cssi/SciSciGPT-SciSciNet huggingface dataset:
Russia's oil potential: prospects and implications

Since the late 1990s, Russia's oil production has experienced a steady resurgence. As a result, Moscow has regained its status as a major oil producer, exporter and crucial player in global energy markets. This paper argues that this rising Russian role should neither be overestimated nor underestimated. It examines some of the main characteristics of the country's oil industry, notably the investment climate, production-sharing agreement regime and pipeline capacity. Russia's rising production is likely to enhance global energy security. Moscow and other major oil producers share a similar goal — stability of the oil market and international economy. Today's energy market should not be analysed in zero-sum terms. Major oil producers coordinate their policy with each other and with major consumers.
Integrated marketing communications in markets with uncertainty and competition

Firms frequently utilize multiple communications instruments as part of their marketing campaign. Interactions between these instruments suggest that firms should apply Integrated Marketing Communications (IMC) to benefit from the synergies. We review different IMC models and then present a stochastic IMC model for which explicit closed-loop solutions of the optimal advertising and market share are obtained. This enables us to understand the role of firm and market parameters such as synergy on the optimal advertising budget and allocation. For the proposed and existing IMC models, we show that the budget and allocation decisions can be made independently, greatly simplifying the implementation of IMC. We also show that there is an optimal long-run market share that the firm should try to maintain through appropriate use of IMC. Finally, the model and results are generalized to multiple (>2) instruments and multiple competitors.
CEO deal-making activities and compensation

Abstract Using transactions generally overlooked in the compensation literature—joint ventures, strategic alliances, seasoned equity offerings (SEOs), and spin-offs—we find that, beyond compensation for increases in firm size or complexity, chief executive officers (CEOs) are rewarded for their deal-making activities. Boards pay CEOs for the core motivation of the deal, as well as for deal volume. We find that compensating for volume instead of core value creation occurs under weak board monitoring and that in deal-making firms, neither CEO turnover nor pay-for-performance responds to underperformance. We introduce an input monitoring explanation for these results: boards compensate for deal volume because of their inability to perfectly monitor outputs.
Evidence from Auditors about Managers' and Auditors' Earnings Management Decisions

This paper reports analyses of data obtained using a field‐based questionnaire in which 253 auditors from one Big 5 firm recalled and described 515 specific experiences they had with clients who they believe were attempting to manage earnings. This approach enables us to analyze separately managers' decisions about how to attempt earnings management and auditors' decisions about whether to prevent earnings management by requiring adjustment of the financial statements. Our results indicate that managers are more likely to attempt earnings management, and auditors are less likely to adjust earnings management attempts, which are structured (not structured) with respect to precise (imprecise) standards. We also find that managers are more likely to make attempts that increase current‐year income, but auditors are more likely to require that those attempts be adjusted, that managers are more likely to make attempts that decrease current‐year income with unstructured transactions and/or when standards are imp...
BACK TO THE ORDINALIST REVOLUTION: BEHAVIORAL ECONOMIC CONCERNS IN EARLY MODERN CONSUMER CHOICE THEORY

The paper argues that theoretical work on consumer choice theory during the early 20th century addressed some of the same issues discussed in recent behavioral economics. This is not generally recognized because the discussion was tied up with the integrability question, the theoretical framework did not involve risky choice or expected utility theory, and the relevant evidence was introspective rather than experimental. The paper makes the case for the similarity and discusses why it is important.
Consent to Retaliation: A Civil Recourse Theory of Contractual Liability

In the ancient Near East, contracts were often solemnized by hacking up a goat. The ritual was an enacted penalty clause: “If I breach this contract, let it be done to me as we are doing to the goat.” This Article argues that we are not so far removed from our goat-hacking forbearers. Legal scholars have argued that contractual liability is best explained by the morality of promise making, or by the need to create optimal incentives in contractual performance. In contrast, this Article argues for the simpler, rawer claim that contractual liability consists of consent to retaliation in the event of breach. In the ancient ritual with the goat, the consented-to retaliation consisted of self-help violence against life and limb. The private law in effect domesticates and civilizes retaliation by replacing private warfare with civil recourse through the courts. It thus facilitates the social cooperation made possible by the ancient threats of retaliation, while avoiding the danger of escalation and violence that such private violence presented. This civil recourse theory of contractual liability provides an explanation for a number of remedial doctrines that have proven difficult for rival interpretations of contract law to explain—including the penaltyclause doctrine, limitations on expectation damages, and the basic privatelaw structure of contractual liability. Finally, this Article responds to some of the most powerful objections that might be made against a civil recourse theory of contractual liability.
Time to SARS-CoV-2 clearance among patients with cancer and COVID-19.

Background For cancer patients, coronavirus disease 19 (COVID-19) infection can lead to delays in cancer therapy both due to the infection itself and due to the need to minimize exposure to other patients and to staff. Clearance guidelines have been proposed, but expected time to clearance has not been established. Methods We identified all patients at a tertiary care hospital cancer center between 25 March 2020 and 6 June 2020 with a positive nasopharyngeal reverse transcriptase polymerase chain reaction (RT-PCR) test for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a cancer-related visit within 3 years, and at least one follow-up assay. We determined the time to clearance using American Society of Clinical Oncology (ASCO), the UK National Institute for Health and Care Excellence (UK-NICE), and Centers for Disease Control and Prevention (CDC) criteria. A matched non-cancer comparison cohort was also identified. Results Thirty-two cancer patients were identified. Nineteen were cleared by ASCO criteria, with estimated median time to clearance of 50 days. Fourteen patients resumed chemotherapy prior to clearance. Using UK-NICE criteria, median time to clearance would have been 31 days, and using CDC criteria, it would have been 13 days. The matched non-cancer cohort had similar clearance time, but with less frequent testing. Conclusion SARS-CoV-2 clearance times differ substantially depending on the criteria used and may be prolonged in cancer patients. This could lead to a delay in cancer care, increased use of clearance testing, and extension of infection control precautions.
Autonomous Robotic Suction to Clear the Surgical Field for Hemostasis Using Image-Based Blood Flow Detection

Autonomous robotic surgery has seen significant progression over the last decade with the aims of reducing surgeon fatigue, improving procedural consistency, and perhaps one day take over surgery itself. However, automation has not been applied to the critical surgical task of controlling tissue and blood vessel bleeding–known as hemostasis. The task of hemostasis covers a spectrum of bleeding sources and a range of blood velocity, trajectory, and volume. In an extreme case, an un-controlled blood vessel fills the surgical field with flowing blood. In this work, we present the first, automated solution for hemostasis through development of a novel probabilistic blood flow detection algorithm and a trajectory generation technique that guides autonomous suction tools towards pooling blood. The blood flow detection algorithm is tested in both simulated scenes and in a real-life trauma scenario involving a hemorrhage that occurred during thyroidectomy. The complete solution is tested in a physical lab setting with the da Vinci Research Kit (dVRK) and a simulated surgical cavity for blood to flow through. The results show that our automated solution has accurate detection, a fast reaction time, and effective removal of the flowing blood. Therefore, the proposed methods are powerful tools to clearing the surgical field which can be followed by either a surgeon or future robotic automation developments to close the vessel rupture.
Quantum Disordered State of Magnetic Charges in Nanoengineered Honeycomb Lattice.

A quantum magnetic state due to magnetic charges is never observed, even though they are treated as quantum mechanical variables in theoretical calculations. Here, the occurrence of a novel quantum disordered state of magnetic charges in a nanoengineered magnetic honeycomb lattice of ultra-small connecting elements is demonstrated. The experimental research, performed using spin resolved neutron scattering, reveals a massively degenerate ground state, comprised of low integer and energetically forbidden high integer magnetic charges, that manifests cooperative paramagnetism at low temperature. The system tends to preserve the degenerate configuration even under large magnetic field application. It exemplifies the robustness of disordered correlation of magnetic charges in a 2D honeycomb lattice. The realization of quantum disordered ground state elucidates the dominance of exchange energy, which is enabled due to the nanoscopic magnetic element size in nanoengineered honeycomb. Consequently, an archetypal platform is envisaged to study quantum mechanical phenomena due to emergent magnetic charges.
BayesFactorFMRI: Implementing Bayesian Second-Level fMRI Analysis with Multiple Comparison Correction and Bayesian Meta-Analysis of fMRI Images with Multiprocessing

BayesFactorFMRI is a tool developed with R and Python to allow neuroimaging researchers to conduct Bayesian second-level analysis and Bayesian meta-analysis of fMRI image data with multiprocessing. This tool expedites computationally intensive Bayesian fMRI analysis through multiprocessing. Its GUI allows researchers who are not experts in computer programming to feasibly perform Bayesian fMRI analysis. BayesFactorFMRI is available via Zenodo and GitHub for download. It would be widely reused by neuroimaging researchers who intend to analyse their fMRI data with Bayesian analysis with better sensitivity compared with classical analysis while improving performance by distributing analysis tasks into multiple processors.
Byzantine Lattice Agreement in Asynchronous Systems

We study the Byzantine lattice agreement (BLA) problem in asynchronous distributed message passing systems. In the BLA problem, each process proposes a value from a join semi-lattice and needs to output a value also in the lattice such that all output values of correct processes lie on a chain despite the presence of Byzantine processes. We present an algorithm for this problem with round complexity of O(log f) which tolerates f < n/5 Byzantine failures in the asynchronous setting without digital signatures, where n is the number of processes. This is the first algorithm which has logarithmic round complexity for this problem in asynchronous setting. Before our work, Di Luna et al give an algorithm for this problem which takes O(f) rounds and tolerates f < n/3 Byzantine failures. We also show how this algorithm can be modified to work in the authenticated setting (i.e., with digital signatures) to tolerate f < n/3 Byzantine failures.
Error-Correcting Codes for Noisy Duplication Channels

Because of its high data density and longevity, DNA is emerging as a promising candidate for satisfying increasing data storage needs. Compared to conventional storage media, however, data stored in DNA is subject to a wider range of errors resulting from various processes involved in the data storage pipeline. In this article, we consider correcting duplication errors for both exact and noisy tandem duplications of a given length $k$ . An exact duplication inserts a copy of a substring of length $k$ of the sequence immediately after that substring, e.g., $\mathsf {ACGT} \to \mathsf {ACG\underline {ACG}T}$ , where $k=3$ , while a noisy duplication inserts a copy suffering from substitution noise, e.g., $\mathsf {ACGT} \to \mathsf {ACG\underline {A \color {Red}{T}}GT}$ . Specifically, we design codes that can correct any number of exact duplication and one noisy duplication errors, where in the noisy duplication case the copy is at Hamming distance 1 from the original. Our constructions rely upon recovering the duplication root of the stored codeword. We characterize the ways in which duplication errors manifest in the root of affected sequences and design efficient codes for correcting these error patterns. We show that the proposed construction is asymptotically optimal, in the sense that it has the same asymptotic rate as optimal codes correcting exact duplications only.
Risk preference heterogeneity in group contests

Abstract We analyze the first model of a group contest with players that are heterogeneous in their risk preferences. In our model, individuals’ preferences are represented by a utility function exhibiting a generalized form of constant absolute risk aversion, allowing us to consider any combination of risk-averse, risk-neutral, and risk-loving players. We begin by proving equilibrium existence and uniqueness under both linear and convex investment costs. Then, we explore how the sorting of a compatible set of players by their risk attitudes into competing groups affects aggregate investment. With linear costs, a balanced sorting (i.e., minimizing the variance in risk attitudes across groups) always produces an aggregate investment level that is at least as high as an unbalanced sorting (i.e., maximizing the variance in risk attitudes across groups). Under convex costs, however, identifying which sorting is optimal is more nuanced and depends on preference and cost parameters.
Silencing Antibiotic Resistance with Antisense Oligonucleotides.

Antisense technologies consist of the utilization of oligonucleotides or oligonucleotide analogs to interfere with undesirable biological processes, commonly through inhibition of expression of selected genes. This field holds a lot of promise for the treatment of a very diverse group of diseases including viral and bacterial infections, genetic disorders, and cancer. To date, drugs approved for utilization in clinics or in clinical trials target diseases other than bacterial infections. Although several groups and companies are working on different strategies, the application of antisense technologies to prokaryotes still lags with respect to those that target other human diseases. In those cases where the focus is on bacterial pathogens, a subset of the research is dedicated to produce antisense compounds that silence or reduce expression of antibiotic resistance genes. Therefore, these compounds will be adjuvants administered with the antibiotic to which they reduce resistance levels. A varied group of oligonucleotide analogs like phosphorothioate or phosphorodiamidate morpholino residues, as well as peptide nucleic acids, locked nucleic acids and bridge nucleic acids, the latter two in gapmer configuration, have been utilized to reduce resistance levels. The major mechanisms of inhibition include eliciting cleavage of the target mRNA by the host’s RNase H or RNase P, and steric hindrance. The different approaches targeting resistance to β-lactams include carbapenems, aminoglycosides, chloramphenicol, macrolides, and fluoroquinolones. The purpose of this short review is to summarize the attempts to develop antisense compounds that inhibit expression of resistance to antibiotics.
Collective Excitations and Optical Response of Ultrathin Carbon-Nanotube Films

We present a theoretical study of the collective quasiparticle excitations responsible for the electromagnetic response of ultrathin plane-parallel homogeneous periodic single-wall carbon-nanotube arrays and weakly inhomogeneous single-wall carbon-nanotube films. We show that in addition to varying film composition, the collective response can be controlled by varying the film thickness. For single-type nanotube arrays, the real part of the dielectric response shows a broad negative refraction band near a quantum interband transition of the constituent nanotube, whereby the system behaves as a hyperbolic metamaterial at higher frequencies than those classical plasma oscillations have to offer. By decreasing nanotube diameters it is possible to push this negative refraction into the visible region, and using weakly inhomogeneous multitype nanotube films broadens its bandwidth.
Consistent Sparse Deep Learning: Theory and Computation

Deep learning has been the engine powering many successes of data science. However, the deep neural network (DNN), as the basic model of deep learning, is often excessively over-parameterized, caus...
Recent LIGO-Virgo discoveries

The LIGO and Virgo gravitational-wave detectors carried out the first half of their third observing run from April through October 2019. During this period, they detected 39 new signals from the co...
Independent and combined effects of exercise training and metformin on insulin sensitivity in individuals with prediabetes

OBJECTIVE Physical activity or metformin enhances insulin sensitivity and opposes the progression from prediabetes to type 2 diabetes. The combination may be more effective because each treatment stimulates AMP-activated protein kinase activity in skeletal muscle. We evaluated the effects of exercise training plus metformin on insulin sensitivity in men and women with prediabetes, compared with each treatment alone. RESEARCH DESIGN AND METHODS For 12 weeks, men and women with prediabetes were assigned to the following groups: placebo (P), 2,000 mg/day metformin (M), exercise training with placebo (EP), or exercise training with metformin (EM) ( n = 8 per group). Before and after the intervention, insulin sensitivity was measured by euglycemic hyperinsulinemic (80 mU/m2/min) clamp enriched with [6,6-2H]glucose. Changes due to intervention were compared across groups by repeated-measures ANOVA. RESULTS All three interventions increased insulin sensitivity ( P < 0.05) relative to the control group. The mean rise was 25–30% higher after EP than after either EM or M, but this difference was not significant. CONCLUSIONS Insulin sensitivity was considerably higher after 12 weeks of exercise training and/or metformin in men and women with prediabetes. Subtle differences among condition means suggest that adding metformin blunted the full effect of exercise training.
Nuclear Magnetic Resonance Diffusometry of Linear and Branched Wormlike Micelles

Diffusion studies using nuclear magnetic resonance (NMR) spectroscopy were conducted on two model surfactant solutions of cetyltrimethylammonium bromide/sodium salicylate (CTAB/NaSal) and cetylpyri...
Inmate Legal Information Requests Analysis: Empirical Data to Inform Library Purchases in Correctional Institutions

ABSTRACTThe introduction of legal content to Google Scholar made United States case law and law journal articles accessible to an unprecedented extent. With case law freely available and accurate bibliographic information for articles, could Google Scholar be accurate and complete enough for correctional institutions to forgo purchasing either print publications or fee-based services for these materials? This article empirically assesses whether Google Scholar can reliably answer the questions of inmates in a correctional facility, the Baltimore City Detention Center. As a comparison, the same questions are tested in Westlaw Correctional, a subscription database marketed to correctional institutions.
Greenness, texture, and spatial relationships predict floristic diversity across wetlands of the conterminous United States

Abstract Plant diversity safeguards wetland ecosystem functions, stability, and resilience, but is threatened by habitat loss and degradation. Remote sensing could support the cost-effective management of biodiversity by providing consistent and frequent data at large scales. While identifying individual species from remote sensing datasets with low spatial and spectral resolution is challenging, studies can focus on factors known to correlate with or promote diversity. We tested the predictive potential of such factors — maximum annual greenness as an indicator of productivity, texture (i.e., spatial arrangement of grey tones) as a proxy for habitat heterogeneity, and spatial autocorrelation — across a dataset of 1115 wetlands in the conterminous United States surveyed by the EPA’s National Wetland Condition Assessment. We used multivariate linear regressions to test whether spectral and spatial metrics derived from two open-source datasets — NASA’s Landsat 5 TM and 7 ETM+ (30 m, 16-day revisit) and USDA’s National Agriculture Inventory Program (1 m, biennial) — can predict wetland plant diversity and richness. Individual texture metrics showed different sensitivity to vegetation evenness, growth form, and spatial distribution and could together predict 35–36% of site variation in richness and diversity. This highlights the impact of habitat heterogeneity on species diversity and spectral variability. While maximum annual greenness and texture metrics had similar predictive capacity, their interactions and combined effects improved the fit of linear models by 11–14%, demonstrating their complementarity. Best results were achieved when including distance-based Moran's Eigenvector Maps (dbMEMs) describing spatial relations among sites at multiple scales and reflecting the role of spatially structured factors (e.g., climate, topography, dispersal) on diversity. Together greenness, texture, and dbMEMs could predict 59% of plant richness and 50% of plant diversity across the entire dataset and up to 71% of the richness of least disturbed sites. These results show the potential of open-source remote sensing datasets to monitor biodiversity resources at a large scale and prioritize the protection and field monitoring of wetlands.
A two-stage service restoration method for electric power distribution systems

Improving the reliability of power distribution systems is critically important for both utilities and customers. This calls for an efficient service restoration module within a distribution management system to support the implementation of self-healing smart grid networks. The emerging smart grid technologies, including distributed generators (DGs) and remote-controlled switches, although enhance the self-healing capability and allow faster recovery, pose additional complexity to the service restoration problem, especially under cold load pickup (CLPU) conditions. In this paper, we propose a novel two-stage restoration framework to generate restoration solutions with a sequence of control actions. The first stage generates a restoration plan that supports both the traditional service restoration using feeder reconfiguration and the grid-forming DG-assisted intentional islanding methods. The second stage generates an optimal sequence of switching operations to bring the outaged system quickly to the final restored configuration. The problem is formulated as a mixed-integer linear program that incorporates system connectivity, operating constraints, and CLPU models. It is demonstrated using a multi-feeder test case that the proposed framework is effective in utilizing all available resources to quickly restore the service and generates an optimal sequence of switching actions to be used by the operator to reach the desired optimal configuration.
Normal Forms for Manifolds of Normally Hyperbolic Singularities and Asymptotic Properties of Nearby Transitions

This paper contains theory on two related topics relevant to manifolds of normally hyperbolic singularities. First, theorems on the formal and $$ C^k $$ normal forms for these objects are proved. Then, the theorems are applied to give asymptotic properties of the transition map between sections transverse to the centre-stable and centre-unstable manifolds of some normally hyperbolic manifolds. A method is given for explicitly computing these so called Dulac maps. The Dulac map is revealed to have similar asymptotic structures as in the case of a saddle singularity in the plane.
Protein design and variant prediction using autoregressive generative models

The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 105-nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.
National and Sub-National Social Distancing Responses to COVID-19

We examine the effectiveness of sub-national actions to control a novel disease, such as COVID-19, in the absence of national policy. Evidence shows that countries where sub-national governments have undertaken unilateral social distancing measures to combat the pandemic with little or no coordination have performed less well in controlling the spread of the disease. We explore analytically whether agreement on a common social distancing policy among sub-national governments, i.e., states or provinces, can lead to a better outcome than if each state or province pursues its own social distancing policy in isolation. A key feature of our model is that it accounts for the inter-jurisdictional spillover effects of each sub-national jurisdiction’s policy choice with respect to social distancing. Our results show that, in the absence of a national mandatory agreement, a sub-national agreement with sufficient coordination of social distancing policy among states yields a more effective and efficient control of a pandemic compared to states choosing policy unilaterally. These findings strongly support calls for greater cooperation among and assistance for sub-national governments to improve the effectiveness of their social distancing efforts in controlling the pandemic.
Worth the effort? A practical examination of random effects in hidden Markov models for animal telemetry data

Hidden Markov models (HMMs) that include individual-level random effects have recently been promoted for inferring animal movement behaviour from biotelemetry data. These "mixed HMMs" come at significant cost in terms of implementation and computation, and discrete random effects have been advocated as a practical alternative to more computationally-intensive continuous random effects. However, the performance of mixed HMMs has not yet been sufficiently explored to justify their widespread adoption, and there is currently little guidance for practitioners weighing the costs and benefits of mixed HMMs for a particular research objective. We performed an extensive simulation study comparing the performance of a suite of fixed and random effect models for individual heterogeneity in the hidden state process of a 2-state HMM. We focused on sampling scenarios more typical of telemetry studies, which often consist of relatively long time series (30-250 observations per animal) for relatively few individuals (5-100 animals). We generally found mixed HMMs did not improve state assignment relative to standard HMMs. Reliable estimation of random effects required larger sample sizes than are often feasible in telemetry studies. Continuous random effect models performed reasonably well with data generated under discrete random effects, but not vice versa. Random effects accounting for unexplained individual variation can improve estimation of state transition probabilities and measurable covariate effects, but discrete random effects are a relatively poor (and potentially misleading) approximation for continuous variation. When weighing the costs and benefits of mixed HMMs, three important considerations are study objectives, sample size, and model complexity. HMM applications often focus on state assignment with little emphasis on heterogeneity in state transition probabilities, in which case random effects in the hidden state process simply may not be worth the additional effort. However, if explaining variation in state transition probabilities is a primary objective and sufficient explanatory covariates are not available, then random effects are worth pursuing as a more parsimonious alternative to individual fixed effects. To help put our findings in context and illustrate some potential challenges that practitioners may encounter when applying mixed HMMs, we revisit a previous analysis of long-finned pilot whale biotelemetry data.
Cardinality of wellordered disjoint unions of quotients of smooth equivalence relations

Assume $\mathsf{ZF + AD^+ + V = L(\mathscr{P}(\mathbb{R}))}$. Let $\approx$ denote the relation of being in bijection. Let $\kappa \in \mathrm{ON}$ and $\langle E_\alpha : \alpha < \kappa\rangle$ be a sequence of equivalence relations on $\mathbb{R}$ with all classes countable and for all $\alpha < \kappa$, $\mathbb{R} / E_\alpha \approx \mathbb{R}$. Then the disjoint union $\bigsqcup_{\alpha < \kappa} \mathbb{R} / E_\alpha$ is in bijection with $\mathbb{R} \times \kappa$ and $\bigsqcup_{\alpha < \kappa} \mathbb{R} / E_\alpha$ has the Jonsson property. Assume $\mathsf{ZF + AD^+ + V = L(\mathscr{P}(\mathbb{R}))}$. A set $X \subseteq [\omega_1]^{<\omega_1}$ has a sequence $\langle E_\alpha : \alpha < \omega_1\rangle$ of equivalence relations on $\mathbb{R}$ such that $\mathbb{R} / E_\alpha \approx \mathbb{R}$ and $X \approx \bigsqcup_{\alpha < \omega_1} \mathbb{R} / E_\alpha$ if and only if $\mathbb{R} \sqcup \omega_1$ injects into $X$. Assume $\mathsf{AD}$. Suppose $R \subseteq [\omega_1]^\omega \times \mathbb{R}$ is a relation such that for all $f \in [\omega_1]^\omega$, $R_f = \{x \in \mathbb{R} : R(f,x)\}$ is nonempty and countable. Then there is an uncountable $X \subseteq \omega_1$ and function $\Phi : [X]^\omega \rightarrow \mathbb{R}$ which uniformizes $R$ on $[X]^\omega$: that is, for all $f \in [X]^\omega$, $R(f,\Phi(f))$. Under $\mathsf{AD}$, if $\kappa$ is an ordinal and $\langle E_\alpha : \alpha < \kappa\rangle$ is a sequence of equivalence relations on $\mathbb{R}$ with all classes countable, then $[\omega_1]^\omega$ does not inject into $\bigsqcup_{\alpha < \kappa} \mathbb{R} / E_\alpha$.
The coset construction for particles of arbitrary spin

When a Poincare-invariant system spontaneously breaks continuous internal symmetries, Goldstones’ theorem demands the existence of massless, spin-zero excitations in a one-to-one correspondence with the broken symmetry generators. When a system spontaneously breaks Poincare symmetry, however, the kinds of excitations that satisfy Goldstone’s theorem can be quite unusual. In particular, they may have any spin and need not be particles or even quasiparticles. The standard coset construction used to formulate effective actions of Goldstones, however, is rather restrictive and is incapable of generating the full spectrum of possibilities allowed by Goldstone’s theorem. We propose a (partial) remedy to this problem by postulating a novel coset construction for systems that spontaneously break Poincare symmetry. This new construction is capable of generating effective actions with a wide range of Goldstone excitations — including fermionic degrees of freedom — even when all symmetries are bosonic. To demonstrate its utility, we focus on constructing effective actions for point particles of various spins. We recover the known result that a particle of spin s requires an $$ \mathcal{N} $$ = 2s supersymmetric worldline reparameterization gauge symmetry, which we implement at the level of the coset construction. In the process, we discover that massless particles require a novel kind of inverse Higgs constraint that bears some resemblance to the dynamical inverse Higgs constraints that appear in certain fermi liquid effective field theories. We then consider particles that, in addition to quantum spin, have finite spatial extent and are free to rotate. We derive a novel action for such particles and find a ‘spin-orbital’ coupling between the intrinsic quantum spin and the physical-rotation degrees of freedom.
Robust Reserve Pricing in Auctions Under Mean Constraints

We study a seller who sets a reserve price in a second price auction with uncertainty over the joint distribution of bidders' valuations. The seller only knows the mean of the marginal distribution of each bidder's valuation and the range, and an adversarial Nature chooses the worst-case distribution within this ambiguity set. We use a dual characterization to solve for this distribution. We find that the seller's optimal reserve price tends to be low and converges to zero in probability as the number of bidders increases.
Persistent negative cash flows, staged financing, and the stockpiling of cash balances

Abstract Firms with negative net cash flows (NCFs) play an empirically important role in recent decades’ increase in the average cash-balance ratio of publicly held non-financial firms. Since 1971, negative NCFs have become much more pervasive, persistent, and greater in magnitude, and these patterns hold within the growing set of firms that have high intangible capital. In recent years, firms with negative NCFs tend to build cash balances through frequent equity offerings. The high cash balances tend to be transitory as subsequent negative NCFs lead firms to rapid cash-balance drawdowns, often followed by new stock sales and cash stockpiling of the proceeds. We conclude that funding needs and staged equity financing by negative NCF firms are central features of the secular rise in the average cash-balance ratio.
Absolute 3d Pose Estimation and Length Measurement of Severely Deformed Fish from Monocular Videos in Longline Fishing

Monocular absolute 3D fish pose estimation allows for efficient fish length measurement in the longline fisheries, where fishes are under severe deformation during the catching process. This task is challenging since it requires locating absolute 3D fish keypoints based on a short monocular video clip. Unlike related works, which either require expensive 3D ground-truth data and/or multiple-view images to provide depth information, or are limited to rigid objects, we propose a novel frame-based method to estimate the absolute 3D fish pose and fish length from a single-view 2D segmentation mask. We first introduce a relative 3D fish template. By minimizing an objective function, our method systematically estimates the relative 3D pose of the target fish and fish 2D keypoints in the image. Finally, with a closed-form solution, the relative 3D fish pose can help locate absolute 3D keypoints, resulting in the frame-based absolute fish length measurement, which is further refined based on the statistical temporal inference for the optimal fish length measurement from the video clip. Our experiments show that this method can accurately estimate the absolute 3D fish pose and further measure the absolute length, even outperforming the state-of-the-art multi-view method.
A new elementary proof of the Prime Number Theorem

Let $\Omega(n)$ denote the number of prime factors of $n$. We show that for any bounded $f\colon\mathbb{N}\to\mathbb{C}$ one has \[ \frac{1}{N}\sum_{n=1}^N\, f(\Omega(n)+1)=\frac{1}{N}\sum_{n=1}^N\, f(\Omega(n))+\mathrm{o}_{N\to\infty}(1). \] This yields a new elementary proof of the Prime Number Theorem.
On Symmetry and Quantification: A New Approach to Verify Distributed Protocols

Proving that an unbounded distributed protocol satisfies a given safety property amounts to finding a quantified inductive invariant that implies the property for all possible instance sizes of the protocol. Existing methods for solving this problem can be described as search procedures for an invariant whose quantification prefix fits a particular template. We propose an alternative constructive approach that does not prescribe, a priori, a specific quantifier prefix. Instead, the required prefix is automatically inferred without any search by carefully analyzing the structural symmetries of the protocol. The key insight underlying this approach is that symmetry and quantification are closely related concepts that express protocol invariance under different re-arrangements of its components. We propose symmetric incremental induction, an extension of the finite-domain IC3/PDR algorithm, that automatically derives the required quantified inductive invariant by exploiting the connection between symmetry and quantification. While various attempts have been made to exploit symmetry in verification applications, to our knowledge, this is the first demonstration of a direct link between symmetry and quantification in the context of clause learning during incremental induction. We also describe a procedure to automatically find a minimal finite size, the cutoff, that yields a quantified invariant proving safety for any size.
Canonical tensor scaling

In this paper we generalize the canonical positive scaling of rows and columns of a matrix to the scaling of selected-rank subtensors of an arbitrary tensor. We expect our results and framework will prove useful for sparse-tensor completion required for generalizations of the recommender system problem beyond a matrix of user-product ratings to multidimensional arrays involving coordinates based both on user attributes (e.g., age, gender, geographical location, etc.) and product/item attributes (e.g., price, size, weight, etc.).
Medical Entity Disambiguation Using Graph Neural Networks

Medical knowledge bases (KBs), distilled from biomedical literature and regulatory actions, are expected to provide high-quality information to facilitate clinical decision making. Entity disambiguation (also referred to as entity linking) is considered as an essential task in unlocking the wealth of such medical KBs. However, existing medical entity disambiguation methods are not adequate due to word discrepancies between the entities in the KB and the text snippets in the source documents. Recently, graph neural networks (GNNs) have proven to be very effective and provide state-of-the-art results for many real-world applications with graph-structured data. In this paper, we introduce ED-GNN based on three representative GNNs (GraphSAGE, R-GCN, and MAGNN) for medical entity disambiguation. We develop two optimization techniques to fine-tune and improve ED-GNN. First, we introduce a novel strategy to represent entities that are mentioned in text snippets as a query graph. Second, we design an effective negative sampling strategy that identifies hard negative samples to improve the model's disambiguation capability. Compared to the best performing state-of-the-art solutions, our ED-GNN offers an average improvement of 7.3% in terms of F1 score on five real-world datasets.
Fatigue Life of High-Speed Ball Bearings With Silicon Nitride Balls

Hot-pressed silicon nitride was evaluated as a rolling-element bearing material. The five-ball fatigue tester was used to test 12.7-mm- diameter silicon nitride balls at maximum Hertz stresses ranging from 4.27 x 10 to the 9th power n/sq m to 6.21 x 10 to the 9th power n/sq m at a race temperature of 328K. The fatigue life of NC-132 hot-pressed silicon nitride was found to be equal to typical bearing steels and much greater than other ceramic or cermet materials at the same stress levels. A digital computer program was used to predict the fatigue life of 120-mm- bore angular-contact ball bearings containing either steel or silicon nitride balls. The analysis indicates that there is no improvement in the lives of bearings of the same geometry operating at DN values from 2 to 4 million where silicon nitride balls are used in place of steel balls.
Results from Cascade Thrust Reverser Noise and Suppression Experiments

Results from experimental work on model scale cascade reversers with cold airflow are presented. Sound power level directivity and spectral characteristics for cascade reversers are reported. Effect of cascade exit area ratio, vane profile shape, and emission arc are discussed. Model equivalent diameters varied from 3 to 5 inches, pressure ratios range from 1.15 to 3.0. Depending on the reverser type, acoustic power was proportional to the 4 1/2 to 6th power of ideal jet velocity. Reverser noise peaked at higher frequency and was more omnidirectional than nozzle-alone jet noise. Appreciable reduction in sideline noise was obtained from plane shields. Airfoil-vaned cascades were the most aerodynamically efficient and least noisy reversers. Scaling of cascade reverser data to example aircraft engines showed all cascades above the 95 PNdB sideline goal from STOL aircraft. However, the airfoil-vaned reverser has a good potential for meeting this goal for high-bypass (low pressure ratio) exhausts.
A microbial signature following bariatric surgery is robustly consistent across multiple cohorts.

Bariatric surgery induces significant shifts in the gut microbiota which could potentially contribute to weight loss and metabolic benefits. The aim of this study was to characterize a microbial si...
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly available1.
Performance Analysis of Deep Learning Workloads on a Composable System

A composable infrastructure is defined as resources, such as compute, storage, accelerators and networking, that are shared in a pool and that can be grouped in various configurations to meet application requirements. This freedom to 'mix and match' resources dynamically allows for experimentation early in the design cycle, prior to the final architectural design or hardware implementation of a system. We describe the design of an enterprise composable infrastructure that we have implemented and evaluate the impact of resource dis-aggregation on representative deep learning benchmarks.
Triple Divorce and the Political Context of Islamic Law in India

The 2019 passage of the ‘The Muslim Women Protection of Rights on Marriage Act’ criminalizing the practice of triple-ṭalāq has been actively debated in both political and academic spheres. For some, the act signals a much-awaited victory for the Muslim women of India who have suffered the consequences of instantaneous and irrevocable divorces; while for others, it signals the continued marginalization of the Muslim community and the willingness of the Indian government to encroach upon their rights as a distinct religious community. To understand the passage of this Act in context, this article explores the larger context surrounding debates over Islamic Law in India, prior watershed Supreme Court decisions, and the recent political agenda of the BJP. These explorations reveal that ‘The Muslim Women Protection of Rights on Marriage Act’ is a red herring that, if fully enacted, can exacerbate the social and legal challenges women face when seeking divorce while also encroaching upon the rights of the increasingly politically marginalized Muslim community.
A Hardy–Ramanujan-type inequality for shifted primes and sifted sets

We establish an analog of the Hardy–Ramanujan inequality for counting members of sifted sets with a given number of distinct prime factors. In particular, we establish a bound for the number of shifted primes p + a below x with k distinct prime factors, uniformly for all positive integers k.
Simplification of Power Electronics for Ion Thruster Neutralizers

A need exists for less complex and lower cost ion thruster systems. Design approaches and the demonstration of neutralizer power electronics for relaxed neutralizer keeper, tip heater, and vaporizer requirements are discussed. The neutralizer circuitry is operated from a 200 to 400 V bus and demonstrates an order of magnitude reduction in parts count. Furthermore, a new technique is described for regulating tip heater power and automatically switching over to provide keeper power with only four additional components. A new design to control the flow rate of the neutralizer with one integrated circuit is also presented.
A combinatorial description of the knot concordance invariant epsilon

In this paper, we give a combinatorial description of the concordance invariant &#x1d700; defined by Hom, prove some properties of this invariant using grid homology techniques. We compute the value of &#x1d700; f...
Thermionic reactor systems for electric propulsion.

This paper summarizes the preliminary design studies of unmanned electric propulsion spacecraft, with primary emphasis on the in-core thermionic reactor power subsystem. A 70-kWe power subsystem, with an external-fuel thermionic reactor, is shown integrated into a large L/D (about 20) electric propulsion spacecraft. The 70-kWe spacecraft is designed for launch to earth escape with a Titan-Centaur. Two 300-kWe reactor designs (external-fuel and flashlight designs from Atomic Energy Commission contracted studies) are integrated into 270-kWe electric propulsion spacecraft. The 270-kWe spacecraft are designed for launch to a 700-nmi earth orbit with a Titan III-C/7 booster. The 70-kWe thermionic reactor power subsystem is also conceptually shown as a space base power plant.