Finding Global Optimum for Truth Discovery: Entropy Based Geometric Variance

Truth Discovery is an important problem arising in data analytics related fields such as data mining, database, and big data. It concerns about finding the most trustworthy information from a dataset acquired from a number of unreliable sources. Due to its importance, the problem has been extensively studied in recent years and a number techniques have already been proposed. However, all of them are of heuristic nature and do not have any quality guarantee. In this paper, we formulate the problem as a high dimensional geometric optimization problem, called Entropy based Geometric Variance. Relying on a number of novel geometric techniques (such as Log-Partition and Modified Simplex Lemma), we further discover new insights to this problem. We show, for the first time, that the truth discovery problem can be solved with guaranteed quality of solution. Particularly, we show that it is possible to achieve a (1+eps)-approximation within nearly linear time under some reasonable assumptions. We expect that our algorithm will be useful for other data related applications

Postnatal dysregulation of Notch signal disrupts dendrite development of adult-born neurons in the hippocampus and contributes to memory impairment

Deficits in the Notch pathway are involved in a number of neurologic diseases associated with mental retardation or/and dementia. The mechanisms by which Notch dysregulation are associated with mental retardation and dementia are poorly understood. We found that Notch1 is highly expressed in the adult-born immature neurons in the hippocampus of mice. Retrovirus mediated knockout of notch1 in single adult-born immature neurons decreases mTOR signaling and compromises their dendrite morphogenesis. In contrast, overexpression of Notch1 intracellular domain (NICD), to constitutively activate Notch signaling in single adult-born immature neurons, promotes mTOR signaling and increases their dendrite arborization. Using a unique genetic approach to conditionally and selectively knockout notch 1 in the postnatally born immature neurons in the hippocampus decreases mTOR signaling, compromises their dendrite morphogenesis, and impairs spatial learning and memory. Conditional overexpression of NICD in the postnatally born immature neurons in the hippocampus increases mTOR signaling and promotes dendrite arborization. These data indicate that Notch signaling plays a critical role in dendrite development of immature neurons in the postnatal brain, and dysregulation of Notch signaling in the postnatally born neurons disrupts their development and thus contributes to the cognitive deficits associated with neurological diseases

Distributed and Robust Support Vector Machine

In this paper, we consider the distributed version of Support Vector Machine (SVM) under the coordinator model, where all input data (i.e., points in R^d space) of SVM are arbitrarily distributed among k nodes in some network with a coordinator which can communicate with all nodes. We investigate two variants of this problem, with and without outliers. For distributed SVM without outliers, we prove a lower bound on the communication complexity and give a distributed (1-epsilon)-approximation algorithm to reach this lower bound, where epsilon is a user specified small constant. For distributed SVM with outliers, we present a (1-epsilon)-approximation algorithm to explicitly remove the influence of outliers. Our algorithm is based on a deterministic distributed top t selection algorithm with communication complexity of O(k log (t)) in the coordinator model. Experimental results on benchmark datasets confirm the theoretical guarantees of our algorithms

cDNA sequences reveal considerable gene prediction inaccuracy in the Plasmodium falciparum genome

<p>Abstract</p> <p>Background</p> <p>The completion of the <it>Plasmodium falciparum </it>genome represents a milestone in malaria research. The genome sequence allows for the development of genome-wide approaches such as microarray and proteomics that will greatly facilitate our understanding of the parasite biology and accelerate new drug and vaccine development. Designing and application of these genome-wide assays, however, requires accurate information on gene prediction and genome annotation. Unfortunately, the genes in the parasite genome databases were mostly identified using computer software that could make some erroneous predictions.</p> <p>Results</p> <p>We aimed to obtain cDNA sequences to examine the accuracy of gene prediction <it>in silico</it>. We constructed cDNA libraries from mixed blood stages of <it>P. falciparum </it>parasite using the SMART cDNA library construction technique and generated 17332 high-quality expressed sequence tags (EST), including 2198 from primer-walking experiments. Assembly of our sequence tags produced 2548 contigs and 2671 singletons <it>versus </it>5220 contigs and 5910 singletons when our EST were assembled with EST in public databases. Comparison of all the assembled EST/contigs with predicted CDS and genomic sequences in the PlasmoDB database identified 356 genes with predicted coding sequences fully covered by EST, including 85 genes (23.6%) with introns incorrectly predicted. Careful automatic software and manual alignments found an additional 308 genes that have introns different from those predicted, with 152 new introns discovered and 182 introns with sizes or locations different from those predicted. Alternative spliced and antisense transcripts were also detected. Matching cDNA to predicted genes also revealed silent chromosomal regions, mostly at subtelomere regions.</p> <p>Conclusion</p> <p>Our data indicated that approximately 24% of the genes in the current databases were predicted incorrectly, although some of these inaccuracies could represent alternatively spliced transcripts, and that more genes than currently predicted have one or more additional introns. It is therefore necessary to annotate the parasite genome with experimental data, although obtaining complete cDNA sequences from this parasite will be a formidable task due to the high AT nature of the genome. This study provides valuable information for genome annotation that will be critical for functional analyses.</p

A Practical Control Strategy for the Maglev Self-Excited Resonance Suppression

This paper addresses the control strategy for the suppression of maglev vehicle-bridge interaction resonance, which worsens the ride comfort of vehicle and degrades the safety of the bridge. Firstly, a minimum model containing a flexible bridge and ten levitation units is presented. Based on the minimum model, we pointed out that magnetic flux feedback instead of the traditional current feedback is capable of simplifying the block diagram of the interaction system. Furthermore, considering the uncertainty of the bridge’s modal frequency, the stability of the interaction system is explored according to an improved root-locus technique. Motivated by the positive effects of the mechanical damping of bridges and the feedback channels’ difference between the levitation subsystem and the bridge subsystem, the increment of electrical damping by the additional feedback of vertical velocity of bridge is proposed and several related implementation issues are addressed. Finally, the numerical and experimental results illustrating the stability improvement are provided