Hadronic Rapidity Spectra in Heavy Ion Collisions at SPS and AGS energies in a Quark Combination Model

The quark combination mechanism of hadron production is applied to nucleus-nucleus collisions at the CERN Super Proton Synchrotron (SPS) and BNL Alternating Gradient Synchrotron (AGS). The rapidity spectra of identified hadrons and their spectrum widths are studied. The data of $\pi^{-}$, $K^{\pm}$, $\phi$, $\Lambda$, $\bar{\Lambda}$, $\Xi^{-}$, and $\bar{\Xi}^{+}$ at 80 and 40 AGeV, in particular at 30 and 20 AGeV where the onset of deconfinement is suggested to happen, are consistently described by the quark combination model. However at AGS 11.6 AGeV below the onset the spectra of $\pi^{\pm}$, $K^{\pm}$ and $\Lambda$ can not be simultaneously explained, indicating the disappearance of intrinsic correlation of their production in the constituent quark level. The collision-energy dependence of the rapidity spectrum widths of constituent quarks and the strangeness of the hot and dense quark matter produced in heavy ion collisions are obtained and discussed.Comment: 7 pages, 5 figure

Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design

Federated learning (FL) has achieved great success as a privacy-preserving distributed training paradigm, where many edge devices collaboratively train a machine learning model by sharing the model updates instead of the raw data with a server. However, the heterogeneous computational and communication resources of edge devices give rise to stragglers that significantly decelerate the training process. To mitigate this issue, we propose a novel FL framework named stochastic coded federated learning (SCFL) that leverages coded computing techniques. In SCFL, before the training process starts, each edge device uploads a privacy-preserving coded dataset to the server, which is generated by adding Gaussian noise to the projected local dataset. During training, the server computes gradients on the global coded dataset to compensate for the missing model updates of the straggling devices. We design a gradient aggregation scheme to ensure that the aggregated model update is an unbiased estimate of the desired global update. Moreover, this aggregation scheme enables periodical model averaging to improve the training efficiency. We characterize the tradeoff between the convergence performance and privacy guarantee of SCFL. In particular, a more noisy coded dataset provides stronger privacy protection for edge devices but results in learning performance degradation. We further develop a contract-based incentive mechanism to coordinate such a conflict. The simulation results show that SCFL learns a better model within the given time and achieves a better privacy-performance tradeoff than the baseline methods. In addition, the proposed incentive mechanism grants better training performance than the conventional Stackelberg game approach

Constraint-based automatic symmetry detection

10.1109/ASE.2013.66930622013 28th IEEE/ACM International Conference on Automated Software Engineering, ASE 2013 - Proceedings15-2

Combined 3D-QSAR Modeling and Molecular Docking Studies on Pyrrole-Indolin-2-ones as Aurora A Kinase Inhibitors

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r2cv values of 0.726 and 0.566, and r2 values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed

A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency

Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving collaborative training among different parties. Unlike traditional centralized learning, which requires collecting data from each party, FL allows clients to share privacy-preserving information without exposing private datasets. This approach not only guarantees enhanced privacy protection but also facilitates more efficient and secure collaboration among multiple participants. Therefore, FL has gained considerable attention from researchers, promoting numerous surveys to summarize the related works. However, the majority of these surveys concentrate on methods sharing model parameters during the training process, while overlooking the potential of sharing other forms of local information. In this paper, we present a systematic survey from a new perspective, i.e., what to share in FL, with an emphasis on the model utility, privacy leakage, and communication efficiency. This survey differs from previous ones due to four distinct contributions. First, we present a new taxonomy of FL methods in terms of the sharing methods, which includes three categories of shared information: model sharing, synthetic data sharing, and knowledge sharing. Second, we analyze the vulnerability of different sharing methods to privacy attacks and review the defense mechanisms that provide certain privacy guarantees. Third, we conduct extensive experiments to compare the performance and communication overhead of various sharing methods in FL. Besides, we assess the potential privacy leakage through model inversion and membership inference attacks, while comparing the effectiveness of various defense approaches. Finally, we discuss potential deficiencies in current methods and outline future directions for improvement

Selective malaria antibody screening among eligible blood donors in Jiangsu, China

The risk of transfusion-transmitted malaria is a major concern in many countries. This study investigated the prevalence of malaria antibodies and parasitemia in eligible blood donors in Jiangsu, in Eastern China. Malaria antibodies were detected in 2.13% of the 704 plasma samples studied. We found that the prevalence of malaria antibodies was not significantly correlated with gender, occupation and frequency of donation, but it increased with age. No Plasmodium was observed in red blood cells and no Plasmodium DNA was detected in any of the antibody-positive samples. The prevalence of malaria antibodies was not higher than expected in Eastern China