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Hong Shen

Hong Shen appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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nucl-thastro-ph.HECryptography and Securityeess.SPInformation Theorymath.ITArtificial Intelligencehep-phInformation Retrievalcs.CYData Structures and AlgorithmsHuman-Computer InteractionMachine LearningNetworking and Internet Architectureastro-ph.SRComputer Visioncond-mat.mes-hallcond-mat.mtrl-sciDiscrete Mathematicseess.IVSocial and Information Networks

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Published work

44 published item(s)

preprint2026arXiv

Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios

AI measurement science has a wide variety of methodologies and measurements for comparing AI systems, resulting in what often appear to be "apples-to-oranges" comparisons across AI evaluations. To move toward "apples-to-apples" comparisons in real-world AI evaluations, this work advocates for methodological transparency in evaluation scenarios, operational grounding, and human-centered design (HCD) principles. We propose a repeatable process for transforming high-level use cases to detailed scenarios by eliciting use cases from subject matter experts (SMEs) via a structured AI Use Case Worksheet with six key elements: use case, sector, user (direct and indirect), intended outcomes, expected impacts (positive and negative), and KPIs and metrics. We demonstrate utility of the worksheet and process in the U.S. financial services sector. This paper reports on example high-level AI use cases identified by financial services sector SMEs: cyber defense enablement, developer productivity, financial crime aggregation, suspicious activity report (SAR) filing, credit memo generation, and internal call center support. These AI use cases provided are illustrative of the process and not exhaustive. Central to our work is a three-stage expansion pipeline combining LLM prompting with human reviews to generate 107 scenarios from those use cases elicited from SMEs. This process integrates iterative human reviews at every juncture to ensure operational grounding: for scenario titles and descriptions; for core scenario elements like users, benefits and risks, and metrics; and for scenario narratives and evaluation objectives. Human checkpoints ensure scenarios remain reflective of real-world usage and human needs. We describe a validation rubric to assess scenario quality. By defining key scenario components, this work supports a more consistent and meaningful paradigm for human-centered AI evaluations.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology

The vehicular-to-everything (V2X) technology has recently drawn a number of attentions from both academic and industrial areas. However, the openness of the wireless communication system makes it more vulnerable to identity impersonation and information tampering. How to employ the powerful radio frequency fingerprint (RFF) identification technology in V2X systems turns out to be a vital and also challenging task. In this paper, we propose a novel RFF extraction method for Long Term Evolution-V2X (LTE-V2X) systems. In order to conquer the difficulty of extracting transmitter RFF in the presence of wireless channel and receiver noise, we first estimate the wireless channel which excludes the RFF. Then, we remove the impact of the wireless channel based on the channel estimate and obtain initial RFF features. Finally, we conduct RFF denoising to enhance the quality of the initial RFF. Simulation and experiment results both demonstrate that our proposed RFF extraction scheme achieves a high identification accuracy. Furthermore, the performance is also robust to the vehicle speed.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation

Recently, there have been increasing calls for computer science curricula to complement existing technical training with topics related to Fairness, Accountability, Transparency, and Ethics. In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation. This paper presents an early use of our approach in a college-level computer science course. Through an in-class activity, we report empirical data for the initial effectiveness of our approach. Our results suggest that the use of the Value Cards toolkit can improve students' understanding of both the technical definitions and trade-offs of performance metrics and apply them in real-world contexts, help them recognize the significance of considering diverse social values in the development of deployment of algorithmic systems, and enable them to communicate, negotiate and synthesize the perspectives of diverse stakeholders. Our study also demonstrates a number of caveats we need to consider when using the different variants of the Value Cards toolkit. Finally, we discuss the challenges as well as future applications of our approach.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

"Public(s)-in-the-Loop": Facilitating Deliberation of Algorithmic Decisions in Contentious Public Policy Domains

This position paper offers a framework to think about how to better involve human influence in algorithmic decision-making of contentious public policy issues. Drawing from insights in communication literature, we introduce a "public(s)-in-the-loop" approach and enumerates three features that are central to this approach: publics as plural political entities, collective decision-making through deliberation, and the construction of publics. It explores how these features might advance our understanding of stakeholder participation in AI design in contentious public policy domains such as recidivism prediction. Finally, it sketches out part of a research agenda for the HCI community to support this work.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification

CT-based bronchial tree analysis plays an important role in the computer-aided diagnosis for respiratory diseases, as it could provide structured information for clinicians. The basis of airway analysis is bronchial tree reconstruction, which consists of bronchus segmentation and classification. However, there remains a challenge for accurate bronchial analysis due to the individual variations and the severe class imbalance. In this paper, we propose a region and structure prior embedded framework named BronchusNet to achieve accurate segmentation and classification of bronchial regions in CT images. For bronchus segmentation, we propose an adaptive hard region-aware UNet that incorporates multi-level prior guidance of hard pixel-wise samples in the general Unet segmentation network to achieve better hierarchical feature learning. For the classification of bronchial branches, we propose a hybrid point-voxel graph learning module to fully exploit bronchial structure priors and to support simultaneous feature interactions across different branches. To facilitate the study of bronchial analysis, we contribute~\textbf{BRSC}: an open-access benchmark of \textbf{BR}onchus imaging analysis with high-quality pixel-wise \textbf{S}egmentation masks and the \textbf{C}lass of bronchial segments. Experimental results on BRSC show that our proposed method not only achieves the state-of-the-art performance for binary segmentation of bronchial region but also exceeds the best existing method on bronchial branches classification by 6.9\%.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Energy Efficient Beamforming Optimization for Integrated Sensing and Communication

This paper investigates the optimization of beamforming design in a system with integrated sensing and communication (ISAC), where the base station (BS) sends signals for simultaneous multiuser communication and radar sensing. We aim at maximizing the energy efficiency (EE) of the multiuser communication while guaranteeing the sensing requirement in terms of individual radar beampattern gains. The problem is a complicated nonconvex fractional program which is challenging to be solved. By appropriately reformulating the problem and then applying the techniques of successive convex approximation (SCA) and semidefinite relaxation (SDR), we propose an iterative algorithm to address this problem. In theory, we prove that the introduced relaxation of the SDR is rigorously tight. Numerical results validate the effectiveness of the proposed algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RIS-Assisted Quasi-Static Broad Coverage for Wideband mmWave Massive MIMO Systems

Reconfigurable intelligent surfaces (RISs) can establish favorable wireless environments to combat the severe attenuation and blockages in millimeter-wave (mmWave) bands. However, to achieve the optimal enhancement of performance, the instantaneous channel state information (CSI) needs to be estimated at the cost of a large overhead that scales with the number of RIS elements and the number of users. In this paper, we design a quasi-static broad coverage at the RIS with the reduced overhead based on the statistical CSI. We propose a design framework to synthesize the power pattern reflected by the RIS that meets the customized requirements of broad coverage. For the communication of broadcast channels, we generalize the broad coverage of the single transmit stream to the scenario of multiple streams. Moreover, we employ the quasi-static broad coverage for a multiuser orthogonal frequency division multiplexing access (OFDMA) system, and derive the analytical expression of the downlink rate, which is proved to increase logarithmically with the power gain reflected by the RIS. By taking into account the overhead of channel estimation, the proposed quasi-static broad coverage even outperforms the design method that optimizes the RIS phases using the instantaneous CSI. Numerical simulations are conducted to verify these observations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Hadron-Quark Crossover in Neutron Star within Gaussian Process Regression Method

The equations of state of the neutron star at the hadron-quark crossover region are interpolated with the Gaussian process regression (GPR) method, which can reduce the randomness of present interpolation schemes. The relativistic mean-field (RMF) model and Nambu-Jona-Lasinio (NJL) model are employed to describe the hadronic phase and quark phase, respectively. In the RMF model, the coupling term between $ω$ and $ρ$ mesons is considered to control the density-dependent behaviors of symmetry energy, i.e. the slope of symmetry energy, $L$. Furthermore, the vector interaction between quarks is included in the NJL model to obtain the additional repulsive contributions. Their coupling strengths and the crossover windows are discussed in the present framework under the constraints on the neutron star from gravitational wave detections, massive neutron star measurements, mass-radius simultaneous observation of NICER collaboration, and the neutron skin thickness of $^{208}$Pb from PREX-II. It is found that the slope of symmetry energy, $L$ should be around $50-90$ MeV and the crossover window is $(0.3,~0.6)~\rm fm^{-3}$ with these observables. Furthermore, the uncertainties of neutron star masses and radii in the hadron-quark crossover regions are also predicted by the GPR method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Hadron-quark mixed phase in the quark-meson coupling model

We explore the possibility of a structured hadron-quark mixed phase forming in the interior of neutron stars. The quark-meson coupling (QMC) model, which explicitly incorporates the internal quark structure of the nucleon, is employed to describe the hadronic phase, while the quark phase is described by the same bag model as the one used in the QMC framework, so as to keep consistency between the two coexisting phases. We analyze the effect of the appearance of hadron-quark pasta phases on the neutron-star properties. We also discuss the influence of nuclear symmetry energy and the bag constant B in quark matter on the deconfinement phase transition. For the treatment of the hadron-quark mixed phase, we use the energy minimization method and compare it with the Gibbs construction. The finite-size effects like surface and Coulomb energies are taken into account in the energy minimization method; they play crucial roles in determining the pasta configuration during the hadron-quark phase transition. It is found that the finite-size effects can significantly reduce the region of the mixed phase relative to that of the Gibbs construction. Using a consistent value of B in the QMC model and quark matter, we find that hadron-quark pasta phases are formed in the interior of massive stars, but no pure quark matter can exist.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Hadron-quark Pasta Phase in Massive Neutron Stars

The structured hadron-quark mixed phase, known as the pasta phase, is expected to appear in the core of massive neutron stars. Motivated by the recent advances in astrophysical observations, we explore the possibility of the appearance of quarks inside neutron stars and check its compatibility with current constraints. We investigate the properties of the hadron-quark pasta phases and their influences on the equation of state (EOS) for neutron stars. In this work, we extend the energy minimization (EM) method to describe the hadron-quark pasta phase, where the surface and Coulomb contributions are included in the minimization procedure. By allowing different electron densities in the hadronic and quark matter phases, the total electron chemical potential with the electric potential remains constant, and local ? equilibrium is achieved inside the Wigner-Seitz cell. The mixed phase described in the EM method shows the features lying between the Gibbs and Maxwell constructions, which is helpful for understanding the transition from the Gibbs construction (GC) to the Maxwell construction (MC) with increasing surface tension. We employ the relativistic mean-field model to describe the hadronic matter, while the quark matter is described by the MIT bag model with vector interactions. It is found that the vector interactions among quarks can significantly stiffen the EOS at high densities and help enhance the maximum mass of neutron stars. Other parameters like the bag constant can also affect the deconfinement phase transition in neutron stars. Our results show that hadron-quark pasta phases may appear in the core of massive neutron stars that can be compatible with current observational constraints.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

NEMR: Network Embedding on Metric of Relation

Network embedding maps the nodes of a given network into a low-dimensional space such that the semantic similarities among the nodes can be effectively inferred. Most existing approaches use inner-product of node embedding to measure the similarity between nodes leading to the fact that they lack the capacity to capture complex relationships among nodes. Besides, they take the path in the network just as structural auxiliary information when inferring node embeddings, while paths in the network are formed with rich user informations which are semantically relevant and cannot be ignored. In this paper, We propose a novel method called Network Embedding on the Metric of Relation, abbreviated as NEMR, which can learn the embeddings of nodes in a relational metric space efficiently. First, our NEMR models the relationships among nodes in a metric space with deep learning methods including variational inference that maps the relationship of nodes to a gaussian distribution so as to capture the uncertainties. Secondly, our NEMR considers not only the equivalence of multiple-paths but also the natural order of a single-path when inferring embeddings of nodes, which makes NEMR can capture the multiple relationships among nodes since multiple paths contain rich user information, e.g., age, hobby and profession. Experimental results on several public datasets show that the NEMR outperforms the state-of-the-art methods on relevant inference tasks including link prediction and node classification.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The $ΞN$ interaction constrained by recent $Ξ^-$ hypernuclei experiments

The $ΞN$ interaction is investigated in the quark mean-field (QMF) model based on recent observables of the $Ξ^-+^{14}\rm{N}$ ($_{Ξ^-}^{15}\rm{C}$) system. The experimental data about the binding energy of $1p$-state $Ξ^-$ hyperon in $_{Ξ^-}^{15}\rm{C}$ hypernuclei at KISO, IBUKI, E07-T011, E176-14-03-35 events are conflated as $B_{Ξ^-}(1p)=1.14\pm0.11$ MeV. With this constraint, the coupling strengths between the vector meson and $Ξ$ hyperon are fixed in three QMF parameter sets. Meanwhile, the $Ξ^-$ binding energy of $1s$ state in $_{Ξ^-}^{15}\rm{C}$ is predicted as $B_{Ξ^-}(1s)=5.66\pm0.38$ MeV with the same interactions, which are completely consistent with the data from the KINKA and IRRAWADDY events. Finally, the single $ΞN$ potential is calculated in the symmetric nuclear matter in the framework of QMF models. It is $U_{ΞN}=-11.96\pm 0.85$ MeV at nuclear saturation density, which will contribute to the study on the strangeness degree of freedom in compact star.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Differentially Private k-Means Clustering with Guaranteed Convergence

Iterative clustering algorithms help us to learn the insights behind the data. Unfortunately, this may allow adversaries to infer the privacy of individuals with some background knowledge. In the worst case, the adversaries know the centroids of an arbitrary iteration and the information of n-1 out of n items. To protect individual privacy against such an inference attack, preserving differential privacy (DP) for the iterative clustering algorithms has been extensively studied in the interactive settings. However, existing interactive differentially private clustering algorithms suffer from a non-convergence problem, i.e., these algorithms may not terminate without a predefined number of iterations. This problem severely impacts the clustering quality and the efficiency of a differentially private algorithm. To resolve this problem, in this paper, we propose a novel differentially private clustering framework in the interactive settings which controls the orientation of the movement of the centroids over the iterations to ensure the convergence by injecting DP noise in a selected area. We prove that, in the expected case, algorithm under our framework converges in at most twice the iterations of Lloyd's algorithm. We perform experimental evaluations on real-world datasets to show that our algorithm outperforms the state-of-the-art of the interactive differentially private clustering algorithms with guaranteed convergence and better clustering quality to meet the same DP requirement.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Effects of symmetry energy on equation of state for simulations of core-collapse supernovae and neutron-star mergers

We construct a new equation of state (EOS) for numerical simulations of core-collapse supernovae and neutron-star mergers based on an extended relativistic mean-field model with a small symmetry energy slope $L$, which is compatible with both experimental nuclear data and recent observations of neutron stars. The new EOS table (EOS4) based on the extended TM1 (TM1e) model with $L=40$ MeV is designed in the same tabular form and compared with the commonly used Shen EOS (EOS2) based on the original TM1 model with $L=110.8$ MeV. This is convenient and useful for performing numerical simulations and examining the influences of symmetry energy and its density dependence on astrophysical phenomena. In comparison with the TM1 model used in EOS2, the TM1e model provides a similar maximum neutron-star mass but smaller radius and tidal deformability for a $1.4 M_\odot$ neutron star, which is more consistent with current constraints. By comparing the phase diagram and thermodynamic quantities between EOS4 and EOS2, it is found that the TM1e model predicts relatively larger region of nonuniform matter and softer EOS for neutron-rich matter. Significant differences between EOS4 and EOS2 are observed in the case with low proton fraction, while the properties of symmetric matter remain unchanged.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multi-cell Edge Coverage Enhancement Using Mobile UAV-Relay

Unmanned aerial vehicle (UAV)-assisted communication is a promising technology in future wireless communication networks. UAVs can not only help offload data traffic from ground base stations (GBSs), but also improve the quality of service of cell-edge users (CEUs). In this paper, we consider the enhancement of cell-edge communications through a mobile relay, i.e., UAV, in multi-cell networks. During each transmission period, GBSs first send data to the UAV, and then the UAV forwards its received data to CEUs according to a certain association strategy. In order to maximize the sum rate of all CEUs, we jointly optimize the UAV mobility management, including trajectory, velocity, and acceleration, and association strategy of CEUs to the UAV, subject to minimum rate requirements of CEUs, mobility constraints of the UAV and causal buffer constraints in practice. To address the mixed-integer nonconvex problem, we transform it into two convex subproblems by applying tight bounds and relaxations. An iterative algorithm was proposed to solve the two subproblems in an alternating manner. Numerical results show that the proposed algorithm achieves higher rates of CEUs as compared with existing benchmark schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Nuclear pasta in hot and dense matter and its influence on the equation of state for astrophysical simulations

We explore the properties of nuclear pasta appearing in supernova matter, i.e., matter at finite temperature with a fixed proton fraction. The pasta phases with a series of geometric shapes are studied using the compressible liquid-drop (CLD) model, where nuclear matter separates into a dense liquid phase of nucleons and a dilute gas phase of nucleons and $α$ particles. The equilibrium conditions for two coexisting phases are derived by minimization of the total free energy including the surface and Coulomb contributions, which are clearly different from the Gibbs conditions for phase equilibrium due to the finite-size effects. Compared to the results considering only spherical nuclei, the inclusion of pasta phases can delay the transition to uniform matter and enlarge the region of nonuniform matter in the phase diagram. The thermodynamic quantities obtained in the present calculation with the CLD model are consistent with those in the realistic equation of state table for astrophysical simulations using the Thomas--Fermi approximation. It is found that the density ranges of various pasta shapes depend on both the temperature $T$ and the proton fraction $Y_p$. Furthermore, the nuclear symmetry energy and its density dependence may play crucial roles in determining the properties of pasta phases. Our results suggest that the pasta phase diagram is most sensitively dependent on the symmetry energy slope $L$ especially in the low-$Y_p$ and high-$T$ region.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On Uplink Performance of Multiuser Massive MIMO Relay Network With Limited RF Chains

This paper considers a multiuser massive multiple-input multiple-output uplink with the help of an analog amplify-and-forward relay. The base station equips a large array of $N_d$ antennas but is supported by a far smaller number of radio-frequency chains. By first deriving new results for a cascaded phase-aligned two-hop channel, we obtain a tight bound for the ergodic rate in closed form for both perfect and quantized channel phase information. The rate is characterized as a function of a scaled equivalent signal-to-noise ratio of the two-hop channel. It implies that the source and relay powers can be respectively scaled down as $1/N_d^a$ and $1/N_d^{1-a}~ (0\!\leq\!a\!\leq\!1)$ for an asymptotically unchanged sum rate. Then for the rate maximization, the problem of power allocation is optimized with closed-form solutions. Simulation results verified the observations of our derived results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Properties of neutron star described by a relativistic $ab~ initio$ model

Properties of neutron star are investigated by an available relativistic $ab~ initio$ method, i.e., the relativistic Brueckner-Hartree-Fock (RBHF) model, with the latest high-precision relativistic charge-dependent potentials, pvCD-Bonn A, B, C. The neutron star matter is solved within the beta equilibrium and charge neutrality conditions in the framework of RBHF model. Comparing to the conventional treatment, where the chemical potential of lepton was approximately represented by the symmetry energy of nuclear matter, the equation of state (EOS) of neutron star matter in the present self-consistent calculation with pvCD-Bonn B has striking difference above the baryon number density $n_b=0.55$ fm$^{-3}$. However, these differences influence the global properties of neutron star only about $1\%\sim2\%$. Then, three two-body potentials pvCD-Bonn A, B, C, with different tensor components, are systematically applied in RBHF model to calculate the properties of neutron star. It is found that the maximum masses of neutron star are around $2.21\sim2.30M_\odot$ and the corresponding radii are $R =11.18\sim11.72$ km. The radii of $1.4M_\odot$ neutron star are predicated as $R_{1.4} = 12.34\sim12.91$ km and their dimensionless tidal deformabilities are $Λ_{1.4} = 485\sim 626$. Furthermore, the direct URCA process in neutron star cooling will happen from $n_b=0.414\sim0.530$ fm$^{-3}$ with the proton fractions, $Y_p=0.136\sim0.138$. All of the results obtained from RBHF model only with two-body pvCD-Bonn potentials completely satisfy various constraints from recent astronomical observations of massive neutron stars, gravitational wave detection (GW 170817), and mass-radius simultaneous measurement (NICER).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Protect Edge Privacy in Path Publishing with Differential Privacy

Paths in a given network are a generalised form of time-serial chains in many real-world applications, such as trajectories and Internet flows. Differentially private trajectory publishing concerns publishing path information that is usable to the genuine users yet secure against adversaries to reconstruct the path with maximum background knowledge. The exiting studies all assume this knowledge to be all but one vertex on the path. To prevent the adversaries recovering the missing information, they publish a perturbed path where each vertex is sampled from a pre-defined set with differential privacy (DP) to replace the corresponding vertex in the original path. In this paper, we relax this assumption to be all but one edge on the path, and hence consider the scenario of more powerful adversaries with the maximum background knowledge of the entire network topology and the path (including all the vertices) except one (arbitrary) missing edge. Under such an assumption, the perturbed path produced by the existing work is vulnerable, because the adversary can reconstruct the missing edge from the existence of an edge in the perturbed path. To address this vulnerability and effectively protect edge privacy, instead of publishing a perturbed path, we propose a novel scheme of graph-based path publishing to protect the original path by embedding the path in a graph that contains fake edges and replicated vertices applying the differential privacy technique, such that only the legitimate users who have the full knowledge of the network topology are able to recover the exact vertices and edges of the original path with high probability. We theoretically analyse the performance of our algorithm in differential privacy, utility, and execution efficiency. We also conduct extensive experimental evaluations on a high-performance cluster system to validate our analytical results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Single $Λ_c^+$ hypernuclei within quark mean-field model

The quark mean-field (QMF) model is applied to study the single $Λ^+_c$ hypernuclei. The charm baryon, $Λ^+_c$, is constructed by three constituent quarks, $u, ~d$, and $c$, confined by central harmonic oscillator potentials. The confinement potential strength of charm quark is determined by fitting the experimental masses of charm baryons, $Λ^+_c,~Σ^+_c$, and $Ξ^{++}_{cc}$. The effects of pions and gluons are also considered to describe the baryons at the quark level. The baryons in $Λ^+_c$ hypernuclei interact with each other through exchanging the $σ,~ω$, and $ρ$ mesons between the quarks confined in different baryons. The $Λ^+_c N$ potential in the QMF model is strongly dependent on the coupling constant between $ω$ meson and $Λ^+_c$, $g_{ωΛ^+_c}$. When the conventional quark counting rule is used, i. e., $g_{ωΛ^+_c}=2/3g_{ωN}$, the massive $Λ^+_c$ hypernucleus can exist, whose single $Λ^+_c$ binding energy is smaller with the mass number increasing due to the strong Coulomb repulsion between $Λ^+_c$ and protons. When $g_{ωΛ^+_c}$ is fixed by the latest lattice $Λ^+_c N$ potential, the $Λ^+_c$ hypernuclei only can exist up to $A\sim 50$.

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preprint2020arXiv

Systematic study on the quark-hadron mixed phase in compact stars

We investigate systematically the quark-hadron mixed phase in dense stellar matter, and its influence on compact star structures. The properties of quark matter and hadronic matter are fixed based on various model predictions. Beside adopting constant values, the surface tension $Σ$ for the quark-hadron interface is estimated with the multiple reflection expansion method and equivparticle model. To fix the structures of quark-hadron pasta phases, a continuous dimensionality of the structure is adopted as proposed by Ravenhall, Pethick, and Wilson. The corresponding properties of hybrid stars are then obtained and confronted with pulsar observations. It is found that the correlation between radius and tidal deformability in traditional neutron stars preserves in hybrid stars. For those permitted by pulsar observations, in almost all cases the quark phase persists inside the most massive compact stars. The quark-hadron interface plays an important role on hybrid star structures once quark matter emerges. The surface tension $Σ$ estimated with various methods increases with density, which predicts stiffer EOSs for the quark-hadron mixed phase and increases the maximum mass of hybrid stars. The EOSs of hybrid star matter are well constrained at densities $n\lesssim 0.8$ fm${}^{-3}$, while larger uncertainty is expected at higher densities.

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preprint2020arXiv

The one-pion-exchange potential with contact terms from lattice QCD simulations

The pion-mass-dependent nucleon-nucleon ($NN$) potentials in term of one-pion exchange and contact terms are obtained from the latest lattice QCD simulations of two-nucleon system, which employ the forms of leading order (LO) $NN$ potential from the chiral effective field theory and thus are named as the LO chiral potential in this work. We extract the coefficients of contact terms and cut-off momenta in these potentials, for the first time, by fitting the phase shifts of $^1S_0$ and $^3S_1$ channels generated by the results of HALQCD collaboration with various pion masses from $468.6$ MeV to $1170.9$ MeV. The low-energy constants in the $^1S_0$ and $^3S_1$ channels become weaker and approach each other for larger pion masses. These LO chiral potentials are applied to symmetric nuclear and pure neutron matter within the Brueckner-Hartree-Fock method. At this moment, however, we do not have yet the information of the $P$-wave $NN$ interaction to be provided by the lattice QCD simulations for full description of nuclear matter. Our results will enhance the development of nuclear structure and nuclear matter by controlling the contribution of the pionic effect and illuminate the role of chiral symmetry of the strong interaction in complex system.

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preprint2019arXiv

Influence of density dependence of symmetry energy in hot and dense matter for supernova simulations

We study the influence of density-dependent symmetry energy at high densities in simulations of core-collapse supernovae, black hole formation and proto-neutron star cooling by extending the relativistic mean field (RMF) theory used for the Shen EOS table. We adopt the extended RMF theory to examine the density dependence of the symmetry energy with a small value of the slope parameter $L$, while the original properties of the symmetric nuclear matter are unchanged. In order to assess matter effects at high densities, we perform numerical simulations of gravitational collapse of massive stars adopting the EOS table at high densities beyond $10^{14}$ g/cm$^3$ with the small $L$ value, which is in accord with the experimental and observational constraints, and compare them with the results obtained by using the Shen EOS. Numerical results for 11.2M$_{\odot}$ and 15M$_{\odot}$ stars exhibit minor effects around the core bounce and in the following evolution for 200 ms. Numerical results for 40M$_{\odot}$ and 50M$_{\odot}$ stars reveal a shorter duration toward the black hole formation with a smaller maximum mass for the small $L$ case. Numerical simulations of proto-neutron star cooling over 10 s through neutrino emissions demonstrate increasing effects of the symmetry energy at high densities. Neutrino cooling drastically proceeds in a relatively long timescale with high luminosities and average energies with the small symmetry energy. Evolution toward the cold neutron star is affected because of the different behavior of neutron-rich matter while supernova dynamics around core bounce remains similar in less neutron-rich environments.

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preprint2019arXiv

Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications

We investigate transmission optimization for intelligent reflecting surface (IRS) assisted multi-antenna systems from the physical-layer security perspective. The design goal is to maximize the system secrecy rate subject to the source transmit power constraint and the unit modulus constraints imposed on phase shifts at the IRS. To solve this complicated non-convex problem, we develop an efficient alternating algorithm where the solutions to the transmit covariance of the source and the phase shift matrix of the IRS are achieved in closed form and semi-closed forms, respectively. The convergence of the proposed algorithm is guaranteed theoretically. Simulations results validate the performance advantage of the proposed optimized design.

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preprint2019arXiv

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.

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preprint2016arXiv

Diversified Top-k Similarity Search in Large Attributed Networks

Given a large network and a query node, finding its top-k similar nodes is a primitive operation in many graph-based applications. Recently enhancing search results with diversification have received much attention. In this paper, we explore an novel problem of searching for top-k diversified similar nodes in attributed networks, with the motivation that modeling diversification in an attributed network should consider both the emergence of network links and the attribute features of nodes such as user profile information. We formulate this practical problem as two optimization problems: the Attributed Coverage Diversification (ACD) problem and the r-Dissimilar Attributed Coverage Diversification (r-DACD) problem. Based on the submodularity and the monotonicity of ACD, we propose an efficient greedy algorithm achieving a tight approximation guarantee of 1-1/e. Unlike the expension based methods only considering nodes' neighborhood, ACD generalize the definition of diversification to nodes' own features. To capture diversification in topological structure of networks, the r-DACD problem introduce a dissimilarity constraint. We refer to this problem as the Dissimilarity Constrained Non-monotone Submodular Maximization (DCNSM) problem. We prove that there is no constant-factor approximation for DCNSM, and also present an efficient greedy algorithms achieving $1/ρ$ approximation, where $ρ\leΔ$, $Δ$ is the maximum degree of its dissimilarity based graph. To the best of our knowledge, it is the first approximation algorithm for the Submodular Maximization problem with a distance constraint. The experimental results on real-world attributed network datasets demonstrate the effectiveness of our methods, and confirm that adding dissimilarity constraint can significantly enhance the performance of diversification.

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preprint2016arXiv

The phase transition in hot $Λ$ hypernuclei within relativistic Thomas-Fermi approximation

A self-consistent description for hot $Λ$ hypernuclei in hypothetical big boxes is developed within the relativistic Thomas-Fermi approximation in order to investigate directly the liquid-gas phase coexistence in strangeness finite nuclear systems. We use the relativistic mean-field model for nuclear interactions. The temperature dependence of $Λ$ hyperon density, $Λ$ hyperon radius, excitation energies, specific heat, and the binding energies of $Λ$ hypernuclei from $^{16}_Λ$O to $^{208}_Λ$Pb in phase transition region are calculated by using the subtraction procedure in order to separate the hypernucleus from the surrounding baryon gas. The $Λ$ central density is very sensitive to the temperature. The radii of $Λ$ hyperon at high temperature become very large. In the relativistic Thomas-Fermi approximation with the subtraction procedure, the properties of hypernuclei are independent of the size of the box in which the calculation is performed. The level density parameters of hypernuclei in the present work are confirmed to be almost constant at low temperature. It is also found that the single-$Λ$ binding energies of $Λ$ hypernuclei are largely reduced with increasing temperature.

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preprint2016arXiv

The properties of nuclear matter with lattice $NN$ potential in relativistic Brueckner-Hartree-Fock theory

We study the properties of nuclear matter with lattice nucleon-nucleon ($NN$) potential in the relativistic Brueckner-Hartree-Fock (RBHF) theory. To use this potential in such a microscopic many-body theory, we firstly have to construct a one-boson-exchange potential (OBEP) based on the latest lattice $NN$ potential. Three mesons, pion, $σ$ meson, and $ω$ meson, are considered. Their coupling constants and cut-off momenta are determined by fitting the on-shell behaviors and phase shifts of the lattice force, respectively. Therefore, we obtain two parameter sets of the OBEP potential (named as LOBEP1 and LOBEP2) with these two fitting ways. We calculate the properties of symmetric and pure neutron matter with LOBEP1 and LOBEP2. In non-relativistic Brueckner-Hartree-Fock case, the binding energies of symmetric nuclear matter are around $-3$ and $-5$ MeV at saturation densities, while it becomes $-8$ and $-12$ MeV in relativistic framework with $^1S_0,~^3S_1,$ and $^3D_1$ channels using our two parameter sets. For the pure neutron matter, the equations of state in non-relativistic and relativistic cases are very similar due to only consideration $^1S_0$ channel with isospin $T=1$ case.

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preprint2016arXiv

The quark mean field model with pion and gluon corrections

The properties of nuclear matter and finite nuclei are studied within the quark mean field (QMF) model by taking the effects of pion and gluon into account at the quark level. The nucleon is described as the combination of three constituent quarks confined by a harmonic oscillator potential. To satisfy the spirit of QCD theory, the contributions of pion and gluon on the nucleon structure are treated in second-order perturbation theory. For the nuclear many-body system, nucleons interact with each other by exchanging mesons between quarks. With different constituent quark mass, $m_q$, we determine three parameter sets about the coupling constants between mesons and quarks, named as QMF-NK1, QMF-NK2, and QMF-NK3 by fitting the ground-state properties of several closed-shell nuclei. It is found that all of the three parameter sets can give satisfactory description on properties of nuclear matter and finite nuclei, meanwhile they can also predict the larger neutron star mass around $2.3M_\odot$ without the hyperon degrees of freedom.

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preprint2015arXiv

A Complete Equation of State for Astrophysical Simulations

We construct a complete equation of state (EOS) covering a wide range of temperature, proton fraction, and baryon density for the use of astrophysical simulations. We employ the relativistic mean-field (RMF) theory to describe nuclear interactions and adopt the Thomas-Fermi approximation to describe the nonuniform nuclear matter. The uniform matter and nonuniform matter are studied consistently using the same RMF theory.

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preprint2015arXiv

A Faster Algorithm to Build New Users Similarity List in Neighbourhood-based Collaborative Filtering

Neighbourhood-based Collaborative Filtering (CF) has been applied in the industry for several decades, because of the easy implementation and high recommendation accuracy. As the core of neighbourhood-based CF, the task of dynamically maintaining users' similarity list is challenged by cold-start problem and scalability problem. Recently, several methods are presented on solving the two problems. However, these methods applied an $O(n^2)$ algorithm to compute the similarity list in a special case, where the new users, with enough recommendation data, have the same rating list. To address the problem of large computational cost caused by the special case, we design a faster ($O(\frac{1}{125}n^2)$) algorithm, TwinSearch Algorithm, to avoid computing and sorting the similarity list for the new users repeatedly to save the computational resources. Both theoretical and experimental results show that the TwinSearch Algorithm achieves better running time than the traditional method.

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preprint2015arXiv

A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm

The neighbourhood-based Collaborative Filtering is a widely used method in recommender systems. However, the risks of revealing customers' privacy during the process of filtering have attracted noticeable public concern recently. Specifically, $k$NN attack discloses the target user's sensitive information by creating $k$ fake nearest neighbours by non-sensitive information. Among the current solutions against $k$NN attack, the probabilistic methods showed a powerful privacy preserving effect. However, the existing probabilistic methods neither guarantee enough prediction accuracy due to the global randomness, nor provide assured security enforcement against $k$NN attack. To overcome the problems of current probabilistic methods, we propose a novel approach, Partitioned Probabilistic Neighbour Selection, to ensure a required security guarantee while achieving the optimal prediction accuracy against $k$NN attack. In this paper, we define the sum of $k$ neighbours' similarity as the accuracy metric $α$, the number of user partitions, across which we select the $k$ neighbours, as the security metric $β$. Differing from the present methods that globally selected neighbours, our method selects neighbours from each group with exponential differential privacy to decrease the magnitude of noise. Theoretical and experimental analysis show that to achieve the same security guarantee against $k$NN attack, our approach ensures the optimal prediction accuracy.

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preprint2015arXiv

An Accuracy-Assured Privacy-Preserving Recommender System for Internet Commerce

Recommender systems, tool for predicting users' potential preferences by computing history data and users' interests, show an increasing importance in various Internet applications such as online shopping. As a well-known recommendation method, neighbourhood-based collaborative filtering has attracted considerable attention recently. The risk of revealing users' private information during the process of filtering has attracted noticeable research interests. Among the current solutions, the probabilistic techniques have shown a powerful privacy preserving effect. When facing $k$ Nearest Neighbour attack, all the existing methods provide no data utility guarantee, for the introduction of global randomness. In this paper, to overcome the problem of recommendation accuracy loss, we propose a novel approach, Partitioned Probabilistic Neighbour Selection, to ensure a required prediction accuracy while maintaining high security against $k$NN attack. We define the sum of $k$ neighbours' similarity as the accuracy metric alpha, the number of user partitions, across which we select the $k$ neighbours, as the security metric beta. We generalise the $k$ Nearest Neighbour attack to beta k Nearest Neighbours attack. Differing from the existing approach that selects neighbours across the entire candidate list randomly, our method selects neighbours from each exclusive partition of size $k$ with a decreasing probability. Theoretical and experimental analysis show that to provide an accuracy-assured recommendation, our Partitioned Probabilistic Neighbour Selection method yields a better trade-off between the recommendation accuracy and system security.

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preprint2015arXiv

Efficient Approximation Algorithms for Computing \emph{k} Disjoint Restricted Shortest Paths

Network applications, such as multimedia streaming and video conferencing, impose growing requirements over Quality of Service (QoS), including bandwidth, delay, jitter, etc. Meanwhile, networks are expected to be load-balanced, energy-efficient, and resilient to some degree of failures. It is observed that the above requirements could be better met with multiple disjoint QoS paths than a single one. Let $G=(V,\, E)$ be a digraph with nonnegative integral cost and delay on every edge, $s,\, t\in V$ be two specified vertices, and $D\in\mathbb{Z}_{0}^{+}$ be a delay bound (or some other constraint), the \emph{$k$ Disjoint Restricted Shortest Path} ($k$\emph{RSP})\emph{ Problem} is computing $k$ disjoint paths between $s$ and $t$ with total cost minimized and total delay bounded by $D$. Few efficient algorithms have been developed because of the hardness of the problem. In this paper, we propose efficient algorithms with provable performance guarantees for the $k$RSP problem. We first present a pseudo-polynomial-time approximation algorithm with a bifactor approximation ratio of $(1,\,2)$, then improve the algorithm to polynomial time with a bifactor ratio of $(1+ε,\,2+ε)$ for any fixed $ε>0$, which is better than the current best approximation ratio $(O(1+γ),\, O(1+\frac{1}γ)\})$ for any fixed $γ>0$ \cite{orda2004efficient}. To the best of our knowledge, this is the first constant-factor algorithm that almost strictly obeys the constraint for the $k$RSP problem.

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preprint2015arXiv

Impacts of the Nuclear Symmetry Energy on Neutron Star Crusts

Using the relativistic mean-field theory, we adopt two different methods, namely, the coexisting phase method and the self-consistent Thomas-Fermi approximation, to study the impacts of the nuclear symmetry energy on properties of neutron star crusts within a wide range of densities. It is found that the nuclear symmetry energy and its density slope play an important role in determining the pasta phases and the crust-core transition.

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preprint2015arXiv

Self-consistent Thomas-Fermi Approximation for Equation of State at Subnuclear Densities

The self-consistent Thomas-Fermi approximation is an essential method for studying the non-uniform nuclear matter with relativistic mean-field theory. In this method, the nucleon distribution in the Wigner-Seitz cell is obtained self-consistently. We make a detailed comparison between the present results and previous calculations in the Thomas-Fermi approximation with a parameterized nucleon distribution that has been adopted in the widely used Shen equation of state.

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preprint2015arXiv

Ultrasensitive and broadband MoS2 photodetector driven by ferroelectrics

Photodetectors based on two dimensional materials have attracted growing interest. However, the sensitivity is still unsatisfactory even under high gate voltage. Here we demonstrate a MoS2 photodetector with a poly(vinylidene fluoride-trifluoroethylene) ferroelectric layer in place of the oxide layer in a traditional field effect transistor. The dark current of the photodetector is strongly suppressed by ferroelectric polarization. A high detectivity 2.21012 Jones) and photoresponsitivity (2570 A W) detector has been achieved under ZERO gate bias at a wavelength of 635 nm. Most strikingly, the band gap of few-layer MoS2 can be tuned by the ultra-high electrostatic field from the ferroelectric polarization. With this characteristic, photoresponse wavelengths of the photodetector are extended into the near infrared (0.85-1.55m). A ferroelectrics optoelectronics hybrid structure is an effective way to achieve high performance 2D electronic optoelectronic devices.

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preprint2013arXiv

Constrained Fault-Tolerant Resource Allocation

In the Constrained Fault-Tolerant Resource Allocation (FTRA) problem, we are given a set of sites containing facilities as resources, and a set of clients accessing these resources. Specifically, each site i is allowed to open at most R_i facilities with cost f_i for each opened facility. Each client j requires an allocation of r_j open facilities and connecting j to any facility at site i incurs a connection cost c_ij. The goal is to minimize the total cost of this resource allocation scenario. FTRA generalizes the Unconstrained Fault-Tolerant Resource Allocation (FTRA_{\infty}) [18] and the classical Fault-Tolerant Facility Location (FTFL) [13] problems: for every site i, FTRA_{\infty} does not have the constraint R_i, whereas FTFL sets R_i=1. These problems are said to be uniform if all r_j's are the same, and general otherwise. For the general metric FTRA, we first give an LP-rounding algorithm achieving the approximation ratio of 4. Then we show the problem reduces to FTFL, implying the ratio of 1.7245 from [3]. For the uniform FTRA, we provide a 1.52-approximation primal-dual algorithm in O(n^4) time, where n is the total number of sites and clients. We also consider the Constrained Fault-Tolerant k-Resource Allocation (k-FTRA) problem where additionally the total number of facilities can be opened across all sites is bounded by k. For the uniform k-FTRA, we give the first constant-factor approximation algorithm with a factor of 4. Note that the above results carry over to FTRA_{\infty} and k-FTRA_{\infty}.

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preprint2012arXiv

Effects of nuclear deformation on the form factor for direct dark matter detection

For direct dark matter detections, to extract useful information about the fundamental interaction from data, it is crucial to properly determine the nuclear form factor. The form factor for spin-independent cross section of collisions between dark matter particle and nucleus is thoroughly studied by many authors. When the analysis was carried out, the nuclei are always supposed to be spherically symmetric. In this work, we investigate the effects of deformation of nuclei from a spherical shape to an elliptical shape on the form factor. Our results indicate that as long as the ellipticity is not too large, such effects cannot cause any substantial effects, especially as the nuclei are randomly orientated in a room temperature circumstance one can completely neglect them.

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preprint2011arXiv

Determining the nuclear form factor for detection of dark matter in the relativistic mean field theory

In this work, we derive the nuclear form factor for the spin-independent collision between the WIMPs and nucleus in terms of the relativistic mean field (RMF) theory. Comparison with the traditional form factors which are commonly used in literature is given and it is found that our results are slightly above that of the 2PF model by 4% to 8%, but deviate from the Helm form factor by 15% to 25% for the whole recoil energy spectrum of 0$\sim $ 100 keV. Moreover, taking Xe and Ge as examples, we show the dependence of the form factor on the recoil energy.

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preprint2011arXiv

Unconstrained and Constrained Fault-Tolerant Resource Allocation

First, we study the Unconstrained Fault-Tolerant Resource Allocation (UFTRA) problem (a.k.a. FTFA problem in \cite{shihongftfa}). In the problem, we are given a set of sites equipped with an unconstrained number of facilities as resources, and a set of clients with set $\mathcal{R}$ as corresponding connection requirements, where every facility belonging to the same site has an identical opening (operating) cost and every client-facility pair has a connection cost. The objective is to allocate facilities from sites to satisfy $\mathcal{R}$ at a minimum total cost. Next, we introduce the Constrained Fault-Tolerant Resource Allocation (CFTRA) problem. It differs from UFTRA in that the number of resources available at each site $i$ is limited by $R_{i}$. Both problems are practical extensions of the classical Fault-Tolerant Facility Location (FTFL) problem \cite{Jain00FTFL}. For instance, their solutions provide optimal resource allocation (w.r.t. enterprises) and leasing (w.r.t. clients) strategies for the contemporary cloud platforms. In this paper, we consider the metric version of the problems. For UFTRA with uniform $\mathcal{R}$, we present a star-greedy algorithm. The algorithm achieves the approximation ratio of 1.5186 after combining with the cost scaling and greedy augmentation techniques similar to \cite{Charikar051.7281.853,Mahdian021.52}, which significantly improves the result of \cite{shihongftfa} using a phase-greedy algorithm. We also study the capacitated extension of UFTRA and give a factor of 2.89. For CFTRA with uniform $\mathcal{R}$, we slightly modify the algorithm to achieve 1.5186-approximation. For a more general version of CFTRA, we show that it is reducible to FTFL using linear programming.

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