Miracle-dz/UniFAR-FTUs huggingface dataset:
On Climatic Conditions, Vegetation Types, and Leaf Size in the Galapagos Islands

Studies of vegetation on 10 islands in the Galipagos demonstrate that the pattern of distribution of various vegetation types, ranging from desert scrub to evergreen forest, is closely associated with the pattern of variation in the local climatic conditions. The variation in average leaf size of the plant communities and of different layers in the communities is on the one hand associated with variations in altitude and exposure, and on the other hand with variations in the structure of the communities. The average leaf size of a plant community is considered to be a useful diagnostic feature for classifying the vegetation types. Structural features of the plant communities, for example, stratification and coverage, influence the leaf size of lower layers within the same community: the variation in leaf size of the shrub and of the herb layer is associated with the variation in coverage of the layer above. Variation in leaf size in the Acalypha complex and in Croton scouleri is suggested to be related to the structural variations in the communities: the structure of the plant communities may impose a selection pressure on the species of Acalypha and Croton, whereby they tend to develop the leaf size optimally fitted for the requirements of their particular environment.
PII: S0275-5408(99)00067-8

The Smith (1979) method provides a means of estimating the anterior chamber depth without additional attachments to the slit lamp [Smith, R. J. H. (1979). A new method of estimating the depth of the anterior chamber. Br. J. Ophthalmol. 63, 215±220]. In this study, the 95% intraobserver limits of repeatability and the 95% inter-observer limits of agreement of this method have been determined. The intra-observer limits of repeatability were determined by plotting the difference vs the mean of the estimated anterior chamber depth obtained in two different sessions by each of two examiners, while the inter-observer limits of agreement are represented by a plot of the difference vs the mean estimated anterior chamber depth between the two examiners. For one examiner, the 95% intra-observer limits of repeatability was ÿ0. 36 to 0.58 mm, while for the other examiner the 95% intra-observer limits of repeatability was ÿ0.25 to 0.37 mm. The 95% inter-observer limits of agreement were ÿ0. 31 to 0.23 mm and ÿ0. 41 to 0.25 mm for the first and second sessions respectively. The intra-observer limits of repeatability are comparable with those reported for A-scan ultrasonographic measures of the anterior chamber depth. These results imply that the Smith method can be used with a high degree of repeatability and agreement to clinically monitor longitudinal changes in anterior chamber depth. # 2000 The College of Optometrists. Published by Elsevier Science Ltd. All rights reserved.
DEEPMIR: A DEEP convolutional neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

Background: Cerebral microbleeds (CMBs) and non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Recent advances using quantitative susceptibility mapping (QSM) make it possible to differentiate iron content from mineralization in-vivo using magnetic resonance imaging (MRI). However, automated detection of such lesions is still challenging, making quantification in large cohort bases studies rather limited. Purpose: Development of a fully automated method using deep learning for detecting CMBs and basal ganglia iron deposits using multimodal MRI. Materials and Methods: We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia, which resulted in defining the gold standard. This gold standard was then used to train a deep convolution neural network (CNN) model. Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI which are used in cohort-based studies. The detection performance was then evaluated using the cross-validation principle in order to ensure generalization of the results. Results: With multi-class CNN models, we achieved an average sensitivity and precision of about 0.8 and 0.6, respectively for detecting CMBs. The same framework detected non-hemorrhage iron deposits reaching an average sensitivity and precision of about 0.8. Conclusions: Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data such as QSM can improve the detection of CMB and non-hemorrhage iron deposits.
Economic irreversibility in pandemic control process: Analytically derived general consequence

After the first lockdowns and similar measures against the COVID-19 outbreak, many countries found it difficult to balance infection control with economics. Because previous knowledge was scarce, economists began researching this issue by cost-benefit analysis and found that infection control processes largely affect economic efficiency. Since previous analyses were performed numerically, universally applicable knowledge has not yet been drawn, but it is indispensable for guiding principles of infection control. We study the processes analytically and show explicit solutions and inequalities in the infection-spreading phase for different processes' effects on social costs using the SIR model. We prove that delayed infection control generally costs more than early measures while keeping infected population stationary. Our findings suggest a guiding principle how to balance infection control with economy.
McStas (ii): An overview of components, their use, and advice for user contributions

A key element of the success of McStas is the component layer where users and developers alike are contributing to the description of new physical models and features. In McStas, components realise all physical elements of the simulated instrument from source via optics and samples to detector. In this second review paper of the McStas package, we present an overview of the component classes in McStas: sources, monitors, optics, samples, misc, and contrib. Within each component class we give thorough examples of high-quality components, including their algorithms and example use. We present two example instruments, one for a continuous source and one for a time-of-flight source, that together demonstrate the use of the main component classes. Finally, we give tips and instructions that will allow the reader to write good components and elucidate the pathway of contributing new components to McStas.
Incorporating network based protein complex discovery into automated model construction

We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs. The structural construction of the computational graphs is driven by the use of topological clustering algorithms on protein-protein networks which incorporate inductive biases stemming from network biology research in protein complex discovery. This structurally constrains the hypothesis space over the possible computational graph factorisation whose parameters can then be learned through supervised or unsupervised task settings. The sparse construction of the computational graph enables the differential protein complex activity analysis whilst also interpreting the individual contributions of genes/proteins involved in each individual protein complex. In our experiments analysing a variety of cancer phenotypes, we show that the proposed methods outperform SVM, Fully-Connected MLP, and Randomly-Connected MLPs in all tasks. Our work introduces a scalable method for incorporating large interaction networks as prior knowledge to drive the construction of powerful computational models amenable to introspective study.
Estimation of Copulas via Maximum Mean Discrepancy

Abstract This article deals with robust inference for parametric copula models. Estimation using canonical maximum likelihood might be unstable, especially in the presence of outliers. We propose to use a procedure based on the maximum mean discrepancy (MMD) principle. We derive nonasymptotic oracle inequalities, consistency and asymptotic normality of this new estimator. In particular, the oracle inequality holds without any assumption on the copula family, and can be applied in the presence of outliers or under misspecification. Moreover, in our MMD framework, the statistical inference of copula models for which there exists no density with respect to the Lebesgue measure on , as the Marshall-Olkin copula, becomes feasible. A simulation study shows the robustness of our new procedures, especially compared to pseudo-maximum likelihood estimation. An R package implementing the MMD estimator for copula models is available. Supplementary materials for this article are available online.
Phonemer at WNUT-2020 Task 2: Sequence Classification Using COVID Twitter BERT and Bagging Ensemble Technique based on Plurality Voting

This paper presents the approach that we employed to tackle the EMNLP WNUT-2020 Shared Task 2 : Identification of informative COVID-19 English Tweets. The task is to develop a system that automatically identifies whether an English Tweet related to the novel coronavirus (COVID-19) is informative or not. We solve the task in three stages. The first stage involves pre-processing the dataset by filtering only relevant information. This is followed by experimenting with multiple deep learning models like CNNs, RNNs and Transformer based models. In the last stage, we propose an ensemble of the best model trained on different subsets of the provided dataset. Our final approach achieved an F1-score of 0.9037 and we were ranked sixth overall with F1-score as the evaluation criteria.
Online Zeroth-order Optimisation on Riemannian Manifolds

We study numerical optimisation algorithms that use zeroth-order information to minimise time-varying geodesically-convex cost functions on Riemannian manifolds. In the Euclidean setting, zeroth-order algorithms have received a lot of attention in both the time-varying and time-invariant case. However, the extension to Riemannian manifolds is much less developed. We focus on Hadamard manifolds, which are a special class of Riemannian manifolds with global nonpositive curvature that offer convenient grounds for the generalisation of convexity notions. Specifically, we derive bounds on the expected instantaneous tracking error, and we provide algorithm parameter values that minimise a metric of performance. Our results illustrate how the manifold geometry in terms of the sectional curvature affects these bounds. To the best of our knowledge, these are the first error bounds for online zeroth-order Riemannian optimisation. Lastly, via numerical simulations, we demonstrate the applicability of our algorithm on an online Karcher mean problem.
Magnetic quadri-dipolar stars rotating in vacuum

Main sequence stars and compact objects like white dwarfs and neutron stars are usually embedded in magnetic fields that strongly deviate from a pure dipole located right at the stellar centre. An off-centred dipole can sometimes better adjust existing data and offer a simple geometric picture to include multipolar fields. However, such configurations are usually to restrictive, limiting multipolar components to strength less than the underlying dipole. In this paper, we consider the most general lowest order multipolar combination given by a dipole and a quadrupole magnetic field association in vacuum. Following the general formalism for multipolar field computations, we derive the full electromagnetic field outside a rotating quadridipole. Exact analytical expressions for the Poynting flux and the electromagnetic kick are given. Such geometry is useful to study the magnetosphere of neutron stars for which more and more compelling observations reveals hints for at least quadridipolar fields. We also show that for sufficiently high quadrupole components at the stellar surface, the electromagnetic kick imprinted to a neutron star can reach thousands of km/s for a millisecond period at birth.
Meta-Heuristic Solutions to a Student Grouping Optimization Problem faced in Higher Education Institutions

Combinatorial problems which have been proven to be NP-hard are faced in Higher Education Institutions and researches have extensively investigated some of the well-known combinatorial problems such as the timetabling and student project allocation problems. However, NP-hard problems faced in Higher Education Institutions are not only confined to these categories of combinatorial problems. The majority of NP-hard problems faced in institutions involve grouping students and/or resources, albeit with each problem having its own unique set of constraints. Thus, it can be argued that techniques to solve NP-hard problems in Higher Education Institutions can be transferred across the different problem categories. As no method is guaranteed tooutperform all others in all problems, it is necessary to investigate heuristic techniques for solving lesser-known problems in order to guide stakeholders or software developers to the most appropriate algorithm for each unique class of NP-hard problems faced in Higher Education Institutions. To this end, this study described an optimization problem faced in a real university that involved grouping students for the presentation of semester results. Ordering based heuristics, genetic algorithm and the ant colony optimization algorithm implemented in Python programming language were used to find feasible solutions to this problem, with the ant colony optimization algorithm performing better or equal in 75% of the test instances and the genetic algorithm producing better or equal results in 38% of the test instances.
Determining Primitive Character States for Phylogenetic Reconstruction

The reconstruction of evolutionary trees by either conventional or cladistic methods depends upon the determination of evolutionary directionality (or polarity) of character states. Many criteria have been used to determine primitive states of characters, but these sometimes have duplicated the same concept and sometimes have not been based on evolutionary reasoning. Some apply only to spe- cific taxa. We recognize two broad categories of criteria of primitiveness: first-level criteria, in which nothing is known about any primitive condition; and second-level criteria, in which at least one primitive condition already is known. We recognize nine criteria, the first six being first-level and the last three being second-level cri- teria: (1) fossil evidence; (2) common is primitive; (3) co-occurrence of primitive states; (4) earliest ontogenetic state; (5) minor abnormalities of organogenesis; (6) vestigial organs; (7) association; (8) correlation; and (9) group trends. The many specific criteria of primitiveness (e.g., hypogyny preceding epigyny in the angio- sperms) are regarded as tendency statements of restricted utility. The only absolute criterion of primitiveness is that the oldest character state is primitive, which can never be determined with certainty because we lack the true phylogeny. The criteria, therefore, can only help approximate real phylogenies. Some criteria are more gen- erally useful than others, but their efficacy depends to some extent on the nature of characters and character states within individual taxa.
CoLAKE: Contextualized Language and Knowledge Embedding

With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, most existing models consider shallow, static, and separately pre-trained entity embeddings, which limits the performance gains of these models. Few works explore the potential of deep contextualized knowledge representation when injecting knowledge. In this paper, we propose the Contextualized Language and Knowledge Embedding (CoLAKE), which jointly learns contextualized representation for both language and knowledge with the extended MLM objective. Instead of injecting only entity embeddings, CoLAKE extracts the knowledge context of an entity from large-scale knowledge bases. To handle the heterogeneity of knowledge context and language context, we integrate them in a unified data structure, word-knowledge graph (WK graph). CoLAKE is pre-trained on large-scale WK graphs with the modified Transformer encoder. We conduct experiments on knowledge-driven tasks, knowledge probing tasks, and language understanding tasks. Experimental results show that CoLAKE outperforms previous counterparts on most of the tasks. Besides, CoLAKE achieves surprisingly high performance on our synthetic task called word-knowledge graph completion, which shows the superiority of simultaneously contextualizing language and knowledge representation.
Green's-function formalism for resonant interaction of x rays with nuclei in structured media

The resonant interaction between x-ray photons and nuclei is one of the most exciting subjects in the burgeoning field of x-ray quantum optics. A resourceful platform used to date is thin-film x-ray cavities with embedded layers or M\"ossbauer nuclei such as $^{57}\mathrm{Fe}$. A quantum optical model based on the classical electromagnetic Green's function is developed to investigate theoretically the nuclear response inside the x-ray cavity. The model is versatile and provides an intuitive picture about the influence of the cavity structure on the resulting spectra. We test its predictive powers with the help of the semiclassical coherent scattering formalism simulations and discuss our results on increasing complexity of layer structures.
Error Compensated Distributed SGD Can Be Accelerated

Gradient compression is a recent and increasingly popular technique for reducing the communication cost in distributed training of large-scale machine learning models. In this work we focus on developing efficient distributed methods that can work for any compressor satisfying a certain contraction property, which includes both unbiased (after appropriate scaling) and biased compressors such as RandK and TopK. Applied naively, gradient compression introduces errors that either slow down convergence or lead to divergence. A popular technique designed to tackle this issue is error compensation/error feedback. Due to the difficulties associated with analyzing biased compressors, it is not known whether gradient compression with error compensation can be combined with Nesterov's acceleration. In this work, we show for the first time that error compensated gradient compression methods can be accelerated. In particular, we propose and study the error compensated loopless Katyusha method, and establish an accelerated linear convergence rate under standard assumptions. We show through numerical experiments that the proposed method converges with substantially fewer communication rounds than previous error compensated algorithms.
Training Data Augmentation for Deep Learning RF Systems

Applications of machine learning are subject to three major components that contribute to the final performance metrics. Within the specifics of neural networks, and deep learning specifically, the first two are the architecture for the model being trained and the training approach used. This work focuses on the third component, the data being used during training. The questions that arise are then "what is in the data" and "what within the data matters? " Looking into the Radio Frequency Machine Learning (RFML) field of Modulation Classification, the use of synthetic, captured, and augmented data are examined and compared to provide insights about the quantity and quality of the available data presented. In general, all three data types have useful contributions to a final application, but captured data germane to the intended use case will always provide more significant information and enable the greatest performance. Despite the benefit of captured data, the difficulties that arise from collection often make the quantity of data needed to achieve peak performance impractical. This paper helps quantify the balance between real and synthetic data, offering concrete examples where training data is parametrically varied in size and source.
Analysis of selenoaminoacids and selenoproteins in blood serum of sows fed by selenium fortified feed

Selenium species (inorganic selenium, selenoaminoacids, and selenoproteins) were analyzed using anion exchange and affinity chromatography, which were connected to ICP/MS for the blood serum of sows fed by seleniumfortified feed. The Anion Exchange PRP X-100 column was used for the analysis of inorganic selenium (Se and Se) and selenoaminoacids. The HEP column was used to separate SelP from GPx+SeAlb in selenoproteins. A quantitative analysis was performed using the post-column isotope dilution technique. The lactating sows were divided into three groups and fed by selenium fortified feed (organic 0.3 mg/kg, 0.6 mg/kg and inorganic 0.6 mg/kg) for four weeks. The test groups showed increases in selenoaminoacids compared with the control group, except the inorganic feed group. There was no significant difference between the organic feed groups. All test groups showed increases in selenoproteins. In particular, SelP showed a large increase that was 1.5 times higher than the other proteins. 요 약: 셀레늄 강화 사료를 먹인 모돈에 대하여 혈청에서의 셀레늄 화학종들 즉, 무기셀레늄, 셀레노아 미노산 및 셀레노단백질들을 음이온 교환과 친화 크로마토 그라피를 ICP/MS에 연결사용하여 분석하였 다. 무기 셀레늄(Se와 Se)과 셀레노 아미노산들은 PRP X-100 음이온 교환 컬럼을 사용하였고 셀레노 단백질들의 경우에는 HEP 컬럼을 사용하여 SelP을 GPx+SeAlb로 부터 분리하였다. 정량분석은 후 컬럼 동위원소 희석법을 사용하여 이 들의 농도를 결정하였다. 모돈 실험군을 3 그룹으로 나누고 셀레늄이 강화된 사료(유기셀레늄 0.3 mg/kg, 0.6 mg/kg 및 무기셀레늄 0.6 mg/kg)를 4주 동안 먹였을 때 셀레늄 화학종들의 변화 및 사료와의 상관관계를 알아보았다. 셀레노 아미노산의 경우, 실험군들은 무기셀레늄 강화사료를 제외하고 대조군에 비하여 높은 농도를 보여주었다. 유기셀레늄 강화사료에서는 큰 차이를 ★ Corresponding author Phone : +82-(0)43-230-3732 Fax : +82-(0)43-232-7176 E-mail : pakyn@knue.ac. kr This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons. org/licenses/ by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Analysis of selenoaminoacids and selenoproteins in blood serum of sows fed by selenium fortified feed 197 Vol. 28, No. 3, 2015 보이지 않았다. 셀레노 단백질의 경우는 실험군 모두가 대조군에 비하여 증가를 보여주었는데 특히 SelP 가 다른 단백질에 비하여 1.5배 정도 더 크게 증가한 것으로 나타났다.