Recently flash-based solid-state drives (SSDs) have been widely deployed as cache devices to boost system performance. However, classical SSD cache algorithms (e.g. LRU) replace the cached data frequently to maintain ...
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ISBN:
(纸本)9781450333580
Recently flash-based solid-state drives (SSDs) have been widely deployed as cache devices to boost system performance. However, classical SSD cache algorithms (e.g. LRU) replace the cached data frequently to maintain high hit rates. Such aggressive data updating strategies result in too many writing operations on SSDs and make them wear out quickly, which finally leads to high costs of SSDs for enterprise applications. In this paper, we propose a novel Expiration-Time Driven Cache (ETD-Cache) method to solve this problem. In ETD-Cache, an active data eviction mechanism is adopted. An already cached block leaves the SSD cache if and only if there is no access to it for a time longer than a specified expiration time. This mechanism gives more time for the cached contents to wait for their following accesses and limits the admission of newly arrived blocks to generate less SSD writes. In addition, a low-overhead candidate management module is designed to maintain the most popular data in the system for the potential cache replacement. The simulations driven by a series of typical real-world traces indicate that due to the great reduction on data updating frequency, ETD-Cache lowers the total SSD costs by 98.45% compared with LRU under the same cache hit rate. Copyright 2015 ACM.
data-driven decision in big data era is becoming ubiquitous in electronic grid. In particular, daily collected power consumption records enable workload aware device clustering, which is crucial for critical domain ap...
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This article is the tenth series of the Fungal Diversity Notes,where 114 taxa distributed in three phyla,ten classes,30 orders and 53 families are described and *** described in the present study include one new famil...
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This article is the tenth series of the Fungal Diversity Notes,where 114 taxa distributed in three phyla,ten classes,30 orders and 53 families are described and *** described in the present study include one new family(*** in Dothideomycetes),five new genera(Caatingomyces,Cryptoschizotrema,Neoacladium,Paramassaria and Trochilispora)and 71 new species,(*** thailandica,Amniculicola aquatica,***,Angustimassarina sylvatica,Blackwellomyces lateris,Boubovia gelatinosa,Buellia viridula,Caatingomyces brasiliensis,Calophoma humuli,Camarosporidiella mori,Canalisporium dehongense,Cantharellus brunneopallidus,***,Castanediella meliponae,Coprinopsis psammophila,Cordyceps succavus,Cortinarius minusculus,***,Diaporthe italiana,***,Diatrypella delonicis,Dictyocheirospora aquadulcis,***,Digitodesmium chiangmaiense,Distoseptispora dehongensis,***,Dothiorella styphnolobii,Ellisembia aurea,Falciformispora aquatic,Fomitiporia carpinea,***,Grammothele aurantiaca,***,Hermatomyces bauhiniae,Jahnula queenslandica,Kamalomyces mangrovei,Lecidella yunnanensis,Micarea squamulosa,Muriphaeosphaeria angustifoliae,Neoacladium indicum,Neodidymelliopsis sambuci,Neosetophoma miscanthi,***,Nodulosphaeria aquilegiae,***,Paramassaria samaneae,Penicillium circulare,***,***-pumilae,***,***,Phaeoisaria siamensis,Phaeopoacea asparagicola,Phaeosphaeria penniseti,Plectocarpon galapagoense,Porina sorediata,Pseudoberkleasmium chiangmaiense,Pyrenochaetopsis sinensis,Rhizophydium koreanum,Russula prasina,Sporoschisma chiangraiense,Stigmatomyces chamaemyiae,***,***,***,***,Thysanorea uniseptata,Torula breviconidiophora,***,Trochilispora schefflerae and Vaginatispora palmae).Further,twelve new combinations(*** cryptotrema,Prolixandromyces australi,***,***,***,P.
With the increasing proliferation of the Mobile Social Networks (MSN) and the Location Based Service (LBS), location privacy has attracted broad attention in recent years. Most researches have been done with the assum...
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Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detec...
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Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.
The volume of RDF data increases dramatically within recent years, while cloud computing platforms like Hadoop are supposed to be a good choice for processing queries over huge data sets for their wonderful scalabilit...
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The volume of RDF data increases dramatically within recent years, while cloud computing platforms like Hadoop are supposed to be a good choice for processing queries over huge data sets for their wonderful scalability. Previous work on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect system performance. Specifically, a good partitioning solution would greatly reduce or even to- tally avoid cross-node joins, and significantly cut down the cost in query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture, where Map/Reduce takes charge of the computing tasks, and RDF query engines like RDF-3X store the data and execute join operations. According to the analysis of query workloads, this work proposes a novel algorithm for automatically parti- tioning RDF data and an approximate solution to physically place the partitions in order to reduce data redundancy. It also discusses how to make a good trade-off between query evaluation efficiency and data redundancy. All of these pro- posed approaches have been evaluated by extensive experiments over large RDF data sets.
In order to extract buildings using only gray information, this article proposed an approach for recognizing and extracting buildings from panchromatic high-resolution remotely sensed imagery based on shadows and segm...
In order to extract buildings using only gray information, this article proposed an approach for recognizing and extracting buildings from panchromatic high-resolution remotely sensed imagery based on shadows and segmentation. First, shadows were detected by potential histogram function. Second, the value of neighborhood total variation for each pixel was calculated, and then binarization and annotation were implemented to generate lable regions whose centroids were used as the seeds of the region growing segmentation, candidate buildings were selected from the segmentation result with the constraint of aspect ratio and rectangularity. At last, shadows were processed with open, dilate and corrode operations respectively, buildings were extracted by computing the adjacency relationship of the processed shadows and candidate buildings, and the building boundaries were fitted with the minimum enclosing rectangle. For verifying the validity of the proposed method, eighteen representative sub-images were chosen from PLEIADES images covering Shenzhen, China. Experimental results show that the average precision and recall of the proposed method are 97.95 % and 79.40 % for the object-based evaluation, and are 98.75 % and 83.16 % for the area-based evaluation respectively, and it has more 10 % and 6 % increase in the overall performance for above two evaluation criterion comparing with two other similar methods.
In this paper we explore one of the key aspects in building an emotion recognition system: generating suitable feature representations. We generate feature representations from both acoustic and lexical levels. At the...
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Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of ...
Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided
Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to det...
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ISBN:
(纸本)9781509034857
Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.
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