Continuous Monitoring of Distributed Data Streams over a Time-based Sliding Window

The past decade has witnessed many interesting algorithms for maintaining statistics over a data stream. This paper initiates a theoretical study of algorithms for monitoring distributed data streams over a time-based sliding window (which contains a variable number of items and possibly out-of-order items). The concern is how to minimize the communication between individual streams and the root, while allowing the root, at any time, to be able to report the global statistics of all streams within a given error bound. This paper presents communication-efficient algorithms for three classical statistics, namely, basic counting, frequent items and quantiles. The worst-case communication cost over a window is $O(\frac{k} {\epsilon} \log \frac{\epsilon N}{k})$ bits for basic counting and $O(\frac{k}{\epsilon} \log \frac{N}{k})$ words for the remainings, where $k$ is the number of distributed data streams, $N$ is the total number of items in the streams that arrive or expire in the window, and $\epsilon < 1$ is the desired error bound. Matching and nearly matching lower bounds are also obtained.Comment: 12 pages, to appear in the 27th International Symposium on Theoretical Aspects of Computer Science (STACS), 201

Multi-channel Fourier packet transform of EEG: optimal representation and time-varying coherence

Multi-channel recording of electroencephalogram (EEG) provides a measure of spatial-temporal pattern of cognitive processes. When oscillatory activities are going to be studied, the time-domain EEG signal can be analyzed via Fourier or wavelet transform. However the loss of temporal information after Fourier transform and the unavailability of phase information in wavelet transform limit their applicability in EEG analysis. In this paper, multi-channel Fourier packet transform is introduced. The algorithm resembles the wavelet packet transform by its binary tree search for an optimal selection of orthogonal basis, but extends the application to the multi-channel scenario. It aims to provide a sparse signal representation to localize features in the spatial-spectral-temporal domain. Since the decomposed atoms are spatially coherent components, analysis of time-varying synchrony across scalp locations is then possible.published_or_final_versio

Empirical analysis on the relationship between good import, good export, Tariff and economic growth

Abstract In this research, the objective is studying the relationship between tariff, good import, good export, and economic growth to understand the possible influences of Trump Tariff imposed since 2017. The relationship between tariff with economic growth and good import, good export with economic growth remain ambiguous. This research combined both to study their relationships. The methods of cointegration test and OLS regression are conducted to study the long term and short term relationship with panel data analysis from 2000 to 2017 from 132 countries. The result shows that there is no significant relationship from the tariff, good import, and good export to economic growth. Result suggests that Trump Tariff has insignificant influences to improve trade deficits and furthermore, economic growth

Second order statistics based blind source separation for artifact correction of short ERP epochs

ERP is commonly obtained by averaging over segmented EEC epochs. In case artifacts are present in the raw EEC measurement, pre-processing is required to prevent the averaged ERP waveform being interfered by artifacts. The simplest pre-processing approach is by rejecting trials in which presence of artifact is detected. Alternatively artifact correction instead of rejection can be performed by blind source separation, so that waste of ERP trials is avoided. In this paper, we propose a second order statistics based blind source separation approach to ERP artifact correction. Comparing with blind separation using independent component analysis, second order statistics based method does not rely on higher order statistics or signal entropy, and therefore leads to more robust separation even if only short epochs are available.published_or_final_versio

Genetic and Genomic Approaches to Identify and Study the Targets of Bioactive Small Molecules

AbstractNatural and synthetic bioactive small molecules form the backbone of modern therapeutics. These drugs primarily exert their effect by targeting cellular host or foreign proteins that are critical for the progression of disease. Therefore, a crucial step in the process of recognizing valuable new drug leads is identification of their protein targets; this is often a time consuming and difficult task. This report is intended to provide a comprehensive review of recent developments in genetic and genomic approaches to overcome the hurdle of discovering the protein targets of bioactive small molecules

A generic theory for Majorana zero modes in 2D superconductors

It is well known that non-Abelian Majorana zero modes (MZM) harbor at vortex cores in a $p_{x}+\text{i}p_{y}$ topological superconductor, which can be realized in a 2D spin-orbit coupled system with a single Fermi surface and by proximity coupling to an $s$-wave superconductor. Here we show that existence of non-Abelian MZMs is unrelated to the bulk topology of a 2D superconductor, and propose that such exotic modes can be resulted in much broader range of superconductors, being topological or trivial. For a generic 2D system with multiple Fermi surfaces and gapped out by superconducting pairings, we show that at least a single MZM survives if there are only odd number of Fermi surfaces of which the corresponding superconducting orders have vortices, and such MZM is protected by an emergent Chern-Simons invariant, irrespective of the bulk topology of the superconductor. This result may enrich new experimental schemes for realizing non-Aelian MZMs. In particular, we propose a minimal scheme to realize the MZMs in a 2D superconducting Dirac semimetal with trivial bulk topology, which can be well achieved based on the recent cold atom experiments.Comment: 5 pages, 3 figures, plus Supplementary Materia

A targeted gene panel that covers coding, non-coding and short tandem repeat regions improves the diagnosis of patients with neurodegenerative diseases

Genetic testing for neurodegenerative diseases (NDs) is highly challenging because of genetic heterogeneity and overlapping manifestations. Targeted-gene panels (TGPs), coupled with next-generation sequencing (NGS), can facilitate the profiling of a large repertoire of ND-related genes. Due to the technical limitations inherent in NGS and TGPs, short tandem repeat (STR) variations are often ignored. However, STR expansions are known to cause such NDs as Huntington\u27s disease and spinocerebellar ataxias type 3 (SCA3). Here, we studied the clinical utility of a custom-made TGP that targets 199 NDs and 311 ND-associated genes on 118 undiagnosed patients. At least one known or likely pathogenic variation was found in 54 patients; 27 patients demonstrated clinical profiles that matched the variants; and 16 patients whose original diagnosis were refined. A high concordance of variant calling were observed when comparing the results from TGP and whole-exome sequencing of four patients. Our in-house STR detection algorithm has reached a specificity of 0.88 and a sensitivity of 0.82 in our SCA3 cohort. This study also uncovered a trove of novel and recurrent variants that may enrich the repertoire of ND-related genetic markers. We propose that a combined comprehensive TGPs-bioinformatics pipeline can improve the clinical diagnosis of NDs

Convergence of TOR-nitrogen and Snf1-glucose signaling pathways onto Gln3

Carbon and nitrogen are two basic nutrient sources for cellular organisms. They supply precursors for energy metabolism and metabolic biosynthesis. In the yeast Saccharomyces cerevisiae, distinct sensing and signaling pathways have been described that regulate gene expression in response to the quality of carbon and nitrogen sources, respectively. Gln3 is a GATA-type transcription factor of nitrogen catabolite-repressible (NCR) genes. Previous observations indicate that the quality of nitrogen sources controls the phosphorylation and cytoplasmic retention of Gln3 via the target of rapamycin (TOR) protein. In this study, we show that glucose also regulates Gln3 phosphorylation and subcellular localization, which is mediated by Snf1, the yeast homolog of AMP-dependent protein kinase and a cytoplasmic glucose sensor. Our data show that glucose and nitrogen signaling pathways converge onto Gln3, which may be critical for both nutrient sensing and starvation responses

OMMA enables population-scale analysis of complex genomic features and phylogenomic relationships from nanochannel-based optical maps.

BackgroundOptical mapping is an emerging technology that complements sequencing-based methods in genome analysis. It is widely used in improving genome assemblies and detecting structural variations by providing information over much longer (up to 1 Mb) reads. Current standards in optical mapping analysis involve assembling optical maps into contigs and aligning them to a reference, which is limited to pairwise comparison and becomes bias-prone when analyzing multiple samples.FindingsWe present a new method, OMMA, that extends optical mapping to the study of complex genomic features by simultaneously interrogating optical maps across many samples in a reference-independent manner. OMMA captures and characterizes complex genomic features, e.g., multiple haplotypes, copy number variations, and subtelomeric structures when applied to 154 human samples across the 26 populations sequenced in the 1000 Genomes Project. For small genomes such as pathogenic bacteria, OMMA accurately reconstructs the phylogenomic relationships and identifies functional elements across 21 Acinetobacter baumannii strains.ConclusionsWith the increasing data throughput of optical mapping system, the use of this technology in comparative genome analysis across many samples will become feasible. OMMA is a timely solution that can address such computational need. The OMMA software is available at https://github.com/TF-Chan-Lab/OMTools