Miracle-dz/UniFAR-FTUs huggingface dataset:
Monothermal caloric testing in the screening of vestibular function.

INTRODUCTION The alternate binaural bithermal caloric test (ABBT) is the standard test for evaluation of the dizzy patient. Monothermal caloric testing (MT) has the potential benefits of reducing the administration time and patient discomfort. The goal of the present study was to investigate the role of MT screening in the prediction of ABBT results. METHODS ABBT results of 218 patients having normal otoneurological examination, normal hearing, and normal electronystagmography (ENG) were retrospectively analyzed to generate norms for all subtests. These norms were then employed to calculate the sensitivity and specificity of MT for predicting normal ABBT in a group of 197 consecutive dizzy patients who were referred for vestibular testing. RESULTS The best predictions of ABBT by MT results were achieved when ENG testing showed oculomotor integrity and no spontaneous, positional, or positioning nystagmus. Under these conditions, warm MT lateralization < 32% had 90% sensitivity and 92% specificity for the prediction of normal ABBT. DISCUSSION When no pathology is detected in the other parts of the ENG, warm MT lateralization < 32% can indicate normal ABBT with a 10% probability for a false-negative result. This false negative rate precludes the routine use of warm MT in the clinical realm and its application as a screening tool for possible vestibular deficits in a generally healthy population like aviation or diving candidates. Higher sensitivity may be achieved by lowering the cut-off point of the response asymmetry required for the diagnosis of MT screening failure and the omission of directional preponderance diagnosis from the goals of the screening.
Microsoft Power Point Files: A Secure Steganographic Carrier

One of the major characteristic of cryptography and steganography is unlimited and limited secret data conversion and embedding respectively. In steganography, concealing unlimited secret data in cover media is unfeasible. Majority of researchers have taken image, text, html, executable files as a steganographic cover media. In this proposed work the authors have considered Microsoft Power Point Files as a cover medium. The authors have developed a methodology which consist two techniques for embedding the valuable unlimited data with three layers of security and high robustness. The resultant stego file undoubtedly can be transmitted any where using any channel. Experimental results show the feasibility of the proposed methodology. resultant data can be understood only by those who can get it back into its original form. This technique is known as encryption. A major drawback of this method is that the existence of data is not hidden. Giving enough time the unreadable encrypted data can be obtained in its original form. As a part of solution to this kind of problems, the concept of steganography has considerably given much attention in recent years. The word steganography is a Greek word giving a meaning to it as ‘writing in hiding’. The main goal of steganography is to communicate message securely in a complete undetectable manner (Allan, 2001; Amin, Salleh, Ibrahim, Katmin, & Shamsuddin, 2003; Bender, Gruhl, Morimoto, & Lu, 1996; Dunbar, 2002; Castiglione, De Santis, & Soriente, 2007). It has been emerged as a skill of concealing private information DOI: 10.4018/jdcf.2011100102 International Journal of Digital Crime and Forensics, 3(4), 16-28, October-December 2011 17 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. inside a carrier that can be considered for all intents and ideas (Zou & Shi, 2005; Artz, 2001; Gutub, Al-Qahtani, & Tabakh, 2009; Johnson & Jajodia, 1998). Digital technology gives us a new way to relate steganographic techniques including hiding information in digital medias. It not only goes well beyond simply by embedding a text in an image, but also pertains to other media, including voice, text, binary files and communication channels (Swanson, Zhu, & Tewfik, 1996; Petitcolas, Anderson, & Kuhn, 1999; Anderson & Petitcolas, 1998). It can be mentioned here that steganography, in respect to constructive approach, generally provides vast potential security and legal data hiding. As far as corporate sectors are concerned, if they cannot hide their important information, this may lead to damage extremely the company’s profitability as well as its sustainability. Again, as a destructive approach, even if steganography is not a threat in general, it may have some bad impacts on the areas where one wants to deal with some kind of unwanted illegal matters (Shejul & Kulkarni, 2010; Sahoo & Tiwari, 2008; Sutaone & Khandare, 2008; Liu & Tsai, 2007; Yan & Ping, 2009).
Distributed perfect hashing for very large key sets

A perfect hash function (PHF) h: S → [0, m -- 1] for a key set S ⊆ U of size n, where m ≥ n and U is a key universe, is an injective function that maps the keys of S to unique values. A minimal perfect hash function (MPHF) is a PHF with m = n, the smallest possible range. Minimal perfect hash functions are widely used for memory efficient storage and fast retrieval of items from static sets. In this paper we present a distributed and parallel version of a simple, highly scalable and near-space optimal perfect hashing algorithm for very large key sets, recently presented in [4]. The sequential implementation of the algorithm constructs a MPHF for a set of 1.024 billion URLs of average length 64 bytes collected from the Web in approximately 50 minutes using a commodity PC. The parallel implementation proposed here presents the following performance using 14 commodity PCs: (i) it constructs a MPHF for the same set of 1.024 billion URLs in approximately 4 minutes; (ii) it constructs a MPHF for a set of 14.336 billion 16-byte random integers in approximately 50 minutes with a performance degradation of 20%; (iii) one version of the parallel algorithm distributes the description of the MPHF among the participating machines and its evaluation is done in a distributed way, faster than the centralized function.
Locality-Aware Consistency Maintenance for Heterogeneous P2P Systems

Replication and caching have been deployed widely in current P2P systems. In update-allowed P2P systems, a consistency maintenance mechanism is strongly demanded. Several solutions have been proposed to maintain the consistency of P2P systems. However, they either use too much redundant update messages, or ignore the heterogeneity nature of P2P systems. Moreover, they propagate updated contents on a locality-ignorant structure, which could consume unnecessary backbone bandwidth and delay the convergence of consistency maintenance. This paper presents a locality-aware consistency maintenance scheme for heterogeneous P2P systems. Taking the heterogeneity nature, we form the replica nodes into a locality-aware hierarchical structure: the upper layer is DHT-based and a node in the lower layer attaches to a physically close node in the upper layer. An efficient update tree is built dynamically upon the upper layer to propagate the updated contents. Theoretical analyses and simulation results demonstrate the effectiveness of our scheme. Specially, experiment results show that, compared with gossip-based scheme, our approach reduces the cost by about one order of magnitude.
Simultaneous and fast 3D tracking of multiple faces in video by GPU-based stream processing

In this work, we implement a real-time visual tracker that targets the position and 3D pose of objects in video sequences, specifically faces. Using stream processors for performing the computations as well as efficient sparse-template-based particle filtering allows us to achieve real-time processing even when tracking multiple objects simultaneously in high- resolution video frames. Stream processing is a relatively new computing paradigm that permits the expression and execution of data-parallel algorithms with great efficiency and minimum effort. Using a GPU (graphics processing unit, a consumer-grade stream processor) and the NVIDIA CUDAtrade technology, we can achieve real-time performance even when tracking multiple objects in high-quality videos.
Distributed Non-Stochastic Experts

We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to k sites, and the sites are required to communicate with each other via the coordinator. At each time-step t, one of the k site nodes has to pick an expert from the set {1, . , n}, and the same site receives information about payoffs of all experts for that round. The goal of the distributed system is to minimize regret at time horizon T, while simultaneously keeping communication to a minimum. The two extreme solutions to this problem are: (i) Full communication: This essentially simulates the non-distributed setting to obtain the optimal O(√log(n)T) regret bound at the cost of T communication. (ii) No communication: Each site runs an independent copy – the regret is O(√log(n)kT) and the communication is 0. This paper shows the difficulty of simultaneously achieving regret asymptotically better than √kT and communication better than T. We give a novel algorithm that for an oblivious adversary achieves a non-trivial trade-off: regret O(√k5(1+e)/6T) and communication O(T/ke), for any value of e ∈ (0, 1/5). We also consider a variant of the model, where the coordinator picks the expert. In this model, we show that the label-efficient forecaster of Cesa-Bianchi et al. (2005) already gives us strategy that is near optimal in regret vs communication trade-off.
Single Cell Analysis Facilitates Staging of Blimp1-Dependent Primordial Germ Cells Derived from Mouse Embryonic Stem Cells

The cell intrinsic programming that regulates mammalian primordial germ cell (PGC) development in the pre-gonadal stage is challenging to investigate. To overcome this we created a transgene-free method for generating PGCs in vitro (iPGCs) from mouse embryonic stem cells (ESCs). Using labeling for SSEA1 and cKit, two cell surface molecules used previously to isolate presumptive iPGCs, we show that not all SSEA1+/cKit+ double positive cells exhibit a PGC identity. Instead, we determined that selecting for cKitbright cells within the SSEA1+ fraction significantly enriches for the putative iPGC population. Single cell analysis comparing SSEA1+/cKitbright iPGCs to ESCs and embryonic PGCs demonstrates that 97% of single iPGCs co-express PGC signature genes Blimp1, Stella, Dnd1, Prdm14 and Dazl at similar levels to e9.5–10.5 PGCs, whereas 90% of single mouse ESC do not co-express PGC signature genes. For the 10% of ESCs that co-express PGC signature genes, the levels are significantly lower than iPGCs. Microarray analysis shows that iPGCs are transcriptionally distinct from ESCs and repress gene ontology groups associated with mesoderm and heart development. At the level of chromatin, iPGCs contain 5-methyl cytosine bases in their DNA at imprinted and non-imprinted loci, and are enriched in histone H3 lysine 27 trimethylation, yet do not have detectable levels of Mvh protein, consistent with a Blimp1-positive pre-gonadal PGC identity. In order to determine whether iPGC formation is dependent upon Blimp1, we generated Blimp1 null ESCs and found that loss of Blimp1 significantly depletes SSEA1/cKitbright iPGCs. Taken together, the generation of Blimp1-positive iPGCs from ESCs constitutes a robust model for examining cell-intrinsic regulation of PGCs during the Blimp1-positive stage of development.
A Fuzzy-Based Approach for the Diagnosis of Fault Modes in a Voltage-Fed PWM Inverter Induction Motor Drive

This paper investigates the use of fuzzy logic for fault detection and diagnosis in a pulsewidth modulation voltage source inverter (PWM-VSI) induction motor drive. The proposed fuzzy technique requires the measurement of the output inverter currents to detect intermittent loss of firing pulses in the inverter power switches. For diagnosis purposes, a localization domain made with seven patterns is built with the stator Concordia current vector. One is dedicated to the healthy domain and the six others to each inverter power switch. The fuzzy bases of the proposed technique are extracted from the current analysis of the fault modes in the PWM-VSI. Experimental results on a 1.5-kW induction motor drive are presented to demonstrate the effectiveness of the proposed fuzzy approach.
High frequency modeling of the winding wires of AC machines

The present paper deals with the modeling in high frequency of AC machines windings, when the coils are subjected to fast slopes of MLI voltage. The purpose of this study is twofold: firstly, to develop a network of the electrical circuit, and secondly, to effectuate a behavioral analysis. The different elements of this network will be identified by Finite Element Calculations thanks to COMSOL MULTIPHYSIC software, and a behavioral analysis of this network is simulated by MATLAB SIMULINK. This analysis confirms the occurrence of voltage spikes in the first few turns of the coil. Finally, an improvement to face these surges is proposed, consisting in modification of the coupling of partial coils of the winding. Simulated and experimental results are matching well and highlight the merits of the present approach.
A comparative assessment of the performance of ensemble learning in customer churn prediction

Customer churn is a main concern of most firms in all industries. The aim of customer churn prediction is detecting customers with high tendency to leave a company. Although, many modeling techniques have been used in the field of churn prediction, performance of ensemble methods has not been thoroughly investigated yet. Therefore, in this paper, we perform a comparative assessment of the performance of four popular ensemble methods, i.e., Bagging, Boosting, Stacking, and Voting based on four known base learners, i.e., C4.5 Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Reduced Incremental Pruning to Produce Error Reduction (RIPPER). Furthermore, we have investigated the effectiveness of two different sampling techniques, i.e., oversampling as a representative of basic sampling techniques and Synthetic Minority Over3sampling Technique (SMOTE) as a representative of advanced sampling techniques. Experimental results show that SMOTE doesn't increase predictive performance. In addition, the results show that the application of ensemble learning has brought a significant improvement for individual base learners in terms of three performance indicators i.e., AUC, sensitivity, and specificity. Particularly, in our experiments, Boosting resulted in the best result among all other methods. Among the four ensemble methods Boosting RIPPER and Boosting C4.5 are the two best methods. These results indicate that ensemble methods can be a best candidate for churn prediction tasks.
Kctd12 and Ulk2 Partner to Regulate Dendritogenesis and Behavior in the Habenular Nuclei

The habenular nuclei of the limbic system regulate responses, such as anxiety, to aversive stimuli in the environment. The habenulae receive inputs from the telencephalon via elaborate dendrites that form in the center of the nuclei. The kinase Ulk2 positively regulates dendritogenesis on habenular neurons, and in turn is negatively regulated by the cytoplasmic protein Kctd12. Given that the habenulae are a nexus in the aversive response circuit, we suspected that incomplete habenular dendritogenesis would have profound implications for behavior. We find that Ulk2, which interacts with Kctd12 proteins via a small proline-serine rich domain, promotes branching and elaboration of dendrites. Loss of Kctd12 results in increased branching/elaboration and decreased anxiety. We conclude that fine-tuning of habenular dendritogenesis during development is essential for appropriate behavioral responses to negative stimuli.
The carbohydrate-active enzymes database (CAZy) in 2013

The Carbohydrate-Active Enzymes database (CAZy; http://www.cazy.org) provides online and continuously updated access to a sequence-based family classification linking the sequence to the specificity and 3D structure of the enzymes that assemble, modify and breakdown oligo- and polysaccharides. Functional and 3D structural information is added and curated on a regular basis based on the available literature. In addition to the use of the database by enzymologists seeking curated information on CAZymes, the dissemination of a stable nomenclature for these enzymes is probably a major contribution of CAZy. The past few years have seen the expansion of the CAZy classification scheme to new families, the development of subfamilies in several families and the power of CAZy for the analysis of genomes and metagenomes. This article outlines the changes that have occurred in CAZy during the past 5 years and presents our novel effort to display the resolution and the carbohydrate ligands in crystallographic complexes of CAZymes.
Determining Noise Temperature of a Noise Source Using Calibrated Noise Sources and an RF Attenuator

A system to measure the noise temperature (NT) of noise sources is implemented in the frequency range of 18-26.5 GHz for the case where noise sources commercially available have only one nominal NT, e.g., 9500 K being equivalent to 15-dB excess noise ratio. For the Y-factor method, two noise sources, i.e., a noise source and another with a radio-frequency attenuator, serve as the standard noise sources. The noise power output of the noise sources are measured using a commercial noise figure measurement instrument. Measurement results are presented, and its uncertainty has been evaluated to be 0.23-0.25 dB (k = 2) .
Independent component analysis for automated decomposition of in vivo magnetic resonance spectra

Fully automated methods for analyzing MR spectra would be of great benefit for clinical diagnosis, in particular for the extraction of relevant information from large databases for subsequent pattern recognition analysis. Independent component analysis (ICA) provides a means of decomposing signals into their constituent components. This work investigates the use of ICA for automatically extracting features from in vivo MR spectra. After its limits are assessed on artificial data, the method is applied to a set of brain tumor spectra. ICA automatically, and in an unsupervised fashion, decomposes the signals into interpretable components. Moreover, the spectral decomposition achieved by the ICA leads to the separation of some tissue types, which confirms the biochemical relevance of the components. Magn Reson Med 50:697–703, 2003. © 2003 Wiley‐Liss, Inc.
VLSI Implementation of Enhanced Edge Preserving Impulse Noise Removal Technique

In many applications, image and video signals are corrupted by impulse noise during acquisition or transmission. Hence there is a need for an efficient and consumer friendly impulse noise removal technique. In this paper, an efficient low cost VLSI architecture for the edge preserving impulse noise removal technique has been proposed. The architecture comprises of two line buffers, register banks, impulse noise detector, edge oriented noise filter and impulse arbiter. The storage space required for the proposed hardware is two line buffer rather than full frame memory. Moreover, proposed algorithm involves only fixed size window instead of variable window size. These two greatly reduces storage requirement as well as computation complexity. The impulse noise detector turns off the remaining circuitry if the current pixel is noise free, thus reducing power consumption. Further, the four stage pipeline architecture greatly improves the speed of operation. The implemented edge preserving algorithm results in better visual quality for denoised image. Thus the proposed architecture has less complexity, less storage requirement, low power consumption and improved speed of operation. The architecture has been implemented in Xilinx 9.2i and the results are tabulated for various images.
A Stochastic Dynamic Programming Model for Optimal Use of Local Energy Resources in a Market Environment

The unbundling of power systems and the emergence of electricity markets favor the deployment of distributed generation in electricity networks. Microgrids are low-voltage distribution networks comprising micro-generation, storage devices and controllable loads that can operate interconnected or isolated from the main distribution grid as a controlled entity. This paper proposes a new method, based on stochastic optimization, to optimize the operation of a microgrid. This involves optimization of the production of the local micro- sources and storage and the exchange with the main distribution grid subject to market conditions. The results of the stochastic approach, and a comparison with a simpler deterministic one are presented using as case study a typical microgrid.
Identification of a novel mutation in DKC1 in dyskeratosis congenita

Dyskeratosis congenita (DC) is a rare congenital syndrome characterized by the triad of reticular skin pigmentation, nail dystrophy and mucosal leukoplakia, and the predisposition to bone marrow failure and malignancy. DC is genetically heterogeneous and X‐linked and autosomal forms of the disease exist. Here, we report the clinical description and mutation analysis of a Russian family with X‐linked DC. A novel mutation in DKC1 raised de novo in the maternal grandmother's gamete was found; this mutation is a 2 bp inversion in exon 3: NM_001363:c. 166_167invCT (Leu56Ser). Pediatr Blood Cancer 2009;52:135–137. © 2008 Wiley‐Liss, Inc.
Epidemic processes with immunization.

We study a model of directed percolation (DP) with immunization, i.e., with different probabilities for the first infection and subsequent infections. The immunization effect leads to an additional non-Markovian term in the corresponding field theoretical action. We consider immunization as a small perturbation around the DP fixed point in d<6, where the non-Markovian term is relevant. The immunization causes the system to be driven away from the neighborhood of the DP critical point. In order to investigate the dynamical critical behavior of the model, we consider the limits of low and high first-infection rate, while the second-infection rate remains constant at the DP critical value. Scaling arguments are applied to obtain an expression for the survival probability in both limits. The corresponding exponents are written in terms of the critical exponents for ordinary DP and DP with a wall. We find that the survival probability does not obey a power-law behavior, decaying instead as a stretched exponential in the low first-infection probability limit and to a constant in the high first-infection probability limit. The theoretical predictions are confirmed by optimized numerical simulations in 1+1 dimensions.