cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal rotation of a qubit under dynamic measurement and velocity control

In this article we explore a modification in the problem of controlling the rotation of a two level quantum system from an initial state to a final state in minimum time. Specifically we consider the case where the qubit is being weakly monitored — albeit with an assumption that both the measurement strength as well as the angular velocity are assumed to be control signals. This modification alters the dynamics significantly and enables the exploitation of the measurement backaction to assist in achieving the control objective. The proposed method yields a significant speedup in achieving the desired state transfer compared to previous approaches. These results are demonstrated via numerical solutions for an example problem on a single qubit.
Simplicial Polytope Complexes and Deloopings of $K$-theory

This paper is a continuation of our previous work in which we defined the notion of a polytope complex and its $K$-theory. In this paper we produce formulas for the delooping of a simplicial polytope complex and the cofiber of a morphism of simplicial polytope complexes. Along the way we also prove that the (classical and higher) scissors congruence groups of polytopes in a homogeneous $n$-manifold (with sufficient geometric data) are determined by its local properties.
RankDCG: Rank-Ordering Evaluation Measure

Ranking is used for a wide array of problems, most notably information retrieval (search). Kendall’s τ, Average Precision, and nDCG are a few popular approaches to the evaluation of ranking. When dealing with problems such as user ranking or recommendation systems, all these measures suffer from various problems, including the inability to deal with elements of the same rank, inconsistent and ambiguous lower bound scores, and an inappropriate cost function. We propose a new measure, a modification of the popular nDCG algorithm, named rankDCG, that addresses these problems. We provide a number of criteria for any effective ranking algorithm and show that only rankDCG satisfies them all. Results are presented on constructed and real data sets. We release a publicly available rankDCG evaluation package.
The critical exponent for continuous conventional powers of doubly nonnegative matrices

Abstract We prove that there exists an exponent beyond which all continuous conventional powers of n -by- n doubly nonnegative matrices are doubly nonnegative. We show that this critical exponent cannot be less than n - 2 and we conjecture that it is always n - 2 (as it is with Hadamard powering). We prove this conjecture when n 6 and in certain other special cases. We establish a quadratic bound for the critical exponent in general.
Exploration of Contextuality in a Psychophysical Double-Detection Experiment

The Contextuality-by-Default (CbD) theory allows one to separate contextuality from context-dependent errors and violations of selective influences (aka “no-signaling” or “no-disturbance” principles). This makes the theory especially applicable to behavioral systems, where violations of selective influences are ubiquitous. For cyclic systems with binary random variables, CbD provides necessary and sufficient conditions for noncontextuality, and these conditions are known to be breached in certain quantum systems. We apply the theory of cyclic systems to a psychophysical double-detection experiment, in which observers were asked to determine presence or absence of a signal property in each of two simultaneously presented stimuli. The results, as in all other behavioral and social systems previously analyzed, indicate lack of contextuality. The role of context in double-detection is confined to lack of selectiveness: the distribution of responses to one of the stimuli is influenced by the state of the other stimulus.
Some simple modules for classical groups and p-ranks of orthogonal and Hermitian geometries

Abstract We determine the characters of the simple composition factors and the submodule lattices of certain Weyl modules for classical groups. The results have several applications. The simple modules arise in the study of incidence systems in finite geometries and knowledge of their dimensions yields the p -ranks of these incidence systems.
On the subgroup generated by solutions of Pell's equation

Abstract Equivalence classes of solutions of the Diophantine equation a 2 + m b 2 = c 2 form an infinitely generated abelian group G m , where m is a fixed square-free positive integer. Solutions of Pell's equation x 2 − m y 2 = 1 generate a subgroup P m of G m . We prove that P m and G m / P m have infinite rank for all m > 1 . We also give several examples of m for which G m / P m has nontrivial torsion.
Light and Electromagnetic Waves Teaching in Engineering Education

Suggestions are made for physics laboratory exercises and the physics laboratory curricula are discussed for engineering majors where the properties of light and electromagnetic waves are studied in parallel. It is shown that there are important educational advantages of an experimental study of the properties of microwaves as an example of electromagnetic waves simultaneously with the properties of light: on the one hand, it enables visualisation of the properties of microwaves, and on the other, it provides evidence that light is an electromagnetic wave.
On the fundamental groups of compact Sasakian manifolds

We study the fundamental groups of compact Sasakian manifolds, which we call Sasaki groups. It is shown that all known K$\ddot{a}$hler groups are Sasaki, in particular, all finite groups are Sasaki. On the other hand, we show there exists many restrictions on the fundamental groups of compact Sasakian manifolds. We also study the Abel-Jacobi map of a compact Sasakian manifold and its applications to Sasaki groups.
SBG-sketch: a self-balanced sketch for labeled-graph stream summarization

Applications in various domains rely on processing graph streams, e.g., communication logs of a cloud-troubleshooting system, road-network traffic updates, and interactions on a social network. A labeled-graph stream refers to a sequence of streamed edges of distinct types that form a labeled graph. Due to the large volume and high velocity of these streams, it is often more practical to incrementally build a lossy-compressed version of the graph, and use this lossy version to approximately evaluate graph queries. Challenges arise when the queries are unknown in advance but are associated with filtering predicates based on edge labels. Surprisingly common, and especially challenging, are labeled-graph streams that have highly skewed and unpredictable label-distributions. This paper introduces Self-Balanced Graph Sketch (SBG-Sketch, for short), a graph sketch for summarizing and querying labeled-graph streams, coping with highly imbalanced labels. SBG-Sketch maintains synopsis for both the edge attributes as well as the topology of the streamed graph. SBG-Sketch allows efficient processing of traversal queries, e.g., reachability queries. Experimental results over a variety of real labeled-graph streams show SBG-Sketch to reduce the estimation errors of state-of-the-art methods by up to 99%.
On the discrepancy between two Zagreb indices

Abstract We examine the quantity S ( G ) = ∑ u v ∈ E ( G ) min ( deg u , deg v ) over sets of graphs with a fixed number of edges. The main result shows the maximum possible value of S ( G ) is achieved by three different classes of constructions, depending on the distance between the number of edges and the nearest triangular number. Furthermore we determine the maximum possible value when the set of graphs is restricted to be bipartite, a forest, or to be planar given sufficiently many edges. The quantity S ( G ) corresponds to the difference between two well studied indices, the irregularity of a graph and the sum of the squares of the degrees in a graph. These are known as the first and third Zagreb indices in the area of mathematical chemistry.
Graph partition strategies for generalized mean field inference

An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well as over the choice of tractable families used for the variational approximation. In this paper, we present a novel combination of graph partitioning algorithms with a generalized mean field (GMF) inference algorithm. This combination optimizes over disjoint clustering of variables and performs inference using those clusters. We provide a formal analysis of the relationship between the graph cut and the GMF approximation, and explore several graph partition strategies empirically. Our empirical results provide rather clear support for a weighted version of MinCut as a useful clustering algorithm for GMF inference, which is consistent with the implications from the formal analysis.
Coefficients of a relative of cyclotomic polynomials

Let $N=p_1p_2... p_n$ be a product of $n$ distinct primes. Define $P_N(x)$ to be the polynomial $(1-x^N)\prod_{1\leq i<j\leq n}(1-x^{N/(p_ip_j)})/\prod_{i=1}^n (1-x^{N/p_i})$. (When $n=2$, $P_{pq}(x)$ is the $pq$-th cyclotomic polynomial, and when $n=3$, $P_{pqr}(x)$ is $(1-x)$ times the $pqr$-th cyclotomic polynomial.) Let the height of a polynomial be the maximum absolute value of one of its coefficients. It is well known that the height of $\Phi_{pq}(x)$ is 1, and Gallot and Moree showed that the same is true for $P_{pqr}(x)$ when $n=3$. We show that the coefficients of $P_N(x)$ depend mainly on the relative order of sums of residues of the form $p_j^{-1} \pmod {p_i}$. This allows us to explicitly describe the coefficients of $P_N(x)$ when $n=3$ and show that the height of $P_N(x)$ is at most 2 when $n=4$. We also show that for any $n$ there exist $P_N(x)$ with height 1 but that in general the maximum height of $P_N(x)$ is a function depending only on $n$ with growth rate $2^{n^2/2+O(n\log n)}$.
On the asymptotic distribution of parameters in random weighted staircase tableaux

In this paper, we study staircase tableaux, a combinatorial object introduced due to its connections with the asymmetric exclusion process (ASEP) and Askey-Wilson polynomials. Due to their interesting connections, staircase tableaux have been the object of study in many recent papers. More specific to this paper, the distribution of various parameters in random staircase tableaux has been studied. There have been interesting results on parameters along the main diagonal, however, no such results have appeared for other diagonals. It was conjectured that the distribution of the number of symbols along the kth diagonal is asymptotically Poisson as k and the size of the tableau tend to infinity. We partially prove this conjecture; more specifically we prove it for the second and the third main diagonal.
Affine space over triangulated categories: A further invitation to Grothendieck derivators

Abstract We propose a construction of affine space (or “polynomial rings”) over a triangulated category, in the context of stable derivators.
Achieving optimal backlog in multi-processor cup games

Many problems in processor scheduling, deamortization, and buffer management can be modeled as single- and multi-processor cup games. At the beginning of the single-processor n-cup game, all cups are empty. In each step of the game, a filler distributes 1−є units of water among the cups, and then an emptier selects a cup and removes up to 1 unit of water from it. The goal of the emptier is to minimize the amount of water in the fullest cup, also known as the backlog. The greedy algorithm (i.e., empty from the fullest cup) is known to achieve backlog O(logn), and no deterministic algorithm can do better. We show that the performance of the greedy algorithm can be exponentially improved with a small amount of randomization: After each step and for any k ≥ Ω(logє−1), the emptier achieves backlog at most O(k) with probability at least 1 −O(2−2k). We call our algorithm the smoothed greedy algorithm because if follows from a smoothed analysis of the (standard) greedy algorithm. In each step of the p-processor n-cup game, the filler distributes p(1−є) unit of water among the cups, with no cup receiving more than 1−δ units of water, and then the emptier selects p cups and removes 1 unit of water from each. Proving nontrivial bounds on the backlog for the multi-processor cup game has remained open for decades. We present a simple analysis of the greedy algorithm for the multi-processor cup game, establishing a backlog of O(є−1 logn), as long as δ > 1/poly(n). Turning to randomized algorithms, we find that the backlog drops to constant. Specifically, we show that if є and δ satisfy reasonable constraints, then there exists an algorithm that bounds the backlog after a given step by 3 with probability at least 1 − O(exp(−Ω(є2p)). We prove that our results are asymptotically optimal for constant є, in the sense that no algorithms can achieve better bounds, up to constant factors in the backlog and in p. Moreover, we prove robustness results, demonstrating that our randomized algorithms continue to behave well even when placed in bad starting states.
Boosting with the Logistic Loss is Consistent

This manuscript provides optimization guarantees, generalization bounds, and statistical consistency results for AdaBoost variants which replace the exponential loss with the logistic and similar losses (specifically, twice differentiable convex losses which are Lipschitz and tend to zero on one side). The heart of the analysis is to show that, in lieu of explicit regularization and constraints, the structure of the problem is fairly rigidly controlled by the source distribution itself. The first control of this type is in the separable case, where a distribution-dependent relaxed weak learning rate induces speedy convergence with high probability over any sample. Otherwise, in the nonseparable case, the convex surrogate risk itself exhibits distribution-dependent levels of curvature, and consequently the algorithm's output has small norm with high probability.
Surface temperatures in New York City: Geospatial data enables the accurate prediction of radiative heat transfer

Despite decades of research seeking to derive the urban energy budget, the dynamics of thermal exchange in the densely constructed environment is not yet well understood. Using New York City as a study site, we present a novel hybrid experimental-computational approach for a better understanding of the radiative heat transfer in complex urban environments. The aim of this work is to contribute to the calculation of the urban energy budget, particularly the stored energy. We will focus our attention on surface thermal radiation. Improved understanding of urban thermodynamics incorporating the interaction of various bodies, particularly in high rise cities, will have implications on energy conservation at the building scale, and for human health and comfort at the urban scale. The platform presented is based on longwave hyperspectral imaging of nearly 100 blocks of Manhattan, in addition to a geospatial radiosity model that describes the collective radiative heat exchange between multiple buildings. Despite assumptions in surface emissivity and thermal conductivity of buildings walls, the close comparison of temperatures derived from measurements and computations is promising. Results imply that the presented geospatial thermodynamic model of urban structures can enable accurate and high resolution analysis of instantaneous urban surface temperatures.
Variational Gram Functions: Convex Analysis and Optimization

We introduce a new class of convex penalty functions, called variational Gram functions (VGFs), that can promote pairwise relations, such as orthogonality, among a set of vectors in a vector space. These functions can serve as regularizers in convex optimization problems arising from hierarchical classification, multitask learning, and estimating vectors with disjoint supports, among other applications. We study convexity of VGFs, and give characterizations for their convex conjugates, subdifferentials, proximal operators, and related quantities. We discuss efficient optimization algorithms for regularized loss minimization problems where the loss admits a common, yet simple, variational representation and the regularizer is a VGF. These algorithms enjoy a simple kernel trick, an efficient line search, as well as computational advantages over first order methods based on the subdifferential or proximal maps. We also establish a general representer theorem for such learning problems. Last, numerical experi...
Clustered Hyperbolic Categories

We introduce a class of categories, called clustered hyperbolic categories, which are constructed from a categorized version of preseeds called categorical preseeds using categorical mutations that...
Tight Bounds on the Threshold for Permuted k-Colorability

If each edge (u,v) of a graph G = (V,E) is decorated with a permutation π u,v of k objects, we say that it has a permuted k-coloring if there is a coloring σ:V → {1,…,k} such that σ(v) ≠ π u,v (σ(u)) for all (u,v) ∈ E. Based on arguments from statistical physics, we conjecture that the threshold d k for permuted k-colorability in random graphs G(n,m = dn/2), where the permutations on the edges are uniformly random, is equal to the threshold for standard graph k-colorability. The additional symmetry provided by random permutations makes it easier to prove bounds on d k . By applying the second moment method with these additional symmetries, and applying the first moment method to a random variable that depends on the number of available colors at each vertex, we bound the threshold within an additive constant. Specifically, we show that for any constant e > 0, for sufficiently large k we have
Incidence Theorems and Their Applications

1: Overview 2: Counting Incidences Over the Reals 3: Counting Incidences Over Finite Fields 4: Kakeya Sets 5: Sylvester-Gallai Type Problems. References.
Applications of psychological science for actionable analytics

According to psychological scientists, humans understand models that most match their own internal models, which they characterize as lists of "heuristic"s (i.e. lists of very succinct rules). One such heuristic rule generator is the Fast-and-Frugal Trees (FFT) preferred by psychological scientists. Despite their successful use in many applied domains, FFTs have not been applied in software analytics. Accordingly, this paper assesses FFTs for software analytics. We find that FFTs are remarkably effective in that their models are very succinct (5 lines or less describing a binary decision tree) while also outperforming result from very recent, top-level, conference papers. Also, when we restrict training data to operational attributes (i.e., those attributes that are frequently changed by developers), the performance of FFTs are not effected (while the performance of other learners can vary wildly). Our conclusions are two-fold. Firstly, there is much that software analytics community could learn from psychological science. Secondly, proponents of complex methods should always baseline those methods against simpler alternatives. For example, FFTs could be used as a standard baseline learner against which other software analytics tools are compared.
Overlapping localized exponential time differencing methods for diffusion problems

The paper is concerned with overlapping domain decomposition and exponential time differencing for the diffusion equation discretized in space by cell-centered finite differences. Two localized exponential time differencing methods are proposed to solve the fully discrete problem: the first method is based on Schwarz iteration applied at each time step and involves solving stationary problems in the subdomains at each iteration, while the second method is based on the Schwarz waveform relaxation algorithm in which time-dependent subdomain problems are solved at each iteration. The convergence of the associated iterative solutions to the corresponding fully discrete multidomain solution and to the exact semi-discrete solution is rigorously proved. Numerical experiments are carried out to confirm theoretical results and to compare the performance of the two methods.
Unimodal Gorenstein h-vectors without the Stanley–Iarrobino property

The study of the h-vectors of graded Gorenstein algebras is an important topic in combinatorial commutative algebra, which despite the large amount of literature produced during the last several ye...
Gowers' Ramsey Theorem for generalized tetris operations

We prove a generalization of Gowers' theorem for FIN_k where, instead of the single tetris operation T:FIN_k→FIN_(k−1), one considers all maps from FIN_k to FIN_j for 0≤j≤k arising from nondecreasing surjections f:{0,1,…,k}→{0,1,…,j}. This answers a question of Bartosova and Kwiatkowska. We also describe how to prove a common generalization of such a result and the Galvin–Glazer–Hindman theorem on finite products, in the setting of layered partial semigroups introduced by Farah, Hindman, and McLeod.
Hybrid optimization and Bayesian inference techniques for a non‐smooth radiation detection problem

In this investigation, we propose several algorithms to recover the location and intensity of a radiation source located in a simulated 250 m x 180 m block in an urban center based on synthetic measurements. Radioactive decay and detection are Poisson random processes, so we employ likelihood functions based on this distribution. Due to the domain geometry and the proposed response model, the negative logarithm of the likelihood is only piecewise continuous differentiable, and it has multiple local minima. To address these difficulties, we investigate three hybrid algorithms comprised of mixed optimization techniques. For global optimization, we consider Simulated Annealing (SA), Particle Swarm (PS) and Genetic Algorithm (GA), which rely solely on objective function evaluations; i.e., they do not evaluate the gradient in the objective function. By employing early stopping criteria for the global optimization methods, a pseudo-optimum point is obtained. This is subsequently utilized as the initial value by the deterministic Implicit Filtering method (IF), which is able to find local extrema in non-smooth functions, to finish the search in a narrow domain. These new hybrid techniques combining global optimization and Implicit Filtering address difficulties associated with the non-smooth response, and their performances are shown to significantly decrease the computational time over the global optimization methods alone. To quantify uncertainties associated with the source location and intensity, we employ the Delayed Rejection Adaptive Metropolis (DRAM) and DiffeRential Evolution Adaptive Metropolis (DREAM) algorithms. Marginal densities of the source properties are obtained, and the means of the chains' compare accurately with the estimates produced by the hybrid algorithms.
Formulas for non-holomorphic Eisenstein series and for the Riemann zeta function at odd integers

New expressions are given for the Fourier expansions of non-holomorphic Eisenstein series with weight k. Among other applications, this leads to non-holomorphic analogs of formulas of Ramanujan, Grosswald and Berndt containing Eichler integrals of holomorphic Eisenstein series.
On Nontangential Limits and Shift Invariant Subspaces

In 1998, Conway and Yang wrote a paper (Holomorphic spaces, MSRI Publications, vol 33, pp 201–209, 1998) in which they posed a number of open questions regarding the shift on $$P^t(\mu )$$ spaces. A few of these have been completely resolved, while at least one remains wide open. In this paper, we review some of the solutions, mention some alternate approaches and discuss further the problem that remains unsolved.
Temperedness of measures defined by polynomial equations over local fields

We investigate the asymptotic growth of the canonical measures on the fibers of morphisms between vector spaces over local fields of arbitrary characteristic. For non-archimedean local fields we use a version of the Łojasiewicz inequality (\cite{lojasiewicz1959}, \cite{hormander1958division}) which follows from Greenberg \cite{greenberg1966rational}, \cite{bollaerts1990estimate}, together with the theory of the Brauer group of local fields to construct definite forms of arbitrarily high degree, and to transfer questions at infinity to questions near the origin. We then use these to generalize results of H{o}rmander \cite{hormander1958division} on estimating the growth of polynomials at infinity in terms of the distance to their zero loci. Specifically, when a fiber corresponds to a non-critical value which is stable, i.e. remains non-critical under small perturbations, we show that the canonical measure on the fiber is tempered, which generalizes results of Igusa and Raghavan \cite{igusa1978lectures}, and Virtanen and Weisbart \cite{virtanen2014elementary}.
Delayed Impact of Fair Machine Learning.

Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.
LVIS: Learning from Value Function Intervals for Contact-Aware Robot Controllers

Guided policy search is a popular approach for training controllers for high-dimensional systems, but it has a number of pitfalls. Non-convex trajectory optimization has local minima, and non-uniqueness in the optimal policy itself can mean that independently-optimized samples do not describe a coherent policy from which to train. We introduce LVIS, which circumvents the issue of local minima through global mixed-integer optimization and the issue of non-uniqueness through learning the optimal value function rather than the optimal policy. To avoid the expense of solving the mixed-integer programs to full global optimality, we instead solve them only partially, extracting intervals containing the true cost-to-go from early termination of the branch-and-bound algorithm. These interval samples are used to weakly supervise the training of a neural net which approximates the true cost-to-go. Online, we use that learned cost-to-go as the terminal cost of a one-step model-predictive controller, which we solve via a small mixed-integer optimization. We demonstrate LVIS on piecewise affine models of a cart-pole system with walls and a planar humanoid robot and show that it can be applied to a fundamentally hard problem in feedback control–control through contact.
Chemical Induction of Aminoglycoside Uptake Overcomes Antibiotic Tolerance and Resistance in Staphylococcus aureus

Summary Aminoglycoside antibiotics require proton motive force (PMF) for bacterial internalization. In non-respiring populations, PMF drops below the level required for drug influx, limiting the utility of aminoglycosides against strict and facultative anaerobes. We recently demonstrated that rhamnolipids (RLs), biosurfactant molecules produced by Pseudomonas aeruginosa, potentiate aminoglycoside activity against Staphylococcus aureus. Here, we demonstrate that RLs induce PMF-independent aminoglycoside uptake to restore sensitivity to otherwise tolerant persister, biofilm, small colony variant, and anaerobic populations of S. aureus. Furthermore, we show that this approach represses the rise of resistance, restores sensitivity to highly resistant clinical isolates, and is effective against other Gram-positive pathogens. Finally, while other membrane-acting agents can synergize with aminoglycosides, induction of PMF-independent uptake is uncommon, and distinct to RLs among several compounds tested. In all, small-molecule induction of PMF-independent aminoglycoside uptake circumvents phenotypic tolerance, overcomes genotypic resistance, and expands the utility of aminoglycosides against intrinsically recalcitrant bacterial populations.
Gender Integration and the Promotion of Inclusive Classroom Climates.

The purpose of this article is to highlight the important role that gender plays in organizing and affecting the quality of the classroom climate. We review research showing how students’ peer rela...
A parallel shared-memory implementation of a high-order accurate solution technique for variable coefficient Helmholtz problems

Abstract The recently developed Hierarchical Poincare–Steklov (HPS) method is a high-order discretization technique that comes with a direct solver. Results from previous papers demonstrate the method’s ability to solve Helmholtz problems to high accuracy without the so-called pollution effect. While the asymptotic scaling of the direct solver’s computational cost is the same as the nested dissection method, serial implementations of the solution technique are not practical for large scale numerical simulations. This manuscript presents the first parallel implementation of the HPS method. Specifically, we introduce an approach for a shared memory implementation of the solution technique utilizing parallel linear algebra. This approach is the foundation for future large scale simulations on supercomputers and clusters with large memory nodes. Performance results on a desktop computer (resembling a large memory node) are presented.
Likelihood Ratios for Out-of-Distribution Detection

Discriminative neural networks offer little or no performance guarantees when deployed on data not generated by the same process as the training distribution. On such out-of-distribution (OOD) inputs, the prediction may not only be erroneous, but confidently so, limiting the safe deployment of classifiers in real-world applications. One such challenging application is bacteria identification based on genomic sequences, which holds the promise of early detection of diseases, but requires a model that can output low confidence predictions on OOD genomic sequences from new bacteria that were not present in the training data. We introduce a genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem. We investigate deep generative model based approaches for OOD detection and observe that the likelihood score is heavily affected by population level background statistics. We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics. We benchmark the OOD detection performance of the proposed method against existing approaches on the genomics dataset and show that our method achieves state-of-the-art performance. Finally, we demonstrate the generality of the proposed method by showing that it significantly improves OOD detection when applied to deep generative models of images.
Russian Election Interference and Race-Baiting

Russian interference in the 2016 United States presidential election exposed the nation’s vulnerability to targeted campaign disruption by foreign intelligence actors through social media. The Russian cyber disinformation campaign exploited racial divisions in the United States to undermine public confidence in American electoral processes and institutions, revealing how those divisions can be weaponized. The campaign fed on racial divisions arising from institutionalized state practices that have a disparate discriminatory effect on racial minorities. Successful in their online interference in 2016, Russian operatives continued to stoke these divisions in the 2018 midterm election and have begun to do so in the 2020 presidential election campaign. Russia will continue to stir racial division in future elections, and other states may follow suit. To combat this threat, reframing the manner in which national security institutions address matters of race is necessary. This Article advocates that national security institutions adopt an explicit “racism as national security threat” framework in place of the implicit “minority race as threat” framework that has previously shaped national security institutions’ behavior. It traces how a minority race as threat framework has historically guided national security institutional action in significant ways. Further, it elucidates how a racism as national security threat framework promotes American antidiscrimination law and international human rights law, and how the strategic retrenchment of policies, programs, and practices that engender racial discrimination will reduce American vulnerability to foreign exploitation. Ultimately, this Article seeks to popularize the understanding that racism subverts American national security, and frame the curtailment of institutionalized racism as a national security priority of the United States.
Triton shells of intact erythrocytes

About 40% of human erythrocyte membrane protein is resistant to solubilization in 0.5% Triton X-114. These components comprise a structure called a Triton shell roughly similar in size and shape to the original erythrocyte and thus constitute a cytoskeleton. With increasing concentrations of Triton the lipid content of the Triton shell decreases dramatically, whereas the majority of the protein components remain constant. Exceptions to this rule include proteins contained in band 3, the presumed anion channel, and in band 4 which decrease with increasing Triton concentration. The Triton-insoluble complex includes spectrin (bands 1 and 2), actin (band 5), and bands 3' and 7. Component 3' has an apparent molecular weight of 88,000 daltons as does 3; but unlike 3, it is insensitive to protease treatment of the intact cell, has a low extinction coefficient at 280 nm, and is solubilized from the shells in alkaline water solutions. Component 7 also has a low extinction coefficient at 280 nm. Spectrin alone is solubilized from the Triton shells in isotonic media. The solubilized spectrin contains no bound Triton and coelectrophoreses with spectrin eluted in hypotonic solutions from ghosts. Electron micrographs of fixed Triton shells stained with uranyl acetate show the presence of numerous filaments which appear beaded and are 80--120 A in diameter. The filaments cannot be composed mainly af actin, but enough spectrin is present to form the filaments. Triton shells may provide an excellent source of material useful in the investigation of the erythrocyte cytoskeleton.
Spiking Neural Networks for early prediction in human–robot collaboration:

This article introduces the Turn-Taking Spiking Neural Network (TTSNet), which is a cognitive model to perform early turn-taking prediction about a human or agent’s intentions. The TTSNet framework...
The Inferential Language Comprehension (iLC) Framework: Supporting Children's Comprehension of Visual Narratives

We present an integrated theoretical framework guiding the use of visual narratives in educational settings. We focus specifically on the use of static and dynamic visual narratives to teach and assess inference skills in young children and discuss evidence to support the efficacy of this approach. In doing so, first we review the basis of the integrated framework, which builds on major findings of cognitive, developmental, and language research highlighting that (a) inference skills can be developed in non-reading contexts using different media, (b) inference skills can transfer across different media, and (c) inference skills can be improved using questioning that includes scaffolding and specific feedback. Second, we review instructional and assessment approaches that align with the proposed framework; these approaches are designed to teach or assess inference making skills using visual narratives and interactive questioning. In this context, we discuss how these approaches leverage the unique affordances of static and dynamic visual narratives with respect to unit of meaning (by increasing opportunities to generate inferences), multimodality (by providing opportunities to generate inferences of higher complexity than text), and vocabulary/knowledge demands (by providing vocabulary/knowledge support), while also reviewing evidence for their usability, feasibility, and efficacy to improve educational outcomes. We conclude with important theoretical and practical questions about future work in this area.
Goal congruency dominates reward value in accounting for behavioral and neural correlates of value-based decision-making.

When choosing between options, whether menu items or career paths, we can evaluate how rewarding each one will be, or how congruent it is with our current choice goal (e.g., to point out the best option or the worst one.). Past decision-making research interpreted findings through the former lens, but in these experiments the most rewarding option was always most congruent with the task goal (choosing the best option). It is therefore unclear to what extent expected reward vs. goal congruency can account for choice value findings. To deconfound these two variables, we performed three behavioral studies and an fMRI study in which the task goal varied between identifying the best vs. the worst option. Contrary to prevailing accounts, we find that goal congruency dominates choice behavior and neural activity. We separately identify dissociable signals of expected reward. Our findings call for a reinterpretation of previous research on value-based choice. Decision-making research has confounded the reward value of options with their goal-congruency, as the task goal was always to pick the most rewarding option. Here, authors separately asked participants to select the least rewarding of a set of options, revealing a dominant role for goal congruency.
The Cost of Independence: Evidence from Companies' Decisions to Dismiss Audit Firms as Tax-Service Providers

This study examines whether companies' decisions to dismiss or substantially reduce reliance on their audit firms as tax-service providers in the wake of the Sarbanes-Oxley Act affect tax avoidance...
Narrowing Vaccination Exemption Laws: Lessons From California and Beyond

Delamater and colleagues projected the percentage of California schoolchildren with a vaccination exemption under 3 scenarios: California law as it has stood since the 2015 passage of Senate Bill 2...
Shift in bacterial taxa precedes morphological plasticity in a larval echinoid

Morphological plasticity is an adaptive response to heterogenous environments when a fitness advantage is conferred. Larval sea urchins, for example, are hypothesized to increase individual fitness in dilute feeding environments by elongating their feeding structure relative to their body size. Morphological plasticity for larval sea urchins is also coupled with significant shifts in the associated bacterial community, but whether this change to the associated microbiota occurs before, during, or following the expression of morphological plasticity is unclear. Using the sea urchin Lytechinus variegatus, we compare the temporal pattern of the microbial community and how it relates to the timing of morphological plasticity for larvae cultured in different food concentrations. From prefeeding through the expression of morphological plasticity, we observed that the microbiota associated with L. variegatus larvae exhibit a four-stage successional pattern, where changes in this community shift prior to the expression of the environment-specific morphology. The high food treatment, which results in the short-arm phenotype, deviated from the microbial trajectory of larval siblings prior to measurable morphological plasticity. These data suggest that a holobiont may exhibit shifts in the associated bacterial community corresponding with variation in the feeding environment that could occur in anticipation of or prior to morphological plasticity.
Stack Pressure Considerations for Room Temperature All-Solid-State Lithium Metal Batteries

Author(s): Doux, Jean-Marie; Nguyen, Han; Tan, Darren HS; Banerjee, Abhik; Wang, Xuefeng; Wu, Erik A; Jo, Chiho; Yang, Hedi; Meng, Ying Shirley
Are Government Owned Investment Funds Created Equal? Evidence from Sovereign Wealth Fund Real Estate Acquisitions

Sovereign Wealth Funds (SWFs) are an institutional investor class about which relatively little is known. Even though they have trillions of dollars in assets under management, their (typically) highly secretive nature renders them difficult to analyze in an academic context. We utilize transactional data from the Sovereign Wealth Fund Institute to provide the first academic analysis of SWF real estate investment activity of which we are aware. To better understand this growing investor class, we compare SWFs with their most closely related institutional group, public pension funds (PPFs). While both SWFs and PPFs are state owned investment funds, we find SWFs have lower Stone and Truman (2016) best practice scores (based on fund structure, governance, transparency and accountability, and behavior.) Further, while both SWFs and PPFs show increasing levels of cross-border real estate investment, SWFs are significantly more likely than PPFs to invest across international borders. We find the percentage of SWF cross-border real estate investment to be substantially higher than the percentage of SWF cross-border investment in public and private equity documented in other studies. Moreover, in a subsample of acquisitions in the U.S., cross-border real estate investments are in locations with lower capitalization rates than domestic acquisitions for both SWFs and PPFs, and there is no discernable difference in rates across the two fund types, on average.
The Segal-Bargmann transform on classical matrix Lie groups

Abstract We study the complex-time Segal-Bargmann transform B s , τ K N on a compact type Lie group K N , where K N is one of the following classical matrix Lie groups: the special orthogonal group SO ( N , R ) , the special unitary group SU ( N ) , or the compact symplectic group Sp ( N ) . Our work complements and extends the results of Driver, Hall, and Kemp on the Segal-Bargman transform for the unitary group U ( N ) . We provide an effective method of computing the action of the Segal-Bargmann transform on trace polynomials, which comprise a subspace of smooth functions on K N extending the polynomial functional calculus. Using these results, we show that as N → ∞ , the finite-dimensional transform B s , τ K N has a meaningful limit G s , τ which can be identified as an operator on the space of complex Laurent polynomials.
Stochasticity of replication forks' speeds plays a key role in the dynamics of DNA replication.

Eukaryotic DNA replication is elaborately orchestrated to duplicate the genome timely and faithfully. Replication initiates at multiple origins from which replication forks emanate and travel bi-directionally. The complex spatio-temporal regulation of DNA replication remains incompletely understood. To study it, computational models of DNA replication have been developed in S. cerevisiae. However, in spite of the experimental evidence of forks’ speed stochasticity, all models assumed that forks’ speeds are the same. Here, we present the first model of DNA replication assuming that speeds vary stochastically between forks. Utilizing data from both wild-type and hydroxyurea-treated yeast cells, we show that our model is more accurate than models assuming constant forks’ speed and reconstructs dynamics of DNA replication faithfully starting both from population-wide data and data reflecting fork movement in individual cells. Completion of replication in a timely manner is a challenge due to its stochasticity; we propose an empirically derived modification to replication speed based on the distance to the approaching fork, which promotes timely completion of replication. In summary, our work discovers a key role that stochasticity of the forks’ speed plays in the dynamics of DNA replication. We show that without including stochasticity of forks’ speed it is not possible to accurately reconstruct movement of individual replication forks, measured by DNA combing.
The olfactory basis of orchid pollination by mosquitoes

Mosquitoes are important vectors of disease and require sources of carbohydrates for reproduction and survival. Unlike host-related behaviors of mosquitoes, comparatively less is understood about the mechanisms involved in nectar-feeding decisions, or how this sensory information is processed in the mosquito brain. Here we show that Aedes spp. mosquitoes, including Aedes aegypti, are effective pollinators of the Platanthera obtusata orchid, and demonstrate this mutualism is mediated by the orchid's scent and the balance of excitation and inhibition in the mosquito's antennal lobe (AL). The P. obtusata orchid emits an attractive, nonanal-rich scent, whereas related Platanthera species-not visited by mosquitoes-emit scents dominated by lilac aldehyde. Calcium imaging experiments in the mosquito AL revealed that nonanal and lilac aldehyde each respectively activate the LC2 and AM2 glomerulus, and remarkably, the AM2 glomerulus is also sensitive to N,N-diethyl-meta-toluamide (DEET), a mosquito repellent. Lateral inhibition between these 2 glomeruli reflects the level of attraction to the orchid scents. Whereas the enriched nonanal scent of P. obtusata activates the LC2 and suppresses AM2, the high level of lilac aldehyde in the other orchid scents inverts this pattern of glomerular activity, and behavioral attraction is lost. These results demonstrate the ecological importance of mosquitoes beyond operating as disease vectors and open the door toward understanding the neural basis of mosquito nectar-seeking behaviors.
Dart: Divide and Specialize for Fast Response to Congestion in RDMA-Based Datacenter Networks

Though Remote Direct Memory Access (RDMA) promises to reduce datacenter network latencies significantly compared to TCP (e.g., 10 $\times$ ), end-to-end congestion control in the presence of incasts is a challenge. Targeting the full generality of the congestion problem, previous schemes rely on slow, iterative convergence to the appropriate sending rates (e.g., TIMELY takes 50 RTTs). Several papers have shown that even in oversubscribed datacenter networks most congestion occurs at the receiver. Accordingly, we propose a divide-and-specialize approach, called Dart , which isolates the common case of receiver congestion and further subdivides the remaining in-network congestion into the simpler spatially-localized and the harder spatially-dispersed cases. For receiver congestion, we propose direct apportioning of sending rates (DASR) in which a receiver for $n$ senders directs each sender to cut its rate by a factor of $n$ , converging in only one RTT. For the spatially-localized case, Dart provides fast (under one RTT) response by adding novel switch hardware for in-order flow deflection (IOFD) because RDMA disallows packet reordering on which previous load balancing schemes rely. For the uncommon spatially-dispersed case, Dart falls back to DCQCN. Small-scale testbed measurements and at-scale simulations, respectively, show that Dart achieves 60% (2.5 $\times$ ) and 79% (4.8 $\times$ ) lower $99^{th}$ -percentile latency, and similar and 58% higher throughput than InfiniBand, and TIMELY and DCQCN.