Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological differentiation and crossability among populations of the Turnera ulmifolia L. complex (Turneraceae)

The Turnera ulmifolia complex is a polymorphic assemblage of perennial weeds native to the Neotropics. Thirteen population samples from a wide geographical range, representing six taxonomic varieties, were selected for a comparison of morphological differentiation and crossa- bility. The complex is polyploid with diploid and tetraploid populations exhibiting distyly and hexaploid populations homostyly. Thirty-eight morphological characters were measured on plants grown under uniform glasshouse conditions and seed set data collected from 2195 controlled crosses made in all combinations among the thirteen populations. Discriminant analysis and clus- tering techniques were employed to investigate phenetic relationships among populations and quantitative methods were used to compare the structure of the data from the morphological and crossability studies. Analysis of the phenetic and crossability data sets, using the Kc statistic, in- dicated that these data sets were significantly associated although discrepancies were apparent in the dendrograms produced. A dendrogram generated from the crossability data was composed solely of clusters of populations of the same ploidal levels, while the dendrogram based on phenetic data included one cluster containing populations at all ploidal levels. It is suggested that autopo- lyploidy or segmental allopolyploidy is responsible for the discordance between data sets. Mor- phological, cytological, and crossability data indicate that the complex might be better treated taxonomically as a number of separate species. The use of controlled crosses among differ- entiated populations of plants is a basic exper- imental technique in systematic studies. Cross- ability data are often used to infer genetic or patristic relationships among groups and are useful in demonstrating hybridization poten- tial or the existence of isolating mechanisms. In association with morphological, cytological, and chemical characters, data from artificial hy- bridization studies provide the most common- ly used sources of information in the construc- tion of classifications of flowering plants. Where both morphological and crossability data are available a comparison of the relationships im- plied by the alternative data sets may be help- ful in clarifying taxonomic decision-making as well as providing insights into the processes of evolutionary differentiation among taxa. The Turnera ulmifolia L. complex is variable in morphology, chromosome number, and breeding system. The plants are native throughout the Neotropics and occur as weeds
The Hausdorff dimension of the visible sets of planar continua

For a compact set Γ ⊂ R2 and a point x, we define the visible part of Γ from x to be the set Γx = {u ∈ Γ : [x, u] ∩ Γ = {u}}. (Here [x, u] denotes the closed line segment joining x to u.) In this paper, we use energies to show that if Γ is a compact connected set of Hausdorff dimension larger than one, then for (Lebesgue) almost every point x ∈ R2, the Hausdorff dimension of Γx is strictly less than the Hausdorff dimension of Γ. In fact, for almost every x,
Host morphophysiological conditions and environment abiotic factors correlate with bat flies (Streblidae) prevalence and intensity in Artibeus Leach, 1821 (Phyllostomidae)

The aim of this study was to correlate Streblidae parasitism rates with temperature and humidity as well as sex, age and reproductive condition of Artibeus bats. Streblidae specimens were collected during two years in the Tingua Biological Reserve and in two areas inside buffer zone, located in Nova Iguacu, Rio de Janeiro State and preserved as wet specimens. The abundance of Streblidae species parasitizing Artibeus was analyzed, and no differences were found between them. However, the number of females parasitizing Artibeus fimbriatus was higher. Moreover, regarding sex, Artibeus females were more parasitized, particularly A. fimbriatus and A. lituratus. There was no correlation between mean intensity of infestation and body mass index of Artibeus species, even when correlated to abiotic data. This study contributes to better understand the parasitism on Artibeus by Streblidae, and based on results, it is clear that Streblidae show no preferences in terms of reproductive condition, body size, age, sex, temperature and humidity in parasitizing Artibeus species.
Illuminating Chaucer through Poetry, Manuscript Illuminations, and a Critical Rap Album

Drawing connections between Chaucer, Eminem, and social issues, New York City high school teacher Tom Liam Lynch helped students become familiar with The Canterbury Tales. Students wrote poems of rhymed couplets about today’s social and political issues, created illuminated manuscripts, and recorded a rap CD. A book and album were published for wider distribution.
Human white matter atlas

The axonal membrane and the myelin sheets around axons restrict water diffusion perpendicular to the fiber orientation in white matter but not along the axon’s main axis, which leads to anisotropic diffusion of water. Over the last decade, the quantitative measurement of this anisotropy by using what has been called diffusion tensor imaging (DTI) has become well established in research applications, revealing the macroscopic threedimensional architecture of white matter and the constituent white matter tracts. The depiction of white matter architecture in orientation-based color coding is a visualization approach in which the image brightness depicts diffusion anisotropy and a red/green/blue color scheme indicates tract orientation. Using a multiple region-of-interest (ROI) approach that exploits existing anatomic knowledge of tract trajectories, white matter bundles of interest can be reconstructed non-invasively in vivo. We have created a human white matter brain atlas, consisting of projection, association, and commissural fibers as well as brainstem fibers, examples of which are depicted here.
Implementation of Course Recommender System for Virtual University of Pakistan

Universities working in Pakistan are offering a comprehensive set of degree programs for different levels. Virtual University of Pakistan is country's first institution completely based on modern information and communication technologies. It offers education in many different majors and various areas of study are available. Multiple courses are offered in each program that satisfy several general requirements of degree. Selection of courses that align with competency and interest can become an important factor in determining final score (CGPA) of student. For this purpose, a web-based course recommender system specifically designed for courses offered at Virtual University is developed. User-based collaborative filtering and rating-prediction approach is used for calculation of expected marks and grades. System is tested against 470 currently available courses and simulated data of 2600 students. Test results showed that expected marks are somehow dependent on student's average marks in already studied courses and average marks of similar students in target course. Accuracy of implemented system is measured using Mean Absolute Error for 100 observations. MAE value came out to be in acceptable range.
Analysis of population functional connectivity data via multilayer network embeddings

Abstract Population analyses of functional connectivity have provided a rich understanding of how brain function differs across time, individual, and cognitive task. An important but challenging task in such population analyses is the identification of reliable features that describe the function of the brain, while accounting for individual heterogeneity. Our work is motivated by two particularly important challenges in this area: first, how can one analyze functional connectivity data over populations of individuals, and second, how can one use these analyses to infer group similarities and differences. Motivated by these challenges, we model population connectivity data as a multilayer network and develop the multi-node2vec algorithm, an efficient and scalable embedding method that automatically learns continuous node feature representations from multilayer networks. We use multi-node2vec to analyze resting state fMRI scans over a group of 74 healthy individuals and 60 patients with schizophrenia. We demonstrate how multilayer network embeddings can be used to visualize, cluster, and classify functional regions of the brain for these individuals. We furthermore compare the multilayer network embeddings of the two groups. We identify significant differences between the groups in the default mode network and salience network—findings that are supported by the triple network model theory of cognitive organization. Our findings reveal that multi-node2vec is a powerful and reliable method for analyzing multilayer networks. Data and publicly available code are available at https://github.com/jdwilson4/multi-node2vec.
Clustering of variables for enhanced interpretability of predictive models

A new strategy is proposed for building easy to interpret predictive models in the context of a high-dimensional dataset, with a large number of highly correlated explanatory variables. The strategy is based on a first step of variables clustering using the CLustering of Variables around Latent Variables (CLV) method. The exploration of the hierarchical clustering dendrogram is undertaken in order to sequentially select the explanatory variables in a group-wise fashion. For model setting implementation, the dendrogram is used as the base-learner in an L2-boosting procedure. The proposed approach, named lmCLV, is illustrated on the basis of a toy-simulated example when the clusters and predictive equation are already known, and on a real case study dealing with the authentication of orange juices based on 1H-NMR spectroscopic analysis. In both illustrative examples, this procedure was shown to have similar predictive efficiency to other methods, with additional interpretability capacity. It is available in the R package ClustVarLV.
Stochastic Bayesian Neural Networks

Bayesian neural networks perform variational inference over the weights however calculation of the posterior distribution remains a challenge. Our work builds on variational inference techniques for bayesian neural networks using the original Evidence Lower Bound. In this paper, we present a stochastic bayesian neural network in which we maximize Evidence Lower Bound using a new objective function which we name as Stochastic Evidence Lower Bound. We evaluate our network on 5 publicly available UCI datasets using test RMSE and log likelihood as the evaluation metrics. We demonstrate that our work not only beats the previous state of the art algorithms but is also scalable to larger datasets.
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction

Prediction of the action outcome is a new challenge for a robot collaboratively working with humans. With the impressive progress in video action recognition in recent years, fine-grained action recognition from video data turns into a new concern. Fine-grained action recognition detects subtle differences of actions in more specific granularity and is significant in many fields such as human-robot interaction, intelligent traffic management, sports training, health caring. Considering that the different outcomes are closely connected to the subtle differences in actions, fine-grained action recognition is a practical method for action outcome prediction. In this paper, we explore the performance of convolutional gate recurrent unit (ConvGRU) method on a fine-grained action recognition tasks: predicting outcomes of ball-pitching. Based on sequences of RGB images of human actions, the proposed approach achieved the performance of 79.17% accuracy, which exceeds the current state-of-the-art result. We also compared different network implementations and showed the influence of different image sampling methods, different fusion methods and pre-training, etc. Finally, we discussed the advantages and limitations of ConvGRU in such action outcome prediction and fine-grained action recognition tasks.
Intelligence Primer

This primer explores the exciting subject of intelligence. Intelligence is a fundamental component of all living things, as well as Artificial Intelligence(AI). Artificial Intelligence has the potential to affect all of our lives and a new era for modern humans. This paper is an attempt to explore the ideas associated with intelligence, and by doing so understand the implications, constraints, and potentially the capabilities of future Artificial Intelligence. As an exploration, we journey into different parts of intelligence that appear essential. We hope that people find this useful in determining where Artificial Intelligence may be headed. Also, during the exploration, we hope to create new thought-provoking questions. Intelligence is not a single weighable quantity but a subject that spans Biology, Physics, Philosophy, Cognitive Science, Neuroscience, Psychology, and Computer Science. Historian Yuval Noah Harari pointed out that engineers and scientists in the future will have to broaden their understandings to include disciplines such as Psychology, Philosophy, and Ethics. Fiction writers have long portrayed engineers and scientists as deficient in these areas. Today, modern society, the emergence of Artificial Intelligence, and legal requirements all act as forcing functions to push these broader subjects into the foreground. We start with an introduction to intelligence and move quickly onto more profound thoughts and ideas. We call this a Life, the Universe and Everything primer, after the famous science fiction book by Douglas Adams. Forty-two may very well be the right answer, but what are the questions?
A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and application in predicting total COVID-19 infected cases

The Nonlinear Grey Bernoulli Model NGBM(1, 1) is a recently developed grey model which has various applications in different fields, mainly due to its accuracy in handling small time-series datasets with nonlinear variations. In this paper, to fully improve the accuracy of this model, a novel model is proposed, namely Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1, 1). This model combines the rolling mechanism with the simultaneous optimization of all model parameters (exponential, background value and initial condition). The accuracy of this new model has significantly been proven through forecasting Vietnam's GDP from 2013 to 2018, before it is applied to predict the total COVID-19 infected cases globally by day.
On constant rank constraint qualifications for second-order cone programming

The constant rank constraint qualification introduced by Janin in 1984 for nonlinear programming has been extensively used for sensitivity analysis, global convergence of first- and second-order algorithms, and for computing the derivative of the value function. In this note we discuss naive extensions of constant rank type constraint qualifications to second-order cone programming, which is based on the Approximate-Karush-Kuhn-Tucker necessary optimality condition for this problem and on the application of the reduction approach. Our definitions are strictly weaker than Robinson's constraint qualification, and an application to the global convergence of an augmented Lagrangian algorithm is obtained. Regarding previous attempts to extend constant rank constraint qualifications to second-order cone programming, we show a counter-example to the definitions presented in [Y. Zhang, L. Zhang, New Constraint Qualifications and Optimality Conditions for Second Order Cone Programs. Set-Valued Var. Anal (2019) 27:693-712].
Logarithmically enhanced Euler-Heisenberg Lagrangian contribution to the electron gyromagnetic factor

Contrary to what was previously believed, two-loop radiative corrections to the $g$ factor of an electron bound in a hydrogenlike ion at $O({\ensuremath{\alpha}}^{2}{(Z\ensuremath{\alpha})}^{5})$ exhibit logarithmic enhancement. This previously unknown contribution is due to a long-distance light-by-light scattering amplitude. Taking an effective field theory approach, and using the Euler-Heisenberg Lagrangian, we find $\mathrm{\ensuremath{\Delta}}g=\ensuremath{-}{\left(\frac{\ensuremath{\alpha}}{\ensuremath{\pi}}\right)}^{2}{\left(Z\ensuremath{\alpha}\right)}^{5}\frac{56\ensuremath{\pi}}{135}lnZ\ensuremath{\alpha}$.
Predicting entanglement and coherent times in FMO complex using the HEOM method

Fenna-Matthews-Olson (FMO) bacteriochlorophylls (BChls) are molecules responsible of the high efficiency energy transfer in the photosynthetic process of green sulfur bacteria, controversially associated to quantum phenomena of long lived coherence. This phenomenon is modelled using Quantum Open Systems (QOS) without include memory effects of the surrounding approximated as a phonon bath on thermal equilibrium. This work applies the Hierarchical Equations of Motion method (HEOM), a non-Markovian approach, in the modelling of the system evolution of FMO complex to perform predictions about the coherence times scales together with global and semi-local entanglement during the quantum excitation.
Accelerometric Method for Cuffless Continuous Blood Pressure Measurement

Pulse transit time (PTT) has been widely used for cuffless blood pressure (BP) measurement. But, it requires more than one cardiovascular signals involving more than one sensing device. In this paper, we propose a method for continuous cuffless blood pressure measurement with the help of left ventricular ejection time (LVET). The LVET is estimated using a signal obtained through a micro-electromechanical system (MEMS)-based accelerometric sensor. The sensor acquires a seismocardiogram (SCG) signal at the chest surface, and the LVET information is extracted. Both systolic blood pressure (SBP) and diastolic blood pressure (DBP) are estimated by calibrating the system with the original arterial blood pressure values of the subjects. The proposed method is evaluated using different quantitative measures on the signals collected from ten subjects under the supine position. The performance of the proposed method is also compared with two earlier approaches, where PTT intervals are estimated from electrocardiogram (ECG)-photoplethysmogram (PPG) and SCG-PPG, respectively. The performance results clearly show that the proposed method is comparable with the state-of-the-art methods. Also, the computed blood pressure is compared with the original one, measured through a CNAP system. It gives the mean errors of the estimated systolic BP and diastolic BP within the range of -0.19 +/- 3.3 mmHg and -1.29 +/- 2.6 mmHg, respectively. The mean absolute errors for systolic BP and diastolic BP are 3.2 mmHg and 2.6 mmHg, respectively. The accuracy of BPs estimated from the proposed method satisfies the requirements of the IEEE standard of 5 +/- 8 mmHg deviation, and thus, it may be used for ubiquitous long term blood pressure monitoring.
Multiple View Generation and Classification of Mid-wave Infrared Images using Deep Learning

We propose a novel study of generating unseen arbitrary viewpoints for infrared imagery in the non-linear feature subspace . Current methods use synthetic images and often result in blurry and distorted outputs. Our approach on the contrary understands the semantic information in natural images and encapsulates it such that our predicted unseen views possess good 3D representations. We further explore the non-linear feature subspace and conclude that our network does not operate in the Euclidean subspace but rather in the Riemannian subspace. It does not learn the geometric transformation for predicting the position of the pixel in the new image but rather learns the manifold. To this end, we use t-SNE visualisations to conduct a detailed analysis of our network and perform classification of generated images as a low-shot learning task.
Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration

There has been growing progress on theoretical analyses for provably efficient learning in MDPs with linear function approximation, but much of the existing work has made strong assumptions to enable exploration by conventional exploration frameworks. Typically these assumptions are stronger than what is needed to find good solutions in the batch setting. In this work, we show how under a more standard notion of low inherent Bellman error, typically employed in least-square value iteration-style algorithms, we can provide strong PAC guarantees on learning a near optimal value function provided that the linear space is sufficiently "explorable". We present a computationally tractable algorithm for the reward-free setting and show how it can be used to learn a near optimal policy for any (linear) reward function, which is revealed only once learning has completed. If this reward function is also estimated from the samples gathered during pure exploration, our results also provide same-order PAC guarantees on the performance of the resulting policy for this setting.
Genetic Parameter Estimates for Growth Traits in Saanen Kids

Genetic parameters of test day (from birth to sixth month) body weight and body size parameters of Saanen kids were estimated by random regression with third order Legendre polynomial. The analyses were applied to 2673 test day records of 382 Saanen kids (Twin = 328; Single = 54; Female = 204; Male = 178) in a private dairy goat farm in Samsun, at the black Sea region of Turkey. Permanent environmental variances were static for height at sacrum, height at withers and chest depth, increased for body weight and body length on time. Heritability values ranged from 0.2679 to 0.6135 for body weight and Height at withers. Genetic correlations changed between 0.725 and 0.979 in all traits. The positive high genetic correlations among traits suggested that selection for any one of these traits would result in considerable positive change in other traits.
Asymptotic Enumeration of Hypergraphs by Degree Sequence

We prove an asymptotic formula for the number of k-uniform hypergraphs with a given degree sequence, for a wide range of parameters. In particular, we find a formula that is asymptotically equal to the number of d-regular k-uniform hypergraphs on n vertices provided that dn ≤ c(n/k) for a constant c > 0, and 3 ≤ k < n^c for any C < 1/9. Our results relate the degree sequence of a random k-uniform hypergraph to a simple model of nearly independent binomial random variables, thus extending the recent results for graphs due to the second and third author.
Fast Agent-Based Simulation Framework of Limit Order Books with Applications to Pro-Rata Markets and the Study of Latency Effects

We introduce a new software toolbox, called Multi-Agent eXchange Environment (MAXE), for agent-based simulation of limit order books. Offering both efficient C++ implementations and Python APIs, it allows the user to simulate large-scale agent-based market models while providing user-friendliness for rapid prototyping. Furthermore, it benefits from a versatile message-driven architecture that offers the flexibility to simulate a range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics. Showcasing its utility for research, we employ our simulator to investigate the influence the choice of the matching algorithm has on the behaviour of artificial trader agents in a zero-intelligence model. In addition, we investigate the role of the order processing delay in normal trading on an exchange and in the scenario of a significant price change. Our results include the findings that (i) the variance of the bid-ask spread exhibits a behavior similar to resonance of a damped harmonic oscillator with respect to the processing delay and that (ii) the delay markedly affects the impact a large trade has on the limit order book.