Miracle-dz/UniFAR-FTUs huggingface dataset:
An updated data on the bionomics of pomegranate fruit borer, Deudorix epijarbas (Moore, 1858) (Lepidoptera: Lycaenidae), infesting pomegranates in Kashmir

Pomegranate fruit borer, Deudorix epijarbas (Moore, 1858) (Lepidoptera: Lycaenidae) is a destructive pest of pomegranate affecting the quality as well as quantity of its fruits. The pest and its other allied species are nuisance to pomegranate cultivation throughout India, particularly in hilly state of Jammu & Kashmir. The aim of present investigation was to study the bionomics of pomegranate fruit borer in laboratory, which included the studies on its morphometry and extent of different developmental stages in the life cycle of pest, for developing a sound management plan against it. The results revealed that the eggs are laid by females singly on fruits, stalks, leaves and flower buds. On hatching, emerging larva bores in to fruits and feed on its pulp thus rendering them unfit for trade. The pest passed through five larval instars of different duration and the total larval period was 32.20 ± 1.87 (SD) days. The larval stage was followed by pupal stage which lasted for 9.50 ± 1.84 (SD) days. Furthermore, the longevity of adult male and female butterflies was recorded as 9.20 ± 1.73 (SD) and 13.20 ± 1.43 (SD) days respectively. The study thus provides information necessary for development and implementation of different control strategies in IPM of pomegranate fruit borer.
SHAPE OPTIMIZATION FOR FLUID FLOW PROBLEMS USING BEZIER CURVES AND DESIGNED NUMERICAL EXPERIMENTS

Some problems in constrained shape optimization are considered. The goal in our optimization process is to maximize a measure of device performance computed using CAE, with a CAD compatible representation and specified geometric constraints. This approach illustrates several issues in integration of CAD and CAE systems. We test our ideas on idealized internal flow devices where the underlying device physics is governed by either the Laplace or the Navier-Stokes equations. The geometric shape of the device is represented by Bezier curves. Analysis tools such as the grid generator and the fluid flow solver are treated as a black box. The search pattern during the optimization process is suggested by the design of experiment methodology. The proposed framework is tested with one potential flow problem and two laminar flow diffuser problems.
Foreign direct investment, financial development and economic growth in Asian developing countries

This research investigates the role of domestic financial development in enhancing the positive effects of foreign direct investment (hereafter, FDI) on economic growth in Asian developing countries. In other words, we examine whether countries with a better domestic financial system can utilize FDI more efficiently. The empirical analysis uses balanced panel data of 24 Asian developing countries in the period 1995-2009. This research applies the various models and techniques in panel data regression. Linear static models for panel data, including constant coefficients model or pooled regression model (POOLED), fixed effects regression model (FEM) and random effects regression model (REM) are employed. We analyze all models and employ several kinds of test including poolability test, Hausman test, LM test, fixed effects tests and Wald tests to select the most appropriated estimated model. The research findings show that FDI alone does not have direct effect on economic growth but does have when combined with financial development. Well-developed domestic financial markets promote the process of technological diffusion associated with FDI in Asian developing countries. Therefore, FDI and domestic financial development are complementary in increasing the rate of economic growth in the region. There is a threshold level of domestic financial development above which FDI starts to have positive impacts on economic growth.
Discriminant Analysis of Assessment Tools in Identifying Language Impairment in Korean-English Bilingual Children

531 This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2020 Korean Academy of Speech-Language Pathology and Audiology Discriminant Analysis of Assessment Tools in Identifying Language Impairment in Korean-English Bilingual Children
Laboratory assessment and prospects of repellent potentiation of naturally occurring plant extractives against the lesser grain bore, Rhizopertha dominica Fabricius (Bostrichidae: Coleoptera)

Prevention of food losses during postharvest storage is of paramount economic importance. The use of synthetic chemical insecticides is either not permitted or used restrictively because of the residue problem and health risks to consumers. In view of the above, there is a need for plants that may provide potential alternatives to the currently used insect control agents as they constitute a rich source of bioactive molecules. Available literature indicates that plant extractives could be source for new insecticides. This paper focuses on the current state of new biorational agents for repellent as grain protectants applied in insect-pest management. A screening five naturally occurring indogenous botanicals namely: Acorus calamus Linn. (rhizomes), Annona squamosa Linn. (unripe fruites) and Vitex negundo Linn. (leaves), Gynendropsis gynendra Linn. , (seeds) and Ocimum basilicum Linn. (leaves) were formulated in distilled water. Triton X-100 at the rate of 0.5 percent as emulsifier and benzene at the rate of 5 percent as solvent were used were tested for their larval repellent biopotency against Rhizopertha dominica Fabricius and two control were also used in the present investigation. Over all, the highest R. dominica larval repellency was calculated A. squamosa extract showed the highest larval repellency (99.77 per cent) and placed on top followed by O. basilicum (99.54 per cent), A. calamus (99.38 per cent) G. gynendra (99.22 per cent), V. negundo (99.10 per cent) where as in control using Benezene was showed (99.38 per cent) and in absolute control was seen only 79.25 percent larval repellency and placed at bottom of merit.
The Effect of Corporate Characteristics on Capital Structure in Indonesia

This study aims to determine the effect of corporate characteristics on the company's capital structure, which plays a fundamental role in the proportion of debt and equity financing risks. The research method used is purposive sampling. This research's population is non-financial issuers listed on the Indonesia Stock Exchange with quarterly data for the period of 2010-2017. The analysis is performed using panel data with six independent variables and two control variables. The results of this study indicate that profitability and institutional ownership have a negative effect on capital structure. In contrast, market ratios, firm size, and managerial ownership have a positive effect on capital structure. Debt decision making must consider financial and ownership characteristics, especially if there is institutional or government ownership in the company because company characteristics have a significant effect on the company's capital structure.
Phenolic content and antioxidant activity of eight representative sweet corn varieties grown in South China

ABSTRACT In the present study, phenolic content and antioxidant capacities including cellular antioxidant activity of eight Chinese sweet corn varieties were investigated. The total phenolic content revealed significant varietal differences and phenolics existed mainly in free form. HPLC analysis showed that ferulic and p-coumaric acids were the dominating phenolics in sweet corn. The DPPH scavenging capacity, ferric reducing antioxidant power, and cellular antioxidant activity demonstrated distinct varietal differences, which were positively correlated with their phenolic content, although the orders of antioxidant activity of eight varieties determined by three different methods were not consistent. Sweet corn could be a better substitute for many commonly consumed vegetables in terms of phenolics and antioxidant activity.
Long-term consolidation switches goal proximity coding from hippocampus to retrosplenial cortex

Recent research indicates the hippocampus may code the distance to the goal during navigation of newly learned environments. It is unclear however, whether this also pertains to highly familiar environments where extensive systems-level consolidation is thought to have transformed mnemonic representations. Here we recorded fMRI while University College London and Imperial College London students navigated virtual simulations of their own familiar campus (> 2 years of exposure) and the other campus learned days before scanning. Posterior hippocampal activity tracked the proximity to the goal in the newly learned campus, but not in the familiar campus. By contrast retrosplenial cortex tracked the distance to the goal in the familiar campus, but not in the recently learned campus. These responses were abolished when participants were guided to their goal by external cues. These results open new avenues of research on navigation and consolidation of spatial information and help advance models of how neural circuits support navigation in novel and highly familiar environments. Significance Statement Historically, research on the hippocampal formation has focused on its role in long-term memory and navigation – often in isolation. No study to date has directly compared realistic navigation within familiar with recently learned environments, nor has it been explored how the neural substrates, along with computational codes, may change. In this study, we show for the first time, a shift from hippocampal to cortical coding of distance to a goal during active navigation. This study bridges the gap between memory consolidation and navigation, and paves the way for more functional and realistic understanding of the hippocampus.
Cohomological Obstructions for Mittag-Leffler Problems

This is an extensive survey of the techniques used to formulate generalizations of the Mittag-Leffler Theorem from complex analysis. With the techniques of the theory of differential forms, sheaves and cohomology, we are able to define the notion of a Mittag-Leffler Problem on a Riemann surface as a problem of passage of data from local to global, and discuss characterizations of contexts where these problems have solutions.
Self-Alignment Pretraining for Biomedical Entity Representations

Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.
Photovoltaic (PV) Virtual Inertia and Fast Frequency Regulation in High PV Power Grids

This paper studies the frequency response using PV. Multiple control strategies are considered and simulated in the high PV ERCOT model, including inertia control, synthetic governor control, and AGC control. The impact of different parameters in PV inertia control and their correlation and impact on frequency response are analyzed. The simulation results show that PV farm has potential to provide multiple types of grid service to support system frequency. This paper also proposed a distributed fast frequency control approach that can better leverage the PV headroom reserve to improve the system frequency nadir after contingencies.
Euclid preparation: IX. EuclidEmulator2 – power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

We present a new, updated version of the EuclidEmulator (called EuclidEmulator2), a fast and accurate predictor for the nonlinear correction of the matter power spectrum. 2 per cent level accurate emulation is now supported in the eight-dimensional parameter space of w0waCDM+∑mν models between redshift z = 0 and z = 3 for spatial scales within the range $0.01 \, h\, {\rm Mpc}^{-1}\le k \le 10\, h\, {\rm Mpc}^{-1}$. In order to achieve this level of accuracy, we have had to improve the quality of the underlying N-body simulations used as training data: (i) we use self-consistent linear evolution of non-dark matter species such as massive neutrinos, photons, dark energy, and the metric field, (ii) we perform the simulations in the so-called N-body gauge, which allows one to interpret the results in the framework of general relativity, (iii) we run over 250 high-resolution simulations with 30003 particles in boxes of 1(h−1 Gpc)3 volumes based on paired-and-fixed initial conditions, and (iv) we provide a resolution correction that can be applied to emulated results as a post-processing step in order to drastically reduce systematic biases on small scales due to residual resolution effects in the simulations. We find that the inclusion of the dynamical dark energy parameter wa significantly increases the complexity and expense of creating the emulator. The high fidelity of EuclidEmulator2 is tested in various comparisons against N-body simulations as well as alternative fast predictors such as HALOFIT, HMCode, and CosmicEmu. A blind test is successfully performed against the Euclid Flagship v2.0 simulation. Nonlinear correction factors emulated with EuclidEmulator2 are accurate at the level of $1{{\ \rm per\ cent}}$ or better for $0.01 \, h\, {\rm Mpc}^{-1}\le k \le 10\, h\, {\rm Mpc}^{-1}$ and z ≤ 3 compared to high-resolution dark-matter-only simulations. EuclidEmulator2 is publicly available at https://github.com/miknab/EuclidEmulator2.
Spatio-temporal Features for Generalized Detection of Deepfake Videos

For deepfake detection, video-level detectors have not been explored as extensively as image-level detectors, which do not exploit temporal data. In this paper, we empirically show that existing approaches on image and sequence classifiers generalize poorly to new manipulation techniques. To this end, we propose spatio-temporal features, modeled by 3D CNNs, to extend the generalization capabilities to detect new sorts of deepfake videos. We show that spatial features learn distinct deepfake-method-specific attributes, while spatio-temporal features capture shared attributes between deepfake methods. We provide an in-depth analysis of how the sequential and spatio-temporal video encoders are utilizing temporal information using DFDC dataset arXiv:2006.07397. Thus, we unravel that our approach captures local spatio-temporal relations and inconsistencies in the deepfake videos while existing sequence encoders are indifferent to it. Through large scale experiments conducted on the FaceForensics++ arXiv:1901.08971 and Deeper Forensics arXiv:2001.03024 datasets, we show that our approach outperforms existing methods in terms of generalization capabilities.
Accelerating Reinforcement Learning with Learned Skill Priors

Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for leveraging prior knowledge is to transfer skills learned on prior tasks to the new task. However, as the amount of prior experience increases, the number of transferable skills grows too, making it challenging to explore the full set of available skills during downstream learning. Yet, intuitively, not all skills should be explored with equal probability; for example information about the current state can hint which skills are promising to explore. In this work, we propose to implement this intuition by learning a prior over skills. We propose a deep latent variable model that jointly learns an embedding space of skills and the skill prior from offline agent experience. We then extend common maximum-entropy RL approaches to use skill priors to guide downstream learning. We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets. Videos and code are available at this https URL.
Lagrangian Discretization of Variational Mean Field Games

In this article, we introduce a method to approximate solutions of some variational mean field game problems with congestion, by finite sets of player trajectories. These trajectories are obtained by solving a minimization problem, similar to the initial variational problem. In this discretized problem, congestion is penalized by a Moreau envelop with the 2-Wasserstein distance. Study of this envelop as well as efficient computation of its values and variations is done using semi-discrete optimal transport. We show convergence of the discrete sets of trajectories toward a solution of the mean field game, as well as conditions on the discretization in order to get this convergence. We also show strong convergence (in particular almost everywhere) in some cases for the underlying densities resulting from the Moreau projection of solution to the discrete problem.
Convolutional Autoencoders for Human Motion Infilling

In this paper we propose a convolutional autoencoder to address the problem of motion infilling for 3D human motion data. Given a start and end sequence, motion infilling aims to complete the missing gap in between, such that the filled in poses plausibly forecast the start sequence and naturally transition into the end sequence. To this end, we propose a single, end-to-end trainable convolutional autoencoder. We show that a single model can be used to create natural transitions between different types of activities. Furthermore, our method is not only able to fill in entire missing frames, but it can also be used to complete gaps where partial poses are available (e.g. from end effectors), or to clean up other forms of noise (e.g. Gaussian). Also, the model can fill in an arbitrary number of gaps that potentially vary in length. In addition, no further post-processing on the model’s outputs is necessary such as smoothing or closing discontinuities at the end of the gap. At the heart of our approach lies the idea to cast motion infilling as an inpainting problem and to train a convolutional de-noising autoencoder on image-like representations of motion sequences. At training time, blocks of columns are removed from such images and we ask the model to fill in the gaps. We demonstrate the versatility of the approach via a number of complex motion sequences and report on thorough evaluations performed to better understand the capabilities and limitations of the proposed approach.
Learning Invariances in Neural Networks

Invariances to translations have imbued convolutional neural networks with powerful generalization properties. However, we often do not know a priori what invariances are present in the data, or to what extent a model should be invariant to a given symmetry group. We show how to \emph{learn} invariances and equivariances by parameterizing a distribution over augmentations and optimizing the training loss simultaneously with respect to the network parameters and augmentation parameters. With this simple procedure we can recover the correct set and extent of invariances on image classification, regression, segmentation, and molecular property prediction from a large space of augmentations, on training data alone.
EIGEN: Event Influence GENeration using Pre-trained Language Models

Reasoning about events and tracking their influences is fundamental to understanding processes. In this paper, we present EIGEN - a method to leverage pre-trained language models to generate event influences conditioned on a context, nature of their influence, and the distance in a reasoning chain. We also derive a new dataset for research and evaluation of methods for event influence generation. EIGEN outperforms strong baselines both in terms of automated evaluation metrics (by 10 ROUGE points) and human judgments on closeness to reference and relevance of generations. Furthermore, we show that the event influences generated by EIGEN improve the performance on a "what-if" Question Answering (WIQA) benchmark (over 3% F1), especially for questions that require background knowledge and multi-hop reasoning.
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries

Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches. But can richer supervision help even more? To explore the utility of fine-grained, lexical-level supervision, we introduce SQUALL, a dataset that enriches 11,276 WIKITABLEQUESTIONS English-language questions with manually created SQL equivalents plus alignments between SQL and question fragments. Our annotation enables new training possibilities for encoderdecoder models, including approaches from machine translation previously precluded by the absence of alignments. We propose and test two methods: (1) supervised attention; (2) adopting an auxiliary objective of disambiguating references in the input queries to table columns. In 5-fold cross validation, these strategies improve over strong baselines by 4.4% execution accuracy. Oracle experiments suggest that annotated alignments can support further accuracy gains of up to 23.9%.
Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction

Potential Drug-Drug Interactions (DDI) occur while treating complex or co-existing diseases with drug combinations, which may cause changes in drugs’ pharmacological activity. Therefore, DDI prediction has been an important task in the medical health machine learning community. Graph-based learning methods have recently aroused widespread interest and are proved to be a priority for this task. However, these methods are often limited to exploiting the inter-view drug molecular structure and ignoring the drug’s intra-view interaction relationship, vital to capturing the complex DDI patterns. This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view interactions between molecules simultaneously. MIRACLE treats a DDI network as a multi-view graph where each node in the interaction graph itself is a drug molecular graph instance. We use GCN to encode DDI relationships and a bond-aware attentive message propagating method to capture drug molecular structure information in the MIRACLE learning stage. Also, we propose a novel unsupervised contrastive learning component to balance and integrate the multi-view information. Comprehensive experiments on multiple real datasets show that MIRACLE outperforms the state-of-the-art DDI prediction models consistently.