cssi/SciSciGPT-SciSciNet huggingface dataset:
Dynamical categories and control policy selection

Every autonomous agent operating in realistic settings must deal with incomplete state information. Perceptual limitations often compromise system observability, and may prevent the acquisition of optimal control policies for a given task. This paper addresses the observability problem within the control composition framework, where the agent selects which policy to adopt next from a set of pre-defined control policies. The idea is to treat the agent in its environment as a dynamical system, and augment the perceived state space (situation space) with contextual cues extracted empirically as the agent exercises each of the existing control policies. Contextual cues are provided by the correlation between dynamic features of the agent environment interaction and agent performance. Initial experiments involving an agent with impoverished sensing capabilities in a simulated, dynamic environment demonstrate that relevant contextual information can be extracted and used to enhance the agent's performance.
Continuous digital calibration of pipeline A/D converters

This paper describes a novel continuous calibration technique for pipeline analog-to-digital converters (ADCs). The new scheme utilizes an existing digital calibration algorithm and extends it to work in real time. The goal is to digitally calibrate the pipeline ADCs in the background without interrupting the normal operation of the converter. The concept behind the digital calibration algorithm is described and simulated using a 1-bit/stage pipeline architecture. Dominant static error mechanisms present in pipeline architectures are identified and discussed. These errors are successfully corrected by the implemented digital calibration algorithm. The calibration scheme is transparent to the overall system performance and is demonstrated using a 14-bit ADC with a 1-bit/stage architecture and 16 identical stages. The first seven stages in the pipeline are calibrated. Continuous calibration is realized using a hardware description language and two extra stages located at the end of the pipeline. The extra stages are only used during the calibration process. Verilog implementations of a stage-and-error-correction logic, as well as a finite state machine to control the calibration process, are presented. The real-time digital calibration technique is verified and successfully demonstrated using the Verilog-XL simulator
Omni-resonant space–time wave packets

We describe theoretically and verify experimentally a novel, to the best of our knowledge, class of diffraction-free pulsed optical beams that are “omni-resonant”: they have the remarkable property of transmission through planar Fabry–Perot resonators without spectral filtering, even if their bandwidth far exceeds the cavity linewidth. Ultrashort wave packets endowed with a specific spatiotemporal structure couple to a single resonant mode independent of its linewidth. We confirm that such “space–time” omni-resonant wave packets retain their bandwidth (1.6 nm), spatiotemporal profile (1.3-ps pulse width, 4-µm beam width), and diffraction-free behavior upon transmission through cavities with resonant linewidths of 0.3 nm and 0.15 nm.
Analysis of fast structured dictionary learning

Sparsity-based models and techniques have been exploited in many signal processing and imaging applications. Data-driven methods based on dictionary and sparsifying transform learning enable learning rich image features from data and can outperform analytical models. In particular, alternating optimization algorithms have been popular for learning such models. In this work, we focus on alternating minimization for a specific structured unitary sparsifying operator learning problem and provide a convergence analysis. While the algorithm converges to the critical points of the problem generally, our analysis establishes under mild assumptions, the local linear convergence of the algorithm to the underlying sparsifying model of the data. Analysis and numerical simulations show that our assumptions hold for standard probabilistic data models. In practice, the algorithm is robust to initialization.
On Sufficient Conditions for Mixed Monotonicity

Mixed monotone systems form an important class of nonlinear systems that have recently received attention in the abstraction-based control design area. Slightly different definitions exist in the literature, and it remains a challenge to verify mixed monotonicity of a system in general. In this paper, we first clarify the relation between different existing definitions of mixed monotone systems, and then give two sufficient conditions for mixed monotone functions defined on Euclidean space. These sufficient conditions are more general than the ones from the existing control literature, and they suggest that mixed monotonicity is a very generic property. Some discussions are provided on the computational usefulness of the proposed sufficient conditions.
Data-Driven Optimization of Public Transit Schedule

Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.
A Lattice and Random Intermediate Point Sampling Design for Animal Movement

Animal movement studies have become ubiquitous in animal ecology for estimation of space use and analysis of movement behavior. In these studies, animal movement data are primarily collected at regular time intervals. We propose an irregular sampling design which could lead to greater efficiency and information gain in animal movement studies. Our novel sampling design, called lattice and random intermediate point (LARI), combines samples at regular and random time intervals. We compare the LARI sampling design to regular sampling designs in an example with common black carpenter ant location data, an example with guppy location data, and a simulation study of movement with a point of attraction. We modify a general stochastic differential equation model to allow for irregular time intervals and use this framework to compare sampling designs. When parameters are estimated reasonably well, regular sampling results in greater precision and accuracy in prediction of missing data. However, in each of the data and simulation examples explored in this paper, LARI sampling results in more accurate and precise parameter estimation, and thus better prediction of missing data as well. This result suggests that researchers might gain greater insight into underlying animal movement processes by choosing LARI sampling over regular sampling.
An ADMM Based Framework for AutoML Pipeline Configuration

We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints alongside the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits), and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML & OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical advantages of this framework.
Survival of Patients With Second Primary Hodgkin Lymphoma

Abstract Introduction The increased risk for second malignancies after Hodgkin lymphoma (HL) diagnosis is well known. However, to our knowledge, no study has investigated the outcomes of patients diagnosed with HL after an antecedent malignancy (HL-2). We aimed to investigate overall survival (OS), disease-specific survival (DSS), and correlates of survival in HL-2 using the Surveillance, Epidemiology and End Results (SEER) database. Patients and Methods HL-2 patients (n = 821) identified from the 2000-2014 SEER-18 registries were compared to first primary HL patients (HL-1, n = 31,355) from the same registries. Multivariable, propensity score–matched (PSM), and competing risks regression analyses were conducted to assess the effect of antecedent malignancy on survival. Results Hematologic (n = 309, 37.6%), prostate (n = 169, 20.6%), and breast (n = 76, 9.3%) malignancies were common antecedent malignancies in HL-2. Median latency between antecedent malignancy and HL diagnosis was 39 months. Median ages at HL diagnosis for HL-1 and HL-2 were 36 and 66 years, respectively (P Conclusion The decrement in DSS in HL-2 relative to HL-1 may be related to biological differences in HL, age, and/or other unanalyzed factors. Further study of HL-2 patients is warranted.
Tree Descent Polynomials: Unimodality and Central Limit Theorem

For a poset whose Hasse diagram is a rooted plane forest F, we consider the corresponding tree descent polynomial $$A_F(q)$$, which is a generating function of the number of descents of the labelings of F. When the forest is a path, $$A_F(q)$$ specializes to the classical Eulerian polynomial. We prove that the coefficient sequence of $$A_F(q)$$ is unimodal and that if $$\{T_{n}\}$$ is a sequence of trees with $$|T_{n}| = n$$ and maximal down degree $$D_{n} = O(n^{0.5-\epsilon }),$$ then the number of descents in a labeling of $$T_{n}$$ is asymptotically normal.
Analysis of limited-memory BFGS on a class of nonsmooth convex functions

The limited memory BFGS (L-BFGS) method is widely used for large-scale unconstrained optimization, but its behavior on nonsmooth problems has received little attention. L-BFGS can be used with or without "scaling"; the use of scaling is normally recommended. A simple special case, when just one BFGS update is stored and used at every iteration, is sometimes also known as memoryless BFGS. We analyze memoryless BFGS with scaling, using any Armijo-Wolfe line search, on the function $f(x) = a|x^{(1)}| + \sum_{i=2}^{n} x^{(i)}$, initiated at any point $x_0$ with $x_0^{(1)}\not = 0$. We show that if $a\ge 2\sqrt{n-1}$, the absolute value of the normalized search direction generated by this method converges to a constant vector, and if, in addition, $a$ is larger than a quantity that depends on the Armijo parameter, then the iterates converge to a non-optimal point $\bar x$ with $\bar x^{(1)}=0$, although $f$ is unbounded below. As we showed in previous work, the gradient method with any Armijo-Wolfe line search also fails on the same function if $a\geq \sqrt{n-1}$ and $a$ is larger than another quantity depending on the Armijo parameter, but scaled memoryless BFGS fails under a weaker condition relating $a$ to the Armijo parameter than that implying failure of the gradient method. Furthermore, in sharp contrast to the gradient method, if a specific standard Armijo-Wolfe bracketing line search is used, scaled memoryless BFGS fails when $a\ge 2 \sqrt{n-1} $ regardless of the Armijo parameter. Finally, numerical experiments indicate that similar results hold for scaled L-BFGS with any fixed number of updates.
Oligomeric Ruthenium Polypyridyl Dye for Improved Stability of Aqueous Photoelectrochemical Cells

Water-splitting dye-sensitized photoelectrochemical cells rely on molecular sensitizers to harvest light energy and drive the catalytic reactions necessary to generate hydrogen and oxygen from wate...
The association of prenatal exposure to intensive traffic with early preterm infant neurobehavioral development as reflected by the NICU Network Neurobehavioral Scale (NNNS).

Abstract Introduction Traffic-related air pollution has been shown to be neurotoxic to the developing fetus and in term-born infants during early childhood. It is unknown whether there is an increased risk of adverse neurobehavioral outcome in preterm infants exposed to higher levels of air pollution during the fetal period. Objective To assess the association between prenatal exposure to traffic-related air pollution on early preterm infant neurobehavior. Methods Air pollution exposure was estimated by two methods: density of major roads and density of vehicle-miles traveled (VMT), each at multiple buffering areas around residential addresses. We examined the association between prenatal exposure to traffic-related air pollution and performance on the Neonate Intensive Care Unit (NICU) Network Behavioral Scale (NNNS), a measure of neurobehavioral outcome in infancy for 240 preterm neonates enrolled in the NICU-Hospital Exposures and Long-Term Health cohort. Linear regression analysis was conducted for exposure and individual NNNS subscales. Latent profile analysis (LPA) was applied to classify infants into distinct NNNS phenotypes. Multinomial logistic regression analysis was conducted between exposure and LPA groups. Covariates included gestational age, birth weight z-score, post-menstrual age at NNNS assessment, socioeconomic status, race, delivery type, maternal smoking status, and medical morbidities during the NICU stay. Results Among all 13 NNNS subscales, hypotonia was significantly associated with VMT (104 vehicle-mile/km2) in 150 m (β = 0.01, P-value Conclusion Prenatal exposure to intensive air pollution emitted from major roads may impact early neurodevelopment of preterm infants. Motor development may be particularly sensitive to air pollution-related toxicity.
Infinite games on finite graphs using grossone

In his seminal work, Robert McNaughton [see McNaughton (Ann Pure Appl Log 65:149–184, 1993) and Khoussainov and Nerode (Automata theory and its applications. Birkhauser, Basel, 2001)] developed a model of infinite games played on finite graphs. This paper presents a new model of infinite games played on finite graphs using the grossone paradigm. The new grossone paradigm provides certain advantages such as allowing for draws, which are common in board games, and a more accurate and decisive method for determining the winner.
Worldwide short selling regulations and IPO underpricing

Abstract We study the impact of country-level short selling constraints on IPO underpricing. Examining 17,151 IPOs from 36 countries, we find that IPO underpricing tends to be greater in countries that ban short selling or security lending and in countries where short selling is not practiced. Non-positive first-day returns are more common in countries where short selling is allowed, security lending is allowed, and short selling is commonly practiced. Short selling constraints exacerbate the positive relation between investor sentiment and underpricing. Additional evidence suggests that higher quality information environments may partially alleviate the effects of short sale constraints on underpricing.
The peculiar case of the Standards of Care: Ethical ramifications of deviating from informed consent in transgender-specific healthcare

Summary In this article, we discuss the alternate model of consent that has become dominant for the provision of transgender-specific health care within the United States, referred to here as the Standards of Care model. This model, which requires medical professionals to provide official opinions on a transgender patient's readiness to accept and undergo care, stands in contrast to the majority model of medical consent in the US, namely individually provided informed consent. Here, we review the informed consent model, including the basic ethical components of this model and the essential elements of medical decision-making capacity. We then consider the Standards of Care model. We situate its origins in pathological understandings of gender variance and review the current requirements of the model. Consideration is given to logical inconsistencies within the current Standards of Care model, which holds that gender variance is non-pathological and affirming care is medically necessary, but that adult patients requesting such care require psychiatric diagnoses and are unable to consent to their own care. We then consider the bioethical meaning of the Standards of Care model, which others have proposed cedes some of the patient autonomy offered by informed consent for an inflated reliance on nonmaleficence. We align ourselves with this position. We continue this interpretation to suggest that the Standards of Care model ultimately fails to deliver this proffered nonmaleficence.
Multiethnic Parastates and Nation-Building: The Case of the Transnistrian Imagined Community

This article explores nation-building processes in the Transnistrian imagined community. While some scholars describe Transnistria’s nation-building strategy as a civic, multicultural project, the analysis of recent demographic and educational data corroborated with the close examination of local media content and official discourses—all point to the emergence of a distinct political culture marked by the increasing use of the Russian language in the public sphere, and the politicization of the Moldovan identity. Discourses about ethnic and national identity in the region have evolved as the Transnistrian elites reimagine the political community as part of the Russkii Mir. These circumstances suggest that, in the long run, the breakaway region might function as the southeastern frontline of Russian irredentism with the elites of the Pridnestrovskaia Moldavskaia Respublika continuing to call on the Russian Federation to annex the parastate instead of seeking a peaceful reintegration into Moldova.
A comparative study of the turnover of multiciliated cells in the mouse trachea, oviduct, and brain.

Background In mammals, multiciliated cells (MCCs) line the lumen of the trachea, oviduct, and brain ventricles, where they drive fluid flow across the epithelium. Each MCC population experiences vastly different local environments that may dictate differences in their lifetime and turnover rates. However, with the exception of MCCs in the trachea, the turnover rates of these multiciliated epithelial populations at extended time scales are not well described. Results Here, using genetic lineage-labeling techniques we provide a direct comparison of turnover rates of MCCs in these three different tissues. Conclusion We find that oviduct turnover is similar to that in the airway (~6 months), while multiciliated ependymal cells turnover more slowly.
Do female CEOs make a difference in firm operations? Evidence from Vietnam

This paper examines the effect of gender in corporate leadership on the performance and risk of Vietnamese listed firms. We find that firms with female CEOs generate higher profitability than those with male CEOs. In addition, firms led by female CEOs experience less systematic and idiosyncratic risks as well as lower volatility in their returns on assets. These results are robust under different regression specifications. Our results support the hypothesis that women offer unique perspectives, experiences and work styles that benefit firms and that provide evidence for continuing government efforts to improve gender equality in Vietnam.
Alcohol Effects on Globus Pallidus Connectivity: Role of Impulsivity and Binge Drinking

Despite the harm caused by binge drinking, the neural mechanisms leading to risky and disinhibited intoxication-related behaviors are not well understood. Evidence suggests that the globus pallidus externus (GPe), a substructure within the basal ganglia, participates in inhibitory control processes, as examined in stop-signaling tasks. In fact, studies in rodents have revealed that alcohol can change GPe activity by decreasing neuronal firing rates, suggesting that the GPe may have a central role in explaining impulsive behaviors and failures of inhibition that occur during binge drinking. In this study, twenty-five healthy volunteers underwent intravenous alcohol infusion to achieve a blood alcohol level of 0.08 g/dl, which is equivalent to a binge drinking episode. A resting state functional magnetic resonance imaging scan was collected prior to the infusion and at binge-level exposure. Functional connectivity analysis was used to investigate the association between alcohol-induced changes in GPe connectivity, drinking behaviors, and impulsivity traits. We found that individuals with greater number of drinks or heavy drinking days in the recent past had greater alcohol-induced deficits in GPe connectivity, particularly to the striatum. Our data also indicated an association between impulsivity and alcohol-induced deficits in GPe-frontal/precentral connectivity. Moreover, alcohol induced changes in GPe-amygdala circuitry suggested greater vulnerabilities to stress-related drinking in some individuals. Taken together, these findings suggest that alcohol may interact with impulsive personality traits and drinking patterns to drive alterations in GPe circuitry associated with behavioral inhibition, possibly indicating a neural mechanism by which binge drinking could lead to impulsive behaviors.
Microfoundations and macroeconomics: 20 years

This paper summarizes Horwitz’s contributions in Microfoundations and Macroeconomics: An Austrian Perspective and evaluates its connection with recent contributions in Austrian macroeconomics. The paper emphasizes the connection between relative price, monetary equilibrium, and monetary policy. The paper pays particular attention to the implications of Horwitz’s denomination of capital theory as a “missing link” in economic theory.
Household Innovation and R&D: Bigger than You Think

Despite recent interest in measuring household activities, investment in household RD that is a household intangible asset. This paper takes a step toward valuing household innovation in the U.S. by developing time series of nominal and real investment and capital stocks for household R&D. We find that household investment in R&D was more than 11 percent of R&D funded by the private business sector in 2017 and about half of what businesses spent on R&D to develop new products for consumers. If household R&D were judged to be in scope for GDP, GDP would have been 0.2 percent higher in 2017. We conclude that household R&D is important and warrants closer attention.
Theorems of the alternative for conic integer programming

Abstract Farkas’ Lemma is a foundational result in linear programming, with implications in duality, optimality conditions, and stochastic and bilevel programming. Its generalizations are known as theorems of the alternative. There exist theorems of the alternative for integer programming and conic programming. We present theorems of the alternative for conic integer programming. We provide a nested procedure to construct a function that characterizes feasibility over right-hand sides and can determine which statement in a theorem of the alternative holds.
d-PBWT: Dynamic Positional Burrows-Wheeler Transform

Durbin’s positional Burrows-Wheeler transform (PBWT) [1] is a scalable foundational data structure for modeling population haplotype sequences. It offers efficient algorithms for matching haplotypes that approach theoretically optimal complexity.
Discrete comparison principles for quasilinear elliptic PDE

Abstract Comparison principles are developed for piecewise linear finite element approximations of quasilinear elliptic partial differential equations. We consider the analysis of a class of nonmonotone Leray-Lions problems featuring both nonlinear solution and gradient dependence in the principal coefficient, and a solution dependent lower-order term. Sufficient local and global conditions on the discretization are found for conforming finite element solutions to satisfy a comparison principle, which implies uniqueness of the solution. For problems without a lower-order term, our analysis shows the meshsize is only required to be locally controlled, based on the variance of the computed solution over each element. We include a discussion of the simpler semilinear case where a linear algebra argument allows a sharper mesh condition for the lower order term.
LRRK2 mutation alters behavioral, synaptic, and nonsynaptic adaptations to acute social stress

Parkinson's disease (PD) risk is increased by stress and certain gene mutations, including the most prevalent PD-linked mutation LRRK2-G2019S. Both PD and stress increase risk for psychiatric symptoms, yet it is unclear how PD-risk genes alter neural circuitry in response to stress that may promote psychopathology. Here we show significant differences between adult G2019S knockin and wild-type (wt) mice in stress-induced behaviors, with an unexpected uncoupling of depression-like and hedonia-like responses in G2019S mice. Moreover, mutant spiny projection neurons in nucleus accumbens (NAc) lack an adaptive, stress-induced change in excitability displayed by wt neurons, and instead show stress-induced changes in synaptic properties that wt neurons lack. Some synaptic alterations in NAc are already evident early in postnatal life. Thus G2019S alters the magnitude and direction of behavioral responses to stress that may reflect unique modifications of adaptive plasticity in cells and circuits implicated in psychopathology in humans.NEW & NOTEWORTHY Depression is associated with Parkinson's disease (PD), and environmental stress is a risk factor for both. We investigated how LRRK2-G2019S PD mutation affects depression-like behaviors, synaptic function, and intrinsic neuronal excitability following stress. In response to stress, the mutation drives abnormal synaptic changes, prevents adaptive changes in intrinsic excitability, and leads to aberrant behaviors, thus defining new ways in which PD mutations derail adaptive plasticity in response to stress that may contribute to disease onset.
Experiments on Point-to-Point Control of a Flexible Beam Using Laplace Transform Technique: Part II—Closed-Loop

Using the Laplace domain synthesis technique documented in earlier publications, experiments on the closed-loop point-to-point position control of a flexible beam are presented. The approach presented here is basically an iterative open-loop control. Solution to the robustness problems encountered during actual implementation is also documented.
Minimum power assignment in wireless ad hoc networks with spanner property

Power assignment for wireless ad hoc networks is to assign a power for each wireless node such that the induced communication graph has some required properties. Recently research efforts have focused on finding the minimum power assignment to guarantee the connectivity or fault-tolerance of the network. In this paper, we study a new problem of finding the power assignment such that the induced communication graph is a spanner for the original communication graph when all nodes have the maximum power. Here, a spanner means that the length of the shortest path in the induced communication graph is at most a constant times of the length of the shortest path in the original communication graph. Polynomial time algorithm is given to minimize the maximum assigned power with spanner property. The algorithm also works for any other property that can be tested in polynomial time and is monotone. We then give a polynomial time approximation method to minimize the total transmission radius of all nodes. Finally, we propose two heuristics and conduct extensive simulations to study their performance when we aim to minimize the total assigned power of all nodes.
A NOVEL APPROACH TO ESTIMATION OF PATIENT-SPECIFIC MUSCLE STRENGTH

Current modeling techniques have been used to model the Reverse Total Shoulder Arthroplasty (RTSA) to account for the geometric changes implemented after RTSA. Though these models have provided insight into the effects of geometric changes from RTSA these is still a limitation of understanding muscle function after RTSA on a patient-specific basis. The goal of this study sought to overcome this limitation by developing an approach to calibrate patient-specific muscle strength for an RTSA subject. The approach was performed for both isometric 0° abduction and dynamic abduction. A 12 degree of freedom (DOF) model developed in our previous work was used in conjunction with our clinical data to create a set of patient-specific data (3 dimensional kinematics, muscle activations, muscle moment arms, joint moments, muscle length, muscle velocity, tendon slack length, optimal fiber length, peak isometric force)) that was used in a novel optimization scheme to estimate muscle parameters that correspond to the patient9s muscle strength[4]. The optimization varied to minimize the difference between measured(“in vivo”) and predicted joint moments and measured (“in vivo”) and predicted muscle activations. The predicted joint moments were constructed as a summation of muscle moments. The nested optimization was implemented within matlab (Mathworks). The optimization yields a set of muscle parameters that correspond to the subject9s muscle strength. The abduction activity was optimized. The maximum activation for the muscles within the model ranged between .03–2.4 (Figure 1). The maximum joint moment produced was 11 newton-meters. The joint moments were reproduced to an value of 1. Muscle parameters were calculated for both isometric and dynamic abduction (Figure 2). The muscle parameters produced provided a feasible solution to reproduce the joint moments seen “in vivo” (Figure 3). Current modeling techniques of the upper extremity focus primarily on geometry. In efforts to create patient-specific models we have developed a framework to predict subject-specific strength characteristics. In order to fully understand muscle function we need muscle parameters that correspond to the subject9s strength. This effort in conjunction with patient-specific models that incorporate the patient9s joint configurations, kinematics and bone anatomy hopes to provide a framework to gain insight into muscle tensioning effects after RTSA. With this framework improvements can be made to the surgical implementation and design of RTSA to improve surgical outcomes.
The impact of plant-level resource reallocations and technical progress on U.S. macroeconomic growth☆

Abstract We build up from the plant level an “aggregate(d)” Solow residual by estimating every U.S. manufacturing plant's contribution to the change in aggregate final demand between 1976 and 1996. Our framework uses the Petrin and Levinsohn (2010) definition of aggregate productivity growth, which aggregates plant-level changes to changes in aggregate final demand in the presence of imperfect competition and other distortions/frictions. We decompose these contributions into plant-level resource reallocations and plant-level technical efficiency changes while allowing in the estimation for 459 different production technologies, one for each 4-digit SIC code. On average we find positive aggregate productivity growth of 2.2% in this sector during this period of declining share in U.S. GDP. We find that aggregate reallocation made a larger contribution to growth than aggregate technical efficiency. Our estimates of the contribution of reallocation range from 1.7 % to 2.1 % per year, while our estimates of the average contribution of aggregate technical efficiency growth range from 0.2 % to 0.6 % per year. In terms of cyclicality, the aggregate technical efficiency component has a standard deviation that is roughly from 50% to 100 % larger than that of aggregate total reallocation, pointing to an important role for technical efficiency in macroeconomic fluctuations. Aggregate reallocation is negative in only 3 of the 20 years of our sample, suggesting that the movement of inputs to more highly valued activities on average plays a stabilizing role in manufacturing growth. Our results have implications for both the theoretical literature on growth and alternative indexes of aggregate productivity growth based only on technical efficiency.
An Approach to Analysis of High Level waste Container Performance in an Unsaturated Repository Environment

One of the requirements for the performance of waste packages prescribed in 10CFR 60.113 is that the high level waste must be “substantially completely” contained for a minimum period of 300 to 1000 years. During this period, the radiation and thermal conditions in the engineered barrier system and the near-field environment are dominated by fission product decay. In the present U.S design of the engineered barrier system, the outer container plays a dominant role in maintaining radionuclide containment. A quantitative methodology for analyzing the performance of the container is described in this paper. This methodology enables prediction of the evolution of the waste package environment in terms of temperature fields, stability of liquid water on the container surface, and concentration of aggressive ions such as chloride. The initiation and propagation of localized corrosion is determined by the corrosion potential of the container material and critical potentials for localized corrosion. The coiTOsion potential is estimated from the kinetics of the anodic and cathodic reactions including oxygen diffusion through scale layers formed on the container surface. The methodology described is applicable to a wide range of metals, alloys and environmental conditions.
The APL\360 Terminal System

APL\360 is an experimental interactive system, programmed for IBM System/360 computers, which uses typewriter terminals connected to the central machine by telephone lines for both input and output. The basis of the system is APL [1-4], a truly machine-free programming language which leans heavily on mathematical notions, but does not slavishly follow classical mathematical notation. Correspondingly, the running system, which is completely interpretive, makes no significant concessions to implementation problems.
Incentives and responses under No Child Left Behind: Credible threats and the role of competition

Abstract NCLB mandated the institution of Adequate Yearly Progress (AYP) objectives, and schools are assigned an AYP pass/fail based on performance in these objectives. AYP-fail status is associated with negative publicity and often sanctions. Using data from Wisconsin and alternate regression discontinuity designs, I study the incentives and responses of schools that failed AYP once. Math-induced AYP-failures showed strong improvements in math, while reading-induced AYP-failures showed marked improvements in reading. Consistent with incentives, these schools showed no positive effect in other high stakes objectives. In contrast, test-participation failures showed no effect in either high stakes reading or math, while they showed some evidence of positive (though not statistically significant) effects in test participation. Improvements in reading are associated with parallel effects in low stakes language arts (possibly due to spillover effects), while there is no evidence of effects in low stakes science or social studies. Nor is there evidence of effects on graduation rates. Performance in low stakes grades suffered, and so did performance in weaker subgroups in spite of their inclusion in AYP computations. There is evidence of focus on marginal students around high stakes cutoffs in subject areas AYP-failed schools improved in, but this did not come at the expense of ends. Credibility of threat mattered—AYP-failed schools that faced more competition responded considerably more strongly in the objectives they had incentives in.
Optimized Score Transformation for Fair Classification.

This paper considers fair probabilistic classification where the outputs of primary interest are predicted probabilities, commonly referred to as scores. We formulate the problem of transforming scores to satisfy fairness constraints that are linear in conditional means of scores while minimizing the loss in utility. The formulation can be applied either to post-process classifier outputs or to pre-process training data, thus allowing maximum freedom in selecting a classification algorithm. We derive a closed-form expression for the optimal transformed scores and a convex optimization problem for the transformation parameters. In the population limit, the transformed score function is the fairness-constrained minimizer of cross-entropy with respect to the optimal unconstrained scores. In the finite sample setting, we propose to approach this solution using a combination of standard probabilistic classifiers and ADMM. The transformation parameters obtained from the finite-sample procedure are shown to be asymptotically optimal. Comprehensive experiments comparing to 10 existing methods show that the proposed FairScoreTransformer has advantages for score-based metrics such as Brier score and AUC while remaining competitive for binary label-based metrics such as accuracy.
Turbulent Energy Spectrum via an Interaction Potential

For a large system of identical particles interacting by means of a potential, we find that a strong large scale flow velocity can induce motions in the inertial range via the potential coupling. This forcing lies in special bundles in the Fourier space, which are formed by pairs of particles. These bundles are not present in the Boltzmann, Euler and Navier–Stokes equations, because they are destroyed by the Bogoliubov–Born–Green–Kirkwood–Yvon formalism. However, measurements of the flow can detect certain bulk effects shared across these bundles, such as the power scaling of the kinetic energy. We estimate the scaling effects produced by two types of potentials: the Thomas–Fermi interatomic potential (as well as its variations, such as the Ziegler–Biersack–Littmark potential), and the electrostatic potential. In the near-viscous inertial range, our estimates yield the inverse five-thirds power decay of the kinetic energy for both the Thomas–Fermi and electrostatic potentials. The electrostatic potential is also predicted to produce the inverse cubic power scaling of the kinetic energy at large inertial scales. Standard laboratory experiments confirm the scaling estimates for both the Thomas–Fermi and electrostatic potentials at near-viscous scales. Surprisingly, the observed kinetic energy spectrum in the Earth atmosphere at large scales behaves as if induced by the electrostatic potential. Given that the Earth atmosphere is not electrostatically neutral, we cautiously suggest a hypothesis that the atmospheric kinetic energy spectra in the inertial range are indeed driven by the large scale flow via the electrostatic potential coupling.
Photofield electron emission from an optical fiber nanotip

We demonstrate a nanotip electron source based on a graded index multimode silica optical fiber, tapered at one end to a radius of curvature r ∼50 nm and coated with a thin film of gold. We report observation of laser-induced electron photoemission at tip bias potentials below the onset of dark field emission. Single-photon photofield emission is identified as the emission mechanism that exhibits fast switching times with an upper limit on the order of 1 μs. The explored fiber optic nanotips are flexible back-illuminated emitters, which can be operated in continuous wave and pulsed modes using lasers with photon energies in the visible range or higher. The mechanical flexibility of the source can facilitate externally controlled positioning. Multiple, individually addressable, nanotips may be assembled into a bundle for applications such as computational electron ghost imaging.
Are cycling and walking good for all? Tracking differences in associations among active travel, socioeconomics, gentrification, and self-reported health

Author(s): Barajas, JM; Braun, LM | Abstract: Introduction: While the health benefits of cycling and walking have been well established, questions remain about whether these benefits hold in varying socioeconomic contexts, including across demographic groups and in the context of neighborhood change. This study examines this relationship, identifying associations between cycling or walking and self-reported health, whether socioeconomic status moderates these associations, and whether gentrification influences the potential moderating effects. Methods: This study uses the 2017 US National Household Travel Survey, subset to adults who lived in central cities (n = 91,541). Weighted logistic regression models with interaction terms were fit to estimate self-reported health status separately for cycling and walking. Gentrification was measured using an indicator based on previous research using US Census data. Results: Cycling in the previous week and each additional walking trip were associated with higher odds of reporting better health. Socioeconomic status moderated the positive associations between active transportation and health for a few variables. Cyclists of color had smaller health effects from cycling for utilitarian trips, while race and ethnicity had only a marginal moderating effect on the association between walking and health. Gentrification was an insignificant moderating factor in general. Conclusions: Findings suggest planning efforts that continue to support programs that promote cycling and walking are crucial tools in the public health toolbox. The health gains from active transportation might be experienced in a variety of neighborhood contexts. Nevertheless, infrastructure investments and policy must be attentive to inequities across neighborhoods.
Source Coding for Synthesizing Correlated Randomness

We consider a scenario wherein two parties Alice and Bob are provided $X_1^n$ and $X_2^n$ - samples that are IID from a PMF ${p_{{X_{\text{1}}}{X_{\text{2}}}}}$. Alice and Bob can communicate to Charles over (noiseless) communication links of rate R 1 and R 2 respectively. Their goal is to enable Charles generate samples Yn such that the triple $\left( {X_1^n,X_2^n,{Y^n}} \right)$ has a PMF that is close, in total variation, to $\prod {{p_{{X_1}{X_2}Y}}} $. In addition, the three parties may posses shared common randomness at rate C. We address the problem of characterizing the set of rate triples (R 1 , R 2 ,C) for which the above goal can be accomplished. We provide a set of sufficient conditions, i.e., an achievable rate region for this three party setup. Our work also provides a complete characterization of a point-to-point setup wherein Bob is absent and Charles is provided with side-information.
User Sentiment as a Success Metric: Persistent Biases Under Full Randomization

We study user sentiment (reported via optional surveys) as a metric for fully randomized A/B tests. Both user-level covariates and treatment assignment can impact response propensity. We show that a simple mean comparison produces biased population level estimates and propose a set of consistent estimators for the average and local treatment effects on treated and respondent users. We show that our problem can be mapped onto the intersection of the missing data problem and observational causal inference, and we identify conditions under which consistent estimators exist. Finally, we evaluate the performance of estimators and find that more complicated models do not necessarily provide superior performance as long as models satisfy consistency criteria.
Supplemental Nutrition Assistance Program-Education Improves Food Security Independent of Food Assistance and Program Characteristics

The purpose of this project was to determine whether consistent food assistance program participation or changes in participation over time mediated or moderated the effect of federal nutrition education through the Supplemental Nutrition Assistance Program-Education (SNAP-Ed) on food security and determine the associations of SNAP-Ed program delivery characteristics with change in food security. This secondary analysis used data from a randomized controlled trial from September 2013 through April 2015. SNAP-Ed-eligible participants (n = 328; ≥18 years) in households with children were recruited from 39 counties in Indiana, USA. The dependent variable was one year change in household food security score measured using the United States Household Food Security Survey Module. Assessment of mediation used Barron-Kenny analysis and moderation used interactions of food assistance program use and changes over time with treatment group in general linear regression modeling. Program delivery characteristics were investigated using mixed linear regression modeling. Results showed that neither consistent participation nor changes in food assistance program participation over time mediated nor moderated the effect of SNAP-Ed on food security and neither were SNAP-Ed program delivery characteristics associated with change in food security over the one year study period. SNAP-Ed directly improved food security among SNAP-Ed-eligible Indiana households with children regardless of food assistance program participation and changes over time or varying program delivery characteristics.
Boosting High Dimensional Predictive Regressions with Time Varying Parameters

Abstract High dimensional predictive regressions are useful in wide range of applications. However, the theory is mainly developed assuming that the model is stationary with time invariant parameters. This is at odds with the prevalent evidence for parameter instability in economic time series, but theories for parameter instability are mainly developed for models with a small number of covariates. In this paper, we present two L 2 boosting algorithms for estimating high dimensional models in which the coefficients are modeled as functions evolving smoothly over time and the predictors are locally stationary. The first method uses componentwise local constant estimators as base learner, while the second relies on componentwise local linear estimators. We establish consistency of both methods, and address the practical issues of choosing the bandwidth for the base learners and the number of boosting iterations. In an extensive application to macroeconomic forecasting with many potential predictors, we find that the benefits to modeling time variation are substantial and they increase with the forecast horizon. Furthermore, the timing of the benefits suggests that the Great Moderation is associated with substantial instability in the conditional mean of various economic series.