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We present a novel approach to providing greater insight into the characteristics of an unlabelled dataset, increasing the efficiency with which labelled datasets can be created. We leverage dimension-reduction techniques in combination with autoencoders to create an efficient feature representation for image tiles derived from remote sensing satellite imagery. The proposed methodology consists of two main stages. Firstly, an autoencoder network is utilised to reduce the high-dimensional image tile data into a compact and expressive latentfeature representation. Subsequently, features are further reduced to a two-dimensional embedding space using the manifold learning algorithm Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbour Embedding (t-SNE). This step enables the visualization of the image tile clusters in a 2D plot, providing an intuitive and interactive representation that can be used to aid rapid and geographically distributed image labelling. To facilitate the labelling process, our approach allows users to interact with the 2D visualization and label clusters based on their domain knowledge. In cases where certain classes are not effectively separated, users can re-apply dimension reduction to interactively refine subsets of clusters and achieve better class separation, enabling a comprehensively labelled dataset. We evaluate the proposed approach on real-world remote sensing satellite image datasets and demonstrate its effectiveness in achieving accurate and efficient image tile clustering and labelling. Users actively participate in the labelling process through our interactive approach, leading to enhanced relevance of the labelled data, by allowing domain experts to contribute their expertise and enrich the dataset for improved downstream analysis and applications. [ABSTRACT FROM AUTHOR]

Maize lethal necrosis (MLN) is a devastating synergistic viral disease of maize caused by maize chlorotic mottle virus (MCMV) plus a virus in the Potyviridae family. The disease has severely impacted maize production in East Africa, Asia, and South America over the past decade, and the presence of MCMV and maize infecting potyviruses in the United States is an ongoing concern. In this study, we performed a genome‐wide association study (GWAS) using the Maize 282 Association Mapping Panel (AMP) to characterize the genetic architecture of host response to MCMV and to identify disease‐tolerant lines. The GWAS analysis detected genomic variants on chromosomes 3, 4, 5, and 8 that were significantly associated with response to MCMV. Several lines in the 282 AMP were highly tolerant to infection, including CML333, one of the parental lines used in the maize nested association mapping population. Quantitative trait locus analysis of the B73 × CML333 recombinant inbred line population revealed an MCMV tolerance locus on chromosome 10. These findings further our understanding of the genetic architecture of MCMV tolerance and will facilitate the development of maize lines, hybrids, and cultivars with improved resistance to MLN and MCMV. Core Ideas: A GWAS identified seven maize chlorotic mottle virus (MCMV) tolerance loci in the Maize 282 Association Mapping Panel.Maize lines with strong MCMV tolerance, including CML333, were identified.A MCMV tolerance QTL was mapped in the B73 × CML333 RIL population.New MCMV tolerant lines and tolerance loci will facilitate development of improved maize. [ABSTRACT FROM AUTHOR]

Maize lethal necrosis (MLN) is a devastating virus disease of maize, induced by co‐infection by Maize chlorotic mottle virus (MCMV) and one of several potyviruses. The maize synthetic population OhMCMV‐1 (Cycle‐1 [C1]) (Reg. no. GP‐623, PI 699306) was developed and released by the USDA‐ARS Corn, Soybean, and Wheat Quality Research Unit as a germplasm resource for improved MLN, MCMV, and Sugarcane mosaic virus (SCMV) resistance. The population has undergone one complete cycle of S1 recurrent selection for resistance to MCMV. Cycle‐1 S1 lines were significantly more resistant to MLN, MCMV, and SCMV than Cycle‐0 S1 lines. The elite virus resistance present within OhMCMV‐1 (C1) indicates this population has strong utility for developing new breeding lines, hybrids, and varieties and for helping to mitigate the economic impact of MLN. Core Ideas: Maize lethal necrosis is a devastating virus disease of maize.Genetic enhancement of maize germplasm is desirable for mitigating the impact of virus diseases.Recurrent selection is an effective strategy for improving resistance to maize lethal necrosis.The synthetic population OhMCMV‐1 is a valuable resource for developing maize lines with elite virus resistance. [ABSTRACT FROM AUTHOR]

Background: Since 2013, badger culling has been part of the UK Government's strategy for controlling bovine tuberculosis (bTB) within a high‐risk area (HRA) in England. Government surveillance data now enables an examination of bTB herd incidence and prevalence, its headline indicators, within and outside cull areas over the period 2009–2020. Methods: Analysis compared herd incidence and prevalence data from within and outside badger culling areas. A range of models (GLMs, GLMMs, GAMs and GAMMs) were used to analyse incidence and prevalence in culled and unculled areas using frequentist and Bayesian approaches. Change in incidence across ten county areas within the HRA for the period 2010–2020 was also compared. Results: Analyses based on Defra published data using a variety of statistical methodologies did not suggest that badger culling affected herd bTB incidence or prevalence over the study period. In 9 of 10 counties, bTB incidence peaked and began to fall before badger culling commenced. Limitations: There are limitations around the data available on culling location, temporal information and other confounding factors. As such, further analysis of any future datasets that may be released on bTB levels in areas where badger culling has been implemented is warranted. Conclusion: This examination of government data obtained over a wide area and a long time period failed to identify a meaningful effect of badger culling on bTB in English cattle herds. These findings may have implications for the use of badger culling in current and future bTB control policy. [ABSTRACT FROM AUTHOR]

A fully-parallelized work-time optimal algorithm is presented for computing the exact Euclidean Distance Transform (EDT) of a 2D binary image with the size of $n\times n$. Unlike existing PRAM (Parallel Random Access Machine) and other algorithms, this algorithm is suitable for implementation on modern SIMD (Single Instruction Multiple Data) architectures such as GPUs. As a fundamental operation of 2D EDT, 1D EDT is efficiently parallelized first. Specifically, the GPU algorithm for the 1D EDT, which uses CUDA (Compute Unified Device Architecture) binary functions, such as ballot(), ffs(), clz(), and shfl(), runs in $O(log_{32}n)$ time and performs $O(n)$ work. Using the 1D EDT as a fundamental operation, the fully-parallelized work-time optimal 2D EDT algorithm is designed. This algorithm consists of three steps. Step 1 of the algorithm runs in $O(log_{32}n)$ time and performs $O(N)$ ($N = n^{2}$) of total work on GPU. Step 2 performs $O(N)$ of total work and has an expected time complexity of $O(logn)$ on GPU. Step 3 runs in $O(log_{32}n)$ time and performs $O(N)$ of total work on GPU. As far as we know, this algorithm is the first fully-parallelized and realized work-time optimal algorithm for GPUs. The experimental results show that this algorithm outperforms the prior state-of-the-art GPU algorithms. [ABSTRACT FROM AUTHOR]

Maize lethal necrosis (MLN) disease caused by a combined infection of Maize chlorotic mottle virus (MCMV) and any cereal infecting potyvirus is a threat to food security in Sub-Saharan Africa (SSA). Resistance to potyvirus has been extensively studied and Mdm1 gene for potyvirus resistance on chromosome 6 of maize is linked to Y1 gene for maize endosperm colour. This study is aimed at selecting for coupling-phase recombination of potyvirus resistance and white endosperm colour. White susceptible maize lines CML333 and CML277 were crossed with a yellow resistant line, Pa405, to produce F1 and F2 progenies. Progenies were screened using molecular markers to recover 22 white endosperm recombinants. 22 selections were advanced to F3 recombinant families, and 10 were assayed for their responses to Maize dwarf mosaic virus (MDMV) and Sugarcane mosaic virus (SCMV). Four families segregated for SCMV resistance, selection of homozygous recombinants within these families will provide lines appropriate for improving lines with resistance to SCMV and MLN resistance in SSA. [ABSTRACT FROM AUTHOR]

There is a need for solutions which assist users to understand long time-series data by observing its changes over time, finding repeated patterns, detecting outliers, and effectively labeling data instances. Although these tasks are quite distinct and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system. [ABSTRACT FROM AUTHOR]

A pilot study was conducted in mid-Michigan to evaluate knowledge of appropriate hyperbaric oxygen therapy referrals in "first contact" physicians who did not have additional hyperbaric training. The hypothesis for this study is that many first-contact physicians have little or no exposure to hyperbaric oxygen (HBO2) therapy and its indications. A survey was distributed regarding accepted indications combined with conditions not currently approved or contraindicated for HBO2. Answers were tallied for correct identification of approved indications, missed approved indications, and inappropriate identification of unapproved indications. Ninety surveys were distributed and 62 physicians of various specialties responded. There were notably high percentages of missed indications, as high as 93%. Many emergent/urgent indications were missed as well. The highest percentage of wrong indications was 32%. Very concerning is the 13% who chose refractory pneumothorax as a condition responsive to HBO2 therapy. This study showed significant lack of familiarity of HBO2 treatment indications among physicians who did not have additional hyperbaric training. Inclusion of hyperbaric education during residencies may increase HBO2 referrals and improve outcomes for various disorders. [ABSTRACT FROM AUTHOR]

Collecting sensor data results in large temporal data sets which need to be visualized, analyzed, and presented. Onedimensional time-series charts are used, but these present problems when screen resolution is small in comparison to the data. This can result in severe over-plotting, giving rise for the requirement to provide effective rendering and methods to allow interaction with the detailed data. Common solutions can be categorized as multi-scale representations, frequency based, and lens based interaction techniques. [ABSTRACT FROM AUTHOR]

Biologists studying animals in their natural environment are increasingly using sensors such as accelerometers in animal-attached 'smart' tags because it is widely acknowledged that this approach can enhance the understanding of ecological and behavioural processes. The potential of such tags is tempered by the difficulty of extracting animal behaviour from the sensors which is currently primarily dependent on the manual inspection of multiple time series graphs. This is time consuming and error-prone for the domain expert and is now the limiting factor for realising the value of tags in this area. We introduce TimeClassifier, a visual analytic system for the classification of time series data for movement ecologists. We deploy our system with biologists and report two real-world case studies of its use. [ABSTRACT FROM AUTHOR]

Hyperspectral image visualization reduces color bands to three, but prevailing linear methods fail to address data characteristics, and nonlinear embeddings are computationally demanding. Qualitative evaluation of embedding is also lacking. We propose faithful stochastic proximity embedding (FSPE), which is a scalable and nonlinear dimensionality reduction method. FSPE considers the nonlinear characteristics of spectral signatures, yet it avoids the costly computation of geodesic distances that are often required by other nonlinear methods. Furthermore, we employ a pixelwise metric that measures the quality of hyperspectral image visualization at each pixel. FSPE outperforms the state-of-art methods by at least 12% on average and up to 25% in the qualitative measure. An implementation on graphics processing units is two orders of magnitude faster than the baseline. Our method opens the path to high-fidelity and real-time analysis of hyperspectral images. [ABSTRACT FROM PUBLISHER]

In this paper we introduce Order of Magnitude Markers (OOMMs) as a new technique for number representation. The motivation for this work is that many data sets require the depiction and comparison of numbers that have varying orders of magnitude. Existing techniques for representation use bar charts, plots and colour on linear or logarithmic scales. These all suffer from related problems. There is a limit to the dynamic range available for plotting numbers, and so the required dynamic range of the plot can exceed that of the depiction method. When that occurs, resolving, comparing and relating values across the display becomes problematical or even impossible for the user. With this in mind, we present an empirical study in which we compare logarithmic, linear, scale-stack bars and our new markers for 11 different stimuli grouped into 4 different tasks across all 8 marker types. [ABSTRACT FROM PUBLISHER]

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. [ABSTRACT FROM AUTHOR]

Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a nonuniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine tune the clustering results at runtime while avoiding any time-consuming recomputation. Our method maintains interactive rates even when hundreds of streamlines are used. [ABSTRACT FROM AUTHOR]

We introduce a novel algorithm for progressively removing noise from view-independent photon maps while simultaneously minimizing residual bias. Our method refines a primal set of photons using data from multiple successive passes to estimate the incident flux local to each photon. We show how this information can be used to guide a relaxation step with the goal of enforcing a constant, per-photon flux. Using a reformulation of the radiance estimate, we demonstrate how the resulting blue noise photon distribution yields a radiance reconstruction in which error is significantly reduced. Our approach has an open-ended runtime of the same order as unbiased and asymptotically consistent rendering methods, converging over time to a stable result. We demonstrate its effectiveness at storing caustic illumination within a view-independent framework and at a fidelity visually comparable to reference images rendered using progressive photon mapping. [ABSTRACT FROM AUTHOR]

In this paper, we present a new visual way of exploring state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the time-series is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm. We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities. [ABSTRACT FROM AUTHOR]

A new area of biological research is identifying and grouping patterns of behaviour in wild animals by analysing data obtained through the attachment of tri-axial accelerometers. As these recording devices become smaller and less expensive their use has increased. Currently acceleration data are visualised as 2D time series plots, and analyses are based on summary statistics and the application of Fourier transforms. We develop alternate visualisations of this data so as to analyse, explore and present new patterns of animal behaviour. Our visualisations include interactive spherical scatterplots, spherical histograms, clustering methods, and feature-based state diagrams of the data. We study the application of these visualisation methods to accelerometry data from animal movement. The reaction of biologists to these visualisations is also reported. [ABSTRACT FROM AUTHOR]

Photon mapping is an efficient method for producing high-quality photorealistic images with full global illumination. In this paper, we present a more accurate and efficient approach to final gathering using the photon map based upon the hierarchical evaluation of the photons over each surface. We use the footprint of each gather ray to calculate the irradiance estimate area rather than deriving it from the local photon density. We then describe an efficient method for computing the irradiance from the photon map given an arbitrary estimate area. Finally, we demonstrate how the technique may be used to reduce variance and increase efficiency when sampling diffuse and glossy-specular BRDFs. [ABSTRACT FROM AUTHOR]

Deep networks often possess a vast number of parameters, and their significant redundancy in parameterization has become a widely-recognized property. This presents significant challenges and restricts many deep learning applications, making the focus on reducing the complexity of models while maintaining their powerful performance. In this paper, we present an overview of popular methods and review recent works on compressing and accelerating deep neural networks. We consider not only pruning methods but also quantization methods, and low-rank factorization methods. This review also intends to clarify these major concepts, and highlights their characteristics, advantages, and shortcomings. [ABSTRACT FROM AUTHOR]

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives. [ABSTRACT FROM AUTHOR]

A distance field is a representation where, at each point within the field, we know the distance from that point to the closest point on any object within the domain. In addition to distance, other properties may be derived from the distance field, such as the direction to the surface, and when the distance field is signed, we may also determine if the point is internal or external to objects within the domain. The distance field has been found to be a useful construction within the areas of computer vision, physics, and computer graphics. This paper serves as an exposition of methods for the production of distance fields, and a review of alternative representations and applications of distance fields. In the course of this paper, we present various methods from all three of the above areas, and we answer pertinent questions such as How accurate are these methods compared to each other? How simple are they to implement?, and What is the complexity and runtime of such methods? [ABSTRACT FROM AUTHOR]

If we were to have a Grid infrastructure for visualization, what technologies would be needed to build such an infrastructure, what kind of applications would benefit from it, and what challenges are we facing in order to accomplish this goal? In this survey paper, we make use of the term ‘visual supercomputing’ to encapsulate a subject domain concerning the infrastructural technology for visualization. We consider a broad range of scientific and technological advances in computer graphics and visualization, which are relevant to visual supercomputing. We identify the state-of-the-art technologies that have prepared us for building such an infrastructure. We examine a collection of applications that would benefit enormously from such an infrastructure, and discuss their technical requirements. We propose a set of challenges that may guide our strategic efforts in the coming years. [ABSTRACT FROM AUTHOR]

This work describes the preparation of approximately 13,000 compounds for rapid identification of hits in high-throughput screening (HTS). These compounds were designed as potential serine/threonine or tyrosine kinase inhibitors. The library consists of various scaffolds, e.g., purines, oxindoles, and imidazoles, whereby each core scaffold generally includes the hydrogen bond acceptor/donor properties known to be important for kinase binding. Several of these are based upon literature kinase templates, or adaptations of them to provide novelty. The routes to their preparation are outlined. A variety of automation techniques were used to prepare >500 compounds per scaffold. Where applicable, scavenger resins were employed to remove excess reagents and when necessary, preparative high performance liquid chromatography (HPLC) was used for purification. These compounds were screened against an 'in-house' kinase panel. The success rate in HTS was significantly higher than the corporate compound collection. © 2004 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

An image of a glass prism which is backlit by a point emitter is presented.

A computer graphics of Ben Spencer and Mark W. Jones is presented.