Miracle-dz/UniFAR-FTUs huggingface dataset:
Denoising using local ICA and kernel-PCA

We present a denoising algorithm for enhancing noisy signals based on local independent component analysis (ICA). This is done by applying ICA to the signal in localized delayed coordinates. The components resembling the signals can be detected by various criteria depending on the nature of the signal. Estimators of kurtosis or the variance of the autocorrelation have been considered. The algorithm proposed can favorably be applied to the problem of denoising multidimensional data like images or fMRI data sets. In comparison to denoising algorithms using wavelets, Wiener filters and kernel PCA the local PCA and ICA algorithms perform considerably better. We provide applications of the algorithm to images and the analysis of protein NMR spectra.
Sensor location problem for large-scale complex systems in the framework of the dynamic Signed Directed Graph

Fault detection in large-scale systems is conducted by the use of sensors, thus the sensor location influences the performances of fault detection directly. As the scale of systems increases, traditional input-output models may not work well or may even not be applicable. The signed directed graph (SDG) concept is used to describe large-scale complex systems and the cause-effect relationships among variables. However, SDG cannot express the dynamic propagation properties when describing the fault propagation phenomena. In this paper, time parameters are taken into account within the branches of an SDG, in order to approximately denote the propagation time of the variable changes in the systems. An SDG constructed this way is called a dynamic SDG. As to sensor location, because of the economic and technical limitations, the number of sensors should be limited while meeting the demands of fault detection. We analyzed the main criteria of the fundamental demand such as reachability, detectability and identifiability of faults in the framework of dynamic SDG, and presented an algorithm to describe the fault propagation process using forward inference and obtained a way to locate sensors. These indices guarantee that the faults can be detected in time and identified from each other. The criteria change when the faults propagate; they are much closer to the reality than those in the framework of a traditional SDG. Some general results, which extend the results obtained in the framework of a traditional SDG, were presented. An algorithm was presented to describe the fault propagation process using forward inference and a way to locate the sensors was obtained. Finally, an example of a boiler system was used out to illustrate the proposed method and results.
To err is robotic, to tolerate immunological: fault detection in multirobot systems

Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space.
Tissue-type plasminogen activator improves neurological functions in a rat model of thromboembolic stroke.

BACKGROUND AND PURPOSE The capacity of an intravenous infusion of double-stranded tissue-type plasminogen activator to salvage neurological functions in a rat model of thromboembolic stroke was studied. METHODS The model of thromboembolic stroke was induced by the intracarotid injection of 2-hour-old homologous blood clots to rats. Neurological functions were scored on a 5-point scale 48 hours after the injection of the clots. Infarction size was determined by triphenyltetrazolium chloride staining, and cerebral hemorrhage was examined macroscopically. RESULTS Intravenous infusion of tissue-type plasminogen activator (1 or 5 x 10(5) IU/kg) within 3 hours after embolization significantly improved neurological functions (P < .01) and reduced infarction size (P < .05). Tissue-type plasminogen activator treatment 6 hours after embolization failed to attenuate the neurological status score. Treatment with tissue-type plasminogen activator did not increase the incidence of intracerebral hemorrhage and was not associated with a systemic fibrinolytic state. In comparison with tissue-type plasminogen activator treatment, although urokinase treatment (5 x 10(5) IU/kg) improved neurological functions, it was associated with a systemic fibrinolytic state and a tendency to increase the incidence of intracerebral hemorrhage. CONCLUSIONS These findings in this model suggest that tissue-type plasminogen activator should be given early after the onset of ischemic symptoms to effectively prevent or limit pathological infarction and improve neurological functions without an increase in the incidence of cerebral hemorrhage.
Joint effect of obesity and TNFA variability on asthma: two international cohort studies

Obesity is a risk factor for asthma. Adipose tissue expresses pro-inflammatory molecules including tumour necrosis factor (TNF), and levels of TNF are also related to polymorphisms in the TNF-α (TNFA) gene. The current authors examined the joint effect of obesity and TNFA variability on asthma in adults by combining two population-based studies. The European Community Respiratory Health Survey and the Swiss Cohort Study on Air Pollution and Lung and Heart Disease in Adults used comparable protocols, questionnaires and measures of lung function and atopy. DNA samples from 9,167 participants were genotyped for TNFA -308 and lymphotoxin-α (LTA) +252 gene variants. Obesity and TNFA were associated with asthma when mutually adjusting for their independent effects (odds ratio (OR) for obesity 2.4, 95% confidence interval (CI) 1.7–3.2; OR for TNFA -308 polymorphism 1.3, 95% CI 1.1–1.6). The association of obesity with asthma was stronger for subjects carrying the G/A and A/A TNFA -308 genotypes compared with the more common G/G genotype, particularly among nonatopics (OR for G/A and A/A genotypes 6.1, 95% CI 2.5–14.4; OR for G/G genotype 1.7, 95% CI 0.8–3.3). The present findings provide, for the first time, evidence for a complex pattern of interaction between obesity, a pro-inflammatory genetic factor and asthma.
What Works in Suicide Bereavement: What Helps and What Hurts?

iii ACKNOWLEDGEMENTS . iv TABLE OF Tables . vii CHAPTER 1: INTRODUCTION . 1 Statement of the Problem . 1 Purpose of the Study . 2 Significance of the Problem . 2 Death, Loss, and Bereavement . 3 Grief and Bereavement in Suicide . 5 CHAPTER 2: SYSTEMIC IMPACT OF SUICIDE . 10 Biological . 10 Psychological . 10 Dyadic/Couple Relationship . 11 Family of Origin . 12 Socio-cultural . 13 Attachment . 17 Do Marriage and Family Therapists Avoid Suicide? 20 What I Will Do and Why It Is Important . 21 CHAPTER 3: METHODOLOGY . 23 Rationale for Methodology . 23 Participants . 24 Procedures . 25 Role of the Researcher . 25 Data Analysis Procedures . 26 CHAPTER 4: RESULTS . 29 Helpful Themes . 30 The Funeral . 31 Spiritual Belief and Continuation of Existence . 35 Let It Be Good . 37 Make It Have Meaning . 38 Having Experienced Grief Previously . 40 Love and Attachment . 41 Keeping Memory Alive Through Material Objects . 43 Communication and Talking About the Suicide . 44 Connecting With Other Survivors. 45 Hurtful Themes . 47 Responsibility . 47 Feeling Excluded . 50 Opposite of Connection . 51 Not Being Willing To Talk About It . 52 Pressure to Be/Replace . 53 Addictions . 54 Suicidality and Depression as Connection/Understanding . 54
Realizing Web Application Vulnerability Analysis via AVDL

Apparatus is provided for connecting to a floating structure the upper end of a pipe assembly disposed substantially vertically in an ocean or the like. The pipe can be an upwelling pipe in an ocean thermal energy conversion installation. A downwardly open, elongate hollow socket is connected to the structure, and a mating elongate hollow pin member is connected to the upper end of the pipe assembly. Mating of the pin member in the socket requires only upward linear motion of the pin into the socket. Separate tethers are connectible in parallel to the pin and socket between the pipe assembly and the floating structure for holding the pin member in mated engagement with the socket. The tethers are severable for releasing the pipe assembly, which is negatively buoyant, from the floating structure in the event of an emergency or otherwise.
Multivariate Regression with Incremental Learning of Gaussian Mixture Models

Within the machine learning framework, incremental learning of multivariate spaces is of special interest for on-line applications. In this work, the regression problem for multivariate systems is solved by implementing an efficient probabilistic incremental algorithm. It allows learning high-dimensional redundant non-linear maps by the cumulative acquisition of data from input-output systems. The proposed model is aimed at solving prediction and inference problems. The implementation introduced in this work allows learning from data batches without the need of keeping them in memory afterwards. The learning architecture is built using Incremental Gaussian Mixture Models. The Expectation-Maximization algorithm and general geometric properties of Gaussian distributions are used to train the models. Our current implementation can produce accurate results fitting models in real multivariate systems. Results are shown from testing the algorithm for both situations, one where the incremental learning is demonstrated and the second where the performance to solve the regression problem is evaluated on a toy example.
Global Geometric Affinity for Revealing High Fidelity Protein Interaction Network

Protein-protein interaction (PPI) network analysis presents an essential role in understanding the functional relationship among proteins in a living biological system. Despite the success of current approaches for understanding the PPI network, the large fraction of missing and spurious PPIs and a low coverage of complete PPI network are the sources of major concern. In this paper, based on the diffusion process, we propose a new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network. The main concept defines a diffusion process in which all proteins simultaneously participate to define a similarity metric (global geometric affinity (GGA)) to robustly reflect the internal connectivity among proteins. The robustness of the GGA is attributed to propagating the local connectivity to a global representation of similarity among proteins in a diffusion process. The propagation process is extremely fast as only simple matrix products are required in this computation process and thus our method is geared toward applications in high-throughput PPI networks. Furthermore, we proposed two new approaches that determine the optimal geometric scale of the PPI network and the optimal threshold for assigning the PPI from the GGA matrix. Our approach is tested with three protein-protein interaction networks and performs well with significant random noises of deletions and insertions in true PPIs. Our approach has the potential to benefit biological experiments, to better characterize network data sets, and to drive new discoveries.
A Knowledge-Based Approach for Script Recognition without Training

The approach described is based on an empirical parametric model for the handwriting recognition system. The parameters are so chosen and quantized as to retain only broad shape information, ignoring writer-dependent and other variability. Concatenation of character prototypes generates archetypal reference words for recognition, and training is unnecessary. The recognition scores exceed 90%.
Contention window adaptation to ensure airtime consumption fairness in multirate Wi-Fi networks

In the paper we address a problem of throughput unfairness inherent in the very nature of multirate Wi-Fi networks employing CSMA/CA mechanism. This unfairness exhibits itself through the fact that slow clients consume more airtime to transfer a given amount of data, leaving less airtime for fast clients. The paper introduces analytical models allowing to estimate a fair contention window (CW) size to be used by each station depending on a ratio between station's data rates. Finally, we propose a lightweight distributed algorithm that enables stations to dynamically adapt their CW so that a suboptimal airtime distribution between them is ensured. It prevents network throughput from being dramatically degraded when a slow station is connected to the network.
Participation and Privacy Perception in Virtual Environments: The Role of Sense of Community, Culture and Gender between Italian and Turkish

Advancements in information and communication technologies have enhanced our possibilities to communicate worldwide, eliminating borders and making it possible to interact with people coming from other cultures like never happened before. Such powerful tools have brought us to reconsider our concept of privacy and social involvement in order to make them fit into this wider environment. It is possible to claim that the information and communication technologies (ICT) revolution is changing our world and is having a core role as a mediating factor for social movements (e.g., Arab spring) and political decisions (e.g., Brexit), shaping the world in a faster and shared brand new way. It is then interesting to explore how the perception of this brand new environment (in terms of social engagement, privacy perception and sense of belonging to a community) differs even in similar cultures separated by recent historical reasons. Recent historical events may in effect have shaped a different psychological representation of Participation, Privacy and Sense of Community in ICT environments, determining a different perception of affordances and concerns of these complex behaviors. The aim of this research is to examine the relation between the constructs of Sense of Community, Participation and Privacy compared with culture and gender, considering the changes that have occurred in the last few years with the introduction of the web environment. A questionnaire, including ad hoc created scales for Participation and Privacy, have been administered to 180 participants from Turkey and Italy. In order to highlight the cultural differences in the perception of these two constructs, we have provided a semantic differential to both sub-samples showing interesting outcomes. The results are then discussed while taking into account the recent history of both countries in terms of the widespread of new technologies, political actions and protest movements.
Cross-frequency transfer in a stochastically driven mesoscopic neuronal model

The brain is known to operate in multiple coexisting frequency bands. Increasing experimental evidence suggests that interactions between those distinct bands play a crucial role in brain processes, but the dynamical mechanisms underlying this cross-frequency coupling are still under investigation. Two approaches have been proposed to address this issue. In the first one distinct nonlinear oscillators representing the brain rhythms involved are coupled actively (bidirectionally), whereas in the second one the oscillators are coupled unidirectionally and thus the driving between them is passive. Here we elaborate the latter approach by implementing a stochastically driven network of coupled neural mass models that operate in the alpha range. This model exhibits a broadband power spectrum with 1/fb form, similar to those observed experimentally. Our results show that such a model is able to reproduce recent experimental observations on the effect of slow rocking on the alpha activity associated with sleep. This suggests that passive driving can account for cross-frequency transfer in the brain, as a result of the complex nonlinear dynamics of its underlying oscillators.
Pedagogical Mediation and Virtual Environments of Teaching-Learning

Research in Distance Education has contemplated aspects of credibility, evaluation,evasion, but very little has been done with regard to the movement of actual learning in this modality. The present study proposes to analyze some of the aspects that differentiate the pedagogical mediation in the on-line mode, specifically in Virtual Environments of Teaching-Learning [VETL] and its implications in the learning movement. It is thus understood, how VETL, a Virtual Environment for TeachingLearning, is organized with different tools of digital communication making it possible to perform synchronous and assynchronous interaction for the purpose of developing a program or a course formally set up and sustained by a pedagogical proposal. This study is to demonstrate the implications of an VETL in regard to its pedagogical concept, its utilization and impact on the process of learning of students that are in the mode of Distance Education. Although this investigation is still in progress, it may be stated already, that the distance modality mediated by an VETL turns the teaching-learning process more fluid, dynamic, transversal and creative, than a face-to-face teaching process. It is believed, that VETL may potentialyze time and space, accelerating the movement of learning of the participating students. It is necessary to advance further in the investigation, so as to establish with greater precision the intensity of a learning process that may be generated by an VETL.
Theoretical Convergence Guarantees for Cooperative Coevolutionary Algorithms

Cooperative coevolutionary algorithms have the potential to significantly speed up the search process by dividing the space into parts that can each be conquered separately. However, recent research presented theoretical and empirical arguments that these algorithms tend to converge to suboptimal solutions in the search space, and are thus not fit for optimization tasks. This paper details an extended formal model for cooperative coevolutionary algorithms, and uses it to explore possible reasons these algorithms converge to optimal or suboptimal solutions. We demonstrate that, under specific conditions, this theoretical model will converge to the globally optimal solution. The proofs provide the underlying theoretical foundation for a better application of cooperative coevolutionary algorithms. We demonstrate the practical advantages of applying ideas from this theoretical work to a simple problem domain.
Transcatheter Aortic Valve Implantation

&NA;Older adults with progressively worsening aortic valve stenosis are limited in their treatment options. Conventional treatment replaces the aortic valve via open-heart surgery, introducing numerous risks and increased mortality in older populations with comorbidities. In recent years, however, transcatheter aortic valve implantation (tAVI) has become a viable option for symptomatic, nonsurgical candidates. Although tAVI is available only to a select population, early randomized and nonrandomized clinical trials are offering insight into the new therapy, with promising results as this treatment continues to evolve. Purpose:This article examined the progression of tAVI therapies, from first-generation to third-generation devices and through 4 studies that address the utility of tAVI in symptomatic, nonsurgical candidates, in an effort to prepare nurses in the management of this newly emerging patient population. Methods:A literature search was conducted of studies within the past 5 years that address transfemoral tAVI in adult populations. Transapical approaches were excluded. Results:Early outcomes demonstrate that successful implantation can improve valvular function and patient functional status. Nurses play a critical role in patient education and monitoring both before and after tAVI. Conclusions:With increasing life expectancies and a prediction that many aging adults with aortic valve stenosis will be limited to tAVI, nurses need to gain familiarity with this novel therapy and its role in patient outcomes.
Bringing Order to the Job Market: Efficient Job Offer Categorization in E-Recruitment

E-recruitment uses a range of web-based technologies to find, evaluate, and hire new personnel for organizations. A crucial challenge in this arena lies in the categorization of job offers: candidates and operators often explore and analyze large numbers of offers and profiles through a set of job categories. To date, recruitment organizations define job categories top-down, relying on standardized vocabularies that often fail to capture new skills and requirements that emerge from dynamic labor markets. In order to support e-recruitment, this paper presents a dynamic, bottom-up method to automatically enrich and revise job categories. The method detects novel, highly characterizing terms in a corpus of job offers, leading to a more effective categorization, and is evaluated on real-world data by Multiposting (http://www.multiposting.fr/en), a large French e-recruitment firm.
Music and Emotion: a composer’s perspective

This article takes an experiential and anecdotal look at the daily lives and work of film composers as creators of music. It endeavours to work backwards from what practitioners of the art and craft of music do instinctively or unconsciously, and try to shine a light on it as a conscious process. It examines the role of the film composer in his task to convey an often complex set of emotions, and communicate with an immediacy and universality that often sit outside of common language. Through the experiences of the author, as well as interviews with composer colleagues, this explores both concrete and abstract ways in which music can bring meaning and magic to words and images, and as an underscore to our daily lives.
Slim Accretion Disks: Theory and Observational Consequences

The concept of slim accretion disks emerged over 30 years ago as an answer to several unsolved problems. Since that time there has been a tremendous increase in the amount of observational data where this model applies. However, many critical issues on the theoretical side remain unsolved, as they are inherently difficult. This is the issue of the disk stability under radiation pressure, the role of the magnetic field in the energy transfer inside the disk, the formation (or not) of a warm corona, and outflows. Thus the progress has to be done both through further developments of the model and through careful comparison with the observational data.