Miracle-dz/UniFAR-FTUs huggingface dataset:
Matching Pursuit Analysis of Auditory Receptive Fields' Spectro-Temporal Properties

Gabor filters have long been proposed as models for spectro-temporal receptive fields (STRFs), with their specific spectral and temporal rate of modulation qualitatively replicating characteristics of STRF filters estimated from responses to auditory stimuli in physiological data. The present study builds on the Gabor-STRF model by proposing a methodology to quantitatively decompose STRFs into a set of optimally matched Gabor filters through matching pursuit, and by quantitatively evaluating spectral and temporal characteristics of STRFs in terms of the derived optimal Gabor-parameters. To summarize a neuron's spectro-temporal characteristics, we introduce a measure for the “diagonality,” i.e., the extent to which an STRF exhibits spectro-temporal transients which cannot be factorized into a product of a spectral and a temporal modulation. With this methodology, it is shown that approximately half of 52 analyzed zebra finch STRFs can each be well approximated by a single Gabor or a linear combination of two Gabor filters. Moreover, the dominant Gabor functions tend to be oriented either in the spectral or in the temporal direction, with truly “diagonal” Gabor functions rarely being necessary for reconstruction of an STRF's main characteristics. As a toy example for the applicability of STRF and Gabor-STRF filters to auditory detection tasks, we use STRF filters as features in an automatic event detection task and compare them to idealized Gabor filters and mel-frequency cepstral coefficients (MFCCs). STRFs classify a set of six everyday sounds with an accuracy similar to reference Gabor features (94% recognition rate). Spectro-temporal STRF and Gabor features outperform reference spectral MFCCs in quiet and in low noise conditions (down to 0 dB signal to noise ratio).
GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume

Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation.
Comparison of Nonlinearity Measures based on Time Series Analysis for Nonlinearity Detection

The main purpose of this paper is a study of the eciency of dierent nonlinearity detection methods based on time-series data from a dynamic process as a part of system identication. A very useful concept in measuring the nonlinearity is the denition of a suitable index to measure any deviation from linearity. To analyze the properties of such an index, the observed time series is assumed to be the output of Volterra series driven by a Gaussian input. After reviewing these methods, some modications and new indices are proposed, and a benchmark simulation study is made. Correlation analysis, harmonic analysis and higher order spectrum analysis are selected methods to be investigated in our simulations. Each method has been validated with its own advantages and disadvantages.
Modelling and analysis of multi-sink wireless sensor networks using queuing theory

Multi-sink wireless sensor networks (WSNs) are required by an increasing number of applications due to their advantages such as saving energy and higher reliability. However, little modeling work has taken place for this type of network, particularly with mathematical analysis. Firstly, we show that the input process of the WSN sink node can be modeled as a Poisson process. Then we study the M/G/1/∞ queue model and make an attempt to use it instead of M/G/c/∞ model to analyze the multi-sink WSN. As far as we know, it is the first time it has been shown in this application that the input process is approximately a Poisson Process, and it is the first time that a queuing model has been used to analyze a multi-sink WSN. Simulation results show that it is reasonable to use M/G/1/∞ queue model when the load is not high.
Static Load Distribution for Communication Intensive Parallel Computing in Multiclusters

In this paper, we examine load distributions to minimize total run time in multi-cluster parallel computing algorithms by applying divisible load theory techniques. Even with homogeneous processor speeds, parallel computations in multi-clusters that evenly assign load can run at less than maximum efficiency due to communication heterogeneity. Using a modified version of the LogP parallel computing model, we propose a general technique of assigning load among multiple clusters to minimize the time each processor spends waiting. This technique is used to determine optimal load distribution for spin glass simulation and parallel bucket sort in multi-cluster systems. It also allows fast analysis of the effects of adding processors or clusters to the computation. We experimentally demonstrate the accuracy of our model, and show how it eliminates wait time in multi-cluster parallel computations. Using load distributions derived from our technique results in an execution time decrease of up to 50%, depending on the degree of heterogeneity among clusters and communication characteristics of the computation.
Detecting and Tracking Cells using Network Flow Programming

We propose a novel approach to automatically detecting and tracking cell populations in time-lapse images. Unlike earlier ones that rely on linking a predetermined and potentially under-complete set of detections, we generate an overcomplete set of competing detection hypotheses. We then perform detection and tracking simultaneously by solving an integer program to find an optimal and consistent subset. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging image sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.
On Feedback for Network Coding

In this paper we examine possible ways that feedback can be used, in the context of systems with network coding capabilities. We illustrate, through a number of simple examples, that use of feedback can be employed for parameter adaptation to satisfy QoS requirements as well as for reliability purposes. We also argue that there are benefits in applying network coding to the feedback packets themselves, and finally, we examine the design of acknowledgment packets.
Automated Query-biased and Structure-preserving Text Summarization on Web Documents

Automatic summarization has become an important application recently due to the increased amount of information available on the Web. Summarization techniques can be very useful in improving the effectiveness of Web search. However, the available search engines, such as Google, only display short extracts under the search results, e.g. two lines of text fragments which consist of the query words and their surrounding text. In this paper, we investigate novel summarization techniques to improve the effectiveness of search engines. The proposed system incorporates the structure of the documents, namely the sectional hierarchy, into the output summaries. Different from the previous work, both the structural information and the content to be displayed in the summary are selected in a query-biased way. The system also uses natural language processing techniques for summarization purposes such as identification of phrases as better content carriers than single words.
An approach for pre-filtering images from big data sets

Big Data are now rapidly expanding in various domains as our ability of data generation and collection is growing tremendously with the rapid development of networking and data storage capability. It refers to the practice of collection and processing of complex and massive data sets. Knowledge discovery from such data sets is the major concern and requirement of various organizations. However, analysis of Big Data poses enormous challenges due to its exponential growth factor. In order to address these challenges, MapReduce framework is usually employed for such analysis. This paper proposes a general algorithmic design technique using MapReduce framework for image pre-filtering. The primary focus of our algorithm is to reduce the size of input data set by allowing relevant and non-redundant images based on various constraints for fast and efficient utilization of images in a distributed fashion. This approach achieves significant speedup over conventional techniques and provides much smaller problem instance which can be solved with less resources.
Context and Joins in the Semantic Web

Databases for the life sciences, especially those associated with bioinformatics, are currently very large and complex, and there is every reason to expect that they will continue to increase in size and complexity in the future. Processing such massive databases requires high performance and scalability. Modern distributed relational databases can provide the necessary performance and scalability, but at the cost of using a different logical foundation for the data. While the Semantic Web has been successfully applied to many domains of the life sciences, it has some limitations that could impede further progress in its use, especially with respect to performance and scalability. This position paper discusses two features that would make the Semantic Web more compatible with high performance databases without changing its logical foundations. The two features are a notion of context and the ability to specify joins. This paper discusses these features in more detail as well as suggesting how they could be incorporated into the Semantic Web.
Correlative light and electron microscopic exploration of endocytic organelles

Explorations of endocytic pathways teach us about subcellular routes travelled by pathogenic agents, such as viruses and toxins, and used for targeting of drugs into the interior of cells. In this compendium we show a correlative microscopic approach that uses the photooxidation capacity of fluorescent dyes to oxidize the substrate diaminobenzidine which after osmification is visible in the electron microscope. The method combines the advantages of both light and electron microscopy: light microscopically, it is possible to study cells in vivo, and to see the samples in a survey, which allows recognition of diversities within tissues and cell cultures; the fine structure then can be clarified in the electron microscope.
Equalization of multichannel acoustic system using sub-systems for speech dereverberation

An auto-relation aided multiple-input/output inverse filtering algorithm (A-RAM) is proposed for the inverse filtering of room acoustics in speech dereverberation. In A-RAM, we exploit the relationship between the received reverberant signals and the acoustic impulse responses as well as constraining the reconstructed signals of the two sub-systems. This results in an auto-relation which is then used as a constraint for the adaptive multiple input/output inversion theorem (A-MINT) algorithm. Simulation results, using both synthetic and recorded room impulse responses, show that the proposed A-RAM achieves a fast convergence compared to A-MINT.
Compressing Integers for Fast File Access

Fast access to files of integers is crucial for the efficient resolution of queries to databases. Integers are the basis of indexes used to resolve queries, for example, in large internet search systems, and numeric data forms a large part of most databases, Disk access costs can be reduced by compression, if the cost of retrieving a compressed representation from disk and the CPU cost of decoding such a representation is less than that of retrieving uncompressed data. In this paper we show experimentally that, for large or small collections, storing integers in a compressed format reduces the time required for either sequential stream access or random access. We compare different approaches to compressing integers, including the Elias gamma and delta codes, Golomb coding, and a variable-byte integer scheme. As a conclusion, we recommend that, for fast access to integers, files be stored compressed.
Exploring the learning potential of peer-assessed dictation in the Chinese context

Students play an important role in using assessment for learning. Their perceptions and involvement directly impact on the learning potential of assessment. This paper investigated students’ views on peer-assessed dictation in two Chinese high schools through a qualitative study. Students recognized both benefits and constraints of peer-assessed dictation. They perceived more benefits when they got actively engaged. This paper contributes to the understanding of assessment and learning relationship from student perspectives. It also offers implications for using assessment more productively in the Asian classrooms and a wider context.
Measures of Fault Tolerance in Distributed Simulated Annealing

In this paper, we examine the different measures of Fault Tolerance in a Distributed Simulated Annealing process. Optimization by Simulated Annealing on a distributed system is prone to various sources of failure. We analyse simulated annealing algorithm, its architecture in distributed platform and potential sources of failures. We examine the behaviour of tolerant distributed system for optimization task. We present possible methods to overcome the failures and achieve fault tolerance for the distributed simulated annealing process. We also examine the implementation of Simulated Annealing in MapReduce system and possible ways to prevent failures in reaching the global optima. This paper will be beneficial to those who are interested in implementing a large scale distributed simulated annealing optimization problem of industrial or academic interest. We recommend hybrid tolerance technique to optimize the trade-off between efficiency and availability.
Getting Started with ADOL-C

The C++ package ADOL-C described in this paper facilitates the evaluation of first and higher derivatives of vector functions that are defined by computer programs written in C or C++. The numerical values of derivative vectors are obtained free of truncation errors at mostly a small multiple of the run time and a fix small multiple random access memory required by the given function evaluation program. Derivative matrices are obtained by columns, by rows or in sparse format. This tutorial describes the source code modification required for the application of ADOL-C, the most frequently used drivers to evaluate derivatives and some recent developments.
The Correlation between Leadership Style and Leader Power

Abstract : Data were collected from a military sample of 181 adults to estimate the relationships of Transformational and Transactional leadership style and leader power. Leadership style was measured by the Multifactor Leadership Questionnaire (MLQ) and leader power by the Rahim Leader Power Inventory (RLPI). Confirmatory factor analyses (CFAs) performed separately for the MLQ and RLPI identified single-factor solutions. Using these measurement models the relationship between the single factors was estimated with a correlation of . 57 between the factors representing Leadership Style and Leader Power. The CFA results are contrary to developer's theories of both scales, but are consistent with studies reporting more parsimonious factor analytic solutions. Results from the current study may be the consequence of the well-defined hierarchical structure of the military organization in which the sample was collected. Additional studies are needed in organizations with differing structures ranging from hierarchical to completely flat to determine if organizational structure is related to the factors and factor structure of these two measures.
Learning metaphor through mixed-reality game design and game play

This paper describes a recent program for game-based learning within a mixed-reality environment, the Situated Multimedia Arts Learning Lab [SMALLab]. In the program, our research team collaborated with a 9th grade Language Arts teacher to design and deliver a new learning game and associated curriculum. Through the process of game-design and game-play, students advance their understanding of metaphor. We outline the theoretical basis upon which design decisions were made, and describe the rationale for choosing Language Arts as the subject area for this program. Three goals structure our research: (1) to advance students' understanding of literary devices with an emphasis on metaphor; (2) to engage otherwise under-performing students through game-based learning that is student-centered, collaborative, and based in reflective practice; and (3) to demonstrate effective game-based learning using a mixed-reality platform in a conventional classroom context. Twenty-four students attending a large suburban high school in the southwest United States participated in this learning experience once a week for seven weeks during the Fall of 2007. Our data indicates that these students attained a more globally coherent model of metaphor in the course of their participation, that they found both the game-design and the game-play process stimulating and rewarding, and that, given the necessary scaffolding, a mixed-reality learning environment can be effectively employed to teach standards-based curriculum in a conventional high school classroom.
Do Developers Deprecate APIs with Replacement Messages? A Large-Scale Analysis on Java Systems

As any other software system, frameworks and libraries evolve over time, and so their APIs. Consequently, client systems should be updated to benefit from improved APIs. To facilitate this task and preserve backward compatibility, API elements should always be deprecated with clear replacement messages. However, in practice, there are evidences that API elements are usually deprecated without such messages. In this paper, we study a set of questions regarding the adoption of deprecation messages. Our goal is twofold: to measure the usage of deprecation messages and to investigate whether a tool is needed to recommend such messages. Thus, we verify (i) the frequency of deprecated elements with replacement messages, (ii) the impact of software evolution on such frequency, and (iii) the characteristics of systems which deprecate API elements in a correct way. Our large-scale analysis on 661 real-world Java systems shows that (i) 64% of the API elements are deprecated with replacement messages per system, (ii) there is almost no major effort to improve deprecation messages over time, and (iii) systems that deprecated API elements in a correct way are statistically significantly different from the ones that do not in terms of size and developing community. As a result, we provide the basis for the design of a tool to support client developers on detecting missing deprecation messages.
A New Multineuron Spike Train Metric

The Victor-Purpura spike train metric has recently been extended to a family of multineuron metrics and used to analyze spike trains recorded simultaneously from pairs of proximate neurons. The metric is one of the two metrics commonly used for quantifying the distance between two spike trains; the other is the van Rossum metric. Here, we suggest an extension of the van Rossum metric to a multineuron metric. We believe this gives a metric that is both natural and easy to calculate. Both types of multineuron metric are applied to simulated data and are compared.
Quality of Life among General Populations of Different Countries in the Past 10 Years, with a Focus on Human Development Index: A Systematic Review and Meta-analysis

Background: The current study was conducted to estimate the integrated mean of Quality Of Life (QOL) of the general population of different countries around the world and to compare them on the grounds of the Human Development Index (HDI). Methods: Well-known international databases such as Medline, Scopus, Science Direct, Google Scholar & Google, and domestic databases including SID, IranMedex, Irandoc & Magiran were searched in 2015. The data were analyzed with the Random Effect Model, using Stata 11 software. Results: Ninety-seven studies were selected for the final analysis. The overall QOL mean in the very high HDI subgroup was 74.26 (CI=72.40–76.12), which was the highest value. The lowest mean score was observed in the psychological domain (M=67.37; CI=66.23–68.52). In the high HDI subgroup, the highest mean was observed in the social relationships domain (M=64.16; CI=61.99–66.34), and the lowest mean was observed in the environment domain (M=58.76; CI=56.50–61.03). In the medium HDI subgroup, the highest mean was calculated for the overall QOL score (M=62.62; CI=56.35–68.92), and the lowest mean was estimated for the environment domain (M=56.98; CI=53.54–60.43). The highest mean in the low HDI subgroup was observed in the physical health domain (M=68.17; CI=67.43–70.52), and the lowest mean was calculated for the environment domain (M=53.14; CI=51.57–54.72). There was considerable heterogeneity in all the subgroups and domains; the values reported here are the weighted means of QOL for different countries. Conclusion: Overall, the highest means of various QOL domains were observed in the very high HDI subgroup.