cssi/SciSciGPT-SciSciNet huggingface dataset:
Phase-consistent dynamic mode decomposition from multiple overlapping spatial domains

An extension of dynamic mode decomposition is presented to synthesize globally consistent modes from velocity fields measured from multiple partially overlapping spatial domains.
Traffic Behavior in Cloud Data Centers: A Survey.

Data centers (DCs) nowadays house tens of thousands of servers and switches, interconnected by high-speed communication links. With the rapid growth of cloud DCs, in both size and number, tremendous efforts have been undertaken to efficiently design the network and manage the traffic within these DCs. However, little effort has been made toward measuring, understanding and chattelizing how the network-level traffic of these DCs behave. In this paper, we aim to present a systematic taxonomy and survey of these DC studies. Specifically, our survey first decomposes DC network traffic behavior into two main stages, namely (1) data collection methodologies and (2) research findings, and then classifies and discusses the recent research studies in each stage. Finally, the survey highlights few research challenges related to DC network traffic that require further research investigation.
Maintaining Information Freshness in Power-Efficient Status Update Systems

This paper is motivated by emerging edge computing systems which consist of sensor nodes that acquire and process information and then transmit status updates to an edge receiver for possible further processing. As power is a scarce resource at the sensor nodes, the system is modeled as a tandem computation-transmission queue with power-efficient computing. Jobs arrive at the computation server with rate $\lambda$ as a Poisson process with no available data buffer. The computation server can be in one of three states: (i) OFF: the server is turned off and no jobs are observed or processed, (ii) ON-Idle: the server is turned on but there is no job in the server, (iii) ON-Busy: the server is turned on and a job is processed in the server. These states cost zero, one and $p_{c} > 1$ units of power, respectively. Under a longterm power constraint, the computation server switches from one state to another in sequence: first a deterministic $T_{o}$ time units in OFF state, then waiting for a job arrival in ON-Idle state and then in ON-Busy state for an independent identically distributed compute time duration. The transmission server has a single unit data buffer to save incoming packets and applies last come first serve with discarding as well as a packet deadline to discard a sitting packet for maintaining information freshness, which is measured by the Age of Information (AoI). Additionally, there is a monotonic functional relation between the mean time spent in ON-Busy state and the mean transmission time. We obtain closed-form expressions for average AoI and average peak AoI. Our numerical results illustrate various regimes of operation for best AoI performances optimized over packet deadlines with relation to power efficiency.
Sparsity-promoting optimal control of cyber–physical systems over shared communication networks

Abstract Recent years have seen several new directions in the design of sparse control of cyber–physical systems (CPSs) driven by the objective of reducing communication costs. One common assumption made in these designs is that the communication happens over a dedicated network. For many practical applications, however, communication must occur over shared networks, leading to two critical design challenges, namely — time-delays in the feedback and fair sharing of bandwidth among users. In this paper, we present a set of sparse H 2 control designs under these two design constraints. An essential aspect of our design is that the delay itself can be a function of sparsity, which leads to an interesting pattern of trade-offs in the H 2 performance. We present three distinct algorithms. The first algorithm preconditions the assignable bandwidth to the network and produces an initial guess for a stabilizing controller. This is followed by our second algorithm, which sparsifies this controller while simultaneously adapting the feedback delay and optimizing the H 2 performance using alternating directions method of multipliers (ADMM). The third algorithm extends this approach to a multiple user scenario where an optimal number of communication links, whose total sum is fixed, is distributed fairly among users by minimizing the variance of their H 2 performances. The problem is cast as a difference-of-convex (DC) program with mixed-integer linear program (MILP) constraints. We provide theorems to prove the convergence of these algorithms, followed by validation through numerical simulations.
Existence and uniqueness for the mild solution of the stochastic heat equation with non-Lipschitz drift on an unbounded spatial domain

We prove the existence and uniqueness of the mild solution for a nonlinear stochastic heat equation defined on an unbounded spatial domain. The nonlinearity is not assumed to be globally, or even locally, Lipschitz continuous. Instead the nonlinearity is assumed to satisfy a one-sided Lipschitz condition. First, a strengthened version of the Kolmogorov continuity theorem is introduced to prove that the stochastic convolutions of the fundamental solution of the heat equation and a spatially homogeneous noise grow no faster than polynomially. Second, a deterministic mapping that maps the stochastic convolution to the solution of the stochastic heat equation is proven to be Lipschitz continuous on polynomially weighted spaces of continuous functions. These two ingredients enable the formulation of a Picard iteration scheme to prove the existence and uniqueness of the mild solution.
Enteroendocrine cells couple nutrient sensing to nutrient absorption by regulating ion transport

The ability to absorb ingested nutrients is an essential function of all metazoans and utilizes a wide array of nutrient transporters found on the absorptive enterocytes of the small intestine. A unique population of patients has previously been identified with severe congenital malabsorptive diarrhea upon ingestion of any enteral nutrition. The intestines of these patients are macroscopically normal, but lack enteroendocrine cells (EECs), suggesting an essential role for this rare population of nutrient-sensing cells in regulating macronutrient absorption. Here, we use human and mouse models of EEC deficiency to identify an unappreciated role for the EEC hormone peptide YY in regulating ion-coupled absorption of glucose and dipeptides. We find that peptide YY is required in the small intestine to maintain normal electrophysiology in the presence of vasoactive intestinal polypeptide, a potent stimulator of ion secretion classically produced by enteric neurons. Administration of peptide YY to EEC-deficient mice restores normal electrophysiology, improves glucose and peptide absorption, diminishes diarrhea and rescues postnatal survival. These data suggest that peptide YY is a key regulator of macronutrient absorption in the small intestine and may be a viable therapeutic option to treat patients with electrolyte imbalance and nutrient malabsorption.
Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator.

Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the vast majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real time for early brain injury diagnosis. However, training such models requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data available. In this study we present a principal component analysis-based method that emulates an empirical low-rank substitution for head impact kinematics, while requiring low computational cost. In characterizing our existing data set of 537 head impacts, each consisting of 6 degrees of freedom measurements, we found that only a few modes, e.g., 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set. Furthermore, these modes are predominantly low frequency since over 70% of the angular velocity response can be captured by modes that have frequencies under 40 Hz. We compared our proposed method against existing impact parametrization methods and showed significantly better performance in injury prediction using a range of kinematic-based metrics—such as head injury criterion (HIC), rotational injury criterion (RIC), and brain injury metric (BrIC)—and brain tissue deformation-based metrics—such as brain angle metric (BAM), maximum principal strain (MPS), and axonal fiber strains (FS). In all cases, our approach reproduced injury metrics similar to the ground truth measurements with no significant difference, whereas the existing methods obtained significantly different (p < 0.01) values as well as substantial differences in injury classification sensitivity and specificity. This emulator will enable us to provide the necessary data augmentation to build a head impact kinematic data set of any size. The emulator and corresponding examples are available on our website1.
Identifying and Serving English Learners in Gifted Education: Looking Back and Moving Forward.

Although the number of English learners (ELs) in the United States continues to increase, this population remains underserved by gifted and talented (GT) education programs across the nation. This ...
Adipocytes fail to maintain cellular identity during obesity due to reduced PPARγ activity and elevated TGFβ-SMAD signaling.

Abstract Objective Obesity due to overnutrition causes adipose tissue dysfunction, which is a critical pathological step on the road to type 2 diabetes (T2D) and other metabolic disorders. In this study, we conducted an unbiased investigation into the fundamental molecular mechanisms by which adipocytes transition to an unhealthy state during obesity. Methods We used nuclear tagging and translating ribosome affinity purification (NuTRAP) reporter mice crossed with Adipoq-Cre mice to determine adipocyte-specific 1) transcriptional profiles (RNA-seq), 2) promoter and enhancer activity (H3K27ac ChIP-seq), 3) and PPARγ cistrome (ChIP-seq) profiles in mice fed chow or a high-fat diet (HFD) for 10 weeks. We also assessed the impact of the PPARγ agonist rosiglitazone (Rosi) on gene expression and cellular state of adipocytes from the HFD-fed mice. We integrated these data to determine the transcription factors underlying adipocyte responses to HFD and conducted functional studies using shRNA-mediated loss-of-function approaches in 3T3-L1 adipocytes. Results Adipocytes from the HFD-fed mice exhibited reduced expression of adipocyte markers and metabolic genes and enhanced expression of myofibroblast marker genes involved in cytoskeletal organization, accompanied by the formation of actin filament structures within the cell. PPARγ binding was globally reduced in adipocytes after HFD feeding, and Rosi restored the molecular and cellular phenotypes of adipocytes associated with HFD feeding. We identified the TGFβ1 effector protein SMAD to be enriched at HFD-induced promoters and enhancers and associated with myofibroblast signature genes. TGFβ1 treatment of mature 3T3-L1 adipocytes induced gene expression and cellular changes similar to those seen after HFD in vivo, and knockdown of Smad3 blunted the effects of TGFβ1. Conclusions Our data demonstrate that adipocytes fail to maintain cellular identity after HFD feeding, acquiring characteristics of a myofibroblast-like cell type through reduced PPARγ activity and elevated TGFβ-SMAD signaling. This cellular identity crisis may be a fundamental mechanism that drives functional decline of adipose tissues during obesity.
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks

In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network classifier, extending prior work on the Bayesian Dark Knowledge framework. The proposed framework takes as input "teacher" and student model architectures and a general posterior expectation of interest. The distillation method performs an online compression of the selected posterior expectation using iteratively generated Monte Carlo samples. We focus on the posterior predictive distribution and expected entropy as distillation targets. We investigate several aspects of this framework including the impact of uncertainty and the choice of student model architecture. We study methods for student model architecture search from a speed-storage-accuracy perspective and evaluate down-stream tasks leveraging entropy distillation including uncertainty ranking and out-of-distribution detection.
Stateful Detection of Black-Box Adversarial Attacks

The problem of adversarial examples, evasion attacks on machine learning classifiers, has proven extremely difficult to solve. This is true even in the black-box threat model, as is the case in many practical settings. Here, the classifier is hosted as a remote service and the adversary does not have direct access to the model parameters.This paper argues that in such settings, defenders have a larger space of actions than previously studied. Specifically, we deviate from the implicit assumption made by prior work that a defense must be a stateless function that operates on individual examples, and evaluate the space of stateful defenses.We develop a defense designed to detect the process of generating adversarial examples. By keeping a history of the past queries, a defender can try to identify when a sequence of queries appears to be for the purpose of generating an adversarial example. We then introduce query blinding, a new class of attacks designed to bypass defenses that rely on such a defense approach. We believe that expanding the study of adversarial examples from stateless classifiers to stateful systems is not only more realistic for many black-box settings, but also gives the defender a much-needed advantage in responding to the adversary.
Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data

ABSTRACT In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging. In this work, we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse, noisy velocity data, where equation-based constraints are imposed through the likelihood function and uncertainty of the reconstructed flow can be estimated. Specifically, a Bayesian deep neural network is trained on sparse measurement data to capture the flow field. In the meantime, the violation of physical laws will be penalized on a large number of spatiotemporal points where measurements are not available. A non-parametric variational inference approach is applied to enable efficient physics-constrained Bayesian learning. Several test cases on idealized vascular flows with synthetic measurement data are studied to demonstrate the merit of the proposed method.
Higher order corks

It is shown that any finite list of smooth closed simply-connected 4-manifolds homeomorphic to a given one X can be obtained by removing a single compact contractible submanifold (or cork) from X, and then regluing it by powers of a boundary diffeomorphism. We then use this result to ‘separate’ finite families of corks embedded in a fixed 4-manifold.
Continuous prefetch for interactive data applications

Interactive data visualization and exploration (DVE) applications are often network-bottlenecked due to bursty request patterns, large response sizes, and heterogeneous deployments over a range of networks and devices. This makes it difficult to ensure consistently low response times (
Medicaid Expansions and Fertility in the United States

Beginning in the mid-1980s and extending through the early to mid-1990s, a substantial number of women and children in the United States gained eligibility for Medicaid through a series of income-based expansions. Using natality data from the National Center for Health Statistics, we estimate fertility responses to these eligibility expansions. We follow Currie and Gruber (2001) and measure changes in state Medicaid-eligibility policy by simulating the fraction of a standard population that would qualify for benefits in different states and different time periods. From 1985 to 1996, the fraction of women aged 15–44 who were eligible for Medicaid coverage for a pregnancy increased more than 20 percentage points. When we use a state and year fixed-effects model with a limited set of covariates, our estimates indicate that fertility increases in response to Medicaid expansions. However, after we include fixed effects for demographic characteristics, the estimated relationship diminishes substantially in size and is no longer statistically significant. We conclude that there is no robust relationship between Medicaid expansions and fertility.
Crystalline assemblies and densest packings of a family of truncated tetrahedra and the role of directional entropic forces.

Polyhedra and their arrangements have intrigued humankind since the ancient Greeks and are today important motifs in condensed matter, with application to many classes of liquids and solids. Yet, little is known about the thermodynamically stable phases of polyhedrally shaped building blocks, such as faceted nanoparticles and colloids. Although hard particles are known to organize due to entropy alone, and some unusual phases are reported in the literature, the role of entropic forces in connection with polyhedral shape is not well understood. Here, we study thermodynamic self-assembly of a family of truncated tetrahedra and report several atomic crystal isostructures, including diamond, β-tin, and high-pressure lithium, as the polyhedron shape varies from tetrahedral to octahedral. We compare our findings with the densest packings of the truncated tetrahedron family obtained by numerical compression and report a new space-filling polyhedron, which has been overlooked in previous searches. Interestingly, ...
COD3S: Diverse Generation with Discrete Semantic Signatures.

We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply \method{} to causal generation, the task of predicting a proposition's plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.
The Siegel Upper Half Space is a Marsden–Weinstein Quotient: Symplectic Reduction and Gaussian Wave Packets

We show that the Siegel upper half spaced is identified with the Marsden- Weinstein quotient obtained by symplectic reduction of the cotangent bundle T ∗ R 2d 2 with O(2d)-symmetry. The reduced symplectic form ond corresponding to the standard sym- plectic form on T ∗ R 2d 2 turns out to be a constant multiple of the symplectic form on � d obtained by Siegel. Our motivation is to understand the geometry behind two differ- ent formulations of the Gaussian wave packet dynamics commonly used in semiclassical mechanics. Specifically, we show that the two formulations are related via the symplectic reduction. Mathematics Subject Classification. 37J15, 53D20, 70G45, 81Q05, 81Q20, 81Q70, 81S10.
Photometry as a Proxy for Stellar Activity in Radial Velocity Analyses

Stellar activity remains a limiting factor in measuring precise planet parameters from radial velocity spectroscopy, not least in the search for Earth mass planets orbiting in the habitable zones of Sun-like stars. One approach to mitigate stellar activity is to use combined analyses of both radial velocity and time-series photometry. We present an analysis of simultaneous disk-integrated photometry and radial velocity data of the Sun in order to determine the useful limits of a combined analysis. We find that simple periodogram or autocorrelation analysis of solar photometry give the correct rotation period <50% of the time. We therefore use a Gaussian process to investigate the time variability of solar photometry and to directly compare simultaneous photometry with radial velocity data. We find that the hyperparameter posteriors are relatively stable over 70 years of solar photometry and the amplitude tracks the solar cycle. We observe good agreement between the hyperparameter posteriors for the simultaneous photometry and radial velocity data. Our primary conclusion is a recommendation to include an additional prior in Gaussian process fits to constrain the evolutionary timescale to be greater than the recurrence timescale (ie., the rotation period) to recover more physically plausible and useful results. Our results indicate that such simultaneous monitoring may be a useful tool in enhancing the precision of radial velocity surveys.
Sutured Floer homology and invariants of Legendrian and transverse knots

Using contact-geometric techniques and sutured Floer homology, we present an alternate formulation of the minus and plus versions of knot Floer homology. We further show how natural constructions in the realm of contact geometry give rise to much of the formal structure relating the various versions of Heegaard Floer homology. In addition, to a Legendrian or transverse knot K ⊂ (Y,ξ) we associate distinguished classes EH→(K) ∈ HFK−(−Y,K) and EH←(K) ∈ HFK+(−Y,K), which are each invariant under Legendrian or transverse isotopies of K. The distinguished class EH→ is shown to agree with the Legendrian/transverse invariant defined by Lisca, Ozsvath, Stipsicz and Szabo despite a strikingly dissimilar definition. While our definitions and constructions only involve sutured Floer homology and contact geometry, the identification of our invariants with known invariants uses bordered sutured Floer homology to make explicit computations of maps between sutured Floer homology groups.
Scaling Relations of Compressible MHD Turbulence

We study scaling relations of compressible and strongly magnetized turbulence using isothermal numerical simulations with resolution 5123. We find a good correspondence of our results with the Fleck model of compressible hydrodynamic turbulence. In particular, we find that the density-weighted velocity, i.e., ≡ ρ1/3, proposed by Kritsuk and coworkers obeys the Kolmogorov scaling, i.e., v(k) ~ k-5/3, for the high Mach number turbulence. Similarly, we find that the exponents of the third-order structure functions for stay equal to unity for all Mach numbers studied. The higher order correlations obey the She-Leveque scalings corresponding to the two-dimensional dissipative structures, and this result does not change with the Mach number either. In contrast to velocity , which exhibits different scaling parallel and perpendicular to the local magnetic field, the scaling of is similar in both directions. In addition, we find that the peaks of density create a hierarchy in which both physical and column densities decrease with the scale in accordance to the Fleck predictions. This hierarchy can be related to ubiquitous small ionized and neutral structures (SINS) in the interstellar gas. We believe that studies of statistics of the column density peaks can provide both a consistency check for the turbulence velocity studies and insight into supersonic turbulence, when the velocity information is not available.
AN IMPROVED MODEL FOR REDUCED-ORDER PHYSIOLOGICAL FLUID FLOWS

An improved one-dimensional mathematical model based on the Pulsed Flow Equations (PFE) is derived by integrating the axial component of the momentum equation over the transient Womersley velocity profile, providing a dynamic momentum equation whose coefficients are smoothly varying functions of the spatial variable. The resulting momentum equation along with the continuity equation and pressure-area relation form our reduced-order model for physiological fluid flows in one dimension and are aimed at providing accurate and fast-to-compute global models for physiological systems represented as networks of quasi one-dimensional fluid flows. The consequent nonlinear coupled system of equations is solved by the Lax-Wendroff scheme and is then applied to an open model arterial network of the human vascular system containing the largest 55 arteries. The proposed model with functional coefficients is compared with current classical one-dimensional theories which assume steady state Hagen-Poiseuille velocity profiles, either parabolic or plug-like, throughout the whole arterial tree. The effects of the nonlinear term in the momentum equation and different strategies for bifurcation points in the network, as well as the various lumped parameter outflow boundary conditions for distal terminal points are also analyzed. The results show that the proposed model can be used as an efficient tool for investigating the dynamics of reduced-order models of flows in physiological systems and would, in particular, be a good candidate for the one-dimensional, system-level component of geometric multiscale models of physiological systems.
Neutrinos and Nucleosynthesis in Gamma-Ray Burst Accretion Disks

We calculate the nuclear composition of matter in accretion disks surrounding stellar mass black holes as are thought to accompany gamma-ray bursts (GRBs). We follow a mass element in the accretion disk starting at the point of nuclear dissociation and calculate the evolution of the electron fraction due to electron, positron, electron neutrino, and electron antineutrino captures. We find that the neutronization of the disk material by electron capture can be reversed by neutrino interactions in the inner regions of disks with accretion rates of 1 M☉ s-1 and higher. For these cases the inner disk regions are optically thick to neutrinos, and so to estimate the emitted neutrino fluxes, we find the surface of last scattering for the neutrinos (the equivalent of the proto-neutron star neutrinosphere) for each optically thick disk model. We also estimate the influence of neutrino interactions on the neutron-to-proton ratio in outflows from GRB accretion disks and find that it can be significant even when the disk is optically thin to neutrinos.
Mind Your Ps and Qs: The Interrelation between Period (P) and Mass-ratio (Q) Distributions of Binary Stars

We compile observations of early-type binaries identified via spectroscopy, eclipses, long-baseline interferometry, adaptive optics, common proper motion, etc. Each observational technique is sensitive to companions across a narrow parameter space of orbital periods P and mass ratios q = M_comp/M_1. After combining the samples from the various surveys and correcting for their respective selection effects, we find the properties of companions to O-type and B-type main-sequence (MS) stars differ among three regimes. First, at short orbital periods P = 0.5, and exhibit a small excess of twins q > 0.95. Second, the companion frequency peaks at intermediate periods log P (days) = 3.5 (a = 10 AU), where the binaries have mass ratios weighted toward small values q = 0.2-0.3 and follow a Maxwellian "thermal" eccentricity distribution. Finally, companions with long orbital periods log P (days) = 5.5-7.5 (a = 200-5,000 AU) are outer tertiary components in hierarchical triples, and have a mass ratio distribution across q = 0.1-1.0 that is nearly consistent with random pairings drawn from the initial mass function. We discuss these companion distributions and properties in the context of binary star formation and evolution. We also reanalyze the binary statistics of solar-type MS primaries, taking into account that (30+/-10)% of single-lined spectroscopic binaries likely contain white dwarf companions instead of low-mass stellar secondaries. The mean frequency of stellar companions with q > 0.1 and log P (days) < 8.0 per primary increases from 0.50+/-0.04 for solar-type MS primaries to 2.1+/-0.3 for O-type MS primaries. We fit joint probability density functions f(M_1,q,P,e) to the corrected distributions, which can be incorporated into binary population synthesis studies.
An evaluation of local shape descriptors for 3D shape retrieval

As the usage of 3D models increases, so does the importance of developing accurate 3D shape retrieval algorithms. A common approach is to calculate a shape descriptor for each object, which can then be compared to determine two objects' similarity. However, these descriptors are often evaluated independently and on different datasets, making them difficult to compare. Using the SHREC 2011 Shape Retrieval Contest of Non-rigid 3D Watertight Meshes dataset, we systematically evaluate a collection of local shape descriptors. We apply each descriptor to the bag-of-words paradigm and assess the effects of varying the dictionary's size and the number of sample points. In addition, several salient point detection methods are used to choose sample points; these methods are compared to each other and to random selection. Finally, information from two local descriptors is combined in two ways and changes in performance are investigated. This paper presents results of these experiments.
Holographic Heat Engines as Quantum Heat Engines

Certain solutions of Einstein’s equations in anti-de Sitter spacetime can be engineered, using extended gravitational thermodynamics, to yield ‘holographic heat engines’, devices that turn heat into useful mechanical work. On the other hand, there are constructions (both experimental and theoretical) where a series of operations is performed on a small quantum system, defining what are known as ‘quantum heat engines’. We propose that certain holographic heat engines can be considered models of quantum heat engines, and the possible fruitfulness of this connection is discussed. Motivated by features of quantum heat engines that take a quantum system through analogues of certain classic thermodynamic cycles, some black hole Otto and Diesel cycles are presented and explored for the first time. In the expected regime of overlap, our Otto efficiency formulae are of the form exhibited by quantum and classical heat engines.
Certain abelian varieties bad at only one prime

An abelian surface A ∕ ℚ of prime conductor N is favorable if its 2-division field F is an S 5 -extension over ℚ with ramification index 5 over ℚ 2 . Let A be favorable and let B be a semistable abelian variety of dimension 2 d and conductor N d with B [ 2 ] filtered by copies of A [ 2 ] . We give a sufficient class field theoretic criterion on F to guarantee that B is isogenous to A d . As expected from our paramodular conjecture, we conclude that there is one isogeny class of abelian surfaces for each conductor in { 2 7 7 , 3 4 9 , 4 6 1 , 7 9 7 , 9 7 1 } . The general applicability of our criterion is discussed in the data section.
Automated Delineation of Hospital Service Areas and Hospital Referral Regions by Modularity Optimization.

Objective To develop an automated, data-driven, and scale-flexible method to delineate hospital service areas (HSAs) and hospital referral regions (HRRs) that are up-to-date, representative of all patients, and have the optimal localization of hospital visits. Data Sources The 2011 state inpatient database in Florida from the Healthcare Cost and Utilization Project. Study Design A network optimization method was used to redefine HSAs and HRRs by maximizing patient-to-hospital flows within each HSA/HRR while minimizing flows between them. We first constructed as many HSAs/HRRs as existing Dartmouth units in Florida, and then compared the two by various metrics. Next, we sought to derive the optimal numbers and configurations of HSAs/HRRs that best reflect the modularity of hospitalization patterns in Florida. Principal Findings The HSAs/HRRs by our method are favored over the Dartmouth units in balance of region size and market structure, shape, and most important, local hospitalization. Conclusions The new method is automated, scale-flexible, and effective in capturing the natural structure of the health care system. It has great potential for applications in delineating other health care service areas or in larger geographic regions.
Chiral symmetry breaking in the AdS/CFT correspondence

We study the SU(3)-invariant relevant deformation of D = 4 = 4 SU(N) gauge theory at large N using the AdS/CFT correspondence. At low energies, we obtain a non-supersymmetric gauge theory with three left-handed quarks in the adjoint of SU(N). In terms of the five-dimensional gauged supergravity, there is an unstable critical point in the scalar potential for fluctuations of some fields in a non-trivial representation of the symmetry group SU(3). On the field theory side, this corresponds to dynamical breaking of the SU(3) chiral symmetry down to SO(3). We compute the condensate of the quark bilinear and the two-point correlation function of the spontaneously broken currents from supergravity and find a non-zero ``pion'' decay constant, fπ.
Understanding Large-scale Structure in the SSA22 Protocluster Region Using Cosmological Simulations

We investigate the nature and evolution of large-scale structure within the SSA22 protocluster region at z = 3.09 using cosmological simulations. A redshift histogram constructed from current spectroscopic observations of the SSA22 protocluster reveals two separate peaks at z = 3.065 (blue) and z = 3.095 (red). Based on these data, we report updated overdensity and mass calculations for the SSA22 protocluster. We find δ_(b.gal) = 4.8 ± 1.8 and δ_(r.gal) = 9.5 ± 2.0 for the blue and red peaks, respectively, and δ_(r.gal) = 7.6 ± 1.4 for the entire region. These overdensities correspond to masses of M_b = (0.76 ± 0.17) x 10^(15)h^(-1) M_⊙ M_r = (2.15 ± 0.32) x 10^(15)h^(-1) M_⊙, and M_t = (3.19 ± 0.40) x 10^(15)h^(-1),M_⊙ for the red, blue, and total peaks, respectively. We use the Small MultiDark Planck (SMDPL) simulation to identify comparably massive z ~ 3 protoclusters, and uncover the underlying structure and ultimate fate of the SSA22 protocluster. For this analysis, we construct mock redshift histograms for each simulated z ~ 3 protocluster, quantitatively comparing them with the observed SSA22 data. We find that the observed double-peaked structure in the SSA22 redshift histogram corresponds not to a single coalescing cluster, but rather the proximity of a ~ 10^(15)h^(-1) M_⊙ protocluster and at least one 〉 10^(14)h^(-1) M_⊙ cluster progenitor. Such associations in the SMDPL simulation are easily understood within the framework of hierarchical clustering of dark matter halos. We finally find that the opportunity to observe such a phenomenon is incredibly rare, with an occurrence rate of 7.4h^3 Gpc^(-3).
Linear response of the Lyapunov exponent to a small constant perturbation

In the current work we demonstrate the principal possibility of prediction of the response of the largest Lyapunov exponent of a chaotic dynamical system to a small constant forcing perturbation via a linearized relation, which is computed entirely from the unperturbed dynamics. We derive the formal representation of the corresponding linear response operator, which involves the (computationally infeasible) infinite time limit. We then compute suitable finite-time approximations of the corresponding linear response operator, and compare its response predictions with actual, directly perturbed and measured, responses of the largest Lyapunov exponent. The test dynamical system is a 20-variable Lorenz 96 model, run in weakly, moderately, and strongly chaotic regimes. We observe that the linearized response prediction is a good approximation for the moderately and strongly chaotic regimes, and less so in the weakly chaotic regime due to intrinsic nonlinearity in the response of the Lyapunov exponent, which the linearized approximation is incapable of following.
SEARCHING FOR HYDROGEN IN TYPE Ib SUPERNOVAE

We present synthetic spectral fits of the typical Type Ib SN 1999dn and the hydrogen-rich Ib SN 2000H using the generalized non-local thermodynamic equilibrium stellar atmospheres code PHOENIX. We fit model spectra to five epochs of SN 1999dn ranging from 10 days pre-maximum light to 17 days post-maximum light and to the two earliest epochs of SN 2000H available, maximum light and six days post-maximum. Our goal is to investigate the possibility of hydrogen in Type Ib supernovae (SNe Ib), specifically a feature around 6200 A which has previously been attributed to high-velocity H{alpha}. In earlier work on SN 1999dn we found the most plausible alternative to H{alpha} to be a blend of Si II and Fe II lines which can be adjusted to fit by increasing the metallicity. Our models are simple; they assume a power-law density profile with radius, homologous expansion, and solar compositions. The helium core is produced by 'burning' 4H{yields}He in order to conserve the nucleon number. For models with hydrogen the outer skin of the model consists of a shell of solar composition. The hydrogen mass of the standard solar composition shell is M{sub H} {approx}< 10{sup -3} M{sub sun} in SN 1999dn and M{submore » H} {approx}< 0.2 M{sub sun} for SN 2000H. Our models fit the observed spectra reasonably well, successfully reproducing most features including the characteristic He I absorptions. The hydrogen feature in SN 1999dn is clear, but much more pronounced in SN 2000H. We discuss a possible evolutionary scenario that accounts for the dichotomy in the hydrogen shell mass between these two SNe.« less
Distributed Synthesis of Local Controllers for Networked Systems With Arbitrary Interconnection Topologies

We consider the problem of designing distributed controllers to guarantee dissipativity of a networked system comprised of dynamically coupled subsystems. We require that the control synthesis is carried out locally at the subsystem-level, without explicit knowledge of the dynamics of other subsystems in the network. We solve this problem in two steps. First, we provide distributed subsystem-level dissipativity analysis conditions whose feasibility is sufficient to guarantee dissipativity of the networked system. We then use these conditions to synthesize controllers locally at the subsystem-level, using only the knowledge of the dynamics of that subsystem, and limited information about the dissipativity of the subsystems to which it is dynamically coupled. We show that the subsystem-level controllers synthesized in this manner are sufficient to guarantee dissipativity of the networked dynamical system. We also provide an approach to make this synthesis compositional, that is, when a new subsystem is added to an existing network, only the dynamics of the new subsystem, and information about the dissipativity of the subsystems in the existing network to which it is coupled are used to design a controller for the new subsystem, while guaranteeing dissipativity of the networked system including the new subsystem. Finally, we demonstrate the application of this synthesis in enabling plug-and-play operations of generators in a microgrid by extending our results to networked switched systems.
Varieties of Characters

Let G be a connected reductive affine algebraic group. In this short note we define the variety of G-characters of a finitely generated group Γ and show that the quotient of the G-character variety of Γ by the action of the trace preserving outer automorphisms of G normalizes the variety of G-characters when Γ is a free group, free abelian group, or a surface group.
Learning to decode: Reinforcement learning for decoding of sparse graph-based channel codes

We show in this work that reinforcement learning can be successfully applied to decoding short to moderate length sparse graph-based channel codes. Specifically, we focus on low-density parity check (LDPC) codes, which for example have been standardized in the context of 5G cellular communication systems due to their excellent error correcting performance. These codes are typically decoded via belief propagation iterative decoding on the corresponding bipartite (Tanner) graph of the code via flooding, i.e., all check and variable nodes in the Tanner graph are updated at once. In contrast, in this paper we utilize a sequential update policy which selects the optimum check node (CN) scheduling in order to improve decoding performance. In particular, we model the CN update process as a multi-armed bandit process with dependent arms and employ a Q-learning scheme for optimizing the CN scheduling policy. In order to reduce the learning complexity, we propose a novel graph-induced CN clustering approach to partition the state space in such a way that dependencies between clusters are minimized. Our results show that compared to other decoding approaches from the literature, the proposed reinforcement learning scheme not only significantly improves the decoding performance, but also reduces the decoding complexity dramatically once the scheduling policy is learned.
USING PEANO CURVES TO CONSTRUCT LAPLACIANS ON FRACTALS

We describe a new method to construct Laplacians on fractals using a Peano curve from the circle onto the fractal, extending an idea that has been used in the case of certain Julia sets. The Peano curve allows us to visualize eigenfunctions of the Laplacian by graphing the pullback to the circle. We study in detail three fractals: the pentagasket, the octagasket and the magic carpet. We also use the method for two nonfractal self-similar sets, the torus and the equilateral triangle, obtaining appealing new visualizations of eigenfunctions on the triangle. In contrast to the many familiar pictures of approximations to standard Peano curves, that do no show self-intersections, our descriptions of approximations to the Peano curves have self-intersections that play a vital role in constructing graph approximations to the fractal with explicit graph Laplacians that give the fractal Laplacian in the limit.
BMO spaces associated with semigroups of operators

We study BMO spaces associated with semigroup of operators on noncommutative function spaces (i.e. von Neumann algebras) and apply the results to boundedness of Fourier multipliers on non-abelian discrete groups. We prove an interpolation theorem for BMO spaces and prove the boundedness of a class of Fourier multipliers on noncommutative Lp spaces for all 1 < p < ∞, with optimal constants in p.
THE DISPERSION RELATIONS AND INSTABILITY THRESHOLDS OF OBLIQUE PLASMA MODES IN THE PRESENCE OF AN ION BEAM

An ion beam can destabilize Alfv?n/ion-cyclotron waves and magnetosonic/whistler waves if the beam speed is sufficiently large. Numerical solutions of the hot-plasma dispersion relation have previously shown that the minimum beam speed required to excite such instabilities is significantly smaller for oblique modes with k ? B 0 ? 0 than for parallel-propagating modes with k ? B 0 = 0, where k is the wavevector and B 0 is the background magnetic field. In this paper, we explain this difference within the framework of quasilinear theory, focusing on low-? plasmas. We begin by deriving, in the cold-plasma approximation, the dispersion relation and polarization properties of both oblique and parallel-propagating waves in the presence of an ion beam. We then show how the instability thresholds of the different wave branches can be deduced from the wave-particle resonance condition, the conservation of particle energy in the wave frame, the sign (positive or negative) of the wave energy, and the wave polarization. We also provide a graphical description of the different conditions under which Landau resonance and cyclotron resonance destabilize Alfv?n/ion-cyclotron waves in the presence of an ion beam. We draw upon our results to discuss the types of instabilities that may limit the differential flow of alpha particles in the solar wind.
Does a Standalone, “Cold” (Low-Thermal-Noise), Linear Resistor Exist Without Cooling?

Classical ways of cooling require some of these elements: phase transition, compressor, nonlinearity, valve and/or switch. A recent example is the 2018 patent of Linear Technology Corporation; they...
Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty

Markov state models (MSMs)–or discrete-time master equation models–are a powerful way of modeling the structure and function of molecular systems like proteins. Unfortunately, MSMs with sufficiently many states to make a quantitative connection with experiments (often tens of thousands of states even for small systems) are generally too complicated to understand. Here, I present a Bayesian agglomerative clustering engine (BACE) for coarse-graining such Markov models, thereby reducing their complexity and making them more comprehensible. An important feature of this algorithm is its ability to explicitly account for statistical uncertainty in model parameters that arises from finite sampling. This advance builds on a number of recent works highlighting the importance of accounting for uncertainty in the analysis of MSMs and provides significant advantages over existing methods for coarse-graining Markov state models. The closed-form expression I derive here for determining which states to merge is equivalent to the generalized Jensen-Shannon divergence, an important measure from information theory that is related to the relative entropy. Therefore, the method has an appealing information theoretic interpretation in terms of minimizing information loss. The bottom-up nature of the algorithm likely makes it particularly well suited for constructing mesoscale models. I also present an extremely efficient expression for Bayesian model comparison that can be used to identify the most meaningful levels of the hierarchy of models from BACE.
Hyperscaling violation from supergravity

In recent applications of AdS/CFT to condensed matter physics, a metric that transforms covariantly under dilatation has been argued to signal hyperscaling violation in a dual quantum field theory. We contextualize and introduce large, in some cases infinite, families of supergravity solutions with this property, focusing on scale covariant generalizations of AdS and Schrodinger spacetimes. These embeddings rely on various aspects of dimensional reduction and flux compactification of eleven-dimensional supergravity. Our top-down approach can be viewed as a partial holographic classification of the landscape of strongly coupled, UV complete quantum field theories with hyperscaling violation.
Radiation Pressure Detection and Density Estimate for 2011 MD

We present our astrometric observations of the small near-Earth object 2011 MD (H ∼ 28.0), obtained after its very close fly-by to Earth in 2011 June. Our set of observations extends the observational arc to 73 days, and, together with the published astrometry obtained around the Earth fly-by, allows a direct detection of the effect of radiation pressure on the object, with a confidence of 5σ. The detection can be used to put constraints on the density of the object, pointing to either an unexpectedly low value of ρ=(640±330)kg m{sup −3} (68% confidence interval) if we assume a typical probability distribution for the unknown albedo, or to an unusually high reflectivity of its surface. This result may have important implications both in terms of impact hazard from small objects and in light of a possible retrieval of this target.
The Coulomb branch of (4,4) supersymmetric field theories in two dimensions

We study (4,4) supersymmetric field theories in two dimensions with a one dimensional Coulomb branch. These theories have applications in string theory. Our analysis explains the known relation between A−D−E groups and modular invariants of affine SU(2).