arXiv subjects

Sheng Wang

Publications and source records attributed to Sheng Wang.

At least 307 records · Page 17Linked to original sources

arXiv2016

The design study for a 500 MeV proton synchrotron with CSNS linac as an injector

Using the China Spallation Neutron Source (CSNS) linac as the injector, a 500 MeV proton synchrotron is proposed for multidisciplinary application, such as biology, material and proton therapy. The synchrotron will deliver proton beam with energy from 80 MeV to 500 MeV. A compact lattice design was worked out, and all the important beam dynamics issues were investigated. The 80 MeV H- beam is stripped and injected into the synchrotron by using multi-turn injection. In order to continuously extraction the proton with small beam loss, the achromatic structure is proposed and slow extraction method with RF knock-out is adopted and optimized.

physics.acc-ph↗

arXiv2016

Deep Learning At Scale and At Ease

Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Large deep learning models are developed for learning rich representations of complex data. There are two challenges to overcome before deep learning can be widely adopted in multimedia and other applications. One is usability, namely the implementation of different models and training algorithms must be done by non-experts without much effort especially when the model is large and complex. The other is scalability, that is the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets. To address these two challenges, in this paper, we design a distributed deep learning platform called SINGA which has an intuitive programming model based on the common layer abstraction of deep learning models. Good scalability is achieved through flexible distributed training architecture and specific optimization techniques. SINGA runs on GPUs as well as on CPUs, and we show that it outperforms many other state-of-the-art deep learning systems. Our experience with developing and training deep learning models for real-life multimedia applications in SINGA shows that the platform is both usable and scalable.

cs.LG↗

arXiv2015

Geometry-covering Jammer Localization based on Distance Comprehension in Wireless Sensor Networks

Jamming attacks could cause severe damage to Wireless Sensor Networks (WSNs). Once jamming attack occurs, the most urgent work is to get the position information of the jammer. Then safety measures to eliminate the jamming effects can be devised. In this paper, the jammer localization is conducted by geometric covering method to achieve a low energy consumption. And utilizing the power of the jamming signal received by the boundary nodes, a compensating method is composed to reduce the estimating error of the jamming area. At last the localization is conducted by extracting the minimum covering circle of the compensated victim area. Simulations are conducted to test the localization accuracy with the impact of node density, jamming region and radius. Results show that this localization method achieves both good precision and low energy consumption.

cs.NI↗

arXiv2015

Protein secondary structure prediction using deep convolutional neural fields

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

q-bio.BM↗

arXiv2015

Predicting diverse M-best protein contact maps

Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence information remains very challenging. Recently evolutionary coupling (EC) analysis, which predicts contacts by detecting co-evolved residues (or columns) in a multiple sequence alignment (MSA), has made good progress due to better statistical assessment techniques and high-throughput sequencing. Existing EC analysis methods predict only a single contact map for a given protein, which may have low accuracy especially when the protein under prediction does not have a large number of sequence homologs. Analogous to ab initio folding that usually predicts a few possible 3D models for a given protein sequence, this paper presents a novel structure learning method that can predict a set of diverse contact maps for a given protein sequence, in which the best solution usually has much better accuracy than the first one. Our experimental tests show that for many test proteins, the best out of 5 solutions generated by our method has accuracy at least 0.1 better than the first one when the top L/5 or L/10 (L is the sequence length) predicted long-range contacts are evaluated, especially for protein families with a small number of sequence homologs. Our best solutions also have better quality than those generated by the two popular EC methods Evfold and PSICOV.

q-bio.QM↗

arXiv2015

AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This manuscript presents Deep Convolutional Neural Fields (DeepCNF), a combination of DCNN with Conditional Random Field (CRF), for sequence labeling with highly imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on highly imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. We then test our AUC-maximized DeepCNF on three very different protein sequence labeling tasks: solvent accessibility prediction, 8-state secondary structure prediction, and disorder prediction. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also have similar performance as the other two training methods on the solvent accessibility prediction problem which has three equally-distributed labels. Furthermore, our experimental results also show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks.

stat.ML↗

arXiv2015

Electron emission of stripping foil and collimation system for CSNS/RCS

For the Rapid Cycling Synchrotron of the China Spallation Neutron Source (CSNS/RCS), the electron emission plays an important role in the accelerator limitation. The interactions between the proton beam and the stripping foil were studied, and the electron scattering processes were simulated by the ORBIT and FLUKA codes. Then, the electron energy distribution and the electron yielding rate can be given. Furthermore, the interactions between the proton beam and the collimation system were studied, and the electron scattering processes were simulated. Then, the energy distribution of the primary electron emission can be given and the yielding rate of the primary electron can be obtained.

physics.acc-ph↗

arXiv2015

Study on the injection beam commissioning software for CSNS/RCS

The China Spallation Neutron Source (CSNS) accelerator uses H- stripping and phase space painting method of filling large ring acceptance with the linac beam of small emittance. The beam commissioning software system is the key part of CSNS accelerator. The injection beam commissioning software for CSNS contains three parts currently: painting curve control, injection beam control and injection orbit correction. The injection beam control contains two subsections: single bunch beam calculation and LRBT beam control at the foil. The injection orbit correction also contains two subsections: injection orbit correction by the calculation and injection trim power control.

physics.acc-ph↗

arXiv2015

Study on the transverse painting during the injection process for CSNS/RCS

For the China Spallation Neutron Source (CSNS), a combination of the H- stripping and phase space painting method is used to accumulate a high intensity beam in the Rapid Cycling Synchrotron (RCS). In this paper, firstly, the injection processes with different painting ranges and different painting methods were studied. With the codes ORBIT and MATLAB, the particle distribution and painting image were obtained. Then, the reasonable painting range which is suitable for the aperture size and magnet gap can be selected. Since the real field uniformity of BH3 and BV3 is not completely in conformity with the design requirement, the painting method and painting range also need to be selected to reduce the effects of bad field uniformity.

physics.acc-ph↗

arXiv2015

Fabrication of Artificial Graphene in a GaAs Quantum Heterostructure

The unusual electronic properties of graphene, which are a direct consequence of its two-dimensional (2D) honeycomb lattice, have attracted a great deal of attention in recent years. Creation of artificial lattices that recreate graphene's honeycomb topology, known as artificial graphene, can facilitate the investigation of graphene-like phenomena, such as the existence of massless Dirac fermions, in a tunable system. In this work, we present the fabrication of artificial graphene in an ultra-high quality GaAs/AlGaAs quantum well, with lattice period as small as 50 nm, the smallest reported so far for this type of system. Electron-beam lithography is used to define an etch mask with honeycomb geometry on the surface of the sample, and different methodologies are compared and discussed. An optimized anisotropic reactive ion etching process is developed to transfer the pattern into the AlGaAs layer and create the artificial graphene. The achievement of such high-resolution artificial graphene should allow the observation for the first time of massless Dirac fermions in an engineered semiconductor.

cond-mat.mes-hall↗

arXiv2015

Bermuda: Bidirectional de novo assembly of transcripts with new insights for handling uneven coverage

Motivation: RNA-seq has made feasible the analysis of a whole set of expressed mRNAs. Mapping-based assembly of RNA-seq reads sometimes is infeasible due to lack of high-quality references. However, de novo assembly is very challenging due to uneven expression levels among transcripts and also the read coverage variation within a single transcript. Existing methods either apply de Bruijn graphs of single-sized k-mers to assemble the full set of transcripts, or conduct multiple runs of assembly, but still apply graphs of single-sized k-mers at each run. However, a single k-mer size is not suitable for all the regions of the transcripts with varied coverage. Contribution: This paper presents a de novo assembler Bermuda with new insights for handling uneven coverage. Opposed to existing methods that use a single k-mer size for all the transcripts in each run of assembly, Bermuda self-adaptively uses a few k-mer sizes to assemble different regions of a single transcript according to their local coverage. As such, Bermuda can deal with uneven expression levels and coverage not only among transcripts, but also within a single transcript. Extensive tests show that Bermuda outperforms popular de novo assemblers in reconstructing unevenly-expressed transcripts with longer length, better contiguity and lower redundancy. Further, Bermuda is computationally efficient with moderate memory consumption.

q-bio.GN↗

arXiv2015

Protein Contact Prediction by Integrating Joint Evolutionary Coupling Analysis and Supervised Learning

Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence remains very challenging. Both evolutionary coupling (EC) analysis and supervised machine learning methods are developed to predict contacts, making use of different types of information, respectively. This paper presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning. Different from existing single-family EC analysis that uses residue co-evolution information in only the target protein family, our joint EC analysis uses residue co-evolution in both the target family and its related families, which may have divergent sequences but similar folds. To implement joint EC analysis, we model a set of related protein families using Gaussian graphical models (GGM) and then co-estimate their precision matrices by maximum-likelihood, subject to the constraint that the precision matrices shall share similar residue co-evolution patterns. To further improve the accuracy of the estimated precision matrices, we employ a supervised learning method to predict contact probability from a variety of evolutionary and non-evolutionary information and then incorporate the predicted probability as prior into our GGL framework. Experiments show that our method can predict contacts much more accurately than existing methods, and that our method performs better on both conserved and family-specific contacts.

q-bio.QM↗

arXiv2015

Impedance Measurements of the Extraction Kicker System for the Rapid Cycling Synchrotron of China Spallation Neutron Source

The fast extraction kicker system is one of the most important accelerator components, whose inner structure will be the main source of the impedance in the RCS. It is necessary to understand the kicker impedance before its installation into the tunnel. The conventional and improved wire methods are employed for the benchmarking impedance measurement results. The simulation and measurements confirm that the window-frame ferrite geometry and the end plate are the important structures causing the coupling impedance. The total impedance of the eight modules systems is determined by the scaling law from the measurement and the impedance measurement of the kicker system is summarized.

physics.acc-ph↗

arXiv2015

Theoretical study of the dual harmonic system and its application on the CSNS/RCS

The dual harmonic system has been widely used in high intensity proton synchrotrons to suppress the space charge effect, as well as reduce the beam loss. To investigate the longitudinal beam dynamics in the dual rf system, the potential well, the sub-buckets in the bunch and the multi-solutions of the phase equation have been studied theoretically. Based on these theoretical studis, the optimization of bunching factor and rf voltage waveform are made for the dual harmonic rf system in the upgrade phase of the CSNS/RCS. In the optimization process, the simulation with space charge effect is done by using a newly developed code C-SCSIM.

physics.acc-ph↗

arXiv2014

Optimization parameter design for proton irradiation accelerator

The proton irradiation accelerator is widely founded for industry application, and should be designed as compact, reliable, and easy operate. A 10 MeV proton beam is designed to be injected into the slow circulation ring with the repetition rate of 0.5 Hz for accumulation and acceleration, and then the beam with the energy of 300MeV will be slowly extracted by third order resonance method. For getting a higher intensity and more uniform beam, the height of the injection bump is carefully optimised during the injection period. Besides, in order to make the extracted beam with a more uniform distribution, a RF Knock-out method is adopted, and the RF kicker's amplitude is well optimised.

physics.acc-ph↗

arXiv2014

The study of beam loading effect in the CSNS/RCS

CSNS/RCS accelerates a high-intensity proton beam from 80 MeV to 1.6 GeV. Since the beam current and beam power is high, the beam loading is a severe problem for the stability of the circulating beam in the RCS. To study the beam loading effect in the CSNS/RCS theoretically, the RLC circuit model of the rf cavity, the method of the Fast Fourier Transform and the method of Laplace transform have been employed to obtain the impedance of the rf system, the beam spectrum and the beam-induced voltage, respectively. Based on these physical models, the beam dynamics equations have been revised and a beam loading model has been constructed in the simulation code ORIENT. By using the code, the beam loading effect on the rf system of the CSNS/RCS has been investigated. Some simulation results have been obtained and conclusions have been drawn.

physics.acc-ph↗

arXiv2014

MRFalign: Protein Homology Detection through Alignment of Markov Random Fields

Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/.

q-bio.QM↗

arXiv2013

The Study on the Effects of Chromaticity and Magnetic Field Tracking Errors at CSNS/RCS

The Rapid Cycling Synchrotron (RCS) is a key component of the China Spallation Neutron Source (CSNS). For this type of high intensity proton synchrotron, the chromaticity, space charge effects and magnetic field tracking errors between the quadrupoles and the dipoles can induce beta function distortion and tune shift, and induce resonances. In this paper the combined effects of chromaticity, magnetic field tracking errors and space charge on beam dynamics at CSNS/RCS are studied systemically. 3-D simulations with different magnetic field tracking errors are performed by using the code ORBIT, and the simulation results are compared with the case without tracking errors.

physics.acc-ph↗