Wrennramos8521

Compared with existing learning anchoring-based approaches, the proposed method doesn't require any predefined anchors, while tremendously improving performances and adaptiveness of detectors. The proposed method can be seamlessly integrated to Faster RCNN, RetinaNet, and SSD, improving the detection mAP by 2.8%, 2.1% and 2.3% respectively on MS COCO 2017 test-dev set. Moreover, the differentiable anchoring-based detectors can be directly applied to specific scenarios without any modification of the hyperparameters or using a specialized optimization. Specifically, the differentiable anchoring-based RetinaNet achieves very competitive performances on tiny face detection and text detection tasks, which are not well handled by the conventional and guided anchoring based RetinaNets for the MS COCO dataset.This paper presents an iterative training of neural networks for intra prediction in a block-based image and video codec. First, the neural networks are trained on blocks arising from the codec partitioning of images, each paired with its context. Then, iteratively, blocks are collected from the partitioning of images via the codec including the neural networks trained at the previous iteration, each paired with its context, and the neural networks are retrained on the new pairs. Thanks to this training, the neural networks can learn intra prediction functions that both stand out from those already in the initial codec and boost the codec in terms of rate-distortion. Moreover, the iterative process allows the design of training data cleansings essential for the neural network training. When the iteratively trained neural networks are put into H.265 (HM-16.15), -4.2% of mean BD-rate reduction is obtained, i.e. -1.8% above the state-of-the-art. By moving them into H.266 (VTM-5.0), the mean BD-rate reduction reaches -1.9%.The ubiquitous presence of surveillance cameras severely compromises the security of private information (e.g. passwords) entered via a conventional keyboard interface in public places. We address this problem by proposing dual modulated QR (DMQR) codes, a novel QR code extension via which users can securely communicate private information in public places using their smartphones and a camera interface. Dual modulated QR codes use the same synchronization patterns and module geometry as conventional monochrome QR codes. Within each module, primary data is embedded using intensity modulation compatible with conventional QR code decoding. Specifically, depending on the bit to be embedded, a module is either left white or an elliptical black dot is placed within it. Additionally, for each module containing an elliptical dot, secondary data is embedded by orientation modulation; that is, by using different orientations for the elliptical dots. Because the orientation of the elliptical dots can only be reliably assessed when the barcodes are captured from a close distance, the secondary data provides "proximal privacy" and can be effectively used to communicate private information securely in public settings. read more Tests conducted using several alternative parameter settings demonstrate that the proposed DMQR codes are effective in meeting their objective- the secondary data can be accurately decoded for short capture distances (6 in.) but cannot be recovered from images captured over long distances (>12 in.). Furthermore, the proximal privacy can be adapted to application needs by varying the eccentricity of the elliptical dots used.Transcranial magnetic resonance guided focused ultrasound (tcMRgFUS) is gaining significant acceptance as a non-invasive treatment for motion disorders and shows promise for novel applications such as blood brain barrier opening for tumor treatment. A typical procedure relies on CT derived acoustic property maps to simulate the transfer of ultrasound through the skull. Accurate estimates of the acoustic attenuation in the skull are essential to accurate simulations, but there is no consensus about how attenuation should be estimated from CT images and there is interest in exploring MR as a predictor of attenuation in the skull. In this study we measure the acoustic attenuation at 0.5, 1, and 2.25 MHz in 89 samples taken from two ex-vivo human skulls. CT scans acquired with a variety of x-ray energies, reconstruction kernels, and reconstruction algorithms and MR images acquired with ultra short and zero echo time sequences are used to estimate the average Hounsfield unit value, MR magnitude, and T2* value in each sample. The measurements are used to develop a model of attenuation as a function of frequency and each individual imaging parameter.Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time generative model for 4D light field patches using variational autoencoders to capture the data distribution of light field patches. We develop a generative model conditioned on the central view of the light field and incorporate this as a prior in an energy minimization framework to address diverse light field reconstruction tasks. While pure learning-based approaches do achieve excellent results on each instance of such a problem, their applicability is limited to the specific observation model they have been trained on. On the contrary, our trained light field generative model can be incorporated as a prior into any model-based optimization approach and therefore extend to diverse reconstruction tasks including light field view synthesis, spatial-angular super resolution and reconstruction from coded projections. Our proposed method demonstrates good reconstruction, with performance approaching end-to-end trained networks, while outperforming traditional model-based approaches on both synthetic and real scenes. Furthermore, we show that our approach enables reliable light field recovery despite distortions in the input.Advances in the image-based diagnostics of complex biological and manufacturing processes have brought unsupervised image segmentation to the forefront of enabling automated, on the fly decision making. However, most existing unsupervised segmentation approaches are either computationally complex or require manual parameter selection (e.g., flow capacities in max-flow/min-cut segmentation). In this work, we present a fully unsupervised segmentation approach using a continuous max-flow formulation over the image domain while optimally estimating the flow parameters from the image characteristics. More specifically, we show that the maximum a posteriori estimate of the image labels can be formulated as a continuous max-flow problem given the flow capacities are known. The flow capacities are then iteratively obtained by employing a novel Markov random field prior over the image domain. We present theoretical results to establish the posterior consistency of the flow capacities. We compare the performance of our approach using brain tumor image segmentation, defect identification in additively manufactured components using electron microscopic images, and segmentation of multiple real-world images. Comparative results with several state-of-the-art supervised as well as unsupervised methods suggest that the present method performs statistically similar to the supervised methods, but results in more than 90% improvement in the Dice score when compared to the state-of-the-art unsupervised methods.Five Bifidobacterium strains, VB23T, VB24T, VB25T, VB26T and VB31T, were isolated from chimpanzee (Pan troglodytes), cotton-top tamarin (Saguinus oedipus), Goeldi's marmoset (Callimico goeldii), moustached tamarin (Saguinus mystax) and patas monkey (Erythrocebus patas), respectively, which were kept in two Czech zoos. These strains were isolated from faecal samples and were Gram-positive, non-motile, non-sporulating, anaerobic and fructose-6-phosphate phosphoketolase-positive. Phylogenetic analyses based on 16S rRNA revealed close relatedness between VB23T and Bifidobacterium angulatum LMG 11039T (96.0 %), VB24T and Bifidobacterium pullorum subsp. pullorum DSM 20433T (96.1 %), VB25T and Bifidobacterium goeldii LMG 30939T (96.5 %), VB26T and Bifidobacterium imperatoris LMG 30297T (98.1 %), and VB31T and B. angulatum LMG 11039T (99.40 %). Internal transcribed spacer profiling revealed that VB23T, VB24T, VB25T, VB26T and VB31T had highest similarity to Bifidobacterium breve LMG 13208T (77.2 %), Bifidobacterium longum subsp. infantis ATCC 15697T (85.8 %), Bifidobacterium biavatii DSM 23969T (76.9 %), B. breve LMG 13208T (81.2 %) and B. angulatum LMG 11039T (88.2 %), respectively. Average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) analyses with their closest neighbours supported the independent phylogenetic positions of the strains with values between 86.3 and 94.3 % for ANI and 25.8 and 54.9 % for dDDH. These genomic and phylogenetic analyses suggested that the evaluated strains were novel Bifidobacterium species named Bifidobacterium erythrocebi sp. nov. (VB31T=DSM 109960T=CCUG 73843T), Bifidobacterium moraviense sp. nov. (VB25T=DSM 109958T=CCUG 73842T), Bifidobacterium oedipodis sp. nov. (VB24T=DSM 109957T=CCUG 73932T), Bifidobacterium olomucense sp. nov. (VB26T=DSM 109959T=CCUG 73845T) and Bifidobacterium panos sp. nov. (VB23T=DSM 109963T=CCUG 73840T).Recombineering using bacteriophage lambda Red recombinase (λ-Red) uses homologous recombination to manipulate bacterial genomes and is commonly applied to disrupt genes to elucidate their function. This is often followed by the introduction of a wild-type copy of the gene on a plasmid to complement its function. This is often not, however, at a native copy number and the introduction of a chromosomal version of a gene can be a desirable solution to provide wild-type copy expression levels of an allele in trans. Here, we present a simple methodology based on the λ-Red-based 'gene doctoring' technique, where we developed tools used for chromosomal tagging in a conserved locus downstream of glmS and found no impact on a variety of important phenotypes. The tools described provide an easy, quick and inexpensive method of chromosomal modification for the creation of a library of insertion mutants to study gene function.

Many patients with ischemic stroke present with multiple comorbidities that threaten survival and recovery. This study sought to determine the risks of adverse long-term stroke outcomes associated with multimorbid diabetes mellitus and depression.

Retrospective analysis of prospectively collected data on consecutive patients without premorbid dementia admitted from the community for a first-ever acute ischemic stroke to comprehensive stroke centers across Ontario, Canada (2003-2013). Premorbid histories of diabetes mellitus and depression were ascertained within 5 years before stroke admission. Adjusted hazard ratios (aHR [95% CI]) of admission to long-term care, incident dementia, readmission for stroke or transient ischemic attack and all-cause mortality, over time among those discharged back into the community poststroke.

Among 23 579 stroke admissions, n=20 201 were discharged back into the community. Diabetes mellitus and depression were associated with synergistic hazards of admission to long-term care (X

=5.