Miracle-dz/UniFAR-FTUs huggingface dataset:
Patch Ordering-Based SAR Image Despeckling Via Transform-Domain Filtering

In this paper, we propose a synthetic aperture radar (SAR) image despeckling method based on patch ordering and transform-domain filtering. Logarithmic transformation with bias correction is applied to the original SAR image to transform the multiplicative noise model into the additive model. Then, we adopt a two-stage filtering strategy. The first stage is coarse filtering which can suppress speckle effectively. In this stage, we extract the sliding patches from the logarithmic SAR image, and order them in a smooth way by a simplified patch ordering algorithm specially for SAR images. The ordered patches are filtered by learned simultaneous sparse coding (SSC), a technology recently advanced in image processing. Then, the coarse filtering result is reconstructed from the filtered patches via inverse permutation and subimage averaging. The second stage is refined filtering which can eliminate small artifacts generated by the coarse filtering. In this stage, the sliding patches are extracted from the coarse filtering result and ordered in the same way. Then, we apply 2-D wavelet hard-thresholding to the ordered patches and reconstruct the refined filtering result. The final result is obtained by taking exponential transformation to the refined filtering result. An algorithm based on the proposed strategy is presented in detail and the parameters are selected for fast and effective realization. Experimental results with both simulated images and real SAR images demonstrate that the proposed method achieves state-of-the-art despeckling performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, equivalent number of looks (ENLs), and ratio image.
The Shakura-Sunyaev Disk Can Smoothly Match an Advection-dominated Accretion Flow

We use the standard Runge-Kutta method to solve a set of basic equations describing black hole accretion flows composed of two-temperature plasma. We do not invoke any extra energy transport mechanism, such as thermal conduction, and we do not specify any ad hoc outer boundary condition for the advection-dominated accretion flow (ADAF) solution. We find that in the case of high viscosity and nonzero radiative cooling, the ADAF solution can have an asymptotic approach to the Shakura-Sunyaev disk (SSD) solution and that the SSD-ADAF transition radius is close to the central black hole. Our results further prove the mechanism of thermal instability-triggered SSD-ADAF transition suggested previously by Takeuchi & Mineshige and Gu & Lu.
An intelligent data fusion framework for structural health monitoring

We propose a structural health monitoring framework that is based on the hybrid intelligent systems to compute composite structure health index as structure health indicator. With our proposed health structure monitor framework, it provides a decision-level analysis for structure health monitoring. In contrast, recent studies focus on signal processing, mechanical modelling and computer based systems, which are not robust enough to provide decision-level analysis confronted with heterogeneous data sources. Therefore, data fusion technique is required to alleviate possibilities of data conflicts, lack of conciseness and incompleteness that arise from the heterogeneous data sources to compute composite structure health index, which is essential for risk analysis. Our proposed intelligent data fusion framework is based on hybrid adaptive resonant theory neural network (superior learning from dynamic data) and adaptive fuzzy inference (superior reasoning with dynamic fuzzy rules derived from time series data formulated membership functions) systems. The outcome of our proposed framework is a structure composite health index, an aggregated index via optimal weighted variables. We implement our framework using a set of GPS and Wind velocity sensors output from a real bridge to evaluate our framework. These sensors data used in our study are collected from long cable-bridge. Our case study showed that our proposed intelligent data fusing framework is capable of reporting the structural status correctly.
Small-scale temporal and spatial variations in protistan community composition at the San Pedro Ocean Time-series station off the coast of southern California

Small-scale spatial and temporal variabiliy in protistan community composition was investigated at the USC San Pedro Ocean Time-series (SPOT) station and contrasted with a 10 yr (2000 to 2010) dataset of samples collected at approximately monthly intervals from the same station. Surface seawater samples were collected for 12 consecutive days at the SPOT station, and an addi- tional 17 stations (16 within a grid surrounding the SPOT station and 1 outlying station; 2 to 21 km apart) were sam- pled during 1 of the 12 sampling days, to investigate the small-scale temporal and spatial variability, respectively. Terminal restriction fragment length polymorphism (T-RFLP) analysis of the 18S rRNA gene was used to gen- erate DNA fingerprints of the protistan community, which were used for the calculation of pair-wise Bray-Curtis and Jaccard similarity values at different spatial and temporal scales. Temperature, salinity, pH, wind, and upwelling did not appear to have any significant effect on commu- nity composition, and distance had a weak correlation with the similarity indices generated from spatial sam- ples. Communities separated by the smallest spatial scales (0 to 2 km) had significantly higher average similar- ity than communities separated by small temporal scales (1 to 9 d). Comparisons with a 10 yr dataset of monthly samples revealed significantly lower average similarity values among communities separated by time periods ≥ 1 mo (45 to 52%) compared to communities separated by the smallest spatial (0 to 2 km; 67 to 71%) and temporal (1 d; 64%) scales. Our results indicate that small-scale spatial and day-to-day variability of protistan communi- ties was overshadowed by monthly, seasonal, and inter- annual variabilities.
Bayesian Inference of Divergence Times and Feeding Evolution in Grey Mullets (Mugilidae)

Grey mullets (Mugilidae, Ovalentariae) are coastal fishes found in near-shore environments of tropical, subtropical, and temperate regions within marine, brackish, and freshwater habitats throughout the world. This group is noteworthy both for the highly conserved morphology of its members—which complicates species identification and delimitation—and also for the uncommon herbivorous or detritivorous diet of most mullets. In this study, we first attempt to identify the number of mullet species, and then—for the resulting species—estimate a densely sampled time-calibrated phylogeny using three mitochondrial gene regions and three fossil calibrations. Our results identify two major subgroups of mullets that diverged in the Paleocene/Early Eocene, followed by an Eocene/Oligocene radiation across both tropical and subtropical habitats. We use this phylogeny to explore the evolution of feeding preference in mullets, which indicates multiple independent origins of both herbivorous and detritivorous diets within this group. We also explore correlations between feeding preference and other variables, including body size, habitat (marine, brackish, or freshwater), and geographic distribution (tropical, subtropical, or temperate). Our analyses reveal: (1) a positive correlation between trophic index and habitat (with herbivorous and/or detritivorous species predominantly occurring in marine habitats); (2) a negative correlation between trophic index and geographic distribution (with herbivorous species occurring predominantly in subtropical and temperate regions), and; (3) a negative correlation between body size and geographic distribution (with larger species occurring predominantly in subtropical and temperate regions).
Control of Z-Source Inverter Connected to a Single-Phase AC Utility System

This paper presents a single-phase Z-source inverter as a power conditioning system for a single phase utility connected system. Z-source inverter is a single-stage topology that has buck-boost feature, which is possible because of additional shoot through state introduced in zero state of the conventional inverter pulse width modulation and provides desired output AC voltage. Small distributed generation (DG) system with alternate energy sources requires power conditioning units with low cost, high efficiency and tolerance to wide range of input voltage variation and has to perform various functions such as dc-ac conversion, system control and achieve power quality norms. To meet some of these requirements a two-loop control strategy for ac side control with grid current feedback with PI control and inner filter capacitor current feedback with proportional control and on dc side PID control for Z-source capacitor voltage regulation are employed, which gives good transient response also suppress load and source disturbances effectively. Theoretical analysis of proposed scheme is established and then simulation results are presented to validate proposed control strategy.
Novel Density-Based Clustering Algorithms for Uncertain Data

Density-based techniques seem promising for handling datauncertainty in uncertain data clustering. Nevertheless, someissues have not been addressed well in existing algorithms. Inthis paper, we firstly propose a novel density-based uncertaindata clustering algorithm, which improves upon existing algorithmsfrom the following two aspects: (1) it employs anexact method to compute the probability that the distance betweentwo uncertain objects is less than or equal to a boundaryvalue, instead of the sampling-based method in previouswork; (2) it introduces new definitions of core object probabilityand direct reachability probability, thus reducing thecomplexity and avoiding sampling. We then further improvethe algorithm by using a novel assignment strategy to ensurethat every object will be assigned to the most appropriatecluster. Experimental results show the superiority of our proposedalgorithms over existing ones.
Lecture 2: Covariate Adjustment with Time-Varying Policy Variables

sequences of teacher quality during elementary school may influence adult outcomes dierently. For example teacher assignments in kindergarten may be more consequential, than those in a later grades. It may be that consecutive years of assignment to low quality teachers is especially detrimental, while intermittent exposure to poor teachers is not. Assessing hypotheses likes these requires methods for evaluating the
Optimal Tuning of a Networked Linear Controller Using a Multi-Objective Genetic Algorithm. Application to a Complex Electromechanical Process

A genetic algorithm for optimal tuning of a linear controller is presented. This method is applied to the networked control of a high-performance drilling process, a class of complex electromechanical process. A multi-objective optimisation criterion is presented for maximizing the tool's working life and the material removal rate. A modified mutation operator is implemented to carry out the search among P, PI, PD andPID controllers simultaneously in an efficient way. Simulations and real experiments with the resulting controllers are presented. The results corroborate the advantage of the proposed strategy with regard to a classic optimisation technique.
A State Of the Art Report on Research in Multiple RGB-D sensor Setups

That the Microsoft Kinect, an RGB-D sensor, transformed the gaming and end consumer sector has been anticipated by the developers. That it also impacted in rigorous computer vision research has probably been a surprise to the whole community. Shortly before the commercial deployment of its successor, Kinect One, the research literature fills with resumees and state-of-the art papers to summarize the development over the past 3 years. This particular report describes significant research projects which have built on sensoring setups that include two or more RGB-D sensors in one scene.
Partitioning and mapping on NoC-Based MPSoC: an energy consumption saving approach

Software complexity has increased considerably over recent years, needing special target architectures as MPSoCs to fulfill the heavy memory, communication and computation requirements. Nevertheless, the use of MPSoCs has brought attention to the need for effective methods and tools for parallel software development. Methodologies aggregating partitioning and mapping are normally employed to fulfill the heavy requirements of such systems. This paper explores task-partitioning and processor-mapping methods on homogeneous NoC-Based MPSoC. The effect of both on application's energy consumption is explored alone and jointly. Experiments with several synthetic and four real applications show that the energy consumption is reduced up to 18%, 31.8% or 38.1% when applying partitioning, mapping or both, respectively.
New Hydrocarbon Degradation Pathways in the Microbial Metagenome from Brazilian Petroleum Reservoirs

Current knowledge of the microbial diversity and metabolic pathways involved in hydrocarbon degradation in petroleum reservoirs is still limited, mostly due to the difficulty in recovering the complex community from such an extreme environment. Metagenomics is a valuable tool to investigate the genetic and functional diversity of previously uncultured microorganisms in natural environments. Using a function-driven metagenomic approach, we investigated the metabolic abilities of microbial communities in oil reservoirs. Here, we describe novel functional metabolic pathways involved in the biodegradation of aromatic compounds in a metagenomic library obtained from an oil reservoir. Although many of the deduced proteins shared homology with known enzymes of different well-described aerobic and anaerobic catabolic pathways, the metagenomic fragments did not contain the complete clusters known to be involved in hydrocarbon degradation. Instead, the metagenomic fragments comprised genes belonging to different pathways, showing novel gene arrangements. These results reinforce the potential of the metagenomic approach for the identification and elucidation of new genes and pathways in poorly studied environments and contribute to a broader perspective on the hydrocarbon degradation processes in petroleum reservoirs.
Perceptual Dissimilarity: A Measure to Quantify the Degradation of Medical Images

This paper introduces a novel image quality metric that outperforms other existing and widely used metrics, namely the Root Mean Square Error (RMSE). While objective methods for assessing perceptual image quality to quantify the visibility of errors between a distorted image and a reference image have got utmost importance nowadays, none of the proposed metrics are completely satisfactory. Structural Similarity Index has been found promising in some cases, however for medical imaging (especially for CT and MRI images) it still remains immature. On that perspective, the Perceptual Dissimilarity (PD) measure proposed in this paper has been found satisfactory enough to work with medical imaging demands.
Background and Vocabulary Knowledge as a Factor for Text Comprehension in Second Language

The present study investigated how background knowledge and vocabulary knowledge held by Japanese readers affect reading comprehension of a science text written in English. In Experiment 1, three tests were administered to 175 Japanese high school and college students: a cloze test (to measure language skill), a vocabulary knowledge test, and a background knowledge test. The participants read a text written about the heart and blood circulation to answer text-based questions and inference questions measuring 'text-base' and 'situation model', theoretical concepts proposed by van Dijk and Kintsch (1983). Path analysis indicated that vocabulary knowledge causally affected 'text-base' , which further showed inter-causal relations with 'situation model' . Background knowledge independently and causally influenced 'situation model' (see details in Figure 2). In Experiment 2, 134 high school participants were divided into high(n=37) and low-groups (n=45) of vocabulary knowledge. It was showed that participants with high vocabulary knowledge performed better in free recall and text-based questions than those with low vocabulary knowledge, whereas no significant differences were found in inference questions and text summarization. In Experfment 3, 134 high school participants were divided into high(n=34) and low-groups (n=36) of background knowledge. It was showed that participants with high background knowledge performed better in text-based and inference questions, and text summarization than those with low background knowledge while no significant
A new species of Scolelepis (Polychaeta: Spionidae) from sandy beaches in China, with a review of Chinese Scolelepis species

A new species of Spionidae, Scolelepis (Scolelepis) daphoinos sp. nov., is described and illustrated from northern China seas. The species was frequently encountered and highly abundant on sand beaches, but rarely found in subtidal areas. It was formerly misidentified as S. (S.) squamata (Muller, 1806) in China but differed from the latter species in several details in morphology, including the presence of obvious reddish pigmentation patches and the absence of unidentate hooded hooks. Another two Scolelepis species, S. (S.) lingulata Imajima, 1992 and S. (S.) variegata Imajima, 1992, are reported for the first time from Chinese waters. Two species of Scolelepis, S. (S.) globosa Wu & Chen, 1964 and S. (S.) lefebvrei (Gravier, 1905), were reported previously; therefore, five species in this genus are known from China in total. A key to all Scolelepis species from Chinese waters is provided.
Gibbs Sampling

During the course of the 1990s, the technology generally referred to as Markov chain Monte Carlo (MCMC) has revolutionized the way statistical models are fitted and, in the process, dramatically revised the scope of models which can be entertained. This vignette focuses on the Gibbs sampler. I provide a review of its origins and its crossover into the mainstream statistical literature. I then attempt an assessment of the impact of Gibbs sampling on the research community, on both statisticians and subject area scientists. Finally, I offer some thoughts on where the technology is headed and what needs to be done as we move into the next millennium. The perspective is, obviously, mine, and I apologize in advance for any major omissions. In this regard, my reference list is modest, and again there may be some glaring omissions. I present little technical discussion, as by now detailed presentations are readily available in the literature. The books of Carlin and Louis (2000), Gelman, Carlin, Stern, and Rubin (1995), Robert and Casella (1999), and Tanner (1993) are good places to start. Also, within the world of MCMC, I adopt an informal definition of a Gibbs sampler. Whereas some writers describe “Metropolis steps within Gibbs sampling,” others assert that the blockwise updating implicit in a Gibbs sampler is a special case of a “block-at-a-time’’ Metropolis-Hastings algorithm. For me, the crucial issue is replacement of the sampling of a high-dimensional vector with sampling of lower-dimensional component blocks, thus breaking the so-called curse of dimensionality. In Section 2 I briefly review what the Gibbs sampler is, how it is implemented, and how it is used to provide inference. With regard to Gibbs sampling, Section 3 asks the question “How did it make its way into the mainstream of statistics? ’ Section 4 asks “What has been the impact? ’ Finally, Section 5 asks “Where are we going? ” Here, speculation beyond the next decade seems fanciful.
The Information Culture Challenge : Moving Beyond OAIS

One of the major challenges involved in digital preservation is to understand what the problem of preserving digital objects is in all of its diverse, idiosyncratic instantiations, rather than to look for uniform, technically feasible, economically sustainable, and standard-compliant solutions. We argue that by using a situated, descriptive approach to the investigation of actual environments where information is created, managed and stored, the sociocultural dimension of digital preservation can emerge and be used as a basis for action. This approach provides a way to move beyond the constraints of the Open Archival Information System model.
Visual social media and affectivity: the impact of the image of Alan Kurdi and young people’s response to the refugee crisis in Oslo and Sheffield

ABSTRACT This article focuses on the reception of the image of Alan Kurdi by a group of young volunteers in Oslo and Sheffield, and their understanding of the subsequent civic and political engagement. Methodologically, the study draws on a series of in-depth interviews conducted with a group of young volunteers who saw the photograph when it went viral on Twitter and who had followed the media coverage and participated in on- and offline activities related to the case. To analyse the views of the research participants on the Alan Kurdi case, I draw on feminist theoretical perspectives on affectivity, theoretical literature relating to news images and iconic photography, and online social movement theories. The study shows that the iconic image of Alan Kurdi offered a way of transforming complex and unsettled aspects of the refugee crisis into something concrete and understandable, contributing to galvanize affective resonance and immediate public response among the involved subjects. The infrastructure of social media, enabling the rapid global circulation of the image, contributed to shaping social assemblages to which connective emotions and common meanings were ascribed.
N ov 2 00 3 Gauss-Bonnet gravity renders negative tension branewolds unstable

We show that the Gauss-Bonnet correction to Einstein gravity induces a gravitational tachyon mode, namely an unstable spin 2 fluctuation, in the Randall-Sundrum I model. We demonstrate that this instability is generically related to the presence of a negative tension brane in the setup , with or without Z2-symmetry across it. Indeed it is shown that the tachyon mode is a bound state localised on any negative tension brane of co-dimension one, embedded in anti-de Sitter background. We discuss the possible resolution of this instability by the inclusion of induced gravity terms on the branes or by an effective four-dimensional cosmological constant.
Agent Based Framework for Scalability in Cloud Computing

Cloud computing focuses on delivery of reliable, secure, fault- tolerant, sustainable, and scalable infrastructures for hosting internet-based application services. These applications have different composition, configuration, and deployment requirements. Cloud service providers are willing to provide large scaled computing infrastructure at a cheap prices. Quantifying the performance of scheduling and allocation policy on a Cloud infrastructure (hardware, software, services) for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is an extremely challenging problem to tackle. This problem can be tackle with the help of mobile agents. Mobile agent being a process that can transport its state from one environment to another, with its data intact, and is capable of performing appropriately in the new environment. This work proposes an agent based framework for providing scalability in cloud computing environments supported with algorithms for searching another cloud when the approachable cloud becomes overloaded and for searching closest datacenters with least response time of virtual machine (VM).