390 research outputs found
Sparse Convolution for Approximate Sparse Instance
Computing the convolution of two vectors of dimension is one
of the most important computational primitives in many fields. For the
non-negative convolution scenario, the classical solution is to leverage the
Fast Fourier Transform whose time complexity is . However, the
vectors and could be very sparse and we can exploit such property to
accelerate the computation to obtain the result. In this paper, we show that
when and holds,
we can approximately recover the all index in with point-wise error of in
time. We further show that we can iteratively correct the error and recover all
index in correctly in time
A Convergence Theory for Federated Average: Beyond Smoothness
Federated learning enables a large amount of edge computing devices to learn
a model without data sharing jointly. As a leading algorithm in this setting,
Federated Average FedAvg, which runs Stochastic Gradient Descent (SGD) in
parallel on local devices and averages the sequences only once in a while, have
been widely used due to their simplicity and low communication cost. However,
despite recent research efforts, it lacks theoretical analysis under
assumptions beyond smoothness. In this paper, we analyze the convergence of
FedAvg. Different from the existing work, we relax the assumption of strong
smoothness. More specifically, we assume the semi-smoothness and semi-Lipschitz
properties for the loss function, which have an additional first-order term in
assumption definitions. In addition, we also assume bound on the gradient,
which is weaker than the commonly used bounded gradient assumption in the
convergence analysis scheme. As a solution, this paper provides a theoretical
convergence study on Federated Learning.Comment: BigData 202
An Approximate Algorithm Combining P Systems and Active Evolutionary Algorithms for Traveling Salesman Problems
An approximate algorithm combining P systems and active evolutionary algorithms (AEAPS) to solve traveling salesman problems (TSPs) is proposed in this paper. The novel algorithm uses the same membrane structure, subalgorithms and transporting mechanisms as Nishida’s algorithm, but adopts two classes of active evolution operators and a good initial solution generating method. Computer experiments show that the AEAPS produces better solutions than Nishida’s shrink membrane algorithm and similar solutions with an approximate optimization algorithm integrating P systems and ant colony optimization techniques (ACOPS) in solving TSPs. But the necessary number of iterations using AEAPS is less than both of them
The impact of gratitude interventions on patients with cardiovascular disease: a systematic review
Positive psychological factors play a pivotal role in improving cardiovascular outcomes. Gratitude interventions are among the most effective positive psychological interventions, with potential clinical applications in cardiology practice. To better understand the potential clinical effects of gratitude interventions in cardiovascular disease, four databases (Web of Science, Scopus, PubMed, and PsycArticles) were searched from 2005 to 2023 for relevant studies. Randomized controlled trials of gratitude interventions as the intervention and that reported physiological or psychosocial outcomes were eligible for inclusion. In total, 19 studies were identified, reporting results from 2951 participants from 19 to 71 years old from both healthy populations and those with clinical diagnoses. The studies showed that gratitude not only promotes mental health and adherence to healthy behaviors but also improves cardiovascular outcomes. Gratitude may have a positive impact on biomarkers of cardiovascular disease risk, especially asymptomatic heart failure, cardiovascular function, and autonomic nervous system activity
Asynchronous Spiking Neural P Systems with Multiple Channels and Symbols
Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computation systems, inspired from the way that the neurons process and communicate information by means of spikes. A new variant of SNP systems, which works in asynchronous mode, asynchronous spiking neural P systems with multiple channels and symbols (ASNP-MCS systems, in short), is investigated in this paper. There are two interesting features in ASNP-MCS systems: multiple channels and multiple symbols. That is, every neuron has more than one synaptic channels to connect its subsequent neurons, and every neuron can deal with more than one type of spikes. The variant works in asynchronous mode: in every step, each neuron can be free to fire or not when its rules can be applied. The computational completeness of ASNP-MCS systems is investigated. It is proved that ASNP-MCS systems as number generating and accepting devices are Turing universal. Moreover, we obtain a small universal function computing device that is an ASNP-MCS system with 67 neurons. Specially, a new idea that can solve ``block'' problems is proposed in INPUT modules
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