cssi/SciSciGPT-SciSciNet huggingface dataset:
Development of structure–function coupling in human brain networks during youth

The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as executive function. However, it remains unclear how white-matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure–function coupling using diffusion-weighted imaging and n-back functional MRI data in a sample of 727 individuals (ages 8 to 23 y). We found that spatial variability in structure–function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure–function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n = 294). Moreover, structure–function coupling in rostrolateral prefrontal cortex was associated with executive performance and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.
A quadratically convergent local algorithm on minimizing sums of the largest eigenvalues of a symmetric matrix

In this paper, we consider the problem on minimizing sums of the largest eigenvalues of a symmetric matrix which depends on the decision variable affinely. An important application of this problem is the graph partitioning problem, which arises in layout of circuit boards, computer logic partitioning, and paging of computer programs. Given ∈≥0, we first derive an optimality condition which ensures that the objective function is within ∈ error bound of the solution. This condition may be used as a practical stopping criterion for any algorithm solving the underlying problem. We also show that, in a neighborhood of the minimizer, the optimization problem can be equivalently formulated as a smooth constrained problem. An existing algorithm on minimizing the largest eigenvalue of a symmetric matrix is shown to be applicable here. This algoritm enjoys the property that if started close enough to the minimizer, then it will converge quadratically. To implement a practical algorithm, one needs to incorporate some technique to generate a good starting point. Since the problem is convex, this can be done by using an algorithm for general convex optimization problems (e.g., Kelley's cutting plane method or ellipsoid methods), or an algorithm specific for the optimization problem under consideration (e.g., the algorithm developed by Cullum, Donath, and Wolfe). Such union ensures that the overall algorithm has global convergence with quadratic rate. Finally, the results presented in this paper are readily extended on minimizing sums of the largest eigenvalues of a Hermitian matrix.
Comparing nonorientable three genus and nonorientable four genus of torus knots

We compare the values of the nonorientable three genus (or, crosscap number) and the nonorientable four genus of torus knots. In particular, let T(p,q) be any torus knot with p even and q odd. The ...
Thresholds, valuations, and K-stability

Abstract Let X be a normal complex projective variety with at worst klt singularities, and L a big line bundle on X. We use valuations to study the log canonical threshold of L, as well as another invariant, the stability threshold. The latter generalizes a notion by Fujita and Odaka, and can be used to characterize when a Q-Fano variety is K-semistable or uniformly K-stable. It can also be used to generalize volume bounds due to Fujita and Liu. The two thresholds can be written as infima of certain functionals on the space of valuations on X. When L is ample, we prove that these infima are attained. In the toric case, toric valuations achieve these infima, and we obtain simple expressions for the two thresholds in terms of the moment polytope of L.
Inferring the Demographic History of Inbred Species from Genome-Wide SNP Frequency Data

Demographic inference using the site frequency spectrum (SFS) is a common way to understand historical events affecting genetic variation. However, most methods for estimating demography from the SFS assume random mating within populations, precluding these types of analyses in inbred populations. To address this issue, we developed a model for the expected SFS that includes inbreeding by parameterizing individual genotypes using beta-binomial distributions. We then take the convolution of these genotype probabilities to calculate the expected frequency of biallelic variants in the population. Using simulations, we evaluated the model’s ability to coestimate demography and inbreeding using one- and two-population models across a range of inbreeding levels. We also applied our method to two empirical examples, American pumas (Puma concolor) and domesticated cabbage (Brassica oleracea var. capitata), inferring models both with and without inbreeding to compare parameter estimates and model fit. Our simulations showed that we are able to accurately coestimate demographic parameters and inbreeding even for highly inbred populations (F = 0.9). In contrast, failing to include inbreeding generally resulted in inaccurate parameter estimates in simulated data and led to poor model fit in our empirical analyses. These results show that inbreeding can have a strong effect on demographic inference, a pattern that was especially noticeable for parameters involving changes in population size. Given the importance of these estimates for informing practices in conservation, agriculture, and elsewhere, our method provides an important advancement for accurately estimating the demographic histories of these species.
Application of Clustering Analysis for Investigation of Food Accessibility

Abstract Access to food assistance programs such as food pantries and food banks needs focus in order to mitigate food insecurity. Accessibility to the food assistance programs is impacted by demographics of the population and geography of the location. It hence becomes imperative to define and identify food assistance deserts (Under-served areas) within a given region to find out the ways to improve the accessibility of food. Food banks, the supplier of food to the food agencies serving the people, can manage its resources more efficiently by targeting the food assistance deserts and increase the food supply in those regions. This paper will examine the characteristics and structure of the food assistance network in the region of Ohio by presenting the possible reasons of food insecurity in this region and identify areas wherein food agencies are needed or may not be needed. Gaussian Mixture Model (GMM) clustering technique is employed to identify the possible reasons and address this problem of food accessibility.
Hierarchical Transfer Convolutional Neural Networks for Image Classification

In this paper, we address the issue of how to enhance the generalization performance of convolutional neural networks (CNN) in the early learning stage for image classification. This is motivated by real-time applications that require the generalization performance of CNN to be satisfactory within limited training time. In order to achieve this, a novel hierarchical transfer CNN framework is proposed. It consists of a group of shallow CNNs and a cloud CNN, where the shallow CNNs are trained firstly and then the first layers of the trained shallow CNNs are used to initialize the first layer of the cloud CNN. This method will boost the generalization performance of the cloud CNN significantly, especially during the early stage of training. Experiments using CIFAR-10 and ImageNet datasets are performed to examine the proposed method. Results demonstrate the improvement of testing accuracy is 12% on average and as much as 20% for the CIFAR-10 case while 5% testing accuracy improvement for the ImageNet case during the early stage of learning. It is also shown that universal improvements of testing accuracy are obtained across different settings of dropout and number of shallow CNNs.
Descartes Circle Theorem, Steiner Porism, and Spherical Designs

A Steiner chain of length k consists of k circles tangent to two given non-intersecting circles (the parent circles) and tangent to each other in a cyclic pattern. The Steiner porism states that on...
Limited lookahead in imperfect-information games

Abstract Limited lookahead has been studied for decades in perfect-information games. We initiate a new direction via two simultaneous deviation points: generalization to imperfect-information games and a game-theoretic approach. We study how one should act when facing an opponent whose lookahead is limited. We study this for opponents that differ based on their lookahead depth, based on whether they, too, have imperfect information, and based on how they break ties. We characterize the hardness of finding a Nash equilibrium or an optimal commitment strategy for either player, showing that in some of these variations the problem can be solved in polynomial time while in others it is PPAD-hard, NP-hard, or inapproximable. We proceed to design algorithms for computing optimal commitment strategies—for when the opponent breaks ties favorably, according to a fixed rule, or adversarially. We then experimentally investigate the impact of limited lookahead. The limited-lookahead player often obtains the value of the game if she knows the expected values of nodes in the game tree for some equilibrium—but we prove this is not sufficient in general. Finally, we study the impact of noise in those estimates and different lookahead depths.
Linear Encodings for Polytope Containment Problems

The polytope containment problem is deciding whether a polytope is a contained within another polytope. The complexity heavily depends on how the polytopes are represented. While there exists efficient necessary and sufficient conditions for polytope containment when their hyperplanes are available (H-polytopes), the case when polytopes are represented by affine transformations of H-polytopes, which we refer to as AH-polytopes, is known to be co-NP-complete. In this paper, we provide a sufficient condition for AH-polytope in AHpolytope problem that can be cast as a linear set of constraints with size that grows linearly with the number of hyperplanes of each polytope. These efficient encodings enable us to designate certain components of polytopes as decision variables, and incorporate them into a convex optimization problem. We present the usefulness of our results on applications to the zonotope containment problem, computing polytopic Hausdorff distances, and finding inner approximations to orthogonal projections of polytopes. Illustrative examples are included.
2013 Statement on Human Papillomavirus DNA Test Utilization.

This statement from the Cytopathology Education and Technology Consortium summarizes appropriate and inappropriate uses of human papillomavirus testing in cervical cancer screening based on guidelines from the American Society for Colposcopy and Cervical Pathology and the American Cancer Society.
Uncovering Wholesale Electricity Market Principles

This paper examines, enunciates, and makes explicit a set of market principles historically relied upon by the Federal Energy Regulatory Commission (FERC) to regulate wholesale electricity markets as required under the Federal Power Act (FPA). These identified competitive market principles are supported by policy and legal foundations that run through a myriad of FERC orders and court decisions. This paper seeks to make that history and those implicit market principles explicit by distilling and organizing Commission Orders and court decisions. It concludes that five market principles, each with multiple subprinciples, can be identified as elemental to how FERC understands and implements its statutory authority. Clear articulation of these foundational principles should help guide engaged entities as wholesale power markets continue to evolve. Market Principle 1 states that wholesale market revenues should predominantly flow from well-designed energy and ancillary services markets. Market structures generally are found to be preferable to non-market structures. Moreover, energy and ancillary services markets, in relationship to wholesale capacity markets, are better able to efficiently promote a least-cost resource. Market Principle 2 states that when altering market design, FERC and Independent System Operators (ISOs) should focus on only those services that are clearly needed, and ensure that any market design change does not unduly discriminate between resources. Market design changes focused on technology-neutral and well-defined granular services will help ensure that the design change does not lead to undue discrimination or preference that effectively favors certain resources. When such an impact still occurs, strong evidence showing that the rules are not unreasonable and arbitrary and that no non-unduly discriminatory and preferential alternative exists must support the change. Market Principle 3 states that interventions that distort transparent and accurate pricing should be minimized. Out-of-market interventions, in particular, have the potential to distort price signals and undermine competition. Market Principle 4 states that FERC’s just and reasonable standard strongly favors rate decreasing outcomes. Markets are premised on the economic presumption that competition reduces prices, in furtherance of the just and reasonable standard. Market Principle 5 states that FERC and ISOs should facilitate and not undermine state public policy preferences. FERC and ISOs are not well-situated to serve as decision-makers in determining which state public policy preferences should be given effect. State public policy preferences that do not run afoul of FERC’s authority under the FPA should thus be given full effect.
Small local action of singular integrals on spaces of non-homogeneous type

Fix d≥2 and s∈(0,d). In this paper we introduce a notion called small local action associated to a singular integral operator, which is a necessary condition for the existence of the principal value integral. Our goal is to understand the geometric properties of a measure for which an associated singular integral has small local action. We revisit Mattila's theory of symmetric measures and relate, under the condition that the measure has finite upper density, the existence of small local action to the cost of transporting the measure to a collection of symmetric measures. As an application, we obtain a soft proof of a theorem of Ruiz de Villa and Tolsa on the measures for which the principal value integral associated with the s-Riesz transform exists if s∉Z. Furthermore, we provide a considerable generalization of this theorem if s∈(d−1,d).
Graph-Based Faulted Line Identification Using Micro-PMU Data in Distribution Systems

Motivated by increasing penetration of distributed generators (DGs) and fast development of micro-phasor measurement units ( ${\mu }$ PMUs), this paper proposes a novel graph-based faulted line identification algorithm using a limited number of ${\mu }$ PMUs in distribution networks. The core of the proposed method is to apply advanced distribution system state estimation (DSSE) techniques integrating ${\mu }$ PMU data to the fault location. We propose a distributed DSSE algorithm to efficiently restrict the searching region for the fault source in the feeder between two adjacent ${\mu }$ PMUs. Based on the graph model of the feeder in the reduced searching region, we further perform the DSSE in a hierarchical structure and identify the location of the fault source. Also, the proposed approach captures the impact of DGs on distribution system operation and remains robust against high-level noises in measurements. Numerical simulations verify the accuracy and efficiency of the proposed method under various fault scenarios covering multiple fault types and fault impedances.
The energy of a simplicial complex

Abstract A finite abstract simplicial complex G defines a matrix L, where L ( x , y ) = 1 if two simplicies x , y in G intersect and where L ( x , y ) = 0 if they don't. This matrix is always unimodular so that the inverse g = L − 1 has integer entries g ( x , y ) . In analogy to Laplacians on Euclidean spaces, these Green function entries define a potential energy between two simplices x , y . We prove that the total energy E ( G ) = ∑ x , y g ( x , y ) is equal to the Euler characteristic χ ( G ) of G and that the number of positive minus the number of negative eigenvalues of L is equal to χ ( G ) .
Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides

The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semiconductor’s performance in solar cells, photodiodes, and optoelectronics. The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate. In this work, we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe, and CdS can lead to accurate and generalizable predictive models of defect properties. By converting any semiconductor + impurity system into a set of numerical descriptors, regression models are developed for the impurity formation enthalpy and charge transition levels. These regression models can subsequently predict impurity properties in mixed anion CdX compounds (where X is a combination of Te, Se and S) fairly accurately, proving that although trained only on the end points, they are applicable to intermediate compositions. We make machine-learned predictions of the Fermi-level-dependent formation energies of hundreds of possible impurities in 5 chalcogenide compounds, and we suggest a list of impurities which can shift the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. Machine learning predictions for the dominating impurities compare well with DFT predictions, revealing the power of machine-learned models in the quick screening of impurities likely to affect the optoelectronic behavior of semiconductors.
Age-of-Onset Estimation†

Age of onset estimates are crucial to clinical management and designing disease prevention studies. For many diseases, age is the primary risk factor. Age of onset is censored if a person is unaffected. Two different modeling philosophies are possible: to assume a mixture of susceptible and nonsusceptible individuals in the population, or to assume that all individuals are susceptible and would become affected if only they lived long enough. Variability of age of onset as a function of exposures can be modeled through the hazard. Designs possible to estimate age of onset parameters are: case—control family studies, kin—cohort studies, and the study of high-risk families. Age-specific penetrances can be estimated using a likelihood, pseudolikelihood, marginal likelihood, weighted score, or the method of moments. Keywords: age-specific penetrance; age-specific risk; cumulative risk; cumulative incidence; hazard; genetic counselling
Tie-Breaking Rules and Divisibility in Experimental Duopoly Markets

We investigate pricing behavior of sellers in duopoly markets with posted prices and market power. The two treatment variables are given by tie-breaking rules and divisibility of the price space. The first treatment variable deals with the rule under which demanded units are allocated between sellers in case of a price tie. A change in divisibility is modeled by making the sellers’ price space finer or coarser. We find that the incidence of perfect collusion is significantly higher under the sharing tie-breaking rule than under the random (coin-toss) one, especially when the price space is less divisible.
Transmitters for Digital Television

Digital television for over-the air broadcasting requires high-power transmitters. Transmitters with high peak power and excellent linearity are required. Combined transistors and vacuum tubes are used to amplify the coded orthogonal frequency-division multiplex (COFDM) or eight-level vestigial sideband quadrature amplitude modulation (8 VSB) to kilowatts of RF power. Keywords: digital TV transmitters; digital communications; transmitters; RF power devices; television broadcast
Tidally Distorted, Iron-enhanced Exoplanets Closely Orbiting Their Stars

The transiting planet candidate KOI 1843.03 ($0.6 R_\oplus$ radius, 4.245 hour orbital period, $0.46 M_\odot$ host star) has the shortest orbital period of any planet yet discovered. Here we show, using the first three-dimensional interior structure simulations of ultra-short-period tidally distorted rocky exoplanets, that KOI 1843.03 may be shaped like an American football, elongated along the planet-star axis with an aspect ratio of up to 1.79. Furthermore, for KOI 1843.03 to have avoided tidal disruption (wherein the planet is pulled apart by the tidal gravity of its host star) on such a close-in orbit, KOI 1843.03 must be as iron-rich as Mercury (about 66% by mass iron compared to Mercury's 70% by mass iron, Hauck et al. 2013). Of the ultra-short-period ($P_\mathrm{orb} \lesssim 1$ day) planets with physically-meaningful constraints on their densities characterized to date, just under half (4 out of 9) are iron-enhanced. As more are discovered, we will better understand the diversity of rocky planet compositions and the variety of processes that lead to planetary iron enhancement.
25 – Disease Diagnosis

Publisher Summary The diagnosis of disease is the first step in the treatment of disease. The ability to diagnose improves with prolonged experience. Diagnostic ability, therefore, results from a broad range of both academic and field experience, mixed together with a great deal of common sense. It is necessary for understanding the appearance and function of a healthy tree before a diseased tree can be recognized. A collection of field guides to woody ornamental plants is a helpful aid for rapid field identification. The knowledge of local conditions of weather and soil type is also necessary for an accurate diagnosis of tree disease. The extremes of temperature and normal range of moisture conditions are important factors in both infectious and noninfectious disease. Diagnostic activities can be divided into field diagnosis and diagnosis by telephone, letter, or office visit. In field diagnosis, the tree specialist examines the tree(s) and is able to question the client during the examination. This form of diagnosis allows the specialist to obtain the maximum information about the tree and site, from both direct examination and background information from the client.
Bayesian Approaches to Cure Rate Models

Bayesian approaches to cure rate modeling have gained great popularity in the analysis of survival data. In this chapter, we review several Bayesian approaches, discussing various alternative formulations of the cure rate model, their interpretations, and their properties. Keywords: clonogens; cure rate; improper survival function; mixture model; promotion time; semiparametric model
A meta-analysis of the gender gap(s) in venture funding: Funder- and entrepreneur-driven perspectives

Abstract Using gender homophily and gender socialization as theoretical foundations, the current study takes the position that both funder-driven (supply-side) and entrepreneur-driven (demand-side) processes perpetuate the gender gap in venture funding. Using this positional anchor, I performed a meta-analysis on gender-funding associations. The results show that gender-funding associations are different across funding contexts, which is consistent with what gender homophily and a funder-driven perspective might suggest. However, the nature of the difference depends on whether the outcome is funding amount or funding success. In addition, business size and industry sector were found to fully mediate the relation between entrepreneur gender and funding needed. This finding is consistent with what gender socialization and an entrepreneur-driven perspective might suggest. The mediation results ultimately suggest that female entrepreneurs need less funding for their ventures, which in turn results in less funding amounts but greater funding success. As such, there is one gender gap to the disadvantage of female entrepreneurs (funding amount) and another gender gap to the advantage of female entrepreneurs (funding success). Together, the perspectives and findings presented in this paper provide insights for both research and practice on the gender gap(s) in venture funding.
Heats of Reaction of Natural Rubber with Sulfur

Abstract An adiabatic copper calorimeter was used to determine the heats of vulcanization of pale crepe natural rubber with sulfur for mixtures varying in composition from 0 to 32 per cent added sulfur. The side reaction that produces hydrogen sulfide was avoided by using reaction temperatures near 155° C. Heats of reaction at 25° C and at 155° C are reported. The enthalpy change at 25° C for compounds containing up to about 18 per cent sulfur is given in joules per gram of vulcanizate by the equation, ΔH25=−21.1·S with a standard deviation of 11 J/g. Here S is the percentage of combined sulfur. Above 18 per cent sulfur the heat of reaction at 25° C remains approximately constant at 380 ± 8 J/g. A comparison is made between the heat of vulcanization and the volume change on vulcanization, both as functions of combined sulfur, by making use of data in the literature.
Smart Cities: A Survey on New Developments, Trends, and Opportunities

The continued growth of the population in urban areas has called for smarter cities for the 21st century. While great progress has been made in the last two decades in this regard, remaining challe...
Differential Complex Formation via Paralogs in the Human Sin3 Protein Interaction Network

Despite the continued analysis of HDAC inhibitors in clinical trials, the heterogeneous nature of the protein complexes they target limits our understanding of the beneficial and off-target effects associated with their application. Among the many HDAC protein complexes found within the cell, Sin3 complexes are conserved from yeast to humans and likely play important roles as regulators of transcriptional activity. The presence of two Sin3 paralogs in humans, SIN3A and SIN3B, may result in a heterogeneous population of Sin3 complexes and contributes to our poor understanding of the functional attributes of these complexes. Here, we profile the interaction networks of SIN3A and SIN3B to gain insight into complex composition and organization. In accordance with existing data, we show that Sin3 paralog identity influences complex composition. Additionally, chemical cross-linking MS identifies domains that mediate interactions between Sin3 proteins and binding partners. The characterization of rare SIN3B proteoforms provides additional evidence for the existence of conserved and divergent elements within human Sin3 proteins. Together, these findings shed light on both the shared and divergent properties of human Sin3 proteins and highlight the heterogeneous nature of the complexes they organize.
Energy-Efficient Massive IoT Shared Spectrum Access Over UAV-Enabled Cellular Networks

Data aggregation has become an emerging paradigm to support massive Internet-of-things (IoT), a new and critical use case for fifth-generation new radio (5G-NR). Indeed, data aggregators can complement cellular base stations and process IoT traffic to reduce network congestion. In this paper, we consider using mobile data aggregators, e.g., drones, that collect IoT traffic and aggregate them to the network. Specifically, we first discuss how the spectrum can be shared between cellular users (UEs) and IoT devices in the presence of drones, proposing a time-division duplexing protocol. We use stochastic geometry to analyze this protocol, comparing it to the standard spectrum sharing and orthogonal allocation protocols. We then formulate a stochastic optimization problem to optimize the nominal IoT transmit power, maximizing the average energy-efficiency (EE) of the IoT device subject to interference constraints to protect UEs. Simulations are presented to validate the theoretical insights and the effectiveness of the proposed protocol. It is shown that using drones, to aggregate IoT traffic, improves the EE of IoT devices, yet the EE degrades as their altitudes increases. Equally important, optimizing the transmit power is critical to further improve the EE, while ensuring fair coexistence with UEs.
Non-Boltzmann vibrational energy distributions and coupling to dissociation rate

In this article, we propose a generalized model for nonequilibrium vibrational energy distribution functions. The model can be used, in place of equilibrium (Boltzmann) distribution functions, when deriving reaction rate constants for high-temperature nonequilibrium flows. The distribution model is derived based on the recent ab initio calculations, carried out using potential energy surfaces developed using accurate computational quantum chemistry techniques for the purpose of studying air chemistry at high temperatures. Immediately behind a strong shock wave, the vibrational energy distribution is non-Boltzmann. Specifically, as the gas internal energy rapidly excites to a high temperature, overpopulation of the high-energy tail (relative to a corresponding Boltzmann distribution) is observed in ab initio simulations. As the gas excites further and begins to dissociate, a depletion of the high-energy tail is observed, during a time-invariant quasi-steady state. Since the probability of dissociation is exponentially related to the vibrational energy of the dissociating molecule, the overall dissociation rate is sensitive to the populations of these high vibrational energy states. The non-Boltzmann effects captured by the new model either enhance or reduce the dissociation rate relative to that obtained assuming a Boltzmann distribution. This article proposes a simple model that is demonstrated to reproduce these non-Boltzmann effects quantitatively when compared to ab initio simulations.
Should All Cross-Lingual Embeddings Speak English?

Most of recent work in cross-lingual word embeddings is severely Anglocentric. The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting. With this work, however, we challenge these practices. First, we show that the choice of hub language can significantly impact downstream lexicon induction zero-shot POS tagging performance. Second, we both expand a standard English-centered evaluation dictionary collection to include all language pairs using triangulation, and create new dictionaries for under-represented languages. Evaluating established methods over all these language pairs sheds light into their suitability for aligning embeddings from distant languages and presents new challenges for the field. Finally, in our analysis we identify general guidelines for strong cross-lingual embedding baselines, that extend to language pairs that do not include English.
Evolution in novel environments: Do restored prairie populations experience strong selection?

When populations colonize new habitats, they are likely to experience novel environmental conditions, and as a consequence may experience strong selection. While selection and the resulting evolutionary responses may have important implications for establishment success in colonizing populations, few studies have estimated selection in such scenarios. Here we examined evidence of selection in recently established plant populations in two prairie restorations in close proximity (<15 km apart) using two approaches: (1) we tested for evidence of past selection on a suite of traits in two Chamaecrista fasciculata populations by comparing the restored populations to each other and their shared source population in common gardens to quantify evolutionary responses and (2) we measured selection in the field. We found evidence of past selection on flowering time, specific leaf area, and root nodule production in one of the populations, but detected contemporary selection on only one trait (plant height). Our findings demonstrate that while selection can occur in colonizing populations, resulting in significant trait differences between restored populations in fewer than six generations, evolutionary responses differ across even nearby populations sown with the same source population. Because contemporary measures of selection differed from evolutionary responses to past selection, our findings also suggest that selection likely differs over the early stages of succession that characterize young prairies.
Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment

A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more probable than an ungrammatical sentence). Our work uses ambiguous relative clause attachment to extend such evaluations to cases of multiple simultaneous valid interpretations, where stark grammaticality differences are absent. We compare model performance in English and Spanish to show that non-linguistic biases in RNN LMs advantageously overlap with syntactic structure in English but not Spanish. Thus, English models may appear to acquire human-like syntactic preferences, while models trained on Spanish fail to acquire comparable human-like preferences. We conclude by relating these results to broader concerns about the relationship between comprehension (i.e. typical language model use cases) and production (which generates the training data for language models), suggesting that necessary linguistic biases are not present in the training signal at all.
Using simulated execution in verifying distributed algorithms

This paper presents a methodology for using simulated execution to assist a theorem prover in verifying safety properties of distributed systems. Execution-based techniques such as testing can increase confidence in an implementation, provide intuition about behavior, and detect simple errors quickly. They cannot by themselves demonstrate correctness. However, they can aid theorem provers by suggesting necessary lemmas and providing tactics to structure proofs. This paper describes the use of these techniques in a machine-checked proof of correctness of the Paxos algorithm for distributed consensus .
Minimum Bounded Chains and Minimum Homologous Chains in Embedded Simplicial Complexes

We study two optimization problems on simplicial complexes with homology over ℤ₂, the minimum bounded chain problem: given a d-dimensional complex 𝒦 embedded in ℝ^(d+1) and a null-homologous (d-1)-cycle C in 𝒦, find the minimum d-chain with boundary C, and the minimum homologous chain problem: given a (d+1)-manifold ℳ and a d-chain D in ℳ, find the minimum d-chain homologous to D. We show strong hardness results for both problems even for small values of d; d = 2 for the former problem, and d=1 for the latter problem. We show that both problems are APX-hard, and hard to approximate within any constant factor assuming the unique games conjecture. On the positive side, we show that both problems are fixed-parameter tractable with respect to the size of the optimal solution. Moreover, we provide an O(√{log β_d})-approximation algorithm for the minimum bounded chain problem where β_d is the dth Betti number of 𝒦. Finally, we provide an O(√{log n_{d+1}})-approximation algorithm for the minimum homologous chain problem where n_{d+1} is the number of (d+1)-simplices in ℳ.
Modeling meteoroid densities for spacecraft risk assessment

Abstract This report describes the methodologies and algorithms that NASA’s Meteoroid Environment Office (MEO) currently utilizes to measure meteoroid densities. We discuss the needs for an accurate model for the meteoroid density distribution in its Meteoroid Engineering Model (MEM), and its potential implications for spacecraft design. The recently developed process is heavily derived from that of previous work [4], although subtle differences exist. A thorough comparison of the best-fit densities derived for sixty meteoroids in [4] and with the process described here shows that discrepancies in final densities larger than a factor of two are measured for ~ 20% of our test events. Such a level of discrepancy suggests that any density calculated using similar methods cannot be assumed to represent a unique fit to the data. Additional observational data for meteoroids beyond those utilized in this work will be essential for producing more reliable and repeatable density measurements.
Optimal scheduling of critically loaded multiclass GI/M/n+M queues in an alternating renewal environment

In this paper, we study optimal control problems for multiclass $$GI/M/n+M$$ queues in an alternating renewal (up–down) random environment in the Halfin–Whitt regime. Assuming that the downtimes are asymptotically negligible and only the service processes are affected, we show that the limits of the diffusion-scaled state processes under non-anticipative, preemptive, work-conserving scheduling policies, are controlled jump diffusions driven by a compound Poisson jump process. We establish the asymptotic optimality of the infinite-horizon discounted and long-run average (ergodic) problems for the queueing dynamics. Since the process counting the number of customers in each class is not Markov, the usual martingale arguments for convergence of mean empirical measures cannot be applied. We surmount this obstacle by demonstrating the convergence of the generators of an augmented Markovian model which incorporates the age processes of the renewal interarrival times and downtimes. We also establish long-run average moment bounds of the diffusion-scaled queueing processes under some (modified) priority scheduling policies. This is accomplished via Foster–Lyapunov equations for the augmented Markovian model.
Injection of isotopic anomalies into the early solar nebula

Detailed hydrodynamic calculations are used to constrain models of the early solar system which rely on external injection of isotopically anomalous matter to explain meteorite anomalies. The conditions studied resemble a supernova remnant shell impacting on the outside of an interstellar H I cloud. For injection of either grains or gas, the essential physics of any such injection process is determined before the impinging material can penetrate beyond a skin layer, so that the results for an H I edge should be valid for any configuration which joins the interstellar medium with an H I boundary zone. The effects of initial injection velocity, gas cooling, and magnetic fields are considered. It is shown that grain penetration is limited to a layer with column density on the order of the grain mass/area ratio. Rayleigh-Taylor instabilities of the gas contribute negligibly to penetrate in the early stages, but do lead to the formation of bubbles (diameter approx.10/sup 11/ cm) of anomalous matter, which may be dispersed in later evolution. In the cases studied, the anomalous matter accumulates in the outer regions of the target cloud. The detailed hydrodynamics of these regions indicates that shock heating at impact, differential penetration by grains, andmore » the observed compositions of meteorites can be combined into a consistent scenario in which grain injection by a supernova remnant leads to the formation of anomalous meteorite parent bodies in the outer regions of the early solar nebula. Some considerations regarding turbulence are discussed in relation to these results.« less
Wilf equivalences between vincular patterns in inversion sequences

Abstract Inversion sequences are finite sequences of non-negative integers, where the value of each entry is bounded from above by its position. They provide a useful encoding of permutations. Patterns in inversion sequences have been studied by Corteel–Martinez–Savage–Weselcouch and Mansour–Shattuck in the classical case, where patterns can occur in any positions, and by Auli–Elizalde in the consecutive case, where only adjacent entries can form an occurrence of a pattern. These papers classify classical and consecutive patterns of length 3 into Wilf equivalence classes according to the number of inversion sequences avoiding them. In this paper we consider vincular patterns in inversion sequences, which, in analogy to Babson–Steingrimsson patterns in permutations, require only certain entries of an occurrence to be adjacent, and thus generalize both classical and consecutive patterns. Solving three conjectures of Lin and Yan, we provide a complete classification of vincular patterns of length 3 in inversion sequences into Wilf equivalence classes, and into more restrictive classes that consider the number of occurrences of the pattern and the positions of such occurrences. We find the first known instance of patterns in inversion sequences where these two more restrictive classes do not coincide.
A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System

This letter proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i.e., sparse code multiple access (SCMA). HD-NOMA combines the code-domain and power-domain NOMA schemes by clustering the users in small path loss (strong) and large path loss (weak) groups. The two groups are decoded using successive interference cancellation while within the group users are decoded using the message passing algorithm. To further improve the performance of the system, a spectral-efficiency maximization problem is formulated under a user quality-of-service constraint, which dynamically assigns power and subcarriers to the users. The problem is non-convex and has sparsity constraints. The alternating optimization procedure is used to solve it iteratively. We apply successive convex approximation and reweighted $\ell _1$ minimization approaches to deal with the non-convexity and sparsity constraints, respectively. The performance of the proposed HD-NOMA is evaluated and compared with the conventional SCMA scheme through numerical simulation. The results show the potential of HD-NOMA in increasing the number of uplink users that can be supported in a given time-frequency resource.
Robust tracking of an unknown trajectory with a multi-rotor UAV: A high-gain observer approach

We study a trajectory tracking problem for a multi-rotor in the presence of modeling error and external disturbances. The desired trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume that only position and orientation measurements for the multi-rotor and position measurements for the reference system can be accessed. We adopt an extended high-gain observer (EHGO) estimation framework to estimate the feed-forward term required for trajectory tracking, the multi-rotor states, as well as modeling error and external disturbances. We design an output feedback controller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. We rigorously analyze the proposed controller and establish its stability properties. Finally, we numerically illustrate our theoretical results using the example of a multi-rotor landing on a ground vehicle.
Faster Projection-free Online Learning

In many online learning problems the computational bottleneck for gradient-based methods is the projection operation. For this reason, in many problems the most efficient algorithms are based on the Frank-Wolfe method, which replaces projections by linear optimization. In the general case, however, online projection-free methods require more iterations than projection-based methods: the best known regret bound scales as $T^{3/4}$. Despite significant work on various variants of the Frank-Wolfe method, this bound has remained unchanged for a decade. In this paper we give an efficient projection-free algorithm that guarantees $T^{2/3}$ regret for general online convex optimization with smooth cost functions and one linear optimization computation per iteration. As opposed to previous Frank-Wolfe approaches, our algorithm is derived using the Follow-the-Perturbed-Leader method and is analyzed using an online primal-dual framework.
Monotone Sobolev Functions in Planar Domains: Level Sets and Smooth Approximation

We prove that almost every level set of a Sobolev function in a planar domain consists of points, Jordan curves, or homeomorphic copies of an interval. For monotone Sobolev functions in the plane we have the stronger conclusion that almost every level set is an embedded 1-dimensional topological submanifold of the plane. Here monotonicity is in the sense of Lebesgue: the maximum and minimum of the function in an open set are attained at the boundary. Our result is an analog of Sard’s theorem, which asserts that for a $$C^2$$ -smooth function in a planar domain almost every value is a regular value. As an application, using the theory of p-harmonic functions, we show that monotone Sobolev functions in planar domains can be approximated uniformly and in the Sobolev norm by smooth monotone functions.
On groups with $S^2$ Bowditch boundary

We prove that a relatively hyperbolic pair $(G,P)$ has Bowditch boundary a 2-sphere if and only if it is a 3-dimensional Poincare duality pair. We prove this by studying the relationship between the Bowditch and Dahmani boundaries of relatively hyperbolic groups.
Overlap in Observational Studies with High-Dimensional Covariates

Abstract Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis. Less discussed is the fact that covariate overlap is more difficult to satisfy in this setting. In this paper, we explore the implications of overlap in observational studies with high-dimensional covariates and formalize curse-of-dimensionality argument, suggesting that these assumptions are stronger than investigators likely realize. Our key innovation is to explore how strict overlap restricts global discrepancies between the covariate distributions in the treated and control populations. Exploiting results from information theory, we derive explicit bounds on the average imbalance in covariate means under strict overlap and show that these bounds become more restrictive as the dimension grows large. We discuss how these implications interact with assumptions and procedures commonly deployed in observational causal inference, including sparsity and trimming.
Mathematical validation of a continuum model for relaxation of interacting steps in crystal surfaces in 2 space dimensions

In this paper we study the boundary value problem for the equation $$\text{ div }\left( D(\nabla u)\nabla \left( \text{ div }\left( |\nabla u|^{p-2}\nabla u+\beta \frac{\nabla u}{|\nabla u|}\right) \right) \right) +au=f$$ in the $$z=(x,y)$$ plane. This problem is derived from a continuum model for the relaxation of a crystal surface below the roughing temperature. The mathematical challenge is of twofolds. First, the mobility $$D(\nabla u)$$ is a $$2\times 2$$ matrix whose smallest eigenvalue is not bounded away from 0 below. Second, the equation contains the 1-Laplace operator, whose mathematical properties are still not well-understood. Existence of a weak solution is obtained. In particular, $$|\nabla u|$$ is shown to be bounded when $$p>\frac{4}{3}$$ .
Last-Mile Shared Delivery: A Discrete Sequential Packing Approach

We propose a model for optimizing the last-mile delivery of n packages from a distribution center to their final recipients, using a strategy that combines the use of ride-sharing platforms (e.g., ...