As artificial intelligence (AI) systems become more complex and widespread, they require significant computational power, increasing energy consumption. Addressing this challenge is essential for ensuring the long-ter...
详细信息
Nowadays,high-performancecomputing(HPC)clusters are increasingly *** volumes of job logs recording many years of operation traces have been *** the same time,the HPC cloud makes it possible to access HPC services ***...
详细信息
Nowadays,high-performancecomputing(HPC)clusters are increasingly *** volumes of job logs recording many years of operation traces have been *** the same time,the HPC cloud makes it possible to access HPC services *** executing applications,both HPC end-users and cloud users need to request specific resources for different workloads by *** users are usually not familiar with the hardware details and software layers,as well as the performance behavior of the underlying HPC *** is hard for them to select optimal resource configurations in terms of performance,cost,and energy ***,how to provide on-demand services with intelligent resource allocation is a critical issue in the HPC *** of job characteristics plays a key role for intelligent resource *** paper presents a survey of the existing work and future directions for prediction of job characteristics for intelligent resource allocation in HPC *** first review the existing techniques in obtaining performance and energy consumption data of *** we survey the techniques for single-objective oriented predictions on runtime,queue time,power and energy consumption,cost and optimal resource configuration for input jobs,as well as multi-objective oriented *** conclude after discussing future trends,research challenges and possible solutions towards intelligent resource allocation in HPC systems.
Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating an...
详细信息
In the last few years, quantum computing has evolved as the next big technological breakthrough. Many big companies are actively researching this area and have built open-source frameworks which can be used to develop...
详细信息
In this era, it is observed that electronic system gadgets are developed every day with the most upcoming features which are beneficial and convenient for the user. SoCs, Softcores, and Softprocessors play a vital rol...
详细信息
Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in under...
详细信息
Medical big data with artificial intelligence are vital in advancing digital ***,the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a significant o...
详细信息
Medical big data with artificial intelligence are vital in advancing digital ***,the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a significant obstacle to reproducing previous *** paper aims to develop an easy-to-use time-series multimodal data extraction pipeline,Quick-MIMIC,for standardised data extraction from MIMIC *** method can fully integrate different data structures into a time-series table,including structured,semi-structured,and unstructured *** also introduce two additional modules to Quick-MIMIC,a pipeline parallelization method and data analysis methods,for reducing the data extraction time and presenting the characteristics of the extracted data *** extensive experimental results show that our pipeline can efficiently extract the needed data from the MIMIC dataset and convert it into the correct format for further analytic tasks.
Recently, research on denoising diffusion models has expanded its application to the field of image restoration. Traditional diffusion-based image restoration methods utilize degraded images as conditional input to ef...
A physics-informed neural network (PINN) uses physics-Augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensure its output is consistent with funda...
详细信息
A physics-informed neural network (PINN) uses physics-Augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensure its output is consistent with fundamental physics laws. However, it turns out to be difficult to train an accurate PINN model for many problems in practice. In this article, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs. By analyzing behavior at model initialization, we first show that a PINN of increasing expressiveness induces an initial bias around flat output functions. Notably, this initial solution can be very close to satisfying many physics PDEs, i.e., falling into a localminimum of the PINN loss that onlyminimizes PDE residuals, while still being far from the true solution that jointly minimizes PDE residuals and the initial and/or boundary conditions. It is difficult for gradient descent optimization to escape from such a local minimum trap, often causing the training to stall. We then prove that the sinusoidalmapping of inputs-in an architecture we label as sf-PINN-is effective to increase input gradient variability, thus avoiding being trapped in such deceptive local minimum. The level of variability can be effectively modulated to match high-frequency patterns in the problem at hand. A key facet of this article is the comprehensive empirical study that demonstrates the efficacy of learning in sinusoidal spaces with PINNs for a wide range of forward and inversemodeling problems spanning multiple physics domains. Impact Statement-Falling under the emerging field of physicsinformed machine learning, PINN models have tremendous potential as a unifying AI framework for assimilating physics theory and measurement data. However, they remain infeasible for broad science and engineering applications due to computational cost and training challenges, especially for more complex problems. Instead of focusing on empirical demonstration of appli
Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart *** massive accounts can be divided into diverse categories,such as miners,toke...
详细信息
Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart *** massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this *** benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal ***,the exploration of blockchain account diversity remains *** the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are *** this end,we present the first attempt to demystify the account diversity on *** key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the *** then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its ***,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage *** for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total *** latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.
暂无评论