Symmetry in Chaotic Systems and Circuits

Symmetry can play an important role in the field of nonlinear systems and especially in the design of nonlinear circuits that produce chaos. Therefore, this Special Issue, titled “Symmetry in Chaotic Systems and Circuits”, presents the latest scientific advances in nonlinear chaotic systems and circuits that introduce various kinds of symmetries. Applications of chaotic systems and circuits with symmetries, or with a deliberate lack of symmetry, are also presented in this Special Issue. The volume contains 14 published papers from authors around the world. This reflects the high impact of this Special Issue

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure

Dynamic Complexity and Causality Analysis of Scalp EEG for Detection of Cognitive Deficits

This dissertation explores the potential of scalp electroencephalography (EEG) for the detection and evaluation of neurological deficits due to moderate/severe traumatic brain injury (TBI), mild cognitive impairment (MCI), and early Alzheimer’s disease (AD). Neurological disorders often cannot be accurately diagnosed without the use of advanced imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Non-quantitative task-based examinations are also used. None of these techniques, however, are typically performed in the primary care setting. Furthermore, the time and expense involved often deters physicians from performing them, leading to potential worse prognoses for patients. If feasible, screening for cognitive deficits using scalp EEG would provide a fast, inexpensive, and less invasive alternative for evaluation of TBI post injury and detection of MCI and early AD. In this work various measures of EEG complexity and causality are explored as means of detecting cognitive deficits. Complexity measures include eventrelated Tsallis entropy, multiscale entropy, inter-regional transfer entropy delays, and regional variation in common spectral features, and graphical analysis of EEG inter-channel coherence. Causality analysis based on nonlinear state space reconstruction is explored in case studies of intensive care unit (ICU) signal reconstruction and detection of cognitive deficits via EEG reconstruction models. Significant contributions in this work include: (1) innovative entropy-based methods for analyzing event-related EEG data; (2) recommendations regarding differences in MCI/AD of common spectral and complexity features for different scalp regions and protocol conditions; (3) development of novel artificial neural network techniques for multivariate signal reconstruction; and (4) novel EEG biomarkers for detection of dementia

Dynamics of many-body photon bound states in chiral waveguide QED

We theoretically study the few- and many-body dynamics of photons in chiral waveguides. In particular, we examine pulse propagation through a system of $N$ two-level systems chirally coupled to a waveguide. We show that the system supports correlated multi-photon bound states, which have a well-defined photon number $n$ and propagate through the system with a group delay scaling as $1/n^2$. This has the interesting consequence that, during propagation, an incident coherent state pulse breaks up into different bound state components that can become spatially separated at the output in a sufficiently long system. For sufficiently many photons and sufficiently short systems, we show that linear combinations of $n$-body bound states recover the well-known phenomenon of mean-field solitons in self-induced transparency. For longer systems, however, the solitons break apart through quantum correlated dynamics. Our work thus covers the entire spectrum from few-photon quantum propagation, to genuine quantum many-body (atom and photon) phenomena, and ultimately the quantum-to-classical transition. Finally, we demonstrate that the bound states can undergo elastic scattering with additional photons. Together, our results demonstrate that photon bound states are truly distinct physical objects emerging from the most elementary light-matter interaction between photons and two-level emitters. Our work opens the door to studying quantum many-body physics and soliton physics with photons in chiral waveguide QED.Comment: Updated with new results. 14 pages plus supplementary materia