Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting social learning using networks: a users guide

Controversy over claims of cultures in nonhuman primates and other animals has led to a call for quantitative methods that are able to infer social learning from freely interacting groups of animals. Network‐based diffusion analysis (NBDA) is such a method that infers social transmission of a behavioral trait when the pattern of acquisition follows the social network. As, relative to other animals, primates may be unusual in their heavy reliance on social learning, with learning frequently directed along pathways of association; in this study, we draw attention to the significance of this method for primatologists. We provide a “users guide” to NBDA methodology, discussing the choice of NBDA model and social network, and suggest model selection procedures. We also present the results of simulations that suggest that NBDA works well even when the assumptions of the underlying model are violated. Am. J. Primatol. 73:834–844, 2011. © 2011 Wiley‐Liss, Inc.
Neural Associative Memories for Detecting Analogies : an Application to Structural Safety Management

This paper describes the first achievements and the future developments of an experimental application of connectionist hashed associative memories for realising analogical reasoning. The application field is the management of structural safety, where analogical reasoning is used to retrieve, given the current description of the state of a structure, the closest-matching cases stored in a case base, which can help safety managers to interpret the current situation.
A new method for model selection in speech recognition

A new method based on model selection for acoustic model training is proposed . The MPE trained model and the MLE trained model is used for model selection for the following training. The selection criteria is based on the ratio of the inter-variance to the intra-variance of each model. Besides we also propose a cluster method for the model in order to get the accuracy information for the weight calculation. The experiments demonstrate that the new model can get better performance than any of the directly trained models.
Progress and challenges with clinical cell therapy in neurorestoratology

Cell therapies in the treatment of central nervous system disease and injury, such as spinal cord injury, multiple sclerosis, sequelae of stroke, amyotrophic lateral sclerosis, and cerebral palsy, have been studied in the clinic for the last 10-20 years. Excitingly, many studies have demonstrated that most patients appear to have some functional improvement following administration of different types of cells by different routes with relatively low risk and good tolerability. However, there are some misconceptions that hinder the development of cell-based neurorestorative strategies. It is a considerable challenge but also an opportunity for physicians in neurorestoratology to face these issues. This review briefly outlines the progress made in neurorestoratology, discusses the relevant issues, and attempts to correct the misconceptions.
Critical review of host specificity and its coevolutionary implications in the fig/fig-wasp mutualism

Figs (Ficus spp., Moraceae) and their pollinating wasps (Agaonidae, Chalcidoidea) constitute perhaps the most tightly integrated pollination mutualism that is known. Figs are characterized by extraordinarily high global and local species diversity. It has been proposed that the diversification of this mutualism has occurred through strict-sense coadaptation and cospeciation between pairs of fig and wasp species that are associated in highly specific one-to-one relationships. However, existing studies cast doubt on the generality of this proposition. Here, we review our current knowledge of the evolutionary history of the fig/fig-wasp mutualism. We critically examine the idea that codivergence between figs and their pollinators has been dominated by strict-sense cospeciation. We present phylogenetic and population genetic data from neotropical fig and fig wasp species that suggest that a more accurate model for diversification in this mutualism is that of groups of genetically well defined wasp species coevolving with genetically less well defined (frequently hybridizing) groups of figs. Last, we use our results to assess previously proposed hypotheses on models of speciation in this mutualism.
Circling particles and drafting in optical vortices

We model a system of particles suspended in a viscous fluid circle in optical vortices generated by holographic optical tweezer techniques (Curtis and Grier 2003 Phys. Rev. Lett. 90 133901) and show that hydrodynamic interactions between the circling particles determine their collective motion. We perform a linear-stability analysis to investigate the stability of regular particle clusters and illustrate the limit cycle to which the unstable modes converge. We clarify that drafting of particle doublets is essential for the understanding of the limit cycle.
Satellite retrieval of aerosol absorption over the oceans using sunglint

Aerosol absorption of sunlight, in particular by black carbon ‐ soot and dark organic material produced from incomplete combustion of fossil fuel and from vegetation fires, is emerging as a key component of climate forcing. However, global characterization of black carbon emissions, distribution and absorption of sunlight cannot be determined within a factor of 5. Here we propose that the oceanic sunglint can be used as a bright background against which aerosol absorption can be measured from space. The method can map global ocean glint every 4–10 days and determine the distribution of aerosol absorption optical thickness with an error of ±25% for aerosol optical thickness of 0.2 to 0.4. 18 % of the ocean observations are for this range of optical thicknesses. The monthly average aerosol absorption is estimated to have an error of ±12% and uncertainty in the single scattering albedo of ±0.02. We outline a satellite design to perform the measurements.
Recognition of synonyms by a lexical graph

Semantic relationships between words comprised by thesauri are essential features for IR, text mining and information extraction systems. This paper introduces a new approach to identiﬁcation of semantic relations such as syn-onymy by a lexical graph. The graph is generated from a text corpus by embedding syntactically parsed sentences in the graph structure. The vertices of the graph are lexical items (words), their connection follows the syntactic structure of a sentence. The structure of the graph and distances be-tween vertices can be utilized to deﬁne metrics for identiﬁcation of semantic relations. The approach has been evaluated on a test set of 200 German syn-onym sets. Inﬂuence of size of the text corpus, word generality and frequency has been investigated. Conducted experiments for synonyms demonstrate that the presented methods can be extended to other semantic relations.
Calculation of Pseudospectra by the Arnoldi Iteration

The Arnoldi iteration, usually viewed as a method for calculating eigenvalues, can also be used to estimate pseudospectra. This possibility may be of practical importance, because in applications involving highly nonnormal matrices or operators, such as hydrodynamic stability, pseudospectra may be physically more significant than spectra.
Community Detection Using Slow Mixing Markov Models

The task of \emph{community detection} in a graph formalizes the intuitive task of grouping together subsets of vertices such that vertices within clusters are connected tighter than those in disparate clusters. This paper approaches community detection in graphs by constructing Markov random walks on the graphs. The mixing properties of the random walk are then used to identify communities. We use coupling from the past as an algorithmic primitive to translate the mixing properties of the walk into revealing the community structure of the graph. We analyze the performance of our algorithms on specific graph structures, including the stochastic block models (SBM) and LFR random graphs.
Pattern recognition and classification for multivariate time series

Nowadays we are faced with fast growing and permanently evolving data, including social networks and sensor data recorded from smart phones or vehicles. Temporally evolving data brings a lot of new challenges to the data mining and machine learning community. This paper is concerned with the recognition of recurring patterns within multivariate time series, which capture the evolution of multiple parameters over a certain period of time. Our approach first separates a time series into segments that can be considered as situations, and then clusters the recognized segments into groups of similar context. The time series segmentation is established in a bottom-up manner according the correlation of the individual signals. Recognized segments are grouped in terms of statistical features using agglomerative hierarchical clustering. The proposed approach is evaluated on the basis of real-life sensor data from different vehicles recorded during car drives. According to our evaluation it is feasible to recognize recurring patterns in time series by means of bottom-up segmentation and hierarchical clustering.
ReSPARQL : a SPARQL Extension for Generic Recommendations on RDF-graphs

The abundance of data published using Semantic Web technologies ratifies their high degree of maturity reached. Moreover, the flexibility of the Resource Description Framework (RDF) enables it to model any knowledge within a specific domain. This has given rise to a potential use of RDF data as input for applications which were not originally designed to operate online on Web data sources. Recommender systems are one example of such applications. These aim to predict the taste of a user towards a set of not consumed items and are typically well optimized for fixed domains. The benefit of having a recommender system which takes advantage of Web knowledge is that a user could be assisted in selecting information from the Web and, therefore, reducing the information overload. However, these systems cannot handle the diversity and unstructuredness of Semantic Web data. One of the reasons is that Semantic Web query languages, such as SPARQL, support retrieval of data exclusively based on facts; predictions or suggestions are entities that cannot be explicitly retrieved. In this thesis ReSPARQL will be presented: an extension of the SPARQL syntax and semantics that fills this gap and enables a generic and flexible approach for recommendations over arbitrary RDF-graphs. It supports content-based and collaborative filtering recommendations and allows both paradigms to gain benefit from each other.
Fractal spacetime structure in asymptotically safe gravity

Four-dimensional Quantum Einstein Gravity (QEG) is likely to be an asymptotically safe theory which is applicable at arbitrarily small distance scales. On sub-planckian distances it predicts that spacetime is a fractal with an effective dimensionality of 2. The original argument leading to this result was based upon the anomalous dimension of Newton's constant. In the present paper we demonstrate that also the spectral dimension equals 2 microscopically, while it is equal to 4 on macroscopic scales. This result is an exact consequence of asymptotic safety and does not rely on any truncation. Contact is made with recent Monte Carlo simulations.
The combination of ground-based astrometric compilation catalogues with the HIPPARCOS Catalogue - II. Long-term predictions and short-term predictions

The combination of ground-based astrometric compilation catalogues, such as the FK5 or the GC, with the results of the ESA Astrometric Satellite HIPPARCOS produces for many thousands of stars proper motions which are significantly more accurate than the proper motions derived from the HIPPARCOS observations alone. In Paper I (Wielen et al. 1999, A&A 347, 1046) we have presented a method of combination for single stars (SI mode). The present Paper II derives a combination method which is appropriate for an ensemble of 'apparently single-stars' which contains undetected astrometric binaries. In this case the quasi-instantaneously measured HIPPARCOS proper motions and positions are affected by 'cosmic errors', caused by the orbital motions of the photo-centers of the undetected binaries with respect to their center-of-mass. In contrast, the ground-based data are 'mean values' obtained from a long period of observation. We derive a linear 'long-term prediction' (LTP mode) for epochs far from the HIPPARCOS epoch T_H ~ 1991.25, and a linear 'short-term prediction' (STP mode) for epochs close to T_H. The most accurate prediction for a position at an arbitrary epoch is provided by a smooth, non-linear transition from the STP solution to the LTP solution. We present an example for the application of our method, and we discuss the error budget of our method for the FK6 (a combination of the FK5 with HIPPARCOS) and for the combination catalog GC+HIP. For the basic fundamental stars, the accuracy of the FK6 proper motions in the LTP mode is better than that of the HIPPARCOS proper motions (taking here the cosmic errors into account) by a factor of more than 4.
Constructed Wetlands for Wastewater Treatment

In the field of wastewater treatment, energy-intensive and highly mechanized technologies are giving way to nature-based technologies that utilize solar energy and living organisms. Constructed treatment wetland (CTW) technology has played an important role in bringing about the change. Wetland technology can provide cheap and effective wastewater treatment in both temperate and tropical climates, and are suitable for adoption in both industrialized as well as developing nations. Currently, CTWs are being utilized for removal of a range of pollutants and a broad variety of wastewaters worldwide. The objective of this article is to provide a comprehensive review of the CTW technology and to present the pollutant removal performance experiences gathered through the application of this technology around the world.
DNA-tension dependence of restriction enzyme activity reveals mechanochemical properties of the reaction pathway

Type II restriction endonucleases protect bacteria against phage infections by cleaving recognition sites on foreign double-stranded DNA (dsDNA) with extraordinary specificity. This capability arises primarily from large conformational changes in enzyme and/or DNA upon target sequence recognition. In order to elucidate the connection between the mechanics and the chemistry of DNA recognition and cleavage, we used a single-molecule approach to measure rate changes in the reaction pathway of EcoRV and BamHI as a function of DNA tension. We show that the induced-fit rate of EcoRV is strongly reduced by such tension. In contrast, BamHI is found to be insensitive, providing evidence that both substrate binding and hydrolysis are not influenced by this force. Based on these results, we propose a mechanochemical model of induced-fit reactions on DNA, allowing determination of induced-fit rates and DNA bend angles. Finally, for both enzymes a strongly decreased association rate is obtained on stretched DNA, presumably due to the absence of intradomain dissociation/re-association between non-specific sites (jumping). The obtained results should apply to many other DNA-associated proteins.
Study on Traffic Flow Base on RBF Neural Network

According to charging lathing standard type classification volume of traffic blowing the expense at enormous and the data is inaccurate, But the volume of traffic may obtain easily according to the traffic survey classification from each kind of intelligent toll station. The function neural networks method will charge with because of radial base the rate of flow data change becomes volume of traffic inquiring into a data, Building-up changes a model, Using the MATLAB programming, Obtaining each kind of vehicle type error distinguish ratio and total error distinguish ratio. It can utilize fully thereby advantage of the fee-collecting station, Every fee-collecting station all has the precise writer that various motorcycle type passes in the process charging, it can cut down the cost getting the traffic survey data, Enables the traffic survey the data to obtain the full use.
Calibrating coseismic coastal land-level changes during the 2014 Iquique (Mw=8.2) earthquake (northern Chile) with leveling, GPS and intertidal biota

The April 1st 2014 Iquique earthquake (MW 8.1) occurred along the northern Chile margin where the Nazca plate is subducted below the South American continent. The last great megathrust earthquake here, in 1877 of Mw ~8.8 opened a seismic gap, which was only partly closed by the 2014 earthquake. Prior to the earthquake in 2013, and shortly after it we compared data from leveled benchmarks, deployed campaign GPS instruments, continuous GPS stations and estimated sea levels using the upper vertical level of rocky shore benthic organisms including algae, barnacles, and mussels. Land-level changes estimated from mean elevations of benchmarks indicate subsidence along a ~100-km stretch of coast, ranging from 3 to 9 cm at Corazones (18°30’S) to between 30 and 50 cm at Pisagua (19°30’S). About 15 cm of uplift was measured along the southern part of the rupture at Chanabaya (20°50’S). Land-level changes obtained from benchmarks and campaign GPS were similar at most sites (mean difference 3.7±3.2 cm). Higher differences however, were found between benchmarks and continuous GPS (mean difference 8.5±3.6 cm), possibly because sites were not collocated and separated by several kilometers. Subsidence estimated from the upper limits of intertidal fauna at Pisagua ranged between 40 to 60 cm, in general agreement with benchmarks and GPS. At Chanavaya, the magnitude and sense of displacement of the upper marine limit was variable across species, possibly due to species—dependent differences in ecology. Among the studied species, measurements on lithothamnioid calcareous algae most closely matched those made with benchmarks and GPS. When properly calibrated, rocky shore benthic species may be used to accurately measure land-level changes along coasts affected by subduction earthquakes. Our calibration of those methods will improve their accuracy when applied to coasts lacking pre-earthquake data and in estimating deformation during pre–instrumental earthquakes.
A unifying view of some training algorithms for multilayer perceptrons with FIR filter synapses

Concerns neural network architectures for modelling time-dependent signals. A number of algorithms have been published for multilayer perceptrons with synapses described by finite impulse response (FIR) and infinite impulse response (IIR) filters (the latter case is also known as locally recurrent globally feedforward networks). The derivations of these algorithms have used different approaches in calculating the gradients, and in this paper we present a short, but unifying account of how these different algorithms compare for the FIR case, both in derivation, and performance. A new algorithm is subsequently presented. In this paper, results are compared for the Mackey-Glass chaotic time series (1977) against a number of other methods including a standard multilayer perceptron, and a local approximation method. <<ETX>>
Network Features of the Mammalian Circadian Clock

The mammalian circadian clock is a cell-autonomous system that drives oscillations in behavior and physiology in anticipation of daily environmental change. To assess the robustness of a human molecular clock, we systematically depleted known clock components and observed that circadian oscillations are maintained over a wide range of disruptions. We developed a novel strategy termed Gene Dosage Network Analysis (GDNA) in which small interfering RNA (siRNA)-induced dose-dependent changes in gene expression were used to build gene association networks consistent with known biochemical constraints. The use of multiple doses powered the analysis to uncover several novel network features of the circadian clock, including proportional responses and signal propagation through interacting genetic modules. We also observed several examples where a gene is up-regulated following knockdown of its paralog, suggesting the clock network utilizes active compensatory mechanisms rather than simple redundancy to confer robustness and maintain function. We propose that these network features act in concert as a genetic buffering system to maintain clock function in the face of genetic and environmental perturbation.
Theory of “Jitter” Radiation from Small-Scale Random Magnetic Fields and Prompt Emission from Gamma-Ray Burst Shocks

We demonstrate that the radiation emitted by ultrarelativistic electrons in highly nonuniform, small-scale magnetic fields is different from synchrotron radiation if the electron's transverse deflections in these fields are much smaller than the beaming angle. A quantitative analytical theory of this radiation, which we refer to as jitter radiation, is developed. It is shown that the emergent spectrum is determined by statistical properties of the magnetic field. The jitter radiation theory is then applied to internal shocks of γ-ray bursts (GRBs). The model of a magnetic field in GRBs proposed by Medvedev & Loeb in 1999 is used. The spectral power distribution of radiation produced by the power-law-distributed electrons with a low-energy cutoff is well described by a sharply broken power law: P(ω) ∝ ω1 for ω ≲ ωjm and P(ω) ∝ ω-(p-1)/2 for ω ≳ ωjm, where p is the electron power-law index and ωjm is the jitter break frequency, which is independent of the field strength but depends on the electron density in the ejecta, ωjm ∝ n1/2, as well as on the shock energetics and kinematics. The total emitted power of jitter radiation is, however, equal to that of synchrotron radiation. Since large-scale fields may also be present in the ejecta, we construct a two-component, jitter + synchrotron spectral model of the prompt γ-ray emission. Quite surprisingly, this model seems to be readily capable of explaining several properties of time-resolved spectra of some GRBs, such as (1) the violation of the constraint on the low-energy spectral index called the synchrotron "line of death," (2) the sharp spectral break at the peak frequency, inconsistent with the broad synchrotron bump, (3) the evidence for two spectral subcomponents, and (4) possible existence of emission features called "GRB lines. " We believe these facts strongly support both the existence of small-scale magnetic fields and the proposed radiation mechanism from GRB shocks. As an example, we use the composite model to analyze GRB 910503, which has two spectral peaks. At last, we emphasize that accurate GRB spectra may allow precise determination of fireball properties as early as several minutes after the explosion.
Hyper Radial Basis Function Neural Network for Nonlinear Interference Cancellation

In this paper the neural network based lter for nonlinear interference cancellation is developed. The Hyper Radial Basis Function (HRBF) network with associated Manhattan learning algorithm is proposed for non-linear noise cancellation under assumption that reference noise is available. The HRBF network is a generalization of radial basis function (RBF) and generalized radial basis function (GRBF) networks. This fact is important for our application and allow to achieve better results in nonlinear interference cancellation due to exible approximation properties of HRBF network. The simulation results and comparison with standard RBF network applying for interference cancellation are discussed. Validity, eeec-tiveness and advantages of HRBF network based cancellation system are demonstrated. This paper has not been submitted elsewhere in identical or similar form.