Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Triple Encoder for Fast Open-Set Link Prediction

We improve both the open-set generalization and efficiency of link prediction on knowledge graphs by leveraging the contexts of entities and relations in a novel semantic triple encoder. Most previous methods, e.g., translation-based and GCN-based embedding approaches, were built upon graph embedding models. They simply treat the entities/relations as a closed set of graph nodes regardless of their context semantics, which however cannot provide critical information for the generalization to unseen entities/relations. In this paper, we partition each graph triple and develop a novel context-based encoder that separately maps each part and its context into a latent semantic space. We train this semantic triple encoder by optimizing two objectives specifically designed for link prediction. In particular, (1) We split each triple into two parts, i.e., i) head entity plus relation and ii) tail entity, process both contexts separately by a Transformer encoder, and combine the encoding outputs to derive the prediction. This Siamese-like architecture avoids the combinatorial explosion of candidate triples and significantly improves the efficiency, especially during inference; (2) We cover the contextualized semantics of the triples in the encoder so it can handle unseen entities during inference, which promisingly improves the generalization ability; (3) We train the model by optimizing two complementary objectives defined on the triple, i.e., classification and contrastive losses, for natural and reliable ranking scores during inference. In experiments, we achieve the state-of-the-art or competitive performance on three popular link prediction benchmarks. In addition, we empirically reduce the inference costs by one or two orders of magnitude compared to a recent context-based encoding approach and meanwhile keep a superior quality of prediction.
Accurate Word Alignment Induction from Neural Machine Translation

Despite its original goal to jointly learn to align and translate, prior researches suggest that the state-of-the-art neural machine translation model Transformer captures poor word alignment through its attention mechanism. In this paper, we show that attention weights do capture accurate word alignment, which could only be revealed if we choose the correct decoding step and layer to induce word alignment. We propose to induce alignment with the to-be-aligned target token as the decoder input and present two simple but effective interpretation methods for word alignment induction, either through the attention weights or the leave-one-out measures. In contrast to previous studies, we find that attention weights capture better word alignment than the leave-one-out measures under our setting. Using the proposed method with attention weights, we greatly improve over fast-align on word alignment induction. Finally, we present a multi-task learning framework to train the Transformer model and show that by incorporating GIZA++ alignments into our multi-task training, we can induce significantly better alignments than GIZA++.
Invasion of Rosa rugosa (Rugosa Rose) into coastal plant communities of Brier Island, Nova Scotia

During August and September 2010, we surveyed the entire 20.4 km perimeter of Brier Island, Nova Scotia, for the invasive shrub Rosa rugosa (Rugosa Rose). This island in the outer reaches of the Bay of Fundy of Nova Scotia is geographically isolated and relatively undeveloped. Our objective was to determine the extent and mechanism of the invasion of R. rugosa into different coastal habitats to gain insight into the potential threat to native biodiversity from the unchecked population growth of this monopolizing, rank shrub. Over 300 colonies of R. rugosa with mean height over 1 m occupied 2089 m of the island perimeter within 10 m of the top of the beach. The mean distance between colonies was about 61 m and the maximum distance was 1927 m. At least 33 colonies formed almost impenetrable walls, each over 10 m in length, and 2 colonies occupied about 500 m 2 each. Rosa rugosa had greatest density on a sand–gravel beach on which 88 colonies occupied 22% of the area and 33% of the beach margin. Exponential growth of the population (inferred from aerial photographs from 1970, 1988, and 2000) may be due to the various systems of seed dispersal. Agents include primary (American Mink, Neovison vison , and Red Squirrel, Tamiascurius hudsonicus ) and secondary (an unidentified rodent) biotic dispersers. Longer distance dispersal may include Coyotes ( Canis latrans ), off-road vehicles and deposition of fruits by currents and waves. The main sites of seedling establishment are native habitats, such as dune grass and seashore Seaside Plantain ( Plantago maritima ) zones, albeit modified by this exotic rose. Dispersal of colonies contradicts a hypothesis of dispersal from human habitation along roads and tracks to the coastal habitats. We conclude that R. rugosa is having a significant impact on marine coastal plant communities and has the potential to dominate windswept shrub habitats on coastlines of much of Nova Scotia.
PeerNomination: Relaxing Exactness for Increased Accuracy in Peer Selection

In peer selection agents must choose a subset of themselves for an award or a prize. As agents are self-interested, we want to design algorithms that are impartial, so that an individual agent cannot affect their own chance of being selected. This problem has broad application in resource allocation and mechanism design and has received substantial attention in the artificial intelligence literature. Here, we present a novel algorithm for impartial peer selection, PeerNomination, and provide a theoretical analysis of its accuracy. Our algorithm possesses various desirable features. In particular, it does not require an explicit partitioning of the agents, as previous algorithms in the literature. We show empirically that it achieves higher accuracy than the exiting algorithms over several metrics.
Preventing Posterior Collapse with Levenshtein Variational Autoencoder

Variational autoencoders (VAEs) are a standard framework for inducing latent variable models that have been shown effective in learning text representations as well as in text generation. The key challenge with using VAEs is the {\it posterior collapse} problem: learning tends to converge to trivial solutions where the generators ignore latent variables. In our Levenstein VAE, we propose to replace the evidence lower bound (ELBO) with a new objective which is simple to optimize and prevents posterior collapse. Intuitively, it corresponds to generating a sequence from the autoencoder and encouraging the model to predict an optimal continuation according to the Levenshtein distance (LD) with the reference sentence at each time step in the generated sequence. We motivate the method from the probabilistic perspective by showing that it is closely related to optimizing a bound on the intractable Kullback-Leibler divergence of an LD-based kernel density estimator from the model distribution. With this objective, any generator disregarding latent variables will incur large penalties and hence posterior collapse does not happen. We relate our approach to policy distillation \cite{RossGB11} and dynamic oracles \cite{GoldbergN12}. By considering Yelp and SNLI benchmarks, we show that Levenstein VAE produces more informative latent representations than alternative approaches to preventing posterior collapse.
Hubble Space Telescope Astrometry in the Orion Nebula Cluster: Census of Low-mass Runaways

We present a catalog of high-precision proper motions in the Orion Nebula Cluster (ONC), based on Treasury Program observations with the Hubble Space Telescope’s (HST) ACS/WFC camera. Our catalog contains 2454 objects in the magnitude range of 14.2 < mF775W < 24.7, thus probing the stellar masses of the ONC from ∼0.4 M☉ down to ∼0.02 M☉ over an area of ∼550 arcmin2. We provide a number of internal velocity dispersion estimates for the ONC that indicate a weak dependence on stellar location and mass. There is good agreement with the published velocity dispersion estimates, although nearly all of them (including ours at and mas yr−1) might be biased by the overlapping young stellar populations of Orion A. We identified four new ONC candidate runaways based on HST and the Gaia DR 2 data, all with masses less than ∼1 M☉. The total census of known candidate runaway sources is 10—one of the largest samples ever found in any Milky Way open star cluster. Surprisingly, none of them have tangential velocities exceeding 20 km s−1. If most of them indeed originated in the ONC, it may compel the re-examination of dynamical processes in very young star clusters. It appears that the mass function of the ONC is not significantly affected by the lost runaways.
Nutritive quality of blends of corn with germinated cowpeas (Vigna unguiculata), pigeon pea (Cajanus cajan), and bambarra groundnut (Voandzeia subterranea).

To germinated varieties of cowpea, Oraludi (OR) and akidi ani (AK), pigeon pea (PP), and bambarra goundnut (BG) were combined with sprouted yellow corn in various blends to evaluate their nutritive quality in young rats. The blends possessed acceptable characteristics similar to blends already tested and developed in this laboratory as the sole source of nutrients for infants or supplements for adults
Towards Unsupervised Language Understanding and Generation by Joint Dual Learning

In modular dialogue systems, natural language understanding (NLU) and natural language generation (NLG) are two critical components, where NLU extracts the semantics from the given texts and NLG is to construct corresponding natural language sentences based on the input semantic representations. However, the dual property between understanding and generation has been rarely explored. The prior work is the first attempt that utilized the duality between NLU and NLG to improve the performance via a dual supervised learning framework. However, the prior work still learned both components in a supervised manner; instead, this paper introduces a general learning framework to effectively exploit such duality, providing flexibility of incorporating both supervised and unsupervised learning algorithms to train language understanding and generation models in a joint fashion. The benchmark experiments demonstrate that the proposed approach is capable of boosting the performance of both NLU and NLG. The source code is available at: https://github.com/MiuLab/DuaLUG.
Bats and Disease: Behavioral and Community Responses of Southern Bat Populations during the White-nose Syndrome Epizootic

Understanding the winter behavior of bats in temperate North America can provide insight into how bats react to perturbations caused by weather or disease. Over the course of three winters (2011 – 2013) we collected acoustic data and captured bats outside of five hibernacula in Tennessee, United States. During this time, Pseudogymnoascus destructans, the causative agent of whitenose syndrome, became established in hibernacula throughout the region, allowing us to track disease-related changes in the winter behavior of ten bat species. We determined that bats in the region are active during winter regardless of disease. We recorded activity outside of hibernacula at temperatures as low as -13°C. Although bat activity was best determined by a combination of variables, the strongest factor was mean daily temperature (R = 0.2838, F1,1473 = 583.82, P < 0.0001). Bats that left the hibernacula earlier in evening had lower body condition than those that left two to four hours after sunset (F7,125 = 22.794, P < 0.0001). As time since P. destructans detection at a site increased, we saw a rise in the number of daytime and cold temperature flights (F5,2085 = 137.76, P < 0.0001). Through the use of passive acoustic 10 monitoring and monthly captures, we were able to determine that winter activity was driven by both ambient temperature and the presence of P. destructans.
The Information Bottleneck Problem and its Applications in Machine Learning

Inference capabilities of machine learning (ML) systems skyrocketed in recent years, now playing a pivotal role in various aspect of society. The goal in statistical learning is to use data to obtain simple algorithms for predicting a random variable <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> from a correlated observation <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>. Since the dimension of <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> is typically huge, computationally feasible solutions should summarize it into a lower-dimensional feature vector <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>, from which <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> is predicted. The algorithm will successfully make the prediction if <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is a good proxy of <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula>, despite the said dimensionality-reduction. A myriad of ML algorithms (mostly employing deep learning (DL)) for finding such representations <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> based on real-world data are now available. While these methods are effective in practice, their success is hindered by the lack of a comprehensive theory to explain it. The information bottleneck (IB) theory recently emerged as a bold information-theoretic paradigm for analyzing DL systems. Adopting mutual information as the figure of merit, it suggests that the best representation <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> should be maximally informative about <inline-formula> <tex-math notation="LaTeX">$Y$ </tex-math></inline-formula> while minimizing the mutual information with <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>. In this tutorial we survey the information-theoretic origins of this abstract principle, and its recent impact on DL. For the latter, we cover implications of the IB problem on DL theory, as well as practical algorithms inspired by it. Our goal is to provide a unified and cohesive description. A clear view of current knowledge is important for further leveraging IB and other information-theoretic ideas to study DL models.
On the normalized power prior

The power prior is a popular tool for constructing informative prior distributions based on historical data. The method consists of raising the likelihood to a discounting factor in order to control the amount of information borrowed from the historical data. However, one often wishes to assign this discounting factor a prior distribution and estimate it jointly with the parameters, which in turn necessitates the computation of a normalizing constant. In this article, we are concerned with how to approximately sample from joint posterior of the parameters and the discounting factor. We first show a few important properties of the normalizing constant and then use these results to motivate a bisection‐type algorithm for computing it on a fixed budget of evaluations. We give a large array of illustrations and discuss cases where the normalizing constant is known in closed‐form and where it is not. We show that the proposed method produces approximate posteriors that are very close to the exact distributions and also produces posteriors that cover the data‐generating parameters with higher probability in the intractable case. Our results suggest that the proposed method is an accurate and easy to implement technique to include this normalization, being applicable to a large class of models. They also reinforce the notion that proper inclusion of the normalizing constant is crucial to the drawing of correct inferences and appropriate quantification of uncertainty.
Owning the servers: A design fiction exploring the transformation of fandom into "our own"

This essay serves as a speculative design fiction that traces the history of fan creation platforms from the past to the present and finally to a possible future. Using the history and success of the Archive of Our Own (https://archiveofourown.org/) as a focal point, I consider the importance of owning the servers as a way for fan communities to create and preserve culture and reassert values. Examining one possible legal and technological future with an air of optimism allows us to consider what fannish communities can do now to maintain a path of our own.
Évaluation de la qualité du rétablissement après chirurgie du rachis sous anesthésie générale sans ou avec opiacés : une étude observationnelle

Introduction : les patients programmes pour une chirurgie du rachis ont plusieurs facteurs de risque de douleur severe postoperatoire. L'anesthesie sans opioides ou OFA, combinant des agents non opioides ayant des proprietes analgesiques et/ou anti-hyperalgesiques comme la dexmedetomidine, la ketamine, le magnesium ou la lidocaine, peuvent beneficier a ces patients et ameliorer leur retablissement. Cette etude de cohorte prospective observationnelle evalue la qualite du retablissement, a travers le QoR-15, apres chirurgie du rachis programmee et effectuee sous anesthesie "classique" avec opioide vs OFA. Methodes : nous avons inclus des patients consecutifs programmes en chirurgie du rachis. Le choix de l'anesthesie etait laisse a la discretion de l'anesthesiste en charge du patient, soit l'OFA, selon notre protocole de service, soit une anesthesie classique avec opioides. Le QoR-15 etait evalue la lendemain de l'intervention et compare avec le score preoperatoire. Resultats : soixante-six patients ont ete inclus, 35 sous OFA, 31 sous anesthesie avec opioides. Compare avec le score preoperatoire, le QoR-15 postoperatoire etait significativement diminue dans le groupe avec opioide (- 6.73 %; -17.14 a -0.75%; p = 0.018), contrairement au groupe OFA (+6.00 %; -14.79 a +25.64%; p = 0.335). La douleur au repos et la douleur maximale en 24 h (p = 0.008) ainsi que les nausees et vomissements postoperatoires (p<0.001) etaient moins severes dans le groupe OFA. Conclusion : cette etude observationnelle retrouve que les patients programmes pour une chirurgie du rachis beneficient de l'anesthesie sans opioides en terme de retablissement precoce, de douleur aigue postoperatoire et de controle des nausees et vomissements.
Desarrollo de un sistema integral de adquisición de imágenes por dron, procesamiento y análisis para agricultura de precisión

Resumen En este trabajo se presenta, de forma detallada, el diseño y desarrollo de un sistema completo de procesamiento y análisis de imágenes captadas por un veh́ıculo aéreo no tripulado (dron). El sistema contempla todos los pasos necesarios para realizar el barrido de una gran área, la generación del orto-mosaico, el procesamiento digital de las imágenes y el cálculo y visualización de variables de interés agronómico sobre la superficie relevada.
Long-Term Forecasting for World Oil Production

Natural resources are limited and very important in our industrial life and development. Oil is considered the black gold and it is included in hundreds of industrial fields. In this paper, the oil production amounts produced from the world continents and regions (Africa, Asia, Europe, Middle East, North America and S. & Cent. America) was forecasted. The forecasting is conducted using the following forecasting algorithms: Auto-Regressive Integrated Moving Average (ARIMA), Hybrid ARIMA, and Linear Regression with horizon value equals ten (2017-2026). After evaluating tested algorithms using MAPE it is noticed that Hybrid ARIMA was the best forecasting algorithm to be used over Africa (MAPE 5.05%), Asia (MAPE 1.29%) and Europe (MAPE 2.85%) datasets. While ARIMA was the best forecasting algorithm to be used for the Middle East (MAPE 3.06%) and North America (MAPE 18.25%). Finally, Linear regression was the most appropriate forecasting algorithm to be used for South & Cent. America (MAPE 1.62%). The deviation of the forecasted 10 years (2017-2026) of Africa and Asia is decreasing with comparison to the last 10 years (2007-2016), while the forecasted 10 years to the rest of all region is increasing with comparison to last 10 years.
The effects of lactic acid bacteria and enzyme mixture inoculants on fermentation and nutrient digestibility of sunflower silage.

whole-plant forage crops which is based on lactic acid fermentation under anaerobic conditions, whereby lactic acid bacteria (LAB) convert watersoluble carbohydrates (WSC) into organic acids, mainly lactic acid. As a result, pH decrease and thus forage is preserved for a long time . The application of silage additives has become the conventional implement to control the ensiling process. Although the main objective in using silage additives is to ensure the fermentation process to produce well preserved silages, attention is also paid to methods of reducing ensiling losses and improving aerobic stability of silages during the feed-out period . In order to improve the ensiling process various chemical and biological Kafkas Univ Vet Fak Derg 15 (2): 195-199, 2009 DOI:10.9775/kvfd.2008. 91-A RESEARCH ARTICLE
Development of a rainbow trout intestinal epithelial cell line and its response to lipopolysaccharide

A cell line, RTgutGC, was developed from the intestine of Oncorhynchus mykiss. RTgutGC has an epithelial-like shape, been passaged over 100 times, and cryopreserved successfully. A rainbow trout origin was confirmed by sequencing a 652 bp region of the mitochondrial cytochrome c oxidase I gene. RTgutGC is grown routinely in Leibovitz’s L15 without glutamine supplemented with 10% fetal bovine serum (FBS). Cell viability was evaluated with Alamar blue (AB) for metabolic activity and carboxyfluorescein diacetate acetoxymethyl ester (CFDA AM) for membrane integrity. Viability was unchanged by lipopolysaccharide (LPS) for cultures in FBS. For cultures at low cell densities in L15 without FBS or glutamine, cell viability declined in a LPS dose-dependent manner, allowing calculation of the concentration causing a 50% decline in viability (EC50). When glutamine was present, the EC50 was increased for both AB and CFDA AM. As the cell density increased, LPS became much less cytotoxic and no EC50 could be calculated for very confluent cultures. Only high-density cultures had alkaline phosphatase (AP) activity. Thus, glutamine and possibly AP protect against LPS cytotoxicity. RTgutGC should be a useful in vitro tool for studying problems of nutrition and gastrointestinal health in fish.