Miracle-dz/UniFAR-FTUs huggingface dataset:
Icarus: Source Generator Based Real-time Recognition of Speech in Noisy Stressful and Lombard Eeect Environments Icarus: Source Generator Based Real-time Recognition of Speech in Noisy Stressful and Lombard Eeect Environments

The problem of real-time automatic speech recognition in an adverse environment is addressed in this paper. Though much research has been performed in the area of speech recognition, only limited success has been demonstrated for real-time recognition in noisy stressful environments. The primary reason for this is that the performance of present day recognition algorithms are predicated on the assumptions of the environmental settings in which the algorithms have been formulated and implemented. In this paper, we discuss the eeects of additive background noise on speech quality and recognition parameters, and propose a source generator based framework to address stress and noise. Using this framework, a computationally eecient real-time recognition system called ICARUS is developed. The speech recognition system incorporates direct processing steps to address the eeects of additive noise on the speech signal and stress on the speech production system. Central issues which are addressed include; i) improved characterization of speech spoken in noisy situations involving both parameter estimation methods and analysis of varying speech characteristics spoken in adverse environments (i.e., stress and Lombard eeect), ii) exploration of signal processing strategies tailored to such speech, and iii) demonstration of real-time system performance of the proposed methods. The proposed recognition system was formulated using a digital signal processing platform. Performance evaluations showed an improvement in speech feature representation under stressed speaking conditions, with an average improvement in recognition rate of +17.28% across eleven noisy stressful speaking conditions. (i) une meilleure caract erisation de la parole produite en milieu bruyant, (ii) une exploration des strat egies de traitement du signal les mieux adapt ees a ce type de parole, (iii) la d emonstration des performances de ces m ethodes dans des syst emes temps-r eel, a base de processeurs de traitement de signal. Les evaluations montrent une am elioration moyenne du taux de reconnaissance de +17.28% dans 11 conditions d' emission de parole en milieu bruyant. A HMM state transition matrix B HMM output probability matrix HMM initial state vector d MFCC (NEU(i)) j (tn) represents a single estimated sequence of stress compensated mel-cepstral coeecients over tn, which are to be submitted to word model HMM i. ^ dc j ] i the estimated 35 transformational weighting vectors, one for each HMM i MFCC (LOM) j (tn) represents the input sequence of uncompensated Lombard eeect mel-cepstral coeecients for an unknown word d(x; ^ x) distance metric used for vector quantizer t …
Pricing American currency options in a jump diffusion model

In this article the problem of the American option valuation in a jump diffusion setting is tackeld. The perpetual case is first considered. Without possible discontinuities (i.e. with negative jumps in the call case), known results concerning the currency option value as well the exercise boundary are obtained with a martingale approach. With possible discontinuities of the underlying process at the exercise boundary (i.e. with positive jumps in the call case) original results are derived by relying on first passage time and overshoot associated with a Lévy process. For finite life American currency calls, the formula given by Bates (1991) or Zhang (1995), is rederived in the context of a negative jump size. It is basically an extension of the Barone-Adesi and Whaley (1987) approach. This formula is tested. It is shown that Bates (1996) model generates good results when the process is continuous at the exercise boundary. However, with possible discontinuities results generated by Bates’ (1996) model are less accurate.
Tweeting Traffic: Analyzing Twitter for generating real-time city traffic insights and predictions

Crowd sourced road traffic management is an open, unexplored problem in data science. With the growth of mobile communications and social media networks, more people are expressing their traffic situations in real-time. We explore how this social media data can be analyzed to generate valuable insights, useful for traffic management and city planning. Our method utilizes background knowledge from structured data repositories for entity extraction from tweets. We proceed to use this spatio-temporal data for traffic incident clustering and prediction. With accuracy and precision measurements providing encouraging results, we build on our methods and present our Continuous Traffic Management Dashboard (CTMD) system: an automated computer system for generating real-time, historic and predictive traffic insights.
Bilinguals as “experts”? Comparing performance of mono- to bilingual individuals via a mousetracking paradigm

The question of whether bilingualism conveys a broader advantage in executive functions has recently been controversially discussed, with the empirical findings presenting a complex pattern of positive and null results. Here we present results from three standard tasks measuring executive functions (Flanker; Simon; Spatial Stroop) in which we compared performance of English monolingual to Chinese–English bilingual young adults. Participants provided responses via movement of a computer mouse rather than the conventional key presses, which provides a rich signal of the unfolding response dynamics. Clear differences between bi- and monolinguals emerged, with the former providing more ‘efficient’ responses than the latter. Results are discussed regarding the extent to which these results can be characterised as a genuine “bilingual advantage”.
Dual MicroBlaze rekeying processor for group key management

User group key management is a critical task in secure multicast applications of multimedia contents, such as, the Internet TV, pay per view, satellite TV, etc. These keys must be recalculated, encrypted and redistributed when a user is joined or disjoined to a specific group in order to avoid that users which do not belong to a concrete group can access to the contents of that group. This paper presents a high performance dual MicroBlaze System on Chip (SoC) to perform this management as far as possible. This platform aims to reduce the influence of a user join or disjoin in the reception of the multimedia contents by the rest of users.
MULTIPLE-F0 TRACKING BASED ON A HIGH-ORDER HMM MODEL

This paper is about multiple-F0 tracking and the estimation of the number of harmonic source streams in music sound signals. A source stream is understood as generated from a note played by a musical instrument. A note is described by a hiddenMarkovmodel (HMM) having two states: the attack state and the sustain state. It is proposed to first perform the tracking of F0 candidates using a high-order hidden Markov model, based on a forward-backward dynamic programming scheme. The propagated weights are calculated in the forward tracking stage, followed by an iterative tracking of the most likely trajectories in the backward tracking stage. Then, the estimation of the underlying source streams is carried out by means of iteratively pruning the candidate trajectories in a maximum likelihood manner. The proposed system is evaluated by a specially constructed polyphonic music database. Compared with the frame-based estimation systems, the tracking mechanism improves significantly the accuracy rate.
PET/MRI in Oncological Imaging: State of the Art

Positron emission tomography (PET) combined with magnetic resonance imaging (MRI) is a hybrid technology which has recently gained interest as a potential cancer imaging tool. Compared with CT, MRI is advantageous due to its lack of ionizing radiation, superior soft-tissue contrast resolution, and wider range of acquisition sequences. Several studies have shown PET/MRI to be equivalent to PET/CT in most oncological applications, possibly superior in certain body parts, e.g., head and neck, pelvis, and in certain situations, e.g., cancer recurrence. This review will update the readers on recent advances in PET/MRI technology and review key literature, while highlighting the strengths and weaknesses of PET/MRI in cancer imaging.
Proofs of the Parisi and Coppersmith-Sorkin conjectures for the finite random assignment problem

Suppose that there are n jobs and n machines and it costs c/sub ij/ to execute job i on machine j. The assignment problem concerns the determination of a one-to-one assignment of jobs onto machines so as to minimize the cost of executing all the jobs. The average case analysis of the classical random assignment problem has received a lot of interest in the recent literature, mainly due to the following pleasing conjecture of Parisi: The average value of the minimum-cost permutation in an n /spl times/ n matrix with i.i. d. exp(1) entries equals /spl Sigma//sub i=1//sup n/ 1/(i/sup 2/). D. Coppersmith and G. Sorkin (1999) have generalized Parisi's conjecture to the average value of the smallest k-assignment when there are n jobs and m machines. We prove both conjectures based on a common set of combinatorial and probabilistic arguments.
A New Multiobjective Signal Optimization for Oversaturated Networks

This paper presents a new methodology for optimizing the signal timing controls of oversaturated networks based on the cell transmission model and a goal programming technique with multiple objectives. The proposed model accounts for intersection spillovers, equity in delays, and system throughputs. This new formulation is solved by genetic algorithms to obtain signal timing plans. A case study with a nine-intersection network and a comparison between the proposed model and the throughput-maximizing strategy are examined. It is found that the new method can efficiently minimize spillovers, balance delay equity, and provide reasonable system throughputs in their respective order for oversaturated networks. The result also indicates that the throughput-maximizing strategy does not always yield minimum spillovers for oversaturated networks and occasionally provides a larger difference in average link delay at a spillover intersection than the proposed model does.
A New Watermarking Technique for Multimedia

A robust watermarking scheme for hiding binary or gray-scale watermarks in digital images is proposed in this chapter. Motivated by the fact that a detector response (a correlation value) only provides a soft evidence for convincing jury in courtroom, embedded watermarks are designed to be visually recognizable after retrieval. To strengthen the existence conndence of a watermark, visually signiicant transformed components are selected. In addition, a relocation technique is presented to tackle geometric-distortion-based attacks without using any registration scheme. Finally, a semi-public watermark detector which does not require use of the original source is proposed for the purpose of authentication. Experimental results demonstrate that our approach satisses the common requirements of image watermarking, and that the performance is superb.
RNA helicase DEAD box protein 5 regulates Polycomb repressive complex 2/Hox transcript antisense intergenic RNA function in hepatitis B virus infection and hepatocarcinogenesis

Chronic hepatitis B virus (HBV) infection is a major factor in hepatocellular carcinoma (HCC) pathogenesis by a mechanism not yet understood. Elucidating mechanisms of HBV‐mediated hepatocarcinogenesis is needed to gain insights into classification and treatment of HCC. In HBV replicating cells, including virus‐associated HCCs, suppressor of zeste 12 homolog (SUZ12), a core subunit of Polycomb repressive complex2 (PRC2), undergoes proteasomal degradation. This process requires the long noncoding RNA, Hox transcript antisense intergenic RNA (HOTAIR). Intriguingly, HOTAIR interacts with PRC2 and also binds RNA‐binding E3 ligases, serving as a ubiquitination scaffold. Herein, we identified the RNA helicase, DEAD box protein 5 (DDX5), as a regulator of SUZ12 stability and PRC2‐mediated gene repression, acting by regulating RNA‐protein complexes formed with HOTAIR. Specifically, knockdown of DDX5 and/or HOTAIR enabled reexpression of PRC2‐repressed genes epithelial cell adhesion molecule (EpCAM) and pluripotency genes. Also, knockdown of DDX5 enhanced transcription from the HBV minichromosome. The helicase activity of DDX5 stabilized SUZ12‐ and PRC2‐mediated gene silencing, by displacing the RNA‐binding E3 ligase, Mex‐3 RNA‐binding family member B (Mex3b), from HOTAIR. Conversely, ectopic expression of Mex3b ubiquitinated SUZ12, displaced DDX5 from HOTAIR, and induced SUZ12 down‐regulation. In G2 phase of cells expressing the HBV X protein (HBx), SUZ12 preferentially associated with Mex3b, but not DDX5, resulting in de‐repression of PRC2 targets, including EpCAM and pluripotency genes. Significantly, liver tumors from HBx/c‐myc bitransgenic mice and chronically HBV‐infected patients exhibited a strong negative correlation between DDX5 messenger RNA levels, pluripotency gene expression, and liver tumor differentiation. Notably, chronically infected HBV patients with HCC expressing reduced DDX5 exhibited poor prognosis after tumor resection, identifying DDX5 as an important player in poor prognosis HCC. Conclusion: The RNA helicase DDX5, and E3 ligase Mex3b, are important cellular targets for the design of novel, epigenetic therapies to combat HBV infection and poor prognosis HBV‐associated liver cancer. (Hepatology 2016;64:1033‐1048)
Extending Fine-Grain Scalable Audio Coding to Very Low Bitrates using Overcomplete Dictionaries

Signal representations in overcomplete dictionaries are considered here as an alternative to the traditional transform representations for fine-grain scalable audio coding. Such representations produce sparser decompositions and thus allow better coding efficiency than transform coding at very low bitrates. Moreover, the decomposition algorithms are intrinsically progressive, and flexible enough to allow an efficient transient modeling. We propose in this paper a fine-grain scalable audio coder which works on a large range of bitrates (2kbs to 128kbs). Objective measures as well as informal subjective evaluation show that this coder outperforms a comparable transform-based coder at very low bitrates.
Individual Responses to Completion of Short-Term and Chronic Interval Training: A Retrospective Study

Alterations in maximal oxygen uptake (VO2max), heart rate (HR), and fat oxidation occur in response to chronic endurance training. However, many studies report frequent incidence of “non-responders” who do not adapt to continuous moderate exercise. Whether this is the case in response to high intensity interval training (HIT), which elicits similar adaptations as endurance training, is unknown. The aim of this retrospective study was to examine individual responses to two paradigms of interval training. In the first study (study 1), twenty active men and women (age and baseline VO2max = 24.0±4.6 yr and 42.8±4.8 mL/kg/min) performed 6 d of sprint interval training (SIT) consisting of 4–6 Wingate tests per day, while in a separate study (study 2), 20 sedentary women (age and baseline VO2max = 23.7±6.2 yr and 30.0±4.9 mL/kg/min) performed 12 wk of high-volume HIT at workloads ranging from 60–90% maximal workload. Individual changes in VO2max, HR, and fat oxidation were examined in each study, and multiple regression analysis was used to identify predictors of training adaptations to SIT and HIT. Data showed high frequency of increased VO2max (95%) and attenuated exercise HR (85%) in response to HIT, and low frequency of response for VO2max (65%) and exercise HR (55%) via SIT. Frequency of improved fat oxidation was similar (60–65%) across regimens. Only one participant across both interventions showed non-response for all variables. Baseline values of VO2max, exercise HR, respiratory exchange ratio, and body fat were significant predictors of adaptations to interval training. Frequency of positive responses to interval training seems to be greater in response to prolonged, higher volume interval training compared to similar durations of endurance training.
Recognition of handwritten devanagiri numerals by graph representation and SVM

Feature based representation is inadequate in modeling different writing styles, irregularity in size, complicated structural relationships, cursivenes present in unconstrained handwritten devanagiri numerals. In this paper, this insufficiency is eliminated by giving graph based representation. Moreover, graphs are robust to similarity deformations as well. Graph Dissimilarity Space Embedding is explored to extract features from numeral graphs. The feature vector generated was trained on SVM with RBF Kernel. Efficacy of the method is corroborated by carrying extensive experiments on benchmark dataset. From this study graph based representation seems to be robust and powerful in representing handwritten devanagiri numerals.
Brain spectrin binding to the NMDA receptor is regulated by phosphorylation, calcium and calmodulin

The N‐methyl‐D‐aspartate receptor (NMDA‐R) and brain spectrin, a protein that links membrane proteins to the actin cytoskeleton, are major components of post‐synaptic densities (PSDs). Since the activity of the NMDA‐R channel is dependent on the integrity of actin and leads to calpain‐mediated spectrin breakdown, we have investigated whether the actin‐binding spectrin may interact directly with NMDA‐Rs. Spectrin is reported here to interact selectively in vitro with the C‐terminal cytoplasmic domains of the NR1a, NR2A and NR2B subunits of the NMDA‐R but not with that of the AMPA receptor GluR1. Spectrin binds at NR2B sites distinct from those of α‐actinin‐2 and members of the PSD95/SAP90 family. The spectrin–NR2B interactions are antagonized by Ca2+ and fyn‐mediated NR2B phosphorylation, but not by Ca2+/calmodulin (CaM) or by Ca2+/CaM‐dependent protein kinase II‐mediated NR2B phosphorylation. The spectrin–NR1 interactions are unaffected by Ca2+ but inhibited by CaM and by protein kinase A‐ and C‐mediated phosphorylations of NR1. Finally, in rat synaptosomes, both spectrin and NR2B are loosened from membranes upon addition of physiological concentrations of calcium ions. The highly regulated linkage of the NMDA‐R to spectrin may underlie the morphological changes that occur in neuronal dendrites concurrently with synaptic activity and plasticity.
Making Use of Partial Knowledge About Hidden States in HMMs: An Approach Based on Belief Functions

This paper addresses the problem of parameter estimation and state prediction in hidden Markov models (HMMs) based on observed outputs and partial knowledge of hidden states expressed in the belief function framework. The usual HMM model is recovered when the belief functions are vacuous. Parameters are learned using the evidential expectation-maximization algorithm, a recently introduced variant of the expectation-maximization algorithm for maximum likelihood estimation based on uncertain data. The inference problem, i.e., finding the most probable sequence of states based on observed outputs and partial knowledge of states, is also addressed. Experimental results demonstrate that partial information about hidden states, when available, may substantially improve the estimation and prediction performances.
Adaptable sensor fusion using multiple Kalman filters

This paper presents an integrated sensor fusion strategy for the positioning of an underwater ROV. The use of multiple Kalman filters makes the system highly adaptable by allowing different combinations of sensors without any modification of the models. This algorithm can handle any number of redundant sensors by using multi-filter fusion and can work asynchronously with different sensor data rates through a filter switching process.
Load-frequency control service in a deregulated environment

In a deregulated environment, independent generators and utility generators may or may not participate in the load frequency control of the system. For the purpose of evaluating the performance of such a system, a flexible method has been developed and implemented. The method assumes that load frequency control is performed by an ISO based on parameters defined by the participating generating units. The participating units comprise utility generators and independent power producers. The utilities define the units which will be under load frequency control, while the independent power producers may or may not participate in the load frequency control. For all the units which participate in the load frequency control, the generator owner defines: (a) generation limits, (b) rate of change and (c) economic participation factor. This information is transmitted to the ISO. This scheme allows the utilities to economically dispatch their own system, while at the same time permitting the ISO to control the interconnected system operation.
A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data

Compared with traditional manned airborne photogrammetry, unmanned aerial vehicle remote sensing (UAVRS) has the advantages of lower cost and higher flexibility in data acquisition. It has, therefore, found various applications in fields such as three-dimensional (3D) mapping, emergency management, and so on. However, due to the instability of the UAVRS platforms and the low accuracy of the onboard exterior orientation (EO) observations, the use of direct georeferencing image data leads to large location errors. Light detection and ranging (LiDAR) data, which is highly accurate 3D information, is treated as a complementary data source to the optical images. This paper presents a semi-automatic approach for the registration of UAVRS images and airborne LiDAR data based on linear control features. The presented approach consists of three main components, as follows. (1) Buildings are first separated from the point cloud by the integrated use of height and size filtering and RANdom SAmple Consensus (RANSAC) plane fitting, and the 3D line segments of the building ridges and boundaries are semi-automatically extracted through plane intersection and boundary regularization with manual selections; (2) the 3D line segments are projected to the image space using the initial EO parameters to obtain the approximate locations, and all the corresponding 2D line segments are semi-automatically extracted from the UAVRS images. Meanwhile, the tie points of the UAVRS images are generated using a Forstner operator and least-squares image matching; and (3) by use of the equations derived from the coplanarity constraints of the linear control features and the colinear constraints of the tie points, block bundle adjustment is carried out to update the EO parameters of the UAVRS images in the coordinate framework of the LiDAR data, achieving the co-registration of the two datasets. Experiments were performed to demonstrate the validity and effectiveness of the presented method, and a comparison with the traditional registration method based on LiDAR intensity images showed that the presented method is more accurate, and a sub-pixel accuracy level can be achieved.