cssi/SciSciGPT-SciSciNet huggingface dataset:
Depletion of the AD risk gene SORL1 selectively impairs neuronal endosomal traffic independent of amyloidogenic APP processing

Summary The SORL1 gene encodes for the protein SorLA, a sorting receptor involved in retromer-related endosomal traffic. Many SORL1 genetic variants increase Alzheimer’s disease (AD) risk, and rare loss-of-function truncation mutations have been found to be causal of late-onset AD. SORL1 is expressed in neurons and glia of the central nervous system and loss of SORL1 has been reported in AD tissue. To model the causal loss-of-function mutations, we used CRISPR/Cas9 technology to deplete SORL1 in human induced pluripotent stem cells (hiPSCs) to test the hypothesis that loss of SORL1 contributes to AD pathogenesis by leading to endosome dysfunction. We report that loss of SORL1 in hiPSC-derived neurons leads to early endosome enlargement, a cellular phenotype that is indicative of ‘traffic jams’ and is now considered a hallmark cytopathology AD. We validate defects in neuronal endosomal traffic by showing decreased localization of amyloid-precursor protein (APP) in the trans-Golgi network (TGN), and increased localization of APP in early endosomes, a site of APP cleavage by the β secretase BACE1. Microglia, immune cells of the CNS, which play a role in AD pathology also express SORL1. We therefore tested and found no effect of SORL1 depletion on endosome size or morphology in hiPSC-derived microglia, suggesting a selective effect on neuronal endosomal trafficking. Finally, because BACE1 dependent APP fragments can cause endosome enlargement, we treated SORL1 deficient hiPSC-derived neurons with BACE1 inhibitors and demonstrate that endosome enlargement occurs independent of amyloidogenic APP fragments. Collectively, these findings clarify where and how SORL1 links to AD. Moreover, our data, together with recent findings, underscores how sporadic AD pathways that regulate endosomal trafficking, and autosomal-dominant AD pathways that regulate APP cleavage, independently converge on the defining cytopathology of AD.
Increased temperatures reduce the vectorial capacity of Aedes mosquitoes for Zika virus

Rapid and significant range expansion of both Zika virus (ZIKV) and its Aedes vector species has resulted in ZIKV being declared a global health threat. Mean temperatures are projected to increase ...
Hardness of learning DNFs using halfspaces

The problem of learning t-term DNF formulas (for t = O(1)) has been studied extensively in the PAC model since its introduction by Valiant (STOC 1984). A t-term DNF can be efficiently learnt using a t-term DNF only if t = 1 i.e., when it is an AND, while even weakly learning a 2-term DNF using a constant term DNF was shown to be NP-hard by Khot and Saket (FOCS 2008). On the other hand, Feldman, Guruswami, Raghavendra and Wu (FOCS 2009) showed the hardness of weakly learning a noisy AND using a halfspace – the latter being a generalization of an AND, while Khot and Saket (STOC 2008) showed that an intersection of two halfspaces is hard to weakly learn using any function of constantly many halfspaces. The question of whether a 2-term DNF is efficiently learnable using 2 or constantly many halfspaces remained open. In this work we answer this question in the negative by showing the hardness of weakly learning a 2-term DNF as well as a noisy AND using any function of a constant number of halfspaces. In particular we prove the following. For any constants ν, ζ > 0 and l ∈ N, given a distribution over point-value pairs {0,1}n × {0,1}, it is NP-hard to decide whether, (i) YES Case. There is a 2-term DNF that classifies all the points of the distribution, and an AND that classifies at least 1−ζ fraction of the points correctly. (ii) NO Case. Any boolean function depending on at most l halfspaces classifies at most 1/2 + ν fraction of the points of the distribution correctly. Our result generalizes and strengthens the previous best results mentioned above on the hardness of learning a 2-term DNF, learning an intersection of two halfspaces, and learning a noisy AND.
Constructing Minimal Perfect Hash Functions Using SAT Technology

Minimal perfect hash functions (MPHFs) are used to provide efficient access to values of large dictionaries (sets of key-value pairs). Discovering new algorithms for building MPHFs is an area of active research, especially from the perspective of storage efficiency. The information-theoretic limit for MPHFs is 1/ln 2 ≈ 1.44 bits per key. The current best practical algorithms range between 2 and 4 bits per key. In this article, we propose two SAT-based constructions of MPHFs. Our first construction yields MPHFs near the information-theoretic limit. For this construction, current state-of-the-art SAT solvers can handle instances where the dictionaries contain up to 40 elements, thereby outperforming the existing (brute-force) methods. Our second construction uses XORSAT filters to realize a practical approach with long-term storage of approximately 1.83 bits per key.
Health Reform and Higher Ed: Campuses as Harbingers of Medicaid Universality and Medicare Commonality:

Between 2010 and 2016, the percentage of uninsured higher education students dropped by more than half. All the Affordable Care Act's key access provisions contributed, but the most important facto...
The Past and Future of Econ 101: The John R. Commons Award Lecture

The introductory economics course, often called Econ 101, is where most economists get their start and where many students receive their only exposure to the field. This essay discusses the course’...
Roles for computing in social change

A recent normative turn in computer science has brought concerns about fairness, bias, and accountability to the core of the field. Yet recent scholarship has warned that much of this technical work treats problematic features of the status quo as fixed, and fails to address deeper patterns of injustice and inequality. While acknowledging these critiques, we posit that computational research has valuable roles to play in addressing social problems --- roles whose value can be recognized even from a perspective that aspires toward fundamental social change. In this paper, we articulate four such roles, through an analysis that considers the opportunities as well as the significant risks inherent in such work. Computing research can serve as a diagnostic, helping us to understand and measure social problems with precision and clarity. As a formalizer, computing shapes how social problems are explicitly defined --- changing how those problems, and possible responses to them, are understood. Computing serves as rebuttal when it illuminates the boundaries of what is possible through technical means. And computing acts as synecdoche when it makes long-standing social problems newly salient in the public eye. We offer these paths forward as modalities that leverage the particular strengths of computational work in the service of social change, without overclaiming computing's capacity to solve social problems on its own.
A Comparison of Action Spaces for Learning Manipulation Tasks

Designing reinforcement learning (RL) problems that can produce delicate and precise manipulation policies requires careful choice of the reward function, state, and action spaces. Much prior work on applying RL to manipulation tasks has defined the action space in terms of direct joint torques or reference positions for a joint-space proportional derivative (PD) controller. In practice, it is often possible to add additional structure by taking advantage of model-based controllers that support both accurate positioning and control of the dynamic response of the manipulator. In this paper, we evaluate how the choice of action space for dynamic manipulation tasks affects the sample complexity as well as the final quality of learned policies. We compare learning performance across three tasks (peg insertion, hammering, and pushing), four action spaces (torque, joint PD, inverse dynamics, and impedance control), and using two modern reinforcement learning algorithms (Proximal Policy optimization and Soft Actor-Critic). Our results lend support to the hypothesis that learning references for a task-space impedance controller significantly reduces the number of samples needed to achieve good performance across all tasks and algorithms.
Non-Uniform Robot Densities in Vibration Driven Swarms Using Phase Separation Theory

In robot swarms operating under highly restrictive sensing and communication constraints, individuals may need to use direct physical proximity to facilitate information exchange. However, in certain task-related scenarios, this requirement might conflict with the need for robots to spread out in the environment, e.g., for distributed sensing or surveillance applications. This paper demonstrates how a swarm of minimally-equipped robots can form high-density robot aggregates that coexist with lower robot densities in space. We envision a scenario where a swarm of vibration-driven robots—which sit atop bristles and achieve directed motion by vibrating them—move randomly in an environment while colliding with each other. Theoretical techniques from the study of far-from-equilibrium collectives and statistical mechanics clarify the mechanisms underlying the formation of these high and low density regions. Specifically, we capitalize on a transformation that connects the collective properties of a system of self-propelled particles with that of a well-studied molecular fluid system, thereby inheriting the rich theory of equilibrium thermodynamics. Real robot experiments as well as simulations illustrate how inter-robot collisions can precipitate the formation of non-uniform robot densities in a closed and bounded region.
Adaptive Nonlinear Model Reduction for Fast Power System Simulation

The paper proposes a new adaptive approach to power system model reduction for fast and accurate time-domain simulation. This new approach is a compromise between linear model reduction for faster simulation and nonlinear model reduction for better accuracy. During the simulation period, the approach adaptively switches among detailed and linearly or nonlinearly reduced models based on variations of the system state: it employs unreduced models for the fault-on period, uses weighted column norms of the admittance matrix to decide which functions to be linearized in power system differential-algebraic equations for large changes of the state, and adopts a linearly reduced model for small changes of the state. Two versions of the adaptive model reduction approach are introduced. The first version uses traditional power system partitioning where the model reduction is applied to a defined large external area in a power system and the other area defined as the study area keeps full detailed models. The second version applies the adaptive model reduction to the whole system. The paper also conducts comprehensive case studies comparing simulation results using the proposed adaptively reduced models with the linearly reduced model and coherency-based reduced model on the Northeast Power Coordinating Council 140-bus 48-machine system.
Monomial-Cartesian codes and their duals, with applications to LCD codes, quantum codes, and locally recoverable codes

A monomial-Cartesian code is an evaluation code defined by evaluating a set of monomials over a Cartesian product. It is a generalization of some families of codes in the literature, for instance toric codes, affine Cartesian codes, and J-affine variety codes. In this work we use the vanishing ideal of the Cartesian product to give a description of the dual of a monomial-Cartesian code. Then we use such description of the dual to prove the existence of quantum error correcting codes and MDS quantum error correcting codes. Finally we show that the direct product of monomial-Cartesian codes is a locally recoverable code with t-availability if at least t of the components are locally recoverable codes.
Realization of graded matrix algebras as Leavitt path algebras

While every matrix algebra over a field K can be realized as a Leavitt path algebra, this is not the case for every graded matrix algebra over a graded field. We provide a complete description of graded matrix algebras over a field, trivially graded by the ring of integers, which are graded isomorphic to Leavitt path algebras. As a consequence, we show that there are graded corners of Leavitt path algebras which are not graded isomorphic to Leavitt path algebras. This contrasts a recent result stating that every corner of a Leavitt path algebra of a finite graph is isomorphic to a Leavitt path algebra. If R is a finite direct sum of graded matricial algebras over a trivially graded field and over naturally graded fields of Laurent polynomials, we also present conditions under which R can be realized as a Leavitt path algebra.
Votes For Women: An Economic Perspective on Women’s Enfranchisement

The ratification of the Nineteenth Amendment in 1920 officially granted voting rights to women across the United States. However, many states extended full or partial suffrage to women before the federal amendment. In this paper, we discuss the history of women's enfranchisement using an economic lens. We examine the demand-side, discussing the rise of the women's movement and its alliances with other social movements, and describe how suffragists put pressure on legislators. On the supply side, we draw from theoretical models of suffrage extension to explain why men shared the right to vote with women. Finally, we review empirical studies that attempt to distinguish between competing explanations. We find that no single theory can explain women's suffrage in the US, and note that while the Nineteenth Amendment extended the franchise to women, state-level barriers to voting limited the ability of black women to exercise that right until the Voting Rights Act of 1965.
The Role of Child Perception and Motivation in Political Socialization

Most of what is known regarding political socialization treats parent–child concordance as evidence of transmission. This direct-transmission approach remains agnostic regarding how socialization occurs, whether traits have a role in a child's ability to identify and understand their parent's values or their motivation to adopt their parents’ values. This article advances a perception-adoption approach to unpack these microprocesses of socialization. The authors test their model using three independent studies in the United States that together comprise 4,852 parent–child dyads. They find that the transmission of partisan orientations from parent to child occurs less than half the time, which is qualitatively different from the generally held view. More importantly, the findings provide a greater understanding of how key predictors facilitate the political socialization process. Specifically, politicization improves child perception, but has no role in the child's motivation to adopt parental values. Closeness and parental value strength influence children to want to be like their parents, but do nothing to improve children's ability to recognize their parents' values. And education, previously thought to have little role in transmission, does not influence a child's ability to understand their parent's affiliation, but appears to make children more likely to reject whatever they believe it to be.
A Time-Series Distribution Test System Based on Real Utility Data

In this paper, we provide a time-series distribution test system. This test system is a fully observable distribution grid in Midwest U.S. with smart meters (SM) installed at all end users. Our goal is to share a real U.S. distribution grid model without modification. This grid model is comprehensive and representative since it consists of both overhead lines and underground cables, and it has standard distribution grid components such as capacitor banks, line switches, substation transformers with load tap changer and secondary distribution transformers. An important uniqueness of this grid model is it has one-year smart meter measurements at all nodes, thus bridging the gap between existing test feeders and quasi-static time-series based distribution system analysis.
The singly averaged differential equations of satellite motion for e greater than or equal to 0 and less than 1

The singly-averaged differential equations of motion of a satellite are developed in terms of parameters valid for all eccentricities less than one. The perturbations included in the acceleration model are due to an aspherical central planet (zonal harmonics up to degree 20 and resonant harmonics up to degree and order 20), atmospheric drag for a time-varying atmosphere, third-body gravity (the sun and moon for an earth satellite), solar radiation pressure with shadowing, and impulsive maneuvers. Analytic averaging is used to remove short-period terms due to the aspherical central planet and third-body gravity. Numerical averaging is used to remove short-period terms due to atmospheric drag and solar radiation pressure.
Braid group symmetries of Grassmannian cluster algebras

Let $${{\,\mathrm{\text{ Gr }}\,}}^\circ (k,n) \subset {{\,\mathrm{\text{ Gr }}\,}}(k,n)$$ denote the open positroid stratum in the Grassmannian. We define an action of the extended affine d-strand braid group on $${{\,\mathrm{\text{ Gr }}\,}}^\circ (k,n)$$ by regular automorphisms, for d the greatest common divisor of k and n. The action is by quasi-automorphisms of the cluster structure on $${{\,\mathrm{\text{ Gr }}\,}}^\circ (k,n)$$, determining a homomorphism from the extended affine braid group to the cluster modular group for $${{\,\mathrm{\text{ Gr }}\,}}(k,n)$$. We also define a quasi-isomorphism between the Grassmannian $${{\,\mathrm{\text{ Gr }}\,}}(k,rk)$$ and the Fock–Goncharov configuration space of 2r-tuples of affine flags for $${{\,\mathrm{\text {SL}}\,}}_k$$. This identifies the cluster variables, clusters, and cluster modular groups, in these two cluster structures. Fomin and Pylyavskyy proposed a description of the cluster combinatorics for $${{\,\mathrm{\text{ Gr }}\,}}(3,n)$$ in terms of Kuperberg’s basis of non-elliptic webs. As our main application, we prove many of their conjectures for $${{\,\mathrm{\text{ Gr }}\,}}(3,9)$$ and give a presentation for its cluster modular group. We establish similar results for $${{\,\mathrm{\text{ Gr }}\,}}(4,8)$$. These results rely on the fact that both of these Grassmannians have finite mutation type.
Understanding and optimizing persistent memory allocation

The proliferation of fast, dense, byte-addressable nonvolatile memory suggests that data might be kept in pointer-rich "in-memory" format across program runs and even process and system crashes. For full generality, such data requires dynamic memory allocation, and while the allocator could in principle be "rolled into" each data structure, it is desirable to make it a separate abstraction. Toward this end, we introduce recoverability, a correctness criterion for persistent allocators, together with a nonblocking allocator, Ralloc, that satisfies this criterion. Ralloc is based on the LRMalloc of Leite and Rocha, with three key innovations. First, we persist just enough information during normal operation to permit correct reconstruction of the heap after a full-system crash. Our reconstruction mechanism performs garbage collection (GC) to identify and remedy any failure-induced memory leaks. Second, we introduce the notion of filter functions, which identify the locations of pointers within persistent blocks to mitigate the limitations of conservative GC. Third, to allow persistent regions to be mapped at an arbitrary address, we employ position-independent (offset-based) pointers for both data and metadata. Experiments show Ralloc to be performance-competitive with both Makalu, the state-of-the-art lock-based persistent allocator, and such transient allocators as LRMalloc and JEMalloc. In particular, reliance on GC and offline metadata reconstruction allows Ralloc to pay almost nothing for persistence during normal operation.
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus

In this paper, we propose a distributed off-policy actor critic method to solve multi-agent reinforcement learning problems. Specifically, we assume that all agents keep local estimates of the global optimal policy parameter and update their local value function estimates independently. Then, we introduce an additional consensus step to let all the agents asymptotically achieve agreement on the global optimal policy function. The convergence analysis of the proposed algorithm is provided and the effectiveness of the proposed algorithm is validated using a distributed resource allocation example. Compared to relevant distributed actor critic methods, here the agents do not share information about their local tasks, but instead they coordinate to estimate the global policy function.
A Scalable Method for Estimating the Regional Polygenicity of Complex Traits

A key question in human genetics is understanding the proportion of SNPs modulating a particular phenotype or the proportion of susceptibility SNPs for a disease, termed polygenicity. Previous studies have observed that complex traits tend to be highly polygenic, opposing the previous belief that only a handful of SNPs contribute to a trait [1, 2, 3, 4]. Beyond these genome-wide estimates, the distribution of polygenicity across genomic regions as well as the genomic factors that affect regional polygenicity remain poorly understood.
Approximate graph spectral decomposition with the Variational Quantum Eigensolver

Spectral graph theory is a branch of mathematics that studies the relationships between the eigenvectors and eigenvalues of Laplacian and adjacency matrices and their associated graphs. The Variational Quantum Eigen- solver (VQE) algorithm was proposed as a hybrid quantum/classical algorithm that is used to quickly determine the ground state of a Hamiltonian, and more generally, the lowest eigenvalue of a matrix M ∈ Rnxn. There are many interesting problems associated with the spectral decompositions of associated matrices, such as partitioning, embedding, and the determination of other properties. In this paper, we will expand upon the VQE algorithm to analyze the spectra of directed and undirected graphs. We evaluate runtime and accuracy comparisons (empirically and theoretically) between different choices of ansatz parameters, graph sizes, graph densities, and matrix types, and demonstrate the effectiveness of our approach on Rigetti's QCS platform on graphs of up to 64 vertices, finding eigenvalues of adjacency and Laplacian matrices. We finally make direct comparisons to classical performance with the Quantum Virtual Machine (QVM) in the appendix, observing a superpolynomial runtime improvement of our algorithm when run using a quantum computer.*
Natural History Study of Prognosis

Prognosis refers to the probable outcome of an attack of disease. The traditional definition would describe the evolution of disease in the absence of medical intervention, but nowadays we may also include the term “clinical course” to describe the natural history of a disease that has been affected by medical intervention. Various design considerations are reviewed for the study of natural history, including the use of cohort studies, the various biases that may affect such studies, and the definition of time zero as a clearly defined starting point. Other topics include the analysis of dichotomous outcomes, survival analysis and longitudinal data analysis. Combination of data from several studies, and situations involving multiple outcomes are also mentioned. Keywords: study design; bias; data analysis; data synthesis; multiple outcomes
History of Analysis of Variance

First used by behavioral scientists in the 1930s, use of Analysis of Variance (ANOVA) grew quickly after World War II, and exactly paralleled incorporation of statistical significance testing in general. Detailed consideration of this history suggests several reasons for the pattern of slow growth, followed by rapid institutionalization. In particular, ANOVA stood duty as a warrant of scientific legitimacy among behavioral scientists, a fact that may also be relevant to understanding recent critiques by psychologists of its overuse and misuse. Keywords: ANOVA ; analysis of variance; Fisher; significance testing; history
Predicting the Photoelectron Spectra of Quasi Octahedral Al6Mo- Cluster.

We have recently developed a computational methodology to separate the effects of size, composition, symmetry and fluxionality in explaining the experimental photoelectron spectra of mixed-metal clusters. This methodology was successfully applied first in explaining the observed differences between the spectra of Al13- and Al12Ni- and more recently to explain the measured spectra of AlnMo-, n=3-5,7 clusters. The combination of our approach and new synthesis techniques can be used to prepare cluster-based materials with tunable properties. In this work we use the methodology to predict the spectrum of Al6Mo-. This system was chosen because its neutral counterpart is a perfect octahedron and it is distorted to a D3d symmetry and was not observed in the recent experiments. This high symmetry cluster bridges the less symmetric Al5Mo- and Al7Mo-structures.The measured spectra of Al5Mo- has well defined peaks, while that of Al7Mo-does not. This can be explained by the fluxionality of Al7Mo-, as at least 6 different structures lie within the range that can be reached by thermal effects. We predict that Al6Mo- has well defined peaks, but some broadening is expected as there are two low-lying isomers, one of D3d and the second of D3h symmetry that are only 0.052 eV apart.
A Theory of Outsourced Fundraising: Why Dollars Turn into 'Pennies for Charity'

Charities frequently rely on professional solicitors whose commissions exceed half of the solicited donations. To understand this practice, we propose a principal-agent model in which the charity optimally offers a higher commission to a more efficient solicitor, raising the price of giving significantly. Outsourcing is, therefore, profitable for the charity only if giving is very price-inelastic, which is not supported by empirical evidence. We show that outsourced fundraising can be optimal if: donors are unaware of this practice; the professional solicitor better activates donors’ warm-glow feelings toward the cause; or there is a significant fixed cost of fundraising. We argue that informing the public of the mere existence of paid solicitations may be the most effective policy available.
Do underwriters matter? The impact of the near failure of an equity underwriter

Abstract The financial crisis provides a natural experiment for testing theoretical predictions of the equity underwriter’s role following an initial public offering. Clients of Bear Stearns, Lehman Brothers, Merrill Lynch, and Wachovia saw their stock prices fall almost 5%, on average, on the day it appeared that these institutions might collapse. The decline was more than 1% lower than the abnormal return of other newly public companies, representing a loss in equity value of almost $3 billion. The price impact was worse for companies with fewer monitors, suggesting that underwriters play an important role in monitoring newly public companies. The abnormal return is more negative for clients that are also lending clients, but is not significantly associated with the role of the underwriter as market maker or counterparty to investors.
Banks as Liquidity Providers: An Explanation for the Coexistence of Lending and Deposit-Taking

What ties together the traditional commercial banking activities of deposittaking and lending? We argue that since banks often lend via commitments, their lending and deposit-taking may be two manifestations of one primitive function: the provision of liquidity on demand. There will be synergies between the two activities to the extent that both require banks to hold large balances of liquid assets: If deposit withdrawals and commitment takedowns are imperfectly correlated, the two activities can share the costs of the liquid-asset stockpile. We develop this idea with a simple model, and use a variety of data to test the model empirically. WHAT ARE THE DEF INING CHARACTERISTICS of a bank? Both the legal definition in the United States and the standard answer from economists is that commercial banks are institutions that engage in two distinct types of activities, one on each side of the balance sheet—deposit-taking and lending. More precisely, deposit-taking involves issuing claims that are riskless and demandable, that is, claims that can be redeemed for a fixed value at any time. Lending involves acquiring costly information about opaque borrowers, and extending credit based on this information. A great deal of theoretical and empirical analysis has been devoted to understanding the circumstances under which each of these two activities might require the services of an intermediary, as opposed to being implemented in arm’s-length securities markets. While much has been learned from this work, with few exceptions it has not addressed a fundamental question: why is it important that one institution carry out both functions
A Guided Network Propagation Approach to Identify Disease Genes that Combines Prior and New Information

Summary. A major challenge in biomedical data science is to identify the causal genes underlying complex genetic diseases. Despite the massive influx of genome sequencing data, identifying disease-relevant genes remains difficult as individuals with the same disease may share very few, if any, genetic variants.
4 – Rational Expressions

Publisher Summary This chapter is focuses on simplifying a certain kind of algebraic expression, called “rational expressions” because they are to algebra what rational numbers are to arithmetic. For rational expressions, multiplying the numerator and denominator by the same non-zero expression might change the form of the rational expression, but it would produce an expression equivalent to the original one. The same is true when dividing the numerator and denominator by the same non-zero quantity. The division of a polynomial by a monomial is the simplest kind of polynomial division. This kind of division is similar to long division with whole numbers. Division of a polynomial by a monomial is accomplished by dividing each term of the polynomial by the monomial. The product of two rational expressions is the product of their numerators over the product of their denominators. The quotient of two rational expressions is the product of the first and the reciprocal of the second. The least common denominator (LCD), for a set of denominators is the smallest expression that is divisible by each of the denominators.
Structure–Function Analysis of Interallelic Complementation in ROOTY Transheterozygotes

Auxin is a crucial plant growth regulator. Forward genetic screens for auxin-related mutants have led to the identification of key genes involved in auxin biosynthesis, transport, and signaling. Loss-of-function mutations in genes involved in glucosinolate biosynthesis, a metabolically related route that produces defense compounds, result in auxin overproduction. We identified an allelic series of fertile, hypomorphic Arabidopsis (Arabidopsis thaliana) mutants for the essential glucosinolate biosynthetic gene ROOTY (RTY) that exhibit a range of phenotypic defects characteristic of enhanced auxin production. Genetic characterization of these lines uncovered phenotypic suppression by cyp79b2 cyp79b3, wei2, and wei7 mutations and revealed the phenomenon of interallelic complementation in several RTY transheterozygotes. Structural modeling of RTY elucidated the relationships between structure and function in the RTY homo- and heterodimers, and unveiled the likely structural basis of interallelic complementation. This work underscores the importance of employing true null mutants in genetic complementation studies.
The fine structure of equity-index option dynamics ✩

Abstract We analyze the high-frequency dynamics of S&P 500 equity-index option prices by constructing an assortment of implied volatility measures. This allows us to infer the underlying fine structure behind the innovations in the latent state variables driving the evolution of the volatility surface. In particular, we focus attention on implied volatilities covering a wide range of moneyness (strike/underlying stock price), which load differentially on the different latent state variables. We conduct a similar analysis for high-frequency observations on the VIX volatility index as well as on futures written on it. We find that the innovations over small time scales in the risk-neutral intensity of the negative jumps in the S&P 500 index, which is the dominant component of the short-maturity out-of-the-money put implied volatility dynamics, are best described via non-Gaussian shocks, i.e., jumps. On the other hand, the innovations over small time scales of the diffusive volatility, which is the dominant component in the short-maturity at-the-money option implied volatility dynamics, are best modeled as Gaussian with occasional jumps.
Hole coupling resonator for free electron lasers

Abstract The performance of two mirror resonators with holes for output coupling is studied for free electron laser application using a Fox-Li type code. The mode profiles inside and outside the cavity, the diffraction losses at the mirror edges and apertures, the amount of useful power coupled through the hole, etc., are calculated for the dominant mode for different hole and mirror dimensions. It is found that resonators in near concentric geometry can develop a mode degeneracy in certain cases, which should be avoided for the stability of the free electron laser gain and output. A resonator configuration for a free electron laser at Lawrence Berkeley Laboratory was found which can provide satisfactory performance over a wavelength range from 25 to 50 μm. The possibility of further increasing the tuning range by an adjustable intracavity aperture is discussed.
The Optimal Treatment of Tax Expenditures

Abstract This paper analyzes the optimal treatment of tax expenditures. It develops an optimal tax model where individuals derive utility from spending on a “contribution” good such as charitable giving. The contribution good has also a public good effect on all individuals in the economy. The government imposes linear taxes on earnings and on the contribution good so as to maximize welfare. The government may also finance directly the contribution good out of tax revenue. Optimal tax and subsidy rates on earnings and the contribution good are expressed in terms of empirically estimable parameters and the redistributive tastes of the government. The optimal subsidy on the contribution good is increasing in the size of the price elasticity of contributions, the size of the crowding out effect of public contributions on private contributions, and the size of the public good effect of the contribution good. Numerical simulations show that the optimal subsidy on contributions is fairly sensitive to the size of these parameters but that, in most cases, it should be lower than the earnings tax rate.
Avoiding COVID-19: Aerosol Guidelines

The COVID-19 pandemic has brought into sharp focus the need to understand respiratory virus transmission mechanisms. In preparation for an anticipated influenza pandemic, a substantial body of literature has developed over the last few decades showing that the short-range aerosol route is an important, though often neglected transmission path. We develop a simple mathematical model for COVID-19 transmission via aerosols, apply it to known outbreaks, and present quantitative guidelines for ventilation and occupancy in the workplace.
1-Factorizations of pseudorandom graphs

A $1$-factorization of a graph $G$ is a collection of edge-disjoint perfect matchings whose union is $E(G)$. A trivial necessary condition for $G$ to admit a $1$-factorization is that $|V(G)|$ is even and $G$ is regular; the converse is easily seen to be false. In this paper, we consider the problem of finding $1$-factorizations of regular, pseudorandom graphs. Specifically, we prove that an $(n,d,\lambda)$-graph $G$ (that is, a $d$-regular graph on $n$ vertices whose second largest eigenvalue in absolute value is at most $\lambda$) admits a $1$-factorization provided that $n$ is even, $C_0\leq d\leq n-1$ (where $C_0$ is a universal constant), and $\lambda\leq d^{1-o(1)}$. In particular, since (as is well known) a typical random $d$-regular graph $G_{n,d}$ is such a graph, we obtain the existence of a $1$-factorization in a typical $G_{n,d}$ for all $C_0\leq d\leq n-1$, thereby extending to all possible values of $d$ results obtained by Janson, and independently by Molloy, Robalewska, Robinson, and Wormald for fixed $d$. Moreover, we also obtain a lower bound for the number of distinct $1$-factorizations of such graphs $G$ which is off by a factor of $2$ in the base of the exponent from the known upper bound. This lower bound is better by a factor of $2^{nd/2}$ than the previously best known lower bounds, even in the simplest case where $G$ is the complete graph. Our proofs are probabilistic and can be easily turned into polynomial time (randomized) algorithms.
Inferring Nighttime Satellite Imagery from Human Mobility

Nighttime lights satellite imagery has been used for decades as a uniform, global source of data for studying a wide range of socioeconomic factors. Recently, another more terrestrial source is producing data with similarly uniform global coverage: anonymous and aggregated smart phone location. This data, which measures the movement patterns of people and populations rather than the light they produce, could prove just as valuable in decades to come. In fact, since human mobility is far more directly related to the socioeconomic variables being predicted, it has an even greater potential. Additionally, since cell phone locations can be aggregated in real time while preserving individual user privacy, it will be possible to conduct studies that would previously have been impossible because they require data from the present. Of course, it will take quite some time to establish the new techniques necessary to apply human mobility data to problems traditionally studied with satellite imagery and to conceptualize and develop new real time applications. In this study we demonstrate that it is possible to accelerate this process by inferring artificial nighttime satellite imagery from human mobility data, while maintaining a strong differential privacy guarantee. We also show that these artificial maps can be used to infer socioeconomic variables, often with greater accuracy than using actual satellite imagery. Along the way, we find that the relationship between mobility and light emissions is both nonlinear and varies considerably around the globe. Finally, we show that models based on human mobility can significantly improve our understanding of society at a global scale.
Responsive parallelism with futures and state

Motivated by the increasing shift to multicore computers, recent work has developed language support for responsive parallel applications that mix compute-intensive tasks with latency-sensitive, usually interactive, tasks. These developments include calculi that allow assigning priorities to threads, type systems that can rule out priority inversions, and accompanying cost models for predicting responsiveness. These advances share one important limitation: all of this work assumes purely functional programming. This is a significant restriction, because many realistic interactive applications, from games to robots to web servers, use mutable state, e.g., for communication between threads. In this paper, we lift the restriction concerning the use of state. We present λi4, a calculus with implicit parallelism in the form of prioritized futures and mutable state in the form of references. Because both futures and references are first-class values, λi4 programs can exhibit complex dependencies, including interaction between threads and with the external world (users, network, etc). To reason about the responsiveness of λi4 programs, we extend traditional graph-based cost models for parallelism to account for dependencies created via mutable state, and we present a type system to outlaw priority inversions that can lead to unbounded blocking. We show that these techniques are practical by implementing them in C++ and present an empirical evaluation.
Stability of the Spacetime Positive Mass Theorem in Spherical Symmetry

The rigidity statement of the positive mass theorem asserts that an asymptotically flat initial data set for the Einstein equations with zero ADM mass, and satisfying the dominant energy condition, must arise from an embedding into Minkowski space. In this paper, we address the question of what happens when the mass is merely small. In particular, we formulate a conjecture for the stability statement associated with the spacetime version of the positive mass theorem, and give examples to show how it is basically sharp if true. This conjecture is then established under the assumption of spherical symmetry in all dimensions. More precisely, it is shown that a sequence of asymptotically flat initial data satisfying the dominant energy condition, without horizons except possibly at an inner boundary, and with ADM masses tending to zero must arise from isometric embeddings into a sequence of static spacetimes converging to Minkowski space in the pointed volume preserving intrinsic flat sense. The difference of second fundamental forms coming from the embeddings and initial data must converge to zero in $$L^p$$ , $$1\le p<2$$ . In addition some minor tangential results are also given, including the spacetime version of the Penrose inequality with rigidity statement in all dimensions for spherically symmetric initial data, as well as symmetry inheritance properties for outermost apparent horizons.
Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments.

Modern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological behaviors or to infer biological parameters. Such quantitative analyses can also help to improve experiment designs for particular goals, such as to learn more about specific model mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment design is to use the Fisher information matrix (FIM), which quantifies the expected information a particular experiment will reveal about model parameters. The finite state projection-based FIM (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory systems, whose complex response distributions do not satisfy standard assumptions of Gaussian variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response genes in S. cerevisiae under time-varying MAPK induction. We verify this FSP-FIM analysis and use it to optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters. We then extend the FSP-FIM approach to explore how different measurement times or genetic modifications help to minimize uncertainty in the sensing of extracellular environments, and we experimentally validate the FSP-FIM to rank single-cell experiments for their abilities to minimize estimation uncertainty of NaCl concentrations during yeast osmotic shock. This work demonstrates the potential of quantitative models to not only make sense of modern biological datasets but to close the loop between quantitative modeling and experimental data collection.
Brownian fluctuations of flame fronts with small random advection

We study the effect of small random advection in two models in turbulent combustion. Assuming that the velocity field decorrelates sufficiently fast, we (i) identify the order of the fluctuations o...
1--Meixner random vectors

A definition of d-dimensional n-Meixner random vectors is given first. This definition involves the commutators of their semi-quantum operators. After that we focus on the 1-Meixner random vectors and derive a system of d partial differential equations satisfied by their Laplace transform. We provide a set of necessary conditions for this system to be integrable. We use these conditions to give a complete characterization of all non-degenerate three-dimensional 1-Meixner random vectors. It must be mentioned that the three-dimensional case produces the first example in which the components of a 1-Meixner random vector cannot be reduced, via an injective linear transformation, to three independent classic Meixner random variables.
Effect of Strain Rate on Dynamic Recrystallization in a Magnesium Alloy under Compression at High Temperature

Interrupted uniaxial compression tests were performed on an extruded Mg-Al-Mn magnesium alloy (AM30) at 450°C and various strain rates of 0.001 s−1, 0.1 s−1, 0.5 s−1 and 0.8 s−1. Texture and microstructure evolution were examined using electron back scattered diffraction (EBSD) and X-ray diffraction (XRD) techniques. Twinning was found to be ubiquitous at high temperature but provided that a highly enough strain rate was applied. The results show that the deformation microstructure evolves with strong dependence on the strain rate as twinning triggers and non-basal slips become harder. Schmid’s factor and slip traces analyses elucidated the driving forces controlling dynamic recrystallization mechanisms and emphasized on the role played by extension twinning and non-basal slips.
Successful coronary artery bypass operation in a SARS-COV-2 infected patient with acute coronary syndrome.

The Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is overwhelming healthcare resources and infrastructure worldwide. Cardiac surgical operating capacity during the COVID-19 pandemic is dramatically lower due to postponement or cancellation of elective or semi-urgent procedures. Earlier reports have demonstrated complicated post-operative courses and high fatality rates in patients undergoing emergent cardiothoracic surgery who were diagnosed post-operatively with COVID-19. These reports raise the possibility that active COVID-19 might precipitate a catastrophic pathophysiogical response to infection in the post-operative period and lead to unfavorable surgical outcomes. Hence, it is imperative to screen patients with SARS-CoV-2 infection prior to surgery and to carefully monitor them in the post-operative period to identify any early signs of active COVID-19. In this report, we present the successful outcome of coronary artery bypass grafting (CABG) operation in a patient with asymptomatic SARS-CoV-2 infection presenting with an acute coronary syndrome and requiring urgent surgical intervention. We employed a meticulous strategy to identify subclinical COVID-19 disease, and after confirming the absence of active disease, proceeded with the CABG operation. The patient outcome was successful with the absence of any overt COVID-19 manifestations in the post-operative period.