Does Chatting Really Help? Tweet Analytics and Analyst Forecast Dispersion

Financial analysts use tweet analytics to prepare their forecasts, yet little information that describes how they do so exists. To address this gap, we scrutinize the associative relationships between tweets about a company’s service and the dispersion of analyst forecasts about the same company’s financial performance. We developed three sets of hypotheses. We extracted tweets related to airlines from the Twitter data from Archive Team and analyst forecast data from Institutional Brokers’ Estimate System Academic. We obtained airline-related tweets from nearly 200,000 individual Twitter users about 10 airlines during a 55-month study period and ran multiple regressions to test the associations between tweet characteristics and forecast dispersion. Our results suggest that, when more posters generate more tweets about a company’s service, analysts make less dispersed forecasts. In addition, negative (or non-verified) tweets reduce forecast dispersion to a greater extent than positive (or verified) tweets do. Theoretically, this paper confirms that Twitter can be a useful data source to provide analysts with additional information to prepare their forecasts. Practically, our findings provide empirical evidence about how Twitter data is associated with analyst forecast dispersion. We encourage stakeholders (such as analysts from small firms and individual investors) to extract data from Twitter as a supplement to market information when analyzing data

Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification tasks, most of them overlook the scarcity of labeled graph data in many applications. For example, in bioinformatics, obtaining protein graph labels usually needs laborious experiments. Recently, few-shot learning has been explored to alleviate this problem with only given a few labeled graph samples of test classes. The shared sub-structures between training classes and test classes are essential in few-shot graph classification. Exiting methods assume that the test classes belong to the same set of super-classes clustered from training classes. However, according to our observations, the label spaces of training classes and test classes usually do not overlap in real-world scenario. As a result, the existing methods don't well capture the local structures of unseen test classes. To overcome the limitation, in this paper, we propose a direct method to capture the sub-structures with well initialized meta-learner within a few adaptation steps. More specifically, (1) we propose a novel framework consisting of a graph meta-learner, which uses GNNs based modules for fast adaptation on graph data, and a step controller for the robustness and generalization of meta-learner; (2) we provide quantitative analysis for the framework and give a graph-dependent upper bound of the generalization error based on our framework; (3) the extensive experiments on real-world datasets demonstrate that our framework gets state-of-the-art results on several few-shot graph classification tasks compared to baselines

Capturing the electromagnetic counterparts of binary neutron star mergers through low-latency gravitational wave triggers

© 2016 The Authors. We investigate the prospects for joint low-latency gravitational wave (GW) detection and prompt electromagnetic (EM) follow-up observations of coalescing binary neutron stars (BNSs). For BNS mergers associated with short duration gamma-ray bursts (SGRBs), we for the first time evaluate the feasibility of rapid EM follow-ups to capture the prompt emission, early engine activity, or reveal any potential by-products such as magnetars or fast radio bursts. To achieve our goal, we first simulate a population of coalescing BNSs using realistic distributions of source parameters and estimate the detectability and localization efficiency at different times before merger. We then use a selection of facilities with GW follow-up agreements in place, from low-frequency radio to high-energy y-ray to assess the prospects of prompt follow-up. We quantify our assessment using observational SGRB flux data extrapolated to be within the horizon distances of the advanced GW interferometric detectors LIGO and Virgo and to the prompt phase immediately following the binary merger. Our results illustrate that while challenging, breakthrough multimessenger science is possible with EM follow-up facilities with fast responses and wide fields-of-view. We demonstrate that the opportunity to catch the prompt stage (<5s) of SGRBs can be enhanced by speeding up the detection pipelines of both GW observatories and EM follow-up facilities. We further show that the addition of an Australian instrument to the optimal detector network could possibly improve the angular resolution by a factor of 2 and thereby contribute significantly to GW-EM multimessenger astronomy

Regular Patterns for Proteome-Wide Distribution of Protein Abundance across Species

A proteome of the bio-entity, including cell, tissue, organ, and organism, consists of proteins of diverse abundance. The principle that determines the abundance of different proteins in a proteome is of fundamental significance for an understanding of the building blocks of the bio-entity. Here, we report three regular patterns in the proteome-wide distribution of protein abundance across species such as human, mouse, fly, worm, yeast, and bacteria: in most cases, protein abundance is positively correlated with the protein's origination time or sequence conservation during evolution; it is negatively correlated with the protein's domain number and positively correlated with domain coverage in protein structure, and the correlations became stronger during the course of evolution; protein abundance can be further stratified by the function of the protein, whereby proteins that act on material conversion and transportation (mass category) are more abundant than those that act on information modulation (information category). Thus, protein abundance is intrinsically related to the protein's inherent characters of evolution, structure, and function

Current evidence and future perspectives on HuR and breast cancer development, prognosis, and treatment.

This is the Accepted Manuscript version of the following article, "Ioly Kotta-Loizou, et al., “Current Evidence and Future Perspectives on HuR and Breast Cancer Development, Prognosis, and Treatment”, Neoplasia, Vol. 18(11): 674-688, October 2016." The final published version is available at:https://doi.org/10.1016/j.neo.2016.09.002 Copyright © 2016, Elsevier.Hu-antigen R (HuR) is an RNA-binding posttranscriptional regulator that belongs to the Hu/ELAV family. HuR expression levels are modulated by a variety of proteins, microRNAs, chemical compounds, or the microenvironment, and in turn, HuR affects mRNA stability and translation of various genes implicated in breast cancer formation, progression, metastasis, and treatment. The aim of the present review is to critically summarize the role of HuR in breast cancer development and its potential as a prognosticator and a therapeutic target. In this aspect, all the existing English literature concerning HuR expression and function in breast cancer cell lines, in vivo animal models, and clinical studies is critically presented and summarized. HuR modulates many genes implicated in biological processes crucial for breast cancer formation, growth, and metastasis, whereas the link between HuR and these processes has been demonstrated directly in vitro and in vivo. Additionally, clinical studies reveal that HuR is associated with more aggressive forms of breast cancer and is a putative prognosticator for patients' survival. All the above indicate HuR as a promising drug target for cancer therapy; nevertheless, additional studies are required to fully understand its potential and determine against which types of breast cancer and at which stage of the disease a therapeutic agent targeting HuR would be more effective.Peer reviewedFinal Accepted Versio

Implications For The Origin Of GRB 051103 From LIGO Observations

We present the results of a LIGO search for gravitational waves (GWs) associated with GRB 051103, a short-duration hard-spectrum gamma-ray burst (GRB) whose electromagnetically determined sky position is coincident with the spiral galaxy M81, which is 3.6 Mpc from Earth. Possible progenitors for short-hard GRBs include compact object mergers and soft gamma repeater (SGR) giant flares. A merger progenitor would produce a characteristic GW signal that should be detectable at the distance of M81, while GW emission from an SGR is not expected to be detectable at that distance. We found no evidence of a GW signal associated with GRB 051103. Assuming weakly beamed gamma-ray emission with a jet semi-angle of 30 deg we exclude a binary neutron star merger in M81 as the progenitor with a confidence of 98%. Neutron star-black hole mergers are excluded with > 99% confidence. If the event occurred in M81 our findings support the the hypothesis that GRB 051103 was due to an SGR giant flare, making it the most distant extragalactic magnetar observed to date.Comment: 8 pages, 3 figures. For a repository of data used in the publication, go to: https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=15166 . Also see the announcement for this paper on ligo.org at: http://www.ligo.org/science/Publication-GRB051103/index.ph

Search for Gravitational Wave Bursts from Six Magnetars

Soft gamma repeaters (SGRs) and anomalous X-ray pulsars (AXPs) are thought to be magnetars: neutron stars powered by extreme magnetic fields. These rare objects are characterized by repeated and sometimes spectacular gamma-ray bursts. The burst mechanism might involve crustal fractures and excitation of non-radial modes which would emit gravitational waves (GWs). We present the results of a search for GW bursts from six galactic magnetars that is sensitive to neutron star f-modes, thought to be the most efficient GW emitting oscillatory modes in compact stars. One of them, SGR 0501+4516, is likely similar to 1 kpc from Earth, an order of magnitude closer than magnetars targeted in previous GW searches. A second, AXP 1E 1547.0-5408, gave a burst with an estimated isotropic energy >10(44) erg which is comparable to the giant flares. We find no evidence of GWs associated with a sample of 1279 electromagnetic triggers from six magnetars occurring between 2006 November and 2009 June, in GW data from the LIGO, Virgo, and GEO600 detectors. Our lowest model-dependent GW emission energy upper limits for band-and time-limited white noise bursts in the detector sensitive band, and for f-mode ringdowns (at 1090 Hz), are 3.0 x 10(44)d(1)(2) erg and 1.4 x 10(47)d(1)(2) erg, respectively, where d(1) = d(0501)/1 kpc and d(0501) is the distance to SGR 0501+4516. These limits on GW emission from f-modes are an order of magnitude lower than any previous, and approach the range of electromagnetic energies seen in SGR giant flares for the first time.United States National Science FoundationScience and Technology Facilities Council of the United KingdomMax-Planck-SocietyState of Niedersachsen/GermanyItalian Istituto Nazionale di Fisica NucleareFrench Centre National de la Recherche ScientifiqueAustralian Research CouncilCouncil of Scientific and Industrial Research of IndiaIstituto Nazionale di Fisica Nucleare of ItalySpanish Ministerio de Educacion y CienciaConselleria d'Economia Hisenda i Innovacio of the Govern de les Illes BalearsFoundation for Fundamental Research on Matter supported by the Netherlands Organisation for Scientific ResearchPolish Ministry of Science and Higher EducationFoundation for Polish ScienceRoyal SocietyScottish Funding CouncilScottish Universities Physics AllianceNational Aeronautics and Space Administration NNH07ZDA001-GLASTCarnegie TrustLeverhulme TrustDavid and Lucile Packard FoundationResearch CorporationAlfred P. Sloan FoundationRussian Space AgencyRFBR 09-02-00166aIPN JPL Y503559 (Odyssey), NASA NNG06GH00G, NASA NNX07AM42G, NASA NNX08AC89G (INTEGRAL), NASA NNG06GI896, NASA NNX07AJ65G, NASA NNX08AN23G (Swift), NASA NNX07AR71G (MESSENGER), NASA NNX06AI36G, NASA NNX08AB84G, NASA NNX08AZ85G (Suzaku), NASA NNX09AU03G (Fermi)Astronom

Search for Gravitational Waves from Low Mass Compact Binary Coalescence in LIGO's Sixth Science Run and Virgo's Science Runs 2 and 3

We report on a search for gravitational waves from coalescing compact binaries using LIGO and Virgo observations between July 7, 2009 and October 20, 2010. We searched for signals from binaries with total mass between 2 and 25 solar masses; this includes binary neutron stars, binary black holes, and binaries consisting of a black hole and neutron star. The detectors were sensitive to systems up to 40 Mpc distant for binary neutron stars, and further for higher mass systems. No gravitational-wave signals were detected. We report upper limits on the rate of compact binary coalescence as a function of total mass, including the results from previous LIGO and Virgo observations. The cumulative 90%-confidence rate upper limits of the binary coalescence of binary neutron star, neutron star- black hole and binary black hole systems are 1.3 x 10^{-4}, 3.1 x 10^{-5} and 6.4 x 10^{-6} Mpc^{-3}yr^{-1}, respectively. These upper limits are up to a factor 1.4 lower than previously derived limits. We also report on results from a blind injection challenge.Comment: 11 pages, 5 figures. For a repository of data used in the publication, go to: . Also see the announcement for this paper on ligo.org at: <http://www.ligo.org/science/Publication-S6CBCLowMass/index.php