cssi/SciSciGPT-SciSciNet huggingface dataset:
Time analyticity for the heat equation and Navier-Stokes equations

Abstract We prove the analyticity in time for solutions of two parabolic equations in the whole space, without any decaying or vanishing conditions. One of them involves solutions to the heat equation of exponential growth of order 2 on M. Here M is R d or a complete noncompact manifold with Ricci curvature bounded from below by a constant. An implication is a sharp solvability condition for the Cauchy problem of the backward heat equation, which is a well known ill-posed problem. Another implication is a sharp criteria for time analyticity of solutions down to the initial time. The other pertains bounded mild solutions of the incompressible Navier-Stokes equations in the whole space. There are many long established analyticity results for the Navier-Stokes equations. See for example [17] and [10] for spatial and time analyticity in certain integral sense, [4] for pointwise space-time analyticity of 3 dimensional solutions to the Cauchy problem, and also the pointwise time analyticity results of [22] and [12] under zero boundary condition. Our result seems to be the first general pointwise time analyticity result for the Cauchy problem for all dimensions, whose proof involves only real variable method. The proof involves a method of algebraically manipulating the integral kernel, which appears applicable to other evolution equations.
A hierarchical model of perceptual multistability involving interocular grouping.

Ambiguous visual images can generate dynamic and stochastic switches in perceptual interpretation known as perceptual rivalry. Such dynamics have primarily been studied in the context of rivalry between two percepts, but there is growing interest in the neural mechanisms that drive rivalry between more than two percepts. In recent experiments, we showed that split images presented to each eye lead to subjects perceiving four stochastically alternating percepts (Jacot-Guillarmod et al. Vision research, 133, 37–46, 2017): two single eye images and two interocularly grouped images. Here we propose a hierarchical neural network model that exhibits dynamics consistent with our experimental observations. The model consists of two levels, with the first representing monocular activity, and the second representing activity in higher visual areas. The model produces stochastically switching solutions, whose dependence on task parameters is consistent with four generalized Levelt Propositions, and with experiments. Moreover, dynamics restricted to invariant subspaces of the model demonstrate simpler forms of bistable rivalry. Thus, our hierarchical model generalizes past, validated models of binocular rivalry. This neuromechanistic model also allows us to probe the roles of interactions between populations at the network level. Generalized Levelt’s Propositions hold as long as feedback from the higher to lower visual areas is weak, and the adaptation and mutual inhibition at the higher level is not too strong. Our results suggest constraints on the architecture of the visual system and show that complex visual stimuli can be used in perceptual rivalry experiments to develop more detailed mechanistic models of perceptual processing.
The Control of Politicians in Divided Societies: The Politics of Fear

Autocrats in many developing countries have extracted enormous personal rents from power. In addition, they have imposed inefficient policies including pervasive patronage spending. I present a model in which the presence of ethnic identities and the absence of institutionalized succession processes allow the ruler to elicit support from a sizeable share of the population despite large reductions in welfare. The fear of falling under an equally inefficient and venal ruler that favours another group is enough to discipline supporters. The model predicts extensive use of patronage, ethnic bias in taxation, and spending patterns and unveils a new mechanism through which economic frictions translate into increased rent extraction by the leader. These predictions are consistent with the experiences of bad governance, ethnic bias, wasteful policies, and kleptocracy in post-colonial Africa.
Sudden Stops, Output Drops, and Credit Collapses

This paper proposes a tractable sudden stop model to explain the main patterns in firm-level data from a sample of Southeast Asian firms during the Asian crisis. The model incorporates trend shocks and financial frictions, features that have so far been used separately in the literature on emerging markets. A negative shock to trend productivity growth can account, in our model, for the drop in output and its swift recovery together with the sizable and persistent downward adjustment in firms' Tobin's Q and debt levels. These patterns are broadly shared by most sudden stop episodes as documented in the literature. Trend shocks can also generate, in our model, strong leverage effects in line with the firm-level data and other observations from the literature.
Public Health GIS and the Internet

Abstract Internet access and use of georeferenced public health information for GIS application will be an important and exciting development for the nation's Department of Health and Human Services and other health agencies in this new millennium. Technological progress towards public health geospatial data integration, analysis, and visualization of space-time events using the Web portends eventual robust use of GIS by public health and other sectors of the economy. Increasing Web resources from distributed spatial data portals and global geospatial libraries, and a growing suite of Web integration tools, will provide new opportunities to advance disease surveillance, control and prevention, and insure public access and community empowerment in public health decision making. Emerging supercomputing, data mining, and compression and transmission technologies will play increasingly critical roles in national emergency, catastrophic planning and response, and risk management. Web-enabled public health GIS ...
Chapter 8 – Photogrammetry

This chapter demonstrates various simple photogrammetry techniques that can help local crime scene technicians “get the job done.” Photogrammetry is another tool on the Bat-Belt of crime scene photographers. Photography can record life, as well as crime and accident scenes. With photogrammetry techniques, photography can also be used to extrapolate the precise measurements of the items within a photograph. Most crime scenes, and the evidence within them, are measured with tape measures so that crime scene diagrams can be constructed. As the crime scenes or accident scenes become larger and more elaborate, many departments use sophisticated laser sighting systems combined with computer software, like Total Station, to create very detailed maps of the scenes. Highly trained FBI image analysts can also extrapolate bank robbery subject heights and shotgun lengths with very sophisticated photogrammetric techniques. On many occasions, however, the personnel of local law enforcement agencies can use very elementary photogrammetric techniques. Basic photogrammetry techniques can help determine the location of evidence at smaller crime scenes.
Moving to Economic Opportunity: The Migration Response to the Fracking Boom

In recent years, internal migration rates have fallen to all-time lows, raising questions about why more individuals do not appear to move to encounter more favorable labor market conditions. The fracking oil and gas boom has recently created large, localized economic shocks and corresponding wage increases for men with all levels of education. Fracking can only occur in certain areas exogenously determined by geology, creating a unique situation to identify wage-induced labor migration. In this paper, I first characterize the reduced form migration response to localized fracking booms. The data indicate that migration has been concentrated in North Dakota, with the largest population growth seen among men who are young, unmarried, or moderately educated. Using an instrumental variables (IV) approach to estimate the elasticity of in-migration with respect to wages, I find that a one percent increase in local average wages increased the in-migration rate by 4.6 percent in North Dakota, but only by 3.1 percent in other states. The elasticity in North Dakota is consistently larger across specifications. I find that initial labor market conditions, potential differences in commuting feasibility, and potential non-linear relationships between wages and migration do not seem to explain the geographic difference, while information might play a role. In combination these results suggest that in limited circumstances, there is still substantial migration to potential wage gains, although the magnitude of this response might be severely dampened by external non-market factors, such as the lack of information.
Perception-Aware Motion Planning via Multiobjective Search on GPUs

In this paper we describe a framework towards computing well-localized, robust motion plans through the perception-aware motion planning problem, whereby we seek a low-cost motion plan subject to a separate constraint on perception localization quality. To solve this problem we introduce the Multiobjective Perception-Aware Planning (MPAP) algorithm which explores the state space via a multiobjective search, considering both cost and a perception heuristic. This framework can accommodate a large range of heuristics, allowing those that capture the history dependence of localization drift and represent complex modern perception methods. We present two such heuristics, one derived from a simplified model of robot perception and a second learned from ground-truth sensor error, which we show to be capable of predicting the performance of a state-of-the-art perception system. The solution trajectory from this heuristic-based search is then certified via Monte Carlo methods to be well-localized and robust. The additional computational burden of perception-aware planning is offset by GPU massive parallelization. Through numerical experiments the algorithm is shown to find well-localized, robust solutions in about a second. Finally, we demonstrate MPAP on a quadrotor flying perception-aware and perception-agnostic plans using Google Tango for localization, finding the quadrotor safely executes the perception-aware plan every time, while crashing in over 20% of the perception-agnostic runs due to loss of localization.
Nato's litmus test: Prioritizing Afghanistan

As the first out-of-Europe deployment operation for NATO in its 60 year history Afghanistan has become NATO’s most important mission. Not only is success in Afghanistan crucial for any future hopes of a globally engaged and active NATO, but for the very existence and credibility of NATO. In other words, Afghanistan has become NATO’s litmus test in a post-9/11 environment. As a historic first for NATO, Europe and America have far more at stake in Afghanistan than their own national interests. While the upcoming NATO summit in Riga will certainly offer a chance for the heads of states gathered to rhetorically affirm the Alliance, events in the mountains and villages of Afghanistan are ultimately where NATO’s future will be affirmed.
Internal Targets in the Cyclotron

The large circulating ion current in the cyclotron which ordinarily does not reach the target chamber has been utilized in the preparation in high concentration of extremely active samples of radio-iron and radiophosphorus. By adapting as internal target the probes used in demonstrating the presence of the circulating current, it has been possible to enhance greatly the yields of radioactive materials as compared with those obtained in the usual manner at the target chamber. The construction of the probe, the nature of the circulating current and the manner in which the target material is applied to the probe are discussed. The general applications of the internal target technique are indicated.
Chapter 5 – Poisson Processes

Publisher Summary This chapter reviews Poisson processes. To begin with, Poisson behavior is so pervasive in natural phenomena and the Poisson distribution is amenable to extensive and elaborate analysis to an extent so as to make the Poisson process a cornerstone of stochastic modeling. Two fundamental properties of the Poisson distribution concern the sum of independent Poisson random variables and certain random decompositions of Poisson phenomena. Consequently, the Poisson process entails notions of both independence and the Poisson distribution. The common occurrence of the Poisson distribution in nature is explained by the law of rare events. Poisson point processes often arise in a form where the time parameter is replaced by a suitable spatial parameter. Also, Poisson events occurring in space can best be modeled as a point process. Conditioned on a fixed total number of events in an interval, the locations of those events are uniformly distributed in a certain way; this provides an important tool for computing certain functional on a Poisson process. Both compound Poisson and marked Poisson processes appear often as models of physical phenomena.
Can You Leave High School Behind

Abstract In recent years, many states, including California, Texas, and Oregon, have changed admissions policies to increase access to public universities for students from lower socioeconomic backgrounds. A key concern, however, is how these students will perform. This paper examines the relationship between high school quality and student success in college. Using administrative data from the University of Texas at Austin, we take advantage of the unique policy environment provided by Texas's Top 10% automatic admissions law, which has not only increased the diversity of high schools in the state that send students to the university, but also provides an admission criteria based on a sole observable characteristic: high school class rank. We find that high school characteristics do affect student performance, and these effects seem more pronounced for women and low-income students. In addition, there is little evidence that the effects of high school characteristics decay over students’ time in college.
Rational Minimax Iterations for Computing the Matrix p th Root

In a previous paper by the author, a family of iterations for computing the matrix square root was constructed by exploiting a recursion obeyed by Zolotarev’s rational minimax approximants of the function $$z^{1/2}$$. The present paper generalizes this construction by deriving rational minimax iterations for the matrix pth root, where $$p \ge 2$$ is an integer. The analysis of these iterations is considerably different from the case $$p=2$$, owing to the fact that when $$p>2$$, rational minimax approximants of the function $$z^{1/p}$$ do not obey a recursion. Nevertheless, we show that several of the salient features of the Zolotarev iterations for the matrix square root, including equioscillatory error, order of convergence, and stability, carry over to the case $$p>2$$. A key role in the analysis is played by the asymptotic behavior of rational minimax approximants on short intervals. Numerical examples are presented to illustrate the predictions of the theory.
Generalized coinvariant algebras for wreath products

Abstract Let r be a positive integer and let G n be the reflection group of n × n monomial matrices whose entries are rth complex roots of unity and let k ≤ n . We define and study two new graded quotients R n , k and S n , k of the polynomial ring C [ x 1 , … , x n ] in n variables. When k = n , both of these quotients coincide with the classical coinvariant algebra attached to G n . The algebraic properties of our quotients are governed by the combinatorial properties of k-dimensional faces in the Coxeter complex attached to G n (in the case of R n , k ) and r-colored ordered set partitions of { 1 , 2 , … , n } with k blocks (in the case of S n , k ). Our work generalizes a construction of Haglund, Rhoades, and Shimozono from the symmetric group S n to the more general wreath products G n .
Group Signatures with Efficient Concurrent Join.

A group signature is a basic privacy mechanism. The group joining operation is a critical component of such a scheme. To date all secure group signature schemes either employed a trustedparty aided join operation or a complex joining protocol requiring many interactions between the prospective user and the Group Manager (GM). In addition no efficient scheme employed a join protocol proven secure against adversaries that have the capability to dynamically initiate multiple concurrent join sessions during an attack. This work presents the first efficient group signature scheme with a simple Joining protocol that is based on a “single message and signature response” interaction between the prospective user and the GM. This single-message and signature-response registration paradigm where no other actions are taken, is the most efficient possible join interaction and was originally alluded to in 1997 by Camenisch and Stadler, but its efficient instantiation remained open till now. The fact that joining has two short communication flows and does not require secure channels is highly advantageous: for example, it allows users to easily join by a proxy (i.e., a security officer of a company can send a file with all registration requests in his company and get back their certificates for distribution back to members of the company). It further allows an easy and non-interactive global system re-keying operation as well as straightforward treatment of multi-group signatures. We present a strong security model for group signatures (the first explicitly taking into account concurrent join attacks) and an efficient scheme with a single-message and signature-response join protocol. The present manuscript is a full version containing proofs, minor corrections as well as a more flexible and efficient protocol construction compared to the proceedings version [28]. ∗Computer Science and Engineering Dept., University of Connecticut, Storrs, CT, USA, aggelos@cse.uconn.edu. Research partly supported by NSF CAREER Award CNS-0447808. †RSA Laboratories, Bedford, MA, USA and Computer Science Dept., Columbia University, NY, USA moti@cs.columbia.edu
Machine learning for pattern discovery in management research

Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect patterns that may have gone unnoticed. However, ML models should not be treated as the result of a deductive causal test. To demonstrate the application of ML for pattern discovery, we implement ML algorithms to study employee turnover at a large technology company. We interpret the relationships between variables using partial dependence plots, which uncover surprising nonlinear and interdependent patterns between variables that may have gone unnoticed using traditional methods. To guide readers evaluating ML for pattern discovery, we provide guidance for evaluating model performance, highlight human decisions in the process, and warn of common misinterpretation pitfalls. An online appendix provides code and data to implement the algorithms demonstrated in the paper.
Explainable Object-Induced Action Decision for Autonomous Vehicles

A new paradigm is proposed for autonomous driving. The new paradigm lies between the end-to-end and pipelined approaches, and is inspired by how humans solve the problem. While it relies on scene understanding, the latter only considers objects that could originate hazard. These are denoted as action inducing, since changes in their state should trigger vehicle actions. They also define a set of explanations for these actions, which should be produced jointly with the latter. An extension of the BDD100K dataset, annotated for a set of 4 actions and 21 explanations, is proposed. A new multi-task formulation of the problem, which optimizes the accuracy of both action commands and explanations, is then introduced. A CNN architecture is finally proposed to solve this problem, by combining reasoning about action inducing objects and global scene context. Experimental results show that the requirement of explanations improves the recognition of action-inducing objects, which in turn leads to better action predictions.
A modified mixed domain method for modeling acoustic wave propagation in strongly heterogeneous media

In this paper, phase correction and amplitude compensation are introduced to a previously developed mixed domain method (MDM), which is only accurate for modeling wave propagation in weakly heterogeneous media. Multiple reflections are also incorporated with the one-way model to improve the accuracy. The resulting model is denoted as the modified mixed domain method (MMDM) and is numerically evaluated for its accuracy and efficiency using four distinct cases. It is found that the MMDM is significantly more accurate than the MDM for strongly heterogeneous media, especially when the phase aberrating layer is approximately perpendicular to the acoustic beam. Additionally, a convergence study suggests that the second-order reflection could be sufficient for cases involving high contrast inhomogeneities and large loss values (e.g., skulls). The method developed in this work could facilitate therapeutic ultrasound for treating brain-related diseases and disorders.
The Nonstochastic Control Problem

We consider the problem of controlling an unknown linear dynamical system in the presence of (nonstochastic) adversarial perturbations and adversarial convex loss functions. In contrast to classical control, the a priori determination of an optimal controller here is hindered by the latter's dependence on the yet unknown perturbations and costs. Instead, we measure regret against an optimal linear policy in hindsight, and give the first efficient algorithm that guarantees a sublinear regret bound, scaling as T^{2/3}, in this setting.
Online Batch Decision-Making with High-Dimensional Covariates

We propose and investigate a class of new algorithms for sequential decision making that interacts with \textit{a batch of users} simultaneously instead of \textit{a user} at each decision epoch. This type of batch models is motivated by interactive marketing and clinical trial, where a group of people are treated simultaneously and the outcomes of the whole group are collected before the next stage of decision. In such a scenario, our goal is to allocate a batch of treatments to maximize treatment efficacy based on observed high-dimensional user covariates. We deliver a solution, named \textit{Teamwork LASSO Bandit algorithm}, that resolves a batch version of explore-exploit dilemma via switching between teamwork stage and selfish stage during the whole decision process. This is made possible based on statistical properties of LASSO estimate of treatment efficacy that adapts to a sequence of batch observations. In general, a rate of optimal allocation condition is proposed to delineate the exploration and exploitation trade-off on the data collection scheme, which is sufficient for LASSO to identify the optimal treatment for observed user covariates. An upper bound on expected cumulative regret of the proposed algorithm is provided.
Non-realizability of the Torelli group as area-preserving homeomorphisms

Nielsen realization problem for the mapping class group Mod (S_g) asks whether the natural projection p_g: Homeo + (S_g) → Mod (S_g) has a section. While all the previous results use torsion elements in an essential way, in this paper, we focus on the much more difficult problem of realization of torsion‐free subgroups of Mod (S_g). The main result of this paper is that the Torelli group has no realization inside the area‐preserving homeomorphisms.
No System Is Perfect: Understanding How Registration‐Based Editorial Processes Affect Reproducibility and Investment in Research Quality

The papers in this volume were published through a Registration‐based Editorial Process (REP). Authors submitted proposals to gather and analyze data; successful proposals were guaranteed publication as long as the authors lived up to their commitments, regardless of whether results supported their predictions. To understand how REP differs from the Traditional Editorial Process (TEP), we analyze the papers themselves; conference comments; a survey of conference authors, reviewers, and attendees; and a survey of authors who have successfully published under TEP. We find that REP increases up‐front investment in planning, data gathering, and analysis, but reduces follow‐up investment after results are known. This shift in investment makes individual results more reproducible, but leaves articles less thorough and refined. REP could be improved by encouraging selected forms of follow‐up investment that survey respondents believe are usually used under TEP to make papers more informative, focused, and accurate at little risk of overstatement.
Pattern-Avoiding Permutation Powers

Abstract Recently, Bona and Smith defined strong pattern avoidance, saying that a permutation π strongly avoids a pattern τ if π and π 2 both avoid τ . They conjectured that for every positive integer k , there is a permutation in S k 3 that strongly avoids 123 ⋯ ( k + 1 ) . We use the Robinson–Schensted–Knuth correspondence to settle this conjecture, showing that the number of such permutations is at least k k 3 ∕ 2 + O ( k 3 ∕ log k ) and at most k 2 k 3 + O ( k 3 ∕ log k ) . We enumerate 231-avoiding permutations of order 3, and we give two further enumerative results concerning strong pattern avoidance. We also consider permutations whose powers all avoid a pattern τ . Finally, we study subgroups of symmetric groups whose elements all avoid certain patterns. This leads to several new open problems connecting the group structures of symmetric groups with pattern avoidance.
Who Writes the News? Corporate Press Releases during Merger Negotiations

Firms have an incentive to manage media coverage to influence their stock prices during important corporate events. Using comprehensive data on media coverage and merger negotiations, we find that bidders in stock mergers originate substantially more news stories after the start of merger negotiations, but before the public announcement. This strategy generates a short-lived run-up in bidders' stock prices during the period when the stock exchange ratio is determined, which substantially impacts the takeover price. Our results demonstrate that the timing and content of financial media coverage may be biased by firms seeking to manipulate their stock price.
Maximum Matching in the Online Batch-arrival Model

Consider a two-stage matching problem, where edges of an input graph are revealed in two stages (batches) and in each stage we have to immediately and irrevocably extend our matching using the edges from that stage. The natural greedy algorithm is half competitive. Even though there is a huge literature on online matching in adversarial vertex arrival model, no positive results were previously known in adversarial edge arrival model.For two-stage bipartite matching problem, we show that the optimal competitive ratio is exactly 2/3 in both the fractional and the randomized-integral models. Furthermore, our algorithm for fractional bipartite matching is instance optimal, i.e., it achieves the best competitive ratio for any given first stage graph. We also study natural extensions of this problem to general graphs and to s stages and present randomized-integral algorithms with competitive ratio ½ + 2−O(s).Our algorithms use a novel Instance-Optimal-LP and combine graph decomposition techniques with online primal-dual analysis.
Impact of Connected and Automated Vehicles in a Corridor

Several approaches have been proposed in the literature that allow connected and automated vehicles (CAVs) to coordinate in areas where there is a potential conflict, for example, in intersections, merging at roadways and roundabouts. In this paper, we consider the problem of coordinating CAVs in a corridor consisting of several conflict areas where collision may occur. We derive a solution that yields the optimal control input, in terms of fuel consumption, for each CAV to cross the corridor under the hard safety constraints. We validate the effectiveness of the solution through simulation, and we show that both fuel consumption and travel time can be improved significantly.
Asymmetric Cell Transmission Model-Based, Ramp-Connected Robust Traffic Density Estimation under Bounded Disturbances

In modern transportation systems, traffic congestion is inevitable. To minimize the loss caused by congestion, various control strategies have been developed most of which rely on observing real-time traffic conditions. As vintage traffic sensors are limited, traffic density estimation is very helpful for gaining network-wide observability. This paper deals with this problem by first, presenting a traffic model for stretched highway having multiple ramps built based on asymmetric cell transmission model (ACTM). Second, based on the assumption that the encompassed nonlinearity of the ACTM is Lipschitz, a robust dynamic observer framework for performing traffic density estimation is proposed. Numerical test results show that the observer yields a sufficient performance in estimating traffic densities having noisy measurements, while being computationally faster the Unscented Kalman Filter in performing real-time estimation.
Crystalline aspects of geography of low dimensional varieties I: numerology

This is a modest attempt to study, in a systematic manner, the structure of low dimensional varieties in positive characteristics using p-adic invariants. The main objects of interest in this paper are surfaces and threefolds. There are many results we prove in this paper and not all can be listed in this abstract. Here are some of the results. We prove inequalities related to the Bogomolov–Miyaoka–Yau inequality: in Corollary 4.7 that $$c_1^2\hbox {\ {\char 054}\ }\max (5c_2+6b_1,6c_2)$$ holds for a large class of surfaces of general type. In Theorem 4.17 we prove that for a smooth, projective, Hodge–Witt, minimal surface of general type (with additional assumptions such as slopes of Frobenius on $$H^2_\mathrm{cris}(X)$$ are $$\hbox {\ {\char 062}\ }{1}/{2}$$ ) that $$c_1^2\hbox {\ {\char 054}\ }5c_2$$ . We do not assume any lifting, and novelty of our method lies in our use of slopes of Frobenius and the slope spectral sequence. We also construct new birational invariants of surfaces. Applying our methods to threefolds, we characterize Calabi–Yau threefolds with $$b_3=0$$ . We show that for any Calabi–Yau threefold $$b_2\hbox {\ {\char 062}\ }c_3/2-1$$ and that threefolds which lie on the line $$b_2=c_3/2-1$$ are precisely those with $$b_3=0$$ and threefolds with $$b_2=c_3/2$$ are characterized as Hodge–Witt rigid (included are rigid Calabi–Yau threefolds which have torsion-free crystalline cohomology and whose Hodge–de Rham spectral sequence degenerates).
Estimating Crop Yields With Remote Sensing And Deep Learning

Increasing the accuracy of crop yield estimates may allow improvements in the whole crop production chain, allowing farmers to better plan for harvest, and for insurers to better understand risks of production, to name a few advantages. To perform their predictions, most current machine learning models use NDVI data, which can be hard to use, due to the presence of clouds and their shadows in acquired images, and due to the absence of reliable crop masks for large areas, especially in developing countries. In this paper, we present a deep learning model able to perform pre-season and in-season predictions for five different crops. Our model uses crop calendars, easy-to-obtain remote sensing data and weather forecast information to provide accurate yield estimates.
Small sphere limit of the quasi-local energy with anti de-Sitter space reference

In [13], a new quasi-local energy is introduced for spacetimes with a non-zero cosmological constant. In this article, we study the small sphere limit of this newly defined quasi-local energy for spacetimes with a negative cosmological constant. For such spacetimes, the anti de-Sitter space is used as the reference for the quasi-local energy. Given a point $p$ in a spacetime $N$, we consider a canonical family of surfaces approaching $p$ along its future null cone and evaluate the limit of the quasi-local energy. The optimal embedding equation which identifies the critical points of the quasi-local energy is solved in order to evaluate the limit. Using the optimal embedding, we show that the limit recovers the stress-energy tensor of the matter field at $p$. For vacuum spacetimes, the quasi-local energy vanishes to a higher order. In this case, the limit of the quasi-local energy is related to the Bel-Robinson tensor at $p$.
Exchange rate volatility and pass-through to inflation in South Africa

Does the South African rand's relatively large volatility affect inflation? To shed some light on this question, a standard estimation technique of exchange rate pass‐through to inflation is extended to incorporate exchange rate volatility. Estimated results suggest that higher exchange rate volatility tends to increase core inflation but to a relatively limited extent in South Africa. The finding lends support to the policy of allowing the rand to float freely and work as a shock absorber, consistent with the nation's successful inflation targeting regime.
Bounded Manifold Completion

Abstract Nonlinear dimensionality reduction is an active area of research. In this paper, we present a thematically different approach to detect a low-dimensional manifold that lies within a set of bounds derived from a given point cloud. A matrix representing distances on a low-dimensional manifold is low-rank, and our method is based on current low-rank Matrix Completion (MC) techniques for recovering a partially observed matrix from fully observed entries. MC methods are currently used to solve challenging real-world problems such as image inpainting and recommender systems. Our MC scheme utilizes efficient optimization techniques that employ a nuclear norm convex relaxation as a surrogate for non-convex and discontinuous rank minimization. The method theoretically guarantees on detection of low-dimensional embeddings and is robust to non-uniformity in the sampling of the manifold. We validate the performance of this approach using both a theoretical analysis as well as synthetic and real-world benchmark datasets.
Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life

It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of “incremental learning” addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data. We demonstrate how classification improves over time by incrementally training a classifier on progressive RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model’s knowledge of genomes grows, classification accuracy increases. The proof-of-concept naive Bayes implementation, when updated yearly, now runs in 1/4th of the non-incremental time with no accuracy loss. It is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge. The incremental learning classifier can be efficiently updated without the cost of reprocessing nor the access to the existing database and therefore save storage as well as computation resources.
Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology

Scatterplots are used for a variety of visual analytics tasks, including cluster identification, and the visual encodings used on a scatterplot play a deciding role on the level of visual separation of clusters. For visualization designers, optimizing the visual encodings is crucial to maximizing the clarity of data. This requires accurately modeling human perception of cluster separation, which remains challenging. We present a multi-stage user study focusing on four factors-distribution size of clusters, number of points, size of points, and opacity of points-that influence cluster identification in scatterplots. From these parameters, we have constructed two models, a distance-based model, and a density-based model, using the merge tree data structure from Topological Data Analysis. Our analysis demonstrates that these factors play an important role in the number of clusters perceived, and it verifies that the distance-based and density-based models can reasonably estimate the number of clusters a user observes. Finally, we demonstrate how these models can be used to optimize visual encodings on real-world data.
Airway emergency management in a pediatric hospital before and during the COVID-19 pandemic

Abstract Objective Children's hospitals frequently care for infants with various life-threatening airway anomalies. Management of these infants can be challenging given unique airway anatomy and potential malformations. Airway emergency management must be immediate and precise, often demanding specialized equipment and/or expertise. We developed a Neonatal-Infant Emergency Airway Program to improve medical responses, communication, equipment usage and outcomes for all infants requiring emergent airway interventions in our neonatal and infant intensive care unit (NICU). Patients and methods All patients admitted to our quaternary NICU from 2008 to 2019 were included in this study. Our program consisted of a multidisciplinary airway response team, pager system, and emergency equipment cart. Respiratory therapists present at each emergency event recorded specialist response times, equipment utilization, and outcomes. A multidisciplinary oversite committee reviewed each incident. Results Since 2008, there were 159 airway emergency events in our NICU (~12 per year). Mean specialist response times decreased from 5.9 ± 4.9 min (2008–2012, mean ± SD) to 4.3 ± 2.2 min (2016–2019, p = 0.12), and the number of incidents with response times >5 min decreased from 28.8 ± 17.8% (2008–2012) to 9.3 ± 11.4% (2016–2019, p = 0.04 by linear regression). As our program became more standardized, we noted better equipment availability and subspecialist communication. Few emergency situations (n = 9, 6%) required operating room management. There were 3 patient deaths (2%). Conclusions Our airway safety program, including readily available specialists and equipment, facilitated effective resolution of airway emergencies in our NICU and multidisciplinary involvement enabled rapid and effective changes in response to COVID-19 regulations. A similar program could be implemented in other centers.
Preparing for the Quantum Revolution: What Is the Role of Higher Education?.

Basic skills in physics and engineering are valued by the quantum industry just as much as specific knowledge of quantum information science.
Characterization of projective spaces by Seshadri constants

We prove that an n-dimensional complex projective variety is isomorphic to $${\mathbb {P}}^n$$ if the Seshadri constant of the anti-canonical divisor at some smooth point is greater than n. We also classify complex projective varieties with Seshadri constants equal to n.
Statistical Predictions in String Theory and Deep Generative Models

Generative models in deep learning allow for sampling probability distributions that approximate data distributions. We propose using generative models for making approximate statistical predictions in the string theory landscape. For vacua admitting a Lagrangian description this can be thought of as learning random tensor approximations of couplings. As a concrete proof‐of‐principle, we demonstrate in a large ensemble of Calabi‐Yau manifolds that Kahler metrics evaluated at points in Kahler moduli space are well‐approximated by ensembles of matrices produced by a deep convolutional Wasserstein GAN. Accurate approximations of the Kahler metric eigenspectra are achieved with far fewer than h11 Gaussian draws. Accurate extrapolation to values of h11 outside the training set are achieved via a conditional GAN. Together, these results implicitly suggest the existence of strong correlations in the data, as might be expected if Reid's fantasy is correct.
PROBING THE LOW-REDSHIFT STAR FORMATION RATE AS A FUNCTION OF METALLICITY THROUGH THE LOCAL ENVIRONMENTS OF TYPE II SUPERNOVAE

Type?II supernovae (SNe) can be used as a star formation tracer to probe the metallicity distribution of global low-redshift star formation. We present oxygen and iron abundance distributions of Type?II?SN progenitor regions that avoid many previous sources of bias. Because iron abundance, rather than oxygen abundance, is of key importance for the late stage evolution of the massive stars that are the progenitors of core-collapse supernovae, and because iron enrichment lags oxygen enrichment, we find a general conversion from oxygen abundance to iron abundance. The distributions we present here are the best yet observational standard of comparison for evaluating how different classes of supernovae depend on progenitor metallicity. We spectroscopically measure the gas-phase oxygen abundance near a representative subsample of the hosts of Type?II?SNe from the first-year Palomar Transient Factory (PTF) SN search, using a combination of Sloan Digital Sky Survey (SDSS) spectra near the SN location (9 hosts) and new longslit spectroscopy (25 hosts). The median metallicity of these 34 hosts at or near the SN location is 12+log(O/H) = 8.65, with a median error of 0.09. The median host galaxy stellar mass from fits to SDSS photometry is 109.9 M ?. They do not show a systematic offset in metallicity or mass from a redshift-matched sample of the MPA/JHU value-added catalog. In contrast to previous SN host metallicity studies, this sample is drawn from a single survey. It is also drawn from an areal rather than a targeted survey, so SNe in the lowest-mass galaxies are not systematically excluded. Indeed, the PTF SN search has a slight bias toward following up transients in low mass galaxies. The progenitor region metallicity distribution we find is statistically indistinguishable from the metallicity distribution of Type?II?SN hosts found by targeted surveys and by samples from multiple surveys with different selection functions. Using the relationship between iron and oxygen abundances found for Milky Way disk, bulge, and halo stars, we translate our distribution of Type?II?SN environments as a function of oxygen abundance into an estimate of the iron abundance, since iron varies more steeply than oxygen. We find that though this sample spans only 0.65?dex in oxygen abundance, the gap between the iron and oxygen abundance is 50% wider at the low-metallicity end of our sample than at the high-metallicity end.
Factorization length distribution for affine semigroups II: asymptotic behavior for numerical semigroups with arbitrarily many generators

Abstract For numerical semigroups with a specified list of (not necessarily minimal) generators, we obtain explicit asymptotic expressions, and in some cases quasipolynomial/quasirational representations, for all major factorization length statistics. This involves a variety of tools that are not standard in the subject, such as algebraic combinatorics (Schur polynomials), probability theory (weak convergence of measures, characteristic functions), and harmonic analysis (Fourier transforms of distributions). We provide instructive examples which demonstrate the power and generality of our techniques. We also highlight unexpected consequences in the theory of homogeneous symmetric functions.
ON THE VARIATION OF CURVATURE FUNCTIONALS IN SPACE FORMS WITH APPLICATION TO A GENERALIZED WILLMORE ENERGY

Functionals involving surface curvature are important across a range of scientific disciplines, and their extrema are representative of physically meaningful objects such as atomic lattices and biomembranes. Inspired in particular by the relationship of the Willmore energy to lipid bilayers, we consider a general functional depending on a surface and a symmetric combination of its principal curvatures, and provided the surface is immersed in a 3-D space form of constant sectional curvature. We calculate the first and second variations of this functional, extending known results and providing computationally accessible expressions given entirely in terms of the basic geometric information found in the surface fundamental forms. Further, we motivate and introduce the p-Willmore energy functional, applying the stability criteria afforded by our calculations to prove a result about the p-Willmore energy of spheres.
Numerical simulation of the geometrical-optics reduction of CE2 and comparisons to quasilinear dynamics

Zonal flows have been observed to appear spontaneously from turbulence in a number of physical settings. A complete theory for their behavior is still lacking. Recently, a number of studies have investigated the dynamics of zonal flows using quasilinear (QL) theories and the statistical framework of a second-order cumulant expansion (CE2). A geometrical-optics (GO) reduction of CE2, derived under an assumption of separation of scales between the fluctuations and the zonal flow, is studied here numerically. The reduced model, CE2-GO, has a similar phase-space mathematical structure to the traditional wave-kinetic equation, but that wave-kinetic equation has been shown to fail to preserve enstrophy conservation and to exhibit an ultraviolet catastrophe. CE2-GO, in contrast, preserves nonlinear conservation of both energy and enstrophy. We show here how to retain these conservation properties in a pseudospectral simulation of CE2-GO. We then present nonlinear simulations of CE2-GO and compare with direct sim...
UNCONVENTIONAL LOW-ENERGY EXCITATIONS OF CUPRATE SUPERCONDUCTORS

Recent development in the physics of high-temperature cuprate superconductivity is reviewed, with special emphasis on the phenomena of unconventional and non-universal low-energy excitations of hole- and electron-type cuprate superconductors and the possible physical origin. A phenomenology based on coexisting competing orders with cuprate superconductivity in the ground state appears to provide a consistent account for a wide range of experimental findings, including the presence (absence) of pseudogaps and Fermi arcs above the superconducting transition T_c in hole-type (electron-type) cuprate superconductors and the novel conductance modulations below T_c, particularly in the vortex state. Moreover, the competing order scenario is compatible with the possibility of pre-formed Cooper pairs and significant phase fluctuations in cuprate superconductors. The physical implications of the unified phenomenology and remaining open issues for the microscopic mechanism of cuprate superconductivity are discussed.
Tau decays into two mesons: an overview

We review the state-of-the-art theoretical analyses of tau decays into a pair of mesons and a neutrino. The participant vector and scalar form factors, f + (s ) and f 0 (s ), are described in the frame of Chiral Perturbation Theory with resonances supplemented by dispersion relations, and the physical parameters of the intermediate resonances produced in the decay are extracted through the pole position of f +,0 (s ) in the complex plane. As a side result, we also determine the low-energy observables associated to the form factors. We hope our study to be of interest for present and future experimental analyses of these decays.