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Runtime systems can significantly reduce the cognitive complexity of scientific applications, narrowing the gap between systems engineering and domain science in HPC. One of the most important angles in this is automating data migration in a cluster. Traditional approaches require the application developer to model communication explicitly, for example through MPI primitives. Celerity, a runtime system for accelerator clusters heavily inspired by the SYCL programming model, instead provides a purely declarative approach focused around access patterns. In addition to eliminating the need for explicit data transfer operations, it provides a basis for efficient and dynamic scheduling at runtime. |
We consider the time discretization of a linear parabolic problem by the discontinuous Galerkin (DG) method using piecewise polynomials of degree at most r − 1 in t, for r ≥ 1 and with maximum step size k. It is well known that the spatial L2-norm of the DG error is of optimal order kr globally in time, and is, for r ≥ 2, superconvergent of order k2r− 1 at the nodes. We show that on the n th subinterval (tn− 1,tn), the dominant term in the DG error is proportional to the local right Radau polynomial of degree r. This error profile implies that the DG error is of order kr+ 1 at the right-hand Gauss–Radau quadrature points in each interval. We show that the norm of the jump in the DG solution at the left end point tn− 1 provides an accurate a posteriori estimate for the maximum error ... |
Medical instrument detection is essential for computer-assisted interventions, since it facilitates clinicians to find instruments efficiently with a better interpretation, thereby improving clinical outcomes. This article reviews image-based medical instrument detection methods for ultrasound-guided (US-guided) operations. Literature is selected based on an exhaustive search in different sources, including Google Scholar, PubMed, and Scopus. We first discuss the key clinical applications of medical instrument detection in the US, including delivering regional anesthesia, biopsy taking, prostate brachytherapy, and catheterization. Then, we present a comprehensive review of instrument detection methodologies, including non-machine-learning and machine-learning methods. |
The risk-based access control model is one of the dynamic models that use the security risk as a criterion to decide the access decision for each access request. This model permits or denies access requests dynamically based on the estimated risk value. The essential stage of implementing this model is the risk estimation process. This process is based on estimating the possibility of information leakage and the value of that information. Several researchers utilized different methods for risk estimation but most of these methods were based on qualitative measures, which cannot suit the access control context that needs numeric and precise risk values to decide either granting or denying access. Therefore, this paper presents a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) mod... |
Feature-level-based fusion has attracted much interest. Generally, a dataset can be created in different views, features, or modalities. To improve the classification rate, local information is shared among different views by various fusion methods. However, almost all the methods use the views without considering their common aspects. In this paper, wavelet transform is considered to extract high and low frequencies of the views as common aspects to improve the classification rate. The fusion method for the decomposed parts is based on joint sparse representation in which a number of scenarios can be considered. The presented approach is tested on three datasets. The results obtained by this method prove competitive performance in terms of the datasets compared to the state-of-the-... |
Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for syn... |
We investigate the queue number of posets in terms of their width, that is, the maximum number of pairwise incomparable elements. A long-standing conjecture of Heath and Pemmaraju asserts that every poset of width w has queue number at most w. The conjecture has been confirmed for posets of width w=2 via so-called lazy linear extension. We extend and thoroughly analyze lazy linear extensions for posets of width w>2. Our analysis implies an upper bound of (w−1)2+1 on the queue number of width-w posets, which is tight for the strategy and yields an improvement over the previously best-known bound. Further, we provide an example of a poset that requires at least w+1 queues in every linear extension, thereby disproving the conjecture for posets of width w>2
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Automatically understanding the content of medical images and delivering accurate descriptions is an emerging field of artificial intelligence that combines skills in both computer vision and natural language processing fields. Medical image captioning is involved in various applications related to diagnosis, treatment, report generation and computer-aided diagnosis to facilitate the decision making and clinical workflows. Unlike generic image captioning, medical image captioning highlights the relationships between image objects and clinical findings, which makes it a very challenging task. Although few review papers have already been published in this field, their coverage is still quite limited and only particular problems are addressed. |
Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensate... |
The use of Graphics Processing Units to accelerate computational applications is increasingly being adopted due to its affordability, flexibility and performance. However, achieving top performance comes at the price of restricted data-parallelism models. In the case of sequence alignment, most GPU-based approaches focus on accelerating the Smith-Waterman dynamic programming algorithm due to its regularity. Nevertheless, because of its quadratic complexity, it becomes impractical when comparing long sequences, and therefore heuristic methods are required to reduce the search space. |
