cssi/SciSciGPT-SciSciNet huggingface dataset:
13 – Documentary Evidence

Publisher Summary This chapter focuses on the broad category of documentary evidence. This type of evidence encompasses evidence beyond what the term “documentary evidence” would normally bring to mind. Documentary evidence includes evidence furnished by written instruments, inscriptions, and documents of all kinds. The term is broad enough to encompass every form of writing and applies to both public and private documents. Under the Federal Rules of Evidence, a writing or recording is defined including “letters, words, or numbers, or their equivalent, set down by handwriting, typewriting, printing, photostatting, photographing, magnetic impulse, mechanical or electronic recording, or other form of data compilation.” Originally, a document provided a way of storing information in a fairly permanent manner, but that concept has grown to include movies, phonograph records, video- and audiotapes, computers and their mass storage devices, and photographs. Because the general rules of admissibility apply to all types of evidence, documentary evidence must meet these usual requirements. In addition, there are certain rules that apply uniquely to documentary evidence.
Role of statin therapy in the management of patients with the metabolic syndrome

Patients with the metabolic syndrome (MS) have an increased risk of developing cardiovascular disease (CVD) and diabetes. Treatment goals to reduce this risk include aggressive lipid lowering. However, despite aggressive management with statins, there remains a high residual risk in patients with MS. Clinical trials continue to provide evidence that more aggressive risk factor modification results in improved CVD outcomes, but implementation and achievement of the myriad of goals frequently requires multiple therapeutic approaches. The addition of combination therapy with statins is emerging as the optimal therapeutic approach to further lower the risk of CVD in patients with MS. The metabolic syndrome (MS) describes a clustering of modifiable risk factors associated with an increased risk of developing cardiovascular disease and diabetes [1-4]. These risk factors include various combinations of hypertension, abdominal obesity, glucose intolerance, and atherogenic dyslipidemia [4-7]. The prevalence of the MS represents a growing public health problem, following the global increasing trends in obesity, diabetes and aging of the population. Lifestyle change directed toward weight reduction and increased physical activity is essential and considered to be the first-line approach to reducing the risk factors associated with the MS. However, patients with the MS often require pharmacologic intervention for the management of dyslipidemia, hypertension, and hyperglycemia to further reduce their risk for coronary heart disease (CHD) [8]. The major abnormalities that constitute the atherogenic dyslipidemia associated with the MS include low levels of high-density lipoprotein (HDL) cholesterol, high levels of triglycerides (TG), as well as elevated numbers of small, dense, low-density lipoprotein (LDL) particles [9]. The LDL cholesterol levels may be increased however may also remain within the normal range. In fact, studies have shown that the risk of CHD in patients with MS is increased irrespective of absolute LDL levels [4]. It is the increased density and decreased size of the LDL particles in the MS that is believed to enhance their atherogenicity. It is well accepted that statins, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, lower LDL cholesterol levels and reduce the risk of cardiovascular events in many high-risk patients [7, 8, 10]. Statins have also been shown to increase HDL and lower TG levels. In addition,
A Bound on a Convexity Measure for Point Sets

A planar point set is in convex position precisely when it has a convex polygonization, that is, a polygonization with maximum interior angle measure at most π. We can thus talk about the convexity...
Markov Forecasting Methods for Welfare Caseloads

Abstract Forecasting welfare caseloads has become more important than ever. Unforeseen swings in the caseload complicate the appropriations process. They may also cause logistical problems for service delivery operations. Perhaps most importantly, recent changes in welfare funding mean that fluctuations in welfare caseloads could have important implications for the well-being of program clientele. The purpose of this paper is to introduce a new method for forecasting welfare caseloads. The method exploits the fact that changes in the caseload must be preceded by changes in welfare entries, exits, or both. Based on a leading indicator constructed from entry and exit data, the model predicted roughly 1 year in advance a turning point in the California caseload that occurred late in 2003.
The role of extensive and intensive margins and export growth

Abstract We investigate and compare countries' export growth based on their performance at the extensive and intensive export margins. Our empirical approach is motivated by an extension to the Melitz (2003) model of heterogeneous firms in which exporters are subject to a one-time sunk cost and also a per-period fixed cost. With imperfect information a firm may enter export markets but shortly exit when it learns its per-period fixed costs. We apply this insight to disaggregated export data and confirm that indeed most export relationships are very short lived. We then show that the survival issue is a significant factor in explaining differences in long run export performance. We find that developing countries would experience significantly higher export growth if they were able to improve their performance with respect to the two key components of the intensive margin: survival and deepening.
Role of regulatory dendritic cells in allergy and asthma

Dendritic cells (DCs) are the most efficient inducers of all immune responses, and are capable of either inducing productive immunity or maintaining the state of tolerance to self antigens and allergens. In this review, we summarize the emerging literature on DCs, with emphasis on the regulatory function of DCs in allergy and asthma. In particular, we summarize recent data regarding the relationship between DC subsets and TH1, TH2, and regulatory T (TReg) cells. The diverse functions of DCs have been attributed to distinct lineages of DCs, which arise from common immature precursor cells that differentiate in response to specific maturation-inducing or local microenvironment conditions. These subsets of DCs induce different lineages of T cells, such as TH1, TH2, and TReg cells, including Th1Reg and Th2Reg cells, which regulate allergic diseases and asthma. Subsets of DCs regulate the induction of a variety of T-cell subtypes, which suppress the development of allergy and asthma, thus providing anti-inflammatory responses and protective immunity.
Kinetic Analysis of Lipid Metabolism in Breast Cancer Cells via Nonlinear Optical Microscopy

Investigating the behavior of breast cancer cells via reaction kinetics may help unravel the mechanisms that underlie metabolic changes in tumors. However, obtaining human in vivo kinetic data is challenging because of difficulties associated with measuring these parameters. Nondestructive methods of measuring lipid content in live cells provide a novel approach to quantitatively model lipid synthesis and consumption. In this study, coherent Raman scattering microscopy was used to probe de novo intracellular lipid content. Combining nonlinear optical microscopy and Michaelis-Menten kinetics-based simulations, we isolated fatty acid synthesis/consumption rates and elucidated effects of altered lipid metabolism in T47D breast cancer cells. When treated with 17β-estradiol, the lipid utilization in cancer cells jumped by twofold. Meanwhile, the rate of de novo lipid synthesis in cancer cells treated with 17β-estradiol was increased by 42%. To test the model in extreme metabolic conditions, we treated T47D cells with etomoxir. Our kinetic analysis demonstrated that the rate of key enzymatic reactions dropped by 75%. These results underline the capability to probe lipid alterations in live cells with minimum interruption and to characterize lipid metabolism in breast cancer cells via quantitative kinetic models and parameters.
Arsenic in US correctional facility drinking water, 2006-2011.

Abstract Background Little is known about the quality of drinking water in US correctional facilities (e.g. detention centers, prisons, jails, etc.). Our objective was to determine if incarcerated persons are at risk for chronic, elevated arsenic exposure relative to the non-incarcerated US population, particularly in the Southwestern US where public water and groundwater arsenic concentrations are high compared to the rest of the US. Methods We analyzed 230,158 arsenic monitoring records from 37,086 community water systems (CWSs) from the Environmental Protection Agency's (EPA) Third Six Year Review of Contaminant Occurrence dataset (covering 2006–2011). We compared six-year average arsenic concentrations and the odds of exceeding the EPA's 10 μg/L maximum contaminant level (MCL) for CWSs exclusively serving correctional facilities versus all other CWSs in the Southwestern US, where groundwater arsenic concentrations are high. Results Average six-year water arsenic concentrations were higher for Southwestern correctional facility CWSs (6.41 μg/L, 95% CI 3.48, 9.34) compared to all other Southwestern CWSs (3.11 μg/L, 95% CI 2.97, 3.24) and to other CWSs across the rest of the US (1.39 μg/L, 95% CI 1.35, 1.42). In the Southwest, 26.1% (N = 6) of correctional facility CWSs versus 5.8% (509) of other CWSs reported six-year arsenic averages exceeding 10 μg/L, corresponding to an odds ratio of 5.70 (95% confidence interval 2.24, 14.52). Correctional facility CWSs in the Southwest were also more likely to report six-year averages exceeding 5 μg/L (the MCL for New Jersey and New Hampshire, N = 8, odds ratio 2.77, 95% CI 1.17, 6.54). Discussion Persons incarcerated in the Southwestern US were at disproportionate risk of elevated drinking water arsenic exposure and related disease from 2006 to 2011. Strict enforcement of EPA regulations and additional technical and financial support for CWSs serving correctional facilities in the Southwest is necessary to protect the health and human rights of incarcerated persons.
When2com: Multi-Agent Perception via Communication Graph Grouping

While significant advances have been made for single-agent perception, many applications require multiple sensing agents and cross-agent communication due to benefits such as coverage and robustness. It is therefore critical to develop frameworks which support multi-agent collaborative perception in a distributed and bandwidth-efficient manner. In this paper, we address the collaborative perception problem, where one agent is required to perform a perception task and can communicate and share information with other agents on the same task. Specifically, we propose a communication framework by learning both to construct communication groups and decide when to communicate. We demonstrate the generalizability of our framework on two different perception tasks and show that it significantly reduces communication bandwidth while maintaining superior performance.
Negative Training for Neural Dialogue Response Generation

Although deep learning models have brought tremendous advancements to the field of open-domain dialogue response generation, recent research results have revealed that the trained models have undesirable generation behaviors, such as malicious responses and generic (boring) responses. In this work, we propose a framework named ``Negative Training" to minimize such behaviors. Given a trained model, the framework will first find generated samples that exhibit the undesirable behavior, and then use them to feed negative training signals for fine-tuning the model. Our experiments show that negative training can significantly reduce the hit rate of malicious responses, or discourage frequent responses and improve response diversity.
Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval

Interactive Information Retrieval (IIR) and Reinforcement Learning (RL) share many commonalities, including an agent who learns while interacts, a long-term and complex goal, and an algorithm that explores and adapts. To successfully apply RL methods to IIR, one challenge is to obtain sufficient relevance labels to train the RL agents, which are infamously known as sample inefficient. However, in a text corpus annotated for a given query, it is not the relevant documents but the irrelevant documents that predominate. This would cause very unbalanced training experiences for the agent and prevent it from learning any policy that is effective. Our paper addresses this issue by using domain randomization to synthesize more relevant documents for the training. Our experimental results on the Text REtrieval Conference (TREC) Dynamic Domain (DD) 2017 Track show that the proposed method is able to boost an RL agent's learning effectiveness by 22% in dealing with unseen situations.
Engineering topological models with a general-purpose symmetry-to-Hamiltonian approach

This work develops a methodology for constructing quantum Hamiltonians with desired symmetries. The authors use this technique to design Hamiltonians with topological properties, such as Hamiltonians that give rise to Majorana fermions and quantum spin liquids.
Large lower bounds for the betti numbers of graded modules with low regularity

Suppose that M is a finitely-generated graded module (generated in degree 0) of codimension $$c\ge 3$$ over a polynomial ring and that the regularity of M is at most $$2a-2$$ where $$a\ge 2$$ is the minimal degree of a first syzygy of M. Then we show that the sum of the betti numbers of M is at least $$\beta _0(M)(2^c + 2^{c-1})$$ . Additionally, under the same hypothesis on the regularity, we establish the surprising fact that if $$c \ge 9$$ then the first half of the betti numbers are each at least twice the bound predicted by the Buchsbaum-Eisenbud-Horrocks rank conjecture: for $$1\le i \le \frac{c+1}{2}$$ , $$\beta _i(M) \ge 2\beta _0(M){c \atopwithdelims ()i}$$ .
Evaluation of Sampling Methods for Robotic Sediment Sampling Systems

Analysis of sediments from rivers, lakes, reservoirs, wetlands, and other constructed surface water impoundments is an important tool to characterize the function and health of these systems, but is generally carried out manually. This is costly and can be hazardous and difficult for humans due to inaccessibility, contamination, or availability of required equipment. Robotic sampling systems can ease these burdens, but little work has examined the efficiency of such sampling means and no prior work has investigated the quality of the resulting samples. This article presents an experimental study that evaluates and optimizes sediment sampling patterns applied to a robot sediment sampling system that allows collection of minimally disturbed sediment cores from natural and man-made water bodies for various sediment types. To meet this need, we developed and tested a robotic sampling platform in the laboratory to test functionality under a range of sediment types and operating conditions. Specifically, we focused on three patterns by which a cylindrical coring device was driven into the sediment (linear, helical, and zig-zag) for three sediment types (coarse sand, medium sand, and silt). The results show that the optimal sampling pattern varies depending on the type of sediment and can be optimized based on the sampling objective. We examined two sampling objectives: maximizing the mass of minimally disturbed sediment and minimizing the power per mass of sample. This study provides valuable data to aid in the selection of optimal sediment coring methods for various applications and builds a solid foundation for future field testing under a range of environmental conditions.
Hochschild cohomology and group actions

Given a finite group action on a (suitably enhanced) triangulated category linear over a field, we establish a formula for the Hochschild cohomology of the category of invariants, assuming the order of the group is coprime to the characteristic of the base field. The formula shows that the cohomology splits canonically with one summand given by the invariant subspace of the Hochschild cohomology of the original category. We also prove that Serre functors act trivially on Hochschild cohomology, and combine this with our formula to give a useful mechanism for computing the Hochschild cohomology of fractional Calabi–Yau categories.
Hole-hole Tamm-Dancoff-approximated density functional theory: A highly efficient electronic structure method incorporating dynamic and static correlation

The study of photochemical reaction dynamics requires accurate as well as computationally efficient electronic structure methods for the ground and excited states. While time-dependent density functional theory (TDDFT) is not able to capture static correlation, complete active space self-consistent field methods neglect much of the dynamic correlation. Hence, inexpensive methods that encompass both static and dynamic electron correlation effects are of high interest. Here, we revisit hole-hole Tamm-Dancoff approximated (hh-TDA) density functional theory for this purpose. The hh-TDA method is the hole-hole counterpart to the more established particle-particle TDA (pp-TDA) method, both of which are derived from the particle-particle random phase approximation (pp-RPA). In hh-TDA, the N-electron electronic states are obtained through double annihilations starting from a doubly anionic (N+2 electron) reference state. In this way, hh-TDA treats ground and excited states on equal footing, thus allowing for conical intersections to be correctly described. The treatment of dynamic correlation is introduced through the use of commonly employed density functional approximations to the exchange-correlation potential. We show that hh-TDA is a promising candidate to efficiently treat the photochemistry of organic and biochemical systems that involve several low-lying excited states-particularly those with both low-lying ππ* and nπ* states where inclusion of dynamic correlation is essential to describe the relative energetics. In contrast to the existing literature on pp-TDA and pp-RPA, we employ a functional-dependent choice for the response kernel in pp- and hh-TDA, which closely resembles the response kernels occurring in linear response and collinear spin-flip TDDFT.
Social entrepreneurship and economic thought: a path to rapprochement

Academics, practitioners, and policymakers are devoting heightened attention to social entrepreneurship: the creation and pursuit of innovative opportunities to produce positive externalities that improve conditions harmful to society. Scholars from across the behavioural, managerial, and organisational sciences are increasingly studying social entrepreneurs and their activities. However, one discipline - economics - is underrepresented in social entrepreneurship research. To address the lack of integration between economics and social entrepreneurship, we identify the unique economic characteristics of social entrepreneurship and explain how adopting an economic lens stands to generate important insights about the phenomenon. We then isolate three potential connection points between work in social entrepreneurship and economics: institutional economics and multi-logic hybrid organisations, narrative economics and social entrepreneurship discourse, and the economics of emotion and social entrepreneurs' emotions. Our integration of social entrepreneurship and economics contributes to scholarship in both domains and identifies avenues for research at the intersections of the two disciplines.
Polynomial Approximation of Anisotropic Analytic Functions of Several Variables

Motivated by numerical methods for solving parametric partial differential equations, this paper studies the approximation of multivariate analytic functions by algebraic polynomials. We introduce various anisotropic model classes based on Taylor expansions, and study their approximation by finite dimensional polynomial spaces $${{\mathcal {P}}}_\Lambda $$ described by lower sets $$\Lambda $$ . Given a budget n for the dimension of $${{\mathcal {P}}}_\Lambda $$ , we prove that certain lower sets $$\Lambda _n$$ , with cardinality n, provide a certifiable approximation error that is in a certain sense optimal, and that these lower sets have a simple definition in terms of simplices. Our main goal is to obtain approximation results when the number of variables d is large and even infinite, and so we concentrate almost exclusively on the case $$d=\infty $$ . We also emphasize obtaining results which hold for the full range $$n\ge 1$$ , rather than asymptotic results that only hold for n sufficiently large. In applications, one typically wants n small to comply with computational budgets.
Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches

When interpreting sequencing data from multiple spatial or longitudinal biopsies, detecting sample mix-ups is essential, yet more difficult than in studies of germline variation. In most genomic studies of tumors, genetic variation is detected through pairwise comparisons of the tumor and a matched normal tissue from the sample donor. In many cases, only somatic variants are reported, which hinders the use of existing tools that detect sample swaps solely based on genotypes of inherited variants. To address this problem, we have developed Somalier, a tool that operates directly on alignments and does not require jointly called germline variants. Instead, Somalier extracts a small sketch of informative genetic variation for each sample. Sketches from hundreds of germline or somatic samples can then be compared in under a second, making Somalier a useful tool for measuring relatedness in large cohorts. Somalier produces both text output and an interactive visual report that facilitates the detection and correction of sample swaps using multiple relatedness metrics. We introduce the tool and demonstrate its utility on a cohort of five glioma samples each with a normal, tumor, and cell-free DNA sample. Applying Somalier to high-coverage sequence data from the 1000 Genomes Project also identifies several related samples. We also demonstrate that it can distinguish pairs of whole-genome and RNA-seq samples from the same individuals in the Genotype-Tissue Expression (GTEx) project. Somalier is a tool that can rapidly evaluate relatedness from sequencing data. It can be applied to diverse sequencing data types and genome builds and is available under an MIT license at github.com/brentp/somalier .
Squeeze free space with nonlocal flat optics

There has been substantial interest in miniaturizing optical systems by flat optics. However, one essential optical component, free space, fundamentally cannot be substituted with conventional local flat optics with space-dependent transfer functions, since the transfer function of free space is momentum-dependent instead. Overcoming this difficulty is important to achieve the utmost miniaturization of optical systems. In this work, we show that free space can be substituted with nonlocal flat optics operating directly in the momentum domain. We derive the general criteria for an optical device to replace free space and provide two concrete designs of photonic crystal slab devices. Such devices can substitute much thicker free space. Our work paves the way for the utmost miniaturization of optical systems using a combination of local and nonlocal flat optics.
Selfish algorithm and emergence of collective intelligence

We propose a model for demonstrating spontaneous emergence of collective intelligent behavior from selfish individual agents. Agents' behavior is modeled using our proposed selfish algorithm ($SA$) with three learning mechanisms: reinforced learning ($SAL$), trust ($SAT$) and connection ($SAC$). Each of these mechanisms provides a distinctly different way an agent can increase the individual benefit accrued through playing the prisoner's dilemma game ($PDG$) with other agents. The $SA$ provides a generalization of the self-organized temporal criticality ($SOTC$) model and shows that self-interested individuals can simultaneously produce maximum social benefit from their decisions. The mechanisms in the $SA$ are self-tuned by the internal dynamics and without having a pre-established network structure. Our results demonstrate emergence of mutual cooperation, emergence of dynamic networks, and adaptation and resilience of social systems after perturbations. The implications and applications of the $SA$ are discussed.
Mirror-Like Electrodeposition of Lithium Metal under a Low-Resistance Artificial Solid Electrolyte Interphase Layer.

Nonuniform electrodeposition and dendritic growth of lithium metal coupled to its chemical incompatibility with liquid electrolytes are largely responsible for poor Coulombic efficiency and safety ...
"I Cannot Do All of This Alone": Exploring Instrumental and Prayer Support in Online Health Communities

Instrumental support is critical for patients and family caregivers facing life-threatening illnesses, injuries, or chronic conditions (e.g., cancer). We partner with CaringBridge.org—a prominent online health community for journaling about health crises—to conduct a study of instrumental support in the following two phases: a content analysis of 641 journal updates; and a survey of 991 users. Quantitative results show that: (1) patients and family caregivers prefer to receive different types of support than their care networks prefer to provide; (2) people generally have more trust in their closest social connections than acquaintances or businesses to provide instrumental support; and (3) users rate “prayer support” as the most important support category to them. Building on these results, we discuss design implications to accommodate divergent preferences and to expand instrumental support networks. We also discuss the need for future work to empower family caregivers and to support spirituality, an understudied topic in HCI.
Decompositions of amplituhedra

The (tree) amplituhedron A(n,k,m) is the image in the Grassmannian Gr(k,k+m) of the totally nonnegative part of Gr(k,n), under a (map induced by a) linear map which is totally positive. It was introduced by Arkani-Hamed and Trnka in 2013 in order to give a geometric basis for the computation of scattering amplitudes in N=4 supersymmetric Yang-Mills theory. In the case relevant to physics (m=4), there is a collection of recursively-defined 4k-dimensional BCFW cells in the totally nonnegative part of Gr(k,n), whose images conjecturally "triangulate" the amplituhedron--that is, their images are disjoint and cover a dense subset of A(n,k,4). In this paper, we approach this problem by first giving an explicit (as opposed to recursive) description of the BCFW cells. We then develop sign-variational tools which we use to prove that when k=2, the images of these cells are disjoint in A(n,k,4). We also conjecture that for arbitrary even m, there is a decomposition of the amplituhedron A(n,k,m) involving precisely M(k, n-k-m, m/2) top-dimensional cells (of dimension km), where M(a,b,c) is the number of plane partitions contained in an a x b x c box. This agrees with the fact that when m=4, the number of BCFW cells is the Narayana number N(n-3, k+1).
The Costs of Employment Segregation: Evidence from the Federal Government under Wilson

Author(s): Aneja, Abhay; Xu, Guo | Abstract: We link personnel records of the federal civil service to census data for 1907-1921 to study the segregation of the civil service by race under President Woodrow Wilson. Using a difference-indifferences design to compare the black-white wage gap around Wilson’s presidential transition, we find that the introduction of employment segregation increased the black wage penalty by 7 percentage points. This gap increases over time and is driven by a reallocation of already-serving black civil servants to lower paid positions. Our results thus document significant costs borne by minorities during a unique episode of state-sanctioned discrimination.
Universal secant bundles and syzygies of canonical curves

We introduce a relativization of the secant sheaves from Green and Lazarsfeld (A simple proof of Petri’s theorem on canonical curves, Geometry Today, 1984) and Ein and Lazarsfeld (Inventiones Math 190:603-646, 2012) and apply this construction to the study of syzygies of canonical curves. As a first application, we give a simpler proof of Voisin’s Theorem for general canonical curves. This completely determines the terms of the minimal free resolution of the coordinate ring of such curves. Secondly, in the case of curves of even genus, we enhance Voisin’s Theorem by providing a structure theorem for the last syzygy space, resolving the Geometric Syzygy Conjecture in even genus.
Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery.

Abstract Background Our previous work classified a taxonomy of suturing gestures during a vesicourethral anastomosis of robotic radical prostatectomy in association with tissue tears and patient outcomes. Herein, we train deep learning-based computer vision to automate the identification and classification of suturing gestures for needle driving attempts. Methods Using two independent raters, we manually annotated live suturing video clips to label timepoints and gestures. Identification (2,395 videos) and classification (511 videos) datasets were compiled to train computer vision models to produce 2- and 5-class label predictions, respectively. Networks were trained on inputs of raw red/blue/green pixels as well as optical flow for each frame. Each model was trained on 80/20 train/test splits. Results In this study, all models were able to reliably predict either the presence of a gesture (identification, area under the curve: 0.88) as well as the type of gesture (classification, area under the curve: 0.87) at significantly above chance levels. For both gesture identification and classification datasets, we observed no effect of recurrent classification model choice (long short-term memory unit versus convolutional long short-term memory unit) on performance. Conclusion Our results demonstrate computer vision’s ability to recognize features that not only can identify the action of suturing but also distinguish between different classifications of suturing gestures. This demonstrates the potential to utilize deep learning computer vision toward future automation of surgical skill assessment.
Striking up with the in crowd: When option markets and insiders agree.

Abstract We study whether the trading behavior of corporate insiders provides additional information to the market, after controlling for the public information integrated by sophisticated investors. First, we establish that insiders and option market participants trade in the same direction on average. Second, we show that insidertrading is relatively more informed when the option market sentiment is positive. The marginal information content of insider trades is higher for firms with higher levels of information asymmetry and during time periods when future economic conditions are less certain.
A Dynamized Power Flow Method Based on Differential Transformation

This paper proposes a novel method for solving and tracing power flow solutions with changes of a loading parameter. Different from the conventional continuation power flow method, which repeatedly solves static AC power flow equations, the proposed method extends the power flow model into a fictitious dynamic system by adding a differential equation on the loading parameter. As a result, the original solution curve tracing problem is converted to solving the time domain trajectories of the reformulated dynamic system. A non-iterative algorithm based on differential transformation is proposed to analytically solve the aforementioned dynamized model in form of power series of time. This paper proves that the nonlinear power flow equations in the time domain are converted to formally linear equations in the domain of the power series order after the differential transformation, thus avoiding numerical iterations. Case studies on several test systems including a 2383-bus system show the merits of the proposed method.
Simulation Based Modeling for a Cybersecure Power Grid

The North American bulk power system is one of the most vital infrastructures in modern society as it accounts for virtually all the electricity supplied to the United States, Canada, and a portion of Baja Notre California, Mexico. Cyberattacks, of all forms, are becoming increasingly prominent within power networks and other infrastructures whereas their resolution can consume a significant deal of time and monetary resources. This can be further worsened if there are subsequent physical attacks in the wake of a cyberattack, as the system downtime leaves the government, military, and other critical infrastructures incredibly vulnerable This research aims to investigate if different patterns of cyberattacks could be identified with speed using simulation and machine learning algorithms. More specifically, we design a simulation model that can help better defend against cyber threats.
Combined vapor exposure to THC and alcohol in pregnant rats: Maternal outcomes and pharmacokinetic effects

Cannabis is the most frequently used illicit drug among pregnant women, yet the potential consequences of prenatal cannabis exposure on development are not well understood. Electronic cigarettes have become an increasingly popular route of administration among pregnant women, in part to user's perception that e-cigarettes are a safer route for consuming cannabis products. Importantly, half of pregnant women who consume cannabis also report consuming alcohol, but research investigating co-consumption of these drugs is limited, particularly with current routes of administration. The purpose of this study was to establish a co-exposure vapor inhalation model of alcohol and THC in pregnant rats, to ultimately determine the effects on fetal development. Pregnant Sprague-Dawley rats were exposed to moderate doses of THC via e-cigarettes, alcohol, the combination, or vehicle daily from gestational days 5-20. Importantly, pharmacokinetic interactions of alcohol and THC were observed during pregnancy. Combined exposure consistently increased blood alcohol concentrations, indicating that THC alters alcohol metabolism. In addition, THC levels also increased over the course of pregnancy and THC metabolism was altered by alcohol. Alcohol, but not THC, exposure during pregnancy reduced maternal weight gain, despite no group differences in food intake. Neither prenatal alcohol nor THC exposure altered gestational length, litter size, sex ratio or birth weight. However, prenatal alcohol exposure delayed eye opening, and prenatal THC exposure decreased body weights during adolescence among offspring. These individual and synergistic effects suggest that this novel co-exposure vapor inhalation paradigm can effectively be used to expose pregnant dams, exerting some effects on fetal development, while avoiding nutritional confounds, birth complications, or changes in litter size. With this model, we have demonstrated that combining THC and alcohol alters drug metabolism, which could have important consequences on prenatal development.
Formation of shocks for 2D isentropic compressible Euler

We consider the 2D isentropic compressible Euler equations, with pressure law $p(\rho) = (\sfrac{1}{\gamma}) \rho^\gamma$, with $\gamma >1$. We provide an elementary constructive proof of shock formation from smooth initial datum of finite energy, with no vacuum regions, and with {nontrivial vorticity}. We prove that for initial data which has minimum slope $- {\sfrac{1}{ \eps}}$, for $ \eps>0$ taken sufficiently small relative to the $\OO(1)$ amplitude, there exist smooth solutions to the Euler equations which form a shock in time $\OO(\eps)$. The blowup time and location can be explicitly computed and solutions at the blowup time are of cusp-type, with Holder $C^ {\sfrac{1}{3}}$ regularity. Our objective is the construction of solutions with inherent $\OO(1)$ vorticity at the shock. As such, rather than perturbing from an irrotational regime, we instead construct solutions with dynamics dominated by purely azimuthal wave motion. We consider homogenous solutions to the Euler equations and use Riemann-type variables to obtain a system of forced transport equations. Using a transformation to modulated self-similar variables and pointwise estimates for the ensuing system of transport equations, we show the global stability, in self-similar time, of a smooth blowup profile.
Compact binary coalescences: constraints on waveforms

Gravitational waveforms for compact binary coalescences (CBCs) have been invaluable for detections by the LIGO-Virgo collaboration. They are obtained by a combination of semi-analytical models and numerical simulations. So far systematic errors arising from these procedures appear to be less than statistical ones. However, the significantly enhanced sensitivity of the new detectors that will become operational in the near future will require waveforms to be much more accurate. This task would be facilitated if one has a variety of cross-checks to evaluate accuracy, particularly in the regions of parameter space where numerical simulations are sparse. Currently errors are estimated by comparing the candidate waveforms with the numerical relativity (NR) ones, which are taken to be exact. The goal of this paper is to propose a qualitatively different tool. We show that full non-linear general relativity (GR) imposes an infinite number of sharp constraints on the CBC waveforms. These can provide clear-cut measures to evaluate the accuracy of candidate waveforms against exact GR, help find systematic errors, and also provide external checks on NR simulations themselves.
The efficacy of IL-6 inhibitor Tocilizumab in reducing severe COVID-19 mortality: a systematic review

Background In the absence of highly effective antiviral therapies against SARS-CoV-2, it is crucial to counter the known pathophysiological causes of severe COVID-19. Evaluating the efficacy existing drugs may expedite the development of such therapeutics. Severe COVID-19 is largely the result of a dysregulated immune response characterized by lymphocytopenia, neutrophilia and critical hypercytokinemia, or "cytokine storm," which is largely mediated by the cytokine interleukin-6 (IL-6). The IL-6 inhibitor tocilizumab (TCZ) could potentially suppress the effects of the pro-inflammatory cytokine and thereby lower mortality from the disease. This systematic analysis aimed to investigate and synthesize existing evidence for the efficacy of TCZ in reducing COVID-19 mortality. Methodology PubMed and SearchWorks searches were performed to locate clinical studies with primary data on TCZ treatment for severe COVID-19. Sixteen case-control studies comparing mortality between TCZ and standard of care (SOC) were identified for quantitative synthesis. The systematic analysis was pre-approved through PROSPERO (CRD42020193479). Results Combined mortality for the TCZ-treated and SOC groups were 26.0% and 43.4% respectively. In all but one of the studies, the odds ratio of mortality from COVID-19 pointed towards lower fatality with TCZ vs the SOC. A combined random effects odds ratio calculation yielded an odds ratio of 0.453 (95% CI [0.376-0.547], p < 0.001). Additionally, 18 uncontrolled trials were identified for qualitative analysis producing a raw combined mortality rate of 16.0%. Conclusions Important caveats to this research include the lack of prospective randomized control trials and the absence of data from the large COVATA study from the published literature. However, results from this systematic analysis of published research provide positive evidence for the potential efficacy of TCZ to treat severe COVID-19, validating the ethical basis and merit of ongoing randomized controlled clinical trials.
SPITZER IRAC SECONDARY ECLIPSE PHOTOMETRY OF THE TRANSITING EXTRASOLAR PLANET HAT-P-1b

We report Spitzer/IRAC photometry of the transiting giant exoplanet HAT-P-1b during its secondary eclipse. This planet lies near the postulated boundary between the pM and pL-class of hot Jupiters, and is important as a test of models for temperature inversions in hot Jupiter atmospheres. We derive eclipse depths for HAT-P-1b, in units of the stellar flux, that are: 0.080% ± 0.008% [3.6 μm], 0.135% ± 0.022% [4.5 μm], 0.203% ± 0.031% [5.8 μm], and 0.238% ± 0.040% [8.0 μm]. These values are best fit using an atmosphere with a modest temperature inversion, intermediate between the archetype inverted atmosphere (HD 209458b) and a model without an inversion. The observations also suggest that this planet is radiating a large fraction of the available stellar irradiance on its dayside, with little available for redistribution by circulation. This planet has sometimes been speculated to be inflated by tidal dissipation, based on its large radius in discovery observations, and on a non-zero orbital eccentricity allowed by the radial velocity data. The timing of the secondary eclipse is very sensitive to orbital eccentricity, and we find that the central phase of the eclipse is 0.4999 ± 0.0005. The difference between the expected and observed phase indicates that the orbit is close to circular, with a 3σ limit of |e cos ω| < 0.002.
RESOLVED MILLIMETER EMISSION FROM THE HD 15115 DEBRIS DISK

We have used the Submillimeter Array (SMA) to make 1.3 mm observations of the debris disk surrounding HD 15115, an F-type star with a putative membership in the ? Pictoris moving group. This nearly edge-on debris disk shows an extreme asymmetry in optical scattered light, with an extent almost two times larger to the west of the star than to the east (originally dubbed the ?Blue Needle?). The SMA observations reveal resolved emission that we model as a circumstellar belt of thermal dust emission. This belt extends to a radius of ?110 AU, coincident with the break in the scattered light profile convincingly seen on the western side of the disk. This outer edge location is consistent with the presence of an underlying population of dust-producing planetesimals undergoing a collisional cascade, as hypothesized in ?birth ring? theory. In addition, the millimeter emission shows a feature aligned with the asymmetric western extension of the scattered light disk. If this millimeter extension is real, then mechanisms for asymmetry that affect only small grains, such as interactions with interstellar gas, are disfavored. This tentative feature might be explained by secular perturbations to grain orbits introduced by neutral gas drag, as previously invoked to explain asymmetric morphologies of other, similar debris disks.
Investigating the Selectivity of KcsA Channel by an Image Charge Solvation Method (ICSM) in Molecular Dynamics Simulations

In this paper, we study the selectivity of the potassium channel KcsA by a recently developed image-charge solvation method (ICSM) combined with molecular dynamics simulations. The hybrid solvation model in the ICSM is able to demonstrate atomistically the function of the selectivity filter of the KcsA channel when potassium and sodium ions are considered and their distributions inside the filter are simulated. Our study also shows that the reaction field effect, explicitly accounted for through image charge approximation in the ICSM model, is necessary in reproducing the correct selectivity property of the potassium channels.
Bulk universality of sparse random matrices

We consider the adjacency matrix of the ensemble of Erdős-Renyi random graphs which consists of graphs on N vertices in which each edge occurs independently with probability p. We prove that in the regime pN ≫ 1, these matrices exhibit bulk universality in the sense that both the averaged n-point correlation functions and distribution of a single eigenvalue gap coincide with those of the GOE. Our methods extend to a class of random matrices which includes sparse ensembles whose entries have different variances.
Summing the instantons: Quantum cohomology and mirror symmetry in toric varieties

Abstract We use the gauged linear sigma model introduced by Witten to calculate instanton expansions for correlation functions in topological sigma models with target space a toric variety V or a Calabi-Yau hypersurface M ⊂ V . In the linear model the instanton moduli spaces are relatively simple objects and the correlators are explicitly computable; moreover, the instantons can be summed, leading to explicit solutions for both kinds of models. In the case of smooth V , our results reproduce and clarify an algebraic solution of the V model due to Batyrev. In addition, we find an algebraic relation determining the solution for M in terms of that for V . Finally, we propose a modification of the linear model which computes instanton expansions about any limiting point in the moduli space. In the smooth case this leads to a (second) algebraic solution of the M model. We use this description to prove some conjectures about mirror symmetry, including the previously conjectured “monomial-divisor mirror map” of Aspinwall, Greene and Morrison.
Envelopes with Prescribed Singularities

We prove that quasi-plurisubharmonic envelopes with prescribed analytic singularities in suitable big cohomology classes on compact Kahler manifolds have the optimal $$C^{1,1}$$ regularity on a Zariski open set. This also proves regularity of certain pluricomplex Green’s functions on Kahler manifolds. We then go on to prove the same regularity for envelopes when the manifold is assumed to have boundary. As an application, we answer affirmatively a question of Ross–Witt-Nystrom concerning the Hele-Shaw flow on an arbitrary Riemann surface.
FRB Periodicity: Mild Pulsars in Tight O/B-star Binaries

Periodicities observed in two Fast Radio Burst (FRB) sources (16 days in FRB 180916.J0158+65 and 160 days in FRB 121102) are consistent with that of tight, stellar mass binary systems. In the case of FRB 180916.J0158+65 the primary is an early OB-type star with mass loss rate $\dot{M} \sim 10^{-8}- 10^{-7} M_\odot$ yr$^{-1}$, and the secondary a neutron star. The observed periodicity is not intrinsic to the FRB's source, but is due to the orbital phase-dependent modulation of the absorption conditions in the massive star's wind. The observed relatively narrow FRB activity window implies that the primary's wind dynamically dominates that of the pulsar, $\eta = L_{sd}/(\dot{M} v_w c) \leq 1$, where $L_{sd} $ is pulsar spin-down, $\dot{M}$ is the primary's wind mass loss rate and $v_w$ is its velocity. The condition $\eta \leq 1$ requires mildly powerful pulsar with $L_{sd} \lesssim 10^{37}$ erg $s^{-1}$. The observations are consistent with magnetically-powered radio emission originating in the magnetospheres of strongly magnetized neutron stars, the classical magnetars.