cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Local Langlands correspondences in $\ell$-adic coefficients
Creators: name: Imai, Naoki
Publisher: name: arXiv
Year: 2020
Subjects: subject: Number Theory (math.NT)
subject: Representation Theory (math.RT)
subject: FOS: Mathematics
subject: 11F70, 11F80

description: Let $\ell$ be a prime number different from the residue characteristic of a non-archimedean local field $F$. We give formulations of $\ell$-adic local Langlands correspondences for connected reductive algebraic groups over $F$, which we conjecture to be independent of a choice of an isomorphism between the $\ell$-adic coefficient field and the complex number field.
description: 19 pages
Evaluating urban and nonurban PM2.5 variability under clean air actions in China during 2010–2022 based on a new high-quality dataset
Creators: name: Liu, Boya
name: Wang, Lili
name: Zhang, Lei
name: Bai, Kaixu
name: Chen, Xingfeng
name: Zhao, Guangna
name: Yin, Haitao
name: Chen, Nan
name: Li, Rong
name: Xin, Jinyuan
name: Wang, Yuesi
name: Sun, Yang
name: Hu, Bo
Publisher: name: Taylor & Francis
Year: 2024
Subjects: Medicine
Neuroscience
Biotechnology
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Cancer

description: The air quality in China has changed due to the implementation of clean air actions since 2013. Evaluating the spatial pattern of PM2.5 and the effectiveness of reducing anthropogenic emissions in urban and nonurban areas is crucial. Therefore, the China Long-term Air Pollutant dataset for PM2.5 (CLAP_PM2.5) was generated from 2010 to 2022 with a daily 0.1° resolution using the random forest model and integrating multiple data sources, including extensive in-situ PM2.5 measurements, visibility, satellite retrievals, surface and upper-level meteorological data and other ancillary data. The CLAP_PM2.5 dataset is more reliable and accurate than other public datasets. Analysis of CLAP_PM2.5 from 2010 to 2022 reveals the decrease in positive urban-nonurban PM2.5 differences and higher decreasing rates of PM2.5 in most city clusters in eastern China. Furthermore, separating meteorological and emission contributions to the PM2.5 variability by a meteorological normalization approach indicates that meteorological contribution gradually changed from unfavorable to PM2.5 reduction during 2013–2017 to favorable to decline enhancement during 2018–2022, and in urban regions, meteorological contribution is higher than that in nonurban areas. Overall, the reduction in deweathered PM2.5 concentrations highlights China's significant achievements in terms of comprehensive clean air actions.
Discrete locally finite full groups of Cantor set homeomorphisms
Creators: name: Garrido, Alejandra
name: Reid, Colin D.
Publisher: name: arXiv
Year: 2020
Subjects: subject: Group Theory (math.GR)
subject: Dynamical Systems (math.DS)
subject: FOS: Mathematics
subject: 20F50, 22D05

description: This work is motivated by the problem of finding locally compact group topologies for piecewise full groups (a.k.a.~ topological full groups). We determine that any piecewise full group that is locally compact in the compact-open topology on the group of self-homeomorphisms of the Cantor set must be uniformly discrete, in a precise sense that we introduce here. Uniformly discrete groups of self-homeomorphisms of the Cantor set are in particular countable, locally finite, residually finite and discrete in the compact-open topology. The resulting piecewise full groups form a subclass of the ample groups introduced by Krieger. We determine the structure of these groups by means of their Bratteli diagrams and associated dimension ranges ($K_0$ groups). We show through an example that not all uniformly discrete piecewise full groups are subgroups of the ``obvious'' ones, namely, piecewise full groups of finite groups.
description: 18 pages, 6 figures, accepted version for publication
Persistent increase in canopy temperature in a Eucalypt woodland exposed to elevated CO2
Creators: name: Duursma, Remko
name: Tjoelker, Mark
Publisher: name: F1000Research
Year: 2016

description: One of the most pertinent effects of rising atmospheric CO2 on vegetation is a reduction in water use, as a direct result of stomatal response to CO2. Most of the forest free-air CO2 enrichment (FACE) sites have demonstrated a reduction in leaf-level rates of water use, which in most cases led to a decrease in total water use for the canopy. Arising from energy balance considerations, reductions in stomatal conductance and transpiratory cooling could result in increased leaf temperatures. Measurements of stomatal conductance (gs) are typically conducted with well-mixed cuvette-based systems, which subjects the leaf to conditions different from those normally experienced in terms of radiation load and wind speed and thus leaf temperatures. Alternatively, measurements of sap flow velocity are used to assess CO2 effects on water use, but this is difficult to interpret in terms of water use per unit leaf area when estimates of tree leaf area are uncertain.
Here we use a continuous, three-year record of canopy temperature measured at the EucFACE, a native, mature, Eucalyptus woodland exposed to elevated CO2 (ambient + 150 µmol mol-1 CO2). The site has 3 plots of 25m diameter in elevated CO2, and 3 control plots with ambient CO2 levels. Four infrared thermometers per plot (24 in total) have monitored canopy temperature at minutely intervals for nearly 3 years. We test the hypothesis that reduced stomatal conductance under elevated CO2 increases the leaf temperature, as is expected from basic energy balance considerations.
The difference between leaf and air temperature (ΔT=Tleaf-Tair) was strongly correlated with solar radiation, frequently reaching up to 4 degrees C. Overall for the dataset (over 40 million temperature readings on n = 968 days) there was a significant effect of CO2 on ΔT (p < 0.01). The average increase in ΔT was only 0.1 degrees C with elevated CO2 (evaluated either as daytime average ΔT, or leaf warming at the peak solar input for every day), and so not surprisingly we only found significant differences between CO2 treatments on ca. 8 out of 32 months of the study. However, the effect of CO2 was much larger during periods of very high solar radiation load (up to 0.5 degrees C higher in elevated than ambient CO2 treatment). Preliminary analyses suggest this leaf temperature increase is roughly consistent with expected differences that would arise from up to a 10% reduction in stomatal conductance.
DEM super-resolution framework based on deep learning: decomposing terrain trends and residuals
Creators: name: Wang, Hongen
name: Xiong, Liyang
name: Hu, Guanghui
name: Cao, Haoyu
name: Li, Sijin
name: Tang, Guoan
name: Zhou, Lei
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Space Science
subject: Medicine
subject: Biotechnology
subject: Environmental Sciences not elsewhere classified
subject: Chemical Sciences not elsewhere classified
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Inorganic Chemistry
subject: FOS: Chemical sciences
subject: Mental Health

description: Deep learning-based super-resolution is an essential technique for acquiring high-resolution digital elevation models (DEMs) by enhancing the spatial resolution of low-resolution DEMs. However, current deep learning-based approaches for DEM super-resolution lack comprehensiveness in terrain information reconstruction, resulting in the need to strengthen the rationality of terrain representation. Furthermore, the limited adaptability and extension potential of these approaches restrict their practical applicability and scope, hindering further advancement. As a solution, we introduce a broadly scalable detrending-based deep learning (DTDL) spatially explicit framework for DEM super-resolution. The framework aims to improve DEM reconstruction through data processing and augmentation. It employs detrending to distinguish between large-scale terrain trends and small-scale residuals in DEMs, thereby enhancing the neural network's capacity to learn terrain information. We integrate DTDL with classical super-resolution methods (SRCNN, EDSR, and SRGAN) and conduct experiments in the Alps, Himalayas, and Rockies. The experimental results indicate that the fusion of DTDL with deep learning-based methods enhances the accuracy of terrain reconstruction and the rationality of terrain feature representation, demonstrating strong compatibility and robustness.
Shakespeare’s Principal Collaborator — Himself?
Creators: name: Richmond, Hugh M.
Publisher: name: Humanities Commons
Year: 2021
Subjects: subject: Theater
subject: History
subject: Shakespeare, William, 1564-1616

description: This essay challenges the current dismantling of Shakespeare's oeuvre by the questioning of the texts and authorship of many of his plays, in order to undercut their scripts' authority, thereby authorizing drastic reinterpretations by critics and directors. The essay in contrast seeks to extend the authorship and flexibility of Shakespeare's authorship
An examination of tamarisk shade intolerance, Fountain Creek, Colorado.
Creators: name: Heschel, Shane
name: Dalton, Kathryn
name: Webster, Lena
Publisher: name: F1000Research
Year: 2016

description: Background/Question/Methods:
Riparian ecosystems of the southwestern United States have been invaded by the weedy species, Tamarix ramosissima, which outcompetes native plains cottonwood (Populus deltoides) and sandbar willow (Salix exigua). Various management methods have been used in attempts to eradicate Tamarix populations, including biological control agents, herbicides, and mechanical removal, none of which are fully effective strategies. Based on observations of Tamarix fitness and abundance in Colorado, we hypothesized that Tamarix spp. may be shade-intolerant, and that functional traits of Tamarix may vary depending on their degree of exposure to sunlight (open, canopy-edge, and full canopy). Physiological consequences of shading might have implications for how riparian corridors are managed in parts of the Southwest U.S.

Results/Conclusions:
Hypotheses about physiological and fitness consequences of varying light levels were tested during the 2015 field season at three distinct sites along Fountain Creek, CO. We randomly selected 150 plants across these 3 sites, which varied only in light intensity (similar soil substrates and soil moisture availability). For all 150 tamarisk trees, we measured the following traits: leaf gas exchange with a porometer, chlorophyll content index, photosystem efficiency/leaf fluorescence, leaf temperature, and flower number (estimated). The number of flowering branches (a proxy for fitness) varied significantly between exposure categories, with the most flowers found on plants in the open, and the least under a canopy. Photosynthetic efficiency also varied significantly between exposure categories, with the plants in the canopy exhibiting the highest efficiency and producing more chlorophyll than in open conditions. Plants under a full canopy did seem to have significantly lower gas exchange rates, though. Therefore, while Tamarix may have compensatory mechanisms to deal with decreased light, plants in full shade were less fit than in open areas.
Broadening access? K-12 teacher perceptions of virtual learning and how we consider access when resources are scarce
Creators: name: Collins, Meghan
Publisher: name: F1000Research
Year: 2016

description: Broadening access? K-12 teacher perceptions of virtual learning and a pilot virtual lab using real science data from DRI’s EcoCell facility

Meghan Collins and Jay Arnone

While the literature on education and ecology reports benefits of active learning and hands-on activities, K-12 teachers in under-resourced schools often have trouble implementing these activities due to time and funding constraints. It is common in Nevada to find K-12 teachers paying for classroom materials out of pocket. Outdoor learning experiences, such as garden classrooms or field trips, are not always accessible to under-resourced classrooms. While over the long term we must continue to improve teachers’ access to resources, what tools can be provided now to improve classroom access to active learning tools?

Here, we test the proposition that virtual learning tools can broaden access to active learning and hands-on activities in ecology for classrooms. We explore teacher perceptions of virtual learning in Nevada to determine if these experiences can be a helpful supplement to classroom education. Nevada is a good case study for quantifying and qualifying effectiveness and feasibility of virtual educational tools because of its diverse population and low per-student expenditures in public schools. These slides present K-12 teacher perceptions of virtual learning from a teacher survey on the potential value of and barriers to using virtual learning tools vis-à-vis time and funding constraints. Teacher perceptions are compared with current literature on virtual learning in K-12 to assess gaps between current practice in Nevada and the literature.
Non-bipartite k-common graphs
Creators: name: Kral, Daniel
name: Noel, Jonathan A.
name: Norin, Sergey
name: Volec, Jan
name: Wei, Fan
Publisher: name: arXiv
Year: 2020
Subjects: subject: Combinatorics (math.CO)
subject: FOS: Mathematics

description: A graph H is k-common if the number of monochromatic copies of H in a k-edge-coloring of K_n is asymptotically minimized by a random coloring. For every k, we construct a connected non-bipartite k-common graph. This resolves a problem raised by Jagger, Stovicek and Thomason [Combinatorica 16 (1996), 123-141]. We also show that a graph H is k-common for every k if and only if H is Sidorenko and that H is locally k-common for every k if and only if H is locally Sidorenko.
description: Fix of an incorrectly argued step in the proof of Lemma 12
Shi xu jian : [si shi wu juan] [volume 02]
詩緖箋 : [四十五卷] [第二冊]
Creators: name: (:Null)
Publisher: name: Jinghu : [publisher not identified]
Year: 2017

description: Chu Tang feng xu jian : jiu juan
Chu Tang ya xu jian : wu juan
Chu Tang song xu jian
Sheng Tang feng xu jian : shi er juan
Sheng Tang ya xu jian : er juan.
Impacts of spatial explanatory variables on surface urban heat island intensity between urban and suburban regions in China
Creators: name: Li, Xuecao
name: Liu, Shirao
name: Ma, Qiwei
name: Cao, Wenting
name: Zhang, Huaguo
name: Wang, Zhihao
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Neuroscience
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Mental Health

description: The intensified thermal environment in suburban areas is raising wide concerns for human society and public health due to rapid urbanization. Although the satellite-derived surface urban heat island intensity (SUHII) is a commonly used indicator, it still needs to be determined the SUHII between urban and suburban areas due to the challenges in delineating their boundaries with changes. Thus, a comprehensive analysis of the spatial explanatory variables (SEVs) and SUHII among urban and suburban areas is highly needed. Here, using the long-term satellite observations, we analyzed the spatiotemporal patterns of SUHII in different temporal intervals (i.e. seasonal and diurnal) and the contribution of SEVs in urban and suburban areas. Our results indicate that SUHII shows predominantly increasing trend from 2012–2021 in cities of China. Despite the trends of SEVs (i.e. increasing/decreasing) being relatively consistent in both urban and suburban, the latter shows a higher proportion regarding the trends in various SEVs. Besides, the partial least squares regression (PLSR) model shows that the major contributors to SUHII in urban areas are landscape shape index (LSI), patch density (PD), and the digital elevation model (DEM), while in suburban areas, those critical SEVs are LSI, normalized difference built-up index (NDBI), and DEM. These findings can facilitate the sustainable design of urban planning in a nature-based solution.
Development of NGSEP as an open-source comprehensive solution for analysis of high throughput sequencing data
Creators: name: Hoz, Juan Fernando De La
name: Lobaton, Juan David
name: Perea, Claudia
name: Cruz, Daniel Felipe
name: Quintero, Juan Camilo
name: Izquierdo, Paulo
name: Raatz, Bodo
name: Duitama, Jorge
Publisher: name: F1000Research
Year: 2016

description: The development and availability of high throughput sequencing (HTS) technologies revolutionized the research on genomics allowing to obtain genome-wide data on entire populations of nearly every form of life. A key step to extract relevant information from HTS data was the development of open-source software tools to perform different bioinformatic analyses. However, although even small labs are now able to efficiently produce large amounts of HTS data, comprehensive analysis of these data integrating different solutions remains a challenging task. We initially developed NGSEP as an open-source package that tightly integrates novel java implementations of algorithms for discovery of single nucleotide variants (SNVs), indels, and copy number variants (CNVs), called from a rich interface implemented in an Eclipse Plugin as well as basic command line usage. We built several functions to facilitate users processing HTS reads and genotype calls, including a one step wizard for parallel automated processing of entire populations, genotype filters and statistics, imputation for inbred populations and format conversion for integration with tools for assessment of population structure, GWAS, genomic prediction, among others. Benchmark using first Whole Genome Sequencing (WGS) data from human, yeast and rice samples and later Genotype by Sequencing (GBS) data from cassava and bean populations showed that NGSEP provides similar or better accuracy for SNP detection and genotyping compared to other tools such as GATK, Samtools and Tassel. NGSEP is now a useful software package for different research groups, as demonstrated by download statistics and recent scientific publications.

Continuing our efforts, we implemented in NGSEP three new algorithms to find structural variants (SVs) from WGS data: CNV-seq, for read-depth comparison between two samples; EWT to detect small CNVs; and a novel implementation of the read-pair and split-read strategies for detection of large indels with breakpoint resolution. Experiments with simulated data show that these functions provide similar accuracies compared to other tools such as Delly or Pindel. We now re-branded NGSEP as Next Generation Sequencing Experience Platform because we expanded the interfaces of NGSEP improving its documentation for command line usage and its integration with the Galaxy environment. We also made NGSEP available within the CyVerse (former iPlant) platform and we integrated the variants detector within the DNAnexus platform for SV discovery in the 3000 rice genomes project. We expect that all these development efforts facilitate the use of NGSEP for a growing number of researchers in different fields of basic and applied genomics.
A brief review on cosmological analysis of galaxy surveys with multiple tracers
Creators: name: Wang, Yuting
name: Zhao, Gong-Bo
Publisher: name: arXiv
Year: 2020
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences

description: Galaxy redshift surveys are one of the key probes in modern cosmology. In the data analysis of galaxy surveys, the precision of the statistical measurement is primarily limited by the cosmic variance on large scales. Fortunately, this limitation can in principle be evaded by observing multiple types of biased tracers. In this brief review, we present the idea of the multi-tracer method, outline key steps in the data analysis, and show several worked examples based on the GAMA, BOSS and eBOSS galaxy surveys.
description: 14 pages, 8 figures. Invited review for Research in Astronomy and Astrophysics (RAA)
Predicting climate suitability for three major deciduous trees and forest ecosystems in tropical Asia
Creators: name: Jiban Chandra Deb
name: Phinn, Stuart
name: McAlpine, Clive
name: Butt, Nathalie
Publisher: name: F1000Research
Year: 2016

description: It is already evident from many studies that global climate change is impacting the species distributions. Tropical forests harbor the most diverse habitats of the earth and therefore, assessing climate change impacts on tropical forests is of particularly significant. The deciduous timber trees of tropical Asia (Tectona grandis, Shorea robusta, Dipterocarpus turbinatus) play an important role in the economy of many south and southeast Asian countries. We assessed these species vulnerability to global climate change by modelling their current and future distributions in different climate scenarios. We used MaxEnt species distribution modelling method to assess the bio-climatic variables that influence the distributions of these deciduous trees and forest ecosystems and how the information can be tailored to suit a decision making process by identifying critical habitats for biodiversity conservation and reserves for forest management.
Keynote Q & A
Creators: name: James, Alison
Publisher: name: Innovations in Teaching & Learning Conference Proceedings
Year: 2016
Subjects: active learning; creativity; critical thinking

description: This session provides attendees an opportunity to have an informal Q and A session with the keynote speaker in a smaller setting.
description: Innovations in Teaching & Learning Conference Proceedings, Vol 8 (2016): Cultivating Creative and Reflective Learners
Faculty and Student Perspectives on Redesigned Learning Spaces at Mason
Creators: name: Eby, Kimberly
name: Lukes, Laura
name: Hazel, Stephanie
name: Zhang, Zicheng
Publisher: name: Innovations in Teaching & Learning Conference Proceedings
Year: 2016
Subjects: active learning; active learning classrooms; learning spaces; collaborative learning; scholarship of teaching and learning

description: Mason is reimagining learning spaces on our campuses to foster active and collaborative student learning. This commitment is part of our strategic plan goals for innovative learning and supporting teaching and scholarship excellence. While we currently have 12 classrooms intentionally designed to support active learning, both the Peterson Health Sciences Building and the Robinson rebuild offer significant opportunities for increasing the number of these learning spaces. As part of this initiative we have created an iterative design and implementation process that leverages faculty and student experience feedback. This poster will highlight preliminary findings (e.g., satisfaction, sense of community and connectedness, types of learning experiences, etc.) from both our faculty and student surveys from the 2015-2016 academic year.Â
description: Innovations in Teaching & Learning Conference Proceedings, Vol 8 (2016): Cultivating Creative and Reflective Learners
Engaging Imagination: How (and Why) Creativity and Play Are Essential to University Learning
Creators: name: James, Alison
Publisher: name: Innovations in Teaching & Learning Conference Proceedings
Year: 2016
Subjects: creativity; critical thinking; active learning; reflective practice

description: In the higher education sector, the word â€œcreativityâ€ has become increasingly popular in terms of defining how we want people to think, perform their roles, approach challenges, and explore ideas. How we investigate and expand our creativity may be using methods and approaches that originate with the arts or by importing others from various domains: business, sociology, psychology, and many more. There is therefore a multidirectional flow of traffic around and between the disciplines in terms of where pedagogic practices have come from.

While creativity and innovation may be terms that we aspire to and are comfortable with in our different subject areas, a closely-allied notionâ€”that of playâ€”appears to be more problematic. Some of us are persuaded that play is vital to human learning and discovery at any age; others believe it to be too trivial or childish for tertiary level-study.

In this talk, I will focus on my use of creative, playful, and three-dimensional approaches to wrestle with complex topics presented by learning, teaching, and research issues and professional relationships/structures. Examples will include critical reflection, threshold concepts in the discipline, student exchanges, doctoral research, corporate strategy, team identity, functioning, and many others. I will argue that our conceptions of play within a tertiary setting must be broadened and deepened, while the contribution of creative, imaginative, and playful pedagogies across the disciplines needs to be better understood. This dual goal is essential not only to motivate and engage our students but to re-invigorate our practice as teachers.

Read and download the paper Innovating in the Creative Arts with LEGO
description: Innovations in Teaching & Learning Conference Proceedings, Vol 8 (2016): Cultivating Creative and Reflective Learners
A novel red-edge vegetable index for paddy rice mapping based on Sentinel-1/2 and GF-6 images
Creators: name: Wan, Yiliang
name: Gong, Yueqi
name: Xu, Feng
name: Shi, Wenzhong
name: Gao, Wei
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Medicine
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Plant Biology

description: Accurate paddy rice mapping is crucial for ensuring food security and guiding agricultural production. Vegetation indices are extensively employed to map paddy rice. However, most traditional normalized indices tend to be oversaturated during periods of lush vegetation due to normalization errors, resulting in uncertainties in paddy rice mapping. To address this issue, we introduce a novel red-edge rice index (RERI) in this study; this index comprises information from red, near-infrared, and red-edge bands without normalization. To extract single- and double-cropping rice features from potential rice areas, we employ GF-6 and Sentinel-2 images based on the proposed RERI and the random forest algorithm. The proposed method is validated in the Dingcheng District of Changde city, China, and the results are compared with those based on three normalized vegetation indices. The results show that the RERI yielded the highest levels of the accuracy for all the metrics, achieving an overall accuracy (OA) of 92.50% and a kappa coefficient of 0.8875. The RERI exhibited F1 scores of 92.26% for single-cropping rice, 93.00% for double-cropping rice, and 92.28% for non-rice areas. Our results highlight the potential of using the RERI for rice identification, and the effectiveness of our method for rice extraction is demonstrated.
Random Vector Functional Link Networks for Function Approximation on Manifolds
Creators: name: Needell, Deanna
name: Nelson, Aaron A.
name: Saab, Rayan
name: Salanevich, Palina
name: Schavemaker, Olov
Publisher: name: arXiv
Year: 2020
Subjects: subject: Machine Learning (stat.ML)
subject: Information Theory (cs.IT)
subject: Machine Learning (cs.LG)
subject: Probability (math.PR)
subject: FOS: Computer and information sciences
subject: FOS: Mathematics
subject: 62M45

description: The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural networks, tend to work slowly when all of the network parameters must be iteratively tuned. To counter this, both researchers and practitioners have tried introducing randomness to reduce the learning requirement. Based on the original construction of Igelnik and Pao, single layer neural-networks with random input-to-hidden layer weights and biases have seen success in practice, but the necessary theoretical justification is lacking. In this paper, we begin to fill this theoretical gap. We provide a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with approximation error decaying asymptotically like $O(1/\sqrt{n})$ for the number $n$ of network nodes. We then extend this result to the non-asymptotic setting, proving that one can achieve any desired approximation error with high probability provided $n$ is sufficiently large. We further adapt this randomized neural network architecture to approximate functions on smooth, compact submanifolds of Euclidean space, providing theoretical guarantees in both the asymptotic and non-asymptotic forms. Finally, we illustrate our results on manifolds with numerical experiments.
description: 37 pages, 1 figure
Comparative evaluation of vegetation greenness trends over circumpolar Arctic tundra using multi-sensors satellite datasets
Creators: name: Liu, Caixia
name: Huang, Huabing
name: Liu, Chong
name: Wang, Xiaoyi
name: Wang, Shaohua
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Biotechnology
subject: Ecology
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Inorganic Chemistry
subject: FOS: Chemical sciences
subject: Plant Biology

description: The circumpolar arctic tundra, located at Earth’s highest latitudes, is extremely sensitive to climate warming. Studies on arctic greening, based on satellite data and field measurements, show discrepancies due to differences in spatial resolution across datasets (e.g., Landsat 30-m, MODIS 250-m, and AVHRR GIMMS 8 km). Research on scale effects has been limited, mostly focusing on small areas rather than the entire 7.11 million km² arctic tundra. Our study addresses this by mapping scale effects across the entire tundra using Normalized Difference Vegetation Index (NDVI) measurements. Findings reveal: (1) Landsat data provides detailed spatial trends, identifying 18.7% of the area as significantly greening, whereas GIMMS data detects more browning due to spectral mixing; (2) GIMMS underestimates the greening to browning ratio at 2.2:1, compared to Landsat and MODIS ratios of 14.1:1 and 15.1:1, respectively; (3) Over 93% agreement exists between Landsat and MODIS or GIMMS trends, with discrepancies in limited areas. This highlights the importance of high-resolution data and field studies for accurately understanding vegetation trends across the arctic tundra.
title: A Framework for Self-Evaluation and Increase of Resource-Efficient Production through Digitalization
Creators: name: Haag, Sebastian
name: Bauerdick, Christoph
name: Campitelli, Alessio
name: Anderl, Reiner
name: Abele, Eberhard
name: Schebek, Liselotte
Publisher: name: Elsevier
Year: 2022

description: Modern sensor technology and decreasing hardware costs enable the collection of a wide range of data. Nonetheless, the collection of data itself does not generate value. The collected data must be processed and analysed. Many small and medium-sized enterprises already collect a number of data. However, there is no definite strategy, which data needs to be collected in order to acquire relevant insights into processes. The enormous potential of data analysis and the current lack of its implementation caused the development of this framework. It will assist enterprises to evaluate their own level of digitalization to assess resource use.
Shi xu jian : [si shi wu juan] [volume 07]
詩緖箋 : [四十五卷] [第七冊]
Creators: name: (:Null)
Publisher: name: Jinghu : [publisher not identified]
Year: 2017

description: Chu Tang feng xu jian : jiu juan
Chu Tang ya xu jian : wu juan
Chu Tang song xu jian
Sheng Tang feng xu jian : shi er juan
Sheng Tang ya xu jian : er juan.
Ovarian mature cystic teratoma: MR imaging findings
Creators: name: Tsili, Athina
Publisher: name: EURORAD
Year: 2016
Subjects: subject: Genital / Reproductive system female
subject: MR
subject: MR-Diffusion/Perfusion
subject: Comparative studies
subject: Neoplasia

description: Clinical History Image Findings Discussion Find Diagnosis Differential Diagnosis
description: female, 47 years
description: Clinical History: A 47-year-old woman was referred for an incidentally detected non-cystic left adnexal mass on ultrasound examination. Laboratory analysis, including tumor markers was unremarkable. MRI examination of the pelvis was followed. Final Diagnosis: Mature cystic ovarian teratoma
Reverberation Mapping Of The Most Luminous Quasars At Z~2-3.
Creators: name: Lira, Paulina
name: Botti, Ismael
name: Netzer, Hagai
name: Kaspi, Shai
Publisher: name: Zenodo
Year: 2016
Subjects: Active Galactic Nuclei

description: Reverberation Mapping (RM) provides the only tool to determine Black Holes (BH) masses directly, but so far this method has been applied only to small and intermediate luminosity systems (L<10^46 ergs/s). We are extending these studies by two orders of magnitude, probing the BH-mass of luminous AGN at redshift 2-3, obtaining the measurement of the largest BHs and extending our knowledge of the physics of AGN and their hosts into the most crucial epoch in galaxy evolution. Since 2005 we have been monitoring very luminous Quasars using broad-band imaging with

the SMARTS telescopes and in 2007 we started the RM campaign of our most variable targets obtaining spectroscopic follow-up with the du Pont telescope. After 10 years of data gathering we are finally able to report on the RM for Quasar J221516 as well as several other systems that should yield results in the near future.
Never Have I Ever...Written 2000 words about a Second of Shakespeare on Screen (Preprint)
Creators: name: Iyengar, Sujata
Publisher: name: Humanities Commons
Year: 2022
Subjects: subject: Television
subject: Young adult literature
subject: South Asian diaspora
subject: Shakespeare, William, 1564-1616
subject: Literature--Adaptations

description: This short note demonstrates how the hit Neftlix comedy,   _Never Have I Ever_, created by Mindy Kaling and Lang Fisher and starring Maitreyi Ramakrishnan as Devi Vishwakumar, "wittily extends and conforms to the popular conventions of high-school streaming television–including an obligatory episode that subtly appropriates _Romeo and Juliet._"
Prioritization of soil erosion-prone sub-watersheds using geomorphometric and statistical-based weighted sum priority approach in the middle Omo-Gibe River basin, Southern Ethiopia
Creators: name: Dofee, Abera Abiyo
name: Chand, Pritam
name: Kumar, Rinku
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Biotechnology
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Science Policy

description: Soil erosion, a major factor contributing to land degradation, poses significant environmental challenges across sub-Saharan Africa, including Ethiopia. This study identifies erosion-prone sub-watersheds in southern Ethiopia's middle Omo-Gibe River basin using digital elevation model data, analyzed using a geomorphometric and statistically weighted sum priority approach in a Geographic Information System environment. Additionally, hypsometric analysis, lithology, land use/cover analysis, and field observations were integrated to enhance methodological reliability in assessing soil erosion patterns and subsequent land degradation in the basin. Results highlight sub-watersheds with varying degrees of erosion sensitivity, emphasizing morphometric parameters and landscape characteristics. Furthermore, hypsometric analysis confirms a moderate to high susceptibility of sub-watersheds to soil erosion. Moreover, half of the river basin consists of shrubland, encompassing scrubs, range lands, and small wooded areas. These are less efficient in mitigating erosion compared to forests, especially in the face of significant summer rainfall and ensuing runoff. Statistical prioritization identified 19 sub-watersheds, covering 43% of the basin's total area, as highly prone to soil erosion and land degradation. The study's findings are valuable for Ethiopian watershed management decision-makers, enabling informed resource allocation and targeted interventions to control soil erosion and protect water resources by utilizing the outputs of this study.
Never Have I Ever...Written 2000 words about a Second of Shakespeare on Screen (Preprint)
Creators: name: Iyengar, Sujata
Publisher: name: unknown
Year: 2021
Subjects: subject: Television
subject: Shakespeare, William, 1564-1616
subject: Literature--Adaptations
subject: South Asian diaspora
subject: Young adult literature

description: This short note demonstrates how the hit Neftlix comedy, _Never Have I Ever_, created by Mindy Kaling and Lang Fisher and starring Maitreyi Ramakrishnan as Devi Vishwakumar, "wittily extends and conforms to the popular conventions of high-school streaming television–including an obligatory episode that subtly appropriates _Romeo and Juliet._"
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Creators: name: Qu, Gang
name: Xiao, Li
name: Hu, Wenxing
name: Zhang, Kun
name: Calhoun, Vince D.
name: Wang, Yu-Ping
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Objective: Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks. Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions. Specifically, our model learns a graph embedding from individual brain networks derived from multi-modal data. A manifold-based regularization term is then enforced to consider the relationships of subjects both within and between modalities. Furthermore, we propose the gradient-weighted regression activation mapping (Grad-RAM) and the edge mask learning to interpret the model, which is used to identify significant cognition-related biomarkers. Results: We validate our MGCN model on the Philadelphia Neurodevelopmental Cohort to predict individual wide range achievement test (WRAT) score. Our model obtains superior predictive performance over GCN with a single modality and other competing approaches. The identified biomarkers are cross-validated from different approaches. Conclusion and Significance: This paper develops a new interpretable graph deep learning framework for cognitive ability prediction, with the potential to overcome the limitations of several current data-fusion models. The results demonstrate the power of MGCN in analyzing multi-modal fMRI and discovering significant biomarkers for human brain studies.
ERCC spike in controls – looking for a friend!
Creators: name: Shaw, Sophie
Publisher: name: F1000Research
Year: 2016

description: ERCC Spike In Controls were developed for use as internal controls in RNA sequencing experiments allowing for assessment of library preparation quality, lowest limit of detection, dynamic range and the ability to detect differential expression. However since their introduction, there has been few publications discussing the use of ERCC controls for enforcing cut offs in real data and there are clear disagreements over the viability of use for normalisation. In addition to this, there is an added cost and time to experiments associated with ERCC use. Therefore, the question of whether ERCC controls are really useful is proposed?

This presentation aimed to start a conversation in the community discussing user experience with ERCC controls. Analysis of various data sets using the R package erccdashboard show that results can vary depending on read depth. This variation impacts on the ability to detect low concentration ERCC transcripts and the ability to correctly detect differential expression. Lowest limit of detection (LODR) estimates can vary depending on the number of transcripts detected and can appear incorrectly low if some transcripts are undetected.

Here we ask the community how frequently they utilise the information provided by ERCC controls? What software is used to look at the ERCC counts? Are similar trends detected to those presented here? Are the metrics given used for all reads? Is the impact of low abundance transcripts accounted for? Do you normalise all reads based on the ERCC controls? If anyone has experience of using ERCC controls, then please do get in touch!
Impact of habitat area and isolation on species body-size distribution
Creators: name: Jacquet, Claire
name: Mouillot, David
name: Kulbicki, Michel
name: Gravel, Dominique
Publisher: name: F1000Research
Year: 2016

description: The Theory of Island Biogeography predicts how area and isolation, through colonization and extinction dynamics, influence the equilibrium species richness on insular habitats, such as ponds, forest fragments or coral reefs. However, this theory remains silent about the body-size distribution within species assemblages, a determinant aspect of community structure for ecosystem functioning. To fill this gap, we develop models of island biogeography that predict body-size distribution as a function of habitat area and isolation. We compare the model predictions to the body-size distribution of piscivorous and herbivorous fish found in tropical reefs, using a published database of 991 tropical reef-associated fish species and their occurence in 134 locations worldwide.
Beyond the Discussion Board: Increasing Participation and Engagement in Synchronous and Asynchronous Online Interactions
Creators: name: Baker, Courtney
name: Smucny, Darlene
name: Tripathy, Monisha
Publisher: name: Innovations in Teaching & Learning Conference Proceedings
Year: 2015
Subjects: teaching online; learning strategies; student motivation; reflective teaching practice; graduate student learning; collaborative learning

description: Online instructors are faced with challenges of facilitating four types of interactions in a virtual learning environment: learner-content, learner-instructor, learner-learner, and learner-technology. Research shows that active learner involvement in the online classroom positively impacts student learning, satisfaction, and retention. How can instructors motivate their students to meaningfully engage and interact with their classmates online, synchronously and/or asynchronously? This session is designed for individuals who would like to increase student engagement, interaction, and participation in online courses, through synchronous and asynchronous components. We explore examples of synchronous and asynchronous collaborative learning opportunities in which students are participating via productive online discussions and activities, engaging all learning styles. From online courses in the Mathematics Specialist Program (CEHD), we share best practices and strategies to increase engagement and interactions in synchronous online classes through specific discussion prompts and grouping strategies, using the interactive platform Blackboard Collaborate. From Mason distance education courses (Office of Distance Education), we highlight examples of asynchronous online activities, which promote collaborative and cooperative learning. To benefit from this session, previous experience teaching courses in Blackboard is not required. The information in this session may be transferable to other online learning platforms, across disciplines, and delivery formats.
description: Innovations in Teaching & Learning Conference Proceedings, Vol 7 (2015): The Science of Learning: Using Research to Improve Teaching
Modeling pan-Arctic seasonal and interannual landfast sea ice thickness and snow depth between 1979 and 2021
Creators: name: Wang, Zihan
name: Zhao, Jiechen
name: Cheng, Bin
name: Hui, Fengming
name: Su, Jie
name: Cheng, Xiao
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified

description: Landfast sea ice (LFSI) is sensitive to local climate change, making it an important component of the cryosphere system. In this study, the LFSI around the pan-Arctic domain was simulated from 1979 to 2021 using a well-validated snow and ice thermodynamic model (HIGHTSI) under the framework of the Fast Ice Prediction System (FIPS), forced by the ERA5 reanalysis. The simulation results agree well with the in-situ observations in the Canadian Arctic, with a mean error of −0.06 ± 0.29 m for ice thickness and −0.04 ± 0.12 m for snow depth. A decrease of −2.8 ± 0.4 cm/10a in thickness and −16.2 ± 1.5 km3/a in volume for the Arctic LFSI was modeled during this period. There was significant spatial variability among the different domains, with the fastest decline found in the Vilkitsky Strait. The modeled snow depth shows large interannual and spatial variations, which was confirmed by other modeling results. The spatiotemporal variations in both air temperature and precipitation are the driving factors for the multi-decadal variations in LFSI thickness. The decreasing air temperature during the 2010s aligned with a slower thickness decrease and a slight volume increase for LFSI, which agreed with the pan-Arctic sea ice pattern.
title: Evaluation of High Resolution Imagery and Elevation Data
Creators: name: Powell, Walter A.
name: Laskey, Kathryn Blackmond
name: Adelman, Leonard
name: Johnson, Ryan
name: Altenau, Michael
name: Goldstein, Andrew
name: Visone, Daniel
name: Braswell, Kenneth
Publisher: name: Proceeding of 14th International Command and Control Research and Technology Symposium
Year: 2009
Subjects: subject: High resolution imagery
subject: High resolution data
subject: Geospatial decision support
subject: Military decision-making

description: How does the underlying data affect the ability of warfighters to derive useful information and make decisions? The Army Topographic Engineering Center (TEC) and GMU endeavor to shed light on this question with the third in TEC’s series of value experiments. The fundamental objective of the series is to improve TEC’s support of military personnel in the field through better geospatial products. The third experiment in the series goes in a different direction from the previous two experiments, which were presented at the 12th and 13th ICCRTS. Whereas previous experiments assessed the value of cutting-edge geospatial tools while keeping the data constant, the present experiment evaluated the effect of higher resolution imagery and elevation data while keeping the tools constant. The high resolution data under evaluation was generated from TEC’s Buckeye system, an operational airborne surveillance system. This paper discusses the scope of the third experiment, its hypotheses, its experimental design, and initial results.