cssi/SciSciGPT-SciSciNet huggingface dataset:
Time Delay Analysis of Turbofan Engine Direct and Indirect Combustion Noise Sources

The core noise components of a dual spool turbofan engine were separated by the use of a coherence function. A source location technique based on adjusting the time delay between the combustor pressure sensor signal and the far-field microphone signal to maximize the coherence and remove as much variation of the phase angle with frequency as possible was used. The discovery was made that for the 130o microphone a 90.027 ms time shift worked best for the frequency band from 0 to 200 Hz while a 86.975 ms time shift worked best for the frequency band from 200 to 400 Hz. Hence, the 0 to 200 Hz band signal took more time than the 200 to 400 Hz band signal to travel the same distance. This suggests the 0 to 200 Hz coherent cross spectral density band is partly due to indirect combustion noise attributed to entropy fluctuations, which travel at the flow velocity, interacting with the turbine. The signal in the 200 to 400 Hz frequency band is attributed mostly to direct combustion noise. Results are presented herein for engine power settings of 48, 54, and 60 percent of the maximum power setting
Labor Monopsony and the Limits of the Law

Recent literature has suggested that antitrust regulation is an appropriate response to labor market monopsony. This article qualifies the primacy of antitrust by arguing that a significant degree of labor market power is “frictional,” that is, without artificial barriers to entry or excessive concentration of employment. If monopsony is pervasive under conditions of laissez-faire, antitrust is likely to play only a partial role in remedying it, and other legal and policy instruments to intervene in the labor market will be required.
Choice Set Confounding in Discrete Choice

Standard methods in preference learning involve estimating the parameters of discrete choice models from data of selections (choices) made by individuals from a discrete set of alternatives (the choice set). While there are many models for individual preferences, existing learning methods overlook how choice set assignment affects the data. Often, the choice set itself is influenced by an individual's preferences; for instance, a consumer choosing a product from an online retailer is often presented with options from a recommender system that depend on information about the consumer's preferences. Ignoring these assignment mechanisms can mislead choice models into making biased estimates of preferences, a phenomenon that we call choice set confounding. We demonstrate the presence of such confounding in widely-used choice datasets. To address this issue, we adapt methods from causal inference to the discrete choice setting. We use covariates of the chooser for inverse probability weighting and/or regression controls, accurately recovering individual preferences in the presence of choice set confounding under certain assumptions. When such covariates are unavailable or inadequate, we develop methods that take advantage of structured choice set assignment to improve prediction. We demonstrate the effectiveness of our methods on real-world choice data, showing, for example, that accounting for choice set confounding makes choices observed in hotel booking and commute transportation more consistent with rational utility maximization.
Parallel FFT on ATM-based networks of workstations

We consider parallel computing on a network of workstations using a connection-oriented protocol (e.g., Asynchronous Transfer Mode) for data communication. In a connection-oriented protocol, a virtual circuit of guaranteed bandwidth is established for each pair of communicating workstations. Since all virtual circuits do not have the same guaranteed bandwidth, a parallel application must deal with the unequal bandwidths between workstations. Since most works in the design of parallel algorithms assume equal bandwidths on all the communication links, they often do not perform well when executed on networks of workstations using connection-oriented protocols. In this paper, we first evaluate the performance degradation caused by unequal bandwidths on the execution of conventional parallel algorithms such as the fast Fourier transform and bitonic sort. We then present a strategy based on dynamic redistribution of data points to reduce the bottlenecks caused by unequal bandwidths. We also extend this strategy to deal with processor heterogeneity. Using analysis and simulation we show that there is a considerable reduction in the runtime if the proposed redistribution strategy is adopted. The basic idea presented in this paper can also be used to improve the runtimes of other parallel applications in connection-oriented environments.
How to Apply Multiple Imputation in Propensity Score Matching with Partially Observed Confounders: A Simulation Study and Practical Recommendations

Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. Unfortunately, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms (MDMs). We found that commonly applied missing data methods resulted in biased and inefficient estimates, and we observed large variation in performance across MI-based strategies. Based on our findings, we recommend 1) deriving the PS after applying MI (referred to as MI-derPassive); 2) conducting PSM within each imputed data set followed by averaging the treatment effects to arrive at one summarized finding (INT-within) for mild MDMs and averaging the PSs across multiply imputed datasets before obtaining one treatment effect using PSM (INT-across) for more complex MDMs; 3) a bootstrapped-based variance to account for uncertainty of PS estimation, matching, and imputation; and 4) inclusion of key auxiliary variables in the imputation model.
Optical data implies a null simultaneity test theory parameter in rotating frames

The simultaneity framework describes the relativistic interaction of time with space. The two major proposed simultaneity frameworks are differential simultaneity, in which time is offset with dist...
CuSP: A Customizable Streaming Edge Partitioner for Distributed Graph Analytics

Graph analytics systems must analyze graphs with billions of vertices and edges which require several terabytes of storage. Distributed-memory clusters are often used for analyzing such large graphs since the main memory of a single machine is usually restricted to a few hundreds of gigabytes. This requires partitioning the graph among the machines in the cluster. Existing graph analytics systems use a built-in partitioner that incorporates a particular partitioning policy, but the best policy is dependent on the algorithm, input graph, and platform. Therefore, built-in partitioners are not sufficiently flexible. Stand-alone graph partitioners are available, but they too implement only a few policies. CuSP is a fast streaming edge partitioning framework which permits users to specify the desired partitioning policy at a high level of abstraction and quickly generates highquality graph partitions. For example, it can partition wdc12, the largest publicly available web-crawl graph with 4 billion vertices and 129 billion edges, in under 2 minutes for clusters with 128 machines. Our experiments show that it can produce quality partitions 6× faster on average than the state-of-theart stand-alone partitioner in the literature while supporting a wider range of partitioning policies.
Principal Investigator microgravity services role in ISS acceleration data distribution

Measurement of the microgravity acceleration environment on the International Space Station will be accomplished by two accelerometer systems. The Microgravity Acceleration Measurement System will record the quasi-steady microgravity environment, including the influences of aerodynamic drag, vehicle rotation, and venting effects. Measurement of the vibratory/transient regime comprised of vehicle, crew, and equipment disturbances will be accomplished by the Space Acceleration Measurement System-II. Due to the dynamic nature of the microgravity environment and its potential to influence sensitive experiments, Principal Investigators require distribution of microgravity acceleration in a timely and straightforward fashion. In addition to this timely distribution of the data, long term access to International Space Station microgravity environment acceleration data is required. The NASA Glenn Research Center's Principal Investigator Microgravity Services project will provide the means for real-time and post experiment distribution of microgravity acceleration data to microgravity science Principal Investigators. Real-time distribution of microgravity environment acceleration data will be accomplished via the World Wide Web. Data packets from the Microgravity Acceleration Measurement System and the Space Acceleration Measurement System-II will be routed from onboard the International Space Station to the NASA Glenn Research Center's Telescience Support Center. Principal Investigator Microgravity Services' ground support equipment located at the Telescience Support Center will be capable of generating a standard suite of acceleration data displays, including various time domain and frequency domain options. These data displays will be updated in real-time and will periodically update images available via the Principal Investigator Microgravity Services web page.
An analysis of replies to Trump's tweets

Donald Trump has tweeted thousands of times during his presidency. These public statements are an increasingly important way through which Trump communicates his political and personal views. A better understanding of the way the American public consumes and responds to these tweets is therefore critical. In the present work, we address both consumption of and response to Trump's tweets by studying replies to them on Twitter. With respect to response, we find that a small number of older, white, left-leaning, and female Americans are responsible for the vast majority of replies to Trump's tweets. These individuals also attend to a broader range of Trump's tweets than the rest of the individuals we study. With respect to consumption, we note that Trump's tweets are often viewed not in isolation, but rather in the context of a set of algorithmically-curated replies. These replies may therefore color the way Americans consume Trump's tweets. To this end, we find some evidence that Twitter accounts see replies in line with their political leanings. However, we show that this can be largely, although not entirely, attributed to the fact that Twitter is more likely to show replies by accounts a user follows. As a basis for comparison, all results for Trump are compared and contrasted with replies to Joe Biden's tweets.
Structured Logconcave Sampling with a Restricted Gaussian Oracle

We give algorithms for sampling several structured logconcave families to high accuracy. We further develop a reduction framework, inspired by proximal point methods in convex optimization, which bootstraps samplers for regularized densities to generically improve dependences on problem conditioning $\kappa$ from polynomial to linear. A key ingredient in our framework is the notion of a ``restricted Gaussian oracle'' (RGO) for $g: \mathbb{R}^d \rightarrow \mathbb{R}$, which is a sampler for distributions whose negative log-likelihood sums a quadratic (in a multiple of the identity) and $g$. By combining our reduction framework with our new samplers, we obtain the following bounds for sampling structured distributions to total variation distance $\epsilon$. \begin{itemize} \item For composite densities $\exp(-f(x) - g(x))$, where $f$ has condition number $\kappa$ and convex (but possibly non-smooth) $g$ admits an RGO, we obtain a mixing time of $O(\kappa d \log^3\tfrac{\kappa d}{\epsilon})$, matching the state-of-the-art non-composite bound \cite{LeeST20}. No composite samplers with better mixing than general-purpose logconcave samplers were previously known. \item For logconcave finite sums $\exp(-F(x))$, where $F(x) = \tfrac{1}{n}\sum_{i \in [n]} f_i(x)$ has condition number $\kappa$, we give a sampler querying $\widetilde{O}(n + \kappa\max(d, \sqrt{nd}))$ gradient oracles to $\{f_i\}_{i \in [n]}$. No high-accuracy samplers with nontrivial gradient query complexity were previously known. \item For densities with condition number $\kappa$, we give an algorithm obtaining mixing time $O(\kappa d \log^2\tfrac{\kappa d}{\epsilon})$, improving \cite{LeeST20} by a logarithmic factor with a significantly simpler analysis. We also show a zeroth-order algorithm attains the same query complexity. \end{itemize}
Vapor exposure to Δ9-tetrahydrocannabinol (THC) slows locomotion of the Maine lobster (Homarus americanus).

Abstract Rationale Despite a long history of use in synaptic physiology, the lobster has been a neglected model for behavioral pharmacology. A restaurateur proposed that exposing lobster to cannabis smoke reduces anxiety and pain during the cooking process. It is unknown if lobster gill respiration in air would result in significant Δ9-tetrahydrocannabinol (THC) uptake and whether this would have any detectable behavioral effects. Objective The primary goal was to determine tissue THC levels in the lobster after exposure to THC vapor. Secondary goals were to determine if THC vapor altered locomotor behavior or nociception. Methods Tissue samples were collected (including muscle, brain and hemolymph) from Homarus americanus (N = 3 per group) following 30 or 60 min of exposure to vapor generated by an e-cigarette device using THC (100 mg/mL in a propylene glycol vehicle). Separate experiments assessed locomotor behavior and hot water nociceptive responses following THC vapor exposure. Results THC vapor produced duration-related THC levels in all tissues examined. Locomotor activity was decreased (distance, speed, time-mobile) by 30 min inhalation of THC. Lobsters exhibit a temperature-dependent withdrawal response to immersion of tail, antennae or claws in warm water; this is novel evidence of thermal nociception for this species. THC exposure for 60 min had only marginal effect on nociception under the conditions assessed. Conclusions Vapor exposure of lobsters, using an e-cigarette based model, produces dose-dependent THC levels in all tissues and reduces locomotor activity. Hot water nociception was temperature dependent, but only minimal anti-nociceptive effect of THC exposure was confirmed.
Anomaly Detection in Large Scale Networks with Latent Space Models

We develop a real-time anomaly detection method for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic logistic model with interaction terms fo...
The economics of religious communities

Abstract The religious club model is central to the economics of religion. To expand its scope for application, we develop the first model to combine (i) increasing returns to membership, (ii) discrimination, and (iii) religious competition. Any degree of non-rivalry in religious club goods introduces scale effects which require new analytical techniques. Due to increasing returns, a religious leader faces a trade-off between forming a large inclusive club and screening out less committed types to form a small strict club. Endogenous screening makes religious strictness a non-monotonic function of economic development, which is consistent with the emergence of strict sects following periods of liberalization and economic growth. Blanket discrimination against all community members makes the religious community stricter and more cohesive, explaining the survival of religious sects and minorities under persecution. Stigmatizing actively religious members promotes social integration on the whole, but can create an extreme isolationist sect. Contrary to prior work on religious markets, we uncover a mechanism by which religious competition reduces religious participation and boosts social integration. Thus, attempts to moderate religion by stigmatizing participation and restricting competition could backfire. Finally, our model provides guidance for empirical work on religious discrimination and further extensions of the religious club model.
A SHRINKAGE PRINCIPLE FOR HEAVY-TAILED DATA: HIGH-DIMENSIONAL ROBUST LOW-RANK MATRIX RECOVERY.

This paper introduces a simple principle for robust statistical inference via appropriate shrinkage on the data. This widens the scope of high-dimensional techniques, reducing the distributional conditions from sub-exponential or sub-Gaussian to more relaxed bounded second or fourth moment. As an illustration of this principle, we focus on robust estimation of the low-rank matrix Θ* from the trace regression model Y = Tr(Θ*⊤X) + ϵ. It encompasses four popular problems: sparse linear model, compressed sensing, matrix completion and multi-task learning. We propose to apply the penalized least-squares approach to the appropriately truncated or shrunk data. Under only bounded 2+δ moment condition on the response, the proposed robust methodology yields an estimator that possesses the same statistical error rates as previous literature with sub-Gaussian errors. For sparse linear model and multi-task regression, we further allow the design to have only bounded fourth moment and obtain the same statistical rates. As a byproduct, we give a robust covariance estimator with concentration inequality and optimal rate of convergence in terms of the spectral norm, when the samples only bear bounded fourth moment. This result is of its own interest and importance. We reveal that under high dimensions, the sample covariance matrix is not optimal whereas our proposed robust covariance can achieve optimality. Extensive simulations are carried out to support the theories.
Notch signaling via Hey1 and Id2b regulates Müller glia's regenerative response to retinal injury

Zebrafish Muller glia (MG) respond to retinal injury by suppressing Notch signaling and producing progenitors for retinal repair. A certain threshold of injury-derived signal must be exceeded in order to engage MG in a regenerative response (MG's injury-response threshold). Pan-retinal Notch inhibition expands the zone of injury-responsive MG at the site of focal injury, suggesting that Notch signaling regulates MG's injury-response threshold. We found that Notch signaling enhanced chromatin accessibility and gene expression at a subset of regeneration-associated genes in the uninjured retina. Two Notch effector genes, hey1 and id2b, were identified that reflect bifurcation of the Notch signaling pathway, and differentially regulate MG's injury-response threshold and proliferation of MG-derived progenitors. Furthermore, Notch signaling component gene repression in the injured retina suggests a role for Dll4, Dlb, and Notch3 in regulating Notch signaling in MG and epistasis experiments confirm that the Dll4/Dlb-Notch3-Hey1/Id2b signaling pathway regulates MG's injury-response threshold and proliferation.
Interior Electroneutrality in Nernst–Planck–Navier–Stokes Systems

We consider the limit of vanishing Debye length for ionic diffusion in fluids, described by the Nernst–Planck–Navier–Stokes system. In the asymptotically stable cases of blocking (vanishing normal flux) and uniform selective (special Dirichlet) boundary conditions for the ionic concentrations, we prove that the ionic charge density $$\rho $$ converges in time to zero in the interior of the domain, in the limit of vanishing Debye length ( $$\epsilon \rightarrow 0$$ ). The order of the limits is important; we take first the long time limit and then the limit of vanishing Debye length. Thus, our main new results in these time asymptotically stable cases concern the vanishing of the ionic charge densities in steady states, as $$\epsilon \rightarrow 0$$ . The infinite time behaviors follow as corollaries of our previous works [5, 6]. For the unstable regime of Dirichlet boundary conditions for the ionic concentrations, we prove bounds that are uniform in time and $$\epsilon $$ . We also consider electroneutral boundary conditions, for which we prove that electroneutrality $$\rho \rightarrow 0$$ is achieved at any fixed $$\epsilon > 0$$ , exponentially fast in time in $$L^p$$ , for all $$1\leqq p < \infty $$ . The results hold for two oppositely charged ionic species with arbitrary ionic diffusivities, in bounded domains with smooth boundaries.
A Subsampling-Based Method for Causal Discovery on Discrete Data

Inferring causal directions on discrete and categorical data is an important yet challenging problem. Even though the additive noise models (ANMs) approach can be adapted to the discrete data, the functional structure assumptions make it not applicable on categorical data. Inspired by the principle that the cause and mechanism are independent, various methods have been developed, leveraging independence tests such as the distance correlation measure. In this work, we take an alternative perspective and propose a subsampling-based method to test the independence between the generating schemes of the cause and that of the mechanism. Our methodology works for both discrete and categorical data and does not imply any functional model on the data, making it a more flexible approach. To demonstrate the efficacy of our methodology, we compare it with existing baselines over various synthetic data and real data experiments.
Fibrin polymer on the surface of biomaterial implants drives the foreign body reaction

Abstract Implantation of biomaterials and medical devices in the body triggers the foreign body reaction (FBR) which is characterized by macrophage fusion at the implant surface leading to the formation of foreign body giant cells and the development of the fibrous capsule enveloping the implant. While adhesion of macrophages to the surface is an essential step in macrophage fusion and implanted biomaterials are known to rapidly acquire a layer of host proteins, a biological substrate that is responsible for this process in vivo is unknown. Here we show that mice with genetically imposed fibrinogen deficiency display a dramatic reduction of macrophage fusion on biomaterials implanted intraperitoneally and subcutaneously and are protected from the formation of the fibrin-containing fibrous capsule. Furthermore, macrophage fusion on biomaterials implanted in FibAEK mice that express a mutated form of fibrinogen incapable of thrombin-mediated polymerization was strongly reduced. Despite the lack of fibrin, the capsule was formed in FibAEK mice, although it had a different composition and distinct mechanical properties than that in wild-type mice. Specifically, while mononuclear α-SMA-expressing macrophages embedded in the capsule of both strains of mice secreted collagen, the amount of collagen and its density in the tissue of FibAEK mice was reduced. These data identify fibrin polymer as a key biological substrate driving the development of the FBR.
Geometric combinatorics of polynomials I: The case of a single polynomial

Abstract There are many different algebraic, geometric and combinatorial objects that one can attach to a complex polynomial with distinct roots. In this article we introduce a new object that encodes many of the existing objects that have previously appeared in the literature. Concretely, for every complex polynomial p with d distinct roots and degree at least 2, we produce a canonical compact planar 2-complex that is a compact metric version of a tiled phase diagram. It has a locally metric that is locally Euclidean away from a finite set of interior points indexed by the critical points of p, and each of its 2-cells is a metric rectangle. From this planar rectangular 2-complex one can use metric graphs known as metric cacti and metric banyans to read off several pieces of combinatorial data: a chain in the partition lattice, a cyclic factorization of a d-cycle, a real noncrossing partition (also known as a primitive d-major), and the monodromy permutations for the polynomial. This article is the first in a series.
Modeling Speedup in Multi-OS Environments

For workloads that place strenuous demands on system software, novel operating system designs like unikernels, library OSes, and hybrid runtimes offer a promising path forward. However, while these systems can outperform general-purpose OSes, they have limited ability to support legacy applications. Multi-OS environments, where the application’s execution is split between a control plane and a data plane operating system, can address this challenge, but reasoning about the performance of applications that run in such a split execution environment is currently guided only by expert intuition and empirical analysis. As the level of specialization in system software and hardware continues to increase, there is both a pressing need and ripe opportunity for investigating analytical models that can predict application performance and guide programmers’ intuition when considering multi-OS environments. In this paper we present such a model to place bounds on application speedup, beginning with a simple, intuitive formulation, and progressing to a more refined model. We present an analysis of the model for a diverse set of benchmarks, as well as a prototype tool to project multi-OS speedups for applications on existing systems. Finally, we validate our model on state-of-the-art multi-OS systems, demonstrating that it reliably predicts speedup with 96% average accuracy.
Sampling Multiple Edges Efficiently

We present a sublinear time algorithm that allows one to sample multiple edges from a distribution that is pointwise e-close to the uniform distribution, in an amortized-efficient fashion. We consider the adjacency list query model, where access to a graph G is given via degree and neighbor queries. The problem of sampling a single edge in this model has been raised by Eden and Rosenbaum (SOSA 18). Let n and m denote the number of vertices and edges of G, respectively. Eden and Rosenbaum provided upper and lower bounds of Θ^*(n/√ m) for sampling a single edge in general graphs (where O^*(⋅) suppresses poly(1/e) and poly(log n) dependencies). We ask whether the query complexity lower bound for sampling a single edge can be circumvented when multiple samples are required. That is, can we get an improved amortized per-sample cost if we allow a preprocessing phase? We answer in the affirmative. We present an algorithm that, if one knows the number of required samples q in advance, has an overall cost that is sublinear in q, namely, O^*(√ q ⋅(n/√ m)), which is strictly preferable to O^*(q⋅ (n/√ m)) cost resulting from q invocations of the algorithm by Eden and Rosenbaum. Subsequent to a preliminary version of this work, Tětek and Thorup (arXiv, preprint) proved that this bound is essentially optimal.
A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching Using Deep Reinforcement Learning

Significant development of ride-sharing services presents a plethora of opportunities to transform urban mobility by providing personalized and convenient transportation while ensuring the efficiency of large-scale ride pooling. However, a core problem for such services is route planning for each driver to fulfill the dynamically arriving requests while satisfying given constraints. Current models are mostly limited to static routes with only two rides per vehicle (optimally) or three (with heuristics) (Alonso-Mora et al. , 2017), at least in the initial allocation while not ascertaining that opposite-direction rides are not grouped together. In this paper, we present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework that (1) dynamically generates optimal routes for each vehicle based on online demand, pricing associated with each ride, vehicle capacities and locations. This matching algorithm starts greedily and optimizes over time using an insertion operation, (2) involves drivers in the decision-making process by allowing them to propose a different price based on the expected reward for a particular ride as well as the destination locations for future rides, which is influenced by supply-and-demand computed by the Deep Q-network. (3) allows customers to accept or reject rides based on their set of preferences with respect to pricing and delay windows, vehicle type and carpooling preferences. These (1-3) in tandem with each other enforce grouping rides with the most route-intersections together. (4) Based on demand prediction, our approach re-balances idle vehicles by dispatching them to the areas of anticipated high demand using deep Reinforcement Learning (RL). Our framework is validated using millions of trips extracted from the New York City Taxi public dataset; however, we consider different vehicle types and designed customer utility functions to validate the setup and study different settings. Experimental results show the effectiveness of our approach in real-time and large scale settings.
Feedback Control of Flight Speed to Reduce Unmanned Aerial System Noise

The aim of this initial study is to incorporate an acoustic metric into the flight control system of an unmanned aerial vehicle. This could be used to mitigate the noise impact of unmanned aerial systems operating near residential communities. To incorporate an acoustic metric into a pre-existing flight control system, two things are required: a source noise model, and an acoustic controller. An acoustic model was developed based on Gutin's work to estimate propeller noise. The flight control system was augmented with a controller to reduce propeller noise using feedback control of the commanded flight speed until an acoustic target was met. This control approach focuses on modifying flight speed only, with no perturbation to the trajectory. Multiple flight simulations were performed and the results showed that integrating an acoustic metric into the flight control system of an unmanned aerial system is possible.
Perceived Produce Availability and Child Fruit and Vegetable Intake: The Healthy Communities Study.

Children eat more fruits and vegetables when more are available at home, but less is known about how the neighborhood food environment relates to children’s diet and weight outcomes. The goal of this study was to determine whether parental perception of the food environment (neighborhood and home) is associated with children’s fruit and vegetable (F&V) intake and weight outcomes, and to assess differences by household food security status and household income. Cross-sectional data from the 2013–2015 U.S. Healthy Communities Study included 5138 children, aged 4 to 15 years old, from 130 U.S. communities. Neighborhood and home food environments were assessed with parent-reported, perceived F&V availability scores. Associations were tested with multi-level linear regression models. Parents’ perception of produce availability was associated with household F&V availability ratings (β = 0.09 points, p < 0.001). Household F&V availability was associated with child F&V intake (β = 0.32 cups/day or 25.6 g/day, p < 0.001). A higher child F&V intake was associated with a lower child BMI z-score (β = −0.05, p = 0.002). Weaker relationships were seen for children living in food insecure or low-income households. Optimizing neighborhood and home access to F&V may help children improve diet quality, but may not be as effective for children living in food insecure or low-income households.
Height four formal groups with quadratic complex multiplication

We construct spectral sequences for computing the cohomology of automorphism groups of formal groups with complex multiplication by a $p$-adic number ring. We then compute the cohomology of the group of automorphisms of a height four formal group law which commute with complex multiplication by the ring of integers in the field $\mathbb{Q}_p(\sqrt{p})$, for primes $p>5$. This is a large subgroup of the height four strict Morava stabilizer group. The group cohomology of this group of automorphisms turns out to have cohomological dimension $8$ and total rank $80$. We then run the $K(4)$-local $E_4$-Adams spectral sequence to compute the homotopy groups of the homotopy fixed-point spectrum of this group's action on the Lubin-Tate/Morava spectrum $E_4$.
Quarkyonic Effective Field Theory, Quark-Nucleon Duality and Ghosts

We present a field theoretical description of quarkyonic matter consisting of quark, nucleon and ghost fields coupling to mesonic degrees of freedom. The ghosts are present to cancel over-counting of nucleon states that are Pauli blocked by the quark Fermi sea. Such a theory becomes an effective field theory of nucleons at low baryon density, and as such will reproduce nucleonic matter phenomenology. This theory can accommodate chiral symmetry restoration and the dynamical generation of a shell of nucleons at the Fermi surface. It is valid for finite temperature and density. In such a theory, quark-nucleon duality is accomplished by inclusion of ghost fields so that the nucleons extra degrees of freedom, that are beyond those of quarks, are compensated by the ghost fields.
Restoring broken symmetries for nuclei and reaction fragments

In typical microscopic approaches, particularly when pairing correlations are present, nuclei and nuclear fragments do not have well-defined quantum numbers and symmetries should be restored. I present here a formalism for the simultaneous projection of total particle numbers of a nucleus, particle numbers of reaction fragments, and of the reaction fragment intrinsic spins and of their correlation, and also for their symmetry-restored densities and total energies. These formulas for the symmetry restored quantities, are free of any singularities, unlike those in the previously introduced prescriptions.
Not all peaks are created equal: the early growth of Supermassive Black Holes

In this work, we use the constrained Gaussian realization technique to study the early growth of supermassive black holes (SMBHs) in cosmological hydrodynamic simulations, exploring its relationship with features of the initial density peaks on large scales, ~1 Mpc/h. Our constrained simulations of volume (20 Mpc/h)^3 successfully reconstruct the large-scale structure as well as the black hole growth for the hosts of the rare 10^9 Msun SMBHs found in the BlueTides simulation at z~7. We run a set of simulations with constrained initial conditions by imposing a 5 \sigma_0(R_G) peak on scale of R_G = 1 Mpc/h varying different peak features, such as the shape and compactness as well as the tidal field surrounding the peak. We find that initial density peaks with high compactness and low tidal field induce the most rapid BH growth at early epochs. This is because compact density peaks with a more spherical large scale matter distribution lead to the formation of high density gas clumps in the centers of halos, and thus boost early BH accretion. Moreover, such initially compact density peaks in low tidal field regions also lead to a more compact BH host galaxy morphology. This can explain the tight correlation between BH growth and host galaxy compactness seen in observations.
Automatic Symmetry Discovery with Lie Algebra Convolutional Network

Existing equivariant neural networks for continuous groups require discretization or group representations. All these approaches require detailed knowledge of the group parametrization and cannot learn entirely new symmetries. We propose to work with the Lie algebra (infinitesimal generators) instead of the Lie group.Our model, the Lie algebra convolutional network (L-conv) can learn potential symmetries and does not require discretization of the group. We show that L-conv can serve as a building block to construct any group equivariant architecture. We discuss how CNNs and Graph Convolutional Networks are related to and can be expressed as L-conv with appropriate groups. We also derive the MSE loss for a single L-conv layer and find a deep relation with Lagrangians used in physics, with some of the physics aiding in defining generalization and symmetries in the loss landscape. Conversely, L-conv could be used to propose more general equivariant ansatze for scientific machine learning.
COVID-19 Vaccine Rollouts and the Reproduction of Urban Spatial Inequality: Disparities Within Large U.S. Cities in March and April 2021 by Racial/Ethnic and Socioeconomic Composition

Rollouts of COVID-19 vaccines in the U.S. were opportunities to redress disparities that surfaced during the pandemic. Initial eligibility criteria, however, neglected geographic, racial/ethnic, and socioeconomic considerations. Marginalized populations may have faced barriers to then-scarce vaccines, reinforcing disparities. Inequalities may have subsided as eligibility expanded. Using spatial modeling, we investigate how strongly local vaccination levels were associated with socioeconomic and racial/ethnic composition as authorities first extended vaccine eligibility to all adults. We harmonize administrative, demographic, and geospatial data across postal codes in eight large U.S. cities over three weeks in Spring 2021. We find that, although vaccines were free regardless of health insurance coverage, local vaccination levels in March and April were negatively associated with poverty, enrollment in means-tested public health insurance (e.g., Medicaid), and the uninsured population. By April, vaccination levels in Black and Hispanic communities were only beginning to reach those of Asian and White communities in March. Increases in vaccination were smaller in socioeconomically disadvantaged Black and Hispanic communities than in more affluent, Asian, and White communities. Our findings suggest vaccine rollouts contributed to cumulative disadvantage. Populations that were left most vulnerable to COVID-19 benefited least from early expansions in vaccine availability in large U.S. cities.
Encapsulated Actomyosin Patterns Drive Cell-Like Membrane Shape Changes

Cell shape changes from locomotion to cytokinesis are, to a large extent, driven by myosin-driven remodeling of cortical actin patterns. Passive crosslinkers such as α-actinin and fascin as well actin nucleator Arp2/3 complex largely determine the architecture and connectivity of actin network patterns; consequently, they regulate network remodeling and membrane shape changes. Membrane constriction in animal cell cytokinesis proceeds by assembly and contraction of a contractile ring pattern rich in α-actinin and myosin at the equator of the cell cortex, with which the ring is contiguous. Here we reconstitute actomyosin networks inside cell-sized lipid bilayer vesicles and show that, depending on vesicle size and concentrations of α-actinin and fascin, actomyosin networks assemble into ring and aster-like patterns. Anchoring actin to the membrane enhances the interaction of the contractile networks with lipid membrane but does not change the architecture of the patterns. A membrane-bound actomyosin ring exerts force and constricts the membrane. An Arp2/3 complex-mediated actomyosin cortex is shown to assemble a ring-like pattern at the equatorial cortex and contribute to myosin-driven clustering of the cortex and consequently membrane deformation. An active gel theory unifies a model for the observed membrane constriction and protrusion induced by the membrane-bound actomyosin networks.
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning

In complex systems, we often observe complex global behavior emerge from a collection of agents interacting with each other in their environment, with each individual agent acting only on locally available information, without knowing the full picture. Such systems have inspired development of artificial intelligence algorithms in areas such as swarm optimization and cellular automata. Motivated by the emergence of collective behavior from complex cellular systems, we build systems that feed each sensory input from the environment into distinct, but identical neural networks, each with no fixed relationship with one another. We show that these sensory networks can be trained to integrate information received locally, and through communication via an attention mechanism, can collectively produce a globally coherent policy. Moreover, the system can still perform its task even if the ordering of its inputs is randomly permuted several times during an episode. These permutation invariant systems also display useful robustness and generalization properties that are broadly applicable. Interactive demo and videos of our results: this https URL
Designing Nonrecognition Rules Under the Internal Revenue Code

Nonrecognition rules are a prominent feature of the income tax laws and are a source of considerable complexity and tax planning. Included among the nonrecognition rules contained in the Internal RevenueCode are provisions applying to like kind exchanges, corporate formations, corporate reorganizations, parent-subsidiary liquidations, and partnership formations and distributions. The policies that arguably support the nonrecognition rules include the familiar trio of tax policy concerns—efficiency, equity, and tax administration. None of these policies, however, provide a strong basis for most of the nonrecognition rules as currently formulated. The efficiency case generally lacks evidentiary support. The equity case is complicated by the fact that the rules operate in a second-best world where the tax base deviates from economic income. And the tax administration argument for the rules, while plausible in theory, is compromised because nonrecognition frequently occurs where there are no valuation and liquidity concerns as a result of the receipt of publicly traded property or the presence of related cash sales. This Article generally dispenses with the efficiency and equity bases for the nonrecognition rules because of the aforementioned flaws. As a result, the similar replacement property factor, which is a product of these rationales for nonrecognition and currently is prominent in most nonrecognition provisions, should be generally discarded. This Article proposes a standard for designing nonrecognition rules that generallyignores the similarities or differences in the relinquished and replacement properties, unless the properties are either identical or possess a very high degree of similarity, and instead takes into account the following:presence of difficult-to-value property, presence of illiquid property, use of rules that are narrowly tailored to common transactional forms that are typically selected for significant non-tax reasons, and adherenceto certain corporate tax policies. This Article then applies this standard as a basis for suggesting revisions to the current nonrecognition rules. Included among the recommended reforms are (1) eliminating nonrecognition for like kind exchanges; (2) eliminating the control requirement for shareholders to receive nonrecognition upon transfers to corporations, but generally taxing shareholders on the transfer or receipt of publicly traded property; and (3) permitting nonrecognition in corporate reorganizations irrespective of satisfying continuity of interest or continuity of business enterprise requirements, but taxing shareholderson the receipt of publicly traded stock. Overall, the recommended approach and reforms should serve to rationalize and simplify the nonrecognition rules contained in the Internal Revenue Code.
Secure V2V and V2I Communication in Intelligent Transportation using Cloudlets

Intelligent Transportation System (ITS) is a vision which offers safe, secure and smart travel experience to drivers. This futuristic plan aims to enable vehicles, roadside transportation infrastructures, pedestrian smart-phones and other devices to communicate with one another to provide safety and convenience services. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication in ITS offers ability to exchange speed, heading angle, position and other environment related conditions amongst vehicles and with surrounding smart infrastructures. V2V will enable vehicles to exchange information about speed, location, direction, or brake status with other surrounding vehicles where receiving vehicles will aggregate these messages and make decisions. These on-board applications will warn drivers about accidents, over-speed, slow traffic ahead, aggressive driver, blind spot or a road hazard. V2I will enable road side units (RSUs) or traffic infrastructures to transmit information about bridge permissible height, merging traffic, work zone warning or road hazard detection to complement V2V applications.
Ka-band MMIC beam-steered transmitter array

The authors describe the design and testing of a 32-GHz six-element linear transmitter array utilizing monolithic-microwave-integrated-circuit (MMIC) phase shifters and power amplifiers for a spacecraft array feed for NASA deep-space communication applications. Performance results for individual phase shifters, power amplifiers, and microstrip radiators are reported, and electronic beam steering of the linear array is demonstrated. The array has a beam width of 7.5 degrees and demonstrates acceptable beam steering over +or-8 degrees , which is adequate for the future two-dimensional array. >
Transformers for Modeling Physical Systems

Transformers are widely used in neural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performance for many language related tasks, their applicability outside of the neural language processing field has been minimal. In this work, we propose the use of transformer models for the prediction of dynamical systems representative of physical phenomena. The use of Koopman based embeddings provide a unique and powerful method for projecting any dynamical system into a vector representation which can then be predicted by a transformer model. The proposed model is able to accurately predict various dynamical systems and outperform classical methods that are commonly used in the scientific machine learning literature.
Variations of the Catalan numbers from some nonassociative binary operations

Abstract We investigate certain nonassociative binary operations that satisfy a four-parameter generalization of the associative law. From this we obtain variations of the ubiquitous Catalan numbers and connections to many interesting combinatorial objects such as binary trees, plane trees, lattice paths, and permutations.
The National Ignition Facility Project: An Update

The National Ignition Facility (NIF) consists of 192 forty-centimeter-square laser beams and a 10-m-diameter target chamber. Physical construction began in 1997. The Laser and Target Area Building ...
Testicular Cancer Imaging

Testicular cancer is the most common malignancy in young men with a high survival with appropriate management. Location of the primary tumor and disease extent as determined by imaging, laboratory markers and pathologic results classify patients as low, intermediate or high risk. The staging and management of testicular cancer are reviewed with an emphasis on the imaging appearance of the primary tumor, nodal disease, distant metastases and post treatment changes.
L-carnitine in inborn errors of metabolism: what is the evidence?

Summary: A questionnaire was posted on the electronic mailing-list Metab-l to determine current practice as regards the use of oral L-carnitine in medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, propionic acidaemia (PA) and methylmalonic acidaemia (MMA). Thirty-one centres replied: L-carnitine was used routinely by 94% of respondents in PA and MMA but by only 36% in MCAD deficiency. A search was made for published papers on the use of L-carnitine in organic acidaemias and in MCAD deficiency. The quality of evidence to support the use of L-carnitine was graded according to the scale published by the Scottish Intercollegiate Guideline Network. No high-quality evidence was identified.