Miracle-dz/UniFAR-FTUs huggingface dataset:
Host-Finding by Insect Parasites. 1. Observations on the Finding of Hosts by Alysia manducator, Mormoniella vitripennis and Trichogramma evanescens

3. Finding the area in which the host occurs . 300 (a) Cases in the literature . 300 (b) Experiments with Alysia manducator 302 (c) Experiments with Mormoniella vitripennis 304 (d) Discussion . 305 4. Finding the host in the host-containing area . 306 (a) Introduction . 306 (b) Perception of the host . 307 (c) Movement from the first host . 311 (d) The effect of the turning movement on the chance of finding the next host . 313 5. Discussion . 315 6. Summary . 316 Acknowledgement . 317 References . 317
Assessment the role of village extension worker in North Kordofan Rural Development Project (NKRDP), Sudan

This study was conducted in North Kordofan State. The study covers both Um Ruwaba and Bara localities (NKRDP area). The main objective is to investigate the role of village extension worker (VEW) on increasing the awareness of the communities towards IPM techniques that used to improve production and minimize hazard in the environment in the project area. The study based on primary and secondary data. The primary date was collected from field through constructed questionnaires filled with participant farmers and VEWs by direct interviews. Stratified random sampling technique was used to select 142 participants as sample size. NKRDP was the main sources of the secondary data as well as the institutional sources (MOA and PPD), references and previous studies. The study used SPSS for descriptive statistics and Chi-Squire test was used to test the role of VEWs services. The results showed the allocation and presence of VEW at the project villages. The results have also highlighted the different extension methods used by VEW, such as home and field visits, meetings, FFS, leaflets, and Poster and extension campaign. The results have showed positive role of VEW as sources of pesticides instated of the village traders. Results also indicated the increasing in farmer’s awareness towards the importance and use of seed dressing. The study had also shown an increase in the awareness of the farmers in the IPM of the watermelon bugs campaigns by 90% and the participation for the reason to control the pest by 76%. Results of Chi-squire test revealed significant differences between parameters measured. Finally, the study recommended the establishment of an extension system that can be developed for an effective integrated pest management.
Comportamento de clones amazónicos de seringueira em Tracuateua

Foi estudado o desempenho dos clones de seringueira ( Hevea sp): IAN 717, IAN 873, IAN 3087, IAN 2880, IAN 2388, IAN 4082, Fx 3899, Fx 3864, Fx 3846 e Fx 3810, nas condicoes ecologicas de Tracuateua, PA. Decorridos oito anos da instalacao do experimento, os clones que apresentam melhor desenvolvimento vegetativo sao: Fx 3810, IAN 717 e IAN 3087. Destes destaca-se o clone Fx 3810 com 73% das plantas em condicoes de sangria.
RESEARCH ON THE EFFECTIVENESS OF PREDICTION MODELS IN THE SECURITIES MARKET

The article describes the methods and models of prediction in case of the stock market. There are different methods for assessing and predicting in case of stock market, which are widely used in practice in the present. In this paper, we used the model of Moving Average Convergence/Divergence, prediction model based on a neural network, model of autoregressive integrated moving average (ARIMA) for prediction. We identified the conditions of the use the models as indicators in the case of the market, show examples of application models on the data of the closing prices for the exchange rate from dollar USD to ruble in Russia. In this article analyzes the disadvantages of these models and the causes of these deficiencies. The materials characteristics in this article are analyzing the practical orientation of prediction models in the case of the stock market and interpret the results of mistaken prediction.
International effort to induce somatic embryogenesis in adult pine trees

The genus Pinus includes several species that are economically important and planted outside their natural ranges as plantation species. Somatic embryogenesis (SE), a biotechnological tool for mass propagation of pines, has been reported for many species but only from seed embryos. Cloning the individual adult trees, through SE from vegetative explants, could potentially benefit the forest industry in that only trees with elite characteristics would be planted commercially. The attributes of conifer trees may only be evaluated after many years of growth and often not until the reproductive growth phase. This chapter describes a concerted effort by several research teams in five countries
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks

A recent breakthrough in deep learning theory shows that the training of over-parameterized deep neural networks can be characterized by a kernel function called \textit{neural tangent kernel} (NTK). However, it is known that this type of results does not perfectly match the practice, as NTK-based analysis requires the network weights to stay very close to their initialization throughout training, and cannot handle regularizers or gradient noises. In this paper, we provide a generalized neural tangent kernel analysis and show that noisy gradient descent with weight decay can still exhibit a "kernel-like" behavior. This implies that the training loss converges linearly up to a certain accuracy. We also establish a novel generalization error bound for two-layer neural networks trained by noisy gradient descent with weight decay.
Comparative Analysis of Methodologies for Domain Ontology Development: A Systematic Review

Interlocking Institutional Worlds (IWs) is a concept explaining the need to interoperate between institutions (or players), to solve problems of common interest in a given domain. Managing knowledge in the IWs domain is complex, but promoting knowledge sharing based on standards and common terms agreeable to all players is essential and is something that must be established. In this sense, ontologies, as a conceptual tool and a key component of knowledge-based systems, have been used by organizations for effective knowledge management of the domain of discourse. There are many methodologies that have been proposed by several researchers during the last decade. However, designing a domain ontology for IWs needs a welldefined ontology development methodology. Therefore, in this article, a survey has been conducted to compare ontology development methodologies between 2015 and 2020. The purpose of this survey is to identify limitations and benefits of previously developed ontology development methodologies. The criteria for the comparison of methodologies has been derived from evolving trends in literature. Our findings give some guidelines that help to define a suitable methodology for designing any domain ontology under the domain of interlocking institutional worlds. Keywords—Knowledge management; interlocking institutional worlds; domain ontology; comparison of methodologies; ontology
Attention Is Not Only a Weight: Analyzing Transformers with Vector Norms

Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing. Hence, attention is being extensively studied to investigate various linguistic capabilities of Transformers, focusing on analyzing the parallels between attention weights and specific linguistic phenomena. This paper shows that attention weights alone are only one of the two factors that determine the output of attention and proposes a norm-based analysis that incorporates the second factor, the norm of the transformed input vectors. The findings of our norm-based analyses of BERT and a Transformer-based neural machine translation system include the following: (i) contrary to previous studies, BERT pays poor attention to special tokens, and (ii) reasonable word alignment can be extracted from attention mechanisms of Transformer. These findings provide insights into the inner workings of Transformers.
Constrained Iterative Labeling for Open Information Extraction

A recent state-of-the-art neural open information extraction (OpenIE) system generates extractions iteratively, requiring repeated encoding of partial outputs. This comes at a significant computational cost. On the other hand, sequence labeling approaches for OpenIE are much faster, but worse in extraction quality. In this paper, we bridge this trade-off by presenting an iterative labeling-based system that establishes a new state of the art for OpenIE, while extracting 10x faster. This is achieved through a novel Iterative Grid Labeling (IGL) architecture, which treats OpenIE as a 2-D grid labeling task. We improve its performance further by applying coverage (soft) constraints on the grid at training time. Moreover, on observing that the best OpenIE systems falter at handling coordination structures, our OpenIE system also incorporates a new coordination analyzer built with the same IGL architecture. This IGL based coordination analyzer helps our OpenIE system handle complicated coordination structures, while also establishing a new state of the art on the task of coordination analysis, with a 12.3 pts improvement in F1 over previous analyzers. Our OpenIE system, OpenIE6, beats the previous systems by as much as 4 pts in F1, while being much faster.
Optimizing Deep Neural Networks via Discretization of Finite-Time Convergent Flows

In this paper, we investigate in the context of deep neural networks, the performance of several discretization algorithms for two first-order finite-time optimization flows. These flows are, namely, the rescaled-gradient flow (RGF) and the signed-gradient flow (SGF), and consist of non-Lipscthiz or discontinuous dynamical systems that converge locally in finite time to the minima of gradient-dominated functions. We introduce three discretization methods for these first-order finite-time flows, and provide convergence guarantees. We then apply the proposed algorithms in training neural networks and empirically test their performances on three standard datasets, namely, CIFAR10, SVHN, and MNIST. Our results show that our schemes demonstrate faster convergences against standard optimization alternatives, while achieving equivalent or better accuracy.
Questions for data scientists in software engineering: a replication

In 2014, a Microsoft study investigated the sort of questions that data science applied to software engineering should answer. This resulted in 145 questions that developers considered relevant for data scientists to answer, thus providing a research agenda to the community. Fast forward to five years, no further studies investigated whether the questions from the software engineers at Microsoft hold for other software companies, including software-intensive companies with different primary focus (to which we refer as software-defined enterprises). Furthermore, it is not evident that the problems identified five years ago are still applicable, given the technological advances in software engineering. This paper presents a study at ING, a software-defined enterprise in banking in which over 15,000 IT staff provides in-house software solutions. This paper presents a comprehensive guide of questions for data scientists selected from the previous study at Microsoft along with our current work at ING. We replicated the original Microsoft study at ING, looking for questions that impact both software companies and software-defined enterprises and continue to impact software engineering. We also add new questions that emerged from differences in the context of the two companies and the five years gap in between. Our results show that software engineering questions for data scientists in the software-defined enterprise are largely similar to the software company, albeit with exceptions. We hope that the software engineering research community builds on the new list of questions to create a useful body of knowledge.
Stellar splashback: the edge of the intracluster light

We examine the outskirts of galaxy clusters in the C-EAGLE simulations to quantify the "edges" of the stellar and dark matter distribution. The radius of the steepest slope in the dark matter, otherwise known as the splashback radius, is located at ~r_200m; the strength and location of this feature depends on the recent mass accretion rate, in good agreement with previous work. Interestingly, the stellar distribution (or intracluster light, ICL) also has a well-defined edge, which is directly related to the splashback radius of the halo. Thus, detecting the edge of the ICL can provide an independent measure of the physical boundary of the halo, and the recent mass accretion rate. We show that these caustics can also be seen in the projected density profiles, but care must be taken to account for the influence of substructures and other non-diffuse material, which can bias and/or weaken the signal of the steepest slope. This is particularly important for the stellar material, which has a higher fraction bound in subhaloes than the dark matter. Finally, we show that the "stellar splashback" feature is located beyond current observational constraints on the ICL, but these large projected distances (>>1 Mpc) and low surface brightnesses (mu >> 32 mag/arcsec^2) can be reached with upcoming observational facilities such as the Vera C. Rubin Observatory, the Nancy Grace Roman Space Telescope, and Euclid.
Resource-Enhanced Neural Model for Event Argument Extraction

Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play. Despite great efforts made in prior work, there remain many challenges: (1) Data scarcity. (2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument. (3) Integrating event trigger information into candidate argument representation. For (1), we explore using unlabeled data. For (2), we use Transformer that uses dependency parses to guide the attention mechanism. For (3), we propose a trigger-aware sequence encoder with several types of trigger-dependent sequence representations. We also support argument extraction either from text annotated with gold entities or from plain text. Experiments on the English ACE 2005 benchmark show that our approach achieves a new state-of-the-art.
Learning Nonlinear Dynamics and Chaos: A Universal Framework for Knowledge-Based System Identification and Prediction

We present a universal framework for learning the behavior of dynamical systems from observations. We formulate the learning task as a constrained optimization problem which can be efficiently solved with the adjoint sensitivity method. Our scheme is flexible with regards to the choice of model, and existing knowledge can be readily incorporated for hybrid learning. We demonstrate the effectiveness of our scheme by learning a variety of systems including a stiff Van der Pol oscillator, a chaotic Lorenz system, and the Kuramoto-Sivashinsky equation. We also include examples of hybrid learning and learning from noisy observations.
Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience

Manipulating deformable objects, such as fabric, is a long standing problem in robotics, with state estimation and control posing a significant challenge for traditional methods. In this paper, we show that it is possible to learn fabric folding skills in only an hour of self-supervised real robot experience, without human supervision or simulation. Our approach relies on fully convolutional networks and the manipulation of visual inputs to exploit learned features, allowing us to create an expressive goal-conditioned pick and place policy that can be trained efficiently with real world robot data only. Folding skills are learned with only a sparse reward function and thus do not require reward function engineering, merely an image of the goal configuration. We demonstrate our method on a set of towel-folding tasks, and show that our approach is able to discover sequential folding strategies, purely from trial-and-error. We achieve state-of-the-art results without the need for demonstrations or simulation, used in prior approaches. Videos available at: this https URL
G-DAug: Generative Data Augmentation for Commonsense Reasoning

Recent advances in commonsense reasoning depend on large-scale human-annotated training sets to achieve peak performance. However, manual curation of training sets is expensive and has been shown to introduce annotation artifacts that neural models can readily exploit and overfit to. We propose a novel generative data augmentation technique, G-DAUGˆC, that aims to achieve more accurate and robust learning in a low-resource setting. Our approach generates synthetic examples using pretrained language models and selects the most informative and diverse set of examples for data augmentation. On experiments with multiple commonsense reasoning benchmarks, G-DAUGˆC consistently outperforms existing data augmentation methods based on back-translation, establishing a new state-of-the-art on WinoGrande, CODAH, and CommonsenseQA, as well as enhances out-of-distribution generalization, proving to be robust against adversaries or perturbations. Our analysis demonstrates that G-DAUGˆC produces a diverse set of fluent training examples, and that its selection and training approaches are important for performance.
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users' consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at this https URL.
Further results on the estimation of dynamic panel logit models with fixed effects

Kitazawa (2013, 2016) showed that the common parameters in the panel logit AR(1) model with strictly exogenous covariates and fixed effects are estimable at the root-n rate using the Generalized Method of Moments. Honor\'e and Weidner (2020) extended his results in various directions: they found additional moment conditions for the logit AR(1) model and also considered estimation of logit AR(p) models with p>1. In this note we prove a conjecture in their paper and show that 2^{T}-2T of their moment functions for the logit AR(1) model are linearly independent and span the set of valid moment functions, which is a 2^{T}-2T -dimensional linear subspace of the 2^{T} -dimensional vector space of real valued functions over the outcomes y element of {0,1}^{T}. We also prove that when p=2 and T element of {3,4,5}, there are, respectively, 2^{T}-4(T-1) and 2^{T}-(3T-2) linearly independent moment functions for the panel logit AR(2) models with and without covariates.
Probabilistic Case-based Reasoning in Knowledge Bases

A case-based reasoning (CBR) system solves a new problem by retrieving ‘cases’ that are similar to the given problem. If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs). Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB. Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity. The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization. Our model is non-parametric, growing dynamically as new entities and relations are added to the KB. On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches. Furthermore, we demonstrate the effectiveness of our model in an “open-world” setting where new entities arrive in an online fashion, significantly outperforming state-of-the-art approaches and nearly matching the best offline method.
Super-Human Performance in Online Low-latency Recognition of Conversational Speech

Achieving super-human performance in recognizing human speech has been a goal for several decades, as researchers have worked on increasingly challenging tasks. In the 1990's it was discovered, that conversational speech between two humans turns out to be considerably more difficult than read speech as hesitations, disfluencies, false starts and sloppy articulation complicate acoustic processing and require robust handling of acoustic, lexical and language context, jointly. Early attempts with statistical models could only reach error rates over 50% and far from human performance (WER of around 5.5%). Neural hybrid models and recent attention-based encoder-decoder models have considerably improved performance as such contexts can now be learned in an integral fashion. However, processing such contexts requires an entire utterance presentation and thus introduces unwanted delays before a recognition result can be output. In this paper, we address performance as well as latency. We present results for a system that can achieve super-human performance (at a WER of 5.0%, over the Switchboard conversational benchmark) at a word based latency of only 1 second behind a speaker's speech. The system uses multiple attention-based encoder-decoder networks integrated within a novel low latency incremental inference approach.
DiPair: Fast and Accurate Distillation for Trillion-ScaleText Matching and Pair Modeling

Pre-trained models like BERT ((Devlin et al., 2018) have dominated NLP / IR applications such as single sentence classification, text pair classification, and question answering. However, deploying these models in real systems is highly non-trivial due to their exorbitant computational costs. A common remedy to this is knowledge distillation (Hinton et al., 2015), leading to faster inference. However – as we show here – existing works are not optimized for dealing with pairs (or tuples) of texts. Consequently, they are either not scalable or demonstrate subpar performance. In this work, we propose DiPair — a novel framework for distilling fast and accurate models on text pair tasks. Coupled with an end-to-end training strategy, DiPair is both highly scalable and offers improved quality-speed tradeoffs. Empirical studies conducted on both academic and real-world e-commerce benchmarks demonstrate the efficacy of the proposed approach with speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model.