Miracle-dz/UniFAR-FTUs huggingface dataset:
FIRE EFFECTS ON RESPROUTING OF SHRUBS IN HEADWATERS OF SOUTHEASTERN LONGLEAF PINE SAVANNAS

Woody plants in fire-frequented ecosystems commonly resprout from underground organs after fires. Responses to variation in characteristics of fire regimes may be a function of plant physiological status or fire intensity. Although these hypotheses have been explored for trees in southeastern longleaf pine (Pinus palustris) savannas, responses of other life forms and stages have not been studied. We examined effects of fire season and frequency, geography, habitat, and underground organ morphology on resprouting of shrubs. In 1994, we located replicated sites, each containing two habitats, upslope savannas and downslope seepages, in Louisiana and Florida. Each site, which contained quadrats located along transects within a 30 × 60 m plot, was burned either during the dormant or growing season and then reburned similarly two years later. Maximum fire temperatures were measured, and densities of shrub stems were censused in quadrats before and after fires. Shrubs collectively resprouted more following dorma.
S wave resonance

We use an existing model of the three-body system based on two-body separable interactions to study the three-body channel. For the , , and amplitudes, we have constructed separable potentials based on the most recent results of the HAL QCD Collaboration. They are characterized by the existence of a resonance just below or above the threshold in the H-dibaryon channel, . A three-body resonance appears 2.3 MeV above the threshold. We show that if the H-dibaryon channel is not considered, the S wave resonance disappears. Thus, the possible existence of a resonance would be sensitive to the interaction. The existence or nonexistence of this resonance could be evidenced by measuring, for example, the cross section.
SCREENING APPROACHES FOR METHANE MITIGATING POTENTIAL OF TANNIN-CONTAINING PLANTS UNDER IN VITRO RUMEN ENVIRONMENT

The aim of the present study was to conduct univariate, bivariate and multivariate (principal component analysis, PCA) approaches in the screening of tannin-containing plants from various collection sites for their CH 4 mitigating properties. Plant samples were obtained from various collection sites in different countries, i.e. Indonesia (n = 27 species), Mongolia (n = 14), Switzerland (n = 16) and Germany (n = 3). The plants were incubated in vitro with buffered-rumen fluid at 39 o C for 24 h. Total gas production was recorded as an indicator of feed quality and emission of CH 4 was measured. Results showed that, based on bivariate screening, generally, plants possessed low CH 4 production had low quality or low total gas production except Rhus typhina, i.e. 43 ml/200 mg DM. The loading plot of PCA showed that all phenolic fractions were in the opposite direction with CH 4 and total gas production. Plants clustered together in reverse direction to that of CH 4 were Bergenia crassifolia root and leaf, Swietenia mahagoni, Clidemia hirta, Peltiphyllum peltatum, Acacia villosa and R. typhina. It was conluded that, for tannin-containing plants, screenings based on univariate, bivariate and multivariate approaches in relation to ruminal CH 4 emission led to similar results
Link Prediction Using Supervised Machine Learning based on Aggregated and Topological Features

Link prediction is an important task in social network analysis. There are different characteristics (features) in a social network that can be used for link prediction. In this paper, we evaluate the effectiveness of aggregated features and topological features in link prediction using supervised learning. The aggregated features, in a social network, are some aggregation functions of the attributes of the nodes. Topological features describe the topology or structure of a social network, and its underlying graph. We evaluated the effectiveness of these features by measuring the performance of different supervised machine learning methods. Specifically, we selected five well-known supervised methods including J48 decision tree, multi-layer perceptron (MLP), support vector machine (SVM), logistic regression and Naive Bayes (NB). We measured the performance of these five methods with different sets of features of the DBLP Dataset. Our results indicate that the combination of aggregated and topological features generates the best performance. For evaluation purposes, we used accuracy, area under the ROC curve (AUC) and F-Measure. Our selected features can be used for the analysis of almost any social network. This is because these features provide the important characteristics of the underlying graph of the social networks. The significance of our work is that the selected features can be very effective in the analysis of big social networks. In such networks we usually deal with big data sets, with millions or billions of instances. Using fewer, but more effective, features can help us for the analysis of big social networks.
Lachesis: Automated Generation of Persistent Partitionings for Big Data Applications

Persistent partitioning is effective in improving the performance by avoiding the expensive shuffling operation, while incurring relatively small overhead. However it remains a significant challenge to automate this process for Big Data analytics workloads that extensively use user defined functions. That is because user defined functions coded with an object-oriented language such as Python, Scala, Java, can contain arbitrary code that is opaque to the system and makes it hard to extract and reuse sub-computations for optimizing data placement. In addition, it is also challenging to predict the future workloads that may utilize the partitionings. We propose the Lachesis system, which allows UDFs to be decomposed into analyzable and reusable sub-computations and relies on a deep reinforcement learning model that infers which sub-computations should be used to partition the underlying data. This analysis is then used to automatically optimize the storage of the data across applications.
Parâmetros genéticos para as produções de leite no dia do controle e da primeira lactação de vacas da raça Holandesa

Covariance components for test day records and lactation milk yield using 263.390 records of 32.448 first lactation Holstein cows, were estimated using animal models by REML. Besides the lactation model, two alternative repeatability models (RM) were analyzed. Lactation model included fixed effects of herd-year-season and age of cow with linear and quadratic terms, and random effects of animal and error. The first model for test-day yield (RMF) included the same effects, but fixed effect of contemporary group, defined as herd-year-month of test. Alternatively another model for test-day yield (RMF) used a logarithmic polynomial sub-model for the shape of the lactation curve. Heritability for lactation yield (0.27) was smaller than those estimated by RMF and RMS, 0.30 and 0.43, respectively. Heritability estimates for univariate (0.22-0.36) and bivariate models (0.23-0.33) for test day milk yields were found to be smallest during early and late lactation. Heritability estimate for lactation milk yield when estimated by univariate model (0.27) was smaller than estimates obtained by bivariate models (0.27-0.30). Genetic correlations were higher between consecutive test days than between test days in the beginning and end of lactation. Larger heritability estimates for test day models and large genetic correlations between test day and lactation yield (0.86-0.99) indicate a potential use of test day records in genetic evaluations.
Hierarchical Graph Matching Network for Graph Similarity Computation

Graph edit distance / similarity is widely used in many tasks, such as graph similarity search, binary function analysis, and graph clustering. However, computing the exact graph edit distance (GED) or maximum common subgraph (MCS) between two graphs is known to be NP-hard. In this paper, we propose the hierarchical graph matching network (HGMN), which learns to compute graph similarity from data. HGMN is motivated by the observation that two similar graphs should also be similar when they are compressed into more compact graphs. HGMN utilizes multiple stages of hierarchical clustering to organize a graph into successively more compact graphs. At each stage, the earth mover distance (EMD) is adopted to obtain a one-to-one mapping between the nodes in two graphs (on which graph similarity is to be computed), and a correlation matrix is also derived from the embeddings of the nodes in the two graphs. The correlation matrices from all stages are used as input for a convolutional neural network (CNN), which is trained to predict graph similarity by minimizing the mean squared error (MSE). Experimental evaluation on 4 datasets in different domains and 4 performance metrics shows that HGMN consistently outperforms existing baselines in the accuracy of graph similarity approximation.
How to Document Computer Networks

In computer science, models are made explicit to provide formality and a precise understanding of small, contingent “universes” (e.g., an organization), as constructed from stakeholder requirements. Conceptual modeling is a fundamental discipline in this context whose main concerns are identifying, analyzing and describing the critical concepts of a universe of discourse. In the information systems field, one of the reasons why projects fail is an inability to capture requirements in a way that can be technically used to configure a system. This problem of requirements specification is considered to have “deficiencies in theory”. We apply a recently developed model called the Thinging Machine (TM) model which uniformly integrates static and dynamic modeling features to this problem of requirements specification. The object-Oriented (OO) approach to modeling, as applied in Unified Modeling Language, is by far the most applied and accepted standard in software engineering; nevertheless, new notions in the field may enhance and facilitate a supplementary understanding of the OO model itself. We aim to contribute to the field of conceptual modeling by introducing the TM model’s philosophical foundation of requirements analysis. The TM model has only five generic processes of things (e.g., objects), in which genericity indicates generality, as in the generic Aristotelian concepts based on abstraction. We show the TM model’s viability by applying it to a real business system.
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of scaling is affirmed to be a sure-fire approach for better model quality, there are challenges on the path such as the computation cost, ease of programming, and efficient implementation on parallel devices. GShard is a module composed of a set of lightweight annotation APIs and an extension to the XLA compiler. It provides an elegant way to express a wide range of parallel computation patterns with minimal changes to the existing model code. GShard enabled us to scale up multilingual neural machine translation Transformer model with Sparsely-Gated Mixture-of-Experts beyond 600 billion parameters using automatic sharding. We demonstrate that such a giant model can efficiently be trained on 2048 TPU v3 accelerators in 4 days to achieve far superior quality for translation from 100 languages to English compared to the prior art.
Learning to Format Coq Code Using Language Models

Should the final right bracket in a record declaration be on a separate line? Should arguments to the rewrite tactic be separated by a single space? Coq code tends to be written in distinct manners by different people and teams. The expressiveness, flexibility, and extensibility of Coq's languages and notations means that Coq projects have a wide variety of recognizable coding styles, sometimes explicitly documented as conventions on naming and formatting. In particular, even inexperienced users can distinguish vernacular using the standard library and plain Ltac from idiomatic vernacular using the Mathematical Components (MathComp) library and SSReflect. While coding conventions are important for comprehension and maintenance, they are costly to document and enforce. Rule-based formatters, such as Coq's beautifier, have limited flexibility and only capture small fractions of desired conventions in large verification projects. We believe that application of language models - a class of Natural Language Processing (NLP) techniques for capturing regularities in corpora - can provide a solution to this conundrum. More specifically, we believe that an approach based on automatically learning conventions from existing Coq code, and then suggesting idiomatic code to users in the proper context, can be superior to manual approaches and static analysis tools - both in terms of effort and results. As a first step, we here outline initial models to learn and suggest space formatting in Coq files, with a preliminary implementation for Coq 8.10, and evaluated on a corpus based on MathComp 1.9. 0 which comprises 164k lines of Coq code from four core projects.
Evolutionary Processes in Quantum Decision Theory

The review presents the basics of quantum decision theory, with an emphasis on temporary processes in decision making. The aim is to explain the principal points of the theory. How an operationally-testable, rational choice between alternatives differs from a choice decorated by irrational feelings is elucidated. Quantum-classical correspondence is emphasized. A model of quantum intelligence network is described. Dynamic inconsistencies are shown to be resolved in the frame of the quantum decision theory.
Cyclical Electromechanical Error Denial System Using Matrix Profile

We propose that the Matrix Profile data structure, conventionally applied to large scale time-series data mining, is applicable to the analysis and suppression of cyclical error in electromechanical systems, paving the way for an intelligent family of adaptable control systems which respond to environmental error at a computational cost low enough to be practical in embedded applications. We construct and evaluate the efficacy of a control algorithm utilizing the Matrix Profile, which we call Cyclical Electromechanical Error Denial System (CEEDS).
FT-Raman spectroscopy of the Christmas wreath lichen, Cryptothecia rubrocincta (Ehrenb.:Fr.) Thor

FT-Raman spectra have been obtained from the highly pigmented lichen Cryptothecia rubrocincta from a Brazilian vestigial rainforest habitat. Spectral signatures of the two main lichen substances, chiodectonic acid and confluentic acid, were identified in adjacent zones of the thallus. Of the characteristic zonal colours displayed by the thallus, the outer red zone contained chiodectonic acid and no calcium oxalate, and graded into by a pink zone with calcium oxalate monohydrate (whewellite) in association with chiodectonic acid, to the inside of which is a concentric white zone containing calcium oxalate dihydrate (weddellite); however, chemically differentiated sites (elliptical brown flecks with a major axis of c. 15 μm) in both the pink and red zones contained chiodectonic acid and calcium oxalate monohydrate. The role of Raman spectroscopy in the spatial identification of lichen substances in the thallial structures is demonstrated.
Quantitative Evaluation of Endoscopic SLAM Methods: EndoSLAM Dataset

Deep learning techniques hold promise to improve dense topography reconstruction and pose estimation, as well as simultaneous localization and mapping (SLAM). However, currently available datasets do not support effective quantitative benchmarking. With this paper, we introduce a comprehensive endoscopic SLAM dataset containing both capsule and standard endoscopy recordings. A Panda robotic arm, two different commercially available high precision 3D scanners, two different commercially available capsule endoscopes with different camera properties and two different conventional endoscopy cameras were employed to collect data from eight ex-vivo porcine gastrointestinal (GI)-tract organs. In total, 35 sub-datasets are provided: 18 sub-datasets for colon, 12 sub-datasets for stomach and five sub-datasets for small intestine, while four of these contain polyp-mimicking elevations carried out by an expert gastroenterologist. To exemplify the use-case, SC-SfMLearner was comprehensively benchmarked. The codes and the link for the dataset are publicly available at this https URL. A video demonstrating the experimental setup and procedure is available at this https URL.
ITSELF: Iterative Saliency Estimation fLexible Framework

Saliency object detection estimates the objects that most stand out in an image. The available unsupervised saliency estimators rely on a pre-determined set of assumptions of how humans perceive saliency to create discriminating features. By fixing the pre-selected assumptions as an integral part of their models, these methods cannot be easily extended for specific settings and different image domains. We then propose a superpixel-based ITerative Saliency Estimation fLexible Framework (ITSELF) that allows any user-defined assumptions to be added to the model when required. Thanks to recent advancements in superpixel segmentation algorithms, saliency-maps can be used to improve superpixel delineation. By combining a saliency-based superpixel algorithm to a superpixel-based saliency estimator, we propose a novel saliency/superpixel self-improving loop to iteratively enhance saliency maps. We compare ITSELF to two state-of-the-art saliency estimators on five metrics and six datasets, four of which are composed of natural-images, and two of biomedical-images. Experiments show that our approach is more robust than the compared methods, presenting competitive results on natural-image datasets and outperforming them on biomedical-image datasets.
On pathwise uniqueness for multidimensional McKean--Vlasov equations

Pathwise uniqueness for multi-dimensional stochastic McKean--Vlasov equation is established under moderate regularity conditions on the drift and diffusion coefficients. Both drift and diffusion depend on the marginal measure of the solution. For pathwise uniqueness, the drift is assumed to be Dini-continuous in the state variable, while the diffusion must be Lipschitz, continuous in time and uniformly nondegenerate. The setting is classical McKean--Vlasov, that is, coefficients of the equation are represented as integrals over the marginal distributions of the process.
Vine Water Relations, Gas Exchange, and Vegetative Growth of Seventeen Vitis Species Grown under Irrigated and Nonirrigated Conditions in California

ADDITIONAL INDEX WORDS. grapevines, water potential, photosynthesis, stomatal conductance, drought tolerance ABSTRACT. A comparison was made among 16 native North American Vitis species and Vitis vinifera L. ('Carignane') grown in the San Joaquin Valley of California with or without irrigation over 2 years. Predawn water potential (ΨPD), predawn leaf osmotic potential (Ψπ), midday leaf (Ψl), and stem water potential (Ψstem), stomatal conductance (gs), net CO2 assimilation rate (A), and intrinsic water use efficiency (WUE) were measured on five dates during the growing season the first year of the study and pruning weights were evaluated both years. Net gas exchange and water potential components taken on the last measurement date in 1992 and pruning weights of the nonirrigated species were less (or more negative for Ψ components) than those of the irrigated vines. The 17 Vitis species were ranked according to their relative drought tolerance based upon their performance without irrigation and when compared to their irrigated cohort. The Vitis species considered most drought tolerant were V. californica, V. champinii, V. doaniana, V. longii, V. girdiana, and V. arizonica. Those six species generally had high values of A, gs, and pruning weights and more favorable vine water status at the end of the study than the other species when grown without irrigation. The drought-induced reductions in the measured parameters also were less for those species when compared to their irrigated cohorts. The least drought tolerant species were, V. berlandieri, V. cinerea, V. lincecumii, V. riparia, and V. solonis. The drought-tolerant rankings were generally associated with the species' native habitat and probable soil water availability. sensitivity to changes in air humidity than did 'Shiraz' or 'Merlot' grapevines (Winkel and Rambal, 1990). There has been relatively little work done on the effects of soil water deficits on other Vitis species. The objective of this study was to rank the relative drought tolerance of 17 Vitis species under irrigated and nonirrigated conditions in the San Joaquin Valley of California. This was accomplished by measuring leaf water rela- tions, gas exchange and vine growth and then comparing each species within the nonirrigated portion of the vineyard with one another and with their irrigated cohort. In addition to several North American species, which are used either as commercial rootstocks or parents of other grape rootstocks, this study included several Vitis species indigenous to the arid southwestern United States. It was expected that the diverse, native habitats of the 17 species would have selected for a wide range drought tolerance characteristics that may be of use in future rootstock breeding programs.
Experimental Measurement of Relative Path Probabilities and Stochastic Actions

For diffusive stochastic dynamics, the probability to observe any individual trajectory is vanishingly small, making it unclear how to experimentally validate theoretical results for ratios of path probabilities. We provide the missing link between theory and experiment, by establishing a protocol to extract ratios of path probabilities from measured time series without fitting a model. For experiments on a single colloidal particle in a microchannel, we extract both ratios of path probabilities, and the most probable path for a barrier crossing. While we find excellent agreement with independently calculated predictions based on the Onsager-Machlup stochastic action, our experimental results are inconsistent with the Freidlin-Wentzell stochastic action. The direct experimental measurement of relative path probabilities presented here paves the way for experimental investigation of any theoretical result related to individual stochastic trajectories.
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data

We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean values, allowing the exploration of the manifold of possible reconstructed data and hence characterising the underlying uncertainty. In this setting, direct application of classical variational methods often gives rise to collapsed densities that do not adequately explore the solution space. Instead, we derive our novel reduced entropy condition approximate inference method that results in rich posteriors. We test our model in a data recovery task under the common setting of missing values and noise, demonstrating superior performance to existing variational methods for imputation and de-noising with different real data sets. We further show higher classification accuracy after imputation, proving the advantage of propagating uncertainty to downstream tasks with our model.