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Utilizing Eyes Following like a Study Instrument inside the Deaf Health Reading and writing and Access to Health Details Task: Protocol for a Multisite Mixed Methods Examine along with Initial Final results.
Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose an efficient algorithm for graph partitioning, the "Mutex Watershed". Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.Thanks to the substantial and explosively inscreased instructional videos on the Internet, novices are able to acquire knowledge for completing various tasks. Over the past decade, growing efforts have been devoted to investigating the problem on instructional video analysis. However, the most existing datasets in this area have limitations in diversity and scale, which makes them far from many real-world applications where more diverse activities occur. To address this, we present a large-scale dataset named as "COIN" for COmprehensive INstructional video analysis. Organized with a hierarchical structure, the COIN dataset contains 11,827 videos of 180 tasks in 12 domains (e.g., vehicles, gadgets, etc.) related to our daily life. With a new developed toolbox, all the videos are annotated efficiently with a series of step labels and the corresponding temporal boundaries. In order to provide a benchmark for instructional video analysis, we evaluate plenty of approaches on the COIN dataset under five different settings. Furthermore, we exploit two important characteristics (i.e., task-consistency and ordering-dependency) for localizing important steps in instructional videos. Accordingly, we propose two simple yet effective methods, which can be easily plugged into conventional proposal-based action detection models. We believe the introduction of the COIN dataset will promote the future in-depth research on instructional video analysis for the community. Our dataset, annotation toolbox and source code are available at http//coin-dataset.github.io.Gaussian Graphical Models (GGM) are often used to describe the conditional correlations between the components of a random vector. learn more In this article, we compare two families of GGM inference methods nodewise edge selection and penalised likelihood maximisation. We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one. As a result, we propose a composite procedure that explores a family of graphs with an nodewise numerical scheme and selects a candidate among them with an overall likelihood criterion. We demonstrate that, when the number of observations is small, this selection method yields graphs closer to the truth and corresponding to distributions with better KL divergence with regards to the real distribution than the other two. Finally, we show the interest of our algorithm on two concrete cases first on brain imaging data, then on biological nephrology data. In both cases our results are more in line with current knowledge in each field.This paper presents a hardness-aware deep metric learning (HDML) framework for image clustering and retrieval. Most previous deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative samples for training. However, this mining strategy only utilizes a subset of training data, which may not be enough to characterize the global geometry of the embedding space comprehensively. To address this problem, we perform linear interpolation on embeddings to adaptively manipulate their hardness levels and generate corresponding label-preserving synthetics for recycled training, so that information buried in all samples can be fully exploited and the metric is always challenged with proper difficulty. As a single synthetic for each sample may still be not enough to describe the unobserved distributions of the training data which is crucial for generalization performance, we further extend HDML to generate multiple synthetics for each sample. We propose a randomly hardness-aware deep metric learning (HDML-R) method and an adaptively hardness-aware deep metric learning (HDML-A) method to sample multiple random and adaptive directions, respectively, for hardness-aware synthesis. Extensive experimental results on the widely used CUB-200-2011, Cars196, Stanford Online Products, In-Shop Clothes Retrieval, and VehicleID datasets demonstrate the effectiveness of the proposed framework.OBJECTIVE This work introduces a bio-inspired breastfeeding simulator (BIBS), an experimental apparatus that mimics infant oral behavior and milk extraction, with the application of studying the breastfeeding mechanism in vitro. METHODS The construction of the apparatus follows a clinical study by the authors that collects measurements of natural intra-oral vacuum pressure, the movements of the infant's jaw, tongue and upper palate, as well as nipple deformation and compression on the breast areola. The infant feeding mechanism simulator consists of a self-programmed vacuum pump assembly that replicates the infant's oral vacuum, two linear actuators and a motor-driven gear which represents oral compressive forces. A flexible, transparent and tissue-like breast phantom with bifurcated milk duct structure is designed and developed to work as the lactating human breast model. Bifurcated ducts are connected with a four-outlet manifold under a reservoir filled with milk-mimicking liquid. Piezoelectric sensors and a CCD (charge-coupled device) camera are used to record and measure the in vitro dynamics of the apparatus.
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