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In multiple coordinated views (MCVs), visualizations across views update their content in response to users' interactions in other views. Interactive systems provide direct manipulation to create coordination between views, but are restricted to limited types of predefined templates. By contrast, textual specification languages enable flexible coordination but expose technical burden. To bridge the gap, we contribute Nebula, a grammar based on natural language for coordinating visualizations in MCVs. The grammar design is informed by a novel framework based on a systematic review of 176 coordinations from existing theories and applications, which describes coordination by demonstration, i.e., how coordination is performed by users. With the framework, Nebula specification formalizes coordination as a composition of user- and coordination-triggered interactions in origin and destination views, respectively, along with potential data transformation between the interactions. We evaluate Nebula by demonstrating its expressiveness with a gallery of diverse examples and analyzing its usability on cognitive dimensions.

With the prevalence of embedded GPUs on mobile devices, power-efficient rendering has become a widespread concern for graphics applications. Reducing the power consumption of rendering applications is critical for extending battery life. In this paper, we present a new real-time power-budget rendering system to meet this need by selecting the optimal rendering settings that maximize visual quality for each frame under a given power budget. Our method utilizes two independent neural networks trained entirely by synthesized datasets to predict power consumption and image quality under various workloads. This approach spares time-consuming precomputation or runtime periodic refitting and additional error computation. We evaluate the performance of the proposed framework on different platforms, two desktop PCs and two smartphones. Results show that compared to the previous state of the art, our system has less overhead and better flexibility. Existing rendering engines can integrate our system with negligible costs.

Graphics Processing Units (GPUs) are now a key component of many devices and systems, including those in the cloud and data centers, thus are also subject to side-channel attacks. Existing side-channel attacks on GPUs typically leak information from graphics libraries like OpenGL and CUDA, which require creating contentions within the GPU resource space and are being mitigated with software patches. This paper evaluates potential side channels exposed at a lower-level interface between GPUs and CPUs, namely the graphics interrupts. These signals could indicate unique signatures of GPU workload, allowing a spy process to infer the behavior of other processes. We demonstrate the practicality and generality of such side-channel exploitation with a variety of assumed attack scenarios. Simulations on both Nvidia and Intel graphics adapters showed that our attack could achieve high accuracy, while in-depth studies were also presented to explore the low-level rationale behind such effectiveness. On top of that, we further propose a practical mitigation scheme which protects GPU workloads against the graphics-interrupt-based side-channel attack by piggybacking mask payloads on them to generate interfering graphics interrupt ``noises’'. Experiments show that our mitigation technique effectively prohibited spy processes from inferring user behaviors via analyzing runtime patterns of graphics interrupt with only trivial overhead.

Realistic 3D facial modeling and animation have been increasingly used in many graphics, animation, and virtual reality applications. However, generating realistic fine-scale wrinkles on 3D faces, in particular, on animated 3D faces, is still a challenging problem that is far away from being resolved. In this article we propose an end-to-end system to automatically augment coarse-scale 3D faces with synthesized fine-scale geometric wrinkles. By formulating the wrinkle generation problem as a supervised generation task, we implicitly model the continuous space of face wrinkles via a compact generative model, such that plausible face wrinkles can be generated through effective sampling and interpolation in the space. We also introduce a complete pipeline to transfer the synthesized wrinkles between faces with different shapes and topologies. Through many experiments, we demonstrate our method can robustly synthesize plausible fine-scale wrinkles on a variety of coarse-scale 3D faces with different shapes and expressions.

Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to $95\%$ of the total processing time. (Un)surprisingly, a significant portion (as high as $99\%$) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this paper, we seek to salvage and recycle the results of such operations to avoid repeated computation. Our solution consists of two phases. In the first phase, we generate a reusable plan that determines the opportunity for reuse. The plan is based on a novel reuse discovery mechanism that can identify available results to prevent redundant computation. In the second phase, the plan is executed to produce the subgraph enumeration results. This processing is based on a newly designed reusable parallel search strategy that can efficiently maintain and retrieve the results of set intersection operations. Our implementation on GPUs shows that our approach can achieve up to $5$ times speedups compared with the state-of-the-art GPU solutions.

Showcases the PyQtGraph plotting library and the kinds of scientific visualizations it enables. It's core attributes are highlighted, namely it's speed, python operability, extensibility, and integrability with larger applications.

Augmented reality (AR) offers new ways to visualize information on-the-go. As noted in related work, AR graphics presented via optical see-through AR displays are particularly prone to color blending, whereby intended graphic colors may be perceptually altered by real-world backgrounds, ultimately degrading usability. This work adds to this body of knowledge by presenting a methodology for assessing AR interface color robustness, as quantitatively measured via shifts in the CIE color space, and qualitatively assessed in terms of users' perceived color name. We conducted a human factors study where twelve participants examined eight AR colors atop three real-world backgrounds as viewed through an in-vehicle AR head-up display (HUD); a type of optical see-through display used to project driving-related information atop the forward-looking road scene. Participants completed visual search tasks, matched the perceived AR HUD color against the WCS color palette, and verbally named the perceived color. We present analysis that suggests blue, green, and yellow AR colors are relatively robust, while red and brown are not, and discuss the impact of chromaticity shift and dispersion on outdoor AR interface design. While this work presents a case study in transportation, the methodology is applicable to a wide range of AR displays in many application domains and settings.

This study draws attention to the challenging perception of two public information pictograms, ‘elevator’ and ‘toilet’. Both indicate the location of a destination. Although the semantic information is completely different, both pictograms partly depict the same: front view of standing human figures. In certain contexts (e.g., at airports or train stations), with people in a hurry and with users from different cultures, this can lead to confusion. In addition, the representation of human figures is increasingly being questioned on the basis of public and political discussions on gender issues. Moreover, attention to accessibility is also being incorporated in these two pictograms. Thus, both pictograms are undergoing an evolutionary process in order to meet current requirements. Do more messages require more complex pictograms? As a starting point, we conducted a comprehension test based on the method recommended by iso 9186-1. The results showed trends, but some questions regarding the two pictograms were not clearly understood by the test group members. Therefore, we conducted another test designed to determine the limits of the graphics depicted in these pictograms. We hope this study will help raise awareness about these issues. Finally, we offer five pointers for consideration when designing the elevator pictogram in the future.

Future CPU-manycore heterogeneous systems can provide high peak throughput by integrating thousands of simple, independent, energy-efficient cores in a single die. However, there are two key challenges to translating this high peak throughput into improved end-to-end workload performance: (1) manycore co-processors rely on simple hardware putting significant demands on the software programmer; and (2) manycore co-processors use in-order cores that struggle to tolerate long memory latencies. To address the manycore programmability challenge, this paper presents a dense and sparse tensor processing framework based on PyTorch that enables domain experts to easily accelerate off-the-shelf workloads on CPUmanycore heterogeneous systems. To address the manycore memory latency challenge, we use our extended PyTorch framework to explore the potential for decoupled access/execute (DAE) software and hardware mechanisms. More specifically, we propose two software-only techniques, naive-software DAE and systolic-software DAE, along with a lightweight hardware access accelerator to further improve area-normalized throughput. We evaluate our techniques using a combination of PyTorch operator microbenchmarking and real-world PyTorch workloads running on a detailed register-transfer-level model of a 128-core manycore architecture. Our evaluation on three real-world dense and sparse tensor workloads suggest these workloads can achieve approximately 2-6× performance improvement when scaled to a future 2,000-core CPU-manycore heterogeneous system compared to an 18-core out-of-order CPU baseline, while potentially achieving higher area-normalized throughput and improved energyefficiency compared to general-purpose graphics processing units.

Immersive Virtual Reality (VR) simulations are argued to support students’ learning of complex scientific phenomena via the use of realistic graphics and interactions that students can hardly experience in everyday life. However, the integration of immersive VR simulations in science classrooms introduces new challenges, whilst there is a lack of learning designs to inform practice. As part of this study, we firstly present a learning experience (LX) design seeking to introduce an immersive VR simulation in Physics classrooms to support high-school students’ understanding of the Special Theory of Relativity. Then, we present an empirical investigation on the enactment and evaluation of the proposed LX design, with 109 high-school students (10–11th graders). A mixed-method approach was adopted to evaluate students’ conceptual learning gains along with their perceptions of the learning experience, encompassing the immersive VR simulation and the inquiry-based learning process adopted. We reflect on our LX design aimed at the integration of an immersive VR simulation in an inquiry-based learning environment and we highlight questions for further research.

The US Department of Energy’s fastest supercomputers and forthcoming exascale systems employ Graphics Processing Units (GPUs) to increase the computational performance of compute nodes. However, the complexity of GPU architectures makes tailoring sophisticated applications to achieve high performance on GPU-accelerated systems a major challenge. At best, prior performance tools for GPU code only provide coarse-grained tuning advice at the kernel level. In this article, we describe GPA, a performance advisor that suggests potential code optimizations at a hierarchy of levels, including individual lines, loops, and functions. To gather the fine-grained measurements needed to produce such insights, GPA uses instruction sampling and binary instrumentation to monitor execution of GPU code. At the time of this writing, GPU instruction sampling is only available on NVIDIA GPUs. To understand performance losses, GPA uses data flow analysis to approximately attribute measured instruction stalls back to their causes. GPA then analyzes patterns of stalls using information about a program’s structure and the GPU architecture to identify optimization strategies that address inefficiencies observed. GPA then employs detailed performance models to estimate the potential speedup that each optimization might provide. Experiments with benchmarks and applications show that GPA provides useful advice for tuning GPU code. We applied GPA to analyze and tune a collection of codes on NVIDIA V100 and A100 GPUs. GPA suggested optimizations that it estimates will accelerate performance across the set of codes by a geometric mean of 1.21×. Applying these optimizations suggested by GPA accelerated these codes by a geometric mean of 1.19×.

Graphics processing units (GPUs) have been a popular choice for the acceleration of iterative tomographic reconstruction algorithms. In this work, two dual-GPU approaches to iterative list-mode reconstruction of PET data are studied. The developed techniques split the image into equal-size cubes and distribute them between the GPUs. Benefiting from caching a plane of the image into the fast shared memory, line-driven forward and back projection kernels are implemented incorporating a shift-invariant point spread function (PSF). In the first approach, lines were individually convolved with the PSF kernel whereas the second approach convolves the reconstructed image with the PSF before the forward projection and after the back projection. The algorithms were implemented using two CUDA-compatible devices with no CPU/GPU data transfer during the reconstruction process. List-mode data obtained from Monte Carlo simulations were used to evaluate the methods. One complete iteration of 107 events on an image of 220 × 220 × 110 voxels is done in less than 510 ms for a non-PSF reconstruction and in 520 to 5220 ms for a PSF reconstruction depending on the PSF and the reconstruction parameters. Dual-GPU memory layout, implementation details, sample images, and performance metrics of the developed algorithms are discussed.

The Ubiquitous interconnected smart devices enabled by the recent evolution of low-cost, generic, small-size, powerful computing platforms devised the term Internet of Things (IoT), which cross-cuts many areas of our modern day living. IoT applications go way beyond simple sensing and actuation to sophisticated localized processing and decision-making. The recent advances in embedded systems produced a long list of IoT boards equipped with powerful central processing units (CPUs), and graphics processing units (GPUs). Unfortunately, even with the limited energy consumption and high processing power of such GPUs, CPUs are usually the only computational element utilized by the hosted applications, thus hindering the capabilities of the entire board. This is mainly due to the complicated nature of GPU-based programming. In this paper, we are presenting a case study showing the effect of offloading the computationally intensive part of a latency-sensitive educational game to a low-cost Raspberry Pi’s GPU, thus enabling the board to seamlessly host the entire game operations. Relying mainly on the boards CPU shows very long interaction latency i.e., 4.82 s. By efficiently leveraging the powerful coprocessor, VideoCore GPU, we are able to significantly improve the interaction latency to a fraction of a second, making the game conveniently playable.

Point set registration has been actively studied in computer vision and graphics. Optimization algorithms are at the core of solving registration problems. Traditional optimization approaches are mainly based on the gradient of objective functions. The derivation of objective functions makes it challenging to find optimal solutions for complex optimization models, especially for those applications where accuracy is critical. Learning-based optimization is a novel approach to address this problem, which learns the gradient direction from data sets. However, many learning-based optimization algorithms learn gradient directions via a single feature extracted from the data set, which will cause the updating direction to be vulnerable to perturbations around the data, thus falling into a bad stationary point. This paper proposes the General Discriminative Optimization (GDO) method that updates a gradient path automatically through the trade-off among contributions of different features on updating gradients. We illustrate the benefits of GDO with tasks of 3D point set registrations and show that GDO outperforms the state-of-the-art registration methods in terms of accuracy and robustness to perturbations.

Spatiotemporal data fusion is a cost-effective way to produce remote sensing images with high spatial and temporal resolutions using multisource images. Using spectral unmixing analysis and spatial interpolation, the flexible spatiotemporal data fusion (FSDAF) algorithm is suitable for heterogeneous landscapes and capable of capturing abrupt land-cover changes. However, the extensive computational complexity of FSDAF prevents its use in large-scale applications and mass production. Besides, the domain decomposition strategy of FSDAF causes accuracy loss at the edges of subdomains due to the insufficient consideration of edge effects. In this study, an enhanced FSDAF (cuFSDAF) is proposed to address these problems, and includes three main improvements. First, the TPS interpolator is replaced by an accelerated inverse distance weighted (IDW) interpolator to reduce computational complexity. Second, the algorithm is parallelized based on the compute unified device architecture (CUDA), a widely used parallel computing framework for graphics processing units (GPUs). Third, an adaptive domain decomposition (ADD) method is proposed to improve the fusion accuracy at the edges of subdomains and to enable GPUs with varying computing capacities to deal with datasets of any size. Experiments showed while obtaining similar accuracies to FSDAF and an up-to-date deep-learning-based method, cuFSDAF reduced the computing time significantly and achieved speed-ups of 140.3-182.2 over the original FSDAF program. cuFSDAF is capable of efficiently producing fused images with both high spatial and temporal resolutions to support applications for large-scale and long-term land surface dynamics. Source code and test data available at https://github.com/HPSCIL/cuFSDAF.

Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis. These data-intensive applications have high requirements during hardware implementation that are challenging for conventional hardware platforms such as central processing units (CPUs) and graphics processing units (GPUs). Computation-in-memory (CIM) provides a possible solution for highly efficient morphology operations. In this study, we demonstrate the application of morphology operation with a novel memristor-based auto-detection architecture and demonstrate non-neuromorphic computation on a multi-array-based memristor system. Pixel-by-pixel logic computations with low parallelism are converted to parallel operations using memristors. Moreover, hardware-implemented computer-integrated manufacturing was used to experimentally demonstrate typical defect detection tasks in integrated circuit (IC) manufacturing and medical image analysis. In addition, we developed a new implementation scheme employing a four-layer network to realize small-object detection with high parallelism. The system benchmark based on the hardware measurement results showed significant improvement in the energy efficiency by approximately 358 times and 32 times more than when a CPU and GPU were employed, respectively, exhibiting the advantage of the proposed memristor-based morphology operation.

We propose word-region alignment-guided multimodal neural machine translation (MNMT), a novel model for MNMT that links the semantic correlation between textual and visual modalities using word-region alignment (WRA). Existing studies on MNMT have mainly focused on the effect of integrating visual and textual modalities. However, they do not leverage the semantic relevance between the two modalities. We advance the semantic correlation between textual and visual modalities in MNMT by incorporating WRA as a bridge. This proposal has been implemented on two mainstream architectures of neural machine translation (NMT): the recurrent neural network (RNN) and the transformer. Experiments on two public benchmarks, English--German and English--French translation tasks using the Multi30k dataset and English--Japanese translation tasks using the Flickr30kEnt-JP dataset prove that our model has a significant improvement with respect to the competitive baselines across different evaluation metrics and outperforms most of the existing MNMT models. For example, 1.0 BLEU scores are improved for the English-German task and 1.1 BLEU scores are improved for the English-French task on the Multi30k test2016 set; and 0.7 BLEU scores are improved for the English-Japanese task on the Flickr30kEnt-JP test set. Further analysis demonstrates that our model can achieve better translation performance by integrating WRA, leading to better visual information use.

Alternative text is critical in communicating graphics to people who are blind or have low vision. Especially for graphics that contain rich information, such as visualizations, poorly written or an absence of alternative texts can worsen the information access inequality for people with visual impairments. In this work, we consolidate existing guidelines and survey current practices to inspect to what extent current practices and recommendations are aligned. Then, to gain more insight into what people want in visualization alternative texts, we interviewed 22 people with visual impairments regarding their experience with visualizations and their information needs in alternative texts. The study findings suggest that participants actively try to construct an image of visualizations in their head while listening to alternative texts and wish to carry out visualization tasks (e.g., retrieve specific values) as sighted viewers would. The study also provides ample support for the need to reference the underlying data instead of visual elements to reduce users' cognitive burden. Informed by the study, we provide a set of recommendations to compose an informative alternative text.

Recovery of a 3D head model including the complete face and hair regions is still a challenging problem in computer vision and graphics. In this paper, we consider this problem using only a few multi-view portrait images as input. Previous multi-view stereo methods that have been based, either on optimization strategies or deep learning techniques, suffer from low-frequency geometric structures such as unclear head structures and inaccurate reconstruction in hair regions. To tackle this problem, we propose a prior-guided implicit neural rendering network. Specifically, we model the head geometry with a learnable signed distance field (SDF) and optimize it via an implicit differentiable renderer with the guidance of some human head priors, including the facial prior knowledge, head semantic segmentation information and 2D hair orientation maps. The utilization of these priors can improve the reconstruction accuracy and robustness, leading to a high-quality integrated 3D head model. Extensive ablation studies and comparisons with state-of-the-art methods demonstrate that our method can generate high-fidelity 3D head geometries with the guidance of these priors., 13 pages, 14 figures

The quantification of 3D shape aesthetics has so far focused on specific shape features and manually defined criteria such as the curvature and the rule of thirds respectively. In this paper, we build a model of 3D shape aesthetics directly from human aesthetics preference data and show it to be well aligned with human perception of aesthetics. To build this model, we first crowdsource a large number of human aesthetics preferences by showing shapes in pairs in an online study and then use the same to build a 3D shape multi-view based deep neural network architecture to allow us learn a measure of 3D shape aesthetics. In comparison to previous approaches, we do not use any pre-defined notions of aesthetics to build our model. Our algorithmically computed measure of shape aesthetics is beneficial to a range of applications in graphics such as search, visualization and scene composition.

Autism Spectrum Disorder is affecting children under the age of 6–12 at very huge level. In China 1 out of 9000 children and India 23 out of 10,000 children are affected. These children will not be able to attend regular school and they need special attention. There are a few special centers to teach children affected by autism but they follow traditional teaching method. Our objective is to create a sensor based device to offer customized learning content to each child based on their own needs and interest. The influential note about this device is that it consists of inbuilt sensors that have its own corresponding functions such as when one sensor is clicked, it might play the audio content, or when another button is pressed the video content is displayed. Initially, the default content for learning in all formats that is audio, video, graphics, and images is loaded in the device. Just like how all fingers in our hands are of different sizes, not every child will like to learn the content in the same format. Few might like audios and others might opt for videos. When the user starts learning with this device, the number of clicks on each button is noted and stored. Based on the number of clicks we can understand how the user prefers to learn the subject and the device can be customized as per their preferences. In case if the user likes video content when compared with others and feels that it is easy to learn through videos then the device can be personalized for them. The decision tree model is used to detect the learning style of the child and recommends content accordingly. With the help of these methodologies, the mental health of the child is also monitored and a report is given to the parents and mentors. The learning progress and the improvement of the child in academics are also kept on track constantly.

This chapter presents some technical details about data formats, streaming, optimization of computation, and distributed deployment of optimized learning algorithms. Chapter 22 provides additional optimization details. We show format conversion and working with XML, SQL, JSON, 15 CSV, SAS and other data objects. In addition, we illustrate SQL server queries, describe protocols for managing, classifying and predicting outcomes from data streams, and demonstrate strategies for optimization, improvement of computational performance, parallel (MPI) and graphics (GPU) computing.

The High-Luminosity LHC, which is expected to be operational as of 2029, will increase the LHC’s integrated luminosity by a factor of 10. To achieve this major upgrade, scientists and engineers are optimising many of the collider’s parameters. Several new key technologies, some of which are completely innovative, are being developed within the scope of the project.

CERN actively invests in many activities to accelerate the innovation process. The CERN Knowledge Transfer group provides advice, support, training, networks and infrastructure to ease the transfer of CERN's know-how to industry and eventually to society. More information: https://kt.cern/activities

The applications of CERN technologies extend beyond high-energy physics to a vast range of areas such as aerospace, healthcare, quantum, environment and digital.

Identifying communities in social networks is one of the most important tasks nowadays. Social networks are especially relevant for networks represented by large-scale graphics. At the same time, it is important to use approximate methods that lead to near-optimal rather than optimal results within a given time. In this article, we propose to extract the communities based on the method of maximum likelihood similarity by representing them by letters. The community structure search algorithm is described and the performance of the algorithm is illustrated using examples of different views of the octagonal network.. Calculations were carried out using the Maple program.

The purpose of the article is to familiarize the reader with the content, structure and scientific foundations of the presentation of topics in the manuscript of the future textbook on the Chuvash language for students of grades 10-11 of schools with their native language of instruction. The research material is the manuscript of the textbook "Chuvash language" for grades 10-11 of schools with a native (Chuvash) language of instruction. The comparative-analytical method of research is used in the work. The manuscript was prepared in accordance with the program approved by the resolution of the Federal Educational and Methodological Association (Protocol No. 3/20 of September 17, 2020). This program is aimed at repeating and fixing the material of grades 5-9. In this regard, the manuscript includes relevant material for this purpose. The new topic is mainly the topic of "Language and speech economy". All topics are presented in accordance with the achievements of modern language theory. The author, as well as professor I.A. Andreev, believes that the study of the topic "Phraseology" is inappropriate. Studying a word out of connection with syntax does not reveal the essence of the use of certain affixes. For example, syntactic affixes can take both individual words and phrases. Therefore, morphemics and morphology should be presented in school taking into account syntax.

We propose an explicit GPU-based solver within the material point method (MPM) framework using graphics processing units (GPUs) to resolve elastoplastic problems under two- and three-dimensional configurations (i.e. granular collapses and slumping mechanics). Modern GPU architectures, including Ampere, Turing and Volta, provide a computational framework that is well suited to the locality of the material point method in view of high-performance computing. For intense and non-local computational aspects (i.e. the back-and-forth mapping between the nodes of the background mesh and the material points), we use straightforward atomic operations (the scattering paradigm). We select the generalized interpolation material point method (GIMPM) to resolve the cell-crossing error, which typically arises in the original MPM, because of the C0 continuity of the linear basis function. We validate our GPU-based in-house solver by comparing numerical results for granular collapses with the available experimental data sets. Good agreement is found between the numerical results and experimental results for the free surface and failure surface. We further evaluate the performance of our GPU-based implementation for the three-dimensional elastoplastic slumping mechanics problem. We report (i) a maximum 200-fold performance gain between a CPU- and a single-GPU-based implementation, provided that (ii) the hardware limit (i.e. the peak memory bandwidth) of the device is reached. Furthermore, our multi-GPU implementation can resolve models with nearly a billion material points. We finally showcase an application to slumping mechanics and demonstrate the importance of a three-dimensional configuration coupled with heterogeneous properties to resolve complex material behaviour.

WOS:000722473100001 PMID: 34822119 Chromosomes are bodies that contain human genetic information. Chromosomal disorders can cause structural and functional disorders in individuals. Detecting the metaphase stages of the cells accurately is a crucial step to detect possible defects in chromosomes. Thus, it is vital at this stage to identify the identical chromosome of each chromosome, to perform the pairing process, and to identify problems arising from this process. In this study, it was investigated whether the analyzable metaphase images can be analyzed by using the transfer learning and fine tuning approaches of deep learning models. The weights of VGG16 and InceptionV3 models trained with ImageNet data set were transferred to this problem and the classification process was carried out. True positive ratio values are 99%(+/- 0.9) and 99%(+/- 0.9) for VGG and Inception networks, respectively. The classification performances obtained depending on the changing training set ratios are presented comparatively in figures. F-measure, precision, and recall values obtained for the VGG and Inception networks were observed as 99%(+/- 1.0) and 99%(+/- 1.0), respectively. F-measure, precision, and recall values of VGG and Inceptionv3 networks are also presented with respect to the ratio of training size. The obtained results have compared with the state-of-the-art methods in the literature and supported with the tables and graphics. The training phase was also accelerated by using transfer learning and fine tuning methods. Transfer learning and fine tuning processes have almost similar performance as the models used in the literature and trained from scratch in metaphase Graphical Abstract The Flowchart of the proposed system for classifying metaphase candidates detection.

Objective This study aims to examine the use of decision aids which may improve shared decision making through an exploration of risk apprehension and modes of collaborative communication. Conclusions Decision aids such as graphics have a key role in facilitating shared treatment decision making, perhaps particularly in perinatal mental health care. They are most useful within a trusting, two-way conversation.

The use of optical contrast between different blood particles allows the use of optoacoustic imaging to visualize the distribution of blood particles (erythrocytes, taking into account oxygen saturation), the delivery of drugs to organs through blood vessels. An algorithm for calculating the ultrasonic field obtained as a result of optoacoustic interaction has been developed to speed up calculations on the GPU board. An architecture for fast restoration of an optoacoustic signal based on graphics processing unit (GPU) programming is proposed. The algorithm used in combination with the pre-migration method provides an improvement in the resolution and sharpness of the optoacoustic image of the simulated biological tissues. Thanks to the advanced graphics processing unit (GPU) computing architecture, time-consuming main processing unit (CPU) computing is accelerated with great computational efficiency.

Interferometric synthetic aperture radar (InSAR) technology has the potential to reveal ground surface deformation at high temporal and spatial resolutions, and the InSAR image processing field is progressing toward big data analytics. In this era of big data, as InSAR is usually employed for natural hazard research, processing large InSAR images under time constraints is a fundamental challenge. Graphics processing units (GPUs) have been widely adopted for high-performance computing (HPC) due to their parallel computing capability and low power consumption. Accordingly, we explore the interconnection between InSAR time series processing and GPU hardware for parallel Sentinel-1 InSAR processing. We build a rapid GPU-assisted InSAR processing chain and apply this chain to accelerate the time-consuming optimization steps in Sentinel-1 InSAR processing. This processing chain plays a pivotal role in rapidly calculating InSAR deformation maps using large quantities of Sentinel-1 SAR data and thus can be used in practical applications. We also perform some experiments to demonstrate that the processing chain is appropriate for the deformation monitoring of various natural disasters and is suitable for emergency monitoring and wide-area investigations of potential hazards involving geological disasters.

A novel implementation of the self-consistent field (SCF) procedure specifically designed for high-performance execution on multiple graphics processing units (GPUs) is presented. The algorithm offloads to GPUs the three major computational stages of the SCF, namely, the calculation of one-electron integrals, the calculation and digestion of electron repulsion integrals, and the diagonalization of the Fock matrix, including SCF acceleration via DIIS. Performance results for a variety of test molecules and basis sets show remarkable speedups with respect to the state-of-the-art parallel GAMESS CPU code and relative to other widely used GPU codes for both single and multi-GPU execution. The new code outperforms all existing multi-GPU implementations when using eight V100 GPUs, with speedups relative to Terachem ranging from 1.2× to 3.3× and speedups of up to 28× over QUICK on one GPU and 15× using eight GPUs. Strong scaling calculations show nearly ideal scalability up to 8 GPUs while retaining high parallel efficiency for up to 18 GPUs.

The article considers the artistic potential of computer graphic modeling on the example of modern exhibition events that implement the convergence of traditional and multimedia exhibition facilities in the demonstration of digital art. Modern trends in multimedia transformation of exhibition spaces are outlined. The study resulted in recommendations for the optimal organization of the environment for displaying hybrid as well as digital works of art. The conclusions on the prospects for the development of innovative exhibition spaces will allow to optimize the functioning of the exhibition environment with a growing share of digital graphic content.

Technology using multimedia assistance is expected to be able to boost some changes both in the classroom atmosphere in general and individual in particular either cognitive (learning outcomes) or affective (learning attitudes). One of learning media that is well-known among users in education is multimedia presentations or Power Point which combine text, graphics, images, photos, audio, videos, and animation in an integrated manner. One of learning tools featuring those conveniences is Genially. The current study was preliminary in nature regarding the use of Genially online media presentation and to investigate with respect its influence on self-confidence among male and female students. The study focused on one skill of language in delivering selected topic or idea using English. Using a quantitative approach, this study involved 62 students consisting of 42 males and 20 females of sophomore students from one vocational college in a small city in Indonesia. To determine level of self-confidence, students were given a questionnaire responding 12 statements in 4 answer scales. Output of One Way Anova and regression test calculation showed that the use of PowerPoint Genially influenced self-confidence for both genders. Even though this media was capable to lead more pleasant and enjoyable learning atmosphere, the influence performed by the variables was considered less significant. This means that students still feel less confident to use oral English in delivering selected topics to their friends in the class.

Graphics processing units (GPUs) power today’s fastest supercomputers, are the dominant platform for deep learning, and provide the intelligence for devices ranging from self-driving cars to robots and smart cameras. They also generate compelling photorealistic images at real-time frame rates. GPUs have evolved by adding features to support new use cases. NVIDIA’s GeForce 256, the first GPU, was a dedicated processor for real-time graphics, an application that demands large amounts of floating-point arithmetic for vertex and fragment shading computations and high memory bandwidth. As real-time graphics advanced, GPUs became programmable. The combination of programmability and floating-point performance made GPUs attractive for running scientific applications. Scientists found ways to use early programmable GPUs by casting their calculations as vertex and fragment shaders. GPUs evolved to meet the needs of scientific users by adding hardware for simpler programming, double-precision floating-point arithmetic, and resilience.

There are already many software tools present and many application software's that being developed. These is because the technology needs to be updated according to the requirements. During these busy times, Developing and updating technologies is itself a big challenge. If any bugs occur in future or to keep track of features are added are working properly according to requirements, it needs some tools to keep track of it. For these things to happen, it requires some methodology to improve debug time like ETL (Event Trace Log) for multiple display features. The other things are that, code that is written today has to be written in such a way that it could be used for at least next 10 years. This will reduce the time of adding the same code simultaneously. Collecting the ETL in the encoded form helps in securing the sensitive information present in the trace log. Adding more testing codes in Unit Test Framework (UTF) find more bugs at early stages which will reduce the code cost. Adding MMIO (Memory Mapped I/O) verifier rules as the part of UTF helps in register verification (Simulates real Hardware context). Addition of MMIO rules for existing and new display features in separate xml file for different platforms makes it easy to identify in which platform feature failure in happening. All these methods help in code quality improvements for Intel Windows Graphics Driver.

Work on VR and AR task interaction and visualization paradigms has typically focused on providing information about the current step (a cue) immediately before or during its performance. Some research has also shown benefits to simultaneously providing information about the next step (a precue). We explore whether it would be possible to improve efficiency by precueing information about multiple upcoming steps before completing the current step. To accomplish this, we developed a remote VR user study comparing task completion time and subjective metrics for different levels and styles of precueing in a path-following task. Our visualizations vary the precueing level (number of steps precued in advance) and style (whether the path to a target is communicated through a line to the target, and whether the place of a target is communicated through graphics at the target). Participants in our study performed best when given two to three precues for visualizations using lines to show the path to targets. However, performance degraded when four precues were used. On the other hand, participants performed best with only one precue for visualizations without lines, showing only the places of targets, and performance degraded when a second precue was given. In addition, participants performed better using visualizations with lines than ones without lines.

Learning media is one component that determines the success of learning. Learning media that should be used in the present day is a learning medium that facilitates students to be students and graduates are multi-competent, multi-literate, critical thinking, creative, innovative, able to cooperate and collaborate. Learning media that have these characteristics are learning media that combine, collaborate, or integrate several learning media, for example images, graphics, audio, and so on. Because the media uses several media, the media is referred to as multimedia. The multimedia chosen in speaking learning, especially on presentation, should be relevant to the needs of learning materials and learning objectives at each stage of the speaking, which includes (1) pre-speaking stages (e.g. group presentation videos used to analyze concepts and presentation aspects; e-books, journals online, online articles, e-mails used to determine the topics, develop topics, and compose the content of speech, etc.) (b) speaking stage, and (3) post-speaking stage. The use of multimedia in each stage of speaking facilitates students to succeed in learning

Avalanche warning services publish avalanche condition reports, often called avalanche bulletins, to help backcountry recreationists make informed risk management decisions about when and where to travel in avalanche terrain. To be successful, the information presented in bulletins must be properly understood and applied prior to entering avalanche terrain. However, few avalanche bulletin elements have been empirically tested for their efficacy in communicating hazard information. The objective of this study is to explicitly test the effectiveness of three different graphics representing the aspect and elevation of avalanche problems on users’ ability to apply the information. To address this question, we conducted an online survey that presented participants with one of three graphic renderings of avalanche problem information and asked them to rank a series of route options in order of their exposure to the described hazard. Following completion of route ranking tasks, users were presented with all three graphics and asked to rate how effective they thought the graphics were. Our analysis dataset included responses from 3,056 backcountry recreationists with a variety of backgrounds and avalanche safety training levels. Using a series of generalized linear mixed effects models, our analysis shows that a graphic format that combines the aspect and elevation information for each avalanche problem is the most effective graphic for helping users understand the avalanche hazard conditions because it resulted in higher success in picking the correct exposure ranking, faster completion times, and was rated by users to be the most effective. These results are consistent with existing research on the impact of graphics on cognitive load and can be applied by avalanche warning services to improve the communication of avalanche hazard to readers of their avalanche bulletins.

With the rapid development of digital art and machine science and technology, the production form of modern animation tends to be diversified, and with the important role of animation color in the animation film foil and rendering, more and more attention was paid by the animation design industry. Computer graphic technology is a new type of artistic creation method, and its birth and development are closely related to computer technology. The purpose of this paper is to make some analysis and induction from the perspective of science and technology and culture and to explore the application of Photoshop graphics and image processing technology in the field of animation. First, the definition of “animation” and the extension of the animation category are introduced. Then, the main functions of Photoshop image processing technology are described in detail. According to the research content, this paper designs a self-made experimental short film “future city.” After the simulation of experimental animation film and the setting and positioning of the experimental scene, Photoshop software was used to show the animation scene. The experimental results show that the color changes in the short film “future city.” After the saturation of sunny scenery is reduced by 3 points and the brightness is reduced by 20 points and the filter, mode change, and color adjustment are carried out, a complete rainy scenery can be obtained. The self-made experimental short film has guiding significance for the creation and practice.

Modern heterogeneous CPU-GPU-based mobile architectures, which execute intensive mobile gaming/graphics applications, use software governors to achieve high performance with energy-efficiency. However, existing governors typically utilize simple statistical or heuristic models, assuming linear relationships using a small unbalanced dataset of mobile games; and the limitations result in high prediction errors for dynamic and diverse gaming workloads on heterogeneous platforms. To overcome these limitations, we propose an interpretable machine learning (ML) model enhanced integrated CPU-GPU governor: (1) It builds tree-based piecewise linear models (i.e., model trees) offline considering both high accuracy (low error) and interpretable ML models based on mathematical formulas using a simulatability operation counts quantitative metric. And then (2) it deploys the selected models for online estimation into an integrated CPU-GPU Dynamic Voltage Frequency Scaling governor. Our experiments on a test set of 20 mobile games exhibiting diverse characteristics show that our governor achieved significant energy efficiency gains of over 10% (up to 38%) improvements on average in energy-per-frame with a surprising-but-modest 3% improvement in Frames-per-Second performance, compared to a typical state-of-the-art governor that employs simple linear regression models.

Simulation of water flow animation is a significant and challenging subject in computer graphics. With the continuous development of computational fluid dynamics and computer graphics, many more effective simulation methods have been developed, and fluid animation simulation has developed rapidly. In order to obtain realistic flow animation, one of the key aspects is to simulate flow motion. Based on the two-dimensional Navier-Stokes equations, a mathematical model is established to solve the boundary conditions required by the physical flow field of water. The coordinate transformation formula is introduced to transform the irregular physical area into a regular square calculation area, and then, the specific expressions of the liberalized Navier-Stokes equation, continuity equation, pressure Poisson equation, and dimensionless boundary conditions are given. Using animation software to sequence graphics and images of all kinds of control and direct operation of the relevant instructions, through the computer technology to simulate the flow of animation, based on the stability of fluid simulation method and simulation efficiency, so as to make realistic flow animation. The results show that FluidsNet has considerable performance in accelerating large scene animation simulation on the basis of ensuring the rationality of prediction, and the motion of water wave is realistic. The application of computer successfully simulates water flow.

In an increasingly connected world, consumer video experiences have diversified away from traditional broadcast video into new applications with increased use of non-camera-captured content such as computer screen desktop recordings or animations created by computer rendering, collectively referred to as screen content. There has also been increased use of graphics and character content that is rendered and mixed or overlaid together with camera-generated content. The emerging Versatile Video Coding (VVC) standard, in its first version, addresses this market change by the specification of low-level coding tools suitable for screen content. This is in contrast to its predecessor, the High Efficiency Video Coding (HEVC) standard, where highly efficient screen content support is only available in extension profiles of its version 4. This paper describes the screen content support and the five main low-level screen content coding tools in VVC: transform skip residual coding (TSRC), block-based differential pulse-code modulation (BDPCM), intra block copy (IBC), adaptive color transform (ACT), and the palette mode. The specification of these coding tools in the first version of VVC enables the VVC reference software implementation (VTM) to achieve average bit-rate savings of about 41% to 61% relative to the HEVC test model (HM) reference software implementation using the Main 10 profile for 4:2:0 screen content test sequences. Compared to the HM using the Screen-Extended Main 10 profile and the same 4:2:0 test sequences, the VTM provides about 19% to 25% bit-rate savings. The same comparison with 4:4:4 test sequences revealed bit-rate savings of about 13% to 27% for $Y'C_{B}C_{R}$ and of about 6% to 14% for $R'G'B'$ screen content. Relative to the HM without the HEVC version 4 screen content coding extensions, the bit-rate savings for 4:4:4 test sequences are about 33% to 64% for $Y'C_{B}C_{R}$ and 43% to 66% for $R'G'B'$ screen content.

In this paper, we present approach for recognition of static fingerspelling in Japanese Sign Language on RGB images. Two 3D articulated hand models have been developed to generate synthetic fingerspellings and to extend a dataset consisting of real hand gestures.In the first approach, advanced graphics techniques were employed to rasterize photorealistic gestures using a skinned hand model. In the second approach, gestures rendered using simpler lighting techniques were post-processed by a modified Generative Adversarial Network. In order to avoid generation of unrealistic fingerspellings a hand segmentation term has been added to the loss function of the GAN. The segmentation of the hand in images with complex background was done by proposed ResNet34-based segmentation network. The finger-spelled signs were recognized by an ensemble with both fine-tuned and trained from scratch neural networks. Experimental results demonstrate that owing to sufficient amount of training data a high recognition rate can be attained on RGB images. The JSL dataset with pixel-level hand segmentations is available for download.

Background Liver resection involves surgical removal of a portion of the liver. It is used to treat liver tumors and liver injuries. The complexity and high-risk nature of this surgery prevents novice doctors from practicing it on real patients. Virtual surgery simulation was developed to simulate surgical procedures to enable medical professionals to be trained without requiring a patient, a cadaver, or an animal. Therefore, there is a strong need for the development of a liver resection surgery simulation system. We propose a real-time simulation system that provides realistic visual and tactile feedback for hepatic parenchymal transection. Methods The tetrahedron structure and cluster-based shape matching are used for physical model construction, topology update of a three-dimensional liver model soft deformation simulation, and haptic rendering acceleration. During the liver parenchyma separation simulation, a tetrahedral mesh is used for surface triangle subdivision and surface generation of the surgical wound. The shape-matching cluster is separated via component detection on an undirected graph constructed using the tetrahedral mesh. Results In our system, cluster-based shape matching is implemented on a GPU, whereas haptic rendering and topology updates are implemented on a CPU. Experimental results show that haptic rendering can be performed at a high frequency (> 900 Hz), whereas mesh skinning and graphics rendering can be performed at 45 fps. The topology update can be executed at an interactive rate (> 10 Hz) on a single CPU thread. Conclusion We propose an interactive hepatic parenchymal transection simulation method based on a tetrahedral structure. The tetrahedral mesh simultaneously supports physical model construction, topology update, and haptic rendering acceleration.

This researchs' purpose is to develop environmental literacy through the use of graphic art for preparatory stage students through the design of guiding means. Therefore, the researchers have prepared a list of some environmental problems by reviewing some previous research and studies and the opinions of specialists which have included (5) major issues branching from them (9) environmental problems, which in turn have included (43) concepts. The researchs tools are prepared to include (6) posters with different designs, a brochure with two covers, the design of (2) (folded) magazines, a design of the cover of the face and the back of the notebook. Inside it, contains a number of recycled paper, the design of one desk-calendar which contains 12 designs; inside it, there is a folder design for keeping papers, a CD sticker design, a card design, a design of 4 prints on Cochet paper, environmental games, printing on a mug, printing on a paper folder. A scale of Environmental Literacy has been also designed, including (a Test of Environmental Information, a Scale of Environmental Attitudes and a Scale of Environmental Skills). A program of the proposed activities has been applied to a group of (30) students of the second year of preparatory school. The experimental method has been used for its suitability to the nature of this research. After the application of the guiding means program, a post-application of the scale of environmental literacy has been subsequently conducted to the research group. Results indicate that the average scores of the experimental group has increased after the statistical treatment of these scores and that: There is a statistically significant difference between the mean scores of students on the scale of environmental literacy pre/post application of the guiding means, in favor of the post-application. There are also statistically significant differences regarding the scores of each dimension of the environmental literacy dimensions pre/post application of the program, in favor of the post-application. This indicates the effectiveness of the program of the guiding means produced using graphic art for the development of environmental literacy. The researcher recommends the necessity of including the guiding means designed using graphic art in the schools of the preparatory stages and using the means that have been designed as well.