16,493 research outputs found
Emotion Quantification Using Variational Quantum State Fidelity Estimation
Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset
Quantum Locally Compact Metric Spaces
We introduce the notion of a quantum locally compact metric space, which is
the noncommutative analogue of a locally compact metric space, and generalize
to the nonunital setting the notion of quantum metric spaces introduced by
Rieffel. We then provide several examples of such structures, including the
Moyal plane, as well as compact quantum metric spaces and locally compact
metric spaces. This paper provides an answer to the question raised in the
literature about the proper notion of a quantum metric space in the nonunital
setup and offers important insights into noncommutative geometry for non
compact quantum spaces.Comment: 39 Pages. Changes from v1: Many minor typos corrected, improved
Theorem 3.1
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
Systems approaches and algorithms for discovery of combinatorial therapies
Effective therapy of complex diseases requires control of highly non-linear
complex networks that remain incompletely characterized. In particular, drug
intervention can be seen as control of signaling in cellular networks.
Identification of control parameters presents an extreme challenge due to the
combinatorial explosion of control possibilities in combination therapy and to
the incomplete knowledge of the systems biology of cells. In this review paper
we describe the main current and proposed approaches to the design of
combinatorial therapies, including the empirical methods used now by clinicians
and alternative approaches suggested recently by several authors. New
approaches for designing combinations arising from systems biology are
described. We discuss in special detail the design of algorithms that identify
optimal control parameters in cellular networks based on a quantitative
characterization of control landscapes, maximizing utilization of incomplete
knowledge of the state and structure of intracellular networks. The use of new
technology for high-throughput measurements is key to these new approaches to
combination therapy and essential for the characterization of control
landscapes and implementation of the algorithms. Combinatorial optimization in
medical therapy is also compared with the combinatorial optimization of
engineering and materials science and similarities and differences are
delineated.Comment: 25 page
On the concept of Bell's local causality in local classical and quantum theory
The aim of this paper is to give a sharp definition of Bell's notion of local
causality. To this end, first we unfold a framework, called local physical
theory, integrating probabilistic and spatiotemporal concepts. Formulating
local causality within this framework and classifying local physical theories
by whether they obey local primitive causality --- a property rendering the
dynamics of the theory causal, we then investigate what is needed for a local
physical theory, with or without local primitive causality, to be locally
causal. Finally, comparing Bell's local causality with the Common Cause
Principles and relating both to the Bell inequalities we find a nice
parallelism: Bell inequalities cannot be derived neither from local causality
nor from a common cause unless the local physical theory is classical or the
common cause is commuting, respectively.Comment: 24 pages, 5 figure
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