A Shift Selection Strategy for Parallel Shift-invert Spectrum Slicing in Symmetric Self-consistent Eigenvalue Computation

© 2020 ACM. The central importance of large-scale eigenvalue problems in scientific computation necessitates the development of massively parallel algorithms for their solution. Recent advances in dense numerical linear algebra have enabled the routine treatment of eigenvalue problems with dimensions on the order of hundreds of thousands on the world's largest supercomputers. In cases where dense treatments are not feasible, Krylov subspace methods offer an attractive alternative due to the fact that they do not require storage of the problem matrices. However, demonstration of scalability of either of these classes of eigenvalue algorithms on computing architectures capable of expressing massive parallelism is non-trivial due to communication requirements and serial bottlenecks, respectively. In this work, we introduce the SISLICE method: a parallel shift-invert algorithm for the solution of the symmetric self-consistent field (SCF) eigenvalue problem. The SISLICE method drastically reduces the communication requirement of current parallel shift-invert eigenvalue algorithms through various shift selection and migration techniques based on density of states estimation and k-means clustering, respectively. This work demonstrates the robustness and parallel performance of the SISLICE method on a representative set of SCF eigenvalue problems and outlines research directions that will be explored in future work

Pipelined genetic propagation

© 2015 IEEE.Genetic Algorithms (GAs) are a class of numerical and combinatorial optimisers which are especially useful for solving complex non-linear and non-convex problems. However, the required execution time often limits their application to small-scale or latency-insensitive problems, so techniques to increase the computational efficiency of GAs are needed. FPGA-based acceleration has significant potential for speeding up genetic algorithms, but existing FPGA GAs are limited by the generational approaches inherited from software GAs. Many parts of the generational approach do not map well to hardware, such as the large shared population memory and intrinsic loop-carried dependency. To address this problem, this paper proposes a new hardware-oriented approach to GAs, called Pipelined Genetic Propagation (PGP), which is intrinsically distributed and pipelined. PGP represents a GA solver as a graph of loosely coupled genetic operators, which allows the solution to be scaled to the available resources, and also to dynamically change topology at run-time to explore different solution strategies. Experiments show that pipelined genetic propagation is effective in solving seven different applications. Our PGP design is 5 times faster than a recent FPGA-based GA system, and 90 times faster than a CPU-based GA system

Lentiviral vectors with amplified beta cell-specific gene expression.

An important goal of gene therapy is to be able to deliver genes, so that they express in a pattern that recapitulates the expression of an endogenous cellular gene. Although tissue-specific promoters confer selectivity, in a vector-based system, their activity may be too weak to mediate detectable levels in gene-expression studies. We have used a two-step transcriptional amplification system to amplify gene expression from lentiviral vectors using the human insulin promoter. In this system, the human insulin promoter drives expression of a potent synthetic transcription activator (the yeast GAL4 DNA-binding domain fused to the activation domain of the Herpes simplex virus-1 VP16 activator), which in turn activates a GAL4-responsive promoter, driving the enhanced green fluorescent protein reporter gene. Vectors carrying the human insulin promoter did not express in non-beta-cell lines, but expressed in murine insulinoma cell lines, indicating that the human insulin promoter was capable of conferring cell specificity of expression. The insulin-amplifiable vector was able to amplify gene expression five to nine times over a standard insulin-promoter vector. In primary human islets, gene expression from the insulin-promoted vectors was coincident with insulin staining. These vectors will be useful in gene-expression studies that require a detectable signal and tissue specificity

Optimal Computation of Avoided Words

The deviation of the observed frequency of a word $w$ from its expected frequency in a given sequence $x$ is used to determine whether or not the word is avoided. This concept is particularly useful in DNA linguistic analysis. The value of the standard deviation of $w$, denoted by $std(w)$, effectively characterises the extent of a word by its edge contrast in the context in which it occurs. A word $w$ of length $k>2$ is a $\rho$-avoided word in $x$ if $std(w) \leq \rho$, for a given threshold $\rho < 0$. Notice that such a word may be completely absent from $x$. Hence computing all such words na\"{\i}vely can be a very time-consuming procedure, in particular for large $k$. In this article, we propose an $O(n)$-time and $O(n)$-space algorithm to compute all $\rho$-avoided words of length $k$ in a given sequence $x$ of length $n$ over a fixed-sized alphabet. We also present a time-optimal $O(\sigma n)$-time and $O(\sigma n)$-space algorithm to compute all $\rho$-avoided words (of any length) in a sequence of length $n$ over an alphabet of size $\sigma$. Furthermore, we provide a tight asymptotic upper bound for the number of $\rho$-avoided words and the expected length of the longest one. We make available an open-source implementation of our algorithm. Experimental results, using both real and synthetic data, show the efficiency of our implementation

Transforming growth factor beta (TGF beta) mediates schwann cell death in vitro and in vivo: Examination of c-jun activation, interactions with survival signals, and the relationship of TGF beta-mediated death to schwann cell differentiation

In some situations, cell death in the nervous system is controlled by an interplay between survival factors and negative survival signals that actively induce apoptosis. The present work indicates that the survival of Schwann cells is regulated by such a dual mechanism involving the negative survival signal transforming growth factor beta (TGF beta), a family of growth factors that is present in the Schwann cells themselves. We analyze the interactions between this putative autocrine death signal and previously defined paracrine and autocrine survival signals and show that expression of a dominant negative c-Jun inhibits TGF beta -induced apoptosis. This and other findings pinpoint activation of c-Jun as a key downstream event in TGF beta -induced Schwann cell death. The ability of TGF beta to kill Schwann cells, like normal Schwann cell death in vivo, is under a strong developmental regulation, and we show that the decreasing ability of TGF beta to kill older cells is attributable to a decreasing ability of TGF beta to phosphorylate c-Jun in more differentiated cells

Autophagy: A cyto-protective mechanism which prevents primary human hepatocyte apoptosis during oxidative stress

The role of autophagy in the response of human hepatocytes to oxidative stress remains unknown. Understanding this process may have important implications for the understanding of basic liver epithelial cell biology and the responses of hepatocytes during liver disease. To address this we isolated primary hepatocytes from human liver tissue and exposed them ex vivo to hypoxia and hypoxia-reoxygenation (H-R). We showed that oxidative stress increased hepatocyte autophagy in a reactive oxygen species (ROS) and class III PtdIns3K-dependent manner. Specifically, mitochondrial ROS and NADPH oxidase were found to be key regulators of autophagy. Autophagy involved the upregulation of BECN1, LC3A, Atg7, Atg5 and Atg 12 during hypoxia and H-R. Autophagy was seen to occur within the mitochondria of the hepatocyte and inhibition of autophagy resulted in the lowering a mitochondrial membrane potential and onset of cell death. Autophagic responses were primarily observed in the large peri-venular (PV) hepatocyte subpopulation. Inhibition of autophagy, using 3-methyladenine, increased apoptosis during H-R. Specifically, PV human hepatocytes were more susceptible to apoptosis after inhibition of autophagy. These findings show for the first time that during oxidative stress autophagy serves as a cell survival mechanism for primary human hepatocytes

Families of bianchi modular symbols: critical base-change p-adic L-functions and p-adic Artin formalism

Let $K$ be an imaginary quadratic field. In this article, we study the eigenvariety for $GL(2)/K$, proving an etaleness result for the weight map at non-critical classical points and a smoothness result at base-change classical points. We give three main applications of this. (1) We construct three-variable $p$-adic $L$-functions over the eigenvariety interpolating the (two-variable) $p$-adic $L$-functions of classical Bianchi cusp forms in families. (2) Let $f$ be a $p$-stabilised newform of weight $k$ at least 2 without CM by $K$. We construct a two-variable $p$-adic $L$-function attached to the base-change of $f$ to $K$ under assumptions on $f$ that we conjecture always hold, in particular making no assumption on the slope of $f$. (3) We prove that these base-change $p$-adic $L$-functions satisfy a $p$-adic Artin formalism result, that is, they factorise in the same way as the classical $L$-function under Artin formalism. In an appendix, Carl Wang-Erickson describes a base-change deformation functor and gives a characterisation of its Zariski tangent space

Multi-level caching with delayed-multicast for video-on-demand

Delayed-Multicast is a novel transmission technique to support Video-on-Demand. It introduces buffers within the network to bridge the temporal delays between similar requests thus minimizing the aggregate bandwidth and server load. This paper introduces an improved online algorithm for resource allocation with Delayed-Multicast by utilizing prior knowledge of each clip's popularity. The algorithm is intended to be simple so as to allow for deployment at multiple levels in a distribution network. The result is greater backbone traffic savings and a corresponding reduction in the server load

On local structures of cubicity 2 graphs

A 2-stab unit interval graph (2SUIG) is an axes-parallel unit square intersection graph where the unit squares intersect either of the two fixed lines parallel to the $X$-axis, distance $1 + \epsilon$ ($0 < \epsilon < 1$) apart. This family of graphs allow us to study local structures of unit square intersection graphs, that is, graphs with cubicity 2. The complexity of determining whether a tree has cubicity 2 is unknown while the graph recognition problem for unit square intersection graph is known to be NP-hard. We present a polynomial time algorithm for recognizing trees that admit a 2SUIG representation