In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control (e.g., an FDA-approved, standard ther...
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This study examines the impact of surface roughness and viscosity variation on the couple stress squeeze film characteristics between a cylinder and a rough plate with slip velocity. Two distinct one-dimensional rough...
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Node embeddings aim to associate a vector to every vertex of a graph which can then be used for downstream tasks such as clustering, classification, or link prediction. Many popular node embeddings such as no $d$ e2ve...
Node embeddings aim to associate a vector to every vertex of a graph which can then be used for downstream tasks such as clustering, classification, or link prediction. Many popular node embeddings such as no $d$ e2vec and DeepWalk are based upon counting which nodes frequently co-occur in random walks of the graph. In this paper, we show that the performance of such algorithms can be improved by rewiring the edges of the graph through a variety of network indices before running DeepWalk. These rewirings effectively give the random walker an inductive bias and increase the accuracy of a logistic regression classifier applied to the node embedding on several benchmark data sets.
Enzymes are proteins capable of accelerating biochemical reactions in the metabolism process. A wide range of applications of enzymes has been developed in biotechnology, industry, pharmaceuticals, biofuels. In this p...
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The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing tr...
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Introducing additional tunable parameters to quantum circuits is a powerful way of improving performance without increasing hardware requirements. A recently introduced multiangle extension of the quantum approximate ...
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Chicken eggs can be a significant source of human PFAS exposure. A survey of PFAS in commercial eggs from larger farms across Denmark showed the absence or low contents of PFAS in free-range and barn eggs. However, or...
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This paper presents theoretical and experimental studies on the superconductivity of Pb0.64Bi0.36 alloy, which is a prototype of strongly coupled superconductors and exhibits one of the strongest coupling under ambien...
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This paper extends the design of an autonomous cyber defence (ACD) agent to monitor and actuate within a protected core network segment. The goal is to take advantage of recent developments in AI models to define a hy...
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ISBN:
(数字)9798350373196
ISBN:
(纸本)9798350373202
This paper extends the design of an autonomous cyber defence (ACD) agent to monitor and actuate within a protected core network segment. The goal is to take advantage of recent developments in AI models to define a hybrid architecture that combines deep reinforcement learning (DRL), large language models (LLMs), and rule-based models. The motivation comes from the fact that modern network segments within colored clouds are using software-defined controllers with the means to host ACD agents and other cybersecurity tools implementing hybrid AI models. For example, our ACD agent uses a DRL model and the chatbot uses an LLM to create an interface with human cybersecurity experts. The ACD agent was evaluated against two red agent strategies in a gym environment using a set of actions to defend services in the network (monitor, analyse, decoy, remove, and restore). Our chatbot was developed using retrieval augmented generation and a prompting agent to augment a pre-trained LLM with data from cybersecurity knowledge graphs. We performed a comparative analysis between a baseline implementation and our chatbot using generation/retrieval metrics. The results suggest that both ACD agent and chatbot can potentially enhance the defence of critical networks connected to untrusted infrastructure.
One of the most promising applications of quantum networks is entanglement-assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precisi...
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One of the most promising applications of quantum networks is entanglement-assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precision timekeeping, field sensing, and biological imaging. When measuring multiple spatially distributed parameters, current literature focuses on quantum entanglement in the discrete-variable case and quantum squeezing in the continuous-variable case, distributed amongst all of the sensors in a given network. However, it can be difficult to ensure that all sensors preshare entanglement of sufficiently high fidelity. This work probes the space between fully entangled and fully classical sensing networks by modeling a star network with probabilistic entanglement generation that is attempting to estimate the average of local parameters. The quantum Fisher information is used to determine which protocols best utilize entanglement as a resource for different network conditions. It is shown that without entanglement distillation there is a threshold fidelity below which classical sensing is preferable. For a network with a given number of sensors and links characterized by a certain initial fidelity and probability of success, this work outlines when and how to use entanglement, when to store it, and when it needs to be distilled.
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