Miracle-dz/UniFAR-FTUs huggingface dataset:
Commentaries: Intuition and Evidence: A Reaction to Watson and Clark

Watson and Clark contend that most models of vocal fold function "do not translate well into the everyday voice clinic" and that their scientific "terminology is sometimes jargonistic with inadequate scientific integrity linking to the underlying theoretical fundamentals. " This assertion seems somewhat startling considering the fact that, once one has invested the effort to understand them, most models do an admirable job of informing clinical insight and rehabilitative decisions. Yes, they are usually complex; so are the phenomena they attempt to explain. Simpylng a model does not, unfortunately simplify reality. And, at least in our view, the scientific integrity of the prevalent models is, on the whole, exceptional. As scientists with a strong interest in clinical application, it is the quality of Watson and Clark's model, rather than their perceptions of clinical utility, on which we would prefer to focus. The modern history of vocal rehabilitation is intimately and inextricably linked to the development of models. The importance of the relationship may not be immediately apparent because, to many of us, "models" may seem little more than interminable screens of incomprehensible equations, projected to the accompaniment of a jargon-packed continuo during the voice-science part of a professional meeting. What all the mathematics represents, however, is only a statement-as detailed as seems nec-
Unified sensor management using CPHD filters

The PHD filter propagates a multitarget statistical first moment, the probability hypothesis density (PHD), in place of the full multitarget posterior distribution. It has been the basis of a systematic approach to multisensor, multitarget sensor management based on the posterior expected number of targets (PENT) objective function. The PHD filter has since been generalized to the cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the full probability distribution on target number. The CPHD filter provides more accurate estimates of target number and target states. This paper shows how PENT-based objective functions can be naturally extended for use with the CPHD filter.
Lamination and Within-Area Integration in the Neocortex

i x I General Introduction . 2 II Regional and Species Variations in Cortical Architectur e . 6 A. Introduction . 6 B. Laminar Differentiation . 7 1. Species Variation . 7 2. Regional Variation . 8 3. Computational Considerations . 9 C. Cell Densities and Laminar Thicknesses . 12 1. Overall Thickness and Cells per Unit Area . 12 2. Laminar Distributions . 14 3. Computational Considerations . 16 D. Excitatory Cells and Circuitry . 18 1. Dendrites . 18 2. Axons . 19 E. Inhibitory Cells and Circuitry . 22 1. Overall Percentages . 22 2. Arborizations . 23 3. Computational Considerations . 23 F. Concluding Remarks . 25 G. Appendix: Connectivity Volume Calculations . 26 III Current Ideas on Modes of Cortical Sensory Processing . 27 A. Introduction . 27 B. Early Ideas . 28 1. Gestalt Psychology . 28 2. Hebb’sOrganization of Behavior. 29 C. Primary and Secondary Cortical Processing . 30 D. Reconstruction . 32 1. Theory . 32 2. Model . 32 3. Experiment . 33 E. Attention and Focus . 34
Analysis of an Automatic Repeat Request Scheme Addressing Long Delay Channels

This paper proposes a variation to the classic implementation of Automatic Repeat reQuest (ARQ) which is particularly suitable for the long delay channels, as can be found, for example, in the underwater environment. The proposed technique, called Selective Repeat with a Second Replica ARQ, (SR)^2 ARQ, follows the same rationale as Selective Repeat ARQ, but, upon NACK reception, schedules two retransmissions, one taking place immediately, and the other put in a special queue to be released after further retransmissions, but before new packet transmissions. We propose an exact analysis of this technique, proving its ability of trading throughput for shorter delivery delay; thus, it is suitable for scenarios where the required data rate is not high, but a timely data delivery is very important, as is the case, for example, for underwater monitoring applications.
Automatic thesaurus generation for minority languages: an Irish example

Nous présentons des techniques pour la génération automatique d'un thésaurus irlandais mono-lingue. Ces résultats ontété réalisés en dépit des ressources limitées et, comme le plupart des autres langues minoritaires, de l'absence d'outils pour le traitement de langage naturel. Techniques are presented for the automatic construction of a monolingual Irish language thesaurus. Our results were obtained despite limited resources, including, as is the case for most other minority languages, the lack of sophisticated software tools for natural language processing .
Contrast enhancement for enlarged images based on edge sharpening

Blurring effect is among the major visual degradations resulting from digital image enlargement. We introduce a set of sigmoidal functions to sharpen edges of enlarged images. We show that these functions have properties that are especially desirable for sharpening expanded edges. We then develop an adaptive contrast enhancement scheme based on edge sharpening using these functions. It is shown that this scheme results in images of better contrast and natural-looking sharp edges.
The Effects of Time Budget Pressure and Risk of Error on Auditor Performance

This paper examines the effects of time budget pressure, risk associated with the task, and audit test on three reduced audit quality (RAQ) behaviors. Specifically, the study evaluated RAQ acts using three situations, including the perceived likelihood of rejecting awkward items from a sample (Situation A), accepting doubtful audit evidence (Situation B), and failing to test all items in the sample (Situation C). A 2×2×2 between-subjects design used 103 audit seniors to evaluate the impact of the independent variables on the potential for RAQ acts. The results show that time budget pressure had the largest impact on the propensity to commit RAQ (higher likelihood in the presence of time budget pressure), and that time budget pressure interacted with risk of the audit task in determining the propensity to commit RAQ. Under conditions of high level of time budget pressure, a lower risk of error for the audit task was associated with an increased likelihood of RAQ. The results were found to differ depending on the specific RAQ.
The achievable distortion region of bivariate Gaussian source on Gaussian broadcast channel

We provide a complete characterization of the achievable distortion region for the problem of sending a bivariate Gaussian source over a bandwidth-matched Gaussian broadcast channel, where each receiver is interested in only one component of the source. This setting naturally generalizes the simple single Gaussian source bandwidth-matched broadcast problem for which the uncoded scheme is known to be optimal. We show that a hybrid scheme can achieve the optimum for the bivariate case, but neither an uncoded scheme alone nor a separation-based scheme alone is sufficient.
Analysis and detection of Soft-404 pages

The WWW is continuously growing, but sometimes, not in the best way due to the proliferation of garbage contents, such as Web Spam pages, duplicate content or dead links. Some web servers do not always use the appropriate HTTP response code for dead links making them to be incorrectly identified, producing a problem for search engines. Our analysis has revealed that 7.35% of web servers send a 200 HTTP code when a request for an unknown document is received, instead of a 404 code, which indicates that the document is not found. These web pages are known as Soft-404 pages. Soft-404 pages are a problem for search engines, and their crawling modules, which process and index these pages, with the consequent loss of resources. There are few studies that analyse this problem and try to solve it. In this article we propose a new detection system for Soft-404 pages, called Soft404Detector, which uses a set of content-based heuristics and combines them with a C4.5 classifier. For testing purposes, we built a Soft-404 pages dataset. Our experiments indicate that our system is very effective, achieving a precision of 0.992 and a recall of 0.980 at Soft-404 pages.
On modeling and locomotion of hybrid mechanical systems with impacts

Walking machines are mechanical systems that undergo impacts and changes in dynamic equations and can be viewed as a subclass of hybrid systems. In this work we focus on a class of planar mechanisms that can locomote through plastic impacts and clamping. This setting retains enough structure to investigate discrete phenomena in locomotion. We use a geometric framework to describe smooth phenomena such as inertial and constraint forces and discrete events such as impacts. In this setting hybrid mechanical control systems are described in terms of affine connections and linear jump transition maps. For this class of systems we perform a local controllability analysis. In particular we employ the notion of configuration controllability to identify systems that are able to locomote by clamping. Additionally, we discuss how to adapt smooth motion planning algorithms to this hybrid setting and present some instructive set of gaits.
Strain Imaging Echocardiography: What Imaging Cardiologists Should Know

Despite recent advances in clinical imaging, echocardiography remains as the most accessi-ble and reliable noninvasive. Since knowledge of left ventricular systolic function remains so critically important in determining prognosis; every effort should be made to prevent subjective estimations. The advent of strain imaging echocardiography now offers a readily available and portable imaging tool that not only offers an objective characterization of myocardial dynamics; but also allows for early detection of subclinical left ventricular dysfunction. This review outlines the basic concepts of strain imaging to better understand the mechanism of myocardial function as well their applicability in the least common cardiac diagnosis among current clinical practice.
Experimentally demonstrated a unidirectional electromagnetic cloak designed by topology optimization

Electromagnetic invisible devices usually designed by transformation optics are rather complicated in material parameters and not suitable for general applications. Recently, a topology optimized cloak based on level-set method was proposed to realize nearly perfect cloaking [Fujii et al., Appl. Phys. Lett. 102, 251106 (2013)]. In this work, we experimentally implemented this idea and fabricated a unidirectional cloak with a relative large invisible region made of single dielectric material. Good cloaking performance was verified through measurement which consists very well with numerical simulation. The advantages and disadvantages of this optimization method are also discussed.
RTS Games as Test-Bed for Real-Time AI Research

This article motivates AI research in the area of real‐time strategy (RTS) games and describes the road‐map and the current status of the ORTS project whose goals are to implement an RTS game programming environment and to build AI systems that eventually can outperform human experts in this popular and challenging domain.
ACTIDS: an active strategy for detecting and localizing network attacks

In this work we investigate a new approach for detecting attacks which aim to degrade the network's Quality of Service (QoS). To this end, a new network-based intrusion detection system (NIDS) is proposed. Most contemporary NIDSs take a passive approach by solely monitoring the network's production traffic. This paper explores a complementary approach in which distributed agents actively send out periodic probes. The probes are continuously monitored to detect anomalous behavior of the network. The proposed approach takes away much of the variability of the network's production traffic that makes it so difficult to classify. This enables the NIDS to detect more subtle attacks which would not be detected using the passive approach alone. Furthermore, the active probing approach allows the NIDS to be effectively trained using only examples of the network's normal states, hence enabling an effective detection of zero day attacks. Using realistic experiments, we show that an NIDS which also leverages the active approach is considerably more effective in detecting attacks which aim to degrade the network's QoS when compared to an NIDS which relies solely on the passive approach.
CanvasDB: a local database infrastructure for analysis of targeted- and whole genome re-sequencing projects

CanvasDB is an infrastructure for management and analysis of genetic variants from massively parallel sequencing (MPS) projects. The system stores SNP and indel calls in a local database, designed to handle very large datasets, to allow for rapid analysis using simple commands in R. Functional annotations are included in the system, making it suitable for direct identification of disease-causing mutations in human exome- (WES) or whole-genome sequencing (WGS) projects. The system has a built-in filtering function implemented to simultaneously take into account variant calls from all individual samples. This enables advanced comparative analysis of variant distribution between groups of samples, including detection of candidate causative mutations within family structures and genome-wide association by sequencing. In most cases, these analyses are executed within just a matter of seconds, even when there are several hundreds of samples and millions of variants in the database. We demonstrate the scalability of canvasDB by importing the individual variant calls from all 1092 individuals present in the 1000 Genomes Project into the system, over 4.4 billion SNPs and indels in total. Our results show that canvasDB makes it possible to perform advanced analyses of large-scale WGS projects on a local server. Database URL: https://github.com/UppsalaGenomeCenter/CanvasDB
On Using Pattern Matching Algorithms in MapReduce Applications

In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, they are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW)is utilized for such comparison, a correlation analysis is then applied to DTWs' outcomes to produce feasible similarity patterns. Three real applications (Word Count, Exim Mainlogparsing and Terasort) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on pseudo-distributed MapReduce platforms
Beat Tracking Towards Automatic Musical Accompaniment

In this paper we address the issue of causal rhythmic analysis, primarily towards predicting the locations of musical beats such that they are consistent with a musical audio input. This will be a key component required for a system capable of automatic accompaniment with a live musician. We are implementing our approach as part of the aubio real-time audio library. While performance for this causal system is reduced in comparison to our previous non-causal system, it is still suitable for our intended purpose.
Deep Image Category Discovery using a Transferred Similarity Function

Automatically discovering image categories in unlabeled natural images is one of the important goals of unsupervised learning. However, the task is challenging and even human beings define visual categories based on a large amount of prior knowledge. In this paper, we similarly utilize prior knowledge to facilitate the discovery of image categories. We present a novel end-to-end network to map unlabeled images to categories as a clustering network. We propose that this network can be learned with contrastive loss which is only based on weak binary pair-wise constraints. Such binary constraints can be learned from datasets in other domains as transferred similarity functions, which mimic a simple knowledge transfer. We first evaluate our experiments on the MNIST dataset as a proof of concept, based on predicted similarities trained on Omniglot, showing a 99\% accuracy which significantly outperforms clustering based approaches. Then we evaluate the discovery performance on Cifar-10, STL-10, and ImageNet, which achieves both state-of-the-art accuracy and shows it can be scalable to various large natural images.
Improved Conditional Dependency Networks for Multi-label Classification

Multi-label classification (MLC) is the supervised learning problem where an instance is associated with multiple labels, rather than with a single label. The widely known binary relevance method (BR) for multi-label classification considers each label as an independent binary problem and has been sidelined in the literature due to perceived inadequacy of label correlations. In this paper, we outline several BR-based classification methods and present our improved conditional dependency networks for multi-label classification (ICDN). ICDN inherits the framework of double layer based classifier chain (DCC) to exploit the label correlations in training stage and modifiers the conditional dependency networks (CDN) by initializing the input values of the second layer with the prediction values from the first layer during the testing stage. The main contribution of the algorithm is that it reduces randomization of input for the conditional dependency networks and improves convergence rate. Experiments on benchmark datasets demonstrate that ICDN obtains the best predictive performance across several datasets under several evaluation methods specifically designed for multi-label classification.
An automated Model-based Testing Approach in Software Product Lines Using a Variability Language.

This paper presents the application of an automated testing approach for Software Product Lines (SPL) driven by its state-machine and variability models. Context: Model-based testing provides a technique for automatic generation of test cases using models. Introduction of a variability model in this technique can achieve testing automation in SPL. Method: We use UML and CVL (Common Variability Language) models as input, and JUnit test cases are derived from these models. This approach has been implemented using the UML2 Eclipse Modeling platform and the CVL-Tool. Validation: A model checking tool prototype has been developed and a case study has been performed. Conclusions: Preliminary experiments have proved that our approach can find structural errors in the SPL under test. In our future work we will introduce Object Constraint Language (OCL) constraints attached to the input UML model
Yükseköğretimde İngilizce Okutmanların Örgütsel Sosyalizasyonunun Yordayıcıları

This study aims to investigate the relationship between organizational socialization and various organizational (type of university, training, work conditions, knowledge sharing) and individual (academic degree, teaching experience, length of employment at current work place, job satisfaction, commitment, self-efficacy) level factors, both factors taken together focusing on the content, context and process dimensions of socialization. For this aim, data from 737 public and private university English language instructors were collected with an inventory consisting of three parts made up of self-developed and pre-developed scales. Taking each dimension of organizational socialization as a criterion variable, three sets of predictors were defined for three separate hierarchical regression analyses. The results revealed that socialization of instructors to the organization, department, and task are significantly predicted by several organizational and individual variables. Knowledge sharing and training are the strongest organizational level predictors while job satisfaction, selfefficacy for instructional strategies, and affective commitment are the strongest individual level predictors. The results suggest higher education administrators adapt organizational and individual level managerial strategies to facilitate organizational, department and task socialization in higher education organizations.
The Quantity and the Quality of Party Systems

Previous research claims that the number of parties affects the representation of social cleavages in voting behavior, election turnout, patterns of political conflict, and other party system effects. This article argues that research typically counts the quantity of parties and that often the more important property is the quality of party competition—the polarization of political parties within a party system. The author first discusses why polarization is important to study. Second, the author provides a new measurement of party system polarization based on voter perceptions of party positions in the Comparative Study of Electoral Systems, which includes more than 50 separate elections from established and developing democracies. Third, the author compares party polarization and party fractionalization as influences on cleavage-based and ideological voting and as predictors of turnout levels. The finding is that party polarization is empirically more important in explaining these outcomes.
Amino-Terminal Hydrophobic Region ofHelicobacter pylori Vacuolating Cytotoxin (VacA) Mediates Transmembrane Protein Dimerization

ABSTRACT Helicobacter pylori VacA is a secreted protein toxin that forms channels in lipid bilayers and induces multiple structural and functional alterations in eukaryotic cells. A unique hydrophobic segment at the amino terminus of VacA contains three tandem repeats of a GxxxG motif that is characteristic of transmembrane dimerization sequences. To examine functional properties of this region, we expressed and analyzed ToxR-VacA-maltose binding protein fusions using the TOXCAT system, which was recently developed by W. P. Russ and D. M. Engelman (Proc. Natl. Acad. Sci. USA 96:863–868, 1999) to study transmembrane helix-helix associations in a natural membrane environment. A wild-type VacA hydrophobic region mediated insertion of the fusion protein into the inner membrane of Escherichia coli and mediated protein dimerization. A fusion protein containing a mutant VacA hydrophobic region (in which glycine 14 of VacA was replaced by alanine) also inserted into the inner membrane but dimerized significantly less efficiently than the fusion protein containing the wild-type VacA sequence. Based on these results, we speculate that the wild-type VacA amino-terminal hydrophobic region contributes to oligomerization of the toxin within membranes of eukaryotic cells.