Traditionally, cognition has been considered a uniquely human capability involving perception, memory, learning, reasoning, and problem-solving. However, recent research shows that cognition is a fundamental ability s...
This study aims to place Lorenzo Magnani’s Eco-Cognitive Computationalism within the broader context of current work on information, computation, and cognition. Traditionally, cognition was believed to be exclusive t...
In this paper, we propose machine learning solutions to predict the time of future trips and the possible distance the vehicle will travel. For this prediction task, we develop and investigate four methods. In the fir...
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We introduce an application-specific c ircuit t hat c an b e programmed t o efficiently perform blind carrier phase recovery for different modulation formats. A circuit implementation that supports QPSK/16/32/64QAM is...
The recent technological advances create increased network capacity demand, highlighting the need for new network optimization methods. However, the proposed solutions require broad testing with numerous time-consumin...
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ISBN:
(数字)9783903176638
ISBN:
(纸本)9798350390605
The recent technological advances create increased network capacity demand, highlighting the need for new network optimization methods. However, the proposed solutions require broad testing with numerous time-consuming simulations. Thus, estimation methods based on Machine Learning (ML) are developed to improve this process. In this work, we create a regression network model to predict four resource utilization metrics using the input set of connection requests. Using eXplainable Artificial Intelligence (XAI) tools, we optimize the proposed model for faster inference without a decrease in prediction quality.
Finding a maximum-weight matching is a classical and well-studied problem in computerscience, solvable in cubic time in general graphs. We introduce and consider in this work the “discovery” variant of the bipartit...
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Finding a maximum-weight matching is a classical and well-studied problem in computerscience, solvable in cubic time in general graphs. We introduce and consider in this work the “discovery” variant of the bipartite matching problem (or assignment problem) where edge weights are not provided as input but must be queried, requiring additional and costly computations. Hence, discovery algorithms are developed aiming to minimize the number of queried weights while providing guarantees on the computed solution. We show in this work the hardness of the underlying problem in general while providing several efficient algorithms that can make use of natural assumptions about the order in which the nodes are processed by the greedy algorithms. Our motivations for exploring this problem stem from finding practical solutions to maximum-weight matching in hypergraphs, a problem recently emerging in the formation of peer-to-peer energy sharing communities.
Traffic Weaver is a Python package developed to generate a semi-synthetic signal (time series) with finer granularity, based on averaged time series, in a manner that, upon averaging, closely matches the original sign...
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We propose a new framework for contextual multi-armed bandits based on tree ensembles. Our framework adapts two widely used bandit methods, Upper Confidence Bound and Thompson Sampling, for both standard and combinato...
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Targeted protein degradation (TPD) is a rapidly growing field in modern drug discovery that aims to regulate the intracellular levels of proteins by harnessing the cell’s innate degradation pathways to selectively ta...
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Targeted protein degradation (TPD) is a rapidly growing field in modern drug discovery that aims to regulate the intracellular levels of proteins by harnessing the cell’s innate degradation pathways to selectively target and degrade disease-related proteins. This strategy creates new opportunities for therapeutic intervention in cases where occupancy-based inhibitors have not been successful. Proteolysis-targeting chimeras (PROTACs) are at the heart of TPD strategies, which leverage the ubiquitin-proteasome system for the selective targeting and proteasomal degradation of pathogenic proteins. This unique mechanism can be particularly useful for dealing with proteins that were once deemed "undruggable" using conventional small-molecule drugs. PROTACs are hetero-bifunctional molecules consisting of two ligands, connected by a chemical linker. As the field evolves, it becomes increasingly apparent that the traditional methodologies for designing such complex molecules have limitations. This has led to the use of machine learning (ML) and generative modeling to improve and accelerate the development process. In this review, we aim to provide a thorough exploration of the impact of ML on de novo PROTAC design — an aspect of molecular design that has not been comprehensively reviewed despite its significance. Initially, we delve into the distinct characteristics of PROTAC linker design, underscoring the complexities required to create effective bifunctional molecules capable of TPD. We then examine how ML in the context of fragment-based drug design (FBDD), honed in the realm of small-molecule drug discovery, is paving the way for PROTAC linker design. Our review provides a critical evaluation of the limitations inherent in applying this method to the complex field of PROTAC development. Moreover, we review existing ML works applied to PROTAC design, highlighting pioneering efforts and, importantly, the limitations these studies face. By offering insights into the current
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