Miracle-dz/UniFAR-FTUs huggingface dataset:
Laboratory and field assessments of lethal and sublethal toxicities of acetogenin-based bioinsecticides against Zaprionus indianus (Diptera: Drosophilidae)

Neotropical Annonaceae-derived bioinsecticides have shown promising control of insect pest species. In order to develop an alternative tool for control Zaprionus indianus (Gupta, 1970), an important invasive pest of Brazilian orchards, this study investigated the insecticidal activity of a formulation prepared from ethanolic seed extract of different Annona species (Annona mucosa Jacq., Annona muricata L., and Annona sylvatica A. St.-Hil.) at discriminatory concentration of 2000 mg L-1 in comparison with a limonoid-based bioinsecticide and a synthetic insecticide (spinetoram) as positive controls. In an ingestion and topical application bioassay, the formulation based on A. mucosa caused more than 95% mortality, which was equivalent to the mortality caused by the synthetic insecticide spinetoram. In a free-choice bioassay, A. mucosa (83%), bioinsecticide (88%) and synthetic insecticide (98%) produced a significant reduction in the number of eggs per fruit. In addition, formulations based on A. mucosa, A. sylvatica, A. muricata and bioinsecticide resulted in a 50% reduction in egg viability. In the field, A. mucosa and bioinsecticide negatively influenced the infestation by Z. indianus in figs, producing a significant reduction in the number of larvae per fruit (3.6 and 2.29 larvae per fruit, respectively) in relation to the control treatment (11 larvae per fruit). However, they were significantly inferior to the synthetic insecticide (0.71 larvae per fruit). Thus, A. mucosa, bioinsecticide and spinetoram have the potential to be used in the framework of Z. indianus management.
Uncertain rationality, depth of reasoning and robustness in games with incomplete information

Predictions under common knowledge of payoffs may differ from those under arbitrarily, but finitely, many orders of mutual knowledge; Rubinstein’s (1989) Email game is a seminal example. Weinstein and Yildiz (2007) showed that the discontinuity in the example generalizes: for all types with multiple rationalizable (ICR) actions, there exist similar types with unique rationalizable action. This paper studies how a wide class of departures from common belief in rationality impact Weinstein and Yildiz’s discontinuity. We weaken ICR to ICRλ, where λ is a sequence whose term λn is the probability players attach to (n− 1)th-order belief in rationality. We find that Weinstein and Yildiz’s discontinuity remains when λn is above an appropriate threshold for all n, but fails whenλn converges to 0. That is, if players’ confidence in mutual rationality persists at high orders, the discontinuity persists, but if confidence vanishes at high orders, the discontinuity vanishes.
Towards a robot-enabled, Neuroscience-guided healthy society

In this work, we propose a biologically inspired framework for developing object permanence in robots. In particular, we build upon a previous work on a slowness principle-based visual model (Wiskott and Sejnowski, 2002), which was shown to be adept at tracking salient changes in the environment, while seamlessly “understanding” external causes, and self-emerging structures that resemble the human visual system. We propose an extension to this architecture with a prefrontal cortex-inspired recurrent loop that enables a simple short term memory, allowing the previously reactive system to retain information through time. We argue that object permanence in humans develop in a similar manner, that is, on top a previously matured object concept. Furthermore, we show that the resulting system displays the very behaviors which are thought to be cornerstones of object permanence understanding in humans. Specifically, the system is able to retain knowledge of a hidden object’s velocity, as well as identity, through (finite) occluded periods.
A COMPARATIVE STUDY OF EFFICIENCY IN CENTRAL AND EASTERN EUROPEAN BANKING SYSTEMS

The European integration process has had a great impact on the macroeconomic environment of the new member states, some effects being more visible than other. In this context it becomes interesting to analyse if the EU ascension has triggered an increase of the banks efficiency from a series of CEE countries (two from the first wave and one from the second wave of EU expansion). The analysed period is between 2003 and 2010, covering the first years after the EU ascension of these countries. In order to estimate the efficiency of the banks from our sample we have used a non-parametric technique, namely the Data Envelopment Analysis. The overall results are suggesting that there is a slight increase of the banks operating in these countries estimated efficiency between 2007 and 2010.
Computational Efficiency via Bitstreams

On behalf of the MADLab AFRL University Center of Excellence (UCoE) team for Efficient and Robust Machine Learning (ERML) and the Air Force Research Laboratory center’s team, we would like to share the center’s first quarterly newsletter. The goal of the newsletter is to provide a venue for showcasing research accomplishments, team members and projects, as well as share information about upcoming events with the academic and Air Force machine learning communities.
A Baseline Fine-Grained Entity Extraction System for TAC-KBP2019

For ﬁne-grained entity extraction, we propose a ﬁne-grained entity typing model with a novel attention mechanism and a hybrid type classi-ﬁer. We advance existing methods in two aspects: feature extraction and type prediction. To capture richer contextual information, we adopt contextualized word representations instead of ﬁxed word embeddings used in previous work. In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts. We also develop a hybrid classiﬁcation method beyond binary relevance to exploit type interdependency with latent type representation. Instead of independently predicting each type, we predict a low-dimensional vector that encodes latent type features and reconstruct the type vector from this latent representation.
The influence of prompts on final year medical students' learning process and achievement in ECG interpretation

Objective: ECG interpretation is prone to errors that can lead to relevant misdiagnoses and incorrect treatment. Prompts are one way in lectures to encourage learning from one’s own mistakes and to reduce error rates. Prompts are measures such as questions, hints, and suggestions of content-related or metacognitive nature, which can lead to self-explanation in the learner and thus to a deeper understanding of an issue. The aim of the study was therefore to investigate whether the use of prompts can reduce the error rate in ECG interpretation among students. Method: In a 2x2 experimental test and control group design, N=100 final year medical students carried out ECG interpretation tasks in the form of online case vignettes in CASUS®. In these tasks, justification prompts (B) and error analysis prompts (F) were systematically varied in four groups and the learning success was measured using a knowledge test. In addition, prior knowledge in ECG interpretation, motivation, interest in the topic, subjective confidence in ECG interpretation, and cognitive load was collected. Results: Neither error analysis prompts nor justification prompts had a significant effect on the correct ECG interpretation by students, F(1,96)=1.03, p=. 31. Justification prompts seemed to have a positive effect on the confidence of answering the questions, F(1,96)=10.15, p=. 002, partial η2=. 10; and a negative effect on student motivation, F(1,96)=8.13 , p=. 005, partial η2=. 08; but both with comparable diagnostic accuracy. Conclusion: The present study could not confirm the positive effects of prompts on the error rate in ECG interpretation reported in the literature but showed significant effects on subjective confidence and motivation which should be investigated in further studies.
Problems of the Lightweight Implementation of Probabilistic Programming

We identify two problems and an open research question with Wingate et al. lightweight implementation technique for probabilistic programming. Simple examples demonstrate that common, what should be semantic-preserving program transformations drastically alter the program behavior. We briefly describe an alternative technique that does respect program refactoring. There remains a question of how to be really, formally sure that the MCMC acceptance ratio is computed correctly, especially for models with conditioning and branching.
Robust Design Using Particle Swarm and Genetic Algorithm Optimization

1. Abstract The paper makes use of design points evaluated during a conventional genetic algorithm or particle swarm optimization to perform robust optimization that accounts for uncertainty in the design variables. The design points obtained from the two algorithms are used to construct response surface approximations that provide an estimate of the sensitivity to variation in the design variables. As an example, the robust optimization of a composite laminate is considered. The proposed approach allows the inclusion of the effects of uncertainty at little or no additional computational cost. The paper shows that the response surface approximations can be easily updated without keeping track of the entire history of the population. In addition a simple fading procedure is proposed that can effectively emphasize more recent information when constructing the approximation. The paper includes results from both a genetic algorithm and a particle swarm optimization algorithm and a comparison between the two algorithms.
A Statistical Framework for Modern Network Science

We discuss how sampling design, units, the observation mechanism, and other basic statistical notions figure into modern network data analysis. These considerations pose several new challenges that cannot be adequately addressed by merely extending or generalizing classical methods. Such challenges stem from fundamental differences between the domains in which network data emerge and those for which classical tools were developed. By revisiting these basic statistical considerations, we suggest a framework in which to develop theory and methods for network analysis in a way that accounts for both conceptual and practical challenges of network science. We then discuss how some well known model classes fit within this framework.
Visual Tools for Debugging Neural Language Models

While neural language models are powerful, they need just the right configuration of hyperparameters and a large amount of training data to perform well. When a model under development or training fails to give expected output, it is difficult for the user to debug, as the model is often a black box to the user. In this work, we discuss a set of visual tools designed to support the debugging process of neural language models. These are implemented in our LAnguage Model Visual Inspector (LAMVI) system, an interactive visual environment for exploring and debugging word embedding models.
A MOBILE DEVICE APPLICATION FOR RFID-BASED SECURITY SYSTEMS

The aim of this paper is to present an application allowing PDA mobile devices to be easily integrated into RFID-based security systems. The main goal of the application was to make the PDA devices capable of reading and writing RFID tags attached to different products or persons. Another important issue we focused upon was the communication between the PDA devices and the central security system that is located at the PC level. The communication component allows fast bi-directional data transfer. It is estimated that the PDA devices will considerably improve the overall quality of an RFID-based security system, reducing the time and increasing the speed of reading and writing operations.
STANDARDIZED CATCH RATES FOR SKIPJACK TUNA CAUGHT BY FRENCH AND SPAIN PURSE SEINE IN THE EAST ATLANTIC BETWEEN 10 N AND 10 S

In this study standardized catch rates of skipjack caught by French and Spanish purse-seines were calculated using a delta-lognormal generalized linear model for 1991-2012. Task II catch and effort data by month, 1ox1o square, fleet and fishing mode (FAD and free school) were used in the analysis. The explanatory variables year, month, area, fleet (French or Spain) and the type of fishery (on free school or on FAD) proved to be important to explain the variability of the catch per unit effort. Months where closures occurred (Nov-Feb) were removed from the entire time series for the final model, though results were not substantively different when these months were included. Overall variability of the standardized index was relatively low. There was a slight decreasing trend until 1997, followed by an increase until 2005, and then a slight decrease in 2012. If we rely in these indices, the conclusion would be that the biomass of skipjack has not shown variability but no long-term trend.
Photogrammetry for Creating High Fidelity 3-D Models of Geological and Meteorite Samples

Introduction: Photogrammetry uses twodimensional images to determine accurate information about the surface of an object [1]. Using a suite of images with overlapping features, it is possible to generate a three-dimensional model of the object [1]. This approach to research can provide insight about subjects of interest ranging in size from hand specimens to fullscale outcrops in the field. It has been popular among archaeological studies [2], and has also been used for paleontological [3] and outcrop mapping studies [4]. We aim to use photogrammetric methods to create high-fidelity three-dimensional models of meteorite samples collected by the UK-led Lost Meteorites of Antarctica project [5,6]. We will be using the technique as part of our characterization and classification of the recovered meteorites, providing a detailed record of the samples for curation and for determining sample volumes and densities. Sample volumes will be useful for preliminary classification with a combined electrical conductivity, magnetic susceptibility probe [7]. We aim to understand how these properties vary with sample volume and mass. Sample densities are useful for a variety of reasons, for example to better understand gravimetric data from orbiters [8,9], and to understand material properties of planetary bodies [10]. Sample density is difficult to calculate via non-destructive techniques, so being able to calculate sample volume without even touching the sample is highly beneficial. Most geological studies involve splitting rock samples and performing a range of destructive or semidestructive analyses. Once completed, it is no longer possible to view the sample as a whole. Through the production of detailed, true-colour, three-dimensional sample models, we preserve an accurate record of the pristine sample (Fig. 1). This is especially important when the sample is small or rare such as meteorites and extraterrestrial samples returned by missions. Such models, which can be viewed at multiple scales, may be used to help inform decision-making about sample cutting for preliminary analysis or for understanding the spatial relationship between later scientific discoveries. This technique only subjects the sample to visible light, meaning that there is no modification of the surface, a goal which has also been achieved using three-dimensional laser scanning [8]. Three-dimensional models of rare samples are a powerful tool for teaching and outreach [11,12]. Integration with other digital records, such as thin section photos or computed tomography datasets would be a useful way for students or the public to engage with the samples and sample science in a way which is limited if valuable samples cannot be on display.
Resource centered RDF data management

Since the first publication of RDF researchers developed systems to store and to query RDF data efficiently on secondary storage. First, they mapped the RDF data model to the relational one. As relational databases performed poorly for generic RDF graphs researches focused on native repositories using B-trees to index triples in most cases. However, native repositories still calculate the result of a query by joining single triples. Because queries can be decomposed into star-like graph patterns we argue that managing RDF data as a set of subgraphs has advantages over existing approaches. Thus, we propose a storage model that manages an RDF graph as a collection of subgraphs and evaluates queries by joining the results of star-like subqueries. In cause of the results of some preliminary tests we are confident that a system based on our approach can efficiently process queries.
Boundary switch on/off control approach to simultaneous identification of diffusion coefficient and initial state for one-dimensional heat equation

In this paper, we consider simultaneous reconstruction of diffusion coefficient and initial state for a one-dimensional heat equation through boundary control and measurement. The boundary measurement is proposed to make the system approximately observable, and both the coefficient and initial state are shown to be identifiable by this measurement under a boundary switch on/off control. By a Dirichlet series representation for the observation, we can transform the problem into an inverse process of reconstruction of the spectrum and coefficients for the Dirichlet series in terms of observation. This happens to be the reconstruction of spectral data for an exponential sequence with measurement error, and it enables us to develop an algorithm based on the matrix pencil method in signal analysis. A theoretical error analysis for the algorithm concerning the coefficient reconstruction is carried out for the proposed method. The numerical simulations are presented to verify the proposed algorithm.
Information Technology and Government Decentralization: Experimental Evidence from Paraguay

Standard models of hierarchy assume that agents and middle managers are better informed than principals. We estimate the value of the informational advantage held by supervisors—middle managers—when ministerial leadership—the principal—introduced a new monitoring technology aimed at improving the performance of agricultural extension agents (AEAs) in rural Paraguay. Our approach employs a novel experimental design that elicited treatment‐priority rankings from supervisors before randomization of treatment. We find that supervisors have valuable information—they prioritize AEAs who would be more responsive to the monitoring treatment. We develop a model of monitoring under different scales of treatment roll‐out and different treatment allocation rules. We semiparametrically estimate marginal treatment effects (MTEs) to demonstrate that the value of information and the benefits to decentralizing treatment decisions depend crucially on the sophistication of the principal and on the scale of roll‐out.
The Effect of Annealing Temperature on Structural & Optical Properties of Nanocrystalline SnO2 Thin Films Prepared by Sol-Gel Technique

: In this work, studying the structural and optical Nano crystalline SnO 2 thin films grown on cleaned glass substrates by using sol- gel (dip coating) technique. It is worthy to say that the thickness of the deposited film was of the order of (300-400)nm . The films are annealed in air at , 300 ◦ C, 400 ◦ C and 500 ◦ C temperatures for 60 minutes. The films that are analyses by X-ray diffraction (XRD), Scanning electron microscopy (SEM) , atomic force microscopy and optical absorption spectroscopy technique. The size of crystalline was observed, as well as, so as to increase with increasing annealing temperature . XRD analysis reveals that the whole films are polycrystalline with tetragonal structure with preferred orientation of (110),(101),(200) and (211) . The increase of annealing temperature leads to raise the diffraction peaks and decrease of FWHM. The atomic force microscopy (AFM) and Scanning electron microscopy (SEM) results showed that the average grain size was increase with the increase in annealing temperature. Spectra of transmittance and absorbance was recorded at wavelengths range (300-1000)nm . The optical properties showed high transmission at visible regions. The optical band gap energy was found to be (3.5 , 3.75 , 3,87) eV at annealing temperature (300,400,500 )°C respectively.
How working memory capacity and shifting matter for learning with worked examples—A replication study.

A previous study found that task shifting and fluid intelligence, but not working memory capacity (WMC) and prior knowledge, influenced the worked example effect (Schwaighofer, Buhner, & Fischer, 2016). To increase confidence in these findings, we report a preregistered extended replication study of Schwaighofer et al.’s investigation. University students (N = 231, Mage = 22.40 [SD = 4.33], 87% women) solved statistical problems with textbook materials presented on a laptop in one of four conditions in a 2 × 2 factorial between-subjects design. We compared worked examples versus problem-solving (replication) and with versus without time pressure (extension). Time pressure was added to test whether learners in the original study were able to offload WMC demands, which would explain why the WMC moderation was not found. Results showed that the advantage of worked examples over problem-solving decreased with increasing prior knowledge, suggesting that problem-solving becomes eventually more effective than worked example study. Similarly, the benefit of worked examples over problem-solving decreased with increasing shifting ability of a learner. However, contingencies on WMC or fluid intelligence were not detected. Our extension analysis indicated that the worked example effect was also not contingent on WMC even when learners were under time pressure. These findings underline the important role that task shifting might play in scaffolded learning environments and suggest that trading in the focus on WMC for a broader perspective on cognitive architecture provides novel explanations for instructional effectiveness. Our study further highlights the importance of more customized instructional support. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Soil microorganisms and enzyme activity of grassland ecosystem affected by changes in precipitation pattern and increase in nitrogen deposition–a review

Changes in precipitation pattern and increase in regional nitrogen deposition have turned to 1482 微生物学通报 Microbiol. China 2017, Vol.44, No.6 Tel: 010-64807511; E-mail: tongbao@im.ac.cn; http://journals.im.ac.cn/wswxtbcn be two hot issues in the study on global change in recent years. As grassland ecosystems are widely distributed over the globe and mostly in the ecological fragile zones, they are very sensitive and susceptible to the interference of global change and then lose balance. Soil microorganisms and soil enzyme activity are sensitive indicators of soil quality in natural ecosystems, They can be used to monitor changes of grassland ecosystems in structure and function under the grand background of changes in water and nitrogen. Based on this, a review is presented of progresses of the study on impacts of changes in precipitation pattern and increase in nitrogen deposition, and their interaction on number, biomass and diversity of soil microbes and enzyme activity, so as to provide a scientific basis for better prediction, evaluation and eventual control and maintenance of the stability of grassland ecosystems. At the same time, problems and shortcomings existing in the current work are analyzed and key scientific issues in future research are also discussed and previewed.