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Ke Wang

Publications and source records attributed to Ke Wang.

At least 613 records · Page 34Linked to original sources
arXiv2019

An Air-Stable and Atomically Thin Graphene/Gallium Superconducting Heterostructure

Two-dimensional layered and atomically thin elemental superconductors may be key ingredients in next-generation quantum technologies, if they can be stabilized and integrated into heterostructured devices under ambient conditions. However, atomically thin elemental superconductors are largely unexplored outside ultra-high vacuum due to rapid oxidation, and even 2D layered superconductors require complex encapsulation strategies to maintain material quality. Here we demonstrate environmentally stable, single-crystal, few-atom-thick superconducting gallium, 2D-Ga, produced by confinement heteroepitaxy (CHet) at the interface of epitaxial graphene (EG) and silicon carbide (SiC). 2D-Ga becomes superconducting at 4 K; this elevation over bulk alpha-Ga (Tc~1 K) is primarily attributed to an increased density of states at the Fermi level as the incipient Ga-Ga dimerization seen in alpha-Ga is suppressed by epitaxy to SiC. We also demonstrate the importance of controlling SiC surface morphology (i.e. step height) and defect-engineering in graphene layers prior to intercalation to achieve large-area uniform 2D-Ga layers with isotropic transport properties. This work demonstrates that unique 2D forms of 3D materials can be stabilized at the EG/SiC interface, which represents a scalable route towards air-stable crystalline 2D superconductors as a potential foundation for next-generation quantum technologies.

cond-mat.supr-con↗
arXiv2019

Confinement Heteroepitaxy: Realizing Atomically Thin, Half-van der Waals Materials

Three-dimensional epitaxial heterostructures are based on covalently-bonded interfaces, whereas those from 2-dimensional (2D) materials exhibit van der Waals interactions. Under the right conditions, however, material structures with mixed interfacial van der Waals and covalent bonding may be realized. Atomically thin layers formed at the epitaxial graphene (EG)/silicon carbide (SiC) interface indicate that EG/SiC interfaces provide this unique environment and enable synthesis of a rich palette of 2D materials not accessible with traditional techniques. Here, we demonstrate a method termed confinement heteroepitaxy (CHet), to realize air-stable, structurally unique, crystalline 2D-Ga, In, and Sn at the EG/SiC interface. The first intercalant layer is covalently-bonded to the SiC, and is accompanied by a vertical bonding gradient that ends with van der Waals interactions. Such structures break out of plane centrosymmetry, thereby introducing atomically thin, non-centrosymmetric 2D allotropes of 3D materials as a foundation for tunable superconductivity, topological states, and plasmonic properties.

cond-mat.mtrl-sci↗
arXiv2019

Epitaxial Graphene Intercalation: A Route to Graphene Modulation and Unique 2D Materials

Intercalation of atomic species through epitaxial graphene layers began only a few years following its initial report in 2004. The impact of intercalation on the electronic properties of the graphene is well known; however, the intercalant itself can also exhibit intriguing properties not found in nature. This suggests that a shift in the focus of epitaxial graphene intercalation studies may lead to fruitful exploration of many new forms of traditionally 3D materials. In the following forward-looking review, we summarize the primary techniques used to achieve and characterize EG intercalation, and introduce a new, facile approach to readily achieve metal intercalation at the graphene/silicon carbide interface. We show that simple thermal evaporation-based methods can effectively replace complicated synthesis techniques to realize large-scale intercalation of non-refractory metals. We also show that these methods can be extended to the formation of compound materials based on intercalation. Two-dimensional (2D) silver (2D-Ag) and large-scale 2D gallium nitride (2D-GaNx) are used to demonstrate these approaches.

cond-mat.mtrl-sci↗
arXiv2019

TS-RNN: Text Steganalysis Based on Recurrent Neural Networks

With the rapid development of natural language processing technologies, more and more text steganographic methods based on automatic text generation technology have appeared in recent years. These models use the powerful self-learning and feature extraction ability of the neural networks to learn the feature expression of massive normal texts. Then they can automatically generate dense steganographic texts which conform to such statistical distribution based on the learned statistical patterns. In this paper, we observe that the conditional probability distribution of each word in the automatically generated steganographic texts will be distorted after embedded with hidden information. We use Recurrent Neural Networks (RNNs) to extract these feature distribution differences and then classify those features into cover text and stego text categories. Experimental results show that the proposed model can achieve high detection accuracy. Besides, the proposed model can even make use of the subtle differences of the feature distribution of texts to estimate the amount of hidden information embedded in the generated steganographic text.

cs.MM↗
arXiv2019

Sequential Star Formation in the filamentary structures of Planck Galactic cold clump G181.84+0.31

We present a multi-wavelength study of the Planck cold clump G181.84+0.31, which is located at the northern end of the extended filamentary structure S242. We have extracted 9 compact dense cores from the SCUBA-2 850 um map, and we have identified 18 young stellar objects (YSOs, 4 Class I and 14 Class II) based on their Spitzer, Wide-field Infrared Survey Explorer (WISE) and Two-Micron All-Sky Survey (2MASS) near- and mid-infrared colours. The dense cores and YSOs are mainly distributed along the filamentary structures of G181.84 and are well traced by HCO$^{+}$(1-0) and N$_{2}$H$^{+}$(1-0) spectral-line emission. We find signatures of sequential star formation activities in G181.84: dense cores and YSOs located in the northern and southern sub-structures are younger than those in the central region. We also detect global velocity gradients of about 0.8$\pm$0.05 km s$^{-1}$pc$^{-1}$ and 1.0$\pm$0.05 km s$^{-1}$pc$^{-1}$ along the northern and southern sub-structures, respectively, and local velocity gradients of 1.2$\pm$0.1 km s$^{-1}$pc$^{-1}$ in the central substructure. These results may be due to the fact that the global collapse of the extended filamentary structure S242 is driven by an edge effect, for which the filament edges collapse first and then further trigger star formation activities inward. We identify three substructures in G181.84 and estimate their critical masses per unit length, which are $\sim$ 101$\pm$15 M$_{\odot}$ pc$^{-1}$, 56$\pm$8 M$_{\odot}$ pc$^{-1}$ and 28$\pm$4 M$_{\odot}$ pc$^{-1}$, respectively. These values are all lower than the observed values ($\sim$ 200 M$_{\odot}$ pc$^{-1}$), suggesting that these sub-structures are gravitationally unstable.

astro-ph.SR↗
arXiv2019

COSET: A Benchmark for Evaluating Neural Program Embeddings

Neural program embedding can be helpful in analyzing large software, a task that is challenging for traditional logic-based program analyses due to their limited scalability. A key focus of recent machine-learning advances in this area is on modeling program semantics instead of just syntax. Unfortunately evaluating such advances is not obvious, as program semantics does not lend itself to straightforward metrics. In this paper, we introduce a benchmarking framework called COSET for standardizing the evaluation of neural program embeddings. COSET consists of a diverse dataset of programs in source-code format, labeled by human experts according to a number of program properties of interest. A point of novelty is a suite of program transformations included in COSET. These transformations when applied to the base dataset can simulate natural changes to program code due to optimization and refactoring and can serve as a "debugging" tool for classification mistakes. We conducted a pilot study on four prominent models: TreeLSTM, gated graph neural network (GGNN), AST-Path neural network (APNN), and DYPRO. We found that COSET is useful in identifying the strengths and limitations of each model and in pinpointing specific syntactic and semantic characteristics of programs that pose challenges.

cs.LG↗
arXiv2019

Learning Scalable and Precise Representation of Program Semantics

Neural program embedding has shown potential in aiding the analysis of large-scale, complicated software. Newly proposed deep neural architectures pride themselves on learning program semantics rather than superficial syntactic features. However, by considering the source code only, the vast majority of neural networks do not capture a deep, precise representation of program semantics. In this paper, we present \dypro, a novel deep neural network that learns from program execution traces. Compared to the prior dynamic models, not only is \dypro capable of generalizing across multiple executions for learning a program's dynamic semantics in its entirety, but \dypro is also more efficient when dealing with programs yielding long execution traces. For evaluation, we task \dypro with semantic classification (i.e. categorizing programs based on their semantics) and compared it against two prominent static models: Gated Graph Neural Network and TreeLSTM. We find that \dypro achieves the highest prediction accuracy among all models. To further reveal the capacity of all aforementioned deep neural architectures, we examine if the models can learn to detect deeper semantic properties of a program. In particular given a task of recognizing loop invariants, we show \dypro beats all static models by a wide margin.

cs.PL↗
arXiv2019

Full orientation control of epitaxial MoS2 on hBN assisted by substrate defects

Inversion asymmetry in two-dimensional materials grants them fascinating properties such as spin-coupled valley degrees of freedom and piezoelectricity, but at the cost of inversion domain boundaries if the epitaxy of the grown 2D layer -- on a polar substrate -- cannot adequately distinguish what are often near-degenerate 0° and 180° orientations. We employ first-principles calculations to identify a method to lift this near-degeneracy: the energetic distinction between eclipsed and staggered configurations during nucleation at a point defect in the substrate. For monolayer MoS2 grown on hexagonal boron nitride, the predicted defect complex can be more stable than common MoS2 point defects because it is both a donor-acceptor pair and a Frenkel pair shared between adjacent layers of a 2D heterostack. Orientation control is verified in experiments that achieve ~90% consistency in the orientation of as-grown triangular MoS2 flakes on hBN, as confirmed by aberration-corrected scanning/transmission electron microscopy. This defect-enhanced orientational epitaxy could provide a general mechanism to break the near-degeneracy of 0/180° orientations of polar 2D materials on polar substrates, overcoming a long-standing impediment to scalable synthesis of single-crystal 2D semiconductors.

cond-mat.mes-hall↗
arXiv2019

Machine Learning for Classification of Protein Helix Capping Motifs

The biological function of a protein stems from its 3-dimensional structure, which is thermodynamically determined by the energetics of interatomic forces between its amino acid building blocks (the order of amino acids, known as the sequence, defines a protein). Given the costs (time, money, human resources) of determining protein structures via experimental means such as X-ray crystallography, can we better describe and compare protein 3D structures in a robust and efficient manner, so as to gain meaningful biological insights? We begin by considering a relatively simple problem, limiting ourselves to just protein secondary structural elements. Historically, many computational methods have been devised to classify amino acid residues in a protein chain into one of several discrete secondary structures, of which the most well-characterized are the geometrically regular $α$-helix and $β$-sheet; irregular structural patterns, such as 'turns' and 'loops', are less understood. Here, we present a study of Deep Learning techniques to classify the loop-like end cap structures which delimit $α$-helices. Previous work used highly empirical and heuristic methods to manually classify helix capping motifs. Instead, we use structural data directly--including (i) backbone torsion angles computed from 3D structures, (ii) macromolecular feature sets (e.g., physicochemical properties), and (iii) helix cap classification data (from CAPS-DB)--as the ground truth to train a bidirectional long short-term memory (BiLSTM) model to classify helix cap residues. We tried different network architectures and scanned hyperparameters in order to train and assess several models; we also trained a Support Vector Classifier (SVC) to use as a baseline. Ultimately, we achieved 85% class-balanced accuracy with a deep BiLSTM model.

q-bio.BM↗
arXiv2019

Physical properties and chemical composition of the cores in the California molecular cloud

We aim to reveal the physical properties and chemical composition of the cores in the California molecular cloud (CMC), so as to better understand the initial conditions of star formation. We made a high-resolution column density map (18.2") with Herschel data, and extracted a complete sample of the cores in the CMC with the \textsl{fellwalker} algorithm. We performed new single-pointing observations of molecular lines near 90 GHz with the IRAM 30m telescope along the main filament of the CMC. In addition, we also performed a numerical modeling of chemical evolution for the cores under the physical conditions. We extracted 300 cores, of which 33 are protostellar and 267 are starless cores. About 51\% (137 of 267) of the starless cores are prestellar cores. Three cores have the potential to evolve into high-mass stars. The prestellar core mass function (CMF) can be well fit by a log-normal form. The high-mass end of the prestellar CMF shows a power-law form with an index $α=-0.9\pm 0.1$ that is shallower than that of the Galactic field stellar mass function. Combining the mass transformation efficiency ($\varepsilon$) from the prestellar core to the star of $15\pm 1\%$ and the core formation efficiency (CFE) of 5.5\%, we suggest an overall star formation efficiency of about 1\% in the CMC. In the single-pointing observations with the IRAM 30m telescope, we find that 6 cores show blue-skewed profile, while 4 cores show red-skewed profile. [$\rm {HCO}^{+}$]/[HNC] and [$\rm {HCO}^{+}$]/$\rm [N_{2}H^{+}]$ in protostellar cores are higher than those in prestellar cores; this can be used as chemical clocks. The best-fit chemical age of the cores with line observations is $\sim 5\times 10^4$~years.

astro-ph.GA↗
arXiv2019

Massive and low-mass protostars in massive "starless" cores

The infrared dark clouds (IRDCs) G11.11$-$0.12 and G28.34$+$0.06 are two of the best-studied IRDCs in our Galaxy. These two clouds host clumps at different stages of evolution, including a massive dense clump in both clouds that is dark even at 70 and 100$μ$m. Such seemingly quiescent massive dense clumps have been speculated to harbor cores that are precursors of high-mass stars and clusters. We observed these two "prestellar" regions at 1mm with the Submillimeter Array (SMA) with the aim of characterizing the nature of such cores. We show that the clumps fragment into several low- to high-mass cores within the filamentary structure of the enveloping cloud. However, while the overall physical properties of the clump may indicate a starless phase, we find that both regions host multiple outflows. The most massive core though 70 $μ$m dark in both clumps is clearly associated with compact outflows. Such low-luminosity, massive cores are potentially the earliest stage in the evolution of a massive protostar. We also identify several outflow features distributed in the large environment around the most massive core. We infer that these outflows are being powered by young, low-mass protostars whose core mass is below our detection limit. These findings suggest that low-mass protostars have already formed or are coevally formed at the earliest phase of high-mass star formation.

astro-ph.GA↗
arXiv2019

A Gravitational Wave Background from Primordial Black Hole Lattices in Matter Dominated Era

We use the wide-used \textsf{Einstein Toolkit} to solve the Einstein constraints and then simulate the expansion of primordial black hole lattices (PBHLs) with different value of $f_{\mathrm{PBH}}$ and $m_{\mathrm{PBH}}$. We find that $f_{\mathrm{PBH}}$ plays an important role during the evolution of PBHLs. Since the motion of primordial black holes (PBHs) caused by the expansion of PBHLs occurs at speeds close to that of light, we expect the emission of gravitational waves (GWs) during the expansion of PBHLs. We use both analytical estimates and numerical simulations to cross check the production of GWs in expanding PBHLs.

gr-qc↗
arXiv2019

Enhanced Expressive Power and Fast Training of Neural Networks by Random Projections

Random projections are able to perform dimension reduction efficiently for datasets with nonlinear low-dimensional structures. One well-known example is that random matrices embed sparse vectors into a low-dimensional subspace nearly isometrically, known as the restricted isometric property in compressed sensing. In this paper, we explore some applications of random projections in deep neural networks. We provide the expressive power of fully connected neural networks when the input data are sparse vectors or form a low-dimensional smooth manifold. We prove that the number of neurons required for approximating a Lipschitz function with a prescribed precision depends on the sparsity or the dimension of the manifold and weakly on the dimension of the input vector. The key in our proof is that random projections embed stably the set of sparse vectors or a low-dimensional smooth manifold into a low-dimensional subspace. Based on this fact, we also propose some new neural network models, where at each layer the input is first projected onto a low-dimensional subspace by a random projection and then the standard linear connection and non-linear activation are applied. In this way, the number of parameters in neural networks is significantly reduced, and therefore the training of neural networks can be accelerated without too much performance loss.

cs.LG↗
arXiv2018

Multicomponent kinematics in a massive filamentary IRDC

To probe the initial conditions for high-mass star and cluster formation, we investigate the properties of dense filaments within the infrared dark cloud G035.39-00.33 (IRDC G035.39) in a combined Very Large Array (VLA) and the Green Bank Telescope (GBT) mosaic tracing the NH3 (1,1) and (2,2) emission down to 0.08 pc scales. Using agglomerative hierarchical clustering on multiple line-of-sight velocity component fitting results, we identify seven extended velocity-coherent components in our data, likely representing spatially coherent physical structures, some exhibiting complex gas motions. The velocity gradient magnitude distribution peaks at its mode of 0.35 km/s/pc and has a long tail extending into higher values of 1.5 - 2 km/s/pc, and is generally consistent with those found toward the same cloud in other molecular tracers and with the values found towards nearby low-mass dense cloud cores at the same scales. Contrary to observational and theoretical expectations, we find the non-thermal ammonia line widths to be systematically narrower (by about 20%) than those of N2H+ (1-0) line transition observed with similar resolution. If the observed ordered velocity gradients represent the core envelope solid-body rotation, we estimate the specific angular momentum to be about 2 x 10^21 cm^2/s, similar to the low-mass star-forming cores. Together with the previous finding of subsonic motions in G035.39, our results demonstrate high levels of similarity between kinematics of a high-mass star-forming IRDC and the low-mass star formation regime.

astro-ph.GA↗
arXiv2018

New Methods for Handling Singular Sample Covariance Matrices

The estimation of a covariance matrix from an insufficient amount of data is one of the most common problems in fields as diverse as multivariate statistics, wireless communications, signal processing, biology, learning theory and finance. In a joint work of Marzetta, Tucci and Simon, a new approach to handle singular covariance matrices was suggested. The main idea was to use dimensionality reduction in conjunction with an average over the Stiefel manifold. In this paper we continue with this research and we consider some new approaches to solve this problem. One of the methods is called the Ewens estimator and uses a randomization of the sample covariance matrix over all the permutation matrices with respect to the Ewens measure. The techniques used to attack this problem are broad and run from random matrix theory to combinatorics.

math.PR↗
arXiv2018

Compressed magnetic field in the magnetically-regulated global collapsing clump of G9.62+0.19

How stellar feedback from high-mass stars (e.g., H{\sc ii} regions) influences the surrounding interstellar medium and regulates new star formation is still unclear. To address this question, we observed the G9.62+0.19 complex in 850 $μ$m continuum with the JCMT/POL-2 polarimeter. An ordered magnetic field has been discovered in its youngest clump, the G9.62 clump. The magnetic field strength is determined to be $\sim$1 mG. Magnetic field plays a larger role than turbulence in supporting the clump. However, the G9.62 clump is still unstable against gravitational collapse even if thermal, turbulent, and magnetic field support are taken into account all together. The magnetic field segments in the outskirts of the G9.62 clump seem to point toward the clump center, resembling a dragged-in morphology, indicating that the clump is likely undergoing magnetically-regulated global collapse. However, The magnetic field in its central region is aligned with the shells of the photodissociation regions (PDRs) and is approximately parallel to the ionization (or shock) front, indicating that the magnetic field therein is likely compressed by the expanding H{\sc ii} regions that formed in the same complex.

astro-ph.SR↗
arXiv2018

Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition

We investigate the use of generative adversarial networks (GANs) in speech dereverberation for robust speech recognition. GANs have been recently studied for speech enhancement to remove additive noises, but there still lacks of a work to examine their ability in speech dereverberation and the advantages of using GANs have not been fully established. In this paper, we provide deep investigations in the use of GAN-based dereverberation front-end in ASR. First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset. Second, further adding residual connections in the deep LSTMs can boost the performance as well. Finally, we find that, for the success of GAN, it is important to update the generator and the discriminator using the same mini-batch data during training. Moreover, using reverberant spectrogram as a condition to discriminator, as suggested in previous studies, may degrade the performance. In summary, our GAN-based dereverberation front-end achieves 14%-19% relative CER reduction as compared to the baseline DNN dereverberation network when tested on a strong multi-condition training acoustic model.

cs.SD↗