2,413 research outputs found

    Global Wellposedness for a Modified Critical Dissipative Quasi-Geostrophic Equation

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    In this paper we consider the following modified quasi-geostrophic equation \partial_{t}\theta+u\cdot\nabla\theta+\nu |D|^{\alpha}\theta=0, \quad u=|D|^{\alpha-1}\mathcal{R}^{\bot}\theta,\quad x\in\mathbb{R}^2 with ν>0\nu>0 and α∈]0,1[ ∪ ]1,2[\alpha\in ]0,1[\,\cup \,]1,2[. When α∈]0,1[\alpha\in]0,1[, the equation was firstly introduced by Constantin, Iyer and Wu in \cite{ref ConstanIW}. Here, by using the modulus of continuity method, we prove the global well-posedness of the system with the smooth initial data. As a byproduct, we also show that for every α∈]0,2[\alpha\in ]0,2[, the Lipschitz norm of the solution has a uniform exponential bound.Comment: In this version we extend the range of α\alpha from (0,1) to (0,2), we also show that for every α∈(0,2)\alpha\in (0,2), the Lipschitz norm of the solution has a uniform exponential bound. 27page

    A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition

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    We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and frame-level classification accuracy, kernel-based acoustic models are as effective as their DNN counterparts. However, on token-error-rates DNN models can be significantly better. We have discovered that this might be attributed to DNN's unique strength in reducing both the perplexity and the entropy of the predicted posterior probabilities. Motivated by our findings, we propose a new technique, entropy regularized perplexity, for model selection. This technique can noticeably improve the recognition performance of both types of models, and reduces the gap between them. While effective on Broadcast News, this technique could be also applicable to other tasks.Comment: arXiv admin note: text overlap with arXiv:1411.400

    Race affects SVR12 in a large and ethnically diverse hepatitis C-infected patient population following treatment with direct-acting antivirals: Analysis of a single-center Department of Veterans Affairs cohort.

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    Hepatitis C virus (HCV) infection is a major cause of chronic liver disease. HCV cure has been linked to improved patient outcomes. In the era of direct-acting antivirals (DAAs), HCV cure has become the goal, as defined by sustained virological response 12 weeks (SVR12) after completion of therapy. Historically, African-Americans have had lower SVR12 rates compared to White people in the interferon era, which had been attributed to the high prevalence of non-CC interleukin 28B (IL28B) type. Less is known about the association between race/ethnicity and SVR12 in DAA-treated era. The aim of the study is to evaluate the predictors of SVR12 in a diverse, single-center Veterans Affairs population. We conducted a retrospective study of patients undergoing HCV therapy with DAAs from 2014 to 2016 at the VA Greater Los Angeles Healthcare System. We performed a multivariable logistic regression analysis to determine predictors of SVR12, adjusting for age, HCV genotype, DAA regimen and duration, human immunodeficiency virus (HIV) status, fibrosis, nonalcoholic fatty liver disease (NAFLD) fibrosis score, homelessness, mental health, and adherence. Our cohort included 1068 patients, out of which 401 (37.5%) were White people and 400 (37.5%) were African-American. Genotype 1 was the most common genotype (83.9%, N = 896). In the adjusted models, race/ethnicity and the presence of fibrosis were statistically significant predictors of non-SVR. African-Americans had 57% lower odds for reaching SVR12 (adj.OR = 0.43, 95% CI = 1.5-4.1) compared to White people. Advanced fibrosis (adj.OR = 0.40, 95% CI = 0.26-0.68) was also a significant predictor of non-SVR. In a single-center VA population on DAAs, African-Americans were less likely than White people to reach SVR12 when adjusting for covariates

    Understanding the edge effect in TASEP with mean-field theoretic approaches

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    We study a totally asymmetric simple exclusion process (TASEP) with one defect site, hopping rate q<1q<1, near the system boundary. Regarding our system as a pair of uniform TASEP's coupled through the defect, we study various methods to match a \emph{finite} TASEP and an \emph{infinite} one across a common boundary. Several approximation schemes are investigated. Utilizing the finite segment mean-field (FSMF) method, we set up a framework for computing the steady state current JJ as a function of the entry rate % \alpha and qq. For the case where the defect is located at the entry site, we obtain an analytical expression for J(α,q)J(\alpha, q) which is in good agreement with Monte Carlo simulation results. When the defect is located deeper in the bulk, we refined the scheme of MacDonald, et.al. [Biopolymers, \textbf{6}, 1 (1968)] and find reasonably good fits to the density profiles before the defect site. We discuss the strengths and limitations of each method, as well as possible avenues for further studies.Comment: 16 pages, 4 figure

    Targeted long-read sequencing reveals clonally expanded HBV-associated chromosomal translocations in patients with chronic hepatitis B

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    Chronic HBV; Clonal expansion; Targeted sequencingVHB crónico; Expansión clónica; Secuenciación dirigidaVHB crònic; Expansió clonal; Seqüenciació dirigidaBackground & Aims HBV infects over 257 million people worldwide and is associated with the development of hepatocellular carcinoma (HCC). Integration of HBV DNA into the host genome is likely a key driver of HCC oncogenesis. Here, we utilise targeted long-read sequencing to determine the structure of HBV DNA integrations as well as full isoform information of HBV mRNA with more accurate quantification than traditional next generation sequencing platforms. Methods DNA and RNA were isolated from fresh frozen liver biopsies collected within the GS-US-174-0149 clinical trial. A pan-genotypic panel of biotinylated oligos was developed to enrich for HBV sequences from sheared genomic DNA (∼7 kb) and full-length cDNA libraries from poly-adenylated RNA. Samples were sequenced on the PacBio long-read platform and analysed using a custom bioinformatic pipeline. Results HBV-targeted long-read DNA sequencing generated high coverage data spanning entire integrations. Strikingly, in 13 of 42 samples (31%) we were able to detect HBV sequences flanked by 2 different chromosomes, indicating a chromosomal translocation associated with HBV integration. Chromosomal translocations were unique to each biopsy sample, suggesting that each originated randomly, and in some cases had evidence of clonal expansion. Using targeted long-read RNA sequencing, we determined that upwards of 95% of all HBV transcripts in patients who are HBeAg-positive originate from cccDNA. In contrast, patients who are HBeAg-negative expressed mostly HBsAg from integrations. Conclusions Targeted lso-Seq allowed for accurate quantitation of the HBV transcriptome and assignment of transcripts to either cccDNA or integration origins. The existence of multiple unique HBV-associated inter-chromosomal translocations in non-HCC CHB patient liver biopsies suggests a novel mechanism with mutagenic potential that may contribute to progression to HCC

    Analyzing Norm Violations in Live-Stream Chat

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    Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35\%.Comment: 17 pages, 8 figures, 15 table
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