Miracle-dz/UniFAR-FTUs huggingface dataset:
Representations : A Cross-Sectional Study Piano Students ’ Conceptions of Musical Scores as External

Musical scores are some of the most important learning tools for musicians’ acquisition of musical knowledge. However, despite their educational relevance, very little is known about how music students conceive of these cultural external representations. Given that these conceptions might act as mediators of students’ learning approaches, the importance of knowing these conceptions seems evident in order to eventually change them. The general aim of this investigation was to study the conceptions of piano students at Spanish music conservatories by adopting a developmental-instructional perspective. The participants were 215 students at intermediate and tertiary degree levels, representing three levels of the collapsed variable age– level of instruction. Data were collected by means of a written open-ended task and analyzed by means of descriptive, parametric, and nonparametric statistical methods. The findings suggested that (a) students’ conceptions were more sophisticated at higher age and education levels, (b) each developmental-instructional group typically focused on different musical aspects, which reflected an inclusive and hierarchical logic, and (c) five increasingly sophisticated conceptions could be identified among these students.
XMM-Newton Observation of SNR RX J1713.7–3946

We present the first results of the observations of the supernova remnant RX J1713.7-3946 (also G347.3-0.5) obtained with the EPIC instrument on board the XMM-Newton satellite. We show a 5 pointings mosaiced image of the X-ray synchrotron emission. We characterize this emission by mapping its spectral parameters (absorbing column density NH and photon index). The synchrotron spectrum is flat at the shock and steep in the interior of the remnant. NH is well correlated with the X-ray brightness. A strong NH is found in the southwest rim of RX J1713.7-3946. We suggest that the SNR is interacting with a HI region there.
A semi-partitioned real-time scheduling approach for periodic task systems on multicore platforms

Semi-partitioned scheduling is regarded as a viable alternative to partitioned or global scheduling approaches. Advantage of semi-partitioned scheduling is two-folds: it has reduced runtime overhead compared to global scheduling, and improved schedulability and system utilization factor compared to partitioned scheduling. This paper proposes a new semi-partitioned scheduling algorithm for real-time periodic task systems over multicore platforms. Our proposed algorithm works in two phases. In the first phase, each task from a feasible application task set is statically assigned to a specific processor. If a task can not be partitioned on any processor in the platform, it qualifies as migrating task. In the second phase, processors are clustered together such that, per cluster, the unused fragmented computation power equivalent to at most one processor is available. We provide schedulability analysis and experimental evaluation to support our proposition. Moreover, simulation results show an average difference of 18-folds in the number of task preemptions and 10-folds in the number of task migrations compared to multiprocessor optimal scheduling algorithm PD.
Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes

Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes from in silico target prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, “the foundation of acquired constitution” that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications.
Linear-time, incremental hierarchy inference for compression

Data compression and learning are, in some sense, two sides of the same coin. If we paraphrase Occam's razor by saying that a small theory is better than a larger theory with the same explanatory power, we can characterize data compression as a preoccupation with small, and learning as a preoccupation with better. Nevill-Manning et al. (see Proc. Data Compression Conference, Los Alamitos, CA, p.244-253, 1994) presented an algorithm, since dubbed SEQUITUR, that presents both faces of the compression/learning coin. Its performance as a data compression scheme outstrips other dictionary schemes, and the structures that it learns from sequences as diverse as DNA and music are intuitively compelling. We present three new results that characterize SEQUITUR's computational and compression performance. First, we prove that SEQUITUR operates in time linear in n, the length of the input sequence, despite its ability to build a hierarchy as deep as log(n). Second, we show that a sequence can be compressed incrementally, improving on the non-incremental algorithm that was described by Nevill-Manning et al., and making on-line compression feasible. Third, we present an intriguing result that emerged during benchmarking; whereas PPMC outperforms SEQUITUR on most files in the Calgary corpus, SEQUITUR regains the lead when tested on multimegabyte sequences. We make some tentative conclusions about the underlying reasons for this phenomenon, and about the nature of current compression benchmarking.
Understanding the Role α7 Nicotinic Receptors Play in Dopamine Efflux in Nucleus Accumbens

Neuronal nicotinic acetylcholine receptors (NNRs) of the α7 subtype have been shown to contribute to the release of dopamine in the nucleus accumbens. The site of action and the underlying mechanism, however, are unclear. Here we applied a circuit modeling approach, supported by electrochemical in vivo recordings, to clarify this issue. Modeling revealed two potential mechanisms for the drop in accumbal dopamine efflux evoked by the selective α7 partial agonist TC-7020. TC-7020 could desensitize α7 NNRs located predominantly on dopamine neurons or glutamatergic afferents to them or, alternatively, activate α7 NNRs located on the glutamatergic afferents to GABAergic interneurons in the ventral tegmental area. Only the model based on desensitization, however, was able to explain the neutralizing effect of coapplied PNU-120596, a positive allosteric modulator. According to our results, the most likely sites of action are the preterminal α7 NNRs controlling glutamate release from cortical afferents to the nucleus accumbens. These findings offer a rationale for the further investigation of α7 NNR agonists as therapy for diseases associated with enhanced mesolimbic dopaminergic tone, such as schizophrenia and addiction.
Analytical logical effort formulation for minimum active area under delay constraints

This paper presents a gate sizing method which formulates minimum active area solutions under delay constraints. The method is based on the logical effort delay model. The minimization of transistor widths has direct impact on the power consumption and circuit area reduction. The analytical formulation of the method takes into account the maximum input capacitance, the load to be driven, and the given timing constraint. Electrical simulations show that the proposed method is very precise for a first order approach, as it presents small average errors of 1.48% in power dissipation, 2.28% in delay propagation, and 6.5% in transistor sizes.
An Enhanced Application of Modified PSO for Association Rule Mining

In data mining, association rule learning is a well-liked and well explored technique for finding out interesting relatives in large databases along with variables. It analyzes and present strong rules discovered in databases by means of diverse measures of interestingness. As all know two important parameters, minimal support and confidence, are forever deciding by the decisionmaker him/herself or in the course of trial-and-error; and thus, the previous algorithms be deficient in both objectiveness and competence. As a result, the main purpose of proposed work is to recommend an improved algorithm that can provide feasible threshold values for minimal support and confidence. Earliest particle swarm optimization algorithm investigates for the optimum fitness value of each particle and next discovers equivalent support and confidence as minimal threshold values subsequent to the data are distorted into binary values. To improve the feasibility of the work the modified PSO is developed to provide the feasible values. The modified PSO (Particle Swarm Optimization) algorithm has a number of swarm population size, the number of highest generation, and three predetermined parameters will be determined , . In each generation, the particle’s position value in all measurements will be reserved or be updated by its pbest value or be updated by the gbest value or restored by generating a new random number.
EvolutionarY Construction of Graph-Coloring Programs using Genetic Programming

Genetic programming (GP) is a novel paradigm that simulates the way of solving problems by nature according to Darwin's theory of fitness-driven natural selection. Instead of using bit strings as in genetic algorithm (GA), GP uses tree structures as its computing structures. As computer programs can be represented as trees, GP has been employed as a method of generating computer programs. In the work reported in this paper, GP is applied to the graph-coloring problem, an NP-complete problem which is an abstraction of many real-world practical problems, with expectation of constructing computer programs that are capable of computing approximations of the optimal solutions to many instances of the problem. The resulting computer programs are analyzed and their performance is compared with two existing commonly used approximation algorithms for graph coloring, i.e., the sequ€ntial coloring algorithms with random coloring order and with maximal-to-minimal-degree coloring order.
Bio-inspired Model of Robot Adaptive Learning and Mapping

In this paper we present a model designed on the basis of the neurophysiology of the rat hippocampus to control the navigation of a real robot. The model allows the robot to learn reward locations dynamically moved in different environments, to build a topological map, and to return home autonomously. We describe robot experimentation results from our tests in a T-maze, an 8-arm radial maze and an extended maze
Clustering of Intermediate-Luminosity X-Ray-Selected Active Galactic Nuclei at z ~ 3

We present the first clustering results of X-ray-selected active galactic nuclei (AGNs) at z ∼ 3. Using Chandra X-ray imaging and UVR optical colors from MUSYC photometry in the Extended Chandra Deep Field-South field, we selected a sample of 58 z ∼ 3 AGN candidates. From the optical data we also selected 1385 luminous blue galaxies (LBGs) at 2.8 < z < 3.8 with R < 25.5. We performed autocorrelation and cross-correlation analyses, and here we present results for the clustering amplitudes and dark matter (DM) halo masses of each sample. For the LBGs we find a correlation length of r0,LBG = 6.7 ± 0.5 Mpc, implying a bias value of 3.5 ± 0.3 and DM halo masses of log(Mmin/M☉) = 11.8 ± 0.1. The AGN-LBG cross-correlation yields r0,AGN-LBG = 8.7 ± 1.9 Mpc, implying for AGNs at 2.8 < z < 3.8 a bias value of 5.5 ± 2.0 and DM halo masses of log(Mmin/M☉) = 12.6+ 0.5−0.8. Evolution of DM halos in the ΛCDM cosmology implies that today these z ∼ 3 AGNs are found in high-mass galaxies with a typical luminosity of .
Improving the GPU space of computation under triangular domain problems

There is a stage in the GPU computing pipeline where a grid of thread-blocks is mapped to the problem domain. Normally, this grid is a k-dimensional bounding box that covers a k-dimensional problem no matter its shape. Threads that fall inside the problem domain perform computations, otherwise they are discarded at runtime. For problems with non-square geometry, this is not always the best idea because part of the space of computation is executed without any practical use. Two- dimensional triangular domain problems, alias td-problems, are a particular case of interest. Problems such as the Euclidean distance map, LU decomposition, collision detection and simula- tions over triangular tiled domains are all td-problems and they appear frequently in many areas of science. In this work, we propose an improved GPU mapping function g(lambda), that maps any lambda block to a unique location (i, j) in the triangular domain. The mapping is based on the properties of the lower triangular matrix and it works at a block level, thus not compromising thread organization within a block. The theoretical improvement from using g(lambda) is upper bounded as I < 2 and the number of wasted blocks is reduced from O(n^2) to O(n). We compare our strategy with other proposed methods; the upper-triangular mapping (UTM), the rectangular box (RB) and the recursive partition (REC). Our experimental results on Nvidias Kepler GPU architecture show that g(lambda) is between 12% and 15% faster than the bounding box (BB) strategy. When compared to the other strategies, our mapping runs significantly faster than UTM and it is as fast as RB in practical use, with the advantage that thread organization is not compromised, as in RB. This work also contributes at presenting, for the first time, a fair comparison of all existing strategies running the same experiments under the same hardware.
Equivalence for Rank-Metric and Matrix Codes and Automorphism Groups of Gabidulin Codes

For a growing number of applications, such as cellular, peer-to-peer, and sensor networks, efficient error-free transmission of data through a network is essential. Toward this end, Kötter and Kschischang propose the use of subspace codes to provide error correction in the network coding context. The primary construction for subspace codes is the lifting of rank-metric or matrix codes, a process that preserves the structural and distance properties of the underlying code. Thus, to characterize the structure and error-correcting capability of these subspace codes, it is valuable to perform such a characterization of the underlying rank-metric and matrix codes. This paper lays a foundation for this analysis through a framework for classifying rank-metric and matrix codes based on their structure and distance properties. To enable this classification, we extend work by Berger on equivalence for rank-metric codes to define a notion of equivalence for matrix codes, and we characterize the group structure of the collection of maps that preserve such equivalence. We then compare the notions of equivalence for these two related types of codes and show that matrix equivalence is strictly more general than rank-metric equivalence. Finally, we characterize the set of equivalence maps that fix the prominent class of rank-metric codes known as Gabidulin codes. In particular, we give a complete characterization of the rank-metric automorphism group of Gabidulin codes, correcting work by Berger, and give a partial characterization of the matrix-automorphism group of the expanded matrix codes that arise from Gabidulin codes.
GPU accelerated SPH simulation of fluids for VFX

Fluids are important to the Visual Effects Industry but extremely hard to control and simulate because of the complexity of the governing equations. Fluid solvers can be divided into two categories, those of the Eulerian point of reference and those of the Lagrangian. Both categories have different advantages and weaknesses and hybrid methods are popular. This thesis examines Smoothed Particle Hydrodynamics, a Lagrangian method for physically based uid simulations. To allow the artist the exibility given by shorter simulation times and increased number of iterations, the performance of the solver is key. In order to maximize the speed of the solver it is implemented entirely on the GPU, including collisions, volumetric force fields, sinks and other artist tools. To understand the implementation decisions, it is important to be familiar with the CUDA programming model. Thus, a brief explanation of CUDA is given before the exact implementation of the methods are explained. The results are presented along with a performance comparison as well as a discussion of the different parameters which can be fed to the solver. Some thoughts on possible future extensions can be found in the conclusion.
Biologic Data Analysis Platform Based on the Cloud

To improve the research productivity in bioinformatics study by using effective means of large scale data analysis, there are many obstacles that need to be overcome They are standardization of data collection and analysis, management of computing and storage resources, easiness of parallel programming, and efficiency of data analysis job execution, to name a few. Among these, easiness of parallel programming is a crucial factor that contributes to usability and efficiency of large scale data analysis. This paper describes a biologic data analysis platform based on cloud computing infrastructure. The platform provides an easy-to-use parallel data analysis environment, and ultimately enhances the productivity of bioinformatics research.
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015

This paper presents our Chinese-to-Japanese patent machine translation system for WAT 2015 (Group ID: ntt ) that uses syntactic pre-ordering over Chinese dependency structures. A head word and its modiﬁer words are reordered by hand-written rules or a learning-to-rank model. Our system outperforms baseline phrase-based machine translations and competes with baseline tree-to-string machine translations.
An associative/parallel processor for partial match retrieval using superimposed codes

This paper presents the design and implementation of special hardware for effective use of the method of superimposed codes. It is shown that the method of superimposed codes is particularly well suited to easy design and implementation of fast and modular hardware. The implementation has shown that a performance gain of two orders of magnitude over conventional software implementations is obtained by using the special hardware. This makes the method of superimposed codes extremely attractive for data base system requiring partial match retrieval. We also demonstrate that the associative memory design is easily adaptable to large scale integration which would make such an approach very cost effective and lead to even further gains in performance.
A Payment Mechanism for Publish-Subscribe Systems

Content-based publish-subscribe (pub-sub) networks enable a group o f p blishers to efficiently provide content to a large number of subscribers while allowing the subscribers to remain anonymous to the publishers. To be commercially viable, we need m chanisms for collecting payments from subscribers and distributing them fairly to publi shers. This paper discusses why existing payment systems are not appropriate for pub-sub netw orks and proposes a probabilistic payment mechanism through an untrusted intermediary that a llows publishers to trade-off efficiency for audit precision.
UvA-DARE ( Digital Academic Repository ) Spectroscopic diagnostics of pulsation in rotating stars

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IDENTIFICATION OF MATERIAL DAMAGE IN TWO DIMENSIONAL DOMAINS USING SQUID BASED NDE SYSTEM

Problems on the identification of two-dimensional spatial domains arising in the detection and characterization of material damage are considered. For electromagnetic nondestructive evaluation systems, observations of the magnetic flux from the front surface are used in a output least-square approach. Parameter estimation techniques based on the method of mappings are discussed and approximation schemes are developed applying a finite-element Galerkin approach. Theoretical convergence results for computational techniques are given and results are applied to numerical experiments to demonstrate the efficacy of the proposed schemes.
Probabilistic approach for modeling and identifying driving situations

Intelligent vehicles need increasing knowledge about both their own state and the driving environment. In this work a novel method for interpreting this information by a reliable detection of relevant driving situations and driving maneuvers is proposed. The information of a situation or maneuver is extracted and hence provided for subsequent processing in the applications. As a result of different situation perception and maneuver realization of the drivers, the selected method is based on probabilistic decisions. Furthermore the inaccuracy of this decision is estimated by the inaccuracies of the sensor measurements. This value can be seen as quality measure of the probabilistic situation and maneuver detection. In addition the model allows to derivate requirements on the sensors, while determining a relevance ranking of the separate sensor information regarding the situation decision.
Smart Power Management for an Onboard Wireless Sensors and Actuators Network

The employment of wireless links for spacecraft onboard data communication is a new and challenging research topic. This new technology can be conveniently used for attitude determination and control sensors and actuators. Still, providing energy efficient data collection is of paramount importance to such an onboard wireless sensors and actuators network (OWSAN). This paper proposes a power management scheme based on estimation of the sensor measurements with a Kalman Filter. The power manager schedules the sleep periods on the node to lower the energy consumption of the wireless transmitter and the sensor. The simulation results show that there is a significant change in the energy consumption level of an onboard ADCS sensor.