Miracle-dz/UniFAR-FTUs huggingface dataset:
An Observational Study of Short‐Cycle Lightning Outbreaks in the Inner Core of Typhoon Hato (2017) Before Landfall

This study analyzes microphysical signatures relevant to the short‐cycle lightning activity in the inner core of Typhoon Hato (2017) before landfall in China. Observations reveal that the lightning bursts were accompanied by enhanced inner‐core convection with a behavior cycle of about 3 hr. The coupling between wavenumber‐2 vortex Rossby waves (VRWs) and shear‐forced convective asymmetries resulted in a local updraft enhancement. Furthermore, supercooled liquid water droplets invigorated a striking enhancement of graupel via riming immediately above the freezing level, further enhancing charge separation and lightning generation outside the eyewall. Furthermore, when similar phase‐locking was associated with other propagating VRWs, graupel and updraft volumes were significantly boosted, leading to short‐cycle lightning outbreaks in the inner core.
Web retrieval: Techniques for the aggregation and selection of queries and answers

In this paper we present a set of techniques for grouping and aggregating queries and search results, in the context of an Internet “search engine. '' (1) In the case of the initial grouping of the queries, we consider the Fuzzy c‐Means1 and Kohonen SOM2 techniques. It is proposed that FCM may be more adequate than k‐Means3 for the grouping of certain data types. We evaluate how we can use FCM to calculate the fuzzy membership grades for a set of Web queries and their corresponding results. (2) In the case of the aggregation of data from different information sources (the clustering techniques), we will consider weighted ordered weighted averaging (WOWA). 4 WOWA is used to choose the most adequate cluster and to identify the historical query in that cluster, which is most similar to a new query. We will see that the WOWA operator offers a wide flexibility for data processing. © 2008 Wiley Periodicals, Inc.
Vector solitons and dispersive waves in birefringent optical fibers

We develop a general formalism for investigating the evolution of arbitrarily polarized short pulses inside a birefringent optical fiber. We use it to numerically study the formation of a dispersive wave inside fibers exhibiting medium to high birefringence when a short optical pulse is launched such that it propagates as a vector soliton. We also investigate the polarization evolution of both the vector soliton and dispersive wave generated by it. The results show that, while the polarization of the dispersive wave is controlled by linear birefringence of the fiber, polarization of the vector soliton is affected considerably by the nonlinear birefringence. The coupled nonlinear equations that we solve include both the Raman and Kerr nonlinearities. Moreover, they include the cross-polarization Raman terms that couple the orthogonally polarized components of the vector soliton. Polarization of the vector soliton is found to be affected considerably by the Raman nonlinearity in the case of medium birefringence.
Assessment of Numerical Weather Prediction Model Reforecasts of the Occurrence, Intensity, and Location of Atmospheric Rivers along the West Coast of North America

Atmospheric rivers (ARs)—narrow corridors of high atmospheric water vapor transport—occur globally and are associated with flooding and maintenance of the water supply. Therefore, it is important to improve forecasts of AR occurrence and characteristics. Although prior work has examined the skill of numerical weather prediction (NWP) models in forecasting atmospheric rivers, these studies only cover several years of reforecasts from a handful of models. Here, we expand this previous work and assess the performance of 10–30 years of wintertime (November–February) AR landfall reforecasts from the control runs of nine operational weather models, obtained from the International Subseasonal to Seasonal (S2S) Project database. Model errors along the west coast of North America at leads of 1–14 days are examined in terms of AR occurrence, intensity, and landfall location. Occurrence-based skill approaches that of climatology at 14 days, while models are, on average, more skillful at shorter leads in California, Oregon, and Washington compared to British Columbia and Alaska. We also find that the average magnitude of landfall integrated water vapor transport (IVT) error stays fairly constant across lead times, although overprediction of IVT is common at later lead times. Finally, we show that northward landfall location errors are favored in California, Oregon, and Washington, although southward errors occur more often than expected from climatology. These results highlight the need for model improvements, while helping to identify factors that cause model errors.
Learning Situation Models for Providing Context-aware Services

During the last decade, " intelligent " environments augmented by multiple sensors, including cameras, microphones, and interaction devices have enabled the computer observation of human activity. In order to detect and respond to human activity, a context model describing the environment, its users and their activities must be maintained. In this thesis, context is represented by situation models. A situation is a kind of state described by a number of characteristic roles and relations. A role is played by an entity and entities can be in relations. An entity refers to a person or object. A situation model can be defined and implemented by experts. Two example implementations of this kind are presented in this thesis. However, human behavior evolves over time. A situation model must be constructed and adapted automatically. This thesis addresses the problem by proposing a framework for the automatic acquisition and evolution of different layers of a situation model. Different learning methods are proposed as part of this framework. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The first layer of the framework concerns role learning and detection. Bayesian classifier, support vector machines (SVMs) and a novel hybrid classifier combining SVMs and Bayesian classifier are presented and compared. Based on the results of role detection, a method for unsupervised situation discovery is then proposed. This method takes a multimodal observation stream as input and generates a first segmentation into distinct observation sequences as output. This segmentation and associated situation labels given by an expert or user are the input for supervised situation learning. A supervised situation learning scheme is proposed. This scheme can be applied to different learner classes, generating a situation representation for each situation label and associated observation sequences. Based on the learned situation model, a method for the integration of user preferences is presented. This method adapts the learned situation model by splitting situations according to user feedback given on executed system services. The methods of the framework have been evaluated separately on data sets coming from different augmented environments. The complete framework has been integrated into an intelligent home environment. The implementation has been evaluated and the obtained results validate the proposed approach. Acknowledgments First of all, I would like to thank my supervisor Prof. James L. Crowley and my co-supervisor Dr. Patrick Reignier for giving me the opportunity to …
Design and validation of a real-time spiking-neural-network decoder for brain–machine interfaces

Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain–machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.
Update on neurobiological mechanisms of fear: illuminating the direction of mechanism exploration and treatment development of trauma and fear-related disorders

Fear refers to an adaptive response in the face of danger, and the formed fear memory acts as a warning when the individual faces a dangerous situation again, which is of great significance to the survival of humans and animals. Excessive fear response caused by abnormal fear memory can lead to neuropsychiatric disorders. Fear memory has been studied for a long time, which is of a certain guiding effect on the treatment of fear-related disorders. With continuous technological innovations, the study of fear has gradually shifted from the level of brain regions to deeper neural (micro) circuits between brain regions and even within single brain regions, as well as molecular mechanisms. This article briefly outlines the basic knowledge of fear memory and reviews the neurobiological mechanisms of fear extinction and relapse, which aims to provide new insights for future basic research on fear emotions and new ideas for treating trauma and fear-related disorders.
Rehabilitation nurses’ knowledge, experiences, and perceptions of the provision of psychological care for patients post stroke in Ireland: A cross-sectional study.

Introduction: Early assessment and management of depression and anxiety for patients after stroke is critical. However, suboptimal provision of psychological care is widely reported. Nurses are the largest professional group to care for patients post stroke and have an important role in addressing their emotional wellbeing. Aim: The aim of this paper is to examine nurse’s knowledge, experiences, and perceptions of the provision of psychological care to patients post stroke in Ireland. Methods: A cross-sectional descriptive survey design involving both quantitative and qualitative items was administered to a convenience sample of seventy-four (n=74) nurses working in six Irish community hospital rehabilitation units. Results: Findings demonstrate suboptimal psychological care provision for patients post stroke with no formal pre-defined care pathways. Despite nurse’s knowledge of psychological problems and symptomology, participants revealed receiving limited training in psychological monitoring and no systemic allocation of psychological care duties. This research suggests that pre-defined/formal care pathways; access to psychology expertise; further training and support for members of the Multi-Disciplinary-Team has the potential to improve the provision of psychological care for patients post stroke. Conclusions: This study has implications for the reform and development of rehabilitation services in relation to practice, education, and research. It identifies opportunities to support nurses to improve the delivery of psychological care for patients post stroke.
Psychological Interventions For Psychosis: A Critical Review Of The Current Evidence

In this review, the current evidence for psychological interventions is critically appraised. Studies differ in both the techniques employed in therapy and the specific goals of treatment. Targeted outcomes include measures of overall symptomatology, social functioning and relapse rates. Whilst some researchers have focused on dysfunctional schemas, interventions range from an emphasis on problem solving and coping strategies, to the rational exploration of delusional beliefs and the use of reality testing. In short, there are broad variations in what is being tested and for whom. The review reflects the results of a literature search using the keywords "psychological", "psychosocial", "psychotherapy" and "schizophrenia. " Reference lists of retrieved papers were also examined for further relevant material. Lack of space permits a detailed examination of CBT alone, although an additional, closely related intervention is given consideration. Studies are divided into those focusing on unremitting, medication-resistant symptoms and those focusing on acute episodes of psychosis.
Penerapan Algoritma Decision Tree C5.0 Untuk Memprediksi Tingkat Kematian Pasien Penyakit Gagal Jantung

Penyakit jantung salah satu penyakit paling umum di antara penyakit lainnya, penyakit jantung dapat menyerang semua orang tanpa mengenal jenis kelamin, usia, atau faktor lainnya. Penelitian ini bertujuan untuk menerapkan algoritma C5.0 untuk memprediksi keberlangsungan hidup penderita penyakit gagal jantung serta memprediksi kematian pasien gagal jantung. Sedangkan untuk mengetahui akurasi yang dihasilkan oleh algoritma C5.0 dalam memprediksi akan digunakan confusion matrix. Penelitian ini akan menggunakan 300 dataset heart failure clinical records. Berdasarkan hasil yang didapatkan bahwa akurasi yang dihasilkan oleh algoritma decision tree C5.0 adalah 86,6% dengan perbandingan jumlah data latih dan data testing adalah 80 : 20. Sedangkan untuk presisi yang dihasilkan adalah 89,655 dan sensitifitas yang dihasilkan adalah 25 dengan spesifisitas 96,15
Abnormal Winter Melting of the Arctic Sea Ice Cap Observed by the Spaceborne Passive Microwave Sensors

The spatial size and variation of Arctic sea ice play an important role in Earth’s climate system. These are affected by conditions in the polar atmosphere and Arctic sea temperatures. The Arctic sea ice concentration is calculated from brightness temperature data derived from the Defense Meteorological Satellite program (DMSP) F13 Special Sensor Microwave/Imagers (SSMI) and the DMSP F17 Special Sensor Microwave Imager/Sounder (SSMIS) sensors. Many previous studies point to significant reductions in sea ice and their causes. We investigated the variability of Arctic sea ice using the daily sea ice concentration data from passive microwave observations to identify the sea ice melting regions near the Arctic polar ice cap. We discovered the abnormal melting of the Arctic sea ice near the North Pole during the summer and the winter. This phenomenon is hard to explain only surface air temperature or solar heating as suggested by recent studies. We propose a hypothesis explaining this phenomenon. The heat from the deep sea in Arctic Ocean ridges and/ or the hydrothermal vents might be contributing to the melting of Arctic sea ice. This hypothesis could be verified by the observation of warm water column structure below the melting or thinning arctic sea ice through the project such as Coriolis dataset for reanalysis (CORA).
On a nonlocal dispersive equation modeling particle suspensions

We study a nonlocal, scalar conservation law Ut + ((Ka * u)u)x = 0, modeling sedimentation of particles in a dilute fluid suspension, where Ka{x) = a-1 K(x/a) is a symmetric smoothing kernel, and * represents convolution. We show this to be a dispersive regularization of the Hopf equation, Ut + (u2)x = 0, analogous to KdV and certain dispersive difference schemes. Using the smoothing property of convolution and the physical principle of conservation of mass, we establish the global existence of smooth solutions. In physical applications, boundary considerations impose initial data of shock type. Up to shock formation time, we show that solutions remain close to solutions of the Hopf equation. After shock formation time, we give evidence that dispersive, hydrodynamic effects, interacting with Brownian diffusion, can generate oscillatory interface patterns in place of shocks. We discuss possible relevance to the physical phenomenon of layering.
Effect of three-dimensional collagen membrane culture and presence of cumulus cells on maturation of bovine oocytes

Objective The use of animals as experimental models has been proposed to improve the techniques applied in human reproduction clinics. This prospective and observational study evaluates the effects of the use of cumulus cells and collagen membrane on the maturation process of bovine oocytes. Methods Design and Setting: Bovine oocytes with or without cumulus cells were cultured in maturation medium for 24 hours in the conventional system (2D), central well plates and in the three-dimensional (3D) system. Intervention: The oocytes were positioned in the collagen membrane and matured for the same period. The morphological evaluation was carried out with the parameters of maturation. Main Outcome Measure: Presence or absence of the first polar corpuscle, which were observed and classified as germinal vesicle (GV), meiosis I (MI) and meiosis II (MII). Results The percentage of oocytes in GV was higher (p<0.05) in treatments without cumulus cells than those with cells. The rates of MII were higher (p<0.05) in the treatments with cumulus cells, independent of the culture system. In general, oocytes with presence of cumulus cells have approximately 1.7 times more chances (p<0.001) of reaching MII after MIV than those matured without cells. Conclusions The presence of the cells in the cumulus is essential for the maturation process of bovine oocytes; the three-dimensional collagen membrane culture system is favorable for the maturation process of bovine oocytes.
Anatomical protocol for gait analysis: joint kinematics measurement and its repeatability

International Society of Biomechanics has proposed a general reporting standard for joint kinematics based on anatomical reference frames. Nevertheless, the gait analysis protocols based on this standard are still poorly reported. The purpose of the current study is to propose and preliminarily assess the potential of an anatomically based ISB 6-DOF protocol, which combines the ISB reporting standard together with a marker cluster technique. The proposed technical marker set enables full description of the lower limb kinematics (including three-dimensional ankle-foot complex rotations) according to the current biomechanical recommendation. The marker set provides a clinically acceptable inter-trial repeatability and minimal equipment requirements.
Netodyssey: a framework for real-time windowed analysis of network traffic

Traffic monitoring and analysis is of critical importance for managing and designing modern computer networks, and constitutes nowadays a very active research field. In most of their studies, researchers use techniques and tools that follow a statistical approach to obtain a deeper knowledge about the traffic behaviour. Network administrators also find great value in statistical analysis tools. Many of those tools return similar metrics calculated for common properties of network packets. This dissertation presents NetOdyssey, a framework for the statistical analysis of network traffic. One of the crucial points of differentiation of NetOdyssey from other analysis frameworks is the windowed analysis philosophy behind NetOdyssey. This windowed analysis philosophy allows researchers who seek for a deeper knowledge about networks, to look at traffic as if looking through a window. This approach is crucial in order to avoid the biasing effects of statistically looking at the traffic as a whole. Small fluctuations and irregularities in the network can now be analyzed, because one is always looking through window which has a fixed size: either in number of observations or in the temporal duration of those observations. NetOdyssey is able to capture live traffic from a network card or from a pre-collected trace, thus allowing for real-time analysis or delayed and repetitive analysis. NetOdyssey has a modular architecture making it possible for researchers with reduced programming capabilities to create analysis modules which can be tweaked and easily shared among those who utilize this framework. These modules were thought so that their implementation is optimized according to the windowed analysis philosophy behind NetOdyssey. This optimization makes the analysis process independent from the size of the analysis window, because it only contemplates the v observations coming in and going out of this window. Besides presenting this framework, its architecture and validation, the present Dissertation also presents four different analysis modules: Average and Standard deviation, Entropy, Auto-Correlation and Hurst Parameter estimators. Each of this modules is presented and validated throughout the present dissertation.
Redundancy resolution using a tractrix and its application to real-time simulations of hyper-redundant manipulators, snakes and tying of knots

To realistically simulate the motion of flexible objects such as ropes, strings, snakes, or human hair,one strategy is to discretise the object into a large number of small rigid links connected by rotary or spherical joints. The discretised system is highly redundant and the rotations at the joints (or the motion of the other links) for a desired Cartesian motion of the end of a link cannot be solved uniquely. In this paper, we propose a novel strategy to resolve the redundancy in such hyper-redundant systems.We make use of the classical tractrix curve and its attractive features. For a desired Cartesian motion of the `head'of a link, the `tail' of the link is moved according to a tractrix,and recursively all links of the discretised objects are moved along different tractrix curves. We show that the use of a tractrix curve leads to a more `natural' motion of the entire object since the motion is distributed uniformly along the entire object with the displacements tending to diminish from the `head' to the `tail'. We also show that the computation of the motion of the links can be done in real time since it involves evaluation of simple algebraic, trigonometric and hyperbolic functions. The strategy is illustrated by simulations of a snake, tying of knots with a rope and a solution of the inverse kinematics of a planar hyper-redundant manipulator.
Three-stage estimation method for non-linear multiple time-series

We present the three-stage pseudo maximum likelihood estimation in order to reduce the computational burdens when a copula-based model is applied to multiple time-series in high dimensions. The method is applied to general stationary Markov time series, under some assumptions which include a time-invariant copula as well as marginal distributions, extending the results of Yi and Liao [2010]. We explore, via simulated and real data, the performance of the model compared to the classical vectorial autoregressive model, giving the implications of misspecified assumptions for margins and/or joint distribution and providing tail dependence measures of economic variables involved in the analysis.