arXiv subjects

Ting Chen

Publications and source records attributed to Ting Chen.

180 records · Page 10Linked to original sources

arXiv2016

Two Photon Decays of $η_c$ from Lattice QCD

We present an exploratory lattice study for the two-photon decay of $η_c$ using $N_f=2$ twisted mass lattice QCD gauge configurations generated by the European Twisted Mass Collaboration. Two different lattice spacings of $a=0.067$fm and $a=0.085$fm are used in the study, both of which are of physical size of 2$fm$. The decay widths are found to be $1.025(5)$KeV for the coarser lattice and $1.062(5)$KeV for the finer lattice respectively where the errors are purely statistical. A naive extrapolation towards the continuum limit yields $Γ\simeq 1.122(14)$KeV which is smaller than the previous quenched result and most of the current experimental results. Possible reasons are discussed.

hep-lat↗

arXiv2016

On a geometric inequality related to fractional integration

In this paper we consider a new kind of inequality related to fractional integration, motivated by Gressman's paper. Based on it we investigate its multilinear analogue inequalities. Combining with the Gressman's work on multilinear integral, we establish this new kind of geometric inequalities with bilinear form and multilinear form in more general settings. Moreover, in some cases we also find the best constants and optimisers for these geometric inequalities on Euclidean spaces with Lebesgue measure settings with $L^p$ bounds.

math.FA↗

arXiv2016

On some determinant and matrix inequalities with a geometrical flavour

In this paper we study some determinant inequalities and matrix inequalities which have a geometrical flavour. We first examine some inequalities which place work of Macbeath [13] in a more general setting and also relate to recent work of Gressman [8]. In particular, we establish optimisers for these determinant inequalities. We then use these inequalities to establish our main theorem which gives a geometric inequality of matrix type which improves and extends some inequalities of Christ in [5].

math.FA↗

arXiv2016

Metal-insulator transition in films of doped semiconductor nanocrystals

To fully deploy the potential of semiconductor nanocrystal films as low-cost electronic materials, a better understanding of the amount of dopants required to make their conductivity metallic is needed. In bulk semiconductors, the critical concentration of electrons at the metal-insulator transition is described by the Mott criterion. Here, we theoretically derive the critical concentration $n_c$ for films of heavily doped nanocrystals devoid of ligands at their surface and in direct contact with each other. In the accompanying experiments, we investigate the conduction mechanism in films of phosphorus-doped, ligand-free silicon nanocrystals. At the largest electron concentration achieved in our samples, which is half the predicted $n_c$, we find that the localization length of hopping electrons is close to three times the nanocrystals diameter, indicating that the film approaches the metal-insulator transition.

cond-mat.mes-hall↗

arXiv2016

COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment, and paired-end read LinkAge

The advent of next-generation sequencing (NGS) technologies enables researchers to sequence complex microbial communities directly from environment. Since assembly typically produces only genome fragments, also known as contigs, instead of entire genome, it is crucial to group them into operational taxonomic units (OTUs) for further taxonomic profiling and down-streaming functional analysis. OTU clustering is also referred to as binning. We present COCACOLA, a general framework automatically bin contigs into OTUs based upon sequence composition and coverage across multiple samples. The effectiveness of COCACOLA is demonstrated in both simulated and real datasets in comparison to state-of-art binning approaches such as CONCOCT, GroopM, MaxBin and MetaBAT. The superior performance of COCACOLA relies on two aspects. One is employing $L_{1}$ distance instead of Euclidean distance for better taxonomic identification during initialization. More importantly, COCACOLA takes advantage of both hard clustering and soft clustering by sparsity regularization. In addition, the COCACOLA framework seamlessly embraces customized knowledge to facilitate binning accuracy. In our study, we have investigated two types of additional knowledge, the co-alignment to reference genomes and linkage of contigs provided by paired-end reads, as well as the ensemble of both. We find that both co-alignment and linkage information further improve binning in the majority of cases. COCACOLA is scalable and faster than CONCOCT ,GroopM, MaxBin and MetaBAT. The software is available at https://github.com/younglululu/COCACOLA

q-bio.GN↗

arXiv2015

Dropout Training for SVMs with Data Augmentation

Dropout and other feature noising schemes have shown promising results in controlling over-fitting by artificially corrupting the training data. Though extensive theoretical and empirical studies have been performed for generalized linear models, little work has been done for support vector machines (SVMs), one of the most successful approaches for supervised learning. This paper presents dropout training for both linear SVMs and the nonlinear extension with latent representation learning. For linear SVMs, to deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square (IRLS) algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a re-weighted least square problem, where the re-weights are analytically updated. For nonlinear latent SVMs, we consider learning one layer of latent representations in SVMs and extend the data augmentation technique in conjunction with first-order Taylor-expansion to deal with the intractable expected non-smooth hinge loss and the nonlinearity of latent representations. Finally, we apply the similar data augmentation ideas to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions, and we further develop a non-linear extension of logistic regression by incorporating one layer of latent representations. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs. In addition, the nonlinear SVMs further improve the prediction performance on several image datasets.

cs.LG↗

arXiv2014

Carrier Transport in Films of Alkyl-Ligand-Terminated Silicon Nanocrystals

Silicon nanocrystals (Si NCs) have shown great promise for electroluminescent and photoluminescent applications. In order to optimize the properties of Si NC devices, however, electronic transport in Si NCs films needs to be thoroughly understood. Here we present a systematic study of the temperature and electric field dependence of conductivity in films of alkyl-ligand-terminated Si NCs, which to date have shown the highest potential for device applications. Our measurements suggest that the conductivity is limited by the ionization of rare NCs containing donor impurities. At low bias, this ionization is thermally activated, with an ionization energy equal to twice the NC charging energy. As the bias is increased, the ionization energy is reduced by the electric field, as determined by the Poole-Frenkel effect. At large bias and sufficiently low temperature, we observe cold ionization of electrons from donor-containing NCs, with a characteristic tunneling length of about 1 nm. The temperature- and electric-field-dependent conductance measurements presented here provide a systematic and comprehensive picture for electron transport in lightly doped nanocrystal films.

cond-mat.mes-hall↗

arXiv2012

Self-Assembly of Patchy Particles into Diamond Structures through Molecular Mimicry

Fabrication of diamond structures by self-assembly is a fundamental challenge in making three-dimensional photonic crystals. We simulate a system of model hard particles with attractive patches and show that they can self-assemble into a diamond structure from an initially disordered state. We quantify the extent to which the formation of the diamond structure can be facilitated by "seeding" the system with small diamond crystallites or by introducing a rotation interaction to mimic a carbon-carbon antibonding interaction. Our results suggest patchy particles may serve as colloidal "atoms" and "molecules" for the bottom-up self-assembly of three-dimensional crystals.

cond-mat.stat-mech↗

arXiv2011

Non-Markovian dynamics and strong coupling between atomic transitions and a waveguide continuum edge

In quantum communication and distributed quantum computing, one-dimensional waveguides provide directional transfer of quantum information. A single-mode waveguide has a density-of-states singularity at the lower cut-off frequency, which resembles sharp resonances of a cavity but with non-Markovian dynamics. Thus we put forward schemes of coupling atomic transitions and a waveguide continuum edge. We first present a scheme of spin-photon quantum interface operating in the non-Markovian regime for a $Λ$-type three-level system coupled to a waveguide. Then we show that strong coupling between atomic transitions and a waveguide continuum edge can lead to vacuum Rabi oscillations and bound polariton states.

quant-ph↗

arXiv2011

The Associated Map of the Nonabelian Gauss-Manin Connection

The Gauss-Manin connection for nonabelian cohomology spaces is the isomonodromy flow. We write down explicitly the vector fields of the isomonodromy flow and calculate its induced vector fields on the associated graded space of the nonabelian Hogde filtration. The result turns out to be intimately related to the quadratic part of the Hitchin map.

math.AG↗

arXiv2009

The Case for a Structured Approach to Managing Unstructured Data

The challenge of managing unstructured data represents perhaps the largest data management opportunity for our community since managing relational data. And yet we are risking letting this opportunity go by, ceding the playing field to other players, ranging from communities such as AI, KDD, IR, Web, and Semantic Web, to industrial players such as Google, Yahoo, and Microsoft. In this essay we explore what we can do to improve upon this situation. Drawing on the lessons learned while managing relational data, we outline a structured approach to managing unstructured data. We conclude by discussing the potential implications of this approach to managing other kinds of non-relational data, and to the identify of our field.

cs.DB↗

arXiv2007

Pressure dependence of superconductivity in doped two-leg ladder cuprates

Within the kinetic energy driven superconducting mechanism, the effect of the pressure on superconductivity in doped two-leg ladder cuprates is studied. It is shown that the superconducting transition temperature in doped two-leg ladder cuprate superconductors increases with increasing pressure in the underpressure regime, and reaches a maximum in the optimal pressure, then decreases in the overpressure regime. This domed shape of the pressure dependence of the superconducting transition temperature is similar to that of the pressure dependence of the longitudinal part of the superconducting gap parameter, indicating that superconductivity in doped two-leg ladder cuprates is mainly produced by the development of the pairing correlation along legs.

cond-mat.supr-con↗

arXiv2007

Simulation studies of a phenomenological model for elongated virus capsid formation

We study a phenomenological model in which the simulated packing of hard, attractive spheres on a prolate spheroid surface with convexity constraints produces structures identical to those of prolate virus capsid structures. Our simulation approach combines the traditional Monte Carlo method with a modified method of random sampling on an ellipsoidal surface and a convex hull searching algorithm. Using this approach we identify the minimum physical requirements for non-icosahedral, elongated virus capsids, such as two aberrant flock house virus (FHV) particles and the prolate prohead of bacteriophage $ϕ_{29}$, and discuss the implication of our simulation results in the context of recent experimental findings. Our predicted structures may also be experimentally realized by evaporation-driven assembly of colloidal spheres.

cond-mat.soft↗

arXiv2006

Simulation studies of the self-assembly of cone-shaped particles

We systematically investigate the self-assembly of anisotropic cone-shaped particles decorated by ring-like attractive patches. We demonstrate that the self-assembled clusters, which arise due to the conical particle's anisotropic shape combined with directional attractive interactions, are precise for certain cluster sizes, resulting in a precise packing sequence of clusters of increasing sizes with decreasing cone angles. We thoroughly explore the dependence of cluster packing on cone angle and cooling rate, and categorize the resulting structures as stable and metastable clusters. We also discuss the implication of our simulation results in the context of the Israelachvili packing rule for surfactants, and a recent geometrical packing analysis on hard cones in the limit of large numbers of cones.

cond-mat.mtrl-sci↗

arXiv2006

Simulation Studies of A Phenomenological Model for the Assembly of Elongated Virus Capsids

We extend our previously developed general approach (1) to study a phenomenological model in which the simulated packing of hard, attractive spheres on a prolate spheroid surface with convexity constraints produces structures identical to those of prolate virus capsid structures. Our simulation approach combines the traditional Monte Carlo method with the method of random sampling on an ellipsoidal surface and a convex hull searching algorithm. Using this approach we study the assembly and structural origin of non-icosahedral, elongated virus capsids, such as two aberrant flock house virus (FHV) particles and the prolate prohead of bacteriophage phi29, and discuss the implication of our simulation results in the context of recent experimental findings.

q-bio.BM↗

arXiv2006

A Precise Packing Sequence for Self-Assembled Convex Structures

Molecular simulations of the self-assembly of cone-shaped particles with specific, attractive interactions are performed. Upon cooling from random initial conditions, we find that the cones self assemble into clusters and that clusters comprised of particular numbers of cones (e.g. 4 - 17, 20, 27, 32, 42) have a unique and precisely packed structure that is robust over a range of cone angles. These precise clusters form a sequence of structures at specific cluster sizes- a precise packing sequence - that for small sizes is identical to that observed in evaporation-driven assembly of colloidal spheres. We further show that this sequence is reproduced and extended in simulations of two simple models of spheres self-assembling from random initial conditions subject to certain convexity constraints. This sequence contains six of the most common virus capsid structures obtained in vivo including large chiral clusters, and a cluster that may correspond to several non-icosahedral, spherical virus capsid structures obtained in vivo. Our findings suggest this precise packing sequence results from free energy minimization subject to convexity constraints and is applicable to a broad range of assembly processes.

cond-mat.mtrl-sci↗

arXiv2006

Mining Mass Spectra: Metric Embeddings and Fast Near Neighbor Search

Mining large-scale high-throughput tandem mass spectrometry data sets is a very important problem in mass spectrometry based protein identification. One of the fundamental problems in large scale mining of spectra is to design appropriate metrics and algorithms to avoid all-pair-wise comparisons of spectra. In this paper, we present a general framework based on vector spaces to avoid pair-wise comparisons. We first robustly embed spectra in a high dimensional space in a novel fashion and then apply fast approximate near neighbor algorithms for tasks such as constructing filters for database search, indexing and similarity searching. We formally prove that our embedding has low distortion compared to the cosine similarity, and, along with locality sensitive hashing (LSH), we design filters for database search that can filter out more than 989% of peptides (118 times less) while missing at most 0.29% of the correct sequences. We then show how our framework can be used in similarity searching, which can then be used to detect tight clusters or replicates. On an average, for a cluster size of 16 spectra, LSH only misses 1 spectrum and admits only 1 false spectrum. In addition, our framework in conjunction with dimension reduction techniques allow us to visualize large datasets in 2D space. Our framework also has the potential to embed and compare datasets with post translation modifications (PTM).

q-bio.QM↗

arXiv2001

A Dynamic Programming Approach to De Novo Peptide Sequencing via Tandem Mass Spectrometry

The tandem mass spectrometry fragments a large number of molecules of the same peptide sequence into charged prefix and suffix subsequences, and then measures mass/charge ratios of these ions. The de novo peptide sequencing problem is to reconstruct the peptide sequence from a given tandem mass spectral data of k ions. By implicitly transforming the spectral data into an NC-spectrum graph G=(V,E) where |V|=2k+2, we can solve this problem in O(|V|+|E|) time and O(|V|) space using dynamic programming. Our approach can be further used to discover a modified amino acid in O(|V||E|) time and to analyze data with other types of noise in O(|V||E|) time. Our algorithms have been implemented and tested on actual experimental data.

cs.CE↗