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Artificial intelligence (AI) has emerged as a powerful accelerator of materials discovery, yet most existing models remain problem-specific, requiring additional data collection and retraining for each new property. Here we introduce and…
The Blackboard Architecture provides a mechanism for storing data and logic and using it to make decisions that impact the application environment that the Blackboard Architecture network models. While rule-fact-action networks can…
The graph database (GDB) is an increasingly common storage model for data involving relationships between entries. Beyond its widespread usage in database industries, the advantages of GDBs indicate a strong potential in constructing…
Grid based systems require a database access mechanism that can provide seamless homogeneous access to the requested data through a virtual data access system, i.e. a system which can take care of tracking the data that is stored in…
Storing big data directly on a blockchain poses a substantial burden due to the need to maintain a consistent ledger across all nodes. Numerous studies in decentralized storage systems have been conducted to tackle this particular…
Modern enterprises rely on data management systems to collect, store, and analyze vast amounts of data related with their operations. Nowadays, clusters and hardware accelerators (e.g., GPUs, TPUs) have become a necessity to scale with the…
Traditional entropy-based methods - such as cross-entropy loss in classification problems - have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence…
The encoding of input parameters is one of the fundamental building blocks of neural network algorithms. Its goal is to map the input data to a higher-dimensional space, typically supported by trained feature vectors. The mapping is crucial…
Understanding application resilience (or error tolerance) in the presence of hardware transient faults on data objects is critical to ensure computing integrity and enable efficient application-level fault tolerance mechanisms. However, we…
Layout-Aware Data Scheduler (LADS) data transfer tool, identifies and addresses the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environments. It exploits the underlying storage layout at…
Advances in networks, accelerators, and cloud services encourage programmers to reconsider where to compute -- such as when fast networks make it cost-effective to compute on remote accelerators despite added latency. Workflow and…
The paper suggests a new approach based on blockchain technologies and smart contracts to creation of a distributed system for managing provenance metadata, as well as access rights to data in distributed storages, which is fault-tolerant,…
This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing…
Hippocampal formation (HF) can rapidly adapt to varied environments and build flexible working memory (WM). To mirror the HF's mechanism on generalization and WM, we propose a model named Generalization and Associative Temporary Encoding…
The multi-modal salient object detection model based on RGB-D information has better robustness in the real world. However, it remains nontrivial to better adaptively balance effective multi-modal information in the feature fusion phase. In…
Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item interactions are entirely unavailable. The ID-based approaches completely fail to work. Cold-start recommenders, on the other hand, leverage item…
Data warehousing enables performant access to high-quality data integrated from dynamic data sources. The medallion architecture, a standard for data warehousing, addresses these goals by organizing data into bronze, silver and gold layers,…
As the AI community increasingly adopts large-scale models, it is crucial to develop general and flexible tools to integrate them. We introduce Gather-Attend-Scatter (GATS), a novel module that enables seamless combination of pretrained…
Most salient object detection approaches use U-Net or feature pyramid networks (FPN) as their basic structures. These methods ignore two key problems when the encoder exchanges information with the decoder: one is the lack of interference…
The GLAST LAT calibration infrastructure is designed to accommodate a wide range of time-varying data types, including at a minimum hardware status bits, conversion constants, and alignment for the GLAST LAT instrument and its prototypes.…