cssi/SciSciGPT-SciSciNet huggingface dataset:
In-Duct Viscous Flow Pressure Distribution on a Body with Boundary-Layer Control

Abstract : It is well known that the static pressure distribution of an axisymmetric body operating within the boundaries of a duct such as a wind or water tunnel test section is altered from that observed in a free stream because of solid blockage and horizontal buoyancy effects. Because potential flow solutions can account only for the solid blockage, auxiliary analysis must be performed to account for the viscous (horizontal buoyancy) effects. One such analysis has been performed by this author, where it was assumed that boundary layer development occurred in a natural, flat plate-like manner over the test body and duct walls. In this report, a modification of that analysis is presented which permits one to analyze bodies with boundary layer control. In addition to the theoretical formulation, a numerical example is presented to illustrate the effect on the pressure distribution of a 0.318 m diameter body with surface suction operating in a 1.22 m diameter duct. (Author)
Inverse Optimal Heuristic Control for Imitation Learning

One common approach to imitation learning is behavioral cloning (BC), which employs straightforward supervised learning (i.e., classification) to directly map observations to controls. A second approach is inverse optimal control (IOC), which formalizes the problem of learning sequential decision-making behavior over long horizons as a problem of recovering a utility function that explains observed behavior. This paper presents inverse optimal heuristic control (IOHC), a novel approach to imitation learning that capitalizes on the strengths of both paradigms. It employs long-horizon IOC-style modeling in a low-dimensional space where inference remains tractable, while incorporating an additional descriptive set of BC-style features to guide a higher-dimensional overall action selection. We provide experimental results demonstrating the capabilities of our model on a simple illustrative problem as well as on two real world problems: turn-prediction for taxi drivers, and pedestrian prediction within an office environment.
TESTS FOR PRICE ENDOGENEITY IN DIFFERENTIATED PRODUCT MODELS

We develop simple tests for endogenous prices arising from omitted demand factors in discrete choice models. Our approach only requires one to locate testing proxies that have some correlation with the omitted factors when prices are endogenous. We use the difference between prices and their predicted values given observed demand and supply factors. If prices are exogenous, these proxies should not explain demand given prices and other explanatory variables. We reject exogeneity if these proxies enter significantly in utility as additional explanatory variables. The tests are easy to implement as we show with several Monte Carlos and discuss for three recent demand applications.
Geophysical investigation for the location of a historic Heiau, Kawaihae, Hawaii

There currently is an awareness in Hawaii to preserve historic sites and the culture of ancient Hawaii. One area of cultural significance, Pu’ukohola Heiau National Historic Site, is located in Kawaihae on the northwest coast of Hawaii (Figure 1). The site is administered by the U.S. Department of the Interior’s National Park Service (NPS). A heiau is a massive stone structure which was used as a temple by the ancient Hawaiians. The Pu’ukohola Heiau is the primary structure associated with the founding of the Kingdom of Hawaii. The last major heiau was built in 1791 by Kamehameha the Great to honor, regain the favor, and enlist the services of his family war-god, Ku-ka-ili-moku. An Englishman, John Young, reported that Kamehameha offered 11 human sacrifices at the dedication of the heiau. The principal offering was the body of Keoua Kuahu’ula, Kamehameha’s only serious rival for the control of the island of Hawaii. With his war temple built and acceptable to the war-god, Kamehameha conquered Maui, Lanai, and Molokai islands in 1794 and Oahu in 1795. With the successful conquest of Oahu, Kamehameha founded the Kingdom of Hawaii. Kauai remained independent until 1810, when it joined the Kingdom by mutual consent to forestall invasion by Kamehameha. Following the death of Kamehameha in 1819, the traditional religion was abandoned. The carved wooden images of Pu’ukohola Heiau were discarded and its thatched temple houses, prayer tower and altar destroyed. Today, there are two intact stone structures, or heiaus, that can be visited at the Pu’ukohola Heiau National Historic Site. One structure is about 200 ft long and 100 ft wide and the other about 275 ft long and 50 ft wide. A third, smaller structure, measuring approximately 10 ft by 10 ft, has historically been reported to exist at the site but its exact location is unknown. Some local residents claim that the missing heiau is located in the bay just a few tens of yards offshore from the site while others maintain that it is buried somewhere onshore near the shoreline. A geophysical investigation was performed at the Pu’ukohola Heiau National Historic Site during the period 11-18 August 1993 in order to locate a purported “shark heiau” in the vicinity of the Kawaihae Boat Harbor construction project, South Kohala, Island of Hawaii. This was performed as part of Section 106, National Historic Preservation Act of 1966, as amended, compliance, pursuant to implementing regulations contained in 36 CFR 800, for the proposed construction at Kawaihae Harbor. NPS persoMe desired to noninvasively determine the location of the shark heiau; the heiau will then presumably be investigated and protected. The geophysical investigation was conducted by personnel of the U.S. Army Engineer Waterways Experiment Station (WES) at the request of the U.S. Army Engineer Division, Pacific Ocean.
Sampling-Based Planning of In-Hand Manipulation with External Pushes

This paper presents a sampling-based planning algorithm for in-hand manipulation of a grasped object using a series of external pushes. A high-level sampling-based planning framework, in tandem with a low-level inverse contact dynamics solver, effectively explores the space of continuous pushes with discrete pusher contact switch-overs. We model the frictional interaction between gripper, grasped object, and pusher, by discretizing complex surface/line contacts into arrays of hard frictional point contacts. The inverse dynamics problem of finding an instantaneous pusher motion that yields a desired instantaneous object motion takes the form of a mixed nonlinear complementarity problem. Building upon this dynamics solver, our planner generates a sequence of pushes that steers the object to a goal grasp. We evaluate the performance of the planner for the case of a parallel-jaw gripper manipulating different objects, both in simulation and with real experiments. Through these examples, we highlight the important properties of the planner: respecting and exploiting the hybrid dynamics of contact sticking/sliding/rolling and a sense of efficiency with respect to discrete contact switch-overs.
Causal-Comparative Study†

A nonexperimental design that is employed to explore cause-and-effect relationships is called ex post facto or causal-comparative. The title ex post facto is applied to studies in which data are gathered retrospectively, whereas causal-comparative is used when data are gathered from groups and the independent variable is not manipulated as in an experiment. Keywords: ex post facto; causal-comparative; differential study; after-the-fact natural experiments
Optimization and Learning With Information Streams: Time-varying algorithms and applications

There is a growing cross-disciplinary effort in the broad domain of optimization and learning with streams of data, applied to settings where traditional batch optimization techniques cannot produce solutions at time scales that match the interarrival times of the data points due to computational and/or communication bottlenecks. Special types of online algorithms can handle this situation, and this article focuses on such time-varying optimization algorithms, with emphasis on machine leaning (ML) and signal processing (SP) as well as data-driven control (DDC). Approaches for the design of time-varying or online first-order optimization methods are discussed, with emphasis on algorithms that can handle errors in the gradient, as may arise when the gradient is estimated. Insights into performance metrics and accompanying claims are provided, along with evidence of cases where algorithms that are provably convergent in batch optimization may perform poorly in an online regime. The role of distributed computation is discussed. Illustrative numerical examples for a number of applications of broad interest are provided to convey key ideas.
Chapter 1 – Matrices

Publisher Summary This chapter provides an overview of basic concepts and operations of matrices. A matrix is a rectangular array of elements arranged in horizontal rows and vertical columns. The entries of a matrix are called elements. Any element having its row index equal to its column index is a diagonal element. Thus, the diagonal elements of a matrix are the elements in the 1–1 position, 2–2 position, 3–3 position, and so on, for as many elements of this type that exist. If the matrix has as many rows as columns, p = n, then it is called a square matrix. The elements of a matrix need not be numbers; they can be, and quite often arise physically as, functions, operators, or matrices. A matrix is an entity unto itself; it is not a number.
Voting on prices vs. voting on quantities in a World Climate Assembly

Abstract This paper posits the conceptually useful allegory of a futuristic “World Climate Assembly” that votes on global carbon emissions via the basic principle of majority rule. Two variants are considered. One is to vote on a universal price (or tax) that is internationally harmonized, but the proceeds from which are domestically retained. The other is to vote on the overall quantity of total worldwide emissions, which are then distributed for free (via a pre-decided fractional subdivision formula) as individual allowance permits that are subsequently marketed in an international cap-and-trade system. The model of the paper suggests that the majority-voted price is likely to be less distortionary and easier to enact than the majority-voted total quantity of permits. Some possible implications for climate-change negotiations are noted.
Airports and Urban Growth: Evidence from a Quasi-Natural Policy Experiment

While signicant work has been done to examine the determinants of regional development, there is little evidence on the contribution of air services toward this outcome. This paper exploits the unexpected market changes induced by the 1978 Airline Deregulation Act to bring new evidence on the link between airline trac and local economic growth. Using data for almost 300 Metropolitan Statistical Areas (MSAs) over a two decade time period centered around the policy change, we exploit time variation in long-run growth rates to identify the eects of airline trac on population, income and employment growth. Our results suggest that air service has a signicant
Rev-erbα heterozygosity produces a dose-dependent phenotypic advantage in mice

Numerous mutational studies have demonstrated that circadian clock proteins regulate behavior and metabolism. Nr1d1(Rev-erbα) is a key regulator of circadian gene expression and a pleiotropic regulator of skeletal muscle homeostasis and lipid metabolism. Loss of Rev-erbα expression induces muscular atrophy, high adiposity, and metabolic syndrome in mice. Here we show that, unlike knockout mice, Nr1d1 heterozygous mice are not susceptible to muscular atrophy and in fact paradoxically possess larger myofiber diameters and improved neuromuscular function, compared to wildtype mice. Heterozygous mice lacked dyslipidemia, a characteristic of Nr1d1 knockout mice and displayed increased whole-body fatty-acid oxidation during periods of inactivity (light cycle). Heterozygous mice also exhibited higher rates of glucose uptake when fasted, and had elevated basal rates of gluconeogenesis compared to wildtype and knockout littermates. Rev-erbα ablation suppressed glycolysis and fatty acid-oxidation in white-adipose tissue (WAT), whereas partial Rev-erbα loss, curiously stimulated these processes. Our investigations revealed that Rev-erbα dose-dependently regulates glucose metabolism and fatty acid oxidation in WAT and muscle.
Unbinding Diversity Measures in Archaeology Using GIS

Thirty years ago, a seminal volume drew together leaders in the field who were working on quantifying diversity in archaeological assemblages, Quantifying Diversity in Archaeology (Leonard and Jones, eds. 1989). While several papers identified space as a critical factor in structuring diversity, this concept was largely unaddressed and the analyses presented remained intrasite in scope. This was certainly due, in part, to the analytic and conceptual limitations of spatial analysis in archaeology in the late 1980s. Quantifying Diversity in Archaeology came out just before the geospatial revolution hit full swing in the field and the capabilities of today’s geospatial technologies were unimaginable at the time. Now, thirty years later, the desire to analyze diversity across multiple sites from entire regions is easy to realize computationally. Nevertheless, key challenges identified in the original volume about quantifying diversity persist. While one can measure variation to identify diversity, we still need to understand what that means and how the categories we use to compute diversity are related to actual variation in past behavior(s) of interest. We explore various geospatial approaches that can help untether diversity analyses from traditional site-based frameworks. We argue that centering the experiences of the people producing and, importantly, perceiving the variation extant in their landscape is essential, as these personal experiences structured the past diversity we aim to study.
Relay Selection for 5G New Radio Via Artificial Neural Networks

Millimeter-wave supplies an alternative frequency band of wide bandwidth to better realize pillar technologies of enhanced mobile broadband (eMBB) and ultra-reliable and lowlatency communication (uRLLC) for 5G- new radio (5G-NR). When using mmWave frequency band, relay stations to assist the coverage of base stations in radio access network (RAN) emerge as an attractive technique. However, relay selection to result in the strongest link becomes the critical technology to facilitate RAN using mmWave. A disruptive approach toward relay selection is to take advantage of existing operating data and apply appropriate artificial neural networks (ANN) and deep learning algorithms to alleviate severe fading in mmWave band. In this paper, we apply classification techniques using ANN with multilayer perception to predict the path loss of multiple transmitted links and base on a certain loss level, and thus execute effective relay selection, which also recommends the handover to an appropriate path. ANN with multilayer perception are compared with other ML algorithms to demonstrate effectiveness for relay selection in 5G-NR.
Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights

Applied work often studies the effect of a binary variable (“treatment”) using linear models with additive effects. I study the interpretation of the OLS estimands in such models when treatment eff...
Templates and recurrences: better together

This paper is the confluence of two streams of ideas in the literature on generating numerical invariants, namely: (1) template-based methods, and (2) recurrence-based methods. A template-based method begins with a template that contains unknown quantities, and finds invariants that match the template by extracting and solving constraints on the unknowns. A disadvantage of template-based methods is that they require fixing the set of terms that may appear in an invariant in advance. This disadvantage is particularly prominent for non-linear invariant generation, because the user must supply maximum degrees on polynomials, bases for exponents, etc. On the other hand, recurrence-based methods are able to find sophisticated non-linear mathematical relations, including polynomials, exponentials, and logarithms, because such relations arise as the solutions to recurrences. However, a disadvantage of past recurrence-based invariant-generation methods is that they are primarily loop-based analyses: they use recurrences to relate the pre-state and post-state of a loop, so it is not obvious how to apply them to a recursive procedure, especially if the procedure is non-linearly recursive (e.g., a tree-traversal algorithm). In this paper, we combine these two approaches and obtain a technique that uses templates in which the unknowns are functions rather than numbers, and the constraints on the unknowns are recurrences. The technique synthesizes invariants involving polynomials, exponentials, and logarithms, even in the presence of arbitrary control-flow, including any combination of loops, branches, and (possibly non-linear) recursion. For instance, it is able to show that (i) the time taken by merge-sort is O(n log(n)), and (ii) the time taken by Strassen’s algorithm is O(nlog2(7)).
Circuit-Based Intrinsic Methods to Detect Overfitting

The focus of this paper is on intrinsic methods to detect overfitting. By intrinsic methods, we mean methods that rely only on the model and the training data, as opposed to traditional methods (we call them extrinsic methods) that rely on performance on a test set or on bounds from model complexity. We propose a family of intrinsic methods, called Counterfactual Simulation (CFS), which analyze the flow of training examples through the model by identifying and perturbing rare patterns. By applying CFS to logic circuits we get a method that has no hyper-parameters and works uniformly across different types of models such as neural networks, random forests and lookup tables. Experimentally, CFS can separate models with different levels of overfit using only their logic circuit representations without any access to the high level structure. By comparing lookup tables, neural networks, and random forests using CFS, we get insight into why neural networks generalize. In particular, we find that stochastic gradient descent in neural nets does not lead to "brute force" memorization, but finds common patterns (whether we train with actual or randomized labels), and neural networks are not unlike forests in this regard. Finally, we identify a limitation with our proposal that makes it unsuitable in an adversarial setting, but points the way to future work on robust intrinsic methods.
Does learning require memorization? a short tale about a long tail

State-of-the-art results on image recognition tasks are achieved using over-parameterized learning algorithms that (nearly) perfectly fit the training set and are known to fit well even random labels. This tendency to memorize seemingly useless training data labels is not explained by existing theoretical analyses. Memorization of the training data also presents significant privacy risks when the training data contains sensitive personal information and thus it is important to understand whether such memorization is necessary for accurate learning. We provide a simple conceptual explanation and a theoretical model demonstrating that for natural data distributions memorization of labels is necessary for achieving close-to-optimal generalization error. The model is motivated and supported by the results of several recent empirical works. In our model, data is sampled from a mixture of subpopulations and the frequencies of these subpopulations are chosen from some prior. The model allows to quantify the effect of not fitting the training data on the generalization performance of the learned classifier and demonstrates that memorization is necessary whenever frequencies are long-tailed. Image and text data are known to follow such distributions and therefore our results establish a formal link between these empirical phenomena. Our results also have concrete implications for the cost of ensuring differential privacy in learning.
Learning to Segment 3D Point Clouds in 2D Image Space

In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively and efficiently project such point clouds into a 2D image space so that traditional 2D convolutional neural networks (CNNs) such as U-Net can be applied for segmentation. To this end, we are motivated by graph drawing and reformulate it as an integer programming problem to learn the topology-preserving graph-to-grid mapping for each individual point cloud. To accelerate the computation in practice, we further propose a novel hierarchical approximate algorithm. With the help of the Delaunay triangulation for graph construction from point clouds and a multi-scale U-Net for segmentation, we manage to demonstrate the state-of-the-art performance on ShapeNet and PartNet, respectively, with significant improvement over the literature. Code is available at https://github.com/Zhang-VISLab.
Applying machine learning to investigate long-term insect–plant interactions preserved on digitized herbarium specimens

Author(s): Meineke, Emily K; Tomasi, Carlo; Yuan, Song; Pryer, Kathleen M | Abstract: Premise:Despite the economic significance of insect damage to plants (i.e., herbivory), long-term data documenting changes in herbivory are limited. Millions of pressed plant specimens are now available online and can be used to collect big data on plant-insect interactions during the Anthropocene. Methods:We initiated development of machine learning methods to automate extraction of herbivory data from herbarium specimens by training an insect damage detector and a damage type classifier on two distantly related plant species (Quercus bicolor and Onoclea sensibilis). We experimented with (1) classifying six types of herbivory and two control categories of undamaged leaf, and (2) detecting two of the damage categories for which several hundred annotations were available. Results:Damage detection results were mixed, with a mean average precision of 45% in the simultaneous detection and classification of two types of damage. However, damage classification on hand-drawn boxes identified the correct type of herbivory 81.5% of the time in eight categories. The damage classifier was accurate for categories with 100 or more test samples. Discussion:These tools are a promising first step for the automation of herbivory data collection. We describe ongoing efforts to increase the accuracy of these models, allowing researchers to extract similar data and apply them to biological hypotheses.
Ridge Regularizaton: an Essential Concept in Data Science

Ridge or more formally l2 regularization shows up in many areas of statistics and machine learning. It is one of those essential devices that any good data scientist needs to master for their craft...
Characters of Renner monoids and their Hecke algebras

This paper gives a general algorithm for computing the character table of any Renner monoid Hecke algebra, by adapting and generalizing techniques of Solomon used to study the rook monoid. The char...
Heat pipes in space and on earth

The heat pipe is a closed tube whose inner surfaces are lined with a porous capillary wick. The wick is saturated with the liquid phase of a working fluid. The heat supplied at one end of the tube, the evaporator, causes evaporation of the working fluid. The vapor will pass to the other end of the tube, the condenser, where it will condense and release the latent heat of vaporization to a heat sink in that section of the pipe. Problems concerning a design of heat pipes for space applications are related to certain difficulties regarding the prediction of device performance under zero-g conditions. Heat pipes are usually tested on the ground under the influence of gravity, and then their performance is extrapolated to space. A description is presented of the approaches used to insure good heat pipe performance in space. Attention is given to an international heat pipe experiment conducted to accumulate zero-g performance data for several new and unique heat pipe designs, heat pipes for ATS-6, cryogenic heat pipes, and future activities.
Risk Interactions of Coronavirus Infection across Age Groups after the Peak of COVID-19 Epidemic.

Background: The COVID-19 pandemic has incurred significant disease burden worldwide, particularly on the elderly population. This study aims to explore how risks of coronavirus infection interact across age groups using data from South Korea. Methods: Daily new COVID-19 cases from 10 March to 30 April 2020 were scraped from online open sources. A multivariate vector autoregressive model for time series of count data was used to examine the risk interactions across age groups. Case counts from previous days were included as predictors to dynamically examine the change of risk patterns. Results: In South Korea, the risk of coronavirus infection among elderly people was significantly affected by other age groups. An increase in virus infection among people aged 20–39 was associated with a double risk of infection among elderly people. Meanwhile, an increase in virus infection among elderly people was also significantly associated with risks of infection among other age groups. The risks of infection among younger people were relatively unaffected by that of other age groups. Conclusions: Protecting elderly people from coronavirus infection could not only reduce the risk of infection among themselves but also ameliorate the risks of virus infection among other age groups. Such interventions should be effective and for the long term.
Optimal Control of Wave Energy Converters Using Epsilon-Trig Regularization Method*

The wave energy converter (WEC) devices provide access to a renewable energy source. Developing control strategies to harvest maximum wave energy requires solving a constrained optimal control problem. It is shown that singular control arcs may constitute part (or the entire) of extremal trajectories. Characterizing the optimal control structure, especially with the possibility of many switches between regular and singular control arcs, is challenging due to lack of a priori information about: 1) optimal sequence as well as number of the regular and singular control arcs, and 2) the corresponding optimal switch times (from a regular to a singular arc and vice versa). This investigation demonstrates the application of a recently developed construct, the Epsilon-Trig Regularization Method (ETRM), to the problem of maximizing energy harvesting for a point-absorber WEC model in the presence of control constraints. Utility of the ETRM for the WEC problem is demonstrated by comparing its high-quality results against those in the literature for a number of test cases.
No-Regret Prediction in Marginally Stable Systems

We consider the problem of online prediction in a marginally stable linear dynamical system subject to bounded adversarial or (non-isotropic) stochastic perturbations. This poses two challenges. Firstly, the system is in general unidentifiable, so recent and classical results on parameter recovery do not apply. Secondly, because we allow the system to be marginally stable, the state can grow polynomially with time; this causes standard regret bounds in online convex optimization to be vacuous. In spite of these challenges, we show that the online least-squares algorithm achieves sublinear regret (improvable to polylogarithmic in the stochastic setting), with polynomial dependence on the system's parameters. This requires a refined regret analysis, including a structural lemma showing the current state of the system to be a small linear combination of past states, even if the state grows polynomially. By applying our techniques to learning an autoregressive filter, we also achieve logarithmic regret in the partially observed setting under Gaussian noise, with polynomial dependence on the memory of the associated Kalman filter.
Delay-Optimal Distributed Edge Computing in Wireless Edge Networks

By integrating edge computing with parallel computing, distributed edge computing (DEC) makes use of distributed devices in edge networks to perform computing in parallel, which can substantially reduce service delays. In this paper, we explore DEC that exploits distributed edge devices connected by a wireless network to perform a computation task offloaded from an end device. In particular, we study the fundamental problem of minimizing the delay of executing a distributed algorithm of the computation task. We first establish some structural properties of the optimal communication scheduling policy. Then, given these properties, we characterize the optimal computation allocation policy, which can be found by an efficient algorithm. Next, based on the optimal computation allocation, we characterize the optimal scheduling order of communications for some special cases, and develop an efficient algorithm with a finite approximation ratio to find it for the general case. Last, based on the optimal computation allocation and communication scheduling, we further show that the optimal selection of devices can be found efficiently for some special cases. Our results provide some useful insights for the optimal computation-communication codesign. We evaluate the performance of the theoretical findings using simulations.
Functional Ratings in Sports

In this paper, we present a new model for ranking sports teams. Our model uses all scoring data from all games to produce a functional rating by the method of least squares. The functional rating can be interpreted as a team average point differential adjusted for strength of schedule. Using two team’s functional ratings we can predict the expected point differential at any time in the game. We looked at three variations of our model accounting for home-court advantage in different ways. We use the 2018–2019 NCAA Division 1 men’s college basketball season to test the models and determined that home-court advantage is statistically important but does not differ between teams.
From the Peierls–Nabarro model to the equation of motion of the dislocation continuum

Abstract We consider a semi-linear integro-differential equation in dimension one associated to the half Laplacian whose solution represents the atom dislocation in a crystal. The equation comprises the evolutive version of the classical Peierls–Nabarro model. We show that for a large number of dislocations, the solution, properly rescaled, converges to the solution of a well known equation called by Head (1972) ”the equation of motion of the dislocation continuum”. The limit equation is a model for the macroscopic crystal plasticity with density of dislocations. In particular, we recover the so called Orowan’s law which states that dislocations move at a velocity proportional to the effective stress.
Efflux Pump Antibiotic Binding Site Mutations Are Associated with Azithromycin Nonsusceptibility in Clinical Neisseria gonorrhoeae Isolates

Lyu and Moseng et al. used cryo-electron microscopy to characterize key residues involved in drug binding by mosaic-like MtrD efflux pump alleles in Neisseria gonorrhoeae (1). Isogenic experiments introducing key MtrD substitutions R714G and K823E increased macrolide MICs, leading the authors to predict that nonmosaic MtrD “gonococcal strains bearing both the mtrR promoter and amino acid changes at MtrD positions 714 or 823 could lead to clinically significant levels of Azi nonsusceptibility resistance.” We tested this hypothesis by analyzing a global meta-analysis collection of 4,852 N. gonorrhoeae genomes (2). In support of their prediction, we identified clinical isolates with novel nonmosaic MtrD …
How Central Office Leaders Influence School Leaders’ Decision-Making: Unpacking Power Dynamics in Two School-Based Decision-Making Systems

While past studies on school district decentralization found that central office leaders can limit school leaders’ decision-making power, the studies did not examine how they do so. We investigated...
Do privately-owned prisons increase incarceration rates?

Abstract This article measures the effect of establishing private prisons on incarceration-related outcomes in the United States. We develop a model to show that enforcement authorities faced with capacity constraints or who are more susceptible to bribes set non-optimal sanction levels which may increase total number of incarcerated individuals and each individual's sentence length. Using instrumental variables regressions on state and individual data from 1989 to 2008, we find evidence showing a rise in private prison beds per capita increases the number of incarcerated individuals per capita and average sentence lengths. The effect is more likely for crime types with more sentencing leeway such as fraud, regulatory, drug, and weapons crimes. There is evidence showing the effect of private prisons is more pronounced in states where prison capacity constraints are met or exceeded and if the state is more corrupt.
Resolution Enhancement in Scanning Electron Microscopy using Deep Learning

We present a deep learning-based framework to perform image super-resolution in scanning electron microscopy. The technique was demonstrated to perform a resolution enhancement using a standard resolution test target and hydrogel samples.
Varenicline rescues nicotine-induced decrease in motivation for sucrose reinforcement.

Abstract Varenicline is one of the top medications used for smoking cessation and is often prescribed before termination of nicotine use. The effect of this combined nicotine and varenicline use on the reward system and motivation for primary reinforcement is underexplored. The goal of this study was to assess the effects of nicotine and varenicline on motivation for a food reinforcer. In Experiment 1, we first assessed the responding for sucrose after pretreatment with nicotine (0, 0.1, or 0.4 mg/kg) and varenicline (0.0, 0.1, 1.0 mg/kg) using a behavioral economics approach. The responding for sucrose was then assessed using a progressive ratio schedule of reinforcement after pretreatment with all possible combinations of nicotine and varenicline doses. In Experiment 2, rats were assessed for the consumption of sucrose in home cages after pretreatment with nicotine and varenicline. We found that (a) nicotine decreased economic demand for sucrose, (b) varenicline rescued nicotine-induced reduction in economic demand for sucrose, and (c) history of varenicline treatment predicted responding for sucrose on a progressive ratio schedule of reinforcement where rats with a history of varenicline treatment responded significantly lower for sucrose across nicotine doses than rats that had not been exposed to varenicline. The results of Experiment 2 largely confirmed that nicotine decreases motivation for sucrose using a passive consumption protocol and that varenicline rescues this effect. Overall, these findings suggest that varenicline interacts with the effects of nicotine by restoring nicotine-induced reduction in motivation for appetitive rewards.
Fair Play as an Injury Prevention Intervention: Do Yellow Card Accumulation Policies Reduce High School Soccer Injuries?

Objectives Evaluate yellow card policies' (YCPs) effectiveness in reducing competition contact injuries (CCIs). Design Retrospective cohort. Setting High schools. Participants Soccer players from High School (HS) Reporting Information Online participating schools, 2005/06 to 2017/18. Independent variables Athlete exposure (AE), CCIs from HS competitions collected from states with/without YCPs. Main outcome measures Rate and rate ratio (RR) of athlete-athlete CCIs recorded by athletic trainers were compared between states with/without YCPs and pre-YCPs/post-YCPs among the states with YCPs using Poisson regressions. Proportions of severe athlete-athlete CCIs were also described in states with/without YCPs. Results Fifteen states implemented YCPs between 2005/06 and 2017/18; 901 athlete-athlete CCIs occurred during 352 775 competition AEs in states with YCPs, and 3525 injuries during 1 459 708 competition AEs in states without YCPs. There was no significant difference in injury rates between schools in states with/without YCPs (RR 1.07; 95% confidence interval [CI]: 0.97-1.17). Among state with YCPs, injury rates were not significantly different between pre-YCP and post-YCP implementation (RR 1.15; 95% CI: 0.98-1.34). Although a significantly lower proportion of injuries resulting in >3 weeks' time loss (TL) occurred in states with YCPs (injury proportion ratio 0.81; 95% CI: 0.66-0.997), no significant differences were observed in proportions of other severe athlete-athlete CCIs between states with/without YCPs. Conclusions Yellow card policies were ineffective in lowering HS soccer athlete-athlete CCI rates, although injuries resulting in >3 weeks' TL were less prevalent in states with YCPs. Implementation of YCPs alone, without proper enforcement, may not be a sufficient injury prevention strategy.
Towards biomimicry of a bat-style perching maneuver on structures: the manipulation of inertial dynamics

The flight characteristics of bats remarkably have been overlooked in aerial drone designs. Unlike other animals, bats leverage the manipulation of inertial dynamics to exhibit aerial flip turns when they perch. Inspired by this unique maneuver, this work develops and uses a tiny robot called Harpoon to demonstrate that the preparation for upside-down landing is possible through: 1) reorientation towards the landing surface through zero-angular-momentum turns and 2) reaching to the surface through shooting a detachable landing gear. The closed-loop manipulations of inertial dynamics takes place based on a symplectic description of the dynamical system (body and appendage), which is known to exhibit an excellent geometric conservation properties.
Integrated Nonreciprocal Photonic Devices With Dynamic Modulation

Nonreciprocal components, such as isolators and circulators, are crucial components for photonic systems. In this article, we review theoretical and experimental progress toward developing nonreciprocal photonic devices based on dynamic modulation. In particular, we focus on approaches that operate at optical wavelengths and device architectures that have the potential for chip-scale integration. We first discuss the requirements for constructing an isolator or circulator using dynamic modulation. We review a number of different isolator and circulator architectures, including waveguide and resonant devices, and describe their underlying operating principles. We then compare these device architectures from a system-level performance perspective, considering how their figures of merit, such as footprint, bandwidth, isolation, and insertion loss scale with respect to device parameters.
Oxygen depletion in FLASH ultra-high-dose-rate radiotherapy: A molecular dynamics simulation.

Purpose We present a first-principles molecular dynamics (MD) simulation and expound upon a mechanism of oxygen depletion hypothesis to explain the mitigation of normal tissue injury observed in ultra-high-dose-rate (UHDR) FLASH radiotherapy. Methods We simulated damage to a segment of DNA (also representing other bio-molecules such as RNA and proteins) in a simulation box filled with H2O and O2 molecules. Attoseconds physical interactions (ionizations, electronic and vibrational excitations) were simulated by using the Monte Carlo track structure code Geant4-DNA. Immediately after ionization, ab initio Car-Parrinello molecular dynamics (CPMD) simulation was used to identify which H2O and O2 molecules surrounding the DNA-molecule were converted into reactive oxygen species (ROS). Sub-sequently, the femto- to nano-second reactions of ROS were simulated by using MD with Reactive Force Field (ReaxFF), to illustrate ROS merging into new types of non-reactive oxygen species (NROS) due to strong coupling among ROS. A coarse-grained model was constructed to describe the relevant collective phenomenon at the macroscopic level on ROS aggregation and formation of NROS agglomerates consistent with the underlying microscopic pathways obtained from MD simulations. Results Time-dependent molecular simulations revealed the formation of metastable and transient spaghetti-like complexes among ROS generated at UHDR. At the higher ROS densities produced under UHDR, stranded chains (i.e., NROS) are produced, mediated through attractive electric polarity forces, hydrogen bonds, and magnetic dipole-dipole interactions among hydroxyl (·OH) radicals. NROS tend to be less mobile than cellular biomolecules as opposed to the isolated and sparsely dense ROS generated at conventional dose rates (CDR). We attribute this effect to the suppression of bio-molecular damage induced per particle track. At a given oxygen level, as the dose rate increases, the size and number of NROS chains increase, and correspondingly the population of toxic ROS components decreases. Similarly, at a given high dose rate, as the oxygen level increases, so do the size and number of NROS chains until an optimum level of oxygen is reached. Beyond that level, the amount of oxygen present may be sfficient to saturate the production of NROS chains, thereby reversing the sparing effects of UHDRs. Conclusions We showed that oxygen depletion, hypothesized to lead to lower normal-tissue toxicity at FLASH dose rates, takes place within femto- to nano-seconds after irradiation. The mechanism is governed by the slow dynamics of chains of ROS complexes (NROS). Under physoxic (≈ 4-5% oxygen) conditions (i.e., in normal tissues), NROS are more abundant than in hypoxic conditions (e.g., 10-15%), oxygen depletion by UHDRs may not be sufficient for tissue sparing.
Automatic learning of topological phase boundaries

Topological phase transitions, which do not adhere to Landau's phenomenological model (i.e., a spontaneous symmetry breaking process and vanishing local order parameters), have been actively researched in condensed matter physics. Machine learning of topological phase transitions has generally proved difficult due to the global nature of the topological indices. Only recently has the method of diffusion maps been shown to be effective at identifying changes in topological order. However, previous diffusion map results required adjustments of two hyperparameters: a data length scale and the number of phase boundaries. In this article we introduce a heuristic that requires no such tuning. This heuristic allows computer programs to locate appropriate hyperparameters without user input. We demonstrate this method's efficacy by drawing remarkably accurate phase diagrams in three physical models: the Haldane model of graphene, a generalization of the Su-Schreiffer-Haeger model, and a model for a quantum ring with tunnel junctions. These diagrams are drawn, without human intervention, from a supplied range of model parameters.
Ultrasensitive and Selective Detection of SARS-CoV-2 using Thermotropic Liquid Crystals and Image-based Machine Learning.

Rapid, robust virus detection techniques with ultrahigh sensitivity and selectivity are required for the outbreak of the pandemic coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Here, we report that the femtomolar concentrations of single-stranded ribonucleic acid (ssRNA) of SARS-CoV-2 trigger ordering transitions in liquid crystal (LC) films decorated with cationic surfactant and complementary 15-mer single-stranded deoxyribonucleic acid (ssDNA) probe. More importantly, the sensitivity of the LC to the severe acute respiratory syndrome (SARS) ssRNA, with a 3 base pair-mismatch compared to the SARS-CoV-2 ssRNA, is measured to decrease by seven orders of magnitude, suggesting that the LC ordering transitions depend strongly on the targeted oligonucleotide sequence. Finally, we design a LC-based diagnostic kit and a smartphone-based application (App) to enable automatic detection of SARS-CoV-2 ssRNA, which could be used for reliable self-test of SARS-CoV-2 at home without the need for complex equipment or procedures.
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization

This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently. Arguably one of the most popular paradigms to tackle this problem is convex relaxation, which achieves remarkable efficacy in practice. However, the theoretical support of this approach is still far from optimal in the noisy setting, falling short of explaining its empirical success. We make progress towards demystifying the practical efficacy of convex relaxation vis-a-vis random noise. When the rank and the condition number of the unknown matrix are bounded by a constant, we demonstrate that the convex programming approach achieves near-optimal estimation errors - in terms of the Euclidean loss, the entrywise loss, and the spectral norm loss - for a wide range of noise levels. All of this is enabled by bridging convex relaxation with the nonconvex Burer-Monteiro approach, a seemingly distinct algorithmic paradigm that is provably robust against noise. More specifically, we show that an approximate critical point of the nonconvex formulation serves as an extremely tight approximation of the convex solution, thus allowing us to transfer the desired statistical guarantees of the nonconvex approach to its convex counterpart.
Resource conservation manifests in the genetic code

Nutrient limitation drives competition for resources across organisms. However, much is unknown about how selective pressures resulting from nutrient limitation shape microbial coding sequences. Here, we study this "resource-driven selection" by using metagenomic and single-cell data of marine microbes, alongside environmental measurements. We show that a significant portion of the selection exerted on microbes is explained by the environment and is associated with nitrogen availability. Notably, this resource conservation optimization is encoded in the structure of the standard genetic code, providing robustness against mutations that increase carbon and nitrogen incorporation into protein sequences. This robustness generalizes to codon choices from multiple taxa across all domains of life, including the human genome.
Accuracy of the Criteria for Hemophagocytic Lymphohistiocytosis

Hemophagocytic lymphohistiocytosis, also known as a hemophagocytic syndrome, is a life-threatening condition that can develop in critically ill patients with malignancies, severe infections, during chemotherapy, and may be associated with currently known or unknown genetic abnormalities; however, this list of potential causes can be extensive. The purpose of this study is to draw attention to the accuracy of its diagnostic criteria, association with a variety of clinical conditions, pathophysiological mechanisms, and outcomes of the diseases. From the medical records in our hospital, we retrospectively extracted 13 cases with hemophagocytosis over a 10-year period. Subsequently, we thoroughly analyzed medical records for the criteria used, the time required for making a diagnosis, adequacy of the criteria, management, and outcomes. We found that not all criteria were used for diagnosis, and the most sensitive and specific tests (genetic study, ferritin, and soluble IL-2r levels) were sometimes bypassed. Late diagnosis delayed management of some patients. Only a few treatment options were used for patient care. The hemophagocytic syndrome is a very rare and fatal entity requiring highly sensitive and specific diagnostic criteria for prompt diagnosis, targeted management, and thorough follow-up. Every patient admitted to the hospital with life-threatening conditions should be suspected and tested for the hemophagocytic syndrome as early as possible. The criteria for hemophagocytic lymphohistiocytosis should be revised, with the most sensitive and specific ones being done in all cases. Subsequently, each patient should be tested for the presence of genetic abnormalities that correlate with the syndrome.