Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most stateof-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error and 17.3% top-1 error

The devil is in the decoder

Many machine vision applications require predictions for every pixel of the input image (for example semantic segmentation, boundary detection). Models for such problems usually consist of encoders which decreases spatial resolution while learning a high-dimensional representation, followed by decoders who recover the original input resolution and result in low-dimensional predictions. While encoders have been studied rigorously, relatively few studies address the decoder side. Therefore this paper presents an extensive comparison of a variety of decoders for a variety of pixel-wise prediction tasks. Our contributions are: (1) Decoders matter: we observe significant variance in results between different types of decoders on various problems. (2) We introduce a novel decoder: bilinear additive upsampling. (3) We introduce new residual-like connections for decoders. (4) We identify two decoder types which give a consistently high performance

Soluble and Cell-Associated Insulin Receptor Dysfunction Correlates with Severity of HAND in HIV-Infected Women

Blood sugar metabolism abnormalities have been identified in HIV-infected individuals and associated with HIV-associated neurocognitive disorders (HAND). These abnormalities may occur as a result of chronic HIV infection, long-term use of combined antiretroviral treatment (CART), aging, genetic predisposition, or a combination of these factors, and may increase morbidity and mortality in this population.To determine if changes in soluble and cell-associated insulin receptor (IR) levels, IR substrate-1 (IRS-1) levels, and IRS-1 tyrosine phosphorylation are associated with the presence and severity of HAND in a cohort of HIV-seropositive women.This is a retrospective cross-sectional study using patient database information and stored samples from 34 HIV-seropositive women and 10 controls without history of diabetes from the Hispanic-Latino Longitudinal Cohort of Women. Soluble IR subunits [sIR, ectodomain (α) and full-length or intact (αβ)] were assayed in plasma and CSF samples by ELISA. Membrane IR levels, IRS-1 levels, and IRS-1 tyrosine phosphorylation were analyzed in CSF white cell pellets (WCP) using flow cytometry. HIV-seropositive women had significantly increased levels of intact or full-length sIR in plasma (p<0.001) and CSF (p<0.005) relative to controls. Stratified by HAND, increased levels of full-length sIR in plasma were associated with the presence (p<0.001) and severity (p<0.005) of HAND. A significant decrease in IRS-1 tyrosine-phosphorylation in the WCP was also associated with the presence (p<0.02) and severity (p<0.02) of HAND.This study provides evidence that IR secretion is increased in HIV-seropositive women, and increased IR secretion is associated with cognitive impairment in these women. Thus, IR dysfunction may have a role in the progression of HAND and could represent a biomarker for the presence and severity of HAND

The impact of maternal HIV infection on cord blood lymphocyte subsets and cytokine profile in exposed non-infected newborns

<p>Abstract</p> <p>Background</p> <p>Children born to HIV+ mothers are exposed intra-utero to several drugs and cytokines that can modify the developing immune system, and influence the newborn's immune response to infections and vaccines. We analyzed the relation between the distribution of cord blood lymphocyte subsets and cytokine profile in term newborns of HIV+ mothers using HAART during pregnancy and compared them to normal newborns.</p> <p>Methods</p> <p>In a prospective, controlled study, 36 mother-child pairs from HIV+ mothers and 15 HIV-uninfected mothers were studied. Hematological features and cytokine profiles of mothers at 35 weeks of pregnancy were examined. Maternal and cord lymphocyte subsets as well as B-cell maturation in cord blood were analyzed by flow cytometry. The non-stimulated, as well as BCG- and PHA-stimulated production of IL2, IL4, IL7, IL10, IL12, IFN-γ and TNF-alpha in mononuclear cell cultures from mothers and infants were quantified using ELISA.</p> <p>Results</p> <p>After one year follow-up none of the exposed infants became seropositive for HIV. An increase in B lymphocytes, especially the CD19/CD5+ ones, was observed in cord blood of HIV-exposed newborns. Children of HIV+ hard drug using mothers had also an increase of immature B-cells. Cord blood mononuclear cells of HIV-exposed newborns produced less IL-4 and IL-7 and more IL-10 and IFN-γ in culture than those of uninfected mothers. Cytokine values in supernatants were similar in infants and their mothers except for IFN-γ and TNF-alpha that were higher in HIV+ mothers, especially in drug abusing ones. Cord blood CD19/CD5+ lymphocytes showed a positive correlation with cord IL-7 and IL-10. A higher maternal age and smoking was associated with a decrease of cord blood CD4+ cells.</p> <p>Conclusions</p> <p>in uninfected infants born to HIV+ women, several immunological abnormalities were found, related to the residual maternal immune changes induced by the HIV infection and those associated with antiretroviral treatment. Maternal smoking was associated to changes in cord CD3/CD4 lymphocytes and maternal hard drug abuse was associated with more pronounced changes in the cord B cell line.</p

Updated research nosology for HIV-associated neurocognitive disorders

In 1991, the AIDS Task Force of the American Academy of Neurology published nomenclature and research case definitions to guide the diagnosis of neurologic manifestations of HIV-1 infection. Now, 16 years later, the National Institute of Mental Health and the National Institute of Neurological Diseases and Stroke have charged a working group to critically review the adequacy and utility of these definitional criteria and to identify aspects that require updating. This report represents a majority view, and unanimity was not reached on all points. It reviews our collective experience with HIV-associated neurocognitive disorders (HAND), particularly since the advent of highly active antiretroviral treatment, and their definitional criteria; discusses the impact of comorbidities; and suggests inclusion of the term asymptomatic neurocognitive impairment to categorize individuals with subclinical impairment. An algorithm is proposed to assist in standardized diagnostic classification of HAND

K Nearest Neighbor Classification with Local Induction of the Simple Value Difference Metric

The classical k nearest neighbor (k-nn) classification assumes that a fixed global metric is defined and searching for nearest neighbors is always based on this global metric. In the paper we present a model with local induction of a metric. Any test object induces a local metric from the neighborhood of this object and selects k nearest neighbors according to this locally induced metric. To induce both the global and the local metric we use the weighted Simple Value Difference Metric (SVDM). The experimental results show that the proposed classification model with local induction of a metric reduces classification error up to several times in comparison to the classical k-nn method