cssi/SciSciGPT-SciSciNet huggingface dataset:
Optimal versus Robust Inference in Nearly Integrated Non-Gaussian Models

Elliott, Rothenberg, and Stock (1996, Econometrica 64, 813–836) derive a class of point-optimal unit root tests in a time series model with Gaussian errors. Other authors have proposed “robust” tests that are not optimal for any model but perform well when the error distribution has thick tails. I derive a class of point-optimal tests for models with non-Gaussian errors. When the true error distribution is known and has thick tails, the point-optimal tests are generally more powerful than the tests of Elliott et al. (1996) and also than the robust tests. However, when the true error distribution is unknown and asymmetric, the point-optimal tests can behave very badly. Thus there is a trade-off between robustness to unknown error distributions and optimality with respect to the trend coefficients.This paper could not have been written without the encouragement of Thomas Rothenberg. This is based on my dissertation, which he supervised. I also thank Don Andrews, Jack Porter, Jim Stock, and seminar participants at the University of Pennsylvania, the University of Toronto, the University of Montreal, Princeton University, and the meetings of the Econometric Society in UCLA. Comments of three anonymous referees greatly improved the exposition of the paper. I owe special thanks to Gary Chamberlain for helping me to understand these results.
Agency Conflicts in Closed-End Funds: The Case of Rights Offerings

We study 120 rights offerings by closed-end funds from 1988-1998. On average, rights offerings are announced when funds trade at a premium. This premium turns into a discount over the course of the offering. The premium decline is more severe when increases in the investment advisor's compensation are larger and when the fund uses affiliated brokerdealers to solicit subscriptions to the offer. A clinical analysis shows that rights offerings allow investment advisors to sidestep fee rebates and increase pecuniary benefits to affiliated entities. Overall, our results suggest the presence of significant conflicts of interest in rights offerings by closed-end funds.
Currency Union, Free-Trade Areas, and Business Cycle Synchronization

Since the 1970s the characteristics of international business cycles have changed and deeper economic integration has modi…ed the features of cross-country comovement. We formally test for correlation shifts in measures of real economic activity and economic/…nancial integration. In Europe we …nd some statistically signi…cant evidence of higher correlations following the creation of the EMU in 1999 for several subgroups of countries. We detect signi…cantly more pronounced correlations between Mexico and the US and between Mexico and Canada in North America after the enforcement of the NAFTA in 1994. Results are derived from an econometric framework based on nonparametric iterated stationary bootstrap methods, whose statistical reliability and performance we assess through Monte Carlo simulations.
Agreements, conventions, and language

The question whether and in what way languages and language use involve convention is addressed, With special reference to David Lewis's account of convention in general. Data are presented which show that Lewis has not captured the sense of 'convention' involved when we speak of adopting a linguistic convention. He has, In effect, attempted an account of social conventions. An alternative account of social convention and an account of linguistic convention are sketched.
Monetary Policy in a Financial Crisis

What are the economic effects of an interest rate cut when an economy is in the midst of a financial crisis? Under what conditions will a cut stimulate output and employment, and raise welfare? Under what conditions will a cut have the opposite effects? We answer these questions in a general class of open economy models, where a financial crisis is modeled as a time when collateral constraints are suddenly binding. We find that when there are frictions in adjusting the level of output in the traded good sector and in adjusting the rate at which that output can be used in other parts of the economy, then a cut in the interest rate is most likely to result in a welfare-reducing fall in output and employment. When these frictions are absent, a cut in the interest rate improves asset positions and promotes a welfare-increasing economic expansion.
Multi-population mortality models: A factor copula approach

Abstract Modeling mortality co-movements for multiple populations have significant implications for mortality/longevity risk management. A few two-population mortality models have been proposed to date. They are typically based on the assumption that the forecasted mortality experiences of two or more related populations converge in the long run. This assumption might be justified by the long-term mortality co-integration and thus be applicable to longevity risk modeling. However, it seems too strong to model the short-term mortality dependence. In this paper, we propose a two-stage procedure based on the time series analysis and a factor copula approach to model mortality dependence for multiple populations. In the first stage, we filter the mortality dynamics of each population using an ARMA–GARCH process with heavy-tailed innovations. In the second stage, we model the residual risk using a one-factor copula model that is widely applicable to high dimension data and very flexible in terms of model specification. We then illustrate how to use our mortality model and the maximum entropy approach for mortality risk pricing and hedging. Our model generates par spreads that are very close to the actual spreads of the Vita III mortality bond. We also propose a longevity trend bond and demonstrate how to use this bond to hedge residual longevity risk of an insurer with both annuity and life books of business.
Identifying the Presence and Cause of Fashion Cycles in Data

AbstractFashions and conspicuous consumption play an important role in marketing. In this article, the author presents a three-pronged framework to analyze fashion cycles in data composed of (1) algorithmic methods for identifying cycles, (2) a statistical framework for identifying cycles, and (3) methods for examining the drivers of such cycles. In the first module, the author identifies cycles by pattern-matching the amplitude and length of cycles observed to a user-specified definition. In the second module, the author defines the conditional monotonicity property, derives conditions under which a data-generating process satisfies it, and demonstrates its role in generating cycles. A key challenge in estimating this model is the presence of endogenous lagged dependent variables, which the author addresses using system generalized method of moments estimators. Third, the author presents methods that exploit the longitudinal and geographic variations in agents’ economic and cultural capital to examine th...
Looking for Work: Solicitation by Labor Board Employees

This Article provides a comprehensive survey of cases where improper "solicitation" of charges by National Labor Relations Board agents has been alleged as a defense by Respondents in those cases. The Board is empowered only to act on charges brought before it by individuals, labor organizations, or employers. The author shows that the Board is without authority—either by statute, by its own rules and regulations, or from the courts—to engage in "solicitation, " which includes initiating charges, fil ing complaints which are beyond the scope of the original charge, or acting upon events which happened greater than six months previous to the filing of the original charge. The author finds, however, that in nearly fifty years of attempts by Respondents to prove such wrongdoings, the Board has never found its agents to have overstepped their authority by engaging in acts of improper solicitation. The author proposes the framework of a test to determine when Board agents have acted excessively.
Estimating equilibrium real interest rates in real time

Abstract We use a range of simple models and 22 years of real-time data vintages for the U.S. to assess the difficulties of estimating the equilibrium real interest rate in real time. Model specifications differ according to whether the time-varying equilibrium real rate is linked to trend growth, and whether potential output and growth are defined by the CBO's estimates or treated as unobserved variables. Our results reveal a high degree of specification uncertainty, an important one-sided filtering problem, and considerable imprecision due to data uncertainty. Also, the link between trend growth and the equilibrium real rate is shown to be quite weak. Overall, we conclude that statistical estimates of the equilibrium real rate will be difficult to use reliably in practical policy applications.
Does Government Ownership Affect the Cost of Debt? Evidence from Privatization

We explore whether government ownership affects the cost of debt using a sample of fully and partially privatized companies. On average across firms, a one-percentage-point decrease in government ownership is associated with an increase in the credit spread, used as a proxy for the cost of debt, by three-quarters of a basis point. However, fully privatized companies exhibit lower credit spreads than partially privatized firms, indicating the cost of a lengthy privatization process. Empirical evidence suggests that these findings result from decreasing government guarantees, firm performance improvements, ownership uncertainty, and bondholder-shareholder conflicts. ( JEL G32, G38, L33)
Fertility timing, wages, and human capital.

Women who have first births relatively late in life earn higher wages. This papers offers an explanation of this fact based on a simple life-cycle model of human capital investment and timing of first birth. The model yields conditions (that are plausibly satisfied) under which late childbearers will tend to invest more heavily in human capital than early childbearers. The empirical analysis finds results consistent with the higher wages of late childbearers arising primarily through greater measurable human capital investment.
Structural bias in the sentencing of felony defendants

As incarceration rates have risen in the US, so has the overrepresentation of African Americans and Latinos among prison inmates. Whether and to what degree these disparities are due to bias in the criminal courts remains a contentious issue. This article pursues two lines of argument toward a structural account of bias in the criminal law, focusing on (1) cumulative disadvantages that may accrue over successive stages of the criminal justice process, and (2) the contexts of racial disadvantage in which courts are embedded. These arguments are tested using case-level data on male defendants charged with felony crimes in urban US counties in 2000. Multilevel binary and ordinal logit models are used to estimate contextual effects on pretrial detention, guilty pleas, and sentence severity, and cumulative effects are estimated as conditional probabilities that are allowed to vary by race across all three outcomes. Results yield strong, but qualified, evidence of cumulative disadvantage accruing to black and Latino defendants, but do not support the contextual hypotheses. When the cumulative effects of bias are taken into account, the estimated probability of the average African American or Latino felon going to prison is 26% higher than that of the average Anglo.
Do markets “discipline” all banks equally?

Purpose – The purpose of this paper is to investigate whether the bond market disciplines all banks equally, in the sense of demanding the same relative risk premium across banks of different risk over the business cycle.Design/methodology/approach – To test this hypothesis, the paper compares the difference between the credit spreads in the primary market of bank and firm bonds with the same credit rating issued during expansions with that same difference of spreads for bonds issued during recessions.Findings – The paper finds that during recessions investors demand higher risk premiums. Importantly, the paper finds that the impact of recessions is not uniform across banks – it affects riskier banks more than safer ones. In other words, in recessions investors are relatively more demanding on riskier banks than on safer ones.Originality/value – These findings are novel. They also have important policy implications because they show that a bond‐issuance policy aimed at promoting market discipline could af...
Lawyer as Peacemaker: A Christian Response to Rambo Litigation

This article examines and critiques Rambo lawyering. The practice of law has evolved so that the cornerstone principle of client loyalty, together with the economic incentives inherent in law practice, not only create strong motivations for lawyers to pursue their clients' causes vigorously, but also allow lawyers to easily absolve themselves of any moral obligation for their activities as their clients' representatives. Vigorous advocacy is an indispensible part of the modern judicial system, and it is generally believed that truth and justice will be served as long as there are vigorous advocates on both sides and the profession's code of ethics is not violated. This creates a tendency toward moral neutrality that can create conflicts for Christian lawyers, especially those involved in trial advocacy, who may feel bound by their faith to exceed the bare ethical standards of the profession. After providing an in-depth description of Rambo lawyering, the article discusses these conflicts in the context of the six transforming initiatives that comprise Jesus's teaching in the Sermon on the Mount (turn the other cheek, give your tunic and cloak, go the second mile, give to those who ask, love your enemy, and pray for those who persecute you). The article concludes by providing suggestions for ways that Jesus's teaching might inform and transform Christian lawyers from Rambo lawyers to peacemakers. These include adopting deontological decision-making principles (rather than teleological reasoning), examining motiviations (their own and their clients'), and approaching their work as lawyers with a greater degree of modesty, humility, and humanity.
Who takes Risks When and Why: Determinants of Changes in Investor Risk Taking*

We use data from a repeated survey panel that was run with real online broker customers in September 2008, December 2008, and March 2009. In all three surveys subjects' risk attitudes, risk expectations, return expectations, and risk taking behavior, i.e. the proportion of wealth they are willing to invest into the stock market compared to a risk free asset, were elicited. Using this unique dataset we analyze whether risk taking, risk attitudes, and expectations change from one quarter to the other and whether the latter two have an impact on risk taking behavior. Our results indicate that risk taking behavior decreases substantially from September to December and from December to March. Similarly, risk expectations and return expectations also change substantially from one survey to the next one. In contrast, various measures of risk attitudes are fairly stable over the time periods. Interestingly, observed changes in risk taking behavior can primarily be attributed to changes in risk and return expectations but not to changes in past performance or changes in risk attitudes. Moreover, our findings are valuable for practitioners - who are urged by MiFID (2006) to elicit their customers' risk profiles and risk preferences - since we show that risk attitudes remain fairly stable and that changes in investment behavior can mainly be attributed to changes in expectations. Lastly, we illustrate that overconfidence seems to be a fairly stable construct between September and December and tends to decrease slightly from December to March.
House Prices and Inflation

The present paper examines the long-run impact of inflation on homeowner equity by investigating the relationship between house prices and the prices of nonhousing goods and services, rather than return series and inflation rates as in previous empirical studies on the inflation hedging ability of real estate. There are two reasons for this methodological departure from prior practice: (1) while the total return on housing cannot be accurately measured, the total return on housing is fully reflected in housing prices, and (2) given that using returns or differencing a time series leads to a loss of long-run information contained in the series, valuable long-run information can be captured by using prices. Also, unlike previous related studies, we exclude housing costs from goods and services prices to avoid potential bias in estimating how inflation affects housing prices. Monthly data series are collected for existing and for new house prices as well as the consumer price index excluding housing costs for the period 1968–2000. Based on both autoregressive distributed lag (ARDL) models and recursive regressions, the empirical results yield estimated Fisher coefficients that are consistently greater than one over the sample period. Thus, we infer that house prices are a stable inflation hedge in the long run.
Incentives and Opportunities to Manage Earnings around Option Grants

This study examines discretionary accruals imbedded in quarterly earnings announcements that precede executive stock option grants. Prior research indicates that managers attempt to increase the value of their option pay (by depressing the option’s exercise price) through a variety of strategies including timing voluntary disclosures, influencing option grant dates, or managing accruals. This study extends the research by jointly examining managerial incentives and opportunities to pursue an accruals-based strategy. We find evidence that discretionary accruals are lower when option pay is high and when concurrent firm performance is poor (incentive factors), but only when firms issue grants following earnings announcements relatively infrequently (opportunity factor). For firms that follow a predictable grant schedule, managers behave as if they believe that investors will discount earnings-based signals preceding the grant. Our results suggest that the decision to pursue an option-related strategy is influenced by economic tradeoffs. From a policy perspective, our results have relevance for the ongoing debate over option compensation practices, appropriate disclosure to investors, and the quality of corporate earnings. JEL Classifications: J33, M40
Evaluating the Demsetzian Trend in Copyright Law

Copyright law provides an excellent case study with which to evaluate Harold Demsetz's theory of property rights. Regardless of how one feels about the relationship between property and intellectual property, it is hard to escape the fact that intellectual property rights have expanded and grown more property-like and more privatized in recent decades. In this article, I critique the undeniable Demsetzian trend in copyright law and challenge some of the fundamental premises upon which rest the normative arguments for continued privatization and propertization of intellectual resources. First, I focus on the perceived benefits of internalizing externalities, arguing that externalities do not necessarily distort incentives or, more generally, the market allocation of resources. For many externalities, there is no efficiency benefit to internalization (whether accomplished by Pigouvian taxes/subsidies or property rights). In the end, the benefits of internalization must be carefully assessed rather than assumed. The view that increasing the degree of internalization through private property rights inevitably leads to increased incentives to invest in creation or distribution is not well-established in either theory or practice. Second, I focus on the frequently-invoked solution of efficient licensing and the "logic" that property rights should be extended "into every corner in which people derive enjoyment and value...[so that] signals of consumer preference [may] trigger and direct [producers'] investments" (Goldstein, 1994). I argue that a fundamental flaw in this logic undermines the efficient licensing hypothesis. Social demand for individuals' access to and use of copyright protected works often exceeds private demand. Purchasers'/licensees' willingness to pay reflects only their private demand and does not take into account value that others might realize as a result of their use. As I explain, many uses of copyrighted works generate value for third-parties. Finally, drawing from the first two points, I argue that, from a Coasean perspective, both externalities and property rights have symmetrical and reciprocal potentials to distort the market allocation of resources. A priori and devoid of context, one cannot say that the potential distortions caused by a property right, externality, or incremental change in a property right have a net positive or negative effect on social welfare.
Cautious Risk Takers: Investor Preferences and Demand for Active Management

Actively managed mutual funds have distinct return distributions from their passive benchmarks and our theoretical analysis using tail-sensitive risk preferences suggests that active value and growth funds may serve to reduce downside risk and capture upside potential, respectively. Furthermore, tail-sensitivity measures estimated from the empirical pricing kernel have significant explanatory power for active fund flows, even after controlling for business cycles and market-wide sentiment. Finally, active funds, unlike their passive counterparts, have exposures to option strategies hedging downside risk or capturing upside potential.
Power spectra of TASEPs with a localized slow site

The totally asymmetric simple exclusion process (TASEP) with a localized defect is revisited in this paper with attention paid to the power spectra of the particle occupancy N(t). Intrigued by the oscillatory behaviors in the power spectra of an ordinary TASEP in high/low density (HD/LD) phases observed previously, we introduce a single slow site with hopping rate q < 1 to the system. As the power spectrum contains time-correlation information on the particle occupancy of the system, we are particularly interested in how the defect affects fluctuation in particle number of the left and right subsystems as well as that of the entire system. Exploiting Monte Carlo simulations, we observe the disappearance of oscillations when the defect is located at the center of the system. When the defect is off-center, oscillations are restored. To explore the origin of such a phenomenon, we use a linearized Langevin equation to calculate the power spectrum for the sublattices and the whole lattice. We provide insights into the interactions between the sublattices coupled through the defect site for both simulation and analytical results.
Further Evidence On Expectations And The Demand for Money During the German Hyperinflation

Probably no event in monetary history has been more studied than the German hyperinflation of the early 1920's. Economists have been attracted to study this episode since it provides an environment that is close to a controlled experiment which is so rare in the study of social sciences. This paper provides further evidence on the role of expectations in effecting the demand for money during the German hyperinflation. One of the difficulties in studying empirically the role of expectations is the lack of an observable variable measuring expectations. This paper examines three measures of expectations that are derived from observed data from the market for foreign exchange. The first measure is based on the hypothesis that the forward exchange rate measures the expected future spot exchange rate and thereby provides an observable measure of the market's expectations concerning the depreciation of the currency. The other two measures distinguish between the forward exchange rate and the expected exchange rate and are based on the supplementary hypothesis that rational behavior requires expectations to be unbiased. Accordingly, the measures of expectations are constructed by using the forward exchange rate along with the information on the systematic relationship between forward and spot exchange rates. The various measures are then used in estimating the demand for money. The emphasis on measures of expectations that are based on data from the foreign exchange markets reflects the belief that in an inflationary economy with flexible exchange rates one of the relevant substitutes for holding domestic money is foreign exchange.
Is Novelty Obsolete - Chronicling the Irrelevance of the Invention Date in U.S. Patent Law

Introduction 54 I. Keying Rights to the Effective Filing Date: An Introduction to the Debate 61 II. Use of Invention-Date-Based Novelty Rights During Ex Parte Patent Prosecution 64 A. A Study of the Prevalence of Invention-Date-Based Novelty Assertions in Patent Prosecution Based on the File Histories of 21,000+ Patent Applications 69 1. The Data Set 69 2. The Pervasiveness (or Lack Thereof) of Rule 131 Affidavit Practice 70 3. The Effectiveness and Distribution of Rule 131 Declarations 72 4. The Timing of Rule 131 Declarations 76 B. The Applicant Opportunity to Assert Invention-Date-Based Rights 77 C. Provisional Patent Applications as a Functional Substitute for Invention-Date-Based Novelty Rights 79 D. Analysis of the Use of Invention-Date-Based Novelty Rights During Prosecution of Higher Value Patents 84 E. Survey of Reliance on Invention-Date-Based Novelty Rights During Patent Prosecution 86 1. Survey Design and Dissemination 86 2. U.S. Attorney and U.S. Patent Agent Participation in Rule 131 Declarations 88 3. Support for Moving to a First-to-File System 90 4. Strategies and Pitfalls of Asserting First-Inventor Rights During Patent Prosecution 95 5. Survey Conclusions 99
Tax and Spending Limits in Colorado

In 1992, Colorado voters approved the Taxpayers Bill of Rights (TABOR), which severely limits the growth of revenues and spending by state and local governments. TABOR was layered over two earlier revenue-restricting measures. Throughout the 1990s, the state enjoyed a robust economy and the effects of these restrictions were largely masked. However, government funding for several basic services in Colorado was rapidly slipping. When Colorado went into recession, the effects of these restrictions were fully felt for the first time, but neither the governor, legislature, nor policy nonprofits have been able to find a way out of the budget bind.
Training Deep Neural Networks with Constrained Learning Parameters

Today's deep learning models are primarily trained on CPUs and GPUs. Although these models tend to have low error, they consume high power and utilize large amount of memory owing to double precision floating point learning parameters. Beyond the Moore's law, a significant portion of deep learning tasks would run on edge computing systems, which will form an indispensable part of the entire computation fabric. Subsequently, training deep learning models for such systems will have to be tailored and adopted to generate models that have the following desirable characteristics: low error, low memory, and low power. We believe that deep neural networks (DNNs), where learning parameters are constrained to have a set of finite discrete values, running on neuromorphic computing systems would be instrumental for intelligent edge computing systems having these desirable characteristics. To this extent, we propose the Combinatorial Neural Network Training Algorithm (CoNNTrA), that leverages a coordinate gradient descent-based approach for training deep learning models with finite discrete learning parameters. Next, we elaborate on the theoretical underpinnings and evaluate the computational complexity of CoNNTrA. As a proof of concept, we use CoNNTrA to train deep learning models with ternary learning parameters on the MNIST, Iris and ImageNet data sets and compare their performance to the same models trained using Backpropagation. We use following performance metrics for the comparison: (i) Training error; (ii) Validation error; (iii) Memory usage; and (iv) Training time. Our results indicate that CoNNTTA models use 32 × less memory and have errors at par with the Backpropagation models.
Enterprise Resource Planning Systems and XBRL Reporting Quality

Enterprise resource planning (ERP) systems are indispensable for a majority of larger and midsize firms, and have changed the way accounting information is collected, stored, processed, and disseminated. Although most ERP systems integrate an eXtensible Business Reporting Language (XBRL) component in their core modules, little research has examined how ERP systems affect the quality of XBRL filings. Using unique data from branch level ERP implementation, we find that the degree of ERP adoption among a firm’s branches is negatively associated with the firm’s XBRL filing errors and positively associated with XBRL reporting comparability, which in turn facilitates external users’ access to the firm’s XBRL filings in the SEC’s EDGAR. These results suggest that ERP systems improve XBRL reporting quality. Moreover, our results indicate that ERP can mitigate the negative effect of extension taxonomies on XBRL reporting quality, which highlights the importance of the ERP system in a complicated XBRL reporting environment.
Higher-Order Moment-Based Anomaly Detection

The identification of anomalies is a critical component of operating complex, large-scale and geographically distributed cyber-physical systems. While designing anomaly detectors, it is common to assume Gaussian noise models to maintain tractability; however, this assumption can lead to the actual false alarm rate being significantly higher than expected. Here we design a distributionally robust threshold of detection using finite and fixed higher-order moments of the detection measure data such that it guarantees the actual false alarm rate to be upper bounded by the desired one. Further, we bound the states reachable through the action of a stealthy attack and identify the trade-off between this impact of attacks that cannot be detected and the worst-case false alarm rate. Through numerical experiments, we illustrate how knowledge of higher-order moments results in a tightened threshold, thereby restricting an attacker’s potential impact.
Criteria for projected discovery and exclusion sensitivities of counting experiments

The projected discovery and exclusion capabilities of particle physics and astrophysics/cosmology experiments are often quantified using the median expected p-value or its corresponding significance. We argue that this criterion leads to flawed results, which for example can counterintuitively project lessened sensitivities if the experiment takes more data or reduces its background. We discuss the merits of several alternatives to the median expected significance, both when the background is known and when it is subject to some uncertainty. We advocate for standard use of the “exact Asimov significance” $$Z^\mathrm{A}$$ detailed in this paper.
Variational Filtering with Copula Models for SLAM

The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled through a multivariate Gaussian distribution, but there are many situations where that assumption is unrealistic. Our paper shows how it is possible to relax this assumption and perform simultaneous localization and mapping (SLAM) with a larger class of distributions, whose multivariate dependency is represented with a copula model. We integrate the distribution model with copulas into a Sequential Monte Carlo estimator and show how unknown model parameters can be learned through gradient-based optimization. We demonstrate our approach is effective in settings where Gaussian assumptions are clearly violated, such as environments with uncertain data association and nonlinear transition models.
Insecure Land Tenure, Social Protection, and Resource Misallocation: Evidence from China’s Agricultural Sector

AbstractSmallholder farmers usually face insecure land rights and limited social protection, so farmland serves as an important safety net against income shocks. The safety net value of farmland ma...
Breaking Barriers: Maximizing Array Utilization for Compute in-Memory Fabrics

Compute in-memory (CIM) is a promising technique that minimizes data transport, the primary performance bottleneck and energy cost of most data intensive applications. This has found wide-spread adoption in accelerating neural networks for machine learning applications. Utilizing a crossbar architecture with emerging non-volatile memories (eNVM) such as dense resistive random access memory (RRAM) or phase change random access memory (PCRAM), various forms of neural networks can be implemented to greatly reduce power and increase on chip memory capacity. However, compute in-memory faces its own limitations at both the circuit and the device levels. Although compute in-memory using the crossbar architecture can greatly reduce data transport, the rigid nature of these large fixed weight matrices forfeits the flexibility of traditional CMOS and SRAM based designs. In this work, we explore the different synchronization barriers that occur from the CIM constraints. Furthermore, we propose a new allocation algorithm and data flow based on input data distributions to maximize utilization and performance for compute-in memory based designs. We demonstrate a $\boldsymbol{7.47}\times$ performance improvement over a naive allocation method for CIM accelerators on ResNet18.
CLOSED FORM OPTIMAL EXERCISE BOUNDARY OF THE AMERICAN PUT OPTION

We present three models of stock price with time-dependent interest rate, dividend yield, and volatility, respectively, that allow for explicit forms of the optimal exercise boundary of the finite ...
Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty

Sudden changes in the dynamics of robotic tasks, such as contact with an object or the latching of a door, are often viewed as inconvenient discontinuities that make manipulation difficult. However, when these transitions are well-understood, they can be leveraged to reduce uncertainty or aid manipulation—for example, wiggling a screw to determine if it is fully inserted or not. Current model-free reinforcement learning approaches require large amounts of data to learn to leverage such dynamics, scale poorly as problem complexity grows, and do not transfer well to significantly different problems. By contrast, hierarchical POMDP planning-based methods scale well via plan decomposition, work well on novel problems, and directly consider uncertainty, but often rely on precise hand-specified models and task decompositions. To combine the advantages of these opposing paradigms, we propose a new method, MICAH, which given unsegmented data of an object’s motion under applied actions, (1) detects changepoints in the object motion model using action-conditional inference, (2) estimates the individual local motion models with their parameters, and (3) converts them into a hybrid automaton that is compatible with hierarchical POMDP planning. We show that model learning under MICAH is more accurate and robust to noise than prior approaches. Further, we combine MICAH with a hierarchical POMDP planner to demonstrate that the learned models are rich enough to be used for performing manipulation tasks under uncertainty that require the objects to be used in novel ways not encountered during training.
An obstacle to sub-AdS holography for SYK-like models

We argue that “stringy” effects in a putative gravity-dual picture for SYK-like models are related to the branching time, a kinetic coefficient defined in terms of the retarded kernel. A bound on the branching time is established assuming that the leading diagrams are ladders with thin rungs. Thus, such models are unlikely candidates for sub-AdS holography. In the weak coupling limit, we derive a relation between the branching time, the Lyapunov exponent, and the quasiparticle lifetime using two different approximations.
Recovering a Clipped Signal in Sparseland

In many data acquisition systems it is common to observe signals whose amplitudes have been clipped. We present two new algorithms for recovering a clipped signal by leveraging the model assumption that the underlying signal is sparse in the frequency domain. Both algorithms employ ideas commonly used in the field of Compressive Sensing (CS); the first is a modified version of Reweighted l1 minimization, and the second is a modification of a simple greedy algorithm known as Trivial Pursuit. An empirical investigation shows that both approaches can recover signals with significant levels of clipping; indeed, both methods perform much better (compared with others) than one would anticipate based on CS experience.
Aerial imagery pile burn detection using deep learning: The FLAME dataset

Abstract Wildfires are one of the costliest and deadliest natural disasters in the US, causing damage to millions of hectares of forest resources and threatening the lives of people and animals. Of particular importance are risks to firefighters and operational forces, which highlights the need for leveraging technology to minimize danger to people and property. FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires along with methods for fire detection and segmentation which can help firefighters and researchers to develop optimal fire management strategies. This paper provides a fire image dataset collected by drones during a prescribed burning piled detritus in an Arizona pine forest. The dataset includes video recordings and thermal heatmaps captured by infrared cameras. The captured videos and images are annotated, and labeled frame-wise to help researchers easily apply their fire detection and modeling algorithms. The paper also highlights solutions to two machine learning problems: (1) Binary classification of video frames based on the presence [and absence] of fire flames. An Artificial Neural Network (ANN) method is developed that achieved a 76% classification accuracy. (2) Fire detection using segmentation methods to precisely determine fire borders. A deep learning method is designed based on the U-Net up-sampling and down-sampling approach to extract a fire mask from the video frames. Our FLAME method approached a precision of 92%, and recall of 84%. Future research will expand the technique for free burning broadcast fire using thermal images.
Engineering dynamical sweet spots to protect qubits from 1/f noise

Superconducting qubits provide a promising architecture for scalability in quantum information processing, but their coherence times are currently limited by environmental noise, miring such processors in the noisy intermediate-scale regime. Operating at ``sweet spots'' (turning points in a qubit's microwave spectrum) can substantially reduce the dephasing due to $1/f$ flux noise. The authors extend this concept to boost noise mitigation with an external drive, yielding $d\phantom{\rule{0}{0ex}}y\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}m\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}l$ sweet spots and turning static sweet ``spots'' into manifolds. This simple, powerful approach adds flexibility to the choice of operating points, and could enhance coherence times by more than an order of magnitude.
Exploration of the Dark Sector with the Fermilab Dimuon Experiment

Searches for dark-matter particles at the GeV mass scale has been receiving much attention in the last several years, partly motivated by the failure of direct and indirect searches of heavier candidates to produce a signal. The SpinQuest dimuon experiment in the 120-GeV Main-Injector proton beam at Fermilab, currently in the commissioning stage, is uniquely equipped to search for dark photons and dark Higgs particles produced in a 5-m long iron beam dump with masses in the range 0.2 - 10 GeV, running in a parasitic mode. This only requires a modest upgrade of a displaced-vertex trigger with acceptance for dark-sector particles decaying into dimuons inside or downstream of the dump. We discuss the physics reach of such a run, the status, and some additional future prospects.
What’s in a name? The incidence of gasoline excise taxes versus gasoline carbon levies

Abstract Legislators often attach specific names to individual taxes to help explain their purpose, increase transparency, and ease public backlash over tax increases. It may be politically beneficial, but does the simple act of naming a tax and attaching it to a specific set of benefits have an actual effect in the marketplace? Do consumers respond differently to tax-induced price increases depending on what that tax is? In this article, a natural experiment is used to evaluate tax incidence after the introduction of two gasoline taxes in Alberta - 1) an increase in the generic excise tax and 2) an environmentally-targeted “carbon levy”. While similar on the cost side, the taxes were very different in name and transparency on the benefit side. Results show that benefit-side transparency can matter – responses were lower and incidence higher for the more transparent carbon levy than with the less transparent excise tax.
AMPGAN v2: Machine Learning-Guided Design of Antimicrobial Peptides.

Antibiotic resistance is a critical public health problem. Each year ∼2.8 million resistant infections lead to more than 35,000 deaths in the U.S. alone. Antimicrobial peptides (AMPs) show promise in treating resistant infections. However, applications of known AMPs have encountered issues in development, production, and shelf-life. To drive the development of AMP-based treatments it is necessary to create design approaches with higher precision and selectivity towards resistant targets. Previously we developed AMPGAN and obtained proof-of-concept evidence for the generative approach to design AMPs with experimental validation. Building on the success of AMPGAN, we present AMPGAN v2 a bidirectional conditional generative adversarial network (BiCGAN) based approach for rational AMP design. AMPGAN v2 uses generator-discriminator dynamics to learn data driven priors and controls generation using conditioning variables. The bidirectional component, implemented using a learned encoder to map data samples into the latent space of the generator, aids iterative manipulation of candidate peptides. These elements allow AMPGAN v2 to generate of candidates that are novel, diverse, and tailored for specific applications—making it an efficient AMP design tool.
The Content of Their Characters - J.D. Salinger, Holden Caulfield and Fredrik Colting

This paper analyzes J. D. Salinger's recent suit against Fredrik Colting for infringing Salinger's copyright in, 'The Catcher in the Rye' and its character Holden Caulfield. The case has been widely noticed because the Second Circuit extended to copyright cases a heightened standard for injunctive relief that requires evidence of irreparable harm. Meanwhile, however, the court's certainty that Salinger should prevail on the merits has escaped much critique. To begin, I argue that the district court misread Colting's novel by mistaking his metafiction for a conventional sequel. I suggest two practical litigation strategies to avoid this outcome. Next, I fault the Second Circuit for adopting this error and further asserting that Colting's novel irreparably harmed Salinger by invading his "right not to speak." This rhetoric, if taken seriously, distorts the meaning of Section 107 of the Copyright Act and undermines the policy of the fair use defense. Paradoxically, it also enables district courts to issue injunctive relief even though plaintiffs have no evidence that an alleged infringement is causing them commercial harm.
Insight into the human pathodegradome of the V8 protease from Staphylococcus aureus

Summary Staphylococcus aureus possesses ten extracellular proteases with mostly unknown targets in the human proteome. To assist with bacterial protease target discovery, we have applied and compared two N-terminomics methods to investigate cleavage of human serum proteins by S. aureus V8 protease, discovering 85 host-protein targets. Among these are virulence-relevant complement, iron sequestration, clotting cascade, and host protease inhibitor proteins. Protein cleavage sites have been identified, providing insight into the disruption of host protein function by V8. Complement proteins are cleaved within peptidase and sushi domains, and host protease inhibitors are cleaved outside their protease-trapping motifs. Our data highlight the potential for further application of N-terminomics in discovery of bacterial protease substrates in other host niches and provide omics-scale insight into the role of the V8 protease in S. aureus pathogenesis.
Convergence of Gibbs Sampling: Coordinate Hit-and-Run Mixes Fast

The Gibbs Sampler is a general method for sampling high-dimensional distributions, dating back to Turchin, 1971. In each step of the Gibbs Sampler, we pick a random coordinate and re-sample that coordinate from the distribution induced by fixing all other coordinates. While it has become widely used over the past half-century, guarantees of efficient convergence have been elusive. We show that for a convex body $K$ in $\mathbb{R}^{n}$ with diameter $D$, the mixing time of the Coordinate Hit-and-Run (CHAR) algorithm on $K$ is polynomial in $n$ and $D$. We also give a lower bound on the conductance of CHAR, showing that it is strictly worse than hit-and-run or the ball walk in the worst case.
Marijuana legalization and household spending on food and alcohol

Utilizing the Consumer Expenditure Interview Survey from 2005 to 2019, I study spending on food and alcohol following recreational marijuana law (RML). Exploiting differences in the timing of the passage of RMLs and employing two-way fixed-effects methods, I find that households located in states adopting these laws increase their quarterly spending on food, which is driven mainly by spending on food consumed away from home. Legalization of recreational marijuana also leads to increased quarterly spending on alcohol. These findings suggest a complementarity between food, alcohol, and marijuana.
The positive Dressian equals the positive tropical Grassmannian

The Dressian and the tropical Grassmannian parameterize abstract and realizable tropical linear spaces; but in general, the Dressian is much larger than the tropical Grassmannian. There are natural positive notions of both of these spaces -- the positive Dressian, and the positive tropical Grassmannian (which we introduced roughly fifteen years ago) -- so it is natural to ask how these two positive spaces compare. In this paper we show that the positive Dressian equals the positive tropical Grassmannian. Using the connection between the positive Dressian and regular positroidal subdivisions of the hypersimplex, we use our result to give a new "tropical" proof of da Silva's 1987 conjecture (first proved in 2017 by Ardila-Rincon-Williams) that all positively oriented matroids are realizable. We also show that the finest regular positroidal subdivisions of the hypersimplex consist of series-parallel matroid polytopes, and achieve equality in Speyer's f-vector theorem. Finally we give an example of a positroidal subdivision of the hypersimplex which is not regular, and make a connection to the theory of tropical hyperplane arrangements.
Peeking Inside the Black Box: Inventor Turnover and Patent Termination:

This study examines the consequences of inventor turnover for the termination of the patents they produced beforehand. The proponents of the knowledge-based view consider firms repositories of know...