Proper Matter Collineations of Plane Symmetric Spacetimes

We investigate matter collineations of plane symmetric spacetimes when the energy-momentum tensor is degenerate. There exists three interesting cases where the group of matter collineations is finite-dimensional. The matter collineations in these cases are either four, six or ten in which four are isometries and the rest are proper.Comment: 10 pages, LaTex, accepted for publication in Modern Physics Letters

Statefinder Parameters for Tachyon Dark Energy Model

In this paper we study the statefinder parameters for the tachyon dark energy model. There are two kinds of stable attractor solutions in this model. The statefinder diagrams characterize the properties of the tachyon dark energy model. Our results show that the evolving trajectories of the attractor solutions lie in the total region and pass through the LCDM fixed point, which is different from other dark energy model.Comment: 5 pages, 5 figures, accepted by MPL

Power-law cosmological solution derived from DGP brane with a brane tachyon field

By studying a tachyon field on the DGP brane model, in order to embed the 4D standard Friedmann equation with a brane tachyon field in 5D bulk, the metric of the 5D spacetime is presented. Then, adopting the inverse square potential of tachyon field, we obtain an expanding universe with power-law on the brane and an exact 5D solution.Comment: 8 pages, 1 figure, accepted by IJMP

catena-Poly[[chloridomercury(II)]-μ-1,4-diaza­bicyclo­[2.2.2]octane-κ2 N:N′-[chlorido­mercury(II)]-di-μ-chlorido]

In the title coordination polymer, [Hg2Cl4(C6H12N2)]n, each HgII center within the chain is four-coordinated by one terminal Cl atom, two bridging μ2-Cl atoms, and one N-atom donor from a μ2-1,4-diaza­bicyclo­[2.2.2]octane (μ2-daco) ligand in a distorted tetra­hedral geometry. The daco ligand acts as an end-to-end bridging ligand and bridges adjacent HgII centers, forming a chain running along [001]. Weak C—H⋯Cl hydrogen-bonding inter­actions link the chains into a three-dimensional network. Comparison of the structural differences with previous findings suggests that the space between the two N donors, as well as the skeletal rigidity in N-heterocyclic linear ligands, may play an important role in the construction of such supra­molecular networks

SDT: A Low-cost and Topology-reconfigurable Testbed for Network Research

Network experiments are essential to network-related scientific research (e.g., congestion control, QoS, network topology design, and traffic engineering). However, (re)configuring various topologies on a real testbed is expensive, time-consuming, and error-prone. In this paper, we propose \emph{Software Defined Topology Testbed (SDT)}, a method for constructing a user-defined network topology using a few commodity switches. SDT is low-cost, deployment-friendly, and reconfigurable, which can run multiple sets of experiments under different topologies by simply using different topology configuration files at the controller we designed. We implement a prototype of SDT and conduct numerous experiments. Evaluations show that SDT only introduces at most 2\% extra overhead than full testbeds on multi-hop latency and is far more efficient than software simulators (reducing the evaluation time by up to 2899x). SDT is more cost-effective and scalable than existing Topology Projection (TP) solutions. Further experiments show that SDT can support various network research experiments at a low cost on topics including but not limited to topology design, congestion control, and traffic engineering.Comment: This paper will be published in IEEE CLUSTER 2023. Preview version onl

A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks

Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy. It has gained substantial popularity in recent years, especially since the enforcement of data protection laws and regulations in many countries. To foster the application of FL, a variety of FL frameworks have been proposed, allowing non-experts to easily train ML models. As a result, understanding bugs in FL frameworks is critical for facilitating the development of better FL frameworks and potentially encouraging the development of bug detection, localization and repair tools. Thus, we conduct the first empirical study to comprehensively collect, taxonomize, and characterize bugs in FL frameworks. Specifically, we manually collect and classify 1,119 bugs from all the 676 closed issues and 514 merged pull requests in 17 popular and representative open-source FL frameworks on GitHub. We propose a classification of those bugs into 12 bug symptoms, 12 root causes, and 18 fix patterns. We also study their correlations and distributions on 23 functionalities. We identify nine major findings from our study, discuss their implications and future research directions based on our findings

Discriminative Elastic-Net Regularized Linear Regression

In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zeroone matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of theses methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available datasets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html

Operational research on malaria control and elimination: a review of projects published between 2008 and 2013.

A literature review for operational research on malaria control and elimination was conducted using the term 'malaria' and the definition of operational research (OR). A total of 15 886 articles related to malaria were searched between January 2008 and June 2013. Of these, 582 (3.7%) met the definition of operational research. These OR projects had been carried out in 83 different countries. Most OR studies (77%) were implemented in Africa south of the Sahara. Only 5 (1%) of the OR studies were implemented in countries in the pre-elimination or elimination phase. The vast majority of OR projects (92%) were led by international or local research institutions, while projects led by National Malaria Control Programmes (NMCP) accounted for 7.8%. With regards to the topic under investigation, the largest percentage of papers was related to vector control (25%), followed by epidemiology/transmission (16.5%) and treatment (16.3%). Only 19 (3.8%) of the OR projects were related to malaria surveillance. Strengthening the capacity of NMCPs to conduct operational research and publish its findings, and improving linkages between NMCPs and research institutes may aid progress towards malaria elimination and eventual eradication world-wide