Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel adaptive scheme of discrimination between internal faults and inrush currents of transformer using mathematical morphology

This paper addresses a new relaying scheme for identifying the inrush current and short circuit current caused by internal fault on the basis of sudden changes detection using novel morphological gradient (NMG), transient current signals are then extracted by use of morphological opening and closing transform. Based on different characteristics of transient signals between the short circuit current and the inrush current, the transformer protection method is proposed. This method can avoid the symmetrical inrush current and saturation of current transformer (CTs). The results of Electrical Power Dynamic Laboratory (EPDL) indicate that the proposed technique can also deal with the sampled data contaminated with various kinds of noises and DC components and is stable during internal faults with external shunt capacitance in a long EHV transmission line
Baseline BAL neutrophilia predicts early mortality in idiopathic pulmonary fibrosis.

BACKGROUND The prognostic value of BAL fluid cell count differential in patients with idiopathic pulmonary fibrosis (IPF) is unknown. We hypothesized that baseline BAL fluid cell count differential (ie, elevated levels of neutrophils and eosinophils, or reduced levels of lymphocytes) would predict higher mortality among persons with IPF. METHODS We evaluated the association of BAL fluid cell count differential and mortality among 156 persons with surgical lung biopsy-proven IPF who underwent bronchoscopy with BAL and cell count differential measurements at presentation. Vital status was obtained among all participants. Cox regression analysis evaluated the association of BAL fluid cell count differential and mortality. RESULTS After controlling for known clinical predictors of mortality, we found that each doubling of baseline BAL fluid neutrophil percentage was associated with a 30% increased risk of mortality (adjusted hazard ratio [HR], 1.28; 95% confidence interval [CI], 1.01 to 1.62; adjusted p = 0.04) in the first year after presentation. We observed no association with BAL fluid lymphocyte percentage and mortality (adjusted HR per doubling, 0.99; 95% CI, 0.76 to 1.29; p = 0.93) or eosinophil percentage and mortality (adjusted HR per doubling, 0.99; 95% CI, 0.69 to 1.40; p = 0.95). CONCLUSIONS Increased BAL fluid neutrophil percentage is an independent predictor of early mortality among persons with IPF. Alternatively, BAL fluid lymphocyte and eosinophil percentages were not associated with mortality. The clinical utility of BAL at the time of diagnosis of IPF should be reconsidered.
Maturation-Dependent Vulnerability of Perinatal White Matter in Premature Birth

Survivors of premature birth have a predilection for perinatal brain injury, especially to periventricular cerebral white matter. Periventricular white matter injury (PWMI) is now the most common cause of brain injury in preterm infants and the leading cause of chronic neurological morbidity. The spectrum of chronic PWMI includes focal cystic necrotic lesions (periventricular leukomalacia) and diffuse myelination disturbances. Recent neuroimaging studies support that the incidence of periventricular leukomalacia is declining, whereas focal or diffuse noncystic injury is emerging as the predominant lesion. In a significant number of infants, PWMI appears to be initiated by perturbations in cerebral blood flow that reflect anatomic and physiological immaturity of the vasculature. Ischemic cerebral white matter is susceptible to pronounced free radical–mediated injury that particularly targets immature stages of the oligodendrocyte lineage. Emerging experimental data supports that pronounced ischemia in the periventricular white matter is necessary but not sufficient to generate the initial injury that leads to PWMI. The developmental predilection for PWMI to occur during prematurity appears to be related to both the timing of appearance and regional distribution of susceptible oligodendrocyte progenitors. Injury to oligodendrocyte progenitors may contribute to the pathogenesis of PWMI by disrupting the maturation of myelin-forming oligodendrocytes. There has been substantial recent progress in the understanding of the cellular and molecular pathogenesis of PWMI. The oligodendrocyte progenitor is a key target for preventive strategies to reduce ischemic cerebral white matter injury in premature infants.
Subliminal: The New Unconscious and What it Teaches Us

Leonard Mlodinow, the best-selling author of "The Drunkard's Walk" and coauthor of "The Grand Design" (with Stephen Hawking) and "War of the Worldviews" (with Deepak Chopra) here examines how the unconscious mind shapes our experience of the world, and how, for instance, we often misperceive everything from our relationships with family, friends and business associates, the reasons for our investment decisions, to our own past. Your preference in politicians, the amount of tip you give the waiter - all our judgments and perceptions - reflect the workings of our mind on two levels, the conscious, of which we are aware, and the unconscious, which is hidden from us. The latter has long been the subject of speculation, but over the past two decades scientific researchers have developed remarkable new tools for probing the hidden, or subliminal, workings of the mind. The result of this explosion of research is a new science of the unconscious, and a sea change in our understanding of how the mind affects the way we live. These cutting-edge discoveries have revealed that the way we experience life - our perception, behavior, memory, and social judgment - is largely driven by the mind's subliminal processes and not by the conscious ones, as we have long believed. Employing his trademark wit and his lucid, accessible explanations of the most obscure scientific subjects, Leonard Mlodinow takes us on a tour of this research, unraveling the complexities of the subliminal self, increasing our understanding of how the human mind works, and how we interact with friends, strangers, spouses and coworkers. In the process he changes our view of ourselves and the world around us. Leonard Mlodinow received his PhD in theoretical physics from the University of California, Berkeley, was an Alexander von Humboldt Fellow at the Max Planck Institute, and now teaches at the California Institute of Technology. His previous books include "War of the Worldviews" (with Deepak Chopra); the two national best sellers "The Grand Design" (with Stephen Hawking) and "The Drunkard's Walk" (a "New York Times" Notable Book and short-listed for the Royal Society Prize for Science Books); "Feynman's Rainbow"; and "Euclid's Window". He also wrote for the television series "MacGyver" and "Star Trek: The Next Generation".
Searching Heterogeneous Human Digital Memory Archives

Advances in digital storage technologies mean that vast digital archives of ones personal life experiences can now be generated. These personal archives (Human Digital Memories (HDMs)) can contain many types of data in various media created or accessed by the individual. These archives are of little benefit if an individual cannot locate and retrieve significant items from them. Existing search techniques are not sufficient for retrieval of items from these new unstructured spaces. This research proposes to develop effective HDM search by the integration of rich sources of context data, such as biometric information, with items in the HDM as a method to aid effective retrieval in this new domain.
Bi-level dimensionality reduction methods using feature selection and feature extraction

Variety of feature selection methods have been developed in the literature, which can be classified into three main categories: filter, wrapper and hybrid approaches. Filter methods apply an independent test without involving any learning algorithm, while wrapper methods require a predetermined learning algorithm for feature subset evaluation. Filter and wrapper methods have their drawbacks and are complementary to each other. The filter approaches have low computational cost with insufficient reliability in classification while wrapper methods tend to have superior classification accuracy but require great computational effort. The methods proposed in this paper are bi-level dimensionality reduction methods that integrate filter method and feature extraction method with the aim to improve the classification performance of the features selected. In the two approaches proposed, in level 1 of dimensionality reduction, feature are selected based on mutual correlation and in level 2 selected features are used to extract features using PCA or LPP. To evaluate the performance of the proposed methods several experiments are conducted on standard datasets and the results obtained show superiority of the proposed methods over single level dimensionality reduction techniques (feature selection based on Mutual correlation, PCA and LPP).
DSM‐IV‐TR: Diagnostic and Statistical Manual of Mental Disorders

The Diagnostic and Statistical Manual of Mental Disorders (DSM), published by the American Psychiatric Association (APA), is used for clinical, research, and educational purposes. The focus of the DSM is on diagnostic codes for psychological disorders and on medical record keeping. The DSM is also used for communication of information to governmental agencies, private insurers, and the World Health Organization (WHO). The DSM is now in its fourth edition. In 1952, the first edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-I) was recognized as the manual of mental health categorization. It developed as a variant of the “Mental Disorders” section of the sixth edition of the World Health Organization's International Classification of Diseases (ICD). Keywords: abnormal; psychopathology
Measuring of ESR with test 1 is more useful than the Westergren method in rheumatoid arthritis.

We read with interest the article by Cha et al1 on the comparison among erythrocyte sedimentation rate (ESR) measurement methods. In this article, the authors stated that TEST 1 ESR measurements better reflect the presence of inflammation than do Westergren data in patients with malignancy, autoimmune disease, or infection. We measured the ESR using the TEST 1 analyzer and Westergren …
A wideband multi-mode SAW filter employing pitch-modulated IDTs on cu-grating/15°YX-LiNbO3-substrate structure

This paper describes an attempt to develop extremely wideband multi-mode surface acoustic wave (MMS) filters in the gigahertz range. The Cu-grating/15°YX-LiNbO3 (15-LN)-substrate structure is employed. First, a design tool developed for the present purpose is detailed. Precise simulation is performed using a modified coupling-of-modes model, in which the coupling between propagating surface and bulk acoustic waves is taken into account. Parameters necessary for the simulation are determined experimentally. Next, this simulation tool is used to design a wideband MMS filter employing pitch-modulated IDTs proposed by the authors. It is shown that a fractional bandwidth of more than 12% is achievable by successfully using six SAW resonances supported in the MMS structure. The designed MMS filter was fabricated on a Cu-grating/15-LN-substrate structure. The passband width of 12.6% and the minimum insertion loss of 1.2 dB were experimentally obtained around 850 MHz. The measured result was in good agreement with the simulation.
Tracking Subpixel Targets with Critically Sampled Optical Sensors

Abstract : In many remote sensing applications, the area of a scene sensed by a single pixel can often be measured in square meters. This means that many objects of interest in a scene are smaller than a single pixel in the resulting image. Current tracking methods rely on robust object detection using multi-pixel features. A subpixel object does not provide enough information for these methods to work. This dissertation presents a method for tracking subpixel objects in image sequences captured from a stationary sensor that is critically sampled spatially. Using template matching, we estimate the maximum a posteriori probability of the target state over a sequence of images. A distance transform is used to calculate the motion prior in linear time, dramatically decreasing computation requirements. We compare the results of this method to a previously state-of-the-art track-before-detect particle filter designed for tracking small, low contrast objects using both synthetic and real-world imagery. Results show our method produces more accurate state estimates and higher detection rates than the current state of the art methods at signal-to-noise ratios as low as 3dB.
3D Face Mesh Modeling for 3D Face Recognition

Face recognition has rapidly emerged as an important area of research within many scientific and engineering disciplines. It has attracted research institutes, commercial industries, and numerous government agencies. This fact is evident by the existence of large number of face recognition conferences such as the International Conference on Automatic Face and Gesture and the Biometric Consortium conference. Special issues of well known journals, are being dedicated to face modeling and recognition, such as the journal of Computer Vision and Image Understanding (CVIU), and the systematic empirical evaluations of face recognition techniques including the FERET (Phillips et al., 2000), XM2VTS (Messer et al., 1999), FRVT 2000 (Blackburn et al., 2000), FRVT 2002 (Phillips et al., 2002), and FRVT 2006, which evolved substantially in the last few years. There are few reasons for this trend; first the demands for machine automations, securities, and law enforcements have created a wide range of commercial applications. The second is the availability of feasible technologies developed by researchers in the areas of image processing, pattern recognition, neural network, computer vision, and computer graphics. Another reason for this growing interest is to help us better understand ourselves through the fields of psychology and cognitive science which targeted the perception of faces in the brain. Because our natural face recognition abilities are very powerful, the study of the brain system could offer important guidance in the development of automatic face recognition. Research with animals has shown that these capabilities are not unique to humans. Sheep, for example, are known to have a remarkable memory for faces (Kendrick et al., 2000). In addition, we constantly use our faces while interacting with each others in a conversation. Face gesturing helps us understand what is being said. Facial expression is an important cue in understanding a person’s emotional state. In sign languages, faces also convey meanings that are essential part of the language. A wealth of 2D image-based algorithms has been published in the last few decades (Zhaho et al., 2003). Due to the numerous limitations of 2D approaches, 3D range image-based algorithms are born. Generally, 3D facial range image or data is rich, yet making full use of its high resolution for face recognition is very challenging. It is difficult to extract
Botulinum Toxin A for Controlling Obesity

Rapid growth of the overweight population and the number of obese individuals in recent decades suggests that current strategies based on diet, exercise, and pharmacological knowledge are not sufficient to address this epidemic. Obesity is the result of a high caloric intake and energy storage, not counterbalanced by an equally important energy expense. Botulinum toxin type A (BoNT-A) use is rapidly expanding to include treatment of a variety of ophthalmological, gastrointestinal, urological, orthopedic, dermatological, secretory, painful, and cosmetic disorders. Many studies evaluating the effect of BoNT-A in gastric antrum e/o fundus for the treatment of obesity have been published. This treatment modality was based on the observation that gastric injection of BoNT-A in laparatomized rats induced a significant reduction of food intake and body weight. These studies have been published yielding debated results. Differences in the selection of patients, the doses of BoNT-A, the method of administration of the toxin, and the instruments of evaluation of some parameters among these studies may be the cause. In this review, it will study the state-of-the-art use of BoNT-A in obesity basic science models and review the clinical evidence on the therapeutic applications of BoNT-A for obesity.
Infinite Relational Modeling of Functional Connectivity in Resting State fMRI

Functional magnetic resonance imaging (fMRI) can be applied to study the functional connectivity of the neural elements which form complex network at a whole brain level. Most analyses of functional resting state networks (RSN) have been based on the analysis of correlation between the temporal dynamics of various regions of the brain. While these models can identify coherently behaving groups in terms of correlation they give little insight into how these groups interact. In this paper we take a different view on the analysis of functional resting state networks. Starting from the definition of resting state as functional coherent groups we search for functional units of the brain that communicate with other parts of the brain in a coherent manner as measured by mutual information. We use the infinite relational model (IRM) to quantify functional coherent groups of resting state networks and demonstrate how the extracted component interactions can be used to discriminate between functional resting state activity in multiple sclerosis and normal subjects.
Competitive Regulation of Nucleolin Expression by HuR and miR-494

ABSTRACT The RNA-binding protein (RBP) nucleolin promotes the expression of several proliferative proteins. Nucleolin levels are high in cancer cells, but the mechanisms that control nucleolin expression are unknown. Here, we show that nucleolin abundance is controlled posttranscriptionally via factors that associate with its 3′ untranslated region (3′UTR). The RBP HuR was found to interact with the nucleolin (NCL) 3′UTR and specifically promoted nucleolin translation without affecting nucleolin mRNA levels. In human cervical carcinoma HeLa cells, analysis of a traceable NCL 3′UTR bearing MS2 RNA hairpins revealed that NCL RNA was mobilized to processing bodies (PBs) after silencing HuR, suggesting that the repression of nucleolin translation may occur in PBs. Immunoprecipitation of MS2-tagged NCL 3′UTR was used to screen for endogenous repressors of nucleolin synthesis. This search identified miR-494 as a microRNA that potently inhibited nucleolin expression, enhanced NCL mRNA association with argonaute-containing complexes, and induced NCL RNA transport to PBs. Importantly, miR-494 and HuR functionally competed for modulation of nucleolin expression. Moreover, the promotion of cell growth previously attributed to HuR was due in part to the HuR-elicited increase in nucleolin expression. Our collective findings indicate that nucleolin expression is positively regulated by HuR and negatively regulated via competition with miR-494.
Composing architectural crosscutting structures in xADL

Designing a software architecture is about defining and composing highlevel design structures. Whereas describing several structures is is fairly well supported, both non-aspect-oriented and aspectoriented Architectural Description Languages (ADLs) fall short when it comes to documenting relations or compositions between architecturallyrelevant crosscutting structures. This makes in turn separation of important concerns in the architecture hard, thereby increasing maintenance overhead and reducing reuse capabilities. This paper identifies and analyzes examples of crosscutting structures in an architecture for an industrial Automatic Guided Vehicle Transportation System (AGVTS). This analysis allowed us to determine and introduce an initial set of structural composition operators into xADL, namely substructure, mapping, and unification. The operators’ feasibility have been assessed while refactoring the existing AGVTS architecture. Based on a real maintenance scenario, we also investigate to what extent these explicit compositions led (or not) to enhanced architectural changeability for evolving the distribution strategy.
Efficient nested loop pipelining in high level synthesis using polyhedral bubble insertion

Loop pipelining is a key transformation in high-level synthesis tools as it helps maximizing both computational throughput and hardware utilization. Nevertheless, it somewhat looses its efficiency when dealing with small trip-count inner loops, as the pipeline latency overhead quickly limits its efficiency. Even if it is possible to overcome this limitation by pipelining the execution of a whole loop nest, the applicability of nested loop pipelining has so far been limited to a very narrow subset of loops, namely perfectly nested loops with constant bounds. In this work we propose to extend the applicability of nested-loop pipelining to imperfectly nested loops with affine dependencies by leveraging on the so-called polyhedral model. We show how such loop nest can be analyzed, and under certain conditions, how one can modify the source code in order to allow nested loop pipeline to be applied using a method called polyhedral bubble insertion. We also discuss the implementation of our method in a source-to-source compiler specifically targeted at High-Level Synthesis tools.
Robotic Telepresence at Scale

Telepresence robots offer a relatively new way for people to project their presence remotely. However, these experiences have only been studied in controlled or small scale installations. To broaden our understanding of the successes and limitations of telepresence robots in large-scale venues, we conducted a study at CHI 2016 where five factors increased over past research: (1) number of local attendees; (2) ratio of remote users to systems; (3) variety of activities; (4) time zone differences; and, (5) environment size. Our results reveal that unlike small-scale venues and situations, remote users take a more socially isolated and functional approach to remote attendance while combating challenges around scheduling and large navigational spaces. Our results reveal new opportunities for thinking about the design of robot personalization, availability, and navigation for systems targeted at large-scale public contexts.
ZnO Quasi-1D Nanostructures: Synthesis, Modeling, and Properties for Applications in Conductometric Chemical Sensors

One-dimensional metal oxide nanostructures such as nanowires, nanorods, nanotubes, and nanobelts gained great attention for applications in sensing devices. ZnO is one of the most studied oxides for sensing applications due to its unique physical and chemical properties. In this paper, we provide a review of the recent research activities focused on the synthesis and sensing properties of pure, doped, and functionalized ZnO quasi-one dimensional nanostructures. We describe the development prospects in the preparation methods and modifications of the surface structure of ZnO, and discuss its sensing mechanism. Next, we analyze the sensing properties of ZnO quasi-one dimensional nanostructures, and summarize perspectives concerning future research on their synthesis and applications in conductometric sensing devices.
Predictions of turbulent secondary flows using the v 2f model By

Turbulent secondary flows, e.g., turbulent flows with streamwise mean vorticity (Bradshaw 1987) arise in many applications including ducts, jets in cross-flow, turbomachinery cooling passages, wall jets, etc. For statsitically stationary flows, streamwise vorticity can be generated by two essentially different mechanisms: deflection of existing mean vorticity or Reynolds stress imbalance. These are typically referred to as Prandtl’s first and second kind of secondary flows. The exact equation for the mean streamwise vorticity component is