cssi/SciSciGPT-SciSciNet huggingface dataset:
Emergent Elements of Inspiratory Rhythmogenesis: Network Synchronization and Synchrony Propagation

Summary We assessed the mechanism of mammalian breathing rhythmogenesis in the preBotzinger complex (preBotC) in vitro, where experimental tests remain inconsistent with hypotheses of canonical rhythmogenic cellular or synaptic mechanisms, i.e., pacemaker neurons or inhibition. Under rhythmic conditions, in each cycle, an inspiratory burst emerges as (presumptive) preBotC rhythmogenic neurons transition from aperiodic uncorrelated population spike activity to become increasingly synchronized during preinspiration (for ∼50–500 ms), which can trigger inspiratory bursts that propagate to motoneurons. In nonrhythmic conditions, antagonizing GABAA receptors can initiate this synchronization while inducing a higher conductance state in nonrhythmogenic preBotC output neurons. Our analyses uncover salient features of preBotC network dynamics where inspiratory bursts arise when and only when the preBotC rhythmogenic subpopulation strongly synchronizes to drive output neurons. Furthermore, downstream propagation of preBotC network activity, ultimately to motoneurons, is dependent on the strength of input synchrony onto preBotC output neurons exemplifying synchronous propagation of network activity.
A time-dependent radiative model for the atmosphere of the eccentric transiting planets

We present a time-dependent radiative model for the atmosphere of the transiting planets that take into account the eccentricity of their orbit. We investigate the temporal tem- perature and flux variations due to the planet-star distance variability. We will also discuss observational aspects with Spitzer measurements.
Inhibition of the polyamine synthesis enzyme ornithine decarboxylase sensitizes triple-negative breast cancer cells to cytotoxic chemotherapy

Treatment of patients with triple-negative breast cancer (TNBC) is limited by a lack of effective molecular therapies targeting this disease. Recent studies have identified metabolic alterations in cancer cells that can be targeted to improve responses to standard-of-care chemotherapy regimens. Using MDA-MB-468 and SUM-159PT TNBC cells, along with LC-MS/MS and HPLC metabolomics profiling, we found here that exposure of TNBC cells to the cytotoxic chemotherapy drugs cisplatin and doxorubicin alter arginine and polyamine metabolites. This alteration was because of a reduction in the levels and activity of a rate-limiting polyamine biosynthetic enzyme, ornithine decarboxylase (ODC). Using gene silencing and inhibitor treatments, we determined that the reduction in ODC was mediated by its negative regulator antizyme, targeting ODC to the proteasome for degradation. Treatment with the ODC inhibitor difluoromethylornithine (DFMO) sensitized TNBC cells to chemotherapy, but this was not observed in receptor-positive breast cancer cells. Moreover, TNBC cell lines had greater sensitivity to single-agent DFMO, and ODC levels were elevated in TNBC patient samples. The alterations in polyamine metabolism in response to chemotherapy, as well as DFMO-induced preferential sensitization of TNBC cells to chemotherapy, reported here suggest that ODC may be a targetable metabolic vulnerability in TNBC.
Optimal Corrective Dispatch of Uncertain Virtual Energy Storage Systems

High penetrations of intermittent renewable energy resources in the power system require large balancing reserves for reliable operations. Aggregated and coordinated behind-the-meter loads can provide these fast reserves, but represent energy-constrained and uncertain reserves (in their energy state and capacity). To optimally dispatch uncertain, energy-constrained reserves, optimization-based techniques allow one to develop an appropriate trade-off between closed-loop performance and robustness of the dispatch. Therefore, this paper investigates the uncertainty associated with energy-constrained aggregations of flexible, behind-the-meter distributed energy resources (DERs). The uncertainty studied herein is associated with estimating the state of charge and the capacity of an aggregation of DERs (i.e., a virtual energy storage system or VESS). To that effect, a risk-based chance constrained control strategy is developed that optimizes the operational risk of unexpectedly saturating the VESS against deviating generators from their scheduled set-points. The controller coordinates energy-constrained VESSs to minimize unscheduled participation of and overcome ramp-rate limited generators for balancing variability from renewable generation, while taking into account grid conditions. To illustrate the effectiveness of the proposed method, simulation-based analysis is carried out on an augmented IEEE RTS-96 network with uncertain energy resources and temperature-based dynamic line ratings.
The subjectivist-contrarian position

James Buchanan endorses a radical version of subjectivism at several points in his body of work. I distinguish two distinct criteria for subjectivism in Buchanan. First, Buchanan rejects the realism of preferences, arguing that they serve only as an analytical tool for understanding choice. Second, Buchanan adopts an Austrian perspective inspired by Kirzner and Shackle on the importance of creativity. In addition to reconstructing Buchanan’s subjectivism, I examine how it relates to more recent work in political philosophy, behavioral economics, and psychology in order to ascertain the value Buchanan’s radical approach might still hold.
Well-posedness for degenerate elliptic PDE arising in optimal learning strategies

We derive a comparison principle for a degenerate elliptic partial differential equation without boundary conditions which arises naturally in optimal learning strategies. Our argument is direct and exploits the degeneracy of the differential operator to construct (logarithmically) diverging barriers.
Deep Learning for Massive MIMO With 1-Bit ADCs: When More Antennas Need Fewer Pilots

This letter considers uplink massive MIMO systems with 1-bit analog-to-digital converters (ADCs) and develops a deep-learning based channel estimation framework. In this framework, the prior channel estimation observations and deep neural networks are leveraged to learn the non-trivial mapping from quantized received measurements to channels. For that, we derive the sufficient length and structure of the pilot sequence to guarantee the existence of this mapping function. This leads to the interesting, and counter-intuitive , observation that when more base-station antennas are employed, our proposed deep learning approach achieves better channel estimation performance, for the same pilot sequence length. Equivalently, for the same channel estimation performance, this means that when more antennas are employed, fewer pilots are required. This observation is also analytically proved for some special channel models. Simulation results confirm our observations and show that more antennas lead to better channel estimation in terms of the normalized mean squared error and the receive signal-to-noise ratio per antenna.
Holographic Space-time and Quantum Information

The formalism of Holographic Space-time (HST) is a translation of the principles of Lorentzian geometry into the language of quantum information. Intervals along time-like trajectories, and their associated causal diamonds, completely characterize a Lorentzian geometry. The Bekenstein-Hawking-'t Hooft-Jacobson-Fischler-Susskind-Bousso Covariant Entropy Principle, equates the logarithm of the dimension of the Hilbert space associated with a diamond to one quarter of the area of the diamond's holographic screen, measured in Planck units. The most convincing argument for this principle is Jacobson's derivation of Einstein's equations as the hydrodynamic expression of this entropy law. In that context, the null energy condition (NEC) is seen to be the analog of the local law of entropy increase. The quantum version of Einstein's relativity principle is a set of constraints on the mutual quantum information shared by causal diamonds along different time-like trajectories. The implementation of this constraint for trajectories in relative motion is the greatest unsolved problem in HST. The other key feature of HST is its claim that, the degrees of freedom localized in the bulk of a diamond are constrained states of variables defined on the holographic screen. This principle gives a simple explanation of otherwise puzzling features of BH entropy formulae, and resolves the firewall problem for black holes in Minkowski space. It motivates a covariant version of the CKN\cite{ckn} bound on the regime of validity of quantum field theory (QFT) and a detailed picture of the way in which QFT emerges as an approximation to the exact theory.
Chapter 7 – Statistical Physics

Statistical physics is based on the idea that the probability or statistical weight of a particular macroscopic state depends upon the number of possible ways the state can be constructed out of its microscopic constituents. The number of distinct ways a macroscopic state can be formed depends in turn upon whether or not the constituents are distinguishable. In Maxwell-Boltzmann’s statistics, the constituents are distinguishable, and the number of ways a particular distribution of particles can be constructed depends upon the number of ways the particles can be selected for each energy range and how many different ways the particles can then be assigned to the microscopic states in that range. Maxwell-Boltzmann statistics leads to the distribution law nrgr=NZe−ϵr/kBT, where nr is the number of particles with energy ϵr and gr is the number of microscopic states with energy ϵr. The partition function Z is Z=∑rgre−ϵr/kBT. This form of statistics can be used to describe the velocity and energy distributions of the particles of a gas and to derive Planck’s formula for black-body radiation. Macroscopic systems composed of indistinguishable components are described by two other kinds of statistics. Bose-Einstein statistics applies when the component parts of a macroscopic system do not satisfy the Pauli exclusion principle and Fermi-Dirac statistics apply when the components do satisfy the Pauli exclusion principle. These two forms of statistics lead to the following distribution laws nrgr=1eαeϵr/kBT−1(Bose-Einstein), nrgr=1eαeϵr/kBT+1(Fermi-Dirac). An important connection has been established between the intrinsic angular momentum or spin of particles and the form of statistics they follow. Particles with zero or integral spin follow Bose-Einstein statistics, while particles with half-integer spin follow Fermi-Dirac statistics. Bose-Einstein statistics can be used to describe the radiation field within a black-body and the collective phenomena associated with the condensation of a macroscopic system into its lowest quantum state. Fermi-Dirac statistics enables us to understand how the electronic characteristics of metals and semiconductors are related to the dynamical properties of their charge carriers.
Tests of Conditional Predictive Ability

We propose a framework for out-of-sample predictive ability testing and forecast selection designed for use in the realistic situation in which the forecasting model is possibly misspecified, due to unmodeled dynamics, unmodeled heterogeneity, incorrect functional form, or any combination of these. Relative to the existing literature (Diebold and Mariano (1995) and West (1996)), we introduce two main innovations: (i) We derive our tests in an environment where the finite sample properties of the estimators on which the forecasts may depend are preserved asymptotically. (ii) We accommodate conditional evaluation objectives (can we predict which forecast will be more accurate at a future date?), which nest unconditional objectives (which forecast was more accurate on average?), that have been the sole focus of previous literature. As a result of (i), our tests have several advantages: they capture the effect of estimation uncertainty on relative forecast performance, they can handle forecasts based on both nested and nonnested models, they allow the forecasts to be produced by general estimation methods, and they are easy to compute. Although both unconditional and conditional approaches are informative, conditioning can help fine-tune the forecast selection to current economic conditions. To this end, we propose a two-step decision rule that uses current information to select the best forecast for the future date of interest. We illustrate the usefulness of our approach by comparing forecasts from leading parameter-reduction methods for macroeconomic forecasting using a large number of predictors.
Chapter 22 – Confidentiality

This chapter looks at the Data Protection Act and other legislation governing confidential information. It explains how information may be shared, for example for research. It deals with the ethical obligations placed on pharmacists and looks at when disclosure may be required by law.
Chapter 3 – Developmental Systems

Multicellular organisms begin their lives as single cells that divide to form groups of cells that undergo progressive morphological changes. Although they are all genetically identical, successive daughter cells give rise to all the diverse cell types that will constitute the multicellular insect. The various cells that make up the compound eye are substantially different from the cells that are found in the Malpighian tubules, yet they all are derived from the same single cell and express differing portions of the genome. The mechanism of transforming the simple, unorganized egg into a complex and highly ordered multicellular organism has long fascinated biologists.
Split sample empirical likelihood

Abstract Empirical likelihood offers a nonparametric approach to estimation and inference, which replaces the probability density-based likelihood function with a function defined by estimating equations. While this eliminates the need for a parametric specification, the restriction of numerical optimization greatly decreases the applicability of empirical likelihood for large data problems. A solution to this problem is the split sample empirical likelihood; this variant utilizes a divide and conquer approach, allowing for parallel computation of the empirical likelihood function. The results show the asymptotic distribution of the estimators and test statistics derived from the split sample empirical likelihood are the same seen in standard empirical likelihood yet have significantly decreased computational times.
Propagation of singularities for the wave equation on edge manifolds

We investigate the geometric propagation and diffraction of singularities of solutions to the wave equation on manifolds with edge singularities. This class of manifolds includes, and is modeled on, the product of a smooth manifold and a cone over a compact fiber. Our main results are a general diffractive theorem showing that the spreading of singularities at the edge only occurs along the fibers and a more refined geometric theorem showing that for appropriately regular (nonfocusing) solutions, the main singularities can only propagate along geometrically determined rays. Thus, for the fundamental solution with initial pole sufficiently close to the edge, we are able to show that the regularity of the diffracted front is greater than that of the incident wave
A Pareto Model for OLAP View Size Estimation

On-Line Analytical Processing (OLAP) aims at gaining useful information quickly from large amounts of data residing in a data warehouse. To improve the quickness of response to queries, pre-aggregation is a useful strategy. However, it is usually impossible to pre-aggregate along all combinations of the dimensions. The multi-dimensional aspects of the data lead to combinatorial explosion in the number and potential storage size of the aggregates. We must selectively pre-aggregate. Cost/benefit analysis involves estimating the storage requirements of the aggregates in question. We present an original algorithm for estimating the number of rows in an aggregate based on the Pareto distribution model. We test the Pareto Model Algorithm empirically against four published algorithms, and conclude the Pareto Model Algorithm is consistently the best of these algorithms for estimating view size.
Some transformational extensions of Montague grammar

Richard Montague’s work on English, as represented in Montague (1970a), (1970b), (1972), represents the first systematic attempt to apply the logician’s methods of formal syntax and semantics to natural language. With few exceptions,1 linguists and logicians had previously been agreed, although for different reasons, that the apparatus developed by logicians for treating the syntax and semantics of artificially constructed formal languages, while obviously fruitful within its restricted domain, was not in any direct way applicable to the analysis of natural languages. Logicians seem to have felt that natural languages were too unsystematic, too full of vagueness and ambiguity, to be amenable to their rigorous methods, or if susceptible to formal treatment, only at great cost.2 Linguists, on the other hand, emphasize their own concern for psychological reality, and the logicians’ lack of it, in eschewing the logicians’ approach: linguists, at least those of the Chomskyan school, are searching for a characterization of the class of possible human languages, hoping to gain thereby some insight into the structure of the mind, and the formal languages constructed by logicians appear to depart radically from the structures common to actual natural languages.
Chapter 10 – Advanced Functions

The chapter, “Advanced Functions” covers more advanced features of and types of functions. The concept of having a variable number of input and/or output arguments to a function is introduced; this is implemented using cell arrays. Function handles, and their use both with anonymous functions and function functions are introduced. Recursive functions are introduced and examples of recursive functions that return values and that only print are demonstrated.
COVID-19 Worries and Behavior Changes in Older and Younger Men and Women.

OBJECTIVES: The case fatality rate of COVID-19 is higher amongst older adults than younger adults and is also higher amongst men than women. However, worry, which is a key motivator of behavioral health changes, occurs less frequently for older than younger adults, and less frequently for men than women. Building on this, we tested whether older adults - and particularly older men -- would report the least amount of COVID-19 worry and also fewer COVID-19 behavior changes. METHODS: From March 23-31, 2020, we administered an online questionnaire assessing COVID-19 perceptions, worries, and behavior changes. Participants were a convenience sample of United States residents, who were community-dwelling younger adults (18-35) or older adults (65 to 81). Analyses included 146 younger adults (68 men, 78 women) and 156 older adults (82 men, 74 women). Participants was predominately White, living in suburban/urban areas, and had completed some college. RESULTS: During the early phase of the outbreak in the United States, older adults perceived the risks of COVID-19 to be higher than did younger adults. Despite this, older men were comparatively less worried about COVID-19 than their younger counterparts. Compared to the other participants, older men had also implemented the fewest behavior changes. DISCUSSION: Interventions are needed to increase COVID-19 behavior changes in older men. These results also highlight the importance of understanding emotional-responses to COVID-19, as these are predictive of their behavioral responses.
Effects of ESD transients on the properties of GMR heads

Abstract Simulated ESD transients with pulsewidths from 0.1 to 10 ns were applied to GMR heads. From 1 to 10 ns essentially no changes in failure voltages are observed, but below approximately 1 ns, failure voltages increase. The time marking a transition between the two regimes, ∼1 ns, and the dependence of failure voltage on either side are qualitatively consistent with the expected transition from adiabatic failure for short pulses to a more steady-state thermal failure for longer pulses, but true adiabatic behavior is not clear for short pulses. The ratio in voltages inducing resistance versus amplitude failure remains constant over all pulsewidths and for both voltage polarities, suggesting thermal degradation is dominant. Although magnetic failures occur at lower voltages than resistive, no observations here suggest that either the magnitude or direction of the pulse-induced magnetic field is a critical parameter in head failure.
A Quaternion-Based Approach to Construct Quaternary Periodic Complementary Pairs

Two arrays form a periodic complementary pair if the sum of their periodic autocorrelations is a delta function. Finding such pairs, however, is challenging for large arrays whose entries are constrained to a small alphabet. One such alphabet is the quaternary set which contains the complex fourth roots of unity. In this letter, we propose a technique to construct periodic complementary pairs defined over the quaternary set using perfect quaternion arrays. We show how new pairs of quaternary sequences, matrices, and four-dimensional arrays that satisfy a periodic complementary property can be constructed with our method.
Linear magnetoresistance induced by intra-scattering semiclassics of Bloch electrons

Weak-field magnetoresistance has seen a revived interest due to the distinct role played by the momentum-space Berry curvature of Bloch electrons. While most previous studies in this regard focus on the inter-scattering motion of semiclassical Bloch electrons in electromagnetic fields, the intra-scattering effects of the semiclassical dynamics augmented by the Berry curvature, magnetic moment, and shift vector on the magnetoresistance have been largely overlooked. Here, we uncover that these intra-scattering effects, which are neglected in the field-independent relaxation time approximation to the Boltzmann collision integral, can be as important as the inter-scattering ones. Concrete calculations on the two-dimensional gapped Dirac model show that the sign of the negative linear magnetoresistance given by the Berry curvature alone is reversed when one considers the magnetic moment and shift vector.
Identification and estimation of the SEIRD epidemic model for COVID-19.

Abstract This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run forecasts. Second, I show that the basic reproduction number R 0 can be identified from the data, conditional on epidemiologic parameters, and propose several nonlinear SUR approaches to estimate R 0 . I examine the performance of these methods using Monte Carlo studies and demonstrate that they yield fairly accurate estimates of R 0 . Next, I apply these methods to estimate R 0 for the US, California, and Japan, and document heterogeneity in the value of R 0 across regions. My estimation approach accounts for possible underreporting of the number of cases. I demonstrate that if one fails to take underreporting into account and estimates R 0 from the reported cases data, the resulting estimate of R 0 may be biased downward and the resulting forecasts may exaggerate the long run number of deaths. Finally, I discuss how auxiliary information from random tests can be used to calibrate the initial parameters of the model and narrow down the range of possible forecasts of the future number of deaths.
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring

In multimodal traffic monitoring, we gather traffic statistics for distinct transportation modes, such as pedestrians, cars and bicycles, in order to analyze and improve people's daily mobility in terms of safety and convenience. On account of its robustness to bad light and adverse weather conditions, and inherent speed measurement ability, the radar sensor is a suitable option for this application. However, the sparse radar data from conventional commercial radars make it extremely challenging for transportation mode classification. Thus, we propose to use a high-resolution millimeter-wave(mmWave) radar sensor to obtain a relatively richer radar point cloud representation for a traffic monitoring scenario. Based on a new feature vector, we use the multivariate Gaussian mixture model (GMM) to do the radar point cloud segmentation, i.e. ‘point-wise’ classification, in an unsupervised learning environment. In our experiment, we collected radar point clouds for pedestrians and cars, which also contained the inevitable clutter from the surroundings. The experimental results using GMM on the new feature vector demonstrated a good segmentation performance in terms of the intersection-over-union (IoU) metrics. The detailed methodology and validation metrics are presented and discussed.
FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds.

Predicting RNA three-dimensional structures from sequence could accelerate understanding of the growing number of RNA molecules being discovered across biology. Rosetta's Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) has shown promise in community-wide blind RNA-Puzzle trials, but lack of a systematic and automated benchmark has left unclear what limits FARFAR performance. Here, we benchmark FARFAR2, an algorithm integrating RNA-Puzzle-inspired innovations with updated fragment libraries and helix modeling. In 16 of 21 RNA-Puzzles revisited without experimental data or expert intervention, FARFAR2 recovers native-like structures more accurate than models submitted during the RNA-Puzzles trials. Remaining bottlenecks include conformational sampling for >80-nucleotide problems and scoring function limitations more generally. Supporting these conclusions, preregistered blind models for adenovirus VA-I RNA and five riboswitch complexes predicted native-like folds with 3- to 14 A root-mean-square deviation accuracies. We present a FARFAR2 webserver and three large model archives (FARFAR2-Classics, FARFAR2-Motifs, and FARFAR2-Puzzles) to guide future applications and advances.
Learning Geocentric Object Pose in Oblique Monocular Images

An object's geocentric pose, defined as the height above ground and orientation with respect to gravity, is a powerful representation of real-world structure for object detection, segmentation, and localization tasks using RGBD images. For close-range vision tasks, height and orientation have been derived directly from stereo-computed depth and more recently from monocular depth predicted by deep networks. For long-range vision tasks such as Earth observation, depth cannot be reliably estimated with monocular images. Inspired by recent work in monocular height above ground prediction and optical flow prediction from static images, we develop an encoding of geocentric pose to address this challenge and train a deep network to compute the representation densely, supervised by publicly available airborne lidar. We exploit these attributes to rectify oblique images and remove observed object parallax to dramatically improve the accuracy of localization and to enable accurate alignment of multiple images taken from very different oblique viewpoints. We demonstrate the value of our approach by extending two large-scale public datasets for semantic segmentation in oblique satellite images. All of our data and code are publicly available.
High-Toughness Inorganic Solid Electrolytes via the Use of Reduced Graphene Oxide

Summary Ceramic solid electrolytes are important emerging materials with the potential to enable the safe use of Li-metal anodes. However, they suffer from inherently low fracture toughness, which significantly limits battery performance and reliability. While small electrolyte dimensions are generally needed for faster ion transport, these length scales also restrict the approaches that can be used to engineer higher fracture resistance. Inspired by the toughening that reduced graphene oxide provides to polymers and engineering ceramics, this study explores the use of rGO to enhance the toughness of an oxide-based lithium-ion conductor. Materials with a greater than 2-fold enhancement in the average KIC are demonstrated. To our knowledge, this is the toughest ceramic solid electrolyte yet reported. Based on these results, an analytical framework is developed to provide guidelines for the design of ultra-tough solid electrolytes using 2D materials.
Engaging undergraduate students in computational chemistry research: A tutorial for new assistant professors

In this article, we provide advice and insights, based on our own experiences, for computational chemists who are beginning new tenure-track positions at primarily undergraduate institutions. We each followed different routes to obtain our tenure-track positions, but we all experienced similar challenges when getting started in our new position. In this article, we discuss our approaches to seven areas that we all found important for engaging undergraduate students in our computational chemistry research, including setting up computational resources, recruiting research students, training research students, designing student projects, managing the lab, mentoring students, and student conference participation. KEYWORDS — undergraduate research, computational chemistry, primarily undergraduate institution, tenure-track position, career pathways
Dynamic Geometric Set Cover and Hitting Set

We investigate dynamic versions of geometric set cover and hitting set where points and ranges may be inserted or deleted, and we want to efficiently maintain an (approximately) optimal solution for the current problem instance. While their static versions have been extensively studied in the past, surprisingly little is known about dynamic geometric set cover and hitting set. For instance, even for the most basic case of one-dimensional interval set cover and hitting set, no nontrivial results were known. The main contribution of our paper are two frameworks that lead to efficient data structures for dynamically maintaining set covers and hitting sets in ℝ¹ and ℝ². The first framework uses bootstrapping and gives a (1+e)-approximate data structure for dynamic interval set cover in ℝ¹ with O(n^α/e) amortized update time for any constant α > 0; in ℝ², this method gives O(1)-approximate data structures for unit-square (and quadrant) set cover and hitting set with O(n^(1/2+α)) amortized update time. The second framework uses local modification, and leads to a (1+e)-approximate data structure for dynamic interval hitting set in ℝ¹ with O(1/e) amortized update time; in ℝ², it gives O(1)-approximate data structures for unit-square (and quadrant) set cover and hitting set in the partially dynamic settings with O(1) amortized update time.
Adaptive level set topology optimization using hierarchical B-splines

This paper presents an adaptive discretization strategy for level set topology optimization of structures based on hierarchical B-splines. This work focuses on the influence of the discretization approach and the adaptation strategy on the optimization results and computational cost. The geometry of the design is represented implicitly by the iso-contour of a level set function. The extended finite element method is used to predict the structural response. The level set function and the state variable fields are discretized by hierarchical B-splines. While first-order B-splines are used for the state variable fields, up to third-order B-splines are considered for discretizing the level set function. The discretizations of the design and the state variable fields are locally refined along the material interfaces and selectively coarsened within the bulk phases. For locally refined meshes, truncated B-splines are considered. The properties of the proposed mesh adaptation strategy are studied for level set topology optimization where either the initial design is comprised of a uniform array of inclusions or inclusions are generated during the optimization process. Numerical studies employing static linear elastic material/void problems in 2D and 3D demonstrate the ability of the proposed method to start from a coarse mesh and converge to designs with complex geometries, reducing the overall computational cost. Comparing optimization results for different B-spline orders suggests that higher interpolation order promote the development of smooth designs and suppress the emergence of small features, without providing an explicit feature size control. A distinct advantage of cubic over quadratic B-splines is not observed.
CEO outside directorships and managerial efficiency: The role of host board capital

Research Question/Issue Do CEO outside board directorships improve or reduce CEO managerial efficiency? What is the role of host board capital in this relationship? Research Findings/Insights We explore the value of CEO outside directorships on managerial efficiency by identifying a unique empirical setting where a CEO's number of outside board seats is exogenously decreased by a merger that eliminates the board of a host firm the CEO is serving on. Using this event as our empirical instrument, we find that outside directorships decrease a CEO's managerial efficiency while host board capital reduces this effect. Theoretical/Academic Implications Proponents view outside board service as a valuable leadership development tool to mentor the CEO while others promulgating an agency view argue that outside board service detracts from the CEO's ability to improve the efficiency of the home firm as it leads to busyness of the CEO. We shed light on this debate by arguing that CEO outside directorships might reduce CEO's managerial efficiency; however, this effect might be contingent on the characteristics of the host board. Practitioner/Policy Implications Our findings offer insights to practitioners about the value of multiple directorships and the importance of the host board capital while informing policy makers about the potential restrictions on outside board assignments.
Game Theory Cancer Models of Cancer Cell-Stromal Cell Dynamics using Interacting Particle Systems

We describe an evolutionary game theory model that has been used to predict the population dynamics of interacting cancer and stromal cells. We first consider the mean field case assuming homogeneo...
Enumeration of planar Tangles

A planar Tangle is a smooth simple closed curve piecewise defined by quadrants of circles with constant curvature. We can enumerate Tangles by counting their dual graphs, which consist of a certain family of polysticks. The number of Tangles with a given length or area grows exponentially, and we show the existence of their growth constants by comparing Tangles to two families of polyominoes.
Regional Moment Tensor Inversion Using Rotational Observations

There are benefits from the addition of rotational motions to translational displacements for moment tensor (MT) inversions. The rotational radiation pattern is orthogonal to the shear radiation pa...
Social Learning with Beliefs in a Parallel Network

Consider a social learning problem in a parallel network, where N distributed agents make independent selfish binary decisions, and a central agent aggregates them together with a private signal to make a final decision. In particular, all agents have private beliefs for the true prior, based on which they perform binary hypothesis testing. We focus on the Bayes risk of the central agent, and counterintuitively find that a collection of agents with incorrect beliefs could outperform a set of agents with correct beliefs. We also consider many-agent asymptotics (i.e., N is large) when distributed agents all have identical beliefs, for which it is found that the central agent's decision is polarized and beliefs determine the limit value of the central agent's risk. Moreover, it is surprising that when all agents believe a certain prior-agnostic constant belief, it achieves globally optimal risk as N→∞.
Spectral extended finite element method for band structure calculations in phononic crystals

Abstract In this paper, we compute the band structure of one- and two-dimensional phononic composites using the extended finite element method (X-FEM) on structured higher-order (spectral) finite element meshes. On using partition-of-unity enrichment in finite element analysis, the X-FEM permits use of structured finite element meshes that do not conform to the geometry of holes and inclusions. This eliminates the need for remeshing in phononic shape optimization and topology optimization studies. In two dimensions, we adopt a rational Bezier representation of curved (circular) geometries, and construct suitable material enrichment functions to model two-phase composites. A Bloch-formulation of the elastodynamic phononic eigenproblem is adopted. Efficient computation of weak form integrals with polynomial integrands is realized via the homogeneous numerical integration scheme—a method that uses Euler's homogeneous function theorem and Stokes's theorem to reduce integration to the boundary of the domain. Ghost penalty stabilization is used on finite elements that are cut by a hole. Band structure calculations on perforated (circular holes, elliptical holes, and holes defined as a level set) materials as well as on two-phase phononic crystals are presented that affirm the sound accuracy and optimal convergence of the method on structured, higher-order spectral finite element meshes. Several numerical examples are presented to demonstrate the advantages of p-refinement made possible by the spectral extended finite element method. In these examples, fourth-order spectral extended finite elements deliver O ( 10 − 8 ) accuracy in frequency calculations with more than thirty-fold fewer degrees-of-freedom when compared to quadratic finite elements.
Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance

Balanced excitation and inhibition is widely observed in cortex. How does this balance shape neural computations and stimulus representations? This question is often studied using computational models of neuronal networks in a dynamically balanced state. But balanced network models predict a linear relationship between stimuli and population responses. So how do cortical circuits implement nonlinear representations and computations? We show that every balanced network architecture admits stimuli that break the balanced state and these breaks in balance push the network into a “semi-balanced state” characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state produces nonlinear stimulus representations and nonlinear computations, is unavoidable in networks driven by multiple stimuli, is consistent with cortical recordings, and has a direct mathematical relationship to artificial neural networks. Several studies show that neurons in the cerebral cortex receive an approximate balance between excitatory (positive) and inhibitory (negative) synaptic input. What are the implications of this balance on neural representations? Earlier studies develop the theory of a “balanced state” that arises naturally in large scale computational models of neural circuits. This balanced state encourages simple, linear relationships between stimuli and neural responses. However, we know that the cortex must implement nonlinear representations. We show that the classical balanced state is fragile and easily broken in a way that produces a new state, which we call the “semi-balanced state.” In this semi-balanced state, input to some neurons is imbalanced by excessive inhibition—which transiently silences these neurons—but no neurons receive excess excitation and balance is maintained the sub-network of non-silenced neurons. We show that stimulus representations in the semi-balanced state are nonlinear, improve the network’s computational power, and have a direct relationship to artificial neural networks widely used in machine learning.
Mixed Effects Envelope Models

When multiple measures are collected repeatedly over time, redundancy typically exists among responses. The envelope method was recently proposed to reduce the dimension of responses without loss of information in regression with multivariate responses. It can gain substantial efficiency over the standard least squares estimator. In this paper, we generalize the envelope method to mixed effects models for longitudinal data with possibly unbalanced design and time-varying predictors. We show that our model provides more efficient estimators than the standard estimators in mixed effects models. Improved accuracy and efficiency of the proposed method over the standard mixed effects model estimator are observed in both the simulations and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study.
Entrainment and Dynamics of Ocean‐derived Impurities within Europa’s Ice Shell

Compositional heterogeneities within Europa’s ice shell likely impact the dynamics and habitability of the ice and subsurface ocean, but the total inventory and distribution of impurities within th...
Structure of numerical algorithms and advanced mechanics

Most elementary numerical schemes found useful for solving classical trajectory problems are canonical transformations. This fact should be made more widely known among teachers of computational physics and Hamiltonian mechanics. From the perspective of advanced mechanics, unlike that of numerical schemes, there are no bewildering number of seemingly arbitrary elementary schemes based on Taylor's expansion. There are only two canonical first and second order algorithms, on the basis of which one can comprehend the structures of higher order symplectic and non-symplectic schemes. This work shows that most elementary algorithms up to the fourth-order can be derived from canonical transformations and Poisson brackets of advanced mechanics.
The Liquid Metallic Hydrogen Model of the Sun and the Solar Atmosphere IV. On the Nature of the Chromosphere

The chromosphere is the site of weak emission lines characterizing the flash spectrum observed for a few seconds during a total eclipse. This layer of the solar atmosphere is known to possess an opaque Hemission and a great number of spicules, which can extend well above the photosphere. A stunning variety of hydrogen emission lines have been observed in this region. The production of these lines has provided the seventeenth line of evidence that the Sun is comprised of condensed matter (Robitaille P.M. Liquid Metallic Hydrogen II: A critical assessment of current and primordial helium levels in Sun. Progr. Phys., 2013, v. 2, 35-47). Contrary to the gaseous solar models, the simplest mechanism for the production of emission lines is the evaporation of excited atoms from condensed surfaces existing within the chromosphere, as found in spicules. This is reminiscent of the chemiluminescence which occurs during the condensation of silver clusters (Konig L., Rabin I., Schultze W., and Ertl G. Chemiluminescence in the Agglomeration of Metal Clusters. Science, v. 274, no. 5291, 1353-1355). The process associated with spicule formation is an exothermic one, requiring the transport of energy away from the site of condensation. As atoms leave localized surfaces, their electrons can occupy any energy level and, hence, a wide variety of emission lines are produced. In this regard, it is hypothesized that the presence of hydri des on the Sun can also fa- cilitate hydrogen condensation in the chromosphere. The associated line emission from main group and transition elements constitutes the thirtie th line of evidence that the Sun is condensed matter. Condensation processes also help to explain why spicules manifest an apparently constant temperature over their entire lengt h. Since the corona supports magnetic field lines, the random orientations associated wi th spicule formation suggests that the hydrogen condensates in the chromosphere are not metallic in nature. Spicules provide a means, not to heat the corona, but rather, for condensed hydrogen to rejoin the photospheric layer of the Sun. Spicular velocities of fo rmation are known to be essentially independent of gravitational effects and highly supportive of the hypothesis that true condensation processes are being observed. The presence of spicules brings into question established chromospheric densities and provides additional support for condensation processes in the chromosphere, the seventh line of evidence that the Sun is comprised of condensed matter. In order to explain the occurrence of the dark lines in the solar spectrum, we must assume that the solar atmosphere incloses a luminous nucleus, producing a continuous spectrum, the brightness of which ex- ceeds a certain limit. The most probable supposi- tion which can be made respecting the Sun's consti- tution is, that it consists of a solid or liquid nucleus, heated to a temperature of the brightest whiteness, surrounded by an atmosphere of somewhat lower temperature.
Wronskian-type formula for inhomogeneous TQ equations

It is known that the transfer-matrix eigenvalues of the isotropic open Heisenberg quantum spin-1/2 chain with nondiagonal boundary magnetic fields satisfy a $$TQ$$ equation with an inhomogeneous term. We derive a discrete Wronskian-type formula relating a solution of this inhomogeneous $$TQ$$ equation to the corresponding solution of a dual inhomogeneous $$TQ$$ equation.
Role of diversity-generating retroelements for regulatory pathway tuning in cyanobacteria

Cyanobacteria maintain extensive repertoires of regulatory genes that are vital for adaptation to environmental stress. Some cyanobacterial genomes have been noted to encode diversity-generating retroelements (DGRs), which promote protein hypervariation through localized retrohoming and codon rewriting in target genes. Past research has shown DGRs to mainly diversify proteins involved in cell-cell attachment or viral-host attachment within viral, bacterial, and archaeal lineages. However, these elements may be critical in driving variation for proteins involved in other core cellular processes. Members of 31 cyanobacterial genera encode at least one DGR, and together, their retroelements form a monophyletic clade of closely-related reverse transcriptases. This class of retroelements diversifies target proteins with unique domain architectures: modular ligand-binding domains often paired with a second domain that is linked to signal response or regulation. Comparative analysis indicates recent intragenomic duplication of DGR targets as paralogs, but also apparent intergenomic exchange of DGR components. The prevalence of DGRs and the paralogs of their targets is disproportionately high among colonial and filamentous strains of cyanobacteria. We find that colonial and filamentous cyanobacteria have recruited DGRs to optimize a ligand-binding module for apparent function in signal response or regulation. These represent a unique class of hypervariable proteins, which might offer cyanobacteria a form of plasticity to adapt to environmental stress. This analysis supports the hypothesis that DGR-driven mutation modulates signaling and regulatory networks in cyanobacteria, suggestive of a new framework for the utility of localized genetic hypervariation.
Is there valuable private information in credit ratings

Abstract Credit rating agencies (CRAs) contend their ratings contain a quantitative assessment based on hard information, and a qualitative adjustment based on private information. We study if the qualitative portion of ratings, generated with the companies’ own private information, contains valuable information for equity investors. We generate predicted ratings based on hard information alone and form portfolios of stocks based on the difference between observed and predicted ratings. Over a sample from 1998 to 2018, we find that stock portfolios formed on the basis of private information generate 2% to 4% in annual risk-adjusted returns. We also find that companies with positive private information have better future accounting performance. Our results suggest that CRAs bring valuable information to the market and investors could benefit from it.