Miracle-dz/UniFAR-FTUs huggingface dataset:
A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation

The commonly used framework for unsupervised machine translation builds initial translation models of both translation directions, and then performs iterative back-translation to jointly boost their translation performance. The initialization stage is very important since bad initialization may wrongly squeeze the search space, and too much noise introduced in this stage may hurt the final performance. In this paper, we propose a novel retrieval and rewriting based method to better initialize unsupervised translation models. We first retrieve semantically comparable sentences from monolingual corpora of two languages and then rewrite the target side to minimize the semantic gap between the source and retrieved targets with a designed rewriting model. The rewritten sentence pairs are used to initialize SMT models which are used to generate pseudo data for two NMT models, followed by the iterative back-translation. Experiments show that our method can build better initial unsupervised translation models and improve the final translation performance by over 4 BLEU scores. Our code is released at https://github.com/Imagist-Shuo/RRforUNMT.git.
Language-aware Interlingua for Multilingual Neural Machine Translation

Multilingual neural machine translation (NMT) has led to impressive accuracy improvements in low-resource scenarios by sharing common linguistic information across languages. However, the traditional multilingual model fails to capture the diversity and specificity of different languages, resulting in inferior performance compared with individual models that are sufficiently trained. In this paper, we incorporate a language-aware interlingua into the Encoder-Decoder architecture. The interlingual network enables the model to learn a language-independent representation from the semantic spaces of different languages, while still allowing for language-specific specialization of a particular language-pair. Experiments show that our proposed method achieves remarkable improvements over state-of-the-art multilingual NMT baselines and produces comparable performance with strong individual models.
The Effect of Ocean Acidification on Calcifying Organisms in Marine Ecosystems: An Organism to Ecosystem Perspective

Ocean acidification (OA), a consequence of anthropogenic carbon dioxide emissions, poses a serious threat to marine organisms in tropical, open-ocean, coastal, deep-sea, and high-latitude sea ecosystems. The diversity of taxonomic groups that precipitate calcium carbonate from seawater are at particularly high risk. Here we review the rapidly expanding literature concerning the biological and ecological impacts of OA on calcification, using a cross-scale, process-oriented approach. In comparison to calcification, we find that areas such as fertilization, early life-history stages, and interaction with synergistic stressors are understudied. Although understanding the long-term consequences of OA are critical, available studies are largely short-term experiments that do not allow for tests of long-term acclimatization or adaptation. Future research on the phenotypic plasticity of contemporary organisms and interpretations of performance in the context of current environmental heterogeneity of pCO2 will gre.
Neural Graph Matching Networks for Chinese Short Text Matching

Chinese short text matching usually employs word sequences rather than character sequences to get better performance. However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hurts the final matching performance. To address this problem, we propose neural graph matching networks, a novel sentence matching framework capable of dealing with multi-granular input information. Instead of a character sequence or a single word sequence, paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism. Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models.
The Vulturine Parrot, Psittrichas fulgidus, a threatened New Guinea endemic: notes on its biology and conservation

Summary Vulturine Parrots Psittrichas fulgidus are endemic to the island of New Guinea. A review of the literature and specimen holdings indicate the species mostly occurs 500 to 1800 m a.s. l. Observations suggest the species occurs in low densities. The species is a highly specialized frugivore feeding on just a few species of figs (Ficus, Moraceae). These parrots are heavily exploited (often by destroying scarce nesting cavities) in Papua New Guinea for their feathers, used in traditional adornment. This practice might not be sustainable and four possible conservation measures are suggested: (1) increase field studies; (2) restrict transport of live birds on domestic flights; (3) replace use of Psittrichas feathers with dyed chicken feathers; (4) instruct tourists not to purchase handicrafts containing Psittrichas or other animal parts.
Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent researches made successful attempts to solve the complete ABSA problem with a unified framework. However, the interactive relations among three subtasks are still under-exploited. We argue that such relations encode collaborative signals between different subtasks. For example, when the opinion term is “delicious”, the aspect term must be “food” rather than “place”. In order to fully exploit these relations, we propose a Relation-Aware Collaborative Learning (RACL) framework which allows the subtasks to work coordinately via the multi-task learning and relation propagation mechanisms in a stacked multi-layer network. Extensive experiments on three real-world datasets demonstrate that RACL significantly outperforms the state-of-the-art methods for the complete ABSA task.
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation

To address the challenge of policy learning in open-domain multi-turn conversation, we propose to represent prior information about dialog transitions as a graph and learn a graph grounded dialog policy, aimed at fostering a more coherent and controllable dialog. To this end, we first construct a conversational graph (CG) from dialog corpora, in which there are vertices to represent “what to say” and “how to say”, and edges to represent natural transition between a message (the last utterance in a dialog context) and its response. We then present a novel CG grounded policy learning framework that conducts dialog flow planning by graph traversal, which learns to identify a what-vertex and a how-vertex from the CG at each turn to guide response generation. In this way, we effectively leverage the CG to facilitate policy learning as follows: (1) it enables more effective long-term reward design, (2) it provides high-quality candidate actions, and (3) it gives us more control over the policy. Results on two benchmark corpora demonstrate the effectiveness of this framework.
Assessment of Continuing Education Needs Among Critical Care Nurses in Remote Québec, Canada.

BACKGROUND This study analyzed the needs of critical care nurses in remote regions of Québec regarding continuing education (CE). METHOD A web-based cross-sectional survey was conducted between May and June 2018. RESULTS A total of 78 nurses completed the survey and reported their CE needs were not being met. Only 21.9% of participants reported a satisfaction level ≥ 6 on a scale of 1 to 10 regarding the offering of CE activities in their region. The most common factors identified as barriers to participation in CE activities were working hours (68.2%), distance and travel time (68.2%), released time to attend CE activities (65.2%), costs of CE activities (57.6%), and financial support (51.5%). CONCLUSION This study provides insights into CE needs among critical care nurses. Shortcomings could be addressed by increasing CE activities in remote regions as well as the proportion of critical care-specific CE activities. Moreover, time and expense coverage should be offered by employers. [J Contin Educ Nurs. 2020;51(7):322-330. ].
Phylogeography of the rare myrmecophagous butterfly Acrodipsas cuprea (Lepidoptera : Lycaenidae) from pinned museum specimens

Acrodipsas cuprea is the only polymorphic species in an uncommon myrmecophagous radiation within the Lycaenidae in Australia. A small cryptic butterfly, it is found almost exclusively on hilltop sites in eastern Australia where several geographically localised morphotypes are recognised. This study used mitochondrial DNA sequences extracted from museum specimens to reveal whether A. cuprea morphotypes evolved in isolation, such that intermediate populations resulted from secondary contact, or whether the distribution of morphotypes was maintained solely by selection across a primary contact zone. Genetic data were also used to assess the species' dispersal ability and to explain the historical processes resulting in its extant distribution. Analysis of molecular variance found that 81% of genetic variation was distributed among colour morphs, suggesting that different wing colours had evolved in isolation. Mixed haplotypes and morphotypes in a zone separating the otherwise discrete morphological populations, particularly along the Great Dividing Range in south-east Queensland, suggests a more recent secondary admixture. Isolation by distance detected by nested clade analysis and pairwise comparisons confirmed the limited dispersal ability of A. cuprea, which was more evident in the southern part of its range. Genetic relationships among haplotypes suggest that the species originated in the drier inland areas of central Queensland, before expanding to the coast and south to Victoria.
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks

Affective tasks such as sentiment analysis, emotion classification, and sarcasm detection have been popular in recent years due to an abundance of user-generated data, accurate computational linguistic models, and a broad range of relevant applications in various domains. At the same time, many studies have highlighted the importance of text preprocessing, as an integral step to any natural language processing prediction model and downstream task. While preprocessing in affective systems is well-studied, preprocessing in word vector-based models applied to affective systems, is not. To address this limitation, we conduct a comprehensive analysis of the role of preprocessing techniques in affective analysis based on word vector models. Our analysis is the first of its kind and provides useful insights of the importance of each preprocessing technique when applied at the training phase, commonly ignored in pretrained word vector models, and/or at the downstream task phase.
Modeling Morphological Typology for Unsupervised Learning of Language Morphology

This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology. By modeling morphological processes including suffixation, prefixation, infixation, and full and partial reduplication with constrained stem change rules, our system effectively constrains the search space and offers a wide coverage in terms of morphological typology. The system is tested on nine typologically and genetically diverse languages, and shows superior performance over leading systems. We also investigate the effect of an oracle that provides only a handful of bits per language to signal morphological type.
Recovery of Neodymium (III) from Aqueous Phase by Chitosan-Manganese-Ferrite Magnetic Beads

Neodymium is a key rare-earth element applied to modern devices. The purpose of this study is the development of a hybrid biomaterial based on chitosan (CS) and manganese ferrite (MF) for the recovery of Nd(III) ions from the aqueous phase. The preparation of the beads was performed in two stages; first, MF particles were obtained by the assessment of three temperatures during the co-precipitation synthesis, and the best nano-MF crystallites were incorporated into CS to obtain the hybrid composite material (CS-MF). The materials were characterized by FTIR, XRD, magnetization measurements, and SEM-EDX. The adsorption experiments included pH study, equilibrium study, kinetics study, and sorption–desorption reusability tests. The results showed that for MF synthesis, 60 °C is an appropriate temperature to obtain MF crystals of ~30 nm with suitable magnetic properties. The final magnetic CS-MF beads perform maximum adsorption at pH 4 with a maximum adsorption capacity of 44.29 mg/g. Moreover, the material can be used for up to four adsorption–desorption cycles. The incorporation of MF improves the sorption capacity of the neat chitosan. Additionally, the magnetic properties enable its easy separation from aqueous solutions for further use. The material obtained represents an enhanced magnetic hybrid adsorbent that can be applied to recover Nd(III) from aqueous solutions.
Semi-supervised Contextual Historical Text Normalization

Historical text normalization, the task of mapping historical word forms to their modern counterparts, has recently attracted a lot of interest (Bollmann, 2019; Tang et al., 2018; Lusetti et al., 2018; Bollmann et al., 2018;Robertson and Goldwater, 2018; Bollmannet al., 2017; Korchagina, 2017). Yet, virtually all approaches suffer from the two limitations: 1) They consider a fully supervised setup, often with impractically large manually normalized datasets; 2) Normalization happens on words in isolation. By utilizing a simple generative normalization model and obtaining powerful contextualization from the target-side language model, we train accurate models with unlabeled historical data. In realistic training scenarios, our approach often leads to reduction in manually normalized data at the same accuracy levels.
MYO, a Candidate Gene for Haploid Induction in Maize Causes Male Sterility

Doubled haploid technology is highly successful in maize breeding programs and is contingent on the ability of maize inducers to efficiently produce haploids. Knowledge of the genes involved in haploid induction is important for not only developing better maize inducers, but also to create inducers in other crops. The main quantitative trait loci involved in maize haploid induction are qhir1 and qhir8. The gene underlying qhir1 has been discovered and validated by independent research groups. Prior to initiation of this study, the gene associated with qhir8 had yet to be recognized. Therefore, this research focused on characterizing positional candidate genes underlying qhir8. Pursuing this goal, a strong candidate for qhir8, GRMZM2G435294 (MYO), was silenced by RNAi. Analysis of crosses with these heterozygous RNAi-transgenic lines for haploid induction rate revealed that the silencing of MYO significantly enhanced haploid induction rate by an average of 0.6% in the presence of qhir1. Recently, GRMZM2G465053 (ZmDMP) was identified by map-based gene isolation and shown to be responsible for qhir8. While our results suggest that MYO may contribute to haploid induction rate, results were inconsistent and only showing minor increases in haploid induction rate compared to ZmDMP. Instead, reciprocal crosses clearly revealed that the silencing of MYO causes male sterility.
Wood Warbler Phylloscopus sibilatrix nest provisioning rates are correlated with seasonal caterpillar availability in British Oak Quercus woodlands

Capsule Maximum provisioning rates occurred during the seasonal peak in caterpillar availability, controlling for brood age and time of day. Aims To determine if the seasonal availability of caterpillars influenced Wood Warbler Phylloscopus sibilatrix nest provisioning rates. Methods Remote cameras were used to monitor nest visits at 14 nests in 8 British oak Quercus woodlands during 2013. The timing of the caterpillar peak was estimated by monitoring the production of caterpillar faecal pellets under oak trees. Results Maximum provisioning rates occurred when the date of provisioning coincided with the date of peak caterpillar availability, but the percentage change in provisioning rates during asynchrony was dependent on brood age. Younger broods experienced a greater decline in provisioning rates than older broods during asynchrony. Provisioning rates peaked in the morning and declined during the day, but when caterpillar availability was low there was little within-day variation. Conclusion Provisioning rates show seasonal variation that is correlated with caterpillar availability. Previous studies suggest that Wood Warbler reproductive fitness is independent of synchrony between the breeding cycle and the caterpillar peak, however, the implications for adult fitness are unknown and should be investigated further.
Full Proceeding Paper ALKALINE PHOSPHATASE ACTIVITY OF GRAPTOPHYLLUM PICTUM AND SPHILANTHES ACMELLA FRACTIONS AGAINST MC3T3­E1 CELLS AS MARKER OF OSTEOBLAST DIFFERENTIATION CELLS

The hexane, ethyl acetate, n‐butanol and water fractions from Graptophyllum pictum and Spilanthes acmella were evaluated the stimulative activity on alkaline phosphatase (ALP) of MC3T3‐E1 osteoblast cells. ALP activity is a marker of osteoblast differentiation. Among the tested, the n‐butanol and water fractions of Graptophyllum pictum showed the activity to 112% and 122% respectively, otherwise the n‐butanol and water fractions of Spilanthes acmella showed the activity to 126% and 127% respectively.
Edible Leafy Plants from Mexico as Sources of Antioxidant Compounds, and Their Nutritional, Nutraceutical and Antimicrobial Potential: A Review

A review of indigenous Mexican plants with edible stems and leaves and their nutritional and nutraceutical potential was conducted, complemented by the authors’ experiences. In Mexico, more than 250 species with edible stems, leaves, vines and flowers, known as “quelites,” are collected or are cultivated and consumed. The assessment of the quelite composition depends on the chemical characteristics of the compounds being evaluated; the protein quality is a direct function of the amino acid content, which is evaluated by high-performance liquid chromatography (HPLC), and the contribution of minerals is evaluated by atomic absorption spectrometry, inductively coupled plasma-optical emission spectrometry (ICP-OES) or ICP mass spectrometry. The total contents of phenols, flavonoids, carotenoids, saponins and other general compounds have been analyzed using UV-vis spectrophotometry and by HPLC. For the determination of specific compounds such as phenolic compounds, flavonoids, organic acids and other profiles, it is recommended to use HPLC-DAD, UHPLC-DAD, UFLC-PDA or gas chromatography-mass spectrometry. The current biochemical analysis and biological evaluations were performed to understand the mechanisms of action that lead to decreased glucose levels and lipid peroxidation, increased hypoglycemic and antitumor activity, immune system improvement, increased antibacterial and antifungal activity and, in some cases, anti-Helicobacter pylori activity.
Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar †

Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the F1 score of 0.90 and area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.99 over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar.
Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence.
Navigating Athletic Training Position Statements: The Strength of Recommendation Taxonomy System.

*Department of Exercise Science, University of South Carolina, Columbia; †Department of Orthopaedics & Sports Medicine, University of South Florida, Tampa; ‡Department of Interdisciplinary Health Sciences; School of Osteopathic Medicine in Arizona; Research Support, A.T. Still University, Mesa, AZ; §Department of Kinesiology, University of Connecticut, Storrs; ||Department of Exercise Science and Athletic Training, Ithaca College, NY