cssi/SciSciGPT-SciSciNet huggingface dataset:
Virtual enriching operators

We construct bounded linear operators that map $$H^1$$ conforming Lagrange finite element spaces to $$H^2$$ conforming virtual element spaces in two and three dimensions. These operators are useful for the analysis of nonstandard finite element methods.
Delineating and validating higher-order dimensions of psychopathology in the Adolescent Brain Cognitive Development (ABCD) study.

Hierarchical dimensional systems of psychopathology promise more informative descriptions for understanding risk and predicting outcome than traditional diagnostic systems, but it is unclear how many major dimensions they should include. We delineated the hierarchy of childhood and adult psychopathology and validated it against clinically relevant measures. Participants were 9987 9- and 10-year-old children and their parents from the Adolescent Brain Cognitive Development (ABCD) study. Factor analyses of items from the Child Behavior Checklist and Adult Self-Report were run to delineate hierarchies of dimensions. We examined the familial aggregation of the psychopathology dimensions, and the ability of different factor solutions to account for risk factors, real-world functioning, cognitive functioning, and physical and mental health service utilization. A hierarchical structure with a general psychopathology (‘p’) factor at the apex and five specific factors (internalizing, somatoform, detachment, neurodevelopmental, and externalizing) emerged in children. Five similar dimensions emerged also in the parents. Child and parent p-factors correlated highly (r = 0.61, p < 0.001), and smaller but significant correlations emerged for convergent dimensions between parents and children after controlling for p-factors (r = 0.09−0.21, p < 0.001). A model with child p-factor alone explained mental health service utilization (R2 = 0.23, p < 0.001), but up to five dimensions provided incremental validity to account for developmental risk and current functioning in children (R2 = 0.03−0.19, p < 0.001). In this first investigation comprehensively mapping the psychopathology hierarchy in children and adults, we delineated a hierarchy of higher-order dimensions associated with a range of clinically relevant validators. These findings hold important implications for psychiatric nosology and future research in this sample.
Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites.

In the coming years, the satellite broadband market will experience significant increases in the service demand, especially for the mobility sector, where demand is burstier. Many of the next generation of satellites will be equipped with numerous degrees of freedom in power and bandwidth allocation capabilities, making manual resource allocation impractical and inefficient. Therefore, it is desirable to automate the operation of these highly flexible satellites. This paper presents a novel power allocation approach based on Deep Reinforcement Learning (DRL) that represents the problem as continuous state and action spaces. We make use of the Proximal Policy Optimization (PPO) algorithm to optimize the allocation policy for minimum Unmet System Demand (USD) and power consumption. The performance of the algorithm is analyzed through simulations of a multibeam satellite system, which show promising results for DRL to be used as a dynamic resource allocation algorithm.
On rigid varieties with projective reduction

In this paper, we study smooth proper rigid varieties which admit formal models whose special fibers are projective. The main theorem asserts that the identity components of the associated rigid Picard varieties will automatically be proper. Consequently, we prove that p-adic Hopf varieties will never have a projective reduction. The proof of our main theorem uses the theory of moduli of semistable coherent sheaves.
Microbiology and clinical characteristics of industrial oil burns.

Abstract Introduction Infections complicating burns generally transition from Gram-positive to Gram-negatives over the first couple weeks, but this depends on multiple factors. The microbiology of infections complicating crude oil (CO) and hydraulic fracturing (FRAC) burns is unknown. Methods We performed a retrospective study of patients with industrial thermal burns hospitalized >2 days with ≥1 day in the ICU between 4/2011–11/2016. Burns were oil-related (ORB; CO or FRAC) or non-oil related (NORB). Epidemiology and microbiology during the first 15 hospital days was compared. Results 149 patients were included, with 11 FRAC and 24 CO. CO burns were more severely burned than those with FRAC and NORB (p Conclusion ORB were associated with more severe burns and unique microbiology. FRAC burns had longer to initial positive culture, potentially suggesting our current methodology is inadequate to diagnose infections associated with FRAC.
Davies’ method for heat-kernel estimates: An extension to the semi-elliptic setting

We consider a class of constant-coefficient partial differential operators on a finite-dimensional real vector space which exhibit a natural dilation invariance. Typically, these operators are anisotropic, allowing for different degrees in different directions. The heat kernels associated to these so-called positive-homogeneous operators are seen to arise naturally as the limits of convolution powers of complex-valued measures, just as the classical heat kernel appears in the central limit theorem. Building on the functional-analytic approach developed by E. B. Davies for higher-order uniformly elliptic operators with measurable coefficients, we formulate a general theory for (anisotropic) self-adjoint variable-coefficient operators, each comparable to a positive-homogeneous operator, and study their associated heat kernels. Specifically, under three abstract hypotheses, we show that the heat kernels satisfy off-diagonal (Gaussian type) estimates involving the Legendre-Fenchel transform of the operator's principle symbol. Our results extend those of E. B. Davies and G. Barbatis and partially extend results of A. F. M. ter Elst and D. Robinson.
Continuous focal translation enhances rate of point-scan volumetric microscopy

Two-Photon Laser-Scanning Microscopy is a powerful tool for exploring biological structure and function due to its ability to optically section through a sample with a tight focus. While it is possible to obtain 3D image stacks by moving a stage, this per-frame imaging process is time consuming. Here, we present a method for an easy-to-implement and inexpensive modification of an existing two-photon microscope to rapidly image in 3D using an electrically tunable lens to create a tessellating scan pattern which repeats with the volume rate. Using appropriate interpolating algorithms, the volumetric imaging rate can be increased by a factor up to four-fold. This capability provides the expansion of the two-photon microscope into the third dimension for faster volumetric imaging capable of visualizing dynamics on timescales not achievable by traditional stage-stack methods.
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation

Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations. Many works have shown that an SNN for inference can be formed by copying the weights from a trained Artificial Neural Network (ANN) and setting the firing threshold for each layer as the maximum input received in that layer. These type of converted SNNs require a large number of time-steps to achieve competitive accuracy which diminishes the energy savings. The number of time-steps can be reduced by training SNNs with spike-based backpropagation from scratch, but that is computationally expensive and slow. To address these challenges, we present a computationally-efficient training technique for deep SNNs. We propose a hybrid training methodology: 1) take a converted SNN and use its weights and thresholds as an initialization step for spike-based backpropagation, and 2) perform incremental spike-timing dependent backpropagation (STDB) on this carefully initialized network to obtain an SNN that converges within few epochs and requires fewer time-steps for input processing. STDB is performed with a novel surrogate gradient function defined using neuron's spike time. The weight update is proportional to the difference in spike timing between the current time-step and the most recent time-step the neuron generated an output spike. The SNNs trained with our hybrid conversion-and-STDB training perform at 10X-25X fewer number of time-steps and achieve similar accuracy compared to purely converted SNNs. The proposed training methodology converges in less than 20 epochs of spike-based backpropagation for most standard image classification datasets, thereby greatly reducing the training complexity compared to training SNNs from scratch. We perform experiments on CIFAR-10, CIFAR-100 and ImageNet datasets for both VGG and ResNet architectures. We achieve top-1 accuracy of 65.19% for ImageNet dataset on SNN with 250 time-steps, which is 10X faster compared to converted SNNs with similar accuracy.
Towards a Directed Homotopy Type Theory

Abstract In this paper, we present a directed homotopy type theory for reasoning synthetically about (higher) categories and directed homotopy theory. We specify a new 'homomorphism' type former for Martin-Lof type theory which is roughly analogous to the identity type former originally introduced by Martin-Lof. The homomorphism type former is meant to capture the notions of morphism (from the theory of categories) and directed path (from directed homotopy theory) just as the identity type former is known to capture the notions of isomorphism (from the theory of groupoids) and path (from homotopy theory). Our main result is an interpretation of these homomorphism types into C at , the category of small categories. There, the interpretation of each homomorphism type hom C ( a , b ) is indeed the set of morphisms between the objects a and b of the category C . We end the paper with an analysis of the interpretation in C at with which we argue that our homomorphism types are indeed the directed version of Martin-Lof's identity types
Hydrodynamics from free-streaming to thermalization and back again

Abstract We study the evolution of the Knudsen and Reynolds numbers in (0+1)-dimensionally expanding fluids with Bjorken symmetry for systems whose microscopic mean free path rises more quickly with time than usually assumed. This allows us to explore within a simple 1-dimensional model the transition from initially thermalizing to ultimately decoupling dynamics. In all cases studied the dynamics is found to be controlled by a hydrodynamic attractor for the Reynolds number whose trajectory as a function of Knudsen number makes a characteristic turn as the dynamics changes from thermalizing to decoupling. We argue that this feature is robust and should also manifest itself in realistic 3-dimensional simulations of expanding heavy-ion collision fireballs.
Joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problem. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework ours, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of ours for MTS forecasting with missing values and its robustness under various missing ratios.
Dislocation drag from phonon wind in an isotropic crystal at large velocities

ABSTRACTThe anharmonic interaction and scattering of phonons by a moving dislocation, the photon wind, imparts a drag force vB(v,T,ρ) on the dislocation. In early studies, the drag coefficient B wa...
On stringy de Sitter spacetimes

We reexamine a family of models with a 3+1-dimensional de Sitter spacetime obtained in the standard tree-level low-energy limit of string theory with a non-trivial anisotropic axion-dilaton background. While such limiting approximations are encouraging but incomplete, our analysis reveals a host of novel features, and shows these models to relate standard and well understood supersymmetric string theory solutions. Finally, we conjecture that this de Sitter spacetime naturally arises by including more of the stringy degrees of freedom, such as a recently advanced variant of the non-commutative phase-space formalism, as well as the analytic continuation of a complex two-dimensional Fano variety arising as a small resolution in a Calabi-Yau 5-fold.
An Impedance-Modulated Code-Division Microwave SQUID Multiplexer

Large arrays of cryogenic detectors, including transition-edge sensors (TESs) or magnetic micro-calorimeters (MMCs), are needed for future experiments across a wide range of applications. Complexities in integration and cryogenic wiring have driven efforts to develop cryogenic readout technologies with large multiplexing factors while maintaining minimal readout noise. One such example is the microwave SQUID multiplexer (μmux), which couples an incoming TES or magnetic calorimeter signal to a unique GHz-frequency resonance that is modulated in frequency. Here, we present a hybrid scheme combining the microwave SQUID multiplexer with code division multiplexing: the impedance-modulated code-division multiplexer (Z-CDM), which may enable an order of magnitude increase in multiplexing factor particularly for low-bandwidth signal applications.
Using long short-term memory neural networks to analyze SEC 13D filings: a recipe for human and machine interaction

We implement an efficient methodology for extracting themes from Securities Exchange Commission 13D filings using aspects of human‐assisted active learning and long short‐term memory (LSTM) neural networks. Sentences from the ‘Purpose of Transaction’ section of each filing are extracted and a randomly chosen subset is labelled based on six filing themes that the existing literature on shareholder activism has shown to have an impact on stock returns. We find that an LSTM neural network that accepts sentences as input performs significantly better, with precision of 77%, than an alternately specified neural network that uses the common bag of words approach. This indicates that both sentence structure and vocabulary are important in classifying SEC 13D filings. Our study has important implications, as it addresses the recent cautions raised in the literature that analysis of finance and accounting‐related text sources should move beyond bag‐of‐words approaches to alternatives that incorporate the analysis of word sense and meaning reflecting context.
A Second-Quantized Kolmogorov–Chentsov Theorem via the Operator Product Expansion

We establish a direct connection between two fundamental topics: one in probability theory and one in quantum field theory. The first topic is the problem of pointwise multiplication of random Schwartz distributions which has been the object of recent progress thanks to Hairer’s theory of regularity structures and the theory of paracontrolled distributions introduced by Gubinelli, Imkeller and Perkowski. The second topic is Wilson’s operator product expansion which is a general property of models of quantum field theory and a cornerstone of the bootstrap approach to conformal field theory. Our main result is a general theorem for the almost sure construction of products of random distributions by mollification and suitable additive as well as multiplicative renormalizations. The hypothesis for this theorem is the operator product expansion with precise bounds for pointwise correlations. We conjecture these bounds to be universal features of quantum field theories with gapped dimension spectrum. Our theorem can accommodate logarithmic corrections, anomalous scaling dimensions and even lack of translation invariance. However, it only applies to fields with short distance singularities that are milder than white noise. As an application, we provide a detailed treatment of a scalar conformal field theory of mean field type, i.e., the fractional massless free field also known as the fractional Gaussian field.
Low-Cost Stochastic Number Generators for Stochastic Computing

Stochastic unary computing provides low-area circuits. However, the required area consuming stochastic number generators (SNGs) in these circuits can diminish their overall gain in area, particularly if several SNGs are required. We propose area-efficient SNGs by sharing the permuted output of one linear feedback shift register (LFSR) among several SNGs. With no hardware overhead, the proposed architecture generates stochastic bit streams with minimum stochastic computing correlation (SCC). Compared to the circular shifting approach presented in prior work, our approach produces stochastic bit streams with 67% less average SCC when a 10-bit LFSR is shared between two SNGs. To generalize our approach, we propose an algorithm to find a set of $m$ permutations ( $n > m > 2$ ) with a minimum pairwise SCC, for an $n$ -bit LFSR. The search space for finding permutations with an exact minimum SCC grows rapidly when $n$ increases and it is intractable to perform a search algorithm using accurately calculated pairwise SCC values, for $n > 9$ . We propose a similarity function that can be used in the proposed search algorithm to quickly find a set of permutations with SCC values close to the minimum one. We evaluate our approach for several applications. The results show that, compared to prior work, it achieves lower mean-squared error (MSE) with the same (or even lower) area. Additionally, based on simulation results, we show that replacing the comparator component of an SNG circuit with a weighted binary generator can reduce SCC.
Allogeneic Chimeric Antigen Receptor-Invariant Natural Killer T Cells Exert Both Direct and Indirect Antitumor Effects through Host CD8 T Cell Cross-Priming

Introduction Chimeric antigen receptor (CAR) T cells have shown impressive results in B-cell malignancies but, unfortunately, their use is still limited by the logistical and financial burdens related to the need of generating autologous cell products. The development of universal allogeneic CAR T cells to be used off-the-shelf has faced major limitations, namely the risk of Graft-versus-Host-Disease (GvHD) induction and the rejection of the cells by the host immune system. Invariant Natural Killer-T (iNKT) cells are innate lymphocytes deprived of GvHD induction potential and displaying antitumor effects, both directly and indirectly, through enhancement of CD8 T cell responses. Preclinical studies in xenogeneic mouse models demonstrated the feasibility of using iNKT cells as a platform for CAR-based therapies, and two clinical trials are currently ongoing. Objective To assess the immunoadjuvant effect of allogeneic CD19-specific iNKT CAR on the host immune system. Methods We transduced murine iNKT cells from FVB/N (H-2Kq) mice with a CD19-specific CD28/CD3ζ CAR and assessed their antitumor effect in vitro and in vivo. Results CD19-iNKT CAR had a direct cytotoxic effect in vitro against the CD19+ A20 lymphoma cells and significantly improved survival of mice after administration to major histocompatibility complex (MHC)–mismatched, immunodeficient BALB/c (H-2Kd) Rag2−/− gamma-chain−/− mice receiving A20 cells (2 × 10e4; Figure 1A) without inducing any signs of GvHD. To test the efficacy of iNKT CAR cells in the presence of host immune cells, we employed BALB/c mice receiving sublethal irradiation (4.4 Gy), resulting in only a partial and transient lymphopenia. The antitumor effect of allogeneic iNKT CAR cells was greatly enhanced (Figure 1B) suggesting the participation of host cells in the antitumor effect. Interestingly, the iNKT CAR effect was partially abrogated when we employed as recipients BALB/c BATF3−/− mice, in which CD8 T cell cross-priming is impaired as a result of the absence of BATF3-dependent CD103+ CD8a+ dendritic cells known to play a role in iNKT cell interactions with other immune effector cells (Figure 1C). Co-administration of allogeneic FVB/N iNKT CAR with autologous BALB/c CD8 T cells at the time of transfer of T-cell-depleted autologous bone marrow cells and A20 lymphoma cells into lethally irradiated BALB/c recipients resulted in a synergistic effect (Figure 1D). Due to their immuno-adjuvant effect on host CD8 T cells, low numbers of allogeneic iNKT CAR outperformed allogeneic conventional CAR T (Tcon CAR) when administered to sublethally irradiated BALB/c mice receiving A20 cells (Figure 1E-F). Conclusion Collectively, these results demonstrate the potent immunoadjuvant effect exerted by allogeneic iNKT CAR cells toward the host immune system suggesting that iNKT CAR cells are an attractive off the shelf product for adoptive immunotherapy.
Micro-Computed Tomography Analysis of Damage in Notched Composite Laminates Under Multi-Axial Fatigue

Abstract The broad application of polymer composites in engineering demands the deep understanding of the main damage mechanisms under realistic loading conditions and the development of proper physics-based models. Towards this goal, this study presents a comprehensive characterization of the main damage mechanisms in a selection of notched composite structures under multiaxial fatigue loading. Thanks to a synergistic combination of X-ray micro-computed tomography (μ-CT) and Digital Image Correlation (DIC), the main failure modes are identified while the crack volume associated to each mechanism is characterized. This study provides unprecedented quantitative data for the development and validation of computational models to capture the fatigue behavior of polymer composite structures.
Value-laden disciplinary shifts in machine learning

As machine learning models are increasingly used for high-stakes decision making, scholars have sought to intervene to ensure that such models do not encode undesirable social and political values. However, little attention thus far has been given to how values influence the machine learning discipline as a whole. How do values influence what the discipline focuses on and the way it develops? If undesirable values are at play at the level of the discipline, then intervening on particular models will not suffice to address the problem. Instead, interventions at the disciplinary-level are required. This paper analyzes the discipline of machine learning through the lens of philosophy of science. We develop a conceptual framework to evaluate the process through which types of machine learning models (e.g. neural networks, support vector machines, graphical models) become predominant. The rise and fall of model-types is often framed as objective progress. However, such disciplinary shifts are more nuanced. First, we argue that the rise of a model-type is self-reinforcing-it influences the way model-types are evaluated. For example, the rise of deep learning was entangled with a greater focus on evaluations in compute-rich and data-rich environments. Second, the way model-types are evaluated encodes loaded social and political values. For example, a greater focus on evaluations in compute-rich and data-rich environments encodes values about centralization of power, privacy, and environmental concerns.
Training in Task Space to Speed Up and Guide Reinforcement Learning

Recent breakthroughs in the reinforcement learning (RL) community have made significant advances towards learning and deploying policies on real world robotic systems. However, even with current state-of-the-art algorithms and computational resources, these methods are still plagued with high sample complexity, and thus long training times, especially for high degree of freedom (DOF) systems. There are also concerns arising from lack of perceived stability or robustness from emerging policies. This paper aims at mitigating these drawbacks by: (1) modeling a complex, high DOF system with a representative simple one, (2) making explicit use of forward and inverse kinematics without forcing the RL algorithm to “learn” them on its own, and (3) learning locomotion policies in Cartesian space instead of joint space. In this paper, these methods are applied to JPL’s Robosimian, but they can be readily used on any system with a base and end effector(s). These locomotion policies can be produced in just a few minutes, trained on a single laptop. We compare the robustness of the resulting learned policies to those of other control methods.
Computational high frequency scattering from high-contrast heterogeneous media

This article considers the computational (acoustic) wave propagation in strongly heterogeneous structures beyond the assumption of periodicity. A high contrast between the constituents of microstructured multiphase materials can lead to unusual wave scattering and absorption, which are interesting and relevant from a physical viewpoint, for instance, in the case of crystals with defects. We present a computational multiscale method in the spirit of the Localized Orthogonal Decomposition and provide its rigorous a priori error analysis for two-phase diffusion coefficients that vary between $1$ and very small values. Special attention is paid to the extreme regimes of high frequency, high contrast, and their previously unexplored coexistence. A series of numerical experiments confirms the theoretical results and demonstrates the ability of the multiscale approach to efficiently capture relevant physical phenomena.
Cortical functional connectivity indexes arousal state during sleep and anesthesia

Disruption of cortical connectivity likely contributes to loss of consciousness (LOC) during both sleep and general anesthesia, but the degree of overlap in the underlying mechanisms is unclear. Both sleep and anesthesia comprise states of varying levels of arousal and consciousness, including states of largely maintained conscious experience (sleep: N1, REM; anesthesia: sedated but responsive) as well as states of substantially reduced conscious experience (sleep: N2/N3; anesthesia: unresponsive). Here, we tested the hypotheses that (1) cortical connectivity will exhibit clear changes when transitioning into states of reduced consciousness, and (2) these changes will be similar for arousal states of comparable levels of consciousness during sleep and anesthesia. Using intracranial recordings from five adult neurosurgical patients, we compared resting state cortical functional connectivity (as measured by weighted phase lag index, wPLI) in the same subjects across arousal states during natural sleep [wake (WS), N1, N2, N3, REM] and propofol anesthesia [pre-drug wake (WA), sedated/responsive (S), and unresponsive (U)]. Analysis of alpha-band connectivity indicated a transition boundary distinguishing states of maintained and reduced conscious experience in both sleep and anesthesia. In wake states WS and WA, alpha-band wPLI within the temporal lobe was dominant. This pattern was largely unchanged in N1, REM, and S. Transitions into states of reduced consciousness N2, N3, and U were characterized by dramatic changes in connectivity, with dominant connections shifting to prefrontal cortex. Secondary analyses indicated similarities in reorganization of cortical connectivity in sleep and anesthesia. Shifts from temporal to frontal cortical connectivity may reflect impaired sensory processing in states of reduced consciousness. The data indicate that functional connectivity can serve as a biomarker of arousal state and suggest common mechanisms of LOC in sleep and anesthesia.
A dialectical reading of Adam Smith on wealth and happiness

Abstract In this essay I consider the relationship between wealth and happiness in Adam Smith by a close reading of a famous section of The Theory of Moral Sentiments (TMS IV.i.8-10). I interpret Smith as presenting an open-ended dialectic between the pursuit of wealth and the pursuit of happiness with the goal of contributing to his readers’ moral education. Within the dialectic Smith places some important social analysis, which he uses to stimulate reflection and synthesis. Upon reflection and observation of the tensions within the passages and the larger scope of his works, Smith pushes the reader to confront a question: given the knowledge—knowledge that one acquires upon reading TMS IV.i.8-10—that (1) people tend to be deceived into thinking that pursuing and acquiring wealth will make them happy, and that (2) acting on their deception has beneficial unintended consequences, how should one proceed in one's own pursuit of wealth and happiness?
Inferring putative ancient whole-genome duplications in the 1000 Plants (1KP) initiative: access to gene family phylogenies and age distributions

BACKGROUND Polyploidy, or whole-genome duplications (WGDs), repeatedly occurred during green plant evolution. To examine the evolutionary history of green plants in a phylogenomic framework, the 1KP project sequenced >1,000 transcriptomes across the Viridiplantae. The 1KP project provided a unique opportunity to study the distribution and occurrence of WGDs across the green plants. As an accompaniment to the capstone publication, this article provides expanded methodological details, results validation, and descriptions of newly released datasets that will aid researchers who wish to use the extended data generated by the 1KP project. RESULTS In the 1KP capstone analyses, we used a total evidence approach that combined inferences of WGDs from Ks and phylogenomic methods to infer and place 244 putative ancient WGDs across the Viridiplantae. Here, we provide an expanded explanation of our approach by describing our methodology and walk-through examples. We also evaluated the consistency of our WGD inferences by comparing them to evidence from published syntenic analyses of plant genome assemblies. We find that our inferences are consistent with whole-genome synteny analyses and our total evidence approach may minimize the false-positive rate throughout the dataset. CONCLUSIONS We release 383,679 nuclear gene family phylogenies and 2,306 gene age distributions with Ks plots from the 1KP capstone paper. These resources will be useful for many future analyses on gene and genome evolution in green plants.
Over-rotation intervals of bimodal interval maps

We describe all bimodal over-twist patterns and give an algorithm allowing one to determine what the left endpoint of the over-rotation interval of a given bimodal map is. We then define a new clas...
Bounds on continuous Scott rank

An analog of Nadel's effective bound for the continuous Scott rank of metric structures, developed by Ben Yaacov, Doucha, Nies, and Tsankov, will be established: Let $\mathscr{L}$ be a language of continuous logic with code $\hat{\mathscr{L}}$. Let $\Omega$ be a weak modulus of uniform continuity with code $\hat{\Omega}$. Let $\mathcal{D}$ be a countable $\mathscr{L}$-pre-structure. Let $\bar{\mathcal{D}}$ denote the completion structure of $\mathcal{D}$. Then $\mathrm{SR}_\Omega(\bar{D}) \leq \omega_1^{\hat{\mathscr{L}}\oplus\hat{\Omega}\oplus\mathcal{D}}$, the Church-Kleene ordinal relative to $\hat{\mathscr{L}}\oplus\hat{\Omega}\oplus\mathcal{D}$.
AutoToon: Automatic Geometric Warping for Face Cartoon Generation

Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces. This task is typically left to artists, as it has proven difficult to capture subjects’ unique characteristics well using automated methods. Recent development of deep end-to-end methods has achieved promising results in capturing style and higher-level exaggerations. However, a key part of caricatures, face warping, has remained challenging for these systems. In this work, we propose AutoToon, the first supervised deep learning method that yields high-quality warps for the warping component of caricatures. Completely disentangled from style, it can be paired with any stylization method to create diverse caricatures. In contrast to prior art, we leverage an SENet and spatial transformer module and train directly on artist warping fields, applying losses both prior to and after warping. As shown by our user studies, we achieve appealing exaggerations that amplify distinguishing features of the face while preserving facial detail.
Measuring Quality in High-Impact Practices.

High-impact practices (HIPs), such as undergraduate research, internships, and senior-capstone projects, are prominent within the academy. Scholars surmise aspects of HIP quality (e.g., student effort, peer collaboration, and faculty interaction) are related to desired outcomes for students (e.g., engagement, GPA, and satisfaction). Using data from the 2015 administration of the National Survey of Student Engagement, respondents who participated in these three HIPs were asked additional questions regarding quality of experience. Results from this study indicate that increased levels of expectations, faculty interaction, and real-world application are related to increases in outcomes; however, these relationships are not consistent among underserved populations.
Reproducible and transparent research practices in published neurology research

The objective of this study was to evaluate the nature and extent of reproducible and transparent research practices in neurology publications. The NLM catalog was used to identify MEDLINE-indexed neurology journals. A PubMed search of these journals was conducted to retrieve publications over a 5-year period from 2014 to 2018. A random sample of publications was extracted. Two authors conducted data extraction in a blinded, duplicate fashion using a pilot-tested Google form. This form prompted data extractors to determine whether publications provided access to items such as study materials, raw data, analysis scripts, and protocols. In addition, we determined if the publication was included in a replication study or systematic review, was preregistered, had a conflict of interest declaration, specified funding sources, and was open access. Our search identified 223,932 publications meeting the inclusion criteria, from which 400 were randomly sampled. Only 389 articles were accessible, yielding 271 publications with empirical data for analysis. Our results indicate that 9.4% provided access to materials, 9.2% provided access to raw data, 0.7% provided access to the analysis scripts, 0.7% linked the protocol, and 3.7% were preregistered. A third of sampled publications lacked funding or conflict of interest statements. No publications from our sample were included in replication studies, but a fifth were cited in a systematic review or meta-analysis. Currently, published neurology research does not consistently provide information needed for reproducibility. The implications of poor research reporting can both affect patient care and increase research waste. Collaborative intervention by authors, peer reviewers, journals, and funding sources is needed to mitigate this problem.
Pre- and post-sequencing recommendations for functional annotation of human fecal metagenomes

Shotgun metagenomes are often assembled prior to annotation of genes which biases the functional capacity of a community towards its most abundant members. For an unbiased assessment of community function, short reads need to be mapped directly to a gene or protein database. The ability to detect genes in short read sequences is dependent on pre- and post-sequencing decisions. The objective of the current study was to determine how library size selection, read length and format, protein database, e-value threshold, and sequencing depth impact gene-centric analysis of human fecal microbiomes when using DIAMOND, an alignment tool that is up to 20,000 times faster than BLASTX. Using metagenomes simulated from a database of experimentally verified protein sequences, we find that read length, e-value threshold, and the choice of protein database dramatically impact detection of a known target, with best performance achieved with longer reads, stricter e-value thresholds, and a custom database. Using publicly available metagenomes, we evaluated library size selection, paired end read strategy, and sequencing depth. Longer read lengths were acheivable by merging paired ends when the sequencing library was size-selected to enable overlaps. When paired ends could not be merged, a congruent strategy in which both ends are independently mapped was acceptable. Sequencing depths of 5 million merged reads minimized the error of abundance estimates of specific target genes, including an antimicrobial resistance gene. Shotgun metagenomes of DNA extracted from human fecal samples sequenced using the Illumina platform should be size-selected to enable merging of paired end reads and should be sequenced in the PE150 format with a minimum sequencing depth of 5 million merge-able reads to enable detection of specific target genes. Expecting the merged reads to be 180-250 bp in length, the appropriate e-value threshold for DIAMOND would then need to be more strict than the default. Accurate and interpretable results for specific hypotheses will be best obtained using small databases customized for the research question.
Perinatal Oral Health: A Novel Collaborative Initiative to Improve Access, Attitudes, Comfort Level, and Knowledge of Pregnant Women and Dental Providers

Objectives The objectives of this program were to increase access to dental care among pregnant women and to improve dental students' exposure, comfort level, and knowledge of the potential impact of poor oral health on pregnancy outcomes. Study Design Through collaborative efforts of a School of Dentistry and a School of Medicine, the Oral Health Pregnancy Day Initiative (OHPDI) was developed. Dental students were educated on the impact poor oral health may have on pregnancy outcomes and the importance of access to care. Pregnant women received perinatal oral health education and needed dental care. Results Thirty-four pregnant women presented for the OHPDI. Thirty-nine dental students participated. Eighty-five percent of students reported they learned how poor oral health may have a negative impact on pregnancy and birth outcomes; 79% agreed as a result of the event they were more likely to treat pregnant women. Ninety-four percent of pregnant women reported not having a dentist and 100% received perinatal oral health education and needed dental care. Eighty-eight pregnant women were seen subsequent to the OHPDI. Conclusion This initiative resulted in increased students' knowledge, exposure, and comfort level to treating pregnant women and pregnant women received needed oral health care education and dental treatment.
Constraint Clustering Based Islanding Scheme For Power Networks

Controlled islanding, which splits the whole power system into islands, is an effective strategy against rolling blackout during severe disturbances. Finding the islanding solutions in a real-time manner is complicated because of the combinatorial explosion of the solution space occurs for a large power network. In this work, a computationally efficient controlled islanding algorithm is proposed that uses constrained spectral clustering while addressing the generator coherency problem. The objective function used in this controlled islanding algorithm is the minimal power-flow disruption. The sole constraint applied to this solution is related to generator coherency. An undirected edge-weighted graph is created based on absolute values of apparent power flow and constraints related to transmission line availability and coherent generator groups are included by altering the edge weights of the graph and using a subspace projection. Spectral clustering is then applied to the constrained solution subspace to find the islanding solution. The methodology is tested on an IEEE-39 test system with a fully dynamic model. Simulation results demonstrate the efficacy of our approach.
Efficient compressive and Bayesian characterization of biphoton frequency spectra

Frequency-bin qudits constitute a promising tool for quantum information processing, but their high dimensionality can make for tedious characterization measurements. Here we introduce and compare compressive sensing and Bayesian mean estimation for recovering the spectral correlations of entangled photon pairs. Using a conventional compressive sensing algorithm, we reconstruct joint spectra with up to a 26-fold reduction in measurement time compared to the equivalent raster scan. Applying a custom Bayesian model to the same data, we then additionally realize reliable and consistent quantification of uncertainty. These efficient methods of biphoton characterization should advance our ability to use the high degree of parallelism and complexity afforded by frequency-bin encoding.
Public perceptions of the tax avoidance of corporations and the wealthy

Corporate taxation has historically relied on two views of taxation: the entity view and the aggregative view. The entity view emphasizes that the corporation as a separate entity should pay taxes. The aggregative view instead views the corporation as a conduit for its shareholders and focuses on payments by the shareholders. The public may hold either view or a mixture of views—these perceptions are likely to influence the design of tax policy. One way to distinguish between them is to explore the causal relation between attitudes toward tax avoidance by the wealthy and corporations. For example, suppose in the data that perceptions of the tax avoidance of the wealthy and of corporations are correlated, but that changes in the perceptions about corporate tax avoidance do not cause changes in the perception of tax avoidance by the wealthy. This would provide evidence for an entity view of taxation, because new evidence of corporate tax avoidance would not cause any change in perceptions of avoidance by the wealthy, who are the owners of the corporation. The public would thus not connect avoidance by corporations to their owners. On the other hand, if changes in the perceptions about corporate tax avoidance cause changes in the perception of tax avoidance by the wealthy, this would provide evidence for an aggregate view. In this case, new evidence of corporate avoidance would be reflected in the perceptions of avoidance of shareholders of the corporation. We use data on public perceptions of whether corporation and the wealthy are paying their fair share from the Pew Research Center to explore this issue. Using graph-theoretic methods for determining causal structures within recursive systems, we provide evidence that public attitudes against tax avoidance by the wealthy cause public attitudes toward corporate tax avoidance, but not vice versa. These statistical results provide evidence for the entity view of corporate taxation.
Institutional Investor Attention and Firm Disclosure

We study how short-term changes in institutional owner attention affect managers' disclosure choices. Holding institutional ownership constant and controlling for industry-quarter effects, we find ...
Economic Values of Pork Attributes: Hedonic Price Analysis of Experimental Auction Data

Many fresh foods, such as fruits, vegetables, and meats, exhibit nearly continuous quality differences that cause consumers to value similar, but not identical, packages or lots of the food differently. Fresh pork chops are among the foods that typically exhibit these quality differences. Thus, although several packages of pork chops may be available at a particular price per pound, consumers will typically select among them based on perceived or expected quality differences. How consumers perceive and value these quality differences is of great interest to pork producers and grocery store managers. For example, it is unclear whether consumers perceive the same attributes when viewing advertising as they perceive in the actual product. Also unclear is how appearance relates to consumers' perceptions of taste or how attribute values change as consumers obtain new information (advertising versus appearance versus taste) about the product. Answers to these questions might signal changes in genetics and management of pork production and in the type of pork that grocery stores stock in meat counters. This may also provide advertisers, retailers, and other pork marketers with new information about fresh pork demand and how consumers perceive and value pork quality attributes. Such information is essential to effective pork advertising and marketing programs, such as the "Other White Meat"? campaign of the National Pork Producer's Council (NPPC). Methods of experimental economics the structured application of experimental methods to analyses of economic phenomena have recently become popular among economists as an effective means of examining consumer preferences and valuations (e.g., Hoffman et al.; Buhr et al.). However, these methods have historically been limited to binomial consumer choices (e.g., Shogren). In these studies, consumers have considered a single characteristic or set of characteristics which they observe at only one of two levels. As a result, experimental economics has been of limited use in valuing the multiple attributes that contribute to the nearly continuous variation observed in fresh pork quality. Melton et al., however, have extended the methods of experimental economics to analyze consumer preferences for multidimensional products. They integrated statistical methods of experimental design and methods of experimental economics so that it is possible to identify differences in consumers' preferences for products that exhibit continuous variance in multiple quality attributes or characteristics. They illustrated the method with an experimental auction of fresh pork chops that vary in color, marbling, and size. In this study, methods of hedonic price analysis are applied to derive new estimates of the economic value of these particular pork traits. Going a step beyond traditional approaches, the effects of socioeconomic variables on an attribute's economic value are also considered.
From Local to Global: External Validity in a Fertility Natural Experiment

We study issues related to external validity for treatment effects using over 100 replications of the Angrist and Evans natural experiment on the effects of sibling sex composition on fertility and...