320 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Flip: Data-Centric Edge CGRA Accelerator

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    Coarse-Grained Reconfigurable Arrays (CGRA) are promising edge accelerators due to the outstanding balance in flexibility, performance, and energy efficiency. Classic CGRAs statically map compute operations onto the processing elements (PE) and route the data dependencies among the operations through the Network-on-Chip. However, CGRAs are designed for fine-grained static instruction-level parallelism and struggle to accelerate applications with dynamic and irregular data-level parallelism, such as graph processing. To address this limitation, we present Flip, a novel accelerator that enhances traditional CGRA architectures to boost the performance of graph applications. Flip retains the classic CGRA execution model while introducing a special data-centric mode for efficient graph processing. Specifically, it exploits the natural data parallelism of graph algorithms by mapping graph vertices onto processing elements (PEs) rather than the operations, and supporting dynamic routing of temporary data according to the runtime evolution of the graph frontier. Experimental results demonstrate that Flip achieves up to 36×\times speedup with merely 19% more area compared to classic CGRAs. Compared to state-of-the-art large-scale graph processors, Flip has similar energy efficiency and 2.2×\times better area efficiency at a much-reduced power/area budget

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Primena Big Data analitike za istraživanje prostorno-vremenske dinamike ljudske populacije

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    With the rapid growth of the volume of available data related to human dynamics, it became more challenging to research and investigate topics that could reveal novel knowledge in the area. In present time people tend to live mostly in large cities, where knowledge about human dynamics, habits and behaviour could lead to better city organisation, energy efficiency, transport organisation and overall better quality and more sustainable living. Human dynamics could be reasoned from many different aspects, but all of them have three elements in common: time, space and data volume. Human activity and interaction could not be inspected without space and time component because everything is happening somewhere at some time. Also, with huge smartphone adoption now terabytes of data related to human dynamic are available. Although data is sensitive to personal information, true owners of the data is either telecom operator company, social media company or any other company that provides the applications that are used on the mobile phone. If such data is to be opened to public or scientific community to conduct a research with it, it needs to be anonimized first.Another challenge of user generated data is data set volume. Data is usually very large in size (Volume), it comes from different sources and in different formats (Variety) and it is generated in real-time and it evolves very fast (Velocity). These are three V's of Big Data, and such data sets need to be approached with specially designed Big Data technologies.In the research presented in this thesis we assembled Big Data technologies, Graph Theory and space-time dependent human dynamic data.Са све већом и већом количином података која је доступна везано за динамику људске популације, постаје све више изазовно да се спроведе истраживање у овој области које би донело ново знање. У данашње време људи масовно живе у великим градовима где би знање о људској динамици, навикама и понашању могло значајно да унапреди организацију градова, енергетску ефикасност, транспорт и свеукупно квалитетнији и више одржив животни стил. Динамика људске популације може да се посматра са више аспеката, али сви они имају три заједничка елемента: време, простор и количину података. Људска активност и интеракције не могу се посматрати одвојено од просторне и временске компоненте јер се све дешава негде и у неко време. Такође, са великим присуством паметних телефона данас су доступни терабајти података о људској динамици. Иако су подаци осетљиви због приватности корисника, прави власници података су заправо телеком компаније, или компаније друштвених мрежа или неке друге компаније које развијају корисничке апликације за паметне телефоне. Ако би се такви подаци отварали за јавност или научну заједницу морали би прво да буду анонимизовани. Други изазов везан за кориснички генерисане податке је величина података. Подаци су обично веома велики меморијски (енг. „Volume“), долазе из различитих извора и у различитим форматима (енг. „Variety“) и генерисани су реалном времену и мењају се  еома брзо (енг. „Velocity“). Ово су три „V“ Великих података, и такви подаци захтевају посебан приступ аналитици са специјално дизајнираним алатима за Аналитику великих података. У оквиру истраживања које је презентовано у овој тези објединили смо Аналитику великих података, Теорију графова и просторно-временски зависне податке о људској динамици.Sa sve većom i većom količinom podataka koja je dostupna vezano za dinamiku ljudske populacije, postaje sve više izazovno da se sprovede istraživanje u ovoj oblasti koje bi donelo novo znanje. U današnje vreme ljudi masovno žive u velikim gradovima gde bi znanje o ljudskoj dinamici, navikama i ponašanju moglo značajno da unapredi organizaciju gradova, energetsku efikasnost, transport i sveukupno kvalitetniji i više održiv životni stil. Dinamika ljudske populacije može da se posmatra sa više aspekata, ali svi oni imaju tri zajednička elementa: vreme, prostor i količinu podataka. LJudska aktivnost i interakcije ne mogu se posmatrati odvojeno od prostorne i vremenske komponente jer se sve dešava negde i u neko vreme. Takođe, sa velikim prisustvom pametnih telefona danas su dostupni terabajti podataka o ljudskoj dinamici. Iako su podaci osetljivi zbog privatnosti korisnika, pravi vlasnici podataka su zapravo telekom kompanije, ili kompanije društvenih mreža ili neke druge kompanije koje razvijaju korisničke aplikacije za pametne telefone. Ako bi se takvi podaci otvarali za javnost ili naučnu zajednicu morali bi prvo da budu anonimizovani. Drugi izazov vezan za korisnički generisane podatke je veličina podataka. Podaci su obično veoma veliki memorijski (eng. „Volume“), dolaze iz različitih izvora i u različitim formatima (eng. „Variety“) i generisani su realnom vremenu i menjaju se  eoma brzo (eng. „Velocity“). Ovo su tri „V“ Velikih podataka, i takvi podaci zahtevaju poseban pristup analitici sa specijalno dizajniranim alatima za Analitiku velikih podataka. U okviru istraživanja koje je prezentovano u ovoj tezi objedinili smo Analitiku velikih podataka, Teoriju grafova i prostorno-vremenski zavisne podatke o ljudskoj dinamici

    Extracting Multi-objective Multigraph Features for the Shortest Path Cost Prediction: Statistics-based or Learning-based?

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    Efficient airport airside ground movement (AAGM) is key to successful operations of urban air mobility. Recent studies have introduced the use of multi-objective multigraphs (MOMGs) as the conceptual prototype to formulate AAGM. Swift calculation of the shortest path costs is crucial for the algorithmic heuristic search on MOMGs, however, previous work chiefly focused on single-objective simple graphs (SOSGs), treated cost enquires as search problems, and failed to keep a low level of computational time and storage complexity. This paper concentrates on the conceptual prototype MOMG, and investigates its node feature extraction, which lays the foundation for efficient prediction of shortest path costs. Two extraction methods are implemented and compared: a statistics-based method that summarises 22 node physical patterns from graph theory principles, and a learning-based method that employs node embedding technique to encode graph structures into a discriminative vector space. The former method can effectively evaluate the node physical patterns and reveals their individual importance for distance prediction, while the latter provides novel practices on processing multigraphs for node embedding algorithms that can merely handle SOSGs. Three regression models are applied to predict the shortest path costs to demonstrate the performance of each. Our experiments on randomly generated benchmark MOMGs show that (i) the statistics-based method underperforms on characterising small distance values due to severe overestimation, (ii) a subset of essential physical patterns can achieve comparable or slightly better prediction accuracy than that based on a complete set of patterns, and (iii) the learning-based method consistently outperforms the statistics-based method, while maintaining a competitive level of computational complexity

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Alarm reduction and root cause inference based on association mining in communication network

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    With the growing demand for data computation and communication, the size and complexity of communication networks have grown significantly. However, due to hardware and software problems, in a large-scale communication network (e.g., telecommunication network), the daily alarm events are massive, e.g., millions of alarms occur in a serious failure, which contains crucial information such as the time, content, and device of exceptions. With the expansion of the communication network, the number of components and their interactions become more complex, leading to numerous alarm events and complex alarm propagation. Moreover, these alarm events are redundant and consume much effort to resolve. To reduce alarms and pinpoint root causes from them, we propose a data-driven and unsupervised alarm analysis framework, which can effectively compress massive alarm events and improve the efficiency of root cause localization. In our framework, an offline learning procedure obtains results of association reduction based on a period of historical alarms. Then, an online analysis procedure matches and compresses real-time alarms and generates root cause groups. The evaluation is based on real communication network alarms from telecom operators, and the results show that our method can associate and reduce communication network alarms with an accuracy of more than 91%, reducing more than 62% of redundant alarms. In addition, we validate it on fault data coming from a microservices system, and it achieves an accuracy of 95% in root cause location. Compared with existing methods, the proposed method is more suitable for operation and maintenance analysis in communication networks
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