Miracle-dz/UniFAR-FTUs huggingface dataset:
3 Vertex Attributes Indices Input Assembly Triangles Raster Operations Fragment Shading Rasterizer Vertex Shading Clipping / Culling Primitive Assembly Triangle Setup Framebuffer Geometry Processing Rasterization Primitive Processing Vertex Processing Fragment Processing

Fig. 1. Various scenes rendered by our software graphics pipeline in real-time on a GPU. (a) A smooth triangulation of the water surface in an animated ocean scene is achieved via a custom pipeline extension that allows the mesh topology to dynamically adapt to the underlying heightfield. (b) Scene geometry captured from video games like this still frame from Total War: Shogun 2 is used to evaluate the performance of our approach on real-world triangle distributions. (c) Many techniques such as mipmapping rely on the ability to compute screen-space derivatives during fragment shading. Our pipeline architecture can support derivative estimation based on pixel quad shading, used here to render a textured model of a heart with trilinear filtering; lower mipmap levels are filled with a checkerboard pattern to visualize the effect. Total War: Shogun 2 screenshot courtesy of The Creative Assembly; used with permission.
Particle Swarm Optimization Algorithms Applied to Antenna and Microwave Design Problems

Particle Swarm Optimization (PSO) is an evolutionary optimization algorithm inspired by the social behavior of birds flocking and fish schooling. Numerous PSO variants have been proposed in the literature for addressing different problem types. In this chapter, the authors apply different PSO variants to common antenna and microwave design problems. The Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithms are applied to real-valued optimization problems. Correspondingly, discrete PSO optimizers such as the binary PSO (binPSO) and the Boolean PSO with velocity mutation (BPSO-vm) are used to solve discrete-valued optimization problems. In case of a multi-objective optimization problem, the authors apply two multi-objective PSO variants. Namely, these are the Multi-Objective PSO (MOPSO) and the Multi-Objective PSO with Fitness Sharing (MOPSO-fs) algorithms. The design examples presented here include microwave absorber design, linear array synthesis, patch antenna design, and dual-band base station antenna optimization. The conclusion and a discussion on future trends complete the chapter. DOI: 10.4018/978-1-4666-2666-9.ch006
Assimilation in Land Surface Hydrology : A General Theory

There has been enormous developments in the theory of data assimilation ever since estimation theory was introduced. The majority of the applications have been directed towards oceanic and atmospheric research. Ocean and atmosphere prognostic equations are well established along with the proper (and formal) parameterization schemes. However, land surface hydrology su ers from a lack of formalization that makes such an application difcult. There have been numerous developments in land data assimilation (McLaughlin, 1995). In the recent past simple methods for surface temperature assimilation have been developed (Lakshmi, 2000) which take into account the sensitivity nature of hydrological variables (Lakshmi and Susskind, 2001) in order to adjust alll the hydrological variables associated with the water and energy budget. In this paper, we attempt to examine the basis for the development of data assimilation in hydrology, i.e. a close examination of the prognostic equations as well as a few examples of data assimilation for land surface variables. Finally, an interesting example of understanding and underscoring the importance of dependence of soil hydraulic properties on soil moisture is presented.
Sea Experiments on Autonomous tracking of Oil Spill using a Robotic Platform

The environmental impact of oil and gas leaks during the extraction, handling, and temporary storage of oil from offshore wells; or during the offshore transportation of oil via flow line, underwater pipeline, or tanker has never been more visible to the public – with the recent disaster in the Gulf of Mexico – yet the solutions currently available do not meet all the requirements in terms of performance and reliability. This research focuses on the problem of autonomous oil spill tracking in oceanic marine environments. We describe a sensor-based guidance, navigation, and control system (GNC) for oil spill tracking by autonomous surface vehicle (ASV) in unsteady and uncertain environments. First, we describe the design and development of a yacht-shaped ASV that can track spilled oil on the sea surface using data supplied by onboard sensors to control rudder angle and sail area for navigation. Secondly, we describe an autonomous ASV decisionmaking algorithm for target speed and direction based on a complete time history of the scanned area around the ASV by the oil detection sensor. Finally, we describe field experiments conducted at the Eigashima beach, Kobe to validate the performance of the ASV with regard to autonomous oil spill tracking using GNCS based on onboard sensors data for tracking artificial oil targets.
A testing method proposal to improve the reliability of lifetime tests applied on electricity meters

Electricity meters are devices responsible for the billing function, so they are considered critical elements of a smart grid and are designed to have high reliability and long service life under normal operating conditions. There are established standards that describe accelerated life tests and the life characteristics of a product. In this sense, this article presents a study on the application of these tests, highlighting the main elements that influence them. The study concludes with a proposal for an accelerated life test model that aims to improve the reliability, adding evidence, to the tests performed.
Improvement of Central Composite Design in Modeling and Optimization of Simulation Experiments

Simulation modeling can be used to solve real world problems. It provides an understanding of a complex system. To develop a simplified model of process simulation, a suitable experimental design is required to be able to capture surface characteristics. This paper presents the experimental design and algorithm used to model the process simulation for optimization problem. The CO2 liquefaction based on external refrigeration with two refrigeration circuits was used as a simulation case study. Latin Hypercube Sampling (LHS) was purposed to combine with existing Central Composite Design (CCD) samples to improve the performance of CCD in generating the second order model of the system. The second order model was then used as the objective function of the optimization problem. The results showed that adding LHS samples to CCD samples can help capture surface curvature characteristics. Suitable number of LHS sample points should be considered in order to get an accurate nonlinear model with minimum number of simulation experiments. Keywords—Central composite design, CO2 liquefaction, Latin Hypercube Sampling, simulation – based optimization.
A New Approach to Detect Aircrafts in Remote Sensing Images Based on Corner and Edge Information Fusion

This paper presents a new method to detect aircrafts in remote-sensing image,in which the edge and corner features are used.Firstly,the whole image is operated by canny edge detector and then the OTSU binarization is employed to segment it.By these preprocessing procedures,the main information in the image is enhanced.Then Harris corner detection is conducted on the image,finding the areas where the corner quantity meets the standard.Finally the false alarms are removed by a region-growing clustering method.Experiments on 60 remote-sensing images are performed,including 238 airplanes.Totally 220 airplanes are detected,18 airplanes are missed and 48 false alarms are reported.The experimental results demonstrate that the newly proposed approach can effectively solve aircraft detection problem in complex background,and have a good detection performance.
New CXCR1/CXCR2 inhibitors represent an effective treatment for kidney or head and neck cancers sensitive or refractory to reference treatments

Clear cell Renal Cell (RCC) and Head and Neck Squamous Cell Carcinomas (HNSCC) are characterized by a pro-angiogenic/pro-inflammatory context. Despite conventional or targeted therapies, metastatic RCC and HNSCC remain incurable. Alternative treatments to reference therapies (sunitinib, a multi tyrosine kinase inhibitor for RCC or cisplatin for HNSCC) are urgently needed on relapse. Here, we described the relevance of targeting the ELR+CXCL cytokines receptors, CXCR1/2, for the treatment of these two cancer types. Methods: The relevance to patient treatment was evaluated by correlating the ELR+CXCL/CXCR1/2 levels to survival using online available data. We report herein the synthesis of new pharmacological inhibitors of CXCR1/2 with anti-proliferation/survival activity. The latter was evaluated with the XTT assay with leukemic, breast, RCC and HNSCC cell lines. Their relevance as an alternative treatment was tested on sunitinib- and cisplatin- resistant cells. The most efficient compound was then tested in a mouse model of RCC and HNSCC. Results: RCC and HNSCC expressed the highest amounts of CXCR1/2 of all cancers. High levels of ELR+CXCL cytokines (CXCL1, 2, 3, 5, 6, 7, 8) correlated to shorter survival. Among the 33 synthesized and tested molecules, compound C29 reduced ELR+CXCL/CXCR1/2-dependent proliferation and migration of endothelial cells. C29 exerted an anti-proliferation/survival activity on a panel of cancer cells including naive and resistant RCC and HNSCC cells. C29 reduced the growth of experimental RCC and HNSCC tumors by decreasing tumor cell proliferation, angiogenesis and ELR+/CXCL-mediated inflammation. Conclusion: Our study highlights the relevance of new CXCR1/2 inhibitors for the treatment of RCC or HNSCC as first-line treatment or at relapse on reference therapies.
Abtin Namiranian 3 D Simulation of a 5 MW Wind Turbine

In the present work, the influence of turbulence and gravity forces on the tower and the rotor of a 5MW onshore wind turbine has been investigated. A full geometry of the turbine has been designed and simulated in a virtual wind farm and then the fluid loads are imported into the structural part by fluid structure interaction (FSI) method. The final results are shown that the gravity force is significantly higher than turbulence loads and should be considered in designing of the large wind turbines.
Opportunities and Challenges of Online Monitoring Systems for Sodium Leak Detection with Internet of Things

In recent years, Internet of Things (IoT) has attra c ed a large amount of research interest due to the remarkable development of Information Communication Technologies (ICT) [1,2]. The IoT allows users to integrate the communication network into various th ings such as sensors, actuators blend with the environme nt around us and new services and applications, such a s a smart power grid [3], smart building energy management [4], and connected vehicles [5] etc., ar e enabled by sharing a lot of information. One of the promising application domains of IoT is online monitoring system for leak detection. From a small size thermal-hydraulic experimental facility to huge size pipeline networks (gas, oil, steam, and w ater etc.), leakage might occur during its lifetime and it is significant to detect such leakage. Especially, lea k detection and corrective action are indispensable t o Sodium-cooled Fast Reactor (SFR) or sodium thermalhydraulic test facility using sodium as a coolant, since accurate leak detection is necessary to guarantee s af operation of the systems. In this paper we have focused on design issues considering integration of IoT into online monitori ng system for sodium leak detection. This paper is organized as follows. In Section 2, some preliminar ies about IoT and leak detection systems are provided. Issues on online monitoring systems with IoT are provided in Section 3 and the conclusion is given i n Section 4.
Hierarchical behavior planning in distributed decision making systems

Distributed decision making has become the predominate methodology of handling autonomous systems for our researchers. Behavior planning involves deciding at a certain time what each distributed decision making system should do and where it should go for the benefit of a certain objective. It deals with task coordination and resource allocation. In this dissertation, we focus on mathematically modelling and analysis the behavior planning in distributed decision making systems. We construct an distributed dynamic resource environment where each resource bears its individual properties. In the planning problem, there are two types of distributed decision making systems, Centers and Agents, which have different sensing, communication, and consuming capabilities. To make plans for these systems so that the total resource consumption is maximized, first we propose Instruction Planner, which applies the Lagrangian Relaxation method to relax the capacity constraints and decompose the planning problem. Meanwhile, we develop the “earliest expiration date” scheduling algorithm to make the results feasible so that no capacity constraints are violated in all the plans. Next in order to tack the coupling issue between scheduling and resource allocation more efficiently, we present Incentive Planner. In Incentive Planner, we functionally distribute the planning problem into three levels. In each level, we define its independent function and objective. Then Anticipation Theorem and Incentive Theorem are introduced and proved so that the decisions
Wireless Information and Energy Transfer for Outdoor to Indoor Multicarrier SUDAS

We study the resource allocation in an amplify and forward (AF) relay assisted orthogonal frequency division multiple access (OFDMA) system employing a shared user equipment (UE)-side distributed antenna (SUDAS). It simultaneously utilizes both unlicensed (in the UE to AF relay link) and licensed frequency bands (AF relay to BS link), hence exploiting the advantages of both MIMO over licensed band with its favorable propagation conditions and millimeter wave band with larger bandwidths, to improve achievable rate of the future 5G communication systems. The users in the system have no energy of their own, so for transmitting information to the base station in the uplink, they first need to harvest energy from the AF relays in the downlink. For this purpose, we present a time switching based MIMO-OFDMA (TS) protocol. To explore the system performance limits, we formulate a non-convex optimization problem to maximize the end-to-end achievable information rate of the system. Simulation results illustrate that the proposed system achieves a significant performance gain over the conventional systems exploiting only licensed bands.
The Functional Design of Scientific Computing Library

Owl is an emerging library developed in the OCaml language for scientific and engineering computing. It focuses on providing a comprehensive set of high-level numerical functions so that developers can quickly build up any data analytical applications. After over one-year intensive development and continuous optimisation, Owl has evolved into a powerful software system with competitive performance to mainstream numerical libraries. Meanwhile, Owl’s overall architecture remains simple and elegant, its small code base can be easily managed by a small group of developers. In this paper, we first present Owl’s design, core components, and its key functionality. We show that Owl benefits greatly from OCaml’s module system which not only allows us to write concise generic code with superior performance, but also leads to a very unique design to enable parallel and distributed computing. OCaml’s static type checking significantly reduces potential bugs and accelerates the development cycle. We also share the knowledge and lessons learnt from building up a full-fledge system for scientific computing with the functional programming community.
Non-homogeneity validation for hyper-parameters optimization of human brain image regression approaches.

Functional MRI brain map is characterized by precise positioning,but low signal-to-noise ratio and large volume of data.We analyse the hyper-parameter optimization of general regression model on fMRI brain data,based on the non-homogeneity validation method.To acknowledge the discrepancy in ability of different regression approaches(including ridge regression,support vector regression and Elman Recurrent Neural Network) we compare them over every feature rating of external environment.The original data is obtained from PBAIC2006.The experiment results show that this method has good stability and generalization;also it can be used in the field which lacks of knowledge of the relevant fields.At the same time,we find the linear method is simple,effective in the fMRI regression mission,and has higher predictive ability but lower calculating costs than other methods under many feature rating of external environment.
A PDE approach for automatic thickness estimation using partial volume classification

Isotropic (c) Anisotropic ; (b) shows iso-contours of temperature and (c) shows larger temperature change in PVC voxels near 7912 A PDE approach for automatic thickness estimation using partial volume classification Easswar Balasubramaniam, Anand Joshi, David Shattuck, and Richard Leahy Electrical Engineering, University of Southern California, Los Angeles, CA, United States, Neurology, University of California, Los Angeles, CA, United States
A computational method for detection of structural variants using Deviant Reads and read pair Orientation: DevRO

Background Next generation sequencing (NGS) technology has made it possible to perform high-resolution screens for structural variants. Computational methods for detection of structural variants utilize paired-end mapping information, depth of coverage, split reads, or some combination of such data. The available methods are particularly designed to detect structural variants in single genomes or multiple genomes in a pairwise manner. The aim of this study was to develop a bioinformatics pipeline for detection of large structural variants using multiple pooled populations. Results Here we describe the method “DevRO”, developed to enable identification of structural variants using short insert paired-ends and long-range mate-pairs. DevRO uses paired-end mapping information from both types of libraries for identification of inversions, deletions and duplications followed by read depth information to screen for copy number variants. DevRO can detect structural variants in multiple populations without the need for pairwise comparisons. It uses a combined approach based on (i) paired-end mapping and (ii) depth of coverage that gives power to the study as compared to traditional methods that are based on either of these. DevRO is also designed to detect deletions in the reference assembly, which is an added functionality as compared to available methods. Conclusion We report a bioinformatics pipeline “DevRO” for detection of structural variants using paired-end mapping and depth of coverage methods tested on sequencing reads from multiple pooled rabbit populations. This method is useful when large numbers of populations have been re-sequenced as compared to traditional methods that can detect structural variants in a pairwise manner.
Detecting Facial Wrinkles based on Gabor Filter using Geometric Constraints

Facial wrinkles play vital feature of old age human skin which might be merged in many image based applications associated with aging. A wrinkle on face is 3D feature of the skin and seem as refined to not continuities as well as no cracks in encompassing texture of the skin. Though, wrinkles on the facial will simply be detected by clarification/ achievement conditions in 2D pictures because of the precise for finding nature of skin deep properties and its texture on the surface. Existing approached present old age skin for image-based analysis supported the study of wrinkles as texture and not as curved is a not continuity / crack option. Our proposed system contains settled algorithmic supported image morphology and Gabor filters to advance outcome of wrinkles detection. The proposed system contains image Gabor filter bank and hessian filter to focus on the skillful curved is a not continuities in texture of skin caused by wrinkles. Main purpose of our proposed system is to improve detection rate and to minimize miss ratio. Experiments are conducted on sets of different images and results are compared with those of existing system. Experiments show that the proposed algorithm provides visually better results than existing system results.
ANALYTICAL SEISMIC VULNERABILITY ASSESSMENT OF LOWRISE STEEL MRFs

The Global Earthquake Model (GEM; http://www.globalquakemodel.org/) is a grand effort to proffer a comprehensive open source tool for large scale loss assessment studies. For this to be accomplished, an analytical seismic vulnerability assessment methodology needs to be developed that links ground shaking with repair cost for a building class. The test bed for the present study is a set of low/mid-rise steel moment-resisting frames (SMRFs) designed for high seismicity US regions and selected appropriately so as to represent all important aspects within their class. The structural analysis was performed using Incremental Dynamic Analysis (IDA). On that premise, the selection of a single Intensity Measure (IM) to parameterize IDA results and, eventually, vulnerability curves needs to be tackled. It was demonstrated that scalar IMs can have an overall satisfactory performance. Once the uncertain structural response is defined in terms of interstory drifts and floor accelerations, across a wide range of intensities, the methodology proceeds to the vulnerability estimation and consequently to loss assessment. The end product of this study is a high-quality set of vulnerability curves whose weighted moments are taken as the uncertain vulnerability function of the investigated building class.
Effects of Swimming Exercise and Joint Mobilization on HSP 70 Levels in Osteoarthritic Rats

Methods: : MIA was injected into SD rats to induce osteoarthritis. These rats were divided into 4 groups: control group (n=30), no further treatment after the MIA injection ; experimental group I(n=30), performed swimming exercise after the MIA injection experimental group II (n=30), underwent joint mobilization after the MIA injection and experimental group III (n=30), performed swimming exercise and underwent joint mobilization after the MIA injection. For the histologic and pathophysiologic evaluation, safranin-O staining and for the immunohistochemical evaluation, the expression of HSP 70 in articular cartilage was analyzed 1, 7, 14, and 21 days after the MIA injection.
Chinese Sentence Similarity Computing Based on Semantic Dependency Relationship Analysis

Sentence similarity computation is very important in all the fields of Natural Language Processing.In Multi-document Summarization Technology,sentence similarity computation is a key problem.As we know,a Chinese sentence can be presented by many kinds of style,if we want to describe what a sentence means,we should dip into the semantic level and consider about the dependency structure.In this paper,we applied a method that based on semantic dependency relationship analysis to compute sentence similarity,and the experiment result of this method is satisfied.