Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Graph Convolutional Networks Fully Exploiting Graph Structure?

Graph Convolutional Networks (GCNs) generalize the idea of deep convolutional networks to graphs, and achieve state-of-the-art results on many graph related tasks. GCNs rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours. In this paper we formalize four levels of structural information injection, and use them to show that GCNs ignore important long-range dependencies embedded in the overall topology of a graph. Our proposal includes a novel regularization technique based on random walks with restart, called RWRReg, which encourages the network to encode long-range information into the node embeddings. RWRReg is further supported by our theoretical analysis, which demonstrates that random walks with restart empower aggregation-based strategies (i.e., the Weisfeiler-Leman algorithm) with long-range information. We conduct an extensive experimental analysis studying the change in performance of several state-of-the-art models given by the four levels of structural information injection, on both transductive and inductive tasks. The results show that the lack of long-range structural information greatly affects performance on all considered models, and that the information extracted by random walks with restart, and exploited by RWRReg, gives an average accuracy improvement of more than $5\%$ on all considered tasks.
$K_{r+1}$-saturated graphs with small spectral radius

For a graph $H$, a graph $G$ is $H$-saturated if $G$ does not contain $H$ as a subgraph but for any $e \in E(\overline{G})$, $G+e$ contains $H$. In this note, we prove a sharp lower bound for the number of paths and walks on length $2$ in $n$-vertex $K_{r+1}$-saturated graphs. We then use this bound to give a lower bound on the spectral radii of such graphs which is asymptotically tight for each fixed $r$ and $n\to\infty$.
EFFECT OF SOME BIOLOGICAL CONTROL AGENTS IN REDUCING THE DISEASE INCIDENCE AND SEVERITY OF WHITE MOLD DISEASE ON EGGPLANT CAUSED BY SCLEROTINIA SCLEROTIORUM

The study aimed to isolating and diagnosing the pathogen of white mold on eggplant and evaluating the efficiency of some plant extracts and biological agents against pathogens under field conditions. The results of isolation and diagnosis showed three isolates of Sclerotinia sclerotiorum. The results of the field experiment showed that all the treatment used in the experiment, which included P. fluorescens, Effective Microorganism EM-1 and the Water hyacinth extract, reduced the negative effects of S. sclerotiorum and clearly protected eggplant plants from white mold disease. Resulted in a significant reduction in the percentage of infection and the severity of infection and in different rates compared with the treatment of S. sclerotiorum disease alone, which had a treatment rate of 100% and severity of infection 56.67%, where the treatment of integration between the biological product EM-1 and P. fluorescens and Aqueous extract of Water hyacinth in the reduction of infection rate, amounting to 16.67% and the severity of the injury 10.00%. Which was positively reflected in the increase in the rate of plant height, wet and dry weight of eggplant plants and the superiority of the treatment of interference between the extract of Water hyacinth and bacteria. P. fluorescens and EM-1 increased plant rate of 152.00 cm and wet dry weight 626.67 and 160.42 g, respectively. The total increase of eggplant was 29.96 kg compared with S. sclerotiorum, which gave a weight of 11.77 kg.
Biogas Purification by Alkali-based Column Washing Extracted from Wood Ash and Banana Stalks

The production of biogas as an energy source from organic waste is a viable option for waste recovery and reduction of greenhouse gases. Before any use of biogas, however, hydrogen sulfide and carbon must be removed to preserve consumer health and the life of the equipment. In this work, we were talking about purifying the biogas by washing on a column with an alkali solution produced from wood ash and ash from the wood stalk. To do this, a functional analysis of the purification system was made. After characterization of the samples, the alkalis were extracted, which were subsequently characterized. The next step was to carry out biogas purification tests from the alkali crystals. The extraction yield of alkali is 7% for the ash of meat grillers and 12% with the ash of banana stalk. The biogas was washed with an alkali concentration of 0.1 g/ml. The optimal residence time offering the best washing is 23 min for CO2 and 32 min for H2S. This study found that the alkali on the banana boom is better. During the washing test, a CO2 absorption capacity of 4.52g/ml of the alkali solution and 20.45 ppm / ml of H2S was recorded. By making an extrapolation It follows that for these quantities of CO2 and H2S absorbed per milliliter, during a domestic installation, for a volume of alkali of 1000 ml, we will have a volume of CO2 of 2,53m3 and a biogas volume of 6.33 m3.
Out-of-Vocabulary Embedding Imputation with Grounded Language Information by Graph Convolutional Networks.

Due to the ubiquitous use of embeddings as input representations for a wide range of natural language tasks, imputation of embeddings for rare and unseen words is a critical problem in language processing. Embedding imputation involves learning representations for rare or unseen words during the training of an embedding model, often in a post-hoc manner. In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph. This is in contrast to existing approaches which typically make use of vector space properties or subword information. We propose an online method to construct a graph from grounded information and design an algorithm to map from the resulting graphical structure to the space of the pre-trained embeddings. Finally, we evaluate our approach on a range of rare and unseen word tasks across various domains and show that our model can learn better representations. For example, on the Card-660 task our method improves Pearson's and Spearman's correlation coefficients upon the state-of-the-art by 11% and 17.8% respectively using GloVe embeddings.
A Semiparametric Approach to Interpretable Machine Learning

Black box models in machine learning have demonstrated excellent predictive performance in complex problems and high-dimensional settings. However, their lack of transparency and interpretability restrict the applicability of such models in critical decision-making processes. In order to combat this shortcoming, we propose a novel approach to trading off interpretability and performance in prediction models using ideas from semiparametric statistics, allowing us to combine the interpretability of parametric regression models with performance of nonparametric methods. We achieve this by utilizing a two-piece model: the first piece is interpretable and parametric, to which a second, uninterpretable residual piece is added. The performance of the overall model is optimized using methods from the sufficient dimension reduction literature. Influence function based estimators are derived and shown to be doubly robust. This allows for use of approaches such as double Machine Learning in estimating our model parameters. We illustrate the utility of our approach via simulation studies and a data application based on predicting the length of stay in the intensive care unit among surgery patients.
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation

Generalization Performance of Deep Learning models trained using Empirical Risk Minimization can be improved significantly by using Data Augmentation strategies such as simple transformations, or using Mixed Samples. We attempt to empirically analyze the impact of such strategies on the transfer of generalization between teacher and student models in a distillation setup. We observe that if a teacher is trained using any of the mixed sample augmentation strategies, such as MixUp or CutMix, the student model distilled from it is impaired in its generalization capabilities. We hypothesize that such strategies limit a model's capability to learn example-specific features, leading to a loss in quality of the supervision signal during distillation. We present a novel Class-Discrimination metric to quantitatively measure this dichotomy in performance and link it to the discriminative capacity induced by the different strategies on a network's latent space.
Citotoxicidade e genotoxicidade da água do rio Subaé (Humildes, Bahia, Brasil) usando Allium cepa L. como bioindicador

Over the years, the Subae River in Bahia has been polluted by the discharge of toxic industrial waste into its waters. This study used the Allium cepa test to investigate the cytotoxic and genotoxic effects caused by the river water that that runs through the city of Humildes-BA. Water samples were collected in three points (PI, PII and PIII) for physical and chemical analysis. Onions bulbs were immersed in triplicate water samples for 72 h to induce germination. The root tips were kept at 4 °C for 12-h, then fixed in Carnoy for 12-h and stained using the Feulgen technique. We analyzed 6500 cells of each treatment. Cytotoxicity was assessed by the mitotic index (MI) and genotoxicity by chromosomal abnormalities (CA). The tests revealed that PI and PIII had DO values, BOD5 and COD changed. The toxicity test indicated that root growth was inhibited by the water drawn from PI (10.3%) and PII (10.6%) compared to the negative control (11.9%). CAs were found in 6.2% of the cells relating to PI, 6.0% relating to PII, and 5.4% relating to PIII, respectively. These values were higher than those found in the positive control (3%), indicating a genotoxic effect at the points studied. From the results, it can be inferred that the waters of the Rio Subae may contain substances that are probably affecting the cell cycle of A. cepa. It is important that other analyses be conducted in different seasons, in order to better monitor local environmental conditions.
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series

This paper is aimed at the problem of predicting the land subsidence or upheave in an area, using GNSS position time series. Since machine learning algorithms have presented themselves as strong prediction tools in different fields of science, we employ them to predict the next values of the GNSS position time series. For this reason, we present an algorithm that takes advantage of the machine learning algorithms for the prediction of positions in a GNSS time series. The proposed algorithm has two steps-preprocessing and prediction. In the preprocessing phase, the periodic tidal and atmospheric signals in the time series are removed and coordinates are transferred to the local coordinate system. In the prediction phase, eight different machine learning algorithms are used, namely, multilayer perceptron, Bayesian neural network, radial basis functions, Gaussian processes, k-nearest neighbor, generalized regression neural network, classification and regression trees, and support vector regression. We show the superiority of the Gaussian processes algorithm, compared to other methods, in 14 different real GNSS time series studies. The proposed algorithm can achieve up to 4 millimeters in accuracy, with the average accuracy as 2 centimeters across all time series.
$\gamma$-Regret for Non-Episodic Reinforcement Learning.

Reinforcement learning (RL) has traditionally been understood from an episodic perspective; the concept of non-episodic RL, where there is no restart and therefore no reliable recovery, remains elusive. A fundamental question in non-episodic RL is how to measure the performance of a learner and derive algorithms to maximize such performance. Conventional wisdom is to maximize the difference between the average reward received by the learner and the maximal long-term average reward. In this paper, we argue that if the total time budget is relatively limited compared to the complexity of the environment, such comparison may fail to reflect the finite-time optimality of the learner. We propose a family of measures, called $\gamma$-regret, which we believe to better capture the finite-time optimality. We give motivations and derive lower and upper bounds for such measures.
High-dimensional generalized semiparametric model for longitudinal data

This paper considers the problem of estimation in the generalized semiparametric model for longitudinal data when the number of parameters diverges with the sample size. A penalization type of generalized estimating equation method is proposed, while we use the regression spline to approximate the nonparametric component. The proposed procedure involves the specification of the posterior distribution of the random effects, which cannot be evaluated in a closed form. However, it is possible to approximate this posterior distribution by producing random draws from the distribution using a Metropolis algorithm. Under some regularity conditions, the resulting estimators enjoy the oracle properties, under the high-dimensional regime. Simulation studies are carried out to assess the performance of our proposed method, and two real data sets are analyzed for procedure demonstration.
Effect of Type of Feeder, Feeder Space, and Bird Density Under Intermittent Lighting Regimens with Broilers

Abstract Three 49-day broiler experiments were carried out to study the relationship of intermittent light (1/3 hr L:1-2/3 hr D, recycled), feeder space requirements, types of feeders, and bird density on broiler performance. In Experiment 1, performance was comparable between broilers exposed to continuous (CL) and intermittent lighting (IL) if the hanging tube feeder space allowance per bird with IL exceeded 1 cm. In Experiment 2, broiler performance was not significantly affected by trough or hanging tube type feeders with CL or IL with feeder space allowance ranging from 2, 2.54, and 2.74 cm per bird. In Experiment 3, mean male and combined sex body weights and feed conversion were comparable between broilers provided IL, . 056 m2 of floor space, and feeder space of either 2.54 or 5.08 cm and broilers provided . 093 m2 of floor space, 2.54 cm feeder space, and with either IL or CL. Mean female body weights and mean feed consumption per broiler for the IL, . 056 m2 of floor space, 2.54 cm feeder space group were significantly depressed when compared to broilers provided either CL or IL, . 093 m2 of floor space, and 2.54 cm feeder space. Increasing the feeder space from 2.54 to 5.08 cm with IL and . 056 m2 of floor space seems to correct the growth depressing effect.
Learning Mixtures of Plackett-Luce Models with Features from Top-$l$ Orders

Plackett-Luce model (PL) is one of the most popular models for preference learning. In this paper, we consider PL with features and its mixture models, where each alternative has a vector of features, possibly different across agents. Such models significantly generalize the standard PL, but are not as well investigated in the literature. We extend mixtures of PLs with features to models that generate top-$l$ and characterize their identifiability. We further prove that when PL with features is identifiable, its MLE is consistent with a strictly concave objective function under mild assumptions. Our experiments on synthetic data demonstrate the effectiveness of MLE on PL with features with tradeoffs between statistical efficiency and computational efficiency when $l$ takes different values. For mixtures of PL with features, we show that an EM algorithm outperforms MLE in MSE and runtime.
Carbon, nitrogen and pH regulate the production and activity of a polygalacturonase isozyme produced by Penicillium expansum

The influence of carbon, nitrogen and pH on polygalacturonase (PG) activity produced by Penicillium expansum were investigated. P. expansum mycelial growth was greatest on lyophilized lyophilised fruit tissue and the highest PG activity occurred in apple pectin medium. Nitrogen source influenced PG activity and was highest with ammonia while the greatest mycelial mass was supported by glutamate or glutamine. PG activity and mycelial mass peaked 5 five days after inoculation as polyuronide content decreased and the pH and ammonium levels increased in apple pectin medium. A single active PG isozyme with an isoelectric point of ∼7.6 was produced in apple pectin medium and a partial cDNA clone was obtained that was most homologous to the pggII gene from Penicillium. griseoroseum. The results from this study indicate that P. expansum can modulate the activity of PG in response to nutrient sources and ambient pH through signalling pathways that modulate nutrient acquisition, uptake and metabolism.
Provable Guarantees for General Two-sided Sequential Matching Markets

Two-sided markets have become increasingly more important during the last years, mostly because of their numerous applications in housing, labor and dating. Consumer-supplier matching platforms pose several technical challenges, specially due to the trade-off between recommending suitable suppliers to consumers and avoiding collisions among consumers' preferences. In this work, we study a general version of the two-sided sequential matching model introduced by Ashlagi et al. (2019). The setting is the following: we (the platform) offer a menu of suppliers to each consumer. Then, every consumer selects, simultaneously and independently, to match with a supplier or to remain unmatched. Suppliers observe the subset of consumers that selected them, and choose either to match a consumer or leave the system. Finally, a match takes place if both the consumer and the supplier sequentially select each other. Each agent's behavior is probabilistic and determined by a regular discrete choice model. Our objective is to choose an assortment family that maximizes the expected cardinality of the matching. Given the computational complexity of the problem, we show several provable guarantees for the general model, which in particular, significantly improve the approximation factors previously obtained.
Viral Induction of Low Frequency Interferon-α Producing Cells

The human peripheral blood mononuclear cells responsible for IFN-alpha production in response to viral stimuli have been most often described as either monocytes (as typified by the response to Sendai virus) or as a light density, HLA-DR+ population which is negative for most cell surface markers characteristic of mature T cells, B cells, monocytes, or natural killer cells (as typified by the response to Herpes simplex virus (HSV)). The frequency of IFN-alpha-producing cells (IPC) responding to Sendai virus is typically 10-fold or more higher than those responding to HSV. In the current study, we have used ELISpot assays to determine the frequency of IPC responding to DNA and RNA viruses including HSV, Sendai, vesicular stomatitis virus, cytomegalovirus, adenovirus, SV40, influenza, measles, mumps, Newcastle disease virus (NDV) and human immunodeficiency virus (HIV). The enveloped viruses but not the nonenveloped viruses (adenovirus and SV40) elicited an IFN-alpha response. The frequency of IPC for each of the other viruses was more similar to the low frequency HSV-responding population than to the higher frequency Sendai virus response. These included several viruses in the same family as Sendai virus, namely the paramyxo viruses measles, mumps, and NDV. IPC were also tested for sensitivity to the lysosomotropic drug chloroquine, which diminishes IFN-alpha produced in response to HSV but not Sendai virus. With the exception of Sendai virus, chloroquine treatment abrogated the majority of IFN-alpha produced and IPC against each of the viruses. We conclude that low frequency, nonmonocytic NIPC account for the majority of IFN-alpha production in response to different viruses.
Factors that Influence the Customer Adoption of Fintech in Hyderabad, India

In the era of digitalisation, financial institutions are transforming in a sea way, providing new variant of services, and adopting new technology to meet customer expectations. Fintech (an innovation enabled with new technology to provide new applications, services, processes, services and business models by financial service companies) is emerging fast in this digitisation process, and financial service firms are at great advantage applying the same. Penetration of E- commerce and smart phone boosted Fintech in the cash driven economy like India. The existing studies on this subject are limited needing further probe into the matter. This is primarily the reason for undertaking the present study. The study aimed at identifying the factors influencing bank customers in the adoption of Fintech in Hyderabad. This study is empirical in nature based on a sample of respondents, selected by adopting probability random sampling method. The data have been analysed using ANOVA, Exploratory Factor Analysis and Multi Variant Regression Model. The results show that there is no significant difference between venture capital backed funding among select continents. This study identified three factors Conducive, Adaptability and Security which influence the usage of Fintech. Conducive is the most enabling influencing factor for usage and adoption of Fintech among bank customers. It is hoped that the findings of this empirical study, based on a reliable sample and valid statistical analysis would have facilitated adding to the existing contributions on the subject.
Quantifying size-asymmetric growth among individual beech trees

We modelled the growth of individual trees in populations of European beech (Fagus sylvatica L.) grown under different thinning regimes using a modified Richards equation. The effect of competition on growth was modelled by coupling the n individual equations simultaneously with a saturation term. By assuming that the growth of an individual within the population is a function of its size to a power a, a measure of the growth advantage of larger individuals (size-asymmetric growth) is provided. If a> 1, larger trees have a disproportionate advantage in growth and by inference, in competition. The degree of size-asymmetric growth, a, exceeded one in stands with large size variability and increased significantly at increasing density. This suggests that the predominant mode of competition is size asymmetric and that this size asymmetry increases with density. A measure of growth asymmetry is more informative than static measures of size inequality in understanding the growth dynamics of managed forest stands. Since a provides a measure of the relative importance of above- versus below-ground competition, it may be useful in interpreting the growth dynamics of forest
Effects of Early Protein Intake on Development and Subsequent Egg Production of Broiler Breeder Hens

Abstract This study examined the influence of crude protein (CP) intake to 6 wk of age on skeletal and body component development of female broiler breeders, as well as subsequent egg production to 33 wk. Chicks were provided isocaloric (2865 kcal ME/kg) feeds containing 12 (low), 16 (medium), or 20% (high) CP in limited allotments intended to result in similar 6-wk body weights (BW). At 6 wk, BW, uniformity, breast weight, and shank and keel lengths were generally increased in proportion to CP intake; conversely, abdominal fat and mortality were decreased. In general, these effects were reduced or nullified by 14 wk and thereafter. Although most physical characteristics were similar among treatments once laying commenced, birds previously provided greater early CP intakes produced more eggs and laid fewer floor eggs to 33 wk.