Many authors recognize the limitations of hierarchical Grid scheduling in scalable environments, and proposed peer-to-peer solutions to this problem. However, most peerto-peer grid resource management systems allow only to discover available resources at the time of the request. We claim that peer-to-peer techniques have the potential for actual Grid scheduling, where each resource maintains a schedule of its future allocation to jobs. We present such a protocol, which additionally allows users to specify desired properties about the requested schedules.

Federated stream processing systems, which utilise nodes from multiple independent domains, can be found increasingly in multi-provider cloud deployments, internet-of-things systems, collaborative sensing applications and large-scale grid systems. To pool resources from several sites and take advantage of local processing, submitted queries are split into query fragments, which are executed collaboratively by different sites. When supporting many concurrent users, however, queries may exhaust available processing resources, thus requiring constant load shedding. Given that individual sites have autonomy over how they allocate query fragments on their nodes, it is an open challenge how to ensure global fairness on processing quality experienced by queries in a federated scenario.We describe THEMIS, a federated stream processing system for resource-starved, multi-site deployments. It executes queries in a globally fair fashion and provides users with constant feedback on the experienced processing quality for their queries. THEMIS associates stream data with its source information content (SIC), a metric that quantifies the contribution of that data towards the query result, based on the amount of source data used to generate it. We provide the BALANCE-SIC distributed load shedding algorithm that balances the SIC values of result data. Our evaluation shows that the BALANCE-SIC algorithm yields balanced SIC values across queries, as measured by Jain's Fairness Index. Our approach also incurs a low execution time overhead.

<div>Proteochemometric (PCM) models of protein-ligand activity combine information from both the ligands and the proteins to which they bind. Several methods inspired by the field of natural language processing (NLP) have been proposed to represent protein sequences. </div><div>Here, we present PCM benchmark results on three multi-protein datasets: protein kinases, rhodopsin-like GPCRs (ChEMBL binding and functional assays), and cytochrome P450 enzymes. Keeping ligand descriptors fixed, we evaluate our own protein embeddings based on subword-segmented language models trained on mammalian sequences against pre-existing NLP-based descriptors, protein-protein similarity matrices derived from multiple sequence alignments (MSA), dummy protein one-hot encodings, and a combination of NLP-based and MSA-based descriptors. Our results show that performance gains over one-hot encodings are small and combining NLP-based and MSA-based descriptors increases predictive performance consistently across different splitting strategies. This work has been presented at the 3rd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry in September 2020.</div>

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