Complex network theory has focused on properties of networks with realvalued edge weights. However, in signal transfer networks, such as those representing the transfer of light across an interferometer, complexvalued edge weights are needed to represent the manipulation of the signal in both magnitude and phase. These complexvalued edge weights introduce interference into the signal transfer, but it is unknown how such interference affects network properties such as smallworldness. To address this gap, we have introduced a smallworld interferometer network model with complexvalued edge weights and generalized existing network measures to define the interferometric clustering coefficient, the apparent path length, and the interferometric smallworld coefficient. Using highperformance computing resources, we generated a large set of smallworld interferometers over a wide range of parameters in system size, nearestneighbor count, and edgeweight phase and computed their interferometric network measures. We found that the interferometric smallworld coefficient depends significantly on the amount of phase on complexvalued edge weights: for small edge weight phases, constructive interference led to a higher interferometric smallworld coefficient; while larger edgeweight phases induced destructive interference which led to a lower interferometric smallworld coefficient. Thus, for the smallworld interferometer model, interferometric measures are necessary to capture the effect of interference on signal transfer. This model is an example of the type of problem that necessitates interferometric measures, and applies to any wavebased network including quantum networks.
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Abstract 
Complex network theory has focused on properties of networks with realvalued edge weights. However, in signal transfer networks, such as those representing the transfer of light across an interferometer, complexvalued edge weights are needed to represent the manipulation of the signal in both magnitude and phase. These complexvalued edge weights introduce interference into the signal transfer, but it is unknown how such interference affects network properties such as smallworldness. To address this gap, we have introduced a smallworld interferometer network model with complexvalued edge weights and generalized existing network measures to define the interferometric clustering coefficient, the apparent path length, and the interferometric smallworld coefficient. Using highperformance computing resources, we generated a large set of smallworld interferometers over a wide range of parameters in system size, nearestneighbor count, and edgeweight phase and computed their interferometric network measures. We found that the interferometric smallworld coefficient depends significantly on the amount of phase on complexvalued edge weights: for small edgeweight phases, constructive interference led to a higher interferometric smallworld coefficient; while larger edgeweight phases induced destructive interference which led to a lower interferometric smallworld coefficient. Thus, for the smallworld interferometer model, interferometric measures are necessary to capture the effect of interference on signal transfer. This model is an example of the type of problem that necessitates interferometric measures, and applies to any wavebased network including quantum networks.more » « lessFree, publiclyaccessible full text available October 25, 2024

Abstract We use complex network theory to study a class of photonic continuous variable quantum states that present both multipartite entanglement and nonGaussian statistics. We consider the intermediate scale of several dozens of modes at which such systems are already hard to characterize. In particular, the states are built from an initial imprinted cluster state created via Gaussian entangling operations according to a complex network structure. We then engender nonGaussian statistics via multiple photon subtraction operations acting on a single node. We replicate in the quantum regime some of the models that mimic realworld complex networks in order to test their structural properties under local operations. We go beyond the already known singlemode effects, by studying the emergent network of photonnumber correlations via complex networks measures. We analytically prove that the imprinted network structure defines a vicinity of nodes, at a distance of four steps from the photonsubtracted node, in which the emergent network changes due to photon subtraction. We show numerically that the emergent structure is greatly influenced by the structure of the imprinted network. Indeed, while the mean and the variance of the degree and clustering distribution of the emergent network always increase, the higher moments of the distributions are governed by the specific structure of the imprinted network. Finally, we show that the behaviour of nearest neighbours of the subtraction node depends on how they are connected to each other in the imprinted structure.more » « less

Abstract We present JBrowse 2, a generalpurpose genome annotation browser offering enhanced visualization of complex structural variation and evolutionary relationships. It retains core features of JBrowse while adding new views for synteny, dotplots, breakpoints, gene fusions, and wholegenome overviews. It allows users to share sessions, open multiple genomes, and navigate between views. It can be embedded in a web page, used as a standalone application, or run from Jupyter notebooks or R sessions. These improvements are enabled by a groundup redesign using modern web technology. We describe application functionality, use cases, performance benchmarks, and implementation notes for web administrators and developers.
Free, publiclyaccessible full text available December 1, 2024 
Social network analysis (SNA) has been gaining traction as a technique for quantitatively studying student collaboration. We analyze networks, constructed from student selfreports of collaboration on homework assignments, in two courses from the University of Colorado Boulder and one course from the Colorado School of Mines. All three courses occurred during the COVID19 pandemic, which allows for a comparison between the course at the Colorado School of Mines (in a fully remote format) with results from a previous prepandemic study of student collaboration at the Colorado School of Mines (in a hybrid format). We compute nodal centrality measures and calculate the correlation between student centrality and performance. Results varied widely between each of the courses studied. The course at the Colorado School of Mines had strong correlations between many centrality measures and performance which matched the patterns seen in the prepandemic study. The courses at the University of Colorado Boulder showed weaker correlations, and one course showed nearly no correlations at all between students' connectivity to their classmates and their performance. Taken together, the results from the trio of courses indicate that the context and environment in which the course is situated play a more important role in fostering a correlation between student collaboration and course performance than the format (remote, hybrid, inperson) of the course. Additionally, we conducted a short study on the effect that missing nodes may have on the correlations calculated from the measured networks. This investigation showed that missing nodes tend to shift correlations towards zero, providing evidence that the statistically significant correlations measured in our networks are not spurious.more » « less

Abstract The inverse scattering transform allows explicit construction of solutions to many physically significant nonlinear wave equations. Notably, this method can be extended to fractional nonlinear evolution equations characterized by anomalous dispersion using completeness of suitable eigenfunctions of the associated linear scattering problem. In anomalous diffusion, the mean squared displacement is proportional to t α , α > 0, while in anomalous dispersion, the speed of localized waves is proportional to A α , where A is the amplitude of the wave. Fractional extensions of the modified Korteweg–deVries (mKdV), sineGordon (sineG) and sinhGordon (sinhG) and associated hierarchies are obtained. Using symmetries present in the linear scattering problem, these equations can be connected with a scalar family of nonlinear evolution equations of which fractional mKdV (fmKdV), fractional sineG (fsineG), and fractional sinhG (fsinhG) are special cases. Completeness of solutions to the scalar problem is obtained and, from this, the nonlinear evolution equation is characterized in terms of a spectral expansion. In particular, fmKdV, fsineG, and fsinhG are explicitly written. Onesoliton solutions are derived for fmKdV and fsineG using the inverse scattering transform and these solitons are shown to exhibit anomalous dispersion.more » « less

Marschall, Tobias (Ed.)
Abstract Motivation JBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Webbased genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization.
Results We have developed a package that provides a Python interface to JBrowse 2’s suite of embeddable components, including the primary Linear Genome View. The package enables users to quickly set up, launch and customize JBrowse views from Jupyter notebooks. In addition, users can share their data via Google’s Colab notebooks, providing reproducible interactive views.
Availability and implementation JBrowse Jupyter is released under the Apache License and is available for download on PyPI. Source code and demos are available on GitHub at https://github.com/GMOD/jbrowsejupyter.

Abstract Quantum cellular automata (QCA) evolve qubits in a quantum circuit depending only on the states of their neighborhoods and model how rich physical complexity can emerge from a simple set of underlying dynamical rules. The inability of classical computers to simulate large quantum systems hinders the elucidation of quantum cellular automata, but quantum computers offer an ideal simulation platform. Here, we experimentally realize QCA on a digital quantum processor, simulating a onedimensional Goldilocks rule on chains of up to 23 superconducting qubits. We calculate calibrated and errormitigated population dynamics and complex network measures, which indicate the formation of smallworld mutual information networks. These networks decohere at fixed circuit depth independent of system size, the largest of which corresponding to 1,056 twoqubit gates. Such computations may enable the employment of QCA in applications like the simulation of stronglycorrelated matter or beyondclassical computational demonstrations.more » « less