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Published in 2021 at "Journal of the Royal Society Interface"
DOI: 10.1098/rsif.2020.0985
Abstract: One of the main objectives of synthetic biology is the development of molecular controllers that can manipulate the dynamics of a given biochemical network that is at most partially known. When integrated into smaller compartments,…
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Keywords:
control biochemical;
robust control;
networks via;
biochemical reaction ... See more keywords
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Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3207197
Abstract: For having an in-depth study and analysis of various network’s structural properties such as interconnection, extensibility, availability, centralization, vulnerability and reliability, we require distance based graph theoretic parameters. Numerous parameters like distance based dimensions help…
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Keywords:
networks via;
fractional dimensions;
symmetric networks;
study convexo ... See more keywords
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Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3263210
Abstract: We analyze the data-dependent capacity of neural networks and assess anomalies in inputs from the perspective of networks during inference. The notion of data-dependent capacity allows for analyzing the knowledge base of a model populated…
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Keywords:
networks via;
neural networks;
via gradient;
probing purview ... See more keywords
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Published in 2023 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2022.3232148
Abstract: By flexibly manipulating the radio propagation environment, reconfigurable intelligent surface (RIS) is a promising technique for future wireless communications. However, the single-side coverage and double-fading attenuation faced by conventional RISs largely restrict their applications. To…
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Keywords:
networks via;
multi functional;
surface;
noma networks ... See more keywords
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Published in 2018 at "IEEE Control Systems Letters"
DOI: 10.1109/lcsys.2018.2821240
Abstract: Controllability is one of the most important properties of a Boolean control network (BCN). Essentially there are two kinds of controls: 1) networked inputs and 2) free logical inputs. This letter considers controllability of BCNs…
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Keywords:
via mixed;
networks via;
mixed controls;
boolean networks ... See more keywords
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Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3165618
Abstract: Deep graph networks (DGNs) are a family of machine learning models for structured data which are finding heavy application in life sciences (drug repurposing, molecular property predictions) and on social network data (recommendation systems). The…
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Keywords:
deep graph;
via input;
networks via;
explaining deep ... See more keywords