Miracle-dz/UniFAR-FTUs huggingface dataset:
Prototypical Clustering Networks for Dermatological Disease Diagnosis

We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis. Dermatological diagnosis poses two major challenges for standard off-the-shelf techniques: First, the data distribution is typically extremely long tailed. Second, intra-class variability is often large. To address the first issue, we formulate the problem as low-shot learning, where once deployed, a base classifier must rapidly generalize to diagnose novel conditions given very few labeled examples. To model diverse classes effectively, we propose Prototypical Clustering Networks (PCN), an extension to Prototypical Networks that learns a mixture of prototypes for each class. Prototypes are initialized for each class via clustering and refined via an online update scheme. Classification is performed by measuring similarity to a weighted combination of prototypes within a class, where the weights are the inferred cluster responsibilities. We demonstrate the strengths of our approach in effective diagnosis on a realistic dataset of dermatological conditions.
Prediction of financial distress with text mining and hidden Markov model

The financial distress of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss. It is important to establish an effective early warning system for prediction of financial distress. Financial news contains a lot of unstructured text data about the financial status of the business. Therefore, this paper takes into account the unstructured text data to the early warning system for sentimental analysis. In the section of financial indicators, this study uses the seven major categories of financial ratios in the ZETA model of Altman (2000). We use logistic regression and random forest to establish our model. However, the weakness of ZETA model is that the prediction accuracy will be greatly dropped over two years. This study introduces a hidden Markov model to improve the long-term prediction accuracy of the model. This paper provides a hybrid method which integrates text mining and hidden Markov model for prediction of financial distress for listed companies in Taiwan.
Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification

In this paper, we propose a wide contextual residual network (WCRN) with active learning (AL) for remote sensing image (RSI) classification. Although ResNets have achieved great success in various applications (e.g. RSI classification), its performance is limited by the requirement of abundant labeled samples. As it is very difficult and expensive to obtain class labels in real world, we integrate the proposed WCRN with AL to improve its generalization by using the most informative training samples. Specifically, we first design a wide contextual residual network for RSI classification. We then integrate it with AL to achieve good machine generalization with limited number of training sampling. Experimental results on the University of Pavia and Flevoland datasets demonstrate that the proposed WCRN with AL can significantly reduce the needs of samples.
Assessing the Performance of Different Irrigation Methods by Satellite Indicators in Southern Italy

Three vegetation indexes derived from remote sensing (NDVI, SAVI and EVI) were used to assess, in a citrus orchard, the performance of three irrigation treatments: control (full irrigation), regulated deficit irrigation and partial root-zone drying. In addition, midday steam water potential and canopy temperature were measured for comparison with the vegetation indexes values. No statistically significant differences were found among treatment in the evaluated parameters, indicating the irrigation treatments did not induce a severe soil water deficit affecting tree water status. The good agreement found between the ground measurements and the indexes derived from remote sensing highlights the potentialities of this technology for assessing the performance of different irrigation methods.
The scattering parameters of boreholed antennas in the UHF band

In this work a semi-empirical model is presented to characterize the transfer function and scattering parameters of a vertical dipole boreholed in a salt mine, in the 370–430 MHz bandwidth. This is achieved by combining measurements from a vector network analyzer with a signal flow graph modelling of the antenna coupling with the environment, and the effects of radio wave propagation in layered media.
Enabling Strategies for Big Data Analytics in Hybrid Infrastructures

A huge volume of data is produced every day by social networks (e.g. Facebook, Instagram, Whatsapp, etc.), sensors, mobile devices and other applications. Although the Cloud computing scenario has grown rapidly in recent years, it still suffers from a lack of the kind of standardization that involves the resource management for Big Data applications, such as the case of MapReduce. In this context, the users face a big challenge in attempting to understand the requirements of the application and how to consolidate the resources properly. This scenario raises significant challenges in the different areas: systems, infrastructure, platforms as well as providing several research opportunities in Big Data Analytics. This work proposes the use of hybrid infrastructures such as Cloud and Volunteer Computing for Big Data processing and analysis. In addition, it provides a data distribution model that improves the resource management of Big Data applications in hybrid infrastructures. The results indicate the feasibility of hybrid infrastructures since it supports the reproducibility and predictability of Big Data processing by low and high-scale simulation within Hybrid infrastructures.
Joint Tx-Rx Beamforming and Power Allocation for 5G Millimeter-Wave Non-Orthogonal Multiple Access Networks

In this paper, we investigate the combination of non-orthogonal multiple access and millimeter-wave communications (mmWave-NOMA). A downlink cellular system is considered, where an analog phased array is equipped at both the base station and users. A joint Tx-Rx beamforming and power allocation problem is formulated to maximize the achievable sum rate (ASR) subject to a minimum rate constraint for each user. As the problem is non-convex, we propose a sub-optimal solution with three stages. In the first stage, the optimal power allocation with a closed form is obtained for an arbitrary fixed Tx-Rx beamforming. In the second stage, the optimal Rx beamforming with a closed form is designed for an arbitrary fixed Tx beamforming. In the third stage, the original joint Tx-Rx beamforming and power allocation problem is reduced to a Tx beamforming problem by using the previous results, and a boundary-compressed particle swarm optimization (BC-PSO) algorithm is proposed to obtain a sub-optimal solution. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results show that the proposed sub-optimal solution can achieve a significantly better performance in terms of ASR compared with those of the state-of-the-art schemes and the conventional mmWave orthogonal multiple access (mmWave-OMA) system.
Palm Trees Counting in Remote Sensing Imagery Using Regression Convolutional Neural Network

Date palm trees are important economic crops in many countries and counting their numbers in a plantation area is crucial information for predicting the yield of date fruits, determination of insurance and financial aids, etc. In this abstract, a supervised tree counting framework is proposed using Convolutional Neural Network (CNN). The proposed approach casts the counting process into a regression problem, instead of following the classification or detection framework. To further decrease the prediction error of counting, we fine-tuned a pretrained CNN architecture into regression model. As the final output, not only the tree count is estimated for an image, but also its spatial density map is provided. Trained with small image patches cropped from airborne dataset, the proposed method is compared to manual counting and obtains good performance.
Innovation Intensity: From IT Use and Innovative Culture to Organizational Performance

Drawing from past research on information technology (IT) use, organizational culture, and innovation, the present study tests a model exploring the effects of collaborative and experiential culture as well as internally and externally-focused use of IT on innovation intensity and organizational performance. As the innovation process is complex and uncertain, we try to open the black box of innovation intensity by exploring the roles played by the use of IT and the innovation culture. The research model was tested via a structural equation model using PLS with data collected from 395 top executives. Results indicate that intense use of internally and externallyfocused IT positively affect innovation intensity. The results also show that collaborative culture increases innovation intensity. In turn, innovation intensity increases both operational and financial performance.
Monitoring of Sakurajima Volcano, Japan, with Sar Data: From Small Displacement Measurements to Modeling and Forecast

The intense activity of Sakurajima volcano is defined through interferometric analysis of SAR data, ALOS Palsar-2 and Sentinel-1, to obtain the caldera displacement over the observed period, between October 2014 and March 2018. Displacement evolution is derived to investigate its correlation with eruptions and diking. The time series, calibrated through global navigation satellite System (GNSS) measurements, are used to identify the most dramatic events affecting Sakurajima area since 2015. Moreover, modeling of a rapid intrusion occurred in August 2015 is presented.
A Sensor Invariant Atmospheric Correction Method for Satellite Images

Land surface reflectance is the fundamental variable for the most of earth observation (EO) missions, and corrections of the atmospheric disturbs from the cloud, gaseous, aerosol help to get accurate spectral description of earth surface. Unlike the previous empirical ways of atmospheric correction, we propose a data fusion method for atmospheric correction of satellite images, with an initial attempt to include the uncertainty information from different data source. It takes advantage of the high temporal resolution of MODIS observations to get BRDF description of the earth surface as the prior information of the earth surface property, uses the ECMWF CAMS Near-real-time as the prior information of the atmospheric sates, to get optimal estimations of the atmospheric parameters. It guarantees the correction is consistent cross different satellites image tiles and even cross different sensors. The validations against the AERONET sites are also show high correlation at around 0.9, with a RMSE of about 0.02.
Experimental Validation of Compact Tomosar for Vegetation Characterization

The study aims to explore the potentials of compact TomoSAR for vegetation characterization. The compact mode transmits either in linear or circular polarized waves and receives at horizontal and vertical polarization providing a different perspective to the understanding of the target. The goal of this study is to assess and evaluate the performance of compact polarimetric SAR modes to reconstruct the 3D reflectivity of forest volume and estimate the vertical structure in comparison with FP modes. Preliminary investigation of compact TomoSAR is conducted using L-band BIOSAR 2008 dataset consisting of six flight tracks acquired over Krycklan in northern Sweden.
Population Genetics of Butterflies

In a world where natural habitats are fast disappearing and where the landscape is intensively managed, even in nature reserves, it is important to understand the consequences of management and large-scale processes on populations. In this respect, studies of butterflies have been seminal in understanding how landscape structure affects populations (Ehrlich & Hanski 2004; Chapters D1 & D2). From a genetic perspective, the population structure of a given species in a particular landscape depends on a series of parameters : population size in each patch of habitat, movements between these patches, and the level of immigration and emigration to and from the system, together with their respective points of origin or destination. The two key factors determining the population structure of butterfly populations are the spatial distribution of their habitats and the ability of each species to disperse through the different components of the habitat matrix in the landscape. Different species may respond differently from the same change in habitat structure, depending on their movement ability and their habitat choice mainly, but not exclusively, determined by their choice of host plants. Due to the attractiveness of butterflies, and their usefulness as model systems for biological questions, there has been a considerable amount of material written on butterfly biology, largely in Europe. This book synthesizes all relevant and recent knowledge in the field, which is a must for those making use of this taxonomic group as a model system. It is divided into five major parts which deal with habitat use, population ecology and genetics, evolutionary ecology, distribution and phylogeny, and global change and conservation. There are growing numbers of scientific projects and networks in Europe in which the use of butterflies as tools and targets for conservation is central, and application of knowledge is closely related to European cultural landscapes. However, the chapters can also be applied to a wide geographic scope. Written by an international team of experts, this timely book is suitable for students, researchers and enthusiasts.
Gaussian Decomposition of LiDAR Waveform Data Simulated by Dart

Light Detection And Ranging (LiDAR) techniques have been extensively applied in spaceborne, airborne and ground-based platforms. Understanding LiDAR data requires modeling approaches that can precisely account for the physical interactions between the emitted laser pulse and reflecting targets. Diverse LiDAR data types arise from different systems, platforms, and applications. However, most existing physical models consider only single pulse configurations to simulate large footprint LiDAR waveforms, which do not correspond to standard data formats. Hence, in many cases, model outputs are not well adapted to research conducted with actual LiDAR systems, especially for Aerial and Terrestrial Laser Scanning (ALS and TLS) systems. The Discrete Anisotropic Radiation Transfer (DART) model provides accurate and efficient simulations of multiple LiDAR pulses from all platform types. This paper presents the latest development of the DART LiDAR module: Gaussian decomposition of the simulated ALS and TLS waveforms followed by the provision of LiDAR point cloud and waveforms in text and standard ASPRS LAS formats.
Serum and Erythrocyte Biomarkers of Nutrient Status Correlate with Short-Term Α-Carotene, Β-Carotene, Folate, and Vegetable Intakes Estimated by Food Frequency Questionnaire in Military Recruits

Abstract Objective: Food frequency questionnaires (FFQs) estimate habitual dietary intake and require evaluation in populations of interest in order to determine accuracy. Thus, the purpose of this study was to determine agreement between circulating biomarkers and FFQ estimated dietary intake in a military population consuming all meals in a dining facility over 12 weeks. Methods: 2014 Block FFQs were administered and fasted blood samples were drawn to assess nutritional biomarkers at the end of a 12-week training period in male (n = 141) and female (n = 125) Marine recruits undergoing initial military training. FFQ estimates of alpha- and beta-carotene, folate, and fruit and vegetable intake and circulating concentrations of serum alpha- and beta-carotene and serum and erythrocyte folate were measured. Partial correlations were used in the full model, and weighted kappa coefficients were used to determine agreement between ranking quartiles of dietary intake estimates with corresponding biomarker status quartiles. Results: Serum and dietary intake of alpha-carotene were positively associated in males (p = 0.009) and females (p < 0.001), as was serum and intake of beta-carotene (males, p = 0.002; females, p < 0.001). Alpha-carotene was positively associated with vegetable intake in males (p = 0.02) and beta-carotene with vegetable intake in females (p = 0.003). Serum folate in males (p = 0.002) and erythrocyte folate in females (p = 0.02) were associated with dietary folate intake. In females, the relationships between biomarker and dietary estimates yielded significant kappa coefficients. In males, a significant kappa coefficient was observed for erythrocyte folate and dietary intake of folate only. The kappa coefficient for serum and estimated intake of beta-carotene was not significant in males. Conclusion: Twelve-week habitual intake of alpha-and beta-carotene and folate were correlated with circulating biomarkers in a military training population. The 2014 Block FFQ was able to accurately rank females into quartiles of nutrient status based on intake, while males were ranked less accurately than females.
Reduced energy-angle dispersion of output electrons from a nanocrystalline Si emitter with a monolayergraphene surface electrode

It is shown that the angle dispersions of output electrons of nanocrystalline silicon (nc-Si) quasiballistic electron emitter are significantly reduced by using a monolayer graphene as the surface electrode. This is closely linked with a remarkable enhancement in the emission efficiency and a suppression of the electron energy dispersion. The angle distribution curves become more directional at lower temperatures. These results are attributed to high transparency of monolayer graphene for quasiballistic electrons generated in the nc-Si layer. The intrinsic multi-tunneling cascade mode through nc-Si dots can be utilized most effectively by monolayer graphene surface electrode.
Effect of high-intensity interval training on cardiovascular disease risk factors and body composition in psoriatic arthritis: a randomised controlled trial

Background Psoriatic arthritis (PsA) is associated with an accumulation of cardiovascular disease (CVD) risk factors. The aim of this study was to evaluate the effect of high-intensity interval training (HIIT) on CVD risk factors in patients with PsA. Methods We randomly assigned 61 patients with PsA (41 women and 20 men) to an intervention group performing HIIT for 11 weeks or a control group who were instructed to not change their physical exercise habits. Outcomes were assessed at 3 and 9 months with measures on maximal oxygen uptake (VO2max), fat percentage and Body Mass Index (BMI). We used linear mixed models to calculate mean difference with 95% CI between the groups according to the intention-to-treat principle. Results At 3 months, the HIIT group had a 3.72 mL/kg/min (95% CI 2.38 to 5.06) higher VO2max and a 1.28 (95% CI −2.51 to −0.05) lower truncal fat percentage than controls. There was also some evidence that the HIIT group had lower total fat percentage (−0.80; 95% CI −1.71 to 0.10) and slightly lower BMI (−0.31; 95% CI −0.78 to 0.17) than the control group. At 9 months, the HIIT group had still a higher VO2max (3.08; 95% CI 1.63 to 4.53) than the control group, whereas the difference in other factors were small. Conclusion In patients with PsA, 3 months with HIIT was associated with a substantial increase in VO2max and a reduction in truncal fat percentage compared with controls. The beneficial effect on VO2max was also sustained through 9 months. Trial registration number NCT02995460.
Decreased Expression of Sphingosine-1-Phosphate Receptor 1 in the Blood Leukocyte of Rheumatoid Arthritis Patients

Sphingosine-1-phosphate (S1P) plays an important role in trafficking leukocytes and developing immune disorders including autoimmunity. In the synovium of rheumatoid arthritis (RA) patients, increased expression of S1P was reported, and the interaction between S1P and S1P receptor 1 (S1P1) has been suggested to regulate the expression of inflammatory genes and the proliferation of synovial cells. In this study, we investigated the level of S1P1 mRNA expression in the blood leukocytes of RA patients. In contrast to the previous reports, the expression level of this gene was not correlated to their clinical scores, disease durations and ages. However, S1P1 was transcribed at a significantly lower level in the circulating leukocytes of RA patients when compared to age-, and sex-matched healthy controls. Since these data may suggest the participation of S1P1, further studies are needed to determine the role of this receptor in the pathogenesis of RA.
Leveraging Cognitive Models in Planning to Assist Narrative Authoring

My research aims to contribute to research in the narrative authoring domain by using cognitive models in narrative plan generation. These cognitive models determine how actions and events in narrative affect the audience. My research intends to leverage these models in narrative planning and use them to provide intelligent narrative plans that are structured to invoke specific responses from audiences when they experience the narrative. This sort of approach would greatly benefit the enrich growing set of variables of narrative planning.
getCRUCLdata: Use and Explore CRU CL v. 2.0 Climatology Elements in R

The CRU CL v. 2.0 data are a gridded climatology of 1961-1990 monthly means released in 2002 and cover all land areas (excluding Antarctica) at 10 arcminutes (0.1666667 degree) resolution (New et al. 2002) providing precipitation, cv of precipitation, wet-days, mean temperature, mean diurnal temperature range, relative humidity, sunshine, ground-frost, windspeed and elevation. While these data have a high resolution and are freely available, the data format can be cumbersome for working with. Four functions are provided by getCRUCLdata that automate importing these data into R (R Core Team 2016). All of the functions facilitate the calculation of minimum temperature and maximum temperature, and format the data into a tidy data frame (Wickham 2014) in a tibble (Wickham, Francois, and Müller 2017) object or a list of raster stack objects (Hijmans 2016) for use in R or easily exported to a raster format file for use in a geographic information system (GIS). Two functions, get_CRU_df() and get_CRU_stack() provide the ability to easily download CRU CL v. 2.0 data from the CRU website and import the data into R and allow for caching downloaded data. The other two functions, create_CRU_df() and create_CRU_stack() allow the user to easily import the data files from a local disk location and transform them into a tidy data frame tibble or raster stack. The data have applications in applied climatology, biogeochemical modelling, hydrology and agricultural meteorology (New et al. 2002).
An Experiment in Pre-Emphasizing Diversified Deep Neural Classifiers

We explore if adding a pre-emphasis step to diversiﬁed deep auto-encoding based classiﬁers serves to further improve their performance with respect to those obtained just separately using pre-emphasis or diver-siﬁcation. An experiment with a number of well-known databases, selected because they have some complementary characteristics, shows that further improvement does appear, the main condition for it simply being to select general and ﬂexible enough pre-emphasis forms. Other manners of combining diversity and pre-emphasis require more research eﬀort, as well as to investigate if other deep architectures can also obtain beneﬁts from these ideas.