Miracle-dz/UniFAR-FTUs huggingface dataset:
N-Body Simulations of Compact Young Clusters near the Galactic Center

We investigate the dynamical evolution of compact young star clusters (CYCs) near the Galactic center using Aarseth's Nbody6 codes. The relatively small number of stars in the cluster (5000-20,000) makes real-number N-body simulations for these clusters feasible on current workstations. Using Fokker-Planck (F-P) models, Kim, Morris, & Lee made a survey of cluster lifetimes for various initial conditions and have found that clusters with a mass ≲2 × 104 M☉ evaporate in ~10 Myr. These results were, however, to be confirmed by N-body simulations because some extreme cluster conditions, such as strong tidal forces and a large stellar mass range participating in the dynamical evolution, might violate assumptions made in F-P models. Here we find that, in most cases, the CYC lifetimes of previous F-P calculations are 5%-30% shorter than those from the present N-body simulations. The comparison of projected number density profiles and stellar mass functions between N-body simulations and Hubble Space Telescope/NICMOS observations by Figer and colleagues in 1999 suggests that the current tidal radius of the Arches cluster is ~1.0 pc and that the parameters for the initial conditions of that cluster are as follows: total mass of 2 × 104 M☉ and mass function slope for intermediate to massive stars of 1.75 (the Salpeter function has 2.35). We also find that the lower stellar mass limit, the presence of primordial binaries, the amount of initial mass segregation, and the choice of initial density profile (King or Plummer models) do not significantly affect the dynamical evolution of CYCs.
The limits of ideological globalization. Current patterns of "left and right" in different geographical regions

Political thinking in terms of "left and "right" has successfully expanded from its originating Western European context all over the globe. In all included world regions except the Middle East, at least 60 percent of the total population are ready to place themselves on a respective LR-scale. In all regions, left-right self identifications are particularly widespread among the more educated and politically active strata. However, very significant regional divergences cannot be explained neither by different micro-characteristics on the level of respondents nor with the degree of macroeconomic development. Thus, they are likely to emerge from differences in endogenous political culture. In conformity with previous, studies, it was found that issue positions are better in predicting LR values on the left half of the scale, while differences between moderate and extreme rightist positions cannot be grasped well with the indicators at hand. However, this regularity is not valid in East Asia where the right scale section is more highly associated with specific political stances. Highly educated strata show more interregional variance in their degree of ideology than population with less schooling. This result evidently contradicts the widespread theoretical notion that educated strata are more likely to adopt a homogeneous Westernized culture all over the world. Abstract Political thinking in terms of "left and "right" has successfully expanded from its originating Western European context all over the globe. In all included world regions except the Middle East, at least 60 percent of the total population are ready to place themselves on a respective LR-scale. In all regions, left-right self identifications are particularly widespread among the more educated and politically active strata. However, very significant regional divergences cannot be explained neither by different micro-characteristics on the level of respondents nor with the degree of macroeconomic development. Thus, they are likely to emerge from differences in endogenous political culture. In conformity with previous, studies, it was found that issue positions are better in predicting LR values on the left half of the scale, while differences between moderate and extreme rightist positions cannot be grasped well with the indicators at hand. However, this regularity is not valid in East Asia where the right scale section is more highly associated with specific political stances. Highly educated strata show more interregional variance in their degree of ideology than population with less schooling. This result evidently contradicts the widespread theoretical notion that educated strata are more likely to adopt a homogeneous Westernized culture all over the world. __________________________________________________________________________
A new method for the control of discrete nonlinear dynamic systems using neural networks

A new controller design method for nonaffine nonlinear dynamic systems is presented in this paper. An identified neural network model of the nonlinear plant is used in the proposed method. The method is based on a new control law that is developed for any discrete deterministic time-invariant nonlinear dynamic system in a subregion Phi(x) of an asymptotically stable equilibrium point of the plant. The performance of the control law is not necessarily dependent on the distance between the current state of the plant and the equilibrium state if the nonlinear dynamic system satisfies some mild requirements in Phi(x). The control law is simple to implement and is based on a novel linearization of the input-output model of the plant at each instant in time. It can be used to control both minimum phase and nonminimum phase nonaffine nonlinear plants. Extensive empirical studies have confirmed that the control law can be used to control a relatively general class of highly nonlinear multiinput-multioutput (MIMO) plants.
On the power of walking for querying tree-structured data

XSLT is the prime example of an XML query language based on tree-walking. Indeed, stripped down, XSLT is just a tree-walking tree-transducer equipped with registers and look-ahead. Motivated by this connection, we want to pinpoint the computational power of devices based on tree-walking. We show that in the absence of unique identifiers even very powerful extensions of the tree-walking paradigm are not relationally complete. That is, these extensions do not capture all of first-order logic. In contrast, when unique identifiers are available, we show that various restrictions allow to capture LOGSPACE, PTIME, PSPACE, and EXPTIME. These complexity classes are defined w.r. t. a Turing machine model working directly on (attributed) trees. When no attributes are present, relational storage does not add power; whether look-ahead adds power is related to the open question whether tree-walking captures the regular tree languages.
Making sense of visual management through affordance theory

Abstract Visual management is much used within operations management practice, particularly in association with process improvement initiatives in diverse areas such as production and healthcare. The practitioner literature abounds with suggested best practice. However, there is little attempt to theorise about why the design and use of ‘visual’ devices for such process improvement works in practice. Within this paper we describe a novel theory of operation which highlights the role that material and visual artefacts proposed by visual management practitioners play within particular ways of organising work. We develop an innovative way of employing the theory of affordances to explain how first- and second-order affordances, situated around the visual devices at the heart of visual management, connect three domains of action, which we refer to as articulation, communication and coordination. Our analysis of three cases from healthcare, clothing manufacturing and software production help ground the theorisation discussed.
Intelligent video surveillance systems for public spaces – a survey

In recent years, a large number of cameras have been installed in public spaces as a part of intelligent video surveillance systems. Such systems are being continuously developed due to the advancements in the Video Content Analysis algorithms. In this paper, some of the latest state-of-the-art intelligent video surveillance systems will be presented in the context of their most desirable characteristics and features. Due to the variety of the solutions the following categories have been taken into consideration: systems based on object detection, tracking and movement analysis, systems able to warn against, detect and identify abnormal and alarming situations, systems based on vehicle detection and traffic or parking lots analysis, object counting systems, systems based on multiple integrated camera views, privacy preserving systems and systems based on cloud environment. The paper describes several solutions for each category and underlines main functionalities of the current intelligent surveillance systems.
Data Mining Cancer Registries: Retrospective Surveillance of Small Area Time Trends in Cancer Incidence Using BaySTDetect

Space-time modelling of small area data is often used in epidemiology for mapping temporal trends in chronic disease rates. For rare diseases such as cancers, data are sparse, and a Bayesian hierarchical modelling approach is typically adopted in order to smooth the raw disease rates. Although there may be a general temporal trend which affect all areas similarly, abrupt changes may also occur in particular areas due to, for example, emergence of localized risk factor(s) or impact of a new health or screening policy. Detection of areas with ``unusual'' temporal patterns is therefore important to flag-up areas warranting further investigations. In this paper, we present a novel area of application of a recently proposed detection method, Bays Detect, for short time series of small area data. Placed within the Bayesian model choice framework, Bays Detect detects unusual time trends based on comparison of two competing space-time models. The first model is a straightforward multiplicative decomposition of the area effect and the temporal effect, assuming one single temporal pattern across the whole study region. The second model estimates a local time trend, independently for each area. An area-specific model indicator is introduced to select which model offers a better description of the local data. Classification of an area local time trend as ``unusual'' or not is based on the posterior mean of this model indicator, which represents the probability that the common trend model is appropriate for that area. An important feature of the method is that the classification rule can be fine-tuned to control the false detection rate (FDR). Based on previous simulation results, we present some further insights of the model specification in relation to the detection performance in practice. Bays Detect is then applied to data on several different cancers collected by the Thames Cancer Registry in South East England to illustrate its potential in retrospective surveillance.
Speech recognition and temporal processing in middle-aged women.

PURPOSE This study was designed to examine speech understanding ability and temporal processing in middle-aged women with normal or near-normal pure-tone thresholds. RESEARCH DESIGN Speech understanding, temporal processing ability, and self-assessed hearing were measured in groups of younger and middle-aged females. STUDY SAMPLE Participants were younger and middle-aged females (n = 12 per group) with normal hearing through 4000 Hz bilaterally. Subjects were drawn from nonclinical populations. DATA COLLECTION AND ANALYSIS Speech understanding was measured in the presence of steady-state noise and competing speech, with and without perceived spatial separation of the target speech and masker. The Gaps-In-Noise (GIN) test (Musiek et al, 2005) was used to assess temporal resolution ability. In addition, subjects completed a questionnaire with several items from the Speech, Spatial, and Qualities of Hearing Scale (Gatehouse and Noble, 2004) to gauge their subjective ability to understand speech in complex listening situations. Data were analyzed via repeated-measures ANOVA and Pearson r correlations. RESULTS Results showed that the performance of the middle-aged subjects was significantly poorer than that of the younger participants in the presence of a spatially coincident speech masker. Although performance in this listening condition was unrelated to pure-tone thresholds, it was strongly correlated with scores on the GIN test. Speech understanding performance in the presence of a steady-state masker was related to high-frequency pure-tone thresholds. CONCLUSIONS These results suggest that some middle-aged women with little or no pure-tone hearing loss experience listening difficulty in complex environments. Results also suggest a strong relationship between temporal processing and speech understanding in certain competing speech situations.
Multi-modal non-rigid registration of medical images based on mutual information maximization

In this paper, a new multi-modal non-rigid registration technique for medical images is presented. Firstly, the registration problem is outlined and some of the most common approaches reported, then, the proposed algorithm is presented. The proposed technique is based on mutual information maximization and computes a deformation field through a suitable globally smoothed affine piecewise transformation. The algorithm has been conceived with particular attention to computational load and accuracy of results. Experimental results involving intra-patient, inter-patients and atlas images on brain CT and MR (T1, T2 and PD modalities) are reported.
Evaluating the Student–Teacher Relationship Scale in the Greek educational setting: an item parcelling perspective

Abstract The Student–Teacher Relationship Scale (STRS) is one of the most extensively used instruments that evaluate the quality of teacher–child relationships. The STRS has a strong theoretical background based on attachment theory and research on parent–child and teacher–child relationships. The purpose of this study was to examine the dimensional structure of the adapted STRS proposed by Koomen et al. (2012) in the Greek educational settings. Another purpose was to examine the intercorrelations of the Dependency subscale with the Closeness and Conflict subscales of the adapted version of STRS, when applied in a cultural context different from the context, where it was developed originally. We applied confirmatory factor analyses with item parcelling in two independent samples (N1 = 118 teachers, 921 students and N2 = 118 teachers, 925 students). Findings provided evidence for the dimensional structure of the adapted STRS subscales and its validity. Results also showed that the Dependency subscale is positively correlated with Closeness in Greece as a background with more collectivistic characteristics. This finding suggests that the cultural universality of dependency can be challenged and that the possible socio-cultural influences in the nature and interpretation of dependent relationships needs to be further examined.
Analysis of home location estimation with iteration on Twitter following relationship

User's home locations are used by numerous social media applications, such as social media analysis. However, since the user's home location is not generally open to the public, many researchers have been attempting to develop a more accurate home location estimation. A social network that expresses relationships between users is used to estimate the users' home locations. The network-based home location estimation method with iteration, which propagates the estimated locations, is used to estimate more users' home locations. In this study, we analyze the function of network-based home location estimation with iteration while using the social network based on following relationships on Twitter. The results indicate that the function that selects the most frequent location among the friends' location has the best accuracy. Our analysis also shows that the 88% of users, who are in the social network based on following relationships, has at least one correct home location within one-hop (friends and friends of friends). According to this characteristic of the social network, we indicate that twice is sufficient for iteration.
Scale-Accurate 3 D Vehicle Point Cloud Extraction from Single-Camera Traffic Video

Reliable data extraction is essential to achieving a high-level understanding of the processes ongoing in the traffic environment. The ability to extract 3D structural properties of vehicles enables advanced traffic analysis such as vehicle classification and the detection of traffic rule violations, accidents, and near-collisions. A novel method is presented for extracting 3D structural properties of moving vehicles using a single camera. This method operates by tracking features of the vehicle as it travels through the camera’s field of view. Twoframe structure from motion is then used to extract a 3D point cloud representing the structure of the vehicle. In addition, a robust pose estimation algorithm is given for relating the geometry of the street surface to the position of the camera with minimal user interaction. It is shown that the resulting 3D point cloud can be used to accurately approximate the dimensions of vehicles to within a half foot of their true dimensions.
Exploring Turn-taking Cues in Multi-party Human-robot Discussions about Objects

In this paper, we present a dialog system that was exhibited at the Swedish National Museum of Science and Technology. Two visitors at a time could play a collaborative card sorting game together with the robot head Furhat, where the three players discuss the solution together. The cards are shown on a touch table between the players, thus constituting a target for joint attention. We describe how the system was implemented in order to manage turn-taking and attention to users and objects in the shared physical space. We also discuss how multi-modal redundancy (from speech, card movements and head pose) is exploited to maintain meaningful discussions, given that the system has to process conversational speech from both children and adults in a noisy environment. Finally, we present an analysis of 373 interactions, where we investigate the robustness of the system, to what extent the system's attention can shape the users' turn-taking behaviour, and how the system can produce multi-modal turn-taking signals (filled pauses, facial gestures, breath and gaze) to deal with processing delays in the system.
Evaluating Fraud Detection Algorithms Using an Auction Data Generator

Online auction sites are a target for fraud. Researchers have developed fraud detection and prevention methods. However, there are difficulties when using either commercial or synthetic auction data to evaluate the effectiveness of these methods. When using commercial data, it is not possible to accurately identify cases of fraud. Using synthetic data, the conclusions drawn may not extend to the real world. The availability of realistic synthetic auction data, which models real auction data, will be invaluable for effective evaluation of fraud detection algorithms. We present an agent-based simulator that is capable of generating realistic English auction data. The agents and model are based on data collected from the Trade Me online auction site. We evaluate the generated data in two ways to show that it is similar to the Trade Me auction data we have collected. In addition, we demonstrate that the simulator can have additional agents added to simulate fraudulent behaviour, and be used to evaluate fraud detection algorithms: we implement three different fraud behaviours and three detection algorithms, and using the simulator, compare the ability of the detection algorithms to correctly identify fraudulent agents.
Retinal blood vessel extraction using curvelet transform and conditional fuzzy entropy

This work employs multiple thresholds on matched filter response for automatic extraction of blood vessels, specially from a low contrast and non-uniformly illuminated background of retina. Curvelet transform is used first to enhance the finest details along the vessels followed by matched filtering to intensify the blood vessels' response. The conditional fuzzy entropy is then maximized to find the set of optimal thresholds to extract different types of vessel silhouettes from the background. Differential Evolution algorithm is used to specify the optimal combination of the fuzzy parameters. Thresholds thus obtained extract the thin, the medium and the thick vessels from the enhanced image which are then logically OR-ed to obtain the entire vascular tree. Performance evaluated on publicly available DRIVE database is compared with the existing blood vessel extraction methods. Experimental runs demonstrate that the proposed method outperforms the existing methods in detecting various types of vessels.
Multiple collaborative representations for face recognition

Different pixel plays different roles in representing a face image. In this paper, we proposed a novel representation which integrates original and its virtual face image to represent test sample. This method first combines two adjacent columns of an original face image to generate corresponding virtual face image, and then classification algorithm is respectively applied to the original and virtual face images to obtain two different representation results. At last, this method directly integrates the two results to get the final representation result and uses the final result to classify test sample. This method can enlarge the number of training samples for each subject and adequately exploit the detail features of each target image so as to improve the recognition accuracy. The abundant experiments of face recognition show that the new representation method can obtain a higher accuracy than other algorithms.
A new approach for spoken language identification based on sequence kernel SVMs

A new back-end classifier for GMM-LM based language identification systems is proposed in this paper. The proposed system consists of a mapping matrix and a back-end classifier of SVMs as its main parts, located in series after the GMM-LM system. While the mapping matrix maps the language model's output vectors to a new space in which the languages are more separable than before, each SVM in the SVM bank-end classifier separates one language from the others. A new sequence kernel is used for each SVM in the bank-end classifier. As a final stage, a fusion block carries out the task of fusing the SVM bank-end scores with those of the GMM-based LID to achieve higher accuracies. We show that not only our new sequence kernel-based SVMs separate languages more efficiently than common Gaussian mixture and GLDS SVM back-end classifiers, but also our new mapping matrix outperforms common linear discriminant matrix in separating classes from each other and finally the introduction of fusion block leads to even superior performance. The overall accuracy of the LID is noticeably increased in comparison with the other LDA-GMM and LDAGLDS SVM back-end classifiers. Our experiments on 5 languages from OGI-TS Multilanguage task prove our claim.
Exemplar-based joint channel and noise compensation

In this paper two models for channel estimation in exemplar-based noise robust speech recognition are proposed. Building on a compositional model that models noisy speech and a combination of noise and speech atoms, the first model iteratively estimates a filter to best compensate the mismatch with the observed noisy speech. The second model estimates separate filters for the noise and speech atoms. We show that both models enable noise-robust ASR even if the channel characteristics of the noisy speech do not match those of the exemplars in the dictionary. Moreover, the second model, which is able to estimate separate filters for speech and noise, is shown to be robust even in the presence of bandwidth-limited sources.