cssi/SciSciGPT-SciSciNet huggingface dataset:
Communication-Computation Efficient Gradient Coding

This paper develops coding techniques to reduce the running time of distributed learning tasks. It characterizes the fundamental tradeoff to compute gradients (and more generally vector summations) in terms of three parameters: computation load, straggler tolerance and communication cost. It further gives an explicit coding scheme that achieves the optimal tradeoff based on recursive polynomial constructions, coding both across data subsets and vector components. As a result, the proposed scheme allows to minimize the running time for gradient computations. Implementations are made on Amazon EC2 clusters using Python with mpi4py package. Results show that the proposed scheme maintains the same generalization error while reducing the running time by $32\%$ compared to uncoded schemes and $23\%$ compared to prior coded schemes focusing only on stragglers (Tandon et al., ICML 2017).
Time-resolved two-photon excitation of long-lifetime polaritons

Recent studies of two-photon excitation of exciton-polaritons in microcavities have considered the possibility of an allowed absorption process into the 2p-state of the excitons that participate in the polariton effect. Here we report time-resolved measurements of two-photon excitation directly into the lower polariton states, invoking the 1s state of the excitons. Although this process is forbidden by symmetry for light at normal incidence, it is allowed at a nonzero angle of incidence due to state mixing. We examine the polarization dependence of two-photon absorption at finite k both theoretically and experimentally. Previous results should be reevaluated in light of the mechanism observed here.
Finite sample guarantees for PCA in non-isotropic and data-dependent noise

This work obtains novel finite sample guarantees for Principal Component Analysis (PCA). These hold even when the corrupting noise is non-isotropic, and a part (or all of it) is data-dependent. Because of the latter, in general, the noise and the true data are correlated. The results in this work are a significant improvement over those given in our earlier work where this “correlated-PCA” problem was first studied. In the regime analyzed in this work, our results imply that the sample complexity required to achieve subspace recovery error that is a constant fraction of the noise level is near-optimal. We demonstrate the validity of our analysis through numerical experiments.
Communication-aware motion planning for multi-agent systems from signal temporal logic specifications

We propose a mathematical framework for synthesizing motion plans for multi-agent systems that fulfill complex, high-level and formal local specifications in the presence of inter-agent communication. The proposed synthesis framework consists of desired motion specifications in temporal logic (STL) formulas and a local motion controller that ensures the underlying agent not only to accomplish the local specifications but also to avoid collisions with other agents or possible obstacles, while maintaining an optimized communication quality of service (QoS) among the agents. Utilizing a Gaussian fading model for wireless communication channels, the framework synthesizes the desired motion controller by solving a joint optimization problem on motion planning and wireless communication, in which both the STL specifications and the wireless communication conditions are encoded as mixed integer-linear constraints on the variables of the agents' dynamical states and communication channel status. The overall framework is demonstrated by a case study of communication-aware multi-robot motion planning and the effectiveness of the framework is validated by simulation results.
Bounding the maximum of dependent random variables

Abstract: Let Mn be the maximum of n zero-mean gaussian variables X1;::;Xn with covariance matrix of minimum eigenvalue and maximum eigenvalue . Then, for n 70, PrfMn (2 logn 2:5 log(2 logn 2:5)) 1 2 :68 g 1 2 : Bounds are also given for tail probabilities other than 1 . Upper bounds are given for tail probabilities of the maximum of dependent identically dis- tributed variables. As an application, the maximum of purely non-deterministic stationary Gaussian processes is shown to have the same rst order asymp-
PDEs with compressed solutions

Sparsity plays a central role in recent developments in signal processing, linear algebra, statistics, optimization, and other fields. In these developments, sparsity is promoted through the addition of an L^1 norm (or related quantity) as a constraint or penalty in a variational principle. We apply this approach to partial differential equations that come from a variational quantity, either by minimization (to obtain an elliptic PDE) or by gradient flow (to obtain a parabolic PDE). Also, we show that some PDEs can be rewritten in an L^1 form, such as the divisible sandpile problem and signum-Gordon. Addition of an L^1 term in the variational principle leads to a modified PDE where a subgradient term appears. It is known that modified PDEs of this form will often have solutions with compact support, which corresponds to the discrete solution being sparse. We show that this is advantageous numerically through the use of efficient algorithms for solving L^1 based problems.
Robust state feedback control design with probabilistic system parameters

In this paper, a new polynomial chaos based framework for analyzing linear systems with probabilistic parameters is presented. Stability analysis and synthesis of optimal quadratically stabilizing controllers for such systems are presented as convex optimization problems, with exponential mean square stability guarantees. A Monte-Carlo approach for analysis and synthesis is also presented, which is used to benchmark the polynomial chaos based approach. The computational advantage of the polynomial chaos approach is shown with an example based on the design of an optimal EMS-stabilizing controller, for an F-16 aircraft model.
ON THE SUM OF A PRIME AND A FIBONACCI NUMBER

We show that the set of the numbers that are the sum of a prime and a Fibonacci number has positive lower asymptotic density.
Games With Tolerant Players.

A notion of pi-tolerant equilibrium is defined that takes into account that players have some tolerance regarding payoffs in a game. This solution concept generalizes Nash and refines epsilon-Nash equilibrium in a natural way. We show that pi-tolerant equilibrium can explain cooperation in social dilemmas such as Prisoner's Dilemma and the Public Good game. We then examine the structure of particularly cooperative pi-tolerant equilibria, where players are as cooperative as they can be, subject to their tolerances, in Prisoner's Dilemma. To the extent that cooperation is due to tolerance, these results provide guidance to a mechanism designer who has some control over the payoffs in a game, and suggest ways in which cooperation can be increased.
Randomized Rounding for the Largest Simplex Problem

The maximum volume j-simplex problem asks to compute the j-dimensional simplex of maximum volume inside the convex hull of a given set of n points in Qd. We give a deterministic approximation algorithm for this problem which achieves an approximation ratio of ej/2 + o(j). The problem is known to be NP-hard to approximate within a factor of cj for some constant c > 1. Our algorithm also gives a factor ej + o(j) approximation for the problem of finding the principal j x j submatrix of a rank d positive semidefinite matrix with the largest determinant. We achieve our approximation by rounding solutions to a generalization of the D-optimal design problem, or, equivalently, the dual of an appropriate smallest enclosing ellipsoid problem. Our arguments give a short and simple proof of a restricted invertibility principle for determinants.
Multi-agent discrete search with limited visibility

The problem of search by multiple agents to find and localize objects arises in many important applications. In this paper, we study a class of multi-agent search problems in which each agent can access only a subset of a discrete search space, with detection performance that depends only on the location. We show this problem can be reformulated as a minimum cost network optimization problem, and develop a fast specialized algorithm for the solution. We prove that our algorithm is correct, and has worst case computation performance that is faster than general minimum cost flow algorithms. We also address the problem where detection performance depends on both location and agent, which is known to be NP-Hard. We reduce the problem to a submodular maximization problem over a matroid, and provide an approximate algorithm with guaranteed performance. We illustrate the performance of our algorithms with simulations of search problems and compare it with other min-cost flow algorithms.
List-decodable robust mean estimation and learning mixtures of spherical gaussians

We study the problem of list-decodable (robust) Gaussian mean estimation and the related problem of learning mixtures of separated spherical Gaussians. In the former problem, we are given a set T of points in n with the promise that an α-fraction of points in T, where 0G, and no assumptions are made about the remaining points. The goal is to output a small list of candidate vectors with the guarantee that at least one of the candidates is close to the mean of G. In the latter problem, we are given samples from a k-mixture of spherical Gaussians on n and the goal is to estimate the unknown model parameters up to small accuracy. We develop a set of techniques that yield new efficient algorithms with significantly improved guarantees for these problems. Specifically, our main contributions are as follows: List-Decodable Mean Estimation. Fix any d ∈ + and 0Od ((nd/α)) and runtime Od ((n/α)d) that outputs a list of O(1/α) many candidate vectors such that with high probability one of the candidates is within l2-distance Od(α−1/(2d)) from the mean of G. The only previous algorithm for this problem achieved error O(α−1/2) under second moment conditions. For d = O(1/), where >0 is a constant, our algorithm runs in polynomial time and achieves error O(α). For d = Θ(log(1/α)), our algorithm runs in time (n/α)O(log(1/α)) and achieves error O(log3/2(1/α)), almost matching the information-theoretically optimal bound of Θ(log1/2(1/α)) that we establish. We also give a Statistical Query (SQ) lower bound suggesting that the complexity of our algorithm is qualitatively close to best possible. Learning Mixtures of Spherical Gaussians. We give a learning algorithm for mixtures of spherical Gaussians, with unknown spherical covariances, that succeeds under significantly weaker separation assumptions compared to prior work. For the prototypical case of a uniform k-mixture of identity covariance Gaussians we obtain the following: For any >0, if the pairwise separation between the means is at least Ω(k+√log(1/δ)), our algorithm learns the unknown parameters within accuracy δ with sample complexity and running time (n, 1/δ, (k/)1/). Moreover, our algorithm is robust to a small dimension-independent fraction of corrupted data. The previously best known polynomial time algorithm required separation at least k1/4 (k/δ). Finally, our algorithm works under separation of O(log3/2(k)+√log(1/δ)) with sample complexity and running time (n, 1/δ, klogk). This bound is close to the information-theoretically minimum separation of Ω(√logk). Our main technical contribution is a new technique, using degree-d multivariate polynomials, to remove outliers from high-dimensional datasets where the majority of the points are corrupted.
Arithmetic properties of Picard–Fuchs equations and holonomic recurrences

Abstract The coefficient series of the holomorphic Picard–Fuchs differential equation associated with the periods of elliptic curves often have surprising number-theoretic properties. These have been widely studied in the case of the torsion-free, genus zero congruence subgroups of index 6 and 12 (e.g. the Beauville families). Here, we consider arithmetic properties of the Picard–Fuchs solutions associated to general elliptic families, with a particular focus on the index 24 congruence subgroups. We prove that elliptic families with rational parameters admit linear reparametrizations such that their associated Picard–Fuchs solutions lie in Z 〚 t 〛 . A sufficient condition is given such that the same holds for holomorphic solutions at infinity. An Atkin–Swinnerton-Dyer congruence is proven for the coefficient series attached to Γ 1 ( 7 ) . We conclude with a consideration of asymptotics, wherein it is proved that many coefficient series satisfy asymptotic expressions of the form u n ∼ l λ n / n . Certain arithmetic results extend to the study of general holonomic recurrences.
Analysis of AneuRisk65 data: Warped logistic discrimination

We analyze the AneuRisk65 curvature functions using a likelihood-based warping method for sparsely sampled curves, and combine it with logistic regression in order to discriminate subjects with aneurysms at or after the terminal bifurcation of the internal carotid artery (the most life-threatening) from subjects with no aneurysms or aneurysms along the carotid artery (the less serious). Significantly lower misclassification rates are obtained when the warping functions are included in the logistic discrimination model, rather than being treated as mere nuisance parameters.
The Ocean Tensor Package

The Ocean Tensor Package is an open-source package for matrix and tensor operations on CPU and GPU. The package aims to serve as a foundational layer for applications that require dense tensor operations on a variety of device types. All operations are available through a unified interface that is carefully designed to be powerful, extensible, and at the same time easy to use. The package has a modular implementation in C and provides a light-weight Python interface. Modularity of the package facilitates the addition of new operations as well as new device types.
Minimizing Time-to-Rank: A Learning and Recommendation Approach.

Consider the following problem faced by an online voting platform: A user is provided with a list of alternatives, and is asked to rank them in order of preference using only drag-and-drop operations. The platform's goal is to recommend an initial ranking that minimizes the time spent by the user in arriving at her desired ranking. We develop the first optimization framework to address this problem, and make theoretical as well as practical contributions. On the practical side, our experiments on Amazon Mechanical Turk provide two interesting insights about user behavior: First, that users' ranking strategies closely resemble selection or insertion sort, and second, that the time taken for a drag-and-drop operation depends linearly on the number of positions moved. These insights directly motivate our theoretical model of the optimization problem. We show that computing an optimal recommendation is NP-hard, and provide exact and approximation algorithms for a variety of special cases of the problem. Experimental evaluation on MTurk shows that, compared to a random recommendation strategy, the proposed approach reduces the (average) time-to-rank by up to 50%.
Adaptive Multidimensional Integration Based on Rank-1 Lattices

Quasi-Monte Carlo methods are used for numerically integrating multivariate functions. However, the error bounds for these methods typically rely on a priori knowledge of some semi-norm of the integrand, not on the sampled function values. In this article, we propose an error bound based on the discrete Fourier coefficients of the integrand. If these Fourier coefficients decay more quickly, the integrand has less fine scale structure, and the accuracy is higher. We focus on rank-1 lattices because they are a commonly used quasi-Monte Carlo design and because their algebraic structure facilitates an error analysis based on a Fourier decomposition of the integrand. This leads to a guaranteed adaptive cubature algorithm with computational cost \(\mathscr {O}(mb^m)\), where b is some fixed prime number and \(b^m\) is the number of data points.
A sharp estimate for probability distributions

Abstract We consider absolutely continuous probability distributions f ( x ) d x on R ≥ 0 . A result of Feldheim & Feldheim shows, among other things, that if the distribution is not compactly supported, then, using X , Y to denote two independent random variables drawn from f ( x ) d x , there exists z > 0 such that most events in X + Y ≥ 2 z are comprised of a ‘small’ term satisfying min ( X , Y ) ≤ z and a ‘large’ term satisfying max ( X , Y ) ≥ z (as opposed to two ‘large’ terms that are both larger than z ). More formally, they showed lim sup z → ∞ P min ( X , Y ) ≤ z | X + Y ≥ 2 z = 1 . The result fails if the distribution is compactly supported. We prove sup z > 0 P min ( X , Y ) ≤ z | X + Y ≥ 2 z ≥ 1 24 + 8 log 2 ( med ( X ) ‖ f ‖ L ∞ ) , where med X denotes the median. Interestingly, the logarithm is necessary and the result is sharp up to constants; we also discuss some open problems.
Explaining Intergenerational Mobility: The Role of Fertility and Family Transfers

Abstract Poor families have more children and transfer less resources to them. This suggests that family decisions about fertility and transfers dampen intergenerational mobility. To evaluate the quantitative importance of this mechanism, we extend the standard heterogeneous-agent life cycle model with earnings risk and credit constraints to allow for endogenous fertility, family transfers, and education. The model, estimated to the US in the 2000s, implies that a counterfactual flat income-fertility profile would—through the equalization of initial conditions—increase intergenerational mobility by 6%. The impact of a counterfactual constant transfer per child is twice as large.
Robust Archaeal and Bacterial Communities Inhabit Shallow Subsurface Sediments of the Bonneville Salt Flats.

ABSTRACT We report the first census of natural microbial communities of the Bonneville Salt Flats (BSF), a perennial salt pan at the Utah-Nevada border. Environmental DNA sequencing of archaeal and bacterial 16S rRNA genes was conducted on samples from multiple evaporite sediment layers collected from the upper 30 cm of the surface salt crust. Our results show that at the time of sampling (September 2016), BSF hosted a robust microbial community dominated by diverse halobacteria and Salinibacter species. Sequences identical to Geitlerinema sp. strain PCC 9228, an anoxygenic cyanobacterium that uses sulfide as the electron donor for photosynthesis, are also abundant in many samples. We identified taxonomic groups enriched in each layer of the salt crust sediment and revealed that the upper gypsum sediment layer found immediately under the uppermost surface halite contains a robust microbial community. In these sediments, we found an increased presence of Thermoplasmatales, Hadesarchaeota, Nanoarchaeaeota, Acetothermia, Desulfovermiculus, Halanaerobiales, Bacteroidetes, and Rhodovibrio. This study provides insight into the diversity, spatial heterogeneity, and geologic context of a surprisingly complex microbial ecosystem within this macroscopically sterile landscape. IMPORTANCE Pleistocene Lake Bonneville, which covered a third of Utah, desiccated approximately 13,000 years ago, leaving behind the Bonneville Salt Flats (BSF) in the Utah West Desert. The potash salts that saturate BSF basin are extracted and sold as an additive for agricultural fertilizers. The salt crust is a well-known recreational and economic commodity, but the biological interactions with the salt crust have not been studied. This study is the first geospatial analysis of microbially diverse populations at this site using cultivation-independent environmental DNA sequencing methods. Identification of the microbes present within this unique, dynamic, and valued sedimentary evaporite environment is an important step toward understanding the potential consequences of perturbations to the microbial ecology on the surrounding landscape and ecosystem.
Imidazolyl-phenyl (IMP) anions: a modular structure for tuning solubility and coordinating ability

The effect of counteranion upon a cation's solution-phase reactivity depends on a subtle interplay of weak interactions. Although these effects are widely appreciated in synthesis and catalysis, probing and controlling anion-cation interactions remains a significant challenge. Here we report the synthesis, characterisation and reactivity of the IMP anions, a family of anions with a coordinating ability that can be tuned for a given application. The anions are robust, compatible with both strongly basic and acidic media, suitable for isolation of unstable organometallic species, and effective as counteranions for homogeneous catalysis. IMP anions are prepared in two steps: deprotonation of substituted 2-phenylimidazoles with NaH, followed by addition of 2 equiv. B(C6F5)3. The anions prepared feature a range of functionality, including nitro, ester, amide, amine and alcohol groups. Based on the spectroscopic properties of [Pd(IPr)(C(O)C9H6N)] [IMP-R], the coordinating ability of [IMP-R]- ranges between BF4- and BArF4-, depending on the polarity of the R group. Gold complexes of type [L-Au-L'][IMP-R] have been isolated and characterised, resulting in the first X-ray structure of a (η2-diphenylacetylene)Au complex. [(tBuXPhos)Au(MeCN)][IMP-R] catalyses [2 + 2] cyclisation of alkenes and alkynes, as well as the hydroalkoxylation of alkynes. Unlike SbF6- and BArF4-, the [IMP-H]- and [IMP-CF3]- salts are sufficiently soluble to efficiently promote cyclisations in toluene with [(tBuXPhos)Au(MeCN)]+.
Diabetes Risk Perception Among Immigrant and Racial/Ethnic Minority Adults in the United States:

PurposeThe purpose of the study was to examine associations of immigrant and racial/ethnic status with diabetes risk perception among a population-based sample of US adults without diabetes. Racial...
Rare-Event Simulation for Distribution Networks

Catastrophic events can dramatically disrupt distribution networks and supply chains; think, for example, about the threat of a hurricane, which causes significant spikes in demands for gasoline, w...
Secure Control under Partial Observability with Temporal Logic Constraints

This paper studies the synthesis of control policies for an agent that has to satisfy a temporal logic specification in a partially observable environment, in the presence of an adversary. The interaction of the agent (defender) with the adversary is modeled as a partially observable stochastic game. The search for policies is limited to over the space of finite state controllers, which leads to a tractable approach to determine policies. The goal is to generate a defender policy to maximize satisfaction of a given temporal logic specification under any adversary policy. We relate the satisfaction of the specification in terms of reaching (a subset of) recurrent states of a Markov chain. We then present a procedure to determine a set of defender and adversary finite state controllers of given sizes that will satisfy the temporal logic specification. We illustrate our approach with an example.
Management of intellectual asset production in industrial laboratories

AbstractIndustrial laboratories generate profit for their parent companies and in so doing benefit society through spillovers of novel technologies and solutions. However, research’s share of corpo...
Correlation Neglect in Student-to-School Matching

A growing body of evidence suggests that many people struggle with decision-making in the presence of correlation. In typical examples of this problem, decision-makers are presented with multiple signals that are each influenced both by independent components and information from a common source. The process by which signals are generated induces correlation, and optimal decision-making requires taking it into account. In practice, however, experiments like those of Enke and Zimmermann [1] demonstrate that many decision-makers neglect to do so, effectively acting as if these correlated signals are independent. We study the prevalence and consequences of these failures of reasoning in a decision of considerable importance: the application strategies of students applying to schools. Many application processes inherently require students to make forecasts of events determined by common underlying inputs, resulting in correlation structures like those described above. For example, students commonly must whittle a large number of schools down to a smaller set that are applied to or ranked, introducing an incentive to avoid listing two programs with highly correlated admissions decisions. In such environments, a student harboring correlation neglect faces a challenging decision.
Accelerated Saddle Point Refinement through Full Exploitation of Partial Hessian Diagonalization.

Identification and refinement of first order saddle point (FOSP) structures on the potential energy surface (PES) of chemical systems is a computational bottleneck in the characterization of reacti...
Do board gender quotas affect firm value? Evidence from California Senate Bill No. 826

Abstract We examine stock market reactions, direct costs of compliance, and board adjustments to California Senate Bill No. 826 (SB 826), the first mandated board gender diversity quota in the United States. Announcement returns average −1.2% and are robust to the use of multiple methodologies. Returns are more negative when the gap between the mandated number and the pre-SB 826 number of female directors is larger. These negative effects are less severe for firms with a greater supply of female candidates, and for those that can more easily replace male directors or attract female directors. For small firms, the annual direct cost of compliance through board expansion is non-trivial, representing 0.76% of market value. Following SB 826, firms significantly increase female board representation, and the increase is greater for firms in California than control firms in other states.
Existence Theory for a Time-Dependent Mean Field Games Model of Household Wealth

We study a nonlinear system of partial differential equations arising in macroeconomics which utilizes a mean field approximation. This system together with the corresponding data, subject to two moment constraints, is a model for debt and wealth across a large number of similar households, and was introduced in a recent paper of Achdou et al. (Philos Trans R Soc Lond Ser A 372(2028):20130397, 2014). We introduce a relaxation of their problem, generalizing one of the moment constraints; any solution of the original model is a solution of this relaxed problem. We prove existence and uniqueness of strong solutions to the relaxed problem, under the assumption that the time horizon is small. Since these solutions are unique and since solutions of the original problem are also solutions of the relaxed problem, we conclude that if the original problem does have solutions, then such solutions must be the solutions we prove to exist. Furthermore, for some data and for sufficiently small time horizons, we are able to show that solutions of the relaxed problem are in fact not solutions of the original problem. In this way we demonstrate nonexistence of solutions for the original problem in certain cases.
The effect of unemployment on noncognitive skills

Unemployment results in lower levels of cognitive skills and has long-term effects on health and economic well-being. In this paper, I show that unemployment also has negative effects on noncogniti...
A Dynamical Model for Clustered Star Formation in the Galactic Disk

The clustered nature of star formation should produce a high degree of structure in the combined phase and chemical space in the Galactic disk. To date, observed structure of this kind has been mostly limited to bound clusters and moving groups. In this paper we present a new dynamical model of the Galactic disk that takes into account the clustered nature of star formation. This model predicts that the combined phase and chemical space is rich in substructure, and that this structure is sensitive to both the precise nature of clustered star formation and the large-scale properties of the Galaxy. The model self-consistently evolves 4 billion stars over the last 5 Gyr in a realistic potential that includes an axisymmetric component, a bar, spiral arms, and giant molecular clouds (GMCs). All stars are born in clusters with an observationally-motivated range of initial conditions. As direct \textit{N}-body calculations for billions of stars is computationally infeasible, we have developed a method of initializing star cluster particles to mimic the effects of direct \textit{N}-body effects, while the actual orbit integrations are treated as test particles within the analytic potential. We demonstrate that the combination of chemical and phase space information is much more effective at identifying truly co-natal populations than either chemical or phase space alone. Furthermore, we show that co-moving pairs of stars are very likely to be co-natal if their velocity separation is $< 2$ km s$^{-1}$ and their metallicity separation is $< 0.05$ dex. The results presented here bode well for harnessing the synergies between \textit{Gaia} and spectroscopic surveys to reveal the assembly history of the Galactic disk.
Air pollution, neighborhood deprivation, and autism spectrum disorder in the Study to Explore Early Development.

Background To examine whether neighborhood deprivation modifies the association between early life air pollution exposure and autism spectrum disorder (ASD), we used resources from a multisite case-control study, the Study to Explore Early Development. Methods Cases were 674 children with confirmed ASD born in 2003-2006; controls were 855 randomly sampled children born during the same time period and residents of the same geographic areas as cases. Air pollution was assessed by roadway proximity and particulate matter <2.5 μm (PM2.5) exposure during pregnancy and first year of life. To characterize neighborhood deprivation, an index was created based on eight census tract-level socioeconomic status-related parameters. The continuous index was categorized into tertiles, representing low, moderate, and high deprivation. Logistic regression was used to estimate odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Results Neighborhood deprivation modified (Pfor interaction = 0.08) the association between PM2.5 exposure during the first year of life and ASD, with a stronger association for those living in high (OR = 2.42, 95% CI = 1.20, 4.86) rather than moderate (OR=1.21, 95% CI = 0.67, 2.17) or low (OR=1.46, 95% CI = 0.80, 2.65) deprivation neighborhoods. Departure from additivity or multiplicativity was not observed for roadway proximity or exposures during pregnancy. Conclusion These results provide suggestive evidence of interaction between neighborhood deprivation and PM2.5 exposure during the first year of life in association with ASD.
Revealing Hi-C subcompartments by imputing inter-chromosomal chromatin interactions.

Higher-order genome organization and its variation in different cellular conditions remain poorly understood. Recent high-coverage genome-wide chromatin interaction mapping using Hi-C has revealed spatial segregation of chromosomes in the human genome into distinct subcompartments. However, subcompartment annotation, which requires Hi-C data with high sequencing coverage, is currently only available in the GM12878 cell line, making it impractical to compare subcompartment patterns across cell types. Here we develop a computational approach, SNIPER (Subcompartment iNference using Imputed Probabilistic ExpRessions), based on denoising autoencoder and multilayer perceptron classifier to infer subcompartments using typical Hi-C datasets with moderate coverage. SNIPER accurately reveals subcompartments using moderate coverage Hi-C datasets and outperforms an existing method that uses epigenomic features in GM12878. We apply SNIPER to eight additional cell lines and find that chromosomal regions with conserved and cell-type specific subcompartment annotations have different patterns of functional genomic features. SNIPER enables the identification of subcompartments without high-coverage Hi-C data and provides insights into the function and mechanisms of spatial genome organization variation across cell types. Genome-wide mapping of chromatin interactions reveals various levels of 3D genome organization. Here, the authors develop SNIPER, a computational method for identifying subcompartments using Hi-C data with moderate coverage.
AP-S Turnstile

Professor Pendry's (Imperial College, London) observation [1] from 2006 is an example of the excitement in the electromagnetic research community about the potential of metamaterials to create the equivalent of Harry Potter's invisibility cloak. Back then, a research group at Duke University demonstrated how a two-dimensional structure, composed of 10 fiberglass rings covered with sub-wavelength arrays of copper elements, could help "channel" an incident EM wave in the microwave frequency range around a copper cylinder, minimizing a scattered signal. Earlier this year, a group at UC Berkeley raised the stakes by fabricating the "cloak" using nanotechnology (for the sub-wavelength elements), so that the target could be made to "disappear" at wavelengths approaching the visible spectrum. Exciting as these metamaterial-based developments have been, they remain far from real-life applications. A recent report in The Economist discusses what is achievable in practice using the current hide-and-seek military technology. This column presents some highlights from the report
A component-based architecture for problem solving environments

Many problems in science and engineering involve computer modeling of physical phenomena. Problem Solving Environments (PSEs) are software systems designed to hide the intricacies of computer modeling of physical phenomena and guide a researcher towards a meaningful solution. Some computer modeling applications are naturally distributed, require gathering experimental data, processing raw data, plugging the results of this processing into theoretical models, and repeating the process with various sets of model parameters. Others require legacy applications, distributed over a wide-area network, to collaborate towards a common goal.Monolithic PSEs for distributed applications are complex and brittle. The architecture we propose is based upon a distributed object system and an agent framework we have developed during the past few years. The Bond agent framework allows us to assemble an agent from pre-existing components based upon (1) an agent description language, (2) an agent control language, and (3) an agent factory.
The Penetration Rate Effect of Connected and Automated Vehicles in Mixed Traffic Routing

We study the problem of routing Connected and Automated Vehicles (CAVs) in the presence of mixed traffic (coexistence of regular vehicles and CAVs). In this setting, we assume that all CAVs belong to the same fleet, and can be routed using a centralized controller. The routing objective is to minimize a given overall fleet traveling cost (travel time or energy consumption). We assume that regular vehicles (non-CAVs) choose their routing decisions selfishly to minimize their traveling time. We propose an algorithm that deals with the routing interaction between CAVs and regular uncontrolled vehicles. We investigate the effect of assigning system-centric routes under different penetration rates (fractions) of CAVs. To validate our method, we apply the proposed routing algorithms to the Braess Network and to a sub-network of the Eastern Massachusetts (EMA) transportation network using actual traffic data provided by the Boston Region Metropolitan Planning Organization. The results suggest that collaborative routing decisions of CAVs improve not only the cost of CAVs, but also that of the non-CAVs. Furthermore, even a small CAV penetration rate can ease congestion for the entire network.
Expanding the CRISPR Toolbox with ErCas12a in Zebrafish and Human Cells

CRISPR and CRISPR-Cas effector proteins enable the targeting of DNA double-strand breaks to defined loci based on a variable length RNA guide specific to each effector. The guide RNAs are generally similar in size and form, consisting of a ∼20 nucleotide sequence complementary to the DNA target and an RNA secondary structure recognized by the effector. However, the effector proteins vary in protospacer adjacent motif requirements, nuclease activities, and DNA binding kinetics. Recently, ErCas12a, a new member of the Cas12a family, was identified in Eubacterium rectale. Here, we report the first characterization of ErCas12a activity in zebrafish and expand on previously reported activity in human cells. Using a fluorescent reporter system, we show that CRISPR-ErCas12a elicits strand annealing mediated DNA repair more efficiently than CRISPR-Cas9. Further, using our previously reported gene targeting method that utilizes short homology, GeneWeld, we demonstrate the use of CRISPR-ErCas12a to integrate reporter alleles into the genomes of both zebrafish and human cells. Together, this work provides methods for deploying an additional CRISPR-Cas system, thus increasing the flexibility researchers have in applying genome engineering technologies.
Geometric and Measure-Theoretic Shrinking Targets in Dynamical Systems

Abstract We consider both geometric and measure-theoretic shrinking targets for ergodic maps, investigating when they are visible or invisible. Some Baire category theorems are proved, and particular constructions are given when the underlying map is fixed. Open questions about shrinking targets are also described.
FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images

Feature selection is used in machine learning to improve predictions, decrease computation time, reduce noise, and tune models based on limited sample data. In this article, we present FeatureExplorer, a visual analytics system that supports the dynamic evaluation of regression models and importance of feature subsets through the interactive selection of features in high-dimensional feature spaces typical of hyperspectral images. The interactive system allows users to iteratively refine and diagnose the model by selecting features based on their domain knowledge, interchangeable (correlated) features, feature importance, and the resulting model performance.
How do Risk-Based Inspections Impact Auditor Behavior? Experimental Evidence on the PCAOB's Process

I provide theory-based causal evidence on the effects of risk-based regulatory inspections, modeled after the PCAOB's, on auditor behavior in a multi-client setting where clients with relatively hi...
ModEx: A text mining system for extracting mode of regulation of transcription factor-gene regulatory interaction.

Abstract Background Transcription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TF-gene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine. Methods In this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases. Results Our results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage. Availibility: Source code and datasets are available for download on GitHub: https://github.com/samanfrm/modex.
Data Pallets: Containerizing Storage for Reproducibility and Traceability

Trusting simulation output is crucial for Sandia’s mission objectives. We rely on these simulations to perform our high-consequence mission tasks given national treaty obligations. Other science and modeling applications, while they may have high-consequence results, still require the strongest levels of trust to enable using the result as the foundation for both practical applications and future research. To this end, the computing community has developed workflow and provenance systems to aid in both automating simulation and modeling execution as well as determining exactly how was some output was created so that conclusions can be drawn from the data.
ASCOT identifies key regulators of neuronal subtype-specific splicing

Public archives of next-generation sequencing data are growing exponentially, but the difficulty of marshaling this data has led to its underutilization by scientists. Here, we present ASCOT, a resource that uses annotation-free methods to rapidly analyze and visualize splice variants across tens of thousands of bulk and single-cell data sets in the public archive. To demonstrate the utility of ASCOT, we identify novel cell type-specific alternative exons across the nervous system and leverage ENCODE and GTEx data sets to study the unique splicing of photoreceptors. We find that PTBP1 knockdown and MSI1 and PCBP2 overexpression are sufficient to activate many photoreceptor-specific exons in HepG2 liver cancer cells. This work demonstrates how large-scale analysis of public RNA-Seq data sets can yield key insights into cell type-specific control of RNA splicing and underscores the importance of considering both annotated and unannotated splicing events.
R&D Investments and Tax Incentives: The Role of Intra‐Firm Cross‐Border Collaboration

U.S. multinational corporations increasingly use intra‐firm, cross‐border research collaboration to disperse R&D across different countries. This paper investigates the implications of such collaboration on the abilities of firms to garner benefits from R&D tax incentives. We find that the association between R&D intensity and tax incentives is three to five times larger when firms have extensive cross‐border collaboration connected to a country. We also find that the effect is stronger when local intellectual property protection is weaker and when local innovation resources are higher. Our results suggest that cross‐border collaboration helps firms achieve more tax‐efficient R&D investments both by reducing the nontax frictions posed by weak intellectual property protection and by increasing the nontax benefits of foreign R&D. Investissements et incitatifs fiscaux dans le domaine de la R‐D : Le role de la collaboration interne transfrontaliere Les societes multinationales des Etats‐Unis misent de plus en plus sur la collaboration interne transfrontaliere en matiere de recherche pour repartir les activites de R‐D dans divers pays. La presente etude examine les consequences de ce type de collaboration sur la capacite des societes a tirer profit des incitatifs fiscaux a la R‐D. Nous etablissons que l'association entre l'intensite de la R‐D et les incitatifs fiscaux est de trois a cinq fois plus forte lorsqu'une societe compte sur une vaste collaboration transfrontaliere associee a un pays particulier. Nous montrons egalement que l'effet est plus marque lorsque les mesures de protection de la propriete intellectuelle sont moins contraignantes et que les ressources locales en matiere d'innovation sont plus importantes. Nos resultats donnent a penser que la collaboration transfrontaliere aide les societes a faire des investissements en R‐D plus avantageux sur le plan fiscal en reduisant les frictions non fiscales suscitees par la faible protection de la propriete intellectuelle et en bonifiant les avantages non fiscaux des activites de R‐D a l'etranger.
Building Credibility and Cooperation in Low-Trust Settings: Persuasion and Source Accountability in Liberia During the 2014–2015 Ebola Crisis:

How can governments in low-trust settings overcome their credibility deficit when promoting public welfare? To answer this question, we evaluate the effectiveness of the Liberian government’s door-...
Eu-China Bit and FTA Enhance Labor Cooperation and Protection

Presentation at the 17th International Conference in Commemoration of Professor Marco Biagi, "The Collective Dimensions of Employment Relations: Organisational and Regulatory Challenges in a World of Work in Transformation", March 18th-19th, 2019, Marco Biagi Foundation - University of Modena and Reggio Emilia (Modena, Italy).
On the shortest separating cycle

Abstract According to a result of Arkin et al. (2016), given n point pairs in the plane, there exists a simple polygonal cycle that separates the two points in each pair to different sides; moreover, a O ( n ) -factor approximation with respect to the minimum length can be computed in polynomial time. Here the following results are obtained: (I) We extend the problem to geometric hypergraphs and obtain the following characterization of feasibility. Given a geometric hypergraph on points in the plane with hyperedges of size at least 2, there exists a simple polygonal cycle that separates each hyperedge if and only if the hypergraph is 2-colorable. (II) We extend the O ( n ) -factor approximation in the length measure as follows: Given a geometric graph G = ( V , E ) , a separating cycle (if it exists) can be computed in O ( m + n log ⁡ n ) time, where | V | = n , | E | = m . Moreover, a O ( n ) -approximation of the shortest separating cycle can be found in polynomial time. Given a geometric graph G = ( V , E ) in R 3 , a separating polyhedron (if it exists) can be found in O ( m + n log ⁡ n ) time, where | V | = n , | E | = m . Moreover, a O ( n 2 / 3 ) -approximation of a separating polyhedron of minimum perimeter can be found in polynomial time. (III) Given a set of n point pairs in convex position in the plane, we show that a ( 1 + e ) -approximation of a shortest separating cycle can be computed in time n O ( e − 1 / 2 ) . In this regard, we prove a lemma on convex polygon approximation that is of independent interest.
Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes Using Transfer Learning

Smart and agile drones are fast becoming ubiquitous at the edge of the cloud. The usage of these drones is constrained by their limited power and compute capability. In this paper, we present a Transfer Learning (TL) based approach to reduce on-board computation required to train a deep neural network for autonomous navigation via value-based Deep Reinforcement Learning for a target algorithmic performance. A library of 3D realistic meta-environments is manually designed using Unreal Gaming Engine and the network is trained end-to-end. These trained meta-weights are then used as initializers to the network in a test environment and fine-tuned for the last few fully connected layers. Variation in drone dynamics and environmental characteristics is carried out to show robustness of the approach. Using NVIDIA GPU profiler, it was shown that the energy consumption and training latency is reduced by 3.7 $\times$ and 1.8 $\times$ respectively without significant degradation in the performance in terms of average distance traveled before crash i.e. Mean Safe Flight (MSF). The approach is also tested on a real environment using DJI Tello drone and similar results were reported. The code for the approach can be found on GitHub: https://github.com/aqeelanwar/DRLwithTL .