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Quantum computing for swarm robotics: a local-to-global approach.
Philos Trans A Math Phys Eng Sci
January 2025
ICAR, National Research Council (CNR), Palermo, Italy.
Quantum computing is a branch of computer science derived from the fundamental laws of quantum mechanics, such as state superposition, multi-value logic and destructive measure. An open challenge in itself is to re-think in quantum terms classic problems and solving techniques. Another nature-inspired field is the development of swarm-based robotic applications, where the challenge is catching the fundamental laws governing swarm dynamics, such as pattern formation and target reaching.
View Article and Find Full Text PDFNonunitary Gates Using Measurements Only.
Phys Rev Lett
December 2024
Tel Aviv University, School of Physics and Astronomy, Tel Aviv 6997801, Israel.
Measurement-based quantum computation (MBQC) is a universal platform to realize unitary gates, only using measurements that act on a preprepared entangled resource state. By deforming the measurement bases, as well as the geometry of the resource state, we show that MBQC circuits always transmit and act on the input state but generally realize nonunitary logical gates. In contrast to the stabilizer formalism that is often used for unitary gates, we find that ZX-calculus is an ideal computation method for these nonunitary gates.
View Article and Find Full Text PDFObjective: Aim: To determine the specifics of criminal liability for disclosing information about a medical examination for detection of infection with the human immunodeficiency virus or another incurable infectious disease under the criminal legislation of Poland and Ukraine in order to improve the legal protection of the interests of people living with HIV.
Patients And Methods: Materials and Methods: The authors used the decisions of national courts in the field of ensuring the information security of a person living with the immunodeficiency virus, international and national legal acts of Ukraine and Poland. The study was carried out on the basis of a systematic approach using the methods of dialectical and formal logic, general scientific and special legal research methods.
Novel Machine Learning Applications in Peripartum Care: A Scoping Review.
Am J Obstet Gynecol MFM
January 2025
The Josef Buchmann Gynecology and Maternity Center, Sheba Medical Center, Tel Hashomer, Israel; ARC Innovation Center, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel; The Dina Recanati School of Medicine, Reichmann University, Herzliya, Israel.
Objective: Machine learning (ML), a subtype of artificial intelligence (AI), presents predictive modeling and dynamic diagnostic tools to facilitate early interventions and improve decision-making. Considering the global challenges of maternal, fetal, and neonatal morbidity and mortality, ML holds the potential to enable significant improvements in maternal and neonatal health outcomes. We aimed to conduct a comprehensive review of ML applications in peripartum care, summarizing the potential of these tools to enhance clinical decision-making and identifying emerging trends and research gaps.
View Article and Find Full Text PDFTemporal logic inference for interpretable fault diagnosis of bearings via sparse and structured neural attention.
ISA Trans
January 2025
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation.
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