Miracle-dz/UniFAR-FTUs huggingface dataset:
Chapter 1 Restoration in the presence of unknown spatially varying blur

The last two decades brought significant progress in the development of efficient methods for classical deconvolution and super-resolution problems in both the single and multi-image scenarios. Most of these methods work with blurs modelled by convolution, which assumes that the properties of blur are the same 2 Image Restoration: Fundamentals and Recent Advances in the whole image (space-invariant blur). Unfortunately, in practice, the blur is typically spatially variant. The most common types of space-variant blur are defocus, optical aberrations and motion blur caused by either camera motion or motion of objects. Extension of deconvolution methods to spatially varying blur is not straightforward. What makes such restoration problems more challenging than in the case of the space-invariant blur, is a much higher number of unknowns that must be estimated. Consequently, the solution is ill-posed and requires additional constrains that must be chosen depending on the type of blur we wish to suppress. The requirement to remove only certain types of blur while keeping others is surprisingly common. A typical example is the removal of motion blur from portrait pictures taken in low-light conditions while keeping a small depth of focus. Similarly, it is usually desirable to remove optical aberrations but we may wish to preserve motion blur conveying the sensation of speed. In the last few years, despite the complexity of space-variant deblurring, we can observe an increasing effort in this direction of research, including difficult problems such as the blur dependent on the depth of scene or several independently moving objects. In this chapter, we give an overview of the current state of the art in the space-variant restoration, addressing the latest results in both deblurring and super-resolution. The chapter is divided into two main parts. In Sec. 1.2, we describe mathematical models used for spatially varying blur and basic restoration approaches connected with these models. Our purpose is not to give a complete survey of all known algorithms, instead we just briefly outline the models and point out interesting papers that appeared in the last several years to indicate the current trends in the research of space-variant restoration. Among others, we introduce models describing the blur caused by camera motion by three-dimensional kernels analogous to those used in standard deconvolution algorithms and models used for complex scenes consisting of several independently moving objects. The second part of this chapter (Sec. 1.3) details a new approach to space-variant super-resolution for …
Phase Transitions for the Uniform Distribution in the PML Problem and its Bethe Approximation

The pattern maximum likelihood (PML) estimate, introduced by Orlitsky et al., is an estimate of the multiset of probabilities in an unknown probability distribution p, the estimate being obtained from n i.i. d. samples drawn from p. The PML estimate involves solving a difficult optimization problem over the set of all probability mass functions (pmfs) of finite support. In this paper, we describe an interesting phase transition phenomenon in the PML estimate: at a certain sharp threshold, the uniform distribution goes from being a local maximum to being a local minimum for the optimization problem in the estimate. We go on to consider the question of whether a similar phase transition phenomenon also exists in the Bethe approximation of the PML estimate, the latter being an approximation method with origins in statistical physics. We show that the answer to this question is a qualified "Yes". Our analysis involves the computation of the mean and variance of the (i;j)th entry, ai;j , in a random k k non-negative integer matrix A with row and column sums all equal to M , drawn according to a distribution that assigns to A a probability proportional to Q i;j (M ai;j)!
Rings of Short N = 3 Superfields in Three Dimensions and M-theory on Ads 4 × N

In this paper we investigate three–dimensional superconformal gauge theories with N = 3 supersymmetry. Independently from specific models, we derive the shortening conditions for unitary representations of the Osp(3|4) superalgebra and we express them in terms of differential constraints on three dimensional N = 3 superfields. We find a ring structure underlying these short representations, which is just the direct generalization of the chiral ring structure of N =2 theories. When the superconformal field theory is realized on the world–volume of an M2–brane such superfield ring is the counterpart of the ring defined by the algebraic geometry of the 8–dimensional cone transverse to the brane. This and other arguments identify the N =3 superconformal field theory dual to M-theory compactified on AdS4×N. It is an N = 3 gauge theory with SU(N)×SU(N) gauge group coupled to a suitable set of hypermultiplets, with an additional Chern Simons interaction. The AdS/CFT correspondence can be directly verified using the recently worked out Kaluza Klein spectrum of N0,1,0 and we find a perfect match. We also note that besides the usual set of BPS conformal operators dual to the lightest KK states, we find that the composite operators corresponding to certain massive KK modes are organized into a massive spin 3 2 N = 3 multiplet that might be identified with the super-Higgs multiplet of a spontaneously broken N = 4 theory. We investigate this intriguing and inspiring feature in a separate paper. † Supported by EEC under TMR contract ERBFMRX-CT96-0045
A comprehensive study of routing protocols for cognitive radio networks

The rapid development of wireless communication technologies results in the problem of radio spectrum shortage. Additionally, the traditional fixed spectrum allocation scheme can lead to the significant spectrum underutilization. Cognitive radio (CR) is an emerging technology to solve those problems by enabling unlicensed users to opportunistically access the available licensed bands in an intelligent and cautious manner. In cognitive radio networks (CRNs), the data routing is one of the most important issues to be taken into account and requires more study. This paper surveys recent routing protocols for CRNs and introduces the classification of routing techniques as well as presenting significant challenges which need to be considered for routing design. The main purpose of this survey is to provide a comprehensive and critical study on the current routing schemes in order to expose new open issues as well as being utilized to either improve the existing routing techniques or to develop new routing solutions. The possible future research in routing for CRNs is also given.
Defibrillation in Models of Cardiac Muscle

The propagation of electrical activity in cardiac tissue can be modelled by reaction diffusion equations, where a tensor of diffusion coefficients represents anisotropy due to fiber orintation, and excitation is represented by high-order, stiff differential systems. The effects of external electrical stimulation, as in artifical pacemakers, or in defibrillators, requires bidomain models, in wich intra- and extracellular currents are treated separately.
Study of variance-based model of Organizational Learning

Organizational learning is a continuous process of value/knowledge integration that may take place through informative, interactive, and cooptative modes. In this paper, we study the variance based model, and introduce Process Model of Organizational Learning. The process model outlines the learning process and may also explain the anomalies found while comparing the penetration rate and survey data analysis. The variance based model seeks to explain differential learning among LDSSs (the local departments of social services), by measuring the presence or absence of a learning environment in these organizations.
Performance evaluation of a packet reservation media access (PRMA) scheme for broadband fixed wireless access

This paper introduces a packet reservation based MAC layer for broadband fixed wireless access communications. The performance of a reservation based MAC scheme is compared with the performances of Pure-ALOHA and Slotted-ALOHA MAC schemes. A simulation using OPNET Modeler is performed to compare the performance of these MAC schemes in terms of channel throughput and average packet delay.
Opioid Analgesia in P 450 Gene Cluster Knockout Mice : A Search for Analgesia-Relevant Isoforms s

Cytochrome P450 monooxygenases (P450s), which are well-known drug-metabolizing enzymes, are thought to play a signal transduction role in m opioid analgesia and may serve as high-affinity H-cimetidine (HCIM) binding sites in the brain. HCIM binding sites may also be related to opioid or nonopioid analgesia. However, of the more than 100 murine P450 enzymes, the specific isoform(s) responsible for either function have not been identified. Presently, three lines of constitutive P450 gene cluster knockout (KO) mice with full-length deletions of 14 Cyp2c, 9 Cyp2d, and 7 Cyp3a genes were studied for deficiencies in HCIM binding and for opioid analgesia. Liver and brain homogenates from all three genotypes showed normal HCIM binding values, indicating that gene products of Cyp2d, Cyp3a, and Cyp2c are not HCIM-binding proteins. Cyp2d KO and Cyp3a KO mice showed normal antinociceptive responses to a moderate systemic dose of morphine (20 mg/kg, s.c.), thereby excluding 16 P450 isoforms as mediators of opioid analgesia. In contrast, Cyp2c KO mice showed a 41% reduction in analgesic responses following systemically (s.c.) administered morphine. However, the significance of brain Cyp2c gene products in opioid analgesia is uncertain because little or no analgesic deficits were noted in Cyp2c KO mice following intracerebroventricular or intrathecalmorphine administration, respectively. These results show that the gene products of Cyp2d and Cyp3a do not contribute to m opioid analgesia in the central nervous system. A possible role for Cyp2c gene products in opioid analgesia requires further consideration.
Dynamic construction of Random Forests: Evaluation using biomedical engineering problems

The aim of this work is the development of a method for the automatic determination of the optimum number of base classifiers which consists of the Random Forests. The novelty of the proposed method is that it doesn't need to select the classifiers to be in the final ensemble from a pool of classifiers which is known in advance, but determines the number of classifiers dynamically during the growing procedure of the forest. The method is based on the employment of an online fitting procedure and is evaluated using classical Random Forests and its modifications as ensemble methods.
Designing daybuilder: an experimental app to support people with depression

Daybuilder is an experimental smartphone app intended to support people with depression. It was designed in collaboration with six participants who had all received antidepressant medication within the last two years. The Daybuilder prototype was field tested with the same six participants for three to four weeks. All participants were interested in using an application like Daybuilder, immediately or in the future if they were to suffer from depression again. The study has shown it possible to design a smartphone app that people with depression find interesting and potentially supportive in their daily lives and in their clinical treatment. In this project we chose to work directly with the depressive people, without involving clinicians, in an effort to get as close as possible to the participants' needs and concerns. This approach caused certain difficulties in recruitment, required adaptation of design activities, and consideration of certain ethical issues. The seriousness and prevalence of depression in society make it urgent as a next step to do a comprehensive study to clarify possible clinical effects of using smartphone apps for this special user group.
Robust histogram-based feature engineering of time series data

Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and if they are properly analyzed can give a better understanding of the processes that are being monitored. In this paper we propose a novel generic histogram-based method for feature engineering of time series data. The preprocessing phase consists of several steps: deseansonalyzing the time series data, modeling the speed of change with first derivatives, and finally calculating histograms. By doing all of those steps the goal is three-fold: achieve invariance to different factors, good modeling of the data and preform significant feature reduction. This method was applied to the AAIA Data Mining Competition 2015, which was concerned with recognition of activities carried out by firefighters by analyzing body sensor network readings. By doing that we were able to score the third place with predictive accuracy of about 83%, which was about 1% worse than the winning solution.
Allele frequencies of dopamine D4 receptor gene (DRD4) and Catechol-O-methyltransferase (COMT) Val158Met polymorphism are associated with methylphenidate response in adolescents with attention deficit/hyperactivity disorder: a case control preliminary study

ABSTRACT OBJECTIVES: In this study, it was aimed to analyse the relationship between clinical improvement in adolescents with attention deficit/hyperactivity disorder (ADHD) and the presence of allele frequencies of dopamine D4 receptor (DRD4), and Val158Met polymorphism of catechol-O-methyltransferase (COMT) genes. METHODS: Thirty-four adolescents (age range, 13–18 years) with ADHD participated in this study. Thirty-two patients were males and two were females. Du Paul ADHD Rating Scale-Clinician version (ARS) and Clinical Global Impression-severity of impairment (CGI-S) were used for the evaluation of symptom severity. Fifty healthy age-matched adolescents were recruited as controls. RESULTS: When the groups with (n = 9) and without (n = 25) 7-repeat alleles for DRD4 were considered, there was a statistically significant decrease of DuPaul ARS total and hyperactivity scores in those treated with OROS-methylphenidate. When the Val/Met allele-positive group for COMT gene (n = 17) was compared with the Val/Val allele-positive group (n = 13) and Met/Met allele-positive group (n = 4), there was a statistically significant decrease of ARS total scores, ARS attention scores, and CGI scores in adolescents with ADHD treated with OROS-MPH. CONCLUSIONS: Specific data from further studies with a larger sample sizes would provide more insights to replicate the current findings.
Time perspective and alcohol-use indicators in France and the United Kingdom: results across adolescents, university students, and treatment outpatients

Abstract While some research has reported a significant relationship between time perspective and alcohol use, that research has differed considerably in terms of how both variables are operationalized. The present study aimed to investigate this relationship by operationalizing time perspective in three ways, using raw scale scores, using temporal clusters, and using the Deviation from a Balanced Time Perspective approach. Additionally, age-appropriate measures of alcohol-related behavior were used, and alcohol scale scores were assessed as both an ordinal and a categorical measure. Data gathered in adolescents and university students in the United Kingdom were re-analyzed, and new data from a French university sample and a French Treatment Outpatient sample were analyzed. Overall, results using the Alcohol Use Disorders Identification Test revealed a very modest relationship between scores on this measure and the various time perspective approaches. Results using the Adolescent Alcohol Involvement Scale suggested a stronger relationship between scores on this measure and the time perspective approaches. The results are discussed in the context of the extant literature, and methodological concerns specific to the use of the Zimbardo Time Perspective Inventory are discussed.
A Primer on Network Meta-Analysis for Dental Research

In the last decade, a new statistical methodology, namely, network meta-analysis, has been developed to address limitations in traditional pairwise meta-analysis. Network meta-analysis incorporates all available evidence into a general statistical framework for comparisons of all available treatments. A further development in the network meta-analysis is to use a Bayesian statistical approach, which provides a more flexible modelling framework to take into account heterogeneity in the evidence and complexity in the data structure. The aim of this paper is therefore to provide a nontechnical introduction to network meta-analysis for dental research community and raise the awareness of it. An example was used to demonstrate how to conduct a network meta-analysis and the differences between it and traditional meta-analysis. The statistical theory behind network meta-analysis is nevertheless complex, so we strongly encourage close collaboration between dental researchers and experienced statisticians when planning and conducting a network meta-analysis. The use of more sophisticated statistical approaches such as network meta-analysis will improve the efficiency in comparing the effectiveness between multiple treatments across a set of trials.
Late-time decay of gravitational and electromagnetic perturbations along the event horizon

We study analytically, via the Newman-Penrose formalism, the late-time decay of linear electromagnetic and gravitational perturbations along the event horizon (EH) of black holes. We first analyze in detail the case of a Schwarzschild black hole. Using a straightforward local analysis near the EH, we show that, generically, the “ingoing” (s>0) component of the perturbing field dies off along the EH more rapidly than its “outgoing” (s 2M lines both components of the perturbation admit the well-known t-2l-3 decay rate, one finds that along the EH the s 0 component dies off as v-2l-4. We then describe the extension of this analysis to a Kerr black hole. We conclude that for axially symmetric modes the situation is analogous to the Schwarzschild case. However, for non-axially symmetric modes both s>0 and s
Tuning HDF5 for Lustre File Systems

HDF5 is a cross-platform parallel I/O library that is used by a wide variety of HPC applications for the flexibility of its hierarchical object-database representation of scientific data. We describe our recent work to optimize the performance of the HDF5 and MPI-IO libraries for the Lustre parallel file system. We selected three different HPC applications to represent the diverse range of I/O requirements, and measured their performance on three different systems to demonstrate the robustness of our optimizations across different file system configurations and to validate our optimization strategy. We demonstrate that the combined optimizations improve HDF5 parallel I/O performance by up to 33 times in some cases running close to the achievable peak performance of the underlying file system and demonstrate scalable performance up to 40,960-way concurrency.
Mechanisms of Pyrethroid Insecticide-Induced Stimulation of Calcium Influx in Neocortical Neurons

Pyrethroid insecticides bind to voltage-gated sodium channels (VGSCs) and modify their gating kinetics, thereby disrupting neuronal function. Pyrethroids have also been reported to alter the function of other channel types, including activation of voltage-gated calcium channels. Therefore, the present study compared the ability of 11 structurally diverse pyrethroids to evoke Ca2+ influx in primary cultures of mouse neocortical neurons. Nine pyrethroids (tefluthrin, deltamethrin, λ-cyhalothrin, β-cyfluthrin, esfenvalerate, S-bioallethrin, fenpropathrin, cypermethrin, and bifenthrin) produced concentration-dependent elevations in intracellular calcium concentration ([Ca2+]i) in neocortical neurons. Permethrin and resmethrin were without effect on [Ca2+]i. These pyrethroids displayed a range of efficacies on Ca2+ influx; however, the EC50 values for active pyrethroids all were within one order of magnitude. Tetrodotoxin blocked increases in [Ca2+]i caused by all nine active pyrethroids, indicating that the effects depended on VGSC activation. The pathways for deltamethrin- and tefluthrin-induced Ca2+ influx include N-methyl-d-aspartic acid receptors, L-type Ca2+ channels, and reverse mode of operation of the Na+/Ca2+ exchanger inasmuch as antagonists of these sites blocked deltamethrin-induced Ca2+ influx. These data demonstrate that pyrethroids stimulate Ca2+ entry into neurons subsequent to their actions on VGSCs.
Porting and enhancements of a real-time object-oriented operating system

This paper describes our experiences in porting a distributed object oriented real-time operating system called Apertos to a new RISC platform, and some enhancements made to improve its performance. Apertos employs an object-metaobject hierarchy to facilitate object heterogeneity. This meta-hierarchy is intended to support various objects with different requirements such as real-time constraints. As such a system grows, satisfying differing object requirements via the meta-hierarchy with its related communication overheads, it is orthogonal to achieving real-time response performance. To address this, we introduce preemptive scheduling and a hierarchical scheduling solution.
Towards a Comprehensive Insight into the Thematic Organization of the Tor Hidden Services

Tor is a popular 'darknet', a network that aims to conceal its users' identities and online activities. Darknets are composed of host machines that cannot be accessed by conventional means, which is why the content they host is typically not indexed by traditional search engines like Google and Bing. On Tor, web content and other types of services can anonymously be made available as so-called hidden services. Obviously, where anonymity can be a vehicle for whistleblowers and political dissidents to exchange information, the reverse of the medal is that it also attracts malicious actors. In our research, we aim to develop a detailed understanding of what Tor is being used for. We applied classification and topic model-based text mining techniques to the content of over a thousand Tor hidden services in order to model their thematic organization and linguistic diversity. As far as we are aware, this paper presents the most comprehensive content-based analysis of Tor to date.
A data mining approach to predict protein secondary structure

In bioinformatics,proteins are coded by strings, called “primary structures”. Biologists have long enough gathered these primary structures in large databases. Numerous experiments and analyses of primary structures have revealed that the protein primary structure closely correlates with the protein second structure. In this paper, we present a data mining approach based on machine learning techniques to predict protein second structure. Based on majority voting mechanism, the approach combine the predictions of homology analysis classifier, Support vector machine(SVM) classifier and modified Knowledge Discovery in Databases (KDD*) process. They are validated with 2 different datasets. Their predictive accuracy results outperform the best secondary structure predictors by 2.00% on average.