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* Context.--Critically ill patients with acute kidney injury (AKI) requiring renal replacement therapy (RRT) have a poor prognosis. Several urinary AKI biomarkers have been proposed to predict renal recovery, but with limited discriminatory ability. Objective.--To validate the predictive performances of novel biomarkers to identify which critical patients with AKI may successfully wean from RRT. Design.--We prospectively recorded and analyzed clinical variables at several time points: (1) before starting RRT, (2) at the time of weaning off RRT, and (3) 24 hours after stopping RRT. A total of 140 critically ill patients who received RRT at a multicenter referral hospital from August 2016 to January 2019 were enrolled. The outcomes of interest were the ability to wean from RRT and 90-day mortality. Results.--The 90-day mortality rate was 13.6% (19 of 140), and 47.9% (67 of 140) of the patients were successfully weaned from RRT. Cluster analysis showed that the following biomarkers were correlated with estimated glomerular filtration rate at the time of weaning off RRT: urinary neutrophil gelatinase-associated lipocalin, kidney injury molecule 1, hemojuvelin, C-C motif chemokine ligand 14, interleukin 18, and liver-type fatty acid--binding protein (L-FABP). Among these, urinary L-FABP/creatinine (uL-FABP/Cr) at the time of weaning off RRT showed the best predictive performance for mortality (area under the receiver operating characteristic curve = 0.79). Taking mortality as a competing risk, Cox proportional hazards analysis indicated that a low uL-FABP/Cr (log) level was an independent prognostic factor for weaning from RRT (subdistribution hazard ratio, 0.35; P = .01). Conclusions.--uL-FABP/Cr at the time of weaning off RRT could predict weaning from RRT and 90-day mortality. [ABSTRACT FROM AUTHOR]

The article focuses on the transformative impact of big data on Earth science, particularly in hydrology, oceanography, and atmospheric science, highlighting advancements in surface water delineation, oceanic 3D modeling, and atmospheric analyses. It discusses how big data address challenges like replicability, reproducibility, and transitioning raw data into informative products.

• An artificial intelligence platform is applied to the analysis of blastocyst expansion • This platform can be used for single blastocyst selection for transfer • Such selection can result in high sustained implantation rates both with and without PTG-A Can artificial intelligence (AI) discriminate a blastocyst's cellular area from unedited time-lapse image files using semantic segmentation and a deep learning optimized U-Net architecture for use in selecting single blastocysts for transfer? This platform was retrospectively applied to time-lapse files from 101 sequentially transferred single blastocysts that were prospectively selected for transfer by their highest expansion ranking within cohorts using a 10 h expansion assay rather than standard grading. The AI platform provides expansion curves and raw data files to classify and compare blastocyst phenotypes within both cohorts and populations. Of 35 sequential unbiopsied single blastocyst transfers, 23 (65.7%) resulted in a live birth. Of 66 sequential single euploid blastocyst transfers, also selected for their most robust expansion, 49 (74.2%) resulted in live birth. The AI platform revealed that the averaged expansion rate was significantly (P = 0.007) greater in euploid blastocysts that resulted in live births compared with those resulting in failure to give a live birth. The platform further provides a framework to analyse fragmentation phenotypes that can test new hypotheses for developmental regulation during the preimplantation period. AI can be used to quantitatively describe blastocyst expansion from unedited time-lapse image files and can be used to quantitatively rank-order blastocysts for transfer. Early clinical results from such single blastocyst selection suggests that live birth rates without biopsy may be comparable to those found using single euploid blastocysts in younger, good responder patients. [ABSTRACT FROM AUTHOR]

How can the kinetics of human blastocyst expansion be used to evaluate an embryo's ploidy identified using preimplantation genetic testing for aneuploidy (PGT-A)? This was a retrospective observational study of 188 autologous blastocysts from 34 sequential treatment cycles using PGT-A and blastocyst biopsy. Using time-lapse imaging, blastocyst expansion was evaluated using a quantitative standardized expansion assay (qSEA). Trophectoderm cell division was examined in selected, unbiopsied embryos (n = 7) to evaluate the contribution of mitosis to the expansion rate. The averaged euploid blastocyst expansion rate was significantly (52.8%) faster than in aneuploid blastocysts (P = 0.0041). Scatterplots, representing 'expansion maps', revealed that both populations showed a similarly overlapping distribution of blastocyst formation times at 80–140 h from fertilization. Euploidy and aneuploidy were better distinguished in regions of higher and lower expansion, respectively, in expansion maps. Based upon the expansion slopes, rank-ordering of individual embryos within cohorts resulted in more than 90% euploid embryos in the first two ranks in patients less than 35 years of age. Additional detailed time-lapse image analysis provided evidence that rapid expansion was associated with robust, integrative cellular mitosis in trophectoderm cells. The kinetics of human blastocyst expansion are related to an embryo's ploidy. These preliminary observations describe a new quantitative, non-invasive approach to embryo assessment that may be useful to identify single blastocysts for transfer, particularly in younger patient groups. However, this approach may also be useful for euploid embryo selection after PGT-A. The results support the hypothesis that aneuploidy universally impairs general cellular processes, including cell division, in differentiated cells. [ABSTRACT FROM AUTHOR]

EML4-ALK alterations are more common in adenocarcinomas and are rarely found in squamous cell histology. In documented cases, the majority of EML4-ALK translocations are identified in squamous cell histology and occur in patients with no or light smoking history. We report an EML4-ALK4 translocation in a 50-year-old patient with squamous cell carcinoma and an 18 pack-year smoking history. The patient had a near complete response in the CNS to alectinib treatment. Our observation suggests that EML4-ALK genomic testing may be clinically useful in patients with heavy smoking history. [ABSTRACT FROM AUTHOR]

Minimum-cost flow algorithms have recently achieved state-of-the-art results in multi-object tracking. However, they rely on the whole image sequence as input. When deployed in real-time applications or in distributed settings, these algorithms first operate on short batches of frames and then stitch the results into full trajectories. This decoupled strategy is prone to errors because the batch-based tracking errors may propagate to the final trajectories and cannot be corrected by other batches. In this paper, we propose a greedy batch-based minimum-cost flow approach for tracking multiple objects. Unlike existing approaches that conduct batch-based tracking and stitching sequentially, we optimize consecutive batches jointly so that the tracking results on one batch may benefit the results on the other. Specifically, we apply a generalized minimum-cost flows (MCF) algorithm on each batch and generate a set of conflicting trajectories. These trajectories comprise the ones with high probabilities, but also those with low probabilities potentially missed by detectors and trackers. We then apply the generalized MCF again to obtain the optimal matching between trajectories from consecutive batches. Our proposed approach is simple, effective, and does not require training. We demonstrate the power of our approach on data sets of different scenarios. [ABSTRACT FROM PUBLISHER]

Background: Metabolic syndrome is associated with pathological remodeling of the heart and adjacent vessels. The early biochemical and cellular changes underlying the vascular damage are not fully understood. In this study, we sought to establish the nature, extent, and initial timeline of cytochemical derangements underlying reduced ventriculo-arterial compliance in a swine model of metabolic syndrome. Methods: Yorkshire swine (n = 8 per group) were fed a normal diet (ND) or a high-cholesterol (HCD) for 12 weeks. Myocardial function and blood flow was assessed before harvesting the heart. Immuno-blotting and immuno-histochemical staining were used to assess the cellular changes in the myocardium, ascending aorta and left anterior descending artery (LAD). Results: There was significant increase in body mass index, blood glucose and mean arterial pressures (p = 0.002, p = 0.001 and p = 0.024 respectively) in HCD group. At the cellular level there was significant increase in anti-apoptotic factors p-Akt (p = 0.007 and p = 0.002) and Bcl-xL (p = 0.05 and p = 0.01) in the HCD aorta and myocardium, respectively. Pro-fibrotic markers TGF-β (p = 0.01), pSmad1/5 (p = 0.03) and MMP-9 (p = 0.005) were significantly increased in the HCD aorta. The levels of pro-apoptotic p38MAPK, Apaf-1 and cleaved Caspase3 were significantly increased in aorta of HCD (p = 0.03, p = 0.04 and p = 0.007 respectively). Similar changes in coronary arteries were not observed in either group. Functionally, the high cholesterol diet resulted in significant increase in ventricular end systolic pressure and–dp/dt (p = 0.05 and p = 0.007 respectively) in the HCD group. Conclusion: Preclinical metabolic syndrome initiates pro-apoptosis and pro-fibrosis pathways in the heart and ascending aorta, while sparing coronary arteries at this early stage of dietary modification. [ABSTRACT FROM AUTHOR]

A new all-optical microwave photonic filter structure that can realize arbitrary programmable complex coefficients, multiple taps, and shape-invariant frequency tuning over the full free-spectral range (FSR) range is presented. It is based on a new optical RF phase shifter achieved by using a programmable wavelength processor (PWP) comprising an array of liquid crystal on silicon pixels. It manipulates the amplitude and phase of optical spectral components, which enables both single-sideband modulation and arbitrary complex coefficient function to be obtained simply by versatile programming the PWP only, without changing the rest of the structure. Experimental results demonstrate reconfigurable and tunable shape-invariant multitap RF filters with wideband tuning range over the full FSR by software programming of the complex coefficients. [ABSTRACT FROM PUBLISHER]

The formation of the tropylium ion, C7H7+, in the mass spectrum of toluene is a chemical process that has been extensively studied. There is, however, still debate as to the structure of the moieties and the reaction pathways involved. This work presents the first computationally complete reaction schemes for the formation of tropylium from toluene to be reported. The calculations were performed at the HF/6-31G(d, p) and the DFT/B3LYP/6-311++G(2d) levels of theory using Gaussian 03W. The previously unreported optimized structures and energies for a transition state and an intermediate in one scheme and a transition state in the other have been determined. These results are consistent with the previously reported literature and the available experimental data. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2009 [ABSTRACT FROM AUTHOR]

Images, as high-dimensional data, usually embody large variabilities. To classify images for versatile applications, an effective algorithm is necessarily designed by systematically considering the data structure, similarity metric, discriminant subspace, and classifier. In this paper, we provide evidence that, besides the Fisher criterion, graph embedding, and tensorization used in many existing methods, the correlation-based similarity metric embodied in supervised multilinear discriminant subspace learning can additionally improve the classification performance. In particular, a novel discriminant subspace learning algorithm, called correlation tensor analysis (CTA), is designed to incorporate both graph-embedded correlational mapping and discriminant analysis in a Fisher type of learning manner. The correlation metric can estimate intrinsic angles and distances for the locally isometric embedding, which can deal with the case when Euclidean metric is incapable of capturing the intrinsic similarities between data points. CTA learns multiple interrelated subspaces to obtain a low-dimensional data representation reflecting both class label information and intrinsic geometric structure of the data distribution. Extensive comparisons with most popular subspace learning methods on face recognition evaluation demonstrate the effectiveness and superiority of CTA. Parameter analysis also reveals its robustness. [ABSTRACT FROM AUTHOR]

This study tested a method for maintaining human spermatozoa without freezing for subsequent use in intracytoplasmic sperm injection (ICSI). We demonstrated that human sperm stored in electrolyte-free solution maintain their motility and viability for at least 4 and 6 weeks, respectively. We also have shown that preserved spermatozoa are fully functional in ICSI. Sperm chromosome analysis after injection of human sperm into mouse oocytes revealed that two weeks of storage does not negatively affect sperm DNA integrity. A mouse model was used to analyze the ability of preserved sperm to participate in normal embryogenesis. Mouse sperm preserved in electrolyte-free solution in a similar manner as human sperm maintained motility for up to 3 weeks. When mouse spermatozoa stored for 1 week were injected into the oocytes by ICSI, they yielded normal blastoctysts and normal viable fetuses. The results of the study bear significance for human assisted reproduction technologies (ART) and provide clinicians and infertile patients with a new method that can simplify sperm preparation for ICSI, assisting men who are unable to provide semen on the day of assisted fertilization. [ABSTRACT FROM AUTHOR]

A fast turnaround and an accurate computational fluid dynamics (CFD) approach for ship total resistance prediction is developed. The approach consists of a nonlinear free surface potential flow solver (PShip code) with a wet-or-dry transom stern model, and a Reynolds-averaged Navier-Stokes (RANS) equation solver that solves viscous free surface flow with a prescribed free surface given from the PShip. The prescribed free surface RANS predicts a viscous correction to the pressure resistance (viscous form) and viscous flow field around the hull. The viscous free surface flow solved this way avoids the time-consuming RANS iterations to resolve the free surface profile. The method, however, requires employing a flow characteristic-based nonreflecting boundary condition at the free surface. The approach can predict the components of ship resistance, the associated wave profile around the hull, and the sinkage and trim of the ship. Validation of the approach is presented with Wigley, Series 60 (CB = 0.6), and NSWCCD Model 5415 hulls. An overall accuracy of ±2% for ship total resistance prediction is achieved. The approach is applied to evaluating the effects of a stern flap on a DD 968 model on ship performance. An empirical viscous form resistance formula is also devised for a quick ship total resistance estimate. [ABSTRACT FROM AUTHOR]

The 2-norbornyl cation is an old topic in physical-organic chemistry. Whether in classical or non-classical form (partial bridged form) it has been one of the focus of discussion. Currently the experimental data and theoretical calculations favorably support the idea that 2-norbornyl cation is not in the classical form in the stable-ion condition. In this paper, first, we will show that a 3-center-2-electron π-complex is formed by the collapse of 2-norbornyl cation. Further, using different theoretical methods (B3LYP, MP2) with different basis sets (6-31+G, 6-31G(d,p), 6-311G(d,p), 6-311G(2d,p)), we find that there is a trend for the 3-center-2-electron π-complex to approach the Cs symmetry, and this π-complex oscillates within the numerical limits of the perfect Cs symmetrical configuration. The stabilization energies of the π-complex are 13.87 Kcal/mol and 19.47 Kcal/mol by B3LYP/6-31+G and MP2/6-31+G, respectively. Second, our calculations also show that the transition state between 2-norbornyl cation and 3-norbornyl cation is formed by a 3, 2-proton shift, not the generally accepted 3, 2-hydride shift. The activation energy of this 3, 2-proton shift is 10.9 Kcal/mol. Detailed structural changes in the optimization process and the formation of transition state (also a 3-center-2-electron π-complex) between 2-norbornyl cation and 3-norbornyl cation will also be included. [ABSTRACT FROM AUTHOR]

Visual tracking can be treated as a parameter estimation problem that infers target states based on image observations from video sequences. A richer target representation may incur better chances of successful tracking in cluttered and dynamic environments, and thus enhance the robustness. Richer representations can be constructed by either specifying a detailed model of a single cue or combining a set of rough models of multiple cues. Both approaches increase the dimensionality of the state space, which results in a dramatic increase of computation. To investigate the integration of rough models from multiple cues and to explore computationally efficient algorithms, this paper formulates the problem of multiple cue integration and tracking in a probabilistic framework based on a factorized graphical model. Structured variational analysis of such a graphical model factorizes different modalities and suggests a co-inference process among these modalities. Based on the importance sampling technique, a sequential Monte Carlo algorithm is proposed to provide an efficient simulation and approximation of the co-inferencing of multiple cues. This algorithm runs in real-time at around 30 Hz. Our extensive experiments show that the proposed algorithm performs robustly in a large variety of tracking scenarios. The approach presented in this paper has the potential to solve other problems including sensor fusion problems. [ABSTRACT FROM AUTHOR]

This paper presents the methodology and theory for automatic spatial pattern discovery from multiple attributed relational graph samples. The spatial pattern is modelled as a mixture of probabilistic parametric attributed relational graphs. A statistic learning procedure is designed to learn the parameters of the spatial pattern model from the attributed relational graph samples. The learning procedure is formulated as a combinatorial non-deterministic process, which uses the expectation–maximization (EM) algorithm to find the maximum-likelihood estimates for the parameters of the spatial pattern model. The learned model summarizes the samples and captures the statistic characteristics of the appearance and structure of the spatial pattern, which is observed under various conditions. It can be used to detect the spatial pattern in new samples. The proposed approach is applied to unsupervised visual pattern extraction from multiple images in the experiments. [Copyright &y& Elsevier]

We have calculated the structures, the heats of hydrogenation and the resonance energies of benzene isomers. All structures and energies were calculated by using the MMX force fields. Using PC model, the calculated structure parameters of benzene's 8 isomers are generally in good agreements with the experimental data. The heats of hydrogenation and resonance energy of benzene isomers are parallel to those experimental data and need a systematic adjustment of 4.5 kJ/mol. [ABSTRACT FROM AUTHOR]

This paper describes a new clustering based feature extraction method for facial expression recognition. We demonstrate the effectiveness of this method and compare it with commonly used principal component analysis method and linear discriminant analysis method. [Copyright &y& Elsevier]

Digital watermarking is an emerging technique to protect data security and intellectual property right. Identification or verification of watermarking patterns can be achieved by detecting watermarks in received signals. However, one of the biggest challenges in watermarking detection is that the strengths of the watermark signals will change after being distorted by an attacker in a watermarking channel. Meanwhile, the embedding strengths may be adapted to original signals, which are unknown at the receiver end. Further, the original signals are often highly non-Gaussian. Although some work has been done on optimum detection of watermarks, the uncertainty of watermark signal strengths and real statistical behavior of multimedia contents have not been taken into account simultaneously. Much more study is needed to enhance the performance of watermarking systems. Since multiplicative watermarks are robust and well suited for copyright protection, this paper presents our investigation on robust optimum detection of multiplicative watermarks. For sub-band transformed domains such as the discrete cosine transform (DCT), discrete wavelet transform (DWT), and pyramid transform, a class of generalized correlators is constructed based on the generalized Gaussian distributions. Thresholding methods to achieve a given false alarm rate, and the performance analyses are provided. The square-root detector is designed and demonstrated to have near optimal performance for a large set of natural images and can be employed as a "universally optimal" detector or decoder for images and video. The locally most powerful detection method is then extended to DFT domain multiplicative watermarking, with the magnitudes of coefficients modeled by the Weibull distributions. Another class of detectors is built based on this statistical modeling. The robust optimum detection of multiplicative watermarks can be applied to copyright notification, enforcement, and broadcast monitoring. We... [ABSTRACT FROM AUTHOR]

Investigates color-based image segmentation and nonstationary color-based target tracking for vision-based human-computer interaction. Representations of color distributions; Description of a structure adaptive self-organizing map neural network; Formulation of the color tracking problem.

Discusses how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. State of the art in multimedia database management; Association of multimedia content with semantic concepts; Knowledge representations for multimedia content.

Many visual learning tasks are usually confronted by some common difficulties. One of them is the lack of supervised information, due to the fact that labeling could be tedious, expensive or even impossible. Another difficulty is the high dimensionality of the visual data. Fortunately, these difficulties could be alleviated by using a hybrid of labeled and unlabeled training data for learning. Since the unlabeled data characterize the joint probability across different features, they could be used to boost weak classifiers by exploring discriminating features in a self-supervised fashion. This paper proposes a novel method, the Discriminant-EM (D-EM) algorithm, which attacks these difficulties by integrating discriminant analysis with the EM framework in this hybrid formulation. Both linear and nonlinear methods are investigated in this paper. Based on kernel multiple discriminant analysis, the nonlinear D-EM provides better ability to simplify the probabilistic structures of data distributions in a discrimination space. We also propose a novel data-sampling scheme for efficient learning of kernel discriminants. Our experimental results show that D-EM outperforms a variety of supervised and semi-supervised learning algorithms for many visual learning tasks, such as content-based image retrieval, invariant object recognition, and nonstationary color tracking. The proposed approach could be easily applied for many other learning tasks. [Copyright &y& Elsevier]

In this article, a new video coding system is proposed which takes advantage of both intrascale and interscale similarities present in video sequences. Each block in every frame is approximated with a linear combination of members of an adaptive library of blocks constructed for that block. This library is made up of some fixed blocks (e.g., DCT basis blocks) and some adaptive blocks. The adaptive library blocks are blocks of the same size or larger blocks that are shrunk, taken from the previous frame. For each block in every frame, a rate-distortion optimized fully orthogonal matching pursuit algorithm is used to determine how many and which ones of the members of the library constructed for that block should be selected and linearly combined to approximate the block most efficiently in terms of bit rate and rms error. Simulation results on sample gray-scale video sequence “Miss America” suggests that this method has promising coding performance in terms of PSNR and bit rate,especially compared to other published fractal-based video compression methods. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 305–319, 1998 [ABSTRACT FROM AUTHOR]

Presents relevant feedback from two characteristics of content-based image retrieval (CBIR). Details on the experimental results; Description of the multimedia object model; Integrating relevance feedback in CBIR.

Looks at a computational framework, which is used in the extraction of salient patterns, called vortex structures, from 3D turbulent fluid flows, relating to pattern recognition. Detailed information on the vortex structure and its detection; Information on the results of experiments conducted; Other detailed information.

This article presents a model-based approach for nonrigid object motion and deformation analysis from 3D data. The modeling primitives used in this research are the superquadrics, which have already been proven useful in describing a variety of natural and man-made objects. This model-based approach is not only in accordance with the human visual perception process but also able to decouple the large and unstructured nonrigid motion estimation system into simple and well structured subsystems. We develop a recursive algorithm for estimating global motion and object shape, which effectively incorporates a priori knowledge of the object into the estimation procedure and obtains a good estimate of the global motion and object shape even if the given 3D points are distributed bias. After compensating for the global motion of the object, a tensor model of local deformation is introduced and a spherical harmonic surface-fitting algorithm is described such that the localized deformations of the object surface can be characterized. The local deformations of the object are then estimated using tensor-description-based analysis and parametrized by the directions and magnitudes of the extreme deformations in a localized surface element. To illustrate the potential of this model-based approach for nonrigid motion analysis, a real data example is presented using the proposed approach. This example involves estimating the left ventricle motion and deformations from a time sequence of 3D coordinates of coronary artery bifurcation points. The estimation results show the success of the model-based approach even when the given bifurcation points are distributed only on half of the left ventricle surface. [ABSTRACT FROM AUTHOR]

This paper describes a new algorithm which uses Gaussian and mean curvature values of the terrain surface to extract feature points. These points are then used for recognition of particular subregions of the terrain and in estimating relative positions of these subregions in the terrain. The Gaussian and mean curvatures are chosen because they are invariant under rotation and translation. In the Gaussian and mean curvature images, the points of maximum and minimum curvatures are extracted and used for matching. The stability of the position of these points in the presence of noise and with resampling is investigated. The input for this algorithm is 3D digital terrain data. Curvature values are calculated from the data by fitting a quadratic surface over a square window and calculating directional derivatives of this surface. A method of surface fitting that is invariant to sensor-centered coordinate system transformation is suggested and implemented. Real terrain data are used in our experiments. The algorithm is tested with and without the presence of noise, and its performance is described. [ABSTRACT FROM AUTHOR]

We propose a motion estimation system that uses stereo image pairs as the input data. To perform experimental work, we also obtain a sequence of outdoor stereo images taken by two metric cameras. The system consists of four main stages, which are (1) determination of point correspondences on the stereo images, (2) correction of distortions in image coordinates, (3) derivation of 3D point coordinates from 2D correspondences, and (4) estimation of motion parameters based on 3D point correspondences. For the first stage of the system, we use a four-way matching algorithm to obtain matched points on two stereo image pairs at two consecutive brae instants (t, and t,.,). Since the input data are stereo images taken by cameras, it has two types of distortions, which are (i) film distortion and (ii) lens distortion. These two distortions must be corrected before any process can be applied on the matched points. To accomplish this goal, we use (i) bilinear transform for film distortion correction and (ii) lens formulas for lens distortion correction. After correcting the distortions, the results are 2D coordinates of each matched point that can be used to derive 3D coordinates. However, due to data noise, the calculated 3D coordinates do not usually represent a consistent rigid structure that is suitable for motion estimation; therefore, we suggest a procedure to select good 3D point sets as the input for motion estimation. The procedure exploits two constraints, rigidity between different time instants and uniform point distribution across the object on the image. For the last stage, we use an algorithm to estimate the motion parameters. We also wish to know what is the effect of quantization error on the estimated results; therefore an error analysis based on quantization error is performed on the estimated motion parameters. In order to test our system, eight sets of stereo image pairs are extracted from an outdoor stereo image sequence and used as the input data. The experimental results indicate that the proposed system does provide reasonable estimated motion parameters. [ABSTRACT FROM AUTHOR]

Presents a new edge detection scheme that detects two-dimensional (2-D) edges by a curve-segment-based detection functional guided by the zero-crossing contours of the Laplacian-of-Gaussian (LOG) to approach the true edge locations. Optimal 2-D edge detection and edge regularization; Detecting salient edges at different scales.

Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales. However, recent advanced deep convolutional neural network-based methods for image restoration do not take full advantage of self-similarities by relying on self-attention neural modules that only process information at the same scale. To solve this problem, we present a novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid. Inspired by the fact that corruptions, such as noise or compression artifacts, drop drastically at coarser image scales, our attention module is designed to be able to borrow clean signals from their "clean" correspondences at the coarser levels. The proposed pyramid attention module is a generic building block that can be flexibly integrated into various neural architectures. Its effectiveness is validated through extensive experiments on multiple image restoration tasks: image denoising, demosaicing, compression artifact reduction, and super resolution. Without any bells and whistles, our PANet (pyramid attention module with simple network backbones) can produce state-of-the-art results with superior accuracy and visual quality. Our code is available at https://github.com/SHI-Labs/Pyramid-Attention-Networks [ABSTRACT FROM AUTHOR]

The use of a PtO[sub x] thin film as a transient template of Pt electrode in the fabrication of ferroelectric capacitor for memory devices was investigated. Fence-free patterning with a significantly high etch rate, selectivity, and sidewall slope was obtained from dry etching the photoresist-masked PtO[sub x] films using Ar/Cl[sub 2]/O[sub 2] helicon wave plasma. The x-ray photoelectron spectroscopy analysis indicates that the redeposition of PtCl[sub x] etch product can be repressed due to an in situ sidewall passivation with a PtO[sub 2] layer resulting from a further oxidation of the etched surface of PtO[sub x] film by the introduction of oxygen in the etching gas. Due to the chemical instability of oxygen in the PtO[sub x] film, a superior hydrogen blocking effect was also achieved in hydrogen plasma annealing of the Pb(Zr[sub 0.5]Ti[sub 0.5])O[sub 3] (PZT) ferroelectric capacitors with the use of PtO[sub x] as the top electrode, and the hydrogen degradation of the PZT capacitor was repressed. © 2003 American Institute of Physics. [ABSTRACT FROM AUTHOR]

The article discusses data access solutions for Earth science, focusing on the development of NEXUS, a Deep Data Platform, at the Jet Propulsion Laboratory of the U.S. National Aeronautics and Space Administration (NASA). Topics include the performance of Map Reduce paradigm for processing large volumes of data using clustering or cloud environment and NEXUS' handling file-based observational temporal, geospatial artifacts by taking advantage of the elasticity of cloud computing environment.

An Analytics Center Framework (ACF) is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources which subsequently enable the research community to focus on the investigation. The Earth science community is an innovative community. We produce many tools and solutions to improve how we do science. In computer science, a framework is a reusable, semi-complete application that can be specialized to produce custom applications [Johnson:88]. Over the past years, NASA has been developing an open source ACF in collaboration with Apache Software Foundation, called the Apache Science Data Analytics Platform (SDAP). It is a big data analytics platform designed for cloud-based data management, analytics, match up, and data discovery services. It is a community-support, extensible open source GIS platform. The motivation is empowering the Earth and Space Science Informatics community to develop a common big data solution for the cloud and on-premise cluster. This talk describes the Apache SDAP and lesson learned from developing and moving SDAP in production to support various NASA projects. [ABSTRACT FROM AUTHOR]

Purpose: This retrospective observational study compares how different classes of blastocyst genotypes from egg donor cycles differentially blastulate and expand using a standard assay. Methods: Quantitative measurements of expansion utilized a customized neural network that segments all sequential time-lapse images during the first 10 h of expansion. Results: Analyses were performed using two developmental time perspectives using time-lapse imaging. The first was the time to blastocyst formation (tB), which broadly reflects variations in developmental rate. Euploidy peaked at 100–115 h from fertilization. In contrast, aneuploidy peaks flanked this interval bi-modally. These distributions limit ploidy discrimination based upon traditional standard grading features when assessed in real time. In contrast, from the second perspective of progressive blastocyst expansion that is normalized to each individual blastocyst's tB time, euploidy was significantly increased at expansion values > 20,000µ2 across all tB intervals studied. A Cartesian coordinate plot graphically summarizes information useful to rank order blastocysts within cohorts for transfer. Defined aneuploidy subgroups, distinguished by the number and complexity of chromosomes involved, also showed distributive differences from both euploids and from each other. A small subset of clinically significant trisomies did not show discriminating features separating them from other euploids. Conclusion: A standard assay of blastocyst expansion normalized to each individual blastocyst's time of blastocyst formation more usefully discriminates euploidy from aneuploidy than real-time expansion comparisons using absolute developmental time from fertilization. [ABSTRACT FROM AUTHOR]

Subspace clustering methods partition the data that lie in or close to a union of subspaces in accordance with the subspace structure. Such methods with sparsity prior, such as sparse subspace clustering (SSC) (Elhamifar and Vidal in IEEE Trans Pattern Anal Mach Intell 35(11):2765-2781, 2013) with the sparsity induced by the ℓ1-norm, are demonstrated to be effective in subspace clustering. Most of those methods require certain assumptions, e.g. independence or disjointness, on the subspaces. However, these assumptions are not guaranteed to hold in practice and they limit the application of existing sparse subspace clustering methods. In this paper, we propose ℓ0-induced sparse subspace clustering (ℓ0-SSC). In contrast to the required assumptions, such as independence or disjointness, on subspaces for most existing sparse subspace clustering methods, we prove that ℓ0-SSC guarantees the subspace-sparse representation, a key element in subspace clustering, for arbitrary distinct underlying subspaces almost surely under the mild i.i.d. assumption on the data generation. We also present the “no free lunch” theorem which shows that obtaining the subspace representation under our general assumptions can not be much computationally cheaper than solving the corresponding ℓ0 sparse representation problem of ℓ0-SSC. A novel approximate algorithm named Approximate ℓ0-SSC (Aℓ0-SSC) is developed which employs proximal gradient descent to obtain a sub-optimal solution to the optimization problem of ℓ0-SSC with theoretical guarantee. The sub-optimal solution is used to build a sparse similarity matrix upon which spectral clustering is performed for the final clustering results. Extensive experimental results on various data sets demonstrate the superiority of Aℓ0-SSC compared to other competing clustering methods. Furthermore, we extend ℓ0-SSC to semi-supervised learning by performing label propagation on the sparse similarity matrix learnt by Aℓ0-SSC and demonstrate the effectiveness of the resultant semi-supervised learning method termed ℓ0-sparse subspace label propagation (ℓ0-SSLP). [ABSTRACT FROM AUTHOR]

In recent years, our world has experienced significant disruptions due to the COVID-19 pandemic, and Russia's 2022 invasion of Ukraine, impacting human activities and the global environment. This paper explored air quality changes in Ukraine due to COVID-19, and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get approach. During the COVID-19 pandemic, strict quarantine policies in Ukraine led to a 2% reduction in tropospheric NO2 concentration before the lockdown and 4% during the lockdown period. Cities like Kyiv, Donetsk, and Dnipro exhibited reductions of 5%, 11%, and 16%, respectively. Total SO2 column concentration decreased by 6% before the lockdown and 2.5% during the lockdown period, except in high population density areas. Kyiv showed the highest reduction of 17% in SO2 concentration, while Donetsk and Dnipro exhibited an 11% reduction. However, during the Russian invasion, there was a significant increase in tropospheric NO2 concentration in heavily destroyed Kharkiv while most eastern regions experienced a reduction. The total SO2 column was 48% higher before the war but reduced throughout the country after the war, except for in Kyiv and a few central regions. These findings can contribute to analyzing air pollution and building digital twin simulations for future reconstruction scenarios. [ABSTRACT FROM AUTHOR]

Abstract: The World-Wide Web provides tremendous resources in Multimedia Data, Ubiquitous Interconnection, and Storage/Computing Power. In this short note, we raise but not answer the question: Can the WWW help bridge the Semantic Gap in Computer Vision? Even if the answer turns out to be NO, we hope that by exploring these resources, we may gain a deeper understanding of the Semantic Gap challenge. [Copyright &y& Elsevier]

Focuses on the impact of the development in hardware, storage, connectivity and sensor technology to the introduction of large image repositories. Efficacy of man and machine interaction; Challenges of the computer vision in content-based retrieval; Citation of the essential components of image retrieval systems.

Aggregating features in terms of different convolutional blocks or contextual embeddings has been proven to be an effective way to strengthen feature representations for semantic segmentation. However, most of the current popular network architectures tend to ignore the misalignment issues during the feature aggregation process caused by step-by-step downsampling operations and indiscriminate contextual information fusion. In this paper, we explore the principles in addressing such feature misalignment issues and inventively propose Feature-Aligned Segmentation Networks (AlignSeg). AlignSeg consists of two primary modules, i.e., the Aligned Feature Aggregation (AlignFA) module and the Aligned Context Modeling (AlignCM) module. First, AlignFA adopts a simple learnable interpolation strategy to learn transformation offsets of pixels, which can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation. Second, with the contextual embeddings in hand, AlignCM enables each pixel to choose private custom contextual information adaptively, making the contextual embeddings be better aligned. We validate the effectiveness of our AlignSeg network with extensive experiments on Cityscapes and ADE20K, achieving new state-of-the-art mIoU scores of 82.6 and 45.95 percent, respectively. Our source code is available at https://github.com/speedinghzl/AlignSeg. [ABSTRACT FROM AUTHOR]

Link prediction is one of the most fundamental problems in graph modeling and mining. It has been studied in a wide range of scenarios, from uncovering missing links between different entities in databases, to recommending relations between people in social networks. In this problem, we wish to predict unseen links in a growing target network by exploiting existing structures in source networks. Most of the existing methods often assume that abundant links are available in the target network to build a model for link prediction. However, in many scenarios, the target network may be too sparse to enable robust inference process, which makes link prediction challenging with the paucity of link data. On the other hand, in many cases, other (more densely linked) auxiliary networks can be available that contains similar link structure relevant to that in the target network. The linkage information in the existing networks can be used in conjunction with the node attribute information in both networks in order to make more accurate link recommendations. Thus, this paper proposes the use of learning methods to perform link inference by transferring the link information from the source network to the target network. We also note that the source network may contain the link information irrelevant to the target network. This leads to cross-network bias between the networks, which makes the link model built upon the source network misaligned with the link structure of the target network. Therefore, we re-sample the source network to rectify such cross-network bias by maximizing the cross-network relevance measured by the node attributes, as well as preserving as rich link information as possible to avoid the loss of source link structure caused by the re-sampling algorithm. The link model based on the re-sampled source network can make more accurate link predictions on the target network with aligned link structures across the networks. We present experimental results illustrating the effectiveness of the approach. [ABSTRACT FROM PUBLISHER]

We present a semisupervised method for single-pixel classification of hyperspectral images. The proposed method is designed to address the special problematic characteristics of hyperspectral images, namely, high dimensionality of hyperspectral pixels, lack of labeled samples, and spatial variability of spectral signatures. To alleviate these problems, the proposed method features the following components. First, being a semisupervised approach, it exploits the wealth of unlabeled samples in the image by evaluating the confidence probability of the predicted labels, for each unlabeled sample. Second, we propose to jointly optimize the classifier parameters and the dictionary atoms by a task-driven formulation, to ensure that the learned features (sparse codes) are optimal for the trained classifier. Finally, it incorporates spatial information through adding a Laplacian smoothness regularization to the output of the classifier, rather than the sparse codes, making the spatial constraint more flexible. The proposed method is compared with a few comparable methods for classification of several popular data sets, and it produces significantly better classification results. [ABSTRACT FROM PUBLISHER]

Expression recognition from faces with varying pose and illumination conditions is a challenging research area with growing interest. In this paper, we develop a novel supervised super-vector encoding framework to learn discriminative image feature representations. The framework is then validated on the Multi-PIE and BU3D-FE databases for multi-view facial expression recognition. Extensive experiments show that our supervised framework gives significant improvement over the unsupervised counterpart and outperforms the state-of-the-arts. [ABSTRACT FROM AUTHOR]

In this paper, a novel discriminative dictionary learning method is proposed for sparse-representation-based classification (SRC) to label highly dimensional hyperspectral imagery (HSI). In SRC, a dictionary is conventionally constructed using all of the training pixels, which is not only inefficient due to the large size of typical HSI images but also ineffective in capturing class-discriminative information crucial for classification. We address the dictionary design problem with the inspiration from the learning vector quantization technique and propose a hinge loss function that is directly related to the classification task as the objective function for dictionary learning. The resulting online learning procedure systematically “pulls” and “pushes” dictionary atoms so that they become better adapted to distinguish between different classes. In addition, the spatial context for a test pixel within its local neighborhood is modeled using a Bayesian graph model and is incorporated with the sparse representation of a single test pixel in a unified probabilistic framework, which enables further refinement of our dictionary to capture the spatial class dependence that complements the spectral information. Experiments on different HSI images demonstrate that the dictionaries optimized using our method can achieve higher classification accuracy with substantially reduced dictionary size than using the whole training set. The proposed method also outperforms existing dictionary learning methods and attains the state-of-the-art results in both the spectral-only and spatial-spectral settings. [ABSTRACT FROM PUBLISHER]

Abstract: We propose a pose-robust face recognition method to handle the challenging task of face recognition in the presence of large pose difference between gallery and probe faces. The proposed method exploits the sparse property of the representation coefficients of a face image over its corresponding view-dictionary. By assuming the representation coefficients are invariant to pose, we can synthesize for the probe image a novel face image which has smaller pose difference with the gallery faces. Furthermore, face recognition in the presence of pose variations is achieved based on the synthesized face image again via sparse representation. Extensive experiments on CMU Multi-PIE face database are conducted to verify the efficacy of the proposed method. [Copyright &y& Elsevier]

Abstract: Sparsity driven classification method has been popular recently due to its effectiveness in various classification tasks. It is based on the assumption that samples of the same class live in the same subspace, thus a test sample can be well represented by the training samples of the same class. Previous methods model the subspace for each class with either the training samples directly or dictionaries trained for each class separately. Although enabling strong reconstructive ability, these methods may not have desirable discriminative ability, especially when there are high correlations among the samples of different classes. In this paper, we propose to learn simultaneously a discriminative projection and a dictionary that are optimized for the sparse representation based classifier, to extract discriminative information from the raw data while respecting the sparse representation assumption. By formulating the task of projection and dictionary learning into an optimization framework, we can learn the discriminative projection and dictionary effectively. Extensive experiments are carried out on various datasets and the experimental results verify the efficacy of the proposed method. [Copyright &y& Elsevier]