Miracle-dz/UniFAR-FTUs huggingface dataset:
CONTROLLING THE EVOLUTION OF A SIMULATED HURRICANE THROUGH OPTIMAL PERTURBATIONS: INITIAL EXPERIMENTS USING A 4-D VARIATIONAL ANALYSIS SYSTEM

Theoretical and model studies have established that the dynamics governing the atmosphere can be extremely sensitive to small changes in initial conditions (e.g., Rabier et al. 1996). This suggests that the earth’s atmosphere is chaotic. Chaos implies sensitivity to small perturbations. In a realistic numerical weather prediction (NWP) model, since small differences in initial conditions can grow exponentially, small but correctly chosen perturbations induce large changes in the evolution of the simulated weather. Current operational NWP practices—including data assimilation, generation of ensembles, and targeted observations—illustrate this daily (Hoffman 2002). A series of perturbations to the atmosphere might therefore be devised to effectively control the evolution of the atmosphere, if the atmosphere is observed and modeled sufficiently well. Hoffman (2002) hypothesized that as we observe and predict the atmosphere with more and more accuracy, we will become able to effect control of the atmosphere with smaller and smaller perturbations. The question addressed in the present study is how to calculate the optimal perturbations. Theory tells us that perturbations must have a special structure to grow explosively. Hurricanes are a natural focus for weather control experiments. The motivation to control the weather is especially strong in the case of tropical cyclones. The AMS policy statement “Hurricane Research and Forecasting” (AMS 2000) summarizes the hazards of tropical cyclones over land: loss of life and nearly $5 billion (in 1998 dollars) annually in damage due to the storm surge, high winds and flooding. The economic cost continues to rise due to growing population and wealth in coastal regions. Further, hurricanes fit the profile of our hypothesis: hurricanes are difficult to predict. One reason is that hurricanes are very sensitive to specified initial conditions and boundary conditions, especially the sea surface temperature and topography. Warm sea surfaces provide latent and sensible energy for hurricanes to grow and rough land surfaces drain energy by frictional
Verification of Scale Effect of Shallow Foundation in Determination of Bearing Capacity of Sandy Soil

The strength parameters for sandy soil are not depending mainly on level of stresses but they are a linear function of width (B) and embedment depth (u) of footing in Terzaghi's solution of bearing capacity. On the other hand, it was found by as called the non-classical solution conducted by Parkins and Madson (1997) that this relation is nonlinear and the bearing capacity for sandy soil is mainly relating to width and depth of foundation, which is known as scale effect. In this work a field model test was carried out on compacted sandy soil bed for different types of footings with different sizes and geometry for clarification of the two aforementioned approaches. It was found that two approaches are consistent and indicating the scale effect well for bearing capacity of sandy soil. On the other hand, from the field model tests results, it is found that the two determinations give overestimated bearing capacity values, especially for rectangular footing with length to width ratio more than 5 (L/B > 5) specially by Parkins and Madson's approach rather than the Terzaghi's equation.
Forwarding Strategies in ICN networking with a 5G Application

The recent growth of Internet traffic and the forecast of new applications on context of fifth generation of network technology (5G) leads to changes in the communication paradigm of future Internet. The Information Centric Networking (ICN) is a new architecture that can cope with the emerging pattern of communication based on named-data instead of their locations. In ICN architecture, Forwarding Strategies (FS) are a relevant feature to improve the network performance. Therefore, this article examines forwarding strategies for an ICN scenario with new 5G applications such as smart grids networking. The results are performed in the ndnSIM simulator and show that the delaybased forwarding strategy was the most suitable for low latency application. Keywords— Information Centric Networking, Named Data Networking, 5G Applications, Smart Grid Network.
Augmentation for Emotion Recognition Using a Conditional

Due to the lack of electroencephalography (EEG) data, it is hard to build an emotion recognition model with high accuracy from EEG signals using machine learning approach. Inspired by generative adversarial networks (GANs), we introduce a Conditional Wasserstein GAN (CWGAN) framework for EEG data augmentation to enhance EEG-based emotion recognition. A Wasserstein GAN with gradient penalty is adopted to generate realistic-like EEG data in differential entropy (DE) form. Three indicators are used to judge the qualities of the generated high-dimensional EEG data, and only high quality data are appended to supplement the data manifold, which leads to better classification of different emotions. We evaluate the proposed CWGAN framework on two public EEG datasets for emotion recognition, namely SEED and DEAP. The experimental results demonstrate that using the EEG data generated by CWGAN significantly improves the accuracies of emotion recognition models.
M ay 2 01 9 Topological quantum critical point in triple-Weyl semimetal : non-Fermi-liquid behaviors and instabilities

Jing-Rong Wang, Guo-Zhu Liu, ∗ and Chang-Jin Zhang 3, † Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions, High Magnetic Field Laboratory of the Chinese Academy of Sciences, Hefei, Anhui 230031, China Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026, China Institute of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
Analysis of Turbulent Flow Data Based on a Spectral Basis Representation

Through the increase of computing power, Large Eddy Simulations (LES) have become an invaluable design tool for industrial applications. However, the increasing amount of data that is generated in each simulation imposes new requirements on data analysis and compression. Two shortcomings of classical approaches are addressed in this study. First, due to storage limitations, only a limited set of quantities can be extracted from the simulation, but needs to be selected in advance. This requires a-prior knowledge about the flow structure and an experienced user. Second, classical post-processing starts after the simulation is completed. Nevertheless, many effects occur at an early stage of the simulation and can give useful insights into trends. Data I/O and computing time can be reduced, if they are monitored carefully. To address both issues, we apply a spectral basis approach for nonlinear dimensionality reduction to turbulent flow data. The spectral basis is derived from the eigenvectors of the Laplace-Beltrami (LB) operator which are optimal for representing smooth functions on a surface. The solution is projected to the LB basis to achieve a compact representation of the flow with a spectral separation. We demonstrate our approach on the LES of the turbulent flow through a HVAC duct that was studied intensively in the past, e.g. by Jäger, A. et al., 2008. Results are compared to a PCA analysis based on an extensive mesh study regarding their compactness and ability to identify large-scale and dominant flow features at an earlier state during the simulation.
Modeling of Interfacial Dynamics in Turbulent Two-Phase Flows Using the Level Set Method

In the present paper, the dynamics of turbulent two-phase flow is numerically predicted. The numerical modeling presented here is based on a new developed numerical method called Interfacial Marker-Level Set method, which coupled with the Reynolds averaged Navier-Stokes equations to predict the dynamical behavior of turbulent two-phase flow. The governing equations for time-dependent, axisymmetric and incompressible two-phase flow are described in both phases and solved separately using the control volume approach on structured cell-centered collocated grids. The transition from one phase to another is performed through a consistent balance of kinematic and dynamic conditions on the interface separating the two phases. The topological changes of the interface are predicted by applying the level set approach. The performance of linear and non-linear two-equation turbulence models is also investigated. Generally, the developed numerical method demonstrates a remarkable capability to predict the dynamical characteristics of complex turbulent two-phase flow in many industrial applications.
LETTER 2 PTS : A Two-Phase Task Scheduling Algorithm for MapReduce

For an efficient processing of large data in a distributed system, Hadoop MapReduce performs task scheduling such that tasks are distributed with consideration of the data locality. The data locality, however, is limitedly exploited, since it is pursued one node at a time basis without considering the global optimality. In this paper, we propose a novel task scheduling algorithm that globally considers the data locality. Through experiments, we show our algorithm improves the performance of MapReduce in various situations. key words: MapReduce, task scheduling algorithm, data locality
calor FTCS finite difference scheme GPGPU parallel computing for the heat conduction equation

In this paper the advantages of parallel computing are shown by solving the heat conduction equation in two dimensions with the forward in time central in space (FTCS) finite difference method. Two different levels of parallelization are consider and compared with traditional serial procedures. We show in this work the importance of parallel computing when dealing with large problems that are impractical or impossible to solve Vicente Cuellar Moro, Miguel Martín Stickle, Manuel Pastor Pérez Investigación 2 | Revista “Pensamiento Matemático” Volumen X, Número X, Xxx’XX, ISSN 2174-0410 them with a serial computing procedure. In the first section a summary of parallel computing approach is presented. Subsequently, the forward in time central in space (FTCS) finite difference method for the heat conduction equation is outline, describing how the heat flow equation is derived in two dimensions and the particularities of the finite difference numerical technique considered. Then, a specific initial boundary value problem is solved by the FTCS finite difference method and serial and parallel pseudo codes are provided. Finally after results are discussed some conclusions are presented.
Target Tracking with Pan-Tilt-Zoom Cameras

Although there are intrinsic advantages of using pan-tilt-zoom cameras their application in automatic surveillance systems is still scarce. The difficulty of creating background mo els for moving cameras and the difficulty of keeping fitted pose and optical geometrical projection mode ls ar key reasons for the limited use of pan-tilt-zoom cameras. Geometric calibration is a useful tool to ove rcom these difficulties. Once developed the background and projection models, it is possible to design system simulators and surveillance methodologies similarly to the ones commonly available for fixed c ameras. In this work we propose a method for PTZ camera auto-calibration over the c am ra’s zoom range. This method is based on the minimization of re-projection errors of feature poin ts detected in images captured by the camera at different orientations and zoom levels. Results obtained over both synthetic and real data show that a full zoom range, complete field of view, pan-tilt-z oom camera calibration is possible. Also in this work, a simulator capable of generating highly flexible, real data only, test scenarios with multiple events having ground truth motion is proposed. The final contributio n of the present work is a new methodology for automatic surveillance control that resorts to trackin g and prediction of targets’ trajectories to enhance event presence awareness performance. Th is met odology is presented and compared with existing ones, through experiments conducted over real data te sting scenarios with multiple events generated through our simulator. The results obtained reveal a gr eat efficiency and potential of our proposed method in event presence awareness in a given scenar io.
2.4 lm cutoff wavelength avalanche photodiode on InP substrate

An InP-based avalanche photodiode with a cutoff wavelength of 2.4 mm is reported. Type-II GaInAs-GaAsSb quantum wells latticematched to InP were used in the absorption region of the photodiode for long-wavelength absorption. The device exhibited multiplication gain in excess of 30 at room temperature and in excess of 200 at 225 Kelvin with dark current density near breakdown of less than 0.66 mA=cm.
Microscopy of ultracold fermionic lithium

A unique feature of ultra-cold lithium atoms is that their scattering properties are determined by a precisely controllable two-body scattering length, which can be tuned over a wide range by accessing a Feshbach resonance. Non-interacting and interacting Fermi gases can be created. At a critical temperature, the latter can undergo a phase transition to a superfluid, which allows one to study fermionic superfluidity in different interaction regimes with a single system. Its relevant length scale is the Fermi wavelength, which is typically 1μm. In this thesis, we present an experimental apparatus that enables microscopy of a trapped ultra-cold gas of fermionic lithium with an optical resolution of 700nm. In a time sequence of 15s, lithium atoms are cooled to degeneracy by applying optical cooling technics only. The cooling procedure occurs in several steps: First, atoms are Zeeman slowed and magneto-optically trapped. Then, they are transferred to a high finesse resonator dipole trap for a first evaporative cooling step. In order to do microscopy, atoms are transported in a running wave dipole trap over a distance of 27cm to a region of high optical access and cooled below the Fermi temperature. In the final position, atoms are placed between two microscope objectives, which are aligned along their common optical axis. In this configuration, optical micro dipole potentials can be formed by means of the first microscope objective. With the second objective, the created potentials as well as atomic absorption, fluorescence and phase signals of atoms which are trapped in these potentials, can be locally detected and quantitatively characterized. The state of the atomic ensemble is locally revealed by observable quantities such as the density and density fluctuations. Density profiles of non-interacting degenerate Fermi gases and Bose-Einstein condensates of tightly bound pairs will be shown. High resolution microscopy has allowed us to observe the local density and its fluctuations of a trapped non-interacting Fermi gas in-situ. In the case of a degenerate Fermi gas, a suppression of fluctuations has been observed, which is a direct manifestation of the Pauli exclusion principle and hence of the fermionic nature of the atoms. Because the atomic ensembles are in thermal equilibrium when being imaged, the fluctuation-
FABRICATION OF A SUPERHYDROPHOBIC NANOFIBRES BY ELECTROSPINNING

The focus of the present work is to fabricate and investigate the surface characteristics of a hydrophilic polycaprolactone (PCL) and superhydrophobic 1H, 1H, 2H, 2HPerfluorodecyltriethoxysilane (PFDTES)-modified polycaprolactone fibrous membranes through simple electro spinning techinique. The surface properties of the polycaprolactone fibrous were modified from hydrophilic to super-hydrophobic with an addition of a small amount (0.05 vol%) PFDTES solution into a mixed solvent of polycaprolactone and chloroform. The morphology of the nanofibirous was observed by scanning electron microscopy (SEM). The electrospun PFDTES-modified polycaprolactone fibrous showed a maximum water contact angle (WCA) of 158 0 due to increace in surface roughness when compare with the polycaprolactone fibrous roughness, which showed a maximum WCA of 81 0 and an average fiber diameter of 400-700 nm.
... M 0 , p 0 Visualization Block 1 ... Visualization Layer Visualization Block 2 ... Visualization Layer Visualization

Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on the cascade of regressors, e.g., CNN, which has shown promising results. Nonetheless, the cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end training, handcrafted features and slow training speed. To address these issues, we propose a new layer, named visualization layer, that can be integrated into the CNN architecture and enables joint optimization with different loss functions. Extensive evaluation of the proposed method on multiple datasets demonstrates state-of-the-art accuracy, while reducing the training time by more than half compared to the typical cascade of CNNs. In addition, we compare multiple CNN architectures with the visualization layer to further demonstrate the advantage of its utilization.
Computing dense and sparse matrix functions using GPUs

Matrix functions are relevant in many scientific computing applications, and several numerical methods for computing them have been proposed in the last years. We focus on the case that a function of a dense matrix of moderate size must be computed explicitly, but also cover the case of iterative computation of the action of a matrix function on a vector with Krylov methods. Accelerating these computations by exploiting GPU hardware is interesting to reduce the response time of simulations in large-scale scientific applications. The most common functions encountered in applications are the matrix exponential and the matrix square root. We focus on these, and evaluate the gain obtained with different algorithms whose implementation is susceptible of achieving high arithmetic intensity on GPUs. We show results comparing these implementations with thread-parallel CPU implementations.
Exploiting Convolutional Representations for Multiscale Human Settlement Detection

We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level information.
Predicting Movie Success Using Machine Learning Algorithms

Determining to see or not to see a movie can be a challenging decision. In the past, many people have decided to view or not to view a movie based on the reviews from critics. Other people have decided to view or not to view a movie based on what friends and others have said after they’ve watched it. Most people are interested in only watching a movie at the box office if it is deemed successful. This paper proposes a way to predict how successful a movie will be prior to its arrival at the box office. Instead of listening to critics and others on whether a movie will be successful, we have applied machine learning algorithms to make this decision. A total of five machine learning algorithms (K-nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB), Decision Trees (DT), K-means Clustering, and Graphing Theory) were applied to a dataset comprised of movie data from 2 different sources (IMDB and YouTube). This dataset contained 500 randomly selected movies and 28 features.
Implementation of Advanced IDS in Contiki for Highly Secured Wireless Sensor Network

The security threats for 6LoWPAN will be from within the 6LoWPAN network or from the IP network. The security techniques which are used for wireless environment in LoPAN devices as the major line of defense will be more susceptible to intruders due to the weak nature of LoPAN devices in wireless environments. Nodes could prompt insider assaults without being distinguished by any cryptography checking. An intrusion detection system (IDS)[1] is principally required as a moment line of resistance to screen the system operations and bring an alert up if there arises an occurrence of any inconsistency. This project examines potential security dangers in 6LoWPAN and audits the present countermeasures specifically the IDS-based answers for countering insider/inner dangers.
Effect of IBA and cutting types on rooting behavior of Myrica nagi ( Bay Barry ) during monsoon

The investigation was done to study the effect of Indole-3-butyric acid and cutting type on rooting behavior of Myrica nagi under various experiments laid out in Randomized Block Design (factorial) with three replications each. The experiment was conducted in monsoon on cutting types (apical, middle and basal) with response to IBA formulations of 0.50%, 0.75%, 1.00%, 1.25% and 1.50% were tried with 2% captan + 2% sucrosetalc in each treatment. The maximum sprouting (38.89%), callusing (54.44%) and live cutting (47.78%) was recorded in cuttings treated with 1.0% IBA + 2% captan + sucrosetalc (T 5 ) during moonsoon. As far as cutting types (apical, middle, basal) was concerned, basal portion responded better to sprouting, callusing and live cuttings than apical and middle cutting inMyrica nagi.
FEF 2575 and FEV 1 / FVC in Relation to Clinical and Physiologic Parameters in Asthmatic Children with Normal FEV 1 Values

Michael R. Simon, MDa, Vernon M. Chinchilli, PhDb, Brenda R. Phillips, MSb, Christine A. Sorkness, PharmDc, Robert F. Lemanske Jr., MDc, Stanley J. Szefler, MDd, Lynn Taussig, MDe, Leonard B. Bacharier, MDf, and Wayne Morgan, MDg for the Childhood Asthma Research and Education (CARE) Network of the National Heart, Lung, and Blood Institute a Division of Allergy and Clinical Immunology, William Beaumont Hospital, Royal Oak, MI and the Departments of Internal Medicine and Pediatrics, Wayne State University School of Medicine, Detroit, MI