Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum-dash mode-locked lasers for tunable wavelength conversion on a 100 GHz frequency grid

All-optical wavelength converters play a key role for next-generation high-bit-rate optical networks. A high-speed, simple, and compact wavelength converter can resolve contention, reduce wavelength blocking, and enable a multitude of new wavelength routing approaches. We propose the combination of a quantum-dash mode-locked laser (QD-MLL) with a frequency-selective filter to provide (two) tunable wavelength pumps for wavelength conversion of optical channels in a dual-pump four-wave-mixing (FWM) scheme. Such a converter is simple, practical, and offers a wide conversion range, modulation format transparency, and the potential for photonic integration. We examine the performance of the proposed scheme experimentally with a QD-MLL and manually tunable optical filters. Wavelength conversion is achieved via an extremely non-linear semiconductor optical amplifier (SOA), a commercial SOA with very high FWM efficiency. We achieved near -3 dB conversion efficiency across a 9.6 nm tuning range with a signal input power of -3 dBm and a pump input power of 2 dBm. We performed bit error rate (BER) measurement on a 5.6 nm conversion range and obtained error-free transmission (BER <;10-9) with less than 2 dB power penalty.
A decomposition scheme based on error-correcting output codes for ensembles of text categorizers

Error-correcting output codes (ECOC) are commonly used to decompose a multicategory problem into many dichotomies. Therefore, the text categorisation task is performed by an ensemble of binary classifiers instead of a single monolithic classifier. The ensemble performance largely depends on the characteristics of the decomposition. We propose a decomposition approach where both the categories and the classifiers are well separated in order to maximise the decision boundaries and minimise correlated predictions. We apply this design to the El Mundo corpus (newspaper articles in Spanish) and the well-known ModApte split of the Reuters-21578 corpus. The results using ensembles are favourably compared to those using a monolithic classifier.
Product high-order ambiguity function for multicomponent polynomial-phase signal modeling

Parameter estimation and performance analysis issues are studied for multicomponent polynomial-phase signals (PPSs) embedded in white Gaussian noise. Identifiability issues arising with existing approaches are described first when dealing with multicomponent PPS having the same highest order phase coefficients. This situation is encountered in applications such as synthetic aperture radar imaging or propagation of polynomial phase signals through channels affected by multipath and is thus worthy of a careful analysis. A new approach is proposed based on a transformation called product high-order ambiguity function (PHAF). The use of the PHAF offers a number of advantages with respect to the high-order ambiguity function (HAF). More specifically, it removes the identifiability problem and improves noise rejection capabilities. Performance analysis is carried out using the perturbation method and verified by simulation results.
Complexity Analysis in Additive Manufacturing for the Production of Tissue Engineering Constructs

Additive Manufacturing (AM) is a process of making a Three-Dimensional (3D) solid object of virtually any shape from a digital model that is used for both prototyping and distributed manufacturing with applications in many fields, such as dental and medical industries and biotech (human tissue replacement). AM refers to technologies that create objects through a sequential layering process. AM processes have several primary areas of complexity that may not be measured precisely, due to uncertain situations. Therefore, this chapter reports an analytical model for evaluating process complexity that takes into account uncertain situations and additive manufacturing process technologies. The model is able to rank AM processes based on their relative complexities. An illustrative example for several processes is demonstrated in order to present the application of the model.
An Optimized Clustering Algorithm Using Genetic Algorithm and Rough set Theory based on Kohonen self organizing map

The Kohonen self organizing map is an efficient tool in exploratory phase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently, most of the researchers found that to take the uncertainty concerned in cluster analysis, using the crisp boundaries in some clustering operations is not necessary. In this paper, an optimized two-level clustering algorithm based on SOM which employs the rough set theory and genetic algorithm is proposed to defeat the uncertainty problem. The evaluation of proposed algorithm on our gathered poultry diseases data and Iris data expresses more accurate compared with the crisp clustering methods and reduces the errors.
A Formal Theory of Language Development

This paper presents a formal model of language acquisition that, in addition to meeting the traditional learnability condition, makes time-course predictions about child language development. We view language acquisition as a population of human grammars, made available by UG, compete in a selectionist process. Selection is made possible by the variational properties of individual grammars; specifically, their differential compatibility with the primary linguistic data in the environment. We first present developmental facts that are problematic for both a statistical learning approach and traditional learnability models. We then propose a formal model of acquisition in the variational framework, and derive some convergence results. The model receives empirical support from developmental evidence in child language, including the subject drop phenomenon, that a learner is best modeled as a population of grammars in co-existence and competition, rather than a single grammar supplemented by auxiliary assumptions. 1 Learnability and Development A central issue in linguistics and cognitive science is the problem of language acquisition: How does a human child come to acquire her language with such ease, yet without high computational power or favorable learning conditions? As articulated by Chomsky (1965, 1975), the language faculty of the child’s mind/brain undergoes changes in the course of language acquisition, from an initial state S0, the Universal Grammar (UG), to a relatively stable state SL, that of a competent speaker of the language: (1) S0 → S1 → . → SL A theory of language acquisition aims to identify and model the process in (1) with sufficient details and clarity. It is evident that any adequate theory must meet the following empirical conditions: (2) a. Learnability: such a theory must accurately characterize the asymptotic properties of the learner in a particular linguistic environment (Gold 1967), under plausible assumptions about the learner’s computational machinery, the nature of the input data, sample size, and so on. b. Developmental compatibility: the learner modeled in such a theory must exhibit behaviors that are analogous to the actual course of language development (Pinker 1979). In this section of the paper, I present developmental facts that are problematic for both statistical learning (e.g. connectionism) and traditional learnability studies, building motivation for the model developed in the rest of the paper. 1.1 Against statistical learning An empiricist approach to language acquisition has (re)gained popularity in computational linguistics and cognitive science; e.g. (Stolcke 1994; Chen 1995; de Marcken 1996; Bates & Elman 1996; Seidenberg 1997).
The relevance vector machine technique for channel equalization application

The relevance vector machine (RVM) technique is applied to communication channel equalization. It is demonstrated that the RVM equalizer can closely match the optimal performance of the Bayesian equalizer, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique.
Social intelligence, innovation, and enhanced brain size in primates

Despite considerable current interest in the evolution of intelligence, the intuitively appealing notion that brain volume and “intelligence” are linked remains untested. Here, we use ecologically relevant measures of cognitive ability, the reported incidence of behavioral innovation, social learning, and tool use, to show that brain size and cognitive capacity are indeed correlated. A comparative analysis of 533 instances of innovation, 445 observations of social learning, and 607 episodes of tool use established that social learning, innovation, and tool use frequencies are positively correlated with species' relative and absolute “executive” brain volumes, after controlling for phylogeny and research effort. Moreover, innovation and social learning frequencies covary across species, in conflict with the view that there is an evolutionary tradeoff between reliance on individual experience and social cues. These findings provide an empirical link between behavioral innovation, social learning capacities, and brain size in mammals. The ability to learn from others, invent new behaviors, and use tools may have played pivotal roles in primate brain evolution.
Detecting Gradients in Text Images Using the Hough Transform

The use of gradients in text images is nowadays quite frequent. Existing segmentation methods encounter serious problems when it comes to modern text images where gradients might appear in the background or the foreground or both at the same time. This paper presents an approach for lightness gradient areas detection based on the Hough Transform. The issues arising are discussed, and results are presented on a dataset comprising Web images, logos and scanned documents.
Stereoscopic 3D experience optimization using cropping and warping

The purpose of this paper is to optimize the stereoscopic 3D experience when users watch those contents. We propose two factors in stereoscopic experience: visual fatigue and depth perception. In order to optimize stereoscopic experience, we propose five principles to reduce visual fatigue and enhance depth perception and implement them by cropping and warping. Using our methods, time-consuming problems of existing view interpolation methods such as camera calibration, accurate dense depth map, and inpainting are avoided. We also design a GUI that enables users to efficiently edit the stereoscopic video, optimize the stereoscopic experience, and preview the stereoscopic result. The user study shows that our method is successful in optimizing stereoscopic experience. One example is shown in Figure 1.
Existence of different subtypes of nicotinic acetylcholine receptors in the rat habenulo-interpeduncular system

Neuronal nicotinic ACh receptors (nAChRs) are present in the rat medial habenula (MHB) and interpeduncular nucleus (IPN), two brain regions connected through the fasciculus retroflexus (FR). The goal of the present study was to compare the electrophysiological and pharmacological characteristics of nAChRs located at pre- and postsynaptic sites within the MHB-IPN system. nAChRs located on the soma of IPN neurons were studied using patch-clamp techniques and a preparation of acutely isolated neurons. Whole-cell currents evoked by Ach and nicotine showed an intense rectification at positive membrane potentials. nAChR channels were relatively nonselective for cations, had a unitary conductance of 35 pS, and were activated by several nicotinic agonists with the following rank order: cytisine greater than ACh greater than nicotine greater than dimethylphenylpiperazinium (DMPP). They were blocked by mecamylamine, hexamethonium, curare, and dihydro-beta-erythroidine (DHBE), but were insensitive to alpha- bungarotoxin and neuronal bungarotoxin. In contrast, nAChRs recorded on the soma of MHB neurons under equivalent experimental conditions exhibited different characteristics for single-channel conductance and agonist and antagonist sensitivity. The pharmacological properties of presynaptic nAChRs in the IPN were analyzed in a rat brain slice preparation. Stimulation of the FR produced a presynaptic afferent volley recorded in the rostral subnucleus of the IPN. Nicotinic agonists decreased the amplitude of the afferent volley with different efficacies: nicotine greater than cytisine greater than ACh greater than DMPP. The action of nicotine was insensitive to alpha-bungarotoxin and to neuronal bungarotoxin, but was blocked by mecamylamine, hexamethonium, curare, and DHBE, with IC50 values different from those reported for IPN postsynaptic nAChRs. This study thus demonstrates the functional diversity of nAChRs in the rat CNS.
Speckle Suppression in Medical Ultrasound Images Using Two Dimensional GARCH Model

In this paper, we introduce a novel speckle suppression method for medical ultrasound images, based on statistical model of wavelet coefficients. We use two-dimensional generalized autoregressive conditional heteroscedasticity (GARCH) model for statistical modeling of images wavelet coefficients. By using two-dimensional GARCH model on the wavelet coefficients, we are capable of taking into account important characteristics of wavelet coefficients, such as non-stationarity, heavy tailed marginal distribution, and the dependencies between the coefficients. Furthermore, we use minimum mean square error (MMSE) estimator for estimating the clean image wavelet coefficients. Here, to prove the performance of this method in speckle suppression, we have compared our proposed method with various speckle suppression methods applied on actual ultrasound medical images and we verify the performance improvement in utilizing the new strategy.
List of papers

2014. Resonances of Dirac Operators. ISBN This thesis consists of a summary of four papers dealing with resonances of Dirac operators on Euclidean 3-space. In Paper I we show that the Complex Absorbing Potential (CAP) method is valid in the semiclassical limit for resonances sufficiently close to the real line if the potential is smooth and compactly supported. In Paper II we continue the investigations initiated in Paper I but here we study clouds of resonances close to the real line and show that in some sense the CAP method remains valid also for multiple resonances. In Paper III we study perturbations of Dirac operators with smooth decaying scalar potentials and show that these possess many resonances near certain points related to the maximum and the minimum of the potential. In Paper IV we show a trace formula of Poisson type for Dirac operators having compactly supported potentials which is related to resonances. The techniques mainly stem from complex function theory and scattering theory.
A Survey of Content-based Spam Classiﬁers

Unsolicited bulk e-mail (spam) is a growing problem with tangible costs felt by virtually every Internet user. There are many solutions to this problem, ranging from simple blacklisting to advanced text classiﬁ-cation and collaborative ﬁltering. None of these techniques provides a total solution, but new technologies and their application oﬀer increasingly eﬀective ﬁlters. This paper provides an overview and comparison of the advanced spam ﬁltering techniques that classify messages based on their content. This includes statistical classiﬁers, collaborative ﬁlters and combinations of diﬀerent classiﬁers.
On the Undecidability of Fuzzy Description Logics with GCIs with Lukasiewicz t-norm

Recently there have been some unexpected results concerning Fuzzy Description Logics (FDLs) with General Concept Inclusions (GCIs). They show that, unlike the classical case, the DL ALC with GCIs does not have the finite model property under Lukasiewicz Logic or Product Logic and, specifically, knowledge base satisfiability is an undecidable problem for Product Logic. We complete here the analysis by showing that knowledge base satisfiability is also an undecidable problem for Lukasiewicz Logic.
Bidirectional Grid-Tie Flyback Converter Applied to Distributed Power Generation and Street Lighting Integrated System

This paper presents a multifunctional bidirectional converter applied to street lighting and photovoltaic (PV) microgeneration systems. The proposed converter works as an electronic driver supplying a street lighting luminaire based on light emitting diodes (LEDs) at night, from ac single-phase mains with high power factor and reduced harmonic distortion. During daylight hours, the converter injects the produced energy by a PV panel to the grid, working as a grid-tie inverter. The proposed topology is based on the integration of two flyback converters, one for each half-cycle of the grid voltage, avoiding the usual diode bridge rectifier for LED lamps drives, thus providing a bidirectional power flow. A prototype of the proposed electronic stage has been designed, built, and tested, in order to validate the system. Finally, experimental results are presented. In rectifier mode, an efficiency of 86% was achieved, whereas an efficiency of 89% was obtained in inverter mode.
Impact of IL12B Gene rs 3212227 Polymorphism on Fibrosis, Liver Inflammation, and Response to Treatment in Genotype 4 Egyptian Hepatitis C Patients

Introduction. Hepatitis C virus (HCV) infection affects almost 3% of the world's population with the highest prevalence in Egypt (15%). The standard therapy; pegylated interferon (PEG-IFN) and ribavirin, is effective in only 60% of Egyptian patients; moreover it is costly, prolonged, and has severe side effects, so prediction of response is essential to reduce burden of unfavorable treatment. Several viral and host factors have been proved to affect response to the treatment PEG-IFN and ribavirin; the strongest of them is polymorphisms near IL28B; nonetheless, nonresponse in patients with favorable IL28B is still unexplained, which implies the importance of studying other immunological factors that may correlate with response. Interleukin 12 (IL-12) is one of the most important proinflammatory cytokine presented with the initiation of immune response, determining Th1 and Th2 differentiation. A functional single nucleotide polymorphism (A/C) at the 3′ untranslated region (3′UTR) at position 1188 (NCBI SNP database no 3212227) was reported to be associated with responding more efficiently to antiviral combination therapy in HCV genotype 1 infected patients. The present study aims to evaluate association between this polymorphism with fibrosis stages, necroinflammation activity, response to the combined therapy, and gender in Egyptian HCV genotype 4. Material and Methods. A total of 133 Egyptian chronic HCV (CHCV) patients were treated with IFN/RBV and were followed up. IL12B 1188 A/C genotyping was performed using polymerase chain reaction-restriction fragment length polymorphism (PRC-RFLP) analysis. Results. A nonsignificant trend for higher sustained virological response (SVR) was observed in patients homozygote for IL12B 1188 A/C SNP CC genotype (69% SVR versus 30.8% NR) only but not in AC and AA genotypes. No association was detected between IL12B 1188 A/C polymorphism and less severe fibrosis or less liver activity. By stratification of response according to gender genotype, a significant difference in response between males and females was seen among AA genotype carriers only due to high number of non responder females. Conclusion. IL12B CC genotype appears to have some influence on SVR achievement but not on severe fibrosis and severe necroinflamation activity. Females carrying A/A genotype of IL12B 1188 A/C SNP achieve less SVR than those carrying AC and CC genotypes.
Behavioural Economics and Policymaking: Learning from the Early Adopters

This paper critically examines initial applications of Behavioural Economics (BE) to policymaking. It focuses primarily but not exclusively on what can be learnt from the early adopters of policies inspired by BE, notably America and Britain. BE is defined by its inductive scientific approach to economics, which can produce empirical demonstrations that are persuasive to policymakers facing practical problems. The analysis identifies three routes via which BE has influenced policy: (1) the theory of libertarian paternalism (“nudges”), (2) the provision of toolkits for policymakers seeking behavioural change, and (3) the expansion of the skill-set of applied economists (and scientists in related disciplines). The effectiveness of each route is assessed, in terms of the likelihood of successfully integrating scientific advances with policy development. The analysis concludes that (3) is the only route that can adapt to the ongoing and rapid evolution of what is a young science. Successful policy development is more likely where there is expert input and the capacity to engage in applied experimentation, piloting and evaluation. The implication is that countries, including Ireland, are more likely to reap the benefits of BE if they create an active and effective interface between applied economists and policymakers.
Normalizing flows for novelty detection in industrial time series data

Flow-based deep generative models learn data distributions by transforming a simple base distribution into a complex distribution via a set of invertible transformations. Due to the invertibility, such models can score unseen data samples by computing their exact likelihood under the learned distribution. This makes flow-based models a perfect tool for novelty detection, an anomaly detection technique where unseen data samples are classified as normal or abnormal by scoring them against a learned model of normal data. We show that normalizing flows can be used as novelty detectors in time series. Two flow-based models, Masked Autoregressive Flows and Free-form Jacobian of Reversible Dynamics restricted by autoregressive MADE networks, are tested on synthetic data and motor current data from an industrial machine and achieve good results, outperforming a conventional novelty detection method, the Local Outlier Factor.
Multi-voxel pattern analysis in human hippocampal subfields

A complete understanding of the hippocampus depends on elucidating the representations and computations that exist in its anatomically distinct subfields. High-resolution structural and functional MRI scanning is starting to permit insights into hippocampal subfields in humans. In parallel, such scanning has facilitated the use of multi-voxel pattern analysis (MVPA) to examine information present in the distributed pattern of activity across voxels. The aim of this study was to combine these two relatively new innovations and deploy MVPA in the hippocampal subfields. Delineating subregions of the human hippocampus, a prerequisite for our study, remains a significant challenge, with extant methods often only examining part of the hippocampus, or being unable to differentiate CA3 and dentate gyrus (DG). We therefore devised a new high-resolution anatomical scanning and subfield segmentation protocol that allowed us to overcome these issues, and separately identify CA1, CA3, DG, and subiculum (SUB) across the whole hippocampus using a standard 3T MRI scanner. We then used MVPA to examine fMRI data associated with a decision-making paradigm involving highly similar scenes that had relevance for the computations that occur in hippocampal subfields. Intra- and inter-rater scores for subfield identification using our procedure confirmed its reliability. Moreover, we found that decoding of information within hippocampal subfields was possible using MVPA, with findings that included differential effects for CA3 and DG. We suggest that MVPA in human hippocampal subfields may open up new opportunities to examine how different types of information are represented and processed at this fundamental level.