Miracle-dz/UniFAR-FTUs huggingface dataset:
Inversion of the Prosail Model from UAV Data

In this paper, we present an innovative workflow for retrieving biophysical traits of vegetation by means of inverting the radiative transfer model PROSAIL from UAV borne hyperspectral data. The approach makes use of spectral images acquired with a Fabry-Perot interferometer in the visible and near infrared spectral range. Even of the reduced spatial coverage of UAV acquisition, the high spatial resolution of such images makes model inversion computationally highly demanding. To overcome this, we made use of a machine learning method. Firstly, we generated look up tables by means of model forward runs based on variating model parameters according to prior knowledge. Random forests were then trained and applied to radiometrically calibrated UAV borne images. This allowed to retrieve model parameters for the area of interest (AOI). The approach showed to be computationally efficient and usable for ecosystems with high spatial variability.
Beyond Presentation: Shared Slideware Control as a Resource for Collocated Collaboration

Traditional models of slideware assume one presenter controls attention through slide navigation and pointing while a passive audience views the action. This paradigm limits group interactions, curtailing opportunities for attendees to use slides to participate in a collaborative discourse. However, as slideware permeates contexts beyond simple one-to-many presentations, there are growing efforts to shift the dynamics of collocated interactions. Technologies exist to shift the one-to-many information control paradigm to variations that extend functions to multiple attendees. But there is limited detailed research on how to design such multi-person attentional control and facilitate collocated interactions without disrupting existing work practices. We report on a detailed naturalistic case of using presentation in a design meeting, where participants used Office Social, an experimental slideware technology that enabled open access to shared interaction with slides across multiple devices. We explore how the design of Office Social supported informal collaboration. Our video-based analysis reveals how the orderly structures of conversational turn-taking and bodily conduct were used in conjunction with the affordances of the socio-technical ecosystem to organize collective activity. We suggest that supporting collocated interactions should take account of the existing conversational methods for achieving orderly collaboration rather than superimposing prescriptive technological methods of order.
Comparison of Integrated Precipitable Water Derived from Cosmic Occultation Data and Ground GPS Measurements

The integrated Precipitable Water (PW) derived from ground IGS stations is considered as reference because of its high accuracy and long-term stability to validate the PW derived from COSMIC RO sounding data. The one-year comparisons show that both PW are quite consistent for PW below 20 mm, and the PW derived from COSMIC has systemic low bias than ground-GPS PW for PW >40 mm. In general, the rms of PW difference between COSMIC and IGS is about 1.5-3.0mm, and the mean difference is less than 0.5mm, which is acceptable. In addition, the variations of PW difference in the southern hemisphere are obviously fluctuant due to fewer IGS stations over this region, while the mean PW difference is more stable and smaller around 0.5 mm for latitude higher than 25°N and the standard deviation of PW difference is gradually decreases with higher latitude in the northern hemisphere.
Single Frame Super Resolution with Convolutional Neural Network for Remote Sensing Imagery

In this paper, a new convolutional neural networks based super resolution(SR) is proposed. SR has been a hot research area for decades, and it includes two types: single frame based SR and multi-frame based SR. The focus of the paper is to reconstruct the corresponding high resolution image from a given low resolution image. The popular end-to-end learning architecture is improved and no preprocessing and image aggregation are needed. Our network model(RSCNN) uses different convolution kernels for a set of feature maps in the feature mapping step, which ensures the accuracy of reconstruction results under the premise of improving the reconstruction quality. The method is applied to Jilin-l which is the first self-developed commercial remote sensing satellite group in China. The results show the superiority of our method both visually and numerically by comparing with other excellent image super resolution algorithms.
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection

This paper describes the participation of USI-IR in WASSA 2018 Implicit Emotion Shared Task. We propose a relevance feedback approach employing a sequential model (biLSTM) and word embeddings derived from a large collection of tweets. To this end, we assume that the top-k predictions produce at a first classification step are correct (based on the model accuracy) and use them as new examples to re-train the network.
Effective Building Extraction by Learning to Detect and Correct Erroneous Labels in Segmentation Mask

Semantic segmentation is pivotal for remote sensing image analysis. Although existing segmentation techniques perform well on similar landscape images, their generalization capability on an entirely different landscape is extremely poor. One of the primary reasons is that they partially or wholly, neglect the underlying relationship that exist in the joint space of input and output variables. Thus, effectively they lack to impose structure in their output predictions which is necessary for successful segmentation. In this paper, we address this problem and propose a novel solution by modeling the joint distribution of input-output variable which in turn enforces some structure in the initial segmentation mask. To this end, we first detect erroneous labels, in the form of Error maps, in the initial building masks. These Error maps are then used to correct the corresponding erroneous labels through a replacement technique. We evaluate our methodology on the benchmark Inria Aerial Image Labeling dataset, which is a large scale high resolution dataset for building footprint segmentation. In contrast to previous methods, our predicted segmentation masks are much closer to ground truth, owning to the fact that they are able to effectively correct both the large errors as well as the blobby effects. We lastly perform on par with other state-of-the-arts, validating the efficacy of our technique.
A Conditional Generative Adversarial Network to Fuse Sar And Multispectral Optical Data For Cloud Removal From Sentinel-2 Images

In this paper, we present the first conditional generative adversarial network (cGAN) architecture that is specifically designed to fuse synthetic aperture radar (SAR) and optical multi-spectral (MS) image data to generate cloud- and haze-free MS optical data from a cloud-corrupted MS input and an auxiliary SAR image. Experiments on Sentinel-2 MS and Sentinel-l SAR data confirm that our extended SAR-Opt-cGAN model utilizes the auxiliary SAR information to better reconstruct MS images than an equivalent model which uses the same architecture but only single-sensor MS data as input.
Person-independent discomfort detection system for infants

Automatic discomfort detection for infants is important in healthcare, since infants have no ability to express their discomfort. We propose a video analysis system, based on supervised learning and classifying previously unseen infants from the testing set in a fully automated way. The ﬁrst stage of our system consists of fame-based face detection, and then ﬁt a face shape to the detected face area by using a Constrained Local Model (CLM). In the second stage, we analyze expression features by using Elongated Local Binary Patterns (ELBP), and classify expression features with an Support Vector Machine (SVM) for discomfort detection. The key contribution of our system is that the face model is infant-independent by employing a Constrained Local Model without prior knowledge about previously unseen infants. The system detects discomfort with an accuracy of 84 . 3%, a sensitivity of 82 . 4%, and speciﬁcity of 84 . 9% on the testing set containing videos of 11 infants. In addition, in order to increase the robustness of the system to head rotation, we introduce a face recovery method based on the symmetry of the face. With this step, the previous performance parameters increase by 3 . 1 − 3 . 8% tested with videos of 2 infants containing 2 , 010 frames.
A clinician's reference guide for the management of atopic dermatitis in Asians

Background Atopic dermatitis (AD) is a common skin condition among Asians. Recent studies have shown that Asian AD has a unique clinical and immunologic phenotype compared with European/American AD. Objective The Asian Academy of Dermatology and Venereology Expert Panel on Atopic Dermatitis developed this reference guide to provide a holistic and evidence-based approach in managing AD among Asians. Methods Electronic searches were performed to retrieve relevant systematic reviews and guidelines on AD. Recommendations were appraised for level of evidence and strength of recommendation based on the U.K. National Institute for Health and Care Excellence and Scottish Intercollegiate Guidelines Network guidelines. These practice points were based on the consensus recommendations discussed during the Asia Pacific Meeting of Experts in Dermatology held in Bali, Indonesia in October 2016 and April 2017. Results The Expert Panel recommends an approach to treatment based on disease severity. The use of moisturizers is recommended across all levels of AD severity, while topical steroids are recommended only for flares not controlled by conventional skin care and moisturizers. Causes of waning efficacy must be explored before using topical corticosteroids of higher potency. Topical calcineurin inhibitors are recommended for patients who have become recalcitrant to steroid, in chronic uninterrupted use, and when there is steroid atrophy, or when there is a need to treat sensitive areas and pediatric patients. Systemic steroids have a limited role in AD treatment and should be avoided if possible. Educational programs that allow a patient-centered approach in AD management are recommended as an adjunct to conventional therapies. Recommendations on the use of phototherapy, systemic drugs, and emerging treatments are also included. Conclusion The management of AD among Asians requires a holistic approach, integrating evidence-based treatments while considering accessibility and cultural acceptability.
A Novel Approach to Select Anchor Pixels in Sebal Model by Using Inputs from SAR Images

The proper selection of anchor pixels (hot and cold pixels) for calculating the sensible heat flux (H) in SEBAL (Surface Energy Balance Algorithm for Land) is a critical aspect to be considered. The current study proposes a novel method to select the cold and hot pixels using the soil moisture measurements derived from the SAR images. The Water cloud Model (WCM) derived soil moisture values combined with NDVI and Land Surface Temperature values were used to locate the anchor pixels in the current study. The results confirmed that the capability of SAR images combined with optical images will help in the appropriate selection of anchor pixels. The ET values obtained in the study showed good agreement with the ground conditions.
RAPID: Read Acceleration for Improved Performance and Endurance in MLC/TLC NVMs

RAPID is a low-overhead critical-word-first read acceleration architecture for improved performance and endurance in MLC/TLC non-volatile memories (NVMs). RAPID encodes the critical words in a cache line using only the most significant bits (MSbs) of the MLC/TLC NVM cells. Since the MSbs of an NVM cell can be decoded using a single read strobe, the data (i.e., critical words) encoded using the MSbs can be decoded with low latency. System-level SPEC CPU2006 workload evaluations of a TLC RRAM architecture show that RAPID improves read latency by 21%, energy by 24%, and endurance by 2-4× over state-of-the-art striped NVM.
Opinion Mining on Twitter: A Sentiment Analysis of the Iran Deal

Social networks have become an inevitable and integrated part of daily routine for many people. People can freely express their opinions and feelings on almost everything in a large crowd. In this research, we investigate the opinions of the public in relation to one of the most challenging topics in the current international affair: The Iran deal. The deal was reached between Iran and the world powers (China, Russia, United Kingdom, United States, and the European Union in July 2015. As a result of the agreement, Iran agreed to restrict its conflicting program in exchange for lifting some of the restrictions. The viability of the agreement, however, is highly at risk. To analyze the public opinion about the deal, we extracted relevant tweets and performed sentiment analysis by two different approaches (lexicon-based and machine-learning). The results show that the largest number of tweets contained positive sentiments followed by negative and neutral.
A User Study on Learning from Human Demonstration

A significant amount of work has advocated that Learning from Demonstration (LfD) is a promising approach to allow end-users to create behaviors for in-game characters without requiring programming. However, one major problem with this approach is that many LfD algorithms require large amounts of training data, and thus are not practical for learning from human demonstrators. In this paper, we focus on LfD with limited training data, and specifically on the problem of active LfD where the demonstrators are human. We present the results of a user study in comparing SALT, a new active LfD approach, versus a previous state-of-the-art Active LfD algorithm, showing that SALT significantly outperforms it when learning from a limited amount of data in the context of learning to play a puzzle video game.
Designing for Collaborative Infrastructuring

'Infrastructuring' as a concept draws attention to the way in which a designed artefact or system is not the end of the development process. Rather, technology development takes place up to, and including, its successful establishment in an associated body of practices. This places emphasis upon the role of practitioners as designers of their own practice environments. While the activities of professional technology development have been reasonably well discussed and conceptualized, activities that practitioners perform to make things work for them are less acknowledged as a systematic contribution to the successful establishment of the use of IT. This paper considers in particular the so-called 'point of infrastructuring': the moment at which practitioners become aware of opportunities to (re-)design their infrastructures, usually initiated by breakdowns or the recognition of potential innovations and reconsideration of current infrastructural use. From this point onwards, end users themselves may start configuring, tailoring or developing new conventions until a point has been reached in which a new technology usage has been successfully established. However, points of infrastructuring do not only provoke end-user driven in-situ design (or 'infrastructuring') activities. They also evoke so-called 'resonance activities'. These encompass all of the observations and communications that attend upon a point of infrastructure as it becomes visible within a work environment. Examination of resonance activities can be a starting point for a better understanding of the practitioner's activities beyond single infrastructural changes, providing a way of highlighting the relationship between different points of infrastructuring. How to capture and understand resonance activities and how to design technological support for them is still an open question. Based on previous work outlined in the literature and an empirical study that looked at collaborative appropriation activities (the most important aspect of infrastructuring) during 3D printing processes, we outline the concept of sociable technologies as a technological approach for capturing and supporting resonance activities and thus enabling infrastructuring activities more broadly.
Are Raw Brassica Vegetables Healthier Than Cooked Ones? A Randomized, Controlled Crossover Intervention Trial on the Health-Promoting Potential of Ethiopian Kale

The present human intervention trial investigated the health-promoting potential of B. carinata, with a focus on effects of thermal processing on bioactivity. Twenty-two healthy subjects consumed a B. carinata preparation from raw (allyl isothiocyanate-containing) or cooked (no allyl isothiocyanate) leaves for five days in a randomized crossover design. Peripheral blood mononuclear cells were exposed to aflatoxin B1 (AFB1), with or without metabolic activation using human S9 mix, and subsequently analyzed for DNA damage using the comet assay. Plasma was analyzed for total antioxidant capacity and prostaglandin E2 (PGE2) levels. Cooked B. carinata significantly reduced DNA damage induced by AFB1 as compared to baseline levels (+S9 mix: 35%, −S9 mix: 33%, p ≤ 0.01, respectively). Raw B. carinata only reduced DNA damage by S9-activated AFB1 by 21% (p = 0.08). PGE2 plasma levels were significantly reduced in subjects after consuming raw B. carinata. No changes in plasma antioxidant capacity were detectable. A balanced diet, including raw and cooked Brassica vegetables, might be suited to fully exploit the health-promoting potential. These results also advocate the promotion of B. carinata cultivation in Eastern Africa as a measure to combat effects of unavoidable aflatoxin exposure.
Study of Landslide Characteristics Using Time-Series InSAR Technique

Xinmo landslide event, China occurred on 24 June 2017. The head of the landslide area was ∼3,600m above the mean sea level. Then, along the high to low elevation direction, a stable zone (S1) above the head area, an unstable zone (that is divided into an upper unstable subsidence area, UU; and a lower unstable uplift area, LU), and a stable area (S2) before the landslide event were identified. The time-series surface deformation was studied using the SBAS-InSAR technique and Sentinel-1 multi-temporal datasets acquired between November of 2015 and June of 2017. The mean deformation value was −8.3mm in S1, −26.9mm in UU, 27.9mm in LU, and − 1.6mm in S2, respectively. Zoning characteristics of moving earth materials in a typical landslide event were observed. After removing the deformation values in two stable zones, the difference in absolute value of the subsidence and uplift in the unstable zone was about 80mm, 100mm, and 130mm on 24 February 2017, 19 May 2017, and 24 June 2017, respectively. No landslide event occurred in February and May. Thus, with the preliminary findings, a warning might be issued once the absolute value was greater than 100mm.
Fusing semantic labeled camera images and 3D LiDAR data for the detection of urban curbs

This article presents a new approach for detecting curbs in urban environments. It is based on the fusion between semantic labeled images obtained using a convolutional neural network and a LiDAR point cloud. Semantic information will be used in order to exploit context for the detection of urban curbs. Using only the semantic labels associated to 3D points, we will define a set of 3D ROIs in which curbs are most likely to reside, thus reducing the search space for a curb. A traditional curb detection method for the LiDAR sensor is next used to correct the previously obtained ROIs. For this, spatial features are computed and filtered in each ROI using the LiDAR’s high accuracy measurements. The proposed solution works in real time and requires few parameters tuning. It proved independent on the type of the urban road, being capable of providing good curb detection results in straight, curved and intersection shaped roads.
Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks

Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve pathologies classification. These bones can interfere with a broad range of diagnostic tasks on pulmonary disease except for musculoskeletal system. Current conventional method for acquisition of bone suppressed X-rays is dual energy imaging, which captures two radiographs at a very short interval with different energy levels; however, the patient is exposed to radiation twice and the artifacts arise due to heartbeats between two shots. In this paper, we introduce a deep generative model trained to predict bone suppressed images on single energy chest X-rays, analyzing a finite set of previously acquired dual energy chest X-rays. Since the relatively small amount of data is available, such approach relies on the methodology maximizing the data utilization. Here we integrate the following two approaches. First, we use a conditional generative adversarial network that complements the traditional regression method minimizing the pairwise image difference. Second, we use Haar 2D wavelet decomposition to offer a perceptual guideline in frequency details to allow the model to converge quickly and efficiently. As a result, we achieve state-of-the-art performance on bone suppression as compared to the existing approaches with dual energy chest X-rays.
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization

In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep Bayesian Neural Network (BNN). We do so by incorporating a KL divergence penalty term into the training objective of an ensemble, derived from the evidence lower bound used in variational inference. We evaluate the uncertainty estimates obtained from our models for active learning on visual classification. Our approach steadily improves upon active learning baselines as the annotation budget is increased.
Features of Millimeter-Wave Ultra-Wideband Signals Application in Telecommunications

In the paper, the features of millimeter-wave ultra-wideband signals application, related to photon methods of generation and transmission of millimeter-wave signals, as well as the quasi-optical approach for modeling signal loss (amplification) are considered. It is proposed to use more accurate models of millimeter-wave ultra-wideband radio systems, which should correspond to the fundamental physical features of the propagation of millimeter waves, especially in urban conditions. It is shown that when calculating such systems it is necessary to take into account the noise associated with the method of generating millimeter waves.
A Transformer-Based Multi-Source Automatic Post-Editing System

This paper presents our English–German Automatic Post-Editing (APE) system submitted to the APE Task organized at WMT 2018 (Chatterjee et al., 2018). The proposed model is an extension of the transformer architecture: two separate self-attention-based encoders encode the machine translation output (mt) and the source (src), followed by a joint encoder that attends over a combination of these two encoded sequences (encsrc and encmt) for generating the post-edited sentence. We compare this multi-source architecture (i.e, {src, mt} → pe) to a monolingual transformer (i.e., mt → pe) model and an ensemble combining the multi-source {src, mt} → pe and single-source mt → pe models. For both the PBSMT and the NMT task, the ensemble yields the best results, followed by the multi-source model and last the single-source approach. Our best model, the ensemble, achieves a BLEU score of 66.16 and 74.22 for the PBSMT and NMT task, respectively.