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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]

The recent rapid emergence of maize lethal necrosis (MLN), caused by coinfection of maize with Maize chlorotic mottle virus (MCMV) and a second virus usually from the family Potyviridae, is causing extensive losses for farmers in East Africa, Southeast Asia, and South America. Although the genetic basis of resistance to potyviruses is well understood in maize, little was known about resistance to MCMV. The responses of five maize inbred lines (KS23-5, KS23-6, N211, DR, and OhlVI) to inoculation with MCMV, Sugarcane mosaic virus, and MLN were characterized. All five lines developed fewer symptoms than susceptible controls after inoculation with MCMV; however, the virus was detected in systemic leaf tissue from each of the lines similarly to susceptible controls, indicating that the lines were tolerant of MCMV rather than resistant to it. Except for KS23-5, the inbred lines also developed fewer symptoms after inoculation with MLN than susceptible controls. To identify genetic loci associated with MCMV tolerance, large F2 or recombinant inbred populations were evaluated for their phenotypic responses to MCMV. and the most resistant and susceptible plants were genotyped by sequencing. One to four quantitative trait loci (QTL) were identified in each tolerant population using recombination frequency and positional mapping strategies. In contrast to previous studies of vims resistance in maize, the chromosomal positions and genetic character of the QTL were unique to each population. The results suggest that different, genotypespecific mechanisms are associated with MCMV tolerance in maize. These results will allow for the development of markers for marker-assisted selection of MCMV- and MLN-tolerant maize hybrids for disease control. [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]

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]