Miracle-dz/UniFAR-FTUs huggingface dataset:
Explainable and Discourse Topic-aware Neural Language Understanding

Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics. While introducing topical semantics in language models, existing approaches incorporate latent document topic proportions and ignore topical discourse in sentences of the document. This work extends the line of research by additionally introducing an explainable topic representation in language understanding, obtained from a set of key terms correspondingly for each latent topic of the proportion. Moreover, we retain sentence-topic associations along with document-topic association by modeling topical discourse for every sentence in the document. We present a novel neural composite language model that exploits both the latent and explainable topics along with topical discourse at sentence-level in a joint learning framework of topic and language models. Experiments over a range of tasks such as language modeling, word sense disambiguation, document classification, retrieval and text generation demonstrate ability of the proposed model in improving language understanding.
Physico-chemical characteristics and productivity of tomato plants in function of nitrogen sources and doses

The objective of this study was to evaluate the effect of nitrogen sources and rates on the physicochemical characteristics and yield of tomato plants. Forty hybrids were cultivated at 100 and 400 kg ha-1 of N, combined with four sources (urea, ammonium sulfate, ammonium nitrate and calcium), plus a treatment without N application in a randomized complete block design four replicates. Size, stem diameter, number of leaves, SPAD (Soil Plant Analysis Development), leaf nitrogen, number of fruits / plants, fruit firmness, bark thickness and average fruit size, ° BRIX, pH, calcium, potassium and sodium in fruits. There was an increase in SPAD index, Brix and longitudinal diameter of fruits as a function of the N dose. The use of ammonium nitrate and calcium provided stronger fruits. Urea and ammonium nitrate provided the highest pH value in tomato fruits. The application of the 100 kg ha-1 dose of N resulted in the highest potassium content in fruits. The highest productivity was obtained with the application of sources containing ammonium and the lowest in the control treatment. Sources and doses of nitrogen fertilizers influenced growth, productivity and parameters related to tomato quality.
Influence of sowing date on the uptake of and responses to soil and fertilizer nitrogen by the spring wheat cultivar Tonic

SUMMARY At 14 sites in the UK, spring wheat (Triticum aestivum) cv. Tonic, was sown on three or four dates at each site between October and March in the 1988/89, 1989/90 and 1990/91 seasons. Responses to spring-applied fertilizer N over the range 0–320 kg/ha were determined. Earlier sowing did not increase uptake of soil N by the crop. Fertilizer N increased grain N offtake by between 25 and 140 kg/ha and yield by between 0·3 and 5·5 t/ha, although grain yield was less responsive to fertilizer N at later sowing dates. Apparent recovery of fertilizer N (AFR) also decreased as sowing was delayed but there was no effect of delayed sowing on the amount of grain produced from each kg of fertilizer N recovered. Because fertilizer N recovery decreased with later sowing, the amount of fertilizer N needed to produce the optimum economic grain yield was not reduced. Neither AFR nor optimum fertilizer (Nopt) was related to optimum yield. Regression of Nopt on the difference between optimum yield and yield without fertilizer N (△y) explained 77% of the variance in Nopt. There was an inverse relationship between △y and soil mineral N (SMN) in spring; regression of △y, on SMN in spring accounted for 29% of the variance in △y Current advisory systems which adjust economic fertilizer N recommendations according to anticipated yield are not justified by these results. Moreover the adjustments made, based on yield expectation, appear about three times as large as those needed to minimize residues of fertilizer N left unrecovered by the crop and to reduce the risk of nitrate leaching in the following winter.
Do tsetse flies ‘see’ zebras? A field study of the visual response of tsetse to striped targets

Abstract A field study in Zimbabwe of Glossina pallidipes Austen and G. morsitans morsitans Westwood supported Waage's (1981) hypothesis that the striped pattern of zebras may protect them from being bitten by blood‐sucking flies. In addition, the results suggest that the orientation of the stripes may be crucially important for the unattractiveness of zebras.
In vitro characterization of bacteriocin producing Bacillus subtilis from milk samples

Bacillus subtilis was isolated from milk samples. Antibiotic resistance and the antimicrobial activity of B. subtilis were studied. All the four isolates of B. subtilis were sensitive to antibiotics such as streptomycin (25 �g/ml), ampicillin (10 �g/ml), penicillin (10 �g/ml), erythromycin (15 �g/ml), amoxycillin (10 �g/ml). But they were resistant to bacitracin (10 �g/ml). B. subtilis shown antibacterial activity against the selected human pathogens such as Salmonella spp, Streptococcus spp, Klebsiella spp and E. coli. The antimicrobial substance from B. subtilis extracted with organic solvent such as ethyl actetate have also shown antibacterial activity against the human pathogens. The proteineceaous nature of the B. subtilis exerted antimicrobial activity. The amount of protein varied between 0.05 - 0.55 mg/ml and the protein was qualitatively analyzed by SDS-PAGE. The entire samples have shown peptide < 62 kDa.
Forward Prediction for Physical Reasoning

Physical reasoning requires forward prediction: the ability to forecast what will happen next given some initial world state. We study the performance of state-of-the-art forward-prediction models in complex physical-reasoning tasks. We do so by incorporating models that operate on object or pixel-based representations of the world, into simple physical-reasoning agents. We find that forward-prediction models improve the performance of physical-reasoning agents, particularly on complex tasks that involve many objects. However, we also find that these improvements are contingent on the training tasks being similar to the test tasks, and that generalization to different tasks is more challenging. Surprisingly, we observe that forward predictors with better pixel accuracy do not necessarily lead to better physical-reasoning performance. Nevertheless, our best models set a new state-of-the-art on the PHYRE benchmark for physical reasoning.
A DISCRETE-EVENT SIMULATION MODEL FOR DRIVER PERFORMANCE ASSESSMENT: APPLICATION TO AUTONOMOUS VEHICLE COCKPIT DESIGN OPTIMIZATION

Abstract The latest advances in the design of vehicles with the adaptive level of automation pose new challenges in the vehicle-driver interaction. Safety requirements underline the need to explore optimal cockpit architectures with regard to driver cognitive and perceptual workload, eyes-off-the-road time and situation awareness. We propose to integrate existing task analysis approaches into system architecture evaluation for the early-stage design optimization. We built the discrete-event simulation tool and applied it within the multi-sensory (sight, sound, touch) cockpit design industrial project.
Identification of Practices and Capabilities in API Management: A Systematic Literature Review

Traditional organizations are increasingly becoming software producing organizations. This software is enabling them to integrate business processes between different departments and with other organizations through Application Programming Interfaces (APIs). The main task of managing APIs is to ensure that the APIs are easy to use by third parties, such as providing helpful documentation, monitoring API performance, and even monetizing API usage. The knowledge on API management is scattered across academic literature. In this document, we describe a Systematic Literature Review (SLR) that has the goal of collecting API Management practices and capabilities related to API Management, as well as proposing a comprehensive definition of the topic. In the scope of this work, a practice is defined as any practice that has the express goal to improve, encourage and manage the usage of APIs. Capabilities are defined as the ability to achieve a certain goal related to API Management, through the execution of two or more interrelated practices. We follow a standard method for SLRs in software engineering. We managed to collect 24 unique definitions for the topic, 114 practices and 39 capabilities.
Interactive effects of distance and matrix on the movements of a peatland dragonfly

We conducted a mark―release―recapture survey of a peatland dragonfly (Leucorrhinia hudsonica) in each of two years (2002; 2003) in a harvested forest landscape in western Newfoundland, Canada. The odds of an individual male moving between peatlands was influenced by both the distance between peatlands and the type of intervening habitat (the matrix). Specifically, at meso scales ( > 700 m) there was a positive effect of the amount of cut matrix between peatlands on the odds of moving, but at fine scales (<700 m) there was the opposite effect; proportionally fewer individuals moved between peatlands. The odds of moving out of a peatland decreased as the surface area of water in the peatland increased. Multi-state mark―recapture models showed that the daily probability of a male moving between any two peatlands was 1.9% in 2002 and 6.9% in 2003 (n = 1527 and 1280 marked individuals). The results suggest that additional empirical studies that directly measure patterns of movement with respect to landscape structure at multiple spatial scales in other taxa and situations are needed in order to uncover other possible non-linear changes in behavior.
STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

Taxonomies are important knowledge ontologies that underpin numerous applications on a daily basis, but many taxonomies used in practice suffer from the low coverage issue. We study the taxonomy expansion problem, which aims to expand existing taxonomies with new concept terms. We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion. To generate natural self-supervision signals, STEAM samples mini-paths from the existing taxonomy, and formulates a node attachment prediction task between anchor mini-paths and query terms. To solve the node attachment task, it learns feature representations for query-anchor pairs from multiple views and performs multi-view co-training for prediction. Extensive experiments show that STEAM outperforms state-of-the-art methods for taxonomy expansion by 11.6% in accuracy and 7.0% in mean reciprocal rank on three public benchmarks. The code and data for STEAM can be found at https://github.com/yueyu1030/STEAM.
Precise expressions for random projections: Low-rank approximation and randomized Newton

It is often desirable to reduce the dimensionality of a large dataset by projecting it onto a low-dimensional subspace. Matrix sketching has emerged as a powerful technique for performing such dimensionality reduction very efficiently. Even though there is an extensive literature on the worst-case performance of sketching, existing guarantees are typically very different from what is observed in practice. We exploit recent developments in the spectral analysis of random matrices to develop novel techniques that provide provably accurate expressions for the expected value of random projection matrices obtained via sketching. These expressions can be used to characterize the performance of dimensionality reduction in a variety of common machine learning tasks, ranging from low-rank approximation to iterative stochastic optimization. Our results apply to several popular sketching methods, including Gaussian and Rademacher sketches, and they enable precise analysis of these methods in terms of spectral properties of the data. Empirical results show that the expressions we derive reflect the practical performance of these sketching methods, down to lower-order effects and even constant factors.
Temporal Graph Networks for Deep Learning on Dynamic Graphs

Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems. Despite the plethora of different models for deep learning on graphs, few approaches have been proposed thus far for dealing with graphs that present some sort of dynamic nature (e.g. evolving features or connectivity over time). In this paper, we present Temporal Graph Networks (TGNs), a generic, efficient framework for deep learning on dynamic graphs represented as sequences of timed events. Thanks to a novel combination of memory modules and graph-based operators, TGNs are able to significantly outperform previous approaches being at the same time more computationally efficient. We furthermore show that several previous models for learning on dynamic graphs can be cast as specific instances of our framework. We perform a detailed ablation study of different components of our framework and devise the best configuration that achieves state-of-the-art performance on several transductive and inductive prediction tasks for dynamic graphs.
Eficacia del fipronil (EXP 60720A) en el control poblacional de Frankliniella occidentalis Pergande (Thysanoptera: Thripidae) en condiciones de laboratorio

En el presente trabajo se ha estudiado la eficacia del fipronil (insecticida perteneciente al grupo de los pirazoles) en el control del trips Frankliniella occidentalis en condiciones de laboratorio con temperatura 27:19° C (D:N) y fotoperiodo 16:8 h (L:O). Para ello se utilizaron discos de hoja de pimiento (Capsicum annuum) de 2,5 cm de diametro, tratados por inmersion en diferentes concentraciones de fipronil. Como insecticidas de comparacion se utilizaron el formetanato y el acrinatrin por ser los insecticidas de uso mas frecuente aplicados en Espana para el control de trips. De los resultados obtenidos se puede afirmar que a igualdad de dosis de materia activa (5 g/Hl), el fipronil resulto ser mas eficaz y en un plazo de tiempo mas corto que los insecticidas de referencia empleados, necesitandose un tiempo minimo de 13 h de exposicion para obtener un 95 % de mortalidad frente a 588 h y 10.262 h en el caso del formetanato y acrinatrin, respectivamente.
Effects of dietary β‐carotene on the immune response of rainbow trout Oncorhynchus mykiss

SUMMARY: We attempt to assess the impact of different levels of dietary β-carotene on immune function in rainbow trout. Semi-purified diets containing 0, 40, 200, and 400 mg β-carotene/kg dry diet were fed for 12 weeks to fish with average weight of 45 g. In addition to the humoral and cellular immune parameters, growth and feed utilization were examined. There were no marked differences in growth and feed utilization showing that β-carotene was not particularly efficient in enhancing growth of rainbow trout. Of the immune parameters measured, total immunoglobulin was significantly highest for the 200 mg β-carotene fed group. Serum complement activity (alternate pathway) at 200 and 400 mg β-carotene supplementation was significantly higher than that of the unsupplemented group. An increasing trend in lysozyme activity was observed, however, the differences among the groups were not significant. Phagocytic activity was similar among diet groups except at the highest level of supplementation where it was the maximum. Oxygen radical production by peripheral blood leukocytes appeared to be lower at higher levels of carotenoid supplementation. Overall, dietary β-carotene clearly enhanced immune response parameters in rainbow trout such as serum complement activity and total plasma immunoglobulin but did not show a definite influence for the other factors examined in the present study.
Record fusion: A learning approach

Record fusion is the task of aggregating multiple records that correspond to the same real-world entity in a database. We can view record fusion as a machine learning problem where the goal is to predict the "correct" value for each attribute for each entity. Given a database, we use a combination of attribute-level, recordlevel, and database-level signals to construct a feature vector for each cell (or (row, col)) of that database. We use this feature vector alongwith the ground-truth information to learn a classifier for each of the attributes of the database. Our learning algorithm uses a novel stagewise additive model. At each stage, we construct a new feature vector by combining a part of the original feature vector with features computed by the predictions from the previous stage. We then learn a softmax classifier over the new feature space. This greedy stagewise approach can be viewed as a deep model where at each stage, we are adding more complicated non-linear transformations of the original feature vector. We show that our approach fuses records with an average precision of ~98% when source information of records is available, and ~94% without source information across a diverse array of real-world datasets. We compare our approach to a comprehensive collection of data fusion and entity consolidation methods considered in the literature. We show that our approach can achieve an average precision improvement of ~20%/~45% with/without source information respectively.
Zero-Shot Learning with Common Sense Knowledge Graphs

Zero-shot learning relies on semantic class representations such as attributes or pretrained embeddings to predict classes without any labeled examples. We propose to learn class representations from common sense knowledge graphs. Common sense knowledge graphs are an untapped source of explicit high-level knowledge that requires little human effort to apply to a range of tasks. To capture the knowledge in the graph, we introduce ZSL-KG, a framework based on graph neural networks with non-linear aggregators to generate class representations. Whereas most prior work on graph neural networks uses linear functions to aggregate information from neighboring nodes, we find that non-linear aggregators such as LSTMs or transformers lead to significant improvements on zero-shot tasks. On two natural language tasks across three datasets, ZSL-KG shows an average improvement of 9.2 points of accuracy versus state-of-the-art methods. In addition, on an object classification task, ZSL-KG shows a 2.2 accuracy point improvement versus the best methods that do not require hand-engineered class representations. Finally, we find that ZSL-KG outperforms the best performing graph neural networks with linear aggregators by an average of 3.8 points of accuracy across these four datasets.
Runoff losses of cyanazine and metolachlor: effects of soil type and precipitation timing

Abstract The effects of time of precipitation and soil type on runoff losses of cyanazine and metolachlor were studied using a tilted-bed, microplot system. Two silt loam soils, Bosket and Dubbs, and a Sharkey silty clay were evaluated. Rainfall (22 mm h−1) was simulated at 0, 2, and 14 days after treatment (DAT). Time of precipitation did not impact herbicide losses or any of the runoff parameters evaluated in this study. Water runoff occurred sooner and in greater quantities from the surfaces of Bosket and Dubbs silt loam soils than from the surface of Sharkey silty clay. Runoff losses of cyanazine did not vary by soil type. Soil drying produced large cracks in Sharkey silty clay, which greatly reduced runoff in this soil. Combined runoff and leachate losses were highest from Dubbs silt loam. Runoff losses of metolachlor were not affected by soil type. However, regression analyses indicated that time of precipitation and soil type interacted to affect initial metolachlor concentration. At 14 DAT, initial metolachlor concentration was highest in runoff from Sharkey soil. Time of precipitation ranked with respect to initial metolachlor concentration in runoff from Bosket and Dubbs silt loam soils were 0 > 2 > 14 DAT and 0 = 2 > 14 DAT, respectively. Nomenclature: Cyanazine; metolachlor.
Modelling the effect of temperature on carbon mineralization rates across a network of European forest sites (FORCAST)

Temperature is a major environmental variable influencing microbial respiration in soils. Thus, understanding how heterotrophic processes in soils may respond to potential increases in temperature is crucial for the prediction of the response of forest carbon budgets to climate change. We investigated carbon mineralization rates from eight European forest soils in relation to soil temperature. Mineral soil samples were collected from eight mature forest sites in the European network CARBOEUROFLUX and were incubated in the laboratory for ca. 270 days at four temperatures: 4, 10, 20 and 30°C. In all soils, carbon mineralization rates decreased over time when incubated at high temperatures of 20 and 30°C. In this study, we explore the different models available to analyse long‐term incubation data. Carbon mineralization rates were best predicted by a first‐order, two‐compartment model that predicted carbon mineralization as a function of time and temperature using all of the incubation data. We found very small fractions (1–9%) of labile carbon in the upper mineral soils. Despite large differences among sites, we found higher carbon mineralization rates and larger amounts of labile carbon in the broadleaf than in the conifer forest soils. No significant differences in temperature sensitivity among the sites (average Q10 of 2.88 using the two‐compartment model) were observed, as estimated with all methods used. Although not statistically significant, the sensitivities of the rate constant of the labile fractions tended to be higher than those for the rate constant of the recalcitrant fractions. Thus, the results of this modelling exercise suggest that despite large variation among sites, a single temperature sensitivity parameter can be used for a range of soils over the range of temperatures we used (4–30°C).
Experimental design and the outcome and interpretation of diversity-stability relations

The nature of the relationship between diversity and stability has become the subject of intense research effort over the last few decades as the role of diversity as a major driver of ecosystem functioning and stability has come to the forefront of ecological interest. Here, we present a meta-analysis of the impact of twelve experimental design factors on the strength and direction of relations between biotic richness and temporal variability at both the aggregate community- and population-level. Based on 35 studies that report 59 community-level and 36 population-level relations, our results show that biotic richness has a highly general stabilizing effect on community properties that are only marginally affected by the nuances of experimental design. In contrast, experimental design factors have a highly significant effect on mean effect sizes and the resulting interpretation of relations between richness and population-level variability. The strongest dichotomous effect was observed based on the method of calculating the response variable, such that when population variability was calculated as the mean variability of populations across all replicates, biotic richness showed a negative (stabilizing) mean effect size. In contrast, when population variability was calculated on a per replicate basis, biotic richness showed a positive (destabilizing) mean effect size. This latter result suggests that a renewed focus on the mechanisms by which populations can be stabilized (and destabilized) by diversity is needed.