cssi/SciSciGPT-SciSciNet huggingface dataset:
General Rogue Waves in the Boussinesq Equation

We derive general rogue wave solutions of arbitrary orders in the Boussinesq equation by the bilinear Kadomtsev–Petviashvili (KP) reduction method. These rogue solutions are given as Gram determina...
Monetary Policy Implementation: Operational Issues for Countries with Evolving Monetary Policy Frameworks

This paper discusses operational issues for countries that want to reform their monetary policy frameworks. It argues that stabilizing short-term interest rates on a day-to-day basis has significant advantages, and thus that short-term interest rates, not reserve money, in most cases should be the daily operating target, including for countries relying on a money targeting policy strategy. The paper discusses how a policy formulation framework based on monetary aggregates can be combined with an operational framework that ensures more stable and predictable short-term rates to enhance policy transmission. It also discusses how to best configure an interest-rate-based operational framework when markets are underdeveloped and liqudity management capacity is weak.
Mammalian gene expression variability is explained by underlying cell state.

Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.
A phase-field approach for modeling equilibrium solute segregation at the interphase boundary in binary alloys

Abstract A number of experimental and theoretical findings in age hardening alloys suggest that specific solute elements preferentially segregate to and reduce the energy of the interphase boundary (IB). This segregation mechanism can stabilize the precipitation microstructure against coarsening, allowing higher operating temperatures in structural applications. Herein, we present a phase field model of solute segregation to IBs that separate matrix and precipitate phases in binary alloys. The proposed modeling framework is capable of capturing bulk thermodynamics and interfacial free energies, while also accounting for various mass transport mechanisms. Analytical equilibrium solutions of one-dimensional systems are presented, and excess IB quantities are evaluated independent of the Gibbs dividing surface convention. With the aid of the parallel tangent construction, IB segregation isotherms are established in terms of the alloy composition and the model parameters describing the free energy functions. Under the regular solution approximation, computational studies elucidating the dependence of the IB energy and segregation levels on temperature and free energy model parameters are presented. We show that the model is consistent with the Gibbs adsorption equation; therefore, it is possible to compare the adsorption behavior predicted by the model parameters with experiments and atomistic simulations. Future work on extending the model to ternary alloys, and incorporating the effect of elastic interactions on IB segregation is expected.
Cooperative Energy Scheduling for Microgrids under Peak Demand Energy Plans

A cooperative energy scheduling method is proposed that allows joint energy optimization for a group of microgrids to achieve cost savings that the microgrids could not achieve individually. The discussed microgrids may be commercial entities in a distribution network under utility electricity rate plans comprising both Time of Use (ToU) and peak demand charge. Defining a stable operation as a situation where all microgrids would be willing to participate, it is shown that under such rate plans and in particular due to the peak demand charge, a cost distribution that is seemingly fair does not necessarily result in a stable cooperation. These results are derived in this paper using concepts from cooperative games. It is therefore sought to devise a stable cost distribution algorithm that, while maximizing some measure of fairness among the participating microgrids, ensures they all benefit from their participation. A simple case study is presented that demonstrates fairness and stability aspects of the cooperation.
Plan Interdiction Games

We propose a framework for cyber risk assessment and mitigation which models attackers as formal planners and defenders as interdicting such plans. We illustrate the value of plan interdiction problems by first modeling network cyber risk through the use of formal planning, and subsequently formalizing an important question of prioritizing vulnerabilities for patching in the plan interdiction framework. In particular, we show that selectively patching relatively few vulnerabilities allows a network administrator to significantly reduce exposure to cyber risk. More broadly, we have developed a number of scalable approaches for plan interdiction problems, making especially significant advances when attack plans involve uncertainty about system dynamics. However, important open problems remain, including how to effectively capture information asymmetry between the attacker and defender, how to best model dynamics in the attacker-defender interaction, and how to develop scalable algorithms for solving associated plan interdiction games.
Striatal Nurr1 Facilitates the Dyskinetic State and Exacerbates Levodopa-Induced Dyskinesia in a Rat Model of Parkinson’s Disease

The transcription factor Nurr1 has been identified to be ectopically induced in the striatum of rodents expressing l-DOPA-induced dyskinesia (LID). In the present study, we sought to characterize Nurr1 as a causative factor in LID expression. We used rAAV2/5 to overexpress Nurr1 or GFP in the parkinsonian striatum of LID-resistant Lewis or LID-prone Fischer-344 (F344) male rats. In a second cohort, rats received the Nurr1 agonist amodiaquine (AQ) together with l-DOPA or ropinirole. All rats received a chronic DA agonist and were evaluated for LID severity. Finally, we performed single-unit recordings and dendritic spine analyses on striatal medium spiny neurons (MSNs) in drug-naive rAAV-injected male parkinsonian rats. rAAV-GFP injected LID-resistant hemi-parkinsonian Lewis rats displayed mild LID and no induction of striatal Nurr1 despite receiving a high dose of l-DOPA. However, Lewis rats overexpressing Nurr1 developed severe LID. Nurr1 agonism with AQ exacerbated LID in F344 rats. We additionally determined that in l-DOPA-naive rats striatal rAAV-Nurr1 overexpression (1) increased cortically-evoked firing in a subpopulation of identified striatonigral MSNs, and (2) altered spine density and thin-spine morphology on striatal MSNs; both phenomena mimicking changes seen in dyskinetic rats. Finally, we provide postmortem evidence of Nurr1 expression in striatal neurons of l-DOPA-treated PD patients. Our data demonstrate that ectopic induction of striatal Nurr1 is capable of inducing LID behavior and associated neuropathology, even in resistant subjects. These data support a direct role of Nurr1 in aberrant neuronal plasticity and LID induction, providing a potential novel target for therapeutic development.SIGNIFICANCE STATEMENT The transcription factor Nurr1 is ectopically induced in striatal neurons of rats exhibiting levodopa-induced dyskinesia [LID; a side-effect to dopamine replacement strategies in Parkinson's disease (PD)]. Here we asked whether Nurr1 is causing LID. Indeed, rAAV-mediated expression of Nurr1 in striatal neurons was sufficient to overcome LID-resistance, and Nurr1 agonism exacerbated LID severity in dyskinetic rats. Moreover, we found that expression of Nurr1 in l-DOPA naive hemi-parkinsonian rats resulted in the formation of morphologic and electrophysiological signatures of maladaptive neuronal plasticity; a phenomenon associated with LID. Finally, we determined that ectopic Nurr1 expression can be found in the putamen of l-DOPA-treated PD patients. These data suggest that striatal Nurr1 is an important mediator of the formation of LID.
Chapter 3 – Drug-Receptor Theory

This chapter discusses the various mathematical models used to describe and quantify drug response. This begins with consideration of the classical theory which formed the basis of all subsequent models followed by the model of agonism most prevalent in pharmacology at this time, the Black/Leff operational model. In addition, biochemical models are described beginning with the two-state model of ion channel activation, the ternary complex, extended ternary complex and cubic ternary complex models of seven transmembrane receptor function. Concepts from molecular dynamics are finally added to describe probabilistic models of receptor activation.
The Effect of Attending a Small Class in the Early Grades on College‐test Taking and Middle School Test Results: Evidence from Project Star

This paper provides a long-term follow-up analysis of students who participated in the Tennessee STAR experiment. In this experiment, students and their teachers were randomly assigned to small, regular-size, or regular-size classes with a teacher aide in the first four years of school. We analyse the effect of past attendance in small classes on student test scores and whether they took the ACT or SAT college entrance exam. Attending a small class in the early grades is associated with an increased likelihood of taking a college-entrance exam, especially among minority students, and somewhat higher test scores.
A Biomedical Sensor Interface With a sinc Filter and Interference Cancellation

A compact, low-power, digitally-assisted sensor interface for biomedical applications is presented. It exploits oversampling and mixed-signal feedback to reduce system area and power, while making the system more robust to interferers. Antialiasing is achieved using a charge-sampling filter with a sinc frequency response and programmable gain. A mixed-signal feedback loop creates a sharp, programmable notch for interference cancellation. A prototype was implemented in a 0.18-μm CMOS process. The on-chip blocks operate from a 1.5-V supply and consume between 255 nW and 2.5 μW depending on noise and bandwidth requirements.
Global mapping strategies for a synthetic aperture radar system in orbit about Venus

An analysis of the global mapping of Venus using a synthetic aperture radar (SAR) is presented. The geometry of the side-looking radar, the narrow swath width, and the slow rotation of Venus combine to constrain the methods required to produce such a map within the primary mapping mission of 121.5 days. Parametric studies indicate that multiple strategies can satisfy the requirements of the mission with reasonable assumptions for the total recording capacity, the downlink data rate, and the operating time of the SAR on each revolution.
Biases in arginine codon usage correlate with genetic disease risk.

The persistence of hypermutable CGN (CGG, CGA, CGC, CGU) arginine codons at high frequency suggests the possibility of negative selective pressure at these sites and that arginine codon usage could be a predictive indicator of human disease genes. We analyzed arginine codons (CGN, AGG, AGA) from all canonical Ensembl protein coding gene transcripts before comparing the frequency of CGN codons between genes with and without human disease associations and with gnomAD constraint metrics. The frequency of CGN codons among a gene’s total arginine codon count was higher in genes linked to syndromic autism spectrum disorder (ASD) compared with genes not associated with ASD. A comparison of genes annotated as dominant or recessive with control genes not matching either classification revealed a progressive increase in CGN codon frequency. Moreover, CGN frequency was positively correlated with a gene’s probability of loss-of-function intolerance (pLI) score and negatively correlated with observed-over-expected ratios for both loss-of-function and missense variants. Our findings indicate that genes utilizing CGN arginine codons rather than AGG or AGA are more likely to underlie single-gene disorders, particularly for dominant phenotypes, and thus constitute candidate genes for the study of human genetic disease.
21 – Tree Maintenance

Publisher Summary Proper maintenance is essential for the health of shade trees because improper maintenance can kill trees. This chapter discusses different methods used in the maintenance of trees including planting, pruning, wound treatment, spraying for the control of diseases and insects, cavity treatment, the application of fertilizers, and watering. Each of these tree maintenance operations can be useful to the tree if performed properly or can cause harm to the tree if performed in an improper way. The chapter illustrates the examples of injury to shade trees as a result of improper maintenance and shows how such injury can be avoided. The techniques used during planting of a tree determine whether a high quality shade tree will develop or a stunted or diseased tree will result. Tree health and preparation are critical to success in tree planting. Follow-up maintenance is equally critical. Therefore, a proper planting of shade trees involves the selection of healthy stock suitable for the planting site, preparation of site, correct placement of the tree in the soil, and follow-up maintenance.
MCell-R: A Particle-Resolution Network-Free Spatial Modeling Framework

Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making. For this reason, a number of methods have been developed to model spatial heterogeneity and incorporated into widely used modeling platforms. Unfortunately, the standard approaches for specifying and simulating chemical reaction networks become untenable when dealing with multistate, multicomponent systems that are characterized by combinatorial complexity. To address this issue, we developed MCell-R, a framework that extends the particle-based spatial Monte Carlo simulator, MCell, with the rule-based model specification and simulation capabilities provided by BioNetGen and NFsim. The BioNetGen syntax enables the specification of biomolecules as structured objects whose components can have different internal states that represent such features as covalent modification and conformation and which can bind components of other molecules to form molecular complexes. The network-free simulation algorithm used by NFsim enables efficient simulation of rule-based models even when the size of the network implied by the biochemical rules is too large to enumerate explicitly, which frequently occurs in detailed models of biochemical signaling. The result is a framework that can efficiently simulate systems characterized by combinatorial complexity at the level of spatially resolved individual molecules over biologically relevant time and length scales.
State Executive Orders: Nuance in restrictions, revealing suspensions, and decisions to enforce.

In the absence of a large-scale federal response to the COVID-19 pandemic, state and local elected officials have enacted executive orders that include both restrictions to public liberties as well as the suspension of rules and regulations. While these restrictive policy actions have received extensive media attention, the suspensions, including regulatory rollbacks, waivers, and extensions are lesser known. This viewpoint offers insight from a working database that captures the nuance and variation across restrictions, suspensions, and enforcement mechanisms being utilized, at the state level. This article is protected by copyright. All rights reserved.
Stochastic Regret Minimization in Extensive-Form Games

Monte-Carlo counterfactual regret minimization (MCCFR) is the state-of-the-art algorithm for solving sequential games that are too large for full tree traversals. It works by using gradient estimates that can be computed via sampling. However, stochastic methods for sequential games have not been investigated extensively beyond MCCFR. In this paper we develop a new framework for developing stochastic regret minimization methods. This framework allows us to use any regret-minimization algorithm, coupled with any gradient estimator. The MCCFR algorithm can be analyzed as a special case of our framework, and this analysis leads to significantly-stronger theoretical on convergence, while simultaneously yielding a simplified proof. Our framework allows us to instantiate several new stochastic methods for solving sequential games. We show extensive experiments on three games, where some variants of our methods outperform MCCFR.
Understanding the Predictors of Street Robbery Hot Spots: A Matched Pairs Analysis and Systematic Social Observation:

This study examines the environmental predictors that classify street robbery hot spots and control street segments in Indianapolis. Empirical controls were generated by matching each hot spot to a...
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression

We address the problem of solving mixed random linear equations. We have unlabeled observations coming from multiple linear regressions, and each observation corresponds to exactly one of the regression models. The goal is to learn the linear regressors from the observations. Classically, Alternating Minimization (AM) (which is a variant of Expectation Maximization (EM)) is used to solve this problem. AM iteratively alternates between the estimation of labels and solving the regression problems with the estimated labels. Empirically, it is observed that, for a large variety of non-convex problems including mixed linear regression, AM converges at a much faster rate compared to gradient based algorithms. However, the existing theory suggests similar rate of convergence for AM and gradient based methods, failing to capture this empirical behavior. In this paper, we close this gap between theory and practice for the special case of a mixture of $2$ linear regressions. We show that, provided initialized properly, AM enjoys a \emph{super-linear} rate of convergence in certain parameter regimes. To the best of our knowledge, this is the first work that theoretically establishes such rate for AM. Hence, if we want to recover the unknown regressors upto an error (in $\ell_2$ norm) of $\epsilon$, AM only takes $\mathcal{O}(\log \log (1/\epsilon))$ iterations. Furthermore, we compare AM with a gradient based heuristic algorithm empirically and show that AM dominates in iteration complexity as well as wall-clock time.
Taming Pretrained Transformers for Extreme Multi-label Text Classification

We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input text could be a product description on Amazon.com and the labels could be product categories. XMC is an important yet challenging problem in the NLP community. Recently, deep pretrained transformer models have achieved state-of-the-art performance on many NLP tasks including sentence classification, albeit with small label sets. However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue. In this paper, we propose X-Transformer, the first scalable approach to fine-tuning deep transformer models for the XMC problem. The proposed method achieves new state-of-the-art results on four XMC benchmark datasets. In particular, on a Wiki dataset with around 0.5 million labels, the prec@1 of X-Transformer is 77.28%, a substantial improvement over state-of-the-art XMC approaches Parabel (linear) and AttentionXML (neural), which achieve 68.70% and 76.95% precision@1, respectively. We further apply X-Transformer to a product2query dataset from Amazon and gained 10.7% relative improvement on prec@1 over Parabel.
Establishing the Importance of Interaction and Presence to Student Learning in Online Environments

With the growing trends in favor of online course offerings in higher education, it is important that researchers continue to focus on investigating the components vital to effectiveness. Using a survey design, the elements of interaction and presence, and their relationship and influence on student learning in an online course is examined in the current study. The findings of the study suggest that students perceive instructor-learner and learner-content interaction to be more important to their learning as compared to learner-learner interaction. In addition, teaching presence plays a more important role in student learning followed by cognitive presence and then social presence. Again, when it comes to the combination of the factors of interaction and presence, the factors that students perceive to have the most influence on their learning are teaching presence and learner-instructor interaction with the least important factor being learner-learner interaction. Overall, the results of this empirical study have implications for online course design and delivery to ensure student learning in online environments.
Instances of computational optimal recovery: Refined approximability models

Abstract Models based on approximation capabilities have recently been studied in the context of Optimal Recovery. These models, however, are not compatible with overparametrization, since model- and data-consistent functions could then be unbounded. This drawback motivates the introduction of refined approximability models featuring an added boundedness condition. Thus, two new models are proposed in this article: one where the boundedness applies to the target functions (first type) and one where the boundedness applies to the approximants (second type). For both types of models, optimal maps for the recovery of linear functionals are first described on an abstract level before their efficient constructions are addressed. By exploiting techniques from semidefinite programming, these constructions are explicitly carried out on a common example involving polynomial subspaces of C [ − 1 , 1 ] .
Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values

Abstract Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RNN-based models in traffic forecasting has not yet been fully exploited in terms of the predictive power of spatial–temporal data and the capability of handling missing data. In this paper, we focus on RNN-based models and attempt to reformulate the way to incorporate RNN and its variants into traffic prediction models. A stacked bidirectional and unidirectional LSTM network architecture (SBU-LSTM) is proposed to assist the design of neural network structures for traffic state forecasting. As a key component of the architecture, the bidirectional LSTM (BDLSM) is exploited to capture the forward and backward temporal dependencies in spatiotemporal data. To deal with missing values in spatial–temporal data, we also propose a data imputation mechanism in the LSTM structure (LSTM-I) by designing an imputation unit to infer missing values and assist traffic prediction. The bidirectional version of LSTM-I is incorporated in the SBU-LSTM architecture. Two real-world network-wide traffic state datasets are used to conduct experiments and published to facilitate further traffic prediction research. The prediction performance of multiple types of multi-layer LSTM or BDLSTM models is evaluated. Experimental results indicate that the proposed SBU-LSTM architecture, especially the two-layer BDLSTM network, can achieve superior performance for the network-wide traffic prediction in both accuracy and robustness. Further, comprehensive comparison results show that the proposed data imputation mechanism in the RNN-based models can achieve outstanding prediction performance when the model’s input data contains different patterns of missing values.
Analogue Gravity on a Superconducting Chip

We describe how analogues of a Hawking evaporating black hole as well as the Unruh effect for an oscillatory, accelerating photodetector in vacuum may be realized using superconducting, microwave circuits that are fashioned out of Josephson tunnel junction and film bulk acoustic resonator elements. This article is part of a discussion meeting issue 'The next generation of analogue gravity experiments'.
A comprehensive study of Titan's magnetic pile-up region during the Cassini era

Abstract While Saturn’s largest moon Titan is devoid of an intrinsic magnetic field, draping of the giant planet’s magnetospheric field around Titan’s ionosphere generates a pile-up region at the moon’s ramside. In this study, we analyze Cassini magnetic field data from all 126 Titan flybys to systematically characterize the extensions and magnitudes of the field enhancements at its ramside. Along each flyby trajectory, the segments where Cassini crossed the piled-up magnetic field are determined, and altitude profiles of the pile-up strength are also generated. We investigate the dependency of the extension and strength of the pile-up region on various parameters, such as the Saturn local time and the magnetospheric environment to which Titan is exposed. In this way, we generate a comprehensive picture of Titan’s ramside magnetic pile-up region during the Cassini era. Our major findings are: (1) The extensions of the observed pile-up reveal an asymmetry between the Saturn-facing and Saturn-averted sides caused by the large gyro-radii of ionospheric pick-up ions. (2) The observed pile-up radial extensions are nearly independent of Titan’s orbital position. (3) The lower boundary of the pile-up region is located at an altitude of 0.35 Titan radius, denoting the average altitude of the magnetic ionopause. (4) Considering the error bars, the altitude profiles of the pile-up strength show a stable picture, i.e., the radial profile of the field magnitude is independent of Saturn’s magnetospheric environment and local time.
Markovian and Non-Markovian Quantum Measurements

Consecutive measurements performed on the same quantum system can reveal fundamental insights into quantum theory’s causal structure, and probe different aspects of the quantum measurement problem. According to the Copenhagen interpretation, measurements affect the quantum system in such a way that the quantum superposition collapses after each measurement, erasing any memory of the prior state. We show here that counter to this view, un-amplified measurements (measurements where all variables comprising a pointer are in principle controllable) have coherent ancilla density matrices that encode the memory of the entire set of (un-amplified) quantum measurements that came before, and that the chain of this entire set is therefore non-Markovian. In contrast, sequences of amplified measurements (measurements where at least one pointer variable has been lost) are equivalent to a quantum Markov chain. We argue that the non-Markovian nature of quantum measurement has empirical consequences that are incompatible with the assumption of wave function collapse, thus elevating the collapse assumption into a testable hypothesis. Finally, we find that all of the information necessary to reconstruct an arbitrary non-Markovian quantum chain of measurements is encoded on the boundary of that chain (the first and the final measurement), reminiscent of the holographic principle.
Lipid sponge droplets as programmable synthetic organelles

Living cells segregate molecules and reactions in various subcellular compartments known as organelles. Spatial organization is likely essential for expanding the biochemical functions of synthetic reaction systems, including artificial cells. Many studies have attempted to mimic organelle functions using lamellar membrane-bound vesicles. However, vesicles typically suffer from highly limited transport across the membranes and an inability to mimic the dense membrane networks typically found in organelles such as the endoplasmic reticulum. Here, we describe programmable synthetic organelles based on highly stable nonlamellar sponge phase droplets that spontaneously assemble from a single-chain galactolipid and nonionic detergents. Due to their nanoporous structure, lipid sponge droplets readily exchange materials with the surrounding environment. In addition, the sponge phase contains a dense network of lipid bilayers and nanometric aqueous channels, which allows different classes of molecules to partition based on their size, polarity, and specific binding motifs. The sequestration of biologically relevant macromolecules can be programmed by the addition of suitably functionalized amphiphiles to the droplets. We demonstrate that droplets can harbor functional soluble and transmembrane proteins, allowing for the colocalization and concentration of enzymes and substrates to enhance reaction rates. Droplets protect bound proteins from proteases, and these interactions can be engineered to be reversible and optically controlled. Our results show that lipid sponge droplets permit the facile integration of membrane-rich environments and self-assembling spatial organization with biochemical reaction systems.
Reminder avoidance: Why people hesitate to disclose their insecurities to friends.

People seek and receive support from friends through self-disclosure. However, when self-disclosures reveal personal insecurities, do people rely on friends as an audience as they normally do? This research demonstrates that they do not. Five preregistered studies show that disclosers exhibit a weaker preference for friends as an audience when disclosures involve revealing personal insecurities than when they involve revealing other neutral or negative personal information. This effect is observed despite that the only alternative audience available to disclosers in these studies is a stranger. We theorize that such an effect occurs because disclosers anticipate stronger pain associated with being reminded of disclosed contents when their disclosures involve personal insecurities than other types of information and, thus, wish to avoid such reminders from happening. Our findings support this theorizing: (a) Disclosers' weaker preference for friends as an audience for insecurity-provoking (vs. noninsecurity-provoking) disclosure is mediated by how painful they anticipate reminders of disclosed contents to be and (b) disclosers' preference for a particular audience is diminished when the perceived likelihood of disclosed-content reminders associated with that audience is enhanced. An additional preregistered exploratory content-analysis study shows that when disclosing personal insecurities, people disclose less and are less intimate in what they disclose when they imagine a friend (vs. a stranger) as an audience. Altogether, disclosers are ironically found to open up less to friends about personal insecurities-self-aspects that may particularly benefit from friends' support-than about other topics, due to their avoidance of potentially painful disclosed-content reminders. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
What is an appropriate measure of litigation? Quantification, qualification and differentiation of dispute resolution

This article reviews the claims about rates of litigation in the United States, as either “too much” or “too little” (e.g. “The Vanishing Trial”). While we need to understand aggregate litigation rates to assess access to justice, it may be more important to understand litigation rates in the context of differentiated case types. Litigation, in some cases, produces too “brittle” (binary) or costly outcomes, which is what led to the American “A” (alternative/appropriate) Dispute Resolution movement. This movement (now moving across the globe) may provide “process pluralism” with greater flexibility in outcome and cost variations, (now often called “a”ccesible dispute resolution”). However, litigation is still important in a variety of justice-seeking contexts (e.g. for new rights creation, old rights enforcement, and precedent elaboration). This article suggests that the question of how much litigation is appropriate in any legal culture is dependent on a variety of factors that goes beyond simple aggregate counting. The article concludes with a critique of recent American legal practices in restricting litigation through mandatory arbitration, non-disclosure agreements, class action limitations, privatized mass claim settlements, and restrictive jurisdictional interpretations in judicial decision making and legislation. Este articulo repasa las afirmaciones de que hay “demasiados” o “demasiado pocos” litigios en los EE. UU. Si bien es necesario entender las proporciones de litigios agregados para evaluar el acceso a la justicia, tal vez sea mas importante entender las proporciones de litigios en el contexto de tipos de casos diferenciados. En algunas ocasiones, los litigios producen resultados demasiado “fragiles” (binarios) o costosos, lo cual origino el movimiento llamado “American ‘A’ (alternativo/adecuado) Dispute Resolution”. Este movimiento, ahora en expansion por todo el mundo, puede proporcionar “pluralismo procesal” de forma mas flexible con diferentes resultados y costes (lo que ahora se denomina “resolucion de conflictos ‘a’ccesible”). Sin embargo, el acto de litigar sigue siendo importante en varios contextos de busqueda de justicia (por ej., para crear nuevos derechos, para aplicacion de viejos derechos, y para la elaboracion precedente). Este articulo da a entender que la proporcion de litigios apropiada en cualquier cultura juridica depende de varios factores mas alla de un recuento. Se concluye con una critica de practicas juridicas recientes en America, consistentes en la restriccion del litigio por arbitraje obligatorio, acuerdos de confidencialidad, limitaciones en demandas colectivas, liquidacion de reclamaciones colectivas e interpretaciones jurisdiccionales restrictivas en la toma de decisiones judiciales y en la legislacion. Available from: https://doi.org/10.35295/osls.iisl/0000-0000-0000-1146
Physics of parameter correlations around the solar-scale enhancement in neutrino theory with unitarity violation

We discuss physics of the three neutrino flavor transformation with non-unitary mixing matrix, with particular attention to the correlation between the $\nu$SM- and the $\alpha$ parameters which represent effect of unitarity violating (UV) new physics. Toward the goal, a new perturbative framework is created to illuminate the effect of non-unitarity in region of the solar-scale enhanced oscillations. We refute the skepticism about the physical reality of the $\nu$SM CP $\delta$ - $\alpha$ parameter phase correlation by analysis with the SOL convention of $U_{\text{\tiny MNS}}$ in which $e^{ \pm i \delta}$ is attached to $s_{12}$. Then, a comparative study between the solar- and atmospheric-scale oscillation regions allowed by the framework reveals a dynamical $\delta-$(blobs of the $\alpha$ parameters) correlation in the solar oscillation region, in sharp contrast to the ``chiral'' type phase correlation $[e^{- i \delta } \bar{\alpha}_{\mu e}, e^{ - i \delta} \bar{\alpha}_{\tau e}, \bar{\alpha}_{\tau \mu}]$ in the PDG convention seen in the atmospheric oscillation region. An explicit perturbative calculation to first order in the $\nu_{\mu} \rightarrow \nu_{e}$ channel allows us to decompose the UV related part of the probability into the unitary evolution part and the genuine non-unitary part. We observe that the effect of non-unitarity tends to cancel between these two parts, as well as between the different $\alpha_{\beta \gamma}$ parameters.
Medicaid expansion, health insurance coverage, and cost barriers to care among low-income adults with asthma: the Adult Asthma Call-Back Survey.

To examine Medicaid expansion (ME) effects on health insurance coverage (HIC) and cost barriers to medical care among people with asthma. We analyzed 2012–2013 and 2015–2016 data from low-income ad...
The complete vertex p-center problem

The vertex p-center problem consists of locating p facilities among a set of M potential sites such that the maximum distance from any demand to its closest located facility is minimized. The complete vertex p-center problem solves the p-center problem for all p from 1 to the total number of sites, resulting in a multi-objective trade-off curve between the number of facilities and the service distance required to achieve full coverage. This trade-off provides a reference to planners and decision makers, enabling them to easily visualize the consequences of choosing different coverage design criteria for the given spatial configuration of the problem. We present two fast algorithms for solving the complete p-center problem: one using the classical formulation but trimming variables while still maintaining optimality and the other converting the problem to a location set covering problem and solving for all distances in the distance matrix. We also discuss scenarios where it makes sense to solve the problem via brute-force enumeration. All methods result in significant speedups, with the set covering method reducing computation times by many orders of magnitude.
Sequential Bayesian parameter estimation of stochastic dynamic load models

Abstract In this paper we focus on the parameter estimation of dynamic load models with stochastic terms—in particular, load models where protection settings are uncertain, such as in aggregated air conditioning units. We show how the uncertainty in the aggregated protection characteristics can be formulated as a stochastic differential equation with process noise. We cast the parameter inversion within a Bayesian parameter estimation framework, and we present methods to include process noise. We demonstrate the benefits of considering stochasticity in the parameter estimation and the risks of ignoring it.
Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening.

Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as those containing open-shell, 3d transition metals that can be...
Health Expenditures and Quality Health Services: The Efficiency Analysis of Differential Risk Structures of Medicare Accountable Care Organizations (ACOs)

Medicare Accountable Care Organizations (ACOs) represent the nation’s largest initiative of Medicare alternative payment models toward value and health outcomes. The Centers for Medicare & Medicaid...
Hyperproperties for Robotics: Planning via HyperLTL

There is a growing interest on formal methods-based robotic planning for temporal logic objectives. In this work, we extend the scope of existing synthesis methods to hyper-temporal logics. We are motivated by the fact that important planning objectives, such as optimality, robustness, and privacy, (maybe implicitly) involve the interrelation between multiple paths. Such objectives are thus hyperproperties, and cannot be expressed with usual temporal logics like the linear temporal logic (LTL). We show that such hyperproperties can be expressed by HyperLTL, an extension of LTL to multiple paths. To handle the complexity of planning with HyperLTL specifications, we introduce a symbolic approach for synthesizing planning strategies on discrete transition systems. Our planning method is evaluated on several case studies.
Lewis Carroll and the Red Hot Potato: A graph theoretic approach to a linear algebraic identity

Abstract The Lewis Carroll Identity expresses the determinant of a matrix in terms of subdeterminants obtained by deleting one row and column or a pair of rows and columns. Using the matrix tree theorem, we can convert this into an equivalent identity involving sums over pairs of forests. Unlike the Lewis Carroll Identity, the Forest Identity involves no minus signs. In 2011, Vlasev and Yeats (2012) suggested that such a Forest Identity could be proven using edge transfers similar to Zeilberger’s 1997 matrix proof. However, until now, such an algorithm has not yet been developed. In this paper, we provide this edge transfer algorithm and a bijective proof for both the Lewis Carroll Identity and Forest Identity. This bijection is implemented by the Red Hot Potato algorithm, so called because the way edges get tossed back and forth between the two forests is reminiscent of the children’s game of hot potato.
Adaptive Nonlinear Control of Fixed-Wing VTOL with Airflow Vector Sensing

Fixed-wing vertical take-off and landing (VTOL) aircraft pose a unique control challenge that stems from complex aerodynamic interactions between wings and rotors. Thus, accurate estimation of external forces is indispensable for achieving high performance flight. In this paper, we present a composite adaptive nonlinear tracking controller for a fixed- wing VTOL. The method employs online adaptation of linear force models, and generates accurate estimation for wing and rotor forces in real-time based on information from a three-dimensional airflow sensor. The controller is implemented on a custom-built fixed-wing VTOL, which shows improved velocity tracking and force prediction during the transition stage from hover to forward flight, compared to baseline flight controllers.
Evolution of multiple postzygotic barriers between species of the Mimulus tilingii complex.

Species are often defined by their ability to interbreed (i.e., Biological Species Concept), but determining how and why reproductive isolation arises between new species can be challenging. In the Mimulus tilingii species complex, three species (M. caespitosa, M. minor, and M. tilingii) are largely allopatric and grow exclusively at high elevations (>2000 m). The extent to which geographic separation has shaped patterns of divergence among the species is not well understood. In this study, we determined that the three species are morphologically and genetically distinct, yet recently diverged. Additionally, we performed reciprocal crosses within and between the species and identified several strong postzygotic reproductive barriers, including hybrid seed inviability, F1 hybrid necrosis, and F1 hybrid male and female sterility. In this study, such postzygotic barriers are so strong that a cross between any species pair in the M. tilingii complex would cause nearly complete reproductive isolation. We consider how geographical and topographical patterns may have facilitated the evolution of several postzygotic barriers and contributed to speciation of closely related members within the M. tilingii species complex.
Standard errors for panel data models with unknown clusters

Abstract This paper develops a new standard-error estimator for linear panel data models. The proposed estimator is robust to heteroskedasticity, serial correlation, and cross-sectional correlation of unknown forms. The serial correlation is controlled by the Newey–West method. To control for cross-sectional correlations, we propose to use the thresholding method, without assuming the clusters to be known. We establish the consistency of the proposed estimator. Monte Carlo simulations show the method works well. An empirical application is considered.
Dimensionality-Reduction in the Drosophila Wing as Revealed by Landmark-Free Measurements of Phenotype

Organismal phenotypes emerge from a complex set of genotypic interactions. While technological advances in sequencing provide a quantitative description of an organism9s genotype, characterization of an organism9s physical phenotype lags far behind. Here, we relate genotype to the complex and multi-dimensional phenotype of an anatomical structure using the Drosophila wing as a model system. We develop a mathematical approach that enables a robust description of biologically salient phenotypic variation. Analysing natural phenotypic variation, and variation generated by weak perturbations in genetic and environmental conditions during development, we observe a highly constrained set of wing phenotypes. In a striking example of dimensionality reduction, the nature of varieties produced by the Drosophila developmental program is constrained to a single integrated mode of variation in the wing. Our strategy demonstrates the emergent simplicity manifest in the genotype-to-phenotype map in the Drosophila wing and may represent a general approach for interrogating a variety of genotype-phenotype relationships.
A polyhedral approach to bisubmodular function minimization

Abstract We consider minimization problems with bisubmodular objective functions. We propose valid inequalities, namely the poly-bimatroid inequalities, and provide a complete linear description of the convex hull of the epigraph of a bisubmodular function. Furthermore, we develop a cutting plane algorithm for constrained bisubmodular minimization based on the poly-bimatroid inequalities. Our computational experiments on the minimization subproblem in robust coupled sensor placement show that our algorithm can solve highly non-linear problems that do not admit compact mixed-integer linear formulations.
A Survey of Cybersecurity of Digital Manufacturing

The Industry 4.0 concept promotes a digital manufacturing (DM) paradigm that can enhance quality and productivity, which reduces inventory and the lead time for delivering custom, batch-of-one products based on achieving convergence of additive, subtractive, and hybrid manufacturing machines, automation and robotic systems, sensors, computing, and communication networks, artificial intelligence, and big data. A DM system consists of embedded electronics, sensors, actuators, control software, and interconnectivity to enable the machines and the components within them to exchange data with other machines, components therein, the plant operators, the inventory managers, and customers. This article presents the cybersecurity risks in the emerging DM context, assesses the impact on manufacturing, and identifies approaches to secure DM.
Cavity-mediated collective spin-exchange interactions in a strontium superradiant laser.

Laser-cooled and quantum degenerate atoms are being pursued as quantum simulators and form the basis of today’s most precise sensors. A key challenge toward these goals is to understand and control coherent interactions between the atoms. We observe long-range exchange interactions mediated by an optical cavity, which manifest as tunable spin-spin interactions on the pseudo spin-½ system composed of the millihertz linewidth clock transition in strontium. This leads to one-axis twisting dynamics, the emergence of a many-body energy gap, and gap protection of the optical coherence against certain sources of decoherence. Our observations will aid in the future design of versatile quantum simulators and the next generation of atomic clocks that use quantum correlations for enhanced metrology.
Isovariant mappings of degree 1 and the Gap Hypothesis

Unpublished results of S Straus and W Browder state that two notions of homotopy equivalence for manifolds with smooth group actions—isovariant and equivariant— often coincide under a condition called the Gap Hypothesis; the proofs use deep results in geometric topology. This paper analyzes the difference between the two types of maps from a homotopy theoretic viewpoint more generally for degree one maps if the manifolds satisfy the Gap Hypothesis, and it gives a more homotopy theoretic proof of the Straus‐Browder result. 55P91, 57S17; 55R91, 55S15, 55S91
Structure and Evolution of Internally Heated Hot Jupiters

NASA headquarters under the NASA Earth and Space Science Fellowship Program [PLANET14F-0038]; NASA ATP program [NNX16AB26G]