Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of phenotypic variation onto body temperature and flight activity in a polymorphic insect

Abstract According to biophysical principles, colour and size are important phenotypic factors that may influence body temperature and activity in ectothermic insects. In taxa showing female‐limited polymorphism, males and female morphs differ in body colour, size and activity pattern. However, no previous study has evaluated whether such phenotypic and behavioural variation relates to differences between males and female morphs in thermal properties. In the present study, the relationships between body colour, size, activity and body temperature are examined under laboratory and field conditions, for the polymorphic damselfly Enallagma cyathigerum (Charpentier, 1840) (Odonata: Zygoptera). Contrary to expectation, males and female colour morphs of this species do not differ in thermal properties (i.e. heating characteristics or field body temperatures). When questioning phenotype and activity, temperature does not appear to be relevant for understanding the maintenance of female‐limited polymorphism.
Measuring the local dark matter density with LAMOST DR5 and Gaia DR2

We apply the vertical Jeans equation to the kinematics of Milky Way stars in the solar neighbourhood to measure the local dark matter density. More than 90 000 G- and K-type dwarf stars are selected from the cross-matched sample of LAMOST (Large Sky Area Multi-Object Fibre Spectroscopic Telescope) fifth data release and Gaia second data release for our analyses. The mass models applied consist of a single exponential stellar disc, a razor thin gas disc, and a constant dark matter density. We first consider the simplified vertical Jeans equation that ignores the tilt term and assumes a flat rotation curve. Under a Gaussian prior on the total stellar surface density, the local dark matter density inferred from Markov chain Monte Carlo simulations is $0. 0133_{-0.0022}^{+0.0024}\ {\rm M}_{\odot }\, {\rm pc}^{-3}$. The local dark matter densities for subsamples in an azimuthal angle range of −10° < ϕ < 5° are consistent within their 1σ errors. However, the northern and southern subsamples show a large discrepancy due to plateaux in the northern and southern vertical velocity dispersion profiles. These plateaux may be the cause of the different estimates of the dark matter density between the north and south. Taking the tilt term into account has little effect on the parameter estimations and does not explain the north and south asymmetry. Taking half of the difference of σz profiles as unknown systematic errors, we then obtain consistent measurements for the northern and southern subsamples. We discuss the influence of the vertical data range, the scale height of the tracer population, the vertical distribution of stars, and the sample size on the uncertainty of the determination of the local dark matter density.
Deep Tensor CCA for Multi-View Learning

We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The high-order correlation of given multiple views is modeled by covariance tensor, which is different from most CCA formulations relying solely on the pairwise correlations. Parameters of transformations of each view are jointly learned by maximizing the high-order canonical correlation. To solve the resulting problem, we reformulate it as the best sum of rank-1 approximation, which can be efficiently solved by existing tensor decomposition method. DTCCA is a nonlinear extension of tensor CCA (TCCA) via deep networks. Comparing with kernel TCCA, DTCCA not only can deal with arbitrary dimensions of the input data, but also does not need to maintain the training data for computing representations of any given data point. Hence, DTCCA as a unified model can efficiently overcome the scalable issue of TCCA for either high-dimensional multi-view data or a large amount of views, and it also naturally extends TCCA for learning nonlinear representation. Extensive experiments on four multi-view data sets demonstrate the effectiveness of the proposed method.
Identification and Characterization of a Piscine Neuropathy and Nodavirus from Juvenile Atlantic Cod from the Atlantic Coast of North America

Abstract In 1999, disease outbreaks in juvenile Atlantic cod Gadus morhua that showed the classic signs of viral encephalopathy and retinopathy (VER) were reported in Nova Scotia. Brain and retinal tissues from moribund cod showed diffuse degenerative vacuolative encephalopathy and degenerative histiocytic retinitis. The affected brain and retinal tissues were observed to be positive for nodaviral antigens by means of immunohistochemical techniques. We partially characterized a nodavirus-like agent from brain and eye tissues and cell culture using reverse-transcriptase polymerase chain reaction and primer sets originally designed for amplification of white trevally Caranx dentex (also known as striped jack Pseudocaranx dentex) and Atlantic halibut Hippoglossus hippoglossus nervous necrosis virus coat (capsid) proteins. Sequencing of the T2 region of the coat protein revealed high similarities (>85% nucleotide identity) to the coat protein genes of other fish nodavirus strains, especially those of Atlantic.
Continuous Release of Data Streams under both Centralized and Local Differential Privacy

We study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value in the stream can be quite large, leading to enormous DP noise and bad utility. To reduce the maximal value and noise, one way is to estimate a threshold so that values above it can be truncated. The intuition is that, in many scenarios, only a few values are large; thus truncation does not change the original data much. We develop such a method that finds a suitable threshold with DP. Given the threshold, we then propose an online hierarchical method and several post-processing techniques. Building on these ideas, we formalize the steps in a framework for the private publishing of streaming data. Our framework consists of three components: a threshold optimizer that privately estimates the threshold, a perturber that adds calibrated noise to the stream, and a smoother that improves the result using post-processing. Within our framework, we also design an algorithm satisfying the more stringent DP setting called local DP. Using four real-world datasets, we demonstrate that our mechanism outperforms the state-of-the-art by a factor of $6-10$ orders of magnitude in terms of utility (measured by the mean squared error of the typical scenario of answering a random range query).
SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding

Pair-based metric learning has been widely adopted to learn sentence embedding in many NLP tasks such as semantic text similarity due to its efficiency in computation. Most existing works employed a sequence encoder model and utilized limited sentence pairs with a pair-based loss to learn discriminating sentence representation. However, it is known that the sentence representation can be biased when the sampled sentence pairs deviate from the true distribution of all sentence pairs. In this paper, our theoretical analysis shows that existing works severely suffered from a good pair sampling and instance weighting strategy. Instead of one time pair selection and learning on equal weighted pairs, we propose a unified locality weighting and learning framework to learn task-specific sentence embedding. Our model, SentPWNet, exploits the neighboring spatial distribution of each sentence as locality weight to indicate the informative level of sentence pair. Such weight is updated along with pair-loss optimization in each round, ensuring the model keep learning the most informative sentence pairs. Extensive experiments on four public available datasets and a self-collected place search benchmark with 1.4 million places clearly demonstrate that our model consistently outperforms existing sentence embedding methods with comparable efficiency.
DiResNet: Direction-Aware Residual Network for Road Extraction in VHR Remote Sensing Images

The binary segmentation of roads in very high resolution (VHR) remote sensing images (RSIs) has always been a challenging task due to factors such as occlusions (caused by shadows, trees, buildings, etc.) and the intraclass variances of road surfaces. The wide use of convolutional neural networks (CNNs) has greatly improved the segmentation accuracy and made the task end-to-end trainable. However, there are still margins to improve in terms of the completeness and connectivity of the results. In this article, we consider the specific context of road extraction and present a direction-aware residual network (DiResNet) that includes three main contributions: 1) an asymmetric residual segmentation network with deconvolutional layers and a structural supervision to enhance the learning of road topology (DiResSeg); 2) a pixel-level supervision of local directions to enhance the embedding of linear features; and 3) a refinement network to optimize the segmentation results (DiResRef). Ablation studies on two benchmark data sets (the Massachusetts data set and the DeepGlobe data set) have confirmed the effectiveness of the presented designs. Comparative experiments with other approaches show that the proposed method has advantages in both overall accuracy and F1-score. The code is available at: https://github.com/ggsDing/DiResNet.
A fully-distributed proximal-point algorithm for Nash equilibrium seeking with linear convergence rate

We address the Nash equilibrium problem in a partial-decision information scenario, where each agent can only observe the actions of some neighbors, while its cost possibly depends on the strategies of other agents. Our main contribution is the design of a fully-distributed, single-layer, fixed-step algorithm, based on a proximal best-response augmented with consensus terms. To derive our algorithm, we follow an operator-theoretic approach. First, we recast the Nash equilibrium problem as that of finding a zero of a monotone operator. Then, we demonstrate that the resulting inclusion can be solved in a fully-distributed way via a proximal-point method, thanks to the use of a novel preconditioning matrix. Under strong monotonicity and Lipschitz continuity of the game mapping, we prove linear convergence of our algorithm to a Nash equilibrium. Furthermore, we show that our method outperforms the fastest known gradient-based schemes, both in terms of guaranteed convergence rate, via theoretical analysis, and in practice, via numerical simulations.
Seasonal acclimatization of Asparagopsis taxiformis (Rhodophyta) from different biogeographic regions

We studied the photosynthetic acclimatization of two populations of Asparagopsis taxiformis from two biogeographic regions (Santa Catalina Island, California, and Kaneohe Bay, Hawaii) in two seasons. We compared thermal variability between locations and estimated the environmental optima and tolerance limits of photosynthesis to understand the relationship between phenotypic plasticity and thermal variability at each location. Photosynthetic performance was assessed using oxygen evolution and pulsed amplitude modulated fluorometry to quantify responses to thermal variation. A. taxiformis from both locations had similar high temperature tolerances for maximum net photosynthesis (Pnetmax) and maximum relative electron transport rate rETRmax (~30°C), but different cold temperature tolerances. Respiration rates from both locations (summer and winter) always increased above 25°C. Chlorophyll fluorescence yield from dark‐adapted samples from both locations decreased at warmer temperatures in the winter and at colder temperatures during the summer. Populations exposed to greater thermal variability (across both short and long time scales) displayed greater capacity for photosynthetic acclimatization. A. taxiformis from California showed seasonal changes in photosynthetic performance within the environmental temperature range (14–21°C), whereas individuals from Hawaii did not. The narrower temperature tolerance (short‐term response) of subtropical Asparagopsis may make it more susceptible than temperate populations to the predicted increases in global sea surface temperatures. Acclimatization of photosynthesis may play an important role in enabling A. taxiformis to respond to a variable environment and to persist in different biogeographic regions.
Present and past microsatellite variation and assessment of genetic structure in Eurasian badger ( Meles meles ) in Denmark

During the past 50 years the number of badgers (Meles meles) in Denmark has declined by c. 50%. To assess the genetic consequences of the demographic decline, six DNA-microsatellite loci were used to analyse 139 badger tissue-samples, which were collected in 1995–98 from three zones (1, 2 and 3) in Jutland (Denmark). Results from contemporary samples were compared to data obtained from DNA extracted from teeth belonging to 39 badgers collected from zones 1 and 2 in 1957–66. The microsatellites showed a low to moderate polymorphism, and the within area genetic diversity (H E) was relatively low (0.308 ≤H E ≤ 0.441) as compared to other carnivores and mustelids. Genetic differentiation among the three recent sampling zones was significant (FST = 0.072,P 0.05). Despite the drastic recent decline, Danish badgers did not show apparent signs of genetic bottlenecks in both recent and historical samples. The use of molecular techniques which allowed us to work with small amounts of degraded DNA extracted from old teeth, allowed us to exclude the hypothesis that genetic differentiation between zones 1 and 2 was owing to a barrier, which has appeared in the last 50 years. Furthermore, the low genetic variability and therefore the relatively high genetic differentiation shown by Danish badgers were also shown not to be the result of the recent drastic population decline.
Bacterial biofilms: the powerhouse of a microbial fuel cell

Research in the field of microbial fuel cells has exploded in recent years and is providing insights into the specialized biological processes that occur within these systems. Fundamental to the functioning of a microbial fuel cell is the formation of highly specialized bacterial biofilms on the electrode surface. While many different bacterial species have been found to associate with electrodes, to date only a few have been isolated in pure culture with the ability to produce a high current density. One such species, Geobacter sulfurreducens, is speculated to form an electrical conductive biofilm capable of electron transfer across a considerable distance greater than 50 µm. The mechanisms for electron transfer across such distances are only just being understood and may involve a matrix of bacterially produced nanowires and/or c-type cytochromes, but limited due to proton accumulation. Evolutionary and genetic engineering studies are now being employed to increase power output on a per cell basis with differing degrees of success.
Evidence for color phase effects on the breeding and life history of Northern Fulmars

About 15% of Northern Fulmars (Fulmarus glacialis) breeding on the Semidi Islands in 1979 had light-phase plumage; the remainder were dark. Fulmars of different plumage types mated indiscriminantly, but the lighter member of a mixed pair was more likely to be male than female. Pairs that included at least one light-phase member had lower breeding success than dark/dark pairs in one of six years. Constancy of breeding site use differed between light/dark and dark/dark pairs, suggesting dark birds skipped more breeding attempts or had lower over-winter survival than light birds. The apparent effect of breeding experience (assessed by site fidelity) on success also differed between pair types. The polymorphism on the Semidi Islands may result from light-phase fulmars immigrating from the Bering Sea, but there is also evidence to suggest it is maintained by balancing selection in a closed population.
Complete Genome of Lactobacillus iners KY Using Flongle Provides Insight Into the Genetic Background of Optimal Adaption to Vaginal Econiche

Despite the importance of Lactobacillus iners and its unique characteristics for the study of vaginal adaption, its genome and genomic researches for identifying molecular backgrounds of these specific phenotypes are still limited. In this study, the first complete genome of L. iners was constructed using a cost-effective long-read sequencing platform, Flongle from Oxford Nanopore, and comparative genome analysis was conducted using a total of 1,046 strain genomes from 10 vaginal Lactobacillus species. Single-molecule sequencing using Flongle effectively resolved the limitation of the 2nd generation sequencing technologies in dealing with genomic regions of high GC contents, and comparative genome analysis identified three potential core genes (INY, ZnuA, and hsdR) of L. iners which was related to its specific adaption to the vaginal environment. In addition, we performed comparative prophage analysis for 1,046 strain genomes to further identify the species specificity. The number of prophages in L. iners genomes was significantly smaller than other vaginal Lactobacillus species, and one of the specific genes (hsdR) was suggested as the means for defense against bacteriophage. The first complete genome of L. iners and the three specific genes identified in this study will provide useful resources to further expand our knowledge of L. iners and its specific adaption to the vaginal econiche.
“Are You Telling the Truth?” — Testing Individuals’ Ability to Differentiate Between Truth and Deceit in Soccer

In the present paper, we tested the ability of individuals to judge correctly whether athletes are lying or telling the truth. For this purpose, we first generated 28 videos as stimulus material: in half of the videos, soccer players were telling the truth, while in the other half, the same soccer players were lying. Next, we tested the validity of these video clips by asking N = 65 individuals in a laboratory experiment (Study 1a) and N = 52 individuals in an online experiment (Study 1b) to rate the level of veracity of each video clip. Results suggest that participants can distinguish between true and false statements, but only for some clips and not for others, indicating that some players were better at deceiving than others. In Study 2, participants again had to make veracity estimations, but we manipulated the level of information given, as participants (N = 145) were randomly assigned to one of three conditions (regular video clips, mute video clips, and only the audio stream of each statement). The results revealed that participants from the mute condition were less accurate in their veracity ratings. The theoretical and practical implications of these findings are discussed.
Confidence Intervals for Single Coefficient of Variation of Weibull Distribution

The aim of this paper is to propose the new confidence intervals for single coefficient of variation of Weibull distributions, using the concept of the generalized confidence interval (GCI), the fiducial generalized confidence interval (FGCI), and the bootstrap percentile method. The coverage probabilities and the expected lengths were evaluated via Monte Carlo simulation. The simulation results showed that the coverage probabilities of the GCIs and the FGCIs were greater than or close to the nominal confidence level. Both of them were recommended. The coverage probabilities of confidence intervals based on the bootstrap percentile method were under the nominal confidence level. Regarding the expected lengths, they tended to decrease when the sample size was increased. All proposed confidence intervals were applied to some real world data in this study.
A Crowdsourcing Repeated Annotations System for Visual Object Detection

As a fundamental task in compute vision, object detection has been developed rapidly driven by the deep learning. The lack of a large number of images with ground truth annotations has become a chief obstacle to object detection applications in many fields. Eliciting labels from crowds is a potential way to obtain large labeled data. Nonetheless, existing crowdsourced techniques, e.g., Amazon Mechanical Turk (MTurk), often fail to guarantee the quality of the annotations, which have a bad influence on the accuracy of the deep detector. A variety of methods have been developed for ground truth inference and learning from crowds. In this paper, we study strategies to crowd-source repeated labels in support for these methods. The core challenge of building such a system is to reduce the difficulty to annotate multiple objects of interest and improve the data quality as much as possible. We present a system that adopts the turn-based annotation mechanism and consists of three simple sub-tasks: a single object annotation, a quality verification task and a coverage verification task. Experimental results demonstrate that our system is scalable, accurate and can assist the detector of obtaining higher accuracy.
Empirical Research on the Effect of Collaborative Learning in Blended Learning Mode Based on KNN Algorithm

The quality of collaborative learning is one of the essential factors that determine the quality of teaching. Therefore, it is a significant work for educators to explore scientific and reasonable grouping methods. In this paper, first we design a Blended Learning mode in which there are a variety of online and offline learning activities. The quantified learning behavior information becomes the original data and basis for grouping. Then we combined KNN (k-Nearest Neighbor) algorithm and grouping principle to implement grouping for the pilot class. Finally, the effect of this grouping method is demonstrated by comparing the final examination results and analyzing the number of students who have finished the preview. The results show that the class with the new grouping method has achieved good performance in the final examination.
CoreBigBench: Benchmarking big data core operations

Significant effort was put into big data benchmarking with focus on end-to-end applications. While covering basic functionalities implicitly, the details of the individual contributions to the overall performance are hidden. As a result, end-to-end benchmarks could be biased toward certain basic functions. Micro-benchmarks are more explicit at covering basic functionalities but they are usually targeted at some highly specialized functions. In this paper we present CoreBigBench, a benchmark that focuses on the most common big data engines/platforms functionalities like scans, two way joins, common UDF execution and more. These common functionalities are benchmarked over relational and key-value data models which covers majority of data models. The benchmark consists of 22 queries applied to sales data and key-value web logs covering the basic functionalities. We ran CoreBigBench on Hive as a proof of concept and verified that the benchmark is easy to deploy and collected performance data. Finally, we believe that CoreBigBench is a good fit for commercial big data engines performance testing focused on basic engine functionalities not covered in end-to-end benchmarks.