Miracle-dz/UniFAR-FTUs huggingface dataset:
What controls variation in carbon use efficiency among Amazonian tropical forests?

Why do some forests produce biomass more efficiently than others? Variations in Carbon Use Efficiency (CUE: total Net Primary Production (NPP)/ Gross Primary Production (GPP)) may be due to changes in wood residence time (Biomass/NPPwood), temperature, or soil nutrient status. We tested these hypotheses in 14, one ha plots across Amazonian and Andean forests where we measured most key components of net primary production (NPP: wood, fine roots, and leaves) and autotrophic respiration (Ra; wood, rhizosphere, and leaf respiration). We found that lower fertility sites were less efficient at producing biomass and had higher rhizosphere respiration, indicating increased carbon allocation to belowground components. We then compared wood respiration to wood growth and rhizosphere respiration to fine root growth and found that forests with residence times <40 yrs had significantly lower maintenance respiration for both wood and fine roots than forests with residence times >40 yrs. A comparison of rhizosphere respiration to fine root growth showed that rhizosphere growth respiration was significantly greater at low fertility sites. Overall, we found that Amazonian forests produce biomass less efficiently in stands with residence times >40 yrs and in stands with lower fertility, but changes to long‐term mean annual temperatures do not impact CUE.
Phylogenetic analysis of Pisidium (Bivalvia: Sphaeriidae) based on molecular mtDNA and nuclear sequence data

Freshwater ecosystems are threatened due to loss and fragmentation of habitat, pollution, over exploitation of resources, climate change and introduction of non-native species, leading native species to extinction. If in one hand some studies about some freshwater taxonomic groups (e.g., mammals) have received special attention, others have almost no information, especially invertebrates. Bivalves, included in this group, have a central role in freshwater ecosystems such as burrowing, production of shells and filtering activity. Despite their functional relevance in freshwater ecosystems, a lack of knowledge about their basic ecology and genetics still exists. In the present study was given special attention to the genus Pisidium (Bivalvia: Sphaeriidae). Essentially, Pisidium systematics has been developed through morphological studies and 24 different species have been described. In Portugal only 7 are described, namely P. amnicum, P. casertanum, P. henslowanum, P. milium, P. nitidium, P. personatum and P. subtruncatum. In this study, 36 samples were collected from three main Portuguese hydrological basins, Douro, Ave and Cávado in order to do molecular characterization of Pisidium in Portugal. These samples were analised for ITS1 and 16S genes. Through phylogenetic analysis, 25 samples may be classified as P. casertanum, 9 as P. subtruncatum and 2 as P. personatum. Genetic distances also showed some differences among samples which may indicate gene flow between rivers. Samples classified as P. personatum showed a high genetic distance from Genbank sequences, similar when compared values between some other Pisidium species. This may indicate the existence of a new species, or there is no correspondent sequence for this specie on Genbank. On the other hand those species that present similar values of genetic distance may be misidentified. The present study is a starting point for future research that should include more samples per site and from other basins and rivers. More genetic studies should be developed concerning this genus, since in Portugal some species could even be unknown while others are endangered. Furthermore population studies should be taken into account in order to develop management and conservation measures.
Uncovering hidden coral diversity: a new cryptic lobophylliid scleractinian from the Indian Ocean

Extant biodiversity can easily be underestimated owing to the presence of cryptic taxa, even among commonly observed species. Scleractinian corals are challenging to identify because of their ecophenotypic variation and morphological plasticity. In addition, molecular analyses have revealed the occurrence of cryptic speciation. Here, we describe a new cryptic lobophylliid genus and species Paraechinophyllia variabilis gen. nov., sp. nov., which is morphologically similar to Echinophyllia aspera and E. orpheensis. The new taxon occurs in Mayotte Island, Madagascar, the Gulf of Aden and the Red Sea. Six molecular markers (COI, 12S, ATP6‐NAD4, NAD3‐NAD5, histone H3 and ITS) and 46 morphological characters at three different levels (macromorphology, micromorphology and microstructure) were examined. The resulting molecular phylogenetic reconstruction showed that Paraechinophyllia gen. nov. represents a distinct group within the Lobophylliidae that diverged from the lineage leading to Echinophyllia and Oxypora in the Early Miocene, approximately 21.5 Ma. The morphological phylogenetic reconstruction clustered Paraechinophyllia gen. nov., Echinophyllia and Oxypora together in a single clade. A sole morphological character, calice relief, discriminated Paraechinophyllia gen. nov. from the latter two genera, suggesting that limited morphological variation has occurred over a long period. These results highlight the importance of cryptic taxa in reef corals, with implications for population genetics, ecological studies and conservation.
Cuticular hydrocarbon profiles as a taxonomic tool: advantages, limitations and technical aspects

Cuticular hydrocarbons (CHCs) are expressed on an insect's cuticle and are one of the major factors allowing insects to identify members of their own species, colony and gender. As a result of their species-specificity, CHCs are increasingly used to delimit species in addition to more conventional methods, such as morphology or genetic markers, and so play an important role in chemotaxonomy. Species vary in the type of CHCs that they produce, as well as in the relative quantities of shared compounds. This review summarizes not only how taxonomists may differentiate between species based on CHC profiles, but also the incentive for using CHC composition as taxonomic tool. Benefits regarding the identification of cryptic species and early signs of reproductive isolation are then discussed, giving examples from studies of taxonomy, behaviour and biosynthesis. For taxonomic characters to reliably indicate species boundaries, their limitations need to be known. Potential problems caused by environmental effects, intra-species variation in profiles and other technical issues are highlighted, and suggestions are made regarding their avoidance. It remains a challenge to determine the variation beyond which two species can be called independent; a problem shared by most methods of delimitation. Recently, there has been a shift towards using a combination of different taxonomic tools, both molecular and non-molecular, to test observed species differences.
Food habits of Steller sea lions (Eumetopias jubatus) off Oregon and northern California, 1986–2007

We described the diet of the eastern stock of Steller sea lions (Eumetopias jubatus) from 1416 scat samples collected from five sites in Oregon and northern California from 1986 through 2007. A total of 47 prey types from 30 families were identified. The most common prey was Pacific hake (Merluccius productus), followed by salmonids (Oncorhynchus spp.), skates (Rajidae), Pacific lamprey (Lampetra tridentata), herrings (Clupeidae), rockfish (Sebastes spp.), and northern anchovy (Engraulis mordax). Steller sea lion diet composition varied seasonally, annually, and spatially. Hake and salmonids were the most commonly identified prey in scats collected during the summer (breeding season), whereas hake and skate were most common in the nonbreeding season. Continued research on Steller sea lion diet and foraging behavior in the southern extent of their range is necessary to address issues such as climate change, interaction with competing California sea lions, and predation impacts on valuable or sensitive fish stocks.
Complete mitochondrial genome of the Rufous burrowing snake, Achalinus rufescens (Reptilia: Xenodermatidae)

Abstract The complete mitochondrial genome (mitogenome) sequence of Achalinus rufescens was determined by using a PCR-based method. The total length of mitogenome is 17,339 bp and contains 13 protein-coding genes, 22 tRNA genes, 2 ribosome RNA genes and 2 control regions (D-loop). All the protein-coding genes of A. rufescens were distributed on the H-strand, except for the ND6 subunit gene and eight tRNA genes which were encoded on the L-strand. The phylogenetic tree of A. rufescens and 11 other closely species was built. The DNA data presented here will be useful to study the evolutionary relationships and genetic diversity of A. rufescens.
AAC Redwater hard red spring wheat

AAC Redwater is a hard red spring wheat (Triticum aestivum L.) adapted to the shorter season wheat-growing regions of the Canadian prairies. AAC Redwater was evaluated in the Parkland Wheat Cooperative Test in 2009, 2010, and 2011. AAC Redwater was significantly (P < 0.05) higher yielding than Katepwa (6.4%) and AC Splendor (7.5%) and was not significantly different in grain yield compared to CDC Teal and CDC Osler. AAC Redwater had maturity similar to AC Splendor, matured a day earlier than Katepwa, and was significantly earlier maturing (2 d) than CDC Teal and CDC Osler. AAC Redwater was significantly shorter than all check cultivars and had a similar mean lodging score to the check cultivars. AAC Redwater is resistant to moderately resistant to leaf and stem rust, comparable to AC Splendor. AAC Redwater was resistant to intermediate in its field reaction to stripe rust and intermediate in its reaction to the Ug99 stem rust race, similar to CDC Teal. At the time of registration, AAC Redwater met the end-use quality specifications of the Canada Western Red Spring wheat class.
A Dataset of Journalists' Interactions with Their Readership: When Should Article Authors Reply to Reader Comments?

The comment sections of online news platforms are an important space to indulge in political conversations and to discuss opinions. Although primarily meant as forums where readers discuss amongst each other, they can also spark a dialog with the journalists who authored the article. A small but important fraction of comments address the journalists directly, e.g., with questions, recommendations for future topics, thanks and appreciation, or article corrections. However, the sheer number of comments makes it infeasible for journalists to follow discussions around their articles in extenso. A better understanding of this data could support journalists in gaining insights into their audience and fostering engaging and respectful discussions. To this end, we present a dataset of dialogs in which journalists of The Guardian replied to reader comments and identify the reasons why. Based on this data, we formulate the novel task of recommending reader comments to journalists that are worth reading or replying to, i.e., ranking comments in such a way that the top comments are most likely to require the journalists' reaction. As a baseline, we trained a neural network model with the help of a pair-wise comment ranking task. Our experiment reveals the challenges of this task and we outline promising paths for future work. The data and our code are available for research purposes from: https://hpi.de/naumann/projects/repeatability/text-mining.html
Nebula: A Scalable Privacy-Preserving Machine Learning System in Ant Financial

With the rapid growth of data volume, data-driven machine learning models have become a necessary part of many industrial applications. Intuitively, the more high-quality data used for training leads to better model performance. However, in reality, data are usually scattered and isolated in different organizations or companies. Such a "data isolation" problem stimulates both academia and industry to explore the collaborative learning paradigm to build better models jointly with multiple data sources. Despite the potential performance gains, this learning paradigm inevitably faces privacy issues, especially for the Fintech domain where data are sensitive by nature. In this paper, we present a privacy-preserving collaborative learning system in Ant Financial, named Nebula. Our system aims to facilitate privacy-preserving collaborative model training for industrial-scale applications. Our system is built upon a ring-allreduce MPI based distributed framework. On top of that, with some optimization strategies and novel sharing scheme, our system is able to scale up to tens of millions of data samples with hundreds of thousands of features and achieve more than 100x speedup compared with the existing state-of-the-art implementations.
Scientific productivity and collaboration in viticulture and enology in Latin American countries

The aim of this study was to analyze the scientific activity of Latin American researchers in viticulture and oenology through bibliometric analyses of articles included in the Science Citation Index Expanded database for the period of 2006 to 2010. A total of 917 research articles were published in 364 domestic and international journals. We highlight the important growth in the number of research papers published during the period, especially in Brazil, Argentina and Chile, as well as an increasing number of international collaborations, mainly with non-American grape- and-wine producing countries. A social network analysis of collaborations between institutions and countries was also performed. The combined analysis of productivity, collaboration and scientific impact provides a global and integrated vision of the research conducted in this area in Latin America. El objetivo de este trabajo fue analizar la actividad cientifica de los investigadores en los paises latinoamericanos en viticultura y enologia mediante analisis bibliometrico de los articulos incluidos en la base de datos Science Citation Index Expanded durante el periodo 2006 a 2010. Un total de 917 articulos de investigacion fueron publicados en 364 revistas nacionales e internacionales. Destaca un crecimiento importante en los trabajos de investigacion publicados en el periodo, sobre todo en Brasil, Argentina y Chile, asi como una colaboracion internacional cada vez mayor, sobre todo con los paises productores de uva y vino no americanos. Tambien se llevo a cabo un analisis de redes sociales de la colaboracion entre instituciones y paises. El analisis combinado de la productividad, la colaboracion cientifica y el impacto ofrece una vision global e integrada de los paises que investigan en esta area.
Tertiary structure prediction of bromelain from Ananas Comosus using comparative modelling method

Bromelain is a general name for a family of sulfhydryl which can be found in the proteolytic enzyme group from pineapples (Ananas Comosus). This study focuses on the prediction of three dimensional structures (3D) of stem bromelain using the method of comparative modelling. The amino acid sequence of bromelain was obtained from the NCBI database was used as a tool to search for proteins with known 3D structures related to the target sequence. Suitable template was chosen based on >30% sequence similarity and lowest e-value. Based on these criteria, 1YAL was selected as the best template with 55% of sequence similarity and 9x10(-52) of e-value. (c) 2017 The Authors. Published by IASE.
Product Quality Prediction with Convolutional Encoder-Decoder Architecture and Transfer Learning

Mining data collected from industrial manufacturing process plays an important role for intelligent manufacturing in Industry 4.0. In this paper, we propose a deep convolutional model for predicting wafer fabrication quality in an intelligent integrated-circuit manufacturing application. The wafer fabrication quality prediction is motivated by the need for improving product line efficiency and reducing manufacturing cost by detecting potential defective work-in-process (WIP) wafers. This work considers the following two crucial data characteristics for wafer fabrication. First, our model is designed to learn spatial correlation between quality measurements on WIP wafers and fabrication results through an encoder-decoder neural network. Second, we leverage the fact that different products share the same raw manufacturing process to enable the knowledge transferring between prediction models of different products. Performance evaluation on real data sets is conducted to validate the strengths of our model on quality prediction, model interpretability, and feasibility of transferring knowledge.
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors

Embedding entities and relations into continuous vector spaces has attracted a surge of interest in recent years. Most embedding methods assume that all test entities are available during training, which makes it time-consuming to retrain embeddings for newly emerging entities. To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities. In this paper, we propose a novel framework, namely Virtual Neighbor (VN) network, to address three key challenges. Firstly, to reduce the neighbor sparsity problem, we introduce the concept of the virtual neighbors inferred by rules. And we assign soft labels to these neighbors by solving a rule-constrained problem, rather than simply regarding them as unquestionably true. Secondly, many existing methods only use one-hop or two-hop neighbors for aggregation and ignore the distant information that may be helpful. Instead, we identify both logic and symmetric path rules to capture complex patterns. Finally, instead of one-time injection of rules, we employ an iterative learning scheme between the embedding method and virtual neighbor prediction to capture the interactions within. Experimental results on two knowledge graph completion tasks demonstrate that our VN network significantly outperforms state-of-the-art baselines. Furthermore, results on Subject/Object-R show that our proposed VN network is highly robust to the neighbor sparsity problem.
Aspect-Invariant Sentiment Feature Learning: Adversarial Multi-task Learning for Aspect-Based Sentiment Analysis

Neural models with attention mechanisms have achieved remarkable performance in Aspect-based Sentiment Analysis (ABSA). In most previous studies, the information about aspects in sentences is considered important for the ABSA task and therefore various attention mechanisms have been explored to leverage interactions between aspects and context. However, some sentiment expressions carry the same polarity regardless of the aspects they are associated with. In such cases, it is not necessary to incorporate aspect information for ABSA. In fact, in our experiments, we find that blindly leveraging interactions between aspects and context as features may introduce noise when analyzing those aspect-invariant sentiment expressions, especially when facing with limited aspect-related annotated data. Hence, in this paper, we propose an Adversarial Multi-task Learning Framework to identify the aspect-invariant/dependent sentiment expressions automatically without requiring extra annotations. In addition, we use a gating mechanism to control the contribution of representations derived from aspect-invariant and aspect-dependent hidden states when generating the final contextual sentiment representations for the given aspect. This essentially allows the exploitation of aspect-invariant sentiment features for better ABSA results. Experimental results on two benchmark datasets show that extending existing neural models using our proposed framework achieves superior performance. In addition, the aspect-invariant data extracted by our framework can be considered as pivot features for better transfer learning of the ABSA models on unseen aspects.
Elevated Road Network: A Metric Learning Method for Recognizing Whether a Vehicle is on an Elevated Road

Mobile navigation is a critical component in mobile maps. Yawing detection (does a vehicle yaw) is an important task in mobile navigation. In regions containing parallel and close elevated and surface roads, it is hard to detect yawing events using traditional methods, which mainly rely on low-accuracy positions and moving directions. Recognizing whether a vehicle is moving on an elevated road can significantly improve the performance of yawing detection. We propose Elevated Road Network (ERNet), a lightweight and real industrial neural network model for mobile navigation, to solve elevated road recognition fundamentally. For an elevated road fragment and a surface road fragment in the same group (they are parallel and close), ERNet takes four types of high-level features as input and learns two 10-dim descriptors (A and B). In inference stage, ERNet predicts a 10-dim embedding (C) for a position of a vehicle. By comparing ||A-C||2 2 and ||B-C||22, and applying a technique called confidence constraint, we recognize the road type corresponding to the position. Significant improvements on elevated road recognition and yawing detection have been achieved compared with several methods in extensive experiments. ERNet is deployed as part of AMap, the famous mobile map in China, serves drivers in three large cities: Beijing, Shanghai and Guangzhou, and will cover the whole country as soon as possible.
Learning to Distract: A Hierarchical Multi-Decoder Network for Automated Generation of Long Distractors for Multiple-Choice Questions for Reading Comprehension

The task of generating incorrect options for multiple-choice questions is termed as distractor generation problem. The task requires high cognitive skills and is extremely challenging to automate. Existing neural approaches for the task leverage encoder-decoder architecture to generate long distractors. However, in this process two critical points are ignored - firstly, many methods use Jaccard similarity over a pool of candidate distractors to sample the distractors. This often makes the generated distractors too obvious or not relevant to the question context. Secondly, some approaches did not consider the answer in the model, which caused the generated distractors to be either answer-revealing or semantically equivalent to the answer. In this paper, we propose a novel Hierarchical Multi-Decoder Network (HMD-Net) consisting of one encoder and three decoders, where each decoder generates a single distractor. To overcome the first problem mentioned above, we include multiple decoders with a dis-similarity loss in the loss function. To address the second problem, we exploit richer interaction between the article, question, and answer with a SoftSel operation and a Gated Mechanism. This enables the generation of distractors that are in context with questions but semantically not equivalent to the answers. The proposed model outperformed all the previous approaches significantly in both automatic and manual evaluations. In addition, we also consider linguistic features and BERT contextual embedding with our base model which further push the model performance.
Active Hazard Observation via Human in the Loop Social Media Analytics System

We demonstrate AHOM, a system that can Actively Observe Hazards via Monitoring Social Media Streams. AHOM proposes an active way to include the human in the loop of hazard information ac-quisition for social media. Different from state of the art, it supports bi-directional interaction between social media data processing system and social media users, which leads to the establishment of deeper and more accurate situational awareness of hazard events. We demonstrate how AHOM utilizes Twitter streams and bi-directional information exchange with social media users for enhanced hazard observation.