Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning on field data for hydraulic fracturing design optimization

This paper summarizes the efforts of the creation of a digital database of field data from several thousands of multistage hydraulic fracturing jobs on near-horizontal wells from several different oilfields in Western Siberia, Russia. In terms of the number of points (fracturing jobs), the present database is a rare case of an outstandingly representative dataset of thousands of cases, compared to typical databases available in the literature, comprising tens or hundreds of pints at best. The focus is made on data gathering from various sources, data preprocessing and development of the architecture of a database as well as solving fracture design optimization via machine learning. We work with the database composed from reservoir properties, fracturing designs and production data. Both a forward problem (prediction of production rate from fracturing design parameters) and an inverse problem (selecting an optimum set of frac design parameters to maximize production) are considered. A recommendation system is designed for advising a DESC engineer on an optimized fracturing design.
Optimal, Non-Rigid Alignment for Feature-Preserving Mesh Denoising

We present a simple, yet effective method for non-rigid alignment of point clouds. Our focus lies on developing a practical approach that allows us to do efficient, multi-way alignment of millions of points such as those produced by structured light scanners. Starting from an initial displacement field over the combined point cloud, our solution relies on an iterative smoothing scheme on the neighborhood graph of each sub-scan, reducing the Dirichlet energy of the displacement field. We compare a number of schemes for computing the initial displacement field, ranging from estimating the Laplacian of the combined point clouds to more traditional measures such as the point to point distance or point to plane distance.
Dual Notch Microwave Sensors Based on Complementary Metamaterial Resonators

In this paper, three dual notch microwave sensors are presented based on a microstrip transmission line and complementary metamaterial resonators. The main aim of this paper is to compare the constitutive parameters and sensitivity of all three dual notch sensors which are based on complementary symmetric split ring resonator (CS-SRR), complementary asymmetric split ring resonator (CAS-SRR) and complementary bisymmetric split ring resonator (CBS-SRR). The main motivation beyond the presented work is to use dual notches to estimate the relative permittivity of material under test (MUT). Electromagnetic simulation elucidates the origin of dual mode resonance of all the three resonators. Sensitivity analysis is performed on each sensor by using fifteen MUTs with relative permittivity ranges from 1.006 to 16.5 and constant dimensions 10 mm <inline-formula> <tex-math notation="LaTeX">$\times10$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times 1$ </tex-math></inline-formula> mm. To verify the concept, a sensor is fabricated and its response is measured using a vector network analyzer (AV3672). Using curve fitting technique the shift in the resonance frequencies of the fabricated sensor due to interaction with MUT is presented as a function of permittivity. Simulated, measured and formulated results are in good agreement with each other.
A Method for Extracting a Breast Image from a Mammogram Based on Binarization, Scaling and Segmentation

The paper proposes a method for extracting a breast image from a mammogram. For this, mammogram binarization, scaling and segmentation of a binary scaled mammogram with the subsequent selection of the maximum connected component that corresponds to a breast image have been suggested. The proposed binarization uses uniform quantization that simplifies the selection of the threshold value for different mammograms. The proposed binary mammogram scaling uses a fast wavelet transform and an arithmetic mean filter with threshold processing which accelerates further segmentation. The proposed segmentation of binary scaled mammograms uses density clustering to extract connected components that can more accurately extract the breast image. The proposed method for processing a mammogram based on binarization, scaling, and segmentation can be used in various intelligent medical diagnostic systems.
Distributed Traffic Engineering for Multi-Domain Software Defined Networks

The increasing scale of software defined networks (SDN) raises the requirement of distributed control plane, for providing scalable, reliable and high performance network management capabilities. In particular, the flat design of distributed control plane enables the management of networks with multiple independent domains that are incapable of deploying a root controller. However, it is very difficult to avoid policy conflicts between multiple controllers in flat plane due to the lack of arbitration. In this paper, we address the problem of traffic engineering in a flat control plane, and design a distributed traffic engineering algorithm, called DisTE, which can provide max-min fair bandwidth allocation for flows and maximize the resource utilization, using a fully distributed arbitration mechanism. DisTE also preserves the local topology of each domain using the topology aggregation method, and supports consistency by multiple rounds of synchronizations. We examine four strategies for determining the synchronization timings, and find that linearly decreasing interval method provides a better trade-off between network utilization and time costs. Experiments on a 717-switches 5-domain network topology demonstrate that DisTE could drive the link utilization ratio to more than 93%, and reduce up to 95% convergence time at cost of 3% relative error on fairness, compared to the centralized approach.
A (Near) Zero-cost and Universal Method to Combat Multipaths for RFID Sensing

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring ‘multipath-free’ signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost – it requires no extra device. We implement CPIX and evaluate its effectiveness on improving the performance on tag localization. The results show that CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work.
An Adaptive UAV Network for Increased User Coverage and Spectral Efficiency

Unmanned Aerial Vehicles (UAVs) are fast becoming a popular choice in a variety of applications in wireless communication systems. UAV-mounted base stations (UAV-BSs) are an effective and cost-efficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. We present a method of using UAV-BSs to provide coverage to mobile users in a fixed area. We propose an algorithm for predicting the user locations based on their mobility data and clustering the predicted locations, so that one UAV-BS would provide coverage to one user cluster. The proposed method, hence is similar to the UAV-BSs following the users to keep them under the coverage region. Simulation results show that the proposed method increases the user coverage by 47%–72% and increases the spectral efficiency by 43%–55% depending on the scenario and in addition, reduces the number of UAV-BSs required to provide coverage.
ENHANCED BROADBAND OPTICAL TRANSMISSION THROUGH ULTRATHIN METALLIC

We demonstrate the fabrication of metallic nanomesh with hexagonal array of circular holes by nanosphere lithography and measure the optical transmission spectrum of the nanomesh. The optical property of metallic nanomesh is theoretically investigated by the finite-difference time-domain method. The numerical simulation results indicate that the transmission properties of metallic nanomesh can be tuned by changing the hole diameter, period and film thickness. The optimized aluminum nanomesh shows enhanced broadband optical transmission of about 80%. This research provides a significant guidance for the design and fabrication of transparent metallic electrodes in optoelectronic devices.
Health Insurance , a Friend in Need ? Evidence from Financial and Health Diaries in Kenya

Health insurance can protect consumption from health shocks, but it can also crowd out informal transfers. This paper examines whether health insurance improves consumption smoothing in the face of health shocks, and to what extent results depend on households’ access to informal transfers as a risk coping strategy. Using high-frequency panel data on health and finances collected in rural Kenya, we show that mobile money users have stronger access to informal transfers than nonusers. We further find that health shocks induce nonusers of mobile money to lower their nonhealth expenditures by approximately 25 percent in weeks when they are uninsured. These same households are able to smooth consumption in weeks with insurance coverage, due to lower out-of-pocket health expenditures. In contrast, mobile money users are able to smooth consumption when experiencing health shocks even in the absence of health insurance, due to an inflow of informal transfers. For this group, health insurance improves healthcare utilization and does not crowd out the inflow informal transfers during weeks with health shocks. These findings have implications for the design of health insurance and mobile health financing products. 1 IFPRI Discussion Papers contain preliminary material and research results and are circulated in order to stimulate discussion and critical comment. They have not been subject to a formal external review via IFPRI’s Publications Review Committee. Any opinions stated herein are those of the author(s) and are not necessarily representative of or endorsed by the International Food Policy Research Institute. 2 The boundaries and names shown and the designations used on the map(s) herein do not imply official endorsement or acceptance by the International Food Policy Research Institute (IFPRI) or its partners and contributors. 3 Copyright remains with the authors. Notices Health Insurance, a Friend in Need? Evidence from Financial and Health Diaries in Kenya Xin Genga Vera Ideb Wendy Janssensc Berber Kramera,∗ Marijn van der Listb
Performance Analysis of SDP For Secure Internal Enterprises

Security has become of paramount importance in recent times, especially due to the advent of cloud computing and Internet of Things. With so many devices in the mix, users have the choice of working from anywhere they want. But it also raises the possibility of being able to multiply the impact of any attack by using all devices at hand. Another important aspect to consider is the prevention of access to sensitive data by unauthorized users using authorized machines. Software Defined Perimeter (SDP) provides one such solution. It aims to only allow traffic from authorized users and machines to a hidden resource. This paper discusses the SDP concept and analyzes its performance in the event of a Distributed Denial of Service (DDoS) attack under two different environments - one virtual and one real-world. The results indicate that SDP provides a resilient method for protection again DDoS attacks. While it requires slightly more time for connection setup, it is offset by its exceptional performance even under duress.
Catfish: Adaptive RDMA-enabled R-Tree for Low Latency and High Throughput

R-tree is a foundational data structure used in spatial databases and scientific databases. With the advancement of Internet and computer architectures, in-memory data processing for R-tree in distributed systems has become a common platform. We have observed new performance challenges to process R-tree as the amount of multidimensional datasets become increasingly huge. Specifically, an R-tree server can be heavily overloaded while the network and client CPU are lightly loaded, and vice versa. In this paper, we present the design and implementation of Catfish, an RDMA enabled R-tree for low latency and high throughput by adaptively utilizing the available network bandwidth and computing resources to balance the workloads between clients and servers. We design and implement two basic mechanisms of using RDMA for the client-server R-tree. First, in the fast messaging design, we use RDMA writes to send R-tree requests to the server and let server threads process R-tree requests to achieve low query latency. Second, in the RDMA offloading design, we use RDMA reads to offload tree traversal from the server to the client, which rescues the server as it is overloaded. We further develop an adaptive scheme to effectively switch an R-tree search between fast messaging and RDMA offloading, maximizing the overall performance. Our experiments show that the adaptive solution of Catfish on InfiniBand significantly outperforms R-tree that uses only fast messaging or only RDMA offloading in both latency and throughput. Catfish can also deliver up to one order of magnitude performance over the traditional schemes using TCP/IP on 1 Gbps and 40 Gbps Ethernet. We make a strong case to use RDMA to effectively balance workloads in distributed systems for low latency and high throughput.
Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction

Predicting urban flow is essential for city risk assessment and traffic management, which profoundly impacts people's lives and property. Recently, some deep learning models, focusing on capturing spatio-temporal (ST) correlations between urban regions, have been proposed to predict urban flows. However, these models overlook latent region functions that impact ST correlations greatly. Thus, it is necessary to have a framework to assist these deep models in tackling the region function issue. However, it is very challenging because of two problems: 1) how to make deep models predict flows taking into consideration latent region functions; 2) how to make the framework generalize to a variety of deep models. To tackle these challenges, we propose a novel framework that employs matrix factorization for spatio-temporal neural networks (MF-STN), capable of enhancing the state-of-the-art deep ST models. MF-STN consists of two components: 1) a ST feature learner, which obtains features of ST correlations from all regions by the corresponding sub-networks in the existing deep models; and 2) a region-specific predictor, which leverages the learned ST features to make region-specific predictions. In particular, matrix factorization is employed on the neural networks, namely, decomposing the region-specific parameters of the predictor into learnable matrices, i.e., region embedding matrices and parameter embedding matrices, to model latent region functions and correlations among regions. Extensive experiments were conducted on two real-world datasets, illustrating that MF-STN can significantly improve the performance of some representative ST models while preserving model complexity.
Collision Resolution Protocol via Soft Decision Stochastic Retransmission

The recently proposed strongest-user collision resolution (SUCRe) protocol resolves the majority of pilot collisions in crowded machine-type massive MIMO scenarios. We show in this paper that the random access (RA) performance of SUCRe can be improved by replacing the hard decision retransmission rule by a soft decision one, so that each user decides to retransmit or not its pilot sequence with a suitable probability. First, we propose to obtain this probability by mapping the ratio between the signal gain of each UE and the sum of the contenders' signal gains with the aid of a sigmoid function. Then, we propose to determine the retransmission probability of each UE according to the probability of this UE being the strongest contender, while proposing also an approximated probability expression for this event. Our results demonstrate an improvement in terms of a lower number of access attempts and a lower fraction of failed access attempts compared to the SUCRe protocol, while maintaining its features of being decentralized and uncoordinated.
Applied Mathematics and Computation

Recently, tensor train rank, deﬁned by a well-balanced matricization scheme, has been shown the powerful capacity to capture the hidden correlations among different modes of a tensor, leading to great success in tensor completion problem. Most of the high-dimensional data in the real world are more likely to be grossly corrupted with sparse noise. In this paper, based on tensor train rank, we consider a new model for tensor robust principal component analysis which aims to recover a low-rank tensor corrupted by sparse noise. The alternating direction method of multipliers algorithm is developed to solve the proposed model. A tensor augmentation tool called ket augmentation is used to convert lower-order tensors to higher-order tensors to enhance the performance of our method. Experiments of simulated data show the superiority of the proposed method in terms of PSNR and SSIM values. Moreover, experiments of the real rain streaks removal and the real stripe noise removal also illustrate the effectiveness of the proposed method.
Selecting the Optimal Groups: Efficiently Computing Skyline k-Cliques

In many applications, graphs often involve the nodes with multi-dimensional numerical attributes, and it is desirable to retrieve a group of nodes that are both highly connected (e.g., clique) and optimal according to some ranking functions. It is well known that the skyline returns candidates for the optimal objects when ranking functions are not specified. Motivated by this, in this paper we formulate the novel model of skyline k-cliques over multi-valued attributed graphs and develop efficient algorithms to conduct the computation. To verify the group based dominance between two k-cliques, we make use of maximum bipartite matching and develop a set of optimization techniques to improve the verification efficiency. Then, a progressive computation algorithm is developed which enumerates the k-cliques in an order such that a k-clique is guaranteed not to be dominated by those generated after it. Novel pruning and early termination techniques are developed to exclude unpromising nodes or cliques by investigating the structural and attribute properties of the multi-valued attributed graph. Empirical studies on four real datasets demonstrate the effectiveness of the skyline k-clique model and the efficiency of the novel computing techniques.
An Automatic Design Framework of Swarm Pattern Formation based on Multi-objective Genetic Programming

Most existing swarm pattern formation methods depend on a predefined gene regulatory network (GRN) structure that requires designers' priori knowledge, which is difficult to adapt to complex and changeable environments. To dynamically adapt to the complex and changeable environments, we propose an automatic design framework of swarm pattern formation based on multi-objective genetic programming. The proposed framework does not need to define the structure of the GRN-based model in advance, and it applies some basic network motifs to automatically structure the GRN-based model. In addition, a multi-objective genetic programming (MOGP) combines with NSGA-II, namely MOGP-NSGA-II, to balance the complexity and accuracy of the GRN-based model. In evolutionary process, an MOGP-NSGA-II and differential evolution (DE) are applied to optimize the structures and parameters of the GRN-based model in parallel. Simulation results demonstrate that the proposed framework can effectively evolve some novel GRN-based models, and these GRN-based models not only have a simpler structure and a better performance, but also are robust to the complex and changeable environments.
Induction of haploid plants in iris (Iris pseudacorus) by pollen irradiation

This is the first report of successful regeneration of haploid lines in iris (Iris pseudacorus) developed by in situ parthenogenesis followed by embryo rescue. Native genotypes of I. pseudacorus (2n = 38) were prepared as female parents and I. spuria was set as the males. Anthers collected from the pollen donor were irradiated by X-ray with doses of 0, 100, 200, 300, and 400 Gy in 2012. A second round of experiments was conducted in 2013 with 300 and 400 Gy. The ovaries from each pollinated flower were collected and cultured and the produced plantlets were then acclimatized. Chromosome counting and flow cytometry were used to determine the ploidy levels. The viability of irradiated pollens was also evaluated. Pollination with irradiated pollen at doses ranging from 100 to 400 Gy can significantly reduce fruit set, particularly at higher doses (300 and 400 Gy). The embryo production was reduced in I. pseudacorus as doses increased; the highest doses led to reduction in the number of regenerants with the formation of just 6 and 4 embryos at 300 and 400 Gy, respectively. Haploid plantlets were produced when the pollen grains were exposed to 300 and 400 Gy of X-ray. Irradiation at 100 and 200 Gy was found to be insufficient to cause pollen sterility. The best dose for haploid production in I. pseudacorus is hereby found to be 300 or 400 Gy of X-ray.
Capacity Enhancement for Energy-Harvesting Cognitive Radio Networks: A NOMA-Enabled Joint Design

In this paper, a novel three timeslots frame structure is proposed for Energy-Harvesting Cognitive Radio Networks, where the frame structure includes spectrum sensing, energy harvesting and non-orthogonal multiple access uplink transmission. Our goal is to maximize the sum-capacity of secondary users (SUs) by jointly optimizing spectrum sensing duration, energy harvesting duration and data transmission duration. To solve the challenging problem, we first derive the closed form expressions for optimal energy harvesting and data transmission durations by fixing the spectrum sensing duration, and then optimize the spectrum sensing duration by golden section search method. Finally, we obtain a sub-optimal solution through the alternate iteration of two previous steps. Simulation results show that the sum-capacity of SUs under the proposed scheme significantly increases compared to the time division multiple access (TDMA) uplink transmission protocol, especially when the transmitted power of cognitive base station (CBS) increases and the number of SUs is large enough.
Mutual-Preference Driven Truthful Auction Mechanism in Mobile Crowdsensing

Motivating the mobile users to participate in sensing services for efficient data generation and collection is one of the most critical issues in Mobile Crowdsensing Systems (MCSs). Auction based mechanisms are seen to be promising and effective solutions to incentivize mobile users. However, price is not the unique factor dominating participants' contribution in MCSs. Participant's preference for different sensing tasks is also a pivotal factor which should be considered in the auction mechanisms as assigning the least favorite tasks discourages them to participate in future sensing tasks. Unfortunately, participant's preference has been overlooked by most existing works, which motivates us to fill this gap in this paper. We first propose a new concept "mutual preference degree" to capture participant's preference and then design a preference-based auction mechanism (PreAM) to simultaneously guarantee individual rationality, budget feasibility, preference truthfulness, and price truthfulness. Finally, both the theoretical analysis and simulation results demonstrate the effectiveness of PreAM.