Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Fast 3D Reconstruction of Urban Areas from Aerial Nadir Images for a Near Real-Time Remote Sensing System

At Fraunhofer IOSB, a concept for a near real-time airborne 3D mapping system for disaster management and security applications was proposed [1]. In addition to an Airborne Laser Scanner (ALS), a RGB camera facing nadir is installed on that platform. Subsequent RBG pictures are used for reconstructing urban scenes, and an ALS point cloud is used as a reference data set to evaluate this work. For in-flight applications, the implemented 3D reconstruction from overlapping nadir images should operate in near real-time. This paper focuses on computational performance and quality considerations of 3D reconstruction of urban scenes with aerial images. Different subpixel disparity levels are considered for generating 3D models. The run-time and quality for depth map generation with two methods are evaluated as well. The PatchMatch Stereo (PMS) algorithm is compared with the Semi-Global Matching (SGM), the most popular algorithm for 3D reconstruction.
An Approach to Tree Species Classification Using Voxel Neighborhood Density-Based Subsampling of Multiscan Terrestrial Lidar Data

The knowledge on the species of individual trees is ineluctable for accurate forest parameter estimation and related studies. Terrestrial Laser Scanning (TLS) remote sensing systems acquire a huge number of point samples that contain very accurate and detailed three dimensional (3D) information of tree structures. Every tree species has unique internal and external crown structural characteristics that can be modeled from its TLS data. However, methods in the state of the art show reduced performance due to inaccurate modeling of tree structures such as the crown, and the branch, and poor selection of features. The proposed method leverages on the fine internal and external crown structural information in TLS data to achieve species classification. We remove noise and stem points in TLS data using a novel voxel neighborhood density-based technique. Internal and external crown geometric features derived from the branch level, and the crown level, respectively, are provided to a non linear Support Vector Machines (SVM) to achieve species classification, and evaluate feature relevance. All experiments were conducted on a set of 75 manually delineated trees belonging to the Spruce, the Pine, and the Birch species.
Predicted Equation for VO2 Based on a 20-Meter Multistage Shuttle Run Test for Children

Abstract This study compared maximum oxygen consumption (VO2max) on a 20-meter multistage shuttle run test (20-Srt) with a cardiopulmonary exercise test (CPET) to determine a VO2max prediction equation for a 20-Srt in children aged 6–10 years. Eighty healthy children performed the CPET on a treadmill, while the 20-Srt took place on a sports court. Heart rate (HR) was measured and the expired gases were continuously measured breath-by-breath using a portable gas analyzer. The VO2max was lower (p<0.05) in CPET than 20-Srt for all, female, and male participants, respectively (46.3±7.9 vs. 48.7±4.6; 42.7±7.8 vs. 46.7±4.8; 49.3±6.8 vs. 50.4±3.9, mL·kg-1·min-1). The standard error estimates were between 3.0 and 3.6 and considered as not clinically relevant if less than 5 mL·kg-1·min-1. The intraclass correlation coefficient between the VO2 in CPET and in 20-Srt was 0.74 (CI95% 0.55–0.84) and considered moderately reliable. The linear multiple regression excluded sex, body mass index and fat-free mass and retained the maximum speed and age in the predictive equation. The 20-Srt estimates the VO2max with moderate reliability and the predictive equation was VO2maxpred=4.302+(maximum speed*5.613)–(age*1.523) for children aged 6–10 years.
Deep Gaussian Processes for Geophysical Parameter Retrieval

This paper introduces deep Gaussian processes (DGPs) for geo-physical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical) processes, provides a efficient solution that scales well to large datasets, and improves prediction accuracy over standard full and sparse GP models. We give empirical evidence of performance for estimation of surface dew point temperature from infrared sounding data.
Csrs-Siat: A Benchmark Remote Sensing Dataset to Semantic-Enabled and Cross-Scales Scene Recognition

The deep learning has been widely used in scene recognition of remote sensing images. However, the accuracy of deep learning relays on the size of training dataset to the utmost. The remote sensing images have various spatial scales and semantics, which are not fully considered in the existing datasets. In this paper, a benchmark remote sensing dataset named as Cross-Scale Remote Sensing dataset of Shenzhen Institutes of Advanced Technology (CSRS-SIAT) is proposed, which has about 100 classes according to the land cover and land use field, and due to the cross-scale characteristics of remote sensing images, the experiments using traditional and state-of-the-art deep learning algorithms shows that there still need more efforts to achieve better results.
Geostationary Microwave Imaging using Compressive Reflector Antenna

The Compressive Reflector Antenna (CRA) has the ability to span dynamic pseudo-random phase wave-front in space, which can be used to modulate the echo in phase, replacing the traditional Doppler style. That means radiometric observing imaging have the opportunity to be implemented in geostationary orbit, a kind of special geosynchronous orbit. For the extremely low SNR in this geometry, we propose using matched filter to improve the SNR and make the constructing more precision. In imaging aspect, we use the Back Projection and Compressed Sensing techniques to imaging at each range gate. Resampling operation is applied to eliminate the influence from the matched filtering. And some necessary simulations have been done to verify the rationality of observing system and imaging algorithm.
AIS Electronic

Mobile-health application has a great potential to intervene unhealthy life-style. However, the adoption of mobile-health application is still low compared to other types of mobile application. There were several previous studies discussing the usage behavior of mobile-health applications in various perspective. However, there is still few discussing the usage behavior from the intrinsic factors of human, one of which is belief. This study aims to determine the impact of belief consisting of attitudinal, health, and social belief on the intention to adopt mobile-health application. This study used quantitative methodology and Partial Least Square - Structural Equation Modeling (PLS-SEM) with SmartPLS 3.0 software to analyze the data. There were 663 respondents from the online questionnaire distributed. This study found that relative advantage, compatibility, health threat, and social influence affect user’s intention to use mobile health application with relative advantage as the most significant.
Quasi-transparent meshed and circularly polarized patch antenna with metamaterials integrated to a solar cell for S-band CubeSat applications

This study presents a quasi-transparent circularly polarized patch antenna integrated to a solar cell using metamaterial. The disadvantages and challenges related to patch antennas and solar cell integration are point out. Then, in order to compensate these detriments to antenna performance metamaterials with reactive impedance surface (RIS) are proposed. A report of the benefits introduced by these elements is shown. Finally, in order to accomplish quasi- transparency, the antenna is meshed adding slots where the currents densities are insignificant. A final prototype with a meshing that do not influence the antenna parameters was achieved and their features are presented.
Performance Analysis of Different Convolution Algorithms in GPU Environment

Convolutional neural networks (CNNs) have a wide range of applications in image and video recognition, recommender systems and natural language processing. But CNNs are computationally intensive, and its computational cost is hard to accept. In order to speed up the calculations, people focus on optimizing convolution that account for most of the proportion of CNNs' operation. So, many algorithms have been proposed to accelerate the operation of convolution layers. However, each algorithm has its advantages and disadvantages, and there is no one algorithm that can handle all situations. In this paper, we examine the performance of various algorithms in GPU environment. By building a customized CNN model, we have fully explored the impact of the neural structure on the performance of algorithms, including inference/training speed, memory consumption and power consumption. In addition to the algorithms, we also focus on how their implementations in GPU environment affect their performance. We trace the kernel functions of these implementations to further generalize the characteristics of these algorithms. Finally, we summarize the characteristics of each algorithm. , and design a strategy to assigns the appropriate implementation for different convolutional layers in CNNs. With our strategy, we can make AlexNet run 1.2x to 2.8x faster than other strategies in GPU environment. This work has very important meaning for understanding these algorithms and may provide insights for further optimizations of the architecture of GPUs and accelerators.
The 9H-Fluoren Vinyl Ether Derivative SAM461 Inhibits Bacterial Luciferase Activity and Protects Artemia franciscana From Luminescent Vibriosis

Vibrio campbellii is a major pathogen in aquaculture. It is a causative agent of the so-called “luminescent vibriosis,” a life-threatening condition caused by bioluminescent Vibrio spp. that often involves mass mortality of farmed shrimps. The emergence of multidrug resistant Vibrio strains raises a concern and poses a challenge for the treatment of this infection in the coming years. Inhibition of bacterial cell-to-cell communication or quorum sensing (QS) has been proposed as an alternative to antibiotic therapies. Aiming to identify novel QS disruptors, the 9H-fluroen-9yl vinyl ether derivative SAM461 was found to thwart V. campbellii bioluminescence, a QS-regulated phenotype. Phenotypic and gene expression analyses revealed, however, that the mode of action of SAM461 was unrelated to QS inhibition. Further evaluation with purified Vibrio fischeri and NanoLuc luciferases revealed enzymatic inhibition at micromolar concentrations. In silico analysis by molecular docking suggested binding of SAM461 in the active site cavities of both luciferase enzymes. Subsequent in vivo testing of SAM461 with gnotobiotic Artemia franciscana nauplii demonstrated naupliar protection against V. campbellii infection at low micromolar concentrations. Taken together, these findings suggest that suppression of luciferase activity could constitute a novel paradigm in the development of alternative anti-infective chemotherapies against luminescent vibriosis, and pave the ground for the chemical synthesis and biological characterization of derivatives with promising antimicrobial prospects.
Aircraft Target Recognition Using Copula Joint Statistical Model and Sparse Representation Based Classification

This paper proposes a new target recognition method for inverse synthetic aperture radar (ISAR) images. This method is based on joint statistical modeling of the complex wavelet coefficients for ISAR image characterization and the sparse representation based classification (SRC) for the recognition. To extract features from an ISAR image, we first transform it in the complex wavelet domain using the dual-tree complex wavelet transform (DT-CWT). Then, we compute magnitude information for each complex subband. After that, we propose a joint statistical model for magnitude distribution, that takes into account the dependences between different orientations and scales. To do so, we adopt the copula as a multivariate model thanks to its suitability to capture jointly the subband marginal distribution and the dependence structure. For the recognition step, we exploit SRC which recovers the test descriptor to classify over a given dictionary composed by the training descriptors. This method classifies the test sample as the class whose training samples can generate the minimum sparse representation error. Experimental results on ISAR images database show that using copula and sparse classifier improve significantly the recognition rates compared to classical models and classifiers.
A Three-Layer Scattering Model of the Slope Forest Area for Polarimetric SAR Interferometry

Polarimetric SAR interferometry (PolInSAR) is a promising remote sensing technique for extracting forest height in which the random volume over ground (RVoG) model has been extensively applied to polarimetric interferometry SAR data for the retrieval of forest geophysical parameters. The complex interferometric coherence of the RVoG model was originally derived in a simplified way by neglecting some factors, like the terrain slope and the double bounce interaction with the stems or trunks. But in many cases, none of them should be ignored. Therefore, a slope three-layer scattering (STLS) model is proposed to correct the terrain distortion for forest parameters estimation in a sloping forest area. At the same time, this model separates the vertical structure of forest into three layers: ground, tree-trunk and the canopy layer which account for the simultaneous effects of three scattering components on complex coherence. A significant model complexity reduction is achieved by aligning the reference frame along the local terrain slope and changing the corresponding radar geometrical configuration. The STLS model provides a better understanding of microwave scattering process in the terrain slope area compared to traditional RVoG model.
'We can go anywhere'

Ride-hailing services have received attention as part of the growing work around the sharing economy, but the focus of these studies has largely been on drivers. In this paper, we examine how ride-hailing is transforming the transportation practices of one group of passengers - people with visual impairments in metropolitan India. Through a qualitative study consisting of interviews and observations, we examined the use and impact of these services on our target population, who otherwise contend with chaotic, unreliable, and largely inaccessible modes of transportation. We found that ride-hailing services positively affects participants' notions of independence, and we tease out how independence for our participants is not just about 'doing things alone, without help' but is also situated, social and relative. Furthermore, we show how accessibility, in the case of ride-hailing in India, is a socio-technical and collaborative achievement, involving interactions between the passenger, the driver, and the technology.
Ortaokul Öğrencilerinin Türkçe Dersi Akademik Başarıları ile Ders Çalışma Alışkanlıkları Arasındaki İlişki

Arastirmanin amaci, ortaokul ogrencilerinin Turkce dersi akademik basarilari ile calisma aliskanliklari arasindaki iliskiyi belirlemek ve ogrencilerin ders calisma aliskanliklarini cesitli degiskenlere gore karsilastirmaktir. Arastirma, 2014-2015 ogretim yili I. yariyilinda Ankara ili merkez ilcesindeki, iki ozel okulda ogrenim gormekte olan 820 (430’u kiz, 390’i erkek) ortaokul ogrencisinin katilimiyla gerceklesmistir. Arastirmanin verileri Ulug (1981) tarafindan gelistirilen “Calisma Aliskanliklari Envanteri” ile toplanmistir. Arastirmadan elde edilen verilere gore ortaokul ogrencilerinin Turkce dersine yonelik calisma aliskanliklari ve akademik basarilari cinsiyete gore farklilik gostermektedir. Ogrencilerin Turkce dersine yonelik calisma aliskanliklari bakimindan duzeyler arasinda anlamli farkliliklar oldugu, ogrencilerin destek alip almamalarina gore calisma aliskanliklari acisindan aralarinda anlamli farklilik olmadigi, anne ya da baba ogrenim duzeyi farkliligi ile envanterden alinan puanlar arasinda anlamli farklilik olmadigi, ortaokul ogrencilerinin Turkce dersi akademik basari notlari ile ders calisma envanteri puanlari arasinda anlamli, orta duzeyde ve pozitif yonde bir iliski bulunmustur.
Economics of 'Tipping' Button in Social Media: An Empirical Analysis of Content Monetization

As the success of social media platforms heavily depends on the amount and the nature of user-generated content, content monetization has been introduced as a mechanism to incentivize users to generate content. In particular, content contributors can be paid (i.e. tipped) by readers who like the story. We adopted difference-in-differences approach with robustness matching estimator to examine the impact of content monetization. Our results confirm that the content monetization effectively motivate content demand and supply and also improves content quality. Furthermore, such economic incentives have a spillover effect on ordinary weibo users before they are eligible to adopt “tipping” function. However, the verified users who have already been the experts or celebrities in the society may be depressed after open application of the program. This result suggests that start-ups are able to survive and earn profit even in markets that are dominated by famous celebrities because of the monetization mechanism.
Effects of Moderate Alcohol Drinking in Patients with Nonalcoholic Fatty Liver Disease

Whether moderate alcohol intake is beneficial remains an unsolved issue. Recent studies have suggested that moderate alcohol consumption is associated with beneficial effects related to the prevention of cardiovascular diseases. Moderate alcohol consumption leads to a higher risk of hepatocellular carcinoma in patients with chronic viral liver diseases. However, the effects of moderate alcohol intake in patients with nonalcoholic fatty liver disease are unclear. In this review, we analyzed, from various perspectives, the effect of moderate alcohol consumption in patients with nonalcoholic fatty liver disease. We reviewed four cohort studies and seven cross-sectional studies. The results showed that moderate alcohol consumption was negatively related to the incidence of nonalcoholic steatohepatitis and liver fibrosis. However, moderate alcohol consumption was positively associated with the incidence of hepatocellular carcinoma in patients with nonalcoholic fatty liver disease. The results of the analysis of the relationship between moderate alcohol consumption and the levels of triglycerides, total cholesterol, high-density lipoprotein, and hypertension were diverse. More clinical data are needed to draw a conclusion about the effects of moderate alcohol consumption in patients with nonalcoholic fatty liver disease.
biotmle: Targeted Learning for Biomarker Discovery

The biotmle package provides an implementation of a biomarker discovery methodology based on targeted minimum loss-Based estimation (TMLE) (van der Laan and Rose 2011) and a generalization of the moderated t-statistic of (Smyth 2004), designed for use with biological sequencing data (e.g., microarrays, RNA-seq). The statistical approach made available in this package relies on the use of TMLE to rigorously evaluate the association between a set of potential biomarkers and another variable of interest while adjusting for potential confounding from another set of user-specified covariates. The implementation is in the form of a package for the R language for statistical computing (R Core Team 2017).
Enhanced learning for agents in quantum-accessible environments

In this paper we provide a broad framework for describing learning agents in general quantum environments. We analyze the types of classically speciﬁed environments which allow for quantum enhancements in learning, by contrasting environments to quantum oracles. We show that whether or not quantum improvements are at all possible depends on the internal structure of the quantum environment. If the environments have an appropriate structure, we show that near-generic improvements in learning times are possible in a broad range of scenarios.
Gold Nanorod-Based Nanohybrids for Combinatorial Therapeutics

In this work, multifunctional nanocarriers consisting of poly(sodium-4-styrenesulfonate) (PSS)/doxorubicin (DOXO)/poly-l-lysine hydrobromide (PLL)/hyaluronic acid (HA)-coated and (PSS/DOXO/PLL)2/HA-coated gold nanorods were assembled by the layer-by-layer technique with the aims of coupling the plasmonic photothermal properties of the metal nanoparticles for plasmonic hyperthermia and the chemoaction of drug DOXO for potential intended combinatorial cancer therapeutics in the future as well as providing different strategies for the controlled and sustained release of the cargo drug molecules. To do that, DOXO could be successfully loaded onto the hybrid nanoconstructs through electrostatic interactions with high efficiencies of up to ca. 78.3 ± 6.9% for the first formed drug layer and 56 ± 13% for the second one, with a total efficiency for the whole system [(PSS/DOXO/PLL)2/HA-coated NRs] of ca. 65.7 ± 1.4%. Nanohybrid internalization was observed to be enhanced by the outer HA layer, which is able to target the CD44 receptors widely overexpressed in some types of cancers as lung, breast, or ovarian ones. Hence, these nanohybrid systems might be versatile nanoplatforms to simultaneously deliver sufficient heat for therapeutic plasmonic hyperthermia and the anticancer drug. Two controlled mechanisms were proposed to modulate the release of the chemodrug, one by means of the enzymatic degradable character of the PLL layer and another by the modulation of the interactions between the polymeric layers through the exploitation of the optical properties of the hybrid particles under near infrared (NIR) laser irradiation. The combination of this bimodal therapeutic approach exerted a synergistic cytotoxic effect on both HeLa and MDA-MB-231 cancer cells in vitro. Cell death mechanisms were also analyzed, elucidating that plasmonic photothermal therapy induces cell necrosis, whereas DOXO activates the cell apoptotic pathway. Therefore, the present NIR laser-induced targeted cancer thermo/chemotherapy represents a novel targeted anticancer strategy with easy control on demand and suitable therapeutic efficacy.
RBClust: High quality class-specific clustering using rule-based classification

Within a class-labeled dataset, there are typically two or more possible class labels. Class-speciﬁc subsets of the dataset have the same class label for each record. Class-speciﬁc clusters are the groups of similar records within these subsets. There exists many machine learning techniques which require class-speciﬁc clusters. We propose RBClust, a rule based method for ﬁnding class-speciﬁc clusters. We demonstrate that when compared to traditional clustering methods, the proposed method achieves better cluster quality, and computation time is signiﬁcantly lower.
NTT’s Neural Machine Translation Systems for WMT 2018

This paper describes NTT’s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus cleaning, and right-to-left n-best re-ranking techniques. Through our experiments, we identified two keys for improving accuracy: filtering noisy training sentences and right-to-left re-ranking. We also found that the Transformer model requires more training data than the RNN-based model, and the RNN-based model sometimes achieves better accuracy than the Transformer model when the corpus is small.
Antenna Integrated With a Microstrip Filter for 5G Mm-wave Applications

In this work, a filtering mm-wave antenna array is proposed for the future 5G mobile devices. The filtering antenna design is based on the fourth-order coupled line planar filter with a metal lid added in order to increase the filter sharpness and decrease loss. In this paper, the metal lid is modified so the slot antenna is formed at the end of the filter. Finally, the proposed antenna element is combined into a linear phased array on the short edge of the typical mobile ground plane.