20 papers · ranked by Valyu relevance
Amin Sahebi, Marco Barbone, Marco Procaccini, Wayne Luk + 2 more
'Georgi Gaydadjiev' 'Roberto Giorgi'] Processing large-scale graphs is challenging due to the nature of the computation that causes irregular memory access patterns. Managing such irregular accesses may cause significant performance degradation on both CPUs and GPUs. Thus, recent research trends propose graph…
Jose L Figueroa, Richard Allen White
We now exist in the era of massive datasets from genomics, large language models, and all the known knowledge of humanity right at our fingertips. Much of this data is becoming more accessible; however, processing such data remains an ongoing issue across systems including high performance computing (HPC)…
Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai + 6 more
'Wenyuan Yu' 'Wenjie Zhang' 'Xuemin Lin' 'Jingren Zhou'] LINGKAI MENG, Antai College of Economics and Management, Shanghai Jiao Tong University, China YU SHAO, East China Normal University, China LONG YUAN∗ , Nanjing University of Science and Technology, China LONGBIN LAI, Alibaba Group, China PENG CHENG, East China…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Alessandro Margara, Gianpaolo Cugola, Nicolò Felicioni, Stefano Cilloni
'Stefano Cilloni'] Data is a precious resource in today's society, and is generated at an unprecedented and constantly growing pace. The need to store, analyze, and make data promptly available to a multitude of users introduces formidable challenges in modern software platforms. These challenges radically impacted the…
Maxim Lippeveld, Daniel Peralta, Andrew Filby, Yvan Saeys
Due to high resolution and throughput of modern image cytometry platforms, morphologically profiling generated datasets poses a significant computational challenge. Here, we present Scalable Cytometry Image Processing (SCIP), an image processing software aimed at running on distributed high performance computing…
Andrei-Alin Corodescu, Nikolay Nikolov, Akif Quddus Khan, Ahmet Soylu + 4 more
'Ahmet Soylu' 'Mihhail Matskin' 'Amir H. Payberah' 'Dumitru Roman' 'Haipeng Dai'] The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to…
Xianzhi Cao, Chong Chen, Shiwei Li, Chang Lv + 2 more
With the explosive growth of terminal devices, scheduling massive parallel task streams has become a core challenge for distributed platforms. For computing resource providers, enhancing reliability, shortening response times, and reducing costs are significant challenges, particularly in achieving energy efficiency…
Hendrik Reiter, Patrick Rathje, Olaf Landsiedel, Wilhelm Hasselbring
Cyber-Physical Systems Authors: ['Hendrik Reiter' 'Patrick Rathje' 'Olaf Landsiedel' 'Wilhelm Hasselbring'] Abstract. Process Mining is established in research and industry systems to analyze and optimize processes based on event data from information systems. Within this work, we accomodate process mining techniques…
Aneesh Khole, Atharva Thakar, Avadhoot Kulkarni, Hrithik Jadhav + 2 more
'Shreyas Shende' 'Varad Karajkhede'] Abstract— Computer systems have evolved over the years starting from sizable, single-user, slow, and expensive machines to multi-user, fast, cheaper, and small-sized machines. The use of multi-user computer networks has given rise to a new paradigm of computing known as Distributed…
Tamer Z. Emara, Thanh Trinh, Joshua Zhexue Huang
Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss…
Mohammad Dehghani, Zahra Yazdanparast
Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms should be trained with more data. Processing this huge amount of data could be…
David Ferere, Irvin Dongo, Yudith Cardinale, Claudia Campolo
The increasing evolution of computing technologies has fostered the new intelligent concept of Ubiquitous computing (Ubicomp). Ubicomp environments encompass the introduction of new paradigms, such as Internet of Things (IoT), Mobile computing, and Wearable computing, into communication networks, which demands more…
Wilfried Agbeto, Camille Coti, Vladimir Reinharz
Advances in graph algorithmics have allowed in-depth study of many natural objects from molecular biology or chemistry to social networks. Particularly in molecular biology and cheminformatics, understanding complex structures by identifying conserved sub-structures is a key milestone towards the artificial design of…
Ivan Rodriguez-Conde, Celso Campos, Florentino Fdez-Riverola, Antonio Fernández-Caballero + 1 more
'Antonio Fernández-Caballero' 'Juan M. Corchado'] Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the…
Authors not listed
Machine learning models are transforming data-driven research across scientific disciplines, yet their deployment as accessible and reliable web services remains a significant challenge. We introduce the NERDD framework, a scalable, maintainable, and secure microservices platform designed to support the sustainable…
Patrick McKeever, Varun Mittal, Bryce Fukuda, Ka Yee Yeung + 1 more
The exponential growth of omics data requires novel strategies for storage, transfer, and processing of said data. We present a scheduler based on the Temporal.io workflow framework which enables two key optimizations of bioinformatics workflows. Firstly, we enable users to transparently map workflow steps to diverse…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Peter Kraus, Edan Bainglass, Francisco F. Ramirez, Enea Svaluto-Ferro + 7 more
Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We…