Miracle-dz/UniFAR-FTUs huggingface dataset:
Egg parasitoids of the pine processionary moth and their occurrence in Venosta/Vinschgau

The egg parasitoids of the pine processionary moth Thaumetopoea pityocampa are briefly presented, together with a simplified guide to their morphological identification. Three major, two minor, and three rare species are listed. A few of them occur in the outbreak area of the moth in Venosta/Vinschgau, South Tyrol, Italy, that represents a recent expansion territory for the moth. At the beginning of the outbreak (1999) the parasitoids were not abundant and were manly represented by the generalist species. A sudden increase of egg mortality due to the major specialist Baryscapus servadeii was observed in 2003 and contributed substantially to the natural limitation of the moth populations in the following years. A further increase of parasitism is predicted as long as the host density will remain high, as other potential competitors for the eggs are absent or at very low density.
Genetic variability and relationships within the skinks Eumeces algeriensis and Eumeces schneideri using mitochondrial markers

Abstract The skinks Eumeces algeriensis and Eumeces schneideri are two of the most widespread species of the E. schneideri group. Despite this, data on their intra-specific variation are limited. In this study we analyse the genetic variability of these two species across their distribution range using two mitochondrial fragments, 12S rRNA and 16S rRNA. The results confirm the paraphyly of the group, with E. algeriensis more related to Scincopus than to E. schneideri. Eumeces algeriensis shows relatively low levels of genetic diversity, which is distributed in two main lineages, one across Morocco, and the other restricted to the southern region (Anti-Atlas). These results are discordant with the high diversity found in other species from the same area and suggest a relatively recent expansion of E. algeriensis across most of its current range. Regarding E. schneideri, we find high levels of genetic variability and a complex pattern of genetic diversity, concordant with the phylogeographic patterns found recently in other species from the Middle East region, but not with the current intra-specific taxonomy of this group.
Evolution of downflows in the transition region above a sunspot over short time-scales

Context. Downflows with potentially super-sonic velocities have been reported to occur in the transition region above many sunspots; however, how these signatures evolve over short time-scales in both spatial and spectral terms is still unknown and requires further research. Aims. In this article, we investigate the evolution of downflows detected within spectral lines sampling the transition region on time-scales of the order of minutes and we search for clues as to the formation mechanisms of these features in co-temporal imaging data. Methods. For the purposes of this article, we used high-resolution spectral and imaging data sampled by the Interface Region Imaging Spectrograph on the 20 and 21 May 2015 to identify and analyse downflows. Additionally, photospheric and coronal imaging data from the Hinode and Solar Dynamics Observatory satellites were studied to provide context about the wider solar atmosphere. Results. Four downflows were identified and analysed through time. The potential super-sonic components of these downflows had widths of around 2″ and were observed to evolve over time-scales of the order of minutes. The measured apparent downflow velocities were structured both in time and space, with the highest apparent velocities occurring above a bright region detected in Si IV 1400 Å images. Downflows with apparent velocities below the super-sonic threshold that was assumed here were observed to extend a few arcseconds away from the foot-points, suggesting that the potential super-sonic components are linked to larger-scale flows. The electron density and mass flux for these events were found to be within the ranges of 109.6–1010.2 cm−3 and 10−6.81–10−7.48 g cm−2 s−1, respectively. Finally, each downflow formed at the foot-point of thin “fingers”, extending out around 3–5″ in Si IV 1400 Å data with smaller widths (< 1″) than the super-sonic downflow components. Conclusions. Downflows can appear, disappear, and recur within time-scales of less than one hour in sunspots. As the potential super-sonic downflow signatures were detected at the foot-points of both extended fingers in Si IV 1400 SJI data and sub-sonic downflows in Si IV 1394 Å spectra, it is likely that these events are linked to larger-scale flows within structures such as coronal loops.
Spatial Pattern Recognition with Adjacency-Clustering: Improved Diagnostics for Semiconductor Wafer Bin Maps

In semiconductor manufacturing, statistical quality control hinges on an effective analysis of wafer bin maps, wherein a key challenge is to detect how defective chips tend to spatially cluster on a wafer--a problem known as spatial pattern recognition. Detecting defect patterns on a wafer can deliver key diagnostics about the root causes of defects and assist production engineers in mitigating future failures. Recently, there has been a growing interest in mixed-type spatial pattern recognition--when multiple defect patterns, of different shapes, co-exist on the same wafer. Mixed-type spatial pattern recognition entails two central tasks: (1) spatial filtering, to distinguish systematic patterns from random noises; and (2) spatial clustering, to group the filtered patterns into distinct defect types. Observing that spatial filtering is instrumental to high-quality pattern recognition, we propose to use a graph-theoretic method called adjacency-clustering, which leverages spatial dependence among adjacent defective chips to effectively filter the raw wafer bin maps. Tested on real-world data and compared against a state-of-the-art approach, our proposed method achieves at least 49% gain in terms of internal cluster validation quality (i.e., validation without external class labels), and about 6% gain in terms of Normalized Mutual Information--an external cluster validation metric based on external class labels. Interestingly, the margin of improvement appears to be a function of the defect pattern complexity, with larger gains achieved for more complex-shaped patterns. This superior performance is a testament to the method's promising impact to semiconductor manufacturing, as well as other contexts where mixed-type spatial patterns are prevalent.
Face-to-Music Translation Using a Distance-Preserving Generative Adversarial Network with an Auxiliary Discriminator

Learning a mapping between two unrelated domains-such as image and audio, without any supervision is a challenging task. In this work, we propose a distance-preserving generative adversarial model to translate images of human faces into an audio domain. The audio domain is defined by a collection of musical note sounds recorded by 10 different instrument families (NSynth \cite{nsynth2017}) and a distance metric where the instrument family class information is incorporated together with a mel-frequency cepstral coefficients (MFCCs) feature. To enforce distance-preservation, a loss term that penalizes difference between pairwise distances of the faces and the translated audio samples is used. Further, we discover that the distance preservation constraint in the generative adversarial model leads to reduced diversity in the translated audio samples, and propose the use of an auxiliary discriminator to enhance the diversity of the translations while using the distance preservation constraint. We also provide a visual demonstration of the results and numerical analysis of the fidelity of the translations. A video demo of our proposed model's learned translation is available in this https URL.
Discrete distributions from a Markov chain

A discrete-time stochastic process derived from a model of basketball is used to generalize any discrete distribution. The generalized distributions can have one or two more parameters than the parent distribution. Those derived from binomial, Poisson and negative binomial distributions can be underdispersed or overdispersed. The mean can be simply expressed in terms of model parameters, thus making inference for the mean straightforward. Probabilities can be quickly computed, enabling likelihood-based inference. Random number generation is also straightforward. The properties of some of the new distributions are described and their use is illustrated with examples.
Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision

In the sentiment attitude extraction task, the aim is to identify «attitudes» - sentiment relations between entities mentioned in text. In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task. For this task, we adapt attentive context encoders of two types: (1) feature-based; (2) self-based. In our study, we utilize the corpus of Russian analytical texts RuSentRel and automatically constructed news collection RuAttitudes for enriching the training set. We consider the problem of attitude extraction as two-class (positive, negative) and three-class (positive, negative, neutral) classification tasks for whole documents. Our experiments1 with the RuSentRel corpus show that the three-class classification models, which employ the RuAttitudes corpus for training, result in 10% increase and extra 3% by F1, when model architectures include the attention mechanism. We also provide the analysis of attention weight distributions in dependence on the term type.
Grooming and agonistic support: a meta-analysis of primate reciprocal altruism

Grooming and agonistic support are 2 common primate behaviors that have been hypothesized to constitute examples of reciprocal altruism. In particular, because primates often direct their grooming up the dominance hierarchy, it has been suggested that they may exchange grooming for agonistic support. Empirical tests of this hypothesis have resulted in highly inconsistent findings. I synthesized the published literature on the relation between grooming and agonistic support in primates using modern meta-analytical techniques. A meta-analysis of 36 studies carried out on 14 different species showed that a significant positive relation exists between grooming and agonistic support (weighted average r = 0.154, corrected for publication bias). These findings suggest that grooming and agonistic support may have evolved as part of a system of low-cost reciprocal altruism. They also highlight the potential of meta-analysis in tackling the study of behavioral phenomena characterized by low overall frequency and small effect sizes. Copyright 2007.
Formal Synthesis of Lyapunov Neural Networks

We propose an automatic and formally sound method for synthesising Lyapunov functions for the asymptotic stability of autonomous non-linear systems. Traditional methods are either analytical and require manual effort or are numerical but lack of formal soundness. Symbolic computational methods for Lyapunov functions, which are in between, give formal guarantees but are typically semi-automatic because they rely on the user to provide appropriate function templates. We propose a method that finds Lyapunov functions fully automatically—using machine learning—while also providing formal guarantees—using satisfiability modulo theories (SMT). We employ a counterexample-guided approach where a numerical learner and a symbolic verifier interact to construct provably correct Lyapunov neural networks (LNNs). The learner trains a neural network that satisfies the Lyapunov criteria for asymptotic stability over a samples set; the verifier proves via SMT solving that the criteria are satisfied over the whole domain or augments the samples set with counterexamples. Our method supports neural networks with polynomial activation functions and multiple depth and width, which display wide learning capabilities. We demonstrate our method over several non-trivial benchmarks and compare it favourably against a numerical optimisation-based approach, a symbolic template-based approach, and a cognate LNN-based approach. Our method synthesises Lyapunov functions faster and over wider spatial domains than the alternatives, yet providing stronger or equal guarantees.
Efficient callus Regeneration and Multiple shoot induction in Brassica juncea var. Pusa Jaikisan

In the present work an efficient regeneration protocol has been established for mustard (Brassica juncea var Pusa Jaikisan) using hypocotyls of in vitro grown seedlings for callus induction and multiple shoot induction. Different concentrations of phytohormones, auxin (NAA: Naphthalene acetic acid) and cytokinins (BAP: Benzylaminopurine) were used. For callus induction and plant regeneration, the MS medium was supplemented with 3% sucrose and BAP (6-benzylamino purine) and NAA (Naphthalene acetic acid) at different concentrations of 0.2, 0.5, 1.0, 1.5, 2.0 and 2.5 mg/L. About 91.6-100% formation of callus from hypocotyls explants were observed in the MS media supplemented with BAP at 0.5-1.0 mg/L + NAA at 0.5-1.0 mg/L. In these conditions, the explants also produce more number of shoots ranging from 7 to 20 shootlets/explant. It was also observed that at MS medium with BPA at 2.0-2.5 mg/L + NAA at 0.5 mg/L, all the explants (100%) produced callus but shoot induction was found to be very less and about 8.3-33.3% explants showed shoot formation with as less as 1-2 shootlets/explant. This protocol can be further explored for transformation of mustard for incorporation of specific genetic traits for improvement of crops.
Crossmodal Language Grounding in an Embodied Neurocognitive Model

Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions as well as with playful interactions with the environment and caregivers. From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration. However, characterizing the underlying mechanisms in the brain is difficult and explaining the grounding of language in crossmodal perception and action remains challenging. In this paper, we present a neurocognitive model for language grounding which reflects bio-inspired mechanisms such as an implicit adaptation of timescales as well as end-to-end multimodal abstraction. It addresses developmental robotic interaction and extends its learning capabilities using larger-scale knowledge-based data. In our scenario, we utilize the humanoid robot NICO in obtaining the EMIL data collection, in which the cognitive robot interacts with objects in a children's playground environment while receiving linguistic labels from a caregiver. The model analysis shows that crossmodally integrated representations are sufficient for acquiring language merely from sensory input through interaction with objects in an environment. The representations self-organize hierarchically and embed temporal and spatial information through composition and decomposition. This model can also provide the basis for further crossmodal integration of perceptually grounded cognitive representations.
When do neural networks outperform kernel methods?

For a certain scaling of the initialization of stochastic gradient descent (SGD), wide neural networks (NN) have been shown to be well approximated by reproducing kernel Hilbert space (RKHS) methods. Recent empirical work showed that, for some classification tasks, RKHS methods can replace NNs without a large loss in performance. On the other hand, two-layers NNs are known to encode richer smoothness classes than RKHS and we know of special examples for which SGD-trained NN provably outperform RKHS. This is true even in the wide network limit, for a different scaling of the initialization. How can we reconcile the above claims? For which tasks do NNs outperform RKHS? If covariates are nearly isotropic, RKHS methods suffer from the curse of dimensionality, while NNs can overcome it by learning the best low-dimensional representation. Here we show that this curse of dimensionality becomes milder if the covariates display the same low-dimensional structure as the target function, and we precisely characterize this tradeoff. Building on these results, we present the spiked covariates model that can capture in a unified framework both behaviors observed in earlier work. We hypothesize that such a latent low-dimensional structure is present in image classification. We test numerically this hypothesis by showing that specific perturbations of the training distribution degrade the performances of RKHS methods much more significantly than NNs.
Skeletal Muscle Regulatory Volume Response by Monocarboxylate Transporters to Increased Extracellular Lactate

SKELETAL MUSCLE REGULATORY VOLUME RESPONSE BY MONOCARBOXYLATE TRANSPORTERS TO INCREASED EXTRACELLULAR LACTATE Matthew Jameson Leung Advisor: University of Guelph, 2011 Dr. M.I. Lindinger The purpose of this thesis was to investigate the role of lactate in the regulatory volume response of mammalian skeletal muscle to hypertonic challenge-induced cell shrinkage. It was demonstrated that adult mice skeletal muscle single fibres responded to increased extracellular osmolarity in a dose-dependent manner when exposed to NaCl or sucrose challenge. This regulatory response to sucrose and NaCl however was abolished when cells were pre-treated with bumetanide, a specific sodium-potassium-chloride cotransport (NKCC) inhibitor, demonstrating that the NKCC is primarily responsible for eliciting a regulatory volume increase (RVI). When cells were exposed to NaLac treatment, bumetanide incubation did not significantly diminish the ability of the cells to recover volume. Furthermore, these cells lost less volume compared to NaCl or sucrose control. Inhibiting the single muscle fibres with either monocarboxylate transport (MCT) inhibitor phloretin or pCMBS resulted in significantly greater volume loss and impaired volume recovery. Combined MCT inhibition of phloretin or pCMBS with NKCC inhibition (bumetanide) led to unexpected findings, whereby the cells lost very little volume. These data suggest that while skeletal muscle fibres may utilize the NKCC to regulate volume, the ability for these cells to employ the most efficient means of volume regulation involves the inclusion of lactate as well via MCT uptake.
Evaluation of Mexican guava germplasm against root knot nematodes.

Root knot nematodes (Meloidogyne spp.) are one of the main phytosanitary problems of guava orchards in Mexico. At Calvillo, Aguascalientes, almost 90% of the seven-thousand hectares cultivated with guava are infested with nematodes. Although there are chemical and biological strategies to manage this problem, genetic resistance of native guava germplasm under cultivation has never been studied. The purpose of the present study was to determine the degree of susceptibility of a group of guava selections included at the germplasm bank of INIFAP as well as other guava accessions. Air-layering plants of the selections 10, 12, 20, 45, 46, 48, 106, 115, 117, and 118 were obtained, and then placed in pots of 12 kg of capacity. Plants were inoculated with 12 ml J2 plus nematode eggs collected at infested guava orchards of Aguascalientes, Michoacan, and Zacatecas. Results using air-layering plants showed that selections 46, 106, and 45 were more tolerant with 26, 35, and 35% of root damaged, respectively, while the most susceptible was selection 48 with 70% of the roots showing galls. In the group of guava seedlings, four accessions (51, 58, "Boyaca", and "Brazil") showed the highest gall numbers per centimeter of root, whereas the least affected were selection 42 and a Psidium spp. This study provides evidence that guava genetic resistance to nematodes could be present in the germplasm under study, and could be used as rootstocks or hybridization in future works.
Wikipedia and Westminster: Quality and Dynamics of Wikipedia Pages about UK Politicians

Wikipedia is a major source of information providing a large variety of content online, trusted by readers from around the world. Readers go to Wikipedia to get reliable information about different subjects, one of the most popular being living people, and especially politicians. While a lot is known about the general usage and information consumption on Wikipedia, less is known about the life-cycle and quality of Wikipedia articles in the context of politics. The aim of this study is to quantify and qualify content production and consumption for articles about politicians, with a specific focus on UK Members of Parliament (MPs). First, we analyze spatio-temporal patterns of readers' and editors' engagement with MPs' Wikipedia pages, finding huge peaks of attention during election times, related to signs of engagement on other social media (e.g. Twitter). Second, we quantify editors' polarisation and find that most editors specialize in a specific party and choose specific news outlets as references. Finally we observe that the average citation quality is pretty high, with statements on 'Early life and career' missing citations most often (18%).
Designing a Fibrin Scaffold for Lung Resident Mesenchymal Stem Cell Therapy in Emphysema Patients

Based on promising results seen in regenerative therapies for wound healing, this study explores the use of delivering stem cells through a fibrin hydrogel for the treatment of emphysema, an end stage lung disease. To determine an optimal formulation of the biopolymer fibrin, gene expression of lung resident mesenchymal stem cells (LR-MSCs) was studied in three different scaffold formulations. In addition, mechanical modeling was completed to correlate any uncharacteristic behavior with a change in mechanical properties. The results from the study indicated that a 3 mg/mL concentration of fibrinogen with 500U of thrombin may be optimal over the currently used concentrations in the Hoffman laboratory at Tufts Cummings School of Veterinary medicine; however, additional testing needs to be completed to validate mechanical modeling and demonstrate the potency potential of LR-MSCs on secondary cell lines. Table of
$EM-$Graded Rings

The main goal of this article is to introduce the concept of $EM-G-$graded rings. This concept is an extension of the notion of $EM-$rings. Let $G$ be a group and $R$ be a $G-$graded commutative ring. The $G-$gradation of $R$ can be extended to $R[x]$ by taking the components $(R[x])_{\sigma}=R_{\sigma}[x]$. We define $R$ to be $EM-G-$graded ring if every homogeneous zero divisor polynomial has an annihilating content. We provide examples of $EM-G-$graded rings that are not $EM-$rings and we prove some interesting results regarding these rings.
Factors controlling inflorescence primordia formation of grapevine: their role in latent bud fruitfulness? A review

The grapevine (Vitis vinifera L.) is a widely cultivated species of major economic importance for wine production. The quality and quantity of grapes are criteria of prime importance to the wine industry, but they are highly variable from year to year. Unlike many perennial plants, cluster formation unfolds in two seasons: season 1 takes place in the bud until dormancy, and season 2 starts after budbreak in the following year. Season 1 corresponds to the initiation and differentiation of inflorescence primordia, controlled by many exogenous and endogenous factors, which explains up to 60% seasonal variation in yield. Season 2 consists of flowering and fruit development, which explains, respectively, 30% and 10% of seasonal variation in yield. It is therefore essential to understand the impact of these factors to better control the yield. This review aims to summarize past and present knowledge concerning the physiology of latent buds relating to their fruitfulness, and to assess the impact of environmenta.