cssi/SciSciGPT-SciSciNet huggingface dataset:
5 – Special Topics

Publisher Summary This chapter discusses some of the present methods for the treatment of singularities, shocks, and eigenvalue problems. Estimates of the errors between the solution of the differential equation and that of finite difference approximation depend on the boundedness of partial derivatives of some order. The estimates cannot be valid at or near a singular point on the boundary or in the interior of the integration domain. In rectangular coordinates, the most common form of boundary singularity occurs at the corners of the integration domain where the initial line meets the vertical boundary. Finite difference methods have questionable value in the neighborhood of singular points as a region of infection lies adjacent to such points. The effect of such discontinuities does not penetrate deeply into the field of integration provided the methods one use are stable, in which case the errors introduced by the discontinuity decay. There are serious difficulties in obtaining accurate solutions near the points of discontinuity and one must often abandon finite difference processes to obtain this accuracy.
Needs of Rural Schools regarding HIV Education.

This document reviews problems related to sexual activity among rural U.S. teenagers, successes and failures of teen sex education programs, and problems and needs specific to rural areas. In a recent poll, 89% of adults favored sex education, in the schools and 73% supported making birth control information and contraceptives available in school clinics. The likelihood of a teenager tecoming sexually active has less to do with socioeconomic status than with individual teens' values, goals, aspirations, and family environment in which he or she is raised. The primary causes of teens' contracting the HIV virus are ignorance, depression, being from a dysfunctional family, and poor school performance. Few sex education curricula address males and none describes strategies for using rural delivery systems. Successful teenage sexuality programs should deal comprehensively with the issues, last long enough to effect change, and involve students in the learning process. By itself, sex education is not a deterrent to teenage sex. The popular media encourage sexual activity and seldom address its acc:mpanying responsibilities. Classroom programs must be supplemented by community-based efforts. Rural teens are as sexually active as their urban counterparts, but rural areas are less likely to offer HIV education to students. The prevalence of high-risk students in rural areas is high while resources are scarce. The need for effective programs to delay sexual activity and educate teens regarding the HIV virus has never been greater. Community approaches might be assisted by unique rural resources su-.h as a tradition of neighbors helping neighbors and coperative extension szrvices. (TES) *AAWAAAAAAAAxxxxAAAAAAAAAAAAA*AAAX*AAAAAAI.AAAAAAAAAWAAAVEAWAAAAIPA A Reproductions supplied by EDRS are the best that can be made A from the original document. A *AAA.AAWAAAAIPAA*AXAAAAAAI.WAAVEZA**A*AA***AAAA*AAA*AAAVEA*AAAA**AA*A*A "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY 01^1 eTO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC)" U S DEPARTMENT OF EDUCATION Office 01 Educatronal Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER ;ERIC) As document has been reproduced as erved from ice person or organ Mahon orrgmahng a O Moor changes nave been made to improve remouctron cp.ahty Pants of new or opnuons statedenthrsdocu ment do not necessarily represent orlimat OEM posd.on or pohcy NEEDS OF RURAL SCTIOOLS REGARDING HAT EDUCATION Prepared by: Doris Helge, Ph.D. National Rural Development Institute 2 BEST COPY AVAILABLE NEEDS OF RURAL SCHOOLS REGARDING HIV EDUCATION Doris Helge, Ph.D. Director National Rural and Small Schools Consortium (NRSSC) Western Washington University Bellingham, WA 98225 206/676-3576 Copyright C) 1989, National Rural Development Institute All Rights Reserved. No part of this manual may be reproduced, stored in a retrieN,a1 system, or transmitted in any form or by any means, electronic, mechanical photocopying, recording, or otherwise, witliuut the prior permission of the copyright owners.
Chapter 4 – The Long Run Behavior of Markov Chains

Publisher Summary This chapter examines the long run behavior of Markov chains. The most important fact concerning a regular Markov chain is the existence of a limiting probability distribution. In the long run (n →∞)∧, the probability of finding the Markov chain in state j is approximately πj, no matter in which state the chain began at time 0. Thereafter, computing the limiting distribution by raising the transition probability matrix to a high power suffers from being inexact, as n = ∞ is never attained, and it also requires more computational effort than is necessary. Often a phenomenon that is not naturally a Markov process can be modeled as a Markov process by including a part of the past history in the state description. Not all Markov chains are regular. A Markov chain in which each state has period 1 is called aperiodic. A Markov chain is irreducible if all pairs of states communicate.
Natural resource abundance and human capital accumulation

Summary This paper studies the link between resource abundance and human capital accumulation. It reviews the commonly used indicators of resource abundance and human capital accumulation. The case for a form of resource curse in human capital accumulation is not robust to reasonable changes in these indicators. In fact, subsoil wealth and resource rents per capita are shown to be significantly correlated with improved indicators of human capital accumulation. If mineral wealth is what authors have in mind when they refer to natural resource abundance, then they should choose indicators that measure this concept as accurately as possible.
Two-sided altruism and signaling

Abstract When donors and recipients care about each other–two-sided altruism–asymmetry of information about the donor’s income or about the donor’s altruism leads naturally to a signaling game. The desire to signal income or altruism puts upward pressure on transfers.
Fuzzy Cluster Analysis

Usually in cluster analysis, an object is a member of one and only one cluster, a property described as “crisp” membership. Fuzzy cluster analysis allows an object to have partial membership in more than one cluster. This is different from the situation where the membership is actually crisp and each case has a probability of belonging to a particular cluster. Selecting a good membership function is important to the success of the fuzzy clustering methods. The Grade of Membership model – which has a long history of usage in other contexts – is proposed as a straightforward way of performing fuzzy cluster analysis. The Grade of Membership model is illustrated by an example involving gene expression data. Keywords: fuzzy cluster; grade of membership; correntropy; matrix factorization; genomic analysis; gene expression
2 – First-Degree Equations and Inequalities

Publisher Summary This chapter provides an overview on first-degree equations and inequalities. Probably, the most useful tool in algebra is the ability to solve first-degree equations in one variable. The chapter also presents first degree equations and properties of equality. A first-degree equation is any equation that can be put in the form ax + b = c, where a, b, and c are constants. The solution set for an equation is the set of all numbers that, when used in place of the variable, makes the equation a true statement. Two or more equations with the same solution set are called equivalent equations. The addition property of equality states that adding the same quantity to both sides of an equation preserves equality. Or, more importantly, adding the same amount to both sides of an equation never changes the solution set.
A general formula for the optimal level of social insurance

In an influential paper, Baily (1978) showed that the optimal level of unemployment insurance (UI) in a stylized static model depends on only three parameters: risk aversion, the consumption-smoothing benefit of UI, and the elasticity of unemployment durations with respect to the benefit rate. This paper examines the key economic assumptions under which these parameters determine the optimal level of social insurance. I show that a Baily-type expression, with an adjustment for precautionary saving motives, holds in a general class of dynamic models subject to weak regularity conditions. For example, the simple reduced-form formula derived here applies with arbitrary borrowing constraints, durable consumption goods, private insurance arrangements, and search and leisure benefits of unemployment.
Information, competition, and the quality of charities

Abstract Drawing upon the all-pay auction literature, we propose a model of charity competition in which informed giving alone can account for the significant quality heterogeneity across similar charities. Our analysis identifies a negative effect of competition and a positive effect of informed giving on the equilibrium quality of charity. In particular, we show that as the number of charities grows, so does the percentage of charity scams, approaching one in the limit. In light of this and other results, we discuss the need for regulating nonprofit entry and conduct as well as promoting informed giving.
Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19

Context Social distancing is an essential but economically painful measure to flatten the curve of emergent infectious diseases. As the novel coronavirus that causes COVID-19 spread throughout the United States in early 2020, the federal government left to the states the difficult and consequential decisions about when to cancel events, close schools and businesses, and issue stay-at-home orders. Methods The authors present an original, detailed dataset of state-level social distancing policy responses to the epidemic; they then apply event history analysis to study the timing of implementation of five social distancing policies across all 50 states. Results The most important predictor of when states adopted social distancing policies is political: all else equal, states led by Republican governors were slower to implement such policies during a critical window of early COVID-19 response. Conclusions Continuing actions driven by partisanship rather than by public health expertise and scientific recommendations may exact greater tolls on health and broader society.
Testing the Imports-as-Market-Discipline Hypothesis

Abstract It has long been believed that intensified international competition forces domestic firms to behave more competitively. Using Turkish data spanning the course of a dramatic trade liberalization, this notion is tested.
Conditionally Optimal Approximation Algorithms for the Girth of a Directed Graph.

The girth is one of the most basic graph parameters, and its computation has been studied for many decades. Under widely believed fine-grained assumptions, computing the girth exactly is known to require mn^{1-o(1)} time, both in sparse and dense m-edge, n-node graphs, motivating the search for fast approximations. Fast good quality approximation algorithms for undirected graphs have been known for decades. For the girth in directed graphs, until recently the only constant factor approximation algorithms ran in O(n^ω) time, where ω 0 and all sparsities m was only recently obtained by Chechik et al. [STOC 2020]; it is also combinatorial. It is known that a better than 2-approximation algorithm for the girth in dense directed unweighted graphs needs n^{3-o(1)} time unless one uses fast matrix multiplication. Meanwhile, the best known approximation factor for a combinatorial algorithm running in O(mn^{1-e}) time (by Chechik et al.) is 3. Is the true answer 2 or 3? The main result of this paper is a (conditionally) tight approximation algorithm for directed graphs. First, we show that under a popular hardness assumption, any algorithm, even one that exploits fast matrix multiplication, would need to take at least mn^{1-o(1)} time for some sparsity m if it achieves a (2-e)-approximation for any e > 0. Second we give a 2-approximation algorithm for the girth of unweighted graphs running in O(mn^{3/4}) time, and a (2+e)-approximation algorithm (for any e > 0) that works in weighted graphs and runs in O(m√ n) time. Our algorithms are combinatorial. We also obtain a (4+e)-approximation of the girth running in O(mn^{√2-1}) time, improving upon the previous best O(m√n) running time by Chechik et al. Finally, we consider the computation of roundtrip spanners. We obtain a (5+e)-approximate roundtrip spanner on O(n^{1.5}/e²) edges in O(m√n/e²) time. This improves upon the previous approximation factor (8+e) of Chechik et al. for the same running time.
Solutions to a System of Equations for $C^m$ Functions

Fix m≥0, and let A=(Aij(x))1≤i≤N,1≤j≤M be a matrix of semialgebraic functions on Rn or on a compact subset E⊂Rn. Given f=(f1,…,fN)∈C∞(Rn,RN), we consider the following system of equations: ∑j=1MAij(x)Fj(x)=fi(x)for i=1,…,N. In this paper, we give algorithms for computing a finite list of linear partial differential operators such that AF=f admits a Cm(Rn,RM) solution F=(F1,…,FM) if and only if f=(f1,…,fN) is annihilated by the linear partial differential operators.
New results for transition probabilities in two-level systems: The large-detuning regime

Abstract : The problem of calculating transition probabilities in two-level systems is studied in the limit where the detuning is large compared to the inverse duration of the interaction. Within each family, transition probabilities may be calculated from formulae that differ only in the numerical value of a scaling parameter. In cases where the coupling function has a pole in the complex time plane, the families are identified with the order of this singularity. In particular, for poles of first order, a connection with the Rosen-Zenet solution can be made. The analysis is performed via high-order perturbation expansions, which are shown to always converge for two-level systems driven by coupling potentials of finite pulse area.
How Outside Money Makes Governing More Difficult

Little empirical attention has been paid to the possible relationship between the sources of money in campaigns and whether political parties within the legislature are more unified or fragmented. ...
FCHSD2 controls oncogenic ERK1/2 signaling outcome by regulating endocytic trafficking

The evolution of transformed cancer cells into metastatic tumors is, in part, driven by altered intracellular signaling downstream of receptor tyrosine kinases (RTKs). The surface levels and activity of RTKs are governed mainly through clathrin-mediated endocytosis (CME), endosomal recycling, or degradation. In turn, oncogenic signaling downstream of RTKs can reciprocally regulate endocytic trafficking by creating feedback loops in cells to enhance tumor progression. We previously showed that FCH/F-BAR and Double SH3 Domain-Containing Protein (FCHSD2) has a cancer-cell specific function in regulating CME in non-small-cell lung cancer (NSCLC) cells. Here, we report that FCHSD2 loss impacts recycling of the RTKs, epidermal growth factor receptor (EGFR) and proto-oncogene c-Met (MET), and shunts their trafficking into late endosomes and lysosomal degradation. Notably, FCHSD2 depletion results in the nuclear translocation of active extracellular signal-regulated kinase 1 and 2 (ERK1/2), leading to enhanced transcription and up-regulation of EGFR and MET. The small GTPase, Ras-related protein Rab-7A (Rab7), is essential for the FCHSD2 depletion-induced effects. Correspondingly, FCHSD2 loss correlates to higher tumor grades of NSCLC. Clinically, NSCLC patients expressing high FCHSD2 exhibit elevated survival, whereas patients with high Rab7 expression display decreased survival rates. Our study provides new insight into the molecular nexus for crosstalk between oncogenic signaling and RTK trafficking that controls cancer progression.
How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice

Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two important problems. First, these models assume a linear interaction effect that changes at a constant rate with the moderator. Second, estimates of the conditional effects of the independent variable can be misleading if there is a lack of common support of the moderator. Replicating 46 interaction effects from 22 recent publications in five top political science journals, we find that these core assumptions often fail in practice, suggesting that a large portion of findings across all political science subfields based on interaction models are fragile and model dependent. We propose a checklist of simple diagnostics to assess the validity of these assumptions and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation. These statistical routines are available in both R and STATA.
A METHOD FOR CREATING MATERIALS WITH A DESIRED REFRACTION COEFFICIENT

It is proposed to create materials with a desired refraction coefficient in a bounded domain D ⊂ ℝ3 by embedding many small balls with constant refraction coefficients into a given material. The number of small balls per unit volume around every point x ∈ D, i.e., their density distribution, is calculated, as well as the constant refraction coefficients in these balls. Embedding into D small balls with these refraction coefficients according to the calculated density distribution creates in D a material with a desired refraction coefficient.
Host Adaptation Predisposes Pseudomonas aeruginosa to Type VI Secretion System-Mediated Predation by the Burkholderia cepacia Complex

Summary Pseudomonas aeruginosa and Burkholderia cepacia complex (Bcc) species are opportunistic lung pathogens of cystic fibrosis (CF) patients. While P. aeruginosa can initiate long-term infections in younger CF patients, Bcc infections only arise in teenagers and adults. Both P. aeruginosa and Bcc use type VI secretion systems (T6SSs) to mediate interbacterial competition. Here, we show P. aeruginosa isolates from teenage and adult CF patients, but not those from young CF patients, are outcompeted by the epidemic Bcc isolate Burkholderia cenocepacia strain AU1054 in a T6SS-dependent manner. The genomes of susceptible P. aeruginosa isolates harbor T6SS-abrogating mutations, the repair of which, in some cases, rendered the isolates resistant. Moreover, seven of eight Bcc strains outcompeted P. aeruginosa strains isolated from the same patients. Our findings suggest certain mutations that arise as P. aeruginosa adapts to the CF lung abrogate T6SS activity, making P. aeruginosa and its human host susceptible to potentially fatal Bcc superinfection.
Ecologically relevant thermal fluctuations enhance offspring fitness: biological and methodological implications for studies of thermal developmental plasticity

Natural thermal environments are notably complex and challenging to mimic in controlled studies. Consequently, our understanding of the ecological relevance and underlying mechanisms of organismal responses to thermal environments is often limited. For example, studies of thermal developmental plasticity have provided key insights into the ecological consequences of temperature variation, but most laboratory studies use treatments that do not reflect natural thermal regimes. While controlling other important factors, we compared the effects of naturally fluctuating temperatures to commonly used laboratory regimes on development of lizard embryos and offspring phenotypes and survival. We incubated eggs in 4 treatments - 3 that followed procedures commonly used in the literature, and one that precisely mimicked naturally fluctuating nest temperatures. To explore context-dependent effects, we replicated these treatments across two seasonal regimes: relatively cool temperatures from nests constructed early in the season and warm temperatures from late-season nests. We show that natural thermal fluctuations have a relatively small effect on developmental variables but enhance hatchling performance and survival at cooler temperatures. Thus, natural thermal fluctuations are important for successful development and simpler approximations (e.g. repeated sine waves, constant temperatures) may poorly reflect natural systems under some conditions. Thus, the benefits of precisely replicating real-world temperatures in controlled studies may outweigh logistical costs. Although patterns might vary according to study system and research goals, our methodological approach demonstrates the importance of incorporating natural variation into controlled studies and provides biologists interested in thermal ecology with a framework for validating the effectiveness of commonly used methods.
Monotonicity properties of the Poisson approximation to the binomial distribution

Abstract Certain monotonicity properties of the Poisson approximation to the binomial distribution are established. As a natural application of these results, exact (rather than approximate) tests of hypotheses on an unknown value of the parameter p of the binomial distribution are presented.
Scaling Choice Models of Relational Social Data

Many prediction problems on social networks, from recommendations to anomaly detection, can be approached by modeling network data as a sequence of relational events and then leveraging the resulting model for prediction. Conditional logit models of discrete choice are a natural approach to modeling relational events as "choices'' in a framework that envelops and extends many long-studied models of network formation. The conditional logit model is simplistic, but it is particularly attractive because it allows for efficient consistent likelihood maximization via negative sampling, something that isn't true for mixed logit and many other richer models. The value of negative sampling is particularly pronounced because choice sets in relational data are often enormous. Given the importance of negative sampling, in this work we introduce a model simplification technique for mixed logit models that we call "de-mixing'', whereby standard mixture models of network formation---particularly models that mix local and global link formation---are reformulated to operate their modes over disjoint choice sets. This reformulation reduces mixed logit models to conditional logit models, opening the door to negative sampling while also circumventing other standard challenges with maximizing mixture model likelihoods. To further improve scalability, we also study importance sampling for more efficiently selecting negative samples, finding that it can greatly speed up inference in both standard and de-mixed models. Together, these steps make it possible to much more realistically model network formation in very large graphs. We illustrate the relative gains of our improvements on synthetic datasets with known ground truth as well as a large-scale dataset of public transactions on the Venmo platform.
A Unified, Stable, and Accurate Meshfree Framework for Peridynamic Correspondence Modeling—Part II: Wave Propagation and Enforcement of Stress Boundary Conditions

The overarching goal of this work is to develop an accurate, robust, and stable methodology for finite deformation modeling using strong-form peridynamics (PD) and the correspondence modeling framework. We adopt recently developed methods that make use of higher-order corrections to improve the computation of integrals in the correspondence formulation. A unified approach is presented that incorporates the reproducing kernel (RK) and generalized moving least square (GMLS) approximations in PD to obtain non-local gradients of higher-order accuracy. We show, however, that the improved quadrature rule does not suffice to handle instability issues that have proven problematic for the correspondence model-based PD. In Part I of this paper, a bond-associative, higher-order core formulation is developed that naturally provides stability without introducing artificial stabilization parameters. Numerical examples are provided to study the convergence of RK-PD, GMLS-PD, and their bond-associated versions to a local counterpart, as the degree of non-locality (i.e., the horizon) approaches zero. Problems from linear elastostatics are utilized to verify the accuracy and stability of our approach. It is shown that the bond-associative approach improves the robustness of RK-PD and GMLS-PD formulations, which is essential for practical applications. The higher-order, bond-associated model can obtain second-order convergence for smooth problems and first-order convergence for problems involving field discontinuities, such as curvilinear free surfaces. In Part II of this paper, we use our unified PD framework to (a) study wave propagation phenomena, which have proven problematic for the state-based correspondence PD framework, and (b) propose a new methodology to enforce natural boundary conditions in correspondence PD formulations, which should be particularly appealing to coupled problems. Our results indicate that bond-associative methods accompanied by higher-order gradient corrections provide the key ingredients to obtain the necessary accuracy, stability, and robustness characteristics needed for engineering-scale simulations.
Solving parametric radical equations with depth 2 rigorously using the restriction set method

We review the history and previous literature on radical equations and present the rigorous solution theory for radical equations of depth 2, continuing a previous study of radical equations of dep...
Is the devil in the shadow? The effect of institutional quality on income

The positive relationship between institutional quality and “official” income is well documented. It is unclear, however, whether this relationship holds once the “unofficial” economy is accounted for. An improvement in institutional quality tends to shift production out of the shadow and into the official sector, resulting in an increase in official income. However, this shift comes at the expense of the informal economy, potentially negating the perceived gains in output. This paper uses data from 5,506 Brazilian municipalities to explore the effects of institutional quality on total income per person. We additionally estimate the effect of institutions on informal sector size. Our most robust finding suggests that higher‐quality institutions using our broadest measure are significantly related to development. While good institutions do decrease the size of the informal sector, the consequential switch to formality raises overall productivity.
Azumaya algebras and canonical components

Let $M$ be a compact 3-manifold and $\Gamma=\pi_1(M)$. Work of Thurston and Culler--Shalen established the $\mathrm{SL}_2(\mathbb{C})$ character variety $X(\Gamma)$ as fundamental tool in the study of the geometry and topology of $M$. This is particularly the case when $M$ is the exterior of a hyperbolic knot $K$ in $S^3$. The main goals of this paper are to bring to bear tools from algebraic and arithmetic geometry to understand algebraic and number theoretic properties of the so-called canonical component of $X(\Gamma)$, as well as distinguished points on the canonical component, when $\Gamma$ is a knot group. In particular, we study how the theory of quaternion Azumaya algebras can be used to obtain algebraic and arithmetic information about Dehn surgeries, and perhaps of most interest, to construct new knot invariants that lie in the Brauer groups of curves over number fields.
Online Learning over a Finite Action Set with Limited Switching

This paper studies the value of switching actions in the Prediction From Experts problem (PFE) and Adversarial Multiarmed Bandits problem (MAB). First, we revisit the well-studied and practically m...
Representations of Cohomological Hall Algebras and Donaldson–Thomas Theory with Classical Structure Groups

We introduce a new class of representations of the cohomological Hall algebras of Kontsevich and Soibelman, which we call cohomological Hall modules, or CoHM for short. These representations are constructed from self-dual representations of a quiver with contravariant involution $$\sigma $$ and provide a mathematical model for the space of BPS states in orientifold string theory. We use the CoHM to define a generalization of the cohomological Donaldson–Thomas theory of quivers in which the quiver representations have orthogonal or symplectic structure groups. The associated invariants are called orientifold Donaldson–Thomas invariants. We prove the orientifold analogue of the integrality conjecture for $$\sigma $$ -symmetric quivers. We also formulate precise conjectures regarding the geometric meaning of orientifold Donaldson–Thomas invariants and the freeness of the CoHM of a $$\sigma $$ -symmetric quiver. We prove the freeness conjecture for disjoint union quivers, loop quivers and the affine Dynkin quiver of type $${\widetilde{A}}_1$$ . We also verify the geometric conjecture in a number of examples. Finally, we construct explicit Poincare–Birkhoff–Witt-type bases of the CoHM of finite type quivers.
Proximity-dependent Proteomics Reveals Extensive Interactions of Protocadherin-19 with Regulators of Rho GTPases and the Microtubule Cytoskeleton.

Abstract Protocadherin-19 belongs to the cadherin family of cell surface receptors and has been shown to play essential roles in the development of the vertebrate nervous system. Mutations in human Protocadherin-19 (PCDH19) lead to PCDH19 Female-limited epilepsy (PCDH19 FLE) in humans, characterized by the early onset of epileptic seizures in children and a range of cognitive and behavioral problems in adults. Despite being considered the second most prevalent gene in epilepsy, very little is known about the intercellular pathways in which it participates. In order to characterize the protein complexes within which Pcdh19 functions, we generated Pcdh19-BioID fusion proteins and utilized proximity-dependent biotinylation to identify neighboring proteins. Proteomic identification and analysis revealed that the Pcdh19 interactome is enriched in proteins that regulate Rho family GTPases, microtubule binding proteins and proteins that regulate cell divisions. We cloned the centrosomal protein Nedd1 and the RacGEF Dock7 and verified their interactions with Pcdh19 in vitro. Our findings provide the first comprehensive insights into the interactome of Pcdh19, and provide a platform for future investigations into the cellular and molecular biology of this protein critical to the proper development of the nervous system.
Learning an Action-Conditional Model for Haptic Texture Generation

Rich haptic sensory feedback in response to user interactions is desirable for an effective, immersive virtual reality or teleoperation system. However, this feedback depends on material properties and user interactions in a complex, non-linear manner. Therefore, it is challenging to model the mapping from material and user interactions to haptic feedback in a way that generalizes over many variations of the user’s input. Current methodologies are typically conditioned on user interactions, but require a separate model for each material. In this paper, we present a learned action-conditional model that uses data from a vision-based tactile sensor (GelSight) and user’s action as input. This model predicts an induced acceleration that could be used to provide haptic vibration feedback to a user. We trained our proposed model on a publicly available dataset (Penn Haptic Texture Toolkit) that we augmented with GelSight measurements of the different materials. We show that a unified model over all materials outperforms previous methods and generalizes to new actions and new instances of the material categories in the dataset.
Inferring Sparse Preference Lists from Partial Information

Probability distributions over rankings are crucial for the modeling and design of a wide range of practical systems. In this work, we pursue a nonparametric approach that seeks to learn a distribu...
StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction

Using deep learning to analyze mechanical stress distributions has been gaining interest with the demand for fast stress analysis methods. Deep learning approaches have achieved excellent outcomes when utilized to speed up stress computation and learn the physics without prior knowledge of underlying equations. However, most studies restrict the variation of geometry or boundary conditions, making these methods difficult to be generalized to unseen configurations. We propose a conditional generative adversarial network (cGAN) model for predicting 2D von Mises stress distributions in solid structures. The cGAN learns to generate stress distributions conditioned by geometries, load, and boundary conditions through a two-player minimax game between two neural networks with no prior knowledge. By evaluating the generative network on two stress distribution datasets under multiple metrics, we demonstrate that our model can predict more accurate high-resolution stress distributions than a baseline convolutional neural network model, given various and complex cases of geometry, load and boundary conditions.
Coalition-Proof Trade and the Friedman Rule in the Lagos-Wright Model

The Lagos‐Wright model—a monetary model in which pairwise meetings alternate in time with a centralized meeting—has been extensively analyzed, but always using particular trading protocols. Here, trading protocols are replaced by two alternative notions of implementability: one that allows only individual defections and one that also allows cooperative defections in meetings. It is shown that the first‐best allocation is implementable under the stricter notion without taxation if people are sufficiently patient. And, if people are free to skip the centralized meeting, then lump‐sum taxation used to pay interest on money does not enlarge the set of implementable allocations.
Improved Bounds for Distributed Load Balancing

In the load balancing problem, the input is an $n$-vertex bipartite graph $G = (C \cup S, E)$ and a positive weight for each client $c \in C$. The algorithm must assign each client $c \in C$ to an adjacent server $s \in S$. The load of a server is then the weighted sum of all the clients assigned to it, and the goal is to compute an assignment that minimizes some function of the server loads, typically either the maximum server load (i.e., the $\ell_{\infty}$-norm) or the $\ell_p$-norm of the server loads. We study load balancing in the distributed setting. There are two existing results in the CONGEST model. Czygrinow et al. [DISC 2012] showed a 2-approximation for unweighted clients with round-complexity $O(\Delta^5)$, where $\Delta$ is the maximum degree of the input graph. Halldorsson et al. [SPAA 2015] showed an $O(\log{n}/\log\log{n})$-approximation for unweighted clients and $O(\log^2\!{n}/\log\log{n})$-approximation for weighted clients with round-complexity polylog$(n)$. In this paper, we show the first distributed algorithms to compute an $O(1)$-approximation to the load balancing problem in polylog$(n)$ rounds. In the CONGEST model, we give an $O(1)$-approximation algorithm in polylog$(n)$ rounds for unweighted clients. For weighted clients, the approximation ratio is $O(\log{n})$. In the less constrained LOCAL model, we give an $O(1)$-approximation algorithm for weighted clients in polylog$(n)$ rounds. Our approach also has implications for the standard sequential setting in which we obtain the first $O(1)$-approximation for this problem that runs in near-linear time. A 2-approximation is already known, but it requires solving a linear program and is hence much slower. Finally, we note that all of our results simultaneously approximate all $\ell_p$-norms, including the $\ell_{\infty}$-norm.
The ACS Nearby Galaxy Survey Treasury VII. The NGC 4214 Starburst and the Effects of Star Formation History on Dwarf Morphology

We present deep Hubble Space Telescope WFPC2 optical observations obtained as part of the ACS Nearby Galaxy Survey Treasury as well as early release Wide Field Camera 3 ultraviolet and infrared observations of the nearby dwarf starbursting galaxy NGC 4214. Our data provide a detailed example of how covering such a broad range in wavelength provides a powerful tool for constraining the physical properties of stellar populations. The deepest data reach the ancient red clump at M{sub F814W} {approx} - 0.2. All of the optical data reach the main-sequence turnoff for stars younger than {approx}300 Myr and the blue He-burning sequence for stars younger than 500 Myr. The full color-magnitude diagram (CMD) fitting analysis shows that all three fields in our data set are consistent with {approx}75% of the stellar mass being older than 8 Gyr, in spite of showing a wide range in star formation rates at present. Thus, our results suggest that the scale length of NGC 4214 has remained relatively constant for many gigayears. As previously noted by others, we also find the galaxy has recently ramped up production consistent with its bright UV luminosity and its population of UV-bright massive stars. In the central field wemore » find UV point sources with F336W magnitudes as bright as -9.9. These are as bright as stars with masses of at least 52-56 M{sub sun} and ages near 4 Myr in stellar evolution models. Assuming a standard initial mass function, our CMD is well fitted by an increase in star formation rate beginning 100 Myr ago. The stellar populations of this late-type dwarf are compared with those of NGC 404, an early-type dwarf that is also the most massive galaxy in its local environment. The late-type dwarf appears to have a similar high fraction of ancient stars, suggesting that these dominant galaxies may form at early epochs even if they have low total mass and very different present-day morphologies.« less
Network-theoretic approach to sparsified discrete vortex dynamics

We examine discrete vortex dynamics in two-dimensional flow through a network-theoretic approach. The interaction of the vortices is represented with a graph, which allows the use of network-theoretic approaches to identify key vortex-to-vortex interactions. We employ sparsification techniques on these graph representations based on spectral theory to construct sparsified models and evaluate the dynamics of vortices in the sparsified set-up. Identification of vortex structures based on graph sparsification and sparse vortex dynamics is illustrated through an example of point-vortex clusters interacting amongst themselves. We also evaluate the performance of sparsification with increasing number of point vortices. The sparsified-dynamics model developed with spectral graph theory requires a reduced number of vortex-to-vortex interactions but agrees well with the full nonlinear dynamics. Furthermore, the sparsified model derived from the sparse graphs conserves the invariants of discrete vortex dynamics. We highlight the similarities and differences between the present sparsified-dynamics model and reduced-order models.
Indecent Exposure in Seyfert 2 Galaxies: A Close Look

NGC 3147, NGC 4698, and 1ES 1927+654 are active galaxies that are classified as Seyfert 2s, based on the line ratios of strong narrow emission lines in their optical spectra. However, they exhibit rapid X-ray spectral variability and/or little indication of obscuration in X-ray spectral fitting, contrary to expectation from the active galactic nucleus (AGN) unification model. Using optical spectropolarimetry with LRIS and near-infrared spectroscopy with NIRSPEC at the W. M. Keck Observatory, we conducted a deep search for hidden polarized broad Hα and direct broad Paβ or Brγ emission lines in these objects. We found no evidence for any broad emission lines from the active nuclei of these galaxies, suggesting that they are unobscured, completely naked AGNs that intrinsically lack broad-line regions.
Cosimplicial resolutions and homotopy spectral sequences in model categories

We develop a general theory of cosimplicial resolutions, homotopy spectral sequences, and completions for objects in model categories, extending work of Bouseld{Kan and Bendersky{Thompson for ordinary spaces. This is based on a generalized cosimplicial version of the Dwyer{Kan{Stover theory of resolution model categories, and we are able to construct our homotopy spectral sequences and completions using very flexible weak resolutions in the spirit of relative homological algebra. We deduce that our completion functors have triple structures and preserve certain ber squares up to homotopy. We also deduce that the Bendersky{Thompson completions over connective ring spectra are equivalent to Bouseld{Kan completions over solid rings. The present work allows us to show, in a subsequent paper, that the homotopy spectral sequences over arbitrary ring spectra have well-behaved composition pairings.
Holistic framework to help students learn effectively from research-validated self-paced learning tools

Introductory physics students do not fully benefit from using self-study tools unless explicit strategies are employed to ensure engaged learning.
PRECISION SPECTROPHOTOMETRY AT THE LEVEL OF 0.1

Accurate relative spectrophotometry is critical for many science applications. Small wavelength-scale residuals in the flux calibration can significantly impact the measurements of weak emission and absorption features in the spectra. Using Sloan Digital Sky Survey data, we demonstrate that the average spectra of carefully selected red-sequence galaxies can be used as a spectroscopic standard to improve the relative spectrophotometry precision to 0.1% on small wavelength scales (from a few to hundreds of Angstroms). We achieve this precision by comparing stacked spectra across tiny redshift intervals. The redshift intervals must be small enough that any systematic stellar population evolution is minimized and is less than the spectrophotometric uncertainty. This purely empirical technique does not require any theoretical knowledge of true galaxy spectra. It can be applied to all large spectroscopic galaxy redshift surveys that sample a large number of galaxies in a uniform population.
THE EVOLUTION OF LYMAN LIMIT ABSORPTION SYSTEMS TO REDSHIFT SIX

We have measured the redshift evolution of the density of Lyman limit systems (LLSs) in the intergalactic medium over the redshift range 0 < z < 6. We have used two new quasar samples to (1) improve coverage at z ~ 1, with GALEX grism spectrograph observations of 50 quasars with 0.8 < z em < 1.3, and (2) extend coverage to z ~ 6, with Keck ESI spectra of 25 quasars with 4.17 < z em < 5.99. Using these samples together with published data, we find that the number density of LLS per unit redshift, n(z), can be well fit by a simple evolution of the form n(z) = n 3.5[(1 + z)/4.5]γ with n 3.5 = 2.80 ± 0.33 and γ = 1.94+0.36 –0.32 for the entire range 0 < z < 6. We have also reanalyzed the evolution of damped Lyα systems (DLAs) in the redshift range 4 < z < 5 using our high-redshift quasar sample. We find a total of 17 DLAs and sub-DLAs, which we have analyzed in combination with published data. The DLAs with } 20.3$
Splashback in accreting dark matter halos

Recent work has shown that density profiles in the outskirts of dark matter halos can become extremely steep over a narrow range of radius. This behavior is produced by splashback material on its first apocentric passage after accretion. We show that the location of this splashback feature may be understood quite simply, from first principles. We present a simple model, based on spherical collapse, that accurately predicts the location of splashback without any free parameters. The important quantities that determine the splashback radius are accretion rate and redshift.
Bohr density of simple linear group orbits

We show that any nonzero orbit under a noncompact, simple, irreducible linear group is dense in the Bohr compactification of the ambient spa ce.
GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN

Optimal experimental design (OED) seeks experiments expected to yield the most useful data for some purpose. In practical circumstances where experiments are time-consuming or resource-intensive, OED can yield enormous savings. We pursue OED for nonlinear systems from a Bayesian perspective, with the goal of choosing experiments that are optimal for parameter inference. Our objective in this context is the expected information gain in model parameters, which in general can only be estimated using Monte Carlo methods. Maximizing this objective thus becomes a stochastic optimization problem. This paper develops gradient-based stochastic optimization methods for the design of experiments on a continuous parameter space. Given a Monte Carlo estimator of expected information gain, we use infinitesimal perturbation analysis to derive gradients of this estimator. We are then able to formulate two gradient-based stochastic optimization approaches: (i) Robbins-Monro stochastic approximation, and (ii) sample average approximation combined with a deterministic quasi-Newton method. A polynomial chaos approximation of the forward model accelerates objective and gradient evaluations in both cases. We discuss the implementation of these optimization methods, then conduct an empirical comparison of their performance. To demonstrate design in a nonlinear setting with partial differential equation forward models, we use the problem of sensor placement for source inversion. Numerical results yield useful guidelines on the choice of algorithm and sample sizes, assess the impact of estimator bias, and quantify tradeoffs of computational cost versus solution quality and robustness.
“I Know She Invented Fire, but What Has She Done Recently?” or “I Have Come to Praise Ch., not Bury Her!” or On the Second Renaissance of Charm Physics

D 0 oscillations, for which the B factories have found strong evidence, provide a new stage for our search for New Physics in heavy flavour dynamics. While the theoretical verdict on the observed values of x D and y D is ambiguous – they could be fully generated by SM dynamics, yet could contain also a sizable contribution from New Physics – such oscillations can enhance the observability of CP violation driven by New Physics. After emphasizing the unique role of charm among up-type quarks, I sketch the CP phenomenology for partial widths and final state distributions.
Numerically Modeling the First Peak of the Type IIb SN 2016gkg

Many Type IIb supernovae (SNe) show a prominent additional early peak in their light curves, which is generally thought to be due to the shock cooling of extended hydrogen-rich material surrounding the helium core of the exploding star. The recent SN 2016gkg was a nearby Type IIb SN discovered shortly after explosion, which makes it an excellent candidate for studying this first peak. We numerically explode a large grid of extended envelope models and compare these to SN 2016gkg to investigate what constraints can be derived from its light curve. This includes exploring density profiles for both a convective envelope and an optically thick steady-state wind, the latter of which has not typically been considered for Type IIb SNe models. We find that roughly ~ 0.02 M⊙ of extended material with a radius of ≈ 180-260 R⊙ reproduces the photometric light curve data, consistent with pre-explosion imaging. These values are independent of the assumed density profile of this material, although a convective profile provides a somewhat better fit. We infer from our modeling that the explosion must have occurred within ≈2–3 hr of the first observed data point, demonstrating that this event was caught very close to the moment of explosion. Nevertheless, our best-fitting 1D models overpredict the earliest velocity measurements, which suggests that the hydrogen-rich material is not distributed in a spherically symmetric manner. We compare this to the asymmetries that have also been seen in the SN IIb remnant Cas A, and we discuss the implications of this for Type IIb SN progenitors and explosion models.