cssi/SciSciGPT-SciSciNet huggingface dataset:
Boundary Layer Transition Correlations and Aeroheating Predictions for Mars Smart Lander

Laminar and turbulent perfect-gas air, Navier-Stokes computations have been performed for a proposed Mars Smart Lander entry vehicle at Mach 6 over a free stream Reynolds number range of 6.9 x 10(exp 6)/m to 2.4 x 10(exp 7)/m (2.1 x 10(exp 6)/ft to 7.3 x 10(exp 6)/ft) for angles-of-attack of 0-deg, 11-deg, 16-deg, and 20-deg, and comparisons were made to wind tunnel heating data obtained a t the same conditions. Boundary layer edge properties were extracted from the solutions and used to correlate experimental data on the effects of heat-shield penetrations (bolt-holes where the entry vehicle would be attached to the propulsion module during transit to Mars) on boundary-layer transition. A non-equilibrium Martian-atmosphere computation was performed for the peak heating point on the entry trajectory in order to determine if the penetrations would produce boundary-layer transition by using this correlation.
Effect of steam sterilization of soil on two desert plant species

Franseria dumosa Gray and Hilaria rigida (Thurb.) Benth. ex Scribn. seedlings were grown in a glasshouse in potted soil which was collected from the Mojave Desert near Mercury, Nevada. The soil represented areas under living shrubs and also areas between shrubs. Soil was either steam sterilized or not steam sterilized. The sterilization resulted in greatly decreased yields of plants possibly because of induced P deficiency. It was suggested that symbiotic mycorrhizae necessary for P absorption for the species involved might have been eliminated by the sterilization. The effect did not hold for a noncalcareous soil well supplied with available P. Soil sterilization increased both Mn and Zn in plants. There was an interaction in that plants did not grow well in soil from under shrubs regardless of steaming indicating possible allelopathic effects. re]19720711
Ex Post Implementation

We analyze the problem of fully implementing a social choice set in ex post equilibrium. We identify an ex post monotonicity condition that is necessary and—in economic environments—sufficient for full implementation in ex post equilibrium. We also identify an ex post monotonicity no veto condition that is sufficient. Ex post monotonicity is satisfied in all single crossing environments with strict ex post incentive constraints. We show by means of two classic examples that ex post monotonicity does not imply nor is it implied by Maskin monotonicity. The single unit auction with interdependent valuations is shown to satisfy ex post monotonicity but not Maskin monotonicity. We further describe a Pareto correspondence that fails ex post monotonicity but satisfies Maskin monotonicity.
A Raspberry Pi-based, RFID-equipped birdfeeder for the remote monitoring of wild bird populations

Radio-frequency identification (RFID) is an increasingly popular wireless technology that allows researchers to monitor wild bird populations from fixed locations in the field. I have developed an ...
Markets as beneficial constraints on the government

We study the role of anonymous markets in which trades cannot be monitored by the government. We adopt a Mirrlees approach to analyze economies in which agents have private information and a benevolent government controls optimal redistributive tax policy. While unrestricted access to anonymous markets reduces the set of policy instruments available to the government, it also limits the scope of inefficient redistributive policies when the government lacks commitment. Indeed, the restrictions that anonymous markets impose on the optimal fiscal policy, especially on capital taxation and the history-dependence of income taxation, can have positive welfare effects in this case.
Woody Plant Encroachment has a Larger Impact than Climate Change on Dryland Water Budgets

Woody plant encroachment (WPE) into grasslands is a global phenomenon that is associated with land degradation via xerification, which replaces grasses with shrubs and bare soil patches. It remains uncertain how the global processes of WPE and climate change may combine to impact water availability for ecosystems. Using a process-based model constrained by watershed observations, our results suggest that both xerification and climate change augment groundwater recharge by increasing channel transmission losses at the expense of plant available water. Conversion from grasslands to shrublands without creating additional bare soil, however, reduces transmission losses. Model simulations considering both WPE and climate change are used to assess their relative roles in a late 21st century condition. Results indicate that changes in focused channel recharge are determined primarily by the WPE pathway. As a result, WPE should be given consideration when assessing the vulnerability of groundwater aquifers to climate change.
Earthmover-Based Manifold Learning for Analyzing Molecular Conformation Spaces

In this paper, we propose a novel approach for manifold learning that combines the Earthmover's distance (EMD) with the diffusion maps method for dimensionality reduction. We demonstrate the potential benefits of this approach for learning shape spaces of proteins and other flexible macromolecules using a simulated dataset of 3-D density maps that mimic the non-uniform rotary motion of ATP synthase. Our results show that EMD-based diffusion maps require far fewer samples to recover the intrinsic geometry than the standard diffusion maps algorithm that is based on the Euclidean distance. To reduce the computational burden of calculating the EMD for all volume pairs, we employ a wavelet-based approximation to the EMD which reduces the computation of the pairwise EMDs to a computation of pairwise weighted $-\ell_{1}$ distances between wavelet coefficient vectors.
Paths Explored, Paths Omitted, Paths Obscured: Decision Points & Selective Reporting in End-to-End Data Analysis

Drawing reliable inferences from data involves many, sometimes arbitrary, decisions across phases of data collection, wrangling, and modeling. As different choices can lead to diverging conclusions, understanding how researchers make analytic decisions is important for supporting robust and replicable analysis. In this study, we pore over nine published research studies and conduct semi-structured interviews with their authors. We observe that researchers often base their decisions on methodological or theoretical concerns, but subject to constraints arising from the data, expertise, or perceived interpretability. We confirm that researchers may experiment with choices in search of desirable results, but also identify other reasons why researchers explore alternatives yet omit findings. In concert with our interviews, we also contribute visualizations for communicating decision processes throughout an analysis. Based on our results, we identify design opportunities for strengthening end-to-end analysis, for instance via tracking and meta-analysis of multiple decision paths.
4-Nonylphenol in Sierra Nevada glaciers, California, USA

Persistent organic pollutants are stored in environmental reservoirs globally. Tracking the mass and movement of these pollutants is critical for assessing environmental health for human and wildli...
Encoding Musical Style with Transformer Autoencoders

We consider the problem of learning high-level controls over the global structure of generated sequences, particularly in the context of symbolic music generation with complex language models. In this work, we present the Transformer autoencoder, which aggregates encodings of the input data across time to obtain a global representation of style from a given performance. We show it is possible to combine this global representation with other temporally distributed embeddings, enabling improved control over the separate aspects of performance style and melody. Empirically, we demonstrate the effectiveness of our method on various music generation tasks on the MAESTRO dataset and a YouTube dataset with 10,000+ hours of piano performances, where we achieve improvements in terms of log-likelihood and mean listening scores as compared to baselines.
Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool

We present a neural rendering framework that maps a voxelized scene into a high quality image. Highly-textured objects and scene element interactions are realistically rendered by our method, despite having a rough representation as an input. Moreover, our approach allows controllable rendering: geometric and appearance modifications in the input are accurately propagated to the output. The user can move, rotate and scale an object, change its appearance and texture or modify the position of the light and all these edits are represented in the final rendering. We demonstrate the effectiveness of our approach by rendering scenes with varying appearance, from single color per object to complex, high-frequency textures. We show that our rerendering network can generate very detailed images that represent precisely the appearance of the input scene. Our experiments illustrate that our approach achieves more accurate image synthesis results compared to alternatives and can also handle low voxel grid resolutions. Finally, we show how our neural rendering framework can capture and faithfully render objects from real images and from a diverse set of classes.
Cultural roots of family ties

We forward the hypothesis and empirically establish that variations in the strength of family ties are rooted in culture. In particular, we show that individualism is associated with looser family ties. We exploit the associations between contemporary individualism and historical climatic and disease environments to establish a causal relationship. At both the individual- and country-levels, we find strong support that individualism reduces family ties. The estimated effects are economically large and robust to a wide variety of potentially confounding variables.
Draft genome assemblies using sequencing reads from Oxford Nanopore Technology and Illumina platforms for four species of North American Fundulus killifish.

Background Whole-genome sequencing data from wild-caught individuals of closely related North American killifish species (Fundulus xenicus, Fundulus catenatus, Fundulus nottii, and Fundulus olivaceus) were obtained using long-read Oxford Nanopore Technology (ONT) PromethION and short-read Illumina platforms. Findings Draft de novo reference genome assemblies were generated using a combination of long and short sequencing reads. For each species, the PromethION platform was used to generate 30-45× sequence coverage, and the Illumina platform was used to generate 50-160× sequence coverage. Illumina-only assemblies were fragmented with high numbers of contigs, while ONT-only assemblies were error prone with low BUSCO scores. The highest N50 values, ranging from 0.4 to 2.7 Mb, were from assemblies generated using a combination of short- and long-read data. BUSCO scores were consistently >90% complete using the Eukaryota database. Conclusions High-quality genomes can be obtained from a combination of using short-read Illumina data to polish assemblies generated with long-read ONT data. Draft assemblies and raw sequencing data are available for public use. We encourage use and reuse of these data for assembly benchmarking and other analyses.
Testing of Heat Flux Sensors at Cryogenic Temperatures

Normally, in order to characterize multilayer insulation installed onto a test tank, the boil-off of the tank is measured and then heat loads from structural and fluid penetrations are calculated from temperature measurements throughout the system. For the Structural Heat Intercept, Insulation, and Vibration Evaluation Rig testing, it was determined that this approach would have significant uncertainties (over 50%) and that another method was needed to characterize the heat load through the blanket. Heat flux sensors are widely used to measure heat loads and characterize insulation systems at room temperature, however, the heat fluxes measured are usually two orders of magnitude higher than high performance MLI. Three different heat flux sensors were initially checked out on a liquid hydrogen calorimeter. One was chosen for actual implementation and 20 sensors were ordered. Of those sensors, calibration was attempted on 7 of the sensors. The results from testing and calibration are discussed.
An Ounce of Prevention

I look at prevention through an economic lens and make three main points. First, those advocating preventive measures are often asked how much money a given measure saves. This question is misguided. Rather preventive measures can be thought of as insurance, with a certain cost in the present that may or may not pay off in the future. In fact, although most medical preventive measures improve expected health, they do not save money. Various lifestyle and early childhood interventions, however, may both save money and improve health. Second, preventive measures, including medical and lifestyle measures, are heterogeneous in their value, both across measures and, within measure, across individuals. As a result, generalizations in everyday discourse about the value of prevention can be overly broad. Third, health insurance coverage for medical preventive measures should generally be more extensive than coverage for the treatment of a medical condition, though full coverage of preventive services is not necessarily optimal.
A System Level Approach to Discrete-Time Nonlinear Systems

In this paper, we will show that there is a universal connection between the closed-loop and the corresponding realizing controller in nonlinear discrete-time systems: Given an achievable stable closed-loop, we can follow a systematic procedure to construct an internally stable causal controller that realizes the desired closed-loop. In the linear system case, this relationship has been used as a key result in the recently developed System Level Synthesis (SLS), and this paper shows how this relationship finds its analogue in the general nonlinear discrete-time system case. Necessary and sufficient conditions are presented that characterize the entire space of closedloops that are achievable by some causal controller for a given system. Furthermore, we will show that constructing the same causal controller from maps that are not achievable closed-loops, still can stabilize the nonlinear system if they approximate the feasibility conditions well enough. Finally, we will discuss how this method opens up new ways towards robust nonlinear controller synthesis, by exploring two direct applications of this approach: design of trajectory tracking controllers for nonlinear systems using linear SLS controllers, and a method to stably "blend" multiple linear SLS controllers into one nonlinear controller that improves closed-loop performance. The companion paper [1] applies the latter approach to large-scale systems with actuator saturation and safety constraints.
BlurNet: Defense by Filtering the Feature Maps

Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adversarial examples, stemming from small perturbations being added to the input image. Adversarial examples are generated by a malicious adversary by obtaining access to the model parameters, such as gradient information, to alter the input or by attacking a substitute model and transferring those malicious examples over to attack the victim model. Specifically, one of these attack algorithms, Robust Physical Perturbations $(RP_{2})$, generates adversarial images of stop signs with black and white stickers to achieve high targeted misclassification rates against standard-architecture traffic sign classifiers. In this paper, we propose BlurNet, a defense against the RP 2 attack. First, we motivate the defense with a frequency analysis of the first layer feature maps of the network on the LISA dataset, which shows that high frequency noise is introduced into the input image by the RP 2 algorithm. To remove the high frequency noise, we introduce a depthwise convolution layer of standard blur kernels after the first layer. We perform a blackbox transfer attack to show that low-pass filtering the feature maps is more beneficial than filtering the input. We then present various regularization schemes to incorporate this low-pass filtering behavior into the training regime of the network and perform white-box attacks. We conclude with an adaptive attack evaluation to show that the success rate of the attack drops from 90% to 20% with total variation regularization, one of the proposed defenses.
Deep Learning with Gaussian Differential Privacy.

Deep learning models are often trained on data sets that contain sensitive information such as individuals’ shopping transactions, personal contacts, and medical records. An increasingly important line of work therefore has sought to train neural networks subject to privacy constraints that are specified by differential privacy or its divergence-based relaxations. These privacy definitions, however, have weaknesses in handling certain important primitives (composition and subsampling), thereby giving loose or complicated privacy analyses of training neural networks. In this article, we consider a recently proposed privacy definition termed f-differential privacy for a refined privacy analysis of training neural networks. Leveraging the appealing properties of f-differential privacy in handling composition and subsampling, this article derives analytically tractable expressions for the privacy guarantees of both stochastic gradient descent and Adam used in training deep neural networks, without the need of developing sophisticated techniques as did. Our results demonstrate that the f-differential privacy framework allows for a new privacy analysis that improves on the prior analysis , which in turn suggests tuning certain parameters of neural networks for a better prediction accuracy without violating the privacy budget. These theoretically derived improvements are confirmed by our experiments in a range of tasks in image classification, text classification, and recommender systems. Python code to calculate the privacy cost for these experiments is publicly available in the TensorFlow Privacy library.
Evaluation of Hearing in the High-Risk Infant

Preterm infants are at high risk for hearing loss. Included in this discussion are the etiology of hearing loss, methods of evaluation, and management of children with suspected hearing loss.
The Information Game

Students want to know why accounting and auditing are important. The Information Game is a classroom resource that introduces the role of accounting, the value of information, the value of auditing, and the importance of auditor independence in an easy-to-use, fun exercise for the first day of class. Students experience the value of (accounting) information and auditing during the game. Available teaching notes provide (i) additional commentary on the game, (ii) post-game questions, (iii) a suggested script, and (iv) a summary of concepts, analogies, and takeaways that help students appreciate the importance of accounting and auditing in the global market economy. An anonymous survey of those who have used the Information Game suggests that instructors find the game highly effective and generally easy to use. Instructors tend to occasionally refer back to the game’s concepts and takeaways during the remainder of their course.
PTEN Inhibition Ameliorates Muscle Degeneration and Improves Muscle Function in a Mouse Model of Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is caused by a mutation of the muscle membrane protein dystrophin and characterized by severe degeneration of myofibers, progressive muscle wasting, loss of mobility, and, ultimately, cardiorespiratory failure and premature death. Currently there is no cure for DMD. Herein, we report that skeletal muscle-specific knockout (KO) of the phosphatase and tensin homolog (Pten) gene in an animal model of DMD (mdx mice) alleviates myofiber degeneration and restores muscle function without increasing tumor incidence. Specifically, Pten KO normalizes myofiber size and prevents muscular atrophy, and it improves grip strength and exercise performance in mdx mice. Pten KO also reduces fibrosis and inflammation, and it ameliorates muscle pathology in mdx mice. Unbiased RNA sequencing reveals that Pten KO upregulates extracellular matrix and basement membrane components positively correlated with wound healing and suppresses negative regulators of wound healing and lipid biosynthesis, thus improving the integrity of muscle basement membrane at the ultrastructural level. Importantly, pharmacological inhibition of PTEN similarly ameliorates muscle pathology and improves muscle integrity and function in mdx mice. Our findings provide evidence that PTEN inhibition may represent a potential therapeutic strategy to restore muscle function in DMD.
Unified Virtual Oscillator Control for Grid-Forming and Grid-Following Converters

A unified virtual oscillator controller (uVOC) is proposed, which enables a unified analysis, design, and implementation framework for both grid-forming (GFM) and grid-following (GFL) voltage-source converters (VSCs). Oscillator-based GFM controllers, such as dispatchable virtual oscillator control (dVOC), offer a rigorous analytical framework with enhanced synchronization but lack effective fault handling capability, which severely limits practical application. The proposed uVOC facilitates synchronization with an arbitrarily low grid voltage and fast overcurrent limiting; this enables effective fault ride-through unlike existing GFM controllers which typically switch to a back-up controller during the fault. GFM operation with uVOC is achieved in both grid-connected and islanded modes with seamless transitions between the two. In GFL converters, bidirectional power flow control and dc bus voltage regulation are achieved with uVOC. No phase-locked loop (PLL) is required for either GFL or GFM operation circumventing the synchronization issues associated with PLLs in weak grid applications. Detail small-signal models for GFM and GFL operation have been developed, and the systematic design guidelines for controller parameters are provided. The proposed controller is validated through hardware experiments in a hybrid ac–dc microgrid.
The A-fibered Burnside Ring as A-Fibered Biset Functor in Characteristic Zero

Let A be an abelian group such that torn(A) is finite for every n ≥ 1 and let ${\mathbb{K}}$ be a field of characteristic zero containing roots of unity of all orders equal to finite element orders in A. In this paper we prove fundamental properties of the A-fibered Burnside ring functor $B_{\mathbb{K}}^{A}$ as an A-fibered biset functor over K. This includes a description of the composition factors of $B^{A}_{\mathbb{K}}$ and the lattice of subfunctors of $B_{\mathbb{K}}^{A}$ in terms of what we call BA-pairs and a poset structure on their isomorphism classes. Unfortunately, we are not able to classify BA-pairs. The results of the paper extend results of Coskun and Yilmaz for the A-fibered Burnside ring functor restricted to p-groups and results of Bouc in the case that A is trivial, i.e., the case of the Burnside ring functor as a biset functor over fields of characteristic zero. In the latter case, BA-pairs become Bouc’s B-groups which are also not known in general.
Scrambling and decoding the charged quantum information

Some deep conjectures about quantum gravity are closely related to the role of symmetries in the gravitational background, especially for quantum black holes. In this paper, we systematically study the theory of quantum information for a charged, chaotic system. We show how the quantum information in the whole system has been represented by its charge sectors, using the theory of quantum chaos and quantum error correction, with concrete examples in the context of the complex Sachdev-Ye-Kitaev model. We discuss possible implications for black-hole thought experiments and conjectures about quantum gravity in the dynamical setup. We believe this work will have potential applications from theories of quantum gravity to quantum simulation in quantum devices.
Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect.

We present a 3D fully-automatic method for the calibration of partial differential equation (PDE) models of glioblastoma (GBM) growth with “mass effect”, the deformation of brain tissue due to the tumor. We quantify the mass effect, tumor proliferation, tumor migration, and the localized tumor initial condition from a single multiparameteric Magnetic Resonance Imaging (mpMRI) patient scan. The PDE is a reaction-advection-diffusion partial differential equation coupled with linear elasticity equations to capture mass effect. The single-scan calibration model is notoriously difficult because the precancerous (healthy) brain anatomy is unknown. To solve this inherently ill-posed and ill-conditioned optimization problem, we introduce a novel inversion scheme that uses multiple brain atlases as proxies for the healthy precancer patient brain resulting in robust and reliable parameter estimation. We apply our method on both synthetic and clinical datasets representative of the heterogeneous spatial landscape typically observed in glioblastomas to demonstrate the validity and performance of our methods. In the synthetic data, we report calibration errors (due to the ill-posedness and our solution scheme) in the 10%–20% range. In the clinical data, we report good quantitative agreement with the observed tumor and qualitative agreement with the mass effect (for which we do not have a ground truth). Our method uses a minimal set of parameters and provides both global and local quantitative measures of tumor infiltration and mass effect.
Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning

Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It is challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high-dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data. Notably, most DMS data do not contain examples of negative sequences, making it challenging to directly estimate how sequence affects function. Here, we develop a positive-unlabeled (PU) learning framework to infer sequence-function relationships from large-scale DMS data. Our PU learning method displays excellent predictive performance across ten large-scale sequence-function datasets, representing proteins of different folds, functions, and library types. The estimated parameters pinpoint key residues that dictate protein structure and function. Finally, we apply our statistical sequence-function model to design highly stabilized enzymes.
A Network Percolation-based Contagion Model of Flood Propagation and Recession in Urban Road Networks

In this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of floodwaters in urban road networks. A network of urban roads resilient to flooding events is essential for the provision of public services and for emergency response. The spread of floodwaters in urban networks is a complex spatial-temporal phenomenon. This study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of floodwaters in urban road networks. The evolution of floods within networks can be captured based on three macroscopic characteristics-flood propagation rate ([Formula: see text]), flood incubation rate ([Formula: see text]), and recovery rate ([Formula: see text])-in a system of ordinary differential equations analogous to the Susceptible-Exposed-Infected-Recovered (SEIR) model. We integrated the flood contagion model with the network percolation process in which the probability of flooding of a road segment depends on the degree to which the nearby road segments are flooded. The application of the proposed model is verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017. The results show that the model can monitor and predict the fraction of flooded roads over time. Additionally, the proposed model can achieve 90% precision and recall for the spatial spread of the flooded roads at the majority of tested time intervals. The findings suggest that the proposed mathematical contagion model offers great potential to support emergency managers, public officials, citizens, first responders, and other decision-makers for flood forecast in road networks.
Scaling of the strain hardening modulus of glassy polymers with the flow stress

In a recent letter, Govaert et al. examined the relationship between strain hardening modulus Gr and flow stress σflow for five different glassy polymers. In each case, results for Gr at different strain rates or different temperatures were linearly related to the flow stress. They suggested that this linear relation was inconsistent with simulations. Data from previous publications and new results are presented to show that simulations also yield a linear relation between modulus and flow stress. Possible explanations for the change in the ratio of modulus to flow stress with temperature and strain rate are discussed. © 2009 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 47: 1406–1411, 2009
Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks

Some social networks provide explicit mechanisms to allocate social rewards such as reputation based on users’ actions, while the mechanism is more opaque in other networks. Nonetheless, there are always individuals who obtain greater rewards and reputation than their peers. An intuitive yet important question to ask is whether these successful users employ strategic behaviors to become influential. It might appear that the influencers ”have gamed the system.” However, it remains difficult to conclude the rationality of their actions due to factors like the combinatorial strategy space, inability to determine payoffs, and resource limitations faced by individuals. The challenging nature of this question has drawn attention from both the theory and data mining communities. Therefore, in this paper, we are motivated to investigate if resource-limited individuals discover strategic behaviors associated with high payoffs when producing collaborative/interactive content in social networks. We propose a novel framework of Dynamic Dual Attention Networks (DDAN) which models individuals’ content production strategies through a generative process, under the influence of social interactions involved in the process. Extensive experimental results illustrate the model’s effectiveness in user behavior modeling. We make three strong empirical findings: (1) Different strategies give rise to different social payoffs; (2) The best performing individuals exhibit stability in their preference over the discovered strategies, which indicates the emergence of strategic behavior; and (3) The stability of a user’s preference is correlated with high payoffs.
Stability of the Positive Mass Theorem for Graphical Hypersurfaces of Euclidean Space

The rigidity of the positive mass theorem states that the only complete asymptotically flat manifold of nonnegative scalar curvature and zero mass is Euclidean space. We prove a corresponding stability theorem for spaces that can be realized as graphical hypersurfaces in \({\mathbb{R}^{n+1}}\). Specifically, for an asymptotically flat graphical hypersurface \({M^n\subset \mathbb{R}^{n+1}}\) of nonnegative scalar curvature (satisfying certain technical conditions), there is a horizontal hyperplane \({\Pi\subset \mathbb{R}^{n+1}}\) such that the flat distance between M and \({\Pi}\) in any ball of radius \({\rho}\) can be bounded purely in terms of n, \({\rho}\), and the mass of M. In particular, this means that if the masses of a sequence of such graphs approach zero, then the sequence weakly converges (in the sense of currents, after a suitable vertical normalization) to a flat plane in \({\mathbb{R}^{n+1}}\). This result generalizes some of the earlier findings of Lee and Sormani (J Reine Angew Math 686:187–220, 2014) and provides some evidence for a conjecture stated there.
Cleartext Data Transmissions in Consumer IoT Medical Devices

This paper introduces a method to capture network traffic from medical IoT devices and automatically detect cleartext information that may reveal sensitive medical conditions and behaviors. The research follows a three-step approach involving traffic collection, cleartext detection, and metadata analysis. We analyze four popular consumer medical IoT devices, including one smart medical device that leaks sensitive health information in cleartext. We also present a traffic capture and analysis system that seamlessly integrates with a home network and offers a user-friendly interface for consumers to monitor and visualize data transmissions of IoT devices in their homes.
Revised Catalog of GALEX Ultraviolet Sources. I. The All-Sky Survey: GUVcat_AIS

The Galaxy Evolution Explorer (GALEX) imaged the sky in two ultraviolet (UV) bands, far-UV (FUV, λ eff ~ 1528 A), and near-UV (NUV, λ eff ~ 2310 A), delivering the first comprehensive sky surveys at these wavelengths. The GALEX database contains FUV and NUV images, ~500 million source measurements and over 100,000 low-resolution UV spectra. The UV surveys are a unique resource for statistical studies of hot stellar objects, z 2 QSOs, star-forming galaxies, nebulae and the interstellar medium, and provide a roadmap for planning future UV instrumentation and follow-up observing programs. We present science-enhanced, "clean" catalogs of GALEX UV sources, with useful tags to facilitate scientific investigations. The catalogs are an improved and expanded version of our previous catalogs of UV sources (BCScat). With respect to BCScat, we have patched 640 fields for which the pipeline had improperly coadded non-overlapping observations, and we provide a version with a larger sky coverage (about 10%) by relaxing the restriction to the central area of the GALEX field to 11 diameter (GUVcat_AIS_fov055), as well as the cleaner, more restrictive version using only the 1° central portion of each field as in BCScat (GUVcat_AIS_fov050). We added new tags to facilitate selection and cleaning of statistical samples for science applications: we flag sources within the footprint of extended objects (nearby galaxies, stellar clusters) so that these regions can be excluded for estimating source density. As in our previous catalogs, in GUVcat duplicate measurements of the same source are removed, so that each astrophysical object has only one entry. Such a unique-source catalog is needed to study the density and distributions of sources, and to match UV sources with catalogs at other wavelengths. The catalog includes all observations from the All-Sky Imaging Survey (AIS), the survey with the largest area coverage, with both FUV and NUV detectors exposed: over 28,700 fields, made up of a total of 57,000 observations ("visits"). The total area covered, when overlaps are removed and gaps are accounted for, is 24,790 square degrees for GUVcat_AIS_fov055 (GUVcat_AIS_fov055) and 22,125 square degrees for (GUVcat_AIS_fov050). The total numbers of "unique" AIS sources (eliminating duplicate measurements) are 82,992,086 () and 69,772,677 (). The typical depth of the GUVcat_AIS catalog is FUV = 19.9, NUV = 20.8 AB mag.
On the brauer–siegel ratio for abelian varieties over function fields

Hindry has proposed an analog of the classical Brauer–Siegel theorem for abelian varieties over global fields. Roughly speaking, it says that the product of the regulator of the Mordell–Weil group and the order of the Tate–Shafarevich group should have size comparable to the exponential differential height. Hindry–Pacheco and Griffon have proved this for certain families of elliptic curves over function fields using analytic techniques. Our goal in this work is to prove similar results by more algebraic arguments, namely by a direct approach to the Tate–Shafarevich group and the regulator. We recover the results of Hindry–Pacheco and Griffon and extend them to new families, including families of higher-dimensional abelian varieties.
A no-go theorem for the continuum limit of a periodic quantum spin chain

We show that the Hilbert space formed from a block spin renormalization construction of a cyclic quantum spin chain (based on the Temperley–Lieb algebra) does not support a chiral conformal field theory whose Hamiltonian generates translation on the circle as a continuous limit of the rotations on the lattice.
Goto Numbers of a Numerical Semigroup Ring and the Gorensteiness of Associated Graded Rings

The Goto number of a parameter ideal Q in a Noetherian local ring (R, m) is the largest integer q such that Q: m q is integral over Q. The Goto numbers of the monomial parameter ideals of R = k[[x a 1 , x a 2 , …, x a ν ]] are characterized using the semigroup of R. This helps in computing them for classes of numerical semigroup rings, as well as on a case-by-case basis. The minimal Goto number of R and its connection to other invariants is explored. Necessary and sufficient conditions for the associated graded rings of R and R/x a 1 R to be Gorenstein are also given, again using the semigroup of R.
Monotonicity Under Local Operations: Linear Entropic Formulas

All correlation measures, classical and quantum, must be monotonic under local operations. In this paper, we characterize monotonic formulas that are linear combinations of the von Neumann entropies associated with the quantum state of a physical system that has $n$ parts. We show that these formulas form a polyhedral convex cone, which we call the monotonicity cone, and enumerate its facets. We illustrate its structure and prove that it is equivalent to the cone of monotonic formulas implied by strong subadditivity. We explicitly compute its extremal rays for ${n} \leq5$ . We also consider the symmetric monotonicity cone, in which the formulas are required to be invariant under subsystem permutations. We describe this cone fully for all $n$ .
Asymptotics of Mean-Field O(N) Models

We study mean-field classical N-vector models, for integers $$N\ge 2$$ . We use the theory of large deviations and Stein’s method to study the total spin and its typical behavior, specifically obtaining non-normal limit theorems at the critical temperatures and central limit theorems away from criticality. Important special cases of these models are the XY ( $$N=2$$ ) model of superconductors, the Heisenberg ( $$N=3$$ ) model [previously studied in Kirkpatrick and Meckes (J Stat Phys 152:54–92, 2013) but with a correction to the critical distribution here], and the Toy ( $$N=4$$ ) model of the Higgs sector in particle physics.
The role of the magnetorotational instability in the sun

We calculate growth rates for nonaxisymmetric instabilities including the magnetorotational instability (MRI) throughout the Sun. We first derive a dispersion relation for nonaxisymmetric instability including the effects of shear, convective buoyancy, and three diffusivities (thermal conductivity, resistivity, and viscosity). We then use a solar model evolved with the stellar evolution code MESA and angular velocity profiles determined by Global Oscillations Network Group helioseismology to determine the unstable modes present at each location in the Sun and the associated growth rates. The overall instability has unstable modes throughout the convection zone and also slightly below it at middle and high latitudes. It contains three classes of modes: large-scale hydrodynamic convective modes, large-scale hydrodynamic shear modes, and small-scale magnetohydrodynamic shear modes, which may be properly called MRI modes. While large-scale convective modes are the most rapidly growing modes in most of the convective zone, MRI modes are important in both stably stratified and convectively unstable locations near the tachocline at colatitudes θ < 53°. Nonaxisymmetric MRI modes grow faster than the corresponding axisymmetric modes; for some poloidal magnetic fields, the nonaxisymmetric MRI growth rates are similar to the angular rotation frequency Ω, while axisymmetric modes are stabilized. We briefly discuss the saturation of the field produced by MRI modes, finding that the implied field at the base of the convective zone in the Sun is comparable to that derived based on dynamos active in the tachocline and that the saturation of field resulting from the MRI may be of importance even in the upper convection zone.
Holographic entanglement entropy is cutoff-covariant

In the context of the AdS/CFT correspondence, it is often convenient to regulate infinite quantities in asymptotically anti-de Sitter spacetimes by introducing a sharp cutoff at some finite, large value of a particular radial coordinate. This procedure is a priori coordinate dependent, and may not be well-motivated in full, covariant general relativity; however, the fact that physically meaningful quantities such as the entanglement entropy can be obtained by such a regulation procedure suggests some underlying covariance. In this paper, we provide a careful treatment of the radial cutoff procedure for computing holographic entanglement entropy in asymptotically anti-de Sitter spacetimes. We prove two results that are frequently assumed in the literature, but that have not been carefully addressed: (i) that the choice of a “globally minimal surface” among several extremal candidates is independent of the choice of regulator, and (ii) that finite CFT quantities such as the mutual information which involve “divergence-cancelling” sums of entanglement entropies are well-defined under the usual prescription for computing covariant holographic entanglement entropy. Our results imply that the “globally minimal surface” prescription for computing the holographic entanglement entropy is well-posed from the perspective of general relativity, and thus support the widely-held belief that this is the correct prescription for identifying the entanglement wedge of a boundary subregion in AdS/CFT. We also comment on the geometric source of state-dependent divergences in the holographic entanglement entropy, and identify precisely the regime of validity of the “vacuum subtraction” protocol for regulating infinite entanglement entropies in arbitrary states by comparing them to the entanglement entropies of identical regions in the vacuum. Our proofs make use of novel techniques for the covariant analysis of extremal surfaces, which are explained in detail and may find use more broadly in the study of holographic entanglement entropy.
Log Fano varieties over function fields of curves

Consider a smooth log Fano variety over the function field of a curve. Suppose that the boundary has positive normal bundle. Choose an integral model over the curve. Then integral points are Zariski dense, after removing an explicit finite set of points on the base curve.
Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers

In this letter, we propose a modulation classification algorithm, which is based on the received signal’s amplitude for coherent optical receivers. The proposed algorithm classifies the modulation format from several possible candidates by differentiating the cumulative distribution function (cdf) curves of their normalized amplitudes. The candidate with the most similar cdf to the received signal is selected. The measure of similarity is the minimum average distance between these cdfs. Five commonly used quadrature amplitude modulation formats in digital coherent optical systems are employed. Optical back-to-back experiments and extended simulations are carried out to investigate the performance of the proposed algorithm. Results show that the proposed algorithm achieves accurate classification at optical signal-to-noise ratios of interest. Furthermore, it does not require carrier recovery.
A variant of the Bombieri–Vinogradov theorem in short intervals and some questions of Serre

We generalise the classical Bombieri–Vinogradov theorem for short intervals to a non-abelian setting. This leads to variants of the prime number theorem for short intervals where the primes lie in arithmetic progressions that are “twisted” by a splitting condition in a Galois extension of number fields. Using this result in conjunction with the recent work of Maynard, we prove that rational primes with a given splitting condition in a Galois extension L/ exhibit bounded gaps in short intervals. We explore several arithmetic applications related to questions of Serre regarding the non-vanishing Fourier coefficients of cuspidal modular forms. One such application is that for a given modular L-function L(s, f), the fundamental discriminants d for which the d-quadratic twist of L(s, f) has a non-vanishing central critical value exhibit bounded gaps in short intervals.
ROBOSPECT: Automated Equivalent Width Measurement

We present ROBOSPECT, a new code to automatically measure and deblend line equivalent widths for both absorption and emission spectra. We test the accuracy of these measurements against simulated spectra as well as manual equivalent width measurements of five stellar spectra over a range of signal-tonoise values and a set of long slit emission spectra. We find that ROBOSPECT accurately matches both the synthetic and manual measurements, with scatter consistent with the expectations based on the data quality and the results of Cayrel (1988). Subject headings: methods: data analysis – stars: abundances
The Pressure and Temperature Limits of Likely Rocky Exoplanets

The interior composition of exoplanets is not observable, limiting our direct knowledge of their structure, composition, and dynamics. Recently described observational trends suggest that rocky exoplanets, that is, planets without significant volatile envelopes, are likely limited to $<$1.5 Earth radii. We show that given this likely upper limit in the radii of purely-rocky super-Earth exoplanets, the maximum expected core-mantle boundary pressure and adiabatic temperature is relatively moderate, 630 GPa and 5000 K, while the maximum central core pressure varies between 1.5 and 2.5 TPa. We further find that for planets with radii less than 1.5 Earth radii, core-mantle boundary pressure and adiabatic temperature are mostly a function of planet radius and insensitive to planet structure. The pressures and temperatures of rocky exoplanet interiors, then, are less than those explored in recent shock-compression experiments, ab-initio calculations, and planetary dynamical studies. We further show that the extrapolation of relevant equations of state does not introduce significant uncertainties in the structural models of these planets. Mass-radius models are more sensitive to bulk composition than any uncertainty in the equation of state, even when extrapolated to TPa pressures.