cssi/SciSciGPT-SciSciNet huggingface dataset:
Before and beyond the Wilson-Cowan equations.

The Wilson–Cowan equations represent a landmark in the history of computational neuroscience. Along with the insights Wilson and Cowan offered for neuroscience, they crystallized an approach to mod...
An Analytic Framework For Interpreting Investment Regressions In The Presence Of Financial Constraints

A financial constraint that prevents access to external funds induces non-classical measurement error in average q as a proxy for unobservable marginal q. Unlike classical measurement error, this measurement error biases upward the coefficient on average q in a univariate regression of investment on average q. In a multiple regression of investment on average q and cash flow, the coefficient on cash flow is positive. The positive cash-flow coefficient indicates the presence of a financial constraint, but it does not indicate a shortage of liquidity to fund current investment. In addition, the coefficient on average q is biased downward.
Analysis of the vanishing moment method and its finite element approximations for second-order linear elliptic PDEs in non-divergence form

This paper is concerned with continuous and discrete approximations of $W^{2,p}$ strong solutions of second-order linear elliptic partial differential equations (PDEs) in non-divergence form. The continuous approximation of these equations is achieved through the Vanishing Moment Method (VMM) which adds a small biharmonic term to the PDE. The structure of the new fourth-order PDE is a natural fit for Galerkin-type methods unlike the original second order equation since the highest order term is in divergence form. The well-posedness of the weak form of the perturbed fourth order equation is shown as well as error estimates for approximating the strong solution of the original second-order PDE. A $C^1$ finite element method is then proposed for the fourth order equation, and its existence and uniqueness of solutions as well as optimal error estimates in the $H^2$ norm are shown. Lastly, numerical tests are given to show the validity of the method.
Lorentz Violation in the Matter–Gravity Sector

We construct the general Lorentz-violating effective field theory in curved spacetime and the corresponding nonrelativistic Hamiltonian in the Earth’s gravitational field. Applying this general framework to three types of experiments, free-dropping, interferometer, and bound-state experiments, we extract first constraints on certain new coefficients in the matter–gravity sector.
Knowing What Others Know: Coordination Motives in Information Acquisition

We explore how optimal information choices change the predictions of strategic models. When a large number of agents play a game with strategic complementarity, information choices exhibit complementarity as well: If an agent wants to do what others do, they want to know what others know. This makes heterogeneous beliefs di‐cult to sustain and may generate multiple equilibria. In models with substitutability, agents prefer to difierentiate their information choices. We use these theoretical results to examine the role of information choice in recent price-setting models and to propose modeling techniques that ensure equilibrium uniqueness.
Macular Damage in Glaucoma is Associated With Deficits in Facial Recognition.

Purpose This report examines the relationship between glaucomatous macular damage and facial recognition. In addition, it assesses the role of contrast sensitivity (CS) as an intermediary step in the causal pathway between macular damage and impairment of facial recognition. Design Prospective cross-sectional study. Methods This study was conducted in a single tertiary care center. The study population included 144 eyes of 72 participants with a diagnosis of open angle glaucoma in one or both eyes and a visual acuity of 20/40 or better in each eye. The presence or absence of macular damage was determined by comparing corresponding regions of the retinal nerve fiber layer and the retinal ganglion cell layer with spectral-domain optical coherence tomography with the 10-2 visual field (VF). Better and worse eye was determined by 10-2 VF mean deviations (MDs). Interventions were 1) macular function as measured by 10-2 VF and 2) CS as measured by the Freiburg Visual Acuity and Contrast Test (FrACT). The primary outcome measure was the Cambridge Face Memory Test (CFMT) score. Results Regardless of eye, there was a significant correlation between facial recognition and 10-2 VF MD (P Conclusions Even with good central visual acuity, patients with glaucomatous macular damage exhibit diminished facial recognition, which is partly mediated through diminished CS.
Why are Saving Rates of Urban Households in China Rising

From 1995 to 2005, the average urban household saving rate in China rose by 8 percentage points, to about one quarter of disposable income. We use household-level data to explain why households are postponing consumption despite rapid income growth. Tracing cohorts over time indicates a virtual absence of consumption smoothing over the life cycle. The age profile of savings has an unusual U-shaped pattern, with saving rates being the highest among the youngest and oldest households. We find that financial underdevelopment, as reflected in constraints on borrowing and low returns on financial assets, partially accounts for this pattern. Moreover, overall saving rates have increased across all demographic groups. We argue that this can be explained by the rising private burden of expenditures on housing, education, and health care.
The influence of weld strength mismatch on crack-tip constraint in single edge notch bend specimens

Previous work by Dodds and Anderson provides a framework to quantify finite size and crack depth effects on cleavage fracture toughness when failure occurs at deformation levels where J no longer uniquely describes the state of stresses and strains in the vicinity of the crack tip. Size effects on cleavage fracture are quantified by defining a value termed J SSY: the J to which an infinite body must be loaded to achieve the same likelihood of cleavage fracture as in a finite body. In weld metal fracture toughness testing, mismatch between weld metal and baseplate strength can alter deformation patterns, which complicate size and crack depth effects on cleavage fracture toughness. This study demonstrates that there is virtually no effect of ±20 percent mismatch on J SSYif the distance from the crack tip to the weld/plate interface (L min) exceeds 5 mm. At higher levels of overmatch (50 to 100%), it is no longer possible to parameterize the departure of J SSYfor a weldment from that for a homogeneous SE(B) based on L min alone. Weld geometry significantly influences the accuracy with which J SSYfor a welded SE(B) can be approximated by J SSYfor a homogeneous specimen at these extreme overmatch levels.
Wellness: Combating Burnout and Its Consequences in Emergency Medicine

Medicine recognizes burnout as a threat to quality patient care and physician quality of life. This issue exists throughout medicine but is notably prevalent in emergency medicine (EM). Because the concept of "wellness" lacks a clear definition, attempts at ameliorating burnout that focus on achieving wellness make success difficult to achieve and measure. Recent work within the wellness literature suggests that the end goal should be to achieve a culture of wellness by addressing all aspects of the physician's environment. A review of the available literature on burnout and wellness interventions in all medical specialties reveals that interventions focusing on individual physicians have varying levels of success. Efforts to compare these interventions are hampered by a lack of consistent endpoints. Studies with consistent endpoints do not demonstrate clear benefits of achieving them because improving scores on various scales may not equate to improvement in quality of care or physician quality of life. Successful interventions have uncertain, long-term effects. Outside of EM, the most successful interventions focus on changes to systems rather than to individual physicians. Within EM, the number of well-structured interventions that have been studied is limited. Future work to achieve the desired culture of wellness within EM requires establishment of a consistent endpoint that serves as a surrogate for clinical significance, addressing contributors to burnout at all levels, and integrating successful interventions into the fabric of EM.
Chapter 10 – Semiconductor Lasers

The energy (E) available to electrons in semiconductors occur in broad bands that depend upon the magnitude and direction of the wave vector (k). The conduction bands correspond to energies above 0 eV, while the valence bands correspond to negative energies. For Si, Ge, GaAs, and AlAs, the maxima of the valence bands occurs at the symmetry point denoted by Γ. GaAs is one of only a few semiconductors for which the minimum in the conduction band also occurs at Γ and vertical transitions between the valence and conduction bands can occur. A discontinuous jump occurs in the potential energy of electrons at the juncture between two semiconductors. Discontinuities of this kind make it possible to create quantum wells and potential barriers for conduction electrons by forming multilayer structures called heterostructures. For multilayered crystals to perform well in optical and electrical devices, the layers must have the same lattice constants and be free of impurities.
Do Credit Shocks Matter? A Global Perspective

Abstract This paper examines the importance of credit market shocks in driving global business cycles over the period 1988:1–2009:4. We first estimate common components in various macroeconomic and financial variables of the G-7 countries. We then evaluate the role played by credit market shocks using a series of VAR models. Our findings suggest that these shocks have been influential in driving global activity during the latest global recession. Credit shocks originating in the United States also have a significant impact on the evolution of world growth during global recessions.
11 – Neutron chain reactions

Publisher Summary Neutron chain reaction in nuclear fuel, such as uranium, depends on nuclear properties and size, shape, and arrangement of materials. A self-sustaining chain reaction involving neutrons and fission is possible if a critical mass of fuel is accumulated. The value of the multiplication factor, k, indicates whether a reactor is subcritical ( 1). The reactor power, which is proportional to the product of flux and the number of fuel atoms, is readily adjustable. A thermal reactor contains a moderator and operates on slowed neutrons. Reactors are classified according to purpose, neutron energy, moderator and coolant, fuel, arrangement, and structural material. Principal types are the pressurized water reactor, the boiling water reactor, the high-temperature gas-cooled reactor, and the liquid metal fast breeder reactor. Excess fuel is added to a reactor initially to take care of burning during the operating cycle, with adjustable control absorbers maintaining criticality. It is important to take into account, the fission product absorbers such as Xe-135 and the limitations related to thermal and radiation effects.
A Back‐up Quarterback View of Mezzanine Finance

Mezzanine nance is ubiquitous. Traditional arguments describe the purpose of Mezzanine debt as completing the market, speci cally as plugging a nancing gap. According to that view, intermediate seniority claims add to the available menu of risk-return combinations hence helps cater to the needs of heterogeneous investors and generates additional sources of capital for productive investment projects. We describe a completely di erent yet equally fundamental role for Mezzanine debt which creates economic value even in a world where all investors are homogenous and risk-neutral. According to our theory, Mezzanine nanciers serve as substitute managing agents back-up quarterbacks of sorts ready to replace the original borrower when the project underperforms. They enable senior borrowers to provide incentives to the original agent without resorting to ine cient punishment such as foreclosing on the project even when its NPV as a going-concern remains positive. The fact that Mezzanine lenders tend to be industryspecialists while senior borrowers tend to be traditional intermediaries, we argue, constitutes direct evidence that Mezzanine stakeholders provide the expertise senior lending need to e ciently mitigate agency frictions.
Developing Ambitious Mathematics Instruction Through Web-Based Coaching: A Randomized Field Trial:

This article describes and evaluates a web-based coaching program designed to support teachers in implementing Common Core–aligned math instruction. Web-based coaching programs can be operated at r...
Chapter 12 – Operating Systems

Publisher Summary Systems programs control the operation of the computer. Applications programs instruct the computer how to handle an organization's information-processing needs. Systems programmers require an understanding of the internal operation of computer hardware. Applications programmers must understand the information-processing needs of an organization, and how computers might be used to meet those needs. Operating systems are collections of systems programs that allow computer systems to handle many functions previously performed by computer operators. Operating systems are primarily resource managers. They manage hardware, programs, and data. They assign hardware to users, charge for resource usage, run many users efficiently, and guarantee good service to users. Scheduling is one of the most important functions of an operating system. Priority scheduling ensures that the more important jobs are serviced first. User jobs are classified as batch processing, interactive, or real-time. Multiprogramming operating systems allow many jobs to run simultaneously. Storage protection mechanisms help to prevent programs from interfering with one another. Boundary registers prevent programs from accessing a storage address outside the range indicated by the registers.
Bilateral Agreements and Fair Trade Practices: A Policy Analysis of the Colombia- U.S. Free Trade Agreement (2006)

this article brings to the attention of those public servants involved in the design and negotiation of free trade agreements between the United states and developing countries, such as colombia, the potential benefits and drawbacks of negotiating in a bilateral forum. rather than critiquing the free trade agreement for its particular provisions, this article examines the u.s. policy of negotiating bilaterally with developing countries as opposed to multilaterally in the world trade system and what effects such an approach might have on the economic development of the latter. Using an incremental policy analysis, the article critiques the bilateral approach in terms of economic development and fair trade negotiations using the recent colombia-u.s. trade agreement as a case study. the article concludes that a bilateral approach that is disconnected from a broader multilateral context may be detrimental to developing countries and recommends increased oversight of such agreements by the World trade Organization to ensure a higher degree of fairness.
Dynamic Pricing in Retail Gasoline Markets

Supergame models of tacit collusion show that supportable price-cost margins increase with expected future collusive profits, ceteris paribus. As a result, collusive margins will be larger when demand is expected to increase or marginal costs are expected to decline. Using panel data on sales volume and gasoline prices in 43 cities over 72 months, we find behavior consistent with tacit collusion in retail gasoline markets. Controlling for current demand and cost, current margins increase with expected next-month demand and decrease with expected next-month cost. The results are not consistent with intertemporal l inkages due to inventory behavior or customer loyalty.
Examining the Factors Associated With Sexual Violence Against Women in Sierra Leone: A Nationwide Cross-Sectional Study.

In 2019, Sierra Leone declared national emergency over rape and other forms of sexual violence (SV), hence diverting resources from other issues to tackle SV. However, little is known about nationw...
Interpreting Robust Optimization via Adversarial Influence Functions

Robust optimization has been widely used in nowadays data science, especially in adversarial training. However, little research has been done to quantify how robust optimization changes the optimizers and the prediction losses comparing to standard training. In this paper, inspired by the influence function in robust statistics, we introduce the Adversarial Influence Function (AIF) as a tool to investigate the solution produced by robust optimization. The proposed AIF enjoys a closed-form and can be calculated efficiently. To illustrate the usage of AIF, we apply it to study model sensitivity -- a quantity defined to capture the change of prediction losses on the natural data after implementing robust optimization. We use AIF to analyze how model complexity and randomized smoothing affect the model sensitivity with respect to specific models. We further derive AIF for kernel regressions, with a particular application to neural tangent kernels, and experimentally demonstrate the effectiveness of the proposed AIF. Lastly, the theories of AIF will be extended to distributional robust optimization.
FSMI: Fast computation of Shannon mutual information for information-theoretic mapping:

Exploration tasks are embedded in many robotics applications, such as search and rescue and space exploration. Information-based exploration algorithms aim to find the most informative trajectories...
Generative modeling of brain maps with spatial autocorrelation

Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene set enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization.
Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning

This work presents efforts to augment the performance of data-driven machine learning algorithms for reaction template recommendation used in computer-aided synthesis planning software. Often, machine learning models designed to perform the task of prioritizing reaction templates or molecular transformations are focused on reporting high-accuracy metrics for the one-to-one mapping of product molecules in reaction databases to the template extracted from the recorded reaction. The available templates that get selected for inclusion in these machine learning models have been previously limited to those that appear frequently in the reaction databases and exclude potentially useful transformations. By augmenting open-access data sets of organic reactions with explicitly calculated template applicability and pretraining a template-relevance neural network on this augmented applicability data set, we report an increase in the template applicability recall and an increase in the diversity of predicted precursors. The augmentation and pretraining effectively teaches the neural network an increased set of templates that could theoretically lead to successful reactions for a given target. Even on a small data set of well-curated reactions, the data augmentation and pretraining methods resulted in an increase in top-1 accuracy, especially for rare templates, indicating that these strategies can be very useful for small data sets.
HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training using TensorFlow

To reduce the training time of large-scale Deep Neural Networks (DNNs), Deep Learning (DL) scientists have started to explore parallelization strategies like data-parallelism, model-parallelism, and hybrid-parallelism. While data-parallelism has been extensively studied and developed, several problems exist in realizing model-parallelism and hybrid-parallelism efficiently. Four major problems we focus on are: 1) defining a notion of a distributed model across processes, 2) implementing forward/back-propagation across process boundaries that requires explicit communication, 3) obtaining parallel speedup on an inherently sequential task, and 4) achieving scalability without losing out on a model’s accuracy. To address these problems, we create HyPar-Flow—a model-size and model-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that can be used to perform data, model, and hybrid parallel training of any Keras model at scale. We create an internal distributed representation of the user-provided Keras model, utilize TF’s Eager execution features for distributed forward/back-propagation across processes, exploit pipelining to improve performance and leverage efficient MPI primitives for scalable communication. Between model partitions, we use send and recv to exchange layer-data/partial-errors while allreduce is used to accumulate/average gradients across model replicas. Beyond the design and implementation of HyPar-Flow, we also provide comprehensive correctness and performance results on three state-of-the-art HPC systems including TACC Frontera (#5 on Top500.org). For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up to 1.6\(\times \) speedup over Horovod-based data-parallel training, 2) 110\(\times \) speedup over single-node on 128 Stampede2 nodes, and 3) 481\(\times \) speedup over single-node on 512 Frontera nodes.
Cavity piezo-mechanics for superconducting-nanophotonic quantum interface.

Hybrid quantum systems are essential for the realization of distributed quantum networks. In particular, piezo-mechanics operating at typical superconducting qubit frequencies features low thermal excitations, and offers an appealing platform to bridge superconducting quantum processors and optical telecommunication channels. However, integrating superconducting and optomechanical elements at cryogenic temperatures with sufficiently strong interactions remains a tremendous challenge. Here, we report an integrated superconducting cavity piezo-optomechanical platform where 10 GHz phonons are resonantly coupled with photons in a superconducting cavity and a nanophotonic cavity at the same time. Taking advantage of the large piezo-mechanical cooperativity (Cem ~7) and the enhanced optomechanical coupling boosted by a pulsed optical pump, we demonstrate coherent interactions at cryogenic temperatures via the observation of efficient microwave-optical photon conversion. This hybrid interface makes a substantial step towards quantum communication at large scale, as well as novel explorations in microwave-optical photon entanglement and quantum sensing mediated by gigahertz phonons. Hybrid quantum systems would allow interfacing superconducting nodes with optical links. Here, the authors demonstrate an integrated platform where 10 GHz phonons are resonantly coupled with photons in a superconducting cavity and a nanophotonic cavity at the same time, allowing efficient mediation of bidirectional microwave-optical conversion.
On the Convergence of SARAH and Beyond

The main theme of this work is a unifying algorithm, \textbf{L}oop\textbf{L}ess \textbf{S}ARAH (L2S) for problems formulated as summation of $n$ individual loss functions. L2S broadens a recently developed variance reduction method known as SARAH. To find an $\epsilon$-accurate solution, L2S enjoys a complexity of ${\cal O}\big( (n+\kappa) \ln (1/\epsilon)\big)$ for strongly convex problems. For convex problems, when adopting an $n$-dependent step size, the complexity of L2S is ${\cal O}(n+ \sqrt{n}/\epsilon)$; while for more frequently adopted $n$-independent step size, the complexity is ${\cal O}(n+ n/\epsilon)$. Distinct from SARAH, our theoretical findings support an $n$-independent step size in convex problems without extra assumptions. For nonconvex problems, the complexity of L2S is ${\cal O}(n+ \sqrt{n}/\epsilon)$. Our numerical tests on neural networks suggest that L2S can have better generalization properties than SARAH. Along with L2S, our side results include the linear convergence of the last iteration for SARAH in strongly convex problems.
Uncertainty Quantification of Mode Shape Variation Utilizing Multi-Level Multi-Response Gaussian Process

Mode shape information play the essential role in deciding the spatial pattern of vibratory response of a structure. The uncertainty quantification of mode shape, i.e., predicting mode shape variation when the structure is subjected to uncertainty, can provide guidance for robust design and control. Nevertheless, computational efficiency is a challenging issue. Direct Monte Carlo simulation is unlikely to be feasible especially for a complex structure with large number of degrees of freedom. In this research, we develop a new probabilistic framework built upon Gaussian process meta-modeling architecture to analyze mode shape variation. To expedite the generation of input dataset for meta-model establishment, a multi-level strategy is adopted which can blend a large amount of low-fidelity data acquired from order-reduced analysis with a small amount of high-fidelity data produced by high-dimensional full finite element analysis. To take advantage of the intrinsic relation of spatial distribution of mode shape, a multi-response strategy is incorporated to predict mode shape variation at different locations simultaneously. These yield a multi-level, multi-response Gaussian process that can efficiently and accurately quantify the effect of structural uncertainty to mode shape variation. Comprehensive case studies are carried out for demonstration and validation.
Acceleration of catalyst discovery with easy, fast, and reproducible computational alchemy

The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost-efficient calculation scheme called alchemical perturbation density functional theory (APDFT) for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on Kohn-Sham density functional theory reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present bench marking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single-point DFT calculations. Using a single nudged elastic band calculation for CH4 dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single-point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are done, and this shows that simple applications of computational alchemy can significantly impact DFT-driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations.Keywords --- Computational catalysis, density functional theory (DFT), adsorption energies, nudged elastic band calculations, binding energies, barrier heights
Spectral Lower Bounds on the I/O Complexity of Computation Graphs

We consider the problem of finding lower bounds on the I/O complexity of arbitrary computations in a two level memory hierarchy. Executions of complex computations can be formalized as an evaluation order over the underlying computation graph. However, prior methods for finding I/O lower bounds leverage the graph structures for specific problems (e.g matrix multiplication) which cannot be applied to arbitrary graphs. In this paper, we first present a novel method to bound the I/O of any computation graph using the first few eigenvalues of the graph's Laplacian. We further extend this bound to the parallel setting. This spectral bound is not only efficiently computable by power iteration, but can also be computed in closed form for graphs with known spectra. We apply our spectral method to compute closed-form analytical bounds on two computation graphs (the Bellman-Held-Karp algorithm for the traveling salesman problem and the Fast Fourier Transform), as well as provide a probabilistic bound for random Erdos Renyi graphs. We empirically validate our bound on four computation graphs, and find that our method provides tighter bounds than current empirical methods and behaves similarly to previously published I/O bounds.
Adenovirus-mediated ubiquitination alters protein-RNA binding and aids viral RNA processing.

Viruses promote infection by hijacking the ubiquitin machinery of the host to counteract or redirect cellular processes. Adenovirus encodes two early proteins, E1B55K and E4orf6, that together co-opt a cellular ubiquitin ligase complex to overcome host defences and promote virus production. Adenovirus mutants lacking E1B55K or E4orf6 display defects in viral RNA processing and protein production, but previously identified substrates of the redirected ligase do not explain these phenotypes. Here, we used a quantitative proteomics approach to identify substrates of E1B55K/E4orf6-mediated ubiquitination that facilitate RNA processing. While all currently known cellular substrates of E1B55K and E4orf6 are degraded by the proteasome, we uncovered RNA-binding proteins as high-confidence substrates that are not decreased in overall abundance. We focused on two RNA-binding proteins, RALY and hnRNP-C, which we confirm are ubiquitinated without degradation. Knockdown of RALY and hnRNP-C increased levels of viral RNA splicing, protein abundance and progeny production during infection with E1B55K-deleted virus. Furthermore, infection with E1B55K-deleted virus resulted in an increased interaction of hnRNP-C with viral RNA and attenuation of viral RNA processing. These data suggest that viral-mediated ubiquitination of RALY and hnRNP-C relieves a restriction on viral RNA processing and reveal an unexpected role for non-degradative ubiquitination in the manipulation of cellular processes during virus infection. Adenovirus produces two early proteins, E1B55K and E4orf6, that become components of the host cell ubiquitin ligase complex comprising elongin-B and C, cullin-5 and RBX1. The authors identified new protein substrates that are ubiquitinated by the E1B55K–E4orf6 complex and find that hnRNP-C and RALY play an inhibitory role on late viral RNA transcripts and that this is counteracted by ubiquitination due to a reduction on the capacity of these proteins to interact with viral RNA.
Redundancy allocation in finite-length nested codes for nonvolatile memories

In this paper, we investigate the optimum way to allocate redundancy of finite-length nested codes for modern nonvolatile memories suffering from both permanent defects and transient errors (erasures or random errors). A nested coding approach such as partitioned codes can handle both permanent defects and transient errors by using two parts of redundancy: 1) Redundancy to deal with permanent defects and 2) redundancy for transient errors. We consider two different channel models of the binary defect and erasure channel (BDEC) and the binary defect and symmetric channel (BDSC). The transient errors of the BDEC are erasures and the BDSC’s transient errors are modeled by the binary symmetric channel, respectively. Asymptotically, the probability of recovery failure can converge to zero if the capacity region conditions of nested codes are satisfied. However, the probability of recovery failure of finite-length nested codes can be significantly variable for different redundancy allocations even though they all satisfy the capacity region conditions. Hence, we formulate the redundancy allocation problem of finite-length nested codes to minimize the recovery failure probability. We derive the upper bounds on the probability of recovery failure and use them to estimate the optimal redundancy allocation. Numerical results show that our estimated redundancy allocation matches well the optimal redundancy allocation.
Uniqueness of a three-dimensional stochastic differential equation

In order to extend the study of the uniqueness property of multidimensional systems of stochastic differential equations, we look at the following three-dimensional system of equations, of which the two-dimensional case has been well studied: dXt=Ytdt, dYt=Ztdt, dZt=|Xt|αdBt. We prove that if (X0,Y0,Z0)≠(0,0,0) and 34<α<1, then the system of equations has a unique solution in the strong sense.
Electronic cigarette risk beliefs and usage after the vaping illness outbreak

New national survey evidence on electronic cigarette (e-cigarette) risk beliefs indicates that people substantially overestimate the health risks posed by e-cigarettes, both in absolute terms and relative to conventional cigarette risk beliefs. Perceptions of the lung cancer risks and total mortality risks of conventional cigarettes function as prior risk beliefs for e-cigarettes. People believe e-cigarettes are at least 60% as risky as conventional cigarettes. Whether respondents have seen reports of vaping-related illnesses has no significant effect on risk beliefs, but there has been a modest increase in the percentage who believe that e-cigarettes are riskier than cigarettes. Accurate e-cigarette beliefs would significantly increase whether people try, currently use, or exclusively use e-cigarettes. Whereas price and taste are the principal drivers of brand choice for conventional cigarettes, use of e-cigarettes is more closely linked to smoking cessation and concern with environmental tobacco smoke.
Millimeter Wave Beam Recommendation via Tensor Completion

Accurate and fast beam-alignment is essential to cope with the fast-varying environment in millimeter-wave communications. A data-driven approach is a promising solution to reduce the training overhead by leveraging side information and on-the-field measurements. In this work, a two-stage tensor completion algorithm is proposed to predict the received power on a set of possible users' positions, given received power measurements on a small subset of positions. Based on these predictions and on positional side information, a small subset of beams is recommended to reduce the training overhead of beam-alignment. Numerical results evaluated with the Quadriga channel simulator demonstrate that the proposed algorithm achieves correct alignment with high probability using small training overhead: given power measurement on only 20% of the possible positions when using a discrete coverage area, our algorithm attains a probability of correct alignment of 80%, with only 2% of trained beams, as opposed to a state-of-the-art scheme which achieves 50% correct alignment in the same configuration. To the best of our knowledge, this is the first work to consider the beam recommendation problem based on measurements collected on a small subset of positions.
Layered Antiferromagnetism Induces Large Negative Magnetoresistance in the van der Waals Semiconductor CrSBr.

The recent discovery of magnetism within the family of exfoliatable van der Waals (vdW) compounds has attracted considerable interest in these materials for both fundamental research and technological applications. However, current vdW magnets are limited by their extreme sensitivity to air, low ordering temperatures, and poor charge transport properties. Here the magnetic and electronic properties of CrSBr are reported, an air-stable vdW antiferromagnetic semiconductor that readily cleaves perpendicular to the stacking axis. Below its Neel temperature, TN = 132 ± 1 K, CrSBr adopts an A-type antiferromagnetic structure with each individual layer ferromagnetically ordered internally and the layers coupled antiferromagnetically along the stacking direction. Scanning tunneling spectroscopy and photoluminescence (PL) reveal that the electronic gap is ΔE = 1.5 ± 0.2 eV with a corresponding PL peak centered at 1.25 ± 0.07 eV. Using magnetotransport measurements, strong coupling between magnetic order and transport properties in CrSBr is demonstrated, leading to a large negative magnetoresistance response that is unique among vdW materials. These findings establish CrSBr as a promising material platform for increasing the applicability of vdW magnets to the field of spin-based electronics.
The International Aspects of Macroprudential Policy

Countries are making more active use of macroprudential tools than in the past with the goal of improving the resilience of their broader financial systems. A growing body of evidence suggests that...
Uniform bounds on the image of the arboreal Galois representations attached to non-CM elliptic curves

Let $\ell$ be a prime number and let $F$ be a number field and $E/F$ a non-CM elliptic curve with a point $\alpha \in E(F)$ of infinite order. Attached to the pair $(E,\alpha)$ is the $\ell$-adic arboreal Galois representation $\omega_{E,\alpha,\ell^{\infty}} : {\rm Gal}(\overline{F}/F) \to \mathbb{Z}_{\ell}^{2} \rtimes {\rm GL}_{2}(\mathbb{Z}_{\ell})$ describing the action of ${\rm Gal}(\overline{F}/F)$ on points $\beta_{n}$ so that $\ell^{n} \beta_{n} = \alpha$. We give an explicit bound on the index of the image of $\omega_{E,\alpha,\ell^{\infty}}$ depending on how $\ell$-divisible the point $\alpha$ is, and the image of the ordinary $\ell$-adic Galois representation. The image of $\omega_{E,\alpha,\ell^{\infty}}$ is connected with the density of primes $\mathfrak{p}$ for which $\alpha \in E(\mathbb{F}_{\mathfrak{p}})$ has order coprime to $\ell$.
Aerobic Metabolism in Vibrio cholerae Is Required for Population Expansion during Infection.

ABSTRACT Vibrio cholerae replicates to high cell density in the human small intestine, leading to the diarrheal disease cholera. During infection, V. cholerae senses and responds to environmental signals that govern cellular responses. Spatial localization of V. cholerae within the intestine affects nutrient availability and metabolic pathways required for replicative success. Metabolic processes used by V. cholerae to reach such high cell densities are not fully known. We sought to better define the metabolic traits that contribute to high levels of V. cholerae during infection. By disrupting the pyruvate dehydrogenase (PDH) complex and pyruvate formate-lyase (PFL), we could differentiate aerobic and anaerobic metabolic pathway involvement in V. cholerae proliferation. We demonstrate that oxidative metabolism is a key contributor to the replicative success of V. choleraein vivo using an infant mouse model in which PDH mutants were attenuated 100-fold relative to the wild type for colonization. Additionally, metabolism of host substrates, including mucin, was determined to support V. cholerae growth in vitro as a sole carbon source, primarily under aerobic growth conditions. Mucin likely contributes to population expansion during human infection as it is a ubiquitous source of carbohydrates. These data highlight oxidative metabolism as important in the intestinal environment and warrant further investigation of how oxygen and other host substrates shape the intestinal landscape that ultimately influences bacterial disease. We conclude from our results that oxidative metabolism of host substrates is a key driver of V. cholerae proliferation during infection, leading to the substantial bacterial burden exhibited in cholera patients. IMPORTANCEVibrio cholerae remains a challenge in the developing world and incidence of the disease it causes, cholera, is anticipated to increase with rising global temperatures and with emergent, highly infectious strains. At present, the underlying metabolic processes that support V. cholerae growth during infection are less well understood than specific virulence traits, such as production of a toxin or pilus. In this study, we determined that oxidative metabolism of host substrates such as mucin contribute significantly to V. cholerae population expansion in vivo. Identifying metabolic pathways critical for growth can provide avenues for controlling V. cholerae infection and the knowledge may be translatable to other pathogens of the gastrointestinal tract.
Scalable termination detection for distributed actor systems

Automatic garbage collection (GC) prevents certain kinds of bugs and reduces programming overhead. GC techniques for sequential programs are based on reachability analysis. However, testing reachability from a root set is inadequate for determining whether an actor is garbage because an unreachable actor may send a message to a reachable actor. Instead, it is sufficient to check termination (sometimes also called quiescence): an actor is terminated if it is not currently processing a message and cannot receive a message in the future. Moreover, many actor frameworks provide all actors with access to file I/O or external storage; without inspecting an actor's internal code, it is necessary to check that the actor has terminated to ensure that it may be garbage collected in these frameworks. Previous algorithms to detect actor garbage require coordination mechanisms such as causal message delivery or nonlocal monitoring of actors for mutation. Such coordination mechanisms adversely affect concurrency and are therefore expensive in distributed systems. We present a low-overhead reference listing technique (called DRL) for termination detection in actor systems. DRL is based on asynchronous local snapshots and message-passing between actors. This enables a decentralized implementation and transient network partition tolerance. The paper provides a formal description of DRL, shows that all actors identified as garbage have indeed terminated (safety), and that all terminated actors--under certain reasonable assumptions--will eventually be identified (liveness).
An Operando calorimeter for high temperature electrochemical cells

Operando calorimetry has previously been utilized to study degradation, side reactions, and other electrochemical effects in electrochemical cells such as batteries at or near room temperature. Calorimetric data can provide important information on the lifetime and thermal properties of electrochemical cells and can be used in practical engineering applications such as thermal management. High temperature electrochemical cells such as solid oxide fuel cells or electrolyzers can also benefit from operando calorimetry, although to our knowledge no such unit has been developed commercially. Herein, we report an operando calorimeter capable of simultaneous calorimetry and electrochemistry at temperatures up to 1,000 °C and in both oxidizing and reducing atmospheres. The calorimeter is constructed by modifying a commercial apparatus originally designed to study high temperature electrochemical cells in various gas environments. We utilize a grey-box, nonlinear system identification model to analyze calorimetric data and achieve an electrochemical cell power sensitivity of 16.1 +/- 11.7 mW. This operando calorimeter provides the tools needed to study both the thermal and kinetic behavior of electrochemical cells at elevated temperatures.
Complexity of stochastic dual dynamic programming

Stochastic dual dynamic programming is a cutting plane type algorithm for multi-stage stochastic optimization originated about 30 years ago. In spite of its popularity in practice, there does not exist any analysis on the convergence rates of this method. In this paper, we first establish the number of iterations, i.e., iteration complexity, required by a basic dual dynamic programming method for solving single-scenario multi-stage optimization problems, by introducing novel mathematical tools including the saturation of search points. We then refine these basic tools and establish the iteration complexity for an explorative dual dynamic programing method proposed herein and the classic stochastic dual dynamic programming method for solving more general multi-stage stochastic optimization problems under the standard stage-wise independence assumption. Our results indicate that the complexity of these methods mildly increases with the number of stages T, in fact linearly dependent on T for discounted problems. Therefore, they are efficient for strategic decision making which involves a large number of stages, but with a relatively small number of decision variables in each stage. Without explicitly discretizing the state and action spaces, these methods might also be pertinent to the related reinforcement learning and stochastic control areas.
An on-average Maeda-type conjecture in the level aspect

We present a conjecture on the average number of Galois orbits of newforms when fixing the weight and varying the level. This conjecture implies, for instance, that the central L-values (resp. L-derivatives) are nonzero for 100% of even weight prime level newforms with root number +1 (resp. -1).
Effective behavior of cooperative and nonidentical molecular motors

Analytical formulas for effective drift, diffusivity, run times, and run lengths are derived for an intracellular transport system consisting of a cargo attached to two cooperative but not identical molecular motors (for example, kinesin-1 and kinesin-2) which can each attach and detach from a microtubule. The dynamics of the motor and cargo in each phase are governed by stochastic differential equations, and the switching rates depend on the spatial configuration of the motor and cargo. This system is analyzed in a limit where the detached motors have faster dynamics than the cargo, which in turn has faster dynamics than the attached motors. The attachment and detachment rates are also taken to be slow relative to the spatial dynamics. Through an application of iterated stochastic averaging to this system, and the use of renewal-reward theory to stitch together the progress within each switching phase, we obtain explicit analytical expressions for the effective drift, diffusivity, and processivity of the motor-cargo system. Our approach accounts in particular for jumps in motor-cargo position that occur during attachment and detachment events, as the cargo tracking variable makes a rapid adjustment due to the averaged fast scales. The asymptotic formulas are in generally good agreement with direct stochastic simulations of the detailed model based on experimental parameters for various pairings of kinesin-1 and kinesin-2 under assisting, hindering, or no load.
Super-relaxed state of myosin in human skeletal muscle is fiber-type dependent

The myosin super-relaxed state (SRX) in skeletal muscle is hypothesized to play an important role in regulating muscle contractility and thermogenesis in humans but has only been examined in model ...