cssi/SciSciGPT-SciSciNet huggingface dataset:
Transmission Capacity of Wireless Networks

Transmission capacity (TC) is a performance metric for wireless networks that measures the spatial intensity of successful transmissions per unit area, subject to a constraint on the permissible outage probability (where outage occurs when the signal to interference plus noise ratio (SINR) at a receiver is below a threshold). This volume gives a unified treatment of the TC framework that has been developed by the authors and their collaborators over the past decade. The mathematical framework underlying the analysis (reviewed in Section 2) is stochastic geometry: Poisson point processes model the locations of interferers, and (stable) shot noise processes represent the aggregate interference seen at a receiver. Section 3 presents TC results (exact, asymptotic, and bounds) on a simple model in order to illustrate a key strength of the framework: analytical tractability yields explicit performance dependence upon key model parameters. Section 4 presents enhancements to this basic model — channel fading, variable link distances (VLD), and multihop. Section 5 presents four network design case studies well-suited to TC: (i) spectrum management, (ii) interference cancellation, (iii) signal threshold transmission scheduling, and (iv) power control. Section 6 studies the TC when nodes have multiple antennas, which provides a contrast vs. classical results that ignore interference.
Load forecasting based distribution system network reconfiguration — A distributed data-driven approach

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.
Twisting of Siegel paramodular forms

Let Sk(Γpara(N)) be the space of Siegel paramodular forms of level N and weight k. Fix an odd prime p ∤ N and let χ be a nontrivial quadratic Dirichlet character mod p. Based on [Twisting of paramodular vectors, Int. J. Number Theory 10 (2014) 1043–1065], we define a linear twisting map &#x1d4af;χ : Sk(Γpara(N)) → Sk(Γpara(Np4)). We calculate an explicit expression for this twist, give the commutation relations of this map with the Hecke operators and Atkin–Lehner involution for primes l≠p, and prove that the L-function of the twist has the expected form.
Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with a wide range of assumptions and applicability. Patterns of Scalable Bayesian Inference seeks to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. It examines how these techniques can be scaled up to larger problems and scaled out across parallel computational resources. It reviews existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, it characterizes the general principles that have proven successful for designing scalable inference procedures and addresses some of the significant open questions and challenges.
Compact NUMA-aware Locks

Modern multi-socket architectures exhibit non-uniform memory access (NUMA) behavior, where access by a core to data cached locally on a socket is much faster than access to data cached on a remote socket. Prior work offers several efficient NUMA-aware locks that exploit this behavior by keeping the lock ownership on the same socket, thus reducing remote cache misses and inter-socket communication. Virtually all those locks, however, are hierarchical in their nature, thus requiring space proportional to the number of sockets. The increased memory cost renders NUMA-aware locks unsuitable for systems that are conscious to space requirements of their synchronization constructs, with the Linux kernel being the chief example. In this work, we present a compact NUMA-aware lock that requires only one word of memory, regardless of the number of sockets in the underlying machine. The new lock is a variant of an efficient (NUMA-oblivious) MCS lock, and inherits its performant features, such as local spinning and a single atomic instruction in the acquisition path. Unlike MCS, the new lock organizes waiting threads in two queues, one composed of threads running on the same socket as the current lock holder, and another composed of threads running on a different socket(s). We implemented the new lock in user-space as well as integrated it in the Linux kernel's qspinlock, one of the major synchronization constructs in the kernel. Our evaluation using both user-space and kernel benchmarks shows that the new lock has a single-thread performance of MCS, but significantly outperforms the latter under contention, achieving a similar level of performance when compared to other, state-of-the-art NUMA-aware locks that require substantially more space.
Learning Privacy Preserving Encodings Through Adversarial Training

We present a framework to learn privacy-preserving encodings of images that inhibit inference of chosen private attributes, while allowing recovery of other desirable information. Rather than simply inhibiting a given fixed pre-trained estimator, our goal is that an estimator be unable to learn to accurately predict the private attributes even with knowledge of the encoding function. We use a natural adversarial optimization-based formulation for this—training the encoding function against a classifier for the private attribute, with both modeled as deep neural networks. The key contribution of our work is a stable and convergent optimization approach that is successful at learning an encoder with our desired properties—maintaining utility while inhibiting inference of private attributes, not just within the adversarial optimization, but also by classifiers that are trained after the encoder is fixed. We adopt a rigorous experimental protocol for verification wherein classifiers are trained exhaustively till saturation on the fixed encoders. We evaluate our approach on tasks of real-world complexity—learning high-dimensional encodings that inhibit detection of different scene categories—and find that it yields encoders that are resilient at maintaining privacy.
Joint sparsity recovery for spectral compressed sensing

Compressed Sensing (CS) is an effective approach to reduce the required number of samples for reconstructing a sparse signal in an a priori basis, but may suffer severely from the issue of basis mismatch. In this paper we study the problem of simultaneously recovering multiple spectrally-sparse signals that are supported on the same frequencies lying arbitrarily on the unit circle. We propose an atomic norm minimization problem, which can be regarded as a continuous counterpart of the discrete CS formulation and be solved efficiently via semidefinite programming. Through numerical experiments, we show that the number of samples per signal may be further reduced by harnessing the joint sparsity pattern of multiple signals.
Learning conversational systems that interleave task and non-task content

Task-oriented dialog systems have been applied in various tasks, such as automated personal assistants, customer service providers and tutors. These systems work well when users have clear and explicit intentions that are well-aligned to the systems' capabilities. However, they fail if users intentions are not explicit. To address this shortcoming, we propose a framework to interleave nontask content (i.e. everyday social conversation) into task conversations. When the task content fails, the system can still keep the user engaged with the non-task content. We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content. To test the effectiveness of the proposed framework, we developed a movie promotion dialog system. Experiments with human users indicate that a system that interleaves social and task content achieves a better task success rate and is also rated as more engaging compared to a pure task-oriented system.
Bar simplicial modules and secondary cyclic (co)homology

© 2018 European Mathematical Society Publishing House. All rights reserved. In this paper we study the simplicial structure of the complex C.A;B; "/IM/, associated to the secondary Hochschild cohomology. The main ingredient is the simplicial object B.A;B; "/, which plays a role equivalent to that of the bar resolution associated to an algebra. We also introduce the secondary cyclic (co)homology and establish some of its properties (Theorems 4.11 and 5.11).
On the status of the geodesic principle in Newtonian and relativistic physics

A theorem due to Bob Geroch and Pong Soo Jang ["Motion of a Body in General Relativity." Journal of Mathematical Physics 16(1), (1975)] provides a sense in which the geodesic principle has the status of a theorem in General Relativity (GR). I have recently shown that a similar theorem holds in the context of geometrized Newtonian gravitation (Newton-Cartan theory) [Weatherall, J. O. "The Motion of a Body in Newtonian Theories." Journal of Mathematical Physics 52(3), (2011)]. Here I compare the interpretations of these two theorems. I argue that despite some apparent differences between the theorems, the status of the geodesic principle in geometrized Newtonian gravitation is, mutatis mutandis, strikingly similar to the relativistic case.
A generalized algebraic approach to optimizing SC-LDPC codes

Spatially coupled low-density parity-check (SC-LDPC) codes are sparse graph codes that have recently become of interest due to their capacity-approaching performance on memoryless binary input channels. In this paper, we unify all existing SC-LDPC code construction methods under a new generalized description of SC-LDPC codes based on algebraic lifts of graphs. We present an improved low-complexity counting method for the special case of (3,3)-absorbing sets for array-based SC-LDPC codes, which we then use to optimize permutation assignments in SC-LDPC code construction. We show that codes constructed in this way are able to outperform previously published constructions, in terms of the number of dominant absorbing sets and with respect to both standard and windowed decoding.
Lefschetz fibrations and Torelli groups

For each $$g \ge 3$$ and $$h \ge 2$$ , we explicitly construct (1) fiber sum indecomposable relatively minimal genus $$g$$ Lefschetz fibrations over genus $$h$$ surfaces whose monodromies lie in the Torelli group, (2) genus $$g$$ Lefschetz fibrations over genus $$h$$ surfaces that are not fiber sums of holomorphic ones, and (3) fiber sum indecomposable genus $$g$$ surface bundles over genus $$h$$ surfaces whose monodromies are in the Torelli group (provided $$g \ge 4$$ ). The last result amounts to finding explicit irreducible embeddings of surface groups into Torelli groups; in fact we find such embeddings into arbitrary terms of the Johnson filtration.
The solar photosphere : Evidence for condensed matter

The stellar equations of state treat the Sun much like an ideal gas, wherein the photosphere is viewed as a sparse gaseous plasma. The temperatures inferred in the solar interior give some credence to these models, especially since it is counterintuitive that an object with internal temperatures in excess of 1 MK could be existing in the liquid state. Nonetheless, extreme temperatures, by themselves, are insufficient evidence for the states of matter. The presence of magnetic fields and gravity also impact the expected phase. In the end, it is the physical expression of a state that is required in establishing the proper phase of an object The photosphere does not lend itself easily to treatment as a gaseous plasma. The physical evidence can be more simply reconciled with a solar body and a photosphere in the condensed state. A discussion of each physical feature follows: 1) the thermal spectrum, 2) limb darkening, 3) solar collapse, 4) the solar density, 5) seismic activity, 6) mass displacement, 7) the chromosphere and critical opalescence, 8) shape, 9) surface activity, 10) photospheric/coronal flows, 11) photospheric imaging, 12) the solar dynamo, and 13) the presence of Sun spots. The explanation of these findings by the gaseous models often requires an improbable combination of events, such as found in the stellar opacity problem. In sharp contrast, each can be explained with simplicity by the condensed state. This work is an invitation to reconsider the phase of the Sun.
Combinatorial Reductions for the Stanley Depth of $I$ and $S/I$

We develop combinatorial tools to study the relationship between the Stanley depth of a monomial ideal $I$ and the Stanley depth of its compliment, $S/I$. Using these results we are able to prove that if $S$ is a polynomial ring with at most 5 indeterminates and $I$ is a square-free monomial ideal, then the Stanley depth of $S/I$ is strictly larger than the Stanley depth of $I$. Using a computer search, we are able to extend this strict inequality up to polynomial rings with at most 7 indeterminates. This partially answers questions asked by Propescu and Qureshi as well as Herzog.
UNSTABLE ELECTROMAGNETIC MODES IN STRONGLY MAGNETIZED PLASMAS

A theory for instability in the long-time limit, arising from the electron gyro-motion in strongly magnetized plasmas, is presented. The analysis of the electron motion in the presence of a strong magnetic field leads to a theoretical framework similar to that of the Landau damping. Various electromagnetic modes are predicted to be possibly unstable, and the regime where the radiation from this instability would stand out, compared to the incoherent electron–cyclotron radiation, is identified. This instability would be relevant to the inertial confinement fusion and the gamma ray burst.
Infinitely supported Liouville measures of Schreier graphs

We provide equivalent conditions for Liouville property of actions of groups. As an application, we show that there is a Liouville measure for the action of the Thompson group $F$ on dyadic rationals. This result should be compared with a recent result of Kaimanovich, where he shows that the action of the Thompson group F on dyadic rationals is not Liouville for all finitely supported measures. As another application we show that there is a Liouville measure for lamplighter actions. This gives more examples of non-amenable Liouville actions.
Stokes phenomenon, Gelfand–Zeitlin systems and relative Ginzburg–Weinstein linearization

Abstract In 2007, Alekseev–Meinrenken proved that there exists a Ginzburg–Weinstein diffeomorphism from the dual Lie algebra u ( n ) ⁎ to the dual Poisson Lie group U ( n ) ⁎ compatible with the Gelfand–Zeitlin integrable systems. In this paper, we explicitly construct such diffeomorphisms via Stokes phenomenon and Boalch's dual exponential maps. Then we introduce a relative version of the Ginzburg–Weinstein linearization motivated by irregular Riemann–Hilbert correspondence, and generalize the results of Enriquez–Etingof–Marshall to this relative setting. In particular, we prove the connection matrix for a certain irregular Riemann–Hilbert problem satisfies a relative gauge transformation equation of the Alekseev–Meinrenken dynamical r-matrices. This gauge equation is then derived as the semiclassical limit of the relative Drinfeld twist equation.
A solution of the maximality problem for one-parameter dynamical systems

Abstract We prove a maximality theorem for one-parameter dynamical systems that include W*-, C*- and multiplier one-parameter dynamical systems. Our main result is new even for one-parameter actions on commutative multiplier algebras including the algebra C b ( R ) of bounded continuous functions on R acted upon by translations. The methods we develop and use in our characterization of maximality include harmonic analysis, topological vector spaces and operator algebra techniques.
Social Balance and the Bernoulli Equation

Since the 1940s there has been an interest in the question of why social networks often give rise to two antagonistic factions. Recently a dynamical model of how and why such a balance might occur ...
Sensitivity analysis for network aggregative games

We investigate the sensitivity of the Nash equilibrium of constrained network aggregative games to changes in exogenous parameters affecting the cost function of the players. This setting is motivated by two applications. The first is the analysis of interventions by a social planner with a networked objective function while the second is network routing games with atomic players and information constraints.
Nonlocal vertices and analyticity: Landau equations and general Cutkosky rule

We study the analyticity properties of amplitudes in theories with nonlocal vertices of the type occurring in string field theory and a wide class of nonlocal field theory models. Such vertices are given in momentum space by entire functions of rapid decay in certain (including Euclidean) directions ensuring UV finiteness but are necessarily of rapid increase in others. A parametric representation is obtained by integrating out the loop (Euclidean) momenta after the introduction of generalized Schwinger parameters. Either in the original or parametric representation, the well-defined resulting amplitudesare then continued in the complex space of the external momenta invariants. We obtain the alternative forms of the Landau equations determining the singularity surfaces showing that the nonlocal vertices serve as UV regulators but do not affect the local singularity structure. As a result the full set of singularities known to occur in local field theory also occurs here: normal and anomalous thresholds as well as acnodes, crunodes, and cusps that may under certain circumstances appear even on the physical sheet. Singularities of the second type also appear as shown from the parametric representation. We obtain the general Cutkosky discontinuity rule for encircling a singularity by employing contour deformations only in the finite plane. The unitarity condition (optical theorem) is thendiscussed as a special application of the rule across normal thresholds and the hermitian analyticity property of amplitudes.
Persistence in Stochastic Lotka–Volterra Food Chains with Intraspecific Competition

This paper is devoted to the analysis of a simple Lotka–Volterra food chain evolving in a stochastic environment. It can be seen as the companion paper of Hening and Nguyen (J Math Biol 76:697–754, 2018b) where we have characterized the persistence and extinction of such a food chain under the assumption that there is no intraspecific competition among predators. In the current paper, we focus on the case when all the species experience intracompetition. The food chain we analyze consists of one prey and $$n-1$$ predators. The jth predator eats the $$j-1$$ st species and is eaten by the $$j+1$$ st predator; this way each species only interacts with at most two other species—the ones that are immediately above or below it in the trophic chain. We show that one can classify, based on the invasion rates of the predators (which we can determine from the interaction coefficients of the system via an algorithm), which species go extinct and which converge to their unique invariant probability measure. We obtain stronger results than in the case with no intraspecific competition because in this setting we can make use of the general results of Hening and Nguyen (Ann Appl Probab 28:1893–1942, 2018a). Unlike most of the results available in the literature, we provide an in-depth analysis for both non-degenerate and degenerate noise. We exhibit our general results by analyzing trophic cascades in a plant–herbivore–predator system and providing persistence/extinction criteria for food chains of length $$n\le 4$$ .
Learning Causal Structures Using Regression Invariance

We study causal discovery in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary. We introduce the idea of using the invariance of the functional relations of the variables to their causes across a set of environments for structure learning. We define a notion of completeness for a causal inference algorithm in this setting and prove the existence of such algorithm by proposing the baseline algorithm. Additionally, we present an alternate algorithm that has significantly improved computational and sample complexity compared to the baseline algorithm. Experiment results show that the proposed algorithm outperforms the other existing algorithms.
Deterministic and cascadable conditional phase gate for photonic qubits

Previous analyses of conditional ${\ensuremath{\varphi}}_{nl}$-phase gates for photonic qubits that treat cross-phase modulation (XPM) in a causal, multimode, quantum field setting suggest that a large ($\ensuremath{\sim}$$\ensuremath{\pi}$ rad) nonlinear phase shift is always accompanied by fidelity-degrading noise [J. H. Shapiro, Phys. Rev. A 73, 062305 (2006); J. Gea-Banacloche, Phys. Rev. A 81, 043823 (2010)]. Using an atomic $\ensuremath{\vee}$ system to model an XPM medium, we present a conditional phase gate that, for sufficiently small nonzero ${\ensuremath{\varphi}}_{nl}$, has high fidelity. The gate is made cascadable by using a special measurement, i.e., principal-mode projection, to exploit the quantum Zeno effect and preclude the accumulation of fidelity-degrading departures from the principal-mode Hilbert space when both control and target photons illuminate the gate. The nonlinearity of the $\ensuremath{\vee}$ system we study is too weak for this particular implementation to be practical. Nevertheless, the idea of cascading through principal-mode projection is of potential use to overcome fidelity-degrading noise for a wide variety of nonlinear optical primitive gates.
The auto Igusa-zeta function of a plane curve singularity is rational

It is shown that the auto-Igusa zeta function of a plane curve singularity is rational. This gives a new criterion for a plane curve over an algebraically closed field of characteristic zero to be smooth at a point.
Reducing Exploration of Dying Arms in Mortal Bandits

Mortal bandits have proven to be extremely useful for providing news article recommendations, running automated online advertising campaigns, and for other applications where the set of available options changes over time. Previous work on this problem showed how to regulate exploration of new arms when they have recently appeared, but they do not adapt when the arms are about to disappear. Since in most applications we can determine either exactly or approximately when arms will disappear, we can leverage this information to improve performance: we should not be exploring arms that are about to disappear. We provide adaptations of algorithms, regret bounds, and experiments for this study, showing a clear benefit from regulating greed (exploration/exploitation) for arms that will soon disappear. We illustrate numerical performance on the Yahoo! Front Page Today Module User Click Log Dataset.
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG

Given a n-by-d data matrix A, principal component projection (PCP) and principal component regression (PCR), i.e. projection and regression restricted to the top-eigenspace of A, are fundamental problems in machine learning, optimization, and numerical analysis. In this paper we provide the first algorithms that solve these problems in nearly linear time for fixed eigenvalue distribution and large n. This improves upon previous methods which had superlinear running times when either the number of top eigenvalues or gap between the eigenspaces were large. We achieve our results by applying rational polynomial approximations to reduce the problem to solving asymmetric linear systems which we solve by a variant of SVRG. We corroborate these findings with preliminary empirical experiments.
Variational Bayes under Model Misspecification

Variational Bayes (VB) is a scalable alternative to Markov chain Monte Carlo (MCMC) for Bayesian posterior inference. Though popular, VB comes with few theoretical guarantees, most of which focus on well-specified models. However, models are rarely well-specified in practice. In this work, we study VB under model misspecification. We prove the VB posterior is asymptotically normal and centers at the value that minimizes the Kullback-Leibler (KL) divergence to the true data-generating distribution. Moreover, the VB posterior mean centers at the same value and is also asymptotically normal. These results generalize the variational Bernstein--von Mises theorem [29] to misspecified models. As a consequence of these results, we find that the model misspecification error dominates the variational approximation error in VB posterior predictive distributions. It explains the widely observed phenomenon that VB achieves comparable predictive accuracy with MCMC even though VB uses an approximating family. As illustrations, we study VB under three forms of model misspecification, ranging from model over-/under-dispersion to latent dimensionality misspecification. We conduct two simulation studies that demonstrate the theoretical results.
Reconstruction of depth-4 multilinear circuits

We present a deterministic algorithm for reconstructing multilinear ΣΠΣΠ(k) circuits, i.e. multilinear depth-4 circuits with fan-in k at the top + gate. For any fixed k, given black-box access to a polynomial f ∈ F[x1, x2, ..., xn] computable by a multilinear ΣΠΣΠ(k) circuit of size s, the algorithm runs in time quasi-poly(n, s, |F|) and outputs a multilinear ΣΠΣΠ(k) circuit of size quasi-poly(n, s) that computes f. Our result solves an open problem posed in [15] (STOC, 2012). Indeed, prior to our work, efficient reconstruction algorithms for multilinear ΣΠΣΠ(k) circuits were known only for the case of k = 2 [15, 52].
Capital regulation under price impacts and dynamic financial contagion

Abstract We construct a continuous time model for price-mediated contagion precipitated by a common exogenous stress to the banking book of all firms in the financial system. In this setting, firms are constrained so as to satisfy a risk-weight based capital ratio requirement. We use this model to find analytical bounds on the risk-weights for assets as a function of the market liquidity. Under these appropriate risk-weights, we find existence and uniqueness for the joint system of firm behavior and the asset prices. We further consider an analytical bound on the firm liquidations, which allows us to construct exact formulas for stress testing the financial system with deterministic or random stresses. Numerical case studies are provided to demonstrate various implications of this model and analytical bounds.
Learning Mean-Field Games

This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population. It first establishes the existence of a unique Nash Equilibrium to this GMFG, and explains that naively combining Q-learning with the fixed-point approach in classical MFGs yields unstable algorithms. It then proposes a Q-learning algorithm with Boltzmann policy (GMF-Q), with analysis of convergence property and computational complexity. The experiments on repeated Ad auction problems demonstrate that this GMF-Q algorithm is efficient and robust in terms of convergence and learning accuracy. Moreover, its performance is superior in convergence, stability, and learning ability, when compared with existing algorithms for multi-agent reinforcement learning.
Time-Resolved Integrative Optical Imaging of Diffusion during Spreading Depression.

Abstract An improved version of the integrative optical imaging method has been developed that substantially increases the time resolution of diffusion measurements. We present a theory for time-resolved integrative optical imaging that incorporates a time-dependent effective diffusion coefficient in homogeneous anisotropic media and a time-dependent nonspecific linear clearance. The method was applied to measure the very fast changes in extracellular diffusion that occur during spreading depression in rat hippocampal slices. We were able to achieve a time resolution of approximately 1 s, an improvement of at least 10 times compared to the standard methods for extracellular diffusion measurement. We have found that diffusion of a small fluorescent extracellular marker (MW 3000) completely stopped during the maximum direct current shift associated with the spreading depression wave, then gradually resumed over several minutes afterward. The effect of spreading depression on extracellular space is much larger than previously estimated by other methods with lower time resolution.
What’s Alter Got to Do with It? A Consideration of Network Content and the Social Ties That Provide It:

The strength of weak ties is among the most important theories in the social sciences. One paradoxical element of the theory has been widely understood and valued—that weak ties connect disparate r...
A computational approach to the Frobenius–Schur indicators of finite exceptional groups

We prove that the finite exceptional groups F4(q), E7(q)ad, and E8(q) have no irreducible complex characters with Frobenius–Schur indicator − 1, and we list exactly which irreducible characters of ...
The Draft Genome of Kochia scoparia and the Mechanism of Glyphosate Resistance via Transposon-Mediated EPSPS Tandem Gene Duplication

Increased copy number of the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene confers resistance to glyphosate, the world's most-used herbicide. There are typically three to eight EPSPS copies arranged in tandem in glyphosate-resistant populations of the weed kochia (Kochia scoparia). Here, we report a draft genome assembly from a glyphosate-susceptible kochia individual. Additionally, we assembled the EPSPS locus from a glyphosate-resistant kochia plant by sequencing select bacterial artificial chromosomes from a kochia bacterial artificial chromosome library. Comparing the resistant and susceptible EPSPS locus allowed us to reconstruct the history of duplication in the structurally complex EPSPS locus and uncover the genes that are coduplicated with EPSPS, several of which have a corresponding change in transcription. The comparison between the susceptible and resistant assemblies revealed two dominant repeat types. Additionally, we discovered a mobile genetic element with a FHY3/FAR1-like gene predicted in its sequence that is associated with the duplicated EPSPS gene copies in the resistant line. We present a hypothetical model based on unequal crossing over that implicates this mobile element as responsible for the origin of the EPSPS gene duplication event and the evolution of herbicide resistance in this system. These findings add to our understanding of stress resistance evolution and provide an example of rapid resistance evolution to high levels of environmental stress.
Predicting Behavior in Cancer-Afflicted Patient and Spouse Interactions using Speech and Language

Cancer impacts the quality of life of those diagnosed as well as their spouse caregivers, in addition to potentially influencing their day-to-day behaviors. There is evidence that effective communication between spouses can improve well-being related to cancer but it is difficult to efficiently evaluate the quality of daily life interactions using manual annotation frameworks. Automated recognition of behaviors based on the interaction cues of speakers can help analyze interactions in such couples and identify behaviors which are beneficial for effective communication. In this paper, we present and detail a dataset of dyadic interactions in 85 real-life cancer-afflicted couples and a set of observational behavior codes pertaining to interpersonal communication attributes. We describe and employ neural network-based systems for classifying these behaviors based on turn-level acoustic and lexical speech patterns. Furthermore, we investigate the effect of controlling for factors such as gender, patient/caregiver role and conversation content on behavior classification. Analysis of our preliminary results indicates the challenges in this task due to the nature of the targeted behaviors and suggests that techniques incorporating contextual processing might be better suited to tackle this problem.
Reducible Fermi surfaces for non-symmetric bilayer quantum-graph operators

This work constructs a class of non-symmetric periodic Schrodinger operators on metric graphs (quantum graphs) whose Fermi, or Floquet, surface is reducible. The Floquet surface at an energy level is an algebraic set that describes all complex wave vectors admissible by the periodic operator at the given energy. The graphs in this study are obtained by coupling two identical copies of a periodic quantum graph by edges to form a bilayer graph. Reducibility of the Floquet surface for all energies ensues when the coupling edges have potentials belonging to the same asymmetry class. The notion of asymmetry class is defined in this article through the introduction of an entire spectral A-function $a(\lambda)$ associated with a potential--two potentials belong to the same asymmetry class if their A-functions are identical. Symmetric potentials correspond to $a(\lambda)\equiv0$. If the potentials of the connecting edges belong to different asymmetry classes, then typically the Floquet surface is not reducible. An exception occurs when two copies of certain bipartite graphs are coupled; the Floquet surface in this case is always reducible. This includes AA-stacked bilayer graphene.
Asymptotic expansions in a general system of decaying functions for solutions of the Navier–Stokes equations

We study the long-time dynamics of the Navier–Stokes equations in the three-dimensional periodic domains with a body force decaying in time. We introduce appropriate systems of decaying functions and corresponding asymptotic expansions in those systems. We prove that if the force has a large-time asymptotic expansion in Gevrey–Sobolev spaces in such a general system, then any Leray–Hopf weak solution admits an asymptotic expansion of the same type. This expansion is uniquely determined by the force, and independent of the solutions. Various applications of the abstract results are provided which particularly include the previously obtained expansions for the solutions in case of power decay, as well as the new expansions in case of the logarithmic and iterated logarithmic decay.
A large-scale study reveals 24-h operational rhythms in hospital treatment

Hospitals operate 24 h a day, and it is assumed that important clinical decisions occur continuously around the clock. However, many aspects of hospital operation occur at specific times of day, including medical team rounding and shift changes. It is unclear whether this impacts patient care, as no studies have addressed this. We analyzed the daily distribution of ∼500,000 doses of 12 separate drugs in 1,546 inpatients at a major children's hospital in the United States from 2010 to 2017. We tracked both order time (when a care provider places an electronic request for a drug) and dosing time (when the patient receives the drug). Order times were time-of-day-dependent, marked by distinct morning-time surges and overnight lulls. Nearly one-third of all 103,847 orders for treatment were placed between 8:00 AM and 12:00 PM. First doses from each order were also rhythmic but shifted by 2 h. These 24-h rhythms in orders and first doses were remarkably consistent across drugs, diagnosis, and hospital units. This rhythm in hospital medicine coincided with medical team rounding time, not necessarily immediate medical need. Lastly, we show that the clinical response to hydralazine, an acute antihypertensive, is dosing time-dependent and greatest at night, when the fewest doses were administered. The prevailing dogma is that hospital treatment is administered as needed regardless of time of day. Our findings challenge this notion and reveal a potential operational barrier to best clinical care.
The Solar Corona above Active Regions: A Comparison of Extreme Ultraviolet Line Emission with Radio Emission

The observation of extreme ultraviolet (EUV) emission lines of Fe ix through Fe xvi made by Orbiting Solar Observatory-1 are discussed and applied to a study of the solar corona above active regions. Ultraviolet and radio emission are determined and compared for several levels of activity classified according to the type of sunspot group associated with the active region. Both radio emission and line radiation from Fe xvi, the highest stage of ionization of Fe observed, are observed to increase rapidly with the onset of activity and are most intense over an E-spot group early in the lifetime of the active region. As activity diminishes, radiation from Fe xv and Fe xvi becomes relatively more prominent. The observations imply that the coronal temperature reaches a maximum during the period of highest activity, as indicated by sunspot-group complexity and the occurrence of chromospheric flares. A maximum coronal electron temperature of 4.0 × 106 °K is estimated when taking into account the mechanism of dielectronic recombination. Concurrently, the average coronal electron density increases by a factor of 10–12. Both electron temperature and density decrease as activity subsides. The coronal temperature above the remaining Ca ii plage is estimated to be 2.5–3.0 × 106 °K after flare activity has ceased and sunspots have disappeared.
Structured Fusion Networks for Dialog.

Neural dialog models have exhibited strong performance, however their end-to-end nature lacks a representation of the explicit structure of dialog. This results in a loss of generalizability, controllability and a data-hungry nature. Conversely, more traditional dialog systems do have strong models of explicit structure. This paper introduces several approaches for explicitly incorporating structure into neural models of dialog. Structured Fusion Networks first learn neural dialog modules corresponding to the structured components of traditional dialog systems and then incorporate these modules in a higher-level generative model. Structured Fusion Networks obtain strong results on the MultiWOZ dataset, both with and without reinforcement learning. Structured Fusion Networks are shown to have several valuable properties, including better domain generalizability, improved performance in reduced data scenarios and robustness to divergence during reinforcement learning.
Gaussian Binomial Coefficients with Negative Arguments

Loeb showed that a natural extension of the usual binomial coefficient to negative (integer) entries continues to satisfy many of the fundamental properties. In particular, he gave a uniform binomial theorem as well as a combinatorial interpretation in terms of choosing subsets of sets with a negative number of elements. We show that all of this can be extended to the case of Gaussian binomial coefficients. Moreover, we demonstrate that several of the well-known arithmetic properties of binomial coefficients also hold in the case of negative entries. In particular, we show that Lucas’ theorem on binomial coefficients modulo p not only extends naturally to the case of negative entries, but even to the Gaussian case.
A buoyancy–shear–drag-based turbulence model for Rayleigh–Taylor, reshocked Richtmyer–Meshkov, and Kelvin–Helmholtz mixing

Abstract A phenomenological turbulence model for Rayleigh–Taylor, reshocked Richtmyer–Meshkov, and Kelvin–Helmholtz instability-induced mixing is developed using a general buoyancy–shear–drag model. Analytic solutions to the simplified model equations corresponding to each instability are derived separately, which are then used to calibrate the model coefficients to predict specific values of the mixing layer growth parameters and exponents. The buoyancy–shear–drag equations for the bubble and spike mixing layer widths are then solved numerically, and a turbulent diffusivity (or viscosity) is constructed dimensionally as the product of the mixing layer width, h , and its time-derivative, d h ∕ d t . Surrogate turbulent fields are then constructed by multiplying a presumed, approximate self-similar spatial profile by appropriate functions of h and d h ∕ d t . Using several simplifying approximations, the turbulent diffusion equations satisfied by the mean mass fraction and mean shear velocity are solved analytically in a reference frame moving with the mean advection velocity, and it is shown that the mean fields evolve in space and time as expected. The explicit modeling and solution of turbulent transport equations (e.g., for the turbulent kinetic energy and its dissipation rate or a lengthscale) are not required. By using separate equations for the bubble and spike evolution, the model naturally captures the asymmetry induced by an increasing density contrast between the heavy and light fluids. The model also includes molecular dissipation and diffusion, and therefore, can describe transition to fully-developed turbulence if the initial turbulent diffusivity/viscosity is much smaller than the molecular diffusivity/viscosity. This model is applied to constant-acceleration Rayleigh–Taylor, impulsively reshocked Richtmyer–Meshkov, and variable-density Kelvin–Helmholtz mixing layers to demonstrate its utility. It is shown that the numerical solutions of the model calibrated using specific values of the instability growth parameters and exponents: (1) produces mixing layer widths in agreement with the expected self-similar growth power laws; (2) gives spatiotemporal profiles of turbulent fields that are expected and consistent with previous results; (3) predicts the expected power-law growths and decays of the spatially-integrated turbulent fields, and; (4) gives spatiotemporal profiles of the mean fields that are expected and consistent with previous results. This relatively simple model can be implemented numerically, and is expected to be useful for fundamental as well as astrophysical and inertial confinement fusion applications.
Affective and Substance Abuse Disorders Following Abortion by Pregnancy Intention in the United States: A Longitudinal Cohort Study.

Background and Objectives: Psychological outcomes following termination of wanted pregnancies have not previously been studied. Does excluding such abortions affect estimates of psychological distress following abortion? To address this question this study examines long-term psychological outcomes by pregnancy intention (wanted or unwanted) following induced abortion relative to childbirth in the United States. Materials and Methods: Panel data on a nationally-representative cohort of 3935 ever-pregnant women assessed at mean age of 15, 22, and 28 years were examined from the National Longitudinal Survey of Adolescent to Adult Health (Add Health). Relative risk (RR) and incident rate ratios (IRR) for time-dynamic mental health outcomes, conditioned by pregnancy intention and abortion exposure, were estimated from population-averaged longitudinal logistic and Poisson regression models, with extensive adjustment for sociodemographic differences, pregnancy and mental health history, and other confounding factors. Outcomes were assessed using the Diagnostic and Statistical Manual, Version 4, American Psychiatric Association (DSM-IV) diagnostic criteria or another validated index for suicidal ideation, depression, and anxiety (affective problems); drug abuse, opioid abuse, alcohol abuse, and cannabis abuse (substance abuse problems); and summary total disorders. Results: Women who terminated one or more wanted pregnancies experienced a 43% higher risk of affective problems (RR 1.69, 95% CI 1.3–2.2) relative to childbirth, compared to women terminating only unwanted pregnancies (RR 1.18, 95% CI 1.0–1.4). Risks of depression (RR 2.22, 95% CI 1.3–3.8) and suicidality (RR 3.44 95% CI 1.5–7.7) were especially elevated with wanted pregnancy abortion. Relative risk of substance abuse disorders with any abortion was high, at about 2.0, but unaffected by pregnancy intention. Excluding wanted pregnancies artifactually reduced estimates of affective disorders by 72% from unity, substance abuse disorders by 11% from unity, and total disorders by 21% from unity. Conclusions: Excluding wanted pregnancies moderately understates overall risk and strongly understates affective risk of mental health difficulties for women following abortion. Compared to corresponding births, abortions of wanted pregnancies are associated with a greater risk of negative psychological affect, particularly depression and suicide ideation, but not greater risk of substance abuse, than are abortions of unwanted pregnancies. Clinical, research, and policy implications are discussed briefly.
Spooky Work at a Distance: An Interaction-Free Quantum Measurement Driven Engine

We propose an engine fueled by an interaction-free measurement, able to lift a quantum particle in the gravitational potential. The energy is provided by the probe, a single photon, in a seemingly nonlocal way.
Quantitative analysis of ablation technique predicts arrhythmia recurrence following atrial fibrillation ablation.

Background Optimal ablation technique, including catheter-tissue contact during atrial fibrillation (AF) radiofrequency (RF) ablation, is associated with improved procedural outcomes. We used a custom developed software to analyze high-frequency catheter position data to study the interaction between catheter excursion during lesion placement, lesion-set sequentiality, and arrhythmia recurrence. Methods A total of 100 consecutive patients undergoing first-time RF ablation for paroxysmal AF were analyzed. Spatial positioning of the ablation catheter sampled at 60 Hz during RF application was extracted from the CARTO3 system (Biosense Webster Inc, USA) and analyzed using custom-developed MATLAB software to determine precise catheter spatial 3D excursion during RF ablation. The primary end point was freedom from atrial arrhythmia lasting longer than 30 seconds after a single ablation procedure. Results At 1 year, 86% of patients were free from recurrent arrhythmia. There was no significant difference in clinical, echocardiographic, or ablation characteristics between patients with and without recurrent arrhythmia. Analyzing 15,356,998 position data points revealed that lesion-set sequentiality and mean lesion catheter excursion were predictors of arrhythmia recurrence. Analyzing arrhythmia recurrence by mean single-lesion catheter excursion (excursion >2.81 mm) and by sequentiality (using 46% of lesions with interlesion distance >6 mm as cutoff) revealed significantly increased arrhythmia recurrence in the higher excursion group (23% vs 6%, P = .03) and in the less sequential group (24% vs 4%, P = .02). Conclusions Ablation lesion sequentiality measured by catheter interlesion distance and catheter stability measured by catheter excursion during lesion placement are potentially modifiable factors affecting arrhythmia recurrence after RF ablation for AF.