus co-doping which breaks the symmetrical structure. The bonding forms in B-PSWNTs are investigated in detail. Besides, Mulliken charge population and the quantum conductance are also calculated to study the quantum transport characteristics of B-PSWNT hetero-junction. It is found that the position of p-n junction in this hetero-junction will be changed as the applied electric field increase and it performs the characteristics of diode.

cond-mat.mes-hall↗

arXiv2006

Strange axial-vector mesons mixing angle

The masses of the $K_1(^3P_1)$ and $K_1(^1P_1)$ are considered in a nonrelativistic constituent quark model, and the absolute value of the $K_1(^3P_1)-K_1(^1P_1)$ mixing angle is determined to be about $59.29^\circ$. Comparison of the theoretical predictions on the strong decay widths of the $K_1(1270)$ and $K_1(1400)$ in the $^3P_0$ decay model as well as the production ratio of these two states in the $τ$ decay between the available experimental data strongly favors that the $K_1(^3P_1)-K_1(^1P_1)$ mixing angle is about $+59.29^\circ$.

hep-ph↗