Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusion of GNSS and Satellite Radar Interferometry: Determination of 3D Fine-Scale Map of Present-Day Surface Displacements in Italy as Expressions of Geodynamic Processes

We present a detailed map of ground movement in Italy derived from the combination of the Global Navigation Satellite System (GNSS) and Satellite Synthetic Aperture Radar (SAR) interferometry. These techniques are two of the most used space geodetic techniques to study Earth surface deformation. The above techniques provide displacements with respect to different components of the ground point position; GNSSs use the geocentric International Terrestrial Reference System 1989 (ITRS89), whereas the satellite SAR interferometry components are identified by the Lines of Sight (LOSs) between a satellite and ground points. Moreover, SAR interferometry is a differential technique, and for that reason, displacements have no absolute reference datum. We performed datum alignment of InSAR products using precise velocity fields derived from GNSS permanent stations. The result is a coherent ground velocity field with detailed boundaries of velocity patterns that provide new information about the complex geodynamics involved on the Italian peninsula and about local movements.
A Soft-Switching Control for Cascaded Buck-Boost Converters Without Zero-Crossing Detection

This paper proposes a novel soft-switching control without zero-crossing detection for the cascaded buck–boost converters (CBBCs). The proposed soft-switching control consists of two parts: soft-switching modulation and closed-loop control. In the modulation, the employed PWM approach is constructed by introducing a small negative current into the conventional PWM, and the soft-switching modulation based on the negative-current-based PWM is presented by adding a new switching process controlled by a modulation variable in the CBBC. In the closed-loop control, the relationship between the models of the negative-current-based PWM and the soft-switching modulation is deduced, and by utilizing the deduced relationship, the closed-loop control is designed while the PWM is modified to achieve the soft-switching. Meanwhile, to further reduce the ripple current and device losses, the parameter of the proposed control is optimized by refining the restrictive conditions. While the zero-crossing point of the inductor current is calculated with a given algorithm based on a current measurement, the circuit of zero-crossing detection is avoided. The validity and feasibility of the proposed soft-switching control approach are confirmed by the simulations and experiments on a 200-W prototype.
Microwave Photonic Link With Flexible Even-Order and Third-Order Distortion Suppression

A microwave photonic link with flexible suppression of the even-order and third-order distortions is proposed by using a single integrated polarization-multiplexing dual parallel Mach-Zehnder modulator (PM-DPMZM). A 180° electric hybrid is applied to introduce a phase shift of 180° between the even and odder modulation sidebands of the radio frequency (RF) signal. One DC bias voltage of the PM-DPMZM is adjusted to implement not only the optimal suppression of the even-order or third-order distortions but also a simultaneous suppression of these two distortions. The experimental system is carried out. In the best suppression points for the second or third intermodulation distortions (IMD2 or IMD3), the spurious-free dynamic range (SFDR) of IMD2 and IMD3 is improved by 17.1 and 17.7 dB, comparing with the conventional MZM based MPL. In the case of the simultaneous suppression, the SFDR values of IMD2 and IMD3 are enhanced by 10.6 and 6.4 dB. The proposed link has great flexibility, simple structure, and convenient operation, and it can be applied in both the single-octave and multi-octave communication systems.
Estimation of the parameters of Cold Unmagnetized Plasma Media Based on Time-Domain Inverse Scattering Technique

In this paper, a time-domain inverse scattering technique for estimating the resonance frequency of 2-D cold un magnetized plasma scatters is proposed. The resonance frequency properties of the unknown plasma region are estimated by post processing scattering-field measurements, which are obtained when the domain of reconstructing region is illuminated by time harmonic source. The plasma scatters parameters are reconstructed by applying a global optimization technique, which combines the finite-difference time-domain (FDTD) method and the particle swarm optimization (PSO) algorithm. During the inversion, the resonance frequency parameters of the unknown region are expanded by a cosine Fourier series. The numerical results illuminate that the proposed method with well reconstruction accuracy and anti ill-posedness characteristics.
Unsupervised Learning of Accurate Camera Pose and Depth From Video Sequences With Kalman Filter

Unsupervised learning-based scene perception has recently become an important research direction. Most unsupervised methods for scene perception tasks (e.g., dense depth recovery and ego-motion estimation) train the convolutional network via minimizing the photometric error of images, achieving very impressive results. Since the supervision signal is weaker than ground truth data, the existing unsupervised methods generally do poorly inaccurate pose estimation and high-resolution depth map generation. To this end, we present an architecture based on the convolutional neural network and the Kalman filter, which is used for unsupervised learning of accurate ego-motion and high-resolution single view depth, with as fewer parameters as possible. Specifically, we first present pose network (P-CNN) with the decoupled (multi-stream) convolutional network for learning accurate camera pose (translation and rotation vector) to avoid the fault aliasing of multiple camera pose, with a relatively light-weighted network. Then, for the sake of improving the relationship of features between consecutive image pairs, we introduce the Kalman filter into the learning framework to improve the smoothness of the estimated camera pose. Finally, in order to decode a high-resolution depth map with a less uneven and unsmooth area, we adopt a new upsampling module in the encoder–decoder architecture of depth network (D-CNN). The extensive experiments are implemented on the KITTI driving dataset, proving that our method obviously predicts a more accurate camera pose and a clear depth map.
IT-Business Alignments among Different Divisions of Japanese Corporations

The purpose of this paper is empirically investigates whether IT strategies, business strategies and divisions are aligned to meet overall business goals for Japanese corporations, including both large and small and medium enterprises (SMEs), based on Structured based Strategic Alignment Model [1], and make comparison with those of Japanese SMEs studied in 2014. Using 101 valid responses of corporations throughout Japan, this study found Business strategy is positive, strongly, and significantly influence over IT strategy, which is the same as the previous study. HR/Administrative department still have a major influence over some departments such as Logistic, Technology and Manufacturing, but not much so for Marketing. It is positive but weak and not significant relationships between HR departments and both business strategy and IT strategy, which are different from the previous study.
Selective Authentication Based Geographic Opportunistic Routing in Wireless Sensor Networks for Internet of Things Against DoS Attacks

Wireless Sensor Networks (WSNs) have been widely used as the communication system in the Internet of Things (IoT). In addition to the services provided by WSNs, many IoT-based applications require reliable data delivery over unstable wireless links. To guarantee reliable data delivery, existing works exploit geographic opportunistic routing with multiple candidate forwarders in WSNs. However, these approaches suffer from serious Denial of Service (DoS) attacks, where a large number of invalid data are deliberately delivered to receivers to disrupt the normal operations of WSNs. In this paper, we propose a selective authentication-based geographic opportunistic routing (SelGOR) to defend against the DoS attacks, meeting the requirements of authenticity and reliability in WSNs. By analyzing statistic state information (SSI) of wireless links, SelGOR leverages an SSI-based trust model to improve the efficiency of data delivery. Unlike previous opportunistic routing protocols, SelGOR ensures data integrity by developing an entropy-based selective authentication algorithm, and is able to isolate DoS attackers and reduce the computational cost. Specifically, we design a distributed cooperative verification scheme to accelerate the isolation of attackers. This scheme also makes SelGOR avoid duplicate data transmission and redundant signature verification resulting from opportunistic routing. The extensive simulations show that SelGOR provides reliable and authentic data delivery, while it only consumes 50% of the computational cost compared to other related solutions.
Racial and Ethnic Disparities in Access to Care during the Early Years of Affordable Care Act Implementation in California.

Background and Purpose Following the Affordable Care Act (ACA) health insurance expansions, this study asks: did racial/ethnic group disparities in access to care remain? And specifically, did Latinos experience worse access to care after the ACA expansions compared to other racial/ethnic groups? Methods Dataset: 2015 California Health Interview Survey (n=21,034; N=29,083,000). Participants: Adults, ages 18 and older, in California. Analyses: Bivariate chi-square tests and logistic multivariate regressions, including stratification by insurance. Results Bivariate tests showed associations between racial/ethnic group and access to care. Latinos had lowest rates of having a usual source of care among uninsured (49.5%) and job-based coverage (85.2%). One-fifth of uninsured non-Latino whites (21%) report foregoing needed care. In the multivariate models, non-Latino whites had significantly higher odds of having a usual source of care (OR=1.32; p<0.05), but also of foregoing needed care (OR=1.43; p<0.05), than Latinos. Asian Americans had significantly lower odds of visiting a doctor in the past year (OR=0.65; p<0.05) than Latino adults. Conclusion Following the ACA, disparities among racial/ethnic groups have become more complex. While Latino adults still have lower rates of having a usual source of care, Asian American adults have low rates of visiting a doctor, and non-Latino whites have high rates of foregoing needed care. Further research into the causes of difficulties in accessing care is needed, as health insurance expansions did not create health equity in solving access to care problems.
A Design of Broadband Feeding Network for Circularly Polarized Antenna

This paper proposes a design of microstrip broadband feeding network for circularly polarized antenna working within X- and Ku- band. It consists of a broadband Wilkinson power divider and a broadband 90° phase shifter using double T-shaped open stubs loaded transmission line. The phase difference of the two output ports is $90^{\circ}\pm 5^{\circ}$, and their mutual isolation better than 15dB. This feeding network is fabricated and measured for verification.
Achromatic Non-Interferometric Single Grating Neutron Phase Imaging

We demonstrate a simple single grating beam modulation technique, which enables the use of a highly intense neutron beam for phase imaging and thus spatially resolved structural correlation measurements in full analogy to quantum interference based methods. In contrast to interferometric approaches our method is intrinsically achromatic and provides unprecedented flexibility in the choice of experimental parameters. Utilizing merely a macroscopic absorption mask unparalleled length scales become accessible. The results presented, including application to a variety of materials, establish a paradigm shift in phase imaging.
Secure Spatial Network Queries on Cloud Platform

With massive real-world networks added with spatial attributes, query processing in spatial networks has been widely used in real-life applications. Considering the high costs of spatial network queries, outsourcing querying services to cloud provides a cost-effective way for data owners. However, directly outsourcing such services may cause serious privacy concerns. To address this privacy problem, existing studies apply anonymous or differential privacy techniques on spatial network queries, which are not under the semi-trusted secure model. Other existing works primarily focus on query processing over either encrypted spatial or network data, which cannot directly be applied to work out the secure spatial network query problem. To this end, we define and study Secure Spatial Network kNN Query (SSNQ) problem on cloud platform. We first present Basic Secure Spatial Network kNN Query (BSSNQ) method, in which we compute secure kNN for the query node to construct candidate sequences using secure subprotocols. With pre-encrypted network distances, we then compute shortest paths between the query node and each candidate, and securely update the candidate sequences according to Euclidean restriction to derive final query results. To improve the efficiency of BSSNQ, we further propose Heuristic Secure Spatial Network kNN Query (HSSNQ) method, which securely calculates shortest paths from the query node to the visiting nodes iteratively, and use optimistic estimate distances as the key to lead the heuristic search within a priority queue. Thorough analysis shows the security and complexity of the proposed methods, and extensive experimental results on real datasets demonstrate the efficiency of the query performance.
2D Magnetotelluric Inversion using Supervised Descent Method

In this work, we study the 2D magnetotelluric (MT) inversion by a machine learning scheme. Specifically, supervised descent method (SDM), which learns the descent directions of an optimization problem, is applied to MT data inversion. The inversion is composed of offline training and online prediction. In the offline stage, the descent directions can be learned from a set of training data; and in the online stage, data inversion can be achieved by updating the models using the learned directions. SDM offers a more convinient way to incorporate prior information into MT inversion. Furthermore, the learning scheme can achieve fast computation. Numerical tests show that we may be able to reconstruct thin layers from MT data with enough information.
A CTR Prediction Approach for Advertising Based on Embedding Model and Deep Learning

In the field of CTR prediction for advertisement, a lot of surface models such as LR, FM have been widely used in industry. Meanwhile, the studies based on deep learning also launched in this field. Because advertisement dataset contains massive multi-field categorical data, this paper proposes a kind of CTR prediction model based on embedding model and deep learning—FMSDAELR. This proposed model can effectively model multi-field categorical data, extract the complex nonlinear correlated relationship, and capture the important features contributing to CTR prediction for advertisement. The result of experiment based on the real dataset shows that the proposed approach can obtain superior performance than other baseline models.
Autonomous Vehicle Detection and Classification in High Resolution Satellite Imagery

High resolution remote sensing data can provide worldwide images rapidly contrasted with conventional strategies for information accumulation. Therefore tiny objects like cars can be easily detected. Automatic vehicles enumeration research domain plays an important role in various applications including traffic monitoring and management. In this paper, we propose autonomous vehicle detection and classification approach in highway environment. Proposed approach consists mainly from three stages: (i) first, preprocessing operations are applied in order to eliminate noisy objects including soil, vegetation, water. (ii) Then, built-up area index is utilized to detect and delineate road networks. (iii) Finally, Multi-thresholding segmentation is implemented, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Quality percentage assessment is carried over different study areas, illustrating the great efficiency of the proposed approach especially in highway environment.
Memory Cache Attacks on Alluxio Impede High Performance Computing

Alluxio is a popular distributed memory storage system, which becomes increasingly important in big data ecosystem by providing high performance storage for computing framework. We point out that there are security vulnerabilities in cache mechanism, data short-circuit read/write mechanism and consistency check mechanism of Alluxio, which may result in the failure of data availability and integrity. Firstly, we find that the lack of limitation of cached copies in the cache mechanism causes the malicious memory consumption attack. An adversary can craft a malicious job to consume the scarce memory storage at a low cost, which will significantly influence the performance of normal users with a degradation of 20%-59%. Secondly, we are aware that the lack of access control and integrity check on the data block will lead to target file tamper attack, in which a target file cached in memory will be modified and considered correctly as long as the size remains unchanged. The tampered file will be read and thus causes a wrong result. Finally, we discuss some corresponding countermeasures for defending these two kinds of memory cache attacks.
Large-System Analysis of Massive MIMO with Optimal M-MMSE Processing

We consider the uplink of a Massive MIMO network with $L$ cells, each comprising a BS with $M$ antennas and $K$ single-antenna user equipments. Recently, [1] studied the asymptotic spectral efficiency of such networks with optimal multicell minimum mean-squared error (M-MMSE) processing when $M\rightarrow\infty$ and $K$ is kept fixed. Remarkably, [1] proved that, for practical channels with spatial correlation, the spectral efficiency grows unboundedly, even with pilot contamination. In this paper, we extend the analysis from [1] to the alternative regime in which $M, K\rightarrow\infty$ with a given ratio. Tools from random matrix theory are used to compute low-complexity approximations which are proved to be asymptotically tight, but accurate for realistic system dimensions, as shown by simulations.
Rejoinder

Jensen (2018) describes an industrially motivated problem where the goal is to establish requirements for an intermediate upstream measurement (X) to ensure that customer specifications are met on the final measurement (Y). For the challenge, we are given two independent data sets: x1; x2; :::xn1 and y1; y2; :::; yn2: Note that the data represent measurements on different units at the intermediate and final processing steps and we have no paired observations. Jensen (2018) motivates this problem by considering the case where measurement is destructive and it is not possible to obtain paired data, though he also alludes to more general situations. This challenge was reportedly “solved” by Shen, Mao, and Deng (2018) by casting the problem into a missing data framework and applying the EM algorithm (Dempster, Laird, and Rubin 1977). However, we believe the challenge as posed is impossible and the Shen, Mao, and Deng (2018) solution (hereafter called the Shen approach) is a mirage, as we shall explain. First, we define a model and some notation. We follow Shen, Mao, and Deng (2018) who modeled the relationship between the intermediate (X) and final measurements (Y) as Y 1⁄4 b0 þ b1X þ e; [1] and assumed that X Nða; dÞ; e Nð0; r2Þ; and X and e are independent. This model has five parameters ðb0; b1; a; d; rÞ: It is useful to notice that this model setup is equivalent to assuming (X, Y) come from a bivariate normal (BVN) distribution with parameters ðlx; rx; ly; ry; qÞ; where we can translate between the two models using lx 1⁄4 a; rx 1⁄4 d; ly 1⁄4 b0 þ b1a; ry 1⁄4 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b1d 2 þ r2 q and correlation q 1⁄4 b1d ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b1d 2 þ r2 q :
Learning Graph Representation: A Comparative Study

Graphs are ubiquitous and allow us to model entities and the relationships between them. Graph data is often observed directly in the natural world (e.g., telecommunication or social networks), and the success of many machine learning tasks such as classification, link prediction, and many others, depends mainly on learning a useful feature representation from graph. This study investigates several research studies that have been conducted in the field of graph representation learning. The growing attention in graph representation learning and graph embedding in recent years raise the need for comparing the existing methods in terms of methodology and techniques. This paper summarizes the recent techniques and methods used for graph representation learning, and compare them together.
Vortex-dynamical interpretation of anti-phase and in-phase flickering of dual buoyant diffusion flames

Anti-phase and in-phase flickering modes of dual buoyant diffusion flames were numerically investigated and theoretically analyzed in this study. Inspired by the flickering mechanism of a single buoyant diffusion flame, for which the deformation, stretching, or even pinch-off of the flame surface result from the formation and evolution of the toroidal vortices, we attempted to understand the anti-phase and in-phase flickering of dual buoyant diffusion flames from the perspective of vortex dynamics. The interaction between the inner-side shear layers of the two flames was identified to be responsible for the different flickering modes. Specifically, the transition between anti-phase and in-phase flickering modes can be predicted by a unified regime nomogram of the normalized flickering frequency versus a characteristic Reynolds number, which accounts for the viscous effect on vorticity diffusion between the two inner-side shear layers. Physically, the transition of the vortical structures from symmetric (in-phase) to staggered (anti-phase) in a dual-flame system can be interpreted as being similar to the mechanism causing flow transition in the wake of a bluff body and forming the Karman vortex street.
Hybrid fiber alterations in exercising seniors suggest contribution to fast‐to‐slow muscle fiber shift

Human skeletal muscle is composed of a functional and metabolic continuum of slow (Type I) and fast fibers (IIa and IIx). Hybrid fibers co‐expressing different myosin heavy chains are also present and seem to be more prominent in aging muscle. Their role is debated; hybrid fibers were reported either in a transitional state, between slow and fast fibers, or as fixed individual entities. This study examined the fate of hybrid fibers with an endurance exercise intervention in an elderly sedentary population.
Closeness with Robots as Social Partners

HRI researchers have explored how people behave toward technology agents, advancing the concept that people can attain “closeness” with technology itself in addition to a living social partner. Yet the topic of closeness with robots has not been fully explored or organized into a discrete area of study. This seems particularly important to the design of robots that are expressive, to the implementation of technologies that use new social signal processing or reciprocal social touch, and to the study of how people respond to robots. This half-day workshop is a forum to discuss the future of “closeness” with robots, conversational agents, autonomous vehicles, Internet of Things devices and other technologies that act as social partners—designs, applications, responses and societal concerns.