cssi/SciSciGPT-SciSciNet huggingface dataset:
Impact of 3D genome organization, guided by cohesin and CTCF looping, on sex-biased chromatin interactions and gene expression in mouse liver

Several thousand sex-differential distal enhancers have been identified in mouse liver; however, their links to sex-biased genes and the impact of any sex-differences in nuclear organization and chromatin interactions are unknown. To address these issues, we first characterized 1847 mouse liver genomic regions showing significant sex differential occupancy by cohesin and CTCF, two key 3D nuclear organizing factors. These sex-differential binding sites were primarily distal to sex-biased genes but rarely generated sex-differential TAD (topologically associating domain) or intra-TAD loop anchors, and were sometimes found in TADs without sex-biased genes. A substantial subset of sex-biased cohesin-non-CTCF binding sites, but not sex-biased cohesin-and-CTCF binding sites, overlapped sex-biased enhancers. Cohesin depletion reduced the expression of male-biased genes with distal, but not proximal, sex-biased enhancers by >10-fold, implicating cohesin in long-range enhancer interactions regulating sex-biased genes. Using circularized chromosome conformation capture-based sequencing (4C-seq), we showed that sex differences in distal sex-biased enhancer–promoter interactions are common. Intra-TAD loops with sex-independent cohesin-and-CTCF anchors conferred sex specificity to chromatin interactions indirectly, by insulating sex-biased enhancer–promoter contacts and by bringing sex-biased genes into closer proximity to sex-biased enhancers. Furthermore, sex-differential chromatin interactions involving sex-biased gene promoters, enhancers, and lncRNAs were associated with sex-biased binding of cohesin and/or CTCF. These studies elucidate how 3D genome organization impacts sex-biased gene expression in a non-reproductive tissue through both direct and indirect effects of cohesin and CTCF looping on distal enhancer interactions with sex-differentially expressed genes.
Deterministic Intra-Vehicle Communications: Timing and Synchronization.

As we power through to the future, in-vehicle communications' reliance on speed is becoming a challenging predicament. This is mainly due to the ever-increasing number of electronic control units (ECUs), which will continue to drain network capacity, hence further increasing bandwidth demand. For a wired network, a tradeoff between bandwidth requirement, reliability, and cost-effectiveness has been our main motivation in developing a high-speed network architecture that is based on the integration of two time-triggered protocols namely; Time Triggered Ethernet (TT-E) and Time Triggered Controller Area Network (TT-CAN). Therefore, as a visible example of an Internet of Vehicles technology, we present a time triggered communication-based network architecture. The new architecture can provide scalable integration of advanced functionalities, while maintaining safety and high reliability. To comply with the bandwidth requirement, we consider high-speed TT-Ethernet as the main bus (i.e., backbone network) where sub-networks can use more cost-effective and lower bandwidth TT-CAN to communicate with other entities in the network via a gateway. The main challenge in the proposed network architecture has been to resolve interoperability between two entirely different time-triggered protocols, especially in terms of timing and synchronization. In this paper, we first explore the main key drivers of the proposed architecture, which are bandwidth, reliability, and timeliness. We then demonstrate the effectiveness of our gateway design in providing full interoperability between the two time-triggered protocols.
Interpretation of bottom-hole temperature data from CO2 injection projects

Abstract Although bottom-hole temperature (BHT) data is routinely collected with bottom-hole pressure (BHP) data during CO2 injection in geological storage projects, it is seldom analyzed in a quantitative manner. However, the BHP and BHT signals do exhibit correlated behavior. This study proposes a practical approach for using BHT data from warmback (no injection) periods, in conjunction with the amplitude of BHP and BHT changes during the preceding injection event, to estimate formation permeability. The methodology is demonstrated using data from the AEP Mountaineer project in USA where CO2 injection was carried out into a saline aquifer. Estimated permeability values from the proposed approach are in good agreement with those obtained from transient pressure analysis of BHP data from the falloff period.
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis.

Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, most deep learning-based objective outcome prediction and grading paradigms are based on histology or genomics alone and do not make use of the complementary information in an intuitive manner. In this work, we propose Pathomic Fusion, an interpretable strategy for end-to-end multimodal fusion of histology image and genomic (mutations, CNV, RNASeq) features for survival outcome prediction. Our approach models pairwise feature interactions across modalities by taking the Kronecker product of unimodal feature representations, and controls the expressiveness of each representation via a gatingbased attention mechanism. Following supervised learning, we are able to interpret and saliently localize features across each modality, and understand how feature importance shifts when conditioning on multimodal input. We validate our approach using glioma and clear cell renal cell carcinoma datasets from the Cancer Genome Atlas (TCGA), which contains paired wholeslide image, genotype, and transcriptome data with ground truth survival and histologic grade labels. In a 15-fold cross-validation, our results demonstrate that the proposed multimodal fusion paradigm improves prognostic determinations from ground truth grading and molecular subtyping, as well as unimodal deep networks trained on histology and genomic data alone. The proposed method establishes insight and theory on how to train deep networks on multimodal biomedical data in an intuitive manner, which will be useful for other problems in medicine that seek to combine heterogeneous data streams for understanding diseases and predicting response and resistance to treatment. Code and trained models are made available at: https://github.com/mahmoodlab/PathomicFusion.
Partial Information Decomposition and the Information Delta: A Geometric Unification Disentangling Non-Pairwise Information.

Information theory provides robust measures of multivariable interdependence, but classically does little to characterize the multivariable relationships it detects. The Partial Information Decomposition (PID) characterizes the mutual information between variables by decomposing it into unique, redundant, and synergistic components. This has been usefully applied, particularly in neuroscience, but there is currently no generally accepted method for its computation. Independently, the Information Delta framework characterizes non-pairwise dependencies in genetic datasets. This framework has developed an intuitive geometric interpretation for how discrete functions encode information, but lacks some important generalizations. This paper shows that the PID and Delta frameworks are largely equivalent. We equate their key expressions, allowing for results in one framework to apply towards open questions in the other. For example, we find that the approach of Bertschinger et al. is useful for the open Information Delta question of how to deal with linkage disequilibrium. We also show how PID solutions can be mapped onto the space of delta measures. Using Bertschinger et al. as an example solution, we identify a specific plane in delta-space on which this approach’s optimization is constrained, and compute it for all possible three-variable discrete functions of a three-letter alphabet. This yields a clear geometric picture of how a given solution decomposes information.
Information asymmetry and the inefficiency of informal ip strategies within employment relationships

Abstract Employee mobility and betrayal increase the risks of disclosing unpublished technical information. This study builds a theoretical foundation for the loss of unpublished technical information regarding human capital. It analyzes the inefficiency of informal intellectual property (“IP”) strategies, which include non-disclosure agreements (“NDAs”), covenants not to compete (“CNCs”), and trade secrets, from the maximum probable loss approach. It bridges the legal literature and the economics literature by emphasizing and explaining the information asymmetries in employment relationships regarding the informal IP strategies. NDAs need to be supplemented by CNCs or trade secret law. Enforceable CNCs and trade secrets have a reward function, but trade secrets are more efficient in informal IP management and innovation. Beyond the legal risks imposed by informal IP, companies should actively improve employee loyalty and their security culture through employee management.
Motion Optimization for Musculoskeletal Dynamics: A Flatness-Based Polynomial Approach

A new approach for trajectory optimization of musculoskeletal dynamic models is introduced. The model combines rigid-body and muscle dynamics described with a Hill-type model driven by neural control inputs. The objective is to find input and state trajectories that are optimal with respect to a minimum-effort objective and meet constraints associated with musculoskeletal models. The measure of effort is given by the integral of pairwise average forces of the agonist-antagonist muscles. The concepts of flat parameterization of nonlinear systems and sum-of-squares optimization are combined to yield a method that eliminates the numerous set of dynamic constraints present in collocation methods. With terminal equilibrium, optimization reduces to a feasible linear program, and a recursive feasibility proof is given for more general polynomial optimization cases. The methods of the article can be used as a basis for fast, and efficient solvers for hierarchical, and receding-horizon control schemes. Two simulation examples are included to illustrate the proposed methods.
The Manufacturing Data and Machine Learning Platform: Enabling Real-time Monitoring and Control of Scientific Experiments via IoT

IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing experiments. However, IoT devices can vary greatly in the type, volume, and velocity of data they generate, making it challenging to fully realize this potential. Indeed, synergizing diverse IoT data streams in near-real time can require the use of machine learning (ML). In addition, new tools and technologies are required to facilitate the collection, aggregation, and manipulation of sensor data in order to simplify the application of ML models and in turn, fully realize the utility of IoT devices in laboratories. Here we will demonstrate how the use of the Argonne-developed Manufacturing Data and Machine Learning (MDML) platform can analyze and use IoT devices in a manufacturing experiment. MDML is designed to standardize the research and operational environment for advanced data analytics and AI-enabled automated process optimization by providing the infrastructure to integrate AI in cyber-physical systems for in situ analysis. We will show that MDML is capable of processing diverse IoT data streams, using multiple computing resources, and integrating ML models to guide an experiment.
The Future of Fractions.

In the 1970s, the movement to the metric system (which has still not completely occurred in the United States) and the advent of hand-held calculators led some to speculate that decimal representation of numbers would render fractions obsolete. This provocative proposition stimulated Zalman Usiskin to write "The Future of Fractions" in 1979. He challenged this opinion by illustrating how it was based on flawed assumptions and erroneous reasoning. Some twenty-eight years later, Usiskin follows up on his fraction remarks in uSome Thoughts about Fractions.'9 In this work, he bolsters his previous arguments concerning the welfare of fractions and asserts that their study will remain an important part of school mathematics.
Good for the Many or Best for the Few?: A Dilemma in the Design of Algorithmic Advice

Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity. When designing these applications, is it better to offer: a) advice that if strictly adhered to is more likely to result in an individual successfully achieving their goal, even if fewer users will choose to adopt it? or b) advice that is likely to be adopted by a larger number of users, but which is sub-optimal with regard to any particular individual achieving their goal? We identify this dilemma, characterized as Goal-Directed vs. Adoption-Directed advice, and investigate the design questions it raises through an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. We report findings that suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications.
Sensor Switching Control Under Attacks Detectable by Finite Sample Dynamic Watermarking Tests

Control system security is enhanced by the ability to detect malicious attacks on sensor measurements. Dynamic watermarking can detect such attacks on linear time-invariant systems. However, existing theory focuses on attack detection and not on the use of watermarking in conjunction with attack mitigation strategies. In this article, we study the problem of switching between two sets of sensors: One set of sensors has high accuracy but is vulnerable to attack, whereas the second set of sensors has low accuracy but cannot be attacked. The problem is to design a sensor switching strategy based on attack detection by dynamic watermarking. This requires new theory because existing results are not adequate to control or bound the behavior of sensor switching strategies that use finite data. To overcome this, we develop new finite sample hypothesis tests for dynamic watermarking in the case of bounded disturbances, using the modern theory of concentration of measure for random matrices. Our resulting switching strategy is validated with a simulation analysis in an autonomous driving setting, which demonstrates the strong performance of our proposed policy.
Maximum and Shape of Interfaces in 3D Ising Crystals

Dobrushin (1972) showed that the interface of a 3D Ising model with minus boundary conditions above the $xy$-plane and plus below is rigid (has $O(1)$-fluctuations) at every sufficiently low temperature. Since then, basic features of this interface -- such as the asymptotics of its maximum -- were only identified in more tractable random surface models that approximate the Ising interface at low temperatures, e.g., for the (2+1)D Solid-On-Solid model. Here we study the large deviations of the interface of the 3D Ising model in a cube of side-length $n$ with Dobrushin's boundary conditions, and in particular obtain a law of large numbers for $M_n$, its maximum: if the inverse-temperature $\beta$ is large enough, then $M_n / \log n \to 2/\alpha_\beta$ as $n\to\infty$, in probability, where $\alpha_\beta$ is given by a large deviation rate in infinite volume. We further show that, on the large deviation event that the interface connects the origin to height $h$, it consists of a 1D spine that behaves like a random walk, in that it decomposes into a linear (in $h$) number of asymptotically-stationary weakly-dependent increments that have exponential tails. As the number $T$ of increments diverges, properties of the interface such as its surface area, volume, and the location of its tip, all obey CLTs with variances linear in $T$. These results generalize to every dimension $d\geq 3$.
Hausdorff and Wasserstein metrics on graphs and other structured data

Optimal transport is widely used in pure and applied mathematics to find probabilistic solutions to hard combinatorial matching problems. We extend the Wasserstein metric and other elements of optimal transport from the matching of sets to the matching of graphs and other structured data. This structure-preserving form of optimal transport relaxes the usual notion of homomorphism between structures. It applies to graphs, directed and undirected, labeled and unlabeled, and to any other structure that can be realized as a $C$-set for some finitely presented category $C$. We construct both Hausdorff-style and Wasserstein-style metrics on $C$-sets and we show that the latter are convex relaxations of the former. Like the classical Wasserstein metric, the Wasserstein metric on $C$-sets is the value of a linear program and is therefore efficiently computable.
Bridging the Gap Between Nanowires and Josephson Junctions: A Superconducting Device Based on Controlled Fluxon Transfer

The basis for superconducting electronics can broadly be divided between two technologies: the Josephson junction and the superconducting nanowire. While the Josephson junction (JJ) remains the dominant technology due to its high speed and low power dissipation, recently proposed nanowire devices offer improvements such as gain, high fanout, and compatibility with CMOS circuits. Despite these benefits, nanowire-based electronics have largely been limited to binary operations, with devices switching between the superconducting state and a high-impedance resistive state dominated by uncontrolled hotspot dynamics. Unlike the JJ, they cannot increment an output through successive switching and their operation speeds are limited by their slow thermal-reset times. Thus, there is a need for an intermediate device with the interfacing capabilities of a nanowire but a faster, moderated response allowing for modulation of the output. We present a nanowire device based on controlled fluxon transport. We show that the device is capable of responding proportionally to the strength of its input, unlike other nanowire technologies. The device can be operated to produce a multilevel output with distinguishable states, the number of which can be tuned by circuit parameters. Agreement between experimental results and electrothermal circuit simulations demonstrates that the device is classical and may be readily engineered for applications including use as a multilevel memory.
Issues with Core-Collapse Supernova Progenitor Models

The status of core collapse supernoova progenitor models is reviewed with a focus on some of the current uncertainties arising from the difficulties of modeling important macrophysics and microphysics. In particular, I look at issues concerned with modeling convection, the implications of the still uncertain 12C(a , y)l60 reaction rate, the uncertainties involved with the incorporation of mass loss, rotation, and magnetic fields in the stellar models, and the possible generation of global instabilities in stellar models a t the late evolutionary stages.
A critical review of recent progress on negative capacitance field-effect transistors

The elegant simplicity of the device concept and the urgent need for a new "transistor" at the twilight of Moore's law have inspired many researchers in industry and academia to explore the physics and technology of negative capacitance field effect transistor (NC-FET). Although hundreds of papers have been published, the validity of quasi-static NC and the frequency-reliability limits of NC-FET are still being debated. The concept of NC - if conclusively demonstrated - will have broad impacts on device physics and technology development. Here, the authors provide a critical review of recent progress on NC-FETs research and some starting points for a coherent discussion.
What did Erwin mean? The physics of information from the materials genomics of aperiodic crystals and water to molecular information catalysts and life.

Erwin Schrodinger famously and presciently ascribed the vehicle transmitting the hereditary information underlying life to an 'aperiodic crystal'. We compare and contrast this, only later discovered to be stored in the linear biomolecule DNA, with the information-bearing, layered quasi-one-dimensional materials investigated by the emerging field of chaotic crystallography. Despite differences in functionality, the same information measures capture structure and novelty in both, suggesting an intimate coherence between the information character of biotic and abiotic matter-a broadly applicable physics of information. We review layered solids and consider three examples of how information- and computation-theoretic techniques are being applied to understand their structure. In particular, (i) we review recent efforts to apply new kinds of information measures to quantify disordered crystals; (ii) we discuss the structure of ice I in information-theoretic terms; and (iii) we recount recent investigations into the structure of tris(bicyclo[2.1.1]hexeno)benzene, showing how an information-theoretic analysis yields additional insight into its structure. We then illustrate a new Second Law of Thermodynamics that describes information processing in active low-dimensional materials, reviewing Maxwell's Demon and a new class of molecular devices that act as information catalysts. Lastly, we conclude by speculating on how these ideas from informational materials science may impact biology.
Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90

NASA's Kepler Space Telescope was designed to determine the frequency of Earth-sized planets orbiting Sun-like stars, but these planets are on the very edge of the mission's detection sensitivity. Accurately determining the occurrence rate of these planets will require automatically and accurately assessing the likelihood that individual candidates are indeed planets, even at low signal-to-noise ratios. We present a method for classifying potential planet signals using deep learning, a class of machine learning algorithms that have recently become state-of-the-art in a wide variety of tasks. We train a deep convolutional neural network to predict whether a given signal is a transiting exoplanet or a false positive caused by astrophysical or instrumental phenomena. Our model is highly effective at ranking individual candidates by the likelihood that they are indeed planets: 98.8% of the time it ranks plausible planet signals higher than false positive signals in our test set. We apply our model to a new set of candidate signals that we identified in a search of known Kepler multi-planet systems. We statistically validate two new planets that are identified with high confidence by our model. One of these planets is part of a five-planet resonant chain around Kepler-80, with an orbital period closely matching the prediction by three-body Laplace relations. The other planet orbits Kepler-90, a star which was previously known to host seven transiting planets. Our discovery of an eighth planet brings Kepler-90 into a tie with our Sun as the star known to host the most planets.
A Kinetic Study of Amyloid Formation: Fibril Growth and Length Distributions

We propose a kinetic model for the self-aggregation by amyloid proteins. By extending several well-known models for protein aggregation, the time evolution of aggregate concentrations containing r proteins, denoted cr(t), can be written in terms of generalized Smoluchowski kinetics. With this approach, we take into account all possible aggregation and fragmentation reactions involving clusters of any size. Correspondingly, an aggregate of size x + y could be formed by or break up into two smaller constituent aggregates of sizes x and y. The rates of each aggregation or fragmentation reaction, called kernels, are specified in terms of the aggregate size, and we solve cr(t) for large cluster sizes using numerical techniques. We show that by using Smoluchowski kinetics many pathways to fibrillation are possible and quantities, such as the aggregate length distribution at an arbitrary time, can be calculated. We show that the predicted results of the model are in agreement with the experimental observations.
Entrywise convergence of iterative methods for eigenproblems

Several problems in machine learning, statistics, and other fields rely on computing eigenvectors. For large scale problems, the computation of these eigenvectors is typically performed via iterative schemes such as subspace iteration or Krylov methods. While there is classical and comprehensive analysis for subspace convergence guarantees with respect to the spectral norm, in many modern applications other notions of subspace distance are more appropriate. Recent theoretical work has focused on perturbations of subspaces measured in the $\ell_{2 \to \infty}$ norm, but does not consider the actual computation of eigenvectors. Here we address the convergence of subspace iteration when distances are measured in the $\ell_{2 \to \infty}$ norm and provide deterministic bounds. We complement our analysis with a practical stopping criterion and demonstrate its applicability via numerical experiments. Our results show that one can get comparable performance on downstream tasks while requiring fewer iterations, thereby saving substantial computational time.
Crust-cooling Models Are Insensitive to the Crust–Core Transition Pressure for Realistic Equations of State

Neutron stars cooling after sustained accretion outbursts provide unique information about the neutron star crust and underlying dense matter. Comparisons between astronomical observations of these cooling transients and model calculations of neutron star crust cooling have frequently been used to constrain neutron star properties such as the mass, radius, crust composition, and presence of nuclear pasta. These calculations often use a fixed pressure at which the crust-core transition happens, though this quantity depends on the dense matter equation of state. We demonstrate that varying the crust-core transition pressure in a manner consistent with adopting various equations of state results in modest changes to the crust cooling light curve. This validates the approach adopted in most crust cooling studies to date, where the neutron star mass and radius are varied while leaving the crust-core transition pressure constant.
The Universality of Homogeneous Polynomial Forms and Critical Limits

Nourdin et al. (Ann Probab 38(5):1947–1985, 2010) established the following universality result: if a sequence of off-diagonal homogeneous polynomial forms in i.i.d. standard normal random variables converges in distribution to a normal, then the convergence also holds if one replaces these i.i.d. standard normal random variables in the polynomial forms by any independent standardized random variables with uniformly bounded third absolute moment. The result, which was stated for polynomial forms with a finite number of terms, can be extended to allow an infinite number of terms in the polynomial forms. Based on a contraction criterion derived from this extended universality result, we prove a central limit theorem for a strongly dependent nonlinear process whose memory parameter lies at the boundary between short and long memory.
Linear sigma models with torsion

Gauged linear sigma models with (0, 2) supersymmetry allow a larger choice of couplings than models with (2, 2) supersymmetry. We use this freedom to find a fully linear construction of torsional heterotic compactifications, including models with branes. As a non-compact example, we describe a family of metrics which correspond to deformations of the heterotic conifold by turning on H-flux. We then describe compact models which are gauge-invariant only at the quantum level. Our construction gives a generalization of symplectic reduction. The resulting spaces are non-Kahler analogues of familiar toric spaces like complex projective space. Perturbatively conformal models can be constructed by considering intersections.
A Project-Based Course on Software Development for (Engineering) Research

This paper describes the motivation and design of a 10-week graduate course that teaches practices for developing research software; although offered by an engineering program, the content applies broadly to any field of scientific research where software may be developed. Topics taught in the course include local and remote version control, licensing and copyright, structuring Python modules, testing and test coverage, continuous integration, packaging and distribution, open science, software citation, and reproducibility basics, among others. Lectures are supplemented by in-class activities and discussions, and all course material is shared openly via GitHub. Coursework is heavily based on a single, term-long project where students individually develop a software package targeted at their own research topic; all contributions must be submitted as pull requests and reviewed/merged by other students. The course was initially offered in Spring 2018 with 17 students enrolled, and will be taught again in Spring 2019.
Numerical relativity beyond astrophysics

Though the main applications of computer simulations in relativity are to astrophysical systems such as black holes and neutron stars, nonetheless there are important applications of numerical methods to the investigation of general relativity as a fundamental theory of the nature of space and time. This paper gives an overview of some of these applications. In particular we cover (i) investigations of the properties of spacetime singularities such as those that occur in the interior of black holes and in big bang cosmology. (ii) investigations of critical behavior at the threshold of black hole formation in gravitational collapse. (iii) investigations inspired by string theory, in particular analogs of black holes in more than 4 spacetime dimensions and gravitational collapse in spacetimes with a negative cosmological constant.
Improving accuracy of electrochemical capacitance and solvation energetics in first-principles calculations

Reliable first-principles calculations of electrochemical processes require accurate prediction of the interfacial capacitance, a challenge for current computationally efficient continuum solvation methodologies. We develop a model for the double layer of a metallic electrode that reproduces the features of the experimental capacitance of Ag(100) in a non-adsorbing, aqueous electrolyte, including a broad hump in the capacitance near the potential of zero charge and a dip in the capacitance under conditions of low ionic strength. Using this model, we identify the necessary characteristics of a solvation model suitable for first-principles electrochemistry of metal surfaces in non-adsorbing, aqueous electrolytes: dielectric and ionic nonlinearity, and a dielectric-only region at the interface. The dielectric nonlinearity, caused by the saturation of dipole rotational response in water, creates the capacitance hump, while ionic nonlinearity, caused by the compactness of the diffuse layer, generates the capacitance dip seen at low ionic strength. We show that none of the previously developed solvation models simultaneously meet all these criteria. We design the nonlinear electrochemical soft-sphere solvation model which both captures the capacitance features observed experimentally and serves as a general-purpose continuum solvation model.
S-Duality and Noncommutative Gauge Theory

It is conjectured that strongly coupled, spatially noncommutative N = 4 Yang-Mills theory has a dual description as a weakly coupled open string theory in a near critical electric field, and that this dual theory is fully decoupled from closed strings. Evidence for this conjecture is given by the absence of physical closed string poles in the non-planar one-loop open string diagram. The open string theory can be viewed as living in a geometry in which space and time coordinates do not commute.
Suppression of chaos at slow variables by rapidly mixing fast dynamics through linear energy-preserving coupling

Chaotic multiscale dynamical systems are common in many areas of science, one of the examples being the interaction of the low-frequency dynamics in the atmosphere with the fast turbulent weather dynamics. One of the key questions about chaotic multiscale systems is how the fast dynamics affects chaos at the slow variables, and, therefore, impacts uncertainty and predictability of the slow dynamics. Here we demonstrate that the linear slow-fast coupling with the total energy conservation property promotes the suppression of chaos at the slow variables through the rapid mixing at the fast variables, both theoretically and through numerical simulations. A suitable mathematical framework is developed, connecting the slow dynamics on the tangent subspaces to the infinite-time linear response of the mean state to a constant external forcing at the fast variables. Additionally, it is shown that the uncoupled dynamics for the slow variables may remain chaotic while the complete multiscale system loses chaos and becomes completely predictable at the slow variables through increasing chaos and turbulence at the fast variables. This result contradicts the common sense intuition, where, naturally, one would think that coupling a slow weakly chaotic system with another much faster and much stronger mixing system would result in general increase of chaos at the slow variables.
Structural Results for Decentralized Stochastic Control with a Word-of-Mouth Communication

In this paper, we analyze a network of agents that communicate through the "word of mouth," in which, every agent communicates only with its neighbors. We introduce the prescription approach, present some of its properties and show that it leads to a new information state. We also state preliminary structural results for optimal control strategies in systems that evolve using word-of-mouth communication. The proposed approach can be generalized to analyze several decentralized systems.
The densest lattice in twenty-four dimensions

In this research announcement we outline the methods used in our recent proof that the Leech lattice is the unique densest lattice in R24. Complete details will appear elsewhere, but here we illustrate our techniques by applying them to the case of lattice packings in R2, and we discuss the obstacles that arise in higher dimensions.
OPE methods for the holomorphic Higgs portal

We develop a systematic and general approach to study the effective Higgs Lagrangian in a supersymmetric framework in which the Higgs fields in the visible sector couple weakly to another sector. The extra sector may be strongly coupled in general. It is assumed to be superconformal in the ultraviolet, but develop a mass-gap with supersymmetry breaking in the infrared. The main technique used in our approach is that of the operator product expansion (OPE). By using OPE methods we are able to compute the parameters in the Higgs Lagrangian to quadratic order and make general statements that are applicable to many classes of models. Not only does this approach allow us to understand the traditional problems plaguing simple models from a different perspective, it also reveals new possibilities for solutions of these problems. The methods and results of our work should be useful in constructing a viable and natural model of physics beyond the Standard Model.
The direction of gravity

Gravity directs the paths of light rays and the growth of structure. Moreover, gravity on cosmological scales does not simply point down: It accelerates the universal expansion by pulling outward, either due to a highly negative pressure dark energy or an extension of general relativity. We have examined methods to test the properties of gravity through cosmological measurements. We have then considered specific possibilities for a sound gravitational theory based on the Galilean shift symmetry. The evolution of the laws of gravity from the early universe to the present acceleration to the future fate – the paths of gravity – carries rich information on this fundamental force of physics, and on the mystery of dark energy.
Quantum Dynamics of Periodic and Limit-Periodic Jacobi and Block Jacobi Matrices with Applications to Some Quantum Many Body Problems

We investigate quantum dynamics with the underlying Hamiltonian being a Jacobi or a block Jacobi matrix with the diagonal and the off-diagonal terms modulated by a periodic or a limit-periodic sequence. In particular, we investigate the transport exponents. In the periodic case we demonstrate ballistic transport, while in the limit-periodic case we discuss various phenomena, such as quasi-ballistic transport and weak dynamical localization. We also present applications to some quantum many body problems. In particular, we establish for the anisotropic XY chain on \({\mathbb{Z}}\) with periodic parameters an explicit strictly positive lower bound for the Lieb–Robinson velocity.
Fused analytical and iterative reconstruction (AIR) via modified proximal forward-backward splitting: a FDK-based iterative image reconstruction example for CBCT.

This work is to develop a general framework, namely analytical iterative reconstruction (AIR) method, to incorporate analytical reconstruction (AR) method into iterative reconstruction (IR) method, for enhanced CT image quality and reconstruction efficiency. Specifically, AIR is established based on the modified proximal forward–backward splitting (PFBS) algorithm, and its connection to the filtered data fidelity with sparsity regularization is discussed. As a result, AIR decouples data fidelity and image regularization with a two-step iterative scheme, during which an AR-projection step updates the filtered data fidelity term, while a denoising solver updates the sparsity regularization term. During the AR-projection step, the image is projected to the data domain to form the data residual, and then reconstructed by certain AR to a residual image which is then weighted together with previous image iterate to form next image iterate. Intuitively since the eigenvalues of AR-projection operator are close to the unity, PFBS based AIR has a fast convergence. Such an advantage is rigorously established through convergence analysis and numerical computation of convergence rate. The proposed AIR method is validated in the setting of circular cone-beam CT with AR being FDK and total-variation sparsity regularization, and has improved image quality from both AR and IR. For example, AIR has improved visual assessment and quantitative measurement in terms of both contrast and resolution, and reduced axial and half-fan artifacts.
Magnetic Pumping as a Source of Particle Heating and Power-law Distributions in the Solar Wind

Based on the rate of expansion of the solar wind, the plasma should cool rapidly as a function of distance to the Sun. Observations show this is not the case. In this work, a magnetic pumping model is developed as a possible explanation for the heating and the generation of power-law distribution functions observed in the solar wind plasma. Most previous studies in this area focus on the role that the dissipation of turbulent energy on microscopic kinetic scales plays in the overall heating of the plasma. However, with magnetic pumping, particles are energized by the largest-scale turbulent fluctuations, thus bypassing the energy cascade. In contrast to other models, we include the pressure anisotropy term, providing a channel for the large-scale fluctuations to heat the plasma directly. A complete set of coupled differential equations describing the evolution, and energization, of the distribution function are derived, as well as an approximate closed-form solution. Numerical simulations using the VPIC kinetic code are applied to verify the model's analytical predictions. The results of the model for realistic solar wind scenario are computed, where thermal streaming of particles are important for generating a phase shift between the magnetic perturbations and the pressure anisotropy. In turn, averaged over a pump cycle, the phase shift permits mechanical work to be converted directly to heat in the plasma. The results of this scenario show that magnetic pumping may account for a significant portion of the solar wind energization.
On the multi-frequency inverse source problem in heterogeneous media

The inverse source problem where an unknown source is to be identified from knowledge of its radiated wave is studied. The focus is placed on the effect that multi-frequency data have on establishing uniqueness. In particular, it is shown that data obtained from finitely many frequencies are not sufficient. On the other hand, if the frequency varies within a set with an accumulation point, then the source is determined uniquely, even in the presence of highly heterogeneous media. In addition, an algorithm for the reconstruction of the source using multi-frequency data is proposed. The algorithm, based on a subspace projection method, approximates the minimum-norm solution given the available multi-frequency measurements. A few numerical examples are presented.
Robust approach for rotor mapping in cardiac tissue

The motion of and interaction between phase singularities that lie at the centers of spiral waves capture many qualitative and, in some cases, quantitative features of complex dynamics in excitable systems. Being able to accurately reconstruct their position is thus quite important, even if the data are noisy and sparse, as in electrophysiology studies of cardiac arrhythmias, for instance. A recently proposed global topological approach [Marcotte and Grigoriev, Chaos 27, 093936 (2017)] promises to meaningfully improve the quality of the reconstruction compared with traditional, local approaches. Indeed, we found that this approach is capable of handling noise levels exceeding the range of the signal with minimal loss of accuracy. Moreover, it also works successfully with data sampled on sparse grids with spacing comparable to the mean separation between the phase singularities for complex patterns featuring multiple interacting spiral waves.
Cobordisms and Reidemeister torsions of homotopy lens spaces

We show that any 3{dimensional homotopy lens space M 3 that is simplehomotopy equivalent to a lens space L(p;q) is topologically s-cobordant to the lens space. It follows that M has the same multi-signature as L(p;q )a nd the action of 1(M ) on the universal cover of M embeds in a free orthogonal action on S 7 . AMS Classication numbers Primary: 57M60, 57N70 Secondary: 57R65, 57R80
Path statistics, memory, and coarse-graining of continuous-time random walks on networks

Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs.
An algebraic independence result related to a conjecture of Dixmier on binary form invariants

In order to better understand the structure of classical rings of invariants for binary forms, Dixmier proposed, as a conjectural homogeneous system of parameters, an explicit collection of invariants previously studied by Hilbert. We generalize Dixmier’s collection and show that a particular subfamily is algebraically independent. Our proof relies on showing certain alternating sums of products of binomial coefficients are nonzero. Along the way we provide a very elementary proof a la Racah, namely, only using the Chu–Vandermonde Theorem, for Dixon’s Summation Theorem. We also provide explicit computations of invariants, for the binary octavic, which can serve as ideal introductory examples to Gordan’s 1868 method in classical invariant theory.
Bar-Halo Friction in Galaxies. I. Scaling Laws

It has been known for some time that rotating bars in galaxies slow due to dynamical friction against the halo. However, recent attempts to use this process to place constraints on the dark matter density in galaxies and possibly also to drive dark matter out of the center have been challenged. This paper uses simplified numerical experiments to clarify several aspects of the friction mechanism. I explicitly demonstrate the Chandrasekhar scaling of the friction force with bar mass, halo density, and halo velocity dispersion. I present direct evidence that exchanges between the bar and halo orbits at major resonances are responsible for friction and study both individual orbits and the net changes at these resonances. I also show that friction alters the phase space density of particles in the vicinity of a major resonance, which is the reason the magnitude of the friction force depends on the prior evolution. I demonstrate that bar slowdown can be captured correctly in simulations having modest spatial resolution and a practicable numbers of particles. Subsequent papers in this series delineate the dark matter density that can be tolerated in halos of different density profiles.
The Equivalence Principle at Work in Radiation from Unaccelerated Atoms and Mirrors

The equivalence principle is a perennial subject of controversy, especially in connection with radiation by a uniformly accelerated classical charge, or a freely falling charge observed by a supported detector. Recently, related issues have been raised in connection with the Unruh radiation associated with accelerated detectors (including two-level atoms and resonant cavities). A third type of system, very easy to analyze because of conformal invariance, is a two-dimensional scalar field interacting with perfectly reflecting boundaries (mirrors). After reviewing the issues for atoms and cavities, we investigate a stationary mirror from the point of view of an accelerated detector in ‘Rindler space’. In keeping with the conclusions of earlier authors about the electromagnetic problem, we find that a radiative effect is indeed observed; from an inertial point of view, the process arises from a collision of the negative vacuum energy of Rindler space with the mirror. There is a qualitative symmetry under interchange of accelerated and inertial subsystems (a vindication of the equivalence principle), but it hinges on the accelerated detector’s being initially in its own ‘Rindler vacuum’. This observation is consistent with the recent work on the Unruh problem.