cssi/SciSciGPT-SciSciNet huggingface dataset:
Null effect of circulating sphingomyelins on risk for breast cancer: a Mendelian randomization report using Breast Cancer Association Consortium (BCAC) data.

Background: Changes in cellular metabolism are a hallmark of cancer and are linked with sphingolipid synthesis. Due to immense interest in how sphingolipids influence chemoresistance, more is known about the impact of sphingolipids during cancer treatment and progression than about the potential role of sphingolipids in the induction of tumors in humans. Methods: Because estrogen triggers sphingolipid signaling cascades, the causal role of circulating levels of sphingomyelin (a type of sphingolipid) on breast cancer was investigated with a well-powered Mendelian randomization design. Results: The results reveal a null effect (OR = 0.94; 95% CI = 0.85, 1.05; P = 0.30). Conclusion: Despite the role sphingomyelins play during chemoresistance and cancer progression, circulating sphingomyelins do not appear to initiate or protect from breast cancer. This finding comprises the first causal report in humans that sphingomyelins on breast cancer initiation is null. Future investigations of risk in other cancer types are needed to further explore the potential role of sphingolipid biology in cancer etiology.
A New Framework for Online Testing of Heterogeneous Treatment Effect

We propose a new framework for online testing of heterogeneous treatment effects. The proposed test, named sequential score test (SST), is able to control type I error under continuous monitoring and detect multi-dimensional heterogeneous treatment effects. We provide an online p-value calculation for SST, making it convenient for continuous monitoring, and extend our tests to online multiple testing settings by controlling the false discovery rate. We examine the empirical performance of the proposed tests and compare them with a state-of-art online test, named mSPRT using simulations and a real data. The results show that our proposed test controls type I error at any time, has higher detection power and allows quick inference on online A/B testing.
Receding Horizon Control for Drinking Water Networks: The Case for Geometric Programming

Optimal, network-driven control of water distribution networks (WDNs) is very difficult: valve and pump models form nontrivial, combinatorial logic; hydraulic models are nonconvex; water demand patterns are uncertain; and WDNs are naturally of large scale. Prior research on control of WDN addressed major research challenges, yet either i) adopted simplified hydraulic models, WDN topologies, and rudimentary valve/pump modeling or ii) used mixed-integer, nonconvex optimization to solve WDN control problems. The objective of this article is to develop tractable computational algorithms to manage WDN operation, while considering arbitrary topology, flow direction, an abundance of valve types, control objectives, hydraulic models, and operational constraints—all while only using convex, continuous optimization. Specifically, we propose new geometric programming (GP)-based model predictive control (MPC) algorithms, designed to solve the water flow equations and obtain WDN controls, i.e., pump/valve schedules alongside heads and flows. The proposed approach amounts to solving a series of convex optimization problems that graciously scale to large networks. The proposed approach is tested using a 126-node network with many valves and pumps and is shown to outperform traditional, rule-based control. The developed GP-based MPC algorithms, as well as the numerical test results, are all included on Github.
Joins on samples: a theoretical guide for practitioners

Despite decades of research on AQP (approximate query processing), our understanding of sample-based joins has remained limited and, to some extent, even superficial. The common belief in the community is that joining random samples is futile. This belief is largely based on an early result showing that the join of two uniform samples is not an independent sample of the original join, and that it leads to quadratically fewer output tuples. Unfortunately, this early result has little applicability to the key questions practitioners face. For example, the success metric is often the final approximation's accuracy, rather than output cardinality. Moreover, there are many non-uniform sampling strategies that one can employ. Is sampling for joins still futile in all of these settings? If not, what is the best sampling strategy in each case? To the best of our knowledge, there is no formal study answering these questions.This paper aims to improve our understanding of sample-based joins and offer a guideline for practitioners building and using real-world AQP systems. We study limitations of offline samples in approximating join queries: given an offline sampling budget, how well can one approximate the join of two tables? We answer this question for two success metrics: output size and estimator variance. We show that maximizing output size is easy, while there is an information-theoretical lower bound on the lowest variance achievable by any sampling strategy. We then define a hybrid sampling scheme that captures all combinations of stratified, universe, and Bernoulli sampling, and show that this scheme with our optimal parameters achieves the theoretical lower bound within a constant factor. Since computing these optimal parameters requires shuffling statistics across the network, we also propose a decentralized variant in which each node acts autonomously using minimal statistics. We also empirically validate our findings on popular SQL and AQP engines.
High School GPAs and ACT Scores as Predictors of College Completion: Examining Assumptions About Consistency Across High Schools:

High school GPAs (HSGPAs) are often perceived to represent inconsistent levels of readiness for college across high schools, whereas test scores (e.g., ACT scores) are seen as comparable. This stud...
Intermittent Learning: On-Device Machine Learning on Intermittently Powered System

This paper introduces intermittent learning --- the goal of which is to enable energy harvested computing platforms capable of executing certain classes of machine learning tasks effectively and efficiently. We identify unique challenges to intermittent learning relating to the data and application semantics of machine learning tasks, and to address these challenges, we devise 1) an algorithm that determines a sequence of actions to achieve the desired learning objective under tight energy constraints, and 2) propose three heuristics that help an intermittent learner decide whether to learn or discard training examples at run-time which increases the energy efficiency of the system. We implement and evaluate three intermittent learning applications that learn the 1) air quality, 2) human presence, and 3) vibration using solar, RF, and kinetic energy harvesters, respectively. We demonstrate that the proposed framework improves the energy efficiency of a learner by up to 100% and cuts down the number of learning examples by up to 50% when compared to state-of-the-art intermittent computing systems that do not implement the proposed intermittent learning framework.
Orbital Stability of Circumstellar Planets in Binary Systems

Planets that orbit only one of the stars in stellar binary systems (i.e., circumstellar) are dynamically constrained to a limited range of orbital parameters and thus understanding conditions on their stability is of great importance in exoplanet searches. We perform $\sim$700 million N-body simulations to identify how stability regions depend on properties of the binary, as well as, the starting planetary inclination and mean longitude relative to the binary orbit. Moreover, we provide grid interpolation maps and lookup tables for the community to use our results. Through Monte-Carlo methods we determine that planets with a semimajor axis $a_p$ $\lesssim$8\% of the binary semimajor axis $a_{bin}$ will likely be stable given the known distribution of binary star parameters. This estimate varies in the Lidov-Kozai regime or for retrograde orbits to 4\% or 10\% of $a_{bin}$, respectively. Our method to quickly determine the circumstellar stability limit is important for interpreting observations of binaries using direct imaging with JWST, photometry with TESS, or even astrometry with Gaia.
Middle School Children With Attention-Deficit/Hyperactivity Disorder Have a Greater Concussion History.

Objective Examine lifetime history of concussions in middle school student athletes who have attention-deficit/hyperactivity disorder (ADHD). Design Cross-sectional study. Setting Nine middle schools in Virginia, USA. Participants A sample of 1037 middle school students (ages 11-14 years, M = 12.6, SD = 0.93; 45.8% girls) underwent baseline/preseason assessments during the 2017 to 2018 academic year and self-reported their health history, including whether or not they had been diagnosed with ADHD. Athletes were divided into 2 groups, those with ADHD (n = 71; 6.8%) and control subjects (n = 966). Independent variables Self-reported diagnosis of ADHD and self-identified sex. Main outcome measures Self-reported concussion history. Results In the total sample, boys were more likely to report a previous history of concussion than girls [χ(1) = 10.81, P = 0.001; odds ratio (OR) = 1.92; 95% confidence interval (CI), 1.30-2.85]. The rate of previous concussion in children with ADHD (23.9%) was twice the rate of previous concussion among children without ADHD (11.4%) [χ(1) = 9.70, P = 0.002; OR = 2.45; 95% CI, 1.37-4.38]. Approximately 1 in 4 boys with ADHD (24.5%) and 1 in 5 girls with ADHD (22.2%) reported having sustained one or more previous concussions. Conclusions Attention-deficit/hyperactivity disorder is associated with a greater prevalence of previous concussion in middle school children. Further research is needed to understand the risk of sustaining concussion for young athletes with ADHD, as well as short- and long-term outcomes of concussion among young athletes with ADHD.
The soil microbial community alters patterns of selection on flowering time and fitness-related traits in Ipomoea purpurea.

Premise Plant flowering time plays an important role in plant fitness and thus evolutionary processes. Soil microbial communities are diverse and have a large impact, both positive and negative, on the host plant. However, owing to few available studies, how the soil microbial community may influence the evolutionary response of plant populations is not well understood. Here we sought to uncover whether belowground microbial communities act as an agent of selection on flowering and growth traits in the common morning glory, Ipomoea purpurea. Methods We performed a controlled greenhouse experiment in which genetic lines of I. purpurea were planted into either sterilized soils or in soils that were sterilized and inoculated with the microbial community from original field soil. We could thus directly test the influence of alterations to the microbial community on plant growth, flowering, and fitness and assess patterns of selection in both soil microbial environments. Results A more complex soil microbial community resulted in larger plants that produced more flowers. Selection strongly favored earlier flowering when plants were grown in the complex microbial environment than compared to sterilized soil. We also uncovered a pattern of negative correlational selection on growth rate and flowering time, indicating that selection favored different combinations of growth and flowering traits in the simplified versus complex soil community. Conclusions Together, these results suggest the soil microbial community is a selective agent on flowering time and ultimately that soil microbial community influences important plant evolutionary processes.
Homological Algebra of Multivalued Action Functionals

We outline a cohomological treatment for multivalued (classical) action functionals. We point out that an application of Takens' theorem, after Zuckerman, Deligne and Freed, allows to conclude that multivalued functionals yield globally defined variational equations.
Identification of a novel base J binding protein complex involved in RNA polymerase II transcription termination in trypanosomes

Base J, β-D-glucosyl-hydroxymethyluracil, is a modification of thymine DNA base involved in RNA Polymerase (Pol) II transcription termination in kinetoplastid protozoa. Little is understood regarding how specific thymine residues are targeted for J-modification or the mechanism of J regulated transcription termination. To identify proteins involved in J-synthesis, we expressed a tagged version of the J-glucosyltransferase (JGT) in Leishmania tarentolae, and identified four co-purified proteins by mass spectrometry: protein phosphatase (PP1), a homolog of Wdr82, a potential PP1 regulatory protein (PNUTS) and a protein containing a J-DNA binding domain (named JBP3). Gel shift studies indicate JBP3 is a J-DNA binding protein. Reciprocal tagging, co-IP and sucrose gradient analyses indicate PP1, JGT, JBP3, Wdr82 and PNUTS form a multimeric complex in kinetoplastids, similar to the mammalian PTW/PP1 complex involved in transcription termination via PP1 mediated dephosphorylation of Pol II. Using RNAi and analysis of Pol II termination by RNA-seq and RT-PCR, we demonstrate that ablation of PNUTS, JBP3 and Wdr82 lead to defects in Pol II termination at the 3'-end of polycistronic gene arrays in Trypanosoma brucei. Mutants also contain increased antisense RNA levels upstream of transcription start sites, suggesting an additional role of the complex in regulating termination of bi-directional transcription. In addition, PNUTS loss causes derepression of silent Variant Surface Glycoprotein genes involved in host immune evasion. Our results suggest a novel mechanistic link between base J and Pol II polycistronic transcription termination in kinetoplastids.
DeepPR: Incremental Recovery for Interdependent VNFs with Deep Reinforcement Learning

The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on deep reinforcement learning. Our simulation results indicate that DeepPR can obtain 98.4% of the theoretical optimum in certain networks.
Some Philosophical Prehistory of the (Earman-Norton) Hole Argument

The celu of the philosophical literature on the hole argument is the 1987 paper by Earman \& Norton ["What Price Space-time Substantivalism? The Hole Story" Br. J. Phil. Sci.]. This paper has a well-known back-story, concerning work by Stachel and Norton on Einstein's thinking in the years 1913-15. Less well-known is a connection between the hole argument and Earman's work on Leibniz in the 1970s and 1980s, which in turn can be traced to an argument first presented in 1975 by Howard Stein. Remarkably, this thread originates with a misattribution: the argument Earman attributes to Stein, which ultimately morphs into the hole argument, was not the argument Stein gave. The present paper explores this episode and presents some reflections on how it bears on the subsequent literature.
RSS-Based Q-Learning for Indoor UAV Navigation

In this paper, we focus on the potential use of unmanned aerial vehicles (UAVs) for search and rescue (SAR) missions in GPS-denied indoor environments. We consider the problem of navigating a UAV to a wireless signal source, e.g., a smartphone or watch owned by a victim. We assume that the source periodically transmits RF signals to nearby wireless access points. Received signal strength (RSS) at the UAV, which is a function of the UAV and source positions, is fed to a Q-learning algorithm, and the UAV is navigated to the vicinity of the source. Unlike the traditional location-based Q-learning approach that uses the GPS coordinates of the agent, our method uses the RSS to define the states and rewards of the algorithm. It does not require any a priori information about the environment. These, in turn, make it possible to use the UAVs in indoor SAR operations. Two indoor scenarios with different dimensions are created using a ray tracing software. Then, the corresponding heat maps that show the RSS at each possible UAV location are extracted for more realistic analysis. Performance of the RSS-based Q-learning algorithm is compared with the baseline (location-based) Q-learning algorithm in terms of convergence speed, average number of steps per episode, and the total length of the final trajectory. Our results show that the RSS-based Q-learning provides competitive performance with the location-based Q-learning.
Conformational plasticity of the ClpAP AAA+ protease couples protein unfolding and proteolysis.

The ClpAP complex is a conserved bacterial protease that unfolds and degrades proteins targeted for destruction. The ClpA double-ring hexamer powers substrate unfolding and translocation into the ClpP proteolytic chamber. Here, we determined high-resolution structures of wild-type Escherichia coli ClpAP undergoing active substrate unfolding and proteolysis. A spiral of pore loop–substrate contacts spans both ClpA AAA+ domains. Protomers at the spiral seam undergo nucleotide-specific rearrangements, supporting substrate translocation. IGL loops extend flexibly to bind the planar, heptameric ClpP surface with the empty, symmetry-mismatched IGL pocket maintained at the seam. Three different structures identify a binding-pocket switch by the IGL loop of the lowest positioned protomer, involving release and re-engagement with the clockwise pocket. This switch is coupled to a ClpA rotation and a network of conformational changes across the seam, suggesting that ClpA can rotate around the ClpP apical surface during processive steps of translocation and proteolysis. Cryo-EM structures of Escherichia coli ClpAP undergoing active substrate unfolding and proteolysis reveal contacts that drive substrate translocation and a dynamic switch mechanism at the ClpA–ClpP interface.
14 – Fusion reactors

Publisher Summary This chapter describes the fusion reactors that permit the controlled release of fusion energy. The chapter reviews the choices of nuclear reaction, examines the requirements for feasibility and practicality, and describes the physical features of machines that have been tested. Of the many possible nuclear reactions, the one that is employed first involves deuterium and tritium (produced by neutron absorption in lithium). A D-T reactor that yields net energy must exceed the ignition temperature of around 4.4 keV and have a product nτ above about 1014, where nτ is the fuel particle number density and tau is the confinement time. Several experimental machines have been tested, involving an electrical discharge (plasma) that is constrained by electric and magnetic fields. One promising fusion machine, the tokamak, achieves magnetic confinement in a doughnut-shaped structure. Research is also under way on inertial confinement; in which laser beams or charged particle beams cause the explosion of miniature D-T pellets.
Chapter 3 – Solutes

Publisher Summary This chapter deals with the properties of solutes. It compares chemical potentials in the aqueous phases on the two sides of a membrane or across some other region to predict the direction of passive solute fluxes as well as the driving forces leading to such motion. The chapter also shows how the fluxes of charged species can account for the electrical potential differences across biological membranes. Many solute properties are intertwined with those of the ubiquitous solvent, water. The movement of water through the soil to a root and then to its xylem can influence the entry of dissolved nutrients, and the subsequent distribution of these nutrients throughout the plant depends on water movement in the xylem (and the phloem in some cases). In contrast to water, however, solute molecules can carry a net positive or negative electrical charge. For such charged particles, the electrical term must be included in their chemical potential. This leads to a consideration of electrical phenomena in general and an interpretation of the electrical potential differences across membranes in particular. The chapter also shows that the osmotic pressure difference effective in causing a volume flux across a membrane permeable to both water and solutes is generally less than the actual osmotic pressure difference across that membrane.
Estimating the Value of Public Insurance Using Complementary Private Insurance

The welfare associated with public insurance is often difficult to quantify because the demand for coverage is unobserved and thus cannot be used to analyze welfare. However, in many settings, individuals can purchase private insurance to supplement public coverage. This paper outlines an approach to use data and variation from private complementary insurance to quantify welfare associated with counterfactuals related to compulsory public insurance. We then apply this approach using administrative data on disability insurance. Our findings suggests that public disability insurance generates substantial surplus for the sample population, and there may be gains to increasing the generosity of coverage.
The Long-Term Agroecological Research (LTAR) Experiment Supports Organic Yields, Soil Quality, and Economic Performance in Iowa

The Long-Term Agroecological Research (LTAR) experiment, at the Iowa State University (ISU) Neely-Kinyon Farm in Greenfield, IA, was established in 1998 to compare the agronomic, ecological, and economic performance of certified organic cropping systems to conventional counterparts. Cropping systems were designed based on local farmer input and practices. In the second LTAR phase (2002 to 2010), equivalent organic and conventional corn (Zea mays) and soybean (Glycine max) yields were achieved in the organic corn-soybean-oat (Avena sativa)/alfalfa (Medicago sativa) (C-S-O/A) and corn-soybean-oat/alfalfa-alfalfa (C-S-O/A-A) rotations compared to the conventional corn-soybean rotation (C-S). Organic oat and alfalfa yields, at 103 bu/acre and 4.4 tons/acre, respectively, exceeded county averages of 73 bu/acre and 3.3. tons/acre, for the same period. Similar plant protection occurred in organic crops, without the use of petrochemicals, compared to conventional crops maintained with synthetic pesticides. In Fall 2009, soil organic carbon, total nitrogen, and extractable K and Ca were 5.7%, 9.5%, 14.2%, and 10.8% higher in organic soils, respectively. Soil properties related to biologically active organic matter were up to 40% higher in organic soils. Economic returns to land and management in 2010 were $510/acre in the organic C-S-O/A- A rotation compared to $351/acre in the C-S rotation. The LTAR experiment will be continued as a valuable demonstration of the potential for organic crops to achieve comparable yields while increasing carbon sequestration and economic returns compared to conventional corn and soybean rotations.
The "gene dosage effect" hypothesis versus the "amplified developmental instability" hypothesis in Down syndrome

Two hypotheses exist to explain the Down syndrome (DS) phenotype. The “gene dosage effect” hypothesis states that the phenotype is a direct result of the cumulative effect of the imbalance of the individual genes located on the triplicated chromosome or chromosome region. In a nut shell, the phenotype results directly from the overexpression of specific chromosome 21 genes. The “amplified developmental instability” hypothesis contends that most manifestations of DS may be interpreted as the results of a non-specific disturbance of chromosome balance, resulting in a disruption of homeostasis. This hypothesis was proposed in an attempt to explain the similarities between the phenotypes of different aneuploid states and the observation that all of the phenotypic traits in DS are also seen in the general population but at lower frequency, with less severity and usually only present as a single trait. Herein, we review recent data and present evidence to support the theory that the phenotypic traits of aneuploid syndromes, and DS in particular, result from the increased dosage of genes encoded on the triplicated chromosome.
Chapter 5 – Linear Transformations

In Chapter 5, we study functions that map the vectors in one vector space to those in another. We concentrate on a special class of these functions, known as linear transformations. The formal definition of a linear transformation is introduced in Section 5.1 along with several of its fundamental properties. In Section 5.2, we show that the effect of any linear transformation is equivalent to multiplication by a corresponding matrix. In Section 5.3, we examine an important relationship between the dimensions of the domain and the range of a linear transformation, known as the Dimension Theorem. In Section 5.4, we introduce two special types of linear transformations: one-to-one and onto. In Section 5.5, these two types of linear transformations are combined to form isomorphisms, which are used to establish that all n-dimensional vector spaces are in some sense equivalent. Finally, in Section 5.6, we return to the topic of eigenvalues and eigenvectors to study them in the context of linear transformations.
Necessary and Sufficient Null Space Condition for Nuclear Norm Minimization in Low-Rank Matrix Recovery

Low-rank matrix recovery has found many applications in science and engineering such as machine learning, system identification, and Euclidean embedding. However, the low-rank matrix recovery problem is an NP hard problem and thus challenging. A commonly used heuristic approach is the nuclear norm minimization. Recently, some authors established the necessary and sufficient null space conditions for nuclear norm minimization to recover every possible low-rank matrix with rank at most $r$ (the strong null space condition). Oymak et al. established a null space condition for successful recovery of a given low-rank matrix (the weak null space condition) using nuclear norm minimization, and derived the phase transition for the nuclear norm minimization. In this paper, we show that the weak null space condition proposed by Oymak et al. is only a sufficient condition for successful matrix recovery using nuclear norm minimization, and is not a necessary condition as claimed. We further give a weak null space condition for low-rank matrix recovery, which is both necessary and sufficient for the success of nuclear norm minimization. At the core of our derivation are an inequality for characterizing the nuclear norms of block matrices, and the conditions for equality to hold in that inequality.
CYCLIC SILANES. SULFUR-INDUCED PENTACOORDINATION IN A DISILOXANE

Reactions of chlorosilanes with diols capable of forming eight-membered-ring systems led to new cyclic silanes. With tetramethylenedichlorosilane ((CH2)4SiCl2) and S[(t-Bu)2C6H2OH]2, the bicyclic s...
Chapter 12 – Statistics and Experimental Design

This chapter presents the various procedures that can be used to assess variation and difference in datasets, comparison of data to models (i.e. curve fitting), confidence intervals and detection of multiple populations. Hypothesis testing also is described.
8 – FISCAL POLICY

Publisher Summary This chapter provides an overview of fiscal policy. Fiscal policies use government-spending, tax, and transfer-payment tools to fight inflation and unemployment. Stabilization policy is only one function of government. Allocation, distribution, and public-choice needs must also be met. Multiplier analysis describes the effect of fiscal policies on aggregate demand. The same fiscal policy has different effects depending on the shape of the aggregate supply curve. All government spending has the same multiplier, but that does not mean that all spending is the same. Government investments increase aggregate supply and aggregate demand. A few government programs reduce economic efficiency and production potential. All taxes are not the same either. Taxes have different AS effects and are paid by different people. Taxes can be progressive, proportional, or regressive. Tax expenditures are special tax breaks that encourage people to spend their own funds on government-approved programs. Tax expenditures are a controversial tool of fiscal policy. Each fiscal policy tool has both advantages and disadvantages.
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding

Few works in the literature of event extraction have gone beyond individual sentences to make extraction decisions. This is problematic when the information needed to recognize an event argument is spread across multiple sentences. We argue that document-level event extraction is a difficult task since it requires a view of a larger context to determine which spans of text correspond to event role fillers. We first investigate how end-to-end neural sequence models (with pre-trained language model representations) perform on document-level role filler extraction, as well as how the length of context captured affects the models’ performance. To dynamically aggregate information captured by neural representations learned at different levels of granularity (e.g., the sentence- and paragraph-level), we propose a novel multi-granularity reader. We evaluate our models on the MUC-4 event extraction dataset, and show that our best system performs substantially better than prior work. We also report findings on the relationship between context length and neural model performance on the task.
Does female schooling reduce fertility? Evidence from Nigeria

Abstract The literature generally points to a negative relationship between female education and fertility. Citing this pattern, policymakers have advocated educating girls and young women as a means to reduce population growth and foster sustained economic and social welfare in developing countries. This paper tests whether the relationship between fertility and education is indeed causal by investigating the introduction of universal primary education in Nigeria. Exploiting differences in program exposure by region and age, the paper presents reduced form and instrumental variables estimates of the impact of female education on fertility. The analysis suggests that increasing female education by one year reduces early fertility by 0.26 births.
Love, hate and murder: Commitment devices in violent relationships

Many violent relationships are characterized by a high degree of cyclicality: women who are the victims of domestic violence often leave and return multiple times. To explain this we develop a model of time inconsistent preferences in the context of domestic violence. This time inconsistency generates a demand for commitment. We present supporting evidence that women in violent relationships display time inconsistent preferences by examining their demand for commitment devices. We find that no-drop policies - which compel the prosecutor to continue with prosecution even if the victim expresses a desire to drop the charges - result in an increase in reporting. No-drop policies also result in a decrease in the number of men murdered by intimates suggesting that some women in violent relationships move away from an extreme type of commitment device when a less costly one is offered.
Facial Nerve Repair Utilizing Intraoperative Repair Strategies

Objectives To determine whether functional and anatomical outcomes following suture neurorrhaphy are improved by the addition of electrical stimulation with or without the addition of polyethylene glycol (PEG). Methods In a rat model of facial nerve injury, complete facial nerve transection and repair was performed via (a) suture neurorrhaphy alone, (b) neurorrhaphy with the addition of brief (30 minutes) intraoperative electrical stimulation, or (c) neurorrhaphy with the addition electrical stimulation and PEG. Functional recovery was assessed weekly for 16 weeks. At 16 weeks postoperatively, motoneuron survival, amount of regrowth, and specificity of regrowth were assessed by branch labeling and tissue analysis. Results The addition of brief intraoperative electrical stimulation improved all functional outcomes compared to suturing alone. The addition of PEG to electrical stimulation impaired this benefit. Motoneuron survival, amount of regrowth, and specificity of regrowth were unaltered at 16 weeks postoperative in all treatment groups. Conclusion The addition of brief intraoperative electrical stimulation to neurorrhaphy in this rodent model shows promising neurological benefit in the surgical repair of facial nerve injury. Level of evidence Animal study.
Identifying the Influential Inputs for Network Output Variance Using Sparse Polynomial Chaos Expansion

Sensitivity analysis (SA) is an important aspect of process automation. It often aims to identify the process inputs that influence the process output’s variance significantly. Existing SA approaches typically consider the input–output relationship as a black box and conduct extensive random sampling from the actual process or its high-fidelity simulation model to identify the influential inputs. In this article, an alternate, novel approach is proposed using a sparse polynomial chaos expansion-based model for a class of input–output relationships represented as directed acyclic networks. The model exploits the relationship structure by recursively relating a network node to its direct predecessors to trace the output variance back to the inputs. It, thereby, estimates the Sobol indices that measure the influence of each input on the output variance, accurately and efficiently. Theoretical analysis establishes the validity of the model as the prediction of the network output converges in probability to the true output under certain regularity conditions. Empirical evaluation of two manufacturing processes and a flooding process shows that the model estimates the Sobol indices accurately with far fewer observations than state-of-the-art black-box methods. Note to Practitioners —This article is motivated by the problem of automated identification of the inputs that influence the variance of the output for networked processes without feedback control. Such processes arise in various natural and engineered systems, of which manufacturing operations and flood mitigation are of particular interest to us, where the output variance represents the uncertainty in productivity, quality, or cost. Therefore, influential inputs’ identification allows us to quantify the effects of the various process parameters, such as operating conditions and physical properties, in determining the uncertainties in the process outputs. We show that our identification method is guaranteed to quantify the effects accurately and is expected to do so more efficiently (with fewer experimental observations) than widely used stochastic sampling techniques. In the future, we will like to evaluate the usefulness of the developed method on large-scale manufacturing and supply chain networks in actual production facilities as well as on critical infrastructures subject to cascading failures.
Hayek's treatment of legal positivism

Friedrich Hayek devoted the later part of his career to investigating the legal rules required for the existence of a free society. The subject of this paper is Hayek’s treatment of legal positivism, which he thought was the most important intellectual movement responsible for the decline of liberal institutions in Europe in the early twentieth century. As shown in the paper, Hayek’s critique consists of two separate arguments: that legal positivism destroys the rule of law and that it amounts to constructivism. The first claim rests on the assumption that “true” laws comply with the rule of law principle, although the meaning of the adjective true is ambiguous. The second claim holds only for a particular variant of legal positivism. In addition to discussing these issues, the paper provides an assessment of Hayek’s own evolutionary theory of law, which was intended as an alternative to both legal positivism and natural law theory.
Structured Tuning for Semantic Role Labeling

Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores. These improvements are due to expressive input representations, which, at least at the surface, are orthogonal to knowledge-rich constrained decoding mechanisms that helped linear SRL models. Introducing the benefits of structure to inform neural models presents a methodological challenge. In this paper, we present a structured tuning framework to improve models using softened constraints only at training time. Our framework leverages the expressiveness of neural networks and provides supervision with structured loss components. We start with a strong baseline (RoBERTa) to validate the impact of our approach, and show that our framework outperforms the baseline by learning to comply with declarative constraints. Additionally, our experiments with smaller training sizes show that we can achieve consistent improvements under low-resource scenarios.
Wireless User-Generic Ear EEG

In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as vacuum forming, plasma-treatment, and spray coating. A 2.5 × 2.5 cm2 wireless recording module is designed to record and stream data wirelessly to a host computer. Performance was evaluated on three human subjects over three months and compared with clinical-grade wet scalp EEG recordings. Recordings of spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the auditory steady-state response (ASSR), are presented. This is the first wireless in-ear EEG to our knowledge to incorporate a dry multielectrode, user-generic design. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG systems.
Learning to Autofocus

Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a realistic dataset of sufficient size for effective learning. Our dataset is labeled with per-pixel depths obtained from multi-view stereo, following [9]. Using this dataset, we apply modern deep classification models and an ordinal regression loss to obtain an efficient learning-based autofocus technique. We demonstrate that our approach provides a significant improvement compared with previous learned and non-learned methods: our model reduces the mean absolute error by a factor of 3.6 over the best comparable baseline algorithm. Our dataset and code are publicly available.
GraphOpt: Learning Optimization Models of Graph Formation

Formation mechanisms are fundamental to the study of complex networks, but learning them from observations is challenging. In real-world domains, one often has access only to the final constructed graph, instead of the full construction process, and observed graphs exhibit complex structural properties. In this work, we propose GraphOpt, an end-to-end framework that jointly learns an implicit model of graph structure formation and discovers an underlying optimization mechanism in the form of a latent objective function. The learned objective can serve as an explanation for the observed graph properties, thereby lending itself to transfer across different graphs within a domain. GraphOpt poses link formation in graphs as a sequential decision-making process and solves it using maximum entropy inverse reinforcement learning algorithm. Further, it employs a novel continuous latent action space that aids scalability. Empirically, we demonstrate that GraphOpt discovers a latent objective transferable across graphs with different characteristics. GraphOpt also learns a robust stochastic policy that achieves competitive link prediction performance without being explicitly trained on this task and further enables construction of graphs with properties similar to those of the observed graph.
Text-Based Ideal Points.

Ideal point models analyze lawmakers’ votes to quantify their political positions, or ideal points. But votes are not the only way to express a political position. Lawmakers also give speeches, release press statements, and post tweets. In this paper, we introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors. We demonstrate the TBIP with two types of politicized text data: U.S. Senate speeches and senator tweets. Though the model does not analyze their votes or political affiliations, the TBIP separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. One benefit of analyzing texts, as opposed to votes, is that the TBIP can estimate ideal points of anyone who authors political texts, including non-voting actors. To this end, we use it to study tweets from the 2020 Democratic presidential candidates. Using only the texts of their tweets, it identifies them along an interpretable progressive-to-moderate spectrum.
Cardiac Microlesions Form During Severe Bacteremic Enterococcus faecalis Infection.

Enterococcus faecalis is a significant cause of hospital-acquired bacteremia. Herein, the discovery is reported that cardiac microlesions form during severe bacteremic E. faecalis infection in mice. The cardiac microlesions were identical in appearance to those formed by Streptococcus pneumoniae during invasive pneumococcal disease. However, E. faecalis does not encode the virulence determinants implicated in pneumococcal microlesion formation. Rather, disulfide bond forming protein A (DsbA) was found to be required for E. faecalis virulence in a Caenorhabditis elegans model and was necessary for efficient cardiac microlesion formation. Furthermore, E. faecalis promoted cardiomyocyte apoptotic and necroptotic cell death at sites of microlesion formation. Additionally, loss of DsbA caused an increase in proinflammatory cytokines, unlike the wild-type strain, which suppressed the immune response. In conclusion, we establish that E. faecalis is capable of forming cardiac microlesions and identify features of both the bacterium and the host response that are mechanistically involved.
Reduced basis approximations of the solutions to spectral fractional diffusion problems

We consider the numerical approximation of the spectral fractional diffusion problem based on the so called Balakrishnan representation. The latter consists of an improper integral approximated via quadratures. At each quadrature point, a reaction-diffusion problem must be approximated and is the method bottle neck. In this work, we propose to reduce the computational cost using a reduced basis strategy allowing for a fast evaluation of the reaction-diffusion problems. The reduced basis does not depend on the fractional power $s$ for $0<s_{\min}\leq s \leq s_{\max}<1$. It is built offline once for all and used online irrespectively of the fractional power. We analyze the reduced basis strategy and show its exponential convergence. The analytical results are illustrated with insightful numerical experiments.
Learning by Ruling and Trade Disputes

Abstract We explore the implications of judicial learning for trade disputes through a model where both the initiation of disputes and the occurrence of rulings are endogenous, governments bargain “in the shadow of the law,” and the efficiency of the court increases with experience. Judicial learning can explain litigation on the equilibrium path, since going to court today implies future payoff gains for the governments. Our model predicts that where learning is present the likelihood of both disputes and rulings should tend to decrease with court experience. Using detailed data on WTO disputes, we find evidence consistent with significant judicial learning at the WTO, but this learning appears to be article-specific and disputant-specific, rather than general, in scope.
The abyssal origins of North Atlantic decadal predictability

The fundamental mechanisms that explain high subpolar North Atlantic (SPNA) decadal predictability within a particular modeling framework are described. The focus is on the Community Earth System Model (CESM), run in both a historical forced-ocean configuration as well as in a fully coupled configuration initialized from the former. The initialized prediction experiments comprise the CESM Decadal Prediction Large Ensemble (CESM-DPLE)—a 40-member set of retrospective hindcasts documented in Yeager et al. (Bull Am Meteorol Soc 99:1867–1886. https://doi.org/10.1175/bams-d-17-0098.1 , 2018). Heat budget analysis confirms the driving role of advective heat convergence in skillful prediction of SPNA upper ocean heat content out to decadal lead times. The key ocean dynamics are topographically-coupled overturning/gyre fluctuations that are geographically centered over the mid-Atlantic ridge (MAR). Long-lasting predictive skill for ocean heat transport can be related to predictable barotropic gyre and sigma-coordinate AMOC circulations, but depth-coordinate AMOC is far less predictable except in the deepest layers. The foundation of ocean memory (and circulation predictive skill) in CESM-DPLE is Labrador Sea Water thickness, which propagates predictably through interior pathways towards the MAR where large anomalies accumulate and persist. Abyssal thickness anomalies drive predictable decadal changes in the gyre circulation, including changes in sea level gradient and near surface flow, that account for the high predictability of SPNA upper ocean heat content.
Expanding Metrics for Unicritical Semihyperbolic Polynomials

We prove that unicritical polynomials $$f(z)=z^d+c$$ which are semihyperbolic, i.e., for which the critical point 0 is a non-recurrent point in the Julia set, are uniformly expanding on the Julia set with respect to the metric $$\rho (z) |dz|$$ , where $$\rho (z) = 1+{{\,\mathrm{dist}\,}}(z,P(f))^{-1+1/d}$$ has singularities on the postcritical set P(f). We also show that this metric is Holder equivalent to the usual Euclidean metric.