Miracle-dz/UniFAR-FTUs huggingface dataset:
Moths in Pheromone Traps for Cydia Molesta Busck, C. Pomonella L., C. Nigricana F. (Tortricidae, Lepidoptera) in Lithuania

In the period 1998-2000, Cydia molesta, a quarantine pest, was not registered in Lithuania. In the current paper, the data on 64 moth species belonging to 11 families and caught in the pheromone traps for Cydia molesta, C. pomonella and C. nigricana are presented. Argyresthia sorbiella, Pammene inquilina are new species for Lithuania. Such rare species for Lithuania as Pristerognatha fuligana, Pammene spiniana, Cydia gallicana, C. pallifrontana, Cnephasia interjectana were also caught in the traps, and the data on their distribution was supplemented with new facts. The moths that were caught most frequently in the traps for Grapholitha molesta, Cydia pomonella, and Cydia nigricana are pointed out.
A new method to set conservation priorities in biodiversity hotspots

Abstract Prioritization of endemic, threatened species and the habitats where they live is a crucial point of conservation actions, particularly in areas with rich endemic floras. In this study, we have developed a new procedure to define the conservation priorities among endemic plants and habitats by evaluating eight criteria. Five criteria deal with the geographic and ecological range of the evaluated species, whereas the other three refer to threats. After the evaluation of each criterion, we combined the partial scores to obtain a priority index (PI). Finally, we characterized the EU habitat categories of conservation concern on the basis of the average PI value of the plants living in each habitat. We tested the method on a list of 260 endemic plants from a biodiversity hotspot (Sardinia) that had an average PI of 3.66 ± 0.16. Even if the habitat categories that are most rich in endemic plants were rocky habitats, and coastal/halophytic habitats, the most endangered habitat was coastal sand dunes (PI = 6.75 ± 1.15). The method herein presented is complementary with the application of IUCN criteria. This integrated approach is a concrete solution that adapts IUCN criteria and categories to local contexts.
Documentos de Trabajo Interaction Effects in Probit Models, Reinterpreting the Impact of Education on Attitudes towards Immigrants and Free-Trade

Economic literature has examined the impact of education and factors endowment on individual preferences towards free-trade and immigration by focusing on the sign of an interaction term between the educational level and the Gross Domestic Product per capita. The aim of this paper is to re-examine this issue by employing the new method proposed Ai and Norton (2003). Findings showed that attitudes are not fully in line with the Heckscher-Ohlin model. That is, high educated people in richer countries could be against immigration and free-trade if their overall disposition to these issues is bad.
Distribution and abundance of ichthyoplankton in the Manicouagan River estuary, a tributary of the lower St. Lawrence estuary

The species composition and relative abundance of ichthyoplankton were investigated during summer 1986 at four stations along the salinity gradient in the Manicouagan River estuary, a tributary of the lower St. Lawrence estuary. Physical characteristics of water masses indicated the presence of a strong saline front (>10‰ per km) delineating the freshwater and marine section of the Manicouagan estuary. The estuary supports a depauperate ichthyoplankton community, including four species of pelagic fish eggs and eight species of fish larvae. Species richness increased with salinity. The ichthyoplankton fauna can be divided into two distinct groups: freshwater and marine. These two groups result initially from spawning preferences exhibited by the different species abundance of freshwater larvae was maximal at the head of the estuary and marine larvae were most abundant at the most saline station. The length frequency distribution suggests that marine larvae are not effectively retained within the estuary. The Manicouagan estuary cannot be considered as a major spawning site nor an important nursery zone for any fish found in this area.
Anti-diabetic role of quercetin and cinnamon on neurobehavioral alterations and biochemical parameters of induced diabetics rats

We aim was to evaluate the protective effects of the antioxidants cinnamon and quercetin on neurobehavioral alterations and complications, besides biochemical parameters of induced-diabetics Wistar rats. Diabetes was induced by a single intraperitoneal injection of streptozotocin at a dose of (45 mg/kg). The administration of streptozotocin was considered acting on anxiety behaviors and biochemical parameters in adult Wistar rats. On the other hand, the protective role of antioxidants (cinnamon and quercetin) on streptozotocin-induced disorders was also evaluated. Behavioral tests in the open field (OF) revealed that diabetic animals exhibited an anxious behavior and an alteration in the locomotive and exploratory activities when compared to control. The administration of the cinnamon (2g/kg) and Quercetin (0.5g/kg) by gastric gavage reduces anxiety and decreases hyperglycemia-related harm. However, antioxidants cinnamon and quercetin administration significantly alleviated anxious and depressive behaviors.
Estimation of Heritabilities and Clonal Contribution Based on the Flowering Assessment in Two Clone Banks of Pinus koraiensis Sieb. et Zucc

Reproductive characteristics of 161 Korean pine (Pinus koraiensis Sieb. et Zucc.) clones were surveyed at two clone banks for 3 years. These clone banks were established at Yongin and Chunchon (mid-Korea) in 1983. Characteristics in female and male strobili were spatial (between locations) and temporal (among investigated times) variables. Broad sense heritabilities were found to vary between 0.20 0.46 in females and between 0.34 to 0.56 in males. Among 161 clones, 32 clones (20 percent of the total clones) accounted for 42 to 54 percent of clonal contribution in female strobili and 83 to 96 percent in male strobili, suggesting that the clonal contribution for male parents was severely unbalanced compared to that for female parents. The effective population numbers varied depending on time (year), location and sex. The mean values of relative effective population numbers at gamete levels were 0.56 in females and 0.09 in males, respectively, and that value at the clonal level was 0.27 (0.25 at Yongin and 0.29 at Chunchon). The degree of sexual asymmetry (As) varied with a range of 0.03 to 0.24 at Chunchon and 0.07 to 0.44 at Yongin. The pattern of gamete production within clones was highly asymmetrical as compared to that of other conifers. This indicates that P. koraiensis is extremely low in male gamete production compared to female gamete production.
CLASSIFICATION OF MAMMOGRAPHIC MASSES USING RADIAL BASIS FUNCTIONS AND SIMULATED ANNEALING WITH SHAPE, ACUTANCE, AND TEXTURE FEATURES

We investigated the use of a classifier based upon nonlinear and combinational optimization techniques (RBF – radial basis functions and simulated annealing) to classify mammographic masses as malignant or benign. The RBF-simulated annealing network was trained with measures of shape, acutance, and texture. To demonstrate the effectiveness of the classifier in identifying malignant tumors and benign masses in mammograms, the network was trained with the three most effective features of fractional concavity, acutance, and sum entropy. The performance of RBF-simulated annealing was compared with linear discriminant analysis (LDA) in terms of the area ( A z ) under the receiver operating characteristics (ROC) curve. The best result obtained with RBF-simulated annealing was A z = 0.96, which compares well with the result obtained with LDA ( A z = 0.99) using the same features.
Comparison of methods to determine the endogenous amino acid flow at the terminal ileum of the growing rat

The aim was to compare a recently developed peptide alimentation method and the protein-free and regression methods for determining endogenous ileal nitrogen (N) and amino acid excretion in the rat, and to apply the endogenous loss estimates to determine the true ileal digestibility of a meat and bone meal (MBM). Preliminary investigations determined the effect of the time of sampling of digesta after a meal (slaughter method) for rats given a protein-free or an enzymically hydrolysed casein (EHC) based diet, on ileal digesta and endogenous N excretion. There was a significant (P < 0.05) effect of the time of sampling on the amount of digesta collected and the endogenous N excretion for both the EHC and protein-free fed rats. The amount of digesta collected from the terminal 20 cm of ileum and the endogenous N excretion for both the EHC and protein-free fed rats were greatest and least variable at 3 h post-feeding. In the main study, endogenous ileal amino acid excretions were determined in the growing rat fed an EHC-based diet (n = 6) and with subsequent treatment of the digesta using ultrafiltration or in six rats given a protein-free diet or by extrapolation from data for 30 animals given five diets which contained graded levels of MBM as the sole protein source. For the EHC treatment, the ileal digesta precipitate plus retentate was used to determine the endogenous flows. The ultrafiltration step excludes unabsorbed dietary amino acids from the measure of endogenous loss. Chromic oxide was the reference marker in all diets. The endogenous N flows determined by the protein-free and regression methods were similar but both significantly (P < 0.01) lower than those for rats fed the EHC-based diet. The mean endogenous ileal N flows determined by the peptide alimentation method, the protein-free and regression approaches were 1866, 1103 and 1019 μg g−1 freeze dry matter intake, respectively. The endogenous amino acid flows at the terminal ileum were lower when determined using the traditional protein-free or regression methods. The true ileal digestibility coefficients for MBM were considerably higher based on the EHC versus protein-free endogenous flows.
Neural Unsupervised Domain Adaptation in NLP—A Survey

Deep neural networks excel at learning from labeled data and achieve state-of-the-art results on a wide array of Natural Language Processing tasks. In contrast, learning from unlabeled data, especially under domain shift, remains a challenge. Motivated by the latest advances, in this survey we review neural unsupervised domain adaptation techniques which do not require labeled target domain data. This is a more challenging yet a more widely applicable setup. We outline methods, from early traditional non-neural methods to pre-trained model transfer. We also revisit the notion of domain, and we uncover a bias in the type of Natural Language Processing tasks which received most attention. Lastly, we outline future directions, particularly the broader need for out-of-distribution generalization of future NLP.
Distributed Logistic Regression for Massive Data with Rare Events

Large-scale rare events data are commonly encountered in practice. To tackle the massive rare events data, we propose a novel distributed estimation method for logistic regression in a distributed system. For a distributed framework, we face the following two challenges. The first challenge is how to distribute the data. In this regard, two different distribution strategies (i.e., the RANDOM strategy and the COPY strategy) are investigated. The second challenge is how to select an appropriate type of objective function so that the best asymptotic efficiency can be achieved. Then, the under-sampled (US) and inverse probability weighted (IPW) types of objective functions are considered. Our results suggest that the COPY strategy together with the IPW objective function is the best solution for distributed logistic regression with rare events. The finite sample performance of the distributed methods is demonstrated by simulation studies and a real-world Sweden Traffic Sign dataset.
Glaucoma Detection From Raw Circumpapillary OCT Images Using Fully Convolutional Neural Networks

Nowadays, glaucoma is the leading cause of blindness worldwide. We propose in this paper two different deep-learning based approaches to address glaucoma detection just from raw circumpapillary OCT images. The first one is based on the development of convolutional neural networks (CNNs) trained from scratch. The second one lies in fine-tuning some of the most common state-of-the-art CNNs architectures. The experiments were performed on a private database composed of 93 glaucomatous and 156 normal B-scans around the optic nerve head of the retina, which were diagnosed by expert ophthalmologists. The validation results evidence that finetuned CNNs outperform the networks trained from scratch when small databases are addressed. Additionally, the VGG family of networks reports the most promising results, with an area under the ROC curve of 0.96 and an accuracy of 0.92, during the prediction of the independent test set.
In the Eye of the Beholder: Gaze and Actions in First Person Video

We address the task of jointly determining what a person is doing and where they are looking based on the analysis of video captured by a headworn camera. To facilitate our research, we first introduce the EGTEA Gaze+ dataset. Our dataset comes with videos, gaze tracking data, hand masks and action annotations, thereby providing the most comprehensive benchmark for First Person Vision (FPV). Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV. Our method describes the participant's gaze as a probabilistic variable and models its distribution using stochastic units in a deep network. We further sample from these stochastic units, generating an attention map to guide the aggregation of visual features for action recognition. Our method is evaluated on our EGTEA Gaze+ dataset and achieves a performance level that exceeds the state-of-the-art by a significant margin. More importantly, we demonstrate that our model can be applied to larger scale FPV dataset—EPIC-Kitchens even without using gaze, offering new state-of-the-art results on FPV action recognition.
Organic acid profile of commercial sour cassava starch

Organic acids are present in sour cassava starch ("polvilho azedo") and contribute with organoleptic and physical characteristics like aroma, flavor and the exclusive baking property, that differentiate this product from the native cassava starch. Samples of commercial sour cassava starch collected in South and Southeast Brazil were prepared for high performance liquid chromatography (HPLC) analysis. The HPLC equipment had a Biorad Aminex HPX-87H column for organic acid analysis and a refractometric detector. Analysis was carried out with 0.005M sulfuric acid as mobile phase, 0.6ml/min flow rate and column temperature of 60° C. The acids quantified were lactic (0.036 to 0.813 g/100g), acetic (0 to 0.068 g/100g), propionic (0 to 0.013 g/100g) and butyric (0 to 0.057 g/100g), that are produced during the natural fermentation of cassava starch. Results showed large variation among samples, even within the same region. Some samples exhibited high acid levels, mainly lactic acid, but in these neither propionic nor butyric acids were detected. Absence of butyric acid was not expected because this is an important component of the sour cassava starch aroma, and the lack of this acid may suggest that such samples were produced without the natural fermentation step.
Evaluation of CNN-based Automatic Music Tagging Models

Recent advances in deep learning accelerated the development of content-based automatic music tagging systems. Music information retrieval (MIR) researchers proposed various architecture designs, mainly based on convolutional neural networks (CNNs), that achieve state-of-the-art results in this multi-label binary classification task. However, due to the differences in experimental setups followed by researchers, such as using different dataset splits and software versions for evaluation, it is difficult to compare the proposed architectures directly with each other. To facilitate further research, in this paper we conduct a consistent evaluation of different music tagging models on three datasets (MagnaTagATune, Million Song Dataset, and MTG-Jamendo) and provide reference results using common evaluation metrics (ROC-AUC and PR-AUC). Furthermore, all the models are evaluated with perturbed inputs to investigate the generalization capabilities concerning time stretch, pitch shift, dynamic range compression, and addition of white noise. For reproducibility, we provide the PyTorch implementations with the pre-trained models.
Development of low-background photomultiplier tubes for liquid xenon detectors

We successfully developed a new photomultiplier tube (PMT) with a three-inch diameter, convex-shaped photocathode, R13111. Its prominent features include good performance and ultra-low radioactivity. The convex-shaped photocathode realized a large photon acceptance and good timing resolution. Low radioactivity was achieved by three factors: (1) the glass material was synthesized using low-radioactive-contamination material; (2) the photocathode was produced with 39K-enriched potassium; and (3) the purest grade of aluminum material was used for the vacuum seal. As a result each R13111 PMT contains only about 0.4 mBq of 226Ra, less than 2 mBq of 238U, 0.3 mBq of 228Ra, 2 mBq of 40K and 0.2 mBq of 60Co. We also examined and resolved the intrinsic leakage of Xe gas into PMTs that was observed in several older models. We thus succeeded in developing a PMT that has low background, large angular acceptance with high collection efficiency, good timing resolution, and long-term stable operation. These features are highly desirable for experiments searching for rare events beyond the standard model, such as dark matter particle interactions and neutrinoless double beta decay events.
Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space

Learning accurate drug representations is essential for tasks such as computational drug repositioning and prediction of drug side effects. A drug hierarchy is a valuable source that encodes knowledge of relations among drugs in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised) and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the drug-drug similarity information obtained from the hierarchy to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical relations. Both quantitative and qualitative results support that the learned drug embedding can accurately reproduce the chemical structure and recapitulate the hierarchical relations among drugs. Furthermore, our approach can infer the pharmacological properties of novel molecules by retrieving similar drugs from the embedding space. We demonstrate that our drug embedding can predict new uses and discover new side effects of existing drugs. We show that it significantly outperforms comparison methods in both tasks.
Conversion between electron spin and microscopic atomic rotation

We theoretically investigate the microscopic mechanism of conversion between the electron spin and the microscopic local rotation of atoms in crystals. In phonon modes with angular momenta, the atoms microscopically rotate around their equilibrium positions in crystals. In a simple toy model with phonons, we calculate the spin expectation value by using the adiabatic series expansion. We show that the time-averaged spin magnetization is generated by the microscopic local rotation of atoms via the spin-orbit interaction. On the other hand, in the system with a simple vibration of atoms, time-averaged spin magnetization becomes zero due to the time-reversal symmetry. Moreover, the magnitude of the time-averaged spin magnetization depends on the inverse of the difference of instantaneous eigenenergy, and we show that it becomes smaller in band insulators with a larger gap.
ValueNet: A Neural Text-to-SQL Architecture Incorporating Values

Building natural language interfaces for databases has been a long-standing challenge for several decades. The major advantage of these so-called text-to-SQL systems is that end-users can query complex databases without the need to know SQL or the underlying database schema. Due to significant advancements in machine learning, the recent focus of research has been on neural networks to tackle this challenge on complex datasets like Spider. Several recent text-to-SQL systems achieve promising results on this dataset. However, none of them extracts and incorporates values from the user questions for generating SQL statements. Thus, the practical use of these systems in a real-world scenario has not been sufficiently demonstrated yet. In this paper we propose ValueNet light and ValueNet -- the first end-to-end text-to-SQL system incorporating values on the challenging Spider dataset. The main idea of our approach is to use not only metadata information about the underlying database but also information on the base data as input for our neural network architecture. In particular, we propose a novel architecture sketch to extract values from a user question and come up with possible value candidates which are not explicitly mentioned in the question. We then use a neural model based on an encoder-decoder architecture to synthesize the SQL query. Finally, we evaluate our model on the Spider challenge using the Execution Accuracy metric, a more difficult metric than used by most participants of the challenge. Our experimental evaluation demonstrates that ValueNet light and ValueNet reach state-of-the-art results of 64% and 60% accuracy, respectively, for translating from text to SQL, even when applying this more difficult metric than used by previous work.
Stick-Slip model for actin-driven cell protrusions, cell polarisation and crawling

Cell crawling requires the generation of intracellular forces by the cytoskeleton and their transmission to an extracellular substrate through specific adhesion molecules. Crawling cells show many features of excitable systems, such as spontaneous symmetry breaking and crawling in the absence of external cues, and periodic and propagating waves of activity. Mechanical instabilities in the active cytoskeleton network and feedback loops in the biochemical network of activators and repressors of cytoskeleton dynamics have been invoked to explain these dynamical features. Here, we show that the interplay between the dynamics of cell-substrate adhesion and linear cellular mechanics is sufficient to reproduce many non-linear dynamical patterns observed in spreading and crawling cells. Using an analytical formalism of the molecular clutch model of cell adhesion, regulated by local mechanical forces, we show that cellular traction forces exhibit a stick-slip dynamics resulting in periodic waves of protrusion/retraction and propagating waves along the cell edge. This can explain spontaneous symmetry breaking and polarisation of spreading cells, leading to steady crawling or bipedal motion, and bistability, where persistent cell motion requires a sufficiently strong transient external stimulus. The model also highlight the role of membrane tension in providing the long-range mechanical communication across the cell required for symmetry breaking.