cssi/SciSciGPT-SciSciNet huggingface dataset:
Universal Matrix Completion

The problem of low-rank matrix completion has recently generated a lot of interest leading to several results that offer exact solutions to the problem. However, in order to do so, these methods make assumptions that can be quite restrictive in practice. More specifically, the methods assume that: a) the observed indices are sampled uniformly at random, and b) for every new matrix, the observed indices are sampled afresh. In this work, we address these issues by providing a universal recovery guarantee for matrix completion that works for a variety of sampling schemes. In particular, we show that if the set of sampled indices come from the edges of a bipartite graph with large spectral gap (i.e. gap between the first and the second singular value), then the nuclear norm minimization based method exactly recovers all low-rank matrices that satisfy certain incoherence properties. Moreover, we also show that under certain stricter incoherence conditions, O(nr2) uniformly sampled entries are enough to recover any rank-r n × n matrix, in contrast to the O(nr log n) sample complexity required by other matrix completion algorithms as well as existing analyses of the nuclear norm method.
A Relative Theory for Leibniz n-Algebras

In this paper we show that for an n-Filippov algebra &#x1d524;, the tensor power &#x1d524;⊗n-1 is endowed with a structure of anti-symmetric co-representation over the Leibniz algebra &#x1d524;∧n-1. This co-representation is used to define some relative theories for Leibniz n-algebras with n > 2 and obtain exact sequences relating them. As a result, we construct a spectral sequence for the Leibniz homology of Filippov algebras.
On t-Core Towers and t-Defects of Partitions

We study generating functions which count the sizes of t-cores of partitions, and, more generally, the sizes of higher rows in t-core towers. We then use these results to derive an asymptotic results for the average size of the t-defect of partitions, as well as some curious congruences.
Optical and mechanical design of the extreme AO coronagraphic instrument MagAO-X

Here we review the current optical mechanical design of MagAO-X. The project is post-PDR and has finished the design phase. The design presented here is the baseline to which all the optics and mechanics have been fabricated. The optical/mechanical performance of this novel extreme AO design will be presented here for the first time. Some highlights of the design are: 1) a floating, but height stabilized, optical table; 2) a Woofer tweeter (2040 actuator BMC MEMS DM) design where the Woofer can be the current f/16 MagAO ASM or, more likely, fed by the facility f/11 static secondary to an ALPAO DM97 woofer; 3) 22 very compact optical mounts that have a novel locking clamp for additional thermal and vibrational stability; 4) A series of four pairs of super-polished off-axis parabolic (OAP) mirrors with a relatively wide FOV by matched OAP clocking; 5) an advanced very broadband (0.5-1.7μm) ADC design; 6) A Pyramid (PWFS), and post-coronagraphic LOWFS NCP wavefront sensor; 7) a vAPP coronagraph for starlight suppression. Currently all the OAPs have just been delivered, and all the rest of the optics are in the lab. Most of the major mechanical parts are in the lab or instrument, and alignment of the optics has occurred for some of the optics (like the PWFS) and most of the mounts. First light should be in early 2019.
Derivatives of (modified) Fredholm determinants and stability of standing and traveling waves

Abstract Continuing a line of investigation initiated in [F. Gesztesy, Y. Latushkin, K.A. Makarov, Evans functions, Jost functions, and Fredholm determinants, Arch. Rat. Mech. Anal. 186 (2007) 361–421] exploring the connections between Jost and Evans functions and (modified) Fredholm determinants of Birman–Schwinger type integral operators, we here examine the stability index, or sign of the first nonvanishing derivative at frequency zero of the characteristic determinant, an object that has found considerable use in the study by Evans function techniques of stability of standing and traveling wave solutions of partial differential equations (PDE) in one dimension. This leads us to the derivation of general perturbation expansions for analytically-varying modified Fredholm determinants of abstract operators. Our main conclusion, similarly in the analysis of the determinant itself, is that the derivative of the characteristic Fredholm determinant may be efficiently computed from first principles for integral operators with semi-separable integral kernels, which include in particular the general one-dimensional case, and for sums thereof, which appears to offer applications in the multi-dimensional case. A second main result is to show that the multi-dimensional characteristic Fredholm determinant is the renormalized limit of a sequence of Evans functions defined in [G.J. Lord, D. Peterhof, B. Sandstede, A. Scheel, Numerical computation of solitary waves in infinite cylindrical domains, SIAM J. Numer. Anal. 37 (2000) 1420–1454] on successive Galerkin subspaces, giving a natural extension of the one-dimensional results of [F. Gesztesy, Y. Latushkin, K.A. Makarov, Evans functions, Jost functions, and Fredholm determinants, Arch. Rat. Mech. Anal. 186 (2007) 361–421] and answering a question of [J. Niesen, Evans function calculations for a two-dimensional system, presented talk, SIAM Conference on Applications of Dynamical Systems, Snowbird, UT, USA, May 2007] whether this sequence might possibly converge (in general, no, but with renormalization, yes). Convergence is useful in practice for numerical error control and acceleration.
The Non-Uniform k-Center Problem

In this article, we introduce and study the Non-Uniform k-Center (NUkC) problem. Given a finite metric space (X, d) and a collection of balls of radii l r1 ≥ … ≥ rkr, the NUkC problem is to find a placement of their centers in the metric space and find the minimum dilation α, such that the union of balls of radius α ⋅ ri around the ith center covers all the points in X. This problem naturally arises as a min-max vehicle routing problem with fleets of different speeds.The NUkC problem generalizes the classic k-center problem, wherein all the k radii are the same (which can be assumed to be 1 after scaling). It also generalizes the k-center with outliers (kCwO for short) problem, in which there are k balls of radius 1 and e (number of outliers) balls of radius 0. Before this work, there was a 2-approximation and 3-approximation algorithm known for these problems, respectively; the former is best possible unless P=NP.We first observe that no O(1)-approximation to the optimal dilation is possible unless P=NP, implying that the NUkC problem is harder than the above two problems. Our main algorithmic result is an (O(1), O(1))-bi-criteria approximation result: We give an O(1)-approximation to the optimal dilation; however, we may open Θ(1) centers of each radii. Our techniques also allow us to prove a simple (uni-criterion), optimal 2-approximation to the kCwO problem improving upon the long-standing 3-factor approximation for this problem.Our main technical contribution is a connection between the NUkC problem and the so-called firefighter problems on trees that have been studied recently in the TCS community. We show NUkC is at least as hard as the firefighter problem. While we do nt know whether the converse is true, we are able to adapt ideas from recent works [1, 3] in non-trivial ways to obtain our constant factor bi-criteria approximation.
Exact Rosenthal-type inequalities for p=3, and related results

Abstract An exact Rosenthal-type inequality for the third absolute moments is given, as well as a number of related results. Such results are useful in applications to Berry–Esseen bounds.
On pinned fields, interlacements, and random walk on $$({\mathbb {Z}}/N {\mathbb {Z}})^2$$

We define two families of Poissonian soups of bidirectional trajectories on $${\mathbb {Z}}^2$$ , which can be seen to adequately describe the local picture of the trace left by a random walk on the two-dimensional torus $$({\mathbb {Z}}/N {\mathbb {Z}})^2$$ , started from the uniform distribution, run up to a time of order $$(N\log N)^2$$ and forced to avoid a fixed point. The local limit of the latter was recently established in Comets et al. (Commun Math Phys 343:129–164, 2016). Our construction proceeds by considering, somewhat in the spirit of statistical mechanics, a sequence of “finite volume” approximations, consisting of random walks avoiding the origin and killed at spatial scale N, either using Dirichlet boundary conditions, or by means of a suitably adjusted mass. By tuning the intensity u of such walks with N, the occupation field can be seen to have a nontrivial limit, corresponding to that of the actual random walk. Our construction thus yields a two-dimensional analogue of the random interlacements model introduced in Sznitman (Ann Math 171(3):2039–2087, 2010) in the transient case. It also links it to the pinned free field in $${\mathbb {Z}}^2$$ , by means of a (pinned) Ray–Knight type isomorphism theorem.
Equilibrium Characterization for Data Acquisition Games

We study a game between two firms in which each provide a service based on machine learning. The firms are presented with the opportunity to purchase a new corpus of data, which will allow them to potentially improve the quality of their products. The firms can decide whether or not they want to buy the data, as well as which learning model to build with that data. We demonstrate a reduction from this potentially complicated action space to a one-shot, two-action game in which each firm only decides whether or not to buy the data. The game admits several regimes which depend on the relative strength of the two firms at the outset and the price at which the data is being offered. We analyze the game's Nash equilibria in all parameter regimes and demonstrate that, in expectation, the outcome of the game is that the initially stronger firm's market position weakens whereas the initially weaker firm's market position becomes stronger. Finally, we consider the perspective of the users of the service and demonstrate that the expected outcome at equilibrium is not the one which maximizes the welfare of the consumers.
Particle acceleration and fast magnetic reconnection

Mathematics demonstrates that the exponential separation of neighboring magnetic field lines, which naturally increases during an ideal evolution in three dimensions, leads to an exponentially increasing connection-breaking nonideal magnetic field. On a time scale that depends only logarithmically on the magnitude of the nonideal terms, a fast magnetic reconnection will generally occur, which has a rate determined by Alfvenic, not resistive, physics. The traditional assumption that the reconnecting flux must be dissipated by an electric field is false. In three dimensions, an ideal evolution can spatially mix the magnetic flux. Flux mixing conserves magnetic helicity, which limits the energy that can be transferred from the magnetic field to the plasma. The magnetic evolution is quasi-ideal during a fast magnetic reconnection, and the energy loss is given by the dot product of the magnetic field line velocity u → ⊥ with the j → × B → Lorentz force. Energy loss occurs through Alfven waves and two other effects, which are also present in an ideal evolution. One is an effective parallel electric field E | |, which can accelerate particles despite the particle acceleration due to the true parallel electric field E | | being negligible, and a coefficient νK, which gives a rate for exponentiation of the kinetic energy of particle motion along the magnetic field.Mathematics demonstrates that the exponential separation of neighboring magnetic field lines, which naturally increases during an ideal evolution in three dimensions, leads to an exponentially increasing connection-breaking nonideal magnetic field. On a time scale that depends only logarithmically on the magnitude of the nonideal terms, a fast magnetic reconnection will generally occur, which has a rate determined by Alfvenic, not resistive, physics. The traditional assumption that the reconnecting flux must be dissipated by an electric field is false. In three dimensions, an ideal evolution can spatially mix the magnetic flux. Flux mixing conserves magnetic helicity, which limits the energy that can be transferred from the magnetic field to the plasma. The magnetic evolution is quasi-ideal during a fast magnetic reconnection, and the energy loss is given by the dot product of the magnetic field line velocity u → ⊥ with the j → × B → Lorentz force. Energy loss occurs through Alfven waves and t...
A Neural Network for Semi-Supervised Learning on Manifolds

Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold. However, an explicit graph representation is problematic for neural networks operating in the online setting. Here, we propose a feed-forward neural network capable of semi-supervised learning on manifolds without using an explicit graph representation. Our algorithm uses channels that represent localities on the manifold such that correlations between channels represent manifold structure. The proposed neural network has two layers. The first layer learns to build a representation of low-dimensional manifolds in the input data as proposed recently in [8]. The second learns to classify data using both occasional supervision and similarity of the manifold representation of the data. The channel carrying label information for the second layer is assumed to be “silent” most of the time. Learning in both layers is Hebbian, making our network design biologically plausible. We experimentally demonstrate the effect of semi-supervised learning on non-trivial manifolds.
Thresholding Bandit with Optimal Aggregate Regret

We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold $\theta$, with a fixed budget of $T$ trials. We introduce LSA, a new, simple and anytime algorithm that aims to minimize the aggregate regret (or the expected number of mis-classified arms). We prove that our algorithm is instance-wise asymptotically optimal. We also provide comprehensive empirical results to demonstrate the algorithm's superior performance over existing algorithms under a variety of different scenarios.
The practice of data base administration

The position or role of data base administrator has been described and discussed since the earliest specifications for data base management systems. 1 , 2 , 4 Most of the literature on DBA is normative—describing in detail what the DBA function should include. Different authors have focused on the DBA’s role in introducing the data base concept to organizations, 8 the functions that the DBA should perform, 7 the specification of tools and DBMS features needed by DBAs, 3 and how to establish and perform the DBA’s responsibilities within an organization. 5 , 6 In practice, however, the organization and content of the DBA function may be quite different from the ideal. Other factors related to the DBA’s technical and organizational environment may influence the DBA’s role. This paper summarizes the results of an exploratory survey on data base administration 9 which was undertaken as a first step toward identifying and understanding these factors.
Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.
Putting Global Governance in Its Place

Greater interdependence is often taken to require more global governance, but the logic requires scrutiny. Cross-border spillovers do not always call for international rules. The canonical cases fo...
User innovation: a novel framework for studying animal innovation within a comparative context.

Much work has been dedicated to defining and describing animal innovation. Despite this, efforts to compare human and animal innovation have been hindered by perceived fundamental differences between how, and why, humans and animals innovate. Furthermore, there is not a useful framework for comparisons across different taxa. Here, we provide an overview of the current understanding of human ‘user’ innovation, provide some examples of user innovation, and highlight the parallels between animal innovation and user innovation by humans. User innovation, put simply, is the process by which people invent to satisfy their own needs, not necessarily with the aim of distributing their invention, or marketing it for profit. Thus, it is much more closely aligned to the manner in which nonhuman animals innovate. Our intention is that this discussion will help to re-frame how we consider animal innovation and foster more direct comparisons between human and animal innovation, while propagating new avenues for research, both experimental and observational.
A BRIEF INTRODUCTION TO SOFIC ENTROPY THEORY

Sofic entropy theory is a generalization of the classical Kolmogorov-Sinai entropy theory to actions of large class of non-amenable groups called sofic groups. This is a short introduction with a guide to the literature.
Decentralized Optimal Merging Control for Connected and Automated Vehicles

This paper addresses the optimal control of Connected and Automated Vehicles (CAVs) arriving from two roads at a merging point where the objective is to jointly minimize the travel time and energy consumption of each CAV. The solution guarantees that a speed-dependent safety constraint is always satisfied, both at the merging point and everywhere within a control zone which precedes it. We first analyze the case of no active constraints and prove that under certain conditions the safety constraint remains inactive, thus significantly simplifying the determination of an explicit decentralized solution. Our analysis allows us to study the tradeoff between the two objective function components (travel time and energy within the control zone). Simulation examples are included to compare the performance of the optimal controller to a baseline with human-driven vehicles with results showing improvements.
Improving Emergency Department Management of Diabetic Ketoacidosis in Children

BACKGROUND: Diagnostic delays in the pediatric emergency department (ED) can lead to unnecessary interventions and prolonged ED length of stay (LOS), especially in patients with diabetes mellitus evaluated for diabetic ketoacidosis (DKA). At our institution, baseline DKA determination time (arrival to diagnosis) was 86 minutes, and 61% of patients did not meet DKA criteria. Subsequently, intravenous (IV) placement occurred in 85% of patients without DKA. We aimed to use point-of-care (POC) testing to reduce DKA determination time from 86 to 30 minutes and to reduce IV placements in patients without DKA from 85% to 20% over 18 months. METHODS: Four key interventions (POC tests, order panels, provider guidelines, and nursing guidelines) were tested by using plan-do-study-act cycles. DKA determination time was our primary outcome, and secondary outcomes included the percentage of patients receiving IV placement and ED LOS. Process measures included the rate of use of POC testing and order panels. All measures were analyzed on statistical process control charts. RESULTS: Between January 2015 and July 2018, 783 patients with diabetes mellitus were evaluated for DKA. After all 4 interventions, DKA determination time decreased from 86 to 26 minutes (P CONCLUSIONS: Using quality-improvement methodology, we achieved a meaningful reduction in DKA determination time, the percentage of IV placements, and ED LOS.
Observability-Blocking Controllers for Network Synchronization Processes

The design of local state-feedback control systems in dynamical networks to block observability at remote nodes is studied, in the context of a canonical linear network synchronization model. A general design algorithm is presented first, which can be used to ensure unobservability at any group of $m$ nodes using m + 1 local controllers. The algorithm is based on a method for joint eigenvalue-eigenvector assignment, and serves to prevent observability while leaving all eigenvalues and all but one eigenvector of the open loop unchanged. Next, the topological structure of the network is exploited to reduce the number of controllers required for blocking observability; the result is based on blocking observability on node cutsets separating the actuation locations and nodes that must be made unobservable. The results are illustrated with a numerical example.
Simple Analysis of Sparse, Sign-Consistent JL

Allen-Zhu, Gelashvili, Micali, and Shavit construct a sparse, sign-consistent Johnson-Lindenstrauss distribution, and prove that this distribution yields an essentially optimal dimension for the correct choice of sparsity. However, their analysis of the upper bound on the dimension and sparsity requires a complicated combinatorial graph-based argument similar to Kane and Nelson's analysis of sparse JL. We present a simple, combinatorics-free analysis of sparse, sign-consistent JL that yields the same dimension and sparsity upper bounds as the original analysis. Our analysis also yields dimension/sparsity tradeoffs, which were not previously known. As with previous proofs in this area, our analysis is based on applying Markov's inequality to the pth moment of an error term that can be expressed as a quadratic form of Rademacher variables. Interestingly, we show that, unlike in previous work in the area, the traditionally used Hanson-Wright bound is not strong enough to yield our desired result. Indeed, although the Hanson-Wright bound is known to be optimal for gaussian degree-2 chaos, it was already shown to be suboptimal for Rademachers. Surprisingly, we are able to show a simple moment bound for quadratic forms of Rademachers that is sufficiently tight to achieve our desired result, which given the ubiquity of moment and tail bounds in theoretical computer science, is likely to be of broader interest.
Building large free subshifts using the Local Lemma

Gao, Jackson, and Seward proved that every countably infinite group $\Gamma$ admits a nonempty free subshift $X \subseteq 2^\Gamma$. Here we strengthen this result by showing that free subshifts can be "large" in various senses. Specifically, we prove that for any $k \geqslant 2$ and $h < \log_2 k$, there exists a free subshift $X \subseteq k^\Gamma$ of Hausdorff dimension and, if $\Gamma$ is sofic, entropy at least $h$, answering two questions attributed by Gao, Jackson, and Seward to Juan Souto. Furthermore, we establish a general lower bound on the largest "size" of a free subshift $X'$ contained inside a given subshift $X$. A central role in our arguments is played by the Lovasz Local Lemma, an important tool in probabilistic combinatorics, whose relevance to the problem of finding free subshifts was first recognized by Aubrun, Barbieri, and Thomasse.
Upper bounds for constant slope p-adic families of modular forms

We study p-adic families of eigenforms for which the p-th Hecke eigenvalue $$a_p$$ has constant p-adic valuation (“constant slope families”). We prove two separate upper bounds for the size of such families. The first is in terms of the logarithmic derivative of $$a_p$$ while the second depends only on the slope of the family. We also investigate the numerical relationship between our results and the former Gouvea–Mazur conjecture.
A Construction of Lower-Bounded Generalized Twisted Modules for a Grading-Restricted Vertex (Super)Algebra

We give a general, direct and explicit construction of lower-bounded generalized twisted modules satisfying a universal property for a grading-restricted vertex (super)algebra V associated to an automorphism g of V. In particular, when g is the identity, we obtain lower-bounded generalized V-modules satisfying a universal property. Let W be a lower-bounded graded vector space equipped with a set of “generating twisted fields” and a set of “generator twist fields” satisfying a weak commutativity for generating twisted fields, a generalized weak commutativity for one generating twisted field and one generator twist field and some other properties that are relatively easy to verify. We first prove the convergence and commutativity of products of an arbitrary number of generating twisted fields, one twist generator field and an arbitrary number of generating fields for V. Then using the convergence and commutativity, we define a twisted vertex operator map for W and prove that W equipped with this twisted vertex operator map is a lower-bounded generalized g-twisted V-module. Using this result, we give an explicit construction of lower-bounded generalized g-twisted V-modules satisfying a universal property starting from vector spaces graded by weights, $${\mathbb {Z}}_{2}$$-fermion numbers and g-weights (eigenvalues of g) and real numbers corresponding to the lower bounds of the weights of the modules to be constructed. In particular, every lower-bounded generalized g-twisted V-module (every lower-bounded generalized V-module when g is the identity) is a quotient of such a universal lower-bounded generalized g-twisted V-module (a universal lower-bounded generalized V-module).
The Impact of Tribalism on Social Welfare

We explore the impact of mutual altruism among the players belonging to the same set – their tribe – in a partition of all players in arbitrary strategic games upon the quality of equilibria attained. To this end, we introduce the notion of a \(\tau \)-tribal extension of an arbitrary strategic game, in which players’ subjective cost functions are updated to reflect this, and the associated Price of Tribalism, which is the ratio of the social welfare of the worst Nash equilibrium of the tribal extension to that of the optimum of social welfare. We show that in a well-known game of friendship cliques, network contribution games as well as atomic linear congestion games, the Price of Tribalism is higher than the Price of Anarchy of either the purely selfish players or fully altruistic players (i.e. ones who seek to maximise the social welfare). This phenomenon is observed under a variety of equilibrium concepts. In each instance, we present upper bounds on the Price of Tribalism that match the lower bounds established by our example.
Distributed source number estimation for multiple target detection in sensor networks

Multiple target detection in sensor networks is a challenging problem since the signal captured by individual sensor node is normally a linear/nonlinear weighted mixture of the source signals. Independent component analysis (ICA) has been widely used to solve the source estimation problem but most of the algorithms assume the number of sources is fixed and equals to the number of observations which generally is not the case in sensor networks. Even though several methods are put forward for the source number estimation, the centralized scheme hinders their application in sensor networks due to the extremely constrained resource and scalability issues. In this paper, a distributed source number estimation framework is developed, where the local estimation is generated within each cluster and a fusion algorithm is performed to combine the local results. We derive a posterior probability fusion method based on Bayes theorem and compare it with the Dempster rule of combination. Experimental results show that using the distributed framework, the confidence of source number estimation is improved over the centralized approach while at the same time, the network traffic can be significantly reduced and resources can be conserved.
Bayesian Hierarchical Models With Conjugate Full-Conditional Distributions for Dependent Data From the Natural Exponential Family

We introduce a Bayesian approach for analyzing (possibly) high-dimensional dependent data that are distributed according to a member from the natural exponential family of distributions. This probl...
The yellow gene influences Drosophila male mating success through sex comb melanization

Drosophila melanogaster males perform a series of courtship behaviors that, when successful, result in copulation with a female. For over a century, mutations in the yellow gene, named for its effects on pigmentation, have been known to reduce male mating success. Prior work has suggested that yellow influences mating behavior through effects on wing extension, song, and/or courtship vigor. Here, we rule out these explanations, as well as effects on the nervous system more generally, and find instead that the effects of yellow on male mating success are mediated by its effects on pigmentation of male-specific leg structures called sex combs. Loss of yellow expression in these modified bristles reduces their melanization, which changes their structure and causes difficulty grasping females prior to copulation. These data illustrate why the mechanical properties of anatomy, not just neural circuitry, must be considered to fully understand the development and evolution of behavior.
Functionalized azetidines via visible light-enabled aza Paternò-Büchi reactions.

Azetidines are four-membered nitrogen-containing heterocycles that hold great promise in current medicinal chemistry due to their desirable pharmacokinetic effects. However, a lack of efficient synthetic methods to access functionalized azetidines has hampered their incorporation into pharmaceutical lead structures. As a [2+2] cycloaddition reaction between imines and alkenes, the aza Paterno-Buchi reaction arguably represents the most direct approach to functionalized azetidines. Hampered by competing reaction paths accessible upon photochemical excitation of the substrates, the current synthetic utility of these transformations is greatly restricted. We herein report the development of a visible light-enabled aza Paterno-Buchi reaction that surmounts existing limitations and represents a mild solution for the direct formation of functionalized azetidines from imine and alkene containing precursors.
Abel and the Concept of the Genus of a Curve

This talk is about the treatement of the Genus of a Curve by Abel, following the book "Essays in Constructive Mathematics".
K-dominance in multidimensional data: Theory and applications

Abstract We study the problem of k-dominance in a set of d-dimensional vectors, prove bounds on the number of maxima (skyline vectors), under both worst-case and average-case models, perform experimental evaluation using synthetic and real-world data, and explore an application of k-dominant skyline for extracting a small set of top-ranked vectors in high dimensions where the full skylines can be unmanageably large.
Capacities and 1-strict subsets in metric spaces

Abstract In a complete metric space that is equipped with a doubling measure and supports a Poincare inequality, we study strict subsets, i.e. sets whose variational capacity with respect to a larger reference set is finite, in the case p = 1 . Relying on the concept of fine topology, we give a characterization of those strict subsets that are also sets of finite perimeter, and then we apply this to the study of condensers as well as BV capacities. We also apply the theory to prove a pointwise approximation result for functions of bounded variation.
Legal Crises in Public Health.

Since the onset of modern legal conceptions of “public health emergencies” (PHEs) in 2001, government officials at all levels have demonstrated a propensity to declare PHEs in response to a multitude of scenarios (e.g., emerging infectious diseases, natural disasters, terrorism events). Many of these declarations are justified by the circumstances, others perhaps less so. Yet, in addition to the rise of formal PHE declarations, officials, academics, media, and others are increasingly labeling multiple types of threats to human health. Pursuant to a “crisis du jour” mentality, Americans are deluged with an unending series of identified public health threats including diseases, conditions, acts, and behaviors. Against this barrage of exigencies one might perceive that primary health hazards stem largely from rapidly escalating conditions or events. Perceptions, however, do not match reality. Significantly greater risks to health arise from long-standing, entrenched causes. Like climbing ivies, these threats creep along year after year posing substantial societal harms while eluding meaningful law and policy responses centered on assuring the public’s health.
Online Learning for Network Constrained Demand Response Pricing in Distribution Systems

Flexible demand response (DR) resources can be leveraged to accommodate the stochasticity of some distributed energy resources. This paper develops an online learning approach that continuously estimates price sensitivities of residential DR participants and produces such price signals to the DR participants that ensure a desired level of DR capacity. The proposed learning approach incorporates the dispatch decisions on DR resources into the distributionally robust chance-constrained optimal power flow (OPF) framework. This integration is shown to adequately remunerate DR resources and co-optimize the dispatch of DR and conventional generation resources. The distributionally robust chance-constrained formulation only relies on empirical data acquired over time and makes no restrictive assumptions on the underlying distribution of the demand uncertainty. The distributional robustness also allows for robustifying the otpimal solution against systematically misestimating empirically learned parameters. The effectiveness of the proposed learning approach is shown via numerical experiments. The paper is accompanied by the code and data supplement released for public use.
When do appointments of corporate sustainability executives affect shareholder value

Over the last two decades, firms have been appointing corporate sustainability executives (CSEs) to be part of their top management teams. Although there is a vast literature on sustainable practices and their relationships with various measures of firm performance, little is known about the nature of the empirical link between CSE appointments and financial performance. We add to the understanding of this link by estimating the stock market reactions to a sample of 115 announcements of CSE appointments made by firms during the period 2000–2018. Our findings using event study methodology, followed by regression analyses, suggest that although the stock market reaction to CSE appointments is not significantly different from zero, the stock market reacts more, or less positively under certain firm- and industry-specific conditions. We find that the stock market reaction is more positive in instances where the announcing firms faced a prior adverse sustainability-related incident, and less positive when announcing firms operate in industries that face relatively greater levels of regulatory sanctions. Also, we find that the stock market reaction is more positive when firms announce CSE appointments with focused as compared to broad responsibilities. Additionally, we find that CSE appointments are associated with subsequent improvements in operating performance – partly driven by a decrease in total costs and partly by an increase in sales. Overall, our findings support the strategy of appointing CSEs to top management teams and enable executives and stakeholders to more deeply understand the shareholder value and operating performance effects of appointing CSEs.
Catalytic formation of narrow Nb nanowires inside carbon nanotubes

Abstract We propose a previously unexplored way to form Nb nanowires from NbCl3 molecules inside carbon nanotubes (CNTs). We have studied this reaction by ab initio density functional calculations and found it to be catalytically promoted in presence of graphitic carbon. Our results suggest that chemisorption of NbCl3 on the CNT is accompanied by a charge transfer of ≈ 0.5 electrons to the nanotube wall, which significantly weakens the Nb–Cl bond. We found that the bcc structure of Nb is not affected by the small diameter of the nanowire inside a CNT. We have also identified strong covalent bonds between the nanowires and the surrounding nanotube that are accompanied by a similar charge transfer of ≲ 0.5 e from surface Nb atoms to the nanotube. The large electronic density of states of bulk Nb at E F is not changed much in the confined geometry, suggesting that ultra-narrow nanowires of Nb may keep their superconducting behavior and form Josephson junctions in the quasi-1D geometry while being protected from the ambient by the enclosing CNT structure.
Supertranslation Hair of Schwarzschild Black Hole: A Wilson Line Perspective

We demonstrate within the quantum field theoretical framework that an asymptotic particle falling into the black hole implants soft graviton hair on the horizon, conforming with the classical proposal of Hawking, Perry and Strominger. A key ingredient to this result is the construction of gravitational Wilson line dressings of an infalling scalar field, carrying a definite horizon supertranslation charge. It is shown that a typical Schwarzschild state is degenerate, and can be labeled by different soft supertranslation hairs parametrized for radial trajectories by the mass and energy of the infalling particle and its asymptotic point of contact with the horizon. The supertranslation zero modes are also obtained in terms of zero-frequency graviton operators, and are shown to be the expected canonical partners of the linearized horizon charge that enlarge the horizon Hilbert space.
Covariant Radiative Transfer for Black Hole Spacetimes

It has now become possible to study directly, via numerical simulation, the evolution of relativistic, radiation-dominated flows around compact objects. With this in mind we set out explicitly covariant forms of the radiative transfer equation that are suitable for numerical integration in curved spacetime or flat spacetime in curvilinear coordinates. Our work builds on and summarizes in consistent form earlier work by Lindquist, Thorne, Morita and Kaneko, and others. We give explicitly the basic equations in spherical-polar coordinates for Minkowski space and the Kerr spacetime in Kerr-Schild coordinates.
Identifying the essential genes of Mycobacterium avium subsp. hominissuis with Tn-Seq using a rank-based filter procedure

Mycobacterium avium subsp. hominissuis (MAH) is increasingly recognized as a significant cause of morbidity, particularly in elderly patients or those with immune deficiency or underlying lung impairment. Disease due to MAH is particularly difficult to treat, often requiring years of antibiotic therapy. Identification of genes essential for MAH growth may lead to novel strategies for improving curative therapy. Here we have generated saturating genome-wide transposon mutant pools in a strain of MAH (MAC109) and developed a novel computational technique for classifying annotated genomic features based on the in vitro effect of transposon mutagenesis. Our findings may help guide future genetic and biochemical studies of MAH pathogenesis and aid in the identification of new drugs to improve the treatment of these serious infections.
Failure of Atovaquone Prophylaxis for Prevention of Toxoplasmosis in Hematopoietic Cell Transplant Recipients

Introduction Toxoplasma gondii DNA can be detected by polymerase chain reaction (PCR) in up to 16% of hematopoietic cell transplant (HCT) patients post-transplant with attributable mortality of 44-66%. The majority (73-87%) of cases result from reactivation in previously exposed (i.e., Toxoplasma seropositive) recipients and most (67-87%) occur in patients not receiving effective prophylaxis. Clinical data on the efficacy of atovaquone for primary prophylaxis of toxoplasmosis following HCT is scarce. Reports of breakthrough toxoplasmosis in patients receiving atovaquone have raised concerns about its efficacy in high-risk patients such as donor positive (D+)/recipient negative (R-) heart transplant and R+ allogeneic HCT recipients. Objectives We describe three HCT patients with breakthrough toxoplasmosis while receiving atovaquone prophylaxis. Methods & Results We identified 126 (94 allogeneic and 23 autologous) HCT patients (August 2016-June 2019) who received atovaquone for Pneumocystis jirovecii pneumonia (PJP) prophylaxis. Seroprevalence among allogeneic HCT recipients with known Toxoplasmaserostatus (n=84) was 37%. Among 31 Toxoplasma seropositive allogeneic HCT recipients, 14 received atovaquone as initial prophylaxis and 17 received only after developing intolerance to trimethoprim-sulfamethoxazole (TMP-SMX). Three (10%) patients had breakthrough Toxoplasma reactivation, two of them with central nervous system (CNS) disease. Median time to reactivation was 66 (range, 52-67) days post-transplant. In the two cases with positive PCR in blood, Toxoplasma DNA became undetectable at 8 and 20 days of therapy, suggesting that parasitemia can be successfully managed with preemptive treatment. All three patients died. Death of patient #2 was suspected to be due to disseminated toxoplasmosis as PCR blood was positive at time of death. Median time to death from diagnosis of Toxoplasma reactivation was 83 (range, 57-311) days. CMV viremia preceded Toxoplasmareactivation in all cases; CMV may have impacted infection risk . All three patients underwent T cell depletion during conditioning. Absolute and CD4 lymphopenia at the time of diagnosis were a shared feature in these cases (median ALC: 176/mm3). Conclusion Breakthrough toxoplasmosis is a potentially fatal complication in HCT patients receiving atovaquone prophylaxis. TMP-SMX should remain the prophylactic agent of choice and PCR-based pre-emptive therapy should be considered in the pre-engraftment period and in patients at risk not receiving TMP-SMX. Such preventive strategies could improve outcomes in vulnerable Toxoplasma seropositive HCT recipients.
Is Mining an Environmental Disamenity? Evidence from Resource Extraction Site Openings

Extractive industries are often challenged by nearby communities due to their environmental and social impacts. If proximity to resource extraction sites represents a disamenity to households, the opening of new mines should lead to a decrease in housing prices. Using evidence from more than 6000 new extraction sites in Chile, this study addresses whether the heavy environmental and social impacts of digging activities outweigh their local economic benefits to the housing market in emerging economies. Findings from a spatial difference-in-difference nearest-neighbor matching estimator reveal that households near mining activity get compensated with lower rental prices, mostly in places with high perceptions of exposure to environmental pollution. Further analysis suggests that this compensation is lower among new residents of mining towns, which constitutes evidence of a taste-based sorting across space. Results in this study bring to light the need of incorporating welfare effects of potential social and environmental disruptions in future studies addressing the economic impact of new mining operations.
Towards practical differential privacy for SQL queries

Differential privacy promises to enable general data analytics while protecting individual privacy, but existing differential privacy mechanisms do not support the wide variety of features and databases used in real-world SQL-based analytics systems.This paper presents the first practical approach for differential privacy of SQL queries. Using 8.1 million real-world queries, we conduct an empirical study to determine the requirements for practical differential privacy, and discuss limitations of previous approaches in light of these requirements. To meet these requirements we propose elastic sensitivity, a novel method for approximating the local sensitivity of queries with general equijoins. We prove that elastic sensitivity is an upper bound on local sensitivity and can therefore be used to enforce differential privacy using any local sensitivity-based mechanism.We build Flex, a practical end-to-end system to enforce differential privacy for SQL queries using elastic sensitivity. We demonstrate that FLEX is compatible with any existing database, can enforce differential privacy for real-world SQL queries, and incurs negligible (0.03%) performance overhead.
SPEECHMINER: A Framework for Investigating and Measuring Speculative Execution Vulnerabilities.

SPEculative Execution side Channel Hardware (SPEECH) Vulnerabilities have enabled the notorious Meltdown, Spectre, and L1 terminal fault (L1TF) attacks. While a number of studies have reported different variants of SPEECH vulnerabilities, they are still not well understood. This is primarily due to the lack of information about microprocessor implementation details that impact the timing and order of various micro-architectural events. Moreover, to date, there is no systematic approach to quantitatively measure SPEECH vulnerabilities on commodity processors. This paper introduces SPEECHMINER, a software framework for exploring and measuring SPEECH vulnerabilities in an automated manner. SPEECHMINER empirically establishes the link between a novel two-phase fault handling model and the exploitability and speculation windows of SPEECH vulnerabilities. It enables testing of a comprehensive list of exception-triggering instructions under the same software framework, which leverages covert-channel techniques and differential tests to gain visibility into the micro-architectural state changes. We evaluated SPEECHMINER on 9 different processor types, examined 21 potential vulnerability variants, confirmed various known attacks, and identified several new variants.