cssi/SciSciGPT-SciSciNet huggingface dataset:
Kenneth Geddes Wilson, 1936–2013, an appreciation

Kenneth G Wilson made deep and insightful contributions to statistical physics, particle physics, and related fields. He was also a helpful and thoughtful human being.
Incorporating photometric redshift probability density information into real-space clustering measurements

The use of photometric redshifts in cosmology is increasing. Often, however these photo-z are treated like spectroscopic observations, in that the peak of the photometric redshift, rather than the full probability density function (PDF), is used. This overlooks useful information inherent in the full PDF. We introduce a new real-space estimator for one of the most used cosmological statistics, the two-point correlation function, that weights by the PDF of individual photometric objects in a manner that is optimal when Poisson statistics dominate. As our estimator does not bin based on the PDF peak, it substantially enhances the clustering signal by usefully incorporating information from all photometric objects that overlap the redshift bin of interest. As a real-world application, we measure quasi-stellar object (QSO) clustering in the Sloan Digital Sky Survey (SDSS). We find that our simplest binned estimator improves the clustering signal by a factor equivalent to increasing the survey size by a factor of 2–3. We also introduce a new implementation that fully weights between pairs of objects in constructing the cross-correlation and find that this pair-weighted estimator improves clustering signal in a manner equivalent to increasing the survey size by a factor of 4–5. Our technique uses spectroscopic data to anchor the distance scale and it will be particularly useful where spectroscopic data (e.g. from BOSS) overlap deeper photometry (e.g. from Pan-STARRS, DES or the LSST). We additionally provide simple, informative expressions to determine when our estimator will be competitive with the autocorrelation of spectroscopic objects. Although we use QSOs as an example population, our estimator can and should be applied to any clustering estimate that uses photometric objects.
Pulsar Timing Constraints on Cumulative and Individual Mass of Stars in the Galactic Center

We consider the time derivatives of the period P of pulsars at the Galactic Center due to variations in their orbital Doppler shifts. We show that in conjunction with a measurement of a pulsar's proper motion and its projected separation from the supermassive black hole, Sgr A*, measuring two of the three derivatives , , or sets a constraint that allows for the recovery of the complete six phase space coordinates of the pulsar's orbit, as well as the enclosed mass within the orbit. Thus, one can use multiple pulsars at different distances from Sgr A* to determine the radial mass distribution of stars and stellar remnants at the Galactic center. Furthermore, we consider the effect of passing stars on the pulsar's period derivatives and show how it can be exploited to measure the characteristic stellar mass in the Galactic center.
Stability of Spreading Processes over Time-Varying Large-Scale Networks

In this paper, we analyze the dynamics of spreading processes taking place over time-varying networks. A common approach to model time-varying networks is via Markovian random graph processes. This modeling approach presents the following limitation: Markovian random graphs can only replicate switching patterns with exponential inter-switching times, while in real applications these times are usually far from exponential. In this paper, we introduce a flexible and tractable extended family of processes able to replicate, with arbitrary accuracy, any distribution of inter-switching times. We then study the stability of spreading processes in this extended family. We first show that a direct analysis based on Ito's formula provides stability conditions in terms of the eigenvalues of a matrix whose size grows exponentially with the number of edges. To overcome this limitation, we derive alternative stability conditions involving the eigenvalues of a matrix whose size grows linearly with the number of nodes. Based on our results, we also show that heuristics based on aggregated static networks approximate the epidemic threshold more accurately as the number of nodes grows, or the temporal volatility of the random graph process is reduced. Finally, we illustrate our findings via numerical simulations.
An energy-based interaction model for population opinion dynamics with topic coupling

We introduce a new, and quite general variational model for opinion dynamics based on pairwise interaction potentials and a range of opinion evolution protocols ranging from random interactions to ...
Active matter on asymmetric substrates

For collections of particles in a thermal bath interacting with an asymmetric substrate, it is possible for a ratchet effect to occur where the particles undergo a net dc motion in response to an ac forcing. Ratchet effects have been demonstrated in a variety of systems including colloids as well as magnetic vortices in type-II superconductors. Here we examine the case of active matter or self-driven particles interacting with asymmetric substrates. Active matter systems include self-motile colloidal particles undergoing catalysis, swimming bacteria, artificial swimmers, crawling cells, and motor proteins. We show that a ratchet effect can arise in this type of system even in the absence of ac forcing. The directed motion occurs for certain particle-substrate interaction rules and its magnitude depends on the amount of time the particles spend swimming in one direction before turning and swimming in a new direction. For strictly Brownian particles there is no ratchet effect. If the particles reflect off the barriers or scatter from the barriers according to Snell's law there is no ratchet effect; however, if the particles can align with the barriers or move along the barriers, directed motion arises. We also find that under certain motion rules, particles accumulate along the walls of the container in agreement with experiment. We also examine pattern formation for synchronized particle motion. We discuss possible applications of this system for self-assembly, extracting work, and sorting as well as future directions such as considering collective interactions and flocking models.
Reconstructing Redshift Distributions with Cross-correlations: Tests and an Optimized Recipe

Many of the cosmological tests to be performed by planned dark energy experiments will require extremely well-characterized photometric redshift measurements. Current estimates for cosmic shear are that the true mean redshift of the objects in each photo-z bin must be known to better than 0.002(1 + z), and the width of the bin must be known to ~0.003(1 + z) if errors in cosmological measurements are not to be degraded significantly. A conventional approach is to calibrate these photometric redshifts with large sets of spectroscopic redshifts. However, at the depths probed by Stage III surveys (such as DES), let alone Stage IV (LSST, JDEM, and Euclid), existing large redshift samples have all been highly (25%-60%) incomplete, with a strong dependence of success rate on both redshift and galaxy properties. A powerful alternative approach is to exploit the clustering of galaxies to perform photometric redshift calibrations. Measuring the two-point angular cross-correlation between objects in some photometric redshift bin and objects with known spectroscopic redshift, as a function of the spectroscopic z, allows the true redshift distribution of a photometric sample to be reconstructed in detail, even if it includes objects too faint for spectroscopy or if spectroscopic samples are highly incomplete. We test this technique using mock DEEP2 Galaxy Redshift survey light cones constructed from the Millennium Simulation semi-analytic galaxy catalogs. From this realistic test, which incorporates the effects of galaxy bias evolution and cosmic variance, we find that the true redshift distribution of a photometric sample can, in fact, be determined accurately with cross-correlation techniques. We also compare the empirical error in the reconstruction of redshift distributions to previous analytic predictions, finding that additional components must be included in error budgets to match the simulation results. This extra error contribution is small for surveys that sample large areas of sky (>~10°-100°), but dominant for ~1 deg2 fields. We conclude by presenting a step-by-step, optimized recipe for reconstructing redshift distributions from cross-correlation information using standard correlation measurements.
Optimal Stefan problem

We consider the inverse multiphase Stefan problem with homogeneous Dirichlet boundary condition on a bounded Lipschitz domain, where the density of the heat source is unknown in addition to the temperature and the phase transition boundaries. The variational formulation is pursued in the optimal control framework, where the density of the heat source is a control parameter, and the criteria for optimality is the minimization of the $$L_2$$-norm difference of the trace of the solution to the Stefan problem from a temperature measurement on the whole domain at the final time. The state vector solves the multiphase Stefan problem in a weak formulation, which is equivalent to Dirichlet problem for the quasilinear parabolic PDE with discontinuous coefficient. The optimal control problem is fully discretized using the method of finite differences. We prove the existence of the optimal control and the convergence of the discrete optimal control problems to the original problem both with respect to cost functional and control. In particular, the convergence of the method of finite differences for the weak solution of the multidimensional multiphase Stefan problem is proved. The proofs are based on achieving a uniform $$L_{\infty }$$ bound and $$W_2^{1,1}$$ energy estimate for the discrete multiphase Stefan problem.
Plasmoid Instability in Forming Current Sheets

The plasmoid instability has revolutionized our understanding of magnetic reconnection in astrophysical environments. By preventing the formation of highly elongated reconnection layers, it is crucial in enabling the rapid energy conversion rates that are characteristic of many astrophysical phenomena. Most of the previous studies have focused on Sweet-Parker current sheets, which, however, are unattainable in typical astrophysical systems. Here, we derive a general set of scaling laws for the plasmoid instability in resistive and visco-resistive current sheets that evolve over time. Our method relies on a principle of least time that enables us to determine the properties of the reconnecting current sheet (aspect ratio and elapsed time) and the plasmoid instability (growth rate, wavenumber, inner layer width) at the end of the linear phase. After this phase the reconnecting current sheet is disrupted and fast reconnection can occur. The scaling laws of the plasmoid instability are \emph{not} simple power laws, and depend on the Lundquist number ($S$), the magnetic Prandtl number ($P_m$), the noise of the system ($\psi_0$), the characteristic rate of current sheet evolution ($1/\tau$), as well as the thinning process. We also demonstrate that previous scalings are inapplicable to the vast majority of the astrophysical systems. We explore the implications of the new scaling relations in astrophysical systems such as the solar corona and the interstellar medium. In both these systems, we show that our scaling laws yield values for the growth rate, wavenumber, and aspect ratio that are much smaller than the Sweet-Parker based scalings.
The Sgr B2 X-Ray Echo of the Galactic Center Supernova Explosion that Produced Sgr A East

Using a combination of one-dimensional and three-dimensional hydrodynamic simulations, we have carried out the first in-depth analysis of the remnant's evolution and its various interactions: with the stellar winds flowing out from the inner ~2 pc; with the supermassive black hole, Sgr A*; and with the 50 km s-1 molecular cloud behind and to the east of the nucleus. We have found that, unlike previous estimates, a rather "standard" supernova explosion with energy ~1.5 × 1051 ergs would have been sufficient to create the remnant we see today and that the latter is probably only ~1700 yr old. We have found that the passage of the remnant across the black hole would have enhanced the accretion rate onto the central object by less than a factor of 2. Such a small increase cannot explain the current Fe fluorescence observed from the molecular cloud Sgr B2; this fluorescence would have required an increase in the luminosity of Sgr A* by 6 orders of magnitude several hundred years ago. Instead, we have uncovered what appears to be a more plausible scenario for this transient irradiation: the interaction between the expanding remnant and the 50 km s-1 molecular cloud. The first impact would have occurred about 1200 yr after the explosion, producing a 2-200 keV luminosity of ~1039 ergs s-1. During the intervening 300-400 yr, the dissipation of kinetic energy subsided considerably, leading to the much lower luminosity (~1036 ergs s-1 at 2-10 keV) we see today.
A Graphon Approach to Limiting Spectral Distributions of Wigner-type Matrices

We present a new approach, based on graphon theory, to finding the limiting spectral distributions of general Wigner-type matrices. This approach determines the moments of the limiting measures and the equations of their Stieltjes transforms explicitly with weaker assumptions on the convergence of variance profiles than previous results. As applications, we give a new proof of the semicircle law for generalized Wigner matrices and determine the limiting spectral distributions for three sparse inhomogeneous random graph models with sparsity $\omega(1/n)$: inhomogeneous random graphs with roughly equal expected degrees, $W$-random graphs and stochastic block models with a growing number of blocks. Furthermore, we show our theorems can be applied to random Gram matrices with a variance profile for which we can find the limiting spectral distributions under weaker assumptions than previous results.
Disk Masses at the End of the Main Accretion Phase: CARMA Observations and Multi-wavelength Modeling of Class I Protostars

We present imaging observations at the 1.3 mm wavelength of Class I protostars in the Taurus star-forming region, obtained with the CARMA interferometer. Of an initial sample of 10 objects, we detected and imaged millimeter wavelength emission from 9. One of the nine is resolved into two sources and detailed analysis of this binary protostellar system is deferred to a future paper. For the remaining eight objects, we use the CARMA data to determine the basic morphology of the millimeter emission. Combining the millimeter data with 0.9 μm images of scattered light, Spitzer Infrared Spectrograph spectra, and broadband spectral energy distributions (all from the literature), we attempt to determine the structure of the circumstellar material. We consider models including both circumstellar disks and envelopes, and constrain the masses (and other structural parameters) of each of these components. We show that the disk masses in our sample span a range from 0.01 to 0.1 M ☉. The disk masses for our sample are significantly higher than for samples of more evolved Class II objects. Thus, Class I disk masses probably provide a more accurate estimate of the initial mass budget for star and planet formation. However, the disk masses determined here are lower than required by theories of giant planet formation. The masses also appear too low for gravitational instability, which could lead to high mass accretion rates. Even in these Class I disks, substantial particle growth may have hidden much of the disk mass in hard-to-see larger bodies.
Semigroups and one-way functions

We study the complexity classes &#x1d5af; and &#x1d5ad;&#x1d5af; through a semigroup &#x1d5bf;&#x1d5af; ("polynomial-time functions"), consisting of all polynomially balanced polynomial-time computable partial functions. The semigroup &#x1d5bf;&#x1d5af; is non-regular if and only if &#x1d5af; ≠ &#x1d5ad;&#x1d5af;. The one-way functions considered here are based on worst-case complexity (they are not cryptographic); they are exactly the non-regular elements of &#x1d5bf;&#x1d5af;. We prove various properties of &#x1d5bf;&#x1d5af;, e.g. that it is finitely generated. We define reductions with respect to which certain universal one-way functions are &#x1d5bf;&#x1d5af;-complete.
The Rees product of posets

We determine how the flagf-vector of any graded poset changes under the Rees product with the chain, and more generally, any t-ary tree. As a corollary, the Mobius function of the Rees product of any graded poset with the chain, and more generally, the t-ary tree, is exactly the same as the Rees product of its dual with the chain, respectively, t-ary chain. We then study enumerative and homological properties of the Rees product of the cubical lattice with the chain. We give a bijective proof that the Mobius function of this poset can be expressed as n times a signed derangement number. From this we derive a new bijective proof of Jonsson's result that the Mobius function of the Rees product of the Boolean algebra with the chain is given by a derangement number. Using poset homology techniques we find an explicit basis for the reduced homology and determine a representation for the reduced homology of the order complex of the Rees product of the cubical lattice with the chain over the symmetric group.
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers

We propose the first general and practical framework to design certifiable algorithms for robust geometric perception in the presence of a large amount of outliers. We investigate the use of a truncated least squares (TLS) cost function, which is known to be robust to outliers, but leads to hard, nonconvex, and nonsmooth optimization problems. Our first contribution is to show that -for a broad class of geometric perception problems- TLS estimation can be reformulated as an optimization over the ring of polynomials and Lasserre's hierarchy of convex moment relaxations is empirically tight at the minimum relaxation order (i.e., certifiably obtains the global minimum of the nonconvex TLS problem). Our second contribution is to exploit the structural sparsity of the objective and constraint polynomials and leverage basis reduction to significantly reduce the size of the semidefinite program (SDP) resulting from the moment relaxation, without compromising its tightness. Our third contribution is to develop scalable dual optimality certifiers from the lens of sums-of-squares (SOS) relaxation, that can compute the suboptimality gap and possibly certify global optimality of any candidate solution (e.g., returned by fast heuristics such as RANSAC or graduated non-convexity). Our dual certifiers leverage Douglas-Rachford Splitting to solve a convex feasibility SDP. Numerical experiments across different perception problems, including single rotation averaging, shape alignment, 3D point cloud and mesh registration, and high-integrity satellite pose estimation, demonstrate the tightness of our relaxations, the correctness of the certification, and the scalability of the proposed dual certifiers to large problems, beyond the reach of current SDP solvers.
Transporting stochastic direct and indirect effects to new populations.

Transported mediation effects may contribute to understanding how interventions work differently when applied to new populations. However, we are not aware of any estimators for such effects. Thus, we propose two doubly robust, efficient estimators of transported stochastic (also called randomized interventional) direct and indirect effects. We demonstrate their finite sample properties in a simulation study. We then apply the preferred substitution estimator to longitudinal data from the Moving to Opportunity Study, a large-scale housing voucher experiment, to transport stochastic indirect effect estimates of voucher receipt in childhood on subsequent risk of mental health or substance use disorder mediated through parental employment across sites, thereby gaining understanding of drivers of the site differences.
Thermodynamic Black Holes.

Black holes pose great difficulties for theory since gravity and quantum theory must be combined in some as yet unknown way. An additional difficulty is that detailed black hole observational data to guide theorists is lacking. In this paper, I sidestep the difficulties of combining gravity and quantum theory by employing black hole thermodynamics augmented by ideas from the information geometry of thermodynamics. I propose a purely thermodynamic agenda for choosing correct candidate black hole thermodynamic scaled equations of state, parameterized by two exponents. These two adjustable exponents may be set to accommodate additional black hole information, either from astrophysical observations or from some microscopic theory, such as string theory. My approach assumes implicitly that the as yet unknown microscopic black hole constituents have strong effective interactions between them, of a type found in critical phenomena. In this picture, the details of the microscopic interaction forces are not important, and the essential macroscopic picture emerges from general assumptions about the number of independent thermodynamic variables, types of critical points, boundary conditions, and analyticity. I use the simple Kerr and Reissner-Nordstrom black holes for guidance, and find candidate equations of state that embody several the features of these purely gravitational models. My approach may offer a productive new way to select black hole thermodynamic equations of state representing both gravitational and quantum properties.
Delay-Performance Tradeoffs in Causal Microphone Array Processing

In real-time listening enhancement applications, such as hearing aid signal processing, sounds must be processed with no more than a few milliseconds of delay to sound natural to the listener. Listening devices can achieve better performance with lower delay by using microphone arrays to filter acoustic signals in both space and time. Here, we analyze the tradeoff between delay and squared-error performance of causal multichannel Wiener filters for microphone array noise reduction. We compute exact expressions for the delay-error curves in two special cases and present experimental results from real-world microphone array recordings. We find that delay-performance characteristics are determined by both the spatial and temporal correlation structures of the signals.
Characterization of Model-Based Uncertainties in Incompressible Turbulent Flows by Machine Learning

This work determines the inaccuracy of using Reynolds averaged Navier Stokes (RANS) turbulence models in transition to turbulent flow regimes by predicting the model-based discrepancies between RANS and large eddy simulation (LES) models and then incorporates the capabilities of machine learning algorithms to characterize the discrepancies which are defined as a function of mean flow properties of RANS simulations. First, three-dimensional CFD simulations using k-omega Shear Stress Transport (SST) and dynamic one-equation subgrid-scale models are conducted in a wall-bounded channel containing a cylinder for RANS and LES, respectively, to identify the turbulent kinetic energy discrepancy. Second, several flow features such as viscosity ratio, wall-distance based Reynolds number, and vortex stretching are calculated from the mean flow properties of RANS. Then these flow features are regressed onto the discrepancy using a Random Forests regression algorithm. Finally, the discrepancy of the test flow is predicted using the trained algorithm. The results reveal that a significant discrepancy exists between RANS and LES simulations and ML algorithm successfully predicts the increased model uncertainties caused by the employment of k-omega SST turbulence model for transitional fluid flows.
Quantum Optimization of Fully Connected Spin Glasses

Quantum computing promises to be more efficient and significantly faster than today's classical computing. Researchers compare the performance of a quantum annealer with that of classical algorithms for the first time on hard spin-glass problems.
Muon Tracing and Image Reconstruction Algorithms for Cosmic Ray Muon Computed Tomography

Cosmic ray muon-computed tomography ( $\mu$ CT) is a new imaging modality with unique characteristics that could be particularly important for diverse applications including nuclear nonproliferation, spent nuclear fuel monitoring, cargo scanning, and volcano imaging. The strong scattering dependence of muons on atomic number Z in combination with high penetration range could offer a significant advantage over existing techniques when dense, shielded containers must be imaged. However, $\mu$ CT reconstruction using conventional filtered back-projection is limited due to the overly simple assumptions that do not consider the curved path caused by multiple Coulomb scattering prompting the need for more sophisticated approaches to be developed. In this paper, we argue that the use of improved muon tracing and scattering angle projection algorithms as well as an algebraic reconstruction technique should produce muon tomographic images with improved quality–or require fewer muons to produce the same image quality–compared to the case where conventional methods are used. We report on the development and assessment of three novel muon tracing methods and two scattering angle projection methods for $\mu$ CT. Simulated dry storage casks with single and partial missing fuel assemblies were used as numerical examples to assess and compare the proposed methods. The reconstructed images showed an expected improvement in image quality when compared with conventional techniques, even without muon momentum information, which should lead to improved detection capability, even for partial defects.
Precise high-fidelity electron–nuclear spin entangling gates in NV centers via hybrid dynamical decoupling sequences

Color centers in solids, such as the nitrogen-vacancy center in diamond, offer well-protected and well-controlled localized electron spins that can be employed in various quantum technologies. Moreover, the long coherence time of the surrounding spinful nuclei can enable a robust quantum register controlled through the color center. We design pulse sequence protocols that drive the electron spin to generate robust entangling gates with these nuclear memory qubits. We find that compared to using Carr-Purcell-Meiboom-Gill (CPMG) alone, Uhrig decoupling sequence and hybrid protocols composed of CPMG and Uhrig sequences improve these entangling gates in terms of fidelity, spin control range, and spin selectivity. We provide analytical expressions for the sequence protocols and also show numerically the efficacy of our method on nitrogen-vacancy centers in diamond. Our results are broadly applicable to color centers weakly coupled to a small number of nuclear spin qubits.
Constructing convex projective 3-manifolds with generalized cusps

We prove that non-compact finite volume hyperbolic 3-manifolds that satisfy a mild cohomological condition (infinitesimal rigidity) admit a family of properly convex deformations of their complete hyperbolic structure where the ends become generalized cusps of type 1 or type 2. We also discuss methods for controlling which types of cusp occur. Using these methods we produce the first known example of a 1-cusped hyperbolic 3-manifold that admits a convex projective structure with a type 2 cusp. We also use these techniques to produce new 1-cusped manifolds that admit a convex projective structure with a type 1 cusp.
Design of a Large ATM Switch with Trunk Grouping

We describe a large ATM switch with moderate circuit complexity which delivers packets in sequence with a small constant delay. The switch takes advantage of trunk grouping to increase throughput and has the same topology as that of a Banyan network. By using modules that look at k inputs (k>16) at a time and recursively dividing the packets into two groups, the proposed switch achieves a good throughput value that is relatively independent of switch size. Cell loss on the order of 10−10 can be obtained using the switch. The switch has O(n log2n) complexity in the number of modules where n is the number of inputs. The modules of the switch have very regular interconnections, and hence are suitable for dense VLSI implementation.
Tensor Canonical Correlation Analysis With Convergence and Statistical Guarantees

In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of an ad-hoc way of transforming data to vectors due to the comput...
Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data.

Motivation Predictive models of DNA chromatin profile (i.e. epigenetic state), such as transcription factor binding, are essential for understanding regulatory processes and developing gene therapies. It is known that the 3D genome, or spatial structure of DNA, is highly influential in the chromatin profile. Deep neural networks have achieved state of the art performance on chromatin profile prediction by using short windows of DNA sequences independently. These methods, however, ignore the long-range dependencies when predicting the chromatin profiles because modeling the 3D genome is challenging. Results In this work, we introduce ChromeGCN, a graph convolutional network for chromatin profile prediction by fusing both local sequence and long-range 3D genome information. By incorporating the 3D genome, we relax the independent and identically distributed assumption of local windows for a better representation of DNA. ChromeGCN explicitly incorporates known long-range interactions into the modeling, allowing us to identify and interpret those important long-range dependencies in influencing chromatin profiles. We show experimentally that by fusing sequential and 3D genome data using ChromeGCN, we get a significant improvement over the state-of-the-art deep learning methods as indicated by three metrics. Importantly, we show that ChromeGCN is particularly useful for identifying epigenetic effects in those DNA windows that have a high degree of interactions with other DNA windows. Availability and implementation https://github.com/QData/ChromeGCN. Supplementary information Supplementary data are available at Bioinformatics online.
TIDE: A General Toolbox for Identifying Object Detection Errors

We introduce TIDE, a framework and associated toolbox (https://dbolya.github.io/tide/) for analyzing the sources of error in object detection and instance segmentation algorithms. Importantly, our framework is applicable across datasets and can be applied directly to output prediction files without required knowledge of the underlying prediction system. Thus, our framework can be used as a drop-in replacement for the standard mAP computation while providing a comprehensive analysis of each model’s strengths and weaknesses. We segment errors into six types and, crucially, are the first to introduce a technique for measuring the contribution of each error in a way that isolates its effect on overall performance. We show that such a representation is critical for drawing accurate, comprehensive conclusions through in-depth analysis across 4 datasets and 7 recognition models.
Locally Decodable/Correctable Codes for Insertions and Deletions.

Recent efforts in coding theory have focused on building codes for insertions and deletions, called insdel codes, with optimal trade-offs between their redundancy and their error-correction capabilities, as well as efficient encoding and decoding algorithms. In many applications, polynomial running time may still be prohibitively expensive, which has motivated the study of codes with super-efficient decoding algorithms. These have led to the well-studied notions of Locally Decodable Codes (LDCs) and Locally Correctable Codes (LCCs). Inspired by these notions, Ostrovsky and Paskin-Cherniavsky (Information Theoretic Security, 2015) generalized Hamming LDCs to insertions and deletions. To the best of our knowledge, these are the only known results that study the analogues of Hamming LDCs in channels performing insertions and deletions. Here we continue the study of insdel codes that admit local algorithms. Specifically, we reprove the results of Ostrovsky and Paskin-Cherniavsky for insdel LDCs using a different set of techniques. We also observe that the techniques extend to constructions of LCCs. Specifically, we obtain insdel LDCs and LCCs from their Hamming LDCs and LCCs analogues, respectively. The rate and error-correction capability blow up only by a constant factor, while the query complexity blows up by a poly log factor in the block length. Since insdel locally decodable/correctble codes are scarcely studied in the literature, we believe our results and techniques may lead to further research. In particular, we conjecture that constant-query insdel LDCs/LCCs do not exist.
Coronavirus Infections and Deaths by Poverty Status: The Effects of Social Distancing.

We study the spread of COVID-19 infections and deaths by county poverty level in the US. In the beginning of the pandemic, counties with either very low poverty levels or very high poverty levels reported the highest numbers of cases. A U-shaped relationship prevails for counties with high population density while among counties with low population density, only poorer counties report high incidence rates of COVID-19. Second, we discuss the pattern of infections spreading from higher to lower income counties. Third, we show that stay-at-home mandates caused significantly higher reductions in mobility in high income counties that experienced adverse weather shocks than counties that did not. These effects are not present in counties with high poverty rates. Using weather shocks in combination with stay-at-home mandates as an instrument for social distancing, we find that measures taken to promote social distancing helped curb infections in high income counties but not in low income counties. These results have important policy implications for containing the spread of infectious diseases in the future.
Policy Learning With Observational Data

In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.
Monte Carlo analysis of phosphorene nanotransistors

Experimental studies on two-dimensional (2D) materials are still in the early stages, and most of the theoretical studies performed to screen these materials are limited to the room-temperature carrier mobility in the free-standing 2D layers. With the dimensions of devices moving toward nanometer-scale lengths, the room-temperature carrier mobility—an equilibrium concept—may not be the main quantity that controls the performance of devices based on these 2D materials, since electronic transport occurs under strong off-equilibrium conditions. Here we account for these non-equilibrium conditions and, for the case of monolayer phosphorene (monolayer black phosphorus), show the results of device simulations for a short-channel n-MOSFET, using the Monte Carlo method coupled with the Poisson equation, including full bands and full electron–phonon matrix elements obtained from density functional theory. Our simulations reveal significant intrinsic limitations to the performance of phosphorene as a channel material in nanotransistors.
On the correlation matrix of the discrete fourier transform and the fast solution of large toeplitz systems for long-memory time series

We show that for long-memory time series, the Toeplitz system Σn(f)x = b can be solved in O(nlog5/2n) operations using a well-known version of the preconditioned conjugate gradient method, where Σn(f) is the n × n covariance matrix, f is the spectral density, and b is a known vector. Solutions of such systems are needed for optimal linear prediction and interpolation. We establish connections between this preconditioning method and the frequency domain analysis of time series. Indeed, the running time of the algorithm is determined by the rate of increase in the condition number of the correlation matrix of the discrete Fourier transform (DFT) vector, as the sample size tends to ∞. We derive an upper bound for this condition number. The bound is of interest in its own right, because it sheds some light on the widely used but heuristic approximation that the standardized DFT coefficients are uncorrelated with equal variances. We present applications of the preconditioning methodology to the forecasting of ...
Lobotomizing the defense brain

Economists model national defense as a pure public good optimally provided by a benevolent and omnipotent “defense brain” to maximize social welfare. I critically consider five assumptions associated with this view: (1) that defense and security is a pure public good that must be provided by a national government, (2) that state-provided defense is always a “good” and never a “bad”, (3) that the state can provide defense in the optimal quantity and quality, (4) that state expenditures on defense are neutral with respect to private economic activity, and (5) that state-provided defense activities are neutral with respect to domestic political institutions. I discuss an alternative framework—the “individualistic view”—for analyzing defense provision and suggest it is superior for understanding reality.
Enterprise ontologies: Review and an activity theory approach

This paper provides an alternative, theory-based approach for generating an enterprise ontology, using activity theory. Activity theory, based on psychology, provides a template-based approach in capturing the context of individual activity in an organization. In particular, activity theory uses eight key class concepts: activity, outcome, subject, object, community, rules, tools and division of labor, as a means of organizing and capturing context information. Activity theory is compared with other previously proposed enterprise ontologies, including “ARIS,” “REA,” “The Enterprise Ontology,” “TOVE,” and “Enterprise Ontology.” This activity theory approach is found to consider information typically not included in enterprise ontologies, but likely of interest in most any enterprise.
A leverage ratio rule for capital adequacy

Abstract This paper studies the economic foundations for maximum leverage ratio capital adequacy rules. The paper makes three contributions to the literature. First, we show how to determine the maximum leverage ratio such that the probability of insolvency is less than some predetermined quantity. Two, we show that a leverage ratio rule controls for the same risks as does a Value-at-Risk (VaR) capital adequacy rule. Third, we argue that leverage ratio rules are better than VaR rules because they are more intuitive and easier to compare across firms.
The dynamic relation between options trading, short selling, and aggregate stock returns

We examine the information contained in option trading and short selling using a dynamic VAR model. First, we address whether options and shorts are complements or substitutes. Contrary to existing event studies around option listing introductions, we show short selling and options trading are complements rather than substitutes. Second, we examine which group is relatively more informed. The results indicate that options traders are relatively more informed. Finally, we examine if options are redundant. Our results indicate that options markets are non-redundant.
Charitable Choices: The Need for a Uniform Nonprofit Limited Liability Company Act (UNLLCA)

This work is an extension of the analysis conducted in "Papa's Brand New Bag: The Need for IRS Recognition of an Independent Nonprofit Limited Liability Company (NLLC)", which discusses the importance of the limited liability company (LLC) in the nonprofit context and the devices needed to ensure its full enjoyment by the nonprofit sector. Part I examines the history, goals, and purposes for which the ULLCA and other uniform acts were developed. Part II discusses the important role nonprofits play in American society, the economic challenges they face in advancing their charitable purposes, a selection of the current forms of corporate organization available to them, and the limited liability company's not yet fully-realized potential in the nonprofit context. Part III discusses the ULLCA's failure, as presently constructed, to guide states in addressing unique nonprofit characteristics and needs. Specifically, the ULLCA leaves key nonprofit governance matters unaddressed, which puts smaller, less sophisticated nonprofits at risk of failing to meet requirements for Internal Revenue Service (IRS) recognition as an 501 (c) (3) tax exempt entity. In comparing the ULLCA to the MNCA and UUNAA, it becomes evident that the ULC can address the ULLCA's nonprofit deficiencies by including protective provi­sions similar to those in the MNCA and UUNAA in a Uniform Nonprofit Limited Liability Company Act (UNLLCA). Part IV re­futes claims that the UNLLCA is unnecessary, untimely, and encourages an unhealthy proliferation of new entities. Part V con­cludes that the ULC should promulgate a separate UNLLCA to address the unique needs of nonprofits. The appendix proposes key UNLLCA provisions that closely follow the ULLCA where possi­ble and incorporate key protective provisions contained in the MNCA and UUNAA where appropriate.
Policy convergence under approval and plurality voting: the role of policy commitment

Proponents of Approval Voting argue that this electoral rule leads to more centrist outcomes compared to Plurality Voting. This claim has been substantiated by scholarly work using spatial models of political competition. We revisit this issue in the context of a model of political competition in which (1) candidates are policy-motivated; (2) candidacy decisions are endogenous; and (3) candidates can credibly commit to implementing any policy. Under these assumptions we find the opposite to be true – Plurality Voting yields convergence to the median voter’s ideal policy but Approval Voting may not. We argue that this result is driven by the differential incentives for candidate entry under the two voting rules. Our results suggest that whether Approval Voting yields more centrist outcomes vis-a-vis Plurality Voting depends on the possibility of policy commitment on the part of the candidates.
The Impact of Demographics on Policy Outcomes Under Varying Institutional Frameworks

This paper examines the impact that demographics have on policy outcomes. The impact that aldermanic ward-level demographics have on the number of liquor licenses is measured in two US cities. In one city there is a great deal of direct resident involvement in the issuance process, while in the other city, issuance decisions are handled by elected representatives. This research does find that demographics have a significant impact on policy outcomes. However, the paper does not find a significant difference in outcomes between decisions made by elected representatives and those made by the community.
The Predictive Validity of Subjective Probabilities of Survival

Although expectations, or more precisely stubjective probability distribtutions, play a prominent role in models of decision making uinder uincertainty, we have had very little data on them. Based on panel data from the Health and Retirement Stu-dy, we stu-dy the evolution of su-bjective stur-vival probabilities and their ability to predict actual mortality. In panel, respondents modify their survival probabilities in response to new information such as the onset of a new disease condition. Stubjective survival probabilities predict acttual survival: those who surnived in the panel reported sturTival probabilities approximately 50% greater at baseline than those who died. Individual behaviour depends notjust on the current state of the world but also on what individuals expect will happen in the future: individuals choose their desired years of schooling based on expected future income; engage in family planning based on expected fertility; and save for retirement based on their expected length of life. Researchers wishing to understand these behaviours must therefore incorporate expectations into their models.' Despite the importance of expectations, we rarely have data on the expectations of individuals and, therefore, have had to make assumptions about them. Often, individuals are assumed to face probabilities equal to the average population probabilities. For example, when survival probabilities are needed in economic models of savings behaviour, researchers use survival probabilities calculated from life tables. When forecasting future income, average incorne of a similar demographic group is often used. In many cases, however, average values will not be the appropriate measure for most individuals: subjective probabilities are likely to differ across individuals, and individuals will choose their behaviours based on their own probabilities. Studies which assume that individuals base their behaviours on population probabilities may therefore give misleading results. A potential improvement over population probabilities is to base models on an individual's own subjective probability.2 Such probabilities could be constructed
To deceive or not to deceive: The effect of deception on behavior in future laboratory experiments

Experimental economists believe (and enforce the idea) that researchers should not employ deception in the design of experiments. This rule exists in order to protect a public good: the ability of other researchers to conduct experiments and to have participants trust their instructions to be an accurate representation of the game being played. Yet other social sciences, particularly psychology, do not maintain such a rule. We examine whether such a public goods problem exists by purposefully deceiving some participants in one study, informing them of this fact, and then examining whether the deceived participants behave differently in a subsequent study. We find significant differences in the selection of individuals who return to play after being deceived as well as (to a lesser extent) the behavior in the subsequent games, thus providing qualified support for the proscription of deception. We discuss policy implications for the maintenance of separate participant pools.
INVESTOR SENTIMENT AND THE CROSS-SECTION OF STOCK RETURNS

We study how investor sentiment affects the cross-section of stock returns. We predict that a wave of investor sentiment has larger effects on securities whose valuations are highly subjective and difficult to arbitrage. Consistent with this prediction, we find that when beginning-of-period proxies for sentiment are low, subsequent returns are relatively high for small stocks, young stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme growth stocks, and distressed stocks. When sentiment is high, on the other hand, these categories of stock earn relatively low subsequent returns.