cssi/SciSciGPT-SciSciNet huggingface dataset:
Another look at the evidence on money-income causality

Abstract Stock and Watson's widely noted finding that money has statistically significant marginal predictive power with respect to real output (as measured by industrial production), even in a sample extending through 1985 and even in the presence of a short-term interest rate, is not robust to two plausible changes. First, extending the sample through 1990 renders money insignificant within Stock and Watson's chosen specification. Second, using the commercial paper rate in place of the Treasury bill rate renders money insignificant even in the sample ending in 1985. A positive finding is that the difference between the commercial paper rate and the Treasury bill rate does have highly significant predictive value for real output, even in the presence of money, regardless of sample. Alternative results, based on forecast error variance decomposition in a vector autoregression setting, confirm these findings by indicating a small and generally insignificant effect of money and a large and highly significant effect of the paper-bill spread on real output.
On knowing one's chains and decking them with flowers: limits on patient autonomy in The Silent World of Doctor and Patient.

In this article Professor Baron continues the debate started by Jay Katz in his book "The Silent World of Doctor and Patient" on the necessity of exploring further patients' reasons for refusing treatment.
The Benefits of Capture

Observers of the administrative state warn against capture of administrative agencies and lament its disastrous effects. This Article suggests that the term “capture,” applied to a close relationship between industry and regulator, is not useful; by stigmatizing that relationship—judging the relationship as problematic from the start—the stigmatization hides the relationships potential benefits. The literature on capture highlights its negative results: lax enforcement of regulation, weak regulations, and illicit benefits going to industry. This picture, however, is incomplete and in substantial tension with another current strand of literature which encourages collaboration between industry and regulator. The collaboration literature draws on the fact that industry input into the regulatory process has important benefits for the regulatory state. Industry usually has information no one else has and has more incentive to give that information to a friendly regulator. Furthermore, working with industry can substantially improve the impact of regulation; voluntary compliance is cheaper and can be more effective than enforced
Representativity, Civil Society, and the EU Social Dialogue: Lessons from the International Labor Organization

This article addresses representativity questions that arise from the formal insertion of private, functional groups within the European Union (EU) governance via the EU social dialogue. It depicts the representativity debate at the EU social dialogue and suggests that important lessons can be learned through the examination of another institutional context in which similar questions have been raised and addressed: the International Labor Organization (ILO) tripartite structure. In addition, it ascertains that the issue of representativity of the EU social partners would further benefit from viewing it in the broader context of the EU "democratic deficit." The article concludes that such an analysis underscores the need to rethink the current understanding of representativity in the EU social dialogue. In particular, it considers the benefits and disadvantages of expanding the notion of representativity to encompass the internal structural mechanisms of the EU social partners.
The impact of interactions on invasion and colonization resistance in microbial communities

In human microbiota, the prevention or promotion of invasions can be crucial to human health. Invasion outcomes, in turn, are impacted by the composition of resident communities and interactions of resident members with the invader. Here we study how interactions influence invasion outcomes in microbial communities, when interactions are primarily mediated by chemicals that are released into or consumed from the environment. We use a previously developed dynamic model which explicitly includes species abundances and the concentrations of chemicals that mediate species interaction. Using this model, we assessed how species interactions impact invasion by simulating a new species being introduced into an existing resident community. We classified invasion outcomes as resistance, augmentation, displacement, or disruption depending on whether the richness of the resident community was maintained or decreased and whether the invader was maintained in the community or went extinct. We found that as the number of invaders introduced into the resident community increased, disruption rather than augmentation became more prevalent. With more facilitation of the invader by the resident community, resistance outcomes were replaced by displacement and augmentation. By contrast, with more facilitation among residents, displacement outcomes shifted to resistance. When facilitation of the resident community by the invader was eliminated, the majority of augmentation outcomes turned into displacement, while when inhibition of residents by invaders was eliminated, invasion outcomes were largely unaffected. Our results suggest that a better understanding of interactions within resident communities and between residents and invaders is crucial to predicting the success of invasions into microbial communities.
CONSERVATIVE PROTESTANT CHILDREARING: AUTHORITARIAN OR AUTHORITATIVE? *

Recent research on conservative Protestantism suggests that religion has reemerged as an important predictor of childrearing attitudes and practices. This research has focused on the distinctive approach toward discipline among conservative Protestant parents. No study, however, has explored the links between conservative Protestantism and positive parental emotion work (physical and verbal expressions of affection). I suggest, paradoxically, that this subculture is characterized both by strict discipline and an unusually warm and expressive style of parent-child interaction. I review parenting advice offered by conservative Protestant leaders, which encourages parents to engage in positive emotion work with their children. I then analyze data from the 1987-1988 National Survey of Families and Households (NSFH) to determine if religious affiliation and theological conservatism are related to positive parental emotion work. I find that parents with conservative theological beliefs are more likely to praise and hug their children than are parents with less conservative theological views. Modest positive net effects of conservative Protestant affiliation are also found.
The Categorical Lucas Rule and the Nuisance and Background Principles Exception

This article examines the seminal 1992 United States Supreme Court decision, Lucas v. South Carolina Coastal Council, 1 specifically focusing on the Lucas nuisance exception. I surveyed approximately 1,600 reported regulatory takings cases decided since the Lucas decision involving Lucas takings challenges. I identified the statutory nuisance cases in which state and local governments unsuccessfully asserted the Lucas nuisance exception as a defense to the courts' findings of a Lucas taking. This article examines the prospective potential of these cases for assisting private property owners in enhancing private property rights protections within the area of regulatory takings.
Optimal financing and disclosure

I study an entrepreneur who finances a project with uncertain cash flows and who jointly chooses the disclosure and financing policies. In the Bayesian persuasion framework, I show that it is optim...
Reforming the Mining Law: Problems and Prospects

This article addresses longstanding efforts to reform the Mining Law of 1872, the venerated, vilified and antiquated law that governs hard rock mining on federal lands. It discusses how the mining law has changed over the years, and assesses prospects for fundamental legislative reform.
When Is a Mutual Fund Director Independent? The Unexplored Role of Professional Relationships Under Section 2(A)(19) of the Investment Company Act

[An investment company that must register with the Securities and Exchange Commission ("the Commission") is required by the Investment Company Act ("the Act") to have a specified percentage or number of directors who are not "interested" in the company. To be not interested (i.e., to be independent), a director of an investment company is barred by § 2(a)(19) of the Act from inter alia having had, during the last two completed fiscal years of the company, a material business relationship or a material professional relationship with specified parties. The Commission, in interpreting § 2(a)(19), has not clearly distinguished the two types of relationships and has not focused on professional relationships apart from business relationships. The present article contends that this is contrary to the intent of Congress. Accordingly, the article first identifies both the elements of a business relationship and the elements of a professional relationship. Second, three no-action letters are reviewed, in each of which the Commission staff could have found that a proposed arrangement would have created a professional relationship for an investment company director.] "Congress entrusted to the independent directors of investment companies... the primary responsibility for looking after the interests of the funds' shareholders. "1
Constraint Enforcement via Tube-Based MPC Exploiting Switching Restrictions

Dwell-time restrictions are critical to overcoming infeasibility caused by arbitrary switches in constrained time-dependent switched systems, especially when the system equilibrium is mode dependent. However, existing approaches to enforce constraints are not applicable in presence of disturbances. In this letter, a recursively feasible switched tube-based MPC (TMPC) is designed for constrained linear modes subject to polytopic-bounded additive disturbances. Control-invariant sets that are robust to mode switches and disturbances are proposed to select terminal constraints and identify feasible initial states. The method is distinguished by the admission of mode-dependent equilibria, user-specified prediction horizons, non-reliance on a preview of the switching signal, and online computations equal to that of a non-switched TMPC.
Heuristic hyperparameter optimization of deep learning models for genomic prediction.

There is a growing interest among quantitative geneticists and animal breeders in the use of deep learning (DL) for genomic prediction. However, the performance of DL is affected by hyperparameters that are typically manually set by users. These hyperparameters do not simply specify the architecture of the model; they are also critical for the efficacy of the optimization and model-fitting process. To date, most DL approaches used for genomic prediction have concentrated on identifying suitable hyperparameters by exploring discrete options from a subset of the hyperparameter space. Enlarging the hyperparameter optimization search space with continuous hyperparameters is a daunting combinatorial problem. To deal with this problem, we propose using differential evolution (DE) to perform an efficient search of arbitrarily complex hyperparameter spaces in DL models, and we apply this to the specific case of genomic prediction of livestock phenotypes. This approach was evaluated on two pig and cattle datasets with real genotypes and simulated phenotypes (N = 7,539 animals and M = 48,541 markers) and one real dataset (N = 910 individuals and M = 28,916 markers). Hyperparameters were evaluated using cross-validation. We compared the predictive performance of DL models using hyperparameters optimized by DE against DL models with "best practice" hyperparameters selected from published studies and baseline DL models with randomly specified hyperparameters. Optimized models using DE showed a clear improvement in predictive performance across all three datasets. DE optimized hyperparameters also resulted in DL models with less overfitting and less variation in predictive performance over repeated retraining compared to non-optimized DL models.
Evolvability and Constraint in the Primate Basicranium, Shoulder, and Hip and the Importance of Multi-trait Evolution

The scapula shares developmental and functional relationships with traits of the basicranium, vertebral column, humerus, and clavicle. As a limb girdle, it also shares analogous characteristics with the pelvis. Despite these relationships, studies of primate shoulder evolution often focus on traits of the scapula in isolation. Such analyses may lead to spurious conclusions, as they implicitly model the scapula as evolving independent of other anatomical regions. Traits of the shoulder girdle share genetic covariances with each other, as well as potential covariances with dimensions of other skeletal elements. To create accurate models of shoulder evolution, it is imperative to account for the constraints imposed by these sources of covariance. Here, we use evolutionary quantitative methods to test a model in which shoulder morphological evolution is influenced by its developmental and functional covariances with the basicranium in the Colobus genus. This evolutionary relationship is also assessed with morphology of the pelvis to provide context to the evolutionary covariance among traits of the basicranium and shoulder girdle. Our results indicate potential evolutionary implications arising from covariances among the basicranium, shoulder, and pelvis. We further propose that the shoulder and basicranium may be examples of developmental, functional, and genetic covariances among traits that manifest an evolutionary suite of mutually constrained morphologies. We demonstrate novel evolutionary relationships among the shoulder girdle and basicranium that affect not only models of primate shoulder evolution but have broader implications for modeling trait evolution across the skeleton.
Personalized Cardiovascular Disease Risk Mitigation via Longitudinal Inverse Classification

Cardiovascular disease (CVD) is a serious illness affecting millions world-wide and is the leading cause of death in the US. Recent years, however, have seen tremendous growth in the area of personalized medicine, a field of medicine that places the patient at the center of the medical decision-making and treatment process. Many CVD-focused personalized medicine innovations focus on genetic biomarkers, which provide person-specific CVD insights at the genetic level, but do not focus on the practical steps a patient could take to mitigate their risk of CVD development. In this work we propose longitudinal inverse classification, a recommendation framework that provides personalized lifestyle recommendations that minimize the predicted probability of CVD risk. Our framework takes into account historical CVD risk, as well as other patient characteristics, to provide recommendations. Our experiments show that earlier adoption of the recommendations elicited from our framework produce significant CVD risk reduction.
Schizophrenia risk alleles often affect the expression of many genes and each gene may have a different effect on the risk: A mediation analysis.

Variants identified by genome-wide association studies (GWAS) are often expression quantitative trait loci (eQTLs), suggesting they are proxies or are themselves regulatory. Across many data sets, analyses show that variants often affect multiple genes. Lacking data on many tissue types, developmental time points, and homogeneous cell types, the extent of this one-to-many relationship is underestimated. This raises questions on whether a disease eQTL target gene explains the genetic association or is a bystander and puts into question the direction of expression effect of on the risk, since the many variants-regulated genes may have opposing effects, imperfectly balancing each other. We used two brain gene expression data sets (CommonMind and BrainSeq) for mediation analysis of schizophrenia-associated variants. We confirm that eQTL target genes often mediate risk but the direction in which expression affects risk is often different from that in which the risk allele changes expression. Of 38 mediator genes significant in both data sets 33 showed consistent mediation direction (Chi2 test p = 6 × 10-6 ). One might expect that the expression would correlate with the risk allele in the same direction it correlates with the disease. For 15 of these 33 (45%), however, the expression change associated with the risk allele was protective, suggesting the likely presence of other target genes with overriding effects. Our results identify specific risk mediating genes and suggest caution in interpreting the biological consequences of targeted modifications of gene expression, as not all eQTL targets may be relevant to disease while those that are, might have different from expected directions.
Practical verification protocols for analog quantum simulators

Analog quantum simulation is expected to be a significant application of near-term quantum devices. Verification of these devices without comparison to known simulation results will be an important task as the system size grows beyond the regime that can be simulated classically. We introduce a set of experimentally-motivated verification protocols for analog quantum simulators, discussing their sensitivity to a variety of error sources and their scalability to larger system sizes. We demonstrate these protocols experimentally using a two-qubit trapped-ion analog quantum simulator and numerically using models of up to five qubits.
Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies

Many powerful computing technologies rely on implicit and explicit data contributions from the public. This dependency suggests a potential source of leverage for the public in its relationship with technology companies: by reducing, stopping, redirecting, or otherwise manipulating data contributions, the public can reduce the effectiveness of many lucrative technologies. In this paper, we synthesize emerging research that seeks to better understand and help people action this data leverage. Drawing on prior work in areas including machine learning, human-computer interaction, and fairness and accountability in computing, we present a framework for understanding data leverage that highlights new opportunities to change technology company behavior related to privacy, economic inequality, content moderation and other areas of societal concern. Our framework also points towards ways that policymakers can bolster data leverage as a means of changing the balance of power between the public and tech companies.
PARIS: Predicting application resilience using machine learning

Abstract The traditional method to study application resilience to errors in HPC applications uses fault injection (FI), a time-consuming approach. While analytical models have been built to overcome the inefficiencies of FI, they lack accuracy. In this paper, we present PARIS, a machine-learning method to predict application resilience that avoids the time-consuming process of random FI and provides higher prediction accuracy than analytical models. PARIS captures the implicit relationship between application characteristics and application resilience, which is difficult to capture using most analytical models. We overcome many technical challenges for feature construction, extraction, and selection to use machine learning in our prediction approach. Our evaluation on 16 HPC benchmarks shows that PARIS achieves high prediction accuracy. PARIS is up to 450x faster than random FI (49x on average). Compared to the state-of-the-art analytical model, PARIS is at least 63% better in terms of accuracy and has comparable execution time on average.
Feasibility of Social Media Recruitment for Orofacial Cleft Genetic Research.

OBJECTIVE This study assessed the feasibility of unpaid social media advertising to recruit participants affected with an orofacial cleft (OFC) for a genetic study. DESIGN This is a retrospective analysis of recruitment based on enrollment and participation in a genetic study. Participants completed a series of enrollment surveys, provided saliva samples, and completed postparticipation feedback surveys. PARTICIPANTS Participants were eligible if they or a minor in their care were affected by an OFC, the affected participant was not adopted, and the mother of the affected individual had not taken antiseizure medication during pregnancy. MAIN OUTCOME MEASURES Success of recruitment was evaluated from the number of enrolled participants and sample return rate. RESULTS In the first 12 months of recruitment, 313 individuals completed initial screening surveys; of these, 306 participants were eligible. A total of 263 individuals completed all online surveys and were sent DNA sample kits. One hundred sixty-two subject DNA samples were returned within 12 months of sending, for a return rate of 62%. Approximately two-thirds (66.3%) of all returned samples were sent back within the first 6 weeks after receiving DNA kits. CONCLUSIONS Unpaid social media advertising enabled the recruitment of a large cohort of participants in a short time (12 months). The resulting study population was limited in racial and ethnic diversity, suggesting that other recruitment strategies will be needed for studies seeking specific demographic or socioeconomic groups. Nonetheless, social media recruitment was efficient and effective for recruiting participants for a genetic study in comparison to traditional clinic-based modes of recruitment.
Stable DNMT3L overexpression in SH-SY5Y neurons recreates a facet of the genome-wide Down syndrome DNA methylation signature

Down syndrome (DS) is characterized by a genome-wide profile of differential DNA methylation that is skewed towards hypermethylation in most tissues, including brain, and includes pan-tissue differential methylation. The molecular mechanisms involve the overexpression of genes related to DNA methylation on chromosome 21. Here, we stably overexpressed the chromosome 21 gene DNA methyltransferase 3L (DNMT3L) in the human SH-SY5Y neuroblastoma cell line and assayed DNA methylation at over 26 million CpGs by whole genome bisulfite sequencing (WGBS) at three different developmental phases (undifferentiated, differentiating, and differentiated). DNMT3L overexpression resulted in global CpG and CpG island hypermethylation as well as thousands of differentially methylated regions (DMRs). The DNMT3L DMRs were skewed towards hypermethylation and mapped to genes involved in neurodevelopment, cellular signaling, and gene regulation. Consensus DNMT3L DMRs showed that cell lines clustered by genotype and then differentiation phase, demonstrating sets of common genes affected across neuronal differentiation. The hypermethylated DNMT3L DMRs from all pairwise comparisons were enriched for regions of bivalent chromatin marked by H3K4me3 as well as differentially methylated sites from previous DS studies of diverse tissues. In contrast, the hypomethylated DNMT3L DMRs from all pairwise comparisons displayed a tissue-specific profile enriched for regions of heterochromatin marked by H3K9me3 during embryonic development. Taken together, these results support a mechanism whereby regions of bivalent chromatin that lose H3K4me3 during neuronal differentiation are targeted by excess DNMT3L and become hypermethylated. Overall, these findings demonstrate that DNMT3L overexpression during neurodevelopment recreates a facet of the genome-wide DS DNA methylation signature by targeting known genes and gene clusters that display pan-tissue differential methylation in DS.
Nonlocal elastodynamics and fracture

A nonlocal field theory of peridynamic type is applied to model the brittle fracture problem. The elastic fields obtained from the nonlocal model are shown to converge in the limit of vanishing non-locality to solutions of classic plane elastodynamics associated with a running crack. We carry out our analysis for a plate subject to mode one loading. The length of the crack is prescribed a priori and is an increasing function of time.
Tightness and tails of the maximum in 3D Ising interfaces

Consider the 3D Ising model on a box of side length n with minus boundary conditions above the xy-plane and plus boundary conditions below it. At low temperatures, Dobrushin (1972) showed that the interface separating the predominantly plus and predominantly minus regions is localized: its height above a fixed point has exponential tails. Recently, the authors proved a law of large numbers for the maximum height Mn of this interface: for every β large, Mn/logn→cβ in probability as n→∞. Here, we show that the laws of the centered maxima (Mn−E[Mn])n≥1 are uniformly tight. Moreover, even though this sequence does not converge, we prove that it has uniform upper and lower Gumbel tails (exponential right tails and doubly exponential left tails). Key to the proof is a sharp (up to O(1) precision) understanding of the surface large deviations. This includes, in particular, the shape of a pillar that reaches near-maximum height, even at its base, where the interactions with neighboring pillars are dominant.
Verification of Eventual Consensus in Synod Using a Failure-Aware Actor Model

Successfully attaining consensus in the absence of a centralized coordinator is a fundamental problem in distributed multi-agent systems. We analyze progress in the Synod consensus protocol—which does not assume a unique leader—under the assumptions of asynchronous communication and potential agent failures. We identify a set of sufficient conditions under which it is possible to guarantee that a set of agents will eventually attain consensus. First, a subset of the agents must behave correctly and not permanently fail until consensus is reached, and second, at least one proposal must be eventually uninterrupted by higher-numbered proposals. To formally reason about agent failures, we introduce a failure-aware actor model (FAM). Using FAM, we model the identified conditions and provide a formal proof of eventual progress in Synod. Our proof has been mechanically verified using the Athena proof assistant and, to the best of our knowledge, it is the first machine-checked proof of eventual progress in Synod.
Extreme values of the derivative of Blaschke products and hypergeometric polynomials

Abstract A finite Blaschke product, restricted to the unit circle, is a smooth covering map. The maximum and minimum values of the derivative of this map reflect the geometry of the Blaschke product. We identify two classes of extremal Blaschke products: those that maximize the difference between the maximum and minimum of the derivative, and those that minimize it. Both classes turn out to have the same algebraic structure, being the quotient of two hypergeometric polynomials.
Japan's Foreign Assets and Liabilities: Implications for the External Accounts

The composition of Japan's current account balance has changed over time, with an increasing income balance primarily reflecting a growing net foreign asset position and higher corporate saving. A comparison of Japan's income balance with peer countries highlights: (i) relatively high yields on FDI assets, and (ii) very low FDI liabilities in Japan. Panel estimation is used to derive separate exchange rate elasticities for income credit and debit, with novel accounting that disentangles the mechanical from the economic response to exchange rate fluctuations. Despite the changing composition of Japan's current account balance, its response to exchange rate movements still operates mostly through the traditional trade channel, with a small but reinforcing contribution from the income balance.
High-Order Implicit Large-Eddy Simulation of Flow over a Marine Propeller

Abstract We report the first high-order eddy-resolving simulation of flow over a marine propeller using a recently developed high-order sliding-mesh method. This method employs the flux reconstruction framework and a new dynamic curved mortar approach to handle the complex rotating geometries. For a wide range of working conditions, it is validated to predict the loads very accurately against experiments. The method’s low-dissipation characteristic has allowed the capturing of a broad spectrum of turbulence structures for very long distances even on a very coarse grid. Comparison with a previous low-order simulation is also carried out to show the low-dissipation advantage of the present simulations. From detailed load analysis, the major loads and their distributions and time and frequency scales are identified. Visualizations of the instantaneous, phase-averaged, and time-averaged flow fields have revealed the processes of tip vortex formation, major vortex evolutions, and flow instability developments at different working conditions. The effects of different fairwaters on the propeller’s overall performance are also quantitatively assessed.
Real-time Sampling and Estimation on Random Access Channels: Age of Information and Beyond

Real-time sampling and estimation of autoregressive Markov processes is considered in random access channels. Two classes of policies are studied: (i) oblivious policies in which decision making is independent of the source realizations, and (ii) non-oblivious policies in which sources are observed causally for decision making. In the first class, minimizing the expected time-average estimation error is equivalent to minimizing the expected age of information (AoI). Lower and upper bounds are provided for the achievable estimation error in this class and age-based threshold policies are shown to provide a two-fold improvement compared to the state-of-the-art. In the second class, an error-based threshold policy is proposed: a transmitter becomes active when its error exceeds a threshold in which case it transmits probabilistically following slotted ALOHA. A closed-form expression is derived for the estimation error as a function of the peak age, the transmission delay, a term which we call the silence delay, as well as the source realization. It is analyzed approximately by considering the underlying source as a discretized Wiener process. The proposed threshold policy provides a three-fold improvement compared to oblivious policies and its performance is close to that of centralized greedy scheduling.
Self-similarity in the Kepler–Heisenberg Problem

The Kepler–Heisenberg problem is that of determining the motion of a planet around a sun in the Heisenberg group, thought of as a three-dimensional sub-Riemannian manifold. The sub-Riemannian Hamiltonian provides the kinetic energy, and the gravitational potential is given by the fundamental solution to the sub-Laplacian. The dynamics are at least partially integrable, possessing two first integrals as well as a dilational momentum which is conserved by orbits with zero energy. The system is known to admit closed orbits of any rational rotation number, which all lie within the fundamental zero-energy integrable subsystem. Here, we demonstrate that, under mild conditions, zero-energy orbits are self-similar. Consequently, we find that these zero-energy orbits stratify into three families: future collision, past collision, and quasi-periodicity without collision. If a collision occurs, it occurs in finite time.
On the Application of BAC-NOMA to 6G umMTC

This letter studies the application of backscatter communications (BackCom) assisted non-orthogonal multiple access (BAC-NOMA) to the envisioned sixth-generation (6G) ultra-massive machine type communications (umMTC). In particular, the proposed BAC-NOMA transmission scheme can realize simultaneous energy and spectrum cooperation between uplink and downlink users, which is important to support massive connectivity and stringent energy constraints in umMTC. Furthermore, a resource allocation problem for maximizing the uplink throughput and suppressing the interference between downlink and uplink transmission is formulated as an optimization problem and the corresponding optimal resource allocation policy is obtained. Computer simulations are provided to demonstrate the superior performance of BAC-NOMA.
Spirals and skyrmions in antiferromagnetic triangular lattices

We study realizations of spirals and skyrmions in two-dimensional antiferromagnets with a triangular lattice on an inversion-symmetry-breaking substrate. As a possible material realization, we investigate the adsorption of transition-metal atoms (Cr, Mn, Fe, or Co) on a monolayer of ${\mathrm{MoS}}_{2}$, ${\mathrm{WS}}_{2}$, or ${\mathrm{WSe}}_{2}$ and obtain the exchange, anisotropy, and Dzyaloshinskii-Moriya interaction parameters using first-principles calculations. Using energy minimization and parallel-tempering Monte Carlo simulations, we determine the magnetic phase diagrams for a wide range of interaction parameters. We find that skyrmion lattices can appear even with weak Dzyaloshinskii-Moriya interactions, but their stability is hindered by magnetic anisotropy. However, a weak easy plane magnetic anisotropy can be beneficial for stabilizing the skyrmion phase. Our results suggest that Cr/${\mathrm{MoS}}_{2}$, Fe/${\mathrm{MoS}}_{2}$, and Fe/${\mathrm{WSe}}_{2}$ interfaces can host spin spirals formed from the ${120}^{\ensuremath{\circ}}$ antiferromagnetic states. Our results further suggest that for interfaces, such as Fe/${\mathrm{MoS}}_{2}$, the Dzyaloshinskii-Moriya interaction is strong enough to drive the system into a three-sublattice skyrmion lattice in the presence of experimentally feasible external magnetic field.
The Corporate Objective Revisited: The Shareholder Perspective

Shareholder value maximization has spurred a long-standing and heated debate between the proponents of a unified corporate objective function and the supporters of multi-constituency goals of the corporation. We weigh in on the corporate purpose debate from a different point of view: that of the shareholders. Specifically, we seek to critically assess the assumptions about shareholders embedded in the shareholder value maximization doctrine and examine the implications of three contemporary shareholding practices: (a) shareholders investing concurrently in competing firms, (b) residual-risk bearing decoupling, and (c) heterogeneous shareholder interests. Our critique draws out the challenges that contemporary shareholder practices pose for corporate governance and highlights the need for strategic corporate governance, or governance policies and practices that prioritize the sustainable competitive advantage of the firm.
Concurrent disjoint set union

We develop and analyze concurrent algorithms for the disjoint set union (“union-find” ) problem in the shared memory, asynchronous multiprocessor model of computation, with CAS (compare and swap) or DCAS (double compare and swap) as the synchronization primitive. We give a deterministic bounded wait-free algorithm that uses DCAS and has a total work bound of $$O\biggl ( m \cdot \left( \log {\left( \frac{np}{m} + 1 \right) } + \alpha {\left( n, \frac{m}{np} \right) } \right) \biggr )$$ for a problem with n elements and m operations solved by p processes, where $$\alpha $$ is a functional inverse of Ackermann’s function. We give two randomized algorithms that use only CAS and have the same work bound in expectation. The analysis of the second randomized algorithm is valid even if the scheduler is adversarial. Our DCAS and randomized algorithms take $$O(\log n)$$ steps per operation, worst-case for the DCAS algorithm, high-probability for the randomized algorithms. Our work and step bounds grow only logarithmically with p, making our algorithms truly scalable. We prove that for a class of symmetric algorithms that includes ours, no better step or work bound is possible. Our work is theoretical, but Alistarh et al (In search of the fastest concurrent union-find algorithm, 2019), Dhulipala et al (A framework for static and incremental parallel graph connectivity algorithms, 2020) and Hong et al (Exploring the design space of static and incremental graph connectivity algorithms on gpus, 2020) have implemented some of our algorithms on CPUs and GPUs and experimented with them. On many realistic data sets, our algorithms run as fast or faster than all others.
{MIRAGE}: Mitigating Conflict-Based Cache Attacks with a Practical Fully-Associative Design

Shared processor caches are vulnerable to conflict-based side-channel attacks, where an attacker can monitor access patterns of a victim by evicting victim cache lines using cache-set conflicts. Recent mitigations propose randomized mapping of addresses to cache lines to obfuscate the locations of set-conflicts. However, these are vulnerable to new attacks that discover conflicting sets of addresses despite such mitigations, because these designs select eviction-candidates from a small set of conflicting lines. This paper presents Mirage, a practical design for a fully associative cache, wherein eviction candidates are selected randomly from all lines resident in the cache, to be immune to set-conflicts. A key challenge for enabling such designs in large shared caches (containing tens of thousands of cache lines) is the complexity of cache-lookup, as a naive design can require searching through all the resident lines. Mirage achieves full-associativity while retaining practical set-associative lookups by decoupling placement and replacement, using pointer-based indirection from tag-store to data-store to allow a newly installed address to globally evict the data of any random resident line. To eliminate set-conflicts, Mirage provisions extra invalid tags in a skewed-associative tag-store design where lines can be installed without set-conflict, along with a load-aware skew-selection policy that guarantees the availability of sets with invalid tags. Our analysis shows Mirage provides the global eviction property of a fully-associative cache throughout system lifetime (violations of full-associativity, i.e. set-conflicts, occur less than once in 10^4 to 10^17 years), thus offering a principled defense against any eviction-set discovery and any potential conflict based attacks. Mirage incurs limited slowdown (2%) and 17-20% extra storage compared to a non-secure cache.
Anharmonic classical time crystals: A coresonance pattern formation mechanism

The authors describe a classical discrete time crystal phase emerging through the excitation of a pair of modes that coresonate with parametric driving, resulting in a tunable anharmonic frequency response.
Traveling wave solutions in a model for social outbursts in a tension-inhibitive regime

In this work we investigate the existence of non-monotone traveling wave solutions to a reaction-diffusion system modeling social outbursts, such as rioting activity, originally proposed in arXiv:1502.04725v3. The model consists of two scalar values, the level of unrest $u$ and a tension field $v$. A key component of the model is a bandwagon effect in the unrest, provided the tension is sufficiently high. We focus on the so-called tension inhibitive regime, characterized by the fact that the level of unrest has a negative feedback on the tension. This regime has been shown to be physically relevant for the spatiotemporal spread of the 2005 French riots. We use Geometric Singular Perturbation Theory to study the existence of such solutions in two situations. The first is when both $u$ and $v$ diffuse at a very small rate. Here, the time scale over which the bandwagon effect is observed plays a key role. The second case we consider is when the tension diffuses at a much slower rate than the level of unrest. In this case, we are able to deduce that the driving dynamics are modeled by the well-known Fisher-KPP equation.
Reconstruction theorem for monoid schemes

Abstract We aim to reconstruct a monoid scheme X from the category of quasi-coherent sheaves over it. This is much in the vein of Gabriel's original reconstruction theorem. Under some finiteness condition on a monoid schemes X , we show that the localisations of the topos Qc ( X ) of quasi-coherent sheaves on X are in a one-to-one correspondence with open subsets of X, while the elements of X correspond to the topos points of Qc ( X ) . This allows us to reconstruct X from Qc ( X ) .
Conservative Objective Models for Effective Offline Model-Based Optimization

Computational design problems arise in a number of settings, from synthetic biology to computer architectures. In this paper, we aim to solve data-driven model-based optimization (MBO) problems, where the goal is to find a design input that maximizes an unknown objective function provided access to only a static dataset of prior experiments. Such data-driven optimization procedures are the only practical methods in many real-world domains where active data collection is expensive (e.g., when optimizing over proteins) or dangerous (e.g., when optimizing over aircraft designs). Typical methods for MBO that optimize the design against a learned model suffer from distributional shift: it is easy to find a design that "fools" the model into predicting a high value. To overcome this, we propose conservative objective models (COMs), a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs, and uses it for optimization. Structurally, COMs resemble adversarial training methods used to overcome adversarial examples. COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems, including optimizing protein sequences, robot morphologies, neural network weights, and superconducting materials.
Top-k eXtreme Contextual Bandits with Arm Hierarchy

Motivated by modern applications, such as online advertisement and recommender systems, we study the top-$k$ extreme contextual bandits problem, where the total number of arms can be enormous, and the learner is allowed to select $k$ arms and observe all or some of the rewards for the chosen arms. We first propose an algorithm for the non-extreme realizable setting, utilizing the Inverse Gap Weighting strategy for selecting multiple arms. We show that our algorithm has a regret guarantee of $O(k\sqrt{(A-k+1)T \log (|\mathcal{F}|T)})$, where $A$ is the total number of arms and $\mathcal{F}$ is the class containing the regression function, while only requiring $\tilde{O}(A)$ computation per time step. In the extreme setting, where the total number of arms can be in the millions, we propose a practically-motivated arm hierarchy model that induces a certain structure in mean rewards to ensure statistical and computational efficiency. The hierarchical structure allows for an exponential reduction in the number of relevant arms for each context, thus resulting in a regret guarantee of $O(k\sqrt{(\log A-k+1)T \log (|\mathcal{F}|T)})$. Finally, we implement our algorithm using a hierarchical linear function class and show superior performance with respect to well-known benchmarks on simulated bandit feedback experiments using extreme multi-label classification datasets. On a dataset with three million arms, our reduction scheme has an average inference time of only 7.9 milliseconds, which is a 100x improvement.
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits

It has become increasingly common for data to be collected adaptively, for example using contextual bandits. Historical data of this type can be used to evaluate other treatment assignment policies to guide future innovation or experiments. However, policy evaluation is challenging if the target policy differs from the one used to collect data, and popular estimators, including doubly robust (DR) estimators, can be plagued by bias, excessive variance, or both. In particular, when the pattern of treatment assignment in the collected data looks little like the pattern generated by the policy to be evaluated, the importance weights used in DR estimators explode, leading to excessive variance. In this paper, we improve the DR estimator by adaptively weighting observations to control its variance. We show that a t-statistic based on our improved estimator is asymptotically normal under certain conditions, allowing us to form confidence intervals and test hypotheses. Using synthetic data and public benchmarks, we provide empirical evidence for our estimator's improved accuracy and inferential properties relative to existing alternatives.
Convergence of moments of twisted COE matrices

We investigate eigenvalue moments of matrices from the circular orthogonal ensemble multiplicatively perturbed by a permutation matrix. More precisely, we investigate the variance of the sum of the eigenvalues raised to power k for arbitrary but fixed k and in the limit of a large matrix size. We find that when the permutation defining the perturbed ensemble has only long cycles, the answer is universal and approaches the corresponding moment of the circular unitary ensemble with a particularly fast rate: the error is of order 1/N3 and the terms of orders 1/N and 1/N2 disappear due to cancellations. We prove this rate of convergence using Weingarten calculus and classifying the contributing Weingarten functions first in terms of a graph model and then algebraically.
Quasipositive links and Stein surfaces

We study the generalization of quasipositive links from the 3–sphere to arbitrary closed, orientable 3–manifolds. Our main result shows that the boundary of any smooth, properly embedded complex curve in a Stein domain is a quasipositive link. This generalizes a result due to Boileau and Orevkov, and it provides the first half of a topological characterization of links in 3–manifolds which bound complex curves in a Stein filling. Our arguments replace pseudoholomorphic curve techniques with a study of characteristic and open book foliations on surfaces in 3– and 4–manifolds.
A Parallel Batch-Dynamic Data Structure for the Closest Pair Problem

We propose a theoretically-efficient and practical parallel batch-dynamic data structure for the closest pair problem. Our solution is based on a serial dynamic closest pair data structure by Golin et al., and supports batches of insertions and deletions in parallel. For a data set of size n, our data structure supports a batch of insertions or deletions of size m in O(m(1+log ((n+m)/m))) expected work and O(log (n+m)log^*(n+m)) depth with high probability, and takes linear space. The key techniques for achieving these bounds are a new work-efficient parallel batch-dynamic binary heap, and careful management of the computation across sets of points to minimize work and depth. We provide an optimized multicore implementation of our data structure using dynamic hash tables, parallel heaps, and dynamic k-d trees. Our experiments on a variety of synthetic and real-world data sets show that it achieves a parallel speedup of up to 38.57x (15.10x on average) on 48 cores with hyper-threading. In addition, we also implement and compare four parallel algorithms for static closest pair problem, for which we are not aware of any existing practical implementations. On 48 cores with hyper-threading, the static algorithms achieve up to 51.45x (29.42x on average) speedup, and Rabin’s algorithm performs the best on average. Comparing our dynamic algorithm to the fastest static algorithm, we find that it is advantageous to use the dynamic algorithm for batch sizes of up to 20% of the data set. As far as we know, our work is the first to experimentally evaluate parallel closest pair algorithms, in both the static and the dynamic settings.