Small Tiles Research Articles

MaterIA: Single Image High‐Resolution Material Capture in the Wild

AbstractWe propose a hybrid method to reconstruct a physically‐based spatially varying BRDF from a single high resolution picture of an outdoor surface captured under natural lighting conditions with any kind of camera device. Relying on both deep learning and explicit processing, our PBR material acquisition handles the removal of shades, projected shadows and specular highlights present when capturing a highly irregular surface and enables to properly retrieve the underlying geometry. To achieve this, we train two cascaded U‐Nets on physically‐based materials, rendered under various lighting conditions, to infer the spatially‐varying albedo and normal maps. Our network processes relatively small image tiles (512 × 512pixels) and we propose a solution to handle larger image resolutions by solving a Poisson system across these tiles. We complete this pipeline with analytical solutions to reconstruct height, roughness and ambient occlusion.

Ultra-Fast FPGA Implementation of Graph Cut Algorithm With Ripple Push and Early Termination

Graph cut has been a popular approach widely used to solve the minimum cut problem, which is prevalent in computer vision tasks, although not limited to this field. Push-relabel is considered as one of the promising algorithms of graph cut due to its good potential to be parallelized. However, existing implementations often not only fail to fully exploit the available parallelism but also fail to make full use of the application context to reduce redundant computations. Therefore, they are not competent for application scenarios with high resolution and real-time requirements. To address the issue, we propose three novel techniques to achieve an ultra-fast and efficient FPGA implementation of a push-relabel algorithm. First, we propose a ripple push technique that significantly parallelizes push operations so as to accelerate the push-relabel convergence process. Second, we propose an early-termination technique that effectively removes redundant computations of the push-relabel algorithm. We also theoretically prove the correctness of our early-termination technique. Third, we propose a highly parallelized search technique called flood irrigation search (FIS). It quickly judges early termination conditions based on a pixel parallel architecture. Our implementation focuses on performance-sensitive applications that divide large images into small graph tiles with a specific size. Compared to the state-of-the-art FPGA implementations of push-relabel algorithms, experimental results show that our method can at least achieve <inline-formula> <tex-math notation="LaTeX">$8.93\times $ </tex-math></inline-formula> improvement of execution time.

Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure

This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A medium-sized virtual server that has been obtained from a cloud service provider has rather limited computing resources. Tiling will not only improve speedup but can be necessary to reduce the memory footprint. However, a tiling scheme with many small tiles increases overhead and can introduce extra latency due to queued tiles that are waiting to be processed. In a high-throughput computing cluster with hundreds of physical processing cores, more tiles can be processed in parallel, and the optimal strategy will be different. A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading were hereby assessed. Experiments were based on an implementation that relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access.

Built of Tiles, Bricks, and Stone: Aleksandra Kasuba’s Art Career in New York

A refugee from Lithuania, Aleksandra Kasuba (1923–2019) arrived in the U.S. in 1947 and built a successful career in the competitive, male-dominated post-World War II New York’s art world. Beginning with small ceramic tile artworks that evolved into multi-media mosaics, Kasuba’s artistic vision expanded to shaping architectural spaces. From 1953 to 1970, appearances in exhibitions organized by the Museum of Contemporary Craft introduced her art to American and international audiences. Her pivotal solo exhibition at an important New York gallery in 1966 led to complex collaborations with noted architects for projects in major U. S. cities. Her innovative monumental designs in marble, brick and granite were focal points of public buildings and spaces. This article examines her key commissions completed from 1970 to 1986 in Chicago, New York City, Rochester, N.Y., Buffalo, N.Y., and Washington, D.C.

Tile-net for undersampled cardiovascular CINE magnetic resonance imaging

We propose the “Tile-net” method based on dividing an image into smaller tiles. Using the tile as the input to the neural network, the network is simplified substantially. The Tile-net learns at a much faster rate than the networks without tiling. The training and reconstruction times for the Tile-net are reduced by 40% and 33%, respectively compared to the networks without tiling. The Tile-net performance is evaluated through the normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structure similarity index measure (SSIM) and the quality of the reconstructed image for test datasets. The Tile-net does not degrade performance; however, it reduces the NMSE by 0.3% compared to the networks without tiling.

Space-Time Loop Tiling for Dynamic Programming Codes

We present a new space-time loop tiling approach and demonstrate its application for the generation of parallel tiled code of enhanced locality for three dynamic programming algorithms. The technique envisages that, for each loop nest statement, sub-spaces are first generated so that the intersection of them results in space tiles. Space tiles can be enumerated in lexicographical order or in parallel by using the wave-front technique. Then, within each space tile, time slices are formed, which are enumerated in lexicographical order. Target tiles are represented with multiple time slices within each space tile. We explain the basic idea of space-time loop tiling and then illustrate it by means of an example. Then, we present a formal algorithm and prove its correctness. The algorithm is implemented in the publicly available TRACO compiler. Experimental results demonstrate that parallel codes generated by means of the presented approach outperform closely related manually generated ones or those generated by using affine transformations. The main advantage of code generated by means of the presented approach is its enhanced locality due to splitting each larger space tile into multiple smaller tiles represented with time slices.

Digital Numbers Constructed by Fine Patterned Polydopamine on DNA Templates.

Using DNA nanostructures as templates to synthesize shape-controlled polydopamine (PDA) is a promising strategy to realize the fabrication of exquisite PDA nanomaterials. However, previous studies using small DNA tiles as templates could only afford very simple structures such as lines and crosses due to the limited space on the template and the relatively low resolution of the PDA nanopatterns. Therefore, the best resolution of the PDA nanostructures that can be achieved by this technique is carefully investigated. And by connecting several DNA tiles together, larger DNA templates are built up and achieve the synthesis of complicated digital nanopatterned PDA structures.

Multipurpose Retrofitting of a Tower Building in Brescia

The paper aims to report a proper low-impact integrated study approach for the energy and seismic retrofitting of a tower building located in the city centre of Brescia (Italy). The building consists of a group of aggregate buildings built in the 1950s, from which emerged an 11-storey tower. It was built with the technology of the time by means of a perimeter and spine framed structure in reinforced concrete, double perimeter infill with a hollow brick air chamber, external cladding on the main fronts with small Botticino marble tiles. The building is registered by the Ministry of Cultural Heritage for its facade and is located in a seismic zone of category two. When work began, the construction was in a class three; thus, the whole project was updated to obtain the new seismic authorisation. The coating showed progressive decay, with dangerous structural instability of the heavy coating. The design team decided to adopt wider corrective action compared to the observed phenomenon, integrating it with a local seismic and global energy improvement through the insertion of a ventilated wall in the building and the realisation of accurate thermal and energy insulation systems. At the end of the intervention, this building became the first condominium in Italy in 2018, certified in class A and upgraded with sustainability criteria in which the application of the GBC Italy (Green Building Council) Condominiums protocol, currently in its final publication phase for the energy–environmental sustainability of condominiums, has been tested.

Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning

Abstract Medulloblastoma (MB) is a primary central nervous system tumor and the most common malignant brain cancer among children. Neuropathologists perform microscopic inspection of histopathological tissue slides under a microscope to assess the severity of the tumor. This is a timeconsuming task and often infused with observer variability. Recently, pre-trained convolutional neural networks (CNN) have shown promising results for MB subtype classification. Typically, high-resolution images are divided into smaller tiles for classification, while the size of the tiles has not been systematically evaluated. We study the impact of tile size and input strategy and classify the two major histopathological subtypes-Classic and Desmoplastic/Nodular. To this end, we use recently proposed EfficientNets and evaluate tiles with increasing size combined with various downsampling scales. Our results demonstrate using large input tiles pixels followed by intermediate downsampling and patch cropping significantly improves MB classification performance. Our top-performing method achieves the AUC-ROC value of 90.90% compared to 84.53% using the previous approach with smaller input tiles.

Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images.

BackgroundBreast cancer is one of the most common cancers and the leading cause of death from cancer among women worldwide. The genetic predisposition to breast cancer may be associated with a mutation in particular genes such as gene BRCA1/2. Patients who carry a germline pathogenic mutation in BRCA1/2 genes have a significantly increased risk of developing breast cancer and might benefit from targeted therapy. However, genetic testing is time consuming and costly. This study aims to predict the risk of gBRCA mutation by using the whole-slide pathology features of breast cancer H&E stains and the patients’ gBRCA mutation status.MethodsIn this study, we trained a deep convolutional neural network (CNN) of ResNet on whole-slide images (WSIs) to predict the gBRCA mutation in breast cancer. Since the dimensions are too large for slide-based training, we divided WSI into smaller tiles with the original resolution. The tile-based classification was then combined by adding the positive classification result to generate the combined slide-based accuracy. Models were trained based on the annotated tumor location and gBRCA mutation status labeled by a designated breast cancer pathologist. Four models were trained on tiles cropped at 5×, 10×, 20×, and 40× magnification, assuming that low magnification and high magnification may provide different levels of information for classification.ResultsA trained model was validated through an external dataset that contains 17 mutants and 47 wilds. In the external validation dataset, AUCs (95% CI) of DL models that used 40×, 20×, 10×, and 5× magnification tiles among all cases were 0.766 (0.763–0.769), 0.763 (0.758–0.769), 0.750 (0.738–0.761), and 0.551 (0.526–0.575), respectively, while the corresponding magnification slides among all cases were 0.774 (0.642–0.905), 0.804 (0.676–0.931), 0.828 (0.691–0.966), and 0.635 (0.471–0.798), respectively. The study also identified the influence of histological grade to the accuracy of the prediction.ConclusionIn this paper, the combination of pathology and molecular omics was used to establish the gBRCA mutation risk prediction model, revealing the correlation between the whole-slide histopathological images and gRCA mutation risk. The results indicated that the prediction accuracy is likely to improve as the training data expand. The findings demonstrated that deep CNNs could be used to assist pathologists in the detection of gene mutation in breast cancer.

Fast median filters using separable sorting networks

Median filters are a widely-used tool in graphics, imaging, machine learning, visual effects, and even audio processing. Currently, very-small-support median filters are performed using sorting networks, and large-support median filters are handled by O (1) histogram-based methods. However, the constant factor on these O (1) algorithms is large, and they scale poorly to data types above 8-bit integers. On the other hand, good sorting networks have not been described above the 7 X 7 case, leaving us with no fast way to compute integer median filters of modest size, and no fast way to compute floating point median filters for any size above 7 X 7. This paper describes new sorting networks that efficiently compute median filters of arbitrary size. The key idea is that these networks can be factored to exploit the separability of the sorting problem - they share common work across scanlines, and within small tiles of output. We also describe new ways to run sorting networks efficiently, using a sorting-specific instruction set, compiler, and interpreter. The speed-up over prior work is more than an order of magnitude for a wide range of data types and filter sizes. For 8-bit integers, we describe the fastest median filters for all sizes up to 25 X 25 on CPU, and up to 33 X 33 on GPU. For higher-precision types, we describe the fastest median filters at all sizes tested on both CPU and GPU.

Effect of glass cover layer on the ballistic performance of transparent ceramic armor

Transparent ceramics are typically used as the strike-face layer owing to their superior mechanical properties, such as high strength and high hardness. For typical transparent ceramic armor, the ceramic layer is usually divided into small ceramic tiles because of the size limitations. However, the tiled structure is restricted in application due to the discontinuity between ceramic tiles, which can be solved by covering the outer surface with a glass plate. In this study, the effect of the glass cover layer on the ballistic performance of transparent ceramic armor is investigated through a combination of experimental testing and finite element (FE) simulation. The results exhibited that under the impact of 7.62 mm armor-piercing incendiaries, the ceramic tiles were prematurely damaged when the glass cover was added. Besides, the reduction of the ceramic energy dissipation was greater than the energy dissipation of the glass cover itself, thus reducing the ballistic performance of the transparent ceramic armor. The results also revealed that the pre-damage area increased as the thickness of the glass cover increased from 1 mm to 4 mm.

A novel reconstruction framework for an imaging calorimeter for HL-LHC

To sustain the harsher conditions of the high-luminosity LHC, the CMS collaboration is designing a novel endcap calorimeter system. The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will maintain highly-granular information in the readout to help mitigate the effects of pileup. In regions characterised by lower radiation levels, small scintillator tiles with individual on-tile SiPM readout are employed. A unique reconstruction framework (TICL: The Iterative CLustering) is being developed to fully exploit the granularity and other significant detector features, such as particle identification and precision timing, with a view to mitigate pileup in the very dense environment of HL-LHC. The inputs to the framework are clusters of energy deposited in individual calorimeter layers. Clusters are formed by a density-based algorithm. Recent developments and tunes of the clustering algorithm will be presented. To help reduce the expected pressure on the computing resources in the HL-LHC era, the algorithms and their data structures are designed to be executed on GPUs. Preliminary results will be presented on decreases in clustering time when using GPUs versus CPUs. Ideas for machine-learning techniques to further improve the speed and accuracy of reconstruction algorithms will be presented.

Computerized Classification of Prostate Cancer Gleason Scores from Whole Slide Images.

Histological Gleason grading of tumor patterns is one of the most powerful prognostic predictors in prostate cancer. However, manual analysis and grading performed by pathologists are typically subjective and time-consuming. In this paper, we present an automatic technique for Gleason grading of prostate cancer from H&E stained whole slide pathology images using a set of novel completed and statistical local binary pattern (CSLBP) descriptors. First, the technique divides the whole slide image (WSI) into a set of small image tiles, where salient tumor tiles with high nuclei densities are selected for analysis. The CSLBP texture features that encode pixel intensity variations from circularly surrounding neighborhoods are extracted from salient image tiles to characterize different Gleason patterns. Finally, the CSLBP texture features computed from all tiles are integrated and utilized by the multi-class support vector machine (SVM) that assigns patient slides with different Gleason scores such as 6, 7, or ≥ 8. Experiments have been performed on 312 different patient cases selected from the cancer genome atlas (TCGA) and have achieved superior performances over state-of-the-art texture descriptors and baseline methods including deep learning models for prostate cancer Gleason grading.

The Physical Properties and Geochemical of Clay from the Bestari Jaya, Kuala Selangor, Selangor, Malaysia for Potential Usage

The study emphasis on physical properties and geochemical of the clays from Bestari Jaya area to determine its suitability for any commercial application. Nine samples collected from six boreholes as representative of white and dark clay in the study area. All samples examined through the optical properties test and physical properties such as moisture, specific gravity, Atterberg limit and particle size distributions. The geochemical and mineralogical conducted using XRF and XRD analysis. The clays from Bestari Jaya considered of having variation in the brightness from medium to high brightness as shown by Delta L value (51.32-73.49%). The moisture content ranges between 34.50-81.03%. The plasticity index (PI) and plasticity limit (PL) values found in ranges of 18-32% with an average of 22.22%, and 32-46% with an average of 37.11% respectively. The plasticity limit and index reveals that the Bestari Jaya clays can be classify as kaolinite clays that are suitable for pottery and brick making. The specific gravity ranges between 2.49 – 2.70 with an average of 2.61. The average value is similar to the value of pure china clay (2.6). The SiO2 content in Bestari Jaya clays is between 37.49 – 69.96 wt% and Al2O3 is between 18.92 – 33.02 wt%. While the L.O.I values are between 8.71-16.04%. Kaolinite as the dominant mineral phases in all clay samples with composition ranges from 65-97.7% and an average of 73.12%. Apart of that, quartz in ranges of 5.3 – 20.6%, identified in almost all samples. Muscovite, hematite and magnetite occur as accessory minerals. Referring to standard and result obtained on representative sample, the Bestari Jaya clays potentially used in the production of smaller tiles for unexclusive pedestrian traffic. The presence of significance amount of heavy metals makes it unsuitable to use in the pharmaceutical or paper industry.

The Physical Properties and Geochemical of Clay from the Bestari Jaya, Kuala Selangor, Selangor, Malaysia for Potential Usage

The study emphasis on physical properties and geochemical of the clays from Bestari Jaya area to determine its suitability for any commercial application. Nine samples collected from six boreholes as representative of white and dark clay in the study area. All samples examined through the optical properties test and physical properties such as moisture, specific gravity, Atterberg limit and particle size distributions. The geochemical and mineralogical conducted using XRF and XRD analysis. The clays from Bestari Jaya considered of having variation in the brightness from medium to high brightness as shown by Delta L value (51.32-73.49%). The moisture content ranges between 34.50-81.03%. The plasticity index (PI) and plasticity limit (PL) values found in ranges of 18-32% with an average of 22.22%, and 32-46% with an average of 37.11% respectively. The plasticity limit and index reveals that the Bestari Jaya clays can be classify as kaolinite clays that are suitable for pottery and brick making. The specific gravity ranges between 2.49 – 2.70 with an average of 2.61. The average value is similar to the value of pure china clay (2.6). The SiO2 content in Bestari Jaya clays is between 37.49 – 69.96 wt% and Al2O3 is between 18.92 – 33.02 wt%. While the L.O.I values are between 8.71-16.04%. Kaolinite as the dominant mineral phases in all clay samples with composition ranges from 65-97.7% and an average of 73.12%. Apart of that, quartz in ranges of 5.3 – 20.6%, identified in almost all samples. Muscovite, hematite and magnetite occur as accessory minerals. Referring to standard and result obtained on representative sample, the Bestari Jaya clays potentially used in the production of smaller tiles for unexclusive pedestrian traffic. The presence of significance amount of heavy metals makes it unsuitable to use in the pharmaceutical or paper industry.

Mechanics of topologically interlocked material systems under point load: Archimedean and Laves tiling

Topologically interlocked material systems are two-dimensional assemblies of unit elements from which no element can be removed from the assembly without disassembly of the entire system. Consequently, such tile assemblies are able to carry transverse mechanical loads. Archimedean and Laves tilings are investigated as templates for the material system architecture. It is demonstrated under point loads that the architecture significantly affects the force-deflection response. Stiffness, load carrying capacity and toughness varied by a factor of at least three from the system with the poorest performance to the system with the best performance. Across all architectures stiffness, strength and toughness are found to be strongly and linearly correlated. Architecture characterizing parameters and their relationship to the mechanical behavior are investigated. It is shown that the measure of the smallest tile area in an assembly provides the best predictor of mechanical behavior. With small tiles present in the assembly the contact force network structure is well developed and the internal load path is channeled through these stiffest components of the assembly.

Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

A small‐tiled geographic mosaic of coevolution between Eurosta solidaginis and its natural enemies and host plant

AbstractWe identified a geographic mosaic of Eurosta solidaginis fly traits produced by coevolution of the stem gall‐forming fly with both its natural enemies and its host plants at small geographic scales in the presence of gene flow. These tritrophic interactions between the fly with its natural enemies and with its host‐plant Solidago altissima produced what has been termed a small‐tiled geographic mosaic of coevolution. Selection on gall diameter and length varies between prairie and forest habitats due to differences in host plants and natural‐enemy communities. At the prairie–forest ecotone where prairie and forest habitats are intermixed, we found that geographic selection mosaics on gall diameter and length varied on a scale of a few kilometers. Gall diameter variation among sites correlated with selection on gall diameters, indicating local adaptation. In contrast, gall lengths did not correlate with selection, indicating that gene flow may have prevented local adaptation of this trait. Eastern (forest) and western (prairie) subspecies of E. solidaginis have been proposed based on fly wing patterns, and these had intermediate forms in the ecotone indicating gene flow between these subspecies. Variation in wing patterns correlated with gall diameter, indicating that gene flow between prairie and forest fly populations may influence the distribution of gall traits. The ratio of forest to prairie vegetation increases with latitude, but there was no indication of latitudinal clines in gall or wing traits. Our results indicate that selection for differentiation in coevolved traits is strong enough to overcome gene flow in small tiles of habitat. The result is that ecological forces produce a dynamic mosaic of genetically differentiated locally adapted populations. It also indicates that prairie and forest host races of E. solidaginis form a mosaic hybrid zone in this region.

Reconstruction in an imaging calorimeter for HL-LHC

The CMS endcap calorimeter upgrade for the High Luminosity LHC in 2027 uses silicon sensors to achieve radiation tolerance, with the further benefit of a very high readout granularity. Small scintillator tiles with individual SiPM readout are used in regions permitted by the radiation levels. A reconstruction framework is being developed to fully exploit the granularity and other significant features of the detector like precision timing, especially in the high pileup environment of HL-LHC . An iterative clustering framework (TICL) has been put in place, and is being actively developed. The framework takes as input the clusters of energy deposited in individual calorimeter layers delivered by the CLUE algorithm, which has recently been revised and tuned. Mindful of the projected extreme pressure on computing capacity in the HL-LHC era, the algorithms are being designed with modern parallel architectures in mind. Important speedup has recently been obtained for the clustering algorithm by running it on GPUs. Machine learning techniques are being developed and integrated into the reconstruction framework. This paper will describe the approaches being considered and show first results.