cssi/SciSciGPT-SciSciNet huggingface dataset:
Rapid quantitative imaging of high intensity ultrasonic pressure fields.

High intensity focused ultrasound (FUS) is a noninvasive technique for treatment of tissues that can lie deep within the body. There is a need for methods to rapidly and quantitatively map FUS pressure beams for quality assurance and accelerate development of FUS systems and techniques. However, conventional ultrasound pressure beam mapping instruments, including hydrophones and optical techniques, are slow, not portable, and expensive, and most cannot map beams at actual therapeutic pressure levels. Here, a rapid projection imaging method to quantitatively map FUS pressure beams based on continuous-wave background-oriented schlieren (CW-BOS) imaging is reported. The method requires only a water tank, a background pattern, and a camera and uses a multi-layer deep neural network to reconstruct two-dimensional root-mean-square (RMS) projected pressure maps that resolve the ultrasound propagation dimension and one lateral dimension. In this work, the method was applied to collect beam maps over a 3 × 1 cm2 field-of-view with 0.425 mm resolution for focal pressures up to 9 MPa. Results at two frequencies and comparisons to hydrophone measurements show that CW-BOS imaging produces high-resolution quantitative RMS projected FUS pressure maps in under 10 s, the technique is linear and robust to beam rotations and translations, and it can map aberrated beams.
The Tax Elasticity of Capital Gains and Revenue-Maximizing Rates

This paper uses a direct-projections approach to estimate the effect of capital gains taxation on realizations at the state level, and then develops a framework for determining revenue-maximizing rates at the federal level. We find that the elasticity of revenues with respect to the tax rate over a ten-year period is -0.5 to -0.3, indicating that capital gains tax cuts do not pay for themselves, and that a 5 percentage point rate increase would yield $18 to $30 billion in annual federal tax revenue. Our long-run estimates yield revenue-maximizing capital gains tax rates of 38 to 47 percent.
Isoflurane and Carbon Dioxide Elicit Similar Behavioral Responses in Rats.

Euthanasia in rodents is an ongoing topic of debate due to concerns regarding the aversive nature of gases with anesthetic properties such as carbon dioxide (CO2) and isoflurane. The aim of this study was to expand upon previously published work evaluating the aversiveness of CO2 by introducing an isoflurane treatment group in parallel. Aversion was tested using a forced exposure setup and an aversion-avoidance setup. In the first part of the study, 12 naive female Sprague–Dawley rats were exposed during four consecutive days, once to each of four treatments: isoflurane, fox urine, oxygen, and CO2. In the second part of the study, 24 naive female Sprague–Dawley rats and 12 rats from the first experiment were exposed to CO2, isoflurane, or both gases. In the forced exposure study, there were no significant differences between CO2 and isoflurane treatments except in line crosses. Overall, rats were more active in the isoflurane and CO2 treatments compared to the control groups, suggesting that isoflurane and CO2 are similarly aversive. In the aversion-avoidance study, rats previously exposed to isoflurane left the dark chamber significantly earlier compared to naive rats during exposure to isoflurane. We also show that learned aversion to isoflurane is sustained for at least 15 days after initial exposure. Given this result, we suggest that CO2 is superior to isoflurane when euthanizing rodents with prior exposure to isoflurane. Overall, these results confirm previous studies which suggest that care should be taken when considering the serial use of isoflurane as an anesthetic.
Hillslopes in humid‐tropical climates aren't always wet: implications for hydrologic response and landslide initiation in Puerto Rico

The devastating impacts of the widespread flooding and landsliding in Puerto Rico following the September 2017 landfall of Hurricane Maria highlight the enhanced hazard potential from increasingly ...
Non-interactive Zero-Knowledge Arguments for QMA, with Preprocessing

A non-interactive zero-knowledge (NIZK) proof system for a language \(L\in \text {NP} \) allows a prover (who is provided with an instance \(x \in L\), and a witness w for x) to compute a classical certificate \(\pi \) for the claim that \(x\in L\) such that \(\pi \) has the following properties: 1) \(\pi \) can be verified efficiently, and 2) \(\pi \) does not reveal any information about w, besides the fact that it exists (i.e. that \(x \in L\)). NIZK proof systems have recently been shown to exist for all languages in \(\text {NP} \) in the common reference string (CRS) model and under the learning with errors (LWE) assumption.
Continuous reports of sensed hand position during sensorimotor adaptation.

Sensorimotor adaptation operates in an obligatory manner. Qualitatively, subjective reports obtained after adaptation demonstrate that, in many conditions, participants are unaware of significant c...
Quantitative stability for the Heisenberg–Pauli–Weyl inequality

Abstract We prove a quantitative stability result for the Heisenberg–Pauli–Weyl inequality. This leads to a next, and next-to-next order correction terms in the inequality.
Spatio-temporal change of support modeling with R

Spatio-temporal change of support methods are designed for statistical analysis on spatial and temporal domains which can differ from those of the observed data. Previous work introduced a parsimonious class of Bayesian hierarchical spatio-temporal models, which we refer to as STCOS, for the case of Gaussian outcomes. Application of STCOS methodology from this literature requires a level of proficiency with spatio-temporal methods and statistical computing which may be a hurdle for potential users. The present work seeks to bridge this gap by guiding readers through STCOS computations. We focus on the R computing environment because of its popularity, free availability, and high quality contributed packages. The stcos package is introduced to facilitate computations for the STCOS model. A motivating application is the American Community Survey (ACS), an ongoing survey administered by the U.S. Census Bureau that measures key socioeconomic and demographic variables for various populations in the United States. The STCOS methodology offers a principled approach to compute model-based estimates and associated measures of uncertainty for ACS variables on customized geographies and/or time periods. We present a detailed case study with ACS data as a guide for change of support analysis in R, and as a foundation which can be customized to other applications.
Density dependent Resource Budget Model for alternate bearing.

Abstract Alternate bearing, seen in many types of plants, is the variable yield with a strongly biennial pattern. In this paper, we introduce a new model for alternate bearing behavior. Similar to the well-known Resource Budget Model, our model is based on the balance between photosynthesis or other limiting resource accumulation and reproduction processes. We consider two novel features with our model, 1) the existence of a finite capacity in the tree’s resource reservoir and 2) the possibility of having low (but non-zero) yield when the tree’s resource level is low. We achieve the former using a density dependent resource accumulation function, and the latter by removing the concept of the well-defined threshold used in the Resource Budget Model. At the level of an individual tree, our model has a stable two-cycle solution, which is suitable to model plants in which the alternate bearing behavior is pronounced. We incorporate environmental stochasticity by adding two uncorrelated noise terms to the parameters of the model associated with the nutrient accumulation and reproduction processes. Furthermore, we examine the model’s behavior on a system of two coupled trees with direct coupling. Unlike the coupled Resource Budget Model, for which the only stable solution is the out-of-phase solution, our model with direct coupling has stable in-phase period-2 solutions. This suggests that our model might serve to explain spatial synchrony on a larger scale.
Evaluation of treatment effect modification by biomarkers measured pre- and post-randomization in the presence of non-monotone missingness

In vaccine studies, an important research question is to study effect modification of clinical treatment efficacy by intermediate biomarker-based principal strata. In settings where participants entering a trial may have prior exposure and therefore variable baseline biomarker values, clinical treatment efficacy may further depend jointly on a biomarker measured at baseline and measured at a fixed time after vaccination. This makes it important to conduct a bivariate effect modification analysis by both the intermediate biomarker-based principal strata and the baseline biomarker values. Existing research allows this assessment if the sampling of baseline and intermediate biomarkers follows a monotone pattern, i.e., if participants who have the biomarker measured post-randomization would also have the biomarker measured at baseline. However, additional complications in study design could happen in practice. For example, in a dengue correlates study, baseline biomarker values were only available from a fraction of participants who have biomarkers measured post-randomization. How to conduct the bivariate effect modification analysis in these studies remains an open research question. In this article, we propose approaches for bivariate effect modification analysis in the complicated sampling design based on an estimated likelihood framework. We demonstrate advantages of the proposed method over existing methods through numerical studies and illustrate our method with data sets from two phase 3 dengue vaccine efficacy trials.
Dueling Posterior Sampling for Preference-Based Reinforcement Learning

In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Building upon ideas from preference-based bandit learning and posterior sampling in RL, we present Dueling Posterior Sampling (DPS), which employs preference-based posterior sampling to learn both the system dynamics and the underlying utility function that governs the user's preferences. Because preference feedback is provided on trajectories rather than individual state/action pairs, we develop a Bayesian approach to solving the credit assignment problem, translating user preferences to a posterior distribution over state/action reward models. We prove an asymptotic no-regret rate for DPS with a Bayesian logistic regression credit assignment model; to our knowledge, this is the first regret guarantee for preference-based RL. We also discuss possible avenues for extending this proof methodology to analyze other credit assignment models. Finally, we evaluate the approach empirically, showing competitive performance against existing baselines.
Quantifying the employment accessibility benefits of shared automated vehicle mobility services: Consumer welfare approach using logsums

Abstract The goal of this study is to assess and quantify the potential employment accessibility benefits of shared-use automated vehicle (AV) mobility service (SAMS) modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study proposes employing a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. The study further captures heterogeneity of workers using a latent class analysis (LCA) approach to account for different worker clusters valuing different types of employment opportunities differently, in which the socio-demographic characteristics of workers are the LCA model inputs. The accessibility analysis results in Southern California indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits.
Minimum time learning model predictive control

In this paper we present a Learning Model Predictive Control (LMPC) strategy for linear and nonlinear time optimal control problems. Our work builds on existing LMPC methodologies and it guarantees finite time convergence properties for the closed‐loop system. We show how to construct a time varying safe set and terminal cost function using closed‐loop data. The resulting LMPC policy is time varying and it guarantees recursive constraint satisfaction and non‐decreasing performance. Computational efficiency is obtained by convexifing the time‐varying safe set and time‐varying terminal cost function. We demonstrate that, for a class of nonlinear system and convex constraints, the convex LMPC formulation guarantees recursive constraint satisfaction and nondecreasing performance. Finally, we illustrate the effectiveness of the proposed strategies on minimum time obstacle avoidance and racing examples.
Differential effects of intervention timing on COVID-19 spread in the United States

Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.
Uncertainty Propagation in Deep Neural Networks Using Extended Kalman Filtering

Extended Kalman Filtering (EKF) can be used to propagate and quantify input uncertainty through a Deep Neural Network (DNN) assuming mild hypotheses on the input distribution. This methodology yields results comparable to existing methods of uncertainty propagation for DNNs while lowering the computational overhead considerably. Additionally, EKF allows model error to be naturally incorporated into the output uncertainty.
The BHB Stars in the Survey Fields of Rodgers et al. (1993): New Observations and Comparisons with Other Recent Surveys

We study blue horizontal branch (BHB) and RR Lyrae stars in the Rodgers et al. fields and compare their velocity and density distributions with other surveys in the same part of the sky. Photometric data are given for 176 early-type stars in the northern field. We identify fourteen BHB stars and four possible BHB stars, and determine the selection efficiency of the Century survey, the HK survey, and the SDSS survey for BHB stars. We give light curves and γ radial velocities for three type ab RR Lyrae stars in the northern field; comparison with the nearby LONEOS survey shows that there is likely to be an equal number of lower-amplitude type ab RR Lyrae stars that we do not find. There are therefore at least twice as many BHB stars as type ab RR Lyrae stars in the northern field—similar to the ratio in the solar neighborhood. The velocity distribution of the southern field shows no evidence for an anomalous thick disk that was found by Gilmore et al.; the halo velocity peaks at a slightly prograde rotational velocity but there is also a significant retrograde halo component in this field. The velocity distribution in the northern field shows no evidence of Galactic rotation for |Z| ≥ 4 kpc and a slight prograde motion for |Z| < 4 kpc. The space densities of BHB stars in the northern field agree with an extrapolation of the power-law distribution recently derived by de Propris et al. For |Z| < 4 kpc, however, we observe an excess of BHB stars compared with this power law. We conclude that these BHB stars mostly belong to a spatially flattened, non-rotating inner halo component of the Milky Way in confirmation of the Kinman et al. analysis of Century survey BHB stars.
Building Halo Merger Trees from the Q Continuum Simulation

Cosmological N-body simulations rank among the most computationally intensive efforts today. A key challenge is the analysis of structure, substructure, and the merger history for many billions of compact particle clusters, called halos. Effectively representing the merging history of halos is essential for many galaxy formation models used to generate synthetic sky catalogs, an important application of modern cosmological simulations. Generating realistic mock catalogs requires computing the halo formation history from simulations with large volumes and billions of halos over many time steps, taking hundreds of terabytes of analysis data. We present fast parallel algorithms for producing halo merger trees and tracking halo substructure from a single-level, density-based clustering algorithm. Merger trees are created from analyzing the halo-particle membership function in adjacent snapshots, and substructure is identified by tracking the "cores" of merging halos – sets of particles near the halo center. Core tracking is performed after creating merger trees and uses the relationships found during tree construction to associate substructures with hosts. The algorithms are implemented with MPI and evaluated on a Cray XK7 supercomputer using up to 16,384 processes on data from HACC, a modern cosmological simulation framework. We present results for creating merger trees from 101 analysis snapshots taken from the Q Continuum, a large volume, high mass resolution, cosmological simulation evolving half a trillion particles.
RESOLVED SPECTROSCOPY OF M DWARF/L DWARF BINARIES. III. THE 'WIDE' L3.5/L4 DWARF BINARY 2MASS J15500845+1455180AB

We report the identification of 2MASS J15500845+1455180 as a 0. 9 L dwarf visual binary. This source is resolved in Sloan Digital Sky Survey (SDSS) images and in near-infrared imaging with the IRTF SpeX imager/spectrometer. The two components, oriented along a north-south axis, have similar brightnesses in the near-infrared (�K � 0.2 mag), although the fainter northern component is redder in J − K color. Resolved near-infrared spectroscopy indicates spectral types of L3.5 and L4, consistent with its L3 combined-light optical classification based on SDSS data. Physical association is confirmed through common proper motion, common spectrophotometric distances and low probability of chance alignment. The projected physical separation of 2MASS J1550+1455AB, 30� 3 AU at an estimated distance of 33� 3 pc, makes it the widest L dwarf-L dwarf pair identified to date, although such a separation is not unusual among very low-mass field binaries. The angular separation and spectral composition of this system makes it an excellent target for obtaining a precise lithium depletion age, and a potential age standard for low-temperature atmosphere studies. Subject headings: binaries: visual — stars: individual (2MASS J15500845+1455180) — stars: low mass, brown dwarfs
Identifying disease-free chest x-ray images with deep transfer learning

Chest X-rays (CXRs) are among the most commonly used medical image modalities. They are mostly used for screening, and an indication of disease typically results in subsequent tests. As this is mostly a screening test used to rule out chest abnormalities, the requesting clinicians are often interested in whether a CXR is normal or not. A machine learning algorithm that can accurately screen out even a small proportion of the “real normal” exams out of all requested CXRs would be highly beneficial in reducing the workload for radiologists. In this work, we report a deep neural network trained for classifying CXRs with the goal of identifying a large number of normal (disease-free) images without risking the discharge of sick patients. We use an ImageNet-pretrained Inception-ResNet-v2 model to provide the image features, which are further used to train a model on CXRs labelled by expert radiologists. The probability threshold for classification is optimized for 100% precision for the normal class, ensuring no sick patients are released. At this threshold we report an average recall of 50%. This means that the proposed solution has the potential to cut in half the number of disease-free CXRs examined by radiologists, without risking the discharge of sick patients.
Markov Chains for Collaboration

Consider a system of n players in which each initially starts on a different team. At each time step, we select an individual winner and an individual loser randomly and the loser joins the winner'...
The Fluence Distribution of Gamma-Ray Bursts

In the use and interpretation of log N-log S distributions for gamma-ray bursts, burst peak flux has typically been used for S. We consider here the use of the fluence as a measure of S, which may be a more appropriate quantity than the peak flux in such highly variable sources. We demonstrate that the selection effects on the fluence can be determined directly from the BATSE trigger data, and then we use techniques developed elsewhere to account for the important threshold effects and correlations. Applying the appropriate corrections to the distributions, we obtain a fluence distribution that shows a somewhat sharper break than the peak flux distribution, implying a possibly narrower fluence luminosity distribution. If bursts are at cosmological distances, these observations together indicate that evolution of the luminosity function is required.
Exotic decays of heavy B quarks

Heavy vector-like quarks of charge −1/3, B, have been searched for at the LHC through the decays B → bZ, bh, tW. In models where the B quark also carries charge under a new gauge group, new decay channels may dominate. We focus on the case where the B is charged under a U(1)′ and describe simple models where the dominant decay mode is $$ B\to bZ^{\prime}\to b\left(b\overline{b}\right) $$ . With the inclusion of dark matter such models can explain the excess of gamma rays from the Galactic center. We develop a search strategy for this decay chain and estimate that with integrated luminosity of 300 fb−1 the LHC will have the potential to discover both the B and the Z′ for B quarks with mass below ∼ 1.6 TeV, for a broad range of Z′ masses. A high-luminosity run can extend this reach to 2 TeV.
A generalized type semigroup and dynamical comparison

In this paper, we construct and study a semigroup associated to an action of a countable discrete group on a compact Hausdorff space that can be regarded as a higher dimensional generalization of the type semigroup. We study when this semigroup is almost unperforated. This leads to a new characterization of dynamical comparison and thus answers a question of Kerr and Schafhauser. In addition, this paper suggests a definition of comparison for dynamical systems in which neither the acting group is necessarily amenable nor the action is minimal.
Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains

We introduce a class of scalable Bayesian hierarchical models for the analysis of massive geostatistical datasets. The underlying idea combines ideas on high-dimensional geostatistics by partitioni...
Miura maps and inverse scattering for the Novikov–Veselov equation

We use the inverse scattering method to construct classical solu- tions for the Novikov-Veselov (NV) equation, solving a problem posed by Las- sas, Mueller, Siltanen, and Stahel. We exploit Bogadanov's Miura-type map which transforms solutions of the modified Novikov-Veselov (mNV) equation into solutions of the NV equation. We show that the Cauchy data of con- ductivity type considered by Lassas, Mueller, Siltanen, and Stahel correspond precisely to the range of the Miura map, so that it suffices to study the mNV equation. We solve the mNV equation using the scattering transform associ- ated to the defocussing Davey-Stewartson II equation.
Thermoelectric Transport Signatures of Dirac Composite Fermions in the Half-Filled Landau Level

Author(s): Potter, AC; Serbyn, M; Vishwanath, A | Abstract: The half-filled Landau level is expected to be approximately particle-hole symmetric, which requires an extension of the Halperin-Lee-Read (HLR) theory of the compressible state observed at this filling. Recent work indicates that, when particle-hole symmetry is preserved, the composite fermions experience a quantized π-Berry phase upon winding around the composite Fermi surface, analogous to Dirac fermions at the surface of a 3D topological insulator. In contrast, the effective low-energy theory of the composite fermion liquid originally proposed by HLR lacks particle-hole symmetry and has vanishing Berry phase. In this paper, we explain how thermoelectric transport measurements can be used to test the Dirac nature of the composite fermions by quantitatively extracting this Berry phase. First, we point out that longitudinal thermopower (Seebeck effect) is nonvanishing because of the unusual nature of particle-hole symmetry in this context and is not sensitive to the Berry phase. In contrast, we find that off-diagonal thermopower (Nernst effect) is directly related to the topological structure of the composite Fermi surface, vanishing for zero Berry phase and taking its maximal value for π Berry phase. In contrast, in purely electrical transport signatures, the Berry phase contributions appear as small corrections to a large background signal, making the Nernst effect a promising diagnostic of the Dirac nature of composite fermions.
NGC 4656UV: A UV-selected Tidal Dwarf Galaxy Candidate

We report the discovery of a UV-bright tidal dwarf galaxy (TDG) candidate in the NGC 4631/4656 galaxy group, which we designate NGC 4656UV. Using survey and archival data spanning from 1.4 GHz to the ultraviolet, we investigate the gas kinematics and stellar properties of this system. The H I morphologies of NGC 4656UV and its parent galaxy NGC 4656 are extremely disturbed, with significant amounts of counterrotating and extraplanar gas. From UV-FIR photometry, computed using a new method to correct for surface gradients on faint objects, we find that NGC 4656UV has no significant dust opacity and a blue spectral energy distribution. We compute a star formation rate of 0.027 M ⊙ yr-1 from the far-ultraviolet flux and measure a total H I mass of 3.8 × 108 M ⊙ for the object. Evolutionary synthesis modeling indicates that NGC 4656UV is a low-metallicity system whose only major burst of star formation occurred within the last ~260-290 Myr. The age of the stellar population is consistent with a rough timescale for a recent tidal interaction between NGC 4656 and NGC 4631, although we discuss the true nature of the object—whether it is tidal or pre-existing in origin—in the context of its metallicity being a factor of 10 lower than its parent galaxy. We estimate that NGC 4656UV is either marginally bound or unbound. If bound, it contains relatively low amounts of dark matter. The abundance of archival data allows for a deeper investigation into this dynamic system than is currently possible for most TDG candidates.
Resonances for Open Quantum Maps and a Fractal Uncertainty Principle

We study eigenvalues of quantum open baker’s maps with trapped sets given by linear arithmetic Cantor sets of dimensions \({\delta\in (0,1)}\). We show that the size of the spectral gap is strictly greater than the standard bound \({\max(0,{1\over 2}-\delta)}\) for all values of \({\delta}\), which is the first result of this kind. The size of the improvement is determined from a fractal uncertainty principle and can be computed for any given Cantor set. We next show a fractal Weyl upper bound for the number of eigenvalues in annuli, with exponent which depends on the inner radius of the annulus.
Stokes' second problem and reduction of inertia in active fluids

Simulations predict that a pendulum immersed in an active fluid oscillates faster than in a passive fluid due to a reduction of the fluid inertia. The decrease in inertia is mediated by topological defects in the stress field, which can effectively decouple the bulk flow dynamics from the pendulum.
Neutron Electric Dipole Moment on the Lattice

For the neutron to have an electric dipole moment (EDM), the theory of nature must have T, or equivalently CP, violation. Neutron EDM is a very good probe of novel CP violation in beyond the standard model physics. To leverage the connection between measured neutron EDM and novel mechanism of CP violation, one requires the calculation of matrix elements for CP violating operators, for which lattice QCD provides a first principle method. In this paper, we review the status of recent lattice QCD calculations of the contributions of the QCD Θ-term, the quark EDM term, and the quark chromo-EDM term to the neutron EDM.
The stratified structure of spaces of smooth orbifold mappings

We consider four notions of maps between smooth C∞ orbifolds , with compact (without boundary). We show that one of these notions is natural and necessary in order to uniquely define the notion of orbibundle pullback. For the notion of complete orbifold map, we show that the corresponding set of Cr maps between and with the Cr topology carries the structure of a smooth C∞ Banach (r finite)/Frechet (r = ∞) manifold. For the notion of complete reduced orbifold map, the corresponding set of Cr maps between and with the Cr topology carries the structure of a smooth C∞ Banach (r finite)/Frechet (r = ∞) orbifold. The remaining two notions carry a stratified structure: The Cr orbifold maps between and is locally a stratified space with strata modeled on smooth C∞ Banach (r finite)/Frechet (r = ∞) manifolds while the set of Cr reduced orbifold maps between and locally has the structure of a stratified space with strata modeled on smooth C∞ Banach (r finite)/Frechet (r = ∞) orbifolds. Furthermore, we give the explicit relationship between these notions of orbifold map. Applying our results to the special case of orbifold diffeomorphism groups, we show that they inherit the structure of C∞ Banach (r finite)/Frechet (r = ∞) manifolds. In fact, for r finite they are topological groups, and for r = ∞ they are convenient Frechet Lie groups.
High-contrast imager for complex aperture telescopes (HiCAT): 6. software control infrastructure and calibration

High contrast imaging using coronagraphy is one of the main avenues to enable the search for life on extrasolar Earth analogs. The HiCAT testbed aims to demonstrate coronagraphy and wavefront control for segmented on-axis space telescopes as envisioned for a future large UV optical IR mission (LUVOIR). Our software infrastructure enables 24/7 automated operation of high-contrast imaging experiments while monitoring for safe operating parameters, along with graceful shutdown processes for unsafe conditions or unexpected errors. The infrastructure also includes a calibration suite that can run nightly to catch regressions and track optical per- formance changes over time, and a testbed simulator to support software development and testing, as well as optical modeling necessary for high-contrast algorithms. This paper presents a design and implementation of testbed control software to leverage continuous integration whether the testbed is available or not.
An X-ray and Radio Study of the Varying Expansion Velocities in Tycho's Supernova Remnant

We present newly obtained X-ray and radio observations of Tycho's supernova remnant using Chandra and the Karl G. Jansky Very Large Array in 2015 and 2013/14, respectively. When combined with earlier epoch observations by these instruments, we now have time baselines for expansion measurements of the remnant of 12-15 year in the X-rays and 30 year in the radio. The remnant's large angular size allows for proper motion measurements at many locations around the periphery of the blast wave. We find, consistent with earlier measurements, a clear gradient in the expansion velocity of the remnant, despite its round shape. The proper motions on the western and southwestern sides of the remnant are about a factor of two higher than those in the east and northeast. We showed in an earlier work that this is related to an offset of the explosion site from the geometric center of the remnant due to a density gradient in the ISM, and using our refined measurements reported here, we find that this offset is approximately 23′′ towards the northeast. An explosion center offset in such a circular remnant has implications for searches for progenitor companions in other remnants.
A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial

Automated segmentation of medical imaging is of broad interest to clinicians and machine learning researchers alike. The goal of segmentation is to increase efficiency and simplicity of visualization and quantification of regions of interest within a medical image. Image segmentation is a difficult task because of multiparametric heterogeneity within the images, an obstacle that has proven especially challenging in efforts to automate the segmentation of brain lesions from non-contrast head computed tomography (CT). In this research, we have experimented with multiple available deep learning architectures to segment different phenotypes of hemorrhagic lesions found after moderate to severe traumatic brain injury (TBI). These include: intraparenchymal hemorrhage (IPH), subdural hematoma (SDH), epidural hematoma (EDH), and traumatic contusions. We were able to achieve an optimal Dice Coefficient1 score of 0.94 using UNet++ 2D Architecture with Focal Tversky Loss Function, an increase from 0.85 using UNet 2D with Binary Cross-Entropy Loss Function in intraparenchymal hemorrhage (IPH) cases. Furthermore, using the same setting, we were able to achieve the Dice Coefficient score of 0.90 and 0.86 in cases of Extra-Axial bleeds and Traumatic contusions, respectively.
CMB optical depth measurements: Past, present, future

Abstract The polarization of the cosmic microwave background (CMB) is encoded with exactly the same cosmic information as the CMB’s temperature anisotropy. However, polarization has the additional promise of accurately probing the reionization history of the universe and potentially constraining, or detecting, the primordial background of gravitational waves produced by inflation. We demonstrate that these two CMB polarization goals are mutually compatible. A polarimeter optimized to detect the inflationary gravitational wave background signature in the polarization of the CMB is well situated to detect the signatures of realistic first-light scenarios. We also discuss current results and prospects for future CMB polarization experiments.
Theory of twist liquids: Gauging an anyonic symmetry

Abstract Topological phases in ( 2 + 1 ) -dimensions are frequently equipped with global symmetries, like conjugation, bilayer or electric–magnetic duality, that relabel anyons without affecting the topological structures. Twist defects are static point-like objects that permute the labels of orbiting anyons. Gauging these symmetries by quantizing defects into dynamical excitations leads to a wide class of more exotic topological phases referred as twist liquids, which are generically non-Abelian. We formulate a general gauging framework, characterize the anyon structure of twist liquids and provide solvable lattice models that capture the gauging phase transitions. We explicitly demonstrate the gauging of the Z 2 -symmetric toric code, S O ( 2 N ) 1 and S U ( 3 ) 1 state as well as the S 3 -symmetric S O ( 8 ) 1 state and a non-Abelian chiral state we call the “4-Potts” state.
Generalized hydrodynamics revisited

During the past decade a number of attempts to formulate a continuum description of complex states of matter have been proposed to circumvent more cumbersome many-body and simulation methods. Typically these have been quantum systems (e.g., electrons) and the resulting phenomenologies collectively often called "quantum hydrodynamics". However, there is extensive work from the past based in non-equilibrium statistical mechanics on the microscopic origins of macroscopic continuum dynamics that has not been exploited in this context. Although formally exact, its original target was the derivation of Navier-Stokes hydrodynamics for slowly varying states in space and time. The objective here is to revisit that work for the present interest in complex quantum states - possible strong degeneracy, strong coupling, and all space-time scales. The result is an exact representation of generalized hydrodynamics suitable for introducing controlled approximations for diverse specific cases, and for critiquing existing work.
Bulk matter and the boundary quantum null energy condition

We investigate the quantum null energy condition (QNEC) in holographic CFTs, focusing on half-spaces and particular classes of states. We present direct, and in certain cases nonperturbative, calculations for both the diagonal and off-diagonal variational derivatives of entanglement entropy. In d ≥ 3, we find that the QNEC is saturated. We compute relations between the off-diagonal variation of entanglement, boundary relative entropy, and the bulk stress tensor. Strong subadditivity then leads to energy conditions in the bulk. In d = 2, we find that the QNEC is in general not saturated when the Ryu-Takayanagi surface intersects bulk matter. Moreover, when bulk matter is present the QNEC can imply new bulk energy conditions. For a simple class of states, we derive an example that is stronger than the bulk averaged null energy condition and reduces to it in certain limits.
The Poole-Frenkel laws and a pathway to multi-valued memory

We revisit the mechanism of Poole-Frenkel nonohmic conduction in materials of nonvolatile memory. Percolation theory is shown to explain both the Poole and Frenkel dependencies corresponding to the cases of small and large samples compared to the correlation radii of their percolation clusters, respectively. The applied bias modifies a limited number of microscopic resistances forming the percolation pathways. That understanding opens a pathway to multivalued nonvolatile memory and related neural network applications.
ARE MASSIVE ELEMENTARY PARTICLES BLACK HOLES

We use exact results in a new approach to quantum gravity to study the effect of quantum loop corrections on the behavior of the metric of spacetime near the Schwarzschild radius of a massive point particle in the standard model. We show that the classical conclusion that such a system is a black hole is obviated. Phenomenological implications are discussed.
Noisy Feedback and Loss Unlimited Private Communication

Cryptographic protocols often involve the assistance of public side channels to which all parties have perfectly noiseless access. For instance, in the BB84 quantum key distribution protocol, the side channel is used to share the bases in which Alice and Bob encoded or measured their qubits. In this paper, we find that in the case of continuous variable communication, by slightly altering this model such that Eve's copy of the initial round of feedback is corrupted by an iota of noise while keeping Alice's copies noiseless, the capacity can be increased dramatically. Specifically, it is known that the private capacity with noiseless feedback for a pure-loss bosonic channel is at most $-\log(1-\eta)$ bits per mode, where $\eta$ is the transmissivity, in the limit of infinite input photon number. This is a very pessimistic result as there is a finite rate limit even with an arbitrarily large number of input photons. We refer to this as a loss limited rate. However, in our altered model we find that we can achieve a rate of $(1/2) \log(1+4\eta N_{S})$ bits per mode with weak security, where $N_{S}$ is the input photon number. This rate diverges with $N_{S}$ , in sharp contrast to the result for the original model. This suggests that physical considerations behind the eavesdropping model should be taken more seriously, as they can create strong dependencies of the achievable rates on the model. For by a seemingly inconsequential weakening of Eve, we obtain a loss-unlimited rate. Our protocol also works verbatim for arbitrary i.i $\mathrm{d}$ , noise (not even necessarily Gaussian) injected by Eve in every round, and even if Eve is given access to copies of the initial transmission and noise. The error probability of the protocol decays super-exponentially with the blocklength.
Information and Entropy

What is information? Is it physical? We argue that in a Bayesian theory the notion of information must be defined in terms of its effects on the beliefs of rational agents. Information is whatever constrains rational beliefs and therefore it is the force that induces us to change our minds. This problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), which is designed for updating from arbitrary priors given information in the form of arbitrary constraints, includes as special cases both MaxEnt (which allows arbitrary constraints) and Bayes' rule (which allows arbitrary priors). Thus, ME unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme that allows us to handle problems that lie beyond the reach of either of the two methods separately. I conclude with a couple of simple illustrative examples.
A Comparison of Bimolecular Reaction Models for Stochastic Reaction–Diffusion Systems

Stochastic reaction–diffusion models have become an important tool in studying how both noise in the chemical reaction process and the spatial movement of molecules influences the behavior of biological systems. There are two primary spatially-continuous models that have been used in recent studies: the diffusion limited reaction model of Smoluchowski, and a second approach popularized by Doi. Both models treat molecules as points undergoing Brownian motion. The former represents chemical reactions between two reactants through the use of reactive boundary conditions, with two molecules reacting instantly upon reaching a fixed separation (called the reaction-radius). The Doi model uses reaction potentials, whereby two molecules react with a fixed probability per unit time, λ, when separated by less than the reaction radius. In this work, we study the rigorous relationship between the two models. For the special case of a protein diffusing to a fixed DNA binding site, we prove that the solution to the Doi model converges to the solution of the Smoluchowski model as λ→∞, with a rigorous $O(\lambda^{-\frac{1}{2} + \epsilon})$ error bound (for any fixed ϵ>0). We investigate by numerical simulation, for biologically relevant parameter values, the difference between the solutions and associated reaction time statistics of the two models. As the reaction-radius is decreased, for sufficiently large but fixed values of λ, these differences are found to increase like the inverse of the binding radius.
Couplings of a light dilaton and violations of the equivalence principle

Experimental discovery of the dilaton would provide strong evidence for string theory. A light dilaton could show up in current tests of the inverse square law for gravity at sub-millimeter distances. In the large extra dimension scenario, Kaluza-Klein excitations of the dilaton can also contribute to the cooling of supernovae. In order to quantify these effects we compute the couplings of a low energy dilaton to matter. These predominantly arise from the fundamental dilaton coupling to the gluon field strength, which receives a sizable enhancement from QCD scaling. We show that detection of the dilaton will give a direct measurement of the QCD coupling constant at the string scale. Particular attention is paid to the size of equivalence principle violating dilaton couplings.
Projective structures, grafting and measured laminations

We show that grafting any xed hyperbolic surface denes a homeomorphism from the space of measured laminations to Teichmuller space, complementing a result of Scannell-Wolf on grafting by a xed lamination. This result is used to study the relationship between the complex-analytic and geometric coordinate systems for the space of com- plex projective (CP 1 ) structures on a surface. We also study the rays in Teichmuller space associated to the graft- ing coordinates, obtaining estimates for extremal and hyperbolic length functions and their derivatives along these grafting rays. 1 (X) using the Schwarzian derivative, identifying P(S) with the total space of the bundle Q(S)! T(S) of holo- morphic quadratic dierentials. A second, more synthetic geometric description of P(S) is due to Thurston, and proceeds through the operation of grafting { a construction which traces its roots back at least to Klein (Kle, x50, p. 230), with a modern history developed by many authors ((Mas1), (Hej), (ST), (Gol), (GKM),(Tan), (McM), (SW)). The simplest example of grafting may be described as follows. Start with a hyperbolic surface X2 T(S) and a simple closed geodesic on X; then construct a new surface by removing from X and replacing it
Gauge coupling renormalization in RS1

Abstract We compute the 4D low energy effective gauge coupling at one-loop order in the compact Randall–Sundrum scenario with bulk gauge fields and charged matter, within controlled approximations. While such computations are subtle, they can be important for studying phenomenological issues such as grand unification. Ultraviolet divergences are cut-off using Pauli–Villars regularization so as to respect 5D gauge and general coordinate invariance. The structure of these divergences on branes and in the bulk is elucidated by a 5D position-space analysis. The remaining finite contributions are obtained by a careful analysis of the Kaluza–Klein spectrum. We comment on the agreement between our results and expectations based on the AdS/CFT correspondence, in particular logarithmic sensitivity to the 4D Planck scale.