Miracle-dz/UniFAR-FTUs huggingface dataset:
Applications of Seafloor Compliance Measurements in the Faroes-Shetland Basin

Seafl oor compliance is a geophysical prospecting method that uses the seafl oor motion under ocean waves to determine the shear modulus structure of the subsurface. Seafl oor compliance measurements are of interest to petroleum exploration because they are sensitive to fl uid-fi lled regions and because their source is always “on”, which should permit continuous monitoring of regions of interest during exploitation. We review the seafl oor compliance method and previous studies made using the method, and then calculate the sensitivity of seafl oor compliance measurements to sub-surface features of interest in the Faroes-Shetland Basin, particularly to sub-basalt sediments. We fi nd that seafl oor compliance measurements will be sensitive to shear velocity structure from 50 meters to as much as 6 km beneath the seafl oor. They will be sensitive to sub-basalt sediments almost everywhere where the water depth is greater than 0.9 km and in some shallower areas directly south of the Faroe Islands. Assuming sub-basalt sediments start 3.5 km beneath the seafl oor, compliance measurements will constrain their depth and thickness to within 0.1-0.2 km if the water depth is 1 km or more. For the same water depths, if the depth limits of the sub-basalt sediments are already well constrained, seafl oor compliance measurements will constrain their shear velocities to within approximately 0.1 km/s. For water depths greater than 0.05 km, seafl oor compliance measurements should constrain the average shear wave velocity in sediments above the basalt fl ows to within 0.01 km/s. Faroe Islands Exploration Conference: Proceedings of the 1st Conference. Annales Societatis Scientiarum Færoensis, Supplementum 43, Tórshavn, 2005: 32-43 Applications of Seafloor Compliance Measurements in the Faroes-Shetland Basin 33 ticularly sensitive to fluid-rich targets at depth. Much progress has been made recently in modeling seafloor compliance and in understanding its properties (Crawford et al., 1998; Latychev and Edwards, 2003; Hulme et al., 2004). The method has its limitations, in particular a relatively low vertical resolution, but these can be overcome by combining with seismic data (Hulme et al., 2004; Roberts, 2004). In this paper, we review the state of the art in seafloorcompliance measurements, modeling, and data inversion, and investigate what compliance measurements can contribute to studies of the Faroes-Shetland Basin. We first review the seafloor compliance method describe what it is sensitive to and how it is modeled. We then present the data inversion technique we use to calculate subsurface structure from seafloor compliance data. We estimate the sensitivity of seafloor compliance measurements to subsurface features of interest in the Faroes region by applying our data inversion to synthetic Faroes-area compliance data. We calculate the depths to which compliance will be sensitive throughout the Faroes region and identify where compliance measurements can be used to study sub-basalt sediments.
Unification of Multisets with Multiple Labelled Multiset Variables

We look into the problem of unifying multisets containing (first-order) terms and multiple multiset variables. The variables are labelled, meaning that a unifier that places a term in a multiset variable Mi is different from another that places a term in a multiset variable Mj , for i 6= j. We describe a sound, complete, and terminating algorithm for computing the set of all possible unifiers, and analyse its complexity. We also prove an input pre-processing step that avoids the computation of less general unifiers.
State-ofthe-art Object Detection Algorithms

Object detection is one of the fundamental task in computer vision. In this report, I present three state-of-the-art algorithms, Integral Channel Features (ICF) [1] Discriminatively Trained Part Based Models (DPM) [4], and Rich Feature Hierarchies for Convolutional Neural Networks (RCNN) [5]. DPM, which uses a deformable model and latent SVN for training, is the most widely used alorithm for object detection. ICF, which is fast and good on detecting pedestrians, combines different simple transformations and uses AdaBoost for training. RCNN, which uses selective search and convolutional neural network, is currently the best algorithm for PASCAL VOC dataset,
A Recurrent Deep Architecture for Quasi-Optimal Feedback Guidance in Planetary Landing

Precision landing on large planetary bodies is an important technology that enables future human and robotic exploration of the solar system. For example, over the past decade, landing systems for robotic missions have been developed with the specific goal of deploying robotic agents (e.g. rovers, landers) on the planetary surface (e.g. Mars, Moon). Considering the strong interest for sending humans back to the Moon within the next decade, the landing system technology will continue to progress to keep up with the demand for more stringent requirements. Indeed, more demanding planetary exploration requirements implies a technology development program that calls for more precise guidance systems capable of delivering rovers and/or landers with higher and higher degree of precision. In this paper we design, test and validate a deep Recurrent Neural Network (RNN) architecture capable of predicting the fuel-optimal thrust from sequence of states during a powered planetary descent. Here, the principle behind imitation learning (supervised learning) are applied. A set of propellant-optimal open loop landing trajectories are computed using direct transcription methods (e.g. Gauss Pseudo Spectral methods). Such sequences comprise the training set (i.e. the teacher) employed during the learning phase. A Long-Short Term Memory (LSTM) architecture is employed to keep track of what has entered the network before and use such information to better predict the output. The RNN-LSTM architecture is trained validated and tested to evaluate the performance predictive performance. Finally, the results of a Monte Carlo simulations in Moon landing scenarios are provided to show the effectiveness of the proposed methodology.
Factors Explaining the Distribution and Site Densities of the Neosho Madtom ( Noturus placidus ) in the Spring River , Missouri

The Neosho madtom, a Federally-listed threatened species endemic to the Arkansas River system, is presently restricted to selected mainstem reaches of the Neosho, Cottonwood, and Spring rivers in Missouri, Kansas, and Oklahoma. These rivers are affected by anthropogenic factors such as municipal waste discharges and agricultural runoff. The Spring River also drains the Tri-State Mining District, where zinc-lead mining occurred in the past. Our objective was to assess effects of water quality degradation, due mainly to mining-related contaminants, on aquatic communities of the Spring River by comparison with those of the Neosho-Cottonwood system. We found higher densities of N. placidus, finer-textured riffle substrate, and lower concentrations of cadmium and lead in benthic macroinvertebrates in the Neosho-Cottonwood system than in the Spring River. In the Spring River, we found no substrate differences between sites with and without N. placidus; however, taxonomic richness of the benthic macro-invertebrate and fish communities were greater, densities of N. placidus and other fishes were higher, and concentrations of zinc and cadmium in benthic macro-invertebrates were lower at sites with N. placidus. Pore waters from three sites in the Spring River system were toxic to Ceriodaphnia dubia; mortality was greater than 80%, and there was no reproduction. Concentrations of zinc and cadmium in pore waters and sediment were high at these sites relative to non-toxic sites, and had SEM/AVS ratios considered potentially toxic. Toxicity tests, concentrations of metals in benthic macroinvertebrates, toxic unit (UT) modeling of pore waters, and an empirical habitat model support a hypothesis of contaminant involvement in the distribution of N. placidus.
1 Using Criticality of GPU Accesses in Memory Management for CPU-GPU Heterogeneous Multi-core Processors

Heterogeneous chip-multiprocessors with CPU and GPU integrated on the same die allow sharing of critical memory system resources among the CPU and GPU applications. Such architectures give rise to challenging resource scheduling problems. In this paper, we explore memory access scheduling algorithms driven by criticality of GPU accesses in such systems. Different GPU access streams originate from different parts of the GPU rendering pipeline, which behaves very differently from the typical CPU pipeline requiring new techniques for GPU access criticality estimation. We propose a novel queuing network model to estimate the performance-criticality of the GPU access streams. If a GPU application performs below the quality of service requirement (e.g., frame rate in 3D scene rendering), the memory access scheduler uses the estimated criticality information to accelerate the critical GPU accesses. Detailed simulations done on a heterogeneous chip-multiprocessor model with one GPU and four CPU cores running heterogeneous mixes of DirectX, OpenGL, and CPU applications show that our proposal improves the GPU performance by 15% on average without degrading the CPU performance much. Extensions proposed for the mixes containing GPGPU applications, which do not have any quality of service requirement, improve the performance by 7% on average for these mixes.
FLOWER IMAGE SEGMENTATION USING WATERSHED AND MARKER CONTROLLED WATERSHED TRANSFORM DOMAIN ALGORITHMS

Watershed and marker-controlled watershed transform domain methods are the one of the powerful tools for image segmentation. Segmentation and recognition are two primary stages in the development of a fully digitized flower identifier for real time use. This paper limits the following discussion to flower image segmentation only. The objective of this work is to study and explore flower detection and segmentation algorithms with watershed transform. Two variants of watershed transform with morphological gradients and wavelet coefficients are proposed. The flower segmentation problem uses watershed and marker-controlled watershed algorithm during the initial phases. This transformed into a wavelet based fusion model with binary flower images in the later stages giving reasonable segmentation outputs. The segmentation results are analysed both visually and mathematically. The average segmentation distance error (SDE) and structural similarity index (SSIM) on Oxford university flower dataset is around 0.485 and 0.786 respectively.
Characterizing JSON Traffic Patterns on a CDN

Content delivery networks serve a major fraction of the Internet traffic, and their geographically deployed infrastructure makes them a good vantage point to observe traffic access patterns. We perform a large-scale investigation to characterize Web traffic patterns observed from a major CDN infrastructure. Specifically, we discover that responses with application/json content-type form a growing majority of all HTTP requests. As a result, we seek to understand what types of devices and applications are requesting JSON objects and explore opportunities to optimize CDN delivery of JSON traffic. Our study shows that mobile applications account for at least 52% of JSON traffic on the CDN and embedded devices account for another 12% of all JSON traffic. We also find that more than 55% of JSON traffic on the CDN is uncacheable, showing that a large portion of JSON traffic on the CDN is dynamic. By further looking at patterns of periodicity in requests, we find that 6.3% of JSON traffic is periodically requested and reflects the use of (partially) autonomous software systems, IoT devices, and other kinds of machine-to-machine communication. Finally, we explore dependencies in JSON traffic through the lens of ngram models and find that these models can capture patterns between subsequent requests. We can potentially leverage this to prefetch requests, improving the cache hit ratio.
The Long-Term Consequences of the Irish Marriage Bar APRIL 2019

IZA DP No. 12301 APRIL 2019 The Long-Term Consequences of the Irish Marriage Bar A Marriage Bar is the requirement that women in certain jobs must leave that job when they marry. Ireland had a Marriage Bar in place until the 1970s. In 2014/2015, women participating in the The Irish Longitudinal Study on Ageing were asked for the first time in a nationally-representative survey specific questions about their experience of the Marriage Bar. In this paper, we use the information from the TILDA respondents for two purposes. The first is to investigate the extent of the Marriage Bar in Ireland. Our analysis suggests that the Marriage Bar was widespread and not confined to specific sectors or occupations (such as the Civil Service). The second purpose is to investigate the long-term consequences of the Marriage Bar. We do so by comparing the outcomes of women who were affected by the Marriage Bar with the outcomes of women who were not affected by the Marriage Bar. Regression analysis shows that women affected by the Marriage Bar have shorter working lives, lower individual income but higher wealth at present, more children and more educated children. However, there are no statistically significant differences in the current health status of the two groups of women. The differences in long-term outcomes do not appear to be confounded by the endogeneity of marriage, education, employment and occupational choices. JEL Classification: J71, J78, J24
Pulsed Erbium-doped Fiber Laser for Quantum Optics Applications

We demonstrated a pulsed, erbium-doped mode-locked fiber laser for quantum optics application. The center wavelength, 3-dB bandwidth, pulse duration, and repetition rate of the laser was 1554 nm, 5.0 nm, 701 fs, and 13.12 MHz, respectively.
AN ANALYSIS OF PIONEER AND FOLLOWER FIRMS' MARKET AND NON- MARKET ACTIONS IN THE EUROPEAN MOBILE TELEPHONE INDUSTRY*

Abstract This study examines the relationship between erosion of the first-mover’s market share and the differences in competitive behaviour of pioneer firms and followers. Particularly, we pay particular attention to market actions related to innovation, pricing and promotion, and to non-market actions related to judicial issues. The empirical study has been carried out with companies that are present in a dynamic context, such as the European mobile telephone industry. Our results show that when followers take more non-market actions than pioneers the negative effect on the firstentrant’s advantage is more significant. On the contrary, we have not found a significant impact of innovating and pricing actions.
Improving Quality-Of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard—LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRaWAN networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova’s (SOTA), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova’s, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.
Using Decision Trees in Data Mining for Predicting Factors Influencing of Heart Disease

Statistics from the World Health Organization (WHO) shows that heart disease is one of the leading causes of mortality all over the world. Because of the importance of heart disease, in recent years, many studies have been conducted on this disease using data mining. The main objective of this study is to find a better decision tree algorithm and then use the algorithm for extracting rules in predicting heart disease. Cleveland data, including 303 records are used for this study. These data include 13 features and we have categorized them into five classes. In this paper, C5.0 algorithm with a accuracy value of 85.33% has a better performance compared to the rest of the algorithms used in this study. Considering the rules created by this algorithm, the attributes of Trestbps, Restecg, Thalach, Slope, Oldpeak, and CP were extracted as the most influential causes in predicting heart disease. Keywords— Data Mining; Heart Disease; Classification; Decision Tree; C5.0 Algorithm.
PEMBUATAN HAAR-CASCADE DAN LOCAL BINARY PATTERN SEBAGAI SISTEM PENDETEKSI HALANGAN PADA AUTOMATIC GUIDED VEHICLE

Automatic Guided Vehicle (AGV) merupakan salah satu jenis robot yang bekerja mengikuti suatu jalur. Dalam penelitian ini, AGV digunakan di lingkungan perkantoran kampus. Lingkungan kampus menyebabkan jalur yang dilewati sulit dikondisikan dalam kondisi steril karena banyak objek penghalang. Hal ini membuat robot harus memiliki sistem pendeteksi halangan yang mampu membedakan halangan diam dan bergerak. Dalam penelitian ini penghalang diam berupa bak sampah dan penghalang bergerak adalah manusia. Untuk mendeteksi penghalang tersebut digunakan haar-cascade sebagai pencarian kasar dan local binary pattern (LBP) sebagai pencarian halus. Haar-cascade dibuat dengan memanfaatkan opencv haar training. Training dilakukan dengan menggunakan 300 citra positif dan 2317 citra negative pada masing-masing objek. Haar-cascade classifier didapatkan setelah dilakukan training hingga 10 stage. Haar-cascade diuji pada jarak dibawah 4 meter dari objek. Pencarian halus menggunakan LBP dilakukan saat haar-cascade mendeteksi adanya objek lebih dari satu. Dari pengujian yang telah dilakukan, sistem berhasil mendeteksi adanya halangan dengan tingkat keberhasilan 81,7%.
Natural ventilation due to wind action : practice knowledge against experimental airflow visualization

The aim of this paper is to confront the designers practice knowledge on the behavior of natural ventilation due to wind action in housing buildings with complex internal partitions against experimental analogical airflow visualization. The methodology consisted of a survey with a group of architects, followed by experimental airflow visualization in physical scale models of a housing building, developed with a water table. The results from the survey showed that the architects were divided regarding the best and worst natural ventilation performance among the tested housing units. However, the experimental visualization showed that besides the orientation, other factors regarding the building geometry must also be taken into account. The main conclusion is that the architects practice knowledge is not enough for comprehensively describing the natural ventilation in this type of building, and that the equipment used was very effective and useful for doing it.
Machine Learning Algorithms in Chinese Web Filtering : Problems , Evaluation and Enhancement

Web filtering is an inductive process which automatically builds a filter by learning the description of user interest from a set of pre-assigned web pages, and uses it to assign unprocessed web pages. In Chinese web filtering, content similarity analysis is the core problem, while Chinese words divided syncopation technology, ambiguousness of Chinese words and Chinese context-sensitive expression customs are all disturbing factors. The automatic-learning and relativity-analysis abilities of Machine Learning algorithms help solve the above problems and make ML useful in Chinese web filtering. From evaluation of four general ML algorithms (Decision Tree, Rule Induction, Bayesian algorithm and Support Vector Machines) by analyzing their description and generalization abilities and comparing their robustness and efficiency, SVM indicates most fitful for Chinese web filtering. While in practical applications, filtering results can be divided into three levels of relative, similar and homology based on user demand. A model of biased algorithms can be helpful. BSVM (for Biased Support Vector Machines) imports a stimulant function, uses training examples distribution n+/nand an user-adaptable parameter k to express the user bias degree of different classes and can help improve the positive examples filtering efficiency. Key-Words: Chinese web filtering, Machine Learning algorithms, Biased Support Vector Machines
Harmonic Power Angle Monitoring for Unsymmetrical Fault Diagnosis in Distribution Network

In this paper identification of a faulty load bus has been done in a multi-bus power system for double line fault, monitoring the feeder operating points and load angles in presence of various frequencies in harmonic P-δ plane. The usage of a new advanced variable slack bus incorporated converged power flow analysis determines the load angle, real power and feeder operating points for normal and fault in various load buses of the network in presence of some chosen harmonic frequencies in the system. The characteristic operating point shifting of the normal and fault operating points in normal and fault P-δ planes, determines the rule sets for identifying the fault buses in the grid system considered. This analysis can also be extended for other fault analysis in both online and offline conditions of the system.