Miracle-dz/UniFAR-FTUs huggingface dataset:
The Biology of Animal Stress: Basic Principles and Implications for Animal Welfare

The biology of animal stress basic principles and implications for animal welfare that we provide for you will be ultimate to give preference. This reading book is your chosen book to accompany you when in your free time, in your lonely. This kind of book can help you to heal the lonely and get or add the inspirations to be more inoperative. Yeah, book as the widow of the world can be very inspiring manners. As here, this book is also created by an inspiring author that can make influences of you to do more.
Endogenous abscisic acid levels are linked to decreased growth of bush bean plants treated with NaCl

This paper studies the relative importance of endogenous ABA and ion toxicity in the leaf growth inhibition caused by NaCl in salt-adapted and unadapted bush beans. Adaptation to salt-stress was achieved by germination of seeds in 75 mM NaCl, while unadapted plants were germinated in tap water. The adaptation process caused a transitory increase in leaf ABA, Na + and Cl - concentrations, while leaf expansion was inhibited. However, when grown for 8 or 13 days in 75 mM NaCl-containing nutrient solution, primary and first trifoliolate leaves of salt-adapted plants had greater areas than those of unadapted plants. Concentrations of ABA, Na + and Cl - in these leaves were lower in adapted plants, and a strong negative correlation between leaf expansion growth and either leaf Na + , Cl - or ABA concentrations could be established. However, in the second trifoliolate leaves only the ABA, but not the Na + or Cl - , concentrations were significantly correlated with leaf expansion. Out results suggest that salt-induced inhibition of leaf expansion growth in bush beans is mediated by ABA rather than Na + or Cl - toxicity. Moreover, the increase of ABA, induced by the salt-pretreatment, seems to play an important role in limiting the accumulation of Na + and Cl - in the leaves, leading to adaptation of bush beans to salt-stress.
The R-process Alliance: The Peculiar Chemical Abundance Pattern of RAVE J183013.5−455510

We report on the spectroscopic analysis of RAVE J183013.5−455510, an extremely metal-poor star, highly enhanced in CNO, and with discernible contributions from the rapid neutron-capture process. There is no evidence of binarity for this object. At = −3.57, this star has one of the lowest metallicities currently observed, with 18 measured abundances of neutron-capture elements. The presence of Ba, La, and Ce abundances above the solar system r-process predictions suggests that there must have been a non-standard source of r-process elements operating at such low metallicities. One plausible explanation is that this enhancement originates from material ejected at unusually high velocities in a neutron star merger event. We also explore the possibility that the neutron-capture elements were produced during the evolution and explosion of a rotating massive star. In addition, based on comparisons with yields from zero-metallicity faint supernova, we speculate that RAVE J1830−4555 was formed from a gas cloud pre-enriched by both progenitor types. From analysis based on Gaia DR2 measurements, we show that this star has orbital properties similar to the Galactic metal-weak thick-disk stellar population.
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of deploying a new data-collection policy is high, to the point that it can become prohibitive to update the data-collection policy more than a few times during learning. With this view, we propose a novel concept of deployment efficiency, measuring the number of distinct data-collection policies that are used during policy learning. We observe that naively applying existing model-free offline RL algorithms recursively does not lead to a practical deployment-efficient and sample-efficient algorithm. We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN) that can effectively optimize a policy offline using 10-20 times fewer data than prior works. Furthermore, the recursive application of BREMEN is able to achieve impressive deployment efficiency while maintaining the same or better sample efficiency, learning successful policies from scratch on simulated robotic environments with only 5-10 deployments, compared to typical values of hundreds to millions in standard RL baselines. Codes and pre-trained models are available at this https URL .
Thermally Imposed Time Constraints on the Activity of the Desert Lizard Sceloporus Merriami

We investigated the thermal biology of the lizard Sceloporus merriami in a hot, arid locality in west Texas. The effects of the thermal environment on lizard activity were hypothesized to be extensive because S. merriami body temperatures average lower than the body temperatures of any other North American desert iguanid (32.20C). To test this hypothesis, we compared lizard thermal microclimate use and rates of lizard move- ments, feeding, and social displays with estimates of the relative availability of thermal microclimates near 32.20. These comparisons were made throughout the lizards' morning activity period. The thermal environment had a major impact on S. merriami and restricted individual activities (movement rates, feeding strikes, and social displays) to a 2-h period beginning around local sunrise (1000) and to a brief period in late afternoon. In the morning, rates of activities were maximal within 1 h after local sunrise. This activity maximum coincided with the time that the average available environmental temperature approximated the mean body temperature for S. merriami. Also at this time, lizards occupied thermal mi- croclimates randomly with respect to microclimate availability on their home ranges, whereas when activity rates were lower, lizard microclimate use was nonrandom. Thus, when thermally favorable microclimates were most available, lizard activity was least constrained. S. merriami timed the initiation, peak, and cessation of overall and microclimate-specific activity to coincide with the overall and microclimate-specific avail- ability of favorable operative temperatures.
Inhibition of Transthyretin-met30 Expression Using Inosine15.1-Hammerhead Ribozymes in Cell Culture

Hereditary amyloidosis is primarily caused by mutations within the transthyretin gene. More than 75 mutations within transthyretin have been reported in causing amyloidosis. The most common mutation is the val30met mutation in the transthyretin protein (TTR-met30) caused by a mononucleic substitution from G to A (GUC to AUC) in the transthyretin gene resulting in the exchange for the amino acids valine to methionine in the corresponding protein sequence. The aim of this work is the development of a specific cleavage of TTR-met30 mRNA in the cell culture system using hammerhead ribozymes. We showed previously that chemically modified nuclease stable Inosine15.1-Hammerhead ribozymes are able to target the TTR-met30 mRNA with high specificity on the RNA level (Biochem. Biophys. Res. Commun. 260, 313–317, 1999). Now we present data confirming our observations on the cellular level. We used the wild-type human normal (hn) TTR expressing cell line HepG2 and the stable transfected cell line 293-TTR-met30 for TTR-met30 experiments. We cleaved the TTR-met30 and hnTTR mRNA with specific nuclease stable chemically modified Inosine15.1-Hammerhead ribozymes and analyzed the protein after immunoprecipitation and subsequent Western blotting. We were able to downregulate the TTR concentration by 54.5% (100% = 1.5 mg/l TTR) and also specifically to target the TTR-met30 expression in the cell culture system. The therapeutic effect was improved using cationic liposomes resulting in a total downregulation by 92.1 and 62.7% targeting hnTTR mRNA and TTR-met30 mRNA, respectively. The successful employment of Inosine15.1-Hammerhead ribozymes in cell culture is therefore a promising tool for the development of a gene therapeutic strategy for hereditary amyloidosis.
The Heavy-Tail Phenomenon in SGD

In recent years, various notions of capacity and complexity have been proposed for characterizing the generalization properties of stochastic gradient descent (SGD) in deep learning. Some of the popular notions that correlate well with the performance on unseen data are (i) the `flatness' of the local minimum found by SGD, which is related to the eigenvalues of the Hessian, (ii) the ratio of the stepsize $\eta$ to the batch size $b$, which essentially controls the magnitude of the stochastic gradient noise, and (iii) the `tail-index', which measures the heaviness of the tails of the eigenspectra of the network weights. In this paper, we argue that these three seemingly unrelated perspectives for generalization are deeply linked to each other. We claim that depending on the structure of the Hessian of the loss at the minimum, and the choices of the algorithm parameters $\eta$ and $b$, the SGD iterates will converge to a \emph{heavy-tailed} stationary distribution. We rigorously prove this claim in the setting of linear regression: we show that even in a simple quadratic optimization problem with independent and identically distributed Gaussian data, the iterates can be heavy-tailed with infinite variance. We further characterize the behavior of the tails with respect to algorithm parameters, the dimension, and the curvature. We then translate our results into insights about the behavior of SGD in deep learning. We finally support our theory with experiments conducted on both synthetic data and neural networks. To our knowledge, these results are the first of their kind to rigorously characterize the empirically observed heavy-tailed behavior of SGD.
An Efficient Semi-smooth Newton Augmented Lagrangian Method for Elastic Net

Feature selection is an important and active research area in statistics and machine learning. The Elastic Net is often used to perform selection when the features present non-negligible collinearity or practitioners wish to incorporate additional known structure. In this article, we propose a new Semi-smooth Newton Augmented Lagrangian Method to efficiently solve the Elastic Net in ultra-high dimensional settings. Our new algorithm exploits both the sparsity induced by the Elastic Net penalty and the sparsity due to the second order information of the augmented Lagrangian. This greatly reduces the computational cost of the problem. Using simulations on both synthetic and real datasets, we demonstrate that our approach outperforms its best competitors by at least an order of magnitude in terms of CPU time. We also apply our approach to a Genome Wide Association Study on childhood obesity.
Sexual strategies in plants III. A quantitative method for describing the gender of plants

Abstract Gender (maleness or femaleness) is a quantitative phenomenon in plants. For accurate descriptions of gender, numerical estimates of the relative capabilities of plants as pollen and ovule parents have advantages over the verbal morphological descriptions of gender that are traditionally used. Full descriptions require the distribution of gender among subsets of a sexual class (e.g., among individual plants) to be indicated as well as the average condition. Estimates of gender can be based on the pollen and ovule or seed production of each subset independently (phenotypic gender), or else the pollen and ovule or seed production of the whole population can be taken into account to assess the functional gender of any subset. The functional gender of a plant estimates the proportions of its genes which are transmitted through pollen (its maleness) or through ovules (its femaleness). Prospective estimates of functional gender are based on the relative maternal and paternal investments before anthesis .
Genetic control of flagellar activity in Chlamydomonas moewusii (Chlorophyta, Volvocales)

Abstract Twenty-three Chlamydomonas moewusii mutants defective in flagellar activity were isolated following ultraviolet irradiation. (Other mutant types, with motility impaired in various ways, were also obtained, but were not studied genetically.) In most cases, flagellar paralysis could be attributed to mutations of single genes. Three non-motile clones proved each to have sustained two independent mutations affecting the activity of the flagella. The genetic behaviour of sixteen paralysis genes was studied in crosses. A total of about 3000 zygospores yielded tetrads suitable for genetic analysis. One mutation proved completely sex-linked, being genetically inseparable from the mt + locus. None of the others showed any evidence for sex-linkage. There was evidence for at least one locus-specific suppressor, originally present in one wild-type parent. Three mutations mapped at a single locus. Each of three other loci had sustained two mutations. At least eleven gene loci appear to be responsible for flag.
Investigating the Effect of Deictic Movements of a Multi-Robot

ABSTRACT While research on human-robot interaction is ongoing as robots become more readily available and easier to use, the study of interactions between a human and a team of multiple robots represents a relatively new field of research. In particular, how multi-robots could be used for everyday users and how the characteristics of multi-robots would affect human perception and cognition has not been explored. In this paper, we specifically focus on physical affordances generated by the movements of multi-robots, and investigate the effects of deictic movements of multi-robots on information retrieval by conducting a delayed free recall task. We conclude with further discussion of how the movements of the multi-robot reshape the way of people perceiving information and what should be considered to design a multi-robot-based display.
CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data

We study the problem of applying spectral clustering to cluster multi-scale data, which is data whose clusters are of various sizes and densities. Traditional spectral clustering techniques discover clusters by processing a similarity matrix that reflects the proximity of objects. For multi-scale data, distance-based similarity is not effective because objects of a sparse cluster could be far apart while those of a dense cluster have to be sufficiently close. Following [16], we solve the problem of spectral clustering on multi-scale data by integrating the concept of objects' "reachability similarity" with a given distance-based similarity to derive an objects' coefficient matrix. We propose the algorithm CAST that applies trace Lasso to regularize the coefficient matrix. We prove that the resulting coefficient matrix has the "grouping effect" and that it exhibits "sparsity". We show that these two characteristics imply very effective spectral clustering. We evaluate CAST and 10 other clustering methods on a wide range of datasets w.r. t. various measures. Experimental results show that CAST provides excellent performance and is highly robust across test cases of multi-scale data.
CMB constraints on late-universe decaying dark matter as a solution to the H0 tension

It has been suggested that late-universe dark matter decays can alleviate the tension between measurements of H0 in the local universe and its value inferred from cosmic microwave background fluctuations. Decaying dark matter can potentially account for this discrepancy as it reshuffles the energy density between matter and radiation and as a result allows dark energy to become dominant at earlier times. We show that the low multipoles amplitude of the cosmic microwave background anisotropy power spectrum severely constrains the feasibility of late-time decays as a solution to the H0 tension.
Learning the Compositional Visual Coherence for Complementary Recommendations

Complementary recommendations, which aim at providing users product suggestions that are supplementary and compatible with their obtained items, have become a hot topic in both academia and industry in recent years. Existing work mainly focused on modeling the co-purchased relations between two items, but the compositional associations of item collections are largely unexplored. Actually, when a user chooses the complementary items for the purchased products, it is intuitive that she will consider the visual semantic coherence (such as color collocations, texture compatibilities) in addition to global impressions. Towards this end, in this paper, we propose a novel Content Attentive Neural Network (CANN) to model the comprehensive compositional coherence on both global contents and semantic contents. Specifically, we first propose a Global Coherence Learning (GCL) module based on multi-heads attention to model the global compositional coherence. Then, we generate the semantic-focal representations from different semantic regions and design a Focal Coherence Learning (FCL) module to learn the focal compositional coherence from different semantic-focal representations. Finally, we optimize the CANN in a novel compositional optimization strategy. Extensive experiments on the large-scale real-world data clearly demonstrate the effectiveness of CANN compared with several state-of-the-art methods.
Growing Together: Modeling Human Language Learning With n-Best Multi-Checkpoint Machine Translation

We describe our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). We view MT models at various training stages (i.e., checkpoints) as human learners at different levels. Hence, we employ an ensemble of multi-checkpoints from the same model to generate translation sequences with various levels of fluency. From each checkpoint, for our best model, we sample n-Best sequences (n=10) with a beam width =100. We achieve an 37.57 macro F1 with a 6 checkpoint model ensemble on the official shared task test data, outperforming a baseline Amazon translation system of 21.30 macro F1 and ultimately demonstrating the utility of our intuitive method.
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

One-stage detector basically formulates object detection as dense classification and localization. The classification is usually optimized by Focal Loss and the box location is commonly learned under Dirac delta distribution. A recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization, where the predicted quality facilitates the classification to improve detection performance. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization. Two problems are discovered in existing practices, including (1) the inconsistent usage of the quality estimation and classification between training and inference and (2) the inflexible Dirac delta distribution for localization when there is ambiguity and uncertainty in complex scenes. To address the problems, we design new representations for these elements. Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations. The improved representations eliminate the inconsistency risk and accurately depict the flexible distribution in real data, but contain continuous labels, which is beyond the scope of Focal Loss. We then propose Generalized Focal Loss (GFL) that generalizes Focal Loss from its discrete form to the continuous version for successful optimization. On COCO test-dev, GFL achieves 45.0\% AP using ResNet-101 backbone, surpassing state-of-the-art SAPD (43.5\%) and ATSS (43.6\%) with higher or comparable inference speed, under the same backbone and training settings. Notably, our best model can achieve a single-model single-scale AP of 48.2\%, at 10 FPS on a single 2080Ti GPU. Code and models are available at this https URL.
Link Prediction for Temporally Consistent Networks

Dynamic networks have intrinsic structural, computational, and multidisciplinary advantages. Link prediction estimates the next relationship in dynamic networks. However, in the current link prediction approaches, only bipartite or non-bipartite but homogeneous networks are considered. The use of adjacency matrix to represent dynamically evolving networks limits the ability to analytically learn from heterogeneous, sparse, or forming networks. In the case of a heterogeneous network, modeling all network states using a binary-valued matrix can be difficult. On the other hand, sparse or currently forming networks have many missing edges, which are represented as zeros, thus introducing class imbalance or noise. We propose a time-parameterized matrix (TP-matrix) and empirically demonstrate its effectiveness in non-bipartite, heterogeneous networks. In addition, we propose a predictive influence index as a measure of a node's boosting or diminishing predictive influence using backward and forward-looking maximization over the temporal space of the n-degree neighborhood. We further propose a new method of canonically representing heterogeneous time-evolving activities as a temporally parameterized network model (TPNM). The new method robustly enables activities to be represented as a form of a network, thus potentially inspiring new link prediction applications, including intelligent business process management systems and context-aware workflow engines. We evaluated our model on four datasets of different network systems. We present results that show the proposed model is more effective in capturing and retaining temporal relationships in dynamically evolving networks. We also show that our model performed better than state-of-the-art link prediction benchmark results for networks that are sensitive to temporal evolution.
SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network

Learning-to-rank (LTR) is a set of supervised machine learning algorithms that aim at generating optimal ranking order over a list of items. A lot of ranking models have been studied during the past decades. And most of them treat each query document pair independently during training and inference. Recently, there are a few methods have been proposed which focused on mining information across ranking candidates list for further improvements, such as learning multivariant scoring function or learning contextual embedding. However, these methods usually greatly increase computational cost during online inference, especially when with large candidates size in real-world web search systems. What's more, there are few studies that focus on novel design of model structure for leveraging information across ranking candidates. In this work, we propose an effective and efficient method named as SERank which is a Sequencewise Ranking model by using Squeeze-and-Excitation network to take advantage of cross-document information. Moreover, we examine our proposed methods on several public benchmark datasets, as well as click logs collected from a commercial Question Answering search engine, Zhihu. In addition, we also conduct online A/B testing at Zhihu search engine to further verify the proposed approach. Results on both offline datasets and online A/B testing demonstrate that our method contributes to a significant improvement.