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Global Krylov subspace methods are effective iterative solvers for large linear matrix equations. Several Lanczos-type product methods (LTPMs) for solving standard linear systems of equations have been extended to their global versions. However, the GPBiCGstab(L) method, which unifies two well-known LTPMs (i.e., BiCGstab(L) and GPBiCG methods), has been developed recently, and it has been shown that this novel method has superior convergence when compared to the conventional LTPMs. In the present study, we therefore extend the GPBiCGstab(L) method to its global version. Herein, we present not only a naive extension of the original GPBiCGstab(L) algorithm but also its alternative implementation. This variant enables the preconditioning technique to be applied stably and efficiently. ... |
Deep learning has been widely adopted in automatic emotion recognition and has lead to significant progress in the field. However, due to insufficient training data, pre-trained models are limited in their generalisation ability, leading to poor performance on novel test sets. To mitigate this challenge, transfer learning performed by fine-tuning pr-etrained models on novel domains has been applied. However, the fine-tuned knowledge may overwrite and/or discard important knowledge learnt in pre-trained models. In this paper, we address this issue by proposing a PathNet-based meta-transfer learning method that is able to (i) transfer emotional knowledge learnt from one visual/audio emotion domain to another domain and (ii) transfer emotional knowledge learnt from multiple audio emoti... |
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... |
This paper contributes multivariate versions of seven commonly used elastic similarity and distance measures for time series data analytics. Elastic similarity and distance measures can compensate for misalignments in the time axis of time series data. We adapt two existing strategies used in a multivariate version of the well-known Dynamic Time Warping (DTW), namely, Independent and Dependent DTW, to these seven measures. While these measures can be applied to various time series analysis tasks, we demonstrate their utility on multivariate time series classification using the nearest neighbor classifier. On 23 well-known datasets, we demonstrate that each of the measures but one achieves the highest accuracy relative to others on at least one dataset, supporting the value of develo... |
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. |
In modern computing architectures, graph theory is the soul of the play due to the rising core counts. It is indispensable to keep finding a better way to connect the cores. A novel chordal-ring interconnect topology system, Equality, is revisited in this paper to compare with a few previous works. This paper details the procedures for constructing the Equality interconnects, its special routing procedures, the strategies for selecting a configuration, and evaluating its performance using the open-source cycle-accurate BookSim package. Four scenarios representing small- to large-scale computing facilities are presented to assess the network performance. This work shows that in 16,384-endpoint systems, the Equality network turns out to be the most efficient system. The results also s... |
Driving the decisions of stock market investors is among the most challenging financial research problems. Markowitz’s approach to portfolio selection models stock profitability and risk level through a mean–variance model, which involves estimating a very large number of parameters. In addition to requiring considerable computational effort, this raises serious concerns about the reliability of the model in real-world scenarios. This paper presents a hybrid approach that combines itemset extraction with portfolio selection. We propose to adapt Markowitz’s model logic to deal with sets of candidate portfolios rather than with single stocks. We overcome some of the known issues of the Markovitz model as follows: (i) Complexity: we reduce the model complexity, in terms of parameter es... |
The determination of Lagrangian Coherent Structures (LCS) is becoming very important in several disciplines, including cardiovascular engineering, aerodynamics, and geophysical fluid dynamics. From the computational point of view, the extraction of LCS consists of two main steps: The flowmap computation and the resolution of Finite Time Lyapunov Exponents (FTLE). In this work, we focus on the design, implementation, and parallelization of the FTLE resolution. We offer an in-depth analysis of this procedure, as well as an open source C implementation (UVaFTLE) parallelized using OpenMP directives to attain a fair parallel efficiency in shared-memory environments. We have also implemented CUDA kernels that allow UVaFTLE to leverage as many NVIDIA GPU devices as desired in order to rea... |
The management of shared resources in multicore processors is an open problem due to the continuous evolution of these systems. The trend toward increasing the number of cores and organizing them in clusters sets out new challenges not considered in previous works. In this paper, we characterize the use of the shared cache and memory bandwidth of an AMD Rome processor executing multiprogrammed workloads and propose several mechanisms that control the use of these resources to improve the system performance and fairness. Our control mechanisms require no hardware or operating system modifications. We evaluate Balancer on a real system running SPEC CPU2006 and CPU2017 applications. Balancer tuned for performance shows an average increase of 7.1% in system performance and an unfairness... |
