Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized cosine similarity in I-vector based automatic speaker recognition systems

This paper deals with the problem of processing of I-vectors in the text-independent speaker verification systems. A new generalized cosine similarity optimization technique is proposed. The optimization is performed over sets of orthogonal and diagonal matrices. Lie group techniques are used in order to obtain exact orthogonal matrix solution. The experiments have been performed using the NIST 2010 database. It was shown that a substantial equal error rate (EER) reduction can be obtained with the proposed algorithm.
Learning structural uncertainties of nonlinear systems with RBF neural networks via persistently exciting control

This work presents a scheme for learning, online, the actual nonlinearities of systems in canonical form. The proposed architecture comprises of an on-line Radial Basis Function (RBF) neural network identifier and a controller, with the signals issued by the latter guaranteeing the satisfaction of a Persistency of Excitation (PE) condition for the RBF regressors employed. As a consequence, the neural network weight estimates are proven to converge to small neighborhoods of their true values; thus succeeding learning the actual system nonlinearities with quality guarantees. Key characteristic is the isolation between identifier and controller design, increasing the robustness level of the proposed on-line learning scheme. Finally, a simulation study is provided to demonstrate its effectiveness.
Financial Literacy and Macro-economics

Financial literacy, moving to the forefront on account of the economic crisis, is a crucial element of the proper functioning of any society. Our goal is to identify the indicators of financial literacy in topic-related Hungarian and international literature which can then be used to draw macro-economic conclusions and be tested through Hungary’s example. We have identified four main indicators, which are: (1) income, (2) savings, (3) external funds (loans) and (4) the economy’s demand for cash. A number of criticisms can be raised in connection with the above indicators, most importantly the fact that the perception of financial literacy is overly linked to income. In spite of this, all these indicators are conducive to providing information on one of the financial literacy-related issues (self-provision, confidence, planning, long-term thinking, profitability, etc.).
A Contour-Based Method for Logo Detection

This paper presents a new approach for logo detection exploiting contour based features. At first stage, pre-processing, contour detection and line segmentation are done. These processes result in set of Outer Contour Strings (OCSs) describing each graphics and text parts of the documents. Then, the logo detection problem is defined as a region scoring problem. Two types of features, coarse and finer ones, are computed from each OCS. Coarse features catch graphical and domain information about OCSs, such as logo positions and aspect ratios. Finer features characterize the contour regions using a gradient based representation. Using these features, we employ regression fitting to score how likely an OCS takes part of a logo region. A final step of correction helps with the wrong segmentation cases. We present experiments done on the Tobacco-800 dataset, and compare our results with the literature. We obtain interesting results compared to the best systems.
Large margin estimation of n-gram language models for speech recognition via linear programming

We present a novel discriminative training algorithm for n-gram language models for use in large vocabulary continuous speech recognition. The algorithm uses large margin estimation (LME) to build an objective function for maximizing the minimum margin between correct transcriptions and their competing hypotheses, which are encoded as word graphs generated from the Viterbi decoding process. The nonlinear LME objective function is approximated by a linear EM-style auxiliary function that leads to a linear programing problem, which is efficiently solved by convex optimization algorithms. Experimental results have shown that the proposed discriminative training method can outperform the conventional discounting-based maximum likelihood estimation methods. A relative reduction in word error rate of over 2.5% has been observed on the SPINE1 speech recognition task.
MULTILAYER PERCEPTRON , RADIAL BASIS FUNCTION NETWORK , AND SELF – ORGANIZING MAP IN THE PROBLEM OF FACE RECOGNITION

In this contribution, one and two-stage neural networks methods for face recognition are presented. For two-stage systems, the Kohonen self-organizing map is used as a feature extractor and multiplayer perceptron (MLP) or radial basis function (RBF) network are used as classifiers. The results of such recognition are compared with face recognition using a one-stage multilayer perceptron and radial basis function network classifiers.
Testing I/O Failures with Enforcer

Testing application behavior in the presence of I/O failures is extremely difficult. The resources used for testing usually work without failure. Failures typically cannot be initiated on the test suite level and are usually not tested sufficiently. Essentially, each interaction of the application with the environment can result in a failure, making failures hard to test. The Enforcer tool identifies such potential failures and automatically tests all relevant outcomes of such actions. It combines the structure of unit tests, coverage information, and fault injection. By taking advantage of a unit test infrastructure, performance can be improved by orders of magnitude compared to previous approaches. This paper introduces the usage of the Enforcer tool.
Adaptive Blind Separation of Speech Signals: Cocktail Party Problem

In this paper we present an on line adaptive scheme for blind separation of speech signals from their convolutive mixtures This prob lem is often referred as cocktail party problem When multiple speakers speak simultaneously in tele conferencing studio we need to separate out each speaker from their mixtures If mix tures are assumed as instantaneous mixtures then it becomes standard blind source separa tion problem In practical situations however the observed signals from microphones are usually convolutive mixtures due to the propagating source signals through the dynamic medium and parasitic e ects like multiple echoes and reverberation Thus the received signals are weighted sum of mixed and delayed components In other words the re ceived signals at each microphone are the con volutive mixtures of speech signals We present two slightly di erent neural networks i e a dynamic recurrent network and a feedforward feedback network which can separate the con volutive mixtures of speech signals without any knowledge of the propagation media Both the oretical and computer simulation results are provided
Development of a smart job allocation model for a Cloud Service Provider

Cloud Computing is known as a provider of dynamic services. It utilizes a very large, scalable and virtualized resource over the Internet. So many industries have joined this bandwagon nowadays. One of the major research issues is to maintain good Quality of Service (QoS) of a Cloud Service Provider (CSP). The QoS encompasses different parameters, like, smart job allocation strategy, efficient load balancing, response time optimization, reduction in wastage of bandwidth, accountability of the overall system, etc. The efficient allocation strategy of the independent computational jobs among different Virtual Machines (VM) in a Datacenter (DC) is a distinguishable challenge in the Cloud Computing domain and finding out an optimal job allocation strategy guided by a good scheduling heuristic for such an environment is an NP-complete problem. So different heuristic approaches may be used for better result. The related works with other NP- complete problems have shown that solutions guided by heuristic approaches can often be improved by applying local scheduling procedure for allocating independent jobs in the virtual machines (VM) inside a Datacenter (DC), which, when combined with fast construction heuristics, can find out the shorter schedules on benchmark problems than the other solution methodologies found in different literatures, and in significant less time. This paper highlights a smart job allocation strategy for a CSP by applying Round-Robin (RR) scheduling policy.
Spinal Sensorimotor Transformation: Relation between Cutaneous Somatotopy and a Reflex Network

The projection of primary afferents onto spinal interneurons constitutes the first step in sensorimotor transformations performed by spinal reflex systems. Despite extensive studies on spinal somatotopy, uncertainties remain concerning the extent and significance of representational overlap and relation to spinal reflex circuits. To address these issues, the cutaneous projection from the hindpaw and its relation to the topography of lamina V neurons encoding withdrawal reflex strength (“reflex encoders”) was studied in rats. Thin and coarse primary afferent terminations in laminas II and III–IV, respectively, were mapped by wheat germ agglutinin-horseradish peroxidase and choleragenoid tracing. The functional weights of these projections were characterized by mapping nociceptive and tactile field potentials and compared with the topography of reflex encoders. Both anatomical and physiological data indicate that thin and coarse skin afferent input is spatially congruent in the horizontal plane. The representation of the hindpaw in the spinal cord was found to be intricate, with a high degree of convergence between the projections from different skin sites. “Somatotopic disruptions” such as the representation of central pads medial to that of the digits were common. The weight distribution of the cutaneous convergence patterns in laminas III–IV was similar to that of lamina V reflex encoders. This suggests that the cutaneous convergence and features such as somatotopic disruptions have specific relations to the sensorimotor transformations performed by reflex interneurons in the deep dorsal horn. Hence, the spinal somatotopic map may be better understood in light of the topography of such reflex systems.
Mismatch Negativity/P3a Complex in Young People with Psychiatric Disorders: A Cluster Analysis

Background We have recently shown that the event-related potential biomarkers, mismatch negativity (MMN) and P3a, are similarly impaired in young patients with schizophrenia- and affective-spectrum psychoses as well as those with bipolar disorder. A data driven approach may help to further elucidate novel patterns of MMN/P3a amplitudes that characterise distinct subgroups in patients with emerging psychiatric disorders. Methods Eighty seven outpatients (16 to 30 years) were assessed: 19 diagnosed with a depressive disorder; 26 with a bipolar disorder; and 42 with a psychotic disorder. The MMN/P3a complex was elicited using a two-tone passive auditory oddball paradigm with duration deviant tones. Hierarchical cluster analysis utilising frontal, central and temporal neurophysiological variables was conducted. Results Three clusters were determined: the ‘globally impaired’ cluster (n = 53) displayed reduced frontal and temporal MMN as well as reduced central P3a amplitudes; the ‘largest frontal MMN’ cluster (n = 17) were distinguished by increased frontal MMN amplitudes and the ‘largest temporal MMN’ cluster (n = 17) was characterised by increases in temporal MMN only. Notably, 55% of those in the globally impaired cluster were diagnosed with schizophrenia-spectrum disorder, whereas the three patient subgroups were equally represented in the remaining two clusters. The three cluster-groups did not differ in their current symptomatology; however, the globally impaired cluster was the most neuropsychologically impaired, compared with controls. Conclusions These findings suggest that in emerging psychiatric disorders there are distinct MMN/P3a profiles of patient subgroups independent of current symptomatology. Schizophrenia-spectrum patients tended to show the most global impairments in this neurophysiological complex. Two other subgroups of patients were found to have neurophysiological profiles suggestive of quite different neurobiological (and hence, treatment) implications.
Nitric Oxide Synthase Inhibition Attenuates Cardiac Response to Hemodilution with Viscogenic Plasma Expander

Background and Objectives Increased vascular wall shear stress by elevated plasma viscosity significantly enhances the endothelial nitric oxide synthase (eNOS) activity during an acute isovolemic hemodilution. Also the modulation of plasma viscosity has effects on the cardiac function that were revealed if a left ventricular (LV) pressure-volume (PV) measurement was used. The aim of this study was to assess cardiac function responses to nitric oxide synthase (NOS) inhibitors with the presence of an elevated plasma viscosity but a low hematocrit level. Furthermore, systemic parameters were monitored in a murine model. Materials and Methods As test group five anesthetized hamsters were administered with N(G)-nitro-L-arginine methyl ester (L-NAME), NOS inhibitor, whereas five other hamsters were used as control group without L-NAME infusion. The dosage of L-NAME was 10 mg/kg. An isovolemic hemodilution was performed by 40% of estimated blood volume with 6% w/v dextran 2000 kDa, high viscosity plasma expanders (PEs) with viscosity 6.34 cP. LV function was measured and assessed using a 1.4 Fr PV conductance catheter. Results The study results demonstrated that NOS inhibition prevented the normal cardiac adaptive response after hemodilution. The endsystolic pressure increased 14% after L-NAME infusion and maintained higher than at the baseline after hemodilution, whereas it gradually decreased in the animals without L-NAME infusion. The admission of L-NAME significantly decreased the maximum rate of ventricular pressure rise (+dP/dtmax), stroke volume and cardiac output after hemodilution if compared to the control group (p<0.05). Conclusion This finding supports the presumption that nitric oxide induced by an increased plasma viscosity with the use of a high viscosity PE plays a major role in the cardiac function during an acute isovolemic hemodilution.
Histopathological Findings in Hereditary Motor and Sensory Neuropathy of Axonal Type With Onset in Early Childhood Associated With Mitofusin 2 Mutations

Neuropathologic abnormalities can be sufficiently characteristic to suggest the genetic basis of some hereditary neuropathies such as those associated with mutations in MPZ, GJB1, GDAP1, MTMR2, SH3TC2, PRX, FGD4, and LMNA. We analyzed the morphologic features of 9 sural nerve biopsies from 6 patients with mutations of mitofusin 2. All patients presented in early childhood with axonal neuropathies designated as mild or severe motor and sensory neuropathy. In all cases, there was a marked decrease in density of myelinated fibers, mainly of large diameter fibers. These changes were more marked in the second biopsies of 3 patients that were performed from 7 to 19 years after the first biopsies. Neurophysiologic findings were most suggestive of axonal degeneration, but some onion bulbs were present in all cases. Axonal mitochondria were smaller than normal, were round, and were abnormally aggregated. These changes may result from abnormal mitochondrial fusion and fission. The results suggest that these clinical and pathological features may be sufficiently characteristic to suggest the diagnosis of mitofusin 2-related neuropathy.
B0AT2 (SLC6A15) Is Localized to Neurons and Astrocytes, and Is Involved in Mediating the Effect of Leucine in the Brain

The B0AT2 protein is a product of the SLC6A15 gene belonging to the SLC6 subfamily and has been shown to be a transporter of essential branched-chain amino acids. We aimed to further characterize the B0AT2 transporter in CNS, and to use Slc6a15 knock out (KO) mice to investigate whether B0AT2 is important for mediating the anorexigenic effect of leucine. We used the Slc6a15 KO mice to investigate the role of B0AT2 in brain in response to leucine and in particular the effect on food intake. Slc6a15 KO mice show lower reduction of food intake as well as lower neuronal activation in the ventromedial hypothalamic nucleus (VMH) in response to leucine injections compared to wild type mice. We also used RT-PCR on rat tissues, in situ hybridization and immunohistochemistry on mouse CNS tissues to document in detail the distribution of SLC6A15 on gene and protein levels. We showed that B0AT2 immunoreactivity is mainly neuronal, including localization in many GABAergic neurons and spinal cord motor neurons. B0AT2 immunoreactivity was also found in astrocytes close to ventricles, and co-localized with cytokeratin and diazepam binding inhibitor (DBI) in epithelial cells of the choroid plexus. The data suggest that B0AT2 play a role in leucine homeostasis in the brain.
A measurement of Young's modulus and residual stress in MEMS bridges using a surface profiler

This paper addresses a relatively simple method of measuring Young's modulus and residual stress in microelectromechanical system (MEMS) type structures. A surface profilometer is used to measure the deflection of thin film fixed–fixed beams due to the force applied by the profilometer probe. These measurements are analyzed using analytical beam theory. The treatment of end effects and the accuracy of the measurement are discussed. Measurements and results are presented for PECVD grown silicon nitride films.
Adaptive scheduler algorithm for multi-criterion designs in B3G wireless systems

Multi-criterion scheduler algorithms are discussed. An adaptive scheduler control algorithm is provided to explore the tradeoff between the system throughput and different user fairness in both the average number of assigned slots and average transmission rates. We showed that even thoughh it is difficult to achieve unique optimum point to satisfy all design criteria, there exists an operator-defined optimality if the priorities of different design criteria are chosen. All performance are quantified based on 1xEV-DO technologies.
A memetic algorithm combined particle swarm optimization with simulated annealing and its application on multiprocessor scheduling problem

A memetic algorithm, which combines globe search with local search strategies, is presented to deal with the multiprocessor scheduling problem(MSP). During the processes, an improved particle swarm optimization is employed to execute the globe search optimization, and the simulated annealing is adopted to improve the quality of the selected candidates based on a certain strategy. Simulations show that the proposed method performs well on the globe exploration. Experimental results based on MSP show that the algorithm achieved an efficient makespan. Streszczenie. Przedmiotem artykulu jest algorytm memetyczny do optymalizacji szeregowania zadan w systemie wieloprocesorowym (ang. Multiprocessor Scheduling Problem), lączący w sobie strategie wyszukiwania globalnego i lokalnego. W celu optymalizacji wyszukiwania globalnego zastosowano ulepszoną metode optymalizacji PSO (ang. Particle Swarm Optimization) oraz algorytm symulowanego wyzarzania (ang. Simulated Annealing) w celu poprawy jakości wybranych elementow. (Algorytm memetyczny w optymalizacji szeregowania zadan w systemie wieloprocesorowym – optymalizacja PSO i algorytm symulowanego wyzarzania)
Iterative Bi-directional Kalman-DFE equalizer for the high data rate HF waveforms in the HF channel

In this paper, we present the Kalman decision feedback equalizer (Kalman-DFE) as a solution for the high data rate HF waveforms. We show that the basic performance for the Kalman-DFE is far from the standard requirements of the HF and the performance breaks down for the higher constellation. As a result, we add two major enhancements for the Kalman-DFE: the bi-directional and iterative structures. The bi-directional structure successfully prevents the performance break down at the higher constellation and enhances the overall performance. The iterative structure then enhances the performance significantly (about 6dB gain at 1e-5 BER for 64QAM after three iterations). We show that the proposed equalizer in the final form achieves significant performance enhancements over the normal one. Moreover, the proposed iterative Kalman-DFE is much simpler than turbo equalizer, especially for the fractional-spaced model, for two reasons: first, the direct adaptation structure used in Kalman filter is much simpler than the indirect adaptation structure usually used with the turbo structure (it does not require matrix inversion). Secondly, the iterative structure (exchanges hard information) is simpler than turbo (exchanges soft information) as the later requires a lot of changes in both equalizer and decoder to support soft information. We perform the simulations on the MIL-STD-188-110 (Appendix C) waveforms, transmitted over an ITU-R poor channel (commonly used channel to test HF modem).
An Abstract Machine for an Object Calculus an Abstract Machine for an Object Calculus

We present an abstract machine speciication for TyCO (Typed Concurrent Objects), a name-passing calculus that aims at capturing fundamental concepts present in Concurrent Object-Oriented Languages. TyCO has built-in labeled messages and ephemeral object s that communicate asynchronously. Persistent objects are supported through instantiation of recursive classes. Concurrency is pervasive and synchronous communication can be implemented by passing continuations in messages. Strong, static typing is provided by a type inference algorithm that supports a form of predicative polymorphism. This paper describes the abstract machine framework in detail, including how it evolves from TyCO and some important properties it can be shown to possess.
Development of Calorie Restriction Mimetics as Therapeutics for Obesity, Diabetes, Inflammatory and Neurodegenerative Diseases

Calorie restriction (CR) is the most robust intervention that decreases morbidity and mortality, and thereby increases the lifespan of many organisms. Although the signaling pathways involved in the beneficial effects of CR are not yet fully understood. Several candidate pathways and key molecules have been identified. The effects of CR are highly conserved from lower organisms such as yeast to higher mammals such as rodents and monkeys. Recent studies have also demonstrated beneficial effects of CR in humans, although we need much longer studies to evaluate whether CR also increases the lifespan of humans. In reality, it is difficult for us to conduct CR interventions in humans because the subjects must be kept in a state of hunger and the duration of this state needed to achieve a clinically meaningful effect is still unknown. Thus, research in this field is focusing on the development of molecules that mimic the beneficial effects of CR without reducing food intake. Some of these candidate molecules include plant-derived functional chemicals (phyto-chemicals), synthetic small molecules, and endocrine molecules such as adipokines. Several studies have already shown that this research field may yield novel drugs for the treatment of age-related diseases such as diabetes. In this article, we describe the target pathways, candidate molecules, and strategies to develop CR mimetics.