Topology of megagauss magnetic fields and of heat-carrying electrons produced in a high-power laser-solid interaction

The intricate spatial and energy distribution of magnetic fields, self-generated during high power laser irradiation (at Iλ2∼1013-1014W.cm-2.μm2) of a solid target, and of the heat-carrying electron currents, is studied in inertial confinement fusion (ICF) relevant conditions. This is done by comparing proton radiography measurements of the fields to an improved magnetohydrodynamic description that fully takes into account the nonlocality of the heat transport. We show that, in these conditions, magnetic fields are rapidly advected radially along the target surface and compressed over long time scales into the dense parts of the target. As a consequence, the electrons are weakly magnetized in most parts of the plasma flow, and we observe a reemergence of nonlocality which is a crucial effect for a correct description of the energetics of ICF experiments.

Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.

New Results for the MAP Problem in Bayesian Networks

This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian networks, which is the problem of querying the most probable state configuration of some of the network variables given evidence. First, it is demonstrated that the problem remains hard even in networks with very simple topology, such as binary polytrees and simple trees (including the Naive Bayes structure). Such proofs extend previous complexity results for the problem. Inapproximability results are also derived in the case of trees if the number of states per variable is not bounded. Although the problem is shown to be hard and inapproximable even in very simple scenarios, a new exact algorithm is described that is empirically fast in networks of bounded treewidth and bounded number of states per variable. The same algorithm is used as basis of a Fully Polynomial Time Approximation Scheme for MAP under such assumptions. Approximation schemes were generally thought to be impossible for this problem, but we show otherwise for classes of networks that are important in practice. The algorithms are extensively tested using some well-known networks as well as random generated cases to show their effectiveness.

Controlling Surface Topology and Functionality of Electrospun Fibers on the Nanoscale using Amphiphilic Block Copolymers To Direct Mesenchymal Progenitor Cell Adhesion

Surface patterning in three dimensions is of great importance in biomaterials design for controlling cell behavior. A facile one-step functionalization of biodegradable PDLLA fibers using amphiphilic diblock copolymers is demonstrated here to systematically vary the fiber surface composition. The copolymers comprise a hydrophilic poly[oligo(ethylene glycol) methacrylate] (POEGMA), poly[(2-methacryloyloxy)ethyl phosphorylcholine] (PMPC), or poly[2-(dimethylamino)ethyl methacrylate)] (PDMAEMA) block and a hydrophobic poly(l-lactide) (PLA) block. The block copolymer-modified fibers have increased surface hydrophilicity compared to that of PDLLA fibers. Mixtures of PLAPMPC and PLAPOEGMA copolymers are utilized to exploit microphase separation of the incompatible hydrophilic PMPC and POEGMA blocks at the fiber surface. Conjugation of an RGD cell-adhesive peptide to one hydrophilic block (POEGMA) using thiol-ene chemistry produces fibers with domains of cell-adhesive (POEGMA) and cell-inert (PMPC) sites, mimicking the adhesive properties of the extracellular matrix (ECM). Human mesenchymal progenitor cells (hES-MPs) showed much better adhesion to the fibers with surface-adhesive heterogeneity compared to that to fibers with only adhesive or only inert surface chemistries.

Burkholderia cenocepacia and Salmonella enterica ArnT proteins that transfer 4-amino-4-deoxy-L-arabinose to lipopolysaccharide share membrane topology and functional amino acids

We recently demonstrated that incorporation of 4-amino-4-deoxy-l-arabinose (l-Ara4N) to the lipid A moiety of lipopolysaccharide (LPS) is required for transport of LPS to the outer membrane and viability of the Gram-negative bacterium Burkholderia cenocepacia. ArnT is a membrane protein catalyzing the transfer of l-Ara4N to the LPS molecule at the periplasmic face of the inner membrane, but its topology and mechanism of action are not well characterized. Here, we elucidate the topology of ArnT and identify key amino acids that likely contribute to its enzymatic function. PEGylation assays using a cysteineless version of ArnT support a model of 13 transmembrane helices and a large C-terminal region exposed to the periplasm. The same topological configuration is proposed for the Salmonella enterica serovar Typhimurium ArnT. Four highly conserved periplasmic residues in B. cenocepacia ArnT, tyrosine-43, lysine-69, arginine-254 and glutamic acid-493, were required for activity. Tyrosine-43 and lysine-69 span two highly conserved motifs, 42RYA44 and 66YFEKP70, that are found in ArnT homologues from other species. The same residues in S. enterica ArnT are also needed for function. We propose these aromatic and charged amino acids participate in either undecaprenyl phosphate-l-Ara4N substrate recognition or transfer of l-Ara4N to the LPS.

Enabling Robust and Efficient Distributed Computation in Dynamic Peer-to-Peer Networks

Motivated by the need for designing efficient and robust fully-distributed computation in highly dynamic networks such as Peer-to-Peer (P2P) networks, we study distributed protocols for constructing and maintaining dynamic network topologies with good expansion properties. Our goal is to maintain a sparse (bounded degree) expander topology despite heavy {\em churn} (i.e., nodes joining and leaving the network continuously over time). We assume that the churn is controlled by an adversary that has complete knowledge and control of what nodes join and leave and at what time and has unlimited computational power, but is oblivious to the random choices made by the algorithm. Our main contribution is a randomized distributed protocol that guarantees with high probability the maintenance of a {\em constant} degree graph with {\em high expansion} even under {\em continuous high adversarial} churn. Our protocol can tolerate a churn rate of up to $O(n/\poly\log(n))$ per round (where $n$ is the stable network size). Our protocol is efficient, lightweight, and scalable, and it incurs only $O(\poly\log(n))$ overhead for topology maintenance: only polylogarithmic (in $n$) bits needs to be processed and sent by each node per round and any node's computation cost per round is also polylogarithmic. The given protocol is a fundamental ingredient that is needed for the design of efficient fully-distributed algorithms for solving fundamental distributed computing problems such as agreement, leader election, search, and storage in highly dynamic P2P networks and enables fast and scalable algorithms for these problems that can tolerate a large amount of churn.

Fast Byzantine Leader Election in Dynamic Networks

We study the fundamental Byzantine leader election problem in dynamic networks where the topology can change from round to round and nodes can also experience heavy {\em churn} (i.e., nodes can join and leave the network continuously over time). We assume the full information model where the Byzantine nodes have complete knowledge about the entire state of the network at every round (including random choices made by all the nodes), have unbounded computational power and can deviate arbitrarily from the protocol. The churn is controlled by an adversary that has complete knowledge and control over which nodes join and leave and at what times and also may rewire the topology in every round and has unlimited computational power, but is oblivious to the random choices made by the algorithm. Our main contribution is an $O(\log^3 n)$ round algorithm that achieves Byzantine leader election under the presence of up to $O({n}^{1/2 - \epsilon})$ Byzantine nodes (for a small constant $\epsilon > 0$) and a churn of up to \\$O(\sqrt{n}/\poly\log(n))$ nodes per round (where $n$ is the stable network size).The algorithm elects a leader with probability at least $1-n^{-\Omega(1)}$ and guarantees that it is an honest node with probability at least $1-n^{-\Omega(1)}$; assuming the algorithm succeeds, the leader's identity will be known to a $1-o(1)$ fraction of the honest nodes. Our algorithm is fully-distributed, lightweight, and is simple to implement. It is also scalable, as it runs in polylogarithmic (in $n$) time and requires nodes to send and receive messages of only polylogarithmic size per round.To the best of our knowledge, our algorithm is the first scalable solution for Byzantine leader election in a dynamic network with a high rate of churn; our protocol can also be used to solve Byzantine agreement in a straightforward way.We also show how to implement an (almost-everywhere) public coin with constant bias in a dynamic network with Byzantine nodes and provide a mechanism for enabling honest nodes to store information reliably in the network, which might be of independent interest.

DEX: self-healing expanders

Using interviewer random effects to remove selection bias from HIV prevalence estimates

Background: Selection bias in HIV prevalence estimates occurs if non-participation in testing is correlated with HIV status. Longitudinal data suggests that individuals who know or suspect they are HIV positive are less likely to participate in testing in HIV surveys, in which case methods to correct for missing data which are based on imputation and observed characteristics will produce biased results. Methods: The identity of the HIV survey interviewer is typically associated with HIV testing participation, but is unlikely to be correlated with HIV status. Interviewer identity can thus be used as a selection variable allowing estimation of Heckman-type selection models. These models produce asymptotically unbiased HIV prevalence estimates, even when non-participation is correlated with unobserved characteristics, such as knowledge of HIV status. We introduce a new random effects method to these selection models which overcomes non-convergence caused by collinearity, small sample bias, and incorrect inference in existing approaches. Our method is easy to implement in standard statistical software, and allows the construction of bootstrapped standard errors which adjust for the fact that the relationship between testing and HIV status is uncertain and needs to be estimated. Results: Using nationally representative data from the Demographic and Health Surveys, we illustrate our approach with new point estimates and confidence intervals (CI) for HIV prevalence among men in Ghana (2003) and Zambia (2007). In Ghana, we find little evidence of selection bias as our selection model gives an HIV prevalence estimate of 1.4% (95% CI 1.2% – 1.6%), compared to 1.6% among those with a valid HIV test. In Zambia, our selection model gives an HIV prevalence estimate of 16.3% (95% CI 11.0% - 18.4%), compared to 12.1% among those with a valid HIV test. Therefore, those who decline to test in Zambia are found to be more likely to be HIV positive. Conclusions: Our approach corrects for selection bias in HIV prevalence estimates, is possible to implement even when HIV prevalence or non-participation is very high or very low, and provides a practical solution to account for both sampling and parameter uncertainty in the estimation of confidence intervals. The wide confidence intervals estimated in an example with high HIV prevalence indicate that it is difficult to correct statistically for the bias that may occur when a large proportion of people refuse to test.

Pre-Processing Power Traces to Defeat Random Clocking Countermeasures

We describe a pre-processing correlation attack on an FPGA implementation of AES, protected with a random clocking countermeasure that exhibits complex variations in both the location and amplitude of the power consumption patterns of the AES rounds. It is demonstrated that the merged round patterns can be pre-processed to identify and extract the individual round amplitudes, enabling a successful power analysis attack. We show that the requirement of the random clocking countermeasure to provide a varying execution time between processing rounds can be exploited to select a sub-set of data where sufficient current decay has occurred, further improving the attack. In comparison with the countermeasure's estimated security of 3 million traces from an integration attack, we show that through application of our proposed techniques that the countermeasure can now be broken with as few as 13k traces.

Central-Tapped Node Linked Modular Fault-Tolerance Topology for SRM Applications

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) can reduce greenhouse gas emissions while switched reluctance motor (SRM) is one of the promising motor for such applications. This paper presents a novel SRM fault-diagnosis and fault-tolerance operation solution. Based on the traditional asymmetric half-bridge topology for the SRM driving, the central tapped winding of the SRM in modular half-bridge configuration are introduced to provide fault-diagnosis and fault-tolerance functions, which are set idle in normal conditions. The fault diagnosis can be achieved by detecting the characteristic of the excitation and demagnetization currents. An SRM fault-tolerance operation strategy is also realized by the proposed topology, which compensates for the missing phase torque under the open-circuit fault, and reduces the unbalanced phase current under the short-circuit fault due to the uncontrolled faulty phase. Furthermore, the current sensor placement strategy is also discussed to give two placement methods for low cost or modular structure. Simulation results in MATLAB/Simulink and experiments on a 750-W SRM validate the effectiveness of the proposed strategy, which may have significant implications and improve the reliability of EVs/HEVs.

Generating analysis topology using virtual topology operators

Virtual topology operations have been utilized to generate an analysis topology definition suitable for downstream mesh generation. Detailed descriptions are provided for virtual topology merge and split operations for all topological entities, where virtual decompositions are robustly linked to the underlying geometry. Current virtual topology technology is extended to allow the virtual partitioning of volume cells. A valid description of the topology, including relative orientations, is maintained which enables downstream interrogations to be performed on the analysis topology description, such as determining if a specific meshing strategy can be applied to the virtual volume cells. As the virtual representation is a true non-manifold description of the sub-divided domain the interfaces between cells are recorded automatically. Therefore, the advantages of non-manifold modelling are exploited within the manifold modelling environment of a major commercial CAD system without any adaptation of the underlying CAD model. A hierarchical virtual structure is maintained where virtual entities are merged or partitioned. This has a major benefit over existing solutions as the virtual dependencies here are stored in an open and accessible manner, providing the analyst with the freedom to create, modify and edit the analysis topology in any preferred sequence.