arXiv subjects

Ying Zhang

Publications and source records attributed to Ying Zhang.

At least 397 records · Page 22Linked to original sources

arXiv2022

%CRTFASTGEEPWR: a SAS macro for power of the generalized estimating equations of multi-period cluster randomized trials with application to stepped wedge designs

Multi-period cluster randomized trials (CRTs) are increasingly used for the evaluation of interventions delivered at the group level. While generalized estimating equations (GEE) are commonly used to provide population-averaged inference in CRTs, there is a gap of general methods and statistical software tools for power calculation based on multi-parameter, within-cluster correlation structures suitable for multi-period CRTs that can accommodate both complete and incomplete designs. A computationally fast, non-simulation procedure for determining statistical power is described for the GEE analysis of complete and incomplete multi-period cluster randomized trials. The procedure is implemented via a SAS macro, \%CRTFASTGEEPWR, which is applicable to binary, count and continuous responses and several correlation structures in multi-period CRTs. The SAS macro is illustrated in the power calculation of two complete and two incomplete stepped wedge cluster randomized trial scenarios under different specifications of marginal mean model and within-cluster correlation structure. The proposed GEE power method is quite general as demonstrated in the SAS macro with numerous input options. The power procedure and macro can also be used in the planning of parallel and crossover CRTs in addition to cross-sectional and closed cohort stepped wedge trials.

stat.CO↗

arXiv2022

Continuum Skyrme Hartree-Fock-Bogoliubov theory with Green's function method for neutron-rich Ca, Ni, Zr, Sn isotopes

The possible exotic nuclear properties in the neutron-rich Ca, Ni, Zr, and Sn isotopes are explored with the continuum Skyrme Hartree-Fock-Bogoliubov theory formulated with the Green's function method. The available experimental two-neutron separation energies $S_{\rm 2n}$ and one-neutron separation energies $S_{\rm n}$ are well reproduced. Much shorter drip lines predicted by $S_{\rm n}$ are obtained compared with those by $S_{\rm 2n}$. The systematic studies of the neutron pairing energies $-E_{\rm pair}$ shown that values of the odd-$A$ nuclei are much smaller in comparison with those of the neighboring even-even nuclei due to the absent contribution of pairing energy by the unpaired odd neutron. By investigating the single-particle structures, the rms radii and the density distributions, the possible halo structures in the neutron-rich Ca, Ni, and Sn isotopes are predicted, in which the sharp increases of rms radii with significant deviations from the traditional $r\varpropto A^{1/3}$ rule and very diffuse spatial distributions in densities are observed. By analyzing the contributions of different partial waves to the total neutron density $ρ_{lj}(r)/ρ(r)$, the orbitals locating around the Fermi surface especially those with low angular momenta are found the main reason causing the extended nuclear density and large rms radii. Finally, the numbers of the neutrons $N_λ$~($N_0$) occupied above the Fermi surface $λ_n$~(in the continuum) are discussed, the behaviors of which are basically consistent with those of pairing energy, supporting the key role of the pairing correlations in the halo phenomena.

nucl-th↗

arXiv2022

Example-Based Vulnerability Detection and Repair in Java Code

The Java libraries JCA and JSSE offer cryptographic APIs to facilitate secure coding. When developers misuse some of the APIs, their code becomes vulnerable to cyber-attacks. To eliminate such vulnerabilities, people built tools to detect security-API misuses via pattern matching. However, most tools do not (1) fix misuses or (2) allow users to extend tools' pattern sets. To overcome both limitations, we created Seader-an example-based approach to detect and repair security-API misuses. Given an exemplar code pair, Seader compares the snippets to infer any API-misuse template and corresponding fixing edit. Based on the inferred info, given a program, Seader performs inter-procedural static analysis to search for security-API misuses and to propose customized fixes. For evaluation, we applied Seader to 28 codepairs; Seader successfully inferred 21 unique API-misuse templates and related fixes. With these patterns, we applied SEADER to a program benchmark that has 86 known vulnerabilities. Seader detected vulnerabilities with 95% precision, 72% recall, and82% F-score. We also applied Seader to 100 open-source projects and manually checked 77 suggested repairs; 76 of the repairs were correct. Seader can help developers correctly use security APIs.

cs.CR↗

arXiv2022

Detailed Balance for Particle Models of Reversible Reactions in Bounded Domains

In particle-based stochastic reaction-diffusion models, reaction rate and placement kernels are used to decide the probability per time a reaction can occur between reactant particles, and to decide where product particles should be placed. When choosing kernels to use in reversible reactions, a key constraint is to ensure that detailed balance of spatial reaction-fluxes holds at all points at equilibrium. In this work we formulate a general partial-integral differential equation model that encompasses several of the commonly used contact reactivity (e.g. Smoluchowski-Collins-Kimball) and volume reactivity (e.g. Doi) particle models. From these equations we derive a detailed balance condition for the reversible $\textrm{A} + \textrm{B} \leftrightarrows \textrm{C}$ reaction. In bounded domains with no-flux boundary conditions, when choosing unbinding kernels consistent with several commonly used binding kernels, we show that preserving detailed balance of spatial reaction-fluxes at all points requires spatially varying unbinding rate functions near the domain boundary. Brownian Dynamics simulation algorithms can realize such varying rates through ignoring domain boundaries during unbinding and rejecting unbinding events that result in product particles being placed outside the domain.

q-bio.QM↗

arXiv2022

Hot 2DHG states in tellurium

Element semiconductor Te is very popular in both fundamental electronic structure study, and device fabrication research area due to its unique band structure. Specifically, in low temperatures, Te possesses strong quantum oscillations with magnetic field applied in basal plane, either following Shubnikov-de Haas (SdH) oscillation rule or following log-periodic oscillation rule. With magnetic field applied along the [001] direction, the SdH oscillations are attributed to the two-dimensional hole gas (2DHG) surface states. Here we reported an interesting SdH oscillation in Te-based single crystals, with the magnetic field applied along the [001] direction of the crystals, showing the maximum oscillation intensity at ~ 75 K, and still traceable at 200 K, which indicates a rather hot 2DHG state. The nontrivial Berry phase can be also obtained from the oscillations, implying the contribution from topological states. More importantly, the high temperature SdH oscillation phenomena are observed in different Te single crystals samples, and Te single crystals with nonmagnetic/magnetic dopants, showing robustness to bulk defects. Therefore, the oscillation may be contributed by the bulk symmetry protected hot 2DHG states, which will offer a new platform for high-temperature quantum transport studies.

cond-mat.mtrl-sci↗

arXiv2022

Mix-up Self-Supervised Learning for Contrast-agnostic Applications

Contrastive self-supervised learning has attracted significant research attention recently. It learns effective visual representations from unlabeled data by embedding augmented views of the same image close to each other while pushing away embeddings of different images. Despite its great success on ImageNet classification, COCO object detection, etc., its performance degrades on contrast-agnostic applications, e.g., medical image classification, where all images are visually similar to each other. This creates difficulties in optimizing the embedding space as the distance between images is rather small. To solve this issue, we present the first mix-up self-supervised learning framework for contrast-agnostic applications. We address the low variance across images based on cross-domain mix-up and build the pretext task based on two synergistic objectives: image reconstruction and transparency prediction. Experimental results on two benchmark datasets validate the effectiveness of our method, where an improvement of 2.5% ~ 7.4% in top-1 accuracy was obtained compared to existing self-supervised learning methods.

cs.CV↗

arXiv2022

Emotional Conversation Generation with Heterogeneous Graph Neural Network

The successful emotional conversation system depends on sufficient perception and appropriate expression of emotions. In a real-life conversation, humans firstly instinctively perceive emotions from multi-source information, including the emotion flow hidden in dialogue history, facial expressions, audio, and personalities of speakers. Then, they convey suitable emotions according to their personalities, but these multiple types of information are insufficiently exploited in emotional conversation fields. To address this issue, in this paper, we propose a heterogeneous graph-based model for emotional conversation generation. Firstly, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i.e., the dialogue history, its emotion flow, facial expressions, audio, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback. Secondly, we employ an Emotion-Personality-Aware Decoder to generate a response relevant to the conversation context as well as with appropriate emotions, through taking the encoded graph representations, the predicted emotions by the encoder and the personality of the current speaker as inputs. Experiments on both automatic and human evaluation show that our method can effectively perceive emotions from multi-source knowledge and generate a satisfactory response. Furthermore, based on the up-to-date text generator BART, our model still can achieve consistent improvement, which significantly outperforms some existing state-of-the-art models.

cs.CL↗

arXiv2022

Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph q in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In recent years, many advanced techniques for query vertex ordering (i.e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. Instead of using the fixed heuristics to generate the matching order, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduces the number of redundant enumerations. With the help of the reinforcement learning framework, our model is able to consider the long-term benefits rather than only consider the local information at current ordering step.Extensive experiments on six real-life data graphs demonstrate that our proposed matching order generation technique could reduce up to two orders of magnitude of query processing time compared to the state-of-the-art algorithms.

cs.LG↗

arXiv2022

The hyperonic star in relativistic mean-field model

The neutron star as a supernova remnant is attracting high attention recently due to the gravitation wave detection and precise measurements about its mass and radius. In the inner core region of the neutron star, the strangeness degrees of freedom, such as the hyperons, can be present, which is also named as a hyperonic star. In this work, the neutron star consisting of nucleons and leptons, and the hyperonic star including the hyperons will be reviewed in the framework of the relativistic mean-field (RMF) model. The popular non-linear and density-dependent RMF parametrizations in the market will be adopted to investigate the role of strangeness baryons in a hyperonic star on its mass, radius, tidal deformability, and other properties. Finally, the magnitudes of the coupling strengths between mesons and hyperons also will be discussed, which can generate the massive hyperonic star with present RMF parameter sets, when the vector coupling constants are strong.

nucl-th↗

arXiv2022

The nuclear symmetry energy from relativistic Brueckner-Hartree-Fock model

The microscopic mechanisms of the symmetry energy in nuclear matter are investigated in the framework of the relativistic Brueckner-Hartree-Fock (RBHF) model with a high-precision realistic nuclear potential, pvCDBonn A. The kinetic energy and potential contributions to symmetry energy are decomposed. They are explicitly expressed by the nucleon self-energies, which are obtained through projecting the $G$-matrices from the RBHF model into the terms of Lorentz covariants. The nuclear medium effects on the nucleon self-energy and nucleon-nucleon interaction in symmetry energy are discussed by comparing the results from the RBHF model and those from Hartree-Fock and relativistic Hartree-Fock models. It is found that the nucleon self-energy including the nuclear medium effect on the single-nucleon wave function provides a largely positive contribution to the symmetry energy, while {the nuclear medium effect on the nucleon-nucleon interaction, i.e., the effective $G$-matrices generates the negative contribution}. The tensor force plays an essential role in the symmetry energy around the density. The scalar and vector covariant amplitudes of nucleon-nucleon interaction dominate the potential component of the symmetry energy. Furthermore, the isoscalar and isovector terms in the optical potential are extracted from the RBHF model. The isoscalar part is consistent with the results from the analysis of global optical potential, while the isovector one has obvious differences at higher incident energy due to the relativistic effect.

nucl-th↗

arXiv2022

The lineshape of the compact fully heavy tetraquark

Hadrons and their distributions are the most direct observables in experiment, which would shed light on the non-perturbative mystery of quantum chromodynamics (QCD). As the result, any new hadron will challenge our current knowlege on the one hand, and provide additional inputs on the other hand. The fully heavy $cc\bar{c}\bar{c}$ system observed by LHCb recently opens a new era for hadron physics. We first extract the internal structure of the fully heavy tetraquarks directly from the experimental data, within the compact tetraquark picture. By fitting to the di-$J/ψ$ lineshape, we find that the $X(6900)$ is only cusp effect from the $J/ψψ(3770)$ channel. In addition, there is also a cusp slightly below $6.8~\mathrm{GeV}$ stemming from the $J/ψψ^\prime$ channel. The two $0^{++}$ tetraquarks behave as two resonances above the di-$η_c$ and di-$J/ψ$ threshold, respectively. The $2^{++}$ state is a bound state below the di-$J/ψ$ threshold. Furthermore, we find that the $X_{0^{++}}(6035)$ shows a significant structure in the di-$η_c$ lineshape even after the coupled channel effect. This is an unique feature which can distinguish compact $cc\bar{c}\bar{c}$ tetraquark from the loosely hadronic molecules.

hep-ph↗

arXiv2022

Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor

Liver cancer is one of the most common malignant diseases in the world. Segmentation and labeling of liver tumors and blood vessels in CT images can provide convenience for doctors in liver tumor diagnosis and surgical intervention. In the past decades, many state-of-the-art medical image segmentation algorithms appeared during this period. With the development of embedded devices, embedded deployment for medical segmentation and automatic reconstruction brings prospects for future automated surgical tasks. Yet, most of the existing segmentation methods mostly care about the spatial feature context and have a perception defect in the semantic relevance of medical images, which significantly affects the segmentation accuracy of liver tumors and blood vessels. Deploying large and complex models into embedded devices requires a reasonable trade-off between model accuracy, reasoning speed and model capacity. Given these problems, we introduce a multi-scale feature fusion network called TransFusionNet based on Transformer. This network achieved very competitive performance for liver vessel and liver tumor segmentation tasks, meanwhile it can improve the recognition of morphologic margins of liver tumors by exploiting the global information of CT images. Experiments show that in vessel segmentation task TransFusionNet achieved mean Dice coefficients of 0.899 and in liver tumor segmentation task TransFusionNet achieved mean Dice coefficients of 0.961. Compared with the state-of-the-art framework, our model achieves the best segmentation result. In addition, we deployed the model into an embedded micro-structure and constructed an integrated model for liver tumor vascular segmentation and reconstruction. This proprietary structure will be the exclusive component of the future medical field.

eess.IV↗

arXiv2022

The Minimal Flavor Structure from Decomposition of the Fermion Mass Matrix

The minimal flavor structures for both quarks and leptons are proposed to address fermion mass hierarchy and flavor mixings by bi-unitary decomposition of the fermion mass matrix. The real matrix ${\bf M}_0^f$ is completely responsive to family mass hierarchy, which is expressed by a close-to-flat matrix structure. The left-handed unitary phase ${\bf F}_L^f$ provides the origin of CP violation in quark and lepton mixings, which can be explained as a quantum effect between Yukawa interaction states and weak gauge states. The minimal flavor structure is realized by just 10 parameters without any redundancy, corresponding to 6 fermion masses, 3 mixing angles and 1 CP violation in the quark/lepton sector. This approach provides a general flavor structure independent of the specific quark or lepton flavor data. We verify the validation of the flavor structure by reproducing quark/lepton masses and mixings. Some possible scenarios that yield the flavor structure are also discussed.

hep-ph↗

arXiv2022

Sparse Representations of Solutions to a class of Random Boundary Value Problems

We introduce certain sparse representation methods, named as stochastic pre-orthogonal adaptive Fourier decomposition 1 and 2 (SPOAFD1 and SPOAFD2) to solve the Dirichlet boundary value problem and the Cauchy initial value problem of random data. To solve the stochastic boundary value problems the sparse representation is, as the initial step, applied to the random boundary data. Due to the semigroup property of the Poisson and the heat kernel, each entry of the expanding series can be lifted up to compose a solution of the Dirichlet and the Cauchy initial value problem, respectively. The sparse representation gives rise to analytic as well as numerical solutions to the problems with high efficiency.

math.NA↗

arXiv2022

Carbide: Highly Reliable Networks Through Real-Time Multiple Control Plane Composition

Achieving highly reliable networks is essential for network operators to ensure proper packet delivery in the event of software errors or hardware failures. Networks must ensure reachability and routing correctness, such as subnet isolation and waypoint traversal. Existing work in network verification relies on centralized computation at the cost of fault tolerance, while other approaches either build an over-engineered, complex control plane, or compose multiple control planes without providing any guarantee on correctness. This paper presents Carbide, a novel system to achieve high reliability in networks through distributed verification and multiple control plane composition. The core of Carbide is a simple, generic, efficient distributed verification framework that transforms a generic network verification problem to a reachability verification problem on a directed acyclic graph (DAG), and solves the latter via an efficient distributed verification protocol (DV-protocol). Equipped with verification results, Carbide allows the systematic composition of multiple control planes and realization of operator-specified consistency. Carbide is fully implemented. Extensive experiments show that (1) Carbide reduces downtime by 43% over the most reliable individual underlying control plane, while enforcing correctness requirements on all traffic; and (2) by systematically decomposing computation to devices and pruning unnecessary messaging between devices during verification, Carbide scales to a production data center network.

cs.NI↗

arXiv2021

Building an AI-ready RSE Workforce

Artificial Intelligence has been transforming industries and academic research across the globe, and research software development is no exception. Machine learning and deep learning are being applied in every aspect of the research software development lifecycles, from new algorithm design paradigms to software development processes. In this paper, we discuss our views on today's challenges and opportunities that AI has presented on research software development and engineers, and the approaches we, at the University of Florida, are taking to prepare our workforce for the new era of AI.

cs.SE↗

arXiv2021

Decentralized Coordinated State Estimation in Integrated Transmission and Distribution Systems

Current transmission and distribution system states are mostly unobservable to each other, and state estimation is separately conducted in the two systems owing to the differences in network structures and analytical models. The large-scale integration of transmission and active distribution systems calls for an effective solution to global state estimation. Unlike existing independent state estimation methods on both levels of these systems, we propose a decentralized coordinated transmission and distribution system state estimation (C-TDSE) method. This method enables accurate monitoring of the integrated systems with a global reference in a decentralized manner and reconciles the mismatches of voltages and powers on the boundaries of the systems. The comparative analysis on the integrated transmission and distribution systems points to improved estimation results relative to the independent state estimation methods.

eess.SY↗

arXiv2021

SiamPolar: Semi-supervised Realtime Video Object Segmentation with Polar Representation

Video object segmentation (VOS) is an essential part of autonomous vehicle navigation. The real-time speed is very important for the autonomous vehicle algorithms along with the accuracy metric. In this paper, we propose a semi-supervised real-time method based on the Siamese network using a new polar representation. The input of bounding boxes is initialized rather than the object masks, which are applied to the video object detection tasks. The polar representation could reduce the parameters for encoding masks with subtle accuracy loss so that the algorithm speed can be improved significantly. An asymmetric siamese network is also developed to extract the features from different spatial scales. Moreover, the peeling convolution is proposed to reduce the antagonism among the branches of the polar head. The repeated cross-correlation and semi-FPN are designed based on this idea. The experimental results on the DAVIS-2016 dataset and other public datasets demonstrate the effectiveness of the proposed method.

cs.CV↗