arXiv subjects

Ying Zhang

Publications and source records attributed to Ying Zhang.

At least 469 records · Page 26Linked to original sources

arXiv2020

Effects of symmetry energy on the radius and tidal deformability of neutron stars in relativistic mean-field model

The radii and tidal deformabilities of neutron stars are investigated in the framework of relativistic mean-field (RMF) model with different density-dependent behaviors of symmetry energy. To study the effects of symmetry energy on the properties of neutron stars, an $ω$ meson and $ρ$ meson coupling term is included in a popular RMF Lagrangian, i.e. the TM1 parameter set, which is used for the widely used supernova equation of state (EoS) table. The coupling constants relevant to the vector-isovector meson, $ρ$, are refitted by a fixed symmetry energy at subsaturation density and its slope at saturation density, while other coupling constants remain the same as the original ones in TM1 so as to update the supernova EoS table. The radius and mass of maximum neutron stars are not so sensitive to the symmetry energy in these family TM1 parameterizations. However, the radii at intermediate mass region are strongly correlated with the slope of symmetry energy. Furthermore, the dimensionless tidal deformabilities of neutron stars are also calculated within the associated Love number. We find that its value at $1.4 M_\odot$ has a linear correlation to the slope of symmetry energy being different from the previous studied. With the latest constraints of tidal deformabilities from GW170817 event, the slope of symmetry energy at nuclear saturation density should be smaller than $60$ MeV in the family TM1 parameterizations. This fact supports the usage of lower symmetry energy slope for the update supernova EoS, which is applicable to simulations of neutron star merger. Furthermore, the analogous analysis are also done within the family IUFSU parameter sets. It is found that the correlations between the symmetry energy slope with the radius and tidal deformability at $1.4 M_\odot$ have very similar linear relations in these RMF models.

nucl-th↗

arXiv2020

Machine learning on DNA-encoded libraries: A new paradigm for hit-finding

DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value through screening of libraries with up to billions of unique small molecules. We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from a large commercial collection and a virtual library of easily synthesizable compounds. We train models using only DEL selection data and apply automated or automatable filters with chemist review restricted to the removal of molecules with potential for instability or reactivity. We validate this approach with a large prospective study (nearly 2000 compounds tested) across three diverse protein targets: sEH (a hydrolase), ERα (a nuclear receptor), and c-KIT (a kinase). The approach is effective, with an overall hit rate of {\sim}30% at 30 {\textmu}M and discovery of potent compounds (IC50 <10 nM) for every target. The model makes useful predictions even for molecules dissimilar to the original DEL and the compounds identified are diverse, predominantly drug-like, and different from known ligands. Collectively, the quality and quantity of DEL selection data; the power of modern machine learning methods; and access to large, inexpensive, commercially-available libraries creates a powerful new approach for hit finding.

q-bio.QM↗

arXiv2020

Efficient Bitruss Decomposition for Large-scale Bipartite Graphs

Cohesive subgraph mining in bipartite graphs becomes a popular research topic recently. An important structure k-bitruss is the maximal cohesive subgraph where each edge is contained in at least k butterflies (i.e., (2, 2)-bicliques). In this paper, we study the bitruss decomposition problem which aims to find all the k-bitrusses for k >= 0. The existing bottom-up techniques need to iteratively peel the edges with the lowest butterfly support. In this peeling process, these techniques are time-consuming to enumerate all the supporting butterflies for each edge. To relax this issue, we first propose a novel online index -- the BE-Index which compresses butterflies into k-blooms (i.e., (2, k)-bicliques). Based on the BE-Index, the new bitruss decomposition algorithm BiT-BU is proposed, along with two batch-based optimizations, to accomplish the butterfly enumeration of the peeling process in an efficient way. Furthermore, the BiT-PC algorithm is devised which is more efficient against handling the edges with high butterfly supports. We theoretically show that our new algorithms significantly reduce the time complexities of the existing algorithms. Also, we conduct extensive experiments on real datasets and the result demonstrates that our new techniques can speed up the state-of-the-art techniques by up to two orders of magnitude.

cs.DB↗

arXiv2020

Towards Highly Efficient State Estimation with Nonlinear Measurements in Distribution Systems

This letter proposes a novel and highly efficient distribution system state estimation (DSSE) algorithm with nonlinear measurements from supervisory control and data acquisition (SCADA) systems. Conventional DSSE, i.e., a weighted least square (WLS)-based method, requires multiple Monte Carlo simulations for overall accuracy evaluation and has high calculation cost due to the nonlinear iterative process. The proposed method uses the Taylor series of voltages for constructing a linear DSSE model in the interval form and then solve this model by interval arithmetic. Compared with the nonlinear WLS-based method, the proposed method obtains accurate and robust estimates via a single random sampling of measurements and is computationally efficient. The comparative analysis of the IEEE 34-bus distribution system points to improved estimation results relative to the WLS-based method.

eess.SY↗

arXiv2019

Understanding the Teaching Styles by an Attention based Multi-task Cross-media Dimensional modelling

Teaching style plays an influential role in helping students to achieve academic success. In this paper, we explore a new problem of effectively understanding teachers' teaching styles. Specifically, we study 1) how to quantitatively characterize various teachers' teaching styles for various teachers and 2) how to model the subtle relationship between cross-media teaching related data (speech, facial expressions and body motions, content et al.) and teaching styles. Using the adjectives selected from more than 10,000 feedback questionnaires provided by an educational enterprise, a novel concept called Teaching Style Semantic Space (TSSS) is developed based on the pleasure-arousal dimensional theory to describe teaching styles quantitatively and comprehensively. Then a multi-task deep learning based model, Attention-based Multi-path Multi-task Deep Neural Network (AMMDNN), is proposed to accurately and robustly capture the internal correlations between cross-media features and TSSS. Based on the benchmark dataset, we further develop a comprehensive data set including 4,541 full-annotated cross-modality teaching classes. Our experimental results demonstrate that the proposed AMMDNN outperforms (+0.0842 in terms of the concordance correlation coefficient (CCC) on average) baseline methods. To further demonstrate the advantages of the proposed TSSS and our model, several interesting case studies are carried out, such as teaching styles comparison among different teachers and courses, and leveraging the proposed method for teaching quality analysis.

cs.MM↗

arXiv2019

The properties of neutron star from realistic nucleon-nucleon interaction within relativistic Hartree-Fock model

The properties of neutron star are studied in the framework of relativistic Hartree-Fock (RHF) model with realistic nucleon-nucleon ($NN$) interactions, i.e. Bonn potentials. The strong repulsion of $NN$ interaction at short range is properly removed by the unitary correlation operator method (UCOM). Meanwhile, the tensor correlation is neglected due to the very rich neutron environment in neutron star, where the total isospin of two nucleons can be approximately regarded as $T=1$. The equations of state of neutron star matter are calculated in $β$ equilibrium and charge neutrality conditions. The properties of neutron star, such as mass, radius, and tidal deformability are obtained by solving the Tolman-Oppenheimer-Volkoff equation and tidal equation. The maximum masses of neutron from Bonn A, B, C potentials are around $2.2M_\odot$. The radius are $12.40-12.91$ km at $1.4M_\odot$, respectively. The corresponding tidal deformabilities are $Λ_{1.4}=293-355$. All of these properties are satisfied the recent observables from the astronomical and gravitational wave devices and are consistent with the results from the relativistic Brueckner-Hartree-Fock model.

nucl-th↗

arXiv2019

Uniaxial Strain-Induced Anisotropic Charge Transfer in Contact Electrification

Even though contact electrification has been studied for a long time, the mechanism of charge transfer still remains elusive. Most of previous reports only focus on the driving force of charge transfer. However, to better understand the mechanism of charge transfer, we believe contact itself for supplying transferring path that charges taking also need to be understood. Here, we focus on the in-plane symmetry of contact in contact electrification by utilizing a uniaxial strain to change the material's isotropic nature to anisotropic. A clear anisotropic charge transfer is observed by contacting axially stretched rubber films at different rotational angles, which could arise from fluctuation of contacting area in microscale. A universal ellipse model is also proposed for qualitatively describing the anisotropy of contact regardless of the specific driving force of contact electrification.

physics.app-ph↗

arXiv2019

Bayesian truncation errors in equations of state of nuclear matter with chiral nucleon-nucleon potentials

The truncation errors in equations of state (EOSs) of nuclear matter derived from the chiral nucleon-nucleon ($NN$) potentials at different expansion orders are analyzed by a Bayesian model. These EOSs are expanded as functions of a dimensionless parameter, $Q$, which is determined by Fermi momentum, $k_F$ and breakdown scale, $Λ_b$. The degree-of-belief (DoB) intervals predicted by the chiral effective field theory are calculated within the corresponding expansion coefficients and the specific prior probability distribution functions in terms of Bayes theorem. The truncation errors of EOSs, generated by the DoB intervals, exhibit good order-by-order convergences with different chiral expansion order potentials. When DoB is considered as $1σ$ credibility, i.e., $68.27\%$ confidence interval, the truncation errors of binding energy per nucleon in symmetric nuclear matter and pure neutron matter are consistent with the results given by a simple error analysis method proposed by Epelbaum, {\it et al.} Finally, the reasonable values of the breakdown scale $Λ_b$ in nuclear matter are discussed through consistency checks of Bayesian method based on the calculations of success rate.

nucl-th↗

arXiv2019

Task-Oriented Conversation Generation Using Heterogeneous Memory Networks

How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks are usually a great choice and a promising way. However, existing memory networks do not perform well when leveraging heterogeneous information from different sources. In this paper, we propose a novel and versatile external memory networks called Heterogeneous Memory Networks (HMNs), to simultaneously utilize user utterances, dialogue history and background knowledge tuples. In our method, historical sequential dialogues are encoded and stored into the context-aware memory enhanced by gating mechanism while grounding knowledge tuples are encoded and stored into the context-free memory. During decoding, the decoder augmented with HMNs recurrently selects each word in one response utterance from these two memories and a general vocabulary. Experimental results on multiple real-world datasets show that HMNs significantly outperform the state-of-the-art data-driven task-oriented dialogue models in most domains.

cs.CL↗

arXiv2019

Higher Order Langevin Monte Carlo Algorithm

A new (unadjusted) Langevin Monte Carlo (LMC) algorithm with improved rates in total variation and in Wasserstein distance is presented. All these are obtained in the context of sampling from a target distribution $π$ that has a density $\hatπ$ on $\mathbb{R}^d$ known up to a normalizing constant. Moreover, $-\log \hatπ$ is assumed to have a locally Lipschitz gradient and its third derivative is locally Hölder continuous with exponent $β\in (0,1]$. Non-asymptotic bounds are obtained for the convergence to stationarity of the new sampling method with convergence rate $1+ β/2$ in Wasserstein distance, while it is shown that the rate is 1 in total variation even in the absence of convexity. Finally, in the case where $-\log \hatπ$ is strongly convex and its gradient is Lipschitz continuous, explicit constants are provided.

math.ST↗

arXiv2019

The charge-dependent Bonn potentials with pseudovector pion-nucleon coupling

To apply the high-precision realistic nucleon-nucleon ($NN$) potentials on the investigations of relativistic many-body methods, the new versions of charge-dependent Bonn (CD-Bonn) $NN$ potential are constructed within the pseudovector pion-nucleon coupling instead of the pseudoscalar type in the original CD-Bonn potential worked out by Machleidt [Phys. Rev. C 63, 024001 (2001)]. Two effective scalar mesons are introduced, whose coupling constants with nucleon are independently determined at each partial wave for total angular momentum $J\leq 4$, to describe the charge dependence of $NN$ scattering data precisely, while the coupling constants between vector, pseudovector mesons and nucleon are identical in all channels. Three revised CD-Bonn potentials adopting the pseudovector pion-nucleon couplings (pvCD-Bonn) are generated by fitting the Nijmegen PWA phase shift data and deuteron binding energy with different pion-nucleon coupling strengths, which can reproduce the phase shifts at spin-single channels and low-energy $NN$ scattering parameters very well, and provide the significantly different mixing parameters at spin-triplet channels. Furthermore, the $D$-state probabilities of deuteron from these potentials range from $4.22\%$ to $6.05\%$. It demonstrates that these potentials contain different components of tensor force, which will be useful to discuss the roles of tensor force in nuclear few-body and many-body systems.

nucl-th↗

arXiv2019

Improving Captioning for Low-Resource Languages by Cycle Consistency

Improving the captioning performance on low-resource languages by leveraging English caption datasets has received increasing research interest in recent years. Existing works mainly fall into two categories: translation-based and alignment-based approaches. In this paper, we propose to combine the merits of both approaches in one unified architecture. Specifically, we use a pre-trained English caption model to generate high-quality English captions, and then take both the image and generated English captions to generate low-resource language captions. We improve the captioning performance by adding the cycle consistency constraint on the cycle of image regions, English words, and low-resource language words. Moreover, our architecture has a flexible design which enables it to benefit from large monolingual English caption datasets. Experimental results demonstrate that our approach outperforms the state-of-the-art methods on common evaluation metrics. The attention visualization also shows that the proposed approach really improves the fine-grained alignment between words and image regions.

cs.CL↗

arXiv2019

A Survey of Community Search Over Big Graphs

With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real-time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have a better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.

cs.DB↗

arXiv2019

Concury: A Fast and Light-weighted Software Load Balancer

A load balancer (LB) is a vital network function for cloud services to balance the load amongst resources. Stateful software LBs that run on commodity servers provides flexibility, cost-efficiency, and packet consistency. However current designs have two main limitations: 1) states are stored as digests which may cause packet inconsistency due to digest collisions; 2) the data plane needs to update for every new connection, and frequent updates hurt throughput and packet consistency. In this work, we present a new software stateful LB called Concury, which is the first solution to solve these problems. The key innovation of Concury is an algorithmic approach to store and look up large network states with frequent connection arrivals, which is succinct in memory cost, consistent under network changes, and incurs infrequent data plane updates. The evaluation results show that the Concury algorithm provides 4x throughput and consumes less memory compared to other LB algorithms, while providing weighted load balancing and false-hit freedom, for both real and synthetic data center traffic. We implement Concury as a prototype system deployed in CloudLab and show that the throughput of Concury on a single thread can reach 62.5% of the maximum capacity of two 10GbE NICs and that on two threads can reach the maximum capacity.

cs.NI↗

arXiv2019

Multi-microjoule GaSe-based mid-infrared optical parametric amplifier with an ultra-broad idler spectrum covering 4.2-16 μm

We report a multi-microjoule, ultra-broadband mid-infrared optical parametric amplifier based on a GaSe nonlinear crystal pumped at ~2 μm. The generated idler pulse has a flat spectrum spanning from 4.5 to 13.3 μm at -3 dB and 4.2 to 16 μm in the full spectral range, with a central wavelength of 8.8 μm. The proposed scheme supports a sub-cycle Fourier-transform-limited pulse width. A (2+1)-dimensional numerical simulation is employed to reproduce the obtained idler spectrum. To our best knowledge, this is the broadest -3 dB spectrum ever obtained by optical parametric amplifiers in this spectral region. The idler pulse energy is ~3.4 μJ with a conversion efficiency of ~2% from the ~2 μm pump to the idler pulse.

physics.optics↗

arXiv2019

Enhanced low-flux sensitivity (ELFS) effect of neutron-induced displacement damage in bipolar devices: physical mechanism and parametric model

Similar to the enhanced low-dose-rate sensitivity (ELDRS) effect of ionization damage, an enhanced low-flux senstivity (ELFS) effect has been reported in ions/neutron irradiation on n-type silicon or PNP transistors. However, the existing mechanism and simulation dominated by the diffusion dynamics give much higher transition flux than the experimental observations. In this work, we develop a new model based on the annealing of defect clusters for the ELFS effect. Simulations considering Si-interstitial-mediated inter-cluster interactions during their annealing processes successfully reproduce the ELFS effect. The ratio of Si interstitials captured by defect clusters to those dissipating off on the sample edges or re-merging into the bulk is found as the key parameter dominating the enhancement factor (EF) of the ELFS effect. We also establish a compact parametric model based on the mechanism, which is found to provide a good quantitative description of the experimental results. The model predicts the existence of nonsensitive regions at sufficiently low and high fluxes as well as a non-trivial fluence and temperature dependence of the enhancement factor.

physics.space-ph↗

arXiv2019

Efficient Butterfly Counting for Large Bipartite Networks

Bipartite networks are of great importance in many real-world applications. In bipartite networks, butterfly (i.e., a complete 2 x 2 biclique) is the smallest non-trivial cohesive structure and plays a key role. In this paper, we study the problem of efficient counting the number of butterflies in bipartite networks. The most advanced techniques are based on enumerating wedges which is the dominant cost of counting butterflies. Nevertheless, the existing algorithms cannot efficiently handle large-scale bipartite networks. This becomes a bottleneck in large-scale applications. In this paper, instead of the existing layer-priority-based techniques, we propose a vertex-priority-based paradigm BFC-VP to enumerate much fewer wedges; this leads to a significant improvement of the time complexity of the state-of-the-art algorithms. In addition, we present cache-aware strategies to further improve time efficiency while theoretically retaining the time complexity of BFC-VP. Moreover, we also show that our proposed techniques can work efficiently in external and parallel contexts. Our extensive empirical studies demonstrate that the proposed techniques can speed up the state-of-the-art techniques by up to two orders of magnitude for the real datasets.

cs.SI↗

arXiv2019

A Survey and Experimental Analysis of Distributed Subgraph Matching

Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view to the pros and cons of each algorithm mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizations from representative state-of-the-art works. We implement the four strategies with the optimizations based on the common Timely dataflow system for systematic strategy-level comparison. Our implementation covers all representation algorithms. We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.

cs.DB↗