Miracle-dz/UniFAR-FTUs huggingface dataset:
Pulse production in Australia past, present and future

Summary. Several cool- and warm-season pulse crops (grain legumes) are grown in rotation with cereals and pasture forming sustainable farming systems in Australia. Australian pulse production has increased rapidly over the past 25 years to about 2 x 106 t/year, mainly because of the increase in the area and yield of lupin production for stockfeed purposes. Pulses currently comprise only 10% of the cropping areas of Australia and this could be expanded to 16% as there are large areas of soil types suitable for a range of pulse crops and new better-adapted pulse varieties are becoming available. Cool-season pulses will continue to dominate pulse production in Australia and the majority of the expansion will probably come from chickpea and faba bean industries. There appears to be no major constraint to pulse production in Australia that cannot be addressed by breeders, agronomists and farmers. Of the current major pulse crops, field pea faces the most number of difficulties, in particular the lack of disease management options. A recent strategic plan of the Australian pulse industry predicts the production of 4 x 106 t/year by 2005 but this will largely depend upon export demand and pulse prices. It is predicted that the growth in pulse production will come from increased productivity in the existing areas, from 1.0 to 1.4 t/ha, through improvements in crop management and the development of superior varieties. The area of pulse production will also expand by an additional 1.2 x 106 ha probably yielding 1.0 t/ha. If trends in grazing stock prices continue, the increased area under pulse production will mostly come at the expense of those areas under unimproved pasture and continuous cereal cropping.
Robust 3D Tracking Control of an Underactuated Autonomous Airship

The letter presents a new, robust control algorithm for position trajectory tracking in a 3D space, dedicated to underactuated airships. In order to take into account real characteristics of such vehicles, and to reflect practically motivated constraints, the algorithm assumes a highly uncertain system dynamics model. The tracking problem is solved in a uniform way, without dividing it into subtasks considered in 2D spaces, thanks to the introduction of an auxiliary tracking error. The proposed controller is based on the sliding mode approach. Its stability is investigated using Lyapunov theorem. Numerical simulations are conducted in order to verify properties of a closed-loop system for a generic model of the airship. Performance of the control system is examined via experiments in various scenarios using a prototype airship. The obtained results indicate that the control objectives are satisfied in practice with a reasonable accuracy. Moreover, it is shown that the controller is robust to some bounded additive measurement perturbations and delays in the control loop.
Facial Action Unit Detection using 3D Facial Landmarks

In this paper, we propose to detect facial action units (AU) using 3D facial landmarks. Specifically, we train a 2D convolutional neural network (CNN) on 3D facial landmarks, tracked using a shape index-based statistical shape model, for binary and multi-class AU detection. We show that the proposed approach is able to accurately model AU occurrences, as the movement of the facial landmarks corresponds directly to the movement of the AUs. By training a CNN on 3D landmarks, we can achieve accurate AU detection on two state-of-the-art emotion datasets, namely BP4D and BP4D+. Using the proposed method, we detect multiple AUs on over 330,000 frames, reporting improved results over state-of-the-art methods.
Action Governor for Discrete-Time Linear Systems With Non-Convex Constraints

This letter introduces an add-on, supervisory scheme, referred to as Action Governor (AG), for discrete-time linear systems to enforce exclusion-zone avoidance requirements. It does so by monitoring, and minimally modifying when necessary, the nominal control signal to a constraint-admissible one. The AG operates based on set-theoretic techniques and online optimization. This letter establishes its theoretical foundation, discusses its computational realization, and uses two simulation examples to illustrate its effectiveness.
In vitro antibacterial activity of garlic and synergism between garlic and antibacterial drugs

INTRODUCTIONThe continuous spread of multidrug-resistant path-ogens has become a serious threat to public health and a major concern for infection control practition-ers worldwide. In addition to increasing the cost of drug regimens, this scenario has paved way for the reemergence of previously controlled diseases and has contributed substantially to the high frequency of opportunistic and chronic infection cases in de-veloping countries. The slow pace of newer antibi-otic development has provided the need to explore nature in search of phytotherapeutic agents with novel targets and mode of actions. The practice of complementary and alternative medicine is now on the increase in developing countries in response to World Health Organization directives, culminating in several pre-clinical and clinical studies of plants used in folk medicine to treat infections (Ako-Nai et al., 2003; Fernandez et al., 2003). Garlic (
Comparison of exploited and unexploited yellow perch Perca flavescens (Mitchill) populations in Nebraska Sandhill lakes

Exploitation can have a pronounced eAect on fish populations. Yellow perch, Perca flavescens (Mitchill), populations in Nebraska Sandhill lakes were sampled in 1998 and 1999. Three of the 29 lakes containing yellow perch have been closed to fishing for at least10 years.Unexploitedyellowperchpopulationshadfastgrowthrates,butagestructure was similar to exploited populations. For unexploited lakes combined, mortality and condition were not diAerent from exploited lakes. However, one unexploited lake, Marsh Lake, had the fastest growth, highest proportion of older fish and highest condition of all populations sampled. This lake had low interspecific competition and high invertebrate abundance, which likely resulted in fast growth and high condition. However, size structure and growth were also related to lake productivity. Although exploitation may aAect yellow perch populations, other factors (food availability, predators and lake productivity) also
Large-Scale Object Detection in the Wild From Imbalanced Multi-Labels

Training with more data has always been the most stable and effective way of improving performance in deep learn-ing era. As the largest object detection dataset so far, OpenImages brings great opportunities and challenges for object detection in general and sophisticated scenarios. However, owing to its semi-automatic collecting and labeling pipeline to deal with the huge data scale, Open Images dataset suffers from label-related problems that objects may explicitly or implicitly have multiple labels and the label distribution is extremely imbalanced. In this work, we quantitatively analyze these label problems and provide a simple but effective solution. We design a concurrent softmax to handle the multi-label problems in object detection and propose a soft-sampling methods with hybrid training scheduler to deal with the label imbalance. Overall, our method yields a dramatic improvement of 3.34 points, leading to the best single model with 60.90 mAP on the public object detection test set of Open Images. And our ensembling result achieves 67.17mAP, which is 4.29 points higher than the first place method last year.
Credible, Parsimonious and Useful Predator‐Prey Models: A Reply to Abrams, Gleeson, and Sarnelle

We were asked to coordinate our response to criticisms by Abrams (1994), Gleeson (1994), and Sarnelle (1994) of our Special Feature on "ratio-dependent" predator-prey theory (Mattson and Berryman 1992). This has been no easy task, for the Special Feature contained a diversity of views and approaches to modeling predator-prey dynamics. For example, Berryman (1992) arrives at ratio-dependent predation through extension of the logistic equation, Arditi and Ginzburg (1989) through modification of the predator's functional response, and Gutierrez (1992) by combining the physiological process of energy allocation with random search for prey. Thus, although we may not use the same explicit form for the predator-prey equations, we agree on the underlying rationale, that changes in predator and prey numbers should be related to consumer/ resource ratios. Our response is presented in two papers. In the first, Akqakaya et al. (1995) responded to criticism of their ratio-dependent functional response equation. This is because detailed and specific criticism has been directed, almost entirely, at the Arditi-Ginzburg (1989) model. For example, Gleeson (1994) objects to the assumption that predators "divide up" the prey before beginning to feed, and to prey becoming "infinitely available" as predators become infinitely rare, but these objections do not apply to the other models. In this paper we try to explain and clarify the differences and commonalities in our separate approaches, because our critics seem confused on this issue. We then respond to the criticism that ratio-dependent the-
Mineral Retention in Young Broiler Chicks Fed Diets Based on Wheat, Sorghum or Maize

The aim of the present study was to determine the changes in mineral retention of broiler chicks fed diets based on wheat, sorghum or maize during the first two weeks post-hatch. The retention of five major minerals (calcium, phosphorus, potassium, sodium and magnesium) and four trace minerals (iron, manganese, zinc and copper) was determined on days 3, 5, 7, 9 and 14 of age. The retention coefficients of individual minerals differed widely and the retention coefficients of major minerals were found to be considerably higher than those of trace minerals. Age effects were significant (p<0.05) for all minerals. In general, mineral retention coefficients were highest at day 3, declined to day 7, and remained unchanged to day 14. Decline in mineral retention with age was similar on all three diets. The cereal effect was significant (p<0.05) for several minerals, with a general tendency for the sorghum-based diet to have greater retention than maize- or wheat-based diets.
Asymptotic $K$-soliton-like solutions of the Zakharov-Kuznetsov type equations

We study here the Zakharov-Kuznetsov equation in dimension $2$ and $3$ and the modified Zakharov-Kuznetsov equation in dimension $2$. Those equations admit solitons, characterized by their velocity and their shift. Given the parameters of $K$ solitons $R^k$ (with distinct velocities), we prove the existence and uniqueness of a multi-soliton $u$ such that $\left\| u- \sum_{k=1}^K R^k \right\|_{H^1}\rightarrow 0 $ as $t \rightarrow +\infty$. The convergence takes place in $H^s$ with an exponential rate for all $s\geq 0$. The construction is made by successive approximations of the multi-soliton. We use classical arguments to control of $H^1$-norms of the errors (inspired by Martel [21]), and introduce a new ingredient for the control of the $H^s$-norm in dimension $d \geq2$, by a technique close to monotonicity.
Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications

We investigate methods for experimental performance enhancement of auto-encoders based on a recurrent neural network (RNN) for communication over dispersive nonlinear channels. In particular, our focus is on the recently proposed sliding window bidirectional RNN (SBRNN) optical fiber auto-encoder. We show that adjusting the processing window in the sequence estimation algorithm at the receiver improves the reach of simple systems trained on a channel model and applied "as is" to the transmission link. Moreover, the collected experimental data was used to optimize the receiver neural network parameters, allowing to transmit 42Gb/s with bit-error rate (BER) below the 6.7% hard-decision forward error correction threshold at distances up to 70 km as well as 84 Gb/s at 20 km. The investigation of digital signal processing (DSP) optimized on experimental data is extended to pulse amplitude modulation with receivers performing sliding window sequence estimation using a feed-forward or a recurrent neural network as well as classical nonlinear Volterra equalization. Our results show that, for fixed algorithm memory, the DSP based on deep learning achieves an improved BER performance, allowing to increase the reach of the system.
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension

In this paper, we study machine reading comprehension (MRC) on long texts: where a model takes as inputs a lengthy document and a query, extracts a text span from the document as an answer. State-of-the-art models (e.g., BERT) tend to use a stack of transformer layers that are pre-trained from a large number of unlabeled language corpora to encode the joint contextual information of query and document. However, these transformer models can only take as input a fixed-length (e.g., 512) text. To deal with even longer text inputs, previous approaches usually chunk them into equally-spaced segments and predict answers based on each segment independently without considering the information from other segments. As a result, they may form segments that fail to cover complete answers or retain insufficient contexts around the correct answer required for question answering. Moreover, they are less capable of answering questions that need cross-segment information. We propose to let a model learn to chunk in a more flexible way via reinforcement learning: a model can decide the next segment that it wants to process in either direction. We also apply recurrent mechanisms to enable information to flow across segments. Experiments on three MRC tasks – CoQA, QuAC, and TriviaQA – demonstrate the effectiveness of our proposed recurrent chunking mechanisms: we can obtain segments that are more likely to contain complete answers and at the same time provide sufficient contexts around the ground truth answers for better predictions.
Elevation of pancreatic oxidative stress in STR/Ort mice

To gain a better understanding of the causative factors of hyperinsulinaemia, pancreatic oxidative stress in STR/Ort mice and a type 2 diabetic mouse model (db/db) was evaluated. Expression of oxidative stress marker HSP32 in the pancreas of STR/Ort mice was significantly higher than that in the liver, kidney and spleen. The expression level of HSP32 in STR/Ort was significantly higher than in db/m at 5, 15 and 20 weeks of age (P < 0.05) and tended to be higher than that in db/m (P = 0.069). The expression level of HSP47 in STR/Ort was also significantly higher than in db/m at 5 and 10 weeks (P < 0.05) and tended to be higher than that of db/m at 15 (P = 0.086) and 20 weeks (P = 0.052). Taken together, these results suggest that elevation of oxidative stress in the pancreas during early developmental periods may induce hyperinsulinaemia in STR/Ort mice through impaired insulin metabolism and contribute to the initiation and progression of osteoarthritis.
Room-temperature coherent manipulation of single-spin qubits in silicon carbide with a high readout contrast

Abstract Spin defects in silicon carbide (SiC) with mature wafer-scale fabrication and micro/nano-processing technologies have recently drawn considerable attention. Although room-temperature single-spin manipulation of colour centres in SiC has been demonstrated, the typically detected contrast is less than 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\%$\end{document}, and the photon count rate is also low. Here, we present the coherent manipulation of single divacancy spins in 4H-SiC with a high readout contrast (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$-30\%$\end{document}) and a high photon count rate (150 kilo counts per second) under ambient conditions, which are competitive with the nitrogen-vacancy centres in diamond. Coupling between a single defect spin and a nearby nuclear spin is also observed. We further provide a theoretical explanation for the high readout contrast by analysing the defect levels and decay paths. Since the high readout contrast is of utmost importance in many applications of quantum technologies, this work might open a new territory for SiC-based quantum devices with many advanced properties of the host material.
A Text Reassembling Approach to Natural Language Generation

Recent years have seen a number of proposals for performing Natural Language Generation (NLG) based in large part on statistical techniques. Despite having many attractive features, we argue that these existing approaches nonetheless have some important drawbacks, sometimes because the approach in question is not fully statistical (i.e., relies on a certain amount of handcrafting), sometimes because the approach in question lacks transparency. Focussing on some of the key NLG tasks (namely Content Selection, Lexical Choice, and Linguistic Realisation), we propose a novel approach, called the Text Reassembling approach to NLG (TRG), which approaches the ideal of a purely statistical approach very closely, and which is at the same time highly transparent. We evaluate the TRG approach and discuss how TRG may be extended to deal with other NLG tasks, such as Document Structuring, and Aggregation. We discuss the strengths and limitations of TRG, concluding that the method may hold particular promise for domain experts who want to build an NLG system despite having little expertise in linguistics and NLG.
Deep Learning and Bayesian Deep Learning Based Gender Prediction in Multi-Scale Brain Functional Connectivity

Brain gender differences have been known for a long time and are the possible reason for many psychological, psychiatric and behavioral differences between males and females. Predicting genders from brain functional connectivity (FC) can build the relationship between brain activities and gender, and extracting important gender related FC features from the prediction model offers a way to investigate the brain gender difference. Current predictive models applied to gender prediction demonstrate good accuracies, but usually extract individual functional connections instead of connectivity patterns in the whole connectivity matrix as features. In addition, current models often omit the effect of the input brain FC scale on prediction and cannot give any model uncertainty information. Hence, in this study we propose to predict gender from multiple scales of brain FC with deep learning, which can extract full FC patterns as features. We further develop the understanding of the feature extraction mechanism in deep neural network (DNN) and propose a DNN feature ranking method to extract the highly important features based on their contributions to the prediction. Moreover, we apply Bayesian deep learning to the brain FC gender prediction, which as a probabilistic model can not only make accurate predictions but also generate model uncertainty for each prediction. Experiments were done on the high-quality Human Connectome Project S1200 release dataset comprising the resting state functional MRI data of 1003 healthy adults. First, DNN reaches 83.0%, 87.6%, 92.0%, 93.5% and 94.1% accuracies respectively with the FC input derived from 25, 50, 100, 200, 300 independent component analysis (ICA) components. DNN outperforms the conventional machine learning methods on the 25-ICA-component scale FC, but the linear machine learning method catches up as the number of ICA components increases.
Necessary and sufﬁcient conditions for causal feature selection in time series with latent common causes

We study the identiﬁcation of direct and indirect causes on time series and provide necessary and sufﬁcient conditions in the presence of latent variables. Our theoretical results and estimation algorithms require two conditional independence tests for each observed candidate time series to determine whether or not it is a cause of an observed target time series. We provide experimental re-sults in simulations, where the ground truth is known, as well as in real data. Our results show that our method leads to essentially no false positives and relatively low false negative rates, even in confounded environments with non-unique lag effects, outperforming the common method of Granger causality.
Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles

We present a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English. This corpus is derived from the multilingual OpenSubtitles dataset and includes short informal sentences, for which reliable linguistic annotations have been computed. We provide a fully annotated version of the data including morphological analysis, dependency syntax and semantic role labeling in both FrameNet and ProbBank styles. Sentences are aligned between English and Hebrew, both sides include full annotations and the explicit mapping from the English arguments to the Hebrew ones. We train a neural SRL model on this Hebrew resource exploiting the pre-trained multilingual BERT transformer model, and provide the first available baseline model for Hebrew SRL as a reference point. The code we provide is generic and can be adapted to other languages to bootstrap SRL resources.
Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19’s communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This article presents a large-scale study based on data mined from Twitter. Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users’ profiles, meta-data and tweets’ context. The study noted various interesting conclusions including the critical impact in term of reach level of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.