Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic and environmental effects on age at first calving and calving interval of naturally bred Boran (zebu) cows in Ethiopia

Twenty-four years of data from a Boran cattle breeding and improvement ranch in the Rift Valley of Ethiopia were used to study the influence of genetic and environmental factors on age at first calving (AFC) and calving interval (CD using an individual animal model. The mean AFC and Cl were 41-8 months and 442 days, respectively. The h 2 values for AFC were 0·062 and 0·075 when estimated on the original and selected data, from which cows that did not calve after 5 years of age were deleted, respectively. The h 2 values for CI were 0·037 and 0·043 when estimated on the original and selected data, from which cows with AFC higher than 5 years and CI longer than 2 years were deleted, respectively. The corresponding c2 values (ratio of permanent environmental variance to total) were 0·031 and 0·028 for the respective data sets. Parameter estimates of the original data from bivariate analysis were 0·012 (h2), 0·065 (h2), -0·054 and -0·176 for first CI, AFC, their genetic and environmental correlation, respectively. The h2 values estimated in a bivariate analysis of the first three CIs varied between 0·002 and 0·093. Year and season had significant effects on both traits. The annual genetic change for both traits was not significant while the solutions for year effects were highly variable. The regression of the solutions of year effects for AFC on year showed that it increased by about 10 days while the variation in CI was due to random year to year fluctuation.
A Method for Extracting Intact RNA from Fruits Rich in Polysaccharides using Ripe Mango Mesocarp

A rapid and simple procedure is described for efficiently extracting intact RNA from mango (Mangifera indica L. cv. Manila) mesocarp, a tissue rich in poly- saccharides. The RNA can be used for in vitro translation, northern blots, and cDNA synthesis. This method is applicable to other fleshy fruits rich in polysaccharid es.
Does Chinese BERT Encode Word Structure?

Contextualized representations give significantly improved results for a wide range of NLP tasks. Much work has been dedicated to analyzing the features captured by representative models such as BERT. Existing work finds that syntactic, semantic and word sense knowledge are encoded in BERT. However, little work has investigated word features for character languages such as Chinese. We investigate Chinese BERT using both attention weight distribution statistics and probing tasks, finding that (1) word information is captured by BERT; (2) word-level features are mostly in the middle representation layers; (3) downstream tasks make different use of word features in BERT, with POS tagging and chunking relying the most on word features, and natural language inference relying the least on such features.
An Alternative to Backpropagation in Deep Reinforcement Learning

State-of-the-art deep learning algorithms mostly rely on gradient backpropagation to train a deep artificial neural network, which is generally regarded to be biologically implausible. For a network of stochastic units trained on a reinforcement learning task or a supervised learning task, one biologically plausible way of learning is to train each unit by REINFORCE. In this case, only a global reward signal has to be broadcast to all units, and the learning rule given is local, which can be interpreted as reward-modulated spike-timing-dependent plasticity (R-STDP) that is observed biologically. Although this learning rule follows the gradient of return in expectation, it suffers from high variance and cannot be used to train a deep network in practice. In this paper, we propose an algorithm called MAP propagation that can reduce this variance significantly while retaining the local property of learning rule. Different from prior works on local learning rules (e.g. Contrastive Divergence) which mostly applies to undirected models in unsupervised learning tasks, our proposed algorithm applies to directed models in reinforcement learning tasks. We show that the newly proposed algorithm can solve common reinforcement learning tasks at a speed similar to that of backpropagation when applied to an actor-critic network.
Statistical Inference for Online Decision Making via Stochastic Gradient Descent

Abstract Online decision making aims to learn the optimal decision rule by making personalized decisions and updating the decision rule recursively. It has become easier than before with the help of big data, but new challenges also come along. Since the decision rule should be updated once per step, an offline update which uses all the historical data is inefficient in computation and storage. To this end, we propose a completely online algorithm that can make decisions and update the decision rule online via stochastic gradient descent. It is not only efficient but also supports all kinds of parametric reward models. Focusing on the statistical inference of online decision making, we establish the asymptotic normality of the parameter estimator produced by our algorithm and the online inverse probability weighted value estimator we used to estimate the optimal value. Online plugin estimators for the variance of the parameter and value estimators are also provided and shown to be consistent, so that interval estimation and hypothesis test are possible using our method. The proposed algorithm and theoretical results are tested by simulations and a real data application to news article recommendation.
Deep Learning on Real Geophysical Data: A Case Study for Distributed Acoustic Sensing Research

Deep Learning approaches for real, large, and complex scientific data sets can be very challenging to design. In this work, we present a complete search for a finely-tuned and efficiently scaled deep learning classifier to identify usable energy from seismic data acquired using Distributed Acoustic Sensing (DAS). While using only a subset of labeled images during training, we were able to identify suitable models that can be accurately generalized to unknown signal patterns. We show that by using 16 times more GPUs, we can increase the training speed by more than two orders of magnitude on a 50,000-image data set.
EIC (Expert Information Criterion) not AIC: the cautious biologist's guide to model selection

1.A goal of many research programs in biology is to extract meaningful insights from large, complex data sets. Researchers in Ecology, Evolution and Behavior (EEB) often grapple with long-term, observational data sets from which they construct models to address fundamental questions about biology. Similarly, epidemiologists analyze large, complex observational data sets to understand the distribution and determinants of human health and disease. A key difference in the analytical workflows for these two distinct areas of biology is delineation of data analysis tasks and explicit use of causal inference methods, widely adopted by epidemiologists. 2.Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB researchers. 3.The first half of this commentary defines four distinct analytical tasks (description, prediction, association, and causal inference), and the corresponding approaches to data analysis and model selection. The latter half is dedicated to walking the reader through the steps of casual inference, focusing on examples from EEB. 4.Given increasing interest in causal inference and common misperceptions regarding the task of causal inference, we aim to facilitate an exchange of ideas between disciplinary silos and provide a framework for analyses of all data, though particularly relevant for observational data.
Securing Manufacturing Using Blockchain

Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. However, analysis of multiple security data could provide more comprehensive and system-wide anomaly detection in industrial networks. In this paper, we present an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWaT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors. However, multi-source analysis is more robust because it can detect anomalies from multiple sources simultaneously, while achieving comparable precision for each of the sources.
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses

Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications. We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency. Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to state-of-the-art GEC systems. We demonstrate that a factor behind this is the inability of systems to rely on a strong internal language model in low error density domains. We hope this work shall facilitate the development of open-domain GEC models that generalize to different topics and genres.
A Reversible Dynamic Movement Primitive formulation

In this work, a novel Dynamic Movement Primitive (DMP) formulation is proposed which supports reversibility, i.e. backwards reproduction of a learned trajectory. Apart from sharing all favourable properties of the original DMP, decoupling the teaching of position and velocity profiles and bidirectional drivability along the encoded path are also supported. Original DMP have been extensively used for encoding and reproducing a desired motion pattern in several robotic applications. However, they lack reversibility, which is a useful and expedient property that can be leveraged in many scenarios. The proposed formulation is analyzed theoretically and its practical usefulness is showcased in an assembly by insertion experimental scenario.
Spacetime integral bounds for the energy-critical nonlinear wave equation

In this paper we prove a global spacetime bound for the quintic, nonlinear wave equation in three dimensions. This bound depends on the $L_{t}^{\infty} L_{x}^{2}$ and $L_{t}^{\infty} \dot{H}^{2}$ norms of the solution to the quintic problem.
Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark

Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts. Models utilizing these superficial clues gain mirage advantages on the in-domain testing set, which makes the evaluation results over-estimated. The lack of trustworthy evaluation settings and benchmarks stalls the progress of NLI research. In this paper, we propose to assess a model's trustworthy generalization performance with cross-datasets evaluation. We present a new unified cross-datasets benchmark with 14 NLI datasets, and re-evaluate 9 widely-used neural network-based NLI models as well as 5 recently proposed debiasing methods for annotation artifacts. Our proposed evaluation scheme and experimental baselines could provide a basis to inspire future reliable NLI research.
Automatic Analysis and Influence of Hierarchical Structure on Melody, Rhythm and Harmony in Popular Music

Repetition is a basic indicator of musical structure. This study introduces new algorithms for identifying musical phrases based on repetition. Phrases combine to form sections yielding a two-level hierarchical structure. Automatically detected hierarchical repetition structures reveal significant interactions between structure and chord progressions, melody and rhythm. Different levels of hierarchy interact differently, providing evidence that structural hierarchy plays an important role in music beyond simple notions of repetition or similarity. Our work suggests new applications for music generation and music evaluation.
Encoder–Decoder Semantic Segmentation Models for Electroluminescence Images of Thin-Film Photovoltaic Modules

We consider a series of image segmentation methods based on the deep neural networks in order to perform semantic segmentation of electroluminescence (EL) images of thin-film modules. We utilize the encoder–decoder deep neural network architecture. The framework is general such that it can easily be extended to other types of images (e.g., thermography) or solar cell technologies (e.g., crystalline silicon modules). The networks are trained and tested on a sample of images from a database with 6000 EL images of copper indium gallium diselenide thin film modules. We selected two types of features to extract, shunts and so called “droplets. ” The latter feature is often observed in the set of images. Several models are tested using various combinations of encoder–decoder layers, and a procedure is proposed to select the best model. We show exemplary results with the best selected model. Furthermore, we applied the best model to the full set of 6000 images and demonstrate that the automated segmentation of EL images can reveal many subtle features, which cannot be inferred from studying a small sample of images. We believe these features can contribute to process optimization and quality control.
Matched Quantized Min-Sum Decoding of Low-Density Parity-Check Codes

A quantized message passing decoding algorithm for low-density parity-check codes is presented. The algorithm relies on the min approximation at the check nodes, and on modelling the variable node inbound messages as observations of an extrinsic discrete memoryless channel. The performance of the algorithm is analyzed and compared to quantized min-sum decoding by means of density evolution, and almost closes the gap with the performance of the sum-product algorithm. A stability analysis is derived, which highlights the role played by degree-3 variable nodes in the stability condition. Finite-length simulation results confirm large gains predicted by the asymptotic analysis.
Improved Discretization of the Full First-Order Magnetic Field Integral Equation

The inaccuracy of the classical magnetic field integral equation (MFIE) is a long-studied problem. We investigate one of the potential approaches to solve the accuracy problem: higher-order discretization schemes. While these are able to offer increased accuracy, we demonstrate that the accuracy problem may still be present. We propose an advanced scheme based on a weak-form discretization of the identity operator which is able to improve the high-frequency MFIE accuracy considerably — without any significant increase in computational effort or complexity.
Unsupervised Video Anomaly Detection via Flow-based Generative Modeling on Appearance and Motion Latent Features

Surveillance video anomaly detection searches for anomalous events such as crimes or accidents among normal scenes. Since anomalous events occur rarely, there is a class imbalance problem between normal and abnormal data and it is impossible to collect all potential anomalous events, which makes the task challenging. Therefore, performing anomaly detection requires learning the patterns of normal scenes to detect unseen and undefined anomalies. Since abnormal scenes are distinguished from normal scenes by appearance or motion, lots of previous approaches have used an explicit pre-trained model such as optical flow for motion information, which makes the network complex and dependent on the pre-training. We propose an implicit two-path AutoEncoder (ITAE) that exploits the structure of a SlowFast network and focuses on spatial and temporal information through appearance (slow) and motion (fast) encoders, respectively. The two encoders and a single decoder learn normal appearance and behavior by reconstructing normal videos of the training set. Furthermore, with features from the two encoders, we suggest density estimation through flow-based generative models to learn the tractable likelihoods of appearance and motion features. Finally, we show the effectiveness of appearance and motion encoders and their distribution modeling through experiments in three benchmarks which result outperforms the state-of-the-art methods.
Object Tracking Using Spatio-Temporal Future Prediction

Occlusion is a long-standing problem that causes many modern tracking methods to be erroneous. In this paper, we address the occlusion problem by exploiting the current and future possible locations of the target object from its past trajectory. To achieve this, we introduce a learning-based tracking method that takes into account background motion modeling and trajectory prediction. Our trajectory prediction module predicts the target object's locations in the current and future frames based on the object's past trajectory. Since, in the input video, the target object's trajectory is not only affected by the object motion but also the camera motion, our background motion module estimates the camera motion. So that the object's trajectory can be made independent from it. To dynamically switch between the appearance-based tracker and the trajectory prediction, we employ a network that can assess how good a tracking prediction is, and we use the assessment scores to choose between the appearance-based tracker's prediction and the trajectory-based prediction. Comprehensive evaluations show that the proposed method sets a new state-of-the-art performance on commonly used tracking benchmarks.
Higher-twist fermionic operators and DIS structure functions from the 
AdS/CFT
 duality

The role of local higher-twist ($\tau > 3$) spin-1/2 fermionic operators of the strongly coupled ${\cal {N}}=4$ supersymmetric Yang-Mills theory on the symmetric and antisymmetric deep inelastic scattering (DIS) structure functions is investigated. The calculations are carried out in terms of the duality between ${\cal {N}}=4$ SYM theory and type IIB supergravity on AdS$_5 \times S^5$. Particularly, we explicitly obtain the structure functions for single-trace spin-1/2 fermionic operators in the 20$^*$ and 60$^*$ irreducible representations of $SU(4)_R$, corresponding to twists 4 and 5, respectively. We also calculate the contributions of other single-trace spin-1/2 fermionic operators in the 4, 20 and 60 irreducible representations of $SU(4)_R$. New important effects are found in comparison with the minimal twist ($\tau = 3$) case, and they are studied thoroughly.
THIN: THrowable Information Networks and Application for Facial Expression Recognition in the Wild

For a number of machine learning problems, an exogenous variable can be identified such that it heavily influences the appearance of the different classes, and an ideal classifier should be invariant to this variable. An example of such exogenous variable is identity if facial expression recognition (FER) is considered. In this paper, we propose a dual exogenous/endogenous representation. The former captures the exogenous variable whereas the second one models the task at hand (e.g., facial expression). We design a prediction layer that uses a tree-gated deep ensemble conditioned by the exogenous representation. We also propose an exogenous dispelling loss to remove the exogenous information from the endogenous representation. Thus, the exogenous information is used two times in a throwable fashion, first as a conditioning variable for the target task, and second to create invariance within the endogenous representation. We call this method THIN, standing for THrowable Information Networks. We experimentally validate THIN in several contexts where an exogenous information can be identified, such as digit recognition under large rotations and shape recognition at multiple scales. We also apply it to FER with identity as the exogenous variable. We demonstrate that THIN significantly outperforms state-of-the-art approaches on several challenging datasets.
Understanding the Hoarding Behaviors during the COVID-19 Pandemic using Large Scale Social Media Data

The COVID-19 pandemic has affected people’s lives around the world on an unprecedented scale. We intend to investigate hoarding behaviors in response to the pandemic using large-scale social media data. First, we collect hoarding-related tweets shortly after the outbreak of the coronavirus. Next, we analyze the hoarding and anti-hoarding patterns of over 42,000 unique Twitter users in the United States from March 1 to April 30, 2020, and dissect the hoarding-related tweets by age, gender, and geographic location. We find the percentage of women in both hoarding and anti-hoarding groups is higher than that of the general Twitter user population. Furthermore, using topic modeling, we investigate the opinions expressed towards the hoarding behavior by categorizing these topics according to demographic and geographic groups. We also calculate the anxiety scores for the hoarding and anti-hoarding related tweets using a lexical approach. By comparing their anxiety scores with the baseline Twitter anxiety score, we reveal further insights. The LIWC anxiety mean for the hoarding-related tweets is significantly higher than the baseline Twitter anxiety mean. Interestingly, beer has the highest calculated anxiety score compared to other hoarded items mentioned in the tweets.
Nonadiabatic Atomic-Like State Stabilizing Antiferromagnetism and Mott Insulation in MnO

In this paper I report evidence that the antiferromagnetic and insulating ground state of MnO is caused by a nonadiabatic atomic-like motion as it is evidently the case in NiO. In addition, I show that the experimental findings of Goodwin et al. [Phys. Rev. Lett. (2006), 96,~047209] corroborate my suggestion that the rhombohedral-like distortion in antiferromagnetic MnO as well as in antiferromagnetic NiO is an inner distortion of the monoclinic base-centered Bravais lattice of the antiferromagnetic phases.