cssi/SciSciGPT-SciSciNet huggingface dataset:
Competitive Wages in a Match with Ordered Contracts

Following the recently dismissed antitrust lawsuit against the National Residency Matching Program (NRMP), Jeremy Bulow and Jonathan Levin (2006) propose a simple matching model in which firms set impersonal salaries simultaneously before matching with workers, which leads to lower aggregate wages than any competitive outcome. I model a feature of the NRMP, ordered contracts, that allows firms to set several contracts while determining the order in which they try to fill them, which has different properties than standard models with multiple contracts. Furthermore, the low wages of Bulow and Levin are no longer an equilibrium, but competitive wages are. (JEL D86, J31, J41)
Market Work, Wages, and Men's Health

In this paper, we investigate the complex interrelations among work-time, wages, and health identified in the Grossman model of the demand for health. Hansen's generalized method of moments techniques are employed to estimate a 3-equation simultaneous model designed to capture the time dependent character of these interrelationships. We then estimate simpler models with more restrictive assumptions commonly found in the literature and find substantial differences between these estimates and those from our simultaneous model. For example, the positive relationship between work-time and health found in other studies disappears when the relevant simultaneities are taken into account.
The United States Smallpox Bioterrorism Preparedness Plan: Rational Response or Potemkin Planning

Smallpox was a major bogeyman after the terrorist attacks on September 11, 2001. The Bush administration launched a major smallpox vaccination campaign, and the Centers for Disease Control and Prevention (CDC) was directed to develop a national response plan for a smallpox bioterrorism attack. This paper grows out of an article written in 2004 on the failure of that smallpox vaccination campaign in the civilian population and implications for the CDC’s smallpox response plan. Since the 2004 article, public health systems across the United States have lost key personnel and resources due to budget cuts, Hurricane Katrina showed the danger of relying on local emergency response, and the H1N1 vaccination campaign illustrated how hard it is to implement a new vaccination program.The thesis of this paper is that these events further undermine what was already a questionable smallpox response plan. But one more ineffective government response plan would hardly be news. Since the Duck and Cover films of the 1950s, the federal government has attempted to mollify the public with patently inadequate response plans for events that experts understand cannot be mitigated. The official United States nuclear war strategy was called Mutually Assured Destruction because it was assumed that there would be few survivors — making duck and cover little more than a cruel joke to frighten a generation of children.The smallpox response plan is useful to study because the plan itself — unlike duck and cover — is based on good science and experience. It fails because the necessary public health infrastructure has been undermined, and because more than 30 years of politically expedient public health decisionmaking has made it impossible to deal with large scale threats when dealing with those threats has significant political costs. It is a Potemkin plan, which, like the villages in Potemkin’s story, exist only to fool the eye. This paper will show ways to recognize Potemkin planning, which is the first step to realistically facing risk.
Cross-Market Investor Sentiment in Commodity Exchange-Traded Funds

Abstract/Zusammenfassung This study shows how the investor sentiment in the stock market affects prices of commodity exchange-traded funds (ETFs). The study provides quantitative evidence that the tracking errors of commodity ETFs differ in the bullish versus the bearish stock market, and the aggregate tracking error of commodity ETFs is sensitive to the well-known sentiment measures. The study exploits a profitable trading strategy based on investor sentiment in the stock market and commodity market. The sentiment-driven demand for commodity ETFs could exist even after consideration of trading costs, and it is a short-term phenomenon. This unique evidence indicates investor sentiment affects asset valuation across markets. Anleger-Sentiment bei Rohstoff-Exchange-Traded-Funds auf verschiedenen Markten Diese Studie zeigt, wie sich Sentiment-Faktoren am Aktienmarkt auf die Preise von Rohstoff-Exchange-Traded-Funds (ETFs) auswirken. Die Studie liefert quantitative Evidenz dafur, dass die Tracking Errors von ...
Do Americans Play Football

The English approach to property and periodical awards in divorce cases, as exemplified by the decision in the McFarlane and Parlour cases, is compared to the American approach, as exemplified by the Principles of the Law of Family Dissolution, adopted in 2000 by the American Law Institute (ALI). It is suggested that these two English cases seem more alike to English courts than they would to American, because the English analysis places little or no emphasis on a factor that American practice generally regards as important - the duration of the parties` relationship. The ALI`s analysis urges a formulaic approach to financial settlement that emphasizes the relationship`s duration, while rejecting financial need as a useful concept upon which to base a decision rule. In contrast, the English decision in these two cases pays little attention to duration while giving attention to need. The American approach is explained and defended. Finally, O`Brien, a well-known American precedent rejected by Mr Justice Thorpe, is discussed. It is argued that difficulties inherent in the O`Brien rule explain not only its widespread rejection by other American courts, but also why the English decision in Parlour and McFarlane is correct in its somewhat reluctant conclusion that different rules should apply to the division at divorce of capital and of income.
Corporate Payout Policy in Dual-Class Firms

Abstract We examine corporate payout policy in dual-class firms. The expropriation hypothesis predicts that dual-class firms pay out less to shareholders because entrenched managers want to maximize the value of assets under control and the associated private benefits. The pre-commitment hypothesis predicts that dual-class firms pay out more to shareholders because firms use corporate payouts as a pre-commitment device to mitigate agency costs. Our results support the pre-commitment hypothesis. Dual-class firms have higher cash dividend payments and total payouts, and they use more regular cash dividends rather than special dividends or repurchases, compared to their propensity-matched single-class firms. Dual-class firms with severe free cash flow-related agency problems and few growth opportunities rely even more on corporate payouts as a pre-commitment mechanism. We also rule out the alternative explanation that dual-class firms pay out more because super-voting shareholders lack the ability to generate home-made dividends by selling shares since super-voting shares are often non-tradable or very illiquid.
Credit Default Swaps and the Empty Creditor Problem

The empty creditor problem arises when a debtholder has obtained insurance against default but otherwise retains control rights in and outside bankruptcy. We analyze this problem from an ex ante and ex post perspective in a formal model of debt with limited commitment, by comparing contracting outcomes with and without insurance through credit default swaps (CDS). We show that CDS, and the empty creditors they give rise to, have important ex ante commitment benefits: By strengthening creditors’ bargaining power, they raise the debtor’s pledgeable income and help reduce the incidence of strategic default. However, we also show that lenders will over-insure in equilibrium, giving rise to an inefficiently high incidence of costly bankruptcy. We discuss a number of remedies that have been proposed to overcome the inefficiency resulting from excess insurance. ( JEL G30, G32, G33) One of the most significant changes in the debtor-creditor relationship in the
Appraising the Appraisal-Tendency Framework

This article considers the consumer research implications of the Appraisal-Tendency Framework (ATF; Han, Lerner, & Keltner, 2007). This article outlines how the ATF approach could be applied to sequential consumer choices (e.g., effects of emotional responses to stockouts on later decisions) and high-stakes decisions (e.g., medical decisions). This article also proposes several areas in which the ATF might be extended: examining complex sequences of choices with emotional consequences, considering how incidental and integral emotions interact, characterizing how both evaluative and regulatory mechanisms may influence the effects of emotion on judgment and choice, and extending the range of positive emotions and appraisal dimensions considered.
Crisis Bargaining and Nuclear Blackmail

Do nuclear weapons offer coercive advantages in international crisis bargaining? Almost seventy years into the nuclear age, we still lack a complete answer to this question. While scholars have devoted significant attention to questions about nuclear deterrence, we know comparatively little about whether nuclear weapons can help compel states to change their behavior. This study argues that, despite their extraordinary power, nuclear weapons are uniquely poor instruments of compellence. Compellent threats are more likely to be effective under two conditions: first, if a challenger can credibly threaten to seize the item in dispute; and second, if enacting the threat would entail few costs to the challenger. Nuclear weapons, however, meet neither of these conditions. They are neither useful tools of conquest nor low-cost tools of punishment. Using a new dataset of more than 200 militarized compellent threats from 1918 to 2001, we find strong support for our theory: compellent threats from nuclear states are no more likely to succeed, even after accounting for possible selection effects in the data. While nuclear weapons may carry coercive weight as instruments of deterrence, it appears that these effects do not extend to compellence.
Historical patent data: A practitioner's guide

I provide a primer on six recent large-scale historical patent datasets for use in innovation research. I discuss how each dataset is constructed, the types of patent information included in each, and the quality and completeness of each. Throughout, I emphasize when our knowledge of the history of invention is dependent on the data source used and provide recommendations about which dataset is most likely to be best for different contexts. Overall, these datasets paint a remarkably consistent picture of the history of U.S. invention. When the datasets do disagree, these differences tend to be minor, although I highlight some important exceptions. I further describe several "niche" historical patent datasets that allow researchers to study institutional contexts that cannot be studied using modern patent data. Finally, I discuss features of patent data that are not available for the historical patents but are available for modern patents.
Sales-Based Rebate Design

We study rebate mechanisms according to which a monopolist selling a product introduces rebates as function of the volume of buyers. This enables the firm to induce payoff externalities that ordinarily do not exist. The monopoly firm sells an indivisible good to a mass of consumers with uncertain valuations corresponding to two sources of uncertainty: a systemic uncertainty corresponding to the realized quality, and an idiosyncratic uncertainty modeling the diversity of consumers’ tastes. Analyzing the equilibria of the induced global game among the consumers, we show that introducing positive externality via an increasing aggregate reward program reduces the profit. Using variational optimization techniques, we identify several key characteristics of the optimal reward program: the optimal solution is a “full-refund or nothing” policy, fully reimbursing the buyers if the realized quality falls in one of the finitely many refund-eligible intervals. The number of intervals, though finite, grow unboundedly as consumers’ tastes become less diverse and valuations concentrate around the true quality. While finding the optimal reward program is in general an intractable problem, we fully characterize the optimal solution in two important instances: one with constraints on the reward size and the other with bounds on the rate of change of the reward with sales volume. Despite their simple and intuitive structures, the corresponding optimal reward programs asymptotically recover the optimal solution. Our analysis sheds light on the potential role of novel technology-enabled features of crowd-based markets in developing new revenue management strategies.
Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2

The SARS-CoV-2 virus is currently causing a worldwide pandemic with dramatic societal consequences for the humankind In the last decades, disease outbreaks due to such zoonotic pathogens have appeared with an accelerated rate, which calls for an urgent development ofbradaptive (smart) therapeutics Here, we develoa computational strategy to adaptively evolve peptides that could selectively inhibit mutating S protein receptor binding domains (RBDs) of different SARS-CoV-2 viral strains from binding to their human host receptor, angiotensin-converting enzyme 2 (ACE2) Starting from suitable peptide templates, based on selected ACE2 segments (natural RBD binder), we gradually modify the templates by random mutations, while retaining those mutations that maximize their RBD-binding free energies In this adaptive evolution, atomistic molecular dynamics simulations of the template-RBD complexes are iteratively perturbed by the peptide mutations, which are retained under favorable Monte Carlo decisions The computational search will provide librariesbrof optimized therapeutics capable of reducing the SARS-CoV-2 infection on a global scale br
Large-scale extended Granger causality for classification of marijuana users from functional MRI

It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such changes using restingstate fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other timeseries of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying marijuana users from typical controls using 126 adult subjects with a childhood diagnosis of ADHD from the Addiction Connectome Preprocessed Initiative (ACPI) database. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall’s-tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. Within a cross-validation scheme of 100 different training/test (90%/10%) data splits, we obtain a mean accuracy range of [0.714, 0.985] and a mean Area Under the receiver operating characteristic Curve (AUC) range of [0.779, 0.999] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912] and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC as a potential biomarker for marijuana use.
Learning User-Preferred Mappings for Intuitive Robot Control

When humans control drones, cars, and robots, we often have some preconceived notion of how our inputs should make the system behave. Existing approaches to teleoperation typically assume a one-size-fits-all approach, where the designers pre-define a mapping between human inputs and robot actions, and every user must adapt to this mapping over repeated interactions. Instead, we propose a personalized method for learning the human’s preferred or preconceived mapping from a few robot queries. Given a robot controller, we identify an alignment model that transforms the human’s inputs so that the controller’s output matches their expectations. We make this approach data-efficient by recognizing that human mappings have strong priors: we expect the input space to be proportional, reversable, and consistent. Incorporating these priors ensures that the robot learns an intuitive mapping from few examples. We test our learning approach in robot manipulation tasks inspired by assistive settings, where each user has different personal preferences and physical capabilities for teleoperating the robot arm. Our simulated and experimental results suggest that learning the mapping between inputs and robot actions improves objective and subjective performance when compared to manually defined alignments or learned alignments without intuitive priors. The supplementary video showing these user studies can be found at: https://youtu.be/rKHka0_48-Q
Marxism and Retribution

Philosophers have written at great length about the moral problems involved in punishing the innocent — particularly as these problems raise obstacles to an acceptance of the moral theory of Utilitarianism. Punishment of an innocent man in order to bring about good social consequences is, at the very least, not always clearly wrong on utilitarian principles. This being so, utilitarian principles are then to be condemned by any morality that may be called Kantian in character. For punishing an innocent man, in Kantian language, involves using that man as a mere means or instrument to some social good and is thus not to treat him as an end in himself, in accord with his dignity or worth as a person.
Shear Strength of Sands during Rapid Loadings

Triaxial tests with times-to-failure ranging from 5 min to 5 milliseconds have been used to investigate the effect of strain-rate on the strength of dry and saturated sands. New techniques were dev...
Einstein metrics, conformal curvature, and anti-holomorphic involutions

Building on previous results [17, 35], we complete the classification of compact oriented Einstein 4-manifolds with $$\det (W^+) > 0$$ . There are, up to diffeomorphism, exactly 15 manifolds that carry such metrics, and, on each of these manifolds, such metrics sweep out exactly one connected component of the corresponding Einstein moduli space.
Morley Trisectors and the Law of Sines with Reflections

Abstract–We invoke the law of sines to prove Morley’s trisector theorem. Though the sinusoidal function appears, the proof is safe for trigonometric distancing.
TAAL: Tampering Attack on Any Key-based Logic Locked Circuits

Due to the globalization of semiconductor manufacturing and test processes, the system-on-a-chip (SoC) designers no longer design the complete SoC and manufacture chips on their own. This outsourcing of the design and manufacturing of Integrated Circuits (ICs) has resulted in several threats, such as overproduction of ICs, sale of out-of-specification/rejected ICs, and piracy of Intellectual Properties (IPs). Logic locking has emerged as a promising defense strategy against these threats. However, various attacks about the extraction of secret keys have undermined the security of logic locking techniques. Over the years, researchers have proposed different techniques to prevent existing attacks. In this article, we propose a novel attack that can break any logic locking techniques that rely on the stored secret key. This proposed TAAL attack is based on implanting a hardware Trojan in the netlist, which leaks the secret key to an adversary once activated. As an untrusted foundry can extract the netlist of a design from the layout/mask information, it is feasible to implement such a hardware Trojan. All three proposed types of TAAL attacks can be used for extracting secret keys. We have introduced the models for both the combinational and sequential hardware Trojans that evade manufacturing tests. An adversary only needs to choose one hardware Trojan out of a large set of all possible Trojans to launch the TAAL attack.
DeepLocalize: Fault Localization for Deep Neural Networks

Deep neural networks (DNNs) are becoming an integral part of most software systems. Previous work has shown that DNNs have bugs. Unfortunately, existing debugging techniques don't support localizing DNN bugs because of the lack of understanding of model behaviors. The entire DNN model appears as a black box. To address these problems, we propose an approach and a tool that automatically determines whether the model is buggy or not, and identifies the root causes for DNN errors. Our key insight is that historic trends in values propagated between layers can be analyzed to identify faults, and also localize faults. To that end, we first enable dynamic analysis of deep learning applications: by converting it into an imperative representation and alternatively using a callback mechanism. Both mechanisms allows us to insert probes that enable dynamic analysis over the traces produced by the DNN while it is being trained on the training data. We then conduct dynamic analysis over the traces to identify the faulty layer or hyperparameter that causes the error. We propose an algorithm for identifying root causes by capturing any numerical error and monitoring the model during training and finding the relevance of every layer/parameter on the DNN outcome. We have collected a benchmark containing 40 buggy models and patches that contain real errors in deep learning applications from Stack Overflow and GitHub. Our benchmark can be used to evaluate automated debugging tools and repair techniques. We have evaluated our approach using this DNN bug-and-patch benchmark, and the results showed that our approach is much more effective than the existing debugging approach used in the state-of-the-practice Keras library. For 34/40 cases, our approach was able to detect faults whereas the best debugging approach provided by Keras detected 32/40 faults. Our approach was able to localize 21/40 bugs whereas Keras did not localize any faults.
Colossal anomalous Nernst effect in a correlated noncentrosymmetric kagome ferromagnet.

The transverse voltage generated by a temperature gradient in a perpendicularly applied magnetic field, termed the Nernst effect, has promise for thermoelectric applications and for probing electronic structure. In magnetic materials, an anomalous Nernst effect (ANE) is possible in a zero magnetic field. We report a colossal ANE in the ferromagnetic metal UCo0.8Ru0.2Al, reaching 23 microvolts per kelvin. Uranium’s 5f electrons provide strong electronic correlations that lead to narrow bands, a known route to producing a large thermoelectric response. In addition, uranium’s strong spin-orbit coupling produces an intrinsic transverse response in this material due to the Berry curvature associated with the relativistic electronic structure. Theoretical calculations show that in UCo0.8Ru0.2Al at least 148 Weyl nodes, and two nodal lines, exist within 60 millielectron volt of the Fermi level. This work demonstrates that magnetic actinide materials can host strong Nernst and Hall responses due to their combined correlated and topological nature.
A Sense of Self for Unix Processes

A method for anomaly detection is introduced in which ``normal'' is defined by short-range correlations in a process' system calls. Initial experiments suggest that the definition is stable during normal behavior for standard UNIX programs. Further, it is able to detect several common intrusions involving sendmail and lpr. This work is part of a research program aimed at building computer security systems that incorporate the mechanisms and algorithms used by natural immune systems.
A region-based method for causal mediation analysis of DNA methylation data

Exposure to environmental factors can affect DNA methylation at a 5'-cytosine-phosphate-guanine-3' (CpG) site or a genomic region, which can then affect an outcome. In other words, environmental effects on an outcome could be mediated by DNA methylation. To date, single CpG-site-based mediation analysis has been employed extensively. More recently, however, there has been considerable interest in studying differentially methylated regions (DMRs), both because DMRs are more likely to have functional effects than single CpG sites and because testing DMRs reduces multiple testing. In this report, we propose a novel causal mediation approach under the counterfactual framework to test the significance of total (TE), direct (DE), and indirect effects (IE) of predictors on response variable with a methylated region (MR) as the mediator (denoted as MR-Mediation). Functional linear transformation is used to reduce the possible high dimension of the CpG sites in a predefined MR and to account for their location information. In our simulation studies, MR-Mediation retained the desired Type I error rates for TE, DE, and IE tests. Furthermore, MR-Mediation had better power performance than testing mean methylation level as the mediator in most considered scenarios, especially for IE (i.e., mediated effect) test, which could be more interesting than the other two effect tests. We further illustrate our proposed method by analysing the methylation mediated effect of exposure to gun violence on total immunoglobulin E or atopic asthma among participants in the Epigenetic Variation and Childhood Asthma in Puerto Ricans study.
Inverses of Matern Covariances on Grids

We conduct a study of the aliased spectral densities of Matern covariance functions on a regular grid of points, providing clarity on the properties of a popular approximation based on stochastic partial differential equations; while others have shown that it can approximate the covariance function well, we find that it assigns too much power at high frequencies and does not provide increasingly accurate approximations to the inverse as the grid spacing goes to zero, except in the one-dimensional exponential covariance case. We provide numerical results to support our theory, and in a simulation study, we investigate the implications for parameter estimation, finding that the SPDE approximation tends to overestimate spatial range parameters.
Atmospheric leptons. the search for a prompt component

The flux of high-energy (≥GeV) neutrinos consists primarily of those produced by cosmic-ray interactions in the atmosphere. The contribution from extraterrestrial sources is still unknown. Current limits suggest that the observed spectrum is dominated by atmospheric neutrinos up to at least 100 TeV. The contribution of charmed hadrons to the flux of atmospheric neutrinos is important in the context of the search for astrophysical neutrinos because the spectrum of such “prompt” neutrinos is harder than that of “conventional” neutrinos from decay of pions and kaons. The prompt component therefore becomes increasingly important as energy increases. This paper reviews the status of the search for prompt muons and neutrinos with emphasis on the complementary aspects of muons, electron neutrinos and muon neutrinos.
A Machine Checked Model of Idempotent MGU Axioms For Lists of Equational Constraints

We present formalized proofs verifying that the first-order unification algorithm defined over lists of satisfiable constraints generates a most general unifier (MGU), which also happens to be idempotent. All of our proofs have been formalized in the Coq theorem prover. Our proofs show that finite maps produced by the unification algorithm provide a model of the axioms characterizing idempotent MGUs of lists of constraints. The axioms that serve as the basis for our verification are derived from a standard set by extending them to lists of constraints. For us, constraints are equalities between terms in the language of simple types. Substitutions are formally modeled as finite maps using the Coq library Coq.FSets.FMapInterface. Coq’s method of functional induction is the main proof technique used in proving many of the axioms.
The magnetic field across the molecular warped disk of Centaurus A

Magnetic fields are amplified as a consequence of galaxy formation and turbulence-driven dynamos. Galaxy mergers can potentially amplify the magnetic fields from their progenitors, making the magnetic fields dynamically important. However, the effect of mergers on magnetic fields is still poorly understood. We use thermal polarized emission observations to trace the magnetic fields in the molecular disk of the nearest radio active galaxy, Centaurus A, which is thought to be the remnant of a merger. Here, we detect that the magnetic field orientations in the plane of the sky tightly follow the ~3.0 kpc-scale molecular warped disk. Our simple regular large-scale axisymmetric spiral magnetic field model can explain, to some extent, the averaged magnetic field orientations across the disk projected on the sky. Our observations also suggest the presence of small-scale turbulent fields, whose relative strengths increase with velocity dispersion and column density. These results have strong implications for understanding the generation and role of magnetic fields in the formation of galaxies across cosmic time. Thermal polarized emission observations of the molecular disk in Centaurus A detect that the magnetic field orientations in the plane of the sky tightly follow the kiloparsec-scale molecular warped disk and also suggest the presence of small-scale turbulent fields.
Faster Algorithms for Largest Empty Rectangles and Boxes

We revisit a classical problem in computational geometry: finding the largest-volume axis-aligned empty box (inside a given bounding box) amidst n given points in d dimensions. Previously, the best algorithms known have running time O(nlog²n) for d = 2 (by Aggarwal and Suri [SoCG'87]) and near n^d for d ≥ 3. We describe faster algorithms with running time - O(n2^{O(log^*n)}log n) for d = 2, - O(n^{2.5+o(1)}) time for d = 3, and - O(n^{(5d+2)/6}) time for any constant d ≥ 4. To obtain the higher-dimensional result, we adapt and extend previous techniques for Klee’s measure problem to optimize certain objective functions over the complement of a union of orthants.
Power Spectra of Constrained Codes With Level-Based Signaling: Overcoming Finite-Length Challenges

In various practical systems, certain data patterns are prone to errors if written or transmitted. In magnetic recording and communication over transmission lines, data patterns causing consecutive transitions that are not sufficiently separated are prone to errors. In Flash memory with two levels per cell, data patterns causing high–low–high charge levels on adjacent cells are prone to errors. Constrained codes are used to eliminate error-prone patterns, and they can also achieve other goals. Recently, we introduced efficient binary symmetric lexicographically-ordered constrained (LOCO) codes and asymmetric LOCO (A-LOCO) codes to increase density in magnetic recording systems and lifetime in Flash systems by eliminating the relevant detrimental patterns. Due to their application, LOCO and A-LOCO codes are associated with level-based signaling. Studying the power spectrum of a random signal with certain properties is principal for any storage or transmission system. It reveals important properties such as the average signal power at DC, the bandwidth of the signal, and whether there are discrete power components at certain frequencies. In this paper, we first modify a framework from the literature in order to introduce a method to derive the power spectrum of a sequence of constrained data associated with level-based signaling. We apply our method to infinitely long sequences satisfying symmetric and asymmetric constraints. Next, we show how to generalize the method such that it works for a stream of finite-length codewords as well, thus demonstrating how to overcome the associated finite-length challenges. We use the generalized method to devise closed forms for the spectra of finite-length LOCO and A-LOCO codes from their transition diagrams. Our LOCO and A-LOCO spectral derivations can be performed for any code length and can be extended to other constrained codes. We plot these power spectra, and discuss various important spectral properties for both LOCO and A-LOCO codes. We also briefly discuss an alternative method for deriving the power spectrum and introduce an idea towards reaching the spectra of self-clocked codes.
Nonlinear coupling of whistler waves to oblique electrostatic turbulence enabled by cold plasma

Kinetic simulations and theory demonstrate that whistler waves can excite oblique, short-wavelength fluctuations through secondary drift instabilities if a population of sufficiently cold plasma is present. The excited modes lead to heating of the cold populations and damping of the primary whistler waves. The instability threshold depends on the density and temperature of the cold population and can be relatively small if the temperature of the cold population is sufficiently low. This mechanism may thus play a significant role in controlling amplitude of whistlers in the regions of the Earth's magnetosphere where cold background plasma of sufficient density is present.
Dimension distortion by right coset projections in the Heisenberg group

We study the family of vertical projections whose fibers are right cosets of horizontal planes in the Heisenberg group, $\mathbb{H}^n$. We prove lower bounds for Hausdorff dimension distortion of sets under these mappings, with respect to the Euclidean metric and also the natural quotient metric which we show behaves like the Euclidean metric in this context. Our bounds are sharp in a large part of the dimension range, and we give conjectural sharp lower bounds for the remaining range. Our approach also lets us improve the known almost sure lower bound for the standard family of vertical projections in $\mathbb{H}^n$ for $n \geq 2$.
The Fatal Conceit of Foreign Intervention: Evidence from the Afghanistan Papers

The fatal conceit of foreign intervention refers to the limitations faced by governments using discretionary power to address perceived problems in foreign societies. Drawing on evidence from the “Afghanistan Papers”—a collection of internal government documents compiled by the Special Inspector General for Afghanistan Reconstruction (SIGAR) and released in December 2019—we demonstrate how the failure of the U.S.-led intervention in Afghanistan to meet the stated objectives illustrates the fatal conceit of foreign intervention. We explore the limitations faced by foreign government interveners in three stages, reflecting distinct decision nodes within a means-ends framework in which the policies relating to foreign intervention are formulated and implemented.
Active Online Learning with Hidden Shifting Domains

Online machine learning systems need to adapt to domain shifts. Meanwhile, acquiring label at every timestep is expensive. We propose a surprisingly simple algorithm that adaptively balances its regret and its number of label queries in settings where the data streams are from a mixture of hidden domains. For online linear regression with oblivious adversaries, we provide a tight tradeoff that depends on the durations and dimensionalities of the hidden domains. Our algorithm can adaptively deal with interleaving spans of inputs from different domains. We also generalize our results to non-linear regression for hypothesis classes with bounded eluder dimension and adaptive adversaries. Experiments on synthetic and realistic datasets demonstrate that our algorithm achieves lower regret than uniform queries and greedy queries with equal labeling budget.
Fair and Distributed Dynamic Optimal Transport for Resource Allocation over Networks

Optimal transport is a framework that facilitates the most efficient allocation of a limited amount of resources. However, the most efficient allocation scheme does not necessarily preserve the most fairness. In this paper, we establish a framework which explicitly considers the fairness of dynamic resource allocation over a network with heterogeneous participants. As computing the transport strategy in a centralized fashion requires significant computational resources, it is imperative to develop computationally light algorithm that can be applied to large scale problems. To this end, we develop a fully distributed algorithm for fair and dynamic optimal transport with provable convergence using alternating method of multipliers. In the designed algorithm, each corresponding pair of resource supplier and receiver compute their own solutions and update the transport schemes through negotiation iteratively which do not require a central planner. The distributed algorithm can yield a fair and efficient resource allocation mechanism over a network. We corroborate the obtained results through case studies.
A Note on Solving Discretely-Constrained Nash-Cournot Games via Complementarity

Discretely-constrained Nash-Cournot games have attracted attention as they arise in various competitive energy production settings in which players must make one or more discrete decisions. Gabriel et al. (Netw Spat Econ 13(3):307–326 2013) claim that the set of equilibria to a discretely-constrained Nash-Cournot game coincides with the set of solutions to a corresponding discretely-constrained mixed complementarity problem. We show that one direction of this claim is false by providing counterexamples to show that there exist solutions to the discretely-constrained Nash-Cournot game that do not coincide with solutions to the discretely-constrained mixed complementarity problem. The updated theorem in this note formally states that every solution to the discretely-constrained mixed complementarity problem is a solution to the discretely-constrained Nash-Cournot game, but not vice versa.
Lower bounds for superpatterns and universal sequences

Abstract A permutation σ ∈ S n is said to be k-universal or a k-superpattern if for every π ∈ S k , there is a subsequence of σ that is order-isomorphic to π. A simple counting argument shows that σ can be a k-superpattern only if n ≥ ( 1 / e 2 + o ( 1 ) ) k 2 , and Arratia conjectured that this lower bound is best-possible. Disproving Arratia's conjecture, we improve the trivial bound by a small constant factor. We accomplish this by designing an efficient encoding scheme for the patterns that appear in σ. This approach is quite flexible and is applicable to other universality-type problems; for example, we also improve a bound by Engen and Vatter on a problem concerning ( k + 1 ) -ary sequences which contain all k-permutations.
Ordered multiplicity inverse eigenvalue problem for graphs on six vertices

For a graph $G$, we associate a family of real symmetric matrices, $\mathcal{S}(G)$, where for any $M \in \mathcal{S}(G)$, the location of the nonzero off-diagonal entries of $M$ is governed by the adjacency structure of $G$. The ordered multiplicity Inverse Eigenvalue Problem of a Graph (IEPG) is concerned with finding all attainable ordered lists of eigenvalue multiplicities for matrices in $\mathcal{S}(G)$. For connected graphs of order six, we offer significant progress on the IEPG, as well as a complete solution to the ordered multiplicity IEPG. We also show that while $K_{m,n}$ with $\min(m,n)\ge 3$ attains a particular ordered multiplicity list, it cannot do so with arbitrary spectrum.
DRAS-CQSim: A reinforcement learning based framework for HPC cluster scheduling

Abstract For decades, system administrators have been striving to design and tune cluster scheduling policies to improve the performance of high performance computing (HPC) systems. However, the increasingly complex HPC systems combined with highly diverse workloads make such manual process challenging, time-consuming, and error-prone. We present a reinforcement learning based HPC scheduling framework named DRAS-CQSim to automatically learn optimal scheduling policy. DRAS-CQSim encapsulates simulation environments, agents, hyperparameter tuning options, and different reinforcement learning algorithms, which allows the system administrators to quickly obtain customized scheduling policies.
Early turbulence and pulsatile flows enhance diodicity of Tesla's macrofluidic valve.

Microfluidics has enabled a revolution in the manipulation of small volumes of fluids. Controlling flows at larger scales and faster rates, or macrofluidics, has broad applications but involves the unique complexities of inertial flow physics. We show how such effects are exploited in a device proposed by Nikola Tesla that acts as a diode or valve whose asymmetric internal geometry leads to direction-dependent fluidic resistance. Systematic tests for steady forcing conditions reveal that diodicity turns on abruptly at Reynolds number $${\rm{Re}}\approx 200$$ and is accompanied by nonlinear pressure-flux scaling and flow instabilities, suggesting a laminar-to-turbulent transition that is triggered at unusually low $${\rm{Re}}$$ . To assess performance for unsteady forcing, we devise a circuit that functions as an AC-to-DC converter, rectifier, or pump in which diodes transform imposed oscillations into directed flow. Our results confirm Tesla’s conjecture that diodic performance is boosted for pulsatile flows. The connections between diodicity, early turbulence and pulsatility uncovered here can inform applications in fluidic mixing and pumping. Nguyen et al. take a fresh look at Tesla’s concept of an asymmetric fluidic conduit. They show that for alternating flow at high frequencies the device can almost be operated as a diode, enabled by a transition to turbulent-like flow at rather low Reynolds numbers.
Additive Schwarz Preconditioners for a State Constrained Elliptic Distributed Optimal Control Problem Discretized by a Partition of Unity Method

The minimization problem (2) is discretized in [4] by a partition of unity method (PUM). The goal of this paper is to use the ideas in [5] for an obstacle problem of clamped Kirchhoff plates to develop preconditioners for the discrete problems in [4].
The limit of law: factors influencing the decision to make harmful acts illegal

This paper examines factors affecting the decision of whether or not to make certain harmful acts illegal. It considers factors related both to the cost of law enforcement and to the crime commission decision. On the enforcement side, illegality is limited by the existence of fixed notice and response costs, which are unrelated to the harm from the act, and also by costs of imposing punishment. In addition, illegality is limited by a finite marginal productivity of detection, one cause of which is legal error. On the commission side, illegality is limited if offenders have strictly positive benefits from committing the act. The paper concludes by examining how the optimal scope of law is affected by its “expressive function”, the idea that some people are deterred by the mere fact that an act is illegal. We specifically ask how the scope of law changes if more people behave in this way. The answer depends on whether “efficient” violations of the law are possible, which in turn depends on whether offenders’ gains are counted in welfare.
Nucleon matrix element of Weinberg's CP-odd gluon operator from the instanton vacuum

Abstract We calculate the nucleon matrix element of Weinberg's dimension-6 CP-odd gluon operator f a b c ( F ˜ μ ν ) a ( F μ ρ ) b ( F ρ ν ) c in the instanton vacuum. In leading order of the instanton packing fraction, the dimension-6 operator is effectively proportional to the topological charge density ( F ˜ μ ν ) a ( F μ ν ) a , whose nucleon matrix element is given by the flavor-singlet axial charge and constrained by the U ( 1 ) A anomaly. The nucleon matrix element of the dimension-6 operator is obtained substantially larger than in other estimates, because of the strong localization of the nonperturbative gluon fields in the instanton vacuum. We comment on the neutron electric dipole moment induced by CP violation proportional to the dimension-6 operator.
Lorentz invariance of basis tensor gauge theory

Basis tensor gauge theory (BTGT) is a vierbein analog reformulation of ordinary gauge theories in which the vierbein field describes the Wilson line. After a brief review of the BTGT, we clarify th...
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning

Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity. However, three fundamental challenges in designing decentralized optimization algorithms are how to reduce their sample, communication, and memory complexities. In this paper, we propose a gradient-tracking-based stochastic recursive momentum (GT-STORM) algorithm for efficiently solving nonconvex optimization problems. We show that to reach an ϵ2-stationary solution, the total number of sample evaluations of our algorithm is O(m1/2ϵ-3) and the number of communication rounds is O(m1/2ϵ-3), which improve the O(ϵ-4) costs of sample evaluations and communications for the existing decentralized stochastic gradient algorithms. We conduct extensive experiments with a variety of learning models, including non-convex logistical regression and convolutional neural networks, to verify our theoretical findings. Collectively, our results contribute to the state of the art of theories and algorithms for decentralized network optimization.
Measure and dimension of sums and products

We investigate the Lebesgue measure, Hausdorff dimension, and Fourier dimension of sets of the form $RY + Z, $ where $R \subseteq (0,\infty)$ and $Y, Z \subseteq \mathbb{R}^d$. We prove a theorem on the Lebesgue measure and Hausdorff dimension of $RY+Z$; The theorem is a generalized variant of some theorems of Wolff and Oberlin in which $Y$ is the unit sphere, but its proof is much simpler. We also prove a deeper existence theorem: For each $\alpha \in [0,1]$ and for each non-empty compact set $R \subseteq (0,\infty)$, there exists a compact set $Y \subseteq [1,2]$ such that $\dim_F(Y) = \dim_H(Y) = \overline{\dim_M}(Y) = \alpha$ and $\dim_F(RY) \geq \min\{ 1, \dim_F(R) + \dim_F(Y)\}$. This theorem verifies a weak form of a more general conjecture, and it can be used to produce new Salem sets from old ones.
Directional maximal function along the primes

We study a two-dimensional discrete directional maximal operator along the set of the prime numbers. We show existence of a set of vectors, which are lattice points in a sufficiently large annulus, for which the $\ell^2$ norm of the associated maximal operator, with supremum taken over all large scales, grows with an epsilon power in the number of vectors. This paper is a follow-up to a prior work on the discrete directional maximal operator along the integers by the first and third author.