cssi/SciSciGPT-SciSciNet huggingface dataset:
Variability in Positive and Negative Affect Among Adolescents with and without ADHD: Differential Associations with Functional Outcomes.

Abstract Introduction. This study examined whether adolescents with and without attention-deficit/hyperactivity disorder (ADHD) differed in affect variability and whether variability in positive and negative affect was associated with functional outcomes. Method. Participants were 302 adolescents (12-14 years, Mage=13.17, 55% male; 54% diagnosed with ADHD; 82% white) and their caregivers who each completed the 10-item Positive and Negative Affect Scale via daily diaries for approximately two weeks. Multi-informant ratings of emotional, behavioral, social, and academic outcomes were assessed. Results. Adolescents with ADHD were found to experience greater variability in self- and parent-reported positive affect, fear, and distress. For adolescents with ADHD, greater variability in self- and parent-reported positive affect, fear, and distress were associated with more internalizing symptoms, greater variability in parent-reported positive affect was associated with worse social functioning, and greater variability in self- and parent-reported fear was associated with more externalizing symptoms. In contrast, greater variability in self- and parent-reported positive affect, fear, and distress were associated with better social functioning in adolescents without ADHD. Limitations. Future work should examine affect variability in adolescents with ADHD within the same day rather than across days. The limited age range and demographic diversity of our sample may limit generalizability of findings. Conclusions. Findings suggest the significant affect variability found among children with ADHD is also present in adolescents with ADHD and is associated with social and behavior impairment. Interventions for adolescents with ADHD should target emotion regulation abilities to help reduce the extremes of and shifts in affective experiences in this population.
The suppression of superconductivity in MgCNi3 by Ni-site doping

Abstract The effects of partial substitution of Cu and Co for Ni in the intermetallic perovskite superconductor MgCNi3 are reported. Calculation of the expected electronic density of states suggests that electron (Cu) and hole (Co) doping should have different effects. For MgCNi3−xCux, solubility of Cu is limited to approximately 3% (x=0.1). and Tc decreases systematically from 7 to 6 K. For MgCNi3−xCox, solubility of Co is much more extensive, but bulk superconductivity disappears for Co doping of 1% (x=0.03). No signature of long range magnetic ordering is observed in the magnetic susceptibility of the Co doped material.
Logarithmic Regret in the Dynamic and Stochastic Knapsack Problem with Equal Rewards

We study a dynamic and stochastic knapsack problem in which a decision maker is sequentially presented with items arriving according to a Bernoulli process over n discrete time periods. Items have ...
Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network

Recent work has shown that topological enhancements to recurrent neural networks (RNNs) can increase their expressiveness and representational capacity. Two popular enhancements are stacked RNNs, which increases the capacity for learning non-linear functions, and bidirectional processing, which exploits acausal information in a sequence. In this work, we explore the delayed-RNN, which is a single-layer RNN that has a delay between the input and output. We prove that a weight-constrained version of the delayed-RNN is equivalent to a stacked-RNN. We also show that the delay gives rise to partial acausality, much like bidirectional networks. Synthetic experiments confirm that the delayed-RNN can mimic bidirectional networks, solving some acausal tasks similarly, and outperforming them in others. Moreover, we show similar performance to bidirectional networks in a real-world natural language processing task. These results suggest that delayed-RNNs can approximate topologies including stacked RNNs, bidirectional RNNs, and stacked bidirectional RNNs - but with equivalent or faster runtimes for the delayed-RNNs.
The Mechanosensitive Ion Channel MSL10 Potentiates Responses to Cell Swelling in Arabidopsis Seedlings

Summary The ability to respond to unanticipated increases in volume is a fundamental property of cells, essential for cellular integrity in the face of osmotic challenges. Plants must manage cell swelling during flooding, rehydration, and pathogen invasion—but little is known about the mechanisms by which this occurs. It has been proposed that plant cells could sense and respond to cell swelling through the action of mechanosensitive ion channels. Here, we characterize a new assay to study the effects of cell swelling on Arabidopsis thaliana seedlings and to test the contributions of the mechanosensitive ion channel MscS-like10 (MSL10). The assay incorporates both cell wall softening and hypo-osmotic treatment to induce cell swelling. We show that MSL10 is required for several previously demonstrated responses to hypo-osmotic shock, including a cytoplasmic calcium transient within the first few seconds, accumulation of ROS within the first 30 min, and increased transcript levels of mechano-inducible genes within 60 min. We also show that cell swelling induces programmed cell death within 3 h in a MSL10-dependent manner. Finally, we show that MSL10 is unable to potentiate cell swelling-induced death when phosphomimetic residues are introduced into its soluble N terminus. Thus, MSL10 functions as a phospho-regulated membrane-based sensor that connects the perception of cell swelling to a downstream signaling cascade and programmed cell death.
On linking of Lagrangian tori in $\mathbb{R}^4$

We prove some results about linking of Lagrangian tori in the symplectic vector space $(\mathbb{R}^4, \omega)$. We show that certain enumerative counts of holomophic disks give useful information about linking. This enables us to prove, for example, that any two Clifford tori are unlinked in a strong sense. We extend work of Dimitroglou Rizell and Evans on linking of monotone Lagrangian tori to a class of non-monotone tori in $\mathbb{R}^4$ and also strengthen their conclusions in the monotone case in $\mathbb{R}^4$.
Local Deep Implicit Functions for 3D Shape

The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations. Towards this end, we introduce Local Deep Implicit Functions (LDIF), a 3D shape representation that decomposes space into a structured set of learned implicit functions. We provide networks that infer the space decomposition and local deep implicit functions from a 3D mesh or posed depth image. During experiments, we find that it provides 10.3 points higher surface reconstruction accuracy (F-Score) than the state-of-the-art (OccNet), while requiring fewer than 1\% of the network parameters. Experiments on posed depth image completion and generalization to unseen classes show 15.8 and 17.8 point improvements over the state-of-the-art, while producing a structured 3D representation for each input with consistency across diverse shape collections.
Fair Learning with Private Demographic Data

Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations. We give a scheme that allows individuals to release their sensitive information privately while still allowing any downstream entity to learn non-discriminatory predictors. We show how to adapt non-discriminatory learners to work with privatized protected attributes giving theoretical guarantees on performance. Finally, we highlight how the methodology could apply to learning fair predictors in settings where protected attributes are only available for a subset of the data.
Parameterization of factorizable line bundles by K-theory and motivic cohomology

In this note we show how to construct a factorizable line bundle on the affine Grassmannian of a group G starting from a Brylinski-Deligne datum, which is an extension of G by the Zaraski-sheafified $$K_2$$ .
Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data

Abstract Background Processing and quantitative analysis of magnetic resonance spectroscopy (MRS) data are far from standardized and require interfacing with third-party software. Here, we present Osprey, a fully integrated open-source data analysis pipeline for MRS data, with seamless integration of pre-processing, linear-combination modelling, quantification, and data visualization. New Method Osprey loads multiple common MRS data formats, performs phased-array coil combination, frequency-and phase-correction of individual transients, signal averaging and Fourier transformation. Linear combination modelling of the processed spectrum is carried out using simulated basis sets and a spline baseline. The MRS voxel is coregistered to an anatomical image, which is segmented for tissue correction and quantification is performed based upon modelling parameters and tissue segmentation. The results of each analysis step are visualized in the Osprey GUI. The analysis pipeline is demonstrated in 12 PRESS, 11 MEGA-PRESS, and 8 HERMES datasets acquired in healthy subjects. Results Osprey successfully loads, processes, models, and quantifies MRS data acquired with a variety of conventional and spectral editing techniques. Comparison with Existing Method(s) Osprey is the first MRS software to combine uniform pre-processing, linear-combination modelling, tissue correction and quantification into a coherent ecosystem. Compared to existing compiled, often closed-source modelling software, Osprey’s open-source code philosophy allows researchers to integrate state-of-the-art data processing and modelling routines, and potentially converge towards standardization of analysis. Conclusions Osprey combines robust, peer-reviewed data processing methods into a modular workflow that is easily augmented by community developers, allowing the rapid implementation of new methods.
The Sylvester Graphical Lasso (SyGlasso)

This paper introduces the Sylvester graphical lasso (SyGlasso) that captures multiway dependencies present in tensor-valued data. The model is based on the Sylvester equation that defines a generative model. The proposed model complements the tensor graphical lasso (Greenewald et al., 2019) that imposes a Kronecker sum model for the inverse covariance matrix by providing an alternative Kronecker sum model that is generative and interpretable. A nodewise regression approach is adopted for estimating the conditional independence relationships among variables. The statistical convergence of the method is established, and empirical studies are provided to demonstrate the recovery of meaningful conditional dependency graphs. We apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects. We demonstrate that our model can simultaneously estimate both the brain connectivity and its temporal dependencies.
The WD40-repeat protein WDR-48 promotes the stability of the deubiquitinating enzyme USP-46 by inhibiting its ubiquitination and degradation.

Ubiquitination is a reversible post-translational modification that has emerged as a critical regulator of synapse development and function. However, the mechanisms that regulate the deubiquitinating enzymes (DUBs) responsible for the removal of ubiquitin from target proteins are poorly understood. We have previously shown that the DUB ubiquitin-specific protease 46 (USP-46) removes ubiquitin from the glutamate receptor GLR-1 and regulates its trafficking and degradation in Caenorhabditis elegans. We found that the WD40-repeat proteins WDR-20 and WDR-48 bind and stimulate the catalytic activity of USP-46. Here, we identified another mechanism by which WDR-48 regulates USP-46. We found that increased expression of WDR-48, but not WDR-20, promotes USP-46 abundance in mammalian cells in culture and in C. elegans neurons in vivo. Inhibition of the proteasome increased USP-46 abundance, and this effect was nonadditive with increased WDR-48 expression. We found that USP-46 is ubiquitinated and that expression of WDR-48 reduces the levels of ubiquitin–USP-46 conjugates and increases the t1/2 of USP-46. A point-mutated WDR-48 variant that disrupts binding to USP-46 was unable to promote USP-46 abundance in vivo. Finally, siRNA-mediated knockdown of wdr48 destabilizes USP46 in mammalian cells. Together, these results support a model in which WDR-48 binds and stabilizes USP-46 protein levels by preventing the ubiquitination and degradation of USP-46 in the proteasome. Given that a large number of USPs interact with WDR proteins, we propose that stabilization of DUBs by their interacting WDR proteins may be a conserved and widely used mechanism that controls DUB availability and function.
Tightening QC Relaxations of AC Optimal Power Flow Problems via Complex Per Unit Normalization

Optimal power flow (OPF) is a key problem in power system operations. OPF problems that use the nonlinear AC power flow equations to accurately model the network physics have inherent challenges associated with non-convexity. To address these challenges, recent research has applied various convex relaxation approaches to OPF problems. The QC relaxation is a promising approach that convexifies the trigonometric and product terms in the OPF problem by enclosing these terms in convex envelopes. The accuracy of the QC relaxation strongly depends on the tightness of these envelopes. This paper presents two improvements to these envelopes. The first improvement leverages a polar representation of the branch admittances in addition to the rectangular representation used previously. The second improvement is based on a coordinate transformation via a complex per unit base power normalization that rotates the power flow equations. The trigonometric envelopes resulting from this rotation can be tighter than the corresponding envelopes in previous QC relaxation formulations. Using an empirical analysis with a variety of test cases, this paper suggests an appropriate value for the angle of the complex base power. Comparing the results with a state-of-the-art QC formulation reveals the advantages of the proposed improvements.
Declared War and American Victory: A Search for Effective Commitment

This paper argues that the act of formally declaring war entails a measure of explicit commitment on the part of American political actors that raises the cost of failure and motivates politicians to see engagements through to a decisive end, fulfilling the role of a contract or institutional commitment device. It argues that undeclared conflicts, lacking such a device, are more likely to end on less decisive and less favorable terms to the U.S.. On this basis, it explains the emergence of a decades-long trend of protracted, unsuccessful, and indecisive military engagements by the United States as having emerged from the erosion of a constitutionally established separation of powers with respect to the initiation and administration of foreign military conflicts. In defense of this theory, it uses case studies to assess the relevance of its predictions and to weigh potential objections involving selection bias and imperfect information.
The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction.

Motivation The computational prediction of gene function is a key step in making full use of newly sequenced genomes. Function is generally predicted by transferring annotations from homologous genes or proteins for which experimental evidence exists. The 'ortholog conjecture' proposes that orthologous genes should be preferred when making such predictions, as they evolve functions more slowly than paralogous genes. Previous research has provided little support for the ortholog conjecture, though the incomplete nature of the data cast doubt on the conclusions. Results We use experimental annotations from over 40 000 proteins, drawn from over 80 000 publications, to revisit the ortholog conjecture in two pairs of species: (i) Homo sapiens and Mus musculus and (ii) Saccharomyces cerevisiae and Schizosaccharomyces pombe. By making a distinction between questions about the evolution of function versus questions about the prediction of function, we find strong evidence against the ortholog conjecture in the context of function prediction, though questions about the evolution of function remain difficult to address. In both pairs of species, we quantify the amount of information that would be ignored if paralogs are discarded, as well as the resulting loss in prediction accuracy. Taken as a whole, our results support the view that the types of homologs used for function transfer are largely irrelevant to the task of function prediction. Maximizing the amount of data used for this task, regardless of whether it comes from orthologs or paralogs, is most likely to lead to higher prediction accuracy. Availability and implementation https://github.com/predragradivojac/oc. Supplementary information Supplementary data are available at Bioinformatics online.
The Early Food Insecurity Impacts of COVID-19

COVID-19 has disrupted food access and impacted food insecurity, which is associated with numerous adverse individual and public health outcomes. To assess these challenges and understand their impact on food security, we conducted a statewide population-level survey using a convenience sample in Vermont from 29 March to 12 April 2020, during the beginning of a statewide stay-at-home order. We utilized the United States Department of Agriculture six-item validated food security module to measure food insecurity before COVID-19 and since COVID-19. We assessed food insecurity prevalence and reported food access challenges, coping strategies, and perceived helpful interventions among food secure, consistently food insecure (pre-and post-COVID-19), and newly food insecure (post COVID-19) respondents. Among 3219 respondents, there was nearly a one-third increase (32.3%) in household food insecurity since COVID-19 (p < 0.001), with 35.5% of food insecure households classified as newly food insecure. Respondents experiencing a job loss were at higher odds of experiencing food insecurity (OR 3.06; 95% CI, 2.114–0.46). We report multiple physical and economic barriers, as well as concerns related to food access during COVID-19. Respondents experiencing household food insecurity had higher odds of facing access challenges and utilizing coping strategies, including two-thirds of households eating less since COVID-19 (p < 0.001). Significant differences in coping strategies were documented between respondents in newly food insecure vs. consistently insecure households. These findings have important potential impacts on individual health, including mental health and malnutrition, as well as on future healthcare costs. We suggest proactive strategies to address food insecurity during this crisis.
New Monetarism in Continuous Time: Methods and Applications

We develop a New Monetarist model in continuous time where agents trade continuously in competitive markets and infrequently in pairwise meetings. Agents can produce and consume both in flows over time intervals and in discrete quantities at points in time. We detail the methodology to solve individual optimization problems and characterize the full set of perfect foresight equilibria. We illustrate the role of continuous time and the tractability of our approach with three applications: (i) forward guidance with policy announcements; (ii) aggregate demand management and monetary policy; (iii) open-market operations with partially liquid bonds.
Circadian Rhythms of Perineuronal Net Composition

Perineuronal nets (PNNs) are extracellular matrix (ECM) structures that envelop neurons and regulate synaptic functions. Long thought to be stable structures, PNNs have been recently shown to respond dynamically during learning, potentially regulating the formation of new synapses. We postulated that PNNs vary during sleep, a period of active synaptic modification. Notably, PNN components are cleaved by matrix proteases such as the protease cathepsin-S. This protease is diurnally expressed in the mouse cortex, coinciding with dendritic spine density rhythms. Thus, cathepsin-S may contribute to PNN remodeling during sleep, mediating synaptic reorganization. These studies were designed to test the hypothesis that PNN numbers vary in a diurnal manner in the rodent and human brain, as well as in a circadian manner in the rodent brain, and that these rhythms are disrupted by sleep deprivation. In mice, we observed diurnal and circadian rhythms of PNNs labeled with the lectin Wisteria floribunda agglutinin (WFA+ PNNs) in several brain regions involved in emotional memory processing. Sleep deprivation prevented the daytime decrease of WFA+ PNNs and enhances fear memory extinction. Diurnal rhythms of cathepsin-S expression in microglia were observed in the same brain regions, opposite to PNN rhythms. Finally, incubation of mouse sections with cathepsin-S eliminated PNN labeling. In humans, WFA+ PNNs showed a diurnal rhythm in the amygdala and thalamic reticular nucleus (TRN). Our results demonstrate that PNNs vary in a circadian manner and this is disrupted by sleep deprivation. We suggest that rhythmic modification of PNNs may contribute to memory consolidation during sleep.
Near-field plates and the near zone of metasurfaces

A brief, tutorial account is given of the differences between the near and far regions of the elec-tromagnetic field emphasizing the source-dependent behavior of the former and the universal properties of the latter. Field patterns of near-field plates, that is, metasurfaces used for sub-wavelength applications, are discussed in some detail. Examples are given of fields that decay away from the plates in an exponential manner, a ubiquitous feature of many interface problems, and metasurfaces for which the decay is not exponential, but algebraic. It is also shown that a properly designed system of two parallel near-field plates can produce fields that exhibit pseudo minima, which are potentially useful for near-field tweezer-like applications.
Distributionally Robust Tuning of Anomaly Detectors in Cyber-Physical Systems with Stealthy Attacks **

Designing resilient control strategies for mitigating stealthy attacks is a crucial task in emerging cyber-physical systems. In the design of anomaly detectors, it is common to assume Gaussian noise models to maintain tractability; however, this assumption can lead the actual false alarm rate to be significantly higher than expected. We propose a distributionally robust anomaly detector for noise distributions in moment-based ambiguity sets. We design a detection threshold that guarantees that the actual false alarm rate is upper bounded by the desired one by using generalized Chebyshev inequalities. Furthermore, we highlight an important tradeoff between the worst-case false alarm rate and the potential impact of a stealthy attacker by efficiently computing an outer ellipsoidal bound for the attack-reachable states corresponding to the distributionally robust detector threshold. We illustrate this trade-off with a numerical example and compare the proposed approach with a traditional chi-squared detector.
On a class of new nonlocal traffic flow models with look-ahead rules

Abstract This paper presents a new class of one-dimensional (1D) traffic models with look-ahead rules that take into account of two effects: nonlocal slow-down effect and right-skewed non-concave asymmetry in the fundamental diagram. The proposed 1D cellular automata (CA) models with the Arrhenius type look-ahead interactions implement stochastic rules for cars’ movement following the configuration of the traffic ahead of each car. In particular, we take two different look-ahead rules: one is based on the distance from the car under consideration to the car in front of it; the other one depends on the car density ahead. Both rules feature a novel idea of multiple moves, which plays a key role in recovering the non-concave flux in the macroscopic dynamics. Through a semi-discrete mesoscopic stochastic process, we derive the coarse-grained macroscopic dynamics of the CA model. We also design a numerical scheme to simulate the proposed CA models with an efficient list-based kinetic Monte Carlo (KMC) algorithm. Our results show that the fluxes of the KMC simulations agree with the coarse-grained macroscopic averaged fluxes for the different look-ahead rules under various parameter settings.
Does Low Latency Trading Improve Market Efficiency? A Discussion

Abstract Chordia and Miao (2020) provide evidence that low-latency trading (LLT) improves the long-term informational efficiency of stock prices. This discussion raises two primary concerns with their analysis. First, the mechanism through which LLT enhances long-term efficiency is unclear. Second, CM’s measure of LLT trading activity is correlated with non-LLT trading activity, which may in turn cause the documented improvements in efficiency. We close by proposing an alternative explanation—changes in market microstructure have had a bifurcated impact on liquidity, enhancing efficiency for large and liquid stocks, but not for small and illiquid stocks.
Towards Physics-informed Deep Learning for Turbulent Flow Prediction

While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim to predict turbulent flow by learning its highly nonlinear dynamics from spatiotemporal velocity fields of large-scale fluid flow simulations of relevance to turbulence modeling and climate modeling. We adopt a hybrid approach by marrying two well-established turbulent flow simulation techniques with deep learning. Specifically, we introduce trainable spectral filters in a coupled model of Reynolds-averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES), followed by a specialized U-net for prediction. Our approach, which we call Turbulent-Flow Net, is grounded in a principled physics model, yet offers the flexibility of learned representations. We compare our model with state-of-the-art baselines and observe significant reductions in error for predictions 60 frames ahead. Most importantly, our method predicts physical fields that obey desirable physical characteristics, such as conservation of mass, whilst faithfully emulating the turbulent kinetic energy field and spectrum, which are critical for accurate prediction of turbulent flows.
The Realization Problem for Discrete Morse Functions on Trees

We introduce a new notion of equivalence of discrete Morse functions on graphs called persistence equivalence. Two functions are considered persistence equivalent if and only if they induce the sam...
Corporate governance and product market competition: evidence from import tariff reductions

Using large reductions in import tariffs as an exogenous shock to the competitive environment, this paper examines how an increase in foreign competition affects the importance of corporate governance. Consistent with prior studies, weak governance is associated with lower firm value, lower operating performance, lower labor productivity, higher capital expenditures, higher non-production expenses, and higher wages in the absence of increased competition. However, the differences between weak and strong governance firms along each of these dimensions are reduced or eliminated after a tariff cut. The effects are concentrated among firms in previously noncompetitive industries, defined as having sales concentration above the median or import penetration below the median in the year before the tariff cut. These conclusions are supported through the use of multiple alternative measures of operating performance and corporate governance. Altogether, the evidence indicates that an increase in competition can mitigate agency costs and serve as a substitute for traditional corporate governance mechanisms.
Performing Stochastic Computation Deterministically

Stochastic logic performs computation on data represented by random bit-streams. The representation allows complex arithmetic to be performed with very simple logic, but it suffers from high latency and poor precision. Furthermore, the results are always somewhat inaccurate due to random fluctuations. In this paper, we show that randomness is not a requirement for this computational paradigm. If properly structured, the same arithmetical constructs can operate on deterministic bit-streams, with the data represented uniformly by the fraction of 1's versus 0's. This paper presents three approaches for the computation: relatively prime stream lengths, rotation, and clock division. Unlike stochastic methods, all three of our deterministic methods produce completely accurate results. The cost of generating the deterministic streams is a small fraction of the cost of generating streams from random/pseudorandom sources. Most importantly, the latency is reduced by a factor of (1/2n), where n is the equivalent number of bits of precision. When computing in unary, the bit-stream length increases with each level of logic. This is an inevitable consequence of the representation, but it can result in unmanageable bit-stream lengths. We discuss two methods for maintaining constant bit-streams lengths via approximations, based on low-discrepancy sequences. These methods provide the best accuracy and area x delay product. They are fast-converging and therefore offer progressive precision.
Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking

Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment. While their accuracy outperforms other types of depth sensors, such as stereo or time-of-flight cameras, the accurate modeling of LIDAR sensors requires laborious manual calibration that typically does not take into account the interaction of laser light with different surface types, incidence angles and other phenomena that significantly influence measurements. In this work, we introduce a physically plausible model of a 2D continuous-wave LIDAR that accounts for the surface-light interactions and simulates the measurement process in the Hokuyo URG-04LX LIDAR. Through automatic differentiation, we employ gradient-based optimization to estimate model parameters from real sensor measurements.
Election Score Can Be Harder than Winner

Election systems based on scores generally determine the winner by computing the score of each candidate and the winner is the candidate with the best score. It would be natural to expect that computing the winner of an election is at least as hard as computing the score of a candidate. We show that this is not always the case. In particular, we show that for Young elections for dichotomous preferences the winner problem is easy, while determining the score of a candidate is hard. This complexity behavior has not been seen before and is unusual. The easiness of the winner problem for dichotomous Young crucially uses the fact that dichotomous preferences guarantee the transitivity of the majority relation. In addition to dichotomous preferences we also look at single-peaked preferences, the most well-studied domain restriction that guarantees the transitivity of the majority relation. We show that for the three major hard election systems and their natural variants, dichotomous Young is the only case where winner is easy and score is hard. This also solves an open question from Lackner and Peters (AAAI 2017), by providing a polynomial-time algorithm for Dodgson score for single-peaked electorates.
In it to win it? Self-esteem and income-earning among couples

Abstract This paper investigates whether the relative self-esteem level of spouses can lead to within-household competition for inputs and affect economic gender inequality in the home. Using data on smallholder farmer couples from Cote d’Ivoire, we examine the relationship between spouses’ self-esteem and income-earning in agriculture. Although the link between own self-esteem and crop income-earning is positive, we find that one spouse’s self-esteem is negatively related to the other’s income-earning, particularly income-earning in higher-value export-oriented agriculture. Women’s outcomes are more sensitive both to their own self-esteem (positively) and to their partners’ (negatively) than men’s. This negative relationship is driven by individuals during middle age, when self-esteem is considered most stable. A key channel through which self-esteem appears to matter is by increasing control over household land: a crucial input to agricultural production. In addition to confirming the importance of noncognitive skills for poverty reduction in rural settings, our findings highlight the importance of their impact on intra- and inter-household inequality, especially in the presence of interlocking market failures constraining the supply of inputs to production.
Photonuclear and Two-Photon Interactions at High-Energy Nuclear Colliders

Ultraperipheral collisions (UPCs) of heavy ions and protons are the energy frontier for electromagnetic interactions. Both photonuclear and two-photon collisions are studied at collision energies t...
Context-Aware Temporal Logic for Probabilistic Systems

In this paper, we introduce the context-aware probabilistic temporal logic (CAPTL) that provides an intuitive way to formalize system requirements by a set of PCTL objectives with a context-based priority structure. We formally present the syntax and semantics of CAPTL and propose a synthesis algorithm for CAPTL requirements. We also implement the algorithm based on the PRISM-games model checker. Finally, we demonstrate the usage of CAPTL on two case studies: a robotic task planning problem, and synthesizing error-resilient scheduler for micro-electrode-dot-array digital microfluidic biochips.
Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

This article presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Numerical experiments have been devised to verify the performance of the proposed method.
Boundary twists, instabilities, and creation of skyrmions and antiskyrmions

We formulate and study the general boundary conditions dictating the magnetization profile in the vicinity of an interface between magnets with dissimilar properties. Boundary twists in the vicinity of an edge due to Dzyaloshinskii-Moriya interactions have been first discussed by Wilson et al. [Phys. Rev. B 88, 214420 (2013)] and by Rohart and Thiaville [Phys. Rev. B 88, 184422 (2013)]. We show that in general case the boundary conditions lead to the magnetization profile corresponding to the N\'eel, Bloch, or intermediate twist. We explore how such twists can be utilized for creation of skyrmions and antiskyrmions, e.g., in a view of magnetic memory applications. To this end, we study various scenarios of how skyrmions and antiskyrmions can be created from interface magnetization twists due to local instabilities. We also show that a judicious choice of Dzyaloshinskii-Moriya tensor (hence a carefully designed material) can lead to local instabilities generating certain types of skyrmions or antiskyrmions. The local instabilities are shown to appear in solutions of the Bogoliubov-de-Gennes equations describing ellipticity of magnon modes bound to interfaces. In one considered scenario, a skyrmion-antiskyrmion pair can be created due to instabilities at an interface between materials with properly engineered Dzyaloshinskii-Moriya interactions. We use micromagnetics simulations to confirm our analytical predictions.
Dominant contributions to lateral distribution functions in ultra-high energy cosmic ray air showers

Abstract In hadron induced air showers of highest energies (E>1018 eV), the lateral distribution functions of electrons and muons are a superposition of many separate electromagnetic sub-showers, initiated by meson decay at different altitudes and energies. The lateral distribution function is the primary tool for reconstructing the energy of the primary in a UHE cosmic ray shower, so understanding it in detail is a prerequisite for having confidence in the energy determination. We analyze in this paper the dominant contributions to the ground level lateral distribution functions, as a function of the altitude and energy at which the sub-showers are initiated. Far from the core, the dominant contribution to the density of electrons comes from sub-showers initiated at low altitudes and low energies (E
MAPPING OUT THE JET CORRELATION LANDSCAPE: A PERSPECTIVE FROM PHENIX EXPERIMENT

This is a status report on where PHENIX stands in terms of mapping out the landscape of jet correlation in pT, hadron species, . We focus on correlation results at high pT which are sensitive to the quenching of the jet by medium as well as results at low and intermediate pT, which allow studying the response of the medium to the jet.
New 'gauge' theories in six-dimensions

More general constructions are given of six-dimensional theories that look at low energy like six-dimensional super Yang-Mills theory. The constructions start with either parallel fivebranes in Type IIB, or M-theory on (ℂ2 × S1)/Γ for Γ a suitable finite group. Via these constructions, one can obtain six-dimensional theories with any simple gauge group, and SU(r) theories with any rational theta angle. A matrix construction of these theories is also possible.
Negative Energy And Angular Momentum Modes Of Thin Accretion Disks

This work derives the linearized equations of motion, the Lagrangian density, the Hamiltonian density, and the canonical angular momentum density for general perturbations [∝ exp (imφ) with m = 0, ± 1, ...] of a geometrically thin self-gravitating, homentropic fluid disk including the pressure. The theory is applied to “eccentric,” m = ± 1 perturbations of a geometrically thin Keplerian disk. We find m = 1 modes at low frequencies relative to the Keplerian frequency. Further, it is shown that these modes can have negative energy and negative angular momentum. The radial propagation of these low-frequency m = 1 modes can transport angular momentum away from the inner region of a disk and thus increase the rate of mass accretion. Depending on the radial boundary conditions there can be discrete low-frequency, negative-energy, m = 1 modes.
Higgs–photon resonances

We study models that produce a Higgs boson plus photon ( $$h^0\gamma $$ ) resonance at the LHC. When the resonance is a $$Z'$$ boson, decays to $$h^0\gamma $$ occur at one loop. If the $$Z'$$ boson couples at tree level to quarks, then the $$h^0\gamma $$ branching fraction is typically of order $$10^{-5}$$ or smaller. Nevertheless, there are models that would allow the observation of $$Z'\rightarrow \,h^0\gamma $$ at $$\sqrt{s}=13$$ TeV with a cross section times branching fraction larger than 1 fb for a $$Z'$$ mass in the 200–450 GeV range, and larger than 0.1 fb for a mass up to 800 GeV. The one-loop decay of the $$Z'$$ into lepton pairs competes with $$h^0\gamma $$ , even if the $$Z'$$ couplings to leptons vanish at tree level. We also present a model in which a $$Z'$$ boson decays into a Higgs boson and a pair of collimated photons, mimicking an $$h^0\gamma $$ resonance. In this model, the $$h^0\gamma $$ resonance search would be the discovery mode for a $$Z'$$ as heavy as 2 TeV. When the resonance is a scalar, although decay to $$h^0\gamma $$ is forbidden by angular momentum conservation, the $$h^0$$ plus collimated photons channel is allowed. We comment on prospects of observing an $$h^0\gamma $$ resonance through different Higgs decays, on constraints from related searches, and on models where $$h^0$$ is replaced by a nonstandard Higgs boson.
Semiclassical dynamics in the mixed quantum-classical limit.

The semiclassical double Herman-Kluk initial value representation is an accurate approach to computing quantum real time correlation functions, but its applications are limited by the need to evaluate an oscillatory integral. In previous work, we have shown that this “sign problem” can be mitigated using the modified Filinov filtration technique to control the extent to which individual modes of the system contribute to the overall phase of the integrand. Here, we follow this idea to a logical conclusion: we analytically derive a general expression for the mixed quantum-classical limit of the semiclassical correlation function—analytical mixed quantum-classical-initial value representation (AMQC-IVR), where the phase contributions from the “classical” modes of the system are filtered while the “quantum” modes are treated in the full semiclassical limit. We numerically demonstrate the accuracy and efficiency of the AMQC-IVR formulation in calculations of quantum correlation functions and reaction rates using three model systems with varied coupling strengths between the classical and quantum subsystems. We also introduce a separable prefactor approximation that further reduces computational cost but is only accurate in the limit of weak coupling between the quantum and classical subsystems.
Revealing black holes with Gaia

We estimate the population of black holes with luminous stellar companions (BH-LCs) in the Milky Way (MW) observable by Gaia. We evolve a realistic distribution of BH-LC progenitors from zero-age to the current epoch taking into account relevant physics, including binary stellar evolution, BH-formation physics, and star formation rate, to estimate the BH-LC population in the MW today. We predict that Gaia will discover between 3800 and 12,000 BH-LCs by the end of its 5 yr mission, depending on BH natal kick strength and observability constraints. We find that the overall yield, and distributions of eccentricities and masses of observed BH-LCs can provide important constraints on the strength of BH natal kicks. Gaia-detected BH-LCs are expected to have very different orbital properties compared to those detectable via radio, X-ray, or gravitational wave observations.
On the distribution of orbits in affine varieties

Given an affine variety $X$, a morphism $\phi:X\to X$, a point $\alpha\in X$, and a Zariski closed subset $V$ of $X$, we show that the forward $\phi$-orbit of $\alpha$ meets $V$ in at most finitely many infinite arithmetic progressions, and the remaining points lie in a set of Banach density zero. This may be viewed as a weak asymptotic version of the Dynamical Mordell-Lang Conjecture for affine varieties. The results hold in arbitrary characteristic, and the proof uses methods of ergodic theory applied to compact Berkovich spaces.
Lining up the Galactic Center Gamma-Ray Excess

Abstract Dark matter particles annihilating into Standard Model fermions may be able to explain the recent observation of a gamma-ray excess in the direction of the Galactic Center. Recently, a hidden photon model has been proposed to explain this signal. Supplementing this model with a dipole moment operator and a small dark sector mass splitting allows a large cross section to a photon line while avoiding direct detection and other constraints. Comparing the line and continuum cross sections, we find that the line is suppressed only by the relative scales and couplings. Given current constraints on this ratio, a line discovery in the near future could point to a new scale Λ ∼ O ( 1 TeV ) , where we would expect to discover new charged particles. Moreover, such a line would also imply that dark matter can be visible in near-future direct detection experiments. (FERMILAB-PUB-14-205-A-T)
Large N QCD – Continuum Reduction and Chiral Condensate

Continuum reduction in large N QCD enables one to extract physical quantities in the N → ∞ limit of QCD by working in small physics volumes. The computation of chiral condensate is an example of such a calculation.
Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability

In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance (UKF-GPS) is proposed and compared with five existing approaches, including UKF-schol, UKF- $\boldsymbol {\kappa }$ , UKF-modified, UKF- $\boldsymbol {\Delta Q}$ , and the square-root UKF (SR-UKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is found that UKF-schol, UKF- $\boldsymbol {\kappa }$ , and UKF- $\boldsymbol {\Delta Q}$ do not work well in some estimations while UKF-GPS works well in most cases. UKF-modified and SR-UKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.
Strong coupling between on chip notched ring resonator and nanoparticle

We have demonstrated a new photonic structure to achieve strong optical coupling between nanoparticle and photonic molecule by utilizing a notched microring resonators. By creating a notch in the ring resonator and putting a nanoparticle inside the notch, large spectral shifts and splittings at nanometer scale can be achieved, compared to only picometer scale observed by fiber tip evanescently coupled to the surface of microsphere, thereby significantly lowered the quality factor requirement for single nanoparticle detection. The ability for sorting the type of nanoparticles due to very different mode shift and splitting behavior of dielectric and metallic nanoparticles is also emphasized.
Material Targets for Scaling All-Spin Logic

All-spin logic devices are promising candidates to augment and complement beyond-CMOS integrated circuit computing due to non-volatility, ultra-low operating voltages, higher logical efficiency, and high density integration. However, the path to reach lower energy-delay product performance compared to CMOS transistors currently is not clear. We show that scaling and engineering the nanoscale magnetic materials and interfaces is the key to realizing spin logic devices that can surpass energy-delay performance of CMOS transistors. With validated stochastic nano-magnetic and vector spin transport numerical models, we derive the target material and interface properties for the nanomagnets and channels. We identified promising new directions for material engineering/discovery focusing on systematic scaling of magnetic anisotropy (Hk) with saturation magnetization (Ms), use of perpendicular magnetic anisotropy, and interface spin mixing conductance of ferromagnet/spin channel interface (Gmix). We provide systematic targets for scaling spin logic energy-delay product toward a 2 aJ.ns energy-delay product, comprehending the stochastic noise for nanomagnets.
Phase tracking for sub-shot-noise-limited receivers

This article demonstrates a real-time phase tracking method that allows sub-shot-noise receivers to maintain high performance in channels with phase noise.