Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test Case

This paper presents an automatic algorithm for mapping floods. Its main characteristic is that it can detect not only inundated bare soils, but also floodwater in urban areas. The synthetic aperture radar (SAR) observations of the flood that hit the city of Houston (Texas) following the landfall of Hurricane Harvey in 2017 are used to apply and validate the algorithm. The latter consists of a two-step approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for focusing the analysis on the most risk-prone areas. Data provided by the Sentinel-1 mission acquired in both Strip Map and Interferometric Wide Swath modes were used, with a geometric resolution of 5 m and 20 m, respectively. Furthermore, the coherence-based algorithm takes full advantage of the Sentinel-1 mission’s six-day repeat cycle, thereby providing an unprecedented possibility to develop an automatic, high-frequency algorithm for detecting floodwater in urban areas. The results for the Houston case study have been qualitatively evaluated through very-high-resolution optical images acquired almost simultaneously with SAR, crowdsourcing points derived by photointerpretation from Digital Globe and Federal Emergency Management Agency’s (FEMA) inundation model over the area. For the first time the comparison with independent data shows that the proposed approach can map flooded urban areas with high accuracy using SAR data from the Sentinel-1 satellite mission.
Predictive Ecosystem Mapping of South-Eastern Australian Temperate Forests Using Lidar-Derived Structural Profiles and Species Distribution Models

Modern approaches to predictive ecosystem mapping (PEM) have not thoroughly explored the use of ‘characteristic’ gradients, which describe vegetation structure (e.g., light detection and ranging (lidar)-derived structural profiles). In this study, we apply a PEM approach by classifying the dominant stand types within the Central Highlands region of south-eastern Australia using both lidar and species distribution models (SDMs). Similarity percentages analysis (SIMPER) was applied to comprehensive floristic surveys to identify five species which best separated stand types. The predicted distributions of these species, modelled using random forests with environmental (i.e., climate, topography) and optical characteristic gradients (Landsat-derived seasonal fractional cover), provided an ecological basis for refining stand type classifications based only on lidar-derived structural profiles. The resulting PEM model represents the first continuous distribution map of stand types across the study region that delineates ecotone stands, which are seral communities comprised of species typical of both rainforest and eucalypt forests. The spatial variability of vegetation structure incorporated into the PEM model suggests that many stand types are not as continuous in cover as represented by current ecological vegetation class distributions that describe the region. Improved PEM models can facilitate sustainable forest management, enhanced forest monitoring, and informed decision making at landscape scales.
Improved Arabic-Chinese Machine Translation with Linguistic Input Features

This study presents linguistically augmented models of phrase-based statistical machine translation (PBSMT) using different linguistic features (factors) on the top of the source surface form. The architecture addresses two major problems occurring in machine translation, namely the poor performance of direct translation from a highly-inflected and morphologically complex language into morphologically poor languages, and the data sparseness issue, which becomes a significant challenge under low-resource conditions. We use three factors (lemma, part-of-speech tags, and morphological features) to enrich the input side with additional information to improve the quality of direct translation from Arabic to Chinese, considering the importance and global presence of this language pair as well as the limitation of work on machine translation between these two languages. In an effort to deal with the issue of the out of vocabulary (OOV) words and missing words, we propose the best combination of factors and models based on alternative paths. The proposed models were compared with the standard PBSMT model which represents the baseline of this work, and two enhanced approaches tokenized by a state-of-the-art external tool that has been proven to be useful for Arabic as a morphologically rich and complex language. The experiment was performed with a Moses decoder on freely available data extracted from a multilingual corpus from United Nation documents (MultiUN). Results of a preliminary evaluation in terms of BLEU scores show that the use of linguistic features on the Arabic side considerably outperforms baseline and tokenized approaches, the system can consistently reduce the OOV rate as well.
Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon

Secondary forests (SF) are important carbon sinks, removing CO2 from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions, as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of polarimetric (quad-pol) ALOS-2 PALSAR-2 data for estimating AGB in a SF area. Land-use was assessed through Landsat time-series to extract the SF age, period of active land-use (PALU), and frequency of clear cuts (FC) to randomly select the SF plots. A chronosequence of 42 SF plots ranging 3–28 years (20 ha) near the Tapajós National Forest in Pará state was surveyed to quantifying AGB growth. The quad-pol data was explored by testing two regression methods, including non-linear (NL) and multiple linear regression models (MLR). We also evaluated the influence of the past land-use in the retrieving AGB through correlation analysis. The results showed that the biophysical variables were positively correlated with the volumetric scattering, meaning that SF areas presented greater volumetric scattering contribution with increasing forest age. Mean diameter, mean tree height, basal area, species density, and AGB were significant and had the highest Pearson coefficients with the Cloude decomposition (λ3), which in turn, refers to the volumetric contribution backscattering from cross-polarization (HV) (ρ = 0.57–0.66, p-value < 0.001). On the other hand, the historical use (PALU and FC) showed the highest correlation with angular decompositions, being the Touzi target phase angle the highest correlation (Φs) (ρ = 0.37 and ρ = 0.38, respectively). The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing to the NL model (R2 adj. = 0.51; RMSE = 38.7 Mg ha−1) bias = 2.1 ± 37.9 Mg ha−1 by incorporate the angular decompositions, related to historical use, and the contribution volumetric scattering, related to forest structure, in the model. The MLR uses six variables, whose selected polarimetric attributes were strongly related with different structural parameters such as the mean forest diameter, basal area, and the mean forest tree height, and not with the AGB as was expected. The uncertainty was estimated to be 18.6% considered all methodological steps of the MLR model. This approach helped us to better understand the relationship between parameters derived from SAR data and the forest structure and its relation to the growth of the secondary forest after deforestation events.
Efficient and Secure Multiparty Computation from Fixed-Key Block Ciphers

Many implementations of secure computation use fixed-key AES (modeled as a random permutation); this results in substantial performance benefits due to existing hardware support for AES and the ability to avoid recomputing the AES key schedule. Surveying these implementations, however, we find that most utilize AES in a heuristic fashion; in the best case this leaves a gap in the security proof, but in many cases we show it allows for explicit attacks.Motivated by this unsatisfactory state of affairs, we initiate a comprehensive study of how to use fixed-key block ciphers for secure computation—in particular for OT extension and circuit garbling—efficiently and securely. Specifically:•We consider several notions of pseudorandomness for hash functions (e.g., correlation robustness), and show provably secure schemes for OT extension, garbling, and other applications based on hash functions satisfying these notions. •We provide provably secure constructions, in the (non-programmable) random-permutation model, of hash functions satisfying the different notions of pseudorandomness we consider.Taken together, our results provide end-to-end security proofs for implementations of secure-computation protocols based on fixed-key block ciphers (modeled as random permutations). Perhaps surprisingly, at the same time our work also results in noticeable performance improvements over the state-of-the-art.
Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance

Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding the performance of models. Yet no consensus has ever been reached on how to select informative bands, even though many techniques have been proposed for estimating plant properties using the vast array of hyperspectral reflectance. In this study, we designed a series of virtual experiments by introducing a dummy variable (Cd) with convertible specific absorption coefficients (SAC) into the well-accepted leaf reflectance PROSPECT-4 model for evaluating popularly adopted informative bands selection techniques, including stepwise-PLS, genetic algorithms PLS (GA-PLS) and PLS with uninformative variable elimination (UVE-PLS). Such virtual experiments have clearly defined responsible wavelength regions related to the dummy input variable, providing objective criteria for model evaluation. Results indicated that although all three techniques examined may estimate leaf biochemical contents efficiently, in most cases the selected bands, unfortunately, did not exactly match known absorption features, casting doubts on their general applicability. The GA-PLS approach was comparatively more efficient at accurately locating the informative bands (with physical and biochemical mechanisms) for estimating leaf biochemical properties and is, therefore, recommended for further applications. Through this study, we have provided objective evaluations of the potential of PLS regressions, which should help to understand the pros and cons of PLS regression models for estimating vegetation biochemical parameters.
Soil Moisture Monitoring Using Remote Sensing Data and a Stepwise-Cluster Prediction Model: The Case of Upper Blue Nile Basin, Ethiopia

In this study, a residual soil moisture prediction model was developed using the stepwise cluster analysis (SCA) and model prediction approach in the Upper Blue Nile basin. The SCA has the advantage of capturing the nonlinear relationships between remote sensing variables and volumetric soil moisture. The principle of SCA is to generate a set of prediction cluster trees based on a series of cutting and merging process according to a given statistical criterion. The proposed model incorporates the combinations of dual-polarized Sentinel-1 SAR data, normalized difference vegetation index (NDVI), and digital elevation model as input parameters. In this regard, two separate stepwise cluster models were developed using volumetric soil moisture obtained from automatic weather stations (AWS) and Noah model simulation as response variables. The performance of the SCA models have been verified for different significance levels (i.e., α = 0.01 , α = 0.05 , and α = 0.1 ). Thus, the AWS based SCA model with α = 0.05 was found to be an optimal model for predicting volumetric residual soil moisture, with correlation coefficient (r) values of 0. 95 and 0.87 and root mean square error (RMSE) of 0.032 and 0.097 m3/m3 during the training and testing periods, respectively. While in the case of the Noah SCA model an optimal prediction performance was observed when α value was set to 0.01, with r being 0.93 and 0.87 and RMSE of 0.043 and 0.058 m3/m3 using the training and testing datasets, respectively. In addition, our result indicated that the combined use of Sentinel-SAR data and ancillary remote sensing products such as NDVI could allow for better soil moisture prediction. Compared to the support vector regression (SVR) method, SCA shows better fitting and prediction accuracy of soil moisture. Generally, this study asserts that the SCA can be used as an alternative method for remote sensing based soil moisture predictions.
Synthesis of Vegetation Indices Using Genetic Programming for Soil Erosion Estimation

Vegetation Indices (VIs) represent a useful method for extracting vegetation information from satellite images. Erosion models like the Revised Universal Soil Loss Equation (RUSLE), employ VIs as an input to determine the RUSLE soil Cover factor (C). From the standpoint of soil conservation planning, the C factor is one of the most important RUSLE parameters because it measures the combined effect of all interrelated cover and management variables. Despite its importance, the results are generally incomplete because most indices recognize healthy or green vegetation, but not senescent, dry or dead vegetation, which can also be an important contributor to C. The aim of this research is to propose a novel approach for calculating new VIs that are better correlated with C, using field and satellite information. The approach followed by this research is to state the generation of new VIs in terms of a computer optimization problem and then applying a machine learning technique, named Genetic Programming (GP), which builds new indices by iteratively recombining a set of numerical operators and spectral channels until the best composite operator is found. Experimental results illustrate the efficiency and reliability of this approach to estimate the C factor and the erosion rates for two watersheds in Baja California, Mexico, and Zaragoza, Spain. The synthetic indices calculated using this methodology produce better approximation to the C factor from field data, when compared with state-of-the-art indices, like NDVI and EVI.
Structural and functional characterisation of staphylococcal superantigen-like protein 11 (SSL11)

I Acknowledgements.III Table of Figures . IX Table of Tables.X Abbreviations . XI Chapter 1 Introduction . 1 1.1 Overview . 1 1.2 Staphylococcus aureus . 2 1.2. 1 Staphylococci . 2 1.2. 2 Pathogenesis of S. aureus . 2 1.2. 3 The S. aureus genome and antibiotic resistance.2 1.3 Host defenses.3 1.3. 1 The complement system.3 i) The three activation pathways.4 ii) Formation of the membrane attack complex (MAC) . 5 iii) The anaphylotoxins, C3a, C4a and C5a . 5 1.3. 2 Pro-inflammatory responses.6 1.3. 3 Leukocyte recruitment to sites of inflammation.6 i) Leukocyte tethering and rolling . 7 ii) Leukocyte adhesion.8 iii) Extravasation / diapedesis . 9 iv) Paracellular migration . 9 v) Transcellular migration . 10 1.3. 4 Neutrophil killing of bacteria . 11 1.3. 5 Phagocytosis.11 1.3. 6 Oxidative burst . 12 1.3. 7 Lactoferrin . 12 1.3. 8 Neutrophil extracellular traps (NETs). 13 1.4 Host evasion by S. aureus . 13 1.4. 1 Complement evasion by S. aureus . 14 i) Inhibition of the initiation of the complement system . 14 ii) Inhibition of C3 convertase . 15 iii) Inhibition of the complement cascade through C3b.15 iv) Inhibition of chemotaxis by C5a . 15 1.4. 2 Evasion of neutrophil killing by S. aureus . 16 i) Factors that inhibit opsonophagocytosis of S. aureus . 16 ii) Factors that inhibit lysis of S. aureus in the phagolysosome . 16 iii) Toxins that lyse neutrophils . 17 1.4. 3 Other evasion mechanisms.17 1.5 Virulence factors produced by S. aureus . 18 1.5. 1 Exfoliative toxins . 18 1.5. 2 Panton-valentine leukocidin . 18 1.5. 3 Superantigens . 19 i) Three-dimensional structure of classical bacterial SAgs . 21
Decentralized estimation and control for power systems

This thesis presents a decentralized alternative to the centralized state-estimation and control technologies used in current power systems. Power systems span over vast geographical areas, and therefore require a robust and reliable communication network for centralized estimation and control. The supervisory control and data acquisition (SCADA) systems provide such a communication architecture and are currently employed for centralized estimation and control of power systems in a static manner. The SCADA systems operate at update rates which are not fast enough to provide appropriate estimation or control of transient or dynamic events occurring in power systems. Packet-switching based networked control system (NCS) is a faster alternative to SCADA systems, but it suffers from some other problems such as packet dropouts, random time delays and packet disordering. A stability analysis framework for NCS in power systems has been presented in the thesis considering these problems. Some other practical limitations and problems associated with real-time centralized estimation and control are computational bottlenecks, cyber threats and issues in acquiring system-wide parameters and measurements. The aforementioned problems can be solved by a decentralized methodology which only requires local parameters and measurements for estimation and control of a local unit in the system. The cumulative effect of control at all the units should be such that the global oscillations and instabilities in the power system are controlled. Such a decentralized methodology has been presented in the thesis. The method for decentralization is based on a new concept of `pseudo-inputs' in which some of the measurements are treated as inputs. Unscented Kalman filtering (UKF) is applied on the decentralized system for dynamic state estimation (DSE). An extended linear quadratic regulator (ELQR) has been proposed for the optimal control of each local unit such that the whole power system is stabilized and all the oscillations are adequately damped. ELQR requires DSE as a prerequisite. The applicability of integrated system for dynamic estimation and control has been demonstrated on a model 16-machine 68-bus benchmark system.
Delaying Aging and Extending Life – An Ancient Dream Revisited : Using Body Regimens as a Window to Reflect on Aging, Identity, and the Body

The desire to defy the aging process and to prolong the lifespan has long captured the human imagination. Recognized as one of the most ancient known pieces of literature, the Epic of Gilgamesh documents a King’s quest to find immortality. More recent examples include the story of Ponce de Leon’s 16th century search to discover the Fountain of Youth, Sir Francis Bacon’s (1659) assertion that humans are naturally immortal “potens non mori,” and Benjamin Franklin’s desire to be preserved in a vat of madeira until science is capable of life extension. Developments in science and technology, including telomere manipulation, genetic engineering, cloning, nanotechnology, the potential to create new organs from stem cells, and the creation of therapeutic pharmaceuticals that could significantly postpone disease, have served to inspire; aging in the 21st century is no longer regarded by scientists as an inevitable process programmed by evolution (Olshansky et al. 2006). Situated within a detailed historical overview, this qualitative research project explores the experiences of individuals engaged in practices currently implicated in potentially delaying aging and even extending life. Based on information from 44 in-depth interviews, this research explores issues such as lay understandings of the biology of aging, conceptualizations of the inner body, the use of and experience with optimization technologies, and the embodied effects of participation in anti-aging and life-extension body regimens.
Proposing an Integrated Framework of Seven Basic and New Quality Management Tools and Techniques: A Roadmap

In the businesses of today, managers and decision makers are often face diversified and complicated problems. The use and application of quality tools and techniques within an effective problem solving methodology are essential to understand and facilitate improvement in any process. Most of the quality-related problems can be solved with the seven basic quality tools and techniques (QC7). Also the seven new quality tools and techniques (M7) are attracting decision makers and expects and their use is increasingly growing. This paper has proposed a new roadmap to apply seven basic and new quality tools and techniques in an integrative framework. For this purpose, a number of experts have been asked to fill a matrix questionnaire. Two approaches have been used to analyze the questionnaires' results. They have been analyzed and prioritized through simple comparison of their average values and also through permutation. The findings imply that QC7 tend to be mostly precedent to M7.