Miracle-dz/UniFAR-FTUs huggingface dataset:
A MinMax Framework for Robust Design Optimization

1. Abstract Presented in the paper is a comprehensive methodology to cast and deploy moment-based Robust Design Optimization. Robust objectives and constraints can be formulated by means of worst-case bounds on two risk metrics, i.e. quantile and tail conditional expectation of the output distributions. Such bounds can then be refined on the basis of partial knowledge available for the specific problem at hand or by adding higher moment information. The required output moments are estimated by two related reduced quadrature techniques, which use the first four moments of the input distributions. As demonstrated by means of an aircraft sizing test case, the methodology is of immediate implementation and deployment, and features a convenient trade-off in cost and accuracy. It is therefore thought to be very promising for industrial applications.
Graph Neural Networks and its applications

Data structured as graphs exists in multiple domains like biochemistry, image processing, recommender systems and social network analysis to name a few. Several approaches to train machine learning models on graph structured data by using preprocessing techniques exist but they lack the flexibility to completely adapt to the dataset and task at hand. Graph Neural Networks allow to create an end to end machine learning model that is simultaneously trained to learn a representation of graph structured data and to fit a predictive model on it. Graph Neural networks can be applied to tasks that range from clustering or visualization, to classification or regression on graph data, and they can also learn a representation at the node or graph level. They achieved state-of-the-art performance on semi-supervised learning and supervised graph classification. Semi-supervised learning is when a graph contains some labeled nodes and their labels are extended to the rest of the nodes of the graph, for example to classify Reddit posts. Supervised graph classification or regression is applied for predicting the class or the value of some attributes of a whole graph or a subgraph. This work will explore some of the most prominent Graph Neural Network variants and apply them to two tasks: approximation of the community detection Girvan-Newman algorithm and compiled code snippet classification.
ANALYSIS OF GENOME-WIDE DATA ON TRANSCRIPTION FACTOR BINDING AND THEIR TARGET GENE REGULATION: A CASE STUDY OF LIVER X RECEPTORS

Chromatin immunoprecipitation combined with massive parallel sequencing (ChIP-seq) facilitates a genome-wide detection of transcription factor binding sites. In our study, we have built a pipeline for ChIP-seq data analysis, which is comprised of the recently published programs for sequence read alignment and peak finding and in-house developed data analysis tools supplemented with various visualization schemes for summarization of results. As examples from the pipeline, we present in detail two methods developed for in-house use: 1) detection and comparison of common binding locations across two or more samples and 2) a novel application of segmentation in finding clusters of transcription factor binding locations and regulated target genes. We applied the developed methods to the analysis of ChIP-seq data of the nuclear receptors liver X receptor (LXR) α and β chromatin binding and target gene expression in human macrophage-type cells.
Role, definition and dimensions of incomplete construction contract documents

The extent to which documentation incompleteness affects construction is well-recorded in a plethora of anecdotes and data detailing inefficiencies generated by letting contracts based on minimum information (Hughes and Barber, 1992). The inability to list entire contingency ranges, reduced design fee levels, and limited time for document development, all produce documents lacking exhaustive descriptions of parties’ rights and obligations for every contingency (Murray and Langford, 2003; Andi and Minato, 2003; Murdoch and Hughes, 2008). Nevertheless, many parties intentionally produce documents wherein production information, contingencies, obligations, and future duties are intentionally left unspecified, to achieve efficient organization of exchange and minimize transaction governance costs (Crocker and Reynolds, 1993; Al-Najjar, 1995; Saussier, 2000). Researchers call attention to theory focusing on the feasibility of completely describing contingencies and sensitivities to all contract-period events, with limited emphasis on the role of documentation incompleteness (Al-Najjar, 1995). This study, informed by Al-Najjar’s (1995) approach to investigating this incompleteness function, addresses this shortcoming. The approach requires that contractual relations governance be conceptualized as: combining instruments to minimize costs by evaluating transaction attributes. This conceptualization guides the questions to be asked about incomplete documentation. It is suggested that researchers should be concerned with reasons for incompleteness. Of particular interest here is: defining degrees of document incompleteness and understanding influencing factors. Also, are there situations when parties might choose incomplete documents over more easily produced detailed documentation? The TCE framework is considered best for systematically exploring issues surrounding construction contract incompleteness and answering these questions. Accessing a contract database, to review relationships between a regular building promoter and its builders, should allow greater understanding of variability in documentation incompleteness for the chosen project type. These results should supplement limited theoretical guidance on the role of incompleteness and contribute to developing consistent measures for construction documentation incompleteness and its impacts.
Thermo-mechanical problems: Eulerian-Lagrangian approach within Kratos-GiD framework

Fluid-structure interaction and generally coupled problems define the “hot-spots” in the computational mechanics. Resolution of those requires both the tools for modeling/visualization and the efficient solvers. Here we present a combination of GID-pre/post-processor with Kratos-Multi Physics tool. In particular we present a coupled Eulerian-Lagrangian algorithm for solution of complicated thermo-mechanical problems involving thermo-mechanical interaction between an object (or viscous fluid) and the ambience. In the proposed approach the Lagrangian domain is moving on top of the fixed Eulerian mesh, following the core concept of embedded methods. The mechanical interaction is thus taking place on the Eulerian mesh, separated into real, fictitious, and interface sub-domains by the image of the Lagrangian domain onto Eulerian mesh. This approach permits to enjoy all the advantages of the Lagrangian method with respect to interface definitions, while save the computational effort associated with the re-meshing on a major part of the computational domain, that is treated by the Eulerian formulation. Using compressible model for the ambience leads to two important advantages. First, it permits consideration of thermal problems without restriction upon temperature gradients (that exists when Boussinesq hypothesis-based solvers are used). On the other hand, compressibility of the ambience eliminates convergence problem of the FSI and permits the application of standard Dirichlet-Neumann coupling. However the results presented here are obtained by using one-way Dirichlet coupling, justified by the physical properties of the constituents in the problem of interest. From the point of view of pre/post-processing,. our approach has a number of algorithmic peculiarities such as necessity of outputting simultaneously both the Eulerian and Lagrangian meshes as well as possibility of “disabling” parts of the domain. Some of these aspects are successfully tackled with the present version of GiD, while simplification of the implementation of the others still needs to be considered.
The Effect of Foreign Direct And Portfolio Investments on Stock Market Returns in E7 Countries

As a result of financial liberalization boundaries between financial markets have been disappeared and capital movements have accelerated which leads to rise of financial flows from developed to developing countries. Foreign investments can be realized as foreign direct investments indicating the ownership of physical assets in another country or as portfolio investments in assets for the sake of maximum return at minimum risk. The aim of this paper is to investigate the effects of foreign direct and portfolio investments on stock returns in E7 countries (Brazil, China, Indonesia, India, Mexico, Russia, Turkey) for the time period from 2005 to 2016 using panel data analysis. Results indicate a significant and positive relation between foreign portfolio investments and stock market returns whereas a negative relation between foreign direct investment and stock market returns. It is concluded that foreign portfolio investments have very crucial effect in increasing the stock returns in E7 countries.
Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data

Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91.
EFFECT OF SOME CHEMICAL MUTAGENS ON PSEUDOMONAS AERUGINOSA H3 ALGINATE PRODUCTION

This study was aimed to investigate the effect of chemical mutagens on the ability of Pseudomonas aeruginosa H3 in alginate production, using N-methyl-N-nitro-Nnitrosoguanidine (MNNG) and Mitomycin C (MMC). It was achieved by incubating fresh cultures of Pseudomonas aeruginosa H3 with MNNG and MMC separately for 60min. , then 100μl of each culture was spread on LB agar plates and incubated at 37 °C for 24 hrs. From the survival colonies that arise after subjection to lethal effect of mutagen that causes 90% killing or more, thirty colonies were selected randomly and screened for their ability in alginate production. Results showed that chemical mutagenesis using MNNG and MMC causes an increase in the ability of P. aeruginosa H3 in alginate production through out the presence of some alginate over producer mutants. Alginate concentration produced by the most highly over producer mutants H3R78, and H3R97 was 160mg/ml after subjection to MNNG and MMC, respectively in comparison with 70mg/ml produced by the wild type of Pseudomonas aeruginosa H3.Results of FT-IR spectroscopy showed that there are no any structural differences in the chemical structure of alginate produced by the wild type of P. aeruginosa H3 and the over-producer mutants H3R78 and H3R97.
O ct 2 01 9 Direct Laser Ion Acceleration and Above-Threshold Ionization at Intensities from 10 21 W / cm 2 to 3 × 10 23 W / cm 2

Calculations on the dynamics of ions and electrons in near-infrared laser fields at intensities up to 3× 10 W/cm are presented. We explore the acceleration of ions in a laser focus by conservation of canonical momentum during ionization events and by the ponderomotive force in the f /1 focal geometry required to reach such intensity. At intensity exceeding 10 W/cm, highly charged ions are expelled from the laser focus before they can interact with the laser pulse at peak intensity, decreasing the predicted ionization yields of deeply-bound states. We consider the interaction of a tightly-focused, f /1 laser pulse with krypton at an intensity of 3×10 W/cm and a pulse duration of 140 fs. We find that the ions and electrons are accelerated to energies in excess of 2 MeV/nucleon and 1.4 GeV, respectively. Ponderomotive expulsion of the parent ions decreases the total number of ultra-relativistic ATI electrons produced by tunneling ionization from the K-shell states of krypton but does not change their energy spectrum.
Accelerated Machine Learning as a Service for Particle Physics Computing

Large-scale particle physics experiments face challenging demands for highthroughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that requires minimal modification to the current computing model. As an example, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC. Using Microsoft Azure Machine Learning deploying Intel FPGAs to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework deployed as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600-700 inferences per second using an image batch of 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.
SATELLITE BASED MONITORING OF FOREST RESOURCES COMPLIANT WITH REDD+ AND ZERO DEFORESTATION

Natural Forests provide valuable ecosystem services and at the same time, direct and indirect human impacts affect these ecosystems. Especially in tropical countries with a high percentage of remaining natural forests, human induced land use change is one of the main drivers for increasing deforestation rates. The drivers, time scale and impact on forests are quite different between countries, but the necessity of a consistent, trustworthy and accurate monitoring of these resources is relevant to manifold stakeholders such as in the domain of land governance. The application of satellite imagery can greatly facilitate this need. The large area coverage and high spatial resolution of newly launched optical and radar satellite systems offer the opportunity to retrieve forest related information on a wall-to-wall basis. Satellite data based forest monitoring systems can provide evidence of most recent forest area and land cover changes with the capability to map historic events by incorporating archived data sets. Mapping forest extent and changes thereof is a crucial information requirement for managing the commons and customary land, forests and natural resources. The paper presents requirements, challenges and a methodological approach to implement National Forest Monitoring Systems (NFMS) at country level by incorporating user needs.
Machine learning climate variability

While comprehensive Earth System Models (ESMs) are the best tools available to understand climate system details, including its variability and ability to predict changes in the system, the immense computational burden associated with them opens up the possibility that reduced-order dynamical representations of such ESMs will enable and facilitate their use in a broad range of applications. By building on recent developments in deep neural networks, this work employs a hierarchy of such networks, ranging from multilayer perceptron to convolutional long short-term memory networks, to develop reduced-order dynamical descriptions of the spatio-temporal variability of temperature in a particular setting of a popular ESM. Upon evaluating such reduced descriptions from the point of view of predictive skill and predictability of climate more generally, it is evident that the more sophisticated of these network architectures is able to perform skillfully at lead times of up to about a year. Furthermore, this approach succeeds in capturing features of climate that have a basis in climate dynamics and can control predictability, further validating the reduced-order dynamical description. Related issues and further perspectives are also considered.
A Light-weight Approach to Online Detection and Classification of Interference in 802.15.4-based Sensor Networks

With a rapidly increasing number of devices sharing access to the 2.4 GHz ISM band, interference becomes a serious problem for 802.15. 4-based, low-power sensor networks. Consequently, interference mitigation strategies are becoming commonplace. In this paper, we consider the step that precedes interference mitigation: interference detection. We have performed extensive measurements to characterize how different types of interferers affect individual 802.15. 4 packets. From these measurements, we define a set of features which we use to train a neural network to classify the source of interference of a corrupted packet. Our approach is sufficiently light-weight for online use in a resource-constrained sensor network. It does not require additional hardware, nor does it use active spectrum sensing or probing packets. Instead, all information about interferers is gathered from inspecting corrupted packets that are received during the sensor network’s regular operation. Even without considering a history of earlier packets, our approach reaches a mean classification accuracy of 79.8%, with per interferer accuracies of 64.9% for WiFi, 82.6% for Bluetooth, 72.1% for microwave ovens, and 99.6% for packets that are corrupted due to insufficient signal strength.
AN EFFECTIVE NONREDUNDANT SAMPLING REPRESENTATION FOR PLANAR NEAR-FIELD ANTENNA MEASUREMENTS

A nonredundant sampling representation is described for planar near-field antenna measurements. The minimum number of samples required is deduced from the number of unknowns required to solve the linear system of equations considering spherical expansion of radiated antenna fields. The antenna multipole order is computed in the beginning in relation with the size of the antenna following the angular spacing which is then mapped to the planar surface by utilizing the distance between the AUT and the scan plane. The acquired near field on the planar meshed grid is processed using the plane wave based Fast Irregular Antenna Field Transformation Algorithm (FIAFTA). Examples are presented afterwards on the applicability of the proposed sampling representation.
Large-Scale Molecular Dynamics Simulations of Homogeneous Nucleation of Pure Aluminium

Despite the continuous and remarkable development of experimental techniques for the investigation of microstructures and the growth of nuclei during the solidification of metals, there are still unknown territories around this topic. The solidification in nanoscale can be effectively investigated by means of molecular dynamics (MD) simulations which can provide a deep insight into the mechanisms of the formation of nuclei and the induced crystal structures. In this study, MD simulations were performed to investigate the solidification of pure Aluminium and the effects of the cooling rate on the final properties of the solidified material. A large number of Aluminium atoms were used in order to investigate the grain growth over time and the formation of stacking faults during solidification. The number of face-centred cubic (FCC), hexagonal close-packed (HCP) and body-centred cubic (BCC) was recorded during the evolution of the process to illustrate the nanoscale mechanisms initiating solidification. The current investigation also focuses on the exothermic nature of the solidification process which has been effectively captured by means of MD simulations using 3 dimensional representations of the kinetic energy across the simulation domain.
Research for Development of Value Added Services for Connected TV

Recent trends on emerging market [1], [2] shows that the connected TV is becoming very popular in developing countries like India, Philippines. Connected TV can be described as an Internet enabled TV. One such product, referred as Home Infotainment Platform (HIP) [3], combines the functions of a television and a computer, by allowing customers to use their television sets for low-bandwidth video chats and access internet websites. It is now commercially available in India [4] and Philippines [5]. The research presented in this thesis is motivated by the business need to implement some value added services, hereinafter referred as VAS, on top of this product which can work as the Unique Selling Point for the above mentioned product. Main motivation behind the research described in this thesis is to develop some interesting VAS for the product of the company using frugal computing. As a result of that the thesis is not very focused to a particular problem, instead provides a frugal solution for several goals need to be achieved to develop the VAS for the global product. Some of such planned VAS for those connected TVs are video conferencing, Video encryption and watermarking, context based web and TV mash up, video summarization and Electronic Program Guide (EPG) for cable feed channels [8]. This thesis is devoted to the development of those above mentioned VAS for connected TV. As all these services need to be developed on an embedded platform, the primary task is to realize the required video CODEC on the target DSP platform. As H.264 is adjudged as the best video CODEC of the day [9] because of its compression efficiency, video quality, and network friendliness, we have developed most of the VAS on top of H.264 CODEC. Security can be ensured by either encrypting the video or putting a watermark in the video. Context can be extracted at top level by recognizing the channel the user is viewing and then getting the relevant information from the website of that particular channel. On the other hand, textual information in a TV show provide some information related to the show at any particular instant of time.