cssi/SciSciGPT-SciSciNet huggingface dataset:
A Robust Solver for a Mixed Finite Element Method for the Cahn–Hilliard Equation

We develop a robust solver for a mixed finite element convex splitting scheme for the Cahn–Hilliard equation. The key ingredient of the solver is a preconditioned minimal residual algorithm (with a multigrid preconditioner) whose performance is independent of the spacial mesh size and the time step size for a given interfacial width parameter. The dependence on the interfacial width parameter is also mild.
Regionalism in New Democracies: The Authoritarian Origins of Voter–Party Linkages:

We investigate the path-dependent effects of subnational variation in authoritarian state-building policies on voter–party linkages after regime change. We argue that long-term patterns of regional...
Transplanted Cells Are Essential for the Induction But Not the Expression of Cortical Plasticity

Transplantation of even a small number of embryonic inhibitory neurons from the medial ganglionic eminence (MGE) into postnatal visual cortex makes it lose responsiveness to an eye deprived of vision when the transplanted neurons reach the age of the normal critical period of activity-dependent ocular dominance (OD) plasticity. The transplant might induce OD plasticity in the host circuitry or might instead construct a parallel circuit of its own to suppress cortical responses to the deprived eye. We transplanted MGE neurons expressing either archaerhodopsin or channelrhodopsin into the visual cortex of both male and female mice, closed one eyelid for 4-5 d, and, as expected, observed transplant-induced OD plasticity. This plasticity was evident even when the activity of the transplanted cells was suppressed or enhanced optogenetically, demonstrating that the plasticity was produced by changes in the host visual cortex.SIGNIFICANCE STATEMENT Interneuron transplantation into mouse V1 creates a window of heightened plasticity that is quantitatively and qualitatively similar to the normal critical period; that is, short-term occlusion of either eye markedly changes ocular dominance (OD). The underlying mechanism of this process is not known. Transplanted interneurons might either form a separate circuit to maintain the OD shift or might instead trigger changes in the host circuity. We designed experiments to distinguish the two hypotheses. Our findings suggest that while inhibition produced by the transplanted cells triggers this form of plasticity, the host circuity is entirely responsible for maintaining the OD shift.
Beginning Mathematical Writing Assignments

Writing assignments in any mathematics course always present several challenges, particularly in lower-level classes where the students are not expecting to write more than a few words at a time. Developed based on strategies from several sources, the two small writing assignments included in this paper represent a gentle introduction to the writing of mathematics and can be utilized in a variety of low-to-middle level courses in a mathematics major.
Manufacturing in Structural Change in Africa

This paper investigates the scale, causes, and timing of significant episodes of industrialization and deindustrialization in Sub-Saharan Africa. Recent studies have argued that the turning point of manufacturing output and employment shares tends to occur prematurely in this region. The analysis is performed using panel data methods for fractional responses and data from a variety of sources for a panel of 41 African countries. The results overwhelmingly do not support the common finding that Sub-Saharan African countries have begun to deindustrialize. Moreover, the study documents meaningful heterogeneity across Sub-Saharan Africa subregions, with the Southern region being the only subregion to have witnessed deindustrialization. However, this deindustrialization of the Southern subregion does not appear to be occurring prematurely. The study also explores the potential role of the Dutch disease and resource curse hypotheses in understanding Sub-Saharan Africa's manufacturing experience in resource rich countries.
Online rental housing market representation and the digital reproduction of urban inequality

As the rental housing market moves online, the internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a repro...
Neural Lyapunov Control

We propose new methods for learning control policies and neural network Lyapunov functions for nonlinear control problems, with provable guarantee of stability. The framework consists of a learner that attempts to find the control and Lyapunov functions, and a falsifier that finds counterexamples to quickly guide the learner towards solutions. The procedure terminates when no counterexample is found by the falsifier, in which case the controlled nonlinear system is provably stable. The approach significantly simplifies the process of Lyapunov control design, provides end-to-end correctness guarantee, and can obtain much larger regions of attraction than existing methods such as LQR and SOS/SDP. We show experiments on how the new methods obtain high-quality solutions for challenging robot control problems such as path tracking for wheeled vehicles and humanoid robot balancing.
Learning for DC-OPF: Classifying active sets using neural nets

The optimal power flow is an optimization problem used in power systems operational planning to maximize economic efficiency while satisfying demand and maintaining safety margins. Due to uncertainty and variability in renewable energy generation and demand, the optimal solution needs to be updated in response to observed uncertainty realizations or near real-time forecast updates. To address the challenge of computing such frequent real-time updates to the optimal solution, recent literature has proposed the use of machine learning to learn the mapping between the uncertainty realization and the optimal solution. Further, learning the active set of constraints at optimality, as opposed to directly learning the optimal solution, has been shown to significantly simplify the machine learning task, and the learnt model can be used to predict optimal solutions in real-time. In this paper, we propose the use of classification algorithms to learn the mapping between the uncertainty realization and the active set of constraints at optimality, thus further enhancing the computational efficiency of the real-time prediction. We employ neural net classifiers for this task and demonstrate the excellent performance of this approach on a number of systems in the IEEE PES PGLib-OPF benchmark library.
Transition to electronic conduction at the onset of flash in cubic zirconia

Abstract We report in-situ measurements of impedance spectroscopy during flash experiments with single crystals of cubic-yttria stabilized zirconia, conducted in ambient air at 300 °C to 700 °C under a direct current electric field. Platinum electrodes transition from blocking to non-blocking behavior before the onset of flash, indicating a transition to partial electronic conductivity during the incubation period. Even though the conductivity continued to increase with temperature the electron transport remained at a plateau of approximately 0.25. This result has ramifications not only in the fundamental understanding but also in technological significance of the flash phenomenon.
Mitochondrial abundance and function in muscle from beef steers with divergent residual feed intakes.

The objective of this study was to evaluate the relationship between muscle mitochondrial function and residual feed intake (RFI) in growing beef cattle. A 56-day feeding trial was conducted with 81 Angus × Hereford steers (initial BW = 378 ± 43 kg) from the University of California Sierra Foothills Research Station (Browns Valley, CA, USA). All steers were individually fed the same finishing ration (metabolizable energy = 3.28 Mcal/kg DM). Average daily gain (ADG), DM intake (DMI) and RFI were 1.82 ± 0.27, 8.89 ± 1.06 and 0.00 ± 0.55 kg/day, respectively. After the feeding trial, the steers were categorized into high, medium and low RFI groups. Low RFI steers consumed 13.6% less DM (P 0.10). The most extreme individuals from the low and high RFI groups were selected to assess mitochondrial function (n = 5 low RFI and n = 6 high RFI). Mitochondrial respiration was measured using an oxygraph (Hansatech Instruments Ltd., Norfolk, UK). State 3 and State 4 respiration rates were similar between both groups (P > 0.10). Respiratory control ratios (RCRs, i.e., State 3 : State 4 oxygen uptakes) declined with animal age and were greater in low RFI steers (4.90) as compared to high RFI steers (4.26) when adjusted for age by analysis of covariance (P = 0.003). Mitochondrial complex II activity levels per gram of muscle were 42% greater in low RFI steers than in high RFI steers (P = 0.004). These data suggest that skeletal muscle mitochondria have greater reserve respiratory capacity and show greater coupling between respiration and phosphorylation in low RFI than in high RFI steers.
Coding: Statistical Data Masking Techniques†

Coding broadly refers to altering the data in some way. In computer science literature, it may refer to encryption, compression, or error correction. These are relevant for cryptography and cryptanalysis, and for data privacy and homeland security. In statistical confidentiality literature, coding encompasses various data-masking techniques. These techniques generally introduce bias and variance to data. The question is how much and what kind of bias and variance is introduced and how this influences the utility of data-mining algorithms and precision of identification, that is the false positive error rate. This article gives a brief overview of statistical disclosure limitation techniques for data masking, pointing out the likelihood of their use and potential impacts for data privacy. Keywords: statistical disclosure limitation; data confidentiality; data privacy; data masking; data mining; recoding; rounding; suppression; data perturbation; data swapping; synthetic data
THE WAR ON DRUGS 2.0: DARKNET FENTANYL'S RISE AND THE EFFECTS OF REGULATORY AND LAW ENFORCEMENT ACTION

U.S. overdose deaths attributed to synthetic opioids, such as fentanyl, have increased from under 3,000 in 2013 to nearly 20,000 in 2016, making up half of all opioid‐related overdose deaths. Using web scrapes of darknet markets from 2014 to 2016, I provide historical prices for fentanyl and its most popular analogues and find that fentanyl vendors priced fentanyl in 2014 at a 90% discount compared to an equivalent dose of heroin. Using regression discontinuity, I evaluate the effects of two major law enforcement and regulatory events. I find minimal lasting effects of U.S. legal actions intended to disrupt darknet markets, but there are statistically significant indications of a price increase corresponding with regulatory action in China. Despite these indications of some regulatory success, fentanyl prices remained approximately 90% cheaper than heroin. (JEL I18, K42)
The integration of visual and target signals in V4 and IT during visual object search

To find specific objects, the brain must integrate top-down, target-specific signals with visual information about objects in view. However, the exact route of this integration in the ventral visua...
Robust state feedback H/sub 2//H/sub /spl infin// control for systems with real parameter uncertainties

This paper considers the robust H/sub 2//H/sub /spl infin// control problem for time invariant systems with time varying structured parameter uncertainties in the state space model. We present a simple sufficient condition in terms of a Riccati equation to design a robust controller that quadratically stabilizes the closed loop uncertain system and minimizes the H/sub 2/ performance bound with an H/sub /spl infin// disturbance attenuation constraint. Finally, we also include an example and illustrate robustness of the proposed design method.
Test & Roll: Profit-Maximizing A/B Tests

We reframe A/B testing as a decision problem with the goal of maximizing profit and derive the optimal sample size formula.
Efficient quasi-phase-matched frequency conversion in a lithium niobate racetrack microresonator

We demonstrate efficient second harmonic generation in a quasi-phase-matched, high quality factor (Q0 ≈ 5.3 × 105) racetrack microresonator. The observed normalized conversion efficiency is about 3.8% mW−1.
An Attention Mechanism for Musical Instrument Recognition.

While the automatic recognition of musical instruments has seen significant progress, the task is still considered hard for music featuring multiple instruments as opposed to single instrument recordings. Datasets for polyphonic instrument recognition can be categorized into roughly two categories. Some, such as MedleyDB, have strong per-frame instrument activity annotations but are usually small in size. Other, larger datasets such as OpenMIC only have weak labels, i.e., instrument presence or absence is annotated only for long snippets of a song. We explore an attention mechanism for handling weakly labeled data for multi-label instrument recognition. Attention has been found to perform well for other tasks with weakly labeled data. We compare the proposed attention model to multiple models which include a baseline binary relevance random forest, recurrent neural network, and fully connected neural networks. Our results show that incorporating attention leads to an overall improvement in classification accuracy metrics across all 20 instruments in the OpenMIC dataset. We find that attention enables models to focus on (or `attend to') specific time segments in the audio relevant to each instrument label leading to interpretable results.
The Investment Network, Sectoral Comovement, and the Changing U.S. Business Cycle

We argue that the input-output network of investment goods across sectors is an important propagation mechanism for understanding business cycles. First, we show that the empirical network is dominated by a few “investment hubs” that produce the majority of investment goods, are highly volatile, and are strongly correlated with the cycle. Second, we embed this network into a multisector model and show that shocks to investment hubs have large aggregate effects while shocks to non-hubs do not. Finally, we measure realized sector-level productivity shocks in the data, feed them into our model, and find that hub shocks account for a large and increasing share of aggregate fluctuations. This fact allows the model to match the decline in the cyclicality of labor productivity and other business cycle changes since the 1980s. Our model also implies that investment stimulus policies increase employment throughout the economy but have unequal effects across sectors.
Could the universe have an exotic topology

A recent article uncovered a surprising dynamical mechanism at work within the (vacuum) Einstein `flow' that strongly suggests that many closed 3-manifolds that do not admit a locally homogeneous and isotropic metric \textit{at all} will nevertheless evolve, under Einsteinian evolution, in such a way as to be \textit{asymptotically} compatible with the observed, approximate, spatial homogeneity and isotropy of the universe \cite{Moncrief:2015}. Since this previous article, however, ignored the potential influence of \textit{dark-energy} and its correspondent accelerated expansion upon the conclusions drawn, we analyze herein the modifications to the foregoing argument necessitated by the inclusion of a \textit{positive} cosmological constant --- the simplest viable model for dark energy.
Spherical discrepancy minimization and algorithmic lower bounds for covering the sphere

Inspired by the boolean discrepancy problem, we study the following optimization problem which we term Spherical Discrepancy: given m unit vectors v1,...,vm, find another unit vector x that minimizes maxi (x, vi). We show that Spherical Discrepancy is APX-hard and develop a multiplicative weights-based algorithm that achieves optimal worst-case error bounds up to lower order terms. We use our algorithm to give the first non-trivial lower bounds for the problem of covering a hypersphere by hyperspherical caps of uniform volume at least [MATH HERE]. We accomplish this by proving a related covering bound in Gaussian space and showing that in this large cap regime the bound transfers to spherical space. Up to a log factor, our lower bounds match known upper bounds in the large cap regime.
Effects of thrombospondin-4 on pro-inflammatory phenotype differentiation and apoptosis in macrophages.

Thrombospondin-4 (TSP-4) attracted renewed attention recently as a result of assignment of new functions to this matricellular protein in cardiovascular, muscular, and nervous systems. We have previously reported that TSP-4 promotes local vascular inflammation in a mouse atherosclerosis model. A common variant of TSP-4, P387-TSP-4, was associated with increased cardiovascular disease risk in human population studies. In a mouse atherosclerosis model, TSP-4 had profound effect on accumulation of macrophages in lesions, which prompted us to examine its effects on macrophages in more detail. We examined the effects of A387-TSP-4 and P387-TSP-4 on mouse macrophages in cell culture and in vivo in the model of LPS-induced peritonitis. In tissues and in cell culture, TSP-4 expression was associated with inflammation: TSP-4 expression was upregulated in peritoneal tissues in LPS-induced peritonitis, and pro-inflammatory signals, INFγ, GM-CSF, and LPS, induced TSP-4 expression in macrophages in vivo and in cell culture. Deficiency in TSP-4 in macrophages from Thbs4−/− mice reduced the expression of pro-inflammatory macrophage markers, suggesting that TSP-4 facilitates macrophage differentiation into a pro-inflammatory phenotype. Expression of TSP-4, especially more active P387-TSP-4, was associated with higher cellular apoptosis. Cultured macrophages displayed increased adhesion to TSP-4 and reduced migration in presence of TSP-4, and these responses were further increased with P387 variant. We concluded that TSP-4 expression in macrophages increases their accumulation in tissues during the acute inflammatory process and supports macrophage differentiation into a pro-inflammatory phenotype. In a model of acute inflammation, TSP-4 supports pro-inflammatory macrophage apoptosis, a response that is closely related to their pro-inflammatory activity and release of pro-inflammatory signals. P387-TSP-4 was found to be the more active form of TSP-4 in all examined functions.
Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria

Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC 50 . The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.
Inverse Kinematics and Sensitivity Minimization of an n-Stack Stewart Platform

The method of Frobenius Norm (FN) minimization of forward kinematic Jacobians is presented to minimize the sensitivity of a robotic manipulator. We demonstrate the effectiveness of this approach with a Monte Carlo simulation of an Assembler robot. The Assembler is described as a stack of Stewart Platforms designed for in-space assembly to aide NASA in deep space exploration. The translations and rotations between each Stewart Platform define the forward kinematics of the Assember, which analytically determine the end effector position and orientation. However, selecting the poses of each Stewart Platform which yield a desired end effector state, defined as the inverse kinematics, is an underconstrained nonlinear optimization problem for large Assembler stacks.
Technical Debt and Firm Performance

Technical debt refers to the design, development, and implementation shortcuts taken by firms when deploying accounting information systems. Prior system-level studies have shown that such shortcut...
A hydrodynamic exercise in free probability: Setting up free Euler equations

Abstract For the free probability analogue of Euclidean space endowed with the Gaussian measure we apply the approach of Arnold to derive Euler equations for a Lie algebra of non-commutative vector fields which preserve a certain trace. We extend the equations to vector fields satisfying non-commutative smoothness requirements. We introduce a cyclic vorticity and show that it satisfies vorticity equations and that it produces a family of conserved quantities.
Distributed learning of average belief over networks using sequential observations

Abstract This paper addresses the problem of distributed learning of average belief with sequential observations, in which a network of n > 1 agents aim to reach a consensus on the average value of their beliefs, by exchanging information only with their neighbors. Each agent has sequentially arriving samples of its belief in an online manner. The neighbor relationships among the n agents are described by a graph which is possibly time-varying, whose vertices correspond to agents and whose edges depict neighbor relationships. Two distributed online algorithms are introduced for undirected and directed graphs, which are both shown to converge to the average belief almost surely. Moreover, the sequences generated by both algorithms are shown to reach consensus with an O ( 1 ∕ t ) rate with high probability, where t is the number of iterations For undirected graphs, the corresponding algorithm is modified for the case with quantized communication and limited precision of the division operation. It is shown that the modified algorithm causes all n agents to either reach a quantized consensus or enter a small neighborhood around the average of their beliefs. Numerical simulations are then provided to corroborate the theoretical results.
Aerial and Spaceborne Remote Sensing in African Archaeology: A Review of Current Research and Potential Future Avenues

Africa represents a vast region where remote sensing technologies have been largely uneven in their archaeological applications. With impending climate-related risks such as increased coastal erosion and rising sea levels, coupled with rapid urban development, gaps in our knowledge of the human history of this continent are in jeopardy of becoming permanent. Spaceborne and aerial remote sensing instruments are powerful tools for producing relatively complete records of archaeological settlement patterns and human behavior at landscape scales. These sensors allow for massive amounts of information to be recorded and analyzed in short spans of time and offer an effective means to increase survey areas and the discovery of new cultural deposits. In this paper, we review various case studies throughout Africa dealing with aerial and satellite remote sensing applications to landscape archaeology in order to highlight recent developments and future research avenues. Specifically, we argue that (semi)automated remote sensing methods stemming from machine learning developments will prove vital to expanding our knowledge base of Africa’s archaeological record. This is especially important for coastal and island regions of the continent where climate change threatens the survival of much of the archaeological record.
Incidence, risk factors, and outcomes of endoscopic sinus surgery after endoscopic skull-base surgery.

BACKGROUND Patients undergoing endoscopic resection of neoplasms with both sinonasal and skull base involvement can develop chronic rhinosinusitis (CRS) after treatment and may occasionally benefit from additional endoscopic sinus surgery (ESS). We investigate risk factors and outcomes associated with revision ESS (rESS) after endoscopic skull-base surgery (SBS) for neoplasms with combined sinonasal and skull base involvement. METHODS A retrospective review of patients with neoplasms with both sinonasal and skull base involvement who underwent endoscopic resection at a single tertiary care academic institution from 2004 through 2017 was performed. Eighty-three patients were included. Main outcome measures included incidence and timing of revision surgery, Lund-Mackay (LM) scores, and 22-item Sino-Nasal Outcome Test (SNOT-22) scores. RESULTS rESS was performed in 21 (25%) cases, 15 (18%) of which were due to CRS. Time between initial resection and rESS was an average of 42 months (range, 6 to 142 months). Pre-SBS and post-SBS LM scores were not significantly different (5.0 vs 4.7, p = 0.640), although pre-SBS and post-SBS SNOT-22 scores showed significant improvement (32.6 vs 24.5, p = 0.030). Malignant pathology correlated with need for rESS (odds ratio [OR] 5.07, p = 0.04), as well as treatment including chemotherapy (OR 5.10, p = 0.003) and radiation (OR 4.15, p = 0.013). CONCLUSION A significant proportion of patients develop clinically significant sinusitis after endoscopic SBS for neoplasms with combined sinonasal and skull base involvement and may benefit from rESS. Intervention occurred, on average, 3.5 years after initial tumor resection. Malignant pathology, radiation therapy, and chemotherapy correlate with need for rESS.
DLockout: A Design Lockout Technique for Key Obfuscated RTL IP Designs

Intellectual Property (IP) infringement including piracy and overproduction have emerged as significant threats in the semiconductor supply chain. Key-based obfuscation techniques (i.e., logic locking) are widely applied to secure legacy IP from such attacks. However, the fundamental question remains open whether an attacker is allowed an exponential amount of time to seek correct key or could it be useful to lock out the design in a non-destructive manner after several incorrect attempts. In this paper, we address this question with a robust design lockout technique. Specifically, we perform comparisons on obfuscation logic output that reflects the condition (correct or incorrect) of the applied key without changing the system behavior. The proposed approach, when combined with key obfuscation (logic locking) technique, increases the difficulty of reverse engineering key obfuscated RTL module. We provide security evaluation of DLockout against three common side-channel attacks followed by a quantitative assessment of the resilience. We conducted a set of experiments on four datapath intensive IPs and one crypto core for three different key lengths (32-, 64-, and 128-bit) under the typical design corner. On average, DLockout incurs negligible area, power, and delay overheads.
Security Constrained Unit Commitment with Corrective Transmission Switching

Traditionally, power system operations use a static network to deliver power and meet demand optimally. Network topology reconfiguration through transmission switching (TS) has gained significant interest recently to reduce the operational cost of power system operations. However, implementation of TS also causes large disturbance in the network and as a result the use of corrective transmission switching (CTS) in response to power system contingencies is currently being researched extensively. This paper emphasizes the importance of CTS to accomplish flexible transmission in N-1 security-constrained unit commitment (SCUC) model. An N-1 SCUC mathematical model implementing a dynamic network in the post-contingency scenario is proposed as opposed to current industry practices of static network in short-term operations. The proposed model is tested and validated on the IEEE 24-bus system. The proposed model results in cost-effective implementation and leads to overall reduced cost, and congestion reduction in the post-contingency scenario.
Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors

Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme.
Poincaré-Lovelock metrics on conformally compact manifolds

Abstract An important tool in the study of conformal geometry, and the AdS/CFT correspondence in physics, is the Fefferman-Graham expansion of conformally compact Einstein metrics. We show that conformally compact metrics satisfying a generalization of the Einstein equation, Poincare-Lovelock metrics, also have Fefferman-Graham expansions. Moreover we show that conformal classes of metrics that are near that of the round metric on the n-sphere have fillings into the ball satisfying the Lovelock equation, extending the existence result of Graham-Lee for Einstein metrics.
Unpaved Roads to Oklahoma: Researching the Story of My Family’s Mineral Rights

This article chronicles the author’s research into her family’s mineral rights, located in eastern Oklahoma. Tracing ownership back to the Muscogee (Creek) Nation, she explores the circumstances an...
Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City

As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted “salutary sheltering” by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population.
The Flight-to-Liquidity Premium in U.S. Treasury Bond Prices

We examine whether there is a flight-to-liquidity premium in Treasury bond prices by comparing them with prices of bonds issued by Refcorp, a U.S. Government agency. Since Refcorp bonds are, in effect, guaranteed by the Treasury, they have the same credit as Treasury bonds. We find a large liquidity premium in Treasury bonds, which can be more than fifteen percent of the value of some Treasury bonds. We find strong evidence that this liquidity premium is related to changes in consumer con- fidence, flows into equity and money market mutual funds, and changes in foreign ownership of Treasury debt. This suggests that the popularity of Treasury bonds directly affects their value
Extremal growth of Betti numbers and trivial vanishing of (co)homology

A Cohen-Macaulay local ring $R$ satisfies trivial vanishing if $\operatorname{Tor}_i^R(M,N)=0$ for all large $i$ implies $M$ or $N$ has finite projective dimension. If $R$ satisfies trivial vanishing then we also have that $\operatorname{Ext}^i_R(M,N)=0$ for all large $i$ implies $M$ has finite projective dimension or $N$ has finite injective dimension. In this paper, we establish obstructions for the failure of trivial vanishing in terms of the asymptotic growth of the Betti and Bass numbers of the modules involved. These, together with a result of Gasharov and Peeva, provide sufficient conditions for $R$ to satisfy trivial vanishing; we provide sharpened conditions when $R$ is generalized Golod. Our methods allow us to settle the Auslander-Reiten conjecture in several new cases. In the last part of the paper, we provide criteria for the Gorenstein property based on consecutive vanishing of Ext. The latter results improve similar statements due to Ulrich, Hanes-Huneke, and Jorgensen-Leuschke.
1 – THE ECONOMIC APPROACH

Publisher Summary Economics is not an answer but, rather, a way of thinking. Economic thinking is believed to be a powerful tool capable of illuminating a broad range of real-world events. This chapter provides an overview of the basics of economics and illustrates their power. Economics is about people and the choices they make. The unit of analysis in economics is the individual. Individuals group together to form collective organizations such as corporations, labor unions, and governments. However, the choices of individuals still underlie and direct these organizations. Economic theory is developed from fundamental postulates about how individual human beings behave, struggle with the problem of scarcity, and respond to change. The reality of life on planet is that productive resources—that is, resources used to produce goods, are limited. Therefore, goods and services are also limited. In contrast, the desires of human beings are virtually unlimited. These facts confront with the two basic ingredients of an economic topic: (1) scarcity and (2) choice. Scarcity is the term used by economists to indicate that man's desire for a thing exceeds the amount of it that is freely available from nature. As scarcity of productive resources, time, and income limit the alternatives available, choices must be made. Choice is the act of selecting among restricted alternatives. A great deal of economics is about how people choose when the alternatives open to them are restricted. Economic theory, somewhat like a road map or a guidebook, establishes reference points, indicating what to look for and what can be considered significant in economic issues. It helps to understand the interrelationships among complex and seemingly unrelated events in the real world.
Translation of array-based loops to distributed data-parallel programs

Large volumes of data generated by scientific experiments and simulations come in the form of arrays, while programs that analyze these data are frequently expressed in terms of array operations in an imperative, loop-based language. But, as datasets grow larger, new frameworks in distributed Big Data analytics have become essential tools to large-scale scientific computing. Scientists, who are typically comfortable with numerical analysis tools but are not familiar with the intricacies of Big Data analytics, must now learn to convert their loop-based programs to distributed data-parallel programs. We present a novel framework for translating programs expressed as array-based loops to distributed data parallel programs that is more general and efficient than related work. We report on a prototype implementation on top of Spark and evaluate the performance of our system relative to hand-written programs.
Light and Lighting Systems

Lighting is the application of visually effective optical radiation. This chapter provides the reader with some basic information about lighting terminology and technologies (lamps, ballasts, luminaires, and controls), and the results of recent research on the effective use of lighting. This article supplements the articles on color vision; colorimetry; optical radiation sources and standards; and physiological optics. Keywords: photometry; spectral sensitivity; lamps; luminaires; visual performance; efficiency; luminous efficacy
The Passion of John Paul Stevens

At a still-energetic age ninety, the newly retired Justice John Paul Stevens became a public intellectual, more celebrated after leaving the bench in 2010 than he was during nearly 35 years on the U.S. Supreme Court. Freed from the constraints of serving on a collegial court, he spoke and wrote his mind on the Second Amendment, the death penalty, the inadequacies of "originalism," and other topics of public concern. Two months before his death in July 2019 at ninety-nine, he published this third book, the memoir reviewed in this essay: The Making of a Justice: Reflections on My First 94 Years. We realize only now, after his death, how valuable John Paul Stevens was in life. He was moved by the very things now missing from our public life: the power of facts, of logic, and of approaching each problem with an open mind, ready to be persuaded or, of his luck was running, to persuade.
Macro Factors in Bond Risk Premia

Are there important cyclical fluctuations in bond market premiums and, if so, with what macroeconomic aggregates do these premiums vary? We use the methodology of dynamic factor analysis for large datasets to investigate possible empirical linkages between forecastable variation in excess bond returns and macroeconomic fundamentals. We find that “real” and “inflation” factors have important forecasting power for future excess returns on U.S. government bonds, above and beyond the predictive power contained in forward rates and yield spreads. This behavior is ruled out by commonly employed affine term structure models where theforecastability ofbondreturns andbondyields is completely summarized by the cross-section of yields or forward rates. An important implication of these findings is that the cyclical behavior of estimated risk premia in both returns and long-term yields depends importantly on whether the information in macroeconomic factors is included in forecasts of excess bond returns. Without the macro factors, risk premia appear virtually acyclical, whereas with the estimated factors risk premia have a marked countercyclical component, consistent with theories that imply investors must be compensated for risks associated with macroeconomic activity. (JEL E0, E4, G10, G12)
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars

Resistive crossbars designed with nonvolatile memory devices have emerged as promising building blocks for deep neural network (DNN) hardware, due to their ability to compactly and efficiently realize vector–matrix multiplication (VMM), the dominant computational kernel in DNNs. However, a key challenge with resistive crossbars is that they suffer from a range of device and circuit level nonidealities, such as driver resistance, sensing resistance, sneak paths, interconnect parasitics, nonlinearities in the peripheral circuits, stochastic write operations, and process variations. These nonidealities can lead to errors in VMMs, eventually degrading the DNN’s accuracy. It is therefore critical to study the impact of crossbar nonidealities on the accuracy of large-scale DNNs (with millions of neurons and billions of synaptic connections). However, this is challenging because the existing device and circuit models are too slow to use in application-level evaluations. We present RxNN, a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar systems. RxNN splits and maps the computations involved in each DNN layer into crossbar operations, and evaluates them using a fast crossbar model (FCM) that accurately captures the errors arising due to crossbar nonidealities while being four-to-five orders of magnitude faster than circuit simulation. FCM models a crossbar-based VMM operation using three stages—nonlinear models for the input and output peripheral circuits (digital-to-analog and analog-to-digital converters), and an equivalent nonideal conductance matrix for the core crossbar array. We implement RxNN by extending the Caffe machine learning framework and use it to evaluate a suite of six large-scale DNNs developed for the ImageNet Challenge (ILSVRC). Our experiments reveal that resistive crossbar nonidealities can lead to significant accuracy degradations (9.6%–32%) for these large-scale DNNs. To the best of our knowledge, this article is the first quantitative evaluation of the accuracy of large-scale DNNs on resistive crossbar-based hardware. We also demonstrate that RxNN enables fast model-in-the-loop retraining of DNNs to partially mitigate the accuracy degradation.
Effect of drug dose and timing of treatment on the emergence of drug resistance in vivo in a malaria model.

Background and objectives There is a significant interest in identifying clinically effective drug treatment regimens that minimize the de novo evolution of antimicrobial resistance in pathogen populations. However, in vivo studies that vary treatment regimens and directly measure drug resistance evolution are rare. Here, we experimentally investigate the role of drug dose and treatment timing on resistance evolution in an animal model. Methodology In a series of experiments, we measured the emergence of atovaquone-resistant mutants of Plasmodium chabaudi in laboratory mice, as a function of dose or timing of treatment (day post-infection) with the antimalarial drug atovaquone. Results The likelihood of high-level resistance emergence increased with atovaquone dose. When varying the timing of treatment, treating either very early or late in infection reduced the risk of resistance. When we varied starting inoculum, resistance was more likely at intermediate inoculum sizes, which correlated with the largest population sizes at time of treatment. Conclusions and implications (i) Higher doses do not always minimize resistance emergence and can promote the emergence of high-level resistance. (ii) Altering treatment timing affects the risk of resistance emergence, likely due to the size of the population at the time of treatment, although we did not test the effect of immunity whose influence may have been important in the case of late treatment. (iii) Finding the 'right' dose and 'right' time to maximize clinical gains and limit resistance emergence can vary depending on biological context and was non-trivial even in our simplified experiments. Lay summary In a mouse model of malaria, higher drug doses led to increases in drug resistance. The timing of drug treatment also impacted resistance emergence, likely due to the size of the population at the time of treatment.
Double COVID-19 Confirmed Case Fatality Rate in Countries with High Elderly Female Vitamin D Deficiency Prevalence

Abstract A number of clues point to a possible role of vitamin D in fighting COVID-19: a reduction in case growth speed with solar zenith angle, higher fatality rate in black people, lower fatality rate in populations that spend more time outdoors. Yet a direct demonstration that vitamin D deficiency is associated with COVID-19 fatalities has remained elusive. We show here in a comparison of 32 countries that countries with high prevalence of vitamin D deficiency among elderly females show a confirmed case fatality rate twice as high as those with low prevalence. We then show that this effect cannot be explained by differences in life expectancy between countries. A mechanistic role for vitamin D in the severity of COVID-19 is proposed. One Sentence Summary Vitamin D deficiency among elderly females is associated with countrywide COVID-19 confirmed case fatality rates up to twice as high as those of countries with low vitamin D deficiency prevalence.
Songbird ventral basal ganglia sends performance error signals to dopaminergic midbrain

SUMMARY Ventral tegmental area (VTA) dopamine neurons signal prediction error, the difference between actual and predicted outcome, but it remains unclear how error is computed. Here we identify in songbirds a ventral basal ganglia (vBG) region that is required for song learning and that sends prediction error signals to VTA. During singing, vBG neurons heterogeneously encoded song timing, auditory error, predicted error, and the difference between the two (prediction error). Viral tracing revealed inputs to vBG from auditory and vocal motor thalamus, auditory and vocal motor cortex, and VTA. Our findings reveal a classic actor-critic circuit motif in which a ventral critic learns the ‘prediction’ component of a prediction error signal that is relayed by VTA to a dorsal actor (the vocal motor BG nucleus Area X). A circuit motif for computing reward prediction error can compute predicted performance quality during motor sequence learning.
Neuromorphic Nearest Neighbor Search Using Intel's Pohoiki Springs

Neuromorphic computing applies insights from neuroscience to uncover innovations in computing technology. In the brain, billions of interconnected neurons perform rapid computations at extremely low energy levels by leveraging properties that are foreign to conventional computing systems, such as temporal spiking codes and finely parallelized processing units integrating both memory and computation. Here, we showcase the Pohoiki Springs neuromorphic system, a mesh of 768 interconnected Loihi chips that collectively implement 100 million spiking neurons in silicon. We demonstrate a scalable approximate k-nearest neighbor (k-NN) algorithm for searching large databases that exploits neuromorphic principles. Compared to state-of-the-art conventional CPU-based implementations, we achieve superior latency, index build time, and energy efficiency when evaluated on several standard datasets containing over 1 million high-dimensional patterns. Further, the system supports adding new data points to the indexed database online in O(1) time unlike all but brute force conventional k-NN implementations.
Improving I/O Complexity of Triangle Enumeration

In the age of big data, many graph algorithms are now required to operate in external memory and deliver performance that does not significantly degrade with the scale of the problem. One particular area that frequently deals with graphs larger than RAM is triangle listing, where the algorithms must carefully piece together edges from multiple partitions to detect cycles. In recent literature, two competing proposals (i.e., Pagh and PCF) have emerged; however, neither one is universally better than the other. Since little is known about the I/O cost of PCF or how these methods compare to each other, we undertake an investigation into the properties of these algorithms, model their I/O cost, understand their shortcomings, and shed light on the conditions under which each method defeats the other. This insight leads us to develop a novel framework we call Trigon that surpasses the I/O performance of both previous techniques in all graphs and under all RAM conditions.