cssi/SciSciGPT-SciSciNet huggingface dataset:
Approximation Algorithms for Extensible Bin Packing

In a variation of bin packing called extensible bin packing, the number of bins is specified as part of the input, and bins may be extended to hold more than the usual unit capacity. The cost of a bin is 1 if it is not extended, and the size if it is extended. The goal is to pack a set of items of given sizes into the specified number of bins so as to minimize the total cost. Adapting ideas Grotschel et al. (1981), Grotschel et al. (1988), Karmarkar and Karp (1982), Murgolo (1987), we give a fully polynomial time asymptotic approximation scheme (FPTAAS) for extensible bin packing. We close with comments on the complexity of obtaining stronger results.
Using EEG microstates to examine post-encoding quiet rest and subsequent word-pair memory.

Abstract Evidence suggests that the brain preferentially consolidates memories during “offline” periods, in which an individual is not performing a task and their attention is otherwise undirected, including spans of quiet, resting wakefulness. Moreover, research has demonstrated that factors such as the initial encoding strength of information influence which memories receive the greatest benefit. Recent studies have begun to investigate these periods of post-learning quiet rest using EEG microstate analysis to observe the electrical dynamics of the brain during these stretches of memory consolidation, specifically finding an increase in the amount of the canonical microstate D during a post-encoding rest period. Here, we implement an exploratory analysis to probe the activity of EEG microstates during a post-encoding session of quiet rest in order to scrutinize the impact of learning on microstate dynamics and to further understand the role these microstates play in the consolidation of memories. We examined 54 subjects (41 female) as they completed a word-pair memory task designed to use repetition to vary the initial encoding strength of the word-pair memories. In this study, we were able to replicate previous research in which there was a significant increase (p
Parameterization of the Stoner-Wohlfarth model of magnetic hysteresis

Abstract The Stoner-Wohlfarth (SW) is the most used model of magnetic hysteresis, but its computation is time-consuming. We approximate piecewise this model by easy-to-compute analytic functions. Our parametrization is suitable for fast quantitative evaluations and fitting experimental data, which we exemplify. Applicability of this parametrization is as broad as that of the SW model, which can be extended to include more parameters.
Foundations of space-time finite element methods: Polytopes, interpolation, and integration

Abstract The main purpose of this article is to facilitate the implementation of space-time finite element methods in four-dimensional space. In order to develop a finite element method in this setting, it is necessary to create a numerical foundation, or equivalently a numerical infrastructure. This foundation should include a collection of suitable elements (usually hypercubes, simplices, or closely related polytopes), numerical interpolation procedures (usually orthonormal polynomial bases), and numerical integration procedures (usually quadrature rules). It is well known that each of these areas has yet to be fully explored, and in the present article, we attempt to directly address this issue. We begin by developing a concrete, sequential procedure for constructing generic four-dimensional elements (4-polytopes). Thereafter, we review the key numerical properties of several canonical elements: the tesseract, tetrahedral prism, and pentatope. Here, we provide explicit expressions for orthonormal polynomial bases on these elements. Next, we construct fully symmetric quadrature rules with positive weights that are capable of exactly integrating high-degree polynomials, e.g. up to degree 17 on the tesseract. Finally, the quadrature rules are successfully tested using a set of canonical numerical experiments on polynomial and transcendental functions.
Large-scale labeling and assessment of sex bias in publicly available expression data

Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.8% vs. 18.9% in human and 21.6% vs. 31.1% in mouse, respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies, respectively. We leverage our expression-based sex labels to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2–5%). Our results demonstrate limited overall sex bias, while highlighting high bias in specific subfields and underscoring the importance of including sex labels to better understand the underlying biology. We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.
Asymmetric Partisan Voter Turnout Games

Since Downs proposed that the act of voting is irrational in 1957, myriad models have been proposed to explain voting and account for observed turnout patterns. We propose a model in which partisans consider both the instrumental and expressive benefits of their vote when deciding whether or not to abstain in an election, introducing an asymmetry that most other models do not consider. Allowing learning processes within our electorate, we analyze what evolutionarily stable strategies are rationalizable under various conditions. Upon varying electorate size, the partisan split of the electorate, and the degree to which an electorate takes underdog considerations into account in its payoff structure, we find that different equilibria arise. Our model predicts comparative statics that are consistent with voter behavior, specifically affirming a “size effect,” in which turnout decreases as electorate size increases. Furthermore, relaxing some of our preliminary assumptions eliminates some of the discrepancies between the predictions of our model and empirical voter behavior. In particular, our work demonstrates that misperceptions about the partisan split of an electorate may account for high turnout behavior .
Refined Locally Linear Transform-Based Spectral-Domain Gradient Sparsity and Its Applications in Spectral CT Reconstruction

Spectral computed tomography (CT) is extension of the conventional single spectral CT (SSCT) along the energy dimension, which achieves superior energy resolution and material distinguish- ability. However, for the state-of-the-art photon counting detector (PCD) based spectral CT, because the emitted photons with a fixed total number for each X-ray beam are divided into several energy bins, the noise level is increased in each reconstructed channel image, and it further leads to an inaccurate material decomposition. To improve the reconstructed image quality and decomposition accuracy, in this work, we first employ a refined locally linear transform to convert the structural similarity among two-dimensional (2D) spectral CT images to a spectral-dimension gradient sparsity. By combining the gradient sparsity in the spatial domain, a global three-dimensional (3D) gradient sparsity is constructed, then measured with $L_{1}$ -, $L_{0}$ - and trace-norm, respectively. For each sparsity measurement, we propose the corresponding optimization model, develop the iterative algorithm, and verify the effectiveness and superiority with real datasets.
TMR: Evaluating NER Recall on Tough Mentions

We propose the Tough Mentions Recall (TMR) metrics to supplement traditional named entity recognition (NER) evaluation by examining recall on specific subsets of ”tough” mentions: unseen mentions, those whose tokens or token/type combination were not observed in training, and type-confusable mentions, token sequences with multiple entity types in the test data. We demonstrate the usefulness of these metrics by evaluating corpora of English, Spanish, and Dutch using five recent neural architectures. We identify subtle differences between the performance of BERT and Flair on two English NER corpora and identify a weak spot in the performance of current models in Spanish. We conclude that the TMR metrics enable differentiation between otherwise similar-scoring systems and identification of patterns in performance that would go unnoticed from overall precision, recall, and F1.
Damage Identification for The Tree-like Network through Frequency-domain Modeling

Abstract In this paper, we propose a method to identify the damaged component and quantify its damage amount in a large network given its overall frequency response. The identification procedure takes advantage of our previous work which exactly models the frequency response of that large network when it is damaged. As a result, the test shows that our method works well when some noise present in the frequency response measurement. In addition, the effects brought by a damaged component which is located deep inside that large network are also discussed.
Infrared molecular emissions from comets

The possibility of detecting IR molecular line emission from cometary parent molecules is explored. Due to the non-LTE conditions in the inner coma and the large amount of near IR solar flux, IR fluorescence will be a significant source of cometary emission and, in fact, will dominate the grain radiation in a sufficiently high resolution instrument. The detection of this line emission will be difficult due to absorption in the terrestrial atmosphere, but it appears possible to measure cometary H2O emission from airplane altitudes. As IR molecular line emission represents one of the few promising methods of detecting cometary parent molecules directly, further research on this problem should be vigorously pursued. Previously announced in STAR as N83-30344
Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

We create and validate a new measure of an occupation’s exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level, which we call the AI Industry Exposure (AIIE) and a measure of AI exposure at the county level, which we call the AI Geographic Exposure (AIGE). We also describe several ways in which the AIOE can be used to create firm level measures of AI exposure. We validate the measures and describe how they can be used in different applications by management, organization and strategy scholars.
UNC-2 CaV2 Channel Localization at Presynaptic Active Zones Depends on UNC-10/RIM and SYD-2/Liprin-α in Caenorhabditis elegans.

Presynaptic active zone proteins couple calcium influx with synaptic vesicle exocytosis. However, the control of presynaptic calcium channel localization by active zone proteins is not completely understood. In a Caenorhabditis elegans (C. elegans) forward genetic screen, we find that UNC-10/RIM (Rab3-interacting molecule) and SYD-2/Liprin-α regulate presynaptic localization of UNC-2, the CaV2 channel ortholog. We further quantitatively analyzed live animals using endogenously GFP-tagged UNC-2 and active zone components. Consistent with the interaction between RIM and CaV2 in mammals, the intensity and number of UNC-2 channel puncta at presynaptic terminals were greatly reduced in unc-10 mutant animals. To understand how SYD-2 regulates presynaptic UNC-2 channel localization, we analyzed presynaptic localization of endogenous SYD-2, UNC-10, RIMB-1/RIM-BP (RIM binding protein), and ELKS-1. Our analysis revealed that although SYD-2 is the most critical for active zone assembly, loss of SYD-2 function does not completely abolish presynaptic localization of UNC-10, RIMB-1, and ELKS-1, suggesting an existence of SYD-2-independent active zone assembly. UNC-2 localization analysis in double and triple mutants of active zone components show that SYD-2 promotes UNC-2 localization by partially controlling UNC-10 localization, and ELKS-1 and RIMB-1 also contribute to UNC-2 channel localization. In addition, we find that core active zone proteins are unequal in their abundance. Although the abundance of UNC-10 at the active zone is comparable to UNC-2, SYD-2 and ELKS-1 are twice more and RIMB-1 four times more abundant than UNC-2. Together our data show that UNC-10, SYD-2, RIMB-1, and ELKS-1 control presynaptic UNC-2 channel localization in redundant yet distinct manners. SIGNIFICANCE STATEMENT Precise control of neurotransmission is dependent on the tight coupling of the calcium influx through voltage-gated calcium channels (VGCCs) to the exocytosis machinery at the presynaptic active zones. However, how these VGCCs are tethered to the active zone is incompletely understood. To understand the mechanism of presynaptic VGCC localization, we performed a C. elegans forward genetic screen and quantitatively analyzed endogenous active zones and presynaptic VGCCs. In addition to RIM, our study finds that SYD-2/Liprin-α is critical for presynaptic localization of VGCCs. Yet, the loss of SYD-2, a core active zone scaffolding protein, does not completely abolish the presynaptic localization of the VGCC, showing that the active zone is a resilient structure assembled by redundant mechanisms.
Hospital quality-review spending and patient safety: a longitudinal analysis using instrumental variables

Since the landmark Institute of Medicine’s (IOM’s) 2000 report first focused attention to the problem of the safety of inpatient care, it has been a priority of hospital staffs, administrators, and policymakers. Despite remarkable progress in the 20 years since the IOM report, there is still much unknown about how these improvements in safety have been achieved. Using a 12-year (2004–2015) panel of Florida acute-care general hospitals, we estimate the relationship between hospital expenditure on peer (or quality) review and patient-safety outcomes, using a composite measure of patient safety (PSI-90) from the Agency for Healthcare Research and Quality. Our identification strategy to account for endogenous quality-review (QR) expenditure relies on exogeneity from within the hospital, in which we use staffing of non-acute ancillary services as instruments for QR expenditure. Estimation of hospital fixed effects (FE) with instrumental variables (FEIV) yields a statistically significant and beneficial effect of QR expenditure on patient safety. We find that, on average, a standard-deviation ($2.4 million) increase in QR expenditure is associated with a 16% decrease in adverse patient-safety events (i.e. PSI-90). Broadly, this study represents a unique contribution to the literature by examining a direct relationship between hospital peer-review spending and inpatient quality of care.
Effects of early life seizures on coordination of hippocampal–prefrontal networks: Influence of sex and dynamic brain states

Objective Early life seizures (ELSs) alter activity-dependent maturation of neuronal circuits underlying learning and memory. The pathophysiological mechanisms underpinning seizure-induced cognitive impairment are not fully understood, and critical variables such as sex and dynamic brain states with regard to cognitive outcomes have not been explored. We hypothesized that in comparison to control (CTL) rats, ELS rats would exhibit deficits in spatial cognition correlating with impaired dynamic neural signal coordination between the hippocampus and medial prefrontal cortex (mPFC). Methods Male and female rat pups were given 50 flurothyl-induced seizures over 10 days starting at postnatal Day 15. As adults, spatial cognition was tested through active avoidance on a rotating arena. Microwire tetrodes were implanted in the mPFC and CA1 subfield. Single cells and local field potentials were recorded and analyzed in each region during active avoidance and sleep. Results ELS males exhibited avoidance impairments, whereas female rats were unaffected. During avoidance, hippocampus-mPFC coherence was higher in CTL females than CTL males across bandwidths. In comparison to CTL males, ELS male learners exhibit increased coherence within theta bandwidth as well as altered burst-timing in mPFC cell activity. Hippocampus-mPFC coherence levels are predictive of cognitive outcome in the active avoidance spatial task. Significance Spatial cognitive outcome post-ELS is sex-dependent, as females fare better than males. ELS males that learn the task exhibit increased mPFC coherence levels at low-theta frequency, which may compensate for ELS effects on mPFC cell timing. These results suggest that coherence may serve as a biomarker for spatial cognitive outcome post-ELS and emphasize the significance of analyzing sex and dynamic cognition as variables in understanding seizure effects on the developing brain.
An American-Based Study Examining the Relationship Between Participants’ Demographic Profiles and Attitudes Regarding Business Outsourcing Techniques and Strategies

The world is a global marketplace, however much inquiry has been created regarding American-based companies relocating departmental operations offshore to foreign territories. Keating (2012) indicated that popular areas for companies to reposition operations include customer service, information technology, and sales services. The main reason for moving offshore is to cut costs and to maximize on cheaper labor pools in the host country. There are cultural and language barriers that complicate matters, as well as, the media, as reported by Dobbs (2004) in the United States (U.S.) brings supposition by broadcasting the jobs lost by U.S. employees, as well as, home country power diminishing by stronger global players rising and thriving, such as, Brazil, The Philippines, and Mexico. This study quantifiably examined the relationship between participants’ demographic profiles and attitudes regarding business outsourcing techniques and strategies utilizing the Chi-Square and Fisher’s Exact tests, to gain a greater understanding of what these American-based beliefs truly are, in relation to, the sample confined within this study. Areas of concern examined attitudes towards managers, as well as, the U.S. government, regarding engaging in business outsourcing endeavors, enforcing policies to keep jobs in America, operating with a global mindset, helping people in both the home and host countries, competence factors associated with the capabilities to head outsourcing efforts based upon gender, and the training needed to successfully move operations offshore, for example. Significant differences were found in all of the demographic variables of ethnicity, gender, number of children (e.g. family size), age, education level, marital status, and annual household income.
Modelling the impact of travel restrictions on COVID-19 cases in Newfoundland and Labrador

In many jurisdictions, public health authorities have implemented travel restrictions to reduce coronavirus disease 2019 (COVID-19) spread. Policies that restrict travel within countries have been implemented, but the impact of these restrictions is not well known. On 4 May 2020, Newfoundland and Labrador (NL) implemented travel restrictions such that non-residents required exemptions to enter the province. We fit a stochastic epidemic model to data describing the number of active COVID-19 cases in NL from 14 March to 26 June. We predicted possible outbreaks over nine weeks, with and without the travel restrictions, and for contact rates 40-70% of pre-pandemic levels. Our results suggest that the travel restrictions reduced the mean number of clinical COVID-19 cases in NL by 92%. Furthermore, without the travel restrictions there is a substantial risk of very large outbreaks. Using epidemic modelling, we show how the NL COVID-19 outbreak could have unfolded had the travel restrictions not been implemented. Both physical distancing and travel restrictions affect the local dynamics of the epidemic. Our modelling shows that the travel restrictions are a plausible reason for the few reported COVID-19 cases in NL after 4 May.
The trade-off between investment in weapons and fertility is mediated through spermatogenesis in the leaf-footed cactus bug Narnia femorata.

Males have the ability to compete for fertilizations through both precopulatory and postcopulatory intrasexual competition. Precopulatory competition has selected for large weapons and other adaptations to maximize access to females and mating opportunities, while postcopulatory competition has resulted in ejaculate adaptations to maximize fertilization success. Negative associations between these strategies support the hypothesis that there is a trade-off between success at pre- and postcopulatory mating success. Recently, this trade-off has been demonstrated with experimental manipulation. Males of the leaf-footed cactus bug Narniafemorata use hind limbs as the primary weapon in male-male competition. However, males can drop a hind limb to avoid entrapment. When this autotomy occurs during development, they invest instead in large testes. While evolutionary outcomes of the trade-offs between pre- and postcopulatory strategies have been identified, less work has been done to identify proximate mechanisms by which the trade-off might occur, perhaps because the systems in which the trade-offs have been investigated are not ones that have the molecular tools required for exploring mechanism. Here, we applied knowledge from a related model species for which we have developmental knowledge and molecular tools, the milkweed bug Oncopeltus fasciatus, to investigate the proximate mechanism by which autotomized N. femorata males developed larger testes. Autotomized males had evidence of a higher rate of transit amplification divisions in the spermatogonia, which would result more spermatocytes and thus in greater sperm numbers. Identification of mechanisms underlying a trade-off can help our understanding of the direction and constraints on evolutionary trajectories and thus the evolutionary potential under multiple forms of selection.
Big Data: Unleashing information

At present, it is projected that about 4 zettabytes (or 10**21 bytes) of digital data are being generated per year by everything from underground physics experiments to retail transactions to security cameras to global positioning systems. In the U. S., major research programs are being funded to deal with big data in all five sectors (i.e., services, manufacturing, construction, agriculture and mining) of the economy. Big Data is a term applied to data sets whose size is beyond the ability of available tools to undertake their acquisition, access, analytics and/or application in a reasonable amount of time. Whereas Tien (2003) forewarned about the data rich, information poor (DRIP) problems that have been pervasive since the advent of large-scale data collections or warehouses, the DRIP conundrum has been somewhat mitigated by the Big Data approach which has unleashed information in a manner that can support informed — yet, not necessarily defensible or valid — decisions or choices. Thus, by somewhat overcoming data quality issues with data quantity, data access restrictions with on-demand cloud computing, causative analysis with correlative data analytics, and model-driven with evidence-driven applications, appropriate actions can be undertaken with the obtained information. New acquisition, access, analytics and application technologies are being developed to further Big Data as it is being employed to help resolve the 14 grand challenges (identified by the National Academy of Engineering in 2008), underpin the 10 breakthrough technologies (compiled by the Massachusetts Institute of Technology in 2013) and support the Third Industrial Revolution of mass customization.
A short proof of the symmetric determinantal representation of polynomials

Abstract We provide a short proof of the theorem that every real multivariate polynomial has a symmetric determinantal representation, which was first proved in Helton et al. (2006) [4] . We then provide an example using our approach and extend our results from the real field R to an arbitrary field F different from characteristic 2. The new approach we take is only based on elementary results from the theory of determinants, the theory of Schur complements, and basic properties of polynomials.
Asymptotics of Ridge Regression in Convolutional Models

Understanding generalization and estimation error of estimators for simple models such as linear and generalized linear models has attracted a lot of attention recently. This is in part due to an interesting observation made in machine learning community that highly over-parameterized neural networks achieve zero training error, and yet they are able to generalize well over the test samples. This phenomenon is captured by the so called double descent curve, where the generalization error starts decreasing again after the interpolation threshold. A series of recent works tried to explain such phenomenon for simple models. In this work, we analyze the asymptotics of estimation error in ridge estimators for convolutional linear models. These convolutional inverse problems, also known as deconvolution, naturally arise in different fields such as seismology, imaging, and acoustics among others. Our results hold for a large class of input distributions that include i.i.d. features as a special case. We derive exact formulae for estimation error of ridge estimators that hold in a certain high-dimensional regime. We show the double descent phenomenon in our experiments for convolutional models and show that our theoretical results match the experiments.
State Income Tax Changes and the Demand for Municipal Bond Funds

We consider how state income tax changes affect the demand for municipal bonds by in-state investors. A tax increase (decrease) makes investing in municipal bonds more (less) desirable, and theory ...
Molecular determinants for α-tubulin methylation by SETD2.

Post-translational modifications to tubulin are important for many microtubule-based functions inside cells. It was recently shown that methylation of tubulin by the histone methyltransferase SETD2 occurs on mitotic spindle microtubules during cell division, with its absence resulting in mitotic defects. However, the catalytic mechanism of methyl addition to tubulin is unclear. We used a truncated version of human wild type SETD2 (tSETD2) containing the catalytic SET and C-terminal Set2–Rpb1–interacting (SRI) domains to investigate the biochemical mechanism of tubulin methylation. We found that recombinant tSETD2 had a higher activity toward tubulin dimers than polymerized microtubules. Using recombinant single-isotype tubulin, we demonstrated that methylation was restricted to lysine 40 of α-tubulin. We then introduced pathogenic mutations into tSETD2 to probe the recognition of histone and tubulin substrates. A mutation in the catalytic domain (R1625C) allowed tSETD2 to bind to tubulin but not methylate it, whereas a mutation in the SRI domain (R2510H) caused loss of both tubulin binding and methylation. Further investigation of the role of the SRI domain in substrate binding found that mutations within this region had differential effects on the ability of tSETD2 to bind to tubulin versus the binding partner RNA polymerase II for methylating histones in vivo, suggesting distinct mechanisms for tubulin and histone methylation by SETD2. Finally, we found that substrate recognition also requires the negatively charged C-terminal tail of α-tubulin. Together, this study provides a framework for understanding how SETD2 serves as a dual methyltransferase for both histone and tubulin methylation.
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances.

We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances. We address this problem by finding a piecewise linear path for each agent such that the actual trajectories following these paths are guaranteed to satisfy the reach-and-avoid requirement. We show that the spatial tracking error of the actual trajectories of the controlled agents can be pre-computed for any qualified path that considers the minimum duration of each path segment due to actuation limits. Using these bounds, we find a collision-free path for each agent by solving Mixed Integer-Linear Programs and coordinate agents by using the priority-based search. We demonstrate our method by benchmarking in 2D and 3D scenarios with ground vehicles and quadrotors, respectively, and show improvements over the solving time and the solution quality compared to two state-of-the-art multi-agent motion planners.
Decentralized Low-Rank State Estimation for Power Distribution Systems

This article considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomes computationally challenging for large networks. We therefore develop a decentralized algorithm that capitalizes on the popular proximal alternating direction method of multipliers (proximal ADMM). The method allows us to distribute the computation among different areas of the network, leading to a scalable algorithm. By doing all computations at individual control areas and only communicating with neighboring areas, the algorithm eliminates the need for data to be sent to a central processing unit and thus increases efficiency and contributes to the goal of autonomous control of distribution networks. We illustrate the advantages of the proposed algorithm numerically using standard IEEE test cases.
Solutions of DC OPF are Never AC Feasible

In this paper, we analyze the relationship between generation dispatch solutions produced by the DC optimal power flow (DC OPF) problem by the AC optimal power flow (AC OPF) problem. While there has been much previous work in analyzing the approximation error of the DC assumption, the AC feasibility has not been fully explored, although difficulty achieving AC feasibility is known in practice. Here, under some light assumptions, we show that no solution to the DC OPF problem will satisfy the AC power flow constraints. Then, it is demonstrated that even with generation adjustments in DC OPF to account for losses, DC OPF solutions are still not AC feasible. Lastly, the computational benefits of DC OPF are analyzed in comparison with AC OPF.
Learning Personal Style from Few Examples

A key task in design work is grasping the client’s implicit tastes. Designers often do this based on a set of examples from the client. However, recognizing a common pattern among many intertwining variables such as color, texture, and layout and synthesizing them into a composite preference can be challenging. In this paper, we leverage the pattern recognition capability of computational models to aid in this task. We offer a set of principles for computationally learning personal style. The principles are manifested in PseudoClient, a deep learning framework that learns a computational model for personal graphic design style from only a handful of examples. In several experiments, we found that PseudoClient achieves a 79.40% accuracy with only five positive and negative examples, outperforming several alternative methods. Finally, we discuss how PseudoClient can be utilized as a building block to support the development of future design applications.
Modeling and analysis of several classes of self-oscillating inverters. I - State-plane representations. II - Model extension, classification, and duality relationships

The present investigation is concerned with an important class of power conditioning networks, taking into account self-oscillating dc-to-square-wave transistor inverters. The considered circuits are widely used both as the principal power converting and processing means in many systems and as low-power analog-to-discrete-time converters for controlling the switching of the output-stage semiconductors in a variety of power conditioning systems. Aspects of piecewise-linear modeling are discussed, taking into consideration component models, and an equivalent-circuit model. Questions of singular point analysis and state plane representation are also investigated, giving attention to limit cycles, starting circuits, the region of attraction, a hard oscillator, and a soft oscillator.
Community modulated recursive trees and population dependent branching processes

We consider random recursive trees that are grown via community modulated schemes that involve random attachment or degree based attachment. The aim of this paper is to derive general techniques based on continuous time embedding to study such models. The associated continuous time embeddings are not branching processes: individual reproductive rates at each time depend on the composition of the entire population at that time. Using stochastic analytic techniques we show that various key macroscopic statistics of the continuous time embedding stabilize, allowing asymptotics for a host of functionals of the original models to be derived.
Improved Lunar Lander Handling Qualities Through Control Response Type and Display Enhancements

A piloted simulation that studied the handling qualities for a precision lunar landing task from final approach to touchdown is presented. A vehicle model based on NASA's Altair Lunar Lander was used to explore the design space around the nominal vehicle configuration to determine which combination of factors provides satisfactory pilot-vehicle performance and workload; details of the control and propulsion systems not available for that vehicle were derived from Apollo Lunar Module data. The experiment was conducted on a large motion base simulator. Eight Space Shuttle and Apollo pilot astronauts and three NASA test pilots served as evaluation pilots, providing Cooper-Harper ratings, Task Load Index ratings and qualitative comments. Each pilot flew seven combinations of control response types and three sets of displays, including two varieties of guidance and a nonguided approach. The response types included Rate Command with Attitude Hold, which was used in the original Apollo Moon landings, a Velocity Increment Command response type designed for up-and-away flight, three response types designed specifically for the vertical descent portion of the trajectory, and combinations of these. It was found that Velocity Increment Command significantly improved handling qualities when compared with the baseline Apollo design, receiving predominantly Level 1 ratings. This response type could be flown with or without explicit guidance cues, something that was very difficult with the baseline design, and resulted in approximately equivalent touchdown accuracies and propellant burn as the baseline response type. The response types designed to be used exclusively in the vertical descent portion of the trajectory did not improve handling qualities.
Use of Navier-Stokes methods for the calculation of high-speed nozzle flow fields

Flows through three reference nozzles have been calculated to determine the capabilities and limitations of the widely used Navier-Stokes solver, PARC. The nozzles examined have similar dominant flow characteristics as those considered for supersonic transport programs. Flows from an inverted velocity profile (IVP) nozzle, an under expanded nozzle, and an ejector nozzle were examined. PARC calculations were obtained with its standard algebraic turbulence model, Thomas, and the two-equation turbulence model, Chien k-epsilon. The Thomas model was run with the default coefficient of mixing set at both 0.09 and a larger value of 0.13 to improve the mixing prediction. Calculations using the default value substantially underpredicted the mixing for all three flows. The calculations obtained with the higher mixing coefficient better predicted mixing in the IVP and underexpanded nozzle flows but adversely affected PARC's convergence characteristics for the IVP nozzle case. The ejector nozzle case did not converge with the Thomas model and the higher mixing coefficient. The Chien k-epsilon results were in better agreement with the experimental data overall than were those of the Thomas run with the default mixing coefficient, but the default boundary conditions for k and epsilon underestimated the levels of mixing near the nozzle exits.
Does the Size-Arrival Effect Occur With an Active Collision-Avoidance Task in an Immersive 3D Virtual Reality Environment?:

ObjectiveDetermine whether the size-arrival effect (SAE) occurs with immersive, 3D visual experiences and active collision-avoidance responses.BackgroundWhen a small near object and a large far obj...
Indivisible Mixed Manna: On the Computability of MMS+PO Allocations

In this paper we initiate the study of finding fair and efficient allocations of an indivisible mixed manna: Divide m indivisible items among n agents under the fairness notion of maximin share (MMS) and the efficiency notion of Pareto optimality (PO). A mixed manna allows an item to be a good for some agents and a chore for others. The problem of finding $\alpha$-MMS allocation for the (near) best $\alpha\in(0,1]$ for which it exists, remains unresolved even for a goods manna with constantly many agents, while the problem of finding $\alpha$-MMS+PO allocation is unexplored for any $\alpha\in(0,1]$. We make significant progress on the above questions for a mixed manna. First, we show that for any $\alpha>0$, an $\alpha$-MMS allocation may not always exist, thus ruling out solving the problem for a fixed $\alpha$. Second, towards computing $\alpha$-MMS+PO allocation for the best possible $\alpha$, we obtain a dichotomous result: We derive two conditions and show that the problem is tractable under these two conditions, while dropping either renders the problem intractable. The two conditions are: (i) number of agents is a constant, and (ii) for every agent, her absolute value for all the items is at least a constant factor of her total (absolute) value for all the goods or all the chores. In particular, first, for instances satisfying (i) and (ii) we design a PTAS - an efficient algorithm to find an $(\alpha-\epsilon)$-MMS and $\gamma$-PO allocation when given $\epsilon,\gamma>0$, for the highest possible $\alpha\in(0,1]$. Second, we show that if either condition is not satisfied then finding an $\alpha$-MMS allocation for any $\alpha\in(0,1]$ is NP-hard, even when a solution exists for $\alpha=1$. To the best of our knowledge, ours is the first algorithm that ensures both approximate MMS and PO guarantees.
On the geometry of nearly orthogonal lattices

Abstract Nearly orthogonal lattices were formally defined in [4] , where their applications to image compression were also discussed. The idea of “near orthogonality” in 2-dimensions goes back to the work of Gauss. In this paper, we focus on well-rounded nearly orthogonal lattices in R n and investigate their geometric and optimization properties. Specifically, we prove that the sphere packing density function on the space of well-rounded lattices in dimension n ≥ 3 does not have any local maxima on the nearly orthogonal set and has only one local minimum there: at the integer lattice Z n . Further, we show that the nearly orthogonal set cannot contain any perfect lattices for n ≥ 3 , although it contains multiple eutactic (and even strongly eutactic) lattices in every dimension. This implies that eutactic lattices, while always critical points of the packing density function, are not necessarily local maxima or minima even among the well-rounded lattices. We also prove that a (weakly) nearly orthogonal lattice in R n contains no more than 4 n − 2 minimal vectors (with any smaller even number possible) and establish some bounds on coherence of these lattices.
Justificacion de la democracia

El ensayo del profesor Robert Dahl, que reproducimos en las paginas que siguen, es un intento por realizar una justificacion de la democracia que "a pesar de no ser muy rigurosa ni filosofica como para convencer a los teoricos politicos y a los filosofos -segun lo senala el propio autor-, podria despertar un interes mas amplio que los argumentos de tendencia filosofica". La justificacion que aqui se plantea es simplemente aquella que sostiene que la idea de la democracia adoptada activamente por un pueblo tiende a producir los mejores sistemas politicos factibles o, en todo caso, el mejor Estado. En funcion de este objetivo, Dahl desarrolla dos supuestos: el de la igualdad en el valor intrinseco y el de autonomia personal, los cuales constituyen los pilares centrales sobre los que el autor desarrolla su hipotesis de justificacion democratica. Este documento corresponde al ensayo presentado en el Simposio Internacional sobre los Problemas de la Democracia Contemporanea, realizado en Santiago de Chile en julio de 1986, y su contenido es una adaptacion del trabajo "La democracia y sus criticos".
Application of the Level-2 Quantum Lasserre Hierarchy in Quantum Approximation Algorithms.

The Lasserre Hierarchy is a set of semidefinite programs which yield increasingly tight bounds on optimal solutions to many NP-hard optimization problems. The hierarchy is parameterized by levels, with a higher level corresponding to a more accurate relaxation. High level programs have proven to be invaluable components of approximation algorithms for many NP-hard optimization problems. There is a natural analogous quantum hierarchy, which is also parameterized by level and provides a relaxation of many (QMA-hard) quantum problems of interest. In contrast to the classical case, however, there is only one approximation algorithm which makes use of higher levels of the hierarchy. Here we provide the first ever use of the level-2 hierarchy in an approximation algorithm for a particular QMA-complete problem, so-called Quantum Max Cut. We obtain modest improvements on state-of-the-art approximation factors for this problem, as well as demonstrate that the level-2 hierarchy satisfies many physically-motivated constraints that the level-1 does not satisfy. Indeed, this observation is at the heart of our analysis and indicates that higher levels of the quantum Lasserre Hierarchy may be very useful tools in the design of approximation algorithms for QMA-complete problems.
Using Fresh Starts to Nudge Increased Retirement Savings.

Abstract We conducted a field experiment to study the effect of framing future moments in time as new beginnings (or “fresh starts”). University employees (N = 6,082) received mailings with an opportunity to choose between increasing their contributions to a savings plan immediately or at a specified future time point. Framing the future time point in relation to a fresh start date (e.g., the recipient’s birthday, the first day of spring) increased the likelihood that the mailing recipient chose to increase contributions at that future time point without decreasing their likelihood of increasing contributions immediately. Overall, fresh start framing increased retirement plan contributions in the eight months following the mailing. Our findings represent the first experimental demonstration of the benefits of fresh start framing in a consequential field setting.
Frontoparietal pattern similarity analyses of cognitive control in monozygotic twins

The ability to flexibly adapt thoughts and actions in a goal-directed manner appears to rely on cognitive control mechanisms that are strongly impacted by individual differences. A powerful research strategy for investigating the nature of individual variation is to study monozygotic (identical) twins. Evidence of twin effects have been observed in prior behavioral and neuroimaging studies, yet within the domain of cognitive control, it remains to be demonstrated that the neural underpinnings of such effects are specific and reliable. Here, we utilize a multi-task, within-subjects event-related neuroimaging design with functional magnetic resonance imaging to investigate twin effects through multivariate pattern similarity analyses. We focus on fronto-parietal brain regions exhibiting consistently increased activation associated with cognitive control demands across four task domains: selective attention, context processing, multi-tasking, and working memory. Healthy young adult monozygotic twin pairs exhibited increased similarity of within- and cross-task activation patterns in these fronto-parietal regions, relative to unrelated pairs. Twin activation pattern similarity effects were clearest under high control demands, were not present in a set of task-unrelated parcels or due to anatomic similarity, and were primarily observed during the within-trial timepoints in which the control demands peaked. Together, these results indicate that twin similarity in the neural representation of cognitive control may be domain-general but also functionally and temporally specific in relation to the level of control demand. The findings suggest a genetic and/or environmental basis for individual variation in cognitive control function, and highlight the potential of twin-based neuroimaging designs for exploring heritability questions within this domain.
On the prediction of equilibrium states in homogeneous turbulence

A comparison of several commonly used turbulence models (including the K–e model and three second-order closures) is made for the test problem of homogeneous turbulent shear flow in a rotating frame. The time evolution of the turbulent kinetic energy and dissipation rate is calculated for these models and comparisons are made with previously published experiments and numerical simulations. Particular emphasis is placed on examining the ability of each model to predict equilibrium states accurately for a range of the parameter Ω/S (the ratio of the rotation rate to the shear rate). It is found that none of the commonly used second-order closure models yield substantially improved predictions for the time evolution of the turbulent kinetic energy and dissipation rate over the somewhat defective results obtained from the simpler K–e model for the unstable flow regime. There is also a problem with the equilibrium states predicted by the various models. For example, the K–e model erroneously yields equilibrium states that are independent of Ω/S while the Launder, Reece & Rodi model and the Shih-Lumley model predict a flow relaminarization when Ω/S > 0.39 - a result that is contrary to numerical simulations and linear spectral analyses, which indicate flow instability for at least the range 0 [les ] Ω/S [les ] 0.5. The physical implications of the results obtained from the various turbulence models considered herein are discussed in detail along with proposals to remedy the deficiencies based on a dynamical systems approach.