Why is the video analytics accuracy fluctuating, and what can we do about it?

It is a common practice to think of a video as a sequence of images (frames), and re-use deep neural network models that are trained only on images for similar analytics tasks on videos. In this paper, we show that this leap of faith that deep learning models that work well on images will also work well on videos is actually flawed. We show that even when a video camera is viewing a scene that is not changing in any human-perceptible way, and we control for external factors like video compression and environment (lighting), the accuracy of video analytics application fluctuates noticeably. These fluctuations occur because successive frames produced by the video camera may look similar visually, but these frames are perceived quite differently by the video analytics applications. We observed that the root cause for these fluctuations is the dynamic camera parameter changes that a video camera automatically makes in order to capture and produce a visually pleasing video. The camera inadvertently acts as an unintentional adversary because these slight changes in the image pixel values in consecutive frames, as we show, have a noticeably adverse impact on the accuracy of insights from video analytics tasks that re-use image-trained deep learning models. To address this inadvertent adversarial effect from the camera, we explore the use of transfer learning techniques to improve learning in video analytics tasks through the transfer of knowledge from learning on image analytics tasks. In particular, we show that our newly trained Yolov5 model reduces fluctuation in object detection across frames, which leads to better tracking of objects(40% fewer mistakes in tracking). Our paper also provides new directions and techniques to mitigate the camera's adversarial effect on deep learning models used for video analytics applications

Low-Noise MMIC Amplifiers for 120 to 180 GHz

Three-stage monolithic millimeter-wave integrated-circuit (MMIC) amplifiers capable of providing useful amounts of gain over the frequency range from 120 to 180 GHz have been developed as prototype low-noise amplifiers (LNAs) to be incorporated into instruments for sensing cosmic microwave background radiation. There are also potential uses for such LNAs in electronic test equipment, passive millimeter- wave imaging systems, radar receivers, communication receivers, and systems for detecting hidden weapons. The main advantage afforded by these MMIC LNAs, relative to prior MMIC LNAs, is that their coverage of the 120-to-180-GHz frequency band makes them suitable for reuse in a wider variety of applications without need to redesign them. Each of these MMIC amplifiers includes InP transistors and coplanar waveguide circuitry on a 50- mthick chip (see Figure 1). Coplanar waveguide transmission lines are used for both applying DC bias and matching of input and output impedances of each transistor stage. Via holes are incorporated between top and bottom ground planes to suppress propagation of electromagnetic modes in the substrate. On the basis of computational simulations, each of these amplifiers was expected to operate with a small-signal gain of 14 dB and a noise figure of 4.3 dB. At the time of writing this article, measurements of noise figures had not been reported, but on-chip measurements had shown gains approaching their simulated values (see Figure 2)

Bioactive glass coatings on metallic implants for biomedical applications

Article provides a detailed overview of the benefits and issues of metal alloys when used as biomedical implants and how they are improved by bioactive glass-based coatings for biomedical applications

The MRN complex is transcriptionally regulated by MYCN during neural cell proliferation to control replication stress

The MRE11/RAD50/NBS1 (MRN) complex is a major sensor of DNA double strand breaks, whose role in controlling faithful DNA replication and preventing replication stress is also emerging. Inactivation of the MRN complex invariably leads to developmental and/or degenerative neuronal defects, the pathogenesis of which still remains poorly understood. In particular, NBS1 gene mutations are associated with microcephaly and strongly impaired cerebellar development, both in humans and in the mouse model. These phenotypes strikingly overlap those induced by inactivation of MYCN, an essential promoter of the expansion of neuronal stem and progenitor cells, suggesting that MYCN and the MRN complex might be connected on a unique pathway essential for the safe expansion of neuronal cells. Here, we show that MYCN transcriptionally controls the expression of each component of the MRN complex. By genetic and pharmacological inhibition of the MRN complex in a MYCN overexpression model and in the more physiological context of the Hedgehog-dependent expansion of primary cerebellar granule progenitor cells, we also show that the MRN complex is required for MYCN-dependent proliferation. Indeed, its inhibition resulted in DNA damage, activation of a DNA damage response, and cell death in a MYCN- and replication-dependent manner. Our data indicate the MRN complex is essential to restrain MYCN-induced replication stress during neural cell proliferation and support the hypothesis that replication-born DNA damage is responsible for the neuronal defects associated with MRN dysfunctions.Cell Death and Differentiation advance online publication, 12 June 2015; doi:10.1038/cdd.2015.81

Therapeutic Effect of a Poly(ADP-Ribose) Polymerase-1 Inhibitor on Experimental Arthritis by Downregulating Inflammation and Th1 Response

Poly(ADP-ribose) polymerase-1 (PARP-1) synthesizes and transfers ADP ribose polymers to target proteins, and regulates DNA repair and genomic integrity maintenance. PARP-1 also plays a crucial role in the progression of the inflammatory response, and its inhibition confers protection in several models of inflammatory disorders. Here, we investigate the impact of a selective PARP-1 inhibitor in experimental arthritis. PARP-1 inhibition with 5-aminoisoquinolinone (AIQ) significantly reduces incidence and severity of established collagen-induced arthritis, completely abrogating joint swelling and destruction of cartilage and bone. The therapeutic effect of AIQ is associated with a striking reduction of the two deleterious components of the disease, i.e. the Th1-driven autoimmune and inflammatory responses. AIQ downregulates the production of various inflammatory cytokines and chemokines, decreases the antigen-specific Th1-cell expansion, and induces the production of the anti-inflammatory cytokine IL-10. Our results provide evidence of the contribution of PARP-1 to the progression of arthritis and identify this protein as a potential therapeutic target for the treatment of rheumatoid arthritis

Functional Annotation and Identification of Candidate Disease Genes by Computational Analysis of Normal Tissue Gene Expression Data

Background: High-throughput gene expression data can predict gene function through the ‘‘guilt by association’ ’ principle: coexpressed genes are likely to be functionally associated. Methodology/Principal Findings: We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characterized RCGs are enriched in groups of genes associated to similar phenotypes. We exploit this fact to find new candidate disease genes for many OMIM phenotypes of unknown molecular origin. Conclusions/Significance: We predict new functional annotations for many human genes, showing that the integration of different data sets and coexpression measures significantly improves the scope of the results. Combining gene expression data, functional annotation and known phenotype-gene associations we provide candidate genes for several geneti

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

A systematic experimental neuropsychological investigation of the functional integrity of working memory circuits in major depression

Verbal and visuospatial working memory (WM) impairment is a well-documented finding in psychiatric patients suffering from major psychoses such as schizophrenia or bipolar affective disorder. However, in major depression (MDD) the literature on the presence and the extent of WM deficits is inconsistent. The use of a multitude of different WM tasks most of which lack process-specificity may have contributed to these inconsistencies. Eighteen MDD patients and 18 healthy controls matched with regard to age, gender and education were tested using process- and circuit-specific WM tasks for which clear brain-behaviour relationships had been established in prior functional neuroimaging studies. Patients suffering from acute MDD showed a selective impairment in articulatory rehearsal of verbal information in working memory. By contrast, visuospatial WM was unimpaired in this sample. There were no significant correlations between symptom severity and WM performance. These data indicate a dysfunction of a specific verbal WM system in acutely ill patients with MDD. As the observed functional deficit did not correlate with different symptom scores, further, longitudinal studies are required to clarify whether and how this deficit is related to illness acuity and clinical state of MDD patients

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal