Miracle-dz/UniFAR-FTUs huggingface dataset:
The Intersection of Independent Lies: Increasing Realism in Ecological Risk Assessment

ABSTRACT In 1966, Levins presented a philosophical discussion on making inference about populations using clusters of models. In this article we provide an overview of model inference in ecological risk assessment, discuss the benefits and trade-offs of increasing model realism, show the similarities and differences between Levins' model clusters and those used in ecological risk assessment, and present how risk assessment models can incorporate Levins' ideas of truth through independent lies. Two aspects of Levins' philosophy are directly relevant to risk assessment. First, confidence in our interpretation of risk is increased when multiple risk assessments yield similar qualitative results. Second, model clusters should be evaluated to determine if they maximize precision, generality, or realism or a mix of the three. In the later case, the evaluation of each model will differ depending on whether it is more general, precise, or realistic relative to the other models used. We conclude that risk assessments can be strengthened using Levins' idea, but that Levins' caution that model outcome should not be mistaken for truth is still applicable.
Substitutions in Spike and Nucleocapsid proteins of SARS-CoV-2 circulating in Colombia

Introduction: SARS-CoV-2 is a new member of the genus Betacoronavirus, responsible for the COVID-19 pandemics. The virus crossed the species barrier and established in the human population due to its ability to exploit the ACE receptor for virus entry, which is present and abundant in several tissues, including the lung and respiratory tract, gastrointestinal tract and hearth. Virus interaction with the cellular receptor is mediated by the surface protein, known as Spike. Another structural protein of major importance in the Nucleocapsid, directly interacting with the viral RNA to form the ribonucleocapsid, considered a multifunctional protein, and being the target of the most molecular diagnostics assays. Objective: To describe the frequency of substitutions in spike and nucleocapsid proteins of SARS-CoV-2 circulating in Colombia and evaluate the frequency of these substitutions in SARS-CoV-2 sequences from other countries of South America. Materials and methods: Samples of 43 patients were included for viral RNA detection by real-time RT-PCR using the Charite-Berlin protocol for the amplification of the SARS-CoV-2 E and RdRp genes. Genome sequences were obtained through the Oxford Nanopore and Illumina MiSeq technologies, following the artic.network "nCoV-2019 sequencing protocol". Available genomes were consulted from GISAID, GenBank, and Genome sequence archive (GSA) and a total of 371 genomes sequences from South America were included. The genome sequences were aligned with the Muscle tool using the MEGA X software. Substitution matrices of the Colombian sequences respect to the reference genome (NC_045512) at the nucleotide and amino acid levels were generated for the spike and nucleocapsid gene. Results: substitution D614G in the amino acid sequence of spike protein was found in 86.7% of the Colombian sequences; substitutions G181V and D936Y in 2.3%, respectively. Five substitutions were found in the nucleocapsid protein, with substitution R203K and G204R being the most frequent (13.95 %) in Colombia. The substitutions D614G in Spike and R203K-G204R in nucleocapsid have a frequency of 83% and 28% respectively in sequences from South America. Conclusion: Non-synonymous substitutions were found in the spike and nucleocapsid proteins in Colombian genomes, the most frequent being D614G in Spike and R203K-G204R in nucleocapsid. These substitutions are frequent in the genomes reported for other South American countries. It is necessary to continue with genomic surveillance of the changes in Spike and Nucleocapsid proteins during the SARS-CoV-2 pandemic in Colombia and South America, even more considering that these proteins are the most commonly used antigen in serological tests.
The abundance and reproductive activity of common Carabidae in a winter wheat crop

Abstract. 1. The number of common carabid beetles trapped‐irr a wheat field on a clay with flint soil from May to October in.1971–77 differed greatly: Harpalus rufipes and Pterostichus melanarius were numerous every year; but the appearance of H.aeneus, P.madidus, Agonum dorsale, Nebria brevicollis, Notiophilus biguttatus, Bembidion lampros and Trechus quadristriatus was much more variable.
Plots of the cumulative differences between observed and expected values of ordered Bernoulli variates

Many predictions are probabilistic in nature; for example, a prediction could be for precipitation tomorrow, but with only a 30 percent chance. Given both the predictions and the actual outcomes, "reliability diagrams" (also known as "calibration plots") help detect and diagnose statistically significant discrepancies between the predictions and the outcomes. The canonical reliability diagrams are based on histogramming the observed and expected values of the predictions; several variants of the standard reliability diagrams propose to replace the hard histogram binning with soft kernel density estimation using smooth convolutional kernels of widths similar to the widths of the bins. In all cases, an important question naturally arises: which widths are best (or are multiple plots with different widths better)? Rather than answering this question, plots of the cumulative differences between the observed and expected values largely avoid the question, by displaying miscalibration directly as the slopes of secant lines for the graphs. Slope is easy to perceive with quantitative precision even when the constant offsets of the secant lines are irrelevant. There is no need to bin or perform kernel density estimation with a somewhat arbitrary kernel.
Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.
Secure Sum Outperforms Homomorphic Encryption in (Current) Collaborative Deep Learning

Deep learning (DL) approaches are achieving extraordinary results in a wide range of domains but often require a massive collection of private data. Hence, methods for training neural networks on the joint data of different data owners, that keep each party's input confidential, are called for. We address the setting of horizontally distributed data in deep learning, where the participants' vulnerable intermediate results have to be processed in a privacy-preserving manner. The predominant scheme for this setting is based on homomorphic encryption (HE), and it is widely considered to be without alternative. In contrast to this, we demonstrate that a carefully chosen, less complex and computationally less expensive secure sum protocol in conjunction with default secure channels exhibits superior properties in terms of both collusion-resistance and runtime. Finally, we discuss several open research questions in the context of collaborative DL, which possibly might lead back to HE-based solutions.
Word Association and MI-Trigger-based Language Modeling

There exists strong word association in natural language. Based on mutual information, this paper proposes a new MI-Trigger-based modeling approach to capture the preferred relationships between words over a short or long distance. Both the distance-independent(DI) and distancedependent(DD) MI-Trigger-based models are constructed within a window. It is found that proper MI-Trigger modeling is superior to word bigram model and the DD MI-Trigger models have better pertbrmance than the DI MI-Trigger models for the same window size. It is also found that the number of the trigger pairs in an MITrigger model can be kept to a reasonable size without losing too much of its modeling power. Finally, it is concluded that the preferred relationships between words are useful to language disambiguation and can be modeled efficiently by the MI-Trigger-based modeling approach.
Multiple Generative Adversarial Networks Analysis for Predicting Photographers' Retouching

Anyone can take a photo, but not everybody has the ability to retouch their pictures and obtain result close to professional. Since it is not possible to ask experts to retouch thousands of pictures, we thought about teaching a piece of software how to reproduce the work of those said experts. This study aims to explore the possibility to use deep learning methods and more specifically, generative adversarial networks (GANs), to mimic artists' retouching and find which one of the studied models provides the best results.
SemEval-2 Task 9: The Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions

Previous research has shown that the meaning of many noun-noun compounds N1 N2 can be approximated reasonably well by paraphrasing clauses of the form 'N2 that . N1', where '. ' stands for a verb with or without a preposition. For example, malaria mosquito is a 'mosquito that carries malaria'. Evaluating the quality of such paraphrases is the theme of Task 9 at SemEval-2010. This paper describes some background, the task definition, the process of data collection and the task results. We also venture a few general conclusions before the participating teams present their systems at the SemEval-2010 workshop. There were 5 teams who submitted 7 systems.
CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning

More than half of the 7,000 languages in the world are in imminent danger of going extinct. Traditional methods of documenting language proceed by collecting audio data followed by manual annotation by trained linguists at different levels of granularity. This time consuming and painstaking process could benefit from machine learning. Many endangered languages do not have any orthographic form but usually have speakers that are bi-lingual and trained in a high resource language. It is relatively easy to obtain textual translations corresponding to speech. In this work, we provide a multimodal machine learning framework for speech representation learning by exploiting the correlations between the two modalities namely speech and its corresponding text translation. Here, we construct a convolutional neural network audio encoder capable of extracting linguistic representations from speech. The audio encoder is trained to perform a speech-translation retrieval task in a contrastive learning framework. By evaluating the learned representations on a phone recognition task, we demonstrate that linguistic representations emerge in the audio encoder's internal representations as a by-product of learning to perform the retrieval task.
An optimizable scalar objective value cannot be objective and should not be the sole objective

This paper concerns the ethics and morality of algorithms and computational systems, and has been circulating internally at Facebook for the past couple years. The paper reviews many Nobel laureates' work, as well as the work of other prominent scientists such as Richard Dawkins, Andrei Kolmogorov, Vilfredo Pareto, and John von Neumann. The paper draws conclusions based on such works, as summarized in the title. The paper argues that the standard approach to modern machine learning and artificial intelligence is bound to be biased and unfair, and that longstanding traditions in the professions of law, justice, politics, and medicine should help.
Learning DAGs without imposing acyclicity

We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint. In particular, for Gaussian distributions, we frame structural learning as a sparse matrix factorization problem and we empirically show that solving an $\ell_1$-penalized optimization yields to good recovery of the true graph and, in general, to almost-DAG graphs. Moreover, this approach is computationally efficient and is not affected by the explosion of combinatorial complexity as in classical structural learning algorithms.
MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning

Humans are able to create rich representations of their external reality. Their internal representations allow for cross-modality inference, where available perceptions can induce the perceptual experience of missing input modalities. In this paper, we contribute the Multimodal Hierarchical Variational Auto-encoder (MHVAE), a hierarchical multimodal generative model for representation learning. Inspired by human cognitive models, the MHVAE is able to learn modality-specific distributions, of an arbitrary number of modalities, and a joint-modality distribution, responsible for cross-modality inference. We formally derive the model's evidence lower bound and propose a novel methodology to approximate the joint-modality posterior based on modality-specific representation dropout. We evaluate the MHVAE on standard multimodal datasets. Our model performs on par with other state-of-the-art generative models regarding joint-modality reconstruction from arbitrary input modalities and cross-modality inference.
Geographical patterns in the beta diversity of China's woody plants: the influence of space, environment and range size

Beta diversity (i.e. species turnover rate across space) is fundamental for understanding mechanisms controlling large-scale species richness patterns. However, the influences on beta diversity are still a matter of debate. In particular, the relative role of environmental and spatial processes (e.g. environmental niche versus dispersal limitation of species) remains elusive, and the influence of species range size has been poorly tested. Here, using distribution maps of 11 405 woody species in China (ca 9.6 × 106 km2), we investigated 1) the geographical and directional patterns of beta diversity for all woody species and species with different range sizes, and 2) compared the effects of environmental and spatial processes on these patterns. Beta diversity was calculated as the decay of similarity in species composition with increasing distance. Variables representing environmental energy, water availability, climatic seasonality, habitat heterogeneity and human activities were used to evaluate the effects of environmental processes, while spatial distance was used to assess the influence of spatial processes. The results indicated significant directional patterns of beta diversity: the similarity decay along the latitudinal gradient was 1.6–2.3 times faster than that along the longitudinal gradient. Beta diversity also increased with the decrease of species range size. As compared with spatial processes, environmental processes had stronger effects on longitudinal beta diversity and on the beta diversity of widely-ranged species. This was opposite to the larger influence of spatial processes on latitudinal beta diversity and the beta diversity of narrowly-ranged species. These results suggest that the distributions of narrowly-ranged woody species in China may have not reached equilibrium with their environmental niches due to dispersal limitation induced by China's topography and/or their low dispersal ability. The projected rapid climatic changes will likely endanger such species. Species dispersal processes should be taken into account in future conservation strategies in China.
Assessing Intelligence in Artificial Neural Networks

The purpose of this work was to develop of metrics to assess network architectures that balance neural network size and task performance. To this end, the concept of neural efficiency is introduced to measure neural layer utilization, and a second metric called artificial intelligence quotient (aIQ) was created to balance neural network performance and neural network efficiency. To study aIQ and neural efficiency, two simple neural networks were trained on MNIST: a fully connected network (LeNet-300-100) and a convolutional neural network (LeNet-5). The LeNet-5 network with the highest aIQ was 2.32% less accurate but contained 30,912 times fewer parameters than the highest accuracy network. Both batch normalization and dropout layers were found to increase neural efficiency. Finally, high aIQ networks are shown to be memorization and overtraining resistant, capable of learning proper digit classification with an accuracy of 92.51% even when 75% of the class labels are randomized. These results demonstrate the utility of aIQ and neural efficiency as metrics for balancing network performance and size.
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems

In this paper we introduce a recurrent neural network (RNN) based variational autoencoder (VAE) model with a new constrained loss function that can generate more meaningful electroencephalography (EEG) features from raw EEG features to improve the performance of EEG based speech recognition systems. We demonstrate that both continuous and isolated speech recognition systems trained and tested using EEG features generated from raw EEG features using our VAE model results in improved performance and we demonstrate our results for a limited English vocabulary consisting of 30 unique sentences for continuous speech recognition and for an English vocabulary consisting of 2 unique sentences for isolated speech recognition. We compare our method with another recently introduced method described by authors in [1] to improve the performance of EEG based continuous speech recognition systems and we demonstrate that our method outperforms their method as vocabulary size increases when trained and tested using the same data set. Even though we demonstrate results only for automatic speech recognition (ASR) experiments in this paper, the proposed VAE model with constrained loss function can be extended to a variety of other EEG based brain computer interface (BCI) applications.
The well correlated diffuse interstellar bands at λλ 6196, 6614 Å and their possible companions

Considered here is the interrelation between five diffuse interstellar bands (DIBs): $\lambda\lambda$ 5545, 6113, 6196, 6445 and 6614 A. Two DIBs ($\lambda$ 6196 and $\lambda$ 6614 A) have already been known as well correlated with each other; their relation with three other, weaker bands, was investigated for the first time. To accomplish this task high-resolution spectra ($\lambda/\Delta\lambda$ $\approx$ 100,000) with high signal-to-noise ratio (S/N) of 54 hot O -- B stars with reddening 0.12 -- 1.45 mag were used. Analysis of measured equivalent widths has allowed to establish linear dependencies and evaluate linear correlation coefficients as high as 0.968 -- 0.988 between the intensities of these five DIBs. Such a degree of correlation may indicate their common origin. Several spacings in wavenumbers found between these DIBs correspond to the energies of vibrational transitions in some PAHs resulting in IR emissions at $\lambda\lambda$ 16.4, 11.3, 7.7, 6.2 and 3.3 $\mu$m.
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains

Reinforcement learning algorithms have had tremendous successes in online learning settings. However, these successes have relied on low-stakes interactions between the algorithmic agent and its environment. In many settings where RL could be of use, such as health care and autonomous driving, the mistakes made by most online RL algorithms during early training come with unacceptable costs. These settings require developing reinforcement learning algorithms that can operate in the so-called batch setting, where the algorithms must learn from set of data that is fixed, finite, and generated from some (possibly unknown) policy. Evaluating policies different from the one that collected the data is called off-policy evaluation, and naturally poses counter-factual questions. In this project we show how off-policy evaluation and the estimation of treatment effects in causal inference are two approaches to the same problem, and compare recent progress in these two areas.
Distribution of vesicular-arbuscular mycorrhizal hyphae in the rhizospheres of trifoliate orange and Bahia grass seedlings under an intercropping system.

An experiment was conducted under green house conditions to observe the distribution of vesicular-arbuscular mycorrhizal (VAM) hyphae between plants. Special acrylic root boxes with three compartments were constructed. The two outer compartments, into which the trifoliate orange (Poncirus trifoliata Raf.) seedlings and Bahia grass (Paspalum notatum Flugge.) were transplanted, were separated from the center compartment by a barrier made of a nylon screen of 37μm mesh. The inoculation with a VAM fungus, Gigaspora margarita Becker and Hall, was done in the center compartment. The density of hyphae, percentage of infection, and the number of spores were higher in the Bahia grass compartment than those in the compartment with trifoliate orange. A petri dish experiment showed that although the Bahia grass and trifoliate orange root exudates stimulated the hyphal growth of G. margarita, the length of hyphae in the Bahia grass treatment was longer than that of the trifoliate orange. In this experiment it was possible to observe in detail the distribution of hyphae in the rhizosphere of two plants under intercropping system conditions. These hyphae would be related to the interconnection between plants to promote a network system ; the formation and development of this network system is influenced by some root exudates.