Article
Optics
Kaumudibikash Goswami, Fabio Costa
Summary: Quantum mechanics allows operations to be in indefinite causal order, leading to advantages in computational and communication tasks. Through the process matrix formalism, different classical capacities for a bipartite quantum process are formulated, showing a generalization of the Holevo bound. Bidirectional communication cannot exceed the limit of a one-way communication protocol in causally separable processes.
Article
Chemistry, Multidisciplinary
Mohamed Hammad, Samia Allaoua Chelloug, Walaa Alayed, Ahmed A. Abd El-Latif
Summary: This article introduces a novel methodology that combines convolutional neural networks with feature selection techniques based on mutual information for scene recognition. The study aims to address the limitations of conventional methods and improve the precision and dependability of scene classification. The methodology achieves notable levels of accuracy on different datasets, surpassing the performance of other established techniques. This work significantly advances the practical application of computer vision and has important implications for improving scene recognition and interpretation accuracy.
APPLIED SCIENCES-BASEL
(2023)
Article
Physics, Multidisciplinary
Zhuoxuan Ju, Parisa Rafiee, Omur Ozel
Summary: This paper explores the optimization of information urgency in an interactive point-to-point system, introducing the concept of UoI and utilizing a Lyapunov optimization framework to propose a new system model and decision strategy. Numerical studies show significant performance improvements compared to benchmark methods.
Article
Computer Science, Artificial Intelligence
Yunfei He, Dengcheng Yan, Wenxin Xie, Yiwen Zhang, Qiang He, Yun Yang
Summary: This article proposes a novel GNN optimization framework, GNN-MHSIC, which minimizes redundant information propagation and preserves relevant information by introducing the nonparametric dependence method HSIC. Experimental results prove the effectiveness and performance of GNN-MHSIC.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Mathematics, Applied
T. L. Carroll
Summary: A reservoir computer is a computational approach that utilizes a high dimensional dynamical system by connecting nonlinear nodes into a network, allowing for memory and feedback. The fading memory duration is crucial for the reservoir computer's ability to solve specific problems effectively and efficiently.
Article
Physics, Multidisciplinary
Kyle Reing, Greg Ver Steeg, Aram Galstyan
Summary: This paper introduces a new approach to information decomposition called Neural Information Decomposition (NID), which shows promising results in distinguishing higher-order functions from noise on synthetic data. NID proves to be more effective compared to other unsupervised probability models.
Review
Mathematical & Computational Biology
Janos Vegh, Adam Jozsef Berki
Summary: Neural information theory is a fundamental method for modeling dynamic relations in biological systems, but there are fierce debates about the notion, representation, content, and processing of information. By analyzing the differences between communication models published in the past seven decades and the recently developed generalized classical information theory, the operating modes of neurons are revisited and found to resemble a combination of analog and digital modes. This challenges the interpretation of neural information and its processing. Additionally, the active role of transfer channels (axons) may introduce further transmission limits beyond those concluded from information theory.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Alicja Miniak-Gorecka, Krzysztof Podlaski, Tomasz Gwizdalla
Summary: This article introduces a self-optimizing neural network approach based on decision networks for the classification of multi-dimensional patterns. The approach utilizes feature vectors and discriminants to create decision patterns and discusses the influence of neighborhood topology. Experimental results demonstrate the superior performance of the proposed approach in terms of generalization and accuracy compared to the support vector machine method.
PEERJ COMPUTER SCIENCE
(2022)
Article
Astronomy & Astrophysics
Michael R. R. Good, Alessio Lapponi, Orlando Luongo, Stefano Mancini
Summary: Inspired by the behavior of a collapsing black hole's null shell as a perfectly reflecting accelerating mirror, researchers extended the model to include mirror semitransparency and derived implicit expressions for the corresponding Bogolyubov coefficients. By focusing on mirrors accelerated via impulsive force, they obtained explicit analytical forms for the coefficients and used them to calculate particle production. The study also involved analyzing the field transmission properties through a semitransparent moving mirror as a Gaussian quantum channel, evaluating capacities in transmitting classical and quantum information.
Article
Computer Science, Artificial Intelligence
Kuan-Chun Chen, Cheng-Te Li, Kuo-Jung Lee
Summary: This article introduces a novel method called Discretized Differentiable Neural Architecture Search (DDNAS) for learning and classifying text representations. By continuously relaxing the architecture representation and applying gradient descent, DDNAS can optimize the search. Additionally, a discretization layer based on mutual information maximization is proposed to model the latent hierarchical categorization in text representation. Extensive experiments demonstrate that DDNAS consistently outperforms other state-of-the-art NAS methods.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2023)
Article
Engineering, Chemical
Ryan J. Kowalski, Ewa Pietrysiak, Girish M. Ganjyal
Summary: A genetic algorithm model and neural network fitness function were developed to predict screw profile design, which successfully predicted the necessary screw profiles and extrusion conditions. By simulating predicted screw profile designs, quick optimization of desired characteristics in the final extrudates can be achieved.
JOURNAL OF FOOD ENGINEERING
(2021)
Article
Food Science & Technology
Miguel Rebollo-Hernanz, Silvia Canas, Diego Taladrid, Vanesa Benitez, Begona Bartolome, Yolanda Aguilera, Maria A. Martin-Cabrejas
Summary: The study introduced a green sustainable method for extracting phenolic compounds from coffee husk and used RSM and ANNs to model the impact of extraction variables on phenolic compound recovery. Experimental results validated the model's accuracy, demonstrating the potential of phenolic substances in coffee husk.
Article
Computer Science, Artificial Intelligence
Marc Munar-Covas, Sebastia Massanet, Daniel Ruiz-Aguilera
Summary: This article discusses the study of discrete implications and presents sufficient and necessary conditions for preserving the additional properties of fuzzy implication functions through discretization.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Review
Construction & Building Technology
Antonio Salzano, Claudia Mariaserena Parisi, Giovanna Acampa, Maurizio Nicolella
Summary: This research proposes a method supported by BIM tools to improve maintenance processes by integrating Building Condition Assessment (BCA) with Building Information Modeling (BIM) to collect, digitize, and evaluate the physical and performance conditions of assets in order to enhance management and maintenance processes.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Environmental Sciences
Esmail Khosropour, Weria Weisany, Nawroz Abdul-razzak Tahir, Leila Hakimi
Summary: Organic substrates, such as biochar and vermicompost, can reduce the accumulation of cadmium in plant tissues under Cd-contaminated soil conditions. They can enhance the growth and physiological attributes of Berberis integerrima, especially when used in combination, leading to increased chlorophyll content and relative water content.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Editorial Material
Neurosciences
Tatyana O. Sharpee
Article
Biochemical Research Methods
Yilun Zhang, Tatyana O. Sharpee
PLOS COMPUTATIONAL BIOLOGY
(2016)
Editorial Material
Biology
Tatyana O. Sharpee
Article
Neurosciences
Craig A. Atencio, Tatyana O. Sharpee
Article
Mathematical & Computational Biology
Joel T. Kaardal, Frederic E. Theunissen, Tatyana O. SharpeeL
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2017)
Article
Multidisciplinary Sciences
Ryan J. Rowekamp, Tatyana O. Sharpee
NATURE COMMUNICATIONS
(2017)
Article
Multidisciplinary Sciences
Yuansheng Zhou, Brian H. Smith, Tatyana O. Sharpee
Article
Computer Science, Artificial Intelligence
John A. Berkowitz, Tatyana O. Sharpee
NEURAL COMPUTATION
(2019)
Article
Cell & Tissue Engineering
Debha N. Amatya, Sara B. Linker, Ana P. D. Mendes, Renata Santos, Galina Erikson, Maxim N. Shokhirev, Yuansheng Zhou, Tatyana Sharpee, Fred H. Gage, Maria C. Marchetto, Yeni Kim
Article
Neurosciences
Tatyana O. Sharpee
CURRENT OPINION IN NEUROBIOLOGY
(2019)
Review
Behavioral Sciences
Tatyana O. Sharpee, John A. Berkowitz
CURRENT OPINION IN BEHAVIORAL SCIENCES
(2019)
Review
Neurosciences
Tatyana O. Sharpee
CURRENT OPINION IN NEUROBIOLOGY
(2017)
Article
Physics, Fluids & Plasmas
Alexander Kuczala, Tatyana O. Sharpee
Article
Physics, Fluids & Plasmas
Johnatan Aljadeff, David Renfrew, Marina Vegue, Tatyana O. Sharpee