cssi/SciSciGPT-SciSciNet huggingface dataset:
On Perfect Matchings in k-Complexes

Keevash and Mycroft [\emph{Mem.~Amer.~Math.~Soc., 2015}] developed a geometric theory for hypergraph matchings and characterized the dense simplicial complexes that contain a perfect matching. Their proof uses the hypergraph regularity method and the hypergraph blow-up lemma recently developed by Keevash. In this note we give a new proof of their results, which avoids these complex tools. In particular, our proof uses the lattice-based absorbing method developed by the author and a recent probabilistic argument of Kohayakawa, Person and the author.
Insights into the Interaction of Redox Active Organic Molecules and Solvents with the Pristine and Defective Graphene Surfaces from Density Functional Theory

A systematic study is reported on the interaction of two representative redox active organic molecules and two solvent molecules with pristine and defective graphene surfaces as a model of an elect...
The average character degree and an improvement of the Itô-Michler theorem

Abstract The classical Ito-Michler theorem states that the degree of every ordinary irreducible character of a finite group G is coprime to a prime p if and only if the Sylow p-subgroups of G are abelian and normal. In an earlier paper [8] , we used the notion of average character degree to prove an improvement of this theorem for the prime p = 2 . In this follow-up paper, we obtain a full improvement for all primes.
Bias of Homotopic Gradient Descent for the Hinge Loss

Gradient descent is a simple and widely used optimization method for machine learning. For homogeneous linear classifiers applied to separable data, gradient descent has been shown to converge to the maximal-margin (or equivalently, the minimal-norm) solution for various smooth loss functions. The previous theory does not, however, apply to the non-smooth hinge loss which is widely used in practice. Here, we study the convergence of a homotopic variant of gradient descent applied to the hinge loss and provide explicit convergence rates to the maximal-margin solution for linearly separable data.
Adaptive sequential machine learning

A framework previously introduced in Wilson et al. (2018) for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to machine learni...
Methionyl-tRNA synthetase inhibitor has potent in vivo activity in a novel Giardia lamblia luciferase murine infection model

Background Methionyl-tRNA synthetase (MetRS) inhibitors are under investigation for the treatment of intestinal infections caused by Giardia lamblia. Objectives To properly analyse the therapeutic potential of the MetRS inhibitor 1717, experimental tools including a robust cell-based assay and a murine model of infection were developed based on novel strains of G. lamblia that employ luciferase reporter systems to quantify viable parasites. Methods Systematic screening of Giardia-specific promoters and luciferase variants led to the development of a strain expressing the click beetle green luciferase. Further modifying this strain to express NanoLuc created a dual reporter strain capable of quantifying parasites in both the trophozoite and cyst stages. These strains were used to develop a high-throughput cell assay and a mouse infection model. A library of MetRS inhibitors was screened in the cell assay and Compound-1717 was tested for efficacy in the mouse infection model. Results Cell viability in in vitro compound screens was quantified via bioluminescence readouts while infection loads in mice were monitored with non-invasive whole-animal imaging and faecal analysis. Compound-1717 was effective in clearing mice of Giardia infection in 3 days at varying doses, which was supported by data from enzymatic and phenotypic cell assays. Conclusions The new in vitro and in vivo assays based on luciferase expression by engineered G. lamblia strains are useful for the discovery and development of new therapeutics for giardiasis. MetRS inhibitors, as validated by Compound-1717, have promising anti-giardiasis properties that merit further study as alternative therapeutics.
Integration of UAVs with Real Time Operating Systems using UAVCAN

In todays world, the applications of Unmanned Aerial Vehicle (UAV) systems are leaping by extending their scope from military applications on to commercial and medical sectors as well. Owing to this commercialization, the necessity to append external hardware with UAV systems becomes inevitable. This external hardware could aid in enabling wireless data transfer between the UAV system and remote Wireless Sensor Networks (WSN) using low powered architecture like Thread, BLE (Bluetooth Low Energy). The data is being transmitted from the flight controller to the ground control station using MAVLink (Micro Air Vehicle Link) protocol. The ideal aim of this research is to address the issues of integrating different hardware with the flight controller of the UAV system using a light-weight protocol called UAVCAN. This approach would result in reduced wiring and would harness the problem of integrating multiple systems to UAV.
Extending NIST's CAVP Testing of Cryptographic Hash Function Implementations.

This paper describes a vulnerability in Apple’s CoreCrypto library, which affects 11 out of the 12 implemented hash functions: every implemented hash function except MD2 (Message Digest 2), as well as several higher-level operations such as the Hash-based Message Authentication Code (HMAC) and the Ed25519 signature scheme. The vulnerability is present in each of Apple’s CoreCrypto libraries that are currently validated under FIPS 140-2 (Federal Information Processing Standard). For inputs of about \(2^{32}\) bytes (4 GiB) or more, the implementations do not produce the correct output, but instead enter into an infinite loop. The vulnerability shows a limitation in the Cryptographic Algorithm Validation Program (CAVP) of the National Institute of Standards and Technology (NIST), which currently does not perform tests on hash functions for inputs larger than 65 535 bits. To overcome this limitation of NIST’s CAVP, we introduce a new test type called the Large Data Test (LDT). The LDT detects vulnerabilities similar to that in CoreCrypto in implementations submitted for validation under FIPS 140-2.
From theory bias to theory dialogue: embracing cognitive, situated, and critical framings of computational thinking in K-12 CS education

The increased interest in promoting CS education for all has been coalescing around the idea of "computational thinking." Several framings for promoting computational thinking in K-12 education have been proposed by practitioners and researchers that each place different emphases on either (1) skill and competence building, (2) creative expression and participation, or (3) social justice and ethics. We review each framing and how the framings structure the theory space of computational thinking. We then discuss how CS education can leverage the explanatory potential that each framing offers to the implementation and evaluation of learning, teaching, and tools in computing education. Our goal is to help CS education researchers, teachers, and designers unpack and leverage the complexities of this theory space (rather than ignoring it) while also addressing broader educational concerns regarding diversity, providing new directions for how students and teachers can actively participate in designing their digital futures, and directing current computing education efforts towards a more humanistic orientation.
Localization for Random Walks Among Random Obstacles in a Single Euclidean Ball

Place an obstacle with probability $$1-p$$ independently at each vertex of $${\mathbb {Z}}^d$$, and run a simple random walk before hitting one of the obstacles. For $$d\ge 2$$ and p strictly above the critical threshold for site percolation, we condition on the environment where the origin is contained in an infinite connected component free of obstacles, and we show that for environments with probability tending to one as $$n\rightarrow \infty $$ there exists a unique discrete Euclidean ball of volume $$d \log _{1/p} n$$ asymptotically such that the following holds: conditioned on survival up to time n we have that at any time $$t \in [\epsilon _n n,n]$$ (for some $$\epsilon _n\rightarrow _{n\rightarrow \infty } 0$$) with probability tending to one the simple random walk is in this ball. This work relies on and substantially improves a previous result of the authors on localization in a region of volume poly-logarithmic in n for the same problem.
Do Workers Comply with Salary History Bans? A Survey on Voluntary Disclosure, Adverse Selection, and Unraveling

Salary history bans forbid employers from asking job candidates to disclose their salaries. However, applicants can still volunteer this information. Our theoretical model predicts that the effect of these laws varies by how workers comply. Our survey of Americans in the labor force finds candidates fall into three compliance types: 25 percent always disclose their salary whether asked or not, 17 percent never disclose, and 58 percent comply with the ban. Importantly, compliance type varies by demographics (e.g. always-disclosers are more male, compliers are more female), and workers are more likely to disclose as others do the same, which suggests unraveling.
A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret

In this paper, we consider the problem of distributed online convex optimization, where a network of local agents aim to jointly optimize a convex function over a period of multiple time steps. The agents do not have any information about the future. Existing algorithms have established dynamic regret bounds that have explicit dependence on the number of time steps. In this work, we show that we can remove this dependence assuming that the local objective functions are strongly convex. More precisely, we propose a gradient tracking algorithm where agents jointly communicate and descend based on corrected gradient steps. We verify our theoretical results through numerical experiments.
Unc-4 acts to promote neuronal identity and development of the take-off circuit in the Drosophila CNS

The Drosophila ventral nerve cord (VNC) is composed of thousands of neurons born from a set of individually identifiable stem cells. The VNC harbors neuronal circuits required to execute key behaviors, such as flying and walking. Leveraging the lineage-based functional organization of the VNC, we investigated the developmental and molecular basis of behavior by focusing on lineage-specific functions of the homeodomain transcription factor, Unc-4. We found that Unc-4 functions in lineage 11A to promote cholinergic neurotransmitter identity and suppress the GABA fate. In lineage 7B, Unc-4 promotes proper neuronal projections to the leg neuropil and a specific flight-related take-off behavior. We also uncovered that Unc-4 acts peripherally to promote proprioceptive sensory organ development and the execution of specific leg-related behaviors. Through time-dependent conditional knock-out of Unc-4, we found that its function is required during development, but not in the adult, to regulate the above events.
BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance

High throughput sequencing of RNA (RNA-Seq) can provide us with millions of short fragments of RNA transcripts from a sample. How to better recover the original RNA transcripts from those fragments (RNA-Seq assembly) is still a difficult task. For example, RNA-Seq assembly tools typically require hyper-parameter tuning to achieve good performance for particular datasets. This kind of tuning is usually unintuitive and time-consuming. Consequently, users often resort to default parameters, which do not guarantee consistent good performance for various datasets.
Analysis of Data Corrections for the First-Generation Radiofrequency-Penetrable PET Insert for Simultaneous PET/MR

This work focused on studying key data corrections which include annulus-based normalization, attenuation correction using an annulus transmission scan, and variance reduction of random coincidence events for the first-generation radiofrequency-penetrable PET insert for simultaneous PET/MR. The brain dedicated PET system is composed of 16 detector modules. Each module employs an array of 3.2 x 3.2 x 20 mm3 LYSO crystal elements which are 1-1 coupled to arrays of silicon photomultipliers (SiPM). Front-end electronics multiplex the output of 128 pixels to 16 vertical-cavity surface-emitting lasers (VCSEL). The VSCELs generate unique optical output patterns per pixel which are passed via fiber optics to an external DAQ. To achieve RF-penetrability of the body coil RF excitation signal and maintain MRI compatibility, the modules in the assembled system are electrically isolated from the MRI and spaced with 1 mm gaps for the RF fields to enter. Variance reduction was performed on random coincidence data experimentally acquired from a Hoffman phantom whereas the annulus-based normalization and annulus attenuation correction was performed on a cylinder to assess non-uniformity. The reconstruction of the non-normalized cylinder showed a non-uniformity of 54.24%, annulus normalization reduced the non-uniformity to 9.34%, and the addition of attenuation correction yielded 8.63%.
Graph Regularized Tensor Train Decomposition

With the advances in data acquisition technology, tensor objects are collected in a variety of applications including multimedia, medical and hyperspectral imaging. As the dimensionality of tensor objects is usually very high, dimensionality reduction is an important problem. Most of the current tensor dimensionality reduction methods rely on finding low-rank linear representations using different generative models. However, it is well-known that high-dimensional data often reside in a low-dimensional manifold. Therefore, it is important to find a compact representation, which uncovers the low-dimensional tensor structure while respecting the intrinsic geometry. In this paper, we propose a graph regularized tensor train (GRTT) decomposition that learns a low-rank tensor train model that preserves the local relationships between tensor samples. The proposed method is formulated as a non-convex optimization problem on the Stiefel manifold and an efficient algorithm is proposed to solve it. The proposed method is compared to existing tensor based dimensionality reduction methods as well as tensor manifold embedding methods for unsupervised learning applications.
Learning context-aware structural representations to predict antigen and antibody binding interfaces.

Motivation Understanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the 'ground truth' of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. To increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens. Results Our framework leverages three key aspects of antibody-antigen interactions to learn predictive structural representations: (i) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (ii) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (iii) since more data are available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction. Availability and implementation The source code is freely available on github at https://github.com/vamships/PECAN.git.
General DNA Methylation Patterns and Environmentally-Induced Differential Methylation in the Eastern Oyster (Crassostrea virginica)

Epigenetic modification, specifically DNA methylation, is one possible mechanism for intergenerational plasticity. Before inheritance of methylation patterns can be characterized, we need a better understanding of how environmental change modifies the parental epigenome. To examine the influence of experimental ocean acidification on eastern oyster (Crassostrea virginica) gonad tissue, oysters were cultured in the laboratory under control (491 ± 49 µatm) or high (2550 ± 211 µatm) pCO2 conditions for four weeks. DNA from reproductive tissue was isolated from five oysters per treatment, then subjected to bisulfite treatment and DNA sequencing. Irrespective of treatment, DNA methylation was primarily found in gene bodies with approximately 22% of CpGs (2.7% of total cytosines) in the C. virginica genome predicted to be methylated. In response to elevated pCO2, we found 598 differentially methylated loci primarily overlapping with gene bodies. A majority of differentially methylated loci were in exons (61.5%) with less intron overlap (31.9%). While there was no evidence of a significant tendency for the genes with differentially methylated loci to be associated with distinct biological processes, the concentration of these loci in gene bodies, including genes involved in protein ubiquitination and biomineralization suggests DNA methylation may be important for transcriptional control in response to ocean acidification. Changes in gonad methylation also indicate potential for these methylation patterns to be inherited by offspring. Understanding how experimental ocean acidification conditions modify the oyster epigenome, and if these modifications are inherited, allows for a better understanding of how ecosystems will respond to environmental change.
On a conjecture of Voisin on the gonality of very general abelian varieties

Abstract We study orbits for rational equivalence of zero-cycles on very general abelian varieties by adapting a method of Voisin to powers of abelian varieties. We deduce that, for k at least 3, a very general abelian variety of dimension at least 2 k − 2 has covering gonality greater than k. This settles a conjecture of Voisin. We also discuss how upper bounds for the dimension of orbits for rational equivalence can be used to provide new lower bounds on other measures of irrationality. In particular, we obtain a strengthening of the Alzati-Pirola bound on the degree of irrationality of abelian varieties.
10 – Relations and Functions

Publisher Summary This chapter discusses the concepts of relations and functions. Relations and functions have many applications in the real world. A relation is any set of ordered pairs. The set of all first coordinates is called the domain of the relation, and the set of all second coordinates is said to be the range of the relation. There are two ways to specify the ordered pairs in a relation. One method is to list them. The other method is to give the rule for obtaining them. If the ordered pairs of a relation are given in terms of an equation rather than a list, the graph of the relation can be used to determine if the relation is a function or not. Any two ordered pairs with the same first coordinates would lie along a vertical line parallel to the y-axis. Therefore, if a vertical line crosses the graph of a relation in more than one place, the relation cannot be a function. If no vertical line can be found that crosses the graph in more than one place, the relation must be a function. Testing to see if a relation is a function by observing if a vertical line crosses the graph of the relation in more than one place is called the vertical line test.
Cases Relating to Chapter 12 – Hearsay Rule and Exceptions

Publisher Summary This chapter presents several case studies related to hearsay rule and exceptions. These cases include the case of Bell versus State, Gonzalez versus State, Cox versus State, Sadler versus State, State versus Washington, and Sadler versus State. In the first case, Gary Paul Bell appeals his conviction and sentence for attempted kidnapping. Defendant-appellant Bell argues that the trial court should have excluded as hearsay the officer's testimony regarding the victim's account of the crime. The trial court admitted the testimony under the hearsay exception for excited utterances. The question presented in the second case is whether the appellant (Gonzalez) forfeited, by his own misconduct of fatally shooting Maria Herrera during a robbery or the burglary of her home, his right to confront Maria in court about hearsay statements she made before she died. In the third case, James Cox was convicted following a bench trial of domestic battery, a Class A misdemeanor. Cox appeals his conviction. In the fourth case, jury convicted Michael Shawn Sadler of murder and assessed his punishment at thirty years' imprisonment. In the final case, the defendant was convicted of armed robbery and assault with intent to do great bodily harm less than murder. The Court of Appeals reversed the convictions because the accomplice's statement, in which the accomplice identified himself as the shooter, was improperly admitted against defendant.
EXPECTATIONS AND CONTAGION IN SELF‐FULFILLING CURRENCY ATTACKS

This paper shows how expectations-driven contagion of currency crises can arise even if the currency market has a unique equilibrium when viewed in isolation. The model of Morris and Shin (1998) is extended to allow speculators to trade in a second currency market. If speculators believe that a devaluation of this other currency will make a domestic devaluation more likely, they will engage in trades that link the two markets. A sharp devaluation of the other currency will then be propagated to the domestic market and will increase the likelihood of ac risis there, fulfilling the original expectations. Even though this contagion is driven solely by expectations, the model places restrictions on observable variables, and these restrictions are broadly consistent with existing empirical evidence.
Global Yield Curve Dynamics and Interactions: A Dynamic Nelson-Siegel Approach

The popular Nelson-Siegel (1987) yield curve is routinely fit to cross sections of intra-country bond yields, and Diebold and Li (2006) have recently proposed a dynamized version. In this paper we extend Diebold-Li to a global context, modeling a potentially large set of country yield curves in a framework that allows for both global and country-specific factors. In an empirical analysis of term structures of government bond yields for the Germany, Japan, the U.K. and the U.S., we find that global yield factors do indeed exist and are economically important, generally explaining significant fractions of country yield curve dynamics, with interesting differences across countries.
Tax Analysis in an Oligopoly Model

In this article we analyze taxation using the conjectural variations model of oligopoly. We demonstrate the way in which the incidence of a tax depends on the pattern of firm interaction. The results obtained have important implications for the controversy surrounding the question of whether a tax on corporate income can be overshifted. We also study normative aspects of taxation. The focus here is on the errors that can arise in excess burden calculations when incorrect assumptions on market structure are made.
Mandatory Disclosure Quality, Inside Ownership, and Cost of Capital

AbstractThis paper examines whether and how inside ownership mediates the relation between disclosure quality and the cost of capital. Both ownership and more transparent reporting have the potential to align incentives between managers and investors thereby reducing systematic risk. Employing a large global sample across 35 countries over the 1990–2004 period, we show that country-level disclosure regulation is negatively related to (i) inside ownership, and (ii) firms' implied cost of capital and realised returns. We then introduce ownership into the cost of capital model, and also find a negative relation. These relations extend to the systematic component of the cost of capital, estimated from Fama–French portfolio sorts on ownership and disclosure regulation. Thus, while the direct effect of disclosure on cost of capital is negative, the indirect effect via ownership is positive, consistent with disclosure quality and ownership acting as substitutes. Using path analysis to assess the relative magnitu...
Is it ethical to use placebos in osteoporosis clinical trials

Because there are now effective agents for treatment of osteoporosis, the question is being raised as to whether or not it is ethical to have placebo-controlled trials of new agents. It is ethical for patients who are at low risk of serious or irreversible harm to participate in placebo-controlled trials as long as they provide informed consent. Morbidity, mortality, and future fracture risk correlate with the presence of previous fractures, the number of previous fractures, whether or not the fracture is recent, and whether or not the fracture is clinically recognized. Lower-risk subjects who may be allowed to participate in placebo-controlled trials include those with low bone density but without a previous vertebral fracture, those with a single vertebral deformity that was not clinically recognized, and those with a vertebral fracture more than 2 years before. Higher-risk subjects who do not tolerate proven drugs or who have not responded to proven drugs may also participate. Even though it may be ethical for selected subjects to participate in placebo-controlled trials of new therapies for osteoporosis, steps should be taken to minimize their exposure (eg, unbalanced randomization, integration of outcomes, and powering trials to actual events rather than a projected number over 3 years), and treating patients who fracture or who fail to respond.
InGaP/GaAs HBT DC-20 GHz distributed amplifier with compact ESD protection circuits

Abstract This paper presents design considerations and implementation of InGaP/GaAs HBT DC-20 GHz distributed amplifier with compact ESD protection circuits. The inherit benefits of both bandwidth and ESD robustness of distributed amplifiers are first compared to those of single-ended feedback amplifiers. Next, novel on-chip ESD protection circuits are introduced, featuring low capacitance loading for wide bandwidth, low leakage, and good linearity under high RF power. This paper discusses the principle of operation, ESD performance, and RF loading of the ESD protection circuits. The RF performance and ESD robustness of the distributed amplifier with the ESD protection circuits are also presented.
On the cover Ramsey number of Berge hypergraphs

Abstract For a fixed set of positive integers R , we say H is an R -uniform hypergraph, or R -graph, if the cardinality of each edge belongs to R . An R -graph H is covering if every vertex pair of H is contained in some hyperedge. For a graph G = ( V , E ) , a hypergraph H is called a Berge- G , denoted by B G , if there is an injection i : V ( G ) → V ( H ) and a bijection f : E ( G ) → E ( H ) such that for all e = u v ∈ E ( G ) , we have { i ( u ) , i ( v ) } ⊆ f ( e ) . In this note, we define a new type of Ramsey number, namely the cover Ramsey number, denoted as R ˆ R ( B G 1 , B G 2 ) , as the smallest integer n 0 such that for every covering R -uniform hypergraph H on n ≥ n 0 vertices and every 2-edge-coloring (blue and red) of H , there is either a blue Berge- G 1 or a red Berge- G 2 subhypergraph. We show that for every k ≥ 2 , there exists some c k such that for any finite graphs G 1 and G 2 , R ( G 1 , G 2 ) ≤ R ˆ [ k ] ( B G 1 , B G 2 ) ≤ c k ⋅ R ( G 1 , G 2 ) 3 . Moreover, we show that for each positive integer d and k , there exists a constant C = C ( d , k ) such that if G is a graph on n vertices with maximum degree at most d , then R ˆ [ k ] ( B G , B G ) ≤ C n .
Effective field theories as a novel probe of fine-tuning of cosmic inflation

Abstract The leading account of several salient observable features of our universe today is provided by the theory of cosmic inflation. But an important and thus far intractable question is whether inflation is generic, or whether it is finely tuned—requiring very precisely specified initial conditions. In this paper I argue that a recent, model-independent characterization of inflation—that treats inflation as an effective field theory (EFT)—promises to address this question in a thoroughly modern and significantly more comprehensive way than in the existing literature. To motivate and provide context for this claim, I distill three core problems with the theory of inflation, which I dub the permissiveness problem, the initial conditions problem, and the multiverse problem. I argue that the initial conditions problem lies within the scope of EFTs of inflation as they are currently conceived, whereas the other two problems remain largely intractable: their solution must await a more complete description of the very early universe. I highlight recent work that addresses the initial conditions problem within the context of a dynamical systems analysis of a specific (state-of-the-art) EFT of inflation, and conclude with a roadmap for how such work might be extended to realize the promise claimed above.
Regression Analysis of Unmeasured Confounding

When studying the causal effect of $x$ on $y$, researchers may conduct regression and report a confidence interval for the slope coefficient $\beta_{x}$. This common confidence interval provides an assessment of uncertainty from sampling error, but it does not assess uncertainty from confounding. An intervention on $x$ may produce a response in $y$ that is unexpected, and our misinterpretation of the slope happens when there are confounding factors $w$. When $w$ are measured we may conduct multiple regression, but when $w$ are unmeasured it is common practice to include a precautionary statement when reporting the confidence interval, warning against unwarranted causal interpretation. If the goal is robust causal interpretation then we can do something more informative. Uncertainty in the specification of three confounding parameters can be propagated through an equation to produce a confounding interval. Here we develop supporting mathematical theory and describe an example application. Our proposed methodology applies well to studies of a continuous response or rare outcome. It is a general method for quantifying error from model uncertainty. Whereas confidence intervals are used to assess uncertainty from unmeasured individuals, confounding intervals can be used to assess uncertainty from unmeasured attributes.
Weak limits for weighted means of orthogonal polynomials

This article is a first attempt to obtain weak limit formulas for weighted means of orthogonal polynomials. For this, we introduce a new mean Nevai class that guarantees the existence of an equilibrium measure for the limit of the means. We show that for a family of measures in this mean Nevai class also the means of the Christoffel-Darboux kernels and the asymptotic distribution of the roots converge weakly to the same equilibrium measure. As a main example, we study the mean Nevai classes in which the equilibrium measure is the orthogonality measure of the ultraspherical polynomials. The respective weak limit formula can be regarded as an asymptotic weak addition formula for the corresponding class of measures.
Lessons learned from the early performance evaluation of Intel optane DC persistent memory in DBMS

Non-volatile memory (NVM) is an emerging technology, which has the persistence characteristics of large capacity storage devices, while providing the low access latency and byte-addressablity of traditional DRAM memory. In this paper, we provide extensive performance evaluations on a recently released NVM device, Intel Optane DC Persistent Memory (PMem), under different configurations with several micro-benchmark tools. Further, we evaluate OLTP and OLAP database workloads with Microsoft SQL Server 2019 when using PMem as buffer pool or persistent storage. From the lessons learned we share some recommendations for future DBMS design with PMem, e.g. simple hardware or software changes are not enough for the best use of PMem in DBMSs.
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking

Zero-shot transfer learning for multi-domain dialogue state tracking can allow us to handle new domains without incurring the high cost of data acquisition. This paper proposes new zero-short transfer learning technique for dialogue state tracking where the in-domain training data are all synthesized from an abstract dialogue model and the ontology of the domain. We show that data augmentation through synthesized data can improve the accuracy of zero-shot learning for both the TRADE model and the BERT-based SUMBT model on the MultiWOZ 2.1 dataset. We show training with only synthesized in-domain data on the SUMBT model can reach about 2/3 of the accuracy obtained with the full training dataset. We improve the zero-shot learning state of the art on average across domains by 21%.
Covariate Shocks and Child Undernutrition : A Review of Evidence from Low- and Middle-Income Countries

Unexpected adverse events that affect areas or populations widely (covariate shocks) can have major consequences for the welfare of a society. Although the negative effects on households, especially among the poor, are well established in the economics literature, fewer studies have focused on how natural, economic, and social covariate shocks affect individual welfare and particularly child nutrition status. This paper reviews the evidence on the effect of covariate shocks on child nutrition status in low- and middle-income countries, the pathways through which the effect operates, and the relationship between the timing of a child's exposure to a covariate shock and the effect on child nutrition status. The paper also examines whether public interventions can help to mitigate any negative effect and whether the effect of covariate shocks can persist in the long term. Based on findings from the synthesis of evidence, the paper presents considerations and options for public policy and future research.
User-generated nationalism: interactions with religion, race, and partisanship in everyday talk online

This article examines how the nationalist imagination structures cyberspace from the bottom up, or what I call user-generated nationalism. It also looks at the interplay between nationalism and oth...
Reviving the Salter-Swan Small Open Economy Model

Abstract This paper provides microfoundations to the Salter-Swan policy framework, a graphical apparatus designed to ascertain the exchange-rate and fiscal stance of a policymaker with internal and external economic targets. The environment is an infinite-horizon small open economy producing tradable and nontradable goods that takes world prices and world interest rates as given and is populated by optimizing households and firms. The economy is subject to terms of trade, interest rate, and discount factor shocks. The internal target of the government is the unemployment rate and the external target is the current account. Downward nominal wage rigidity and financial frictions serve as the rationale for meaningful policy intervention.
When Mayors Matter: Estimating the Impact of Mayoral Partisanship on City Policy

U.S. cities are limited in their ability to set policy. Can these constraints mute the impact of mayors’ partisanship on policy outcomes? We hypothesize that mayoral partisanship will more strongly affect outcomes in policy areas where there is the less shared authority between local, state, and federal governments. To test this hypothesis, we create a novel dataset combining U.S. mayoral election returns from 1990 to 2006 with city fiscal data. Using regression discontinuity design, we find that cities that elect a Democratic mayor spend a smaller share of their budget on public safety, a policy area where local discretion is high, than otherwise similar cities that elect a Republican or Independent. We find no differences on tax policy, social policy, and other areas that are characterized by significant overlapping authority. These results suggest that models of national policymaking are only partially applicable to U.S. cities. They also have implications for political accountability: mayors may not be able to influence the full range of policies that are nominally local responsibilities.
Connectivity of Inhomogeneous Random K-Out Graphs

We propose inhomogeneous random K-out graphs $\mathbb {H}(n; {\mu }, {K}_{n})$ , where each of the $n$ nodes is assigned to one of $r$ classes independently with a probability distribution ${\mu } = \{\mu _{1}, \ldots, \mu _{r}\}$ . In particular, each node is classified as class $i$ with probability $\mu _{i}>0$ , independently. With ${K}_{n} = \left ({K_{1,n}, \ldots, K_{r,n} }\right)$ , each class $i$ node selects $K_{i,n}$ distinct nodes uniformly at random from among all other nodes. A pair of nodes are adjacent in $\mathbb {H}(n; {\mu }, {K}_{n})$ if at least one selects the other. Without loss of generality, we assume that $K_{1,n} \leq K_{2,n} \leq \ldots \leq K_{r,n}$ . Earlier results on homogeneous random K-out graphs $\mathbb {H}(n; K_{n})$ , where all nodes select the same number $K$ of other nodes, reveal that $\mathbb {H}(n; K_{n})$ is connected with high probability (whp) if $K_{n} \geq 2$ which implies that $\mathbb {H}(n; {\mu }, {K}_{n})$ is connected whp if $K_{1,n} \geq 2$ . In this paper, we investigate the connectivity of inhomogeneous random K-out graphs $\mathbb {H}(n; {\mu }, {K}_{n})$ for the special case when $K_{1,n}=1$ , i.e., when each class 1 node selects only one other node. We show that $\mathbb {H}\left ({n;{\mu },{K}_{n}}\right)$ is connected whp if $K_{r,n}$ is chosen such that $\lim _{n \to \infty } K_{r,n} = \infty $ . However, any bounded choice of the sequence $K_{r,n}$ gives a positive probability of $\mathbb {H}\left ({n;{\mu },{K}_{n}}\right)$ being asymptotically not connected in the limit of large $n$ . Simulation results are provided to validate our results in the finite node regime.
Coordination of OLTC and Smart Inverters for Optimal Voltage Regulation of Unbalanced Distribution Networks

Abstract Photovoltaic (PV) smart inverters can improve the voltage profile of distribution networks. A multi-objective optimization framework for coordination of reactive power injection of smart inverters and tap operations of on-load tap changers (OLTCs) for multi-phase unbalanced distribution systems is proposed. The optimization objective is to minimize voltage deviations and the number of tap operations simultaneously. A novel linearization method is proposed to linearize power flow equations and to convexify the problem, which guarantees convergence of the optimization and less computation costs. The optimization is modeled and solved using mixed-integer linear programming (MILP). The proposed method is validated against conventional rule-based autonomous voltage regulation (AVR) on the highly-unbalanced modified IEEE 37 bus test system and a large California utility feeder. Simulation results show that the proposed method accurately estimates feeder voltage, significantly reduces voltage deviations, mitigates over-voltage problems, and reduces voltage unbalance while eliminating unnecessary tap operations. The robustness of the method is validated against various levels of forecast error. The computational efficiency and scalability of the proposed approach are also demonstrated through the simulations on the large utility feeder.
A note on distortion effects on the strength of bivariate copula tail dependence

Abstract This note presents relationships between a base copula and the copula induced by distortion of the base copula in the strength of tail dependence measured by tail dependence coefficients and tail orders. We derive a theorem that determines the relationships for distortion functions satisfying conditions related to regular variation. In addition to the well-known logarithmic, power and dual-power distortions, Lomax-distribution and Weibull-distribution based distortions are considered. The results can be readily applied to distortions constructed by compositions of the considered distortions.
Ab initio modeling of the energy landscape for screw dislocations in body-centered cubic high-entropy alloys

In traditional body-centered cubic (bcc) metals, the core properties of screw dislocations play a critical role in plastic deformation at low temperatures. Recently, much attention has been focused on refractory high-entropy alloys (RHEAs), which also possess bcc crystal structures. However, unlike face-centered cubic high-entropy alloys (HEAs), there have been far fewer investigations into bcc HEAs, specifically on the possible effects of chemical short-range order (SRO) in these multiple principal element alloys on dislocation mobility. Here, using density functional theory, we investigate the distribution of dislocation core properties in MoNbTaW RHEAs alloys, and how they are influenced by SRO. The average values of the core energies in the RHEA are found to be larger than those in the corresponding pure constituent bcc metals, and are relatively insensitive to the degree of SRO. However, the presence of SRO is shown to have a large effect on narrowing the distribution of dislocation core energies and decreasing the spatial heterogeneity of dislocation core energies in the RHEA. It is argued that the consequences of the mechanical behavior of HEAs is a change in the energy landscape of the dislocations, which would likely heterogeneously inhibit their motion.
MA 3 : Model Agnostic Adversarial Augmentation for Few Shot learning

Despite the recent developments in vision-related problems using deep neural networks, there still remains a wide scope in the improvement of generalizing these models to unseen examples. In this paper, we explore the domain of few-shot learning with a novel augmentation technique. In contrast to other generative augmentation techniques, where the distribution over input images are learnt, we propose to learn the probability distribution over the image transformation parameters which are easier and quicker to learn. Our technique is fully differentiable which enables its extension to versatile data-sets and base models. We evaluate our proposed method on multiple base-networks and 2 data-sets to establish the robustness and efficiency of this method. We obtain an improvement of nearly 4% by adding our augmentation module without making any change in network architectures. We also make the code 1 readily available for usage by the community.
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process

We consider networks, trained via stochastic gradient descent to minimize $\ell_2$ loss, with the training labels perturbed by independent noise at each iteration. We characterize the behavior of the training dynamics near any parameter vector that achieves zero training error, in terms of an implicit regularization term corresponding to the sum over the data points, of the squared $\ell_2$ norm of the gradient of the model with respect to the parameter vector, evaluated at each data point. This holds for networks of any connectivity, width, depth, and choice of activation function. We interpret this implicit regularization term for three simple settings: matrix sensing, two layer ReLU networks trained on one-dimensional data, and two layer networks with sigmoid activations trained on a single datapoint. For these settings, we show why this new and general implicit regularization effect drives the networks towards "simple" models.
COVID-19 Societal Response Captured by Seismic Noise in China and Italy

Seismic noise with frequencies above 1 Hz is often called "cultural noise" and is generally correlated quite well with human activities Recently, cities in mainland China and Italy imposed restrictions on travel and day-to-day activity in response to COVID-19, which gave us an unprecedented opportunity to study the relationship between seismic noise above 1 Hz and human activities Using seismic records from stations in China and Italy, we show that seismic noise above 1 Hz was primarily generated by the local transportation systems The lockdown of the cities and the imposition of travel restrictions led to an similar to 4-12 dB decrease in seismic noise power in mainland China Data also show that different Chinese cities experienced distinct periods of diminished cultural noise, related to differences in local response to the epidemic In contrast, there was only similar to 1-6 dB decrease of seismic noise power in Italy, after the country was put under a lockdown The noise data indicate that traffic flow did not decrease as much in Italy and show how different cities reacted distinctly to the lockdown conditions
3D quadratic NLS equation with electromagnetic perturbations

Abstract In this paper we study the asymptotic behavior of a quadratic Schrodinger equation with electromagnetic potentials. We prove that small solutions scatter. The proof builds on earlier work of the author for quadratic NLS with a non magnetic potential. The main novelty is the use of various smoothing estimates for the linear Schrodinger flow in place of boundedness of wave operators to deal with the loss of derivative. As a byproduct of the proof we obtain boundedness of the wave operator of the linear electromagnetic Schrodinger equation on an L 2 weighted space for small potentials, as well as a dispersive estimate for the corresponding flow.
The space of cubic surfaces equipped with a line

The Cayley–Salmon theorem implies the existence of a 27-sheeted covering space of the parameter space of smooth cubic surfaces, marking each of the 27 lines on each surface. In this paper we compute the rational cohomology of the total space of this cover, using the spectral sequence in the method of simplicial resolution developed by Vassiliev. The covering map is an isomorphism in cohomology (in fact of mixed Hodge structures) and the cohomology ring is isomorphic to that of $${{\,\mathrm{PGL}\,}}(4,{\mathbb {C}})$$ . We derive as a consequence of our theorem that over the finite field $${\mathbb {F}}_q$$ the average number of lines on a smooth cubic surface equals 1 (away from finitely many characteristics); this average is $$1 + O(q^{-1/2})$$ by a standard application of the Weil conjectures.
N-jettiness beam functions at N3LO

We present the first complete calculation for the quark and gluon N-jettiness ( $$ {\mathcal{T}}_N $$ ) beam functions at next-to-next-to-next-to-leading order (N3LO) in perturbative QCD. Our calculation is based on an expansion of the differential Higgs boson and Drell-Yan production cross sections about their collinear limit. This method allows us to employ cutting edge techniques for the computation of cross sections to extract the universal building blocks in question. The class of functions appearing in the matching coefficents for all channels includes iterated integrals with non-rational kernels, thus going beyond the one of harmonic polylogarithms. Our results are a key step in extending the $$ {\mathcal{T}}_N $$ subtraction methods to N3LO, and to resum $$ {\mathcal{T}}_N $$ distributions at N3LL′ accuracy both for quark as well as for gluon initiated processes.
Voluntary Do-Not-Sell Lists - An Innovative Approach to Reducing Gun Suicides.

An Innovative Approach to Reducing Gun Suicides Virginia recently joined Washington State in deciding to allow people to confidentially put their own names into the federal firearm background-check...