Miracle-dz/UniFAR-FTUs huggingface dataset:
Super-Resolution of Remote Sensing Images Based on Transferred Generative Adversarial Network

Single image super-resolution (SR) has been widely studied in recent years as a crucial technique for remote sensing applications. This paper proposes a SR method for remote sensing images based on a transferred generative adversarial network (TGAN). Different from the previous GAN-based SR approaches, the novelty of our method mainly reflects from two aspects. First, the batch normalization layers are removed to reduce the memory consumption and the computational burden, as well as raising the accuracy. Second, our model is trained in a transfer-learning fashion to cope with the insufficiency of training data, which is the crux of applying deep learning methods to remote sensing applications. The model is firstly trained on an external dataset DIV2K and further fine-tuned with the remote sensing dataset. Our experimental results demonstrate that the proposed method is superior to SRCNN and SRGAN in terms of both the objective evaluation and the subjective perspective.
Increased Surgical Complications in Smokers Undergoing Radical Cystectomy1

Background: Not only is smoking a risk factor for the development of bladder cancer, it has also been implicated in increasing surgical morbidity and mortality. In general, the demographic and clinical characteristics of smokers are different to non-smokers which can bias the results of the impact of smoking. Objective: To evaluate the impact of smoking on radical cystectomy outcomes. Methods: Radical cystectomy cases were identified in the National Surgical Quality Improvement Program database from 2007–2015. Smokers were matched with non-smokers using propensity scores in a 1:1 ratio. Multivariate logistic regression was performed to evaluate the overall incidence of Clavien III-V complications. Secondary analysis was performed for the incidence of each complication recorded in NSQIP. Results: A total of 850 smokers undergoing radical cystectomy were matched to 850 non-smokers. The matching process improved the balance of covariates between smokers and non-smokers. The overall incidence of Clavien III-V complications was higher in smokers (13.1% vs 7.4%, p < 0.001). This corresponded to an adjusted odds ratio of 1.9 [95% CI 1.4–2.6, p = 0.028]. Other comorbid conditions worsened post-operative complications amongst smokers. When evaluating each complication recorded in the database, smokers had a higher incidence of wound dehiscence, pneumonia and myocardial infarction. Conclusion: Current smokers have a greater risk of morbidity following radical cystectomy. This should be considered when evaluating safety of surgery and patients should be counselled accordingly. Furthermore, even a short period of pre-operative smoking cessation can improve surgical outcomes.
Printable Aggregate Elements

Aggregating base elements into rigid objects such as furniture or sculptures is a great way for designers to convey a specific look and feel. Unfortunately, there is no existing solution to help model structurally sound aggregates. The challenges stem from the fact that the final shape and its structural properties emerge from the arrangements of the elements, whose sizes are large so that they remain easily identifiable. Therefore there is a very tight coupling between the object shape, structural properties, and the precise layout of the elements. We present the first method to create aggregates of elements that are structurally sound and can be manufactured on 3D printers. Rather than having to assemble an aggregate shape by painstakingly positioning elements one by one, users of our method only have to describe the structural purpose of the desired object. This is done by specifying a set of external forces and attachment points. The algorithm then automatically optimizes a layout of user-provided elements that answers the specified scenario. The elements can have arbitrary shapes: convex, concave, elongated, and can be allowed to deform. Our approach creates connections between elements through small overlaps preserving their appearance, while optimizing for the global rigidity of the resulting aggregate. We formulate a topology optimization problem whose design variables are the positions and orientations of individual elements. Global rigidity is maximized through a dedicated gradient descent scheme. Due to the challenging setting -- number of elements, arbitrary shapes, orientation, and constraints in 3D -- we propose several novel steps to achieve convergence.
Polsar Speckle Filtering Using Iterative MMSE

In this paper, we extend the use of the synthetic aperture radar (SAR) iterative minimum mean square error (IMMSE) technique to polarimetric SAR (Po1SAR) speckle filtering. Experimental results prove that the proposed approach especially reduces speckle in the extended homogenous areas and, meanwhile, better preserves the spatial details and the polarimetric information, compared with other classical methods.
Calibration Challenges for the Biomass P-Band SAR Instrument

The BIOMASS mission gives completely new challenges in external calibration arising from the orbital pattern needed for the tomographic and Pol-InSAR phases of the mission, the strong effects of the ionosphere at P-band, and the lack of pre-existing P-band data except over very limited parts of the globe. Together these create problems that can only be solved by combining infrequent visits to instrumented calibration sites with systematic exploitation of the properties of distributed targets and targets of opportunity. Proposed approaches to performing radiometric and polarimetric calibration are described, together with meeting geolocation accuracy requirements.
DB&IR integration: report on the dagstuhl seminar

This paper is based on a five-day workshop on "Ranked XML Querying" that took place in Schloss Dagstuhl in Germany in March 2008 and was attended by 27 people from three different research communities: database systems (DB), information retrieval (IR), and Web. The seminar title was interpreted in an IR-style "andish" sense (it covered also subsets of {Ranking, XML, Querying}, with larger sets being favored) rather than the DB-style strictly conjunctive manner. So in essence, the seminar really addressed the integration of DB and IR technologies with Web 2.0 being an important target area.
Video Games for Well-Being: A Systematic Review on the Application of Computer Games for Cognitive and Emotional Training in the Adult Population

Background: Although several excellent reviews and meta-analyses have investigated the effect of video game trainings as tools to enhance well-being, most of them specifically focused on the effects of digital games on brain plasticity or cognitive decline in children and seniors. On the contrary, only one meta-analysis results to be focused on the adult population, and it is restricted to examining the effects of training with a particular genre of games (action video games) on cognitive skills of healthy adults. Objectives: This systematic review was aimed to identify research evidences about the impact on cognitive [i.e., processing and reaction times (RTs), memory, task-switching/multitasking, and mental spatial rotation] and emotional skills of video games training in the healthy adult population. Methods: A multi-component analysis of variables related to the study, the video games, and the outcomes of the training was made on the basis of important previous works. Databases used in the search were PsycINFO, Web of Science (Web of Knowledge), PubMed, and Scopus. The search string was: [(“Video Games” OR “Computer Games” OR “Interactive Gaming”)] AND [(“Cognition”) OR (“Cognitive”) OR (“Emotion”) OR (“Emotion Regulation”)] AND [“Training”]. Results: Thirty-five studies met the inclusion criteria and were further classified into the different analysis' variables. The majority of the retrieved studies used commercial video games, and action games in particular, which resulted to be the most commonly used, closely followed by puzzle games. Effect sizes for training with video games on cognitive skills in general ranged from 0.06 to 3.43: from 0.141 to 3.43 for processing and RTs, 0.06 to 1.82 for memory, 0.54 to 1.91 for task switching/multitasking, and 0.3 to 3.2 for mental spatial rotation; regarding video games for the training of emotional skills, effect sizes ranged from 0.201 to 3.01. Conclusion: Overall, findings give evidences of benefits of video games training on cognitive and emotional skills in relation to the healthy adult population, especially on young adults. Efficacy has been demonstrated not only for non-commercial video games or commercial brain-training programs, but for commercial video games as well.
Spatio-Temporal Assessment of Fire Severity in a Protected and Mountainous Ecosystem

Wildfires are one of the major threats to the biodiversity of protected and mountainous landscapes. The severity of these fires is an important factor that influence vegetation recovery. Several remote sensing techniques has been used in previous studies to assess fire severity, but the Normalized Burn Ratio (NBR) has become the standard SI approach to assess burn severity. The Normalized Burn Ratio (NBR) demarcates burnt and unburned pixels in the pre-/post-fire season and, then creates an index of the severity of a burn using multi-spectral sensors, which establishes an ideal tool for fire severity assessment. In this study, the fire severity level is computed using the NBR Index from LANDSAT image. The images of pre-and postfire are used to evaluate the severity level, which is defined as a difference in NBR Index of pre- and post-fire. Results shows that in year 2000, 30704.22 ha of area burned with low to high severity, 31352.13 ha in year 2005,4697 ha in year 2013 and 30009.33 ha in 2017. A hundred field plots and fire records were used to validate the fire severity map for 2013 and 2017 with an accuracy of 74% which supports the use of NBR for assessing fire severity.
High-Order Ionospheric Effects on 3-D GPS Coordinate Estimation in Turkey

The global positioning system (GPS) can navigate and position at any time and weather conditions, but subject to a variety of error sources. The most important one of these errors is the ionospheric delay when the GPS signals propagate through the ionosphere. The first order ionospheric effects can be removed using dual frequency GPS receivers, while the higher order ionospheric (HOI) effects are much smaller than the first order effects, which are generally neglected. However, under highly active solar activities condition, high order ionospheric effects may be big and should be eliminated. In this study, the high order ionospheric effects on 3D GPS coordinate components was investigated with 30 days (June 2011) of GPS data at 8 GPS stations in Turkey. Firstly, the high order ionospheric effects were corrected in the RINEX observation file, and then raw and corrected observations were processed in GAMIT software to obtain precise coordinate components. Results show that average 10, 7 and 24 millimeters changes occurred in the north, east and up components by high-order ionospheric effects, respectively. During the peak solar activity days in 2011, it is observed that the high-order ionospheric effects in the north, east and up components can reach 18, 11 and 43 millimeters change, respectively.
Flat panel UV-C light source with CNT cold cathode electron beam (C-beam)

We developed flat panel UV-C light generation technique with the electron beam pumping technology using carbon nanotube (CNT) emitters. The wide bandgap anode material excited with high performance CNT based cold cathode electron beam (C-beam). Flat panel UV-C light generation efficiencies depend on the anode biases and current densities. With the optimized C-beam and anode, we could obtain UV-C with wavelength of 269.5 nm. Flat panel UV-C light source would be applied to many industries because advantages of flat areal light source.
Nonparametric Bayesian 3-D ISAR Imaging of Space Debris

Space debris damage orbiting spacecraft and astronauts and ISAR imaging is an important method to recognize and classify debris. Compared with 2-D imaging, 3-D imaging is able to provide more information. However, debris with rapid spinning have great migration through range-cells, so common methods are unproductive. A novel method of ISAR 3-D imaging based on nonparametric Bayesian model is proposed aimed at debris with spinning. Firstly, a motion model and a signal model are proposed. Secondly, PSO algorithm is utilized to preprocess the data and obtain the height of target. Finally, Nonparametric Bayesian model is imposed to elaborately reconstruct the target in range and cross-range. For monostatic radar, point-target simulation data and electromagnetism data confirm that the method will obtain refined 3-D imaging results. Meanwhile, this method is capable to surmount the obstacle of Doppler aliasing and data missing caused by rapid spinning.
Spatial Null Estimation in Beam-Space Post-Doppler Stap for Airborne Collocated Mimo Radar

Exploiting Spatial and Doppler nulls is a straightforward way to construct a valid blocking matrix(BM) in the generalized sidelobe canceller(GSC)-based Space-time adaptive processing(STAP). However, spatial nulls of the airborne collocated MIMO radar are not uniformly distributed and their unknown frequencies remain to be estimated. In this work, we develop estimation rules of spatial null for the airborne collocated MIMO radars. The proposed rules provide closed-form solutions of both the number and frequencies of spatial nulls. By using the proposed estimated rules to construct the BM, the resulting GSC-based STAP processor exhibits near-optimum performance and outperforms the conventional estimation rules. Simulations are provided to verify the accuracy of the proposed estimation rule and demanstrate the effectiveness of its application to the GSC-based STAP.
Experience Management with Beliefs, Desires, and Intentions for Virtual Agents

Intelligent interactive narrative systems often use an experience manager to govern the behavior of non-player charactersin a way that guides the story towards its author’s agenda, which may be for entertainment, education, training, or other purposes. For such systems, a central challenge is creating believable virtual characters. The Belief Desire Intention framework is often cited as a goal for researchers in this field; for characters to seem realistic, a human audience should attribute beliefs, desires, and intentions to them. Much of my prior work has focused on belief; my goal for the future is to finish the work on belief, and to implement a new model of desire and intention that explicitly reasons about characters’ commitment to certain plans of action.
AIS Electronic Library (AISeL)

This research explored the sentiment in post, retweet, and reply is more meaningful than just numbers. We analyzed four targets (two politicians and two celebrities) on Twitter to examine our model. The results showed the sentiment ratio of celebrities is higher than politicians. We also provided the h -index to identify high impact of posted topics and the results showed different topics have different impact. The proposed model can appropriately estimate the influence in social media and help companies allocate resources effectively and efficiently.
Fabrication of highly conductive carbon nanotube emitters for long-life cold cathode electron beam

We developed high performance carbon nanotube cold cathode electron source with molybdenum layer for vacuum nanoelectronic devices. With DC-PECVD system for the CNT growth, the high-energy ion radicals in the plasma could etch the Mo layer, and then the etched vapors coated on the CNT emitter. With the patterned Mo layer, a property of CNT emitter were improved as the emission current and the conductivity of emitters
Shared collaborative spaces challenges in inter-organisational collaborative projects

This research in progress is based on a qualitative approach with multiple case studies using Nonaka’s theory of knowledge creation and conceptualisation of a knowledge-creating place, Ba. It addresses the high-level need to make collaboration more effective between university and industry by exploring shared collaborative spaces in collaborative research projects. It uses semi-structured interviews, participant observation, and document analysis to look at the ways in which researchers and industry representatives with different culture within these partnerships create shared collaborative spaces to share information and knowledge. The study will propose a conceptual model explicating effective shared collaborative spaces in the university-industry collaboration (UIC) context according to the active actors (researchers and industry representatives) perspective. The model will be an original contribution to research in the area of knowledge management in UICs’. Finding effective shared collaborative spaces may be lead to more effective collaboration between university and industry. a number of This paper presents the initial findings of one case study. PROTIC (Participatory Research and Ownership with Technology, Information, and Change) is a 5-year collaborative project between Monash University, Oxfam Australia, and Oxfam in Bangladesh (Sarrica et al. 2017). It was launched on June 7, 2015. One hundred smartphones and phone credit are given to the women farmers in each of two villages in Bangladesh. Then they are trained how they can use it. It provides benefits for women farmers in numbers of ways. Trained women are able to interact with agricultural information via smartphones concerned to crop, rice cultivation, fisheries, livestock, poultry, and general horticulture. This information is provided throughout commercial telecommunications companies which are developed by the local community. Women have access to SMS services and phone call if they need to get some information. This project is investigating information system design and socio-technical questions related to the adoption and adaptation of new technologies. PROTIC is a Participatory Action Research (PAR) project, and the outcomes are expected to include recommendations on managing PAR projects and documentation on developing information management systems for resilient farming in Bangladesh.
A Model Driven Approach for Snow Wetness Retrieval with Sentinel-l

In this paper, a novel approach for the retrieval of snow wetness is presented for Sentinel-1 (S-1) data. The approach uses the information on snow proprieties provided by the hydroclimatological model AMUNDSEN and confirmed by comparisons performed at different sematic level to train a regressor that is able to exploit the dual-polarimetric information provided by S-1. The preliminary results obtained for the Rofental in Austria are discussed.
Testing Multi-Sensors Time Series of Lai Estimates to Monitor Rice Phenology: Preliminary Results

Timely and accurate information on crop growth and seasonal dynamics are increasingly needed to develop monitoring systems aimed to detect seasonal anomalies, support site specific management and estimate crop yield at the end of the season. In particular, frequent decametric information nowadays being provided exploiting the new generation of Earth Observation (EO) platforms are fundamental for farm level monitoring. This study presents an analysis aimed at fully exploiting dense time series of EO data derived from the combined use of ESA Sentinel-2A and NASA Landsat-7/8 imageries for crop phenological monitoring. Decametric Leaf Area Index (LAI) maps were generated for the year 2016 by inversion of the PROSAIL radiative transfer model with Gaussian process regression for a study area located in northern of Italy, and used to derive rice crop stages information by exploiting an adaptation of phenological algorithm. Preliminary results showed that occurrence of the main crop development stages can be estimated at parcel level with reasonable accuracy (7 to 15 days mean error depending on the phenological phase) and phenometrics analysis provide information in agreement with the different cultivated varieties and adopted agro-management practices.
Combining the Pulmonary Rehabilitation Decisional Score with the Bode Index and Clinical Opinion in Assigning Priority for Pulmonary Rehabilitation

Abstract Combining objective tools with clinical decision (CD) may help clinicians identify the priority for pulmonary rehabilitation (PR) in patients with COPD. We aimed to assess the specificity, sensitivity and efficiency of a new tool, the Pulmonary Rehabilitation Decisional Score (PRDS), and its correlation with the BODE index (BI) and CD in assigning PR priority. We retrospectively compared the three methods (CD vs. PRDS vs. BI) in 124 patients. We assigned low priority (LP), high priority (HP) and very high priority (VHP) to PR based on a priori scores of PRDS (LP = 0–10; HP = 11–17; VHP ≥18) and BI (LP = 0–2; HP = 3–5; VHP ≥6) and compared these with CD. PR priority assigned by the different methods was similar among groups, but did not often refer to the same subjects. PRDS and BI showed very high concordance with CD in defining VHP (97.8% and 95.6% for PRDS and BI, respectively), but were less concordant with CD in assigning LP and HP. Both PRDS and BI differently evaluated 38/124 cases compared to CD (PRDS underprescribed 18 and overprescribed 20; BI underprescribed 19 and overprescribed 19). However, a direct comparison between PRDS and BI showed that the discordance decreased to 8 underprescriptions and 10 overprescriptions (efficiency ∼85%). An objective instrument such as the PRDS can enhance CD with additional information on new aspects such as disability and fragility. PRDS and BI are nonetheless equally efficient at detecting discrepancies versus CD alone, especially when the priority for PR is defined as low or very high.
Exploring Gameplay With AI Agents

The process of play testing a game is subjective, expensive and incomplete. In this paper, we present a play-testing approach that explores the game space with automated agents and collects data to answer questions posed by the designers. Rather than have agents interacting with an actual game client, this approach recreates the bare bone mechanics of the game as a separate system. Our agent is able to play in minutes what would take testers days of organic gameplay. The analysis of thousands of game simulations exposed imbalances in game actions, identified inconsequential rewards and evaluated the effectiveness of optional strategic choices. Our test case game, The Sims Mobile, was recently released and the findings shown here influenced design changes that resulted in improved player experience.
Deep Learning - a New Approach for Multi-Label Scene Classification in Planetscope and Sentinel-2 Imagery

Motivated by the increasing availability of high-resolution satellite imagery, we developed deep learning models able to efficiently and accurately classify the atmospheric conditions and dominant classes of land cover/land use in commercial PlanetScope imagery acquired over the Amazon rainforest. In specific, we trained deep convolutional neural network (CNN) to perform multi-label scene classification of high-resolution (<10 m) satellite imagery. We also discuss the challenges and opportunities in training deep CNN models for multi-label scene classification. Finally, we investigate the transferability of our PlanetScope-trained models to freely available Sentinel-2 imagery acquired over the wet tropics of Australia. Our best performing model achieved an $F$β of 0.91, which was only 2% short of the top performing model in the Understanding the Amazon from Space Kaggle competition [1]. We also find that our models are suitable for classifying similar resolution satellite imagery, such as Sentinel-2.