1,055 research outputs found
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
Connected Attribute Filtering Based on Contour Smoothness
A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform
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
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 and propagate through the system with a group delay scaling as
. 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 -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
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