Miracle-dz/UniFAR-FTUs huggingface dataset:
An Hybrid Recurrent Convolutional Neural Network for Crop Type Recognition Based on Multitemporal Sar Image Sequences

Agriculture monitoring is a key task for producers, governments and decision makers. The analysis of multitemporal remote sensing data provides a cost-effective way to perform this task. Recurrent Neural Networks (RNNs) have been successfully used in temporal modeling problems, while Convolutional Neural Networks (CNNS) are the state-of-the-art in image classification, mainly due to their ability to capture spatial context. In this work, we propose the use of a hybrid network architecture for crop mapping that combines RNNs and CNNs. We evaluate this architecture experimentally upon a Sentinel-1A database from a tropical region in Brazil. The ability of recurrent networks to model temporal context is compared with the conventional image stacking approach. The impact of using CNN learned features rather than context aware handcrafted features is also investigated. In our analysis the hybrid architecture achieved better average class accuracy than alternative approaches based on image stacking and GLCM features.
Roles and Impacts of the Transplant Pharmacist: A Systematic Review.

Background Pharmacists have been involved in the care of transplant recipients for several decades, and a growing body of literature shows the beneficial effects of clinical pharmacist care on important outcomes for these patients. Objectives The primary objective was to describe the roles and impacts of pharmacists in a solid organ transplant setting. The secondary objective was to describe and rate the pharmacists' interventions. Data Sources Three databases-PubMed, Embase, and Evidence-Based Medicine Reviews-were searched from January 1, 1990, to June 16, 2015. Study Selection and Data Extraction All studies addressing the roles of pharmacists and the impacts of clinical pharmacy services on the care of solid organ transplant recipients were considered. Only studies providing a statistical analysis were included. Design, setting, sample size, patient characteristics, pharmacists' interventions, study bias, and outcomes were extracted for analysis. Data Synthesis Four randomized controlled trials, 4 cohort studies, 3 pre-post studies, and 1 quasi-randomized controlled trial were included in the review, representing a total of 1837 patients. Of the 12 studies included, 8 specifically focused on renal transplant, and 1 each focused on liver, lung, abdominal organ, and general solid organ transplant. The pivotal pharmacist activities leading to the main patient outcomes were medication counselling (n = 8 studies), medication reconciliation (n = 5), and reviewing and optimizing drug therapy (n = 3). Improvements to medication adherence (n = 6 studies), morbidity (n = 4), costs (n = 2), and medication errors (n = 2) were reported. Conclusion Currently available evidence suggests that pharmacists can improve patient outcomes in the solid organ transplant setting. Adherence, morbidity, costs, and medication errors were identified as the main outcomes that were improved by pharmaceutical interventions. Transplant programs need to invest more in this resource.
A Two-Stream Unified Interpretation Network for Heterogeneous Remote Sensing Images Classification

The conventional studies on different types of remote sensing (RS) images classifications are conducted separately. Thanks to the powerful potential of deep learning to automatically learn features from data, exploring a unified method is possible. Moreover, recent research shows that sparse and low-rank representations can convey valuable information for patterns classification. Therefore, this paper presents a two-stream heterogeneous RS images unified interpretation network (HRSIUI-Net). One stream is to transfer the pre-trained fully convolutional network to learn deep multi-scale spatial features of RS data. The other stream is to employ a subspace learning based on graph embedding to learn the sparse and low-rank subspace representations of high-dimensional features. And then, two streams of learned subspace features are integrated for classification combined with an SVM. The experimental results on two typical RS data indicate that HRSIUI-Net can achieve competitive performance.
Relating Sar Tomography to Tropical Forest Biomass Via Lidar Data

Forest biomass is a most important parameter in the context of the global carbon cycle. Mapping above ground biomass (AGB) at a global scale contributes to understanding the dynamics of climate change. Tropical forests are extremely important as they store more biomass. In recent years, SAR tomography has been introduced as a new technique that has shown enormous potential in AGB retrieval. A strong linear relationship between in-situ measurements and tomographic power from 30 m above the terrain was discovered by previous studies carried out in French Guiana. However, the two parameters that determine the linear relationship might vary for different tropical forests. Due to the great difficulty in measuring tropical forest AGB by field surveys, in-situ measurements is unfeasible to relate SAR tomography for mapping global forests AGB. For purpose of solving this problem, we investigate the possibility to use LiDAR derived AGB to find the two parameters of the fit line. Experimental results obtained by processing data from the TropiSAR campaign support the feasibility of the proposed concept.
Office blood pressure measurement types: Different methodology—Different clinical conclusions

The measurement of blood pressure in the office (OBP) remains the basis for hypertension diagnosis and management for more than half a century. Despite the increasing use of out‐of‐office blood pressure measurement using home and less so ambulatory monitoring and their endorsement by hypertension societies, at present and for some time to come it is likely that in many people the diagnosis and management of hypertension will be based on OBP measurement alone. OBP measurement is a very variable method affected by multiple factors, which have major impact on the OBP level, reproducibility and prognostic ability. Thus, there are several types of OBP measurement, depending on the device type, conditions, observer’s presence and the number of readings. The 4 main OBP types are: Type I: Auscultatory OBP in clinical practice; Type II: Automated attended OBP in clinical practice; Type III: Research setting OBP; Type IV: Unattended automated OBP. These OBP types have different standardization level, different reproducibility, different clinical relevance and different thresholds for hypertension diagnosis. Unless the methodological details of OBP measurement are reported, any conclusions based on such measurements in clinical research and in practice are questionable.
The Physics of Self-Complementary Metasurfaces

Basic properties of interaction between electromagnetic waves and self-complementary metasurfaces in recent papers are studied. Some general properties for the transmission and reflection coefficients are derived using linear and circular polarized incident waves. We show a self-complementary metasurface example made of rectangular holes and rectangular patches and the scattering parameters obtained by simulation. This geometry may be used for conversion from circular to linear polarization with any polarization angle, broadband power dividing or birefringence.
Modelling Player Understanding of Non-Player Character Paths

Modelling a player's understanding of NPC movements can be useful for adapting gameplay to different play styles. For stealth games, what a player knows or suspects of enemy movements is important to how they will navigate towards a solution. In this work, we build a uniform abstraction of potential player path knowledge based on their partial observations. We use this representation to compute different path estimates according to different player expectations. We augment our work with a user study that validates what kinds of NPC behaviour a player may expect, and develop a tool that can build and explore appropriate (expected) paths. We find that players prefer short simple paths over long or complex paths with looping or backtracking behaviour.
Alternative Ingredient Recommendation: A Co-occurrence and Ingredient Category Importance Based Approach

As many people will refer to a recipe when cooking, there are several recipe-sharing websites that include lots of recipes and make recipes easier to access than before. However, there is often the case that we could not get all the ingredients listed on the recipe. Prior research on alternative ingredient substitution has built a recommendation system considering the suitability of a recommended ingredient with the remained ingredients. In this paper, in addition to suitability, we also take the diversity of the ingredient categories and the novelty of new combination of ingredients into account. Besides, we combine suitability with novelty as an index, to see whether our method could help find out a new combination of ingredients that is possibly to be a new dish. Our evaluation results show that our proposed method attains a comparable or even better performance on each perspective.
Sox Genes Show Spatiotemporal Expression during Murine Tongue and Eyelid Development

The tongue is a critical organ, involved in functions such as speaking, swallowing, mastication, and degustation. Although Sox genes are known to play critical roles in many biological processes, including organogenesis, the expression of the Sox family members during tongue development remains unclear. We therefore performed a comparative in situ hybridization analysis of 17 Sox genes (Sox1–14, 17, 18, and 21) during murine tongue development. Sox2, 4, 6, 8, 9, 10, 11, 12, and 21 were found to be expressed in the tongue epithelium, whereas Sox2, 4–6, 8–11, 13, and 21 showed expression in the mesenchyme of the developing tongue. Expression of Sox1, 4, 6, 8–12, and 21 were observed in the developing tongue muscle. Sox5 and 13 showed expression only at E12, while Sox1 expression was observed only on E18. Sox6, 8, 9, and 12 showed expression at several stages. Although the expression of Sox2, 4, 10, 11, and 21 was detected during all the four stages of tongue development, their expression patterns differed among the stages. We thus identified a dynamic spatiotemporal expression pattern of the Sox genes during murine tongue development. To understand whether Sox genes are involved in the development of other craniofacial organs through similar roles to those in tongue development, we also examined the expression of Sox genes in eyelid primordia, which also contain epithelium, mesenchyme, and muscle. However, expression patterns and timing of Sox genes differed between tongue and eyelid development. Sox genes are thus related to organogenesis through different functions in each craniofacial organ.
Pattern Strengthened Deep Model for Sar Image Classification

Convolutional Neural Network (CNN) has shown great potential for structural information extraction, however, Synthetic Aperture Radar (SAR) image interpretation via CNN, is still a challenging task due to the pixel- to- pixel variation nature of SAR signal. In this paper, a pattern strengthened deep model for SAR image classification is presented. The main idea underlying this method is to strengthen (or sharpen) the salient patterns contained in SAR image while attenuate the random variation, so as to leverage the enormous potential of deep model. To achieve this goal, max pooling is elaborately designed to form a pattern strengthened deep model. The experimental results on two SAR datasets show that the proposed method increases the classification accuracy by 4.91% and 4.64% compared with the typical CNN, and indicate our innovative attempt is promising for SAR image classification in the case of limited training data.
Land Cover Generation from Optical Image

With growing needs of the global land cover information, high resolution datasets have been released, such as 30m-resolution GLC30 and NLCD 2011. However, these datasets cannot update in time when the ground-truth changes. In this paper, an automatic algorithm is proposed to generate land cover based on convolutional neural network (CNN) using optical image. A land cover translation framework is designed by utilizing the techniques of the fully convolutional network, and it can provide pixel-to-pixel translation of the image. The experiment is carried out on high resolution optical image sourcing from Google earth, and the results demonstrate that the proposed method is able to generate robust and reasonable prediction of the land cover. By testing at several different areas, this methods achieve the average accuracy at 70.2% for 8 types land cover comparing with NLCD 2011 datasets.
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation

Brain cancer can be very fatal, but chances of survival increase through early detection and treatment. Doctors use Magnetic Resonance Imaging (MRI) to detect and locate tumors in the brain, and very carefully analyze scans to segment brain tumors. Manual segmentation is time consuming and tiring for doctors, and it can be difficult for them to notice extremely small abnormalities. Automated segmentations performed by computers offer quicker diagnoses, the ability to notice small details, and more accurate segmentations. Advances in deep learning and computer hardware have allowed for high-performing automated segmentation approaches. However, several problems persist in practice: increased training time, class imbalance, and low performance. In this paper, I propose applying V-Net, a volumetric, fully convolutional neural network, to segment brain tumors in MRI scans from the BraTS Challenges. With this approach, I achieve a whole tumor dice score of 0.89 and train the network in a short time while addressing class imbalance with the use of a dice loss layer. Then, I propose applying an existing technique to improve automated segmentation performance in practice.
User Specific Adaptation in Automatic Transcription of Vocalised Percussion

The goal of this work is to develop an application that enables music producers to use their voice to create drum patterns when composing in Digital Audio Workstations (DAWs). An easy-to-use and user-oriented system capable of automatically transcribing vocalisations of percussion sounds, called LVT - Live Vocalised Transcription, is presented. LVT is developed as a Max for Live device which follows the `segment-and-classify' methodology for drum transcription, and includes three modules: i) an onset detector to segment events in time; ii) a module that extracts relevant features from the audio content; and iii) a machine-learning component that implements the k-Nearest Neighbours (kNN) algorithm for the classification of vocalised drum timbres. Due to the wide differences in vocalisations from distinct users for the same drum sound, a user-specific approach to vocalised transcription is proposed. In this perspective, a given end-user trains the algorithm with their own vocalisations for each drum sound before inputting their desired pattern into the DAW. The user adaption is achieved via a new Max external which implements Sequential Forward Selection (SFS) for choosing the most relevant features for a given set of input drum sounds.
LMU Munich’s Neural Machine Translation Systems at WMT 2018

We present the LMU Munich machine translation systems for the English–German language pair. We have built neural machine translation systems for both translation directions (English→German and German→English) and for two different domains (the biomedical domain and the news domain). The systems were used for our participation in the WMT18 biomedical translation task and in the shared task on machine translation of news. The main focus of our recent system development efforts has been on achieving improvements in the biomedical domain over last year’s strong biomedical translation engine for English→German (Huck et al., 2017a). Considerable progress has been made in the latter task, which we report on in this paper.
Joint Encoding LBP Features from Infrared and Visible-Light Cloud Image Observations for Ground-Based Cloud Classification

Cloud type classification based on ground-based cloud image observations is an important task in atmospheric research. Currently, two kinds of cloud image observations with infrared and visible light images are widely used for cloud classification. However, they are only independently analyzed and simply compared in the current study. The useful information from these two kinds of images is not fully utilized and integrated. The classification performance could be improved if taking full advantage of the complementary information of these two observations. Thus, first, a database containing these two kinds of cloud images with same temporal resolution is released in this study. Then, a two-observation joint encoding strategy of LBP (local binary pattern) features is proposed to implement cloud classification by encoding the joint distribution of LBP patterns in different observations, which captures the correlation between two observations. Experimental results based on this database show the significant superiority of the proposed method compared to the results based on the single observation.
A Remote Sensing Spatiotemporal Fusion Model of Landsat and Modis Data via Deep Learning

In this paper, a novel spatiotemporal fusion model based on deep learning is proposed, which handles the huge spatial resolution gap and the nonlinear mapping between the high spatial resolution (HSR) image and the corresponding high temporal resolution (HTR) image at the same imaging time. Considering the huge spatial resolution gap, a two-layer fusion strategy is adopted. In each layer, the convolutional neural network (CNN) model is employed to exploit the non-linear mapping between the HSR and HTR image and reconstruct the high-spatial and high-temporal (HSHT) resolution images. In the experiment, Landsat data is the representation of the high spatial resolution images, MODIS data is used as the corresponding low spatial resolution images. The experimental results on two different datasets clearly illustrate the superiority of the proposed model.
Joint management format at the mixed-surgical intermediate care unit: an interrupted time series analysis

Background The management format of the mixed-surgical intermediate care unit (IMCU) affects its performance. A format of combined supervision of surgeons with additional critical care certifications and admitting specialists, named the “joint format”, may herein be a promising new model of specialized critical care. This study aims to assess the performance of the joint management format. Methods This observational cohort study compared three IMCU management formats at the stand-alone, mixed-surgical IMCU of a tertiary referral hospital using interrupted time series analyses. All admissions from 2001 until 2015 were included. Predetermined criteria for performance (utilization, efficiency, and safety) were applied to three different management format periods: open (2001–2006), closed (2006–2011), and joint (2011–2015) formats. Results A total of 8894 admissions were analyzed. In terms of case load (utilization), there was an overall increase in the number of surgical patients (0.25%/year) (p<0.001), age (0.38/year) (p<0.001), and readmissions from the ward (0.16%/year) (p<0.001) and from the intensive care unit (ICU) (0.17%/year) (p=0.014). In terms of efficiency, the admission duration decreased (1.58 hours/year) (p<0.001). Transfer to the ICU within 24 hours, readmission within 24 hours from the ward, and unplanned mortality (eg, safety) did not change over time. Discussion At a time of increasingly complex case load, the joint format at the mixed-surgical IMCU is an efficient and safe management format in which the admitting specialist continues to provide specialized care. Specialty-specific supervision at IMCUs is a safe option which should be considered in healthcare policy decisions. Level of evidence Level IV.
Change Detection Based on Fully-Connected Conditional Random Field with Region Potential in Remote Sensing Images

In this paper, a new change detection method based on fully-connected conditional random field (FCCRF) with region potential is proposed. To deal with over-smoothing problem in FCCRF model, we propose to add region boundary constraint into FCCRF model. The proposed method defines the unary potential using the memberships of unsupervised fuzzy C-means clustering, designs the pairwise potential by a linear combination of Gaussian kernels using the complete set of pixels in the multi-temporal images to suppress noise effects, implements the region potential by the mean probability of pixels within image objects to preserve details of object boundary information. Experimental results demonstrate that the proposed method improves the change detection accuracy, turns out to be more robust against noise than traditional approaches.
A stage model for uncovering inertia in big data analytics adoption

Big data and analytics have been credited with being a revolution that will radically transform the way firms do business. Nevertheless, the process of adopting and diffusing big data analytics, as well as actions taken in response to generated insight, require organizational transformation. Yet, as with any form of organizational transformation, there are multiple inhibiting factors that threaten successful change. The purpose of this study is to examine the inertial forces that can hamper the value of big data analytics throughout this process. We draw on a multiple case study approach of 27 firms to examine this question. Building on a stage model of adoption, our findings suggest that inertia is present in different forms, including economic, political, socio-cognitive, negative psychology, and socio- technical. The ways in which firms attempt to mitigate these forces of inertia is elaborated on, and best practices are presented. We conclude the paper by discussing the implications that these findings have for both research and practice.