Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization

© 2015 Elsevier Ltd. All rights reserved. Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request

A parameterisable FPGA-tailored architecture for YOLOv3-Tiny

Object detection is the task of detecting the position of objects in an image or video as well as their corresponding class. The current state of the art approach that achieves the highest performance (i.e. fps) without significant penalty in accuracy of detection is the YOLO framework, and more specifically its latest version YOLOv3. When embedded systems are targeted for deployment, YOLOv3-tiny, a lightweight version of YOLOv3, is usually adopted. The presented work is the first to implement a parameterised FPGA-tailored architecture specifically for YOLOv3-tiny. The architecture is optimised for latency-sensitive applications, and is able to be deployed in low-end devices with stringent resource constraints. Experiments demonstrate that when a low-end FPGA device is targeted, the proposed architecture achieves a 290x improvement in latency, compared to the hard core processor of the device, achieving at the same time a reduction in mAP of 2.5 pp (30.9% vs 33.4%) compared to the original model. The presented work opens the way for low-latency object detection on low-end FPGA devices

Chemical characterization of water-soluble organic carbon aerosols at a rural site in the Pearl River Delta, China, in the summer of 2006

Online measurements of water-soluble organic carbon (WSOC) aerosols were made using a particle-into-liquid sampler (PILS) combined with a total organic carbon (TOC) analyzer at a rural site in the Pearl River Delta region, China, in July 2006. A macroporous nonionic (DAX-8) resin was used to quantify hydrophilic and hydrophobic WSOC, which are defined as the fractions of WSOC that penetrated through and retained on the DAX-8 column, respectively. Laboratory calibrations showed that hydrophilic WSOC (WSOCHPI) included low-molecular aliphatic dicarboxylic acids and carbonyls, saccharides, and amines, while hydrophobic WSOC (WSOCHPO) included longer-chain aliphatic dicarboxylic acids and carbonyls, aromatic acids, phenols, organic nitrates, cyclic acids, and fulvic acids. On average, total WSOC (TWSOC) accounted for 60% of OC, and WSOCHPO accounted for 60% of TWSOC. Both WSOC HIP and WSOCHPO increased with photochemical aging determined from the NOx/NOy ratio. In particular, the average WSOCHPO mass was found to increase by a factor of five within a timescale of ∼10 hours, which was substantially larger than that of WSOCHPI (by a factor of 2-3). The total increase in OC mass with photochemical aging was associated with the large increase in WSOCHPO mass. These results, combined with the laboratory calibrations, suggest that significant amounts of hydrophobic organic compounds (likely containing large carbon numbers) were produced by photochemical processing. By contrast, water-insoluble OC (WIOC) mass did not exhibit significant changes with photochemical aging, suggesting that chemical transformation of WIOC to WSOC was not a dominant process for the production of WSOC during the study period. Copyright 2009 by the American Geophysical Union

Weak stability of l_1-minimization methods in sparse data reconstruction

As one of the most plausible convex optimization methods for sparse data reconstruction, l_1-minimization plays a fundamental role in the development of sparse optimization theory. The stability of this method has been addressed in the literature under various assumptions such as the restricted isometry property, null space property, and mutual coherence. In this paper, we propose a unified means to develop the so-called weak stability theory for 1-minimization methods under the condition called the weak range space property of a transposed design matrix, which turns out to be a necessary and sufficient condition for the standard l_1-minimization method to be weakly stable in sparse data reconstruction. The reconstruction error bounds established in this paper are measured by the so-called Robinson’s constant. We also provide a unified weak stability result for standard l_1-minimization under several existing compressed sensing matrix properties. In particular, the weak stability of l_1-minimization under the constant-free range space property of order k of the transposed design matrix is established for the first time in this paper. Different from the existing analysis, we utilize the classic Ho˙man’s lemma concerning the error bound of linear systems as well as Dudley’s theorem concerning the polytope approximation of the unit l_2-ball to show that l_1-minimization is robustly and weakly stable in recovering sparse data from inaccurate measurements

Laimaphelenchus suberensis sp. nov. associated with Quercus suber in Portugal

Laimaphelenchus suberensis sp. nov. obtained from declining Quercus suber trees of Herdade da Gouveia de Baixo, Alentejo, Portugal, is described and illustrated based on morphological, biometrical and molecular characters. The diagnosis of Laimaphelenchus species has been commonly based on the presence or absence of a vulval flap and on the shape structure of the tail tip. The species described here has been included in the Laimaphelenchus group without vulval flap, and can be distinguished from morphologically similar species by its tail tip shape structure that has a stalk-like terminus and three diffuse tubercles with 4–6 finger-like protrusions. For the molecular analyses, the mitochondrial DNA region from the cytochrome oxidase subunit I (mtCOI), the D2-D3 expansion segments of the large subunit (LSU) and small subunit (SSU) of rRNA gene were amplified and sequenced. Sequences of L. suberensis sp. nov. clustered separately from all Laimaphelenchus spp. with available sequences in Genbank, confirming its identification as a new species. This is the second report of the genus Laimaphelenchus in Portugal, associated with Q. suber: L. heidelbergi and L. suberensis sp. nov.This research was supported by CFE, CIEPQPF and FEDER funds through the ‘Programa Operacional Factores de Competitividade – COMPETE’ and by national funds through FCT–Fundação para a Ciência e a Tecnologia under the projects UID/BIA/04004/2013, PEst-C/EQB/UI0102/2013 and FCOMP-01-0124-008937 (Ref. PTDC/BIA–BEC/102834/2008) and by Instituto do Ambiente, Tecnologia e Vida (IATV). Carla Maleita (SFRH/BPD/85736/2012) and Sofia Costa (SFRH/BPD/ 102438/2014) were financed by MEC National funding and The European Social Fund through POCH (Programa Operacional Capital Humano).info:eu-repo/semantics/publishedVersio

Biotic responses to volatile volcanism and environmental stresses over the Guadalupian-Lopingian (Permian) transition

Biotic extinction during the Guadalupian-Lopingian (G-L) transition is actively debated, with its timing, validity, and causality all questioned. Here, we show, based on detailed sedimentary, paleoecologic, and geochemical analyses of the Penglaitan section in South China, that this intra-Permian biotic crisis began with the demise of a metazoan reef system and extinction of corals and alatoconchid bivalves in the late Guadalupian. A second crisis, among nektonic organisms, occurred around the G-L boundary. Mercury concentration/total organic carbon (Hg/TOC) ratios show two anomalies. The first Hg/TOC peak broadly coincides with the reef collapse and a positive shift in Δ199Hg values during a lowstand interval, which was followed by microbial proliferation. A larger Hg/TOC peak is found just above the G-L boundary and speculatively represents a main eruption episode of the Emeishan large igneous province (ELIP). This volatile volcanism coincided with nektonic extinction, a negative δ13Ccarb excursion, anoxia, and sea-level rise. The temporal coincidence of these phenomena supports a cause-andeffect relationship and indicates that the eruption of the ELIP likely triggered the G-L crisis

Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection

Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivates the development of weakly supervised and few-shot object detection methods. However, these methods largely underperform with respect to their strongly supervised counterpart, as weak training signals \emph{often} result in partial or oversized detections. Towards solving this problem we introduce, for the first time, an online annotation module (OAM) that learns to generate a many-shot set of \emph{reliable} annotations from a larger volume of weakly labelled images. Our OAM can be jointly trained with any fully supervised two-stage object detection method, providing additional training annotations on the fly. This results in a fully end-to-end strategy that only requires a low-shot set of fully annotated images. The integration of the OAM with Fast(er) R-CNN improves their performance by $17\%$ mAP, $9\%$ AP50 on PASCAL VOC 2007 and MS-COCO benchmarks, and significantly outperforms competing methods using mixed supervision.Comment: Accepted at ECCV 2020. Camera-ready version and Appendice

Enhanced performance in polymer photovoltaic cells with chloroform treated indium tin oxide anode modification

Enhanced performance of a poly(3-hexylthiophene):(6,6)-phenyl C61 butyric acid methyl ester bulk heterojunction polymer photovoltaic cell is reported by modifying the indium tin oxide (ITO) anode with chloroform solution. Instead of the traditional UV-ozone treatment, the optimized chloroform modification on ITO anode can result in an enhancement in the power conversion efficiency of an identical device, originating from an increase in the photocurrent with negligible change in the open-circuit voltage. The performance enhancement is attributed to the work function modification of the ITO substrate through the surface incorporation of the chlorine, and thus improved charge collection efficiency. © 2011 American Institute of Physics

Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature Adaptation

In this paper, we look into the problem of estimating per-pixel depth maps from unconstrained RGB monocular night-time images which is a difficult task that has not been addressed adequately in the literature. The state-of-the-art day-time depth estimation methods fail miserably when tested with night-time images due to a large domain shift between them. The usual photo metric losses used for training these networks may not work for night-time images due to the absence of uniform lighting which is commonly present in day-time images, making it a difficult problem to solve. We propose to solve this problem by posing it as a domain adaptation problem where a network trained with day-time images is adapted to work for night-time images. Specifically, an encoder is trained to generate features from night-time images that are indistinguishable from those obtained from day-time images by using a PatchGAN-based adversarial discriminative learning method. Unlike the existing methods that directly adapt depth prediction (network output), we propose to adapt feature maps obtained from the encoder network so that a pre-trained day-time depth decoder can be directly used for predicting depth from these adapted features. Hence, the resulting method is termed as "Adversarial Domain Feature Adaptation (ADFA)" and its efficacy is demonstrated through experimentation on the challenging Oxford night driving dataset. Also, The modular encoder-decoder architecture for the proposed ADFA method allows us to use the encoder module as a feature extractor which can be used in many other applications. One such application is demonstrated where the features obtained from our adapted encoder network are shown to outperform other state-of-the-art methods in a visual place recognition problem, thereby, further establishing the usefulness and effectiveness of the proposed approach.Comment: ECCV 202

High-energy scale revival and giant kink in the dispersion of a cuprate superconductor

In the present photoemission study of a cuprate superconductor Bi1.74Pb0.38Sr1.88CuO6+delta, we discovered a large scale dispersion of the lowest band, which unexpectedly follows the band structure calculation very well. The incoherent nature of the spectra suggests that the hopping-dominated dispersion occurs possibly with the assistance of local spin correlations. A giant kink in the dispersion is observed, and the complete self-energy containing all interaction information is extracted for a doped cuprate in the low energy region. These results recovered significant missing pieces in our current understanding of the electronic structure of cuprates.Comment: 4 pages, 3 figures, submitted to Phys. Rev. Lett. on May 21, 200