Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity to Small Delays of Pathwise Stability for Stochastic Retarded Evolution Equations

In this paper, we shall study the almost sure pathwise exponential stability property for a class of stochastic functional differential equations with delays, possibly, in the highest-order derivative terms driven by multiplicative noise. Instead of establishing a moment exponential stability as the first step and then proceeding to investigate the pathwise stability of the system under consideration, we shall develop a direct approach for this problem. As a consequence, we can show that some systems, which are not exponential momently stable, have the exponential stability not sensitive to small delays in the almost sure sense.
Model Predictive Control Based on System ReIdentification (MPC-SRI) to Control Bio-H 2 Production from Biomass

Compressors and a steam reformer are the important units in biohydrogen from biomass plant. The compressors are useful for achieving high-pressure operating conditions while the steam reformer is the main process to produce H 2 gas. To control them, in this research used a model predictive control (MPC) expected to have better controller performance than conventional controllers. Because of the explicit model empowerment in MPC, obtaining a better model is the main objective before employing MPC. The common way to get the empirical model is through the identification system, so that obtained a first-order plus dead-time (FOPDT) model. This study has already improved that way since used the system reidentification (SRI) based on closed loop mode. Based on this method the results of the compressor pressure control and temperature control of steam reformer were that MPC based on system re-identification (MPC-SRI) has better performance than MPC without system reidentification (MPCWSRI) and the proportional-integral (PI) controller, by % improvement of 73% against MPCWSRI and 75% against the PI controller.
Virtual Reality to Simulate Visual Tasks

Virtual reality (VR) can be used as a tool to analyze the interactions between the visual system of a robotic agent and the environment, with the aim of designing the algorithms to solve the visual tasks necessary to properly behave into the 3D world. The novelty of our approach lies in the use of the VR as a tool to simulate the behavior of vision systems. The visual system of a robot (e.g., an autonomous vehicle, an active vision system, or a driving assistance system) and its interplay with the environment can be modeled through the geometrical relationships between the virtual stereo cameras and the virtual 3D world. Differently from conventional applications, where VR is used for the perceptual rendering of the visual information to a human observer, in the proposed approach, a virtual world is rendered to simulate the actual projections on the cameras of a robotic system. In this way, machine vision algorithms can be quantitatively validated by using the ground truth data provided by the knowledge of both the structure of the environment and the vision system. In computer vision (Trucco & Verri, 1998; Forsyth & Ponce, 2002), in particular for motion analysis and depth reconstruction, it is important to quantitatively assess the progress in the field, but too often the researchers reported only qualitative results on the performance of their algorithms due to the lack of calibrated image database. To overcome this problem, recent works in the literature describe test beds for a quantitative evaluation of the vision algorithms by providing both sequences of images and ground truth disparity and optic flow maps (Scharstein & Szeliski, 2002; Baker et al., 2007). A different approach is to generate image sequences and stereo pairs by using a database of range images collected by a laser range-finder (Yang & Purves, 2003; Liu et al., 2008). In general, the major drawback of the calibrated data sets is the lack of interactivity: it is not possible to change the scene and the camera point of view. In order to face the limits of these approaches, several authors proposed robot simulators equipped with visual sensors and capable to act in virtual environments. Nevertheless, such software tools are capable of accurately simulating the physics of robots, rather than their visual systems. In many works, the stereo vision is intended for future developments (Jørgensen & Petersen, 2008; Awaad et al., 2008), whereas other robot simulators in the literature have a binocular vision system (Okada et al., 2002; Ulusoy et al., 2004), but they work on stereo image pairs where parallel axis cameras are used. More recently, a commercial application (Michel, 2004) and an open source project for cognitive robotics research (Tikhanoff et al., 2008) have been developed both capable to fixate a target, nevertheless the ground truth data are not provided. Virtual Reality to Simulate Visual Tasks for Robotic Systems 4
Entangled Photon Pairs in Disordered Photonic Lattices 2014

Photonic lattices consisting of arrays of evanescently coupled waveguides fabricated with precisely controlled parameters have enabled the study of discrete optical phenomena, both classical and quantum, and the simulation of other physical phenomena governed by the same dynamics. In this dissertation, I have experimentally demonstrated transverse Anderson localization of classical light in arrays with off-diagonal coupling disorder and investigated theoretically and experimentally the propagation of entangled photon pairs through such disordered systems. I discovered a new phenomenon, Anderson co-localization, in which a spatially entangled photon pair in a correlated transversally extended state localizes in the correlation space, though neither photon localizes on its own. When the photons of a pair are in an anti-correlated state, they maintain their anti-correlation upon transmission through the disordered lattice, exhibiting Anderson antilocalization. These states were generated by use of parametric down conversion in a nonlinear crystal. The transition between the correlated and anti-correlated states was also explored by using a lens system in a configuration intermediate between imaging and Fourier transforming. In the course of this research, I discovered a curious aspect of light transmission through such disordered discrete lattices. An excitation wave of a single spatial frequency (transverse momentum) is transmitted through the system and is accompanied by another wave with the same spatial frequency but opposite sign, indicating some form of internal reflection facilitated by the disordered structure.
Models and algorithms for detection of spam and senders of spam

In the paper, we give an overview of several approaches that we use to analyze “spam” (undesired bulk advertising) and the credentials of senders of spam, for the purpose of automatic detection. We use some of these approaches for discrimination between stolen account credentials and “spam-bots” (accounts opened purely for the purpose of distributing spam), since the methods for handling spam senders differs by type of account. Other approaches described in this paper we use to automatically detect classes of spam messages, assisting spam analysts in their work to find the messages, so that the messages may be automatically deleted.
OSDAP-Optimized and Secure Data Aggregation Protocol for Wireless Sensor Networks

Maximum utilization of limited resources (energy, bandwidth and memory ) in wireless sensor networks is a challenge for the researcher community. Addition to this, security in data transmission is also a big constraint which make WSN research more and more attractive. Data aggregation is one of the technique for energy preservation which minimize the computing overhead by eliminating the redundant data. However, some security attacks makes the data aggregation readings false, thus not providing accurate results. Also, there is a tradeoff between security and energy consumption of WSN. If we go for higher security, it also increases the energy consumption (more encryption and decryption causes more energy consumption) and if we try to preserve the energy, we have to compromise security somewhere. In this paper, an optimized and secure protocol of data aggregation is proposed which preserve energy while not compromising the security. Encrypting the data only at leaf nodes, use of privacy homomorphism technique and slicing the data ensures secure and accurate data aggregation. Our theoretical analysis and simulation shows that our protocol is more energy efficient and secure than existing protocol EESSDA.
EPILEPSY PREDICTION USING DEEP LEARNING IN HPC

Deep Learning is one of the most interesting areas of Machine Learning and Pattern Recognition research. The endless possibility of this promising technology continues to progress in recent years and tends to solve lots of human challenging problems. One such challenge is in the autonomous diagnosis of critical diseases that requires the attention of specialized physicians. This work intends to leverage and pursue three key aspects of the Epilepsy/Seizure Diagnosis using machines in Medical Domain, Deep Beliefs Networks and in a High Performance Computing environment. Epilepsy is a chronic disorder, the trademark of which is recurrent, unprovoked seizures. A person is diagnosed with epilepsy if they have two or more unprovoked seizures. The seizures in epilepsy may be related to either a brain injury or a family tendency, but often the cause is completely unknown. At present, there are at least 65 million people in the world who have epilepsy according to the Epilepsy Foundation. Also, 470,000 children in the US alone have epilepsy and 1 in 26 in the US will develop epilepsy at some point in their lifetime which states the seriousness of this problem. Electroencephalography (EEG) monitoring is a noninvasive method to measure the electrical activity of the brain. Because it is noninvasive, this method needs connected electrodes attached to the scalp of the patients. The diagnosis of Epilepsy using this EEG data is complex and the exams take at least one week to be completed and requires the analysis of a trained and specialized professional. Preliminary research states that this kind of method to record brain activity also produces noise in brain wave monitoring. Although some automated diagnosis methods have been introduced in recent years – some of them are related with the use of neural networks applied in EEG in order to support physicians – our hypothesis is that with deep learning technique one can learn when a seizure is happening using data representations. EEG recordings which
Image analysis of workpiece joints with uncertainty for cooperative feature constraints 1

In order to improve the denoising effect of the solder joint image of the uncertainty workpiece, a new method of noise removal for the solder joint image based on the cooperative feature constraint and the radial singular function. The image is decomposed by the cooperative feature constraint through the analysis of the image characteristic constraint coefficient, and skews high-frequency part and the low-frequency part of the feature constraint coefficients. In order to improve the denoising performance, an improved self-adaptive eigenvalue algorithm is proposed by calculating the median absolute variance estimation corresponding to the characteristic coefficient, and the image is subjected to the second denoising. Finally, the median filter is used to smooth the image and get the final denoising image. The results show that compared with the traditional method, the proposed algorithm can not only improve the denoising performance and denoising effect, but also keep the uncertainty of the workpiece solder joint image edge information.
Existential Art Therapy in Children’s Bereavement Groups: Development of a Method

Children and adolescents who have experienced the death of a family member are faced with the emotional journey of grieving their loss, which may be accompanied with existential concerns about life and death in general. Bereavement programs serve to connect children with others who know what losing a family member is like. Art therapy can be used in these bereavement groups to express and give shape to the emotions and memories the participants have. It is a non-verbal way to reflect and find meaning that can come from post-traumatic growth. This capstone thesis project explores an art therapy method for bereaved children and adolescents that draws upon theories in existential therapy. This method was incorporated into bereavement programming for 15 individuals age 4-18 and the results are discussed in this capstone. “Grief never ends. But it changes. It’s a passage, not a place to stay. Grief is not a sign of weakness, nor a lack of faith. It is the price of love. ” — Author Unknown EXISTENTIAL ART THERAPY IN CHILDREN’S BEREAVEMENT GROUPS 3 Existential Art Therapy in Children’s Bereavement Groups: Development of a Method
Unveiling the gene regulatory landscape in diseases through the identification of DNase I-hypersensitive sites

DNase I-hypersensitive sites (DHSs) serve key roles in the regulation of gene transcription as markers of cis-regulatory elements (CREs). Recent advances in next-generation sequencing have enabled the genome-wide location and annotation of DHSs in a variety of cells. Numerous studies have confirmed that DHSs are involved in several processes in cell fate decision and development. DHSs have also been indicated in cancer and inherited diseases as driver distal regulatory elements. Here, the definition of DHSs is reviewed, in addition to high-throughput methods of DHS identification. Furthermore, the function of DHSs in gene expression is probed. The roles of DHSs in disease occurrence are also reviewed and discussed. Concomitant advances in the identification of essential roles of DHSs will assist in disclosing the underlying molecular mechanisms, supplementing gene transcription and enlarging the molecular basis of DHS-related bioprocesses, phenotypes, distinct traits and diseases.
Correlation between long non-coding RNAs (lncRNAs) H19 expression and trastuzumab resistance in breast cancer

Introduction: Trastuzumab resistance is a major obstacle encountered in human epidermal growth factor receptor 2 (HER2)-positive breast cancer therapy. Long non-coding RNAs (lncRNAs) have been confirmed to play important roles in both tumorigenesis and tumor development. However, whether lncRNAs are associated with trastuzumab resistance is not yet clear. Subjects and Methods: We evaluated trastuzumab sensitivity in breast cancer cell lines, SKBR3, HCC1954, and MDA-MB-231. We also evaluated H19 expression in these cell lines after treatment with different trastuzumab concentrations. Besides, H19 was downregulated to investigate its role in cell viability and trastuzumab sensitivity and a trastuzumab resistance cell line was cultured to verify the effect of H19 in trastuzumab resistance. Forty-eight HER2-positive breast cancer patients treated with trastuzumab in the first-line setting were selected retrospectively to explore the relationship between H19 expression and tumor-node-metastasis (TNM) stage as well as trastuzumab resistance. Results: H19 is a trastuzumab-responsive lncRNA and its expression was upregulated in a trastuzumab-resistant breast cancer cell. Downregulation of H19 restored the sensitivity of trastuzumab-resistant cells to this drug. The expression of H19 significantly correlated with TNM stage. Patients with higher expression of H19 showed an evidently shorter progression-free survival than those with low H19 expression. H19 overexpression was negatively correlated to the trastuzumab-therapy response. Conclusions: Our results provide evidence for the H19-mediated regulation of trastuzumab resistance in HER2-positive breast cancer cells. H19 could act as a potential predictive biomarker for HER2-positive breast cancer patients, and downregulation of H19 could reverse trastuzumab resistance and enhance the inhibitory function of this drug.
Inflammatory bowel diseases: a burden in pediatrics

Introduction: Inflammatory bowel disease is a chronic condition of the gastrointestinal tract, comprising mainly Crohn disease (CD) and ulcerative colitis (UC). Both of them are frequently encountered in children, being multifactorial conditions, with an unclear etiology. Patients concerns: We present 4 cases of inflammatory bowel disease (IBD) in children in order to underline the variable evolution depending on the patient's particularities. Diagnosis, interventions and outcomes: The first case, a 13-year-old male patient, with a history of Henoch–Schonlein purpura, was admitted for rectal bleeding and weight loss, with normal laboratory parameters. The colonoscopy and the histopathological examination established the diagnosis of UC. The evolution was initially favorable under corticosteroids and sulfasalazine, but with 3 relapses in 2 years. The second case, a 16-year-old male patient, with a history of lactose intolerance and constipation, was admitted for bloody, diarrheic stools, the laboratory tests pointing out only leukocytosis with neutrophilia. The colonoscopy and histopathological examination established the diagnosis of UC. The patient's evolution was slowly favorable. The third case, a 9-year old male patient, with emotional disorders and babbling, admitted for semiconsistent, bloody stools, with increased inflammatory tests, whose colonoscopy pointed out diffuse edema and hemorrhages, the histopathological examination establishing the diagnosis of CD. The evolution was initially favorable, but with 5 relapses in 3 years. The last case, a 12-year-old male patient, was admitted with diarrheic, bloody stools, refractory to antibiotics, and weight loss, with increased inflammatory tests. The colonoscopy pointed out ulcerations, hemorrhages, and disseminated puss deposits. The histopathological examination established the diagnosis of CD. The patient's evolution was favorable, with only 1 relapse in 3 years. Conclusions: The adequate management, especially the self-management can influence the prognosis of patients with IBD, even though it is unpredictable and burdened by the risk of malignant transformation.
ENHANCED GRAPH BASED NORMALIZED CUT METHODS FOR IMAGE SEGMENTATION

Image segmentation is one of the important steps in digital image processing. Several algorithms are available for segmenting the images, posing many challenges such as precise criteria and efficient computations. Most of the graph based methods used for segmentation depend on local properties of graphs without considering global impressions of image, which ultimately limits segmentation quality. In this paper, we propose an enhanced graph based normalized cut method for extracting global impression and consistencies in the image. We propose a technique to add flexibility to original recursive normalized two way cut method which was further extended to other graph based methods. The results show that the proposed technique improves segmentation quality as well as requires lesser computational time than the regular normalized cut method.
Effizienz oder Moral? Wirtschaft und Gerechtigkeit aus Sicht ökonomischer Laien

Abstract The present article investigates how economic experts and laypersons deal with issues of fairness in the economic area. Most economic experts follow an ethic that can be described as both, deductive and utilitarian. The ethical judgment of an economic system as a whole or a specific economic measure follows its economic consequences. Measures that increase economic wealth are also judged as fair. The putative motives of individuals or institutions are neglected. To the contrary, laypeople follow an ethic that is both, deontic and intuitive. Ethical judgments are heavily influenced by the motives of the acting individuals and institutions. Questions of efficiency are rather neglected. Laypeople tend to accept those measures which are perceived as fair. Using a number of empirical examples we show how these differences in moral reasoning can explain the huge gap between experts and laypeople in their acceptance of different economic policies. Zusammenfassung Der vorliegende Artikel beschäftigt sich mit der Frage, wie Fragen von Gerechtigkeit im wirtschaftlichen Kontext durch ökonomische Experten und ökonomische Laien bewertet werden. Die meisten ökonomischen Experten folgen in ihrem Gerechtigkeitsdenken einer deduktiv utilitaristischen Ethik. Die ethische Bewertung eines Wirtschaftssystems oder einer konkreten wirtschaftspolitischen Maßnahme orientiert sich weitestgehend an seinen wirtschaftlichen Konsequenzen: Maßnahmen, die geeignet sind, den Wohlstand zu steigern, werden auch als gerecht empfunden, wobei die moralischen Intentionen der handelnden Akteure nur eine nachgelagerte Rolle spielen. Ökonomische Laien hingegen folgen einer deontischen und intuitiven Ethik, die sich weitgehend an den Motiven einer Handlung orientiert. Effizienzwirkungen wirtschaftlicher Maßnahmen werden dabei kaum beachtet, was als fair wahrgenommen wird, wird auch akzeptiert. Anhand zahlreicher empirischer Beispiele wird gezeigt, welche unterschiedlichen Urteile durch ökonomische Experten und Laien hieraus erwachsen.
Statistical Inference on Software Reliability Assuming Exponential Fault Correction Time

Abstract Most of the software reliability models are based on reliability growth models which deal only with the fault detection process. In such models it is assumed that faults are corrected immediately after being detected or the time to correct a fault is not taken into account. This assumption may not be realistic in practice. In this paper we propose a software reliability model taking into account the fault correction time. The software failure times as well as correction times are assumed to be exponential. The model is formulated as an alternating renewal process. The properties of the reliability model are studied through the renewal process. The system parameters are estimated by means of the maximum likelihood estimators and the properties of the estimators are discussed. We also propose some large sample tests for the system parameters. Some numerical studies are made to evaluate the power of the tests.
The Mandatory Bid and Company Law in Europe

The article presents the interplay between the mandatory bid rule and (European or national) company law. This rule is explained with reference to both the protection of investors' expectations as to the shareholding structure of the company and the promotion of the market's allocative efficiency. In accordance with the recent Audiolux judgment of the ECJ, the mandatory bid cannot be regarded as a manifestation of an overarching equal treatment principle in European company law. Some recourse to company law is in any case necessary to discern the limits of the mandatory bid. Indeed, the mandatory bid rule puts a heavy burden on collective shareholder action, and thus tips the balance of power between shareholders and management in favour of the latter. This is to be taken into account when determining the scope of application of the rule to joint activities of shareholders. This is also a matter of the internal structure of the company, which is only regulated in national, rather than European, company law. Thus, national company law becomes relevant in the interpretation of the mandatory bid rule in each jurisdiction, while the effet utile of the Takeover Directive must be preserved.