cssi/SciSciGPT-SciSciNet huggingface dataset:
On the iterated estimation of dynamic discrete choice games

We study the first-order asymptotic properties of a class of estimators of the structural parameters in dynamic discrete choice games. We consider K-stage policy iteration (PI) estimators, where K denotes the number of PIs employed in the estimation. This class nests several estimators proposed in the literature. By considering a “pseudo likelihood” criterion function, our estimator becomes the K-pseudo maximum likelihood (PML) estimator in Aguirregabiria and Mira (2002, 2007). By considering a “minimum distance” criterion function, it defines a new K-minimum distance (MD) estimator, which is an iterative version of the estimators in Pesendorfer and Schmidt-Dengler (2008) and Pakes et al. (2007). First, we establish that the K-PML estimator is consistent and asymptotically normal for any K∈N. This complements findings in Aguirregabiria and Mira (2007), who focus on K=1 and K large enough to induce convergence of the estimator. Furthermore, we show under certain conditions that the asymptotic variance of the K-PML estimator can exhibit arbitrary patterns as a function of K. Second, we establish that the K-MD estimator is consistent and asymptotically normal for any K∈N. For a specific weight matrix, the K-MD estimator has the same asymptotic distribution as the K-PML estimator. Our main result provides an optimal sequence of weight matrices for the K-MD estimator and shows that the optimally weighted K-MD estimator has an asymptotic distribution that is invariant to K. The invariance result is especially unexpected given the findings in Aguirregabiria and Mira (2007) for K-PML estimators. Our main result implies two new corollaries about the optimal 1-MD estimator (derived by Pesendorfer and Schmidt-Dengler (2008)). First, the optimal 1-MD estimator is efficient in the class of K-MD estimators for all K∈N. In other words, additional PIs do not provide first-order efficiency gains relative to the optimal 1-MD estimator. Second, the optimal 1-MD estimator is more or equally efficient than any K-PML estimator for all K∈N. Finally, the Appendix provides appropriate conditions under which the optimal 1-MD estimator is efficient among regular estimators.
Indistinguishability Obfuscation from Simple-to-State Hard Problems: New Assumptions, New Techniques, and Simplification.

In this work, we study the question of what set of simple-to-state assumptions suffice for constructing functional encryption and indistinguishability obfuscation (\(i\mathcal {O}\)), supporting all functions describable by polynomial-size circuits. Our work improves over the state-of-the-art work of Jain, Lin, Matt, and Sahai (Eurocrypt 2019) in multiple dimensions.
Optimal couplings between sparse block models

We study the problem of coupling a stochastic block model with a planted bisection to a uniform random graph having the same average degree. Focusing on the regime where the average degree is a constant relative to the number of vertices $n$, we show that the distance to which the models can be coupled undergoes a phase transition from $O(\sqrt{n})$ to $\Omega(n)$ as the planted bisection in the block model varies. This settles half of a conjecture of Bollob\'{a}s and Riordan and has some implications for sparse graph limit theory. In particular, for certain ranges of parameters, a block model and the corresponding uniform model produce samples which must converge to the same limit point. This implies that any notion of convergence for sequences of graphs with $\Theta(n)$ edges which allows for samples from a limit object to converge back to the limit itself must identify these models.
Fair Prediction with Endogenous Behavior

There is great interest in whether machine learning algorithms deployed in consequential domains (e.g. in criminal justice) treat different demographic groups "fairly." However, there are several proposed notions of fairness, typically mutually incompatible. Using criminal justice as an example, we study a model in which society chooses an incarceration rule. Agents of different demographic groups differ in their outside options (e.g. opportunity for legal employment) and decide whether to commit crimes. We show that equalizing type I and type II errors across groups is consistent with the goal of minimizing the overall crime rate; other popular notions of fairness are not.
The Wiring Logic of an Identified Serotonergic Neuron That Spans Sensory Networks.

Serotonergic neurons project widely throughout the brain to modulate diverse physiological and behavioral processes. However, a single-cell resolution understanding of the connectivity of serotonergic neurons is currently lacking. Using a whole-brain EM dataset of a female Drosophila, we comprehensively determine the wiring logic of a broadly projecting serotonergic neuron (the CSDn) that spans several olfactory regions. Within the antennal lobe, the CSDn differentially innervates each glomerulus, yet surprisingly, this variability reflects a diverse set of presynaptic partners, rather than glomerulus-specific differences in synaptic output, which is predominately to local interneurons. Moreover, the CSDn has distinct connectivity relationships with specific local interneuron subtypes, suggesting that the CSDn influences distinct aspects of local network processing. Across olfactory regions, the CSDn has different patterns of connectivity, even having different connectivity with individual projection neurons that also span these regions. Whereas the CSDn targets inhibitory local neurons in the antennal lobe, the CSDn has more distributed connectivity in the LH, preferentially synapsing with principal neuron types based on transmitter content. Last, we identify individual novel synaptic partners associated with other sensory domains that provide strong, top-down input to the CSDn. Together, our study reveals the complex connectivity of serotonergic neurons, which combine the integration of local and extrinsic synaptic input in a nuanced, region-specific manner. SIGNIFICANCE STATEMENT All sensory systems receive serotonergic modulatory input. However, a comprehensive understanding of the synaptic connectivity of individual serotonergic neurons is lacking. In this study, we use a whole-brain EM microscopy dataset to comprehensively determine the wiring logic of a broadly projecting serotonergic neuron in the olfactory system of Drosophila. Collectively, our study demonstrates, at a single-cell level, the complex connectivity of serotonergic neurons within their target networks, identifies specific cell classes heavily targeted for serotonergic modulation in the olfactory system, and reveals novel extrinsic neurons that provide strong input to this serotonergic system outside of the context of olfaction. Elucidating the connectivity logic of individual modulatory neurons provides a ground plan for the seemingly heterogeneous effects of modulatory systems.
SARS-CoV-2 Antibody Responses Do Not Predict COVID-19 Disease Severity.

Objectives Initial reports indicate adequate performance of some serology-based severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) assays. However, additional studies are required to facilitate interpretation of results, including how antibody levels impact immunity and disease course. Methods A total of 967 subjects were tested for IgG antibodies reactive to SARS-CoV-2, including 172 suspected cases of SARS-CoV-2, 656 plasma samples from healthy donors, 49 sera from patients with rheumatic disease, and 90 specimens from individuals positive for polymerase chain reaction (PCR)-based respiratory viral panel. A subgroup of SARS-CoV-2 PCR-positive cases was tested for IgM antibodies by proteome array method. Results All specificity and cross-reactivity specimens were negative for SARS-CoV-2 IgG antibodies (0/795, 0%). Positive agreement of IgG with PCR was 83% of samples confirmed to be more than 14 days from symptom onset, with less than 100% sensitivity attributable to a case with severe immunosuppression. Virus-specific IgM was positive in a higher proportion of cases less than 3 days from symptom onset. No association was observed between mild and severe disease course with respect to IgG and IgM levels. Conclusions The studied SARS-CoV-2 IgG assay had 100% specificity and no adverse cross-reactivity. Measures of IgG and IgM antibodies did not predict disease severity in our patient population.
Zeros of polynomials with four-term recurrence and linear coefficients

This paper investigates the zero distribution of a sequence of polynomials $$\left\{ P_{m}(z)\right\} _{m=0}^{\infty }$$ which satisfy a four-term recurrence whose coefficients are linear polynomials in z. In particular, we study necessary and sufficient conditions for the reality of the zeros of $$P_{m}(z)$$ . Under these conditions, we find an explicit interval containing these zeros, whose union forms a dense subset of this interval.
Asymptotics for the Taylor coefficients of certain infinite products

Let $$(m_1,\ldots ,m_J)$$ and $$(r_1,\ldots ,r_J)$$ be two sequences of J positive integers satisfying $$1\le r_j< m_j$$ for all $$j=1,\ldots ,J$$ . Let $$(\delta _1,\ldots ,\delta _J)$$ be a sequence of J nonzero integers. In this paper, we study the asymptotic behavior of the Taylor coefficients of the infinite product $$\begin{aligned} \prod _{j=1}^J\Bigg (\prod _{k\ge 1}\big (1-q^{r_j+m_j(k-1)}\big )\big (1-q^{-r_j+m_jk}\big )\Bigg )^{\delta _j}. \end{aligned}$$ Our work generalizes many known results, including an asymptotic formula due to Lehner for the partition function arising from the first Rogers–Ramanujan identity. The main technique used here is based on Rademacher’s circle method.
Wireless Network Simplification: The Performance of Routing

This paper explores the network simplification problem for Gaussian full-duplex relay networks with arbitrary topology. Particularly, given an $N$ -relay Gaussian full-duplex network, the network simplification problem seeks to find fundamental guarantees on the capacity of the best subnetwork, among a particular class of subnetworks, as a fraction of the full-network capacity. The focus of this work is the case when the selected subnetwork class is a path from the source to the destination. The main result of the paper shows that for an $N$ -relay Gaussian networks with arbitrary topology, the best route can in the worst case guarantee an approximate fraction $\frac {1}{ \lfloor N/2 \rfloor + 1}$ of the capacity of the full network, independently of the channel coefficients and/or operating SNR. Furthermore, this guarantee is shown to be fundamental, i.e., it is the highest worst-case guarantee that can be provided for routing in relay networks. A key step in the proof of the main result lies in the derivation of a simplification result for antenna selection in MIMO channels that may also be of independent interest. To the best of our knowledge, this is the first result that characterizes the performance of routing in comparison to physical layer cooperation techniques that approximately achieve the network capacity for general wireless network topologies. The results in this paper show that routing can, in the worst case, result in an unbounded gap from the network capacity - or reversely, physical layer cooperation can offer unbounded gains over routing.
Latent Dirichlet Analysis of Categorical Survey Responses

Beliefs are important determinants of an individual’s choices and economic outcomes, so understanding how they comove and differ across individuals is of considerable interest. Researchers often re...
Stopping with congestion and private payoffs

Abstract This paper analyses a two-player stopping game with multiarmed bandits in which each player chooses between learning about the quality of her private risky arm and competing for the use of a single shared safe arm. The qualities of the players’ risky arms are independent. A player whose risky arm produces a success no longer competes for the safe arm. We assume that a player observes her opponent’s actions but not his realised payoffs. She is therefore never certain whether her opponent is still competing for the safe arm. When the players’ prior probabilities of success are sufficiently close, there exists no pure strategy equilibrium, and we characterise the unique mixed strategy equilibrium. Otherwise, the unique equilibrium is in pure strategies. The amount of experimentation performed in equilibrium is inefficiently low but, for many priors, higher than if successes are publicly observed.
Geometry and interior nodal sets of Steklov eigenfunctions

We investigate the geometric properties of Steklov eigenfunctions in smooth manifolds. We derive the refined doubling estimates and Bernstein’s inequalities. For the real analytic manifolds, we are able to obtain the sharp upper bound for the measure of interior nodal sets.
The impacts of body mass on immune cell concentrations in birds.

Body mass affects many biological traits, but its impacts on immune defences are fairly unknown. Recent research on mammals found that neutrophil concentrations disproportionately increased (scaled...
Diverse epithelial cell populations contribute to regeneration of secretory units in injured salivary glands.

Salivary glands exert exocrine secretory function to provide saliva for lubrication and protection of the oral cavity. Its epithelium consists of several differentiated cell types including acinar, ductal and myoepithelial cells that are maintained in a lineage-restricted manner during homeostasis or after mild injuries. Glandular regeneration following a near complete loss of secretory cells, however, may involve cellular plasticity, although the mechanism and extent of such plasticity remain unclear. Here, by combining lineage-tracing experiments with a model of severe glandular injury in the mouse submandibular gland, we show that de novo formation of acini involves induction of cellular plasticity in multiple non-acinar cell populations. Fate-mapping analysis revealed that although ductal stem cells marked by cytokeratin K14 and Axin2 undergo a multipotency switch, they do not make a significant contribution to acinar regeneration. Intriguingly, more than 80% of regenerated acini derive from differentiated cells including myoepithelial and ductal cells that appear to dedifferentiate to a progenitor-like state before re-differentiation to acinar cells. The potential of diverse cell populations serving as a reserve source for acini widens the therapeutic options for hyposalivation.
Anderson acceleration for seismic inversion

ABSTRACTState-of-the-art seismic imaging techniques treat inversion tasks such as full-waveform inversion (FWI) and least-squares reverse time migration (LSRTM) as partial differential equation-con...
DeeplyEssential: a deep neural network for predicting essential genes in microbes

Essential genes are those genes that are critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies. We propose a deep neural network for predicting essential genes in microbes. Our architecture called DeeplyEssential makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. We also expose and study a hidden performance bias that effected previous classifiers. Extensive results show that DeeplyEssential outperform existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes. Deep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.
Description of a proposed on-orbit calibration procedure for SIM based on spacecraft maneuver

The astrometric performance of the SIM relies on precise measurements of the optical pathlength difference of the starlight through the arms of the interferometers that comprise the SIM instrument, and on precise relative distance betweeen a set of fiducials that define the baselines of the interferometers. The accuracy of these measurements can be affected by various phenomena. Some of them are time-dependent, while others are relatively static and repeatable. In this work we are concerned with the instrument errors of the latter type and in their compensation. In particular, a procedure for on-orbit calibration of the instrument error function is defined, and a proof of concept of its viability is presented. On a given grid of stars, the proposed procedure generates approximations of the gradient of the instrument error function at a discrete set of field points corresponding to the star locations via a specialized set of maneuvers of the spacecraft. These gradient approximations are then used to estimate the error function via a least squares procedure in a manner that is very analogous to the wavefront reconstruction problem in adaptive optics systems. An error analysis of the procedure is presented providing further insights into the connections between instrument errors and the grid reduction solution. Finally, numerical results are presented on a randomly generated grid of stars that demonstrate the feasibility of the method.
Machine learning a molecular Hamiltonian for predicting electron dynamics

We develop a computational method to learn a molecular Hamiltonian matrix from matrix-valued time series of the electron density. As we demonstrate for three small molecules, the resulting Hamiltonians can be used for electron density evolution, producing highly accurate results even when propagating 1,000 time steps beyond the training data. As a more rigorous test, we use the learned Hamiltonians to simulate electron dynamics in the presence of an applied electric field, extrapolating to a problem that is beyond the field-free training data. We find that the resulting electron dynamics predicted by our learned Hamiltonian are in close quantitative agreement with the ground truth. Our method relies on combining a reduced-dimensional, linear statistical model of the Hamiltonian with a time-discretization of the quantum Liouville equation within time-dependent Hartree Fock theory. We train the model using a least-squares solver, avoiding numerous, CPU-intensive optimization steps. For both field-free and field-on problems, we quantify training and propagation errors, highlighting areas for future development.
Improved Moser‐Trudinger‐Onofri Inequality under Constraints

A classical result of Aubin states that the constant in Moser-Trudinger-Onofri inequality on $\mathbb{S}^{2}$ can be imporved for furnctions with zero first order moments of the area element. We generalize it to higher order moments case. These new inequalities bear similarity to a sequence of Lebedev-Milin type inequalities on $\mathbb{S}^{1}$ coming from the work of Grenander-Szego on Toeplitz determinants (as pointed out by Widom). We also discuss the related sharp inequality by a perturbation method.
Intermittent Inference with Nonuniformly Compressed Multi-Exit Neural Network for Energy Harvesting Powered Devices

This work aims to enable persistent, event-driven sensing and decision capabilities for energy-harvesting (EH)-powered devices by deploying lightweight DNNs onto EH-powered devices. However, harvested energy is usually weak and unpredictable and even lightweight DNNs take multiple power cycles to finish one inference. To eliminate the indefinite long wait to accumulate energy for one inference and to optimize the accuracy, we developed a power trace-aware and exit-guided network compression algorithm to compress and deploy multi-exit neural networks to EH-powered microcontrollers (MCUs) and select exits during execution according to available energy. The experimental results show superior accuracy and latency compared with state-of-the-art techniques.
A rare complication of ERCP: duodenal perforation due to biliary stent migration

Background and study aims Perforation of the duodenal wall opposing the major papilla due to a migrated pancreatobiliary stent rarely has been described in the literature as a complication of endoscopic retrograde cholangiopancreatography (ERCP). Factors associated with perforation from migrated stents from ERCP are unknown. Patients and methods This was a retrospective, observational study. Patients were identified from January 1, 1994 to May 31, 2019 in a prospectively maintained ERCP database. Results Eleven cases of duodenal perforation from migrated pancreatobiliary stents placed at ERCP were identified during the study period. All cases involved biliary stents, placed for biliary stricture management. The perforating stent was plastic in 10 cases (91 %). This complication occurred in one in 2,293 ERCP procedures in which a pancreatobiliary stent was placed. Conclusion This complication is more common with biliary stents compared to pancreatic stents. This may be related to the angle of exit of biliary stents being more perpendicular to the opposing duodenal wall and the near exclusive use of external pigtail plastic stents in the pancreatic duct. All perforating plastic stents were ≥ 9 cm in length. Longer stents may provide leverage for perforation with a migration event.
Nonlinear Control of Networked Dynamical Systems

We develop a principled mathematical framework for controlling nonlinear, networked dynamical systems. Our method integrates dimensionality reduction, bifurcation theory, and emerging model discovery tools to find low-dimensional subspaces where feed-forward control can be used to manipulate a system to a desired outcome. The method leverages the fact that many high-dimensional networked systems have many fixed points, allowing for the computation of control signals that will move the system between any pair of fixed points. The sparse identification of nonlinear dynamics (SINDy) algorithm is used to fit a nonlinear dynamical system to the evolution on the dominant, low-rank subspace. This then allows us to use bifurcation theory to find collections of constant control signals that will produce the desired objective path for a prescribed outcome. Specifically, we can destabilize a given fixed point while making the target fixed point an attractor. The discovered control signals can be easily projected back to the original high-dimensional state and control space. We illustrate our nonlinear control procedure on established bistable, low-dimensional biological systems, showing how control signals are found that generate switches between the fixed points. We then demonstrate our control procedure for high-dimensional systems on random high-dimensional networks and Hopfield memory networks.
Neural Network Topologies for Sparse Training

The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to store and train them. Research over the past few decades has explored the prospect of sparsifying DNNs before, during, and after training by pruning edges from the underlying topology. The resulting neural network is known as a sparse neural network. More recent work has demonstrated the remarkable result that certain sparse DNNs can train to the same precision as dense DNNs at lower runtime and storage cost. An intriguing class of these sparse DNNs is the X-Nets, which are initialized and trained upon a sparse topology with neither reference to a parent dense DNN nor subsequent pruning. We present an algorithm that deterministically generates sparse DNN topologies that, as a whole, are much more diverse than X-Net topologies, while preserving X-Nets’ desired characteristics.
Presidential Spotlight: Dialoguing the Possible—Creating a Public Record of CSCA Challenges, Lessons Learned, and Envisioning the Future

M. Chad McBride, immediate past president of the Central States Communication Association and Chad Edwards, past executive director share thoughts about helping navigate CSCA through troubling time...
Frequentist coverage properties of uncertainty intervals for weak Poisson signals in the presence of background

We construct uncertainty intervals for weak Poisson signals in the presence of background. We consider the case where a primary experiment yields a realization of the signal plus background, and a second experiment yields a realization of the background. The data acquisition times, for the background-only experiment, Tbg, and the primary experiment, T, are selected so that their ratio, Tbg/T, varies from 1 to 25. The upper choice of 25 is motivated by an experimental study at the National Institute of Standards and Technology (NIST). The expected number of background counts in the primary experiment varies from 0.2 to 2. We construct 90% and 95% confidence intervals based on a propagation-of-errors method as well as two implementations of a Neyman procedure where acceptance regions are constructed based on a likelihood-ratio criterion that automatically determines whether the resulting confidence interval is one-sided or two-sided. In one of the implementations of the Neyman procedure due to Feldman and Cousins (FC), uncertainty in the expected background contribution is neglected. In the other implementation, we account for random uncertainty in the estimated expected background with a parametric bootstrap implementation of a method due to Conrad. We also construct minimum length Bayesian credibility intervals. For each method, we test for the presence of a signal based on the value of the lower endpoint of the uncertainty interval. In general, the propagation-of-errors method performs the worst compared to the other methods according to frequentist coverage and detection probability criteria, and sometimes produces nonsensical intervals where both endpoints are negative. The Neyman procedures generally yield intervals with better frequentist coverage properties compared to the Bayesian method except for some cases where Tbg/T = 1. In general, the Bayesian method yields intervals with lower detection probabilities compared to Neyman procedures. One of the main conclusions is that when Tbg/T is 5 or more and the expected background is 2 or less, the FC method outperforms the other methods considered. For Tbg/T = 1, 2 we observe that the Neyman procedure methods yield false detection probabilities for the case of no signal that are higher than expected given the nominal frequentist coverage of the interval. In contrast, for Tbg/T = 1, 2, the false detection probability of the Bayesian method is less than expected according to the nominal frequentist coverage.
A mathematical theory of cooperative communication

Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction. Prior models of cooperative communication are algorithmic in nature and do not shed light on why cooperation may yield effective belief transmission and what limitations may arise due to differences between beliefs of agents. Through a connection to the theory of optimal transport, we establishing a mathematical framework for cooperative communication. We derive prior models as special cases, statistical interpretations of belief transfer plans, and proofs of robustness and instability. Computational simulations support and elaborate our theoretical results, and demonstrate fit to human behavior. The results show that cooperative communication provably enables effective, robust belief transmission which is required to explain feats of human learning and improve human-machine interaction.
Equations of motion in general relativity and quantum mechanics

In a previous article a relationship was established between the linearized metrics of General Relativity associated with geodesics and the Dirac Equation of quantum mechanics. In this paper the extension of that result to arbitrary curves is investigated. A generalized Dirac equation is derived and shown to be related to the Lie derivative of the momentum along the curve. In addition,the equations of motion are derived from the Hamilton-Jacobi equation associated with the metric and the wave equation associated with the Hamiltonian is then shown not to commute with the Dirac operator. Finally, the Maxwell-Boltzmann distribution is shown to be a consequence of geodesic motion.
Privacy and Utility Aware Data Sharing for Space Situational Awareness From Ensemble and Unscented Kalman Filtering Perspective

This article presents an optimization-based formulation for privacy-utility tradeoff in the ensemble and unscented Kalman filtering framework, focusing on the space situational awareness. Privacy and utility are defined in terms of lower and upper bound on the state estimation error covariance. The synthetic sensor noise is used to satisfy these bounds and is determined by solving an optimization problem. Given privacy and utility bounds, this article present optimization problem formulations to determine the maximum noise for which the utility is satisfied or the estimation errors are upper bounded, the minimum noise for which the privacy is satisfied or the estimation errors are lower bounded, the optimal noise that satisfies utility constraints and maximizes privacy, and the optimal noise that satisfies privacy constraints and minimizes the uncertainty. We demonstrate the application of these formulations to the tracking of the International Space Station and highlight the optimal privacy versus utility tradeoff for this dynamical system.
Merging black hole binaries in galactic nuclei: implications for advanced-LIGO detections

Motivated by the recent detection of gravitational waves from the black hole binary merger GW150914, we study the dynamical evolution of (stellar-mass) black holes in galactic nuclei, where massive star clusters reside. With masses of and sizes of only a few parsecs, nuclear star clusters (NSCs) are the densest stellar systems observed in the local universe and represent a robust environment where black hole binaries can dynamically form, harden, and merge. We show that due to their large escape speeds, NSCs can retain a large fraction of their merger remnants. Successive mergers can then lead to significant growth and produce black hole mergers of several tens of solar masses similar to GW150914 and up to a few hundreds of solar masses, without the need to invoke extremely low metallicity environments. We use a semi-analytical approach to describe the dynamics of black holes in massive star clusters. Our models give a black hole binary merger rate of from NSCs, implying up to a few tens of possible detections per year with Advanced LIGO. Moreover, we find a local merger rate of for high mass black hole binaries similar to GW150914; a merger rate comparable to or higher than that of similar binaries assembled dynamically in globular clusters (GCs). Finally, we show that if all black holes receive high natal kicks, , then NSCs will dominate the local merger rate of binary black holes compared to either GCs or isolated binary evolution.
Topological properties of spaces admitting free group actions

In 1992, David Wright proved a remarkable theorem about which contractible open manifolds are covering spaces. He showed that if a one-ended open manifold M has pro-monomorphic fundamental group at infinity which is not pro-trivial and is not stably Z, then M does not cover any manifold (except itself). In the non-manifold case, Wright's method showed that when a one-ended, simply connected, locally compact ANR X with pro-monomorphic fundamental group at infinity admits an action of Z by covering transformations then the fundamental group at infinity of X is (up to pro-isomorphism) an inverse sequence of finitely generated free groups. We improve upon this latter result, by showing that X must have a stable finitely generated free fundamental group at infinity. Simple examples show that a free group of any finite rank is possible. We also prove that if X (as above), admits a non-cocompact action of Z+Z by covering transformations, then X is simply connected at infinity. Corollary: Every finitely presented one-ended group G which contains an element of infinite order satisfies exactly one of the following: 1) G is simply connected at infinity; 2) G is virtually a surface group; 3) The fundamental group at infinity of G is not pro-monomorphic. Our methods also provide a quick new proof of Wright's open manifold theorem.
An introduction to linear stability analysis for deciphering spatial patterns in signaling networks

Mathematical modeling is now used commonly in the analysis of signaling networks. With advances in high resolution microscopy, the spatial location of different signaling molecules and the spatio-temporal dynamics of signaling microdomains are now widely acknowledged as key features of biochemical signal transduction. Reaction-diffusion mechanisms are commonly used to model such features, often with a heavy reliance on numerical simulations to obtain results. However, simulations are parameter dependent and may not be able to provide an understanding of the full range of the system responses. Analytical approaches on the other hand provide a framework to study the entire phase space. In this tutorial, we provide a largely analytical method for studying reaction-diffusion models and analyzing their stability properties. Using two representative biological examples, we demonstrate how this approach can guide experimental design.
Reconnection-powered Linear Accelerator and Gamma-Ray Flares in the Crab Nebula

The recent discovery of day-long gamma-ray flares in the Crab Nebula, presumed to be synchrotron emission by PeV (1015 eV) electrons in milligauss magnetic fields, presents a strong challenge to particle acceleration models. The observed photon energies exceed the upper limit (~100 MeV) obtained by balancing the acceleration rate and synchrotron radiation losses under standard conditions where the electric field is smaller than the magnetic field. We argue that a linear electric accelerator, operating at magnetic reconnection sites, is able to circumvent this difficulty. Sufficiently energetic electrons have gyroradii so large that their motion is insensitive to small-scale turbulent structures in the reconnection layer and is controlled only by large-scale fields. We show that such particles are guided into the reconnection layer by the reversing magnetic field as they are accelerated by the reconnection electric field. As these electrons become confined within the current sheet, they experience a decreasing perpendicular magnetic field that may drop below the accelerating electric field. This enables them to reach higher energies before suffering radiation losses and hence to emit synchrotron radiation in excess of the 100 MeV limit, providing a natural resolution to the Crab gamma-ray flare paradox.
Quenching of Supermassive Black Hole Growth around the Apparent Maximum Mass

Recent quasar surveys have revealed that supermassive black holes (SMBHs) rarely exceed a mass of M BH ~ a few × 1010 during the entire cosmic history. It has been argued that quenching of the BH growth is caused by a transition of a nuclear accretion disk into an advection-dominated accretion flow, with which strong outflows and/or jets are likely to be associated. We investigate the relationship between the maximum mass of SMBHs and the radio-loudness of quasars with a well-defined sample of ~105 quasars at a redshift range of 0 < z < 2, obtained from the Sloan Digital Sky Surveys DR7 catalog. We find that the number fraction of the radio-loud (RL) quasars increases above a threshold of M BH 2 × 109 , independent of their redshifts. Moreover, the number fraction of RL quasars with lower Eddington ratios (out of all RL quasars), indicating lower accretion rates, increases above the critical BH mass. These observational trends can be natural consequences of the proposed scenario of suppressing BH growth around the apparent maximum mass of ~1010 . The ongoing VLA Sky Survey in radio will allow us to estimate of the exact number fraction of RL quasars more precisely, which gives further insight into the quenching processes for BH growth.
On the State of Water Ice on Saturn's Moon Titan and Implications to Icy Bodies in the Outer Solar System †

The crystalline state of water ice in the Solar System depends on the temperature history of the ice and the influence of energetic particles to which it has been exposed. We measured the infrared absorption spectra of amorphous and crystalline water ice in the 10−50 K and 10−140 K temperature ranges, respectively, and conducted a systematic experimental study to investigate the amorphization of crystalline water ice via ionizing radiation irradiation at doses of up to 160 ± 30 eV per molecule. We found that crystalline water ice can be converted only partially to amorphous ice by electron irradiation. The experiments showed that a fraction of the 1.65 μm band, which is characteristic for crystalline water ice, survived the irradiation, to a degree that strongly depends on the temperature. Quantitative kinetic fits of the temporal evolution of the 1.65 μm band clearly demonstrate that there is a balance between thermal recrystallization and irradiation-induced amorphization, with thermal recrystallizaton ...
Vortical flow development in round ducts across scales for engine inlet applications

Turbofan engine performance depends highly on the characteristics and conditions of the inlet flow. Swirl distortions, caused by non-uniformities in flows arising from boundary layer or ground/fuselage vortex ingestion, are of concern and need to be fully understood to guarantee the efficiency and safety of propulsion systems. To investigate a fundamental single-vortex distortion development in a duct at different Reynolds numbers, a StreamVane distortion generating device was designed and experimentally analyzed in a small-scale low-speed wind tunnel ( $$Re_{\text{D}} \approx 500 \times 10^3$$ ) and in a full-scale engine testing rig ( $$Re_{\text{D}} \approx 3 \times 10^6$$ ). Stereoscopic particle image velocimetry was used to measure the three-component velocity fields at discrete measurement planes downstream of the distortion device. Results show that the main features of the secondary flow are generated very similarly in both scales, and that its axial development is also overall very similar and mostly driven by two-dimensional (2D) vortex dynamics. Induced velocities arising from the proximity of the vortex to the duct wall cause the vortex center to convect circumferentially around the duct, in the same sense as the vortex rotation, as it travels downstream. The unsteady flow in the different measurement planes of the wind-tunnel experiment shows the development of local instabilities related to the turbulent development of the vortex. This work shows that the development of this vane-generated, vortex-dominated flow is largely Reynolds number independent for the covered range, so that details of similar duct-bounded flows can be analyzed in depth in small-scale experiments, decreasing development efforts and cost.
Saddle-point solution of the fingerprinting capacity game under the marking assumption

We study a fingerprinting game in which the collusion channel is unknown. The encoder embeds fingerprints into a host sequence and provides the decoder with the capability to trace back pirated copies to the colluders.
Simple perturbatively traversable wormholes from bulk fermions

A new class of traversable wormholes was recently constructed which relies only on local bulk dynamics rather than an explicit coupling between distinct boundaries. Here we begin with a four-dimensional Weyl fermion field of any mass m propagating on a classical background defined by a Z2 quotient of (rotating) BTZ × S1. This setup allows one to compute the fermion stress-energy tensor exactly. For appropriate boundary conditions around a non-contractible curve, perturbative back-reaction at any m renders the associated wormhole traversable and suggests it can become eternally traversable at the limit where the background becomes extremal. A key technical step is the proper formulation of the method of images for fermions in curved spacetime. We find the stress- energy of spinor fields to have important kinematic differences from that of scalar fields, typically causing the sign of the integrated null stress-energy (and thus in many cases the sign of the time delay/advance) to vary around the throat of the wormhole. Similar effects may arise for higher-spin fields.
Higher spin partition functions via the quasinormal mode method in de Sitter quantum gravity

In this note we compute the 1-loop partition function of spin-$s$ fields on Euclidean de Sitter space $S^{2n+1}$ using the quasinormal mode method. Instead of computing the quasinormal mode frequencies from scratch, we use the analytic continuation prescription $L_{\text{AdS}}\to iL_{\text{dS}}$, appearing in the dS/CFT correspondence, and Wick rotate the normal mode frequencies of fields on thermal $\text{AdS}_{2n+1}$ into the quasinormal mode frequencies of fields on de Sitter space. We compare the quasinormal mode and heat kernel methods of calculating 1-loop determinants, finding exact agreement, and furthermore explicitly relate these methods via a sum over the conformal dimension. We discuss how the Wick rotation of normal modes on thermal $\text{AdS}_{2n+1}$ can be generalized to calculating 1-loop partition functions on the thermal spherical quotients $S^{2n+1}/\mathbb{Z}_{p}$. We further show that the quasinormal mode frequencies encode the group theoretic structure of the spherical spacetimes in question, analogous to the recent analysis made for thermal AdS in (1910.07607) and (1910.11913).
Spectra and Line Profiles of FU Orionis Objects: Comparisons between Boundary Layer Models and Observations

We present solutions for the accretion disks and boundary layers in pre-main-sequence stars undergoing FU Orionis outbursts. These solutions differ from earlier disk solutions in that they include a self-consistent treatment of the boundary layer region. In a previous paper, Popham showed that these stars should stop accreting angular momentum once they spin up to modest rotation rates. Here we show that for reasonable values of α, these low angular momentum accretion rate solutions fit the spectra and line profiles observed in FU Orionis objects better than solutions with high rates of angular momentum accretion. We find solutions that fit the observations of FU Orionis and V1057 Cygni. These solutions have mass accretion rates of 2 and 1 × 10–4 M☉ yr–1, stellar masses of 0.7 and 0.5 M☉, and stellar radii of 5.75 and 5.03 R☉, respectively. They also have modest stellar rotation rates 8-9 × 10–6 s–1, comparable to the observed rotation rates of T Tauri stars, and angular momentum accretion rates of zero. This supports our earlier suggestion that FU Orionis outbursts may regulate the rotation rates of T Tauri stars.
The Spectrum of GRB 930131 (``Superbowl Burst'') from 20 keV to 200 MeV

We have constructed a broadband spectrum for GRB 930131 (the "Superbowl Burst"), ranging from 20 keV to 200 MeV, by combining spectral information from the Gamma Ray Observatory's BATSE, COMPTEL, and EGRET instruments. We present general methods for combining spectra from different time intervals obtained by the same instrument as well as for combining spectra from the same time interval taken by different instruments. The resulting spectrum is remarkably flat (in νFν-space) up to high energies. We find that the spectral shape can be successfully fitted by the shocked synchrotron emission model of Tavani. We present evidence that the flatness of the spectrum at high energies is not due to spectral time variability.
The Geometry of Spontaneous Spiking in Neuronal Networks

The mathematical theory of pattern formation in electrically coupled networks of excitable neurons forced by small noise is presented in this work. Using the Freidlin–Wentzell large-deviation theory for randomly perturbed dynamical systems and the elements of the algebraic graph theory, we identify and analyze the main regimes in the network dynamics in terms of the key control parameters: excitability, coupling strength, and network topology. The analysis reveals the geometry of spontaneous dynamics in electrically coupled network. Specifically, we show that the location of the minima of a certain continuous function on the surface of the unit n-cube encodes the most likely activity patterns generated by the network. By studying how the minima of this function evolve under the variation of the coupling strength, we describe the principal transformations in the network dynamics. The minimization problem is also used for the quantitative description of the main dynamical regimes and transitions between them. In particular, for the weak and strong coupling regimes, we present asymptotic formulas for the network activity rate as a function of the coupling strength and the degree of the network. The variational analysis is complemented by the stability analysis of the synchronous state in the strong coupling regime. The stability estimates reveal the contribution of the network connectivity and the properties of the cycle subspace associated with the graph of the network to its synchronization properties. This work is motivated by the experimental and modeling studies of the ensemble of neurons in the Locus Coeruleus, a nucleus in the brainstem involved in the regulation of cognitive performance and behavior.
Gray Radiation Hydrodynamics with the FLASH Code for Astrophysical Applications

We present the newly-incorporated gray radiation hydrodynamics capabilities of the FLASH code based on a radiation flux-limiter aware hydrodynamics numerical implementation designed specifically for applications in astrophysical problems. The newly incorporated numerical methods consist of changes in the unsplit hydrodynamics solver and adjustments in the flux-limited radiation diffusion unit. Our method can treat problems in both the strong and weak radiation-matter coupling limits as well as transitions between the two regimes. Appropriate extensions in the "Helmholtz" equation of state are implemented to treat two-temperature astrophysical plasmas involving the interaction between radiation and matter and the addition of a new opacity unit based on the OPAL opacity database, commonly used for astrophysical fluids. A set of radiation-hydrodynamics test problems is presented aiming to showcase the new capabilities of FLASH and to provide direct comparison to other codes like CASTRO. To illustrate the capacity of FLASH to simulate phenomena occurring in stellar explosions, such as shock break-out, radiative precursors and supernova ejecta heating due to the decays of radioactive nickel-56 and cobalt-56, we also present 1D supernova simulations and compare the computed lightcurves to those of the SNEC code. The latest public release of FLASH with these enhanced capabilities is available for download and use by the broader astrophysics community.
The Star Formation Histories of Local Group Dwarf Galaxies. II. Searching For Signatures of Reionization

We search for signatures of reionization in the star formation histories (SFHs) of 38 Local Group dwarf galaxies (10{sup 4} < M{sub *} < 10{sup 9} M{sub ☉}). The SFHs are derived from color-magnitude diagrams using archival Hubble Space Telescope/Wide Field Planetary Camera 2 imaging. Only five quenched galaxies (And V, And VI, And XIII, Leo IV, and Hercules) are consistent with forming the bulk of their stars before reionization, when full uncertainties are considered. Observations of 13 of the predicted 'true fossils' identified by Bovill and Ricotti show that only two (Hercules and Leo IV) indicate star formation quenched by reionization. However, both are within the virial radius of the Milky Way and evidence of tidal disturbance complicates this interpretation. We argue that the late-time gas capture scenario posited by Ricotti for the low mass, gas-rich, and star-forming fossil candidate Leo T is observationally indistinguishable from simple gas retention. Given the ambiguity between environmental effects and reionization, the best reionization fossil candidates are quenched low mass field galaxies (e.g., KKR 25).