cssi/SciSciGPT-SciSciNet huggingface dataset:
Plabic R-Matrices

Postnikov's plabic graphs in a disk are used to parametrize totally positive Grassmannians. In recent years plabic graphs have found numerous applications in math and physics. One of the key features of the theory is the fact that if a plabic graph is reduced, the face weights can be uniquely recovered from boundary measurements. On surfaces more complicated than a disk this property is lost. In this paper we undertake a comprehensive study of a certain semi-local transformation of weights for plabic networks on a cylinder that preserve boundary measurements. We call this a plabic R-matrix. We show that plabic R-matrices have underlying cluster algebra structure, generalizing recent work of Inoue-Lam-Pylyavskyy. Special cases of transformations we consider include geometric R-matrices appearing in Berenstein-Kazhdan theory of geometric crystals, and also certain transformations appearing in a recent work of Goncharov-Shen.
IMPDH inhibitors for antitumor therapy in tuberous sclerosis complex

Recent studies in distinct preclinical tumor models have established the nucleotide synthesis enzyme inosine-5'-monophosphate dehydrogenase (IMPDH) as a viable target for antitumor therapy. IMPDH inhibitors have been used clinically for decades as safe and effective immunosuppressants. However, the potential to repurpose these pharmacological agents for antitumor therapy requires further investigation, including direct comparisons of available compounds. Therefore, we tested structurally distinct IMPDH inhibitors in multiple cell and mouse tumor models of the genetic tumor syndrome tuberous sclerosis complex (TSC). TSC-associated tumors are driven by uncontrolled activation of the growth-promoting protein kinase complex mechanistic target of rapamycin (mTOR) complex 1 (mTORC1), which is also aberrantly activated in the majority of sporadic cancers. Despite eliciting similar immunosuppressive effects, the IMPDH inhibitor mizoribine, used clinically throughout Asia, demonstrated far superior antitumor activity compared with the FDA-approved IMPDH inhibitor mycophenolate mofetil (or CellCept, a prodrug of mycophenolic acid). When compared directly to the mTOR inhibitor rapamycin, mizoribine treatment provided a more durable antitumor response associated with tumor cell death. These results provide preclinical support for repurposing mizoribine, over other IMPDH inhibitors, as an alternative to mTOR inhibitors for the treatment of TSC-associated tumors and possibly other tumors featuring uncontrolled mTORC1 activity.
Mourning the Loss of the Land in Le Clézio’s “Villa Aurore” and “Orlamonde”

The popularity of Pablo Servigne and Raphael Stevens’ best-seller Comment tout peut s’effrondrer (2015) undoubtedly speaks to our anxiety about fragile and interrelated economic, political, and env...
Of Bodies Politic and Pecuniary: A Brief History of Corporate Purpose

American corporate law has long drawn a bright line between for-profit and non-profit corporations. In recent years, hybrid or social enterprises have increasingly put this bright-line distinction to the test. This Article asks what we can learn about the purpose of the American business corporation by examining its history and development in the United States in its formative period from roughly 1780-1860. This brief history of corporate purpose suggests that the duty to maximize profits in the for-profit corporation is a relatively recent development. Historically, the American business corporation grew out of an earlier form of corporation that was neither for-profit nor nonprofit in today’s parlance but rather, served a multitude of municipal, religious, charitable, educational, and eventually business purposes in early nineteenth-century New England. The purposes of early American business corporations—rather than maximization of profit to private shareholders—were often overtly public, involving development of local transportation, finance, and other much-needed economic infrastructure. With the rise of factory-based manufacturing, railroads, and other capital-intensive industries in the middle decades of the nineteenth century and the advent of general incorporation statutes, the purpose of the American business corporation shifted fundamentally from public to private. By 1860, the stage was set for the modern firm. This Article concludes that the corporation has no intrinsic purpose. The corporation’s defining features are separate legal personality and the ability to aggregate capital toward any otherwise lawful end, whether for-profit or nonprofit. Social enterprises today more closely resemble the early American business corporation than the profit-maximizing modern firm. Social enterprise should be seen less as a legally uncertain novelty than a return to the business corporation’s nineteenth-century American roots. Finally, this Article suggests potential limitations for social enterprise.
libsbmljs-Enabling web-based SBML tools.

Abstract The SBML standard is used in a number of online repositories for storing systems biology models, yet there is currently no Web-capable JavaScript library that can read and write the SBML format. This is a severe limitation since the Web has become a universal means of software distribution, and the graphical capabilities of modern web browsers offer a powerful means for building rich, interactive applications. Also, there is a growing developer population specialized in web technologies that is poised to take advantage of the universality of the web to build the next generation of tools in systems biology and other fields. However, current solutions require server-side processing in order to support existing standards in modeling. We present libsbmljs, a JavaScript/WebAssembly library for Node.js and the Web with full support for all SBML extensions. Our library is an enabling technology for online SBML editors, model-building tools, and web-based simulators, and runs entirely in the browser without the need for any dedicated server resources. We provide NPM packages, an extensive set of examples, JavaScript API documentation, and an online demo that allows users to read and validate the SBML content of any model in the BioModels and BiGG databases. We also provide instructions and scripts to allow users to build a copy of libsbmljs against any libSBML version. Although our library supports all existing SBML extensions, we cover how to add additional extensions to the wrapper, should any arise in the future. To demonstrate the utility of this implementation, we also provide a demo at https://libsbmljsdemo.github.io/ with a proof-of-concept SBML simulator that supports ODE and stochastic simulations for SBML core models. Our project is hosted at https://libsbmljs.github.io/ , which contains links to examples, API documentation, and all source code files and build scripts used to create libsbmljs. Our source code is licensed under the Apache 2.0 open source license.
AMPK activation by metformin promotes survival of dormant ER+ breast cancer cells

Purpose: Despite adjuvant endocrine therapy for patients with estrogen receptor alpha (ER)-positive breast cancer, dormant residual disease can persist for years and eventually cause tumor recurrence. We sought to deduce mechanisms underlying the persistence of dormant cancer cells to identify therapeutic strategies. Experimental Design: Mimicking the aromatase inhibitor–induced depletion of estrogen levels used to treat patients, we developed preclinical models of dormancy in ER+ breast cancer induced by estrogen withdrawal in mice. We analyzed tumor xenografts and cultured cancer cells for molecular and cellular responses to estrogen withdrawal and drug treatments. Publicly available clinical breast tumor gene expression datasets were analyzed for responses to neoadjuvant endocrine therapy. Results: Dormant breast cancer cells exhibited upregulated 5′ adenosine monophosphate-activated protein kinase (AMPK) levels and activity, and upregulated fatty acid oxidation. While the antidiabetes AMPK-activating drug metformin slowed the estrogen-driven growth of cells and tumors, metformin promoted the persistence of estrogen-deprived cells and tumors through increased mitochondrial respiration driven by fatty acid oxidation. Pharmacologic or genetic inhibition of AMPK or fatty acid oxidation promoted clearance of dormant residual disease, while dietary fat increased tumor cell survival. Conclusions: AMPK has context-dependent effects in cancer, cautioning against the widespread use of an AMPK activator across disease settings. The development of therapeutics targeting fat metabolism is warranted in ER+ breast cancer.
Fundraising and Optimal Policy Rules

This paper develops a simple spatial model of fundraising, in which charities select a target population to solicit donations. First, we show that in a competitive charity market without any intervention, the number of charities in the market and/or the overall net funds raised by charities may be sub-optimal. Next, we analyze whether a social planner can prevent such shortcomings and show that a regulatory mechanism can be designed to achieve socially desirable outcomes. In contrast to the previous literature, our model does not necessarily produce monopoly as the optimal market structure. We show that if fixed costs associated with establishing charities are sufficiently low, then the optimal market structure is not a monopoly. Given the importance of the trade-off between the volume and variety of charitable services, we argue that this result may be of particular interest to policy makers.
Two Analyst-Oriented Computer Languages: EASL, POSE

For approximately three and one-half years a continuous modest-level effort has been maintained at Aerospace Corporation to support the development of analyst-oriented computer languages which could be used for both off-line and interactive applications. Initially, this effort produced a block-oriented macro language called EASL (Engineering Analysis and Simulation Language) [1].
Housing wealth isn't wealth

A fall in house prices due to a change in fundamental value redistributes wealth from those long housing (for whom the fundamental value of the house they own exceeds the present discounted value of their planned future consumption of housing services) to those short housing. In a closed economy representative agent model (the special case when the birth rate is zero, of the Yaari-Blanchard OLG model used in the paper), there is no pure wealth effect on consumption from a change in house prices if this represents a change in their fundamental value. When the birth rate is positive, higher fundamental house prices driven by the housing demand of future generations will boost current consumption. There is a pure wealth effect on consumption from a change in house prices even in the representative agent model, if this reflects a change in the speculative bubble component of house prices. Two other channels through which a fall in house prices can affect aggregate consumption are (1) redistribution effects if the marginal propensity to spend out of wealth differs between those long housing (the old, say) and those short housing (the young, say) and (2) collateral or credit effects due to the collateralisability of housing wealth and the non-collateralisability of human wealth. A decline in house prices reduces the scope for mortgage equity withdrawal. For given sequences of future after-tax labour income and interest rates, a fall in house prices will then depress consumption in the short run while boosting it in the long run.
Aiding Decision-Making to Reduce the Impacts of Climate Change

Utilizing theory and empirical insights from psychology and behavioural economics, this paper examines individuals’ cognitive and motivational barriers to adopting climate change adaptation and mitigation measures that increase consumer welfare. We explore various strategies that take into account the simplified decision-making processes used by individuals and resulting biases. We make these points by working through two examples: (1) investments in energy efficiency products and new technology and (2) adaptation measures to reduce property damage from future floods and hurricanes. In both cases there is a reluctance to undertake these measures due to high and certain upfront costs, delayed and probabilistic benefits, and behavioural biases related to this asymmetry. The use of choice architecture through framing and the use of default options coupled with short-term incentives and long-term contracts can encourage greater investment in these measures.
Knowledge-based facility planning: a review and a framework

The knowledge-based facility planning (KBFP) problem is reviewed. The aim of KBFP is to provide a more comprehensive planning package for users so that their expertise can be augmented with proven knowledge, and yield significantly better plans. The categories reviewed include facilities equipment selection, software model selection, and the generative task of creating a facility planning solution. The employed problem representation and problem-solving techniques are reviewed. Finally, the development of an integrated framework for KBFP is discussed.
The new cosmology: Mid-term report card for inflation

Inflation has been the driving idea in cosmology for two decades and is a pillar of the New Cosmology. The inflationary paradigm has now passed its first round of significant tests, with two of its three basics predictions confirmed at about the 10% level (spatially flat Universe and density perturbations produced from quantum fluctuations with $$| n - 1| \sim {\mathcal{O}}(0.1)$$ ). The Inflationary Paradigm has some of the truth. Over the next decade the precision of these tests, most of which involve measurements of CMB anisotropy and polarization, will improve 30 fold or more(!), testing inflation more sharply and possibly elucidating the underlying cause. Especially important in this regard is detecting the inflation-produced gravitational waves, either directly or through their CMB polarization signature. While inflation has by no means been verified, its successes have raised the bar for competitor theories: Any alternative must feature the two hallmarks of inflation: superluminal expansion and entropy production.
Chapter 4 – Biochemistry of Human Milk

Abstract Human milk is a highly complex composite liquid of nutrients for infant growth, consisting primarily of fat, carbohydrates, and proteins, as well as minerals, vitamins, and other nutrients. The delicate balance of nutrients and the dynamic lactation process make human milk the only food substance during life that is adequate as the sole source of nutrition for a period of time in an infant's life. The biochemistry of human milk changes throughout the stages of breastfeeding and as a function of infant needs and demands for growth and development. There remains a tremendous amount to be learned about the interaction of macronutrients, micronutrients and bioactive factors in human milk as the optimal nutrition for a human infant.
Keynote 48: Is it really for everyone?

Burtness et al. recently published the landmark Keynote-48 study, demonstrating a survival benefit for pembrolizumab monotherapy and pembrolizumab/chemotherapy, compared with cetuximab/chemotherapy, in patients with recurrent/metastatic head and neck squamous cell carcinoma (HNSCC). These data are impactful and practice-changing, and have rapidly been adopted in practice, with increasing numbers of HNSCC patients receiving either pembrolizumab monotherapy, or pembrolizumab / chemotherapy, in the first line recurrent / metastatic setting. Pembrolizumab was approved as a single agent for patients whose tumors express PD□L1 (Combined Positive Score [CPS] ≥1), while pembrolizumab / chemotherapy was approved for use in the United States for all patients irrespective of PD-L1 expression.
Selection Algorithms with Small Groups

We revisit the selection problem, namely that of computing the ith order statistic of n given elements, in particular the classic deterministic algorithm by grouping and partition due to Blum, Floy...
CHAPTER 1 – Geometric Measure Theory

Publisher Summary Geometric measure theory could be described as differential geometry, generalized through measure theory to deal with maps and surfaces that are not necessarily smooth and applied to the calculus of variations. It dates from the 1960 foundational paper of Herbert Federer and Wendell Fleming on Normal and Integral Currents, recognized by the 1986 AMS Steele Prize for a paper of fundamental or lasting importance. This chapter presents an outline of the purpose and basic concepts of geometric measure theory. Along with its successes and advantages, the definition of a surface as a mapping has certain drawbacks: (1) there is an inevitable a priori restriction on the types of singularities that can occur; (2) there is an a priori restriction on the topological complexity; and (3) the natural topology lacks compactness properties. The importance of compactness properties appears in the direct method. The direct method for finding a surface of least area with a given boundary has three steps: (1) take a sequence of surfaces with areas decreasing to the infimum, (2) extract a convergent subsequence, and (3) show that the limit surface is the desired surface of least area. An alternative to surfaces as mappings is provided by rectifiable currents, the m-dimensional, oriented surfaces of geometric measure theory.
High Frequency of SARS-CoV-2 RNAemia and Association With Severe Disease.

Background Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in blood, also known as RNAemia, has been reported, but its prognostic implications are poorly understood. This study aimed to determine the frequency of SARS-CoV-2 RNA in plasma and its association with coronavirus disease 2019 (COVID-19) clinical severity. Methods An analytical cross-sectional study was performed in a single-center tertiary care institution and included consecutive inpatients and outpatients with confirmed COVID-19. The prevalence of SARS CoV-2 RNAemia and the strength of its association with clinical severity variables were examined and included intensive care unit (ICU) admission, invasive mechanical ventilation, and 30-day all-cause mortality. Results Paired nasopharyngeal and plasma samples were included from 85 patients. The median age was 55 years, and individuals with RNAemia were older than those with undetectable SARS-CoV-2 RNA in plasma (63 vs 50 years; P = .04). Comorbidities were frequent including obesity (37.6%), hypertension (30.6%), and diabetes mellitus (22.4%). RNAemia was detected in 28/85 (32.9%) of patients, including 22/28 (78.6%) who required hospitalization. In models adjusted for age, RNAemia was detected more frequently in individuals who developed severe disease including ICU admission (32.1 vs 14.0%; P = .04) and invasive mechanical ventilation (21.4% vs 3.5%; P = .02). All 4 deaths occurred in individuals with detectable RNAemia. An additional 121 plasma samples from 28 individuals with RNAemia were assessed longitudinally, and RNA was detected for a maximum duration of 10 days. Conclusions This study demonstrated a high proportion of SARS-CoV-2 RNAemia, and an association between RNAemia and clinical severity suggesting the potential utility of plasma viral testing as a prognostic indicator for COVID-19.
Data-based RNA-seq simulations by binomial thinning.

With the explosion in the number of methods designed to analyze bulk and single-cell RNA-seq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method’s performance. Rather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets. Using data simulated from a theoretical model can substantially impact the results of a study. We developed more realistic simulation techniques for RNA-seq data. Our tools are available in the seqgendiff R package on the Comprehensive R Archive Network: https://cran.r-project.org/package=seqgendiff .
mPFC spindle cycles organize sparse thalamic activation and recently active CA1 cells during non-REM sleep

Sleep oscillations in the neocortex and hippocampus are critical for the integration of new memories into stable generalized representations in neocortex. However, the role of the thalamus in this process is poorly understood. To determine the thalamic contribution to non-REM oscillations (sharp-wave ripples, SWRs; slow/delta; spindles), we recorded units and local field potentials (LFPs) simultaneously in the limbic thalamus, mPFC, and CA1 in rats. We report that the cycles of neocortical spindles provide a key temporal window that coordinates CA1 SWRs with sparse but consistent activation of thalamic units. Thalamic units were phase-locked to delta and spindles in mPFC, and fired at consistent lags with other thalamic units within spindles, while CA1 units that were active during spatial exploration were engaged in SWR-coupled spindles after behavior. The sparse thalamic firing could promote an incremental integration of recently acquired memory traces into neocortical schemas through the interleaved activation of thalamocortical cells.
Piecewise Linear Regression via a Difference of Convex Functions

We present a new piecewise linear regression methodology that utilizes fitting a difference of convex functions (DC functions) to the data. These are functions $f$ that may be represented as the difference $\phi_1 - \phi_2$ for a choice of convex functions $\phi_1, \phi_2$. The method proceeds by estimating piecewise-liner convex functions, in a manner similar to max-affine regression, whose difference approximates the data. The choice of the function is regularised by a new seminorm over the class of DC functions that controls the $\ell_\infty$ Lipschitz constant of the estimate. The resulting methodology can be efficiently implemented via Quadratic programming even in high dimensions, and is shown to have close to minimax statistical risk. We empirically validate the method, showing it to be practically implementable, and to have comparable performance to existing regression/classification methods on real-world datasets.
Minimax Weight and Q-Function Learning for Off-Policy Evaluation

We provide theoretical investigations into off-policy evaluation in reinforcement learning using function approximators for (marginalized) importance weights and value functions. Our contributions include: (1) A new estimator, MWL, that directly estimates importance ratios over the state-action distributions, removing the reliance on knowledge of the behavior policy as in prior work (Liu et al., 2018). (2) Another new estimator, MQL, obtained by swapping the roles of importance weights and value-functions in MWL. MQL has an intuitive interpretation of minimizing average Bellman errors and can be combined with MWL in a doubly robust manner. (3) Several additional results that offer further insights into these methods, including the sample complexity analyses of MWL and MQL, their asymptotic optimality in the tabular setting, how the learned importance weights depend the choice of the discriminator class, and how our methods provide a unified view of some old and new algorithms in RL.
Extreme Relative Pose Network Under Hybrid Representations

In this paper, we introduce a novel RGB-D based relative pose estimation approach that is suitable for small-overlapping or non-overlapping scans and can output multiple relative poses. Our method performs scene completion and matches the completed scans. However, instead of using a fixed representation for completion, the key idea is to utilize hybrid representations that combine 360-image, 2D image-based layout, and planar patches. This approach offers adaptively feature representations for relative pose estimation. Besides, we introduce a global-2-local matching procedure, which utilizes initial relative poses obtained during the global phase to detect and then integrate geometric relations for pose refinement. Experimental results justify the potential of this approach across a wide range of benchmark datasets. For example, on ScanNet, the rotation translation errors of the top-1/top-5 predictions of our approach are $28.6^{\circ}/0.90m$ and $16.8^{\circ}/0.76m$, respectively. Our approach also considerably boosts the performance of multi-scan reconstruction in few-view reconstruction settings.
ARDA: automatic relational data augmentation for machine learning

Automatic machine learning (AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspects of the machine learning pipeline like model selection, hyperparameter tuning, and feature selection, relatively few works have focused on automatic data augmentation. Automatic data augmentation involves finding new features relevant to the user's predictive task with minimal "human-in-the-loop" involvement.We present ARDA, an end-to-end system that takes as input a dataset and a data repository, and outputs an augmented data set such that training a predictive model on this augmented dataset results in improved performance. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join. We perform an extensive empirical evaluation of different system components and benchmark our feature selection algorithm on real-world datasets.
Probing the interactions between interstitial hydrogen atoms in niobium through density functional theory calculations

Abstract Past experiments about hydrogen absorption in niobium have revealed specific properties about interactions between interstitial hydrogen atoms. It has been reported that there are long-range attractive and short-range repulsive interactions between interstitial hydrogen atoms in niobium. It has also been reported that these interactions are of many-body nature. While previous understanding of these interactions is based on experimental inferences from past experiments, through these calculations, for the first time, we can understand the nature of the interactions at a fundamental level. In this work, we use density functional theory calculations to study the interactions of interstitial hydrogen atoms in niobium. We report here that these interactions are a combination of an attractive, indirect image interaction and a repulsive, direct interaction. Through our calculations, we also infer here that these interactions indeed have many-body characteristics.
Loss of TET2 Affects Proliferation and Drug Sensitivity through Altered Dynamics of Cell-State Transitions

A persistent puzzle in cancer biology is how mutations, which neither alter growth signaling pathways nor directly interfere with drug mechanism, can still recur and persist in tumors. One example is the mutation of the DNA demethylase tet methylcytosine dioxygenase 2 (TET2) in acute myeloid leukemias (AMLs) that frequently persists from diagnosis through remission and relapse, but whose fitness advantage in chemotherapy is unclear. Here, we use isogenic human AML cell lines to show that TET2 loss of function alters the dynamics of transitions between differentiated and stem-like states. A conceptual mathematical model and experimental validation suggest that these altered cell-state dynamics can benefit the cell population by slowing population decay during drug treatment and lowering the number of survivor cells needed to re-establish the initial population. These studies shed light on the functional and phenotypic effects of a TET2 mutation in AML and illustrate how a single gene mutation can alter a cells' phenotypic plasticity. A record of this paper's transparent peer review process is included in the Supplemental Information.
Evading Anderson localization in a one-dimensional conductor with correlated disorder

We show that a one-dimensional disordered conductor with correlated disorder has an extended state and a Landauer resistance that is nonzero in the limit of infinite system size in contrast to the predictions of the scaling theory of Anderson localization. The delocalization transition is not related to any underlying symmetry of the model such as particle-hole symmetry. Moreover, the form of correlated disorder considered here is distinct from other models with delocalization transitions that have been considered in the literature. For a wire of finite length the effect manifests as a sharp transmission resonance that narrows as the length of the wire is increased. Experimental realizations and applications are discussed including the possibility of constructing a narrow-band light filter.
Near data acceleration with concurrent host access

Near-data accelerators (NDAs) that are integrated with the main memory have the potential for significant power and performance benefits. Fully realizing these benefits requires the large available memory capacity to be shared between the host and NDAs in a way that permits both regular memory access by some applications and accelerating others with an NDA, avoids copying data, enables collaborative processing, and simultaneously offers high performance for both host and NDA. We identify and solve new challenges in this context: mitigating row-locality interference from host to NDAs, reducing read/write-turnaround overhead caused by fine-grain interleaving of host and NDA requests, architecting a memory layout that supports the locality required for NDAs and sophisticated address inter-leaving for host performance, and supporting both packetized and traditional memory interfaces. We demonstrate our approach in a simulated system that consists of a multi-core CPU and NDA-enabled DDR4 memory modules. We show that our mechanisms enable effective and efficient concurrent access using a set of microbenchmarks, then demonstrate the potential of the system for the important stochastic variance-reduced gradient (SVRG) algorithm.
The Case for Ethical Non-compete Agreements: Executives Versus Sandwich-Makers

Human capital, the knowledge, skills, and abilities of employees, can be a powerful driver of firm performance, yet the mobility of human capital raises questions over how to protect it. Employee non-compete agreements, which limit an employee’s ability to start or join a rival firm, have received recent attention. While past research considers whether non-competes are effective tools at limiting employee mobility, few have considered if non-competes should be used. Filling this gap, I propose a normative schema for when employee non-competes can be considered ethical. A review of the limited existing literature on the ethics of employee non-competes finds that prior research has mostly focused on questions of property rights, which I propose as being nested within other ethical constructs. Analysis of two real-world illustrative examples of employee non-competes (an executive at Amazon and a Jimmy John’s sandwich-maker) leads to a three-prong approach to evaluating non-competes based on ethical dimensions of power, autonomy, and fairness. I end by proposing—although further research is warranted—a measure of employee-level absorptive capacity, which is closely coupled with an employee’s pre-employment human capital, as an employee-level attribute independent of, although likely coincidental with, the tripartite requirements of power/autonomy/fairness for ethical employee non-compete agreements.
NSVD : Normalized Singular Value Deviation Reveals Number of Latent Factors in Tensor Decomposition.

Tensor decomposition has been shown, time and time again, to be an effective tool in multiaspect data mining, especially in exploratory applications where the interest is in discovering hidden inte...
Interobserver Agreement of Lung Ultrasound Findings of COVID-19.

Background Lung ultrasound (LUS) has received considerable interest in the clinical evaluation of patients with COVID-19. Previously described LUS manifestations for COVID-19 include B-lines, consolidations, and pleural thickening. The interrater reliability (IRR) of these findings for COVID-19 is unknown. Methods This study was conducted between March and June 2020. Nine physicians (hospitalists: n = 4; emergency medicine: n = 5) from 3 medical centers independently evaluated n = 20 LUS scans (n = 180 independent observations) collected from patients with COVID-19, diagnosed via RT-PCR. These studies were randomly selected from an image database consisting of COVID-19 patients evaluated in the emergency department with portable ultrasound devices. Physicians were blinded to any patient information or previous LUS interpretation. Kappa values (κ) were used to calculate IRR. Results There was substantial IRR on the following items: normal LUS scan (κ = 0.79 [95% CI: 0.72-0.87]), presence of B-lines (κ = 0.79 [95% CI: 0.72-0.87]), ≥3 B-lines observed (κ = 0.72 [95% CI: 0.64-0.79]). Moderate IRR was observed for the presence of any consolidation (κ = 0.57 [95% CI: 0.50-0.64]), subpleural consolidation (κ = 0.49 [95% CI: 0.42-0.56]), and presence of effusion (κ = 0.49 [95% CI: 0.41-0.56]). Fair IRR was observed for pleural thickening (κ = 0.23 [95% CI: 0.15-0.30]). Discussion Many LUS manifestations for COVID-19 appear to have moderate to substantial IRR across providers from multiple specialties utilizing differing portable devices. The most reliable LUS findings with COVID-19 may include the presence/count of B-lines or determining if a scan is normal. Clinical protocols for LUS with COVID-19 may require additional observers for the confirmation of less reliable findings such as consolidations.
Data Freshness in Leader-Based Replicated Storage

Leader-based data replication improves consistency in highly available distributed storage systems via sequential writes to the "leader" nodes. After a write has been committed by the leaders, "follower" nodes are written by a multicast mechanism and are only guaranteed to be eventually consistent. With Age of Information (AoI) as the freshness metric, we characterize how the number of leaders affects the freshness of the data retrieved by an instantaneous read query. In particular, we derive the average age of a read query for a deterministic model for the leader writing time and a probabilistic model for the follower writing time. We obtain a closed-form expression for the average age for exponentially distributed follower writing time. Our numerical results show that, depending on the relative speed of the write operation to the two groups of nodes, there exists an optimal number of leaders which minimizes the average age of the retrieved data, and that this number increases as the relative speed of writing on leaders increases.
Numerical study of optical ray retracing in laser-plasma backscatter

Optical ray retracing in stimulated Brillouin backscatter from laser-produced plasmas is studied numerically, using a 2-D propagation code (bounce). This code treats steady-state behavior in the strong damping limit, and includes self-focusing effects. Ray retracing phenomena are grouped into two limiting cases. In the ''whole-beam'' limit, the pump field at the lens has a broad spatial profile that can be focused to a long narrow waist within the plasma. This geometry selectively amplifies only those initial noise components that are propagating back along the axis, where the net gain is highest. The simulations show that such effects exhibit a pronounced threshold due to self-focusing, and disappear when the focal width becomes comparable to the length of the gain medium. In the opposite limit, where the pump radiation at the lens is dominated by small-scale transverse structure, the backscatter can reproduce this structure in detail (i.e., exhibit wavefront reversal) when the far field of the pump produces an interference pattern in the plasma. The plasma then behaves as an active volume hologram as it amplifies the backward-propagating random noise fields.
Association between SARS-CoV-2 Neutralizing Antibodies and Commercial Serological Assays.

Background Commercially available SARS-CoV-2 serological assays based on different viral antigens have been approved for the qualitative determination of anti-SARS-CoV-2 antibodies. However, there are limited published data associating the results from commercial assays with neutralizing antibodies. Methods Sixty-six specimens from 48 patients with PCR-confirmed COVID-19 and a positive result by the Roche Elecsys Anti-SARS-CoV-2, Abbott SARS-CoV-2 IgG, or EUROIMMUN SARS-CoV-2 IgG assays and 5 control specimens were analyzed for the presence of neutralizing antibodies to SARS-CoV-2. Correlation, concordance, positive percent agreement (PPA), and negative percent agreement (NPA) were calculated at several cutoffs. Results were compared in patients categorized by clinical outcomes. Results The correlation between SARS-CoV-2 neutralizing titer (EC50) and the Roche, Abbott, and EUROIMMUN assays was 0.29, 0.47, and 0.46, respectively. At an EC50 of 1:32, the concordance kappa with Roche was 0.49 (95% CI; 0.23-0.75), with Abbott was 0.52 (0.28-0.77), and with EUROIMMUN was 0.61 (0.4-0.82). At the same neutralizing titer, the PPA and NPA for the Roche was 100% (94-100) and 56% (30-80); Abbott was 96% (88-99) and 69% (44-86); and EUROIMMUN was 91% (80-96) and 81% (57-93) for distinguishing neutralizing antibodies. Patients who were intubated, had cardiac injury, or acute kidney injury from COVID-19 infection had higher neutralizing titers relative to those with mild symptoms. Conclusions COVID-19 patients generate an antibody response to multiple viral proteins such that the calibrator ratios on the Roche, Abbott, and EUROIMMUN assays are all associated with SARS-CoV-2 neutralization. Nevertheless, commercial serological assays have poor NPA for SARS-CoV-2 neutralization, making them imperfect proxies for neutralization.
Symmetry-enriched quantum spin liquids in (3 + 1) d

We use the intrinsic one-form and two-form global symmetries of (3+1)d bosonic field theories to classify quantum phases enriched by ordinary (0-form) global symmetry. Different symmetry-enriched phases correspond to different ways of coupling the theory to the background gauge field of the ordinary symmetry. The input of the classification is the higher-form symmetries and a permutation action of the 0-form symmetry on the lines and surfaces of the theory. From these data we classify the couplings to the background gauge field by the 0-form symmetry defects constructed from the higher-form symmetry defects. For trivial two-form symmetry the classification coincides with the classification for symmetry fractionalizations in (2 + 1)d. We also provide a systematic method to obtain the symmetry protected topological phases that can be absorbed by the coupling, and we give the relative ’t Hooft anomaly for different couplings. We discuss several examples including the gapless pure U(1) gauge theory and the gapped Abelian finite group gauge theory. As an application, we discover a tension with a conjectured duality in (3 + 1)d for SU(2) gauge theory with two adjoint Weyl fermions.
Improving Excited State Potential Energy Surfaces via Optimal Orbital Shapes.

We demonstrate that, rather than resorting to high-cost dynamic correlation methods, qualitative failures in excited-state potential energy surface predictions can often be remedied at no additional cost by ensuring that optimal molecular orbitals are used for each individual excited state. This approach also avoids the weighting choices required by state-averaging and dynamic weighting and obviates their need for expensive wave function response calculations when relaxing excited-state geometries. Although multistate approaches are of course preferred near conical intersections, other features of excited-state potential energy surfaces can benefit significantly from our single-state approach. In three different systems, including a double bond dissociation, a biologically relevant amino hydrogen dissociation, and an amino-to-ring intramolecular charge transfer, we show that state-specific orbitals offer qualitative improvements over the state-averaged status quo.
Artificial light at night amplifies seasonal relapse of haemosporidian parasites in a widespread songbird.

Urban habitats can shape interactions between hosts and parasites by altering not only exposure rates but also within-host processes. Artificial light at night (ALAN) is common in urban environment...
Executive Accountants and the Reliability of Financial Reporting

Using hand-collected data from mandatory disclosures of executive officers, we examine financial reporting outcomes associated with delegating significant accounting responsibilities to an executiv...
An equity framework for family, community, and school partnerships

Equity has often been identified as a foundational concept for truly inclusive and reciprocal partnerships among schools, families, and communities. Equity can be difficult for schools to achieve w...
Edge Preserving Maps of the Nonseparating Curve Graphs, Curve Graphs and Rectangle Preserving Maps of the Hatcher-Thurston Graphs

Let R be a compact, connected, orientable surface of genus g with n boundary components with g ≥ 2, n ≥ 0. Let &#x1d4a9;(R) be the nonseparating curve graph, &#x1d49e;(R) be the curve graph and ℋ&#x1d4af; (R) be the Hatch...
Interpreting and Stabilizing Machine-learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing sub-grid-scale physics (e.g. moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the mid-tropospheric moisture, two widely-studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity-wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parametrized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m/s. In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM- vs. GCRM- trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.
A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19

At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risks, financial difficulties, etc. the online media, especially the Twitter platform, is experiencing a high volume of activity related to this pandemic. Among the hot topics, the polarized debates about unconfirmed medicines for the treatment and prevention of the disease have attracted significant attention from online media users. In this work, we present a stance data set, COVID-CQ, of user-generated content on Twitter in the context of COVID-19. We investigated more than 14 thousand tweets and manually annotated the tweet initiators' opinions regarding the use of "chloroquine" and "hydroxychloroquine" for the treatment or prevention of COVID-19. To the best of our knowledge, COVID-CQ is the first data set of Twitter users' stances in the context of the COVID-19 pandemic, and the largest Twitter data set on users' stances towards a claim, in any domain. We have made this data set available to the research community via the Mendeley Data repository. We expect this data set to be useful for many research purposes, including stance detection, evolution and dynamics of opinions regarding this outbreak, and changes in opinions in response to the exogenous shocks such as policy decisions and events.
Monopole Floer homology and the spectral geometry of three-manifolds

We refine some classical estimates in Seiberg–Witten theory, and discuss an application to the spectral geometry of three-manifolds. We show that for any Riemannian metric on a rational homology three-sphere $Y$, the first eigenvalue of the Hodge Laplacian on coexact one-forms is bounded above explicitly in terms of the Ricci curvature, provided that $Y$ is not an $L$-space (in the sense of Floer homology). The latter is a computable purely topological condition, and holds in a variety of examples. Performing the analogous refinement in the case of manifolds with $b_1 \gt 0$, we provide a gauge-theoretic proof of an inequality of Brock and Dunfield relating the Thurston and $L^2$ norms of hyperbolic three-manifolds, first proved using minimal surfaces.
$C^{1,0}$ foliation theory

Transverse 1–dimensional foliations play an important role in the study of codimension-one foliations. In Geom. Topol. Monogr. 19 (2015) 21–72, the authors introduced the notion of flow box decomposition of a 3–manifold M. This is a combinatorial decomposition of M that reflects both the structure of a given codimension-one foliation and that of a given transverse dimension-one foliation, and that is amenable to inductive strategies. In this paper, flow box decompositions are used to extend some classical foliation results to foliations that are not C2. Enhancements of well-known results of Calegari on smoothing leaves, Dippolito on Denjoy blowup of leaves, and Tischler on approximations by fibrations are obtained. The methods developed are not intrinsically 3–dimensional techniques, and should generalize to prove corresponding results for codimension-one foliations in n–dimensional manifolds.
Twisted exponents and twisted Frobenius–Schur indicators for Hopf algebras

ABSTRACTClassically, the exponent of a group is the least common multiple of the orders of its elements. This notion was generalized by Etingof and Gelaki to Hopf algebras. Kashina, Sommerhauser, and Zhu later observed that there is a strong connection between exponents and Frobenius–Schur indicators. In this article, we introduce the notion of twisted exponents and show there is a similar relationship between the twisted exponent and the twisted Frobenius–Schur indicators defined in previous work of the authors. In particular, we exhibit a new formula for the twisted indicators and use it to prove periodicity and rationality statements.