cssi/SciSciGPT-SciSciNet huggingface dataset:
Remarks on approximate harmonic maps in dimension two

For the class of approximate harmonic maps \(u\in W^{1,2}(\Sigma ,N)\) from a closed Riemmanian surface \((\Sigma ,g)\) to a compact Riemannian manifold (N, h), we show that (i) the so-called energy identity holds for weakly convergent approximate harmonic maps \(\{u_n\}:\Sigma \rightarrow N\), with tension fields \(\tau (u_n)\) bounded in the Morrey space \(M^{1,\delta }(\Sigma )\) for some \(0\le \delta <2\); and (ii) if an approximate harmonic map u has tension field \(\tau (u)\in L\log L(\Sigma )\cap M^{1,\delta }(\Sigma )\) for some \(0\le \delta <2\), then \(u\in W^{2,1}(\Sigma , N)\). Based on these estimates, we further establish the bubble tree convergence, referring to energy identity both \(L^{2,1}\) of gradients and \(L^1\)-norm of hessians and the oscillation convergence, for a weakly convergent sequence of approximate harmonic maps \(\{u_n\}\), with tension fields \(\tau (u_n)\) uniformly bounded in \(M^{1,\delta }(\Sigma )\) for some \(0\le \delta <2\) and uniformly integrable in \(L\log L(\Sigma )\).
SnapVX: a network-based convex optimization solver

SnapVX is a high-performance solver for convex optimization problems defined on networks. For problems of this form, SnapVX provides a fast and scalable solution with guaranteed global convergence. It combines the capabilities of two open source software packages: Snap.py and CVXPY. Snap.py is a large scale graph processing library, and CVXPY provides a general modeling framework for small-scale subproblems. SnapVX offers a customizable yet easy-to-use Python interface with "out-of-the-box" functionality. Based on the Alternating Direction Method of Multipliers (ADMM), it is able to efficiently store, analyze, parallelize, and solve large optimization problems from a variety of different applications. Documentation, examples, and more can be found on the SnapVX website at http://snap.stanford.edu/snapvx.
Linear-quadratic optimal control problems for mean-field stochastic differential equations — time-consistent solutions

Linear-quadratic optimal control problems are considered for mean-field stochastic differential equations with deterministic coefficients. Time-inconsistency feature of the problems is carefully investigated. Both open-loop and closed-loop equilibrium solutions are presented for such kind of problems. Open-loop solutions are presented by means of variational method with decoupling of forward-backward stochastic differential equations, which lead to a Riccati equation system lack of symmetry. Closed-loop solutions are presented by means of multi-person differential games, the limit of which leads to a Riccati equation system with a symmetric structure.
Protecting life in the milky way: metals keep the grbs away

The host galaxies of the five local, z<0.25, long-duration gamma-ray bursts, each of which had a well-documented associated supernova, are all faint and metal-poor compared to the population of local star-forming galaxies. We quantify this statement by using a previous analysis of star-forming galaxies (z<0.2) from the Sloan Digital Sky Survey to estimate the fraction of local star formation as a function of host galaxy oxygen abundance. We find that only a small fraction (<25%) of current star formation occurs in galaxies with oxygen abundance 12+log(O/H)<8.6, i.e., about half that of the Milky Way. However, all five low-z GRB hosts have oxygen abundance below this limit, in three cases very significantly so. If GRBs traced local star formation independent of metallicity, the probability of obtaining such low abundances for all five hosts would be P~0.1%. We conclude that GRBs trace only low-metallicity star formation, and that the Milky Way has been too metal rich to host long GRBs for at least the last several billion years. This result has implications for the potential role of GRBs in mass extinctions, for searches for recent burst remnants in the Milky Way and other large galaxies, for non-detections of late radio emission from local core-collapse supernovae, and for the production of cosmic rays in the local Universe. We also find that the isotropic energy release of these five GRBs, E_iso, steeply decreases with increasing host oxygen abundance, suggesting an upper metallicity limit for ``cosmological'' GRBs at ~0.15 Z_solar. (Abridged)
On complete metrizability of the Hausdorff metric topology

There exists a completely metrizable bounded metrizablespaceXwith compatible metricsd, d'so that the hyperspaceCL(X) ofnonempty closed subsets ofXendowed with the Hausdorff metricHd,Hd', resp. isa-favorable,s-favorable, resp. in the strong Choquet game.In particular, there exists a completely metrizable bounded metric space(X, d) such that (CL(X), Hd) is not completely metrizable.
Approximation of elliptic equations with BMO coefficients

We study solution techniques for elliptic equations in divergence form, where the coefficients are only of bounded mean oscillation (BMO). For $|p-2|<\varepsilon$ and a right hand side in $W^{-1}_p$ we show convergence of a finite element scheme, where $\varepsilon$ depends on the oscillation of the coefficients.
Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets

Multimodal machine learning algorithms aim to learn visual-textual correspondences. Previous work suggests that concepts with concrete visual manifestations may be easier to learn than concepts with abstract ones. We give an algorithm for automatically computing the visual concreteness of words and topics within multimodal datasets. We apply the approach in four settings, ranging from image captions to images/text scraped from historical books. In addition to enabling explorations of concepts in multimodal datasets, our concreteness scores predict the capacity of machine learning algorithms to learn textual/visual relationships. We find that 1) concrete concepts are indeed easier to learn; 2) the large number of algorithms we consider have similar failure cases; 3) the precise positive relationship between concreteness and performance varies between datasets. We conclude with recommendations for using concreteness scores to facilitate future multimodal research.
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations

We describe ParaNMT-50M, a dataset of more than 50 million English-English sentential paraphrase pairs. We generated the pairs automatically by using neural machine translation to translate the non-English side of a large parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M can be a valuable resource for paraphrase generation and can provide a rich source of semantic knowledge to improve downstream natural language understanding tasks. To show its utility, we use ParaNMT-50M to train paraphrastic sentence embeddings that outperform all supervised systems on every SemEval semantic textual similarity competition, in addition to showing how it can be used for paraphrase generation.
Polarization of neural rings

Neural rings and ideals, as introduced by Curto, Itskov, Veliz-Cuba, and Youngs, are useful algebraic tools for organizing the combinatorial information of neural codes. However, they are neither g...
Uniform mixing and completely positive sofic entropy

Let G be a countable discrete sofic group. We define a concept of uniform mixing for measure-preserving G-actions and show that it implies completely positive sofic entropy. When G contains an element of infinite order, we use this to produce an uncountable family of pairwise nonisomorphic G-actions with completely positive sofic entropy. None of our examples is a factor of a Bernoulli shift.
Entanglement entropy in SU(N) gauge theory

The entanglement entropy of SU(N) lattice gauge theory is studied exactly in 1+1 space-time dimensions and in Migdal-Kadanoff approximation in higher dimensional space. The existence of a non-analytical behavior reminiscent of a phase transition for a characteristic size of the entangled region is demonstrated for higher dimensional theories.
On asymptotic vanishing behavior of local cohomology

Let R be a standard graded algebra over a field k, with irrelevant maximal ideal $$\mathfrak {m}$$, and I a homogeneous R-ideal. We study the asymptotic vanishing behavior of the graded components of the local cohomology modules $$\{{\text {H}}^{i}_{\mathfrak {m}}(R/I^n)\}_{n\in {\mathbb {N}}}$$ for $$i<\dim R/I$$. We show that, when $${{\,\mathrm{{{char}}}\,}}k= 0$$, R / I is Cohen–Macaulay, and I is a complete intersection locally on $${{\,\mathrm{{Spec}}\,}}R {\setminus }\{\mathfrak {m}\}$$, the lowest degrees of the modules $$\{{\text {H}}^{i}_{\mathfrak {m}}(R/I^n)\}_{n\in {\mathbb {N}}}$$ are bounded by a linear function whose slope is controlled by the generating degrees of the dual of $$I/I^2$$. Our result is a direct consequence of a related bound for symmetric powers of locally free modules. If no assumptions are made on the ideal or the field k, we show that the complexity of the sequence of lowest degrees is at most polynomial, provided they are finite. Our methods also provide a result on stabilization of maps between local cohomology of consecutive powers of ideals.
Analytical Modeling of Wi-Fi and LTE-LAA Coexistence: Throughput and Impact of Energy Detection Threshold

With both small-cell LTE and Wi-Fi networks available as alternatives for deployment in unlicensed bands (notably 5 GHz), the investigation into their coexistence is a topic of active interest, primarily driven by industry groups. 3GPP has recently standardized LTE licensed assisted access (LTE-LAA) that seeks to make LTE more co-existence friendly with Wi-Fi by incorporating similar sensing and back-off features. Nonetheless, the results presented by industry groups offer little consensus on important issues like respective network parameter settings that promote “fair access” as required by 3GPP. Answers to such key system deployment aspects, in turn, require credible analytical models, on which there has been a little progress to date. Accordingly, in one of the first works of its kind, we develop a new framework for estimating the throughput of Wi-Fi and LTE-LAA in coexistence scenarios via suitable modifications to the celebrated Bianchi model. The impact of various network parameters such as energy detection threshold on Wi-Fi and LTE-LAA coexistence is explored as a byproduct and corroborated via a National Instrument experimental test bed that validates the results for LTE-LAA access priority classes 1 and 3.
Robust Distributed Linear Programming

This paper presents a robust, distributed algorithm to solve general linear programs. The algorithm design builds on the characterization of the solutions of the linear program as saddle points of a modified Lagrangian function. We show that the resulting continuous-time saddle-point algorithm is provably correct but, in general, not distributed because of a global parameter associated with the nonsmooth exact penalty function employed to encode the inequality constraints of the linear program. This motivates the design of a discontinuous saddle-point dynamics that, while enjoying the same convergence guarantees, is fully distributed and scalable with the dimension of the solution vector. We also characterize the robustness against disturbances and link failures of the proposed dynamics. Specifically, we show that it is integral-input-to-state stable but not input-to-state stable. The latter fact is a consequence of a more general result, that we also establish, which states that no algorithmic solution for linear programming is input-to-state stable when uncertainty in the problem data affects the dynamics as a disturbance. Our results allow us to establish the resilience of the proposed distributed dynamics to disturbances of finite variation and recurrently disconnected communication among the agents. Simulations in an optimal control application illustrate the results.
A polynomial chaos framework for designing linear parameter varying control systems

Here we use polynomial chaos framework to design controllers for linear parameter varying (LPV) dynamical systems. We assume the scheduling variable to be random and use polynomial chaos approach to synthesize the controller for the resulting linear stochastic dynamical system. The stability of the LPV system is formulated as an exponential mean-square (EMS) stability problem. Two algorithms are presented that guarantee EMS stability of the stochastic system and correspond to parameter dependent and independent Lyapunov functions, respectively. LPV controllers from the polynomial chaos based framework is shown to outperform LPV controller from classical design for an example nonlinear system.
Optimal lower bounds for distributed and streaming spanning forest computation

We show optimal lower bounds for spanning forest computation in two different models:• One wants a data structure for fully dynamic spanning forest in which updates can insert or delete edges amongst a base set of n vertices. The sole allowed query asks for a spanning forest, which the data structure should successfully answer with some given (potentially small) constant probability e > 0. We prove that any such data structure must use Ω(n log3n) bits of memory.• There is a referee and n vertices in a network sharing public randomness, and each vertex knows only its neighborhood; the referee receives no input. The vertices each send a message to the referee who then computes a spanning forest of the graph with constant probability ϵ > 0. We prove the average message length must be Ω(log3n) bits.Both our lower bounds are optimal, with matching upper bounds provided by the AGM sketch [AGM12] (which even succeeds with probability 1 − 1/poly(n)). Furthermore, for the first setting we show optimal lower bounds even for low failure probability δ, as long as δ > 2−n1−ϵ.
Image Captioning with Compositional Neural Module Networks

In image captioning where fluency is an important factor in evaluation, e.g., $n$-gram metrics, sequential models are commonly used; however, sequential models generally result in overgeneralized expressions that lack the details that may be present in an input image. Inspired by the idea of the compositional neural module networks in the visual question answering task, we introduce a hierarchical framework for image captioning that explores both compositionality and sequentiality of natural language. Our algorithm learns to compose a detail-rich sentence by selectively attending to different modules corresponding to unique aspects of each object detected in an input image to include specific descriptions such as counts and color. In a set of experiments on the MSCOCO dataset, the proposed model outperforms a state-of-the art model across multiple evaluation metrics, more importantly, presenting visually interpretable results. Furthermore, the breakdown of subcategories $f$-scores of the SPICE metric and human evaluation on Amazon Mechanical Turk show that our compositional module networks effectively generate accurate and detailed captions.
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform

Companies increasingly expose machine learning (ML) models trained over sensitive user data to untrusted domains, such as end-user devices and wide-access model stores. This creates a need to control the data's leakage through these models. We present Sage, a differentially private (DP) ML platform that bounds the cumulative leakage of training data through models. Sage builds upon the rich literature on DP ML algorithms and contributes pragmatic solutions to two of the most pressing systems challenges of global DP: running out of privacy budget and the privacy-utility tradeoff. To address the former, we develop block composition, a new privacy loss accounting method that leverages the growing database regime of ML workloads to keep training models endlessly on a sensitive data stream while enforcing a global DP guarantee for the stream. To address the latter, we develop privacy-adaptive training, a process that trains a model on growing amounts of data and/or with increasing privacy parameters until, with high probability, the model meets developer-configured quality criteria. Sage's methods are designed to integrate with TensorFlow-Extended, Google's open-source ML platform. They illustrate how a systems focus on characteristics of ML workloads enables pragmatic solutions that are not apparent when one focuses on individual algorithms, as most DP ML literature does.
Single-Column Emulation of Reanalysis of the Northeast Pacific Marine Boundary Layer

An artificial neural network is trained to reproduce thermodynamic tendencies and boundary layer properties from ERA5 HIRES reanalysis data over the summertime Northeast Pacific stratocumulus to trade cumulus transition region. The network is trained prognostically using 7-day forecasts rather than using diagnosed instantaneous tendencies alone. The resulting model, Machine Assisted Reanalysis Boundary Layer Emulation (MARBLE), skillfully reproduces the boundary layer structure and cloud properties of the reanalysis data in 7-day single-column prognostic simulations over withheld testing periods. Radiative heating profiles are well-simulated, and the mean climatology and variability of the stratocumulus to cumulus transition are accurately reproduced. MARBLE more closely tracks the reanalysis than does a comparable configuration of the underlying forecast model.
The Importance of Mesozooplankton Diel Vertical Migration for Sustaining a Mesopelagic Food Web

We used extensive ecological and biogeochemical measurements obtained from quasi-Lagrangian experiments during two California Current Ecosystem Long-Term Ecosystem Research cruises to analyze carbon fluxes between the epipelagic and mesopelagic zones using a linear inverse ecosystem model (LIEM). Measurement constraints on the model include 14C primary productivity, dilution-based microzooplankton grazing rates, gut pigment-based mesozooplankton grazing rates (on multiple zooplankton size classes), 234Th:238U disequilibrium and sediment trap measured carbon export, and metabolic requirements of micronekton, zooplankton, and bacteria. A likelihood approach (Markov Chain Monte Carlo) was used to estimate the resulting flow uncertainties from a sample of potential flux networks. Results highlight the importance of mesozooplankton active transport (i.e., diel vertical migration) for supplying the carbon demand of mesopelagic organisms and sequestering carbon dioxide from the atmosphere. In nine water parcels ranging from a coastal bloom to offshore oligotrophic conditions, mesozooplankton active transport accounted for 18% - 84% (median: 42%) of the total carbon supply to the mesopelagic, with gravitational settling of POC (12% - 55%; median: 37%) and subduction (2% - 32%; median: 14%) providing the majority of the remainder. Vertically migrating zooplankton contributed to downward carbon flux through respiration and excretion at depth and via consumption loses to predatory zooplankton and mesopelagic fish (e.g. myctophids and gonostomatids). Sensitivity analyses showed that the results of the LIEM were robust to changes in nekton metabolic demands, rates of bacterial production, and mesozooplankton gross growth efficiency. This analysis suggests that prior estimates of zooplankton active transport based on conservative estimates of standard (rather than active) metabolism should be revisited.
Interdisciplinary video review: Assessing milestones and providing feedback.

Literature within residency education states that directly observing resident–patient visits with the goal of providing formative and summative feedback to learners is helpful for resident skill de...
Point group symmetry of cadmium arsenide thin films determined by convergent beam electron diffraction

Cadmium arsenide $(\mathrm{C}{\mathrm{d}}_{3}\mathrm{A}{\mathrm{s}}_{2})$ is one of the first materials to be discovered to belong to the class of three-dimensional topological semimetals. Reported room-temperature crystal structures of $\mathrm{C}{\mathrm{d}}_{3}\mathrm{A}{\mathrm{s}}_{2}$ differ subtly in the way the Cd vacancies are arranged within its antifluorite-derived structure, which determines if an inversion center is present and if $\mathrm{C}{\mathrm{d}}_{3}\mathrm{A}{\mathrm{s}}_{2}$ is a Dirac or Weyl semimetal. Here, we apply convergent beam electron diffraction (CBED) to determine the point group of $\mathrm{C}{\mathrm{d}}_{3}\mathrm{A}{\mathrm{s}}_{2}$ thin films grown by molecular-beam epitaxy. Using CBED patterns from multiple zone axes, high-angle annular dark-field images acquired in scanning transmission electron microscopy, and Bloch wave simulations, we show that $\mathrm{C}{\mathrm{d}}_{3}\mathrm{A}{\mathrm{s}}_{2}$ belongs to the tetragonal 4/mmm point group, which is centrosymmetric. The results show that CBED can distinguish very subtle differences in the crystal structure of a topological semimetal, a capability that will be useful for designing materials and thin film heterostructures with topological states that depend on the presence of certain crystal symmetries.
Phylogenetic Shifts in Gene Body Methylation Correlate with Gene Expression and Reflect Trait Conservation

A subset of genes in plant genomes are labeled with DNA methylation specifically at CG residues. These genes, known as gene-body methylated (gbM), have a number of associated characteristics. They tend to have longer sequences, to be enriched for intermediate expression levels, and to be associated with slower rates of molecular evolution. Most importantly, gbM genes tend to maintain their level of DNA methylation between species, suggesting that this trait is under evolutionary constraint. Given the degree of conservation in gbM, we still know surprisingly little about its function in plant genomes or whether gbM is itself a target of selection. To address these questions, we surveyed DNA methylation across eight grass (Poaceae) species that span a gradient of genome sizes. We first established that genome size correlates with genome-wide DNA methylation levels, but less so for genic levels. We then leveraged genomic data to identify a set of 2,982 putative orthologs among the eight species and examined shifts of methylation status for each ortholog in a phylogenetic context. A total of 55% of orthologs exhibited a shift in gbM, but these shifts occurred predominantly on terminal branches, indicating that shifts in gbM are rarely conveyed over time. Finally, we found that the degree of conservation of gbM across species is associated with increased gene length, reduced rates of molecular evolution, and increased gene expression level, but reduced gene expression variation across species. Overall, these observations suggest a basis for evolutionary pressure to maintain gbM status over evolutionary time.
An Investigation of Neurochemical Changes in Chronic Cannabis Users

With the legalization of recreational cannabis (CB) the characterization of how it may impact brain chemistry is essential. Magnetic resonance spectroscopy (MRS) was used to examine neurometabolite concentrations in the dorsal anterior cingulate (dACC) in chronic CB users (N = 26; 10 females) and controls (N = 24; 10 females). The concentrations of glutamate (Glu), total creatine (tCr), choline (Cho), total N-acetylaspartate (tNAA), and myo-inositol (mI) were estimated using LCModel. The ANCOVAs failed to show significant differences between controls and CB users. Regression analyses were then performed on the CB group to model each neurometabolite to determine its relationship to monthly CB use, sex, the interaction between CB use and sex. tCr was found to be predicted by both monthly CB use and sex. While the regression model was not significant the relationship between monthly CB use and Glu appears to be modulated by sex with the effect of monthly use (dose) being stronger in males. tNAA failed to show an effect of CB use but did reveal an effect of sex with females showing larger tNAA levels. Although the results presented are preliminary due to the small sample size they do guide future research. The results presented provide direction for further studies as they suggest that dose may significantly influence the observance of CB effects and that those effects may be modulated by sex. Studies with significantly larger sample sizes designed specifically to examine individuals with varying usage as well as sex effects are necessary.
Artificial Intelligence for Bioinformatics: Applications in Protein Folding Prediction

AI recently shows great promise in the field of bioinformatics, such as protein structure prediction. The Critical Assessment of protein Structure Prediction (CASP) is a nationwide experiment that takes place biannually, which centered around analyzing the best current systems for predicting protein tertiary structures. In this paper, we research on available AI methods and features, and then explore novel methods based on reinforcement learning. Such method will have profound implications for R&D in bioinformatics and add an additional platform to the management of innovation in biotechnology.
The Abetting Bully: Vicarious Bullying and Unethical Leadership in Higher Education

Aim/Purpose: The purpose of this paper is to examine the phenomena of vicarious bullying, or an abetting bully, when a bully’s subordinate is used to inflict abuse on the target. This study examines who is most affected by this multi-faceted organizational abuse in American higher education. Background: Workplace bullying has received international attention. Recent studies in the United States have focused on workplace bullying in higher education. However, workplace bullying emerges from an elaborate social structure. This research article brings the unique perspective of vicarious bullying for analysis. Methodology: A data collection from 729 American higher education professionals was used to answer the following three research questions which were addressed in this study: RQ1: What is the overall prevalence of vicarious bullying in American higher education? RQ2: What is the likelihood of experiencing vicarious bullying in American higher education based on gender? RQ3: What is the likelihood of experiencing vicarious bullying in American higher education based on a woman’s race? A chi-square analysis was used to examine which demographic groups are more susceptible to vicarious bullying. Contribution: This article expands the literature on workplace bullying in American higher education by considering how unethical leadership can contribute to and inspire abetting and vicarious bullies who are enabled to maintain the toxic work culture. Findings: This article expands the literature on workplace bullying in American higher education by considering how unethical leadership can contribute to and inspire abetting and vicarious bullies who are enabled to maintain the toxic work culture. Abetting the Bully Recommendations for Practitioners: Vicarious bullying occurs when the organization fails to curtail managerial abuse. The result is higher turnover for women employees. Working with chief diversity officers and EEO officials can develop policies that stifle this behavior. Recommendations for Researchers: While workplace bullying has gained international attention, the organizational behavior of vicarious bullying is a unique organizational perspective that warrants further study. Impact on Society:Data confirm that women are more likely to leave their organizations to avoid workplace bullying. Women’s departures weaken an organization when they take their insight and knowledge with them. Future Research: Future research can consider the relationship between ethical leadership at the department level and executive level of higher education, and how that might have an impact on the prevalence of workplace bullying.
Dialog State Tracking: A Neural Reading Comprehension Approach.

Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question what is the state of the current dialog? after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of 47.33% on the standard test split, exceeding current state-of-the-art by 11.75%**.
Geographic and genetic variation in susceptibility of Butomus umbellatus to foliar fungal pathogens

Large-scale patterns of plant invasions may reflect regional heterogeneity in biotic and abiotic factors and genetic variation within and between invading populations. Having information on how effects of biotic resistance vary spatially can be especially important when implementing biological control because introduced agents may have different impacts through interactions with host-plant genotype, local environment, or other novel enemies. We conducted a series of field surveys and laboratory studies to determine whether there was evidence of biotic resistance, as foliar fungal pathogens, in two introduced genotypes (triploid G1, diploid G4) of the Eurasian wetland weed, Butomus umbellatus L. in the USA. We tested whether genotypes differed in disease attack and whether spatial patterns in disease incidence were related to geographic location or climate for either genotype. We surveyed 27 B. umbellatus populations (17 G1, 10 G4) to determine disease incidence and associated fungal pathogens. For a subset of plant populations, we isolated foliar fungi and tested pathogenicity of three isolates in laboratory assays. After accounting for location (latitude, climate), G1 plants had lower disease incidence than G4 plants in the field (38% vs. 70%) but similar pathogen richness. In contrast, bioassays revealed G1 plants consistently received a higher damage score and had larger leaf lesions regardless of pathogen. The seemingly contradictory results between the field and laboratory may be due to climatic differences between areas that limit the regional pool of pathogens or their effect on plant genotype. These results demonstrate that two widespread B. umbellatus genotypes exhibit different susceptibility to pathogens and effectiveness of pathogen biological controls may depend on local conditions.
Revisiting thin silicon for photovoltaics: a technoeconomic perspective

Crystalline silicon comprises 90% of the global photovoltaics (PV) market and has sustained a nearly 30% cumulative annual growth rate, yet comprises less than 2% of electricity capacity. To sustain this growth trajectory, continued cost and capital expenditure (capex) reductions are needed. Thinning the silicon wafer well below the industry-standard 160 μm, in principle reduces both manufacturing cost and capex, and accelerates economically-sustainable expansion of PV manufacturing. In this analysis piece, we explore two questions surrounding adoption of thin silicon wafers: (a) What are the market benefits of thin wafers? (b) What are the technological challenges to adopt thin wafers? In this analysis, we re-evaluate the benefits and challenges of thin Si for current and future PV modules using a comprehensive technoeconomic framework that couples device simulation, bottom-up cost modeling, and a sustainable cash-flow growth model. When adopting an advanced technology concept that features sufficiently good surface passivation, the comparable efficiencies are achievable for both 50 μm wafers and 160 μm ones. We then quantify the economic benefits for thin Si wafers in terms of poly-Si-to-module manufacturing capex, module cost, and levelized cost of electricity (LCOE) for utility PV systems. Particularly, LCOE favors thinner wafers for all investigated device architectures, and can potentially be reduced by more than 5% from the value of 160 μm wafers. With further improvements in module efficiency, an advanced device concept with 50 μm wafers could potentially reduce manufacturing capex by 48%, module cost by 28%, and LCOE by 24%. Furthermore, we apply a sustainable growth model to investigate PV deployment scenarios in 2030. It is found that the state-of-the-art industry concept could not achieve the climate targets even with very aggressive financial scenarios, therefore the capex reduction benefit of thin wafers is advantageous to facilitate faster PV adoption. Lastly, we discuss the remaining technological challenges and areas for innovation to enable high-yield manufacturing of high-efficiency PV modules with thin Si wafers.
Learning to Represent Bilingual Dictionaries

Bilingual word embeddings have been widely used to capture the correspondence of lexical semantics in different human languages. However, the cross-lingual correspondence between sentences and words is less studied, despite that this correspondence can significantly benefit many applications such as crosslingual semantic search and textual inference. To bridge this gap, we propose a neural embedding model that leverages bilingual dictionaries. The proposed model is trained to map the lexical definitions to the cross-lingual target words, for which we explore with different sentence encoding techniques. To enhance the learning process on limited resources, our model adopts several critical learning strategies, including multi-task learning on different bridges of languages, and joint learning of the dictionary model with a bilingual word embedding model. We conduct experiments on two new tasks. In the cross-lingual reverse dictionary retrieval task, we demonstrate that our model is capable of comprehending bilingual concepts based on descriptions, and the proposed learning strategies are effective. In the bilingual paraphrase identification task, we show that our model effectively associates sentences in different languages via a shared embedding space, and outperforms existing approaches in identifying bilingual paraphrases.
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.
A self-attention based deep learning method for lesion attribute detection from CT reports

In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity. While these lesion attributes are rich and useful in many downstream clinical applications, how to extract them from the radiology reports is less studied. This paper outlines a novel deep learning method to automatically extract attributes of lesions of interest from the clinical text. Different from classical CNN models, we integrated the multi-head self-attention mechanism to handle the long-distance information in the sentence, and to jointly correlate different portions of sentence representation subspaces in parallel. Evaluation on an in-house corpus demonstrates that our method can achieve high performance with 0.848 in precision, 0.788 in recall, and 0.815 in F-score. The new method and constructed corpus will enable us to build automatic systems with a higher-level understanding of the radiological world.
Performance of a modified feedback loop adaptive array with TVRO satellite signals

Adaptive antenna arrays are commonly used to suppress interfering signal at the receive site. Gupta and Ksienski (1986) proposed modifications to the feedback loops and showed that an adaptive array with modified feedback loops provides the required suppression of weak interfering signals. Ward et al. (see IEEE Trans. Ant. Prop. Vol.AP-36, p.1551, 1988) built an experimental adaptive array and verified the theoretical analysis. The experimental adaptive array uses the modified feedback loops to control the antenna weights. In the present work, the performance of the experimental system is evaluated using signals from an existing geostationary satellite interference environment. To do so, an earth station was build to receive signals from various geostationary satellites. The details of the earth station are given. Using the signals received by the earth station, it is shown that the experimental system can null up to two weak interfering signal without affecting the desired signal. >
Hydrodynamics-dominated wetting phenomena on hybrid superhydrophobic surfaces.

Abstract Hypothesis Because of rich local hysteresis, it is a challenge to determine the global minimum surface energy and consequently, the true apparent contact angle of a liquid droplet on chemically-heterogeneous, rough surfaces. Meanwhile, a specific type of wetting phenomena strongly dominated by the hydrodynamic process is expected to be resulted by the chemically-heterogeneous micro-structures, yet not well studied. Simulations A series of micropatterned hybrid substrates, which have the same pillar dimensions but different pillar spacings, have been studied using continuum simulations. The hybrid feature of the micropatterns presented here has two defining facets: the pillar top-surface is highly hydrophilic (SiO2-based), while the pillar side-surface and substrate bottom surface are hydrophobic (Teflon). Different initializations of a 0.4 µL water droplet have been simulated to mimic the experimental measurements, i.e., releasing a stationary droplet near the substrate, impacting the droplet at various speeds, and setting an initial apparent contact angle of 90°. Findings The simulated contact angles agree with the experiments in the literature. Moreover, this study vividly depicts the hydrodynamics-dominated wetting processes, and captures multiple metastable Cassie-Baxter states. This research is applicable to the design of new hybrid superhydrophobic surfaces with special functionality, such as high controllability at varied hydrodynamic conditions.
Optimal Control and Analysis of a Modified Trojan Y-Chromosome Strategy

Abstract The Trojan Y Chromosome (TYC) strategy is a promising eradication method that attempts to manipulate the female to male ratio to promote the reduction of the population of an invasive species. The manipulation stems from an introduction of sex-reversed males, called supermales, into an ecosystem. The offspring of the supermales is guaranteed to be male. Mathematical models have shown that the population can be driven to extinction with a continuous supply of supermales. In this paper, a new model of the TYC strategy is introduced and analyzed that includes two important modeling characteristics, that are neglected in all previous models. First, the new model includes intraspecies competition for mates. Second, a strong Allee effect is included. Several conclusions about the strategy via optimal control are established. These results have large scale implications for the biological control of invasive species.
A Theory of Uncertainty Variables for State Estimation and Inference

Probability theory forms an overarching framework for modeling uncertainty, and by extension, also in designing state estimation and inference algorithms. While it provides a good foundation to system modeling, analysis, and an understanding of the real world, its application to algorithm design suffers from computational intractability. In this work, we develop a new framework of uncertainty variables to model uncertainty. A simple uncertainty variable is characterized by an uncertainty set, in which its realization is bound to lie, while the conditional uncertainty is characterized by a set map, from a given realization of a variable to a set of possible realizations of another variable. We prove Bayes’ law and the law of total probability equivalents for uncertainty variables. We define a notion of independence, conditional independence, and pairwise independence for a collection of uncertainty variables, and show that this new notion of independence preserves the properties of independence defined over random variables. We then develop a graphical model, namely Bayesian uncertainty network, a Bayesian network equivalent defined over a collection of uncertainty variables, and show that all the natural conditional independence properties, expected out of a Bayesian network, hold for the Bayesian uncertainty network. We also define the notion of point estimate, and show its relation with the maximum a posteriori estimate.
Quantum General Invariance and Loop Gravity

A quantum physical projector is proposed for generally covariant theories which are derivable from a Lagrangian. The projector is the quantum analogue of the integral over the generators of finite one-parameter subgroups of the gauge symmetry transformations which are connected to the identity. Gauge variables are retained in this formalism, thus permitting the construction of spacetime area and volume operators in a tentative spacetime loop formulation of quantum general relativity.
On Supra-SIM Sets of Natural Numbers

We introduce the class of supra-SIM sets of natural numbers. We prove that this class is partition regular and closed under finite-embeddability. We also prove some results on sumsets and SIM sets motivated by their positive Banach density analogues.
Crossing fitness valleys via double substitutions within codons.

Single nucleotide substitutions in protein-coding genes can be divided into synonymous (S), with little fitness effect, and non-synonymous (N) ones that alter amino acids and thus generally have a greater effect. Most of the N substitutions are affected by purifying selection that eliminates them from evolving populations. However, additional mutations of nearby bases potentially could alleviate the deleterious effect of single substitutions, making them subject to positive selection. To elucidate the effects of selection on double substitutions in all codons, it is critical to differentiate selection from mutational biases. We addressed the evolutionary regimes of within-codon double substitutions in 37 groups of closely related prokaryotic genomes from diverse phyla by comparing the fractions of double substitutions within codons to those of the equivalent double S substitutions in adjacent codons. Under the assumption that substitutions occur one at a time, all within-codon double substitutions can be represented as “ancestral-intermediate-final” sequences (where “intermediate” refers to the first single substitution and “final” refers to the second substitution) and can be partitioned into four classes: (1) SS, S intermediate–S final; (2) SN, S intermediate–N final; (3) NS, N intermediate–S final; and (4) NN, N intermediate–N final. We found that the selective pressure on the second substitution markedly differs among these classes of double substitutions. Analogous to single S (synonymous) substitutions, SS double substitutions evolve neutrally, whereas analogous to single N (non-synonymous) substitutions, SN double substitutions are subject to purifying selection. In contrast, NS show positive selection on the second step because the original amino acid is recovered. The NN double substitutions are heterogeneous and can be subject to either purifying or positive selection, or evolve neutrally, depending on the amino acid similarity between the final or intermediate and the ancestral states. The results of the present, comprehensive analysis of the evolutionary landscape of within-codon double substitutions reaffirm the largely conservative regime of protein evolution. However, the second step of a double substitution can be subject to positive selection when the first step is deleterious. Such positive selection can result in frequent crossing of valleys on the fitness landscape.
The GOES-R EUVS model for EUV irradiance variability

The Geostationary Operational Environmental Satellite R (GOES-R) series of four satellites are the next generation NOAA GOES satellites. Once on orbit and commissioned, they are renamed GOES 16–19, making critical terrestrial and space weather measurements through 2035. GOES 16 and 17 are currently on orbit, having been launched in 2016 and 2018, respectively. The GOES-R satellites include the Extreme Ultraviolet (EUV) and X-ray Irradiance Sensors (EXIS) instrument suite, which measures calibrated solar irradiance in eight lines or bands between 25 nm and 285 nm with the Extreme Ultraviolet Sensors (EUVS) instrument. EXIS also includes the X-Ray Sensor (XRS) instrument, which measures solar soft X-ray irradiance at the legacy GOES bands. The EUVS Measurements are used as inputs to the EUVS Model, a solar spectral irradiance model for space weather operations that predicts irradiance in twenty-two 5 nm wide intervals from 5 nm to 115 nm, and one 10 nm wide interval from 117 to 127 nm at 30 s cadence. Once fully operational, NOAA will distribute the EUVS Model irradiance with 1 min latency as a primary space weather data product, ushering in a new era of rapid dissemination and measurement continuity of EUV irradiance spectra. This paper describes the EUVS Model algorithms, data sources, calibration methods and associated uncertainties. Typical model (relative) uncertainties are less than ~5% for variability at time-scales longer than 6 h, and are ~25% for solar flare induced variability. The absolute uncertainties, originating from the instruments used to calibrate the EUVS Model, are ~10%. Examples of model results are presented at both sub-daily and multi-year timescales to demonstrate the model’s capabilities and limitations. Example solar flare irradiances are also modeled.
Dynamical axion misalignment with small instantons

We present a new mechanism to relax the initial misalignment angle of the QCD axion and raise the cosmological bound on the axion decay constant. The QCD axion receives a contribution from small UV instantons during inflation, which raises its mass to the inflationary Hubble scale. This makes the axion start rolling down its potential early on. In the scenario, the standard model Yukawa couplings of quarks are dynamical, being of order one during the inflationary era and reducing to their standard model values once it ends. This means that after inflation the contribution of the small instantons is suppressed, and the axion potential reduces to the standard one from the usual IR instantons. As a result, when the axion starts to oscillate again after inflation, the initial misalignment angle is suppressed due to the dynamics during inflation. While the general idea of dynamical axion misalignment has been discussed in the literature before, we present in detail the major bottleneck on the mismatching between the minima of the axion potentials during and after inflation, and how it is circumvented in our scenario via the Froggatt-Nielsen mechanism. Taking into account of all the constraints, we find that the axion decay constant could be raised to the GUT scale, 1015 GeV, in our scenario.