Miracle-dz/UniFAR-FTUs huggingface dataset:
Diet and Foraging of Rana sauteri and Bufo bankorensis Tadpoles in Subtropical Taiwanese Streams

We compared algae found in the alimentary canal of each species with algae found on tiles placed in the same streams. Algae collected from tiles primarily consisted of diatoms, green algae, and blue-green algae, with diatoms being the dominant taxa at all six of our study sites. Tadpoles of both species consumed mostly diatoms, and the food contents were similar between them. We found high similarities between the algal composition on the tiles and food in the alimentary canals of the tadpoles, suggesting that R. sauteri and B. bankorensis tadpoles are non-selective with respect to the type of algae they ingest.
Mycobacterium avium Chester: Designation of the Neotype Strain

The Judicial Commission has been requested (10) to issue an Opinion conserving the name Mycobacterium avium Chester. None of the strains on which the original description of this organism were based appears to be extant, and therefore strain SSC 1336 (=ATCC 25291) is here designated as the neotype strain of M. avium Chester. This strain was isolated from lesions of a hen and promptly deposited in the American Type Culture Collection for long-term preservation. It belongs to serotype 2, conforms to the current description of the species, and has the approval of the International Subcommittee on the Taxonomy of Mycobacteria for designation as the neotype strain. A description of the neotype strain is given.
Spatio–temporal differentiation in the population structure of Hydrobia ulvae on an intertidal mudflat (Marennes-Oléron Bay, France)

Spatio–temporal changes in density, breeding cycle, growth and population structure of Hydrobia ulvae (Gastropoda: Prosobranchia) were studied by monthly sampling of a wide bare mudflat in Marennes-Oléron Bay. Four stations, located along a cross-shore transect and characterized by different geomorphological structures, were investigated from March 2000 to February 2001. Hydrobia ulvae was mainly distributed in the upper half part of the mudflat and no individuals were found in the lower part. The breeding cycle extended from March to December and showed two annual peaks, in spring and in autumn. Three cohorts were recruited during the year and showed high growth rates during summer; the parameters of the von Bertalanffy model describing the growth curves were equal to kmax=0·47±0·5 mm month−1 and L∞=5·4±0·2 mm. The snail population had similar size–frequency structure along the transect at the beginning of the survey but summer recruitment initiated spatial differentiation. Reproduction occurred in the middle part of the mudflat but recruits mainly settled down at the upper level of the mudflat; new cohorts appeared with increasing individual densities. The middle part of the mudflat was rather dominated by adult individuals which showed large density fluctuations.
The Stata Journal

Spatial dependence exists whenever the expected utility of one unit of analysis is affected by the decisions or behavior made by other units of analysis. Spatial dependence is ubiquitous in social relations and interactions. Yet, there are surprisingly few social science studies accounting for spatial dependence. This holds true for settings in which researchers use monadic data, where the unit of analysis is the individual unit, agent, or actor, and even more true for dyadic data settings, where the unit of analysis is the pair or dyad representing an interaction or a relation between two individual units, agents, or actors. Dyadic data offer more complex ways of modeling spatial-effect variables than do monadic data. The commands described in this article facilitate spatial analysis by providing an easy tool for generating, with one command line, spatial-effect variables for monadic contagion as well as for all possible forms of contagion in dyadic data.
The biogeography and ecology of myelin in marine copepods

Calanoid copepods are ubiquitous in marine environments, where they are an abundant component of the plankton. Interestingly, this crustacean group is characterized by the co-existence of myelinate and amyelinate species. Myelin greatly enhances nervous system function, but requires the production and maintenance of large amounts of cell membranes. The role that myelin plays in the distribution and ecology of calanoids has not been investigated, yet should be significant given the costs and benefits of the adaptation. Surveying the available literature, habitats and ecological zones were identified, and calanoid species were categorized as either myelinate or amyelinate based on phylogeny. The diversity and abundance of the two types of calanoids were determined and compared to an expected ratio of 1:1. Myelinate taxa were predominant in oceanic epipelagic regions at all latitudes, while a majority of vertically migrating mesopelagic calanoids were amyelinate. Temperate and tropical estuaries were typically less diverse, with communities dominated by amyelinate calanoids. However, in many sub-tropical estuaries, myelinate species comprised . 80% of the calanoid population. An enhanced escape response and savings in energy may promote the success of myelinate calanoids in habitats characterized by high predation risk and low standing stocks of phytoplankton. Environments with low food quality may favor amyelinate species, given the cost of producing large quantities of lipid-rich membranes.
Higher Ground? How Groundtruth Labeling Impacts Our Understanding of the Spread of Fake News During the 2016 Election

The spread of fake news in online social media platforms has garnered much public attention and apprehension. Consequently, both the industry and academia alike are investing increased effort to understand, detect, and curb fake news. Yet, researchers differ in what they consider to be fake news sites. In this paper, we ﬁrst aggregate 5 distinct lists of fake news sites, and 3 lists of mainstream news sites published by experts and reputable organizations. Then, using each pair of fake and mainstream news lists as an independent “groundtruth”, we examine i) the prevalence and ii) temporal characteristics of fake news as well as iii) the agenda-setting differences between fake and mainstream news sites. We observe that depending on the groundtruth, the prevalence of fake news varies signiﬁcantly. However, the temporal trends and agenda-setting differences between fake and mainstream news sites remain moderately consistent across different groundtruth lists.
A Deep Long Short-Term Memory Neural Network based Autoencoders for Signal Validation

In the present fourth industrial revolution, the degree of automation is growing significantly and enormous amount of time-series data are collected from industrial systems for analysis. The demand for robust and resilient performance has led to the use of signal validation and on-line monitoring schemes to monitor sensors and equipment conditions using data-drivenbased models built by those time-series data. However, most data-driven models are developed and applied under steady-state or time-invariant conditions since this belongs to most of system’s uptime, but it is vital to have condition monitoring during transient operations as well, which has been less concerned. In this study, transient operations are any non-steady state but timevarying within normal operating conditions, such as start-up, shutdown, and load following modes of the system [1,2]. Recently, Ahmed et al [3] proposed a novel data-driven method, weighted distance Auto Associative Bilateral Kernel Regression (AABKR), for on-line monitoring during transient operation which has been successfully applied to the start-up transient of a nuclear power plant (NPP). However considering the availability of massive industrial time-series data or big data, in this work, the authors seek to explore the possibility and applicability of the deep learning techniques for signal validation and condition monitoring of safety parameters and sensors in NPPs during transient operations. Deep learning is a branch of machine learning algorithms that uses a cascade of many layers of non-linear processing units for feature extraction and transformation. There are various deep learning architectures, which include Restricted Boltzmann Machine (RBM) based deep belief network (DBN), Convolutional Neural Network (CNN), deep Auto-encoders, and deep Recurrent Neural Network (RNN). Since sensor data is a time-series data, this work chose to use a deep recurrent neural network (RNN) architecture based on deep learning technique, long short-term memory (LSTM) to capture the timevarying dependencies of transient data for the purpose of sensors’ signal validation and condition monitoring. The deep LSTM model is developed in form of autoencoder: the encoder-decoder network, such that the multi-dimensional sensors signals can be effectively reconstructed on-line for the purpose of condition monitoring. 2. Methods and Results
Collaborative Design for Fashionable Wearables: A Fashion System Perspective

Wearable technologies are used by only a small part of the consumer market, and their abandonment rates are still high. Aesthetic value and style are essential for making these devices fashionable, thus facilitating their widespread use. Designing fashionable wearables requires a collaboration between professionals working in technology and fashion fields. Although many studies in the literature indicate a need for collaboration, none explored the attitudes of professionals from these fields towards collaborating with each other. Addressing this gap, we conducted in-depth interviews with 4 fashion designers, 1 fashion editor, 3 product developers and 1 public relations manager working in fashion industry. Based on the insights derived from these fashion professionals, we presented stakeholders that should be actively involved in the collaboration, the characteristics of collaboration environment, barriers for a successful collaboration, and two product development process models driven by either fashion and
Dyadic Human Interaction Recognition from Videos using Multi-layer 3D CNN

: Human Activity Recognition aims in recognizing and interpreting the activities of human automatically from videos. In general, the major issues of Human Activity Recognition are dynamic backgrounds, similar action recognition, dyadic human interaction recognition, incremental learning etc. Among these issues, identifying the interactions of human within minimal computation time and reduced misclassification rate is a cumbersome task. Hence, a Dyadic Human Interaction Recognition system is proposed in this paper that utilizes a multi-layer 3D CNN and a kernel-based rv-tSNE transformation algorithm for better classification. Preprocessing techniques like gray scale conversion, pixel normalization, and one-hot encoding have been deployed to handle enormous amount of irregularities in the dataset. The model is trained using multi-layer 3D Convolution Neural Networks (CNN). Additionally, a transformation model named kernel-based rv-tSNE has been incorporated for dimensionality reduction and easier visualization of the data. The requirement of the transformation module is justified by comparing the end results of the system with and without the transformation algorithm. The results demonstrate that the proposed system recognizes the interactions of human with reduced misclassification rate and minimal processing time compared to the benchmarking datasets for various interactions. The proposed system achieves 94.78% and 93.394% for SBU kinect interaction dataset and for the newly recorded AU-Interaction dataset respectively . The proposed system finds its applications in sports event analysis
The Biochar Option to Improve Plant Yields: First Results From Some Field and Pot Experiments in Italy

The pyrolysis conversion of agricultural residues into biochar and its incorporation in agricultural soil, avoids CO2 emissions providing a safe long-term soil carbon sequestration. Furthermore, biochar application to soil seems to increase nutrient stocks in the rooting zone, to reduce nutrient leaching and to improve crop yields. This study reports some preliminary results obtained using biochar in two typical Italian agricultural crops. Two field experiments were made on durum wheat (Triticum durum L.) in Central Italy and maize (Zea mays L.) in Northern Italy. In both the field experiments, an increase in yields (+ 10% and + 6% in terms of grain production, respectively) was detected after a biochar application of 10 t ha-1. A further increase in grain production (+24%) was detected when biochar was added with maize residues. The biochar dose-effect curve was studied on perennial ryegrass (Lolium perenne L.) in a pot experiment. The highest increase of dry matter (+120%) was obtained at a biochar rate of 60 t ha-1 and above this threshold, a general reduction of biomass was observed. Results demonstrate the potential of biochar applications to improve in terms of dry matter production, while pointing out the needs for long-term field studies to better understand the effects of biochar on soil.
The 3/4-Power Law Is Not Universal: Evolution of Isometric, Ontogenetic Metabolic Scaling in Pelagic Animals

Abstract It is commonly assumed that metabolic rate scales with body mass to the 3/4 power. However, during ontogeny, the metabolic rate of pelagic (open-water) animals often scales isometrically (in a 1:1 proportion) with body mass. This is a robust pattern, occurring in five different phyla. It can also be seen by comparing pelagic and benthic (bottom-dwelling) species within phyla, as well as pelagic larvae and benthic adults within species. High energy costs of continual swimming to stay afloat or of rapid rates of growth and reproduction in response to high levels of mortality (predation) in open water, or both, may contribute to the isometric or nearly isometric metabolic scaling of pelagic animals. The observation of isometric scaling and other kinds of metabolic scaling both within and among species suggests that metabolic scaling is not simply the result of physical constraints, but is an evolutionarily malleable trait that responds to ecological circumstances.
A Simulation-Based Method to Determine Model Misspecification: Examples using Natural Mortality and Population Dynamics Models

Abstract Recent developments in the models used in wildlife and fisheries science have allowed the inclusion of a wider range of data than previously. However, the diagnostics of such complex models have not kept pace. We describe a new diagnostic technique based on simulation analysis. Model misspecification was identified through simulation methods that created a distribution of likely parameter values for a model that was correctly specified. If the actual estimate of that parameter is outside the bounds of the simulated distribution, then the model is probably misspecified. We tested the reliability of the new diagnostic by introducing known-model misspecification into complex fisheries stock assessment models. We then compared the results from this new diagnostic with those of a more tradition fisheries diagnostic, namely, retrospective analysis. The simulation-based diagnostic was shown to identify inisspecification affecting the estimated dynamics more reliably than retrospective analysis.
The Effects of Gravitation on the Inter-Media Agenda-Setting Central Process: The Case of the Murder of Hrant Dink

While the first level agenda setting researches focus on the transfer of issue salience from the media to public agenda, second level agenda setting researches interest in the attributes emphasized in the news and their affect on the public agenda. Some of these researches tends to analysis the media agenda. Influences of the news media on each other are studied by the inter-media agenda setting researches at at both the first and second levels. The same researches examine also the effects of different types of media on each other. However there is the problem of lack of a systematic theoretical model. This is caused by the differentiation in the aims of researchers and in their research objects. The other problem in the inter-media agenda setting researches is that there is no research on the agenda setting effects of the newspapers which have different ideological and political identities. This research aims to scrutinize the inter-media agenda-setting effects among various newspapers that have got different points of view. The research is designed to test the central gravitation effects of the mainstream news papers. Here the Murder of Hrant Dink is selected as a case study. Anahtar Kelimeler: Kitle İletişim Araçları Arası Gündem Belirleme, Niteliksel Gündem Belirleme, İkinci Düzey Gündem Belirleme, Hrant Dink.
Performance of short polar codes under ML decoding

: Polar codes are a recently introduced class of codes that achieve the capacity of arbitrary symmetric binary-input channels. This capacity-achieving performance is obtained by encoders and decoders of complexity O ( N log N ) where N is the code block-length. The performance of polar coding under belief propa-hation (BP) decoding has been studied before, using Reed-Muller (RM) codes as a benchmark. This work studies the performance of polar coding under trellis-based maximum-likelihood (ML) decoding, again using RM codes as a benchmark. One ﬁnding is that RM codes perform better than polar codes under ML decoding for certain short codes. On the other hand, polar codes have a lower trellis complexity. A second ﬁnding is that BP decoding offers performance comparable to ML decoding as the block-length is increased.
Voluntary Adoption of the International Integrated Reporting Framework: Capital Market Consequences

The purpose of integrated reports is to improve the quality of information available to capital providers to enable more efficient capital allocation decisions. There is scant evidence to substantiate the proposed capital market benefits of integrated reporting (IR) in voluntary settings as extant empirical results are focused on mandatory IR. This paper examines whether voluntary adoption of the International Integrated Reporting Framework and initiation of integrated reports influence the information environment, cost of equity and firm value. Using an international sample of IR firms and matched non-IR firms, the results provide no evidence of an association between voluntary IR adoption and capital market consequences. These results are robust to controlling for self-selection, to the use of both level and change specifications, to a difference-in-differences design, to alternative model specifications and sample specifications, and to a number of sensitivity analyses. JEL Classification: M40, M41
Molecular Detection and Phylogenetic Relationships of Three Symbiotic Bacteria in Spiders (Araneae) from China

ABSTRACT: Only a few studies have examined the presence of endosymbionts in spiders (Araneae). We used PCR methods to assay for the presence of Wolbachia, Cardinium and Spiroplasma in 1000 spider individuals from China (including 16 families and 37 species). Of the species, 41.4% (15/37) contained one or more endosymbionts, Nephila clavata tested positive for all three endosymbionts, indicating that hereditary symbioses are extremely widespread in spiders. Align ment of the sequences obtained here with previously published alignments allowed us to establish phylogenetic trees for three symbiotic bacteria. The results showed that the Wolbachia found in infected spiders all belong to group A or B. The maximum parsimony(MP) tree showed that all Cardinium and Spiroplasma infecting spiders formed the same branch, indicating a very close genetic relationship between the two genera of symbiotic bacteria that exist in the spiders from different countries. However, the sequences of Spiroplasma from Chinese spiders showed a closer phylogenetic relationship and greater similarity to each other.
The Effect of the Effectiveness of Accounting Information System and Use of Accounting Information Technology on Individual Performance with Computer User Expertise as a Moderating Variable (Survey on Cv. Mahli

: This study aims to examine the effect of the effectiveness of accounting information systems, the use of accounting information technology and the expertise of computer users together on individual performance. This study also aims to examine the effect of computer user expertise on the relationship between the effectiveness of accounting information systems and the use of accounting information technology with individual performance on CV. Mahli Baru Retailindo. The populations in this study were employees at CV. Mahli Baru Retailindo, which numbered 13 outlets in Banda Aceh City and Greater Aceh. In this study, the sources used are primary data and secondary data. Primary data obtained from the results of questionnaires answers to research respondents, while secondary data obtained from other documents. While the research data collection techniques are carried out by documentation techniques. The analytical method used is Multiple Linear Regression Analysis. The results showed that the effectiveness of accounting information systems, the use of accounting information technology and computer user expertise both jointly and separately had an effect on individual performance on the CV. Mahli Baru Retailindo. The next results show that computer user expertise influences the relationship between the effectiveness of accounting information systems and the use of accounting information technology with individual performance on CV. Mahli Baru Retailindo).
Staging Reflections on Ethical Dilemmas in Machine Learning: A Card-Based Design Workshop for High School Students

The increased use of machine learning (ML) in society raises questions of how ethical dilemmas inherent in computational artefacts can be made understandable and explorable for students. To investigate this, we developed a card-based design workshop that allows students to reflect on ethical dilemmas by designing their own ML applications. The workshop was developed in an iterative process engaging four high school classrooms with students aged 16-20. We found that scaffolding students in designing meaningful ML systems served to qualify their ethical reflections. Further students' design processes allowed them to engage with the ethical dilemmas and to tie these to the properties of the technology and to their design decisions. We suggest seeing technology-close discussions about ethics as a goal in design processes, and prototyping as a means to ground these discussions in students' own design decisions, and we contribute a workshop format and design artefacts that allow for this.
Resistance Genes and Selection DNA Markers for Blast Disease in Rice (Oryza sativa L.)

Blast is a serious disease caused by a fungal pathogen Pyricularia oryzae Cavara of rice (Oryza sativa L.). The use of resistant varieties is considered one of the most efficient ways of crop protection from the disease. In addition to a large amount of information accumulated during the long history of genetic studies on resistance to rice blast, recent progress in rice genomics has enabled us to use DNA markers for breeding the resistant varieties by marker assisted selection (MAS). In this report, we summarize the reported rice blast resistance genes and their selection markers to encourage further utilization for breeding. First, we assemble the information about the reported genes with regard to their number, chromosomal locations, patterns of resistance, donor strains, and molecular characterization of the cloned genes by reviewing the literature. In addition, we present some remaining issues about the nomenclature system and identification of the resistance genes. Then, we provide the first assembled list of the reported DNA markers for blast resistance genes, including the sequences of the primer pairs, genetic distances from the resistance genes, and cross combinations of the parental strains used to detect the polymorphisms. This information will help rice breeders to improve the resistance to rice blast by MAS.