cssi/SciSciGPT-SciSciNet huggingface dataset:
Recommendations for Follow-Up After Colonoscopy and Polypectomy: A Consensus Update by the US Multi-Society Task Force on Colorectal Cancer.

Author(s): Gupta, Samir; Lieberman, David; Anderson, Joseph C; Burke, Carol A; Dominitz, Jason A; Kaltenbach, Tonya; Robertson, Douglas J; Shaukat, Aasma; Syngal, Sapna; Rex, Douglas K | Abstract: Colonoscopy is routinely performed for colorectal cancer (CRC) screening, follow-up of other abnormal screening tests, work up of signs and symptoms of gastrointestinal disease, and surveillance after CRC and polyp removal. Post procedure, colonoscopists are expected to provide follow-up recommendations to patients and referring physicians. Recommendations for follow-up after normal colonoscopy among individuals age-eligible for screening, and post-polypectomy among all individuals with polyps are among the most common clinical scenarios requiring guidance1.Risk of metachronous advanced neoplasia is associated with findings on prior colonoscopy. After high quality colonoscopy, patients with no neoplasia detected are at the lowest risk, and those with polyps are risk stratified based on the histology, number, location, and size of polyps detected. Since release of the last US Multi-Society Task Force (Task Force) recommendations for post colonoscopy follow-up and polyp surveillance in 20122, a number of papers have been published on risk of CRC based on colonoscopy findings and patient characteristics, as well as the potential impact of screening and surveillance colonoscopy on outcomes such as incident CRC and polyps. Further, recent studies increasingly reflect the modern era of colonoscopy with more awareness of the importance of quality factors (e.g. adequate bowel preparation, cecal intubation, adequate adenoma detection, complete polyp resection), and utilization of state of the art technologies (e.g. high definition colonoscopes).n Higher quality colonoscopy could impact the importance of previously identified risk factors. Our aim was to review newly available evidence and update recommendations for follow-up after colonoscopy with or without polypectomy.Keywords:nnaftercare, colon polyps, colonoscopy, colon cancer, consensus statement, early detection of cancer, practice guideline
Compositions with restricted parts

Abstract Euler showed that the number of partitions of n into distinct parts equals the number of partitions of n into odd parts. This theorem was generalized by Glaisher and further by Franklin. Recently, Beck made three conjectures on partitions with restricted parts, which were confirmed analytically by Andrews and Chern and combinatorially by Yang. Analogous to Euler’s partition theorem, it is known that the number of compositions of n with odd parts equals the number of compositions of n + 1 with parts greater than one, as both numbers equal the Fibonacci number F n . Recently, Sills provided a bijective proof for this result using binary sequences, and Munagi proved a generalization similar to Glaisher’s result using the zigzag graphs of compositions. Extending Sills’ bijection, we obtain a further generalization which is analogous to Franklin’s result. We establish, both analytically and combinatorially, two closed formulae for the number of compositions with restricted parts appearing in our generalization. We also prove some composition analogues for the conjectures of Beck.
Measuring and optimizing system reliability: a stochastic programming approach

We propose a computational framework to quantify (measure) and to optimize the reliability of complex systems. The approach uses a graph representation of the system that is subject to random failures of its components (nodes and edges). Under this setting, reliability is defined as the probability of finding a path between sources and sink nodes under random component failures and we show that this measure can be computed by solving a stochastic mixed-integer program. The stochastic programming setting allows us to account for system constraints and general probability distributions to characterize failures and allows us to derive optimization formulations that identify designs of maximum reliability. We also propose a strategy to approximately solve these problems in a scalable manner by using purely continuous formulations.
MOD: Minimally Ordered Durable Datastructures for Persistent Memory

Persistent Memory (PM) makes possible recoverable applications that can preserve application progress across system reboots and power failures. Actual recoverability requires careful ordering of cacheline flushes, currently done in two extreme ways. On one hand, expert programmers have reasoned deeply about consistency and durability to create applications centered on a single custom-crafted durable datastructure. On the other hand, less-expert programmers have used software transaction memory (STM) to make atomic one or more updates, albeit at a significant performance cost due largely to ordered log updates. In this work, we propose the middle ground of composable persistent datastructures called Minimally Ordered Durable datastructures (MOD). We prototype MOD as a library of C++ datastructures---currently, map, set, stack, queue and vector---that often perform better than STM and yet are relatively easy to use. They allow multiple updates to one or more datastructures to be atomic with respect to failure. Moreover, we provide a recipe to create additional recoverable datastructures. MOD is motivated by our analysis of real Intel Optane PM hardware showing that allowing unordered, overlapping flushes significantly improves performance. MOD reduces ordering by adapting existing techniques for out-of-place updates (like shadow paging) with space-reducing structural sharing (from functional programming). MOD exposes a Basic interface for single updates and a Composition interface for atomically performing multiple updates. Relative to widely used Intel PMDK v1.5 STM, MOD improves map, set, stack, queue microbenchmark performance by 40%, and speeds up application benchmark performance by 38%.
Fair classification and social welfare

Now that machine learning algorithms lie at the center of many important resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. How do leading notions of fairness as defined by computer scientists map onto longer-standing notions of social welfare? In this paper, we present a welfare-based analysis of fair classification regimes. Our main findings assess the welfare impact of fairness-constrained empirical risk minimization programs on the individuals and groups who are subject to their outputs. We fully characterize the ranges of Δ'e perturbations to a fairness parameter 'e in a fair Soft Margin SVM problem that yield better, worse, and neutral outcomes in utility for individuals and by extension, groups. Our method of analysis allows for fast and efficient computation of "fairness-to-welfare" solution paths, thereby allowing practitioners to easily assess whether and which fair learning procedures result in classification outcomes that make groups better-off. Our analyses show that applying stricter fairness criteria codified as parity constraints can worsen welfare outcomes for both groups. More generally, always preferring "more fair" classifiers does not abide by the Pareto Principle---a fundamental axiom of social choice theory and welfare economics. Recent work in machine learning has rallied around these notions of fairness as critical to ensuring that algorithmic systems do not have disparate negative impact on disadvantaged social groups. By showing that these constraints often fail to translate into improved outcomes for these groups, we cast doubt on their effectiveness as a means to ensure fairness and justice.
At the Intersections: International and Multicultural Higher Education

Within the United States, the areas of international and multicultural higher education have similar goals but different origins, and there have been historical areas of tension between the two even as many scholars and practitioners have sought ways to link them. Much literature on the intersections between the two areas was published in the 1990s and early 2010s, but the landscape of higher education has shifted significantly in recent years; furthermore, most previous research focused on theoretical reasons for why the two areas did not intersect and offered broad suggestions for how they could be merged. This study investigated research questions related to what the language used by international and multicultural offices to describe their work can reveal about how they partner or compete to create inclusive institutional environments, using the lens of Kretzmann and McKnight’s assets-based community development theory. The study used content analysis to analyze data collected from the websites of ten institutions in Massachusetts. Key findings include a slight bias towards international offices in asset-based framing and a significant bias towards multicultural offices in deficit-based framing. These findings largely reflect old tensions between the two areas despite nearly three decades of scholarship on the topic and significant changes in climate for higher education, demonstrating the need for a more intentional dialogue between these areas and a balanced model of framing both areas’ work. This research is currently being expanded upon using semi-structured interviews with administrators who work at the ten institutions used in the content analysis.
Evolutionary and biochemical analyses reveal conservation of the Brassicaceae telomerase ribonucleoprotein complex

The telomerase ribonucleoprotein complex (RNP) is essential for genome stability and performs this role through the addition of repetitive DNA to the ends of chromosomes. The telomerase enzyme is composed of a reverse transcriptase (TERT), which utilizes a template domain in an RNA subunit (TER) to reiteratively add telomeric DNA at the ends of chromosomes. Multiple TERs have been identified in the model plant Arabidopsis thaliana. Here we combine a phylogenetic and biochemical approach to understand how the telomerase RNP has evolved in Brassicaceae, the family that includes A. thaliana. Because of the complex phylogenetic pattern of template domain loss and alteration at the previously characterized A. thaliana TER loci, TER1 and TER2, across the plant family Brassicaceae, we bred double mutants from plants with a template deletion at AtTER1 and T-DNA insertion at AtTER2. These double mutants exhibited no telomere length deficiency, a definitive indication that neither of these loci encode a functional telomerase RNA. Moreover, we determined that the telomerase components TERT, Dyskerin, and the KU heterodimer are under strong purifying selection, consistent with the idea that the TER with which they interact is also conserved. To test this hypothesis further, we analyzed the substrate specificity of telomerase from species across Brassicaceae and determined that telomerase from close relatives bind and extend substrates in a similar manner, supporting the idea that TERs in different species are highly similar to one another and are likely encoded from an orthologous locus. Lastly, TERT proteins from across Brassicaceae were able to complement loss of function tert mutants in vivo, indicating TERTs from other species have the ability to recognize the native TER of A. thaliana. Finally, we immunoprecipitated the telomerase complex and identified associated RNAs via RNA-seq. Using our evolutionary data we constrained our analyses to conserved RNAs within Brassicaceae that contained a template domain. These analyses revealed a highly expressed locus whose disruption by a T-DNA resulted in a telomeric phenotype similar to the loss of other telomerase core proteins, indicating that the RNA has an important function in telomere maintenance.
Learning the Helix Topology of Musical Pitch

To explain the consonance of octaves, music psychologists represent pitch as a helix where azimuth and axial coordinate correspond to pitch class and pitch height respectively. This article addresses the problem of discovering this helical structure from unlabeled audio data. We measure Pearson correlations in the constant-Q transform (CQT) domain to build a K-nearest neighbor graph between frequency subbands. Then, we run the Isomap manifold learning algorithm to represent this graph in a three-dimensional space in which straight lines approximate graph geodesics. Experiments on isolated musical notes demonstrate that the resulting manifold resembles a helix which makes a full turn at every octave. A circular shape is also found in English speech, but not in urban noise. We discuss the impact of various design choices on the visualization: instrumentarium, loudness mapping function, and number of neighbors K.
A machine learning framework for solving high-dimensional mean field game and mean field control problems

Mean field games (MFG) and mean field control (MFC) are critical classes of multiagent models for the efficient analysis of massive populations of interacting agents. Their areas of application span topics in economics, finance, game theory, industrial engineering, crowd motion, and more. In this paper, we provide a flexible machine learning framework for the numerical solution of potential MFG and MFC models. State-of-the-art numerical methods for solving such problems utilize spatial discretization that leads to a curse of dimensionality. We approximately solve high-dimensional problems by combining Lagrangian and Eulerian viewpoints and leveraging recent advances from machine learning. More precisely, we work with a Lagrangian formulation of the problem and enforce the underlying Hamilton-Jacobi-Bellman (HJB) equation that is derived from the Eulerian formulation. Finally, a tailored neural network parameterization of the MFG/MFC solution helps us avoid any spatial discretization. Our numerical results include the approximate solution of 100-dimensional instances of optimal transport and crowd motion problems on a standard work station and a validation using a Eulerian solver in two dimensions. These results open the door to much-anticipated applications of MFG and MFC models that are beyond reach with existing numerical methods.
Reconstruction of Gene Regulatory Networks by Integrating Biological Model and a Recommendation System

Gene Regulatory Networks (GRNs) control many aspects of cellular processes including cell differentiation, maintenance of cell type specific states, signal transduction, and response to stress. Since GRNs provide information that is essential for understanding cell function, the inference of these networks is one of the key challenges in systems biology.
Commensal Oral Rothia mucilaginosa Produces Enterobactin, a Metal-Chelating Siderophore.

ABSTRACT Next-generation sequencing studies of saliva and dental plaque from subjects in both healthy and diseased states have identified bacteria belonging to the Rothia genus as ubiquitous members of the oral microbiota. To gain a deeper understanding of molecular mechanisms underlying the chemical ecology of this unexplored group, we applied a genome mining approach that targets functionally important biosynthetic gene clusters (BGCs). All 45 genomes that were mined, representing Rothia mucilaginosa, Rothia dentocariosa, and Rothia aeria, harbored a catechol-siderophore-like BGC. To explore siderophore production further, we grew the previously characterized R. mucilaginosa ATCC 25296 in liquid cultures, amended with glycerol, which led to the identification of the archetype siderophore enterobactin by using tandem liquid chromatography-mass spectrometry (LC-MS/MS), high-performance liquid chromatography (HPLC), and nuclear magnetic resonance (NMR) spectroscopy. Normally attributed to pathogenic gut bacteria, R. mucilaginosa is the first commensal oral bacterium found to produce enterobactin. Cocultivation studies including R. mucilaginosa or purified enterobactin revealed that enterobactin reduced growth of certain strains of cariogenic Streptococcus mutans and pathogenic strains of Staphylococcus aureus. Commensal oral bacteria were either unaffected, reduced in growth, or induced to grow adjacent to enterobactin-producing R. mucilaginosa or the pure compound. Taken together with Rothia’s known capacity to ferment a variety of carbohydrates and amino acids, our findings of enterobactin production add an additional level of explanation to R. mucilaginosa’s prevalence in the oral cavity. Enterobactin is the strongest Fe(III) binding siderophore known, and its role in oral health requires further investigation. IMPORTANCE The communication language of the human oral microbiota is vastly underexplored. However, a few studies have shown that specialized small molecules encoded by BGCs have critical roles such as in colonization resistance against pathogens and quorum sensing. Here, by using a genome mining approach in combination with compound screening of growth cultures, we identified that the commensal oral community member R. mucilaginosa harbors a catecholate-siderophore BGC, which is responsible for the biosynthesis of enterobactin. The iron-scavenging role of enterobactin is known to have positive effects on the host’s iron pool and negative effects on host immune function; however, its role in oral health remains unexplored. R. mucilaginosa was previously identified as an abundant community member in cystic fibrosis, where bacterial iron cycling plays a major role in virulence development. With respect to iron’s broad biological importance, iron-chelating enterobactin may explain R. mucilaginosa’s colonization success in both health and disease.
Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features.

Accurate segmentation of different sub-regions of gliomas including peritumoral edema, necrotic core, enhancing and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape, segmentation of the sub-regions is very challenging. Recent development using deep learning models has proved its effectiveness in the past several brain segmentation challenges as well as other semantic and medical image segmentation problems. Most models in brain tumor segmentation use a 2D/3D patch to predict the class label for the center voxel and variant patch sizes and scales are used to improve the model performance. However, it has low computation efficiency and also has limited receptive field. U-Net is a widely used network structure for end-to-end segmentation and can be used on the entire image or extracted patches to provide classification labels over the entire input voxels so that it is more efficient and expect to yield better performance with larger input size. Furthermore, instead of picking the best network structure, an ensemble of multiple models, trained on different dataset or different hyper-parameters, can generally improve the segmentation performance. In this study we propose to use an ensemble of 3D U-Nets with different hyper-parameters for brain tumor segmentation. Preliminary results showed effectiveness of this model. In addition, we developed a linear model for survival prediction using extracted imaging and non-imaging features, which, despite the simplicity, can effectively reduce overfitting and regression errors.
Decarbonization of the U.S. electricity sector: Are state energy policy portfolios the solution?

State governments have taken the lead on U.S. energy and climate policy. It is not yet clear, however, whether state energy policy portfolios can generate results in a similar magnitude or manner to their presumed carbon mitigation potential. This article seeks to address this lack of policy evidence and contribute empirical insights on the carbon mitigation effects of state energy portfolios within the U.S. electricity sector. Using a dynamic, long-term electricity dispatch model with U.S. power plant, utility, and transmission and distribution data between 2010 and 2030, this analysis builds a series of state-level policy portfolio scenarios and performs a comparative scenario analysis. Results reveal that state policy portfolios have modest to minimal carbon mitigation effects in the long run if surrounding states do not adopt similar portfolios as well. The difference in decarbonization potential between isolated state policies and larger, more coordinated policy efforts is due in large part to carbon leakage, which is the export of carbon intensive fossil fuel-based electricity across state lines. Results also confirm that a carbon price of $50/metric ton CO2e can generate substantial carbon savings. Although both policy options—an energy policy portfolio or a carbon price—are effective at reducing carbon emissions in the present analysis, neither is as effective alone as when the two strategies are combined.
Chapter 8 – Binary Cycle Power Plants

Binary cycle geothermal power plants are the closest in thermodynamic principle to conventional fossil or nuclear plants in that the working fluid undergoes an actual closed cycle. The working fluid, chosen for its appropriate thermodynamic properties, receives heat from the geofluid, evaporates, expands through a prime mover, condenses, and is returned to the evaporator by means of a feed pump.
Aging sexual stereotypes and sexual expression in mid- and later life: examining the stereotype matching effect

Objectives: Ageist sexual stereotypes may prohibit midlife and older adults from achieving sexual wellness when stereotypical beliefs about aging, sex, and intimacy become internalized over the lif...
Beyond the MDS Bound in Distributed Cloud Storage

Regenerating code is a class of distributed storage codes that can optimally trade the bandwidth with the amount of data stored per node to repair a failed node. There are two extreme points in the optimal regenerating trade-off curve, which correspond to minimum-storage regenerating (MSR) and minimum-bandwidth regenerating (MBR). Recently, Reed-Solomon (RS) code based regenerating codes (RS-RC) were constructed under the product-matrix framework. It can also achieve the maximum distance separable (MDS) property in code regeneration and reconstruction. However, in case that the network is hostile and the storage nodes could be compromised or packets be modified, the storage capacity and the bandwidth required to regenerate or reconstruct the original file can be significantly affected. In this paper, we propose Hermitian code based regenerating codes (H-RC) by developing constructions under the product-matrix framework for minimum storage regenerating (H-MSR) and the minimum bandwidth regenerating (H-MBR). We also propose data regeneration and reconstruction algorithms for both H-MSR and H-MBR codes under both error-free and hostile networks. We demonstrate that the proposed algorithms can also successfully determine the erroneous decodings in hostile networks. Theoretical evaluation shows that our proposed H-RC can detect and correct more errors in hostile networks well beyond the RS-RC with the same code rate. Our analysis shows that the proposed H-RC have lower computational complexity than the RS-RC for both code regeneration and code reconstruction.
The Capital Inflows Problem Revisited: A Stylized Model of Southern Cone Disinflation

In the late 1970s, countries in Latin America's Southern Cone initiated attempts to lower domestic inflation rates through the progressive reduction of a preannounced rate of exchange-rate devaluation. The stabilization programs gave rise to massive capital inflows, real exchange-rate appreciation, and current-account deficits. This paper develops a stylized intertemporal framework in which the effects of a credible preannounced disinflation scheme can be studied. It is shown that even when agents have perfect foresight and markets clear continuously, the "capital inflows" phenomenon and the associated real appreciation may result. While unanticipated, permanent inflation changes are neutral in the paper, anticipated inflation is neutral only in exceptional circumstances. A preannounced disinflation operates by altering the path of an expenditure-based real domestic interest rate that depends on expected changes in the prices of liquidity services and nontradable consumption goods.
Contracts, Intellectual Property Rights, and Multinational Investment in Developing Countries

Abstract The policy debate between multinational firms favoring strong contract law, and host-country governments who often oppose such protection motivates the paper. Local agents (managers) learn the multinational’s technology and can defect to start a rival firm. Contract enforcement, including binding the multinational itself, makes the multinational better off. Outcomes for the host country are more complex, depending on mode switches induced by enforcement. If enforcement induces the multinational to switch from exporting to local production, welfare improves. If local production was occurring anyway, enforcement may result in the loss of rents to local agents and lower welfare.
Chapter 17 – A coupled anisotropic damage model for the inelastic response of composite materials

Publisher Summary This chapter introduces a coupled incremental damage and plasticity theory for rate independent and rate dependent composite materials. This theory allows damage to be path dependent either on the stress history or thermodynamic force conjugate to damage, which is achieved through the use of an incremental damage tensor. Damage and inelastic deformations are incorporated in the proposed model that is used for the analysis of fiber-reinforced metal matrix composite materials. The damage is described kinematically in both the elastic and inelastic domains using the fourth order damage effect tensor, which is a function of the second-order damage tensor. A physical interpretation of the second order damage tensor is presented, which relates to the microcrack porosity within the unit cell. The inelastic deformation behavior with damage is presented here within the framework of thermodynamics with internal state variables. Computational aspects of both the rate independent and rate dependent models are also discussed in the chapter. The Newthon Rapson iterative scheme is used for the overall laminate system. To test the validity of the model, a series of laminated systems (0(8s)), (90(8s)), (0/90)(4s), and (-45/45)(2s) are investigated at both room and elevated temperatures of 538° C and 649° C. The results obtained from the special purpose developed computer program, DVPCALSET(Damage and Viscoplastic Coupled Analysis of Laminate Systems at Elevated Temperatures), are then compared with the available experimental results and other existing theoretical material models.
Data base development and analysis for the social historian: The educational status of the beneficed clergy of the diocese of Hereford, 1289–1539

This paper has attempted to describe a set of machine procedures able to perform sophisticated manipulation on structured text files, a data format particularly well-suited to the needs of the social historian concerned with quantitative analysis. Examples taken from a initial study based on the parish clergy of the diocese of Hereford have served to demonstrate how these programs can be used to prepare data easily in a machine-readable form, and once they have been entered, restructure them in ways conducive to the easy analysis of significant features. The ability to reduce quantifiable aspects of textual data to matrix form is similarly provided. The results of the Hereford project, useful in themselves, also indicate the direction which additions to the package will take. At present little facility is available for automatically “tagging’ identifications. That is, once it has been determined to the user's satisfaction that the John Smith who was presented to Ross rectory in 1490 is the same individual who was collated to a cathedral prebend in the following year, it should be possible to integrate this relationship as a linkage in the data base. At present this is done with cross-referencing in one of the notes fields, but we are beginning to develop more sophisticated and efficient methods of creating linkages between related entries using the list processing capabilities available in SAIL. While the program package is still evolving, all of the routines described in this paper are fully implemented and readers with access to a PDP-10 system who might be interested in using the programs in their own applications are welcome to write for an exportable compiled version of the package. A manual describing the operation of each program is also in preparation.
Chapter 2 – Fundamentals of Fluid Mechanics

Fluid mechanics is the study of fluids at rest and in motion. A fluid is defined as a material that continuously deforms under a constant load. There are five relationships that are most useful in fluid mechanics problems: kinematic, stress, conservation, regulating, and constitutive. The analysis of fluid mechanics problems can be altered depending on the choice of the system of interest and the volume of interest, which govern the simplification of vector quantities. By assuming that a fluid is a continuum, we make the assumption that there are no inhomogeneities within the fluid. Viscosity relates the shear rate to the shear stress. Definition of a fluid as Newtonian depends on whether the viscosity is constant at various shear rates. Newtonian fluids have constant viscosities, whereas non-Newtonian fluids have a nonconstant viscosity. For most biofluid applications, we assume that the fluid is Newtonian.
Chapter 6 – Ventilation Requirements

Publisher Summary The purpose of ventilation is to reduce the danger in a hazardous location to a lower level or to prevent a location in a hazardous area from becoming dangerous. Types of ventilation: natural ventilation and mechanical ventilation. The presence of sufficient ventilation influences the distance and time at which a flammable gas or vapor enters its explosion range, and reaches a non hazardous concentration. Natural ventilation is capable of diluting flammable gases or vapors in the air to safe concentrations. Since the changes in wind direction and the existence of crosswinds are unknown, wind velocities alone are normally used in combination With other factors to determine the traveling distance of a flammable gas or vapor in the air: (1) The size of the source of hazard. (2) The system pressure. (3) The operating mode. (4) To determine the quantity of the flammable gas or vapor released to the atmosphere —small or large. Natural ventilation is present in outdoor locations, and mechanical ventilation can be used in indoor locations.. . There are two types of mechanical ventilation: a “pressure fan” that pressurizes an enclosed space, and an “exhaust or suction fan” that causes a negative air flow in the location. In providing natural or mechanical ventilation, it is important to consider the presence of obstructions. A pressure fan can be installed in an enclosed space. However, setting up the location for a suction fan depends on the vapor density of the flammable product. Safeguards are required because mechanical ventilation can break down. Either safeguards can prevent the failure of the ventilating system or warn against it . There are two types of safeguards available: type “A” and type “B”. The type “A” safeguard is a redundant ventilating system which operates on loss of ventilation. The type “B” safeguard is only an audible and visible alarm system that operates on loss of ventilation.
A Linear and Nonlinear Study of Mira

Both linear and nonlinear calculations of the 331 day, long period variable star Mira have been undertaken to see what radial pulsation mode is naturally selected. Models are similar to those considered in the linear nonadiabatic stellar pulsation study of Ostlie and Cox (1986). Models are considered with masses near one solar mass, luminosities between 4000 and 5000 solar luminosities, and effective temperatures of approximately 3000 K. These models have fundamental mode periods that closely match the pulsation period of Mira. The equation of state for the stellar material is given by the Stellingwerf (1975ab) procedure, and the opacity is obtained from a fit by Cahn that matches the low temperature molecular absorption data for the Population I Ross-Aller 1 mixture calculated from the Los Alamos Astrophysical Opacity Library. For the linear study, the Cox, Brownlee, and Eilers (1966) approximation is used for the linear theory variation of the convection luminosity. For the nonlinear work, the method described by Ostlie (1990) and Cox (1990) is followed. Results showing internal details of the radial fundamental and first overtone modes behavior in linear theory are presented. Preliminary radial fundamental mode nonlinear calculations are discussed. The very tentative conclusion is that neither the fundamental or first overtone mode is excluded from being the actual observed one.
Well-Placed: The Geography of Opportunity and High School Effects on College Attendance

Recent work has broadened the scope of school effectiveness research to consider not only academic achievement but also other outcomes, especially college attendance. This literature has argued that high schools are an important determinant of college attendance, with some contending that high schools matter more for college attendance than for academic achievement. A separate branch of research has illustrated how place-based opportunities facilitate college attendance. We merge these two literatures by asking if schools’ geographic context can explain apparent variation in effectiveness among Wisconsin high schools. We find that geographic context explains more than a quarter of the variance in traditional estimates of school effectiveness on college attendance, because factors like proximity to colleges are strongly associated with college attendance. Accounting for geography is therefore important in order not to overstate high schools’ role in higher education outcomes. Results are based on multilevel models applied to rich administrative data on every Wisconsin public high school entrant between 2006 and 2011.
\(L_0\)-ARM: Network Sparsification via Stochastic Binary Optimization

We consider network sparsification as an \(L_0\)-norm regularized binary optimization problem, where each unit of a neural network (e.g., weight, neuron, or channel, etc.) is attached with a stochastic binary gate, whose parameters are jointly optimized with original network parameters. The Augment-Reinforce-Merge (ARM) [27], a recently proposed unbiased gradient estimator, is investigated for this binary optimization problem. Compared to the hard concrete gradient estimator from Louizos et al. [19], ARM demonstrates superior performance of pruning network architectures while retaining almost the same accuracies of baseline methods. Similar to the hard concrete estimator, ARM also enables conditional computation during model training but with improved effectiveness due to the exact binary stochasticity. Thanks to the flexibility of ARM, many smooth or non-smooth parametric functions, such as scaled sigmoid or hard sigmoid, can be used to parameterize this binary optimization problem and the unbiasness of the ARM estimator is retained, while the hard concrete estimator has to rely on the hard sigmoid function to achieve conditional computation and thus accelerated training. Extensive experiments on multiple public datasets demonstrate state-of-the-art pruning rates with almost the same accuracies of baseline methods. The resulting algorithm \(L_0\)-ARM sparsifies the Wide-ResNet models on CIFAR-10 and CIFAR-100 while the hard concrete estimator cannot. The code is public available at https://github.com/leo-yangli/l0-arm.
Learned garbage collection

Several programming languages use garbage collectors (GCs) to automatically manage memory for the programmer. Such collectors must decide when to look for unreachable objects to free, which can have a large performance impact on some applications. In this preliminary work, we propose a design for a learned garbage collector that autonomously learns over time when to perform collections. By using reinforcement learning, our design can incorporate user-defined reward functions, allowing an autonomous garbage collector to learn to optimize the exact metric the user desires (e.g., request latency or queries per second). We conduct an initial experimental study on a prototype, demonstrating that an approach based on tabular Q learning may be promising.
Estimating normalizing constants for log-concave distributions: algorithms and lower bounds

Estimating the normalizing constant of an unnormalized probability distribution has important applications in computer science, statistical physics, machine learning, and statistics. In this work, we consider the problem of estimating the normalizing constant Z=∫ℝ d e −f(x) d x to within a multiplication factor of 1 ± e for a µ-strongly convex and L-smooth function f, given query access to f(x) and ∇ f(x). We give both algorithms and lowerbounds for this problem. Using an annealing algorithm combined with a multilevel Monte Carlo method based on underdamped Langevin dynamics, we show that O(d 4/3κ + d 7/6κ7/6/e2) queries to ∇ f are sufficient, where κ= L / µ is the condition number. Moreover, we provide an information theoretic lowerbound, showing that at least d 1−o(1)/e2−o(1) queries are necessary. This provides a first nontrivial lowerbound for the problem.
Comparison of Vancomycin Pharmacokinetics in Cystic Fibrosis Patients Pre and Post-lung Transplant:

Background:Vancomycin is commonly used to treat acute cystic fibrosis (CF) exacerbations associated with methicillin-resistant Staphylococcus aureus (MRSA). Multiple studies have demonstrated pharm...
On the enumeration and asymptotic growth of free quasigroup words

This paper counts the number of reduced quasigroup words of a particular length in a certain number of generators. Taking account of the relationship with the Catalan numbers, counting words in a f...
$k$-Schur expansions of Catalan functions

Abstract We make a broad conjecture about the k-Schur positivity of Catalan functions, symmetric functions which generalize the (parabolic) Hall-Littlewood polynomials. We resolve the conjecture with positive combinatorial formulas in cases which address the k-Schur expansion of (1) Hall-Littlewood polynomials, proving the q = 0 case of the strengthened Macdonald positivity conjecture from [24] ; (2) the product of a Schur function and a k-Schur function when the indexing partitions concatenate to a partition, describing a class of Gromov-Witten invariants for the quantum cohomology of complete flag varieties; (3) k-split polynomials, solving a substantial special case of a problem of Broer and Shimozono-Weyman on parabolic Hall-Littlewood polynomials [37] . In addition, we prove the conjecture that the k-Schur functions defined via k-split polynomials [25] agree with those defined in terms of strong tableaux [21] .
Covariate Adaptive False Discovery Rate Control With Applications to Omics-Wide Multiple Testing

Conventional multiple testing procedures often assume hypotheses for different features are exchangeable. However, in many scientific applications, additional covariate information regarding the pa...
A Starting Point for Disability Justice in Legal Education

This article explores how a disability justice framework would provide greater access to law school and therefore the legal profession for disabled students of color; specifically, disabled Black, Indigenous, and Latinx students. Using DisCrit principles formulated by Subini Annamma, David Connor, and Beth Ferri (2013), this article provides suggestions for incorporating a disability justice lens to legal education. In doing so, this article specifically recognizes the work of three disability justice activist-attorney-scholars, Lydia X.Z. Brown, Talila “TL” Lewis, and Katherine Perez, and considers lessons from their advocacy and leadership that can apply in the law school setting.
False discovery and its control in low rank estimation

Models specified by low rank matrices are ubiquitous in contemporary applications. In many of these problem domains, the row–column space structure of a low rank matrix carries information about some underlying phenomenon, and it is of interest in inferential settings to evaluate the extent to which the row–column spaces of an estimated low rank matrix signify discoveries about the phenomenon. However, in contrast with variable selection, we lack a formal framework to assess true or false discoveries in low rank estimation; in particular, the key source of difficulty is that the standard notion of a discovery is a discrete notion that is ill suited to the smooth structure underlying low rank matrices. We address this challenge via a geometric reformulation of the concept of a discovery, which then enables a natural definition in the low rank case. We describe and analyse a generalization of the stability selection method of Meinshausen and Buhlmann to control for false discoveries in low rank estimation, and we demonstrate its utility compared with previous approaches via numerical experiments.
Rating a robo‐rater

Since 2011, Morningstar has issued Morningstar Analyst Ratings on many of the largest mutual funds in the United States. In June 2017, Morningstar launched the Morningstar Quantitative Rating™ to provide a forward‐looking rating on all mutual funds. Morningstar uses a “robo‐rater” machine‐learning model to assign Morningstar Quantitative Ratings. However, the “robo‐rater” cannot utilize the complete set of information available to Morningstar's analyst as it cannot process “soft information.” The purpose of this study is to evaluate if and how this “robo‐rater” is conducive to mutual fund selection. I find no evidence that the “robo‐rater” offers value to investors beyond its assessment of mutual fund expenses and I find that its inability to process “soft information” makes the Morningstar Quantitative Rating™ much less useful than the Morningstar Analyst Rating™. I also examine the relationship between Morningstar Quantitative Rating™ and mutual fund flows and find that the “robo‐rater” has little to no influence on investors' choice of mutual funds. Paper is available through the Wiley Online Library at https://onlinelibrary.wiley.com/doi/full/10.1002/cfp2.1090
Characterizing recent bark beetle-caused tree mortality in the western United States from aerial surveys

Abstract Bark beetle (Curculionidae, Scolytinae) outbreaks have been extensive and severe across the western United States in recent decades, and assessments of tree mortality are critical for documenting patterns and increasing understanding of drivers and impacts. Aerial surveys have produced a rich data set that includes damage severity, year and location of damage, and beetle and tree species. Here we update a data set of bark beetle-caused tree mortality for the western United States to include five additional years (now 1997–2018) and use these data to characterize recent outbreaks and compare with earlier tree mortality. We estimated “mortality area” (MA), the canopy area of beetle-killed trees and a more accurate representation of outbreak impacts than “affected area”, as well as the number of killed trees. Recently, the US Forest Service changed survey approaches, creating challenges for linking observations from the old and new approaches. We compared four methods to harmonize these approaches to produce consistent time series of tree mortality. General similarity of MA occurred in several methods; however, the range of results indicated some uncertainty. Based on limited analyses and a desire to be conservative in damage estimates, we suggest that the FHPR1-R4 method, which produced intermediate mortality among the methods, is most realistic for representing tree mortality from bark beetles. Using this recommended method, we found that bark beetles caused 4.3 Mha of MA and 3.8x109 killed trees when summed across space, time, and bark beetle/host combination (range among methods: 1.5–7.4 Mha and 1.2–6.3 x 109 killed trees). This total mortality area was 4.7% of forest area in the western United States; 28% of this mortality occurred in 2013–2018. Annual tree mortality remained high recently, with values comparable to earlier years, and was a result of combinations of outbreaks of different beetle species in different regions with different timing. Bark beetles continue to be agents of significant forest disturbance in the western United States. Given a range of mortality area results from the different methods, we encourage further evaluation of estimates using independent observations.
Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis

The relationship between primary biliary cholangitis (PBC), a chronic cholestatic autoimmune liver disease, and the peripheral immune system remains to be fully understood. Herein, we performed the first mass cytometry (CyTOF)-based, immunophenotyping analysis of the peripheral immune system in PBC at single-cell resolution. CyTOF was performed on peripheral blood mononuclear cells (PBMCs) from PBC patients (n = 33) and age-/sex-matched healthy controls (n = 33) to obtain immune cell abundance and marker expression profiles. Hierarchical clustering methods were applied to identify immune cell types and subsets significantly associated with PBC. Subsets of gamma-delta T cells (CD3+TCRgd+), CD8+ T cells (CD3+CD8+CD161+PD1+), and memory B cells (CD3-CD19+CD20+CD24+CD27+) were found to have lower abundance in PBC than in control. In contrast, higher abundance of subsets of monocytes and naive B cells were observed in PBC compared to control. Furthermore, several naive B cell (CD3-CD19+CD20+CD24-CD27-) subsets were significantly higher in PBC patients with cirrhosis (indicative of late-stage disease) than in those without cirrhosis. Alternatively, subsets of memory B cells were lower in abundance in cirrhotic relative to non-cirrhotic PBC patients. Future immunophenotyping investigations could lead to better understanding of PBC pathogenesis and progression, and also to the discovery of novel biomarkers and treatment strategies.
The relationship between the intrinsic Čech and persistence distortion distances for metric graphs

Metric graphs are meaningful objects for modeling complex structures that arise in many real-world applications, such as road networks, river systems, earthquake faults, blood vessels, and filamentary structures in galaxies. To study metric graphs in the context of comparison, we are interested in determining the relative discriminative capabilities of two topology-based distances between a pair of arbitrary finite metric graphs: the persistence distortion distance and the intrinsic Cech distance. We explicitly show how to compute the intrinsic Cech distance between two metric graphs based solely on knowledge of the shortest systems of loops for the graphs. Our main theorem establishes an inequality between the intrinsic Cech and persistence distortion distances in the case when one of the graphs is a bouquet graph and the other is arbitrary. The relationship also holds when both graphs are constructed via wedge sums of cycles and edges.
From Nesterov’s Estimate Sequence to Riemannian Acceleration

We propose the first global accelerated gradient method for Riemannian manifolds. Toward establishing our result we revisit Nesterov's estimate sequence technique and develop an alternative analysis for it that may also be of independent interest. Then, we extend this analysis to the Riemannian setting, localizing the key difficulty due to non-Euclidean structure into a certain ``metric distortion.'' We control this distortion by developing a novel geometric inequality, which permits us to propose and analyze a Riemannian counterpart to Nesterov's accelerated gradient method.