Miracle-dz/UniFAR-FTUs huggingface dataset:
Complete genome sequences and phylogenetic analysis of West Nile virus strains isolated from the United States, Europe, and the Middle East.

The complete nucleotide sequences of eight West Nile (WN) virus strains (Egypt 1951, Romania 1996-MQ, Italy 1998-equine, New York 1999-equine, MD 2000-crow265, NJ 2000MQ5488, NY 2000-grouse3282, and NY 2000-crow3356) were determined. Phylogenetic trees were constructed from the aligned nucleotide sequences of these eight viruses along with all other previously published complete WN virus genome sequences. The phylogenetic trees revealed the presence of two genetic lineages of WN viruses. Lineage 1 WN viruses have been isolated from the northeastern United States, Europe, Israel, Africa, India, Russia, and Australia. Lineage 2 WN viruses have been isolated only in sub-Saharan Africa and Madagascar. Lineage 1 viruses can be further subdivided into three monophyletic clades.
A Practical Framework to Study Low-Power Scheduling Algorithms on Real-Time and Embedded Systems

With the advanced technology used to design VLSI (Very Large Scale Integration) circuits, low-power and energy-efficiency have played important roles for hardware and software implementation. Real-time scheduling is one of the fields that has attracted extensive attention to design low-power, embedded/real-time systems. The dynamic voltage scaling (DVS) and CPU shut-down are the two most popular techniques used to design the algorithms. In this paper, we firstly review the fundamental advances in the research of energy-efficient, real-time scheduling. Then, a unified framework with a real Intel PXA255 Xscale processor, namely real-energy, is designed, which can be used to measure the real performance of the algorithms. We conduct a case study to evaluate several classical algorithms by using the framework. The energy efficiency and the quantitative difference in their performance, as well as the practical issues found in the implementation of these algorithms are discussed. Our experiments show a gap between the theoretical and real results. Our framework not only gives researchers a tool to evaluate their system designs, but also helps them to bridge this gap in their future works.
Curve Fitting With Clothoidal Splines.

Clothoids, i.e. curves Z(s) in R 2 whose curvatures x(s) are linear fitting functions of arclength s, have been used for some time for curve fitting purposes in engineering applications. The first part of the paper deals with some basic interpolation problems for clothoids and studies the existence and uniqueness of their solutions. The second part discusses curve fitting problems for clothoidal splines, i.e. C2-curves, which are composed of finitely many clothoids. An iterative method is described for finding a clothoidal spline Z(s) passing through given points Z i ϵ R 2 . i = 0,1,. , n+1, which minimizes the integral ∫ Z x ( s ) 2 d s . This algorithm is superlinearly convergent and needs only 0(n) operations per iteration. A similar algorithm is given for a related problem of smoothing by clothoidal splines.
Multirate Cascaded Discrete-Time Low-Pass ΔΣ Modulator for GSM/Bluetooth/UMTS

This paper shows that multirate processing in a cascaded discrete-time ΔΣ modulator allows to reduce the power consumption by up to 35%. Multirate processing is possible in a discrete-time ΔΣ modulator by its adaptibility with the sampling frequency. The power reduction can be achieved by relaxing the sampling speed of the first stage and increasing it appropriately in the second stage. Furthermore, a cascaded ΔΣ modulator enables the power efficient implementation of multiple communication standards. @The advantages of multirate cascaded ΔΣ modulators are demonstrated by comparing the performance of single-rate and multirate implementations using behavioral-level and circuit-level simulations. This analysis has been further validated with the design of a multirate cascaded triple-mode discrete-time ΔΣ modulator. A 2-1 multirate low-pass cascade, with a sampling frequency of 80 MHz in the first stage and 320 MHz in the second stage, meets the requirements for UMTS. The first stage alone is suitable for digitizing Bluetooth and GSM with a sampling frequency of 90 and 50 MHz respectively. This multimode ΔΣ modulator is implemented in a 1.2 V 90 nm CMOS technology with a core area of 0.076 mm2. Measurement results show a dynamic range of 66/77/85 dB for UMTS/ Bluetooth/GSM with a power consumption of 6.8/3.7/3.4 mW. This results in an energy per conversion step of 1.2/0.74/2.86 pJ.
Electroweak Baryogenesis

Contrary to naive cosmological expectations, all evidence suggests that the universe contains an abundance of matter over antimatter. This article reviews the currently popular scenario in which testable physics, present in the standard model of electroweak interactions and its modest extensions, is responsible for this fundamental cosmological datum. A pedagogical explanation of the motivations and physics behind electroweak baryogenesis is provided, and analytical approaches, numerical studies, up to date developments and open questions in the field are also discussed.
‘I Want to Bring Him from the Aeroplane to Here’: The Meaning of Animals to Children of Refugee or Migrant Backgrounds Resettled in Australia

Separation from animals with whom children have caring relationships can lead to considerable loss and grief, perhaps especially in the case of migration. This article reports on a thematic analysis of interviews undertaken with children of migrant or refugee backgrounds who had resettled in Australia. Findings suggest that children who spoke about animals framed their experiences in ways that either evoked a sense of loss with regard to animals or referred to animals as engendering a safe haven following resettlement. The article concludes by exploring potential service responses and encourages a focus on animals' needs in the context of migration.
Trade, Gravity, and Sudden Stops: On How Commercial Trade Can Increase the Stability of Capital Flows

Financial stability is an important policy objective, since crises are associated with large economic, social and political costs. This paper contributes to the discussion by providing new theoretical and empirical evidence on the causal connection between lack of exposure to commercial trade and proclivity to sudden stops. On the theoretical front, the paper shows how exposure to trade raises the creditworthiness of countries and reduces the probability of sudden stops.
Comparative Analyses of the Secretome from Dedifferentiated and Redifferentiated Adult Articular Chondrocytes

Objective: The main goal of this study was to compare the secretion products derived from human articular chondrocytes established in either long-term monolayer cultures or in scaffold-free 3-dimensional (3-D) cultures. Methods: Stable isotope labeling of amino acids in cell culture (SILAC) was applied to investigate quantitatively the differences between proteins secreted from dedifferentiated and redifferentiated chondrocytes. Proteins in cell supernatants were resolved by 1-D gel electrophoresis and analyzed by mass spectrometry. The results from the proteomic analyses were validated by immunoblotting. Additionally, antibody arrays were used to screen culture supernatants for 79 different morphogens. Results: Quantitative SILAC showed that some relevant growth factors such as CTGF or GAS6 were elevated in monolayers, along with proteins characteristic of a dedifferentiated phenotype such as collagen type I and tenascin. In spheroids, data showed overexpression of some cartilage-specific proteins such as aggrecan, together with important matrix regulators such as chitinase-3–like protein and stromelysin-1. Antibody arrays revealed that chondrocytes in monolayer secrete higher levels of leukocyte-activating agents such as MCP-1 and GRO, whereas the spheroid configuration favors the production of cell morphogens such as MCSF and VEGF. Conclusion: Our results show that some classic dedifferentiation and redifferentiation markers are differentially expressed in 2-D or 3-D culture configurations. Other cell/matrix regulatory molecules are also found to be differentially expressed by chondrocytes in 2-D and 3-D conditions by SILAC and antibody arrays. Our data bring new information for understanding the biology of chondrocytes in general and the process of cartilage tissue reconstruction in particular.
The hyperbolic class of quadratic time-frequency representations. I. Constant-Q warping, the hyperbolic paradigm, properties, and members

The time-frequency (TF) version of the wavelet transform and the "affine" quadratic/bilinear TF representations can be used for a TF analysis with constant-Q characteristic. The paper considers a new approach to constant-Q TF analysis. A specific TF warping transform is applied to Cohen's class of quadratic TF representations, which results in a new class of quadratic TF representations with constant-Q characteristic. The new class is related to a "hyperbolic TF geometry" and is thus called the hyperbolic class (HC). Two prominent TF representations previously considered in the literature, the Bertrand P/sub 0/ distribution and the Altes-Marinovic Q-distribution, are members of the new HC. The authors show that any hyperbolic TF representation is related to both the wideband ambiguity function and a "hyperbolic ambiguity function". It is also shown that the HC is the class of all quadratic TF representations which are invariant to "hyperbolic time-shifts" and TF scalings, operations which are important in the analysis of Doppler-invariant signals and self-similar random processes. The paper discusses the definition of the HC via constant-Q warping, some signal-theoretic fundamentals of the "hyperbolic TF geometry", and the description of the HC by 2D kernel functions. Several members of the HC are considered, and a list of desirable properties of hyperbolic TF representations is given together with the associated kernel constraints. >
Generating Fast Domain-Optimized Planners by Automatically Configuring a Generic Parameterised Planner

When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during preprocessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting planner. By providing many alternatives for these choices and exposing them as parameters, planning systems can in principle be configured to work well on different domains. However, usually planners are used in default configurations that have been chosen because of their good average performance over a set of benchmark domains, with limited experimentation of the potentially huge range of possible configurations. In this work, we propose a general framework for automatically configuring a parameterised planner, showing that substantial performance gains can be achieved. We apply the framework to the well-known LPG planner, which has 62 parameters and over 6.5 × 10 possible configurations. We demonstrate that by using this highly parameterised planning system in combination with the off-the-shelf, state-of-the-art automatic algorithm configuration procedure ParamILS, substantial performance improvements on specific planning domains can be obtained.
Building a Mobile Applications Knowledge Base for the Linked Data Cloud

The number of mobile applications (apps) in major app stores exceeded one million in 2013. While app stores provide a central point for storing app metadata, they often impose restrictions on the access to this information thus limiting the potential to develop tools to search, recommend, and analyze app information. A few projects have circumvented these limitations and managed to create a dataset with a substantial number of apps. However, accessing this information, especially for the purpose of an integrated view, is difficult as there is no common standard for publishing data. We present Mobipedia, an effort to gather this information from various sources and publish it as RDF Linked Data. We describe the status of Mobipedia, which currently has information on more than one million apps that has been extracted from a number of unstructured and semi-structured sources. This paper describes the ontology used to model information, the process for fact extraction, and an overview of applications facilitated by Mobipedia.
RECENT ADVANCES IN ARCHAEOLOGICAL PREDICTIVE MODELING FOR ARCHEOLOGICAL RESEARCH AND CULTURAL HERITAGE MANAGEMENT

The identification of areas that are insignificant for archaeological research can be used for guidance and support in projects that involve decision-making about the use of land and modern development activities. On the other hand, the identification of areas significant for archaeological research can contribute to archaeological knowledge and minimise the risk of unsuccessful excavations. This paper presents a review of the most recent and representative applications of predictive modelling in Archaeology, which demonstrate that predictive models can be successfully exploited by archaeological research and Cultural Heritage Management (CHM).
Crowd-powered find algorithms

We consider the problem of using humans to find a bounded number of items satisfying certain properties, from a data set. For instance, we may want humans to identify a select number of travel photos from a data set of photos to display on a travel website, or a candidate set of resumes that meet certain requirements from a large pool of applicants. Since data sets can be enormous, and since monetary cost and latency of data processing with humans can be large, optimizing the use of humans for finding items is an important challenge. We formally define the problem using the metrics of cost and time, and design optimal algorithms that span the skyline of cost and time, i.e., we provide designers the ability to control the cost vs. time trade-off. We study the deterministic as well as error-prone human answer settings, along with multiplicative and additive approximations. Lastly, we study how we may design algorithms with specific expected cost and time measures.
Plant 4/1 protein: potential player in intracellular, cell-to-cell and long-distance signaling

Originally isolated as a result of its ability to interact with the movement protein of Tomato spotted wilt virus in a yeast two-hybrid system, the 4/1 protein is proving to be an excellent tool for studying intracellular protein trafficking and intercellular communication. Expression of 4/1 in vivo is tightly regulated, first appearing in the veins of the cotyledon and later in the vasculature of the leaf and stem in association with the xylem parenchyma and phloem parenchyma. Structural studies indicate that 4/1 proteins contain as many as five coiled–coil (CC) domains; indeed, the highest level of sequence identity among 4/1 proteins involves their C-terminal CC domains, suggesting that protein–protein interaction is important for biological function. Recent data predict that the tertiary structure of this C-terminal CC domain is strikingly similar to that of yeast protein She2p; furthermore, like She2p, 4/1 protein exhibits RNA-binding activity, and mutational analysis has shown that the C-terminal CC domain is responsible for RNA binding. The 4/1 protein contains a nuclear export signal. Additional microscopy studies involving leptomycin and computer prediction suggest the presence of a nuclear localization signal as well.
Generating product configuration knowledge bases from precise domain extended UML models

The Unified Modeling Language (UML) is an emerging standard conceptual modeling language in Software Engineering processes. UML provides extension mechanisms (stereotypes) to adapt the general modeling language to specific application domains. In addition, UML comprises the standardized expression language Object Constraint Language (OCL) to model additional invariants. In this paper we show how UML can be extended using the standardized extension mechanisms to fit the needs of the domain of knowledge-based product configuration. Starting from a conceptual product model, we define the semantics of the employed extensions based upon a logic theory of configuration in a way that the resulting knowledge bases can be processed by a specialized inference engine. We show especially how the built-in expression language OCL can be employed to enhance the expressiveness of the conceptual models and how such expressions can be translated to the logical model. Finally, we describe a prototype implementation of the presented ideas based on commercial tools.
Entertainment, Intelligent, or Hybrid Programming? An Automated Content Analysis of 12 Years of Political Satire Interviews

ABSTRACT The research considers the balance of intellectual versus entertainment-oriented individuals appearing as interview guests on political satire programs. Specifically, the analysis employs semi-supervised learning to categorize the occupations of individuals appearing on The Daily Show and The Colbert Report between 2003 and 2014 (N = 3,507). The results confirm that satire programs have become an important outlet for politicians and journalists looking to connect with a younger audience. More often than not, these guest appearances privilege a newsworthy and intellectually oriented discussion of contemporary issues and ideas as opposed to the promotion of entertainment content. Important to note, the data set represents the largest body of political satire content analyzed to date and the results confirm the value of applying automated and semi-supervised learning coding within communication research. We conclude by discussing the methodological contributions of our coding process, future directions for political comedy research, and the scalable potential of automated coding efforts.
Relevance and Symmetry

We define a behavioral concept of relevance in the context of decision making under uncertainty. We argue that this concept provides a sensible answer to the question "What probabilistic environments do an individuals' preferences reveal as mattering to her decisions? " under a symmetry assumption. This question has important implications for economic modeling. It is often the case that a modeler desires to restrict the probabilistic environments a decision maker considers. Without a concept of relevant beliefs, it is impossible to check from preferences whether a model is reflecting what the modeler intended. This checking is essential to isolating the effect of changing information while holding tastes fixed. We show that a single concept of relevance delivers this for a wide range of models, including models that allow for ambiguity attitude. We also use symmetry and relevance to provide insight into the foundations of the α-MEU and smooth ambiguity models of decision-making under uncertainty.
Clustering Spikes in Multi-unit Neural Signals into Dominant and Remainder Classes

Spike sorting is a critical component in the brain machine interface. The traditional objective of spike sorting is to classify each spike, or action potential, in a multi-unit waveform into a separate class representing the neuron that fired the spike. A major problem in spike sorting is the variable and unknown number of classes. This paper investigates the efficacy of sorting neural signals into two classes a dominant class, which ideally includes spike occurrences of the most prominent neuron, and a remainder class, which ideally includes spike occurrences of all other distinguishable neurons rather than sorting into the ‘correct’ number of classes. This results in fewer classification errors, since classifications amongst neurons in the remainder class do not have to be made. A simulated neural dataset consisting of 30 multi-unit signals each with 4 distinguishable, or near-field, neurons was created using Matlab. The signals were represented as orthogonal ‘features’ by performing principle component analysis of the spike waveforms. Two clustering algorithms, k-means and expectation maximization using a Gaussian mixture model (GMM EM), were used on the first two principle component scores of each signal. Clustering was performed with the number of classes initialized with values 2, 3, 4, and 5. After sorting, the most prominent cluster was assigned to the dominant class and all other clusters were assigned to the remainder class. The correct and false classifications were determined and analyzed. Initializing the sorting algorithms to sort the data into the number of nearfield neurons or one more than the number of near-field neurons and then determining the dominant and reminder classes had the least classification errors for both algorithms. Initializing the number of classes to one less than the number of near-field neurons caused a marginal increase in errors. Initially sorting the data into two classes resulted in much greater classification error. Overall, the k-means algorithm performed better on average, but the GMM EM performed better when initialized with appropriate seeds.
A Novel Principal Component Analysis Neural Network Algorithm for Fingerprint Recognition in Online Examination System

To solve the authentication problem in online examination system for large-scale, a novel principal component analysis neural network algorithm for fingerprint recognition is presented. Based on the introduction of the basic principles of feature selection and feature extraction for principal component analysis¿Construction of Symmetric subspace model based on principal component analysis neural network, and the convergence of Symmetric subspace algorithm is analyzed.The feasibility of using this algorithm for fingerprint recognition problems is also discussed. Algorithm to meet the convergence conditions and to simplify the complex pre-processing steps, greatly reducing the computational complexity, improve the speed of the identification. Experimental results indicate that the algorithm can obtain a higher recognition rate compared with BP neural network recognition algorithm and this new algorithm presents a feasible and effective way to research on fingerprint recognition algorithm for the examination.
Coal demand estimating in Iran based on socio-economic indicators using particle swarm optimisation and genetic algorithm

The main objective of this research is to investigate Iran's coal demand, projection and supplies by using the structure of the Iranian socio-economic conditions. This study develops a scenario to analyse coal consumption and make future projections based on particle swarm optimisation (PSO) and genetic algorithm (GA) methods. The models developed in two forms (exponential and linear) and applied to the coal demand of Iran. PSO and GA demand estimation models (PSO-DEM and GA-DEM) are developed to estimate the future coal demand values based on population, gross domestic product (GDP), import and export figures. Coal consumption in Iran from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, relative error averages were 2.121% and 10.647% for GA — DEMexponential and GA — DEMlinear and were 1.921% for 3.885% for PSO — DEMexponential and PSO — DEMlinear. Coal demand is forecasted up to year 2030.