Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust dissipative observer design for nonlinear systems

A methodology for the design of optimal robust dissipative observers is introduced. The problem is formulated as the minimization of the effect of additive disturbances on the observation error. The ℋ∞ and the minimal invariant ellipsoid are used as performance criteria. The design procedure is reduced to a finite-dimensional optimization problem subject to a matrix inequality constraint. A comparison example is presented to compare the performance under disturbances of the proposed design methodology with the original one.
A Comparison of (Semantic) Markup Languages

Several languages have been proposed as candidates for semantic markup. We needed to adopt a language for our current research on developing user-oriented tools operating over the Semantic Web. This paper presents the results of our analysis of three candidates that we considered: XML, RDF, and DAML+OIL along with their associated schemas and ontology specifications. The analysis focuses on the expressiveness of each language, and is presented along several dimensions and summarized in a comparison table. A surprising result of our analysis is the decision to adopt XML(Schema) for practical reasons, since it is able to accommodate a relatively expressive set of constructs and is widely known and commercially supported. We also discuss how we plan to complement XML(S) with a small set of conventions, so that we will have an easier transition to other markup languages in the future.
Optimal Color Quantization for Real-Time Object Recognition

Recognizing the reappearance of an object provides a useful functionality for automated scene interpretation.While many modalities will in general contribute to this task, this paper focuses on the use of object color properties. Re-identifying objects between cameras requires a calibration of the respective color spaces, which are a function of both camera and lighting properties. Ways in which such calibration may be accomplished, on-line and without specialist equipment, are investigated. Matching against previously observed objects is an image retrieval problem, for which we develop an explicit representation of the observed color distribution. Color histograms can be built and compared in real time without specialist hardware: we investigate effective representations for classification of object identity. The camera capture noise characteristics are used to define optimal histogram quantization intervals. A model of the object color properties is built using multiple observations of the same object, acquired with the use of a spatial object tracker. Comparative results on real data are presented for single and multi-camera re-identification, using algorithms which may be executed in real time.
A Close to Optimal Adaptive Filter for Sudden System Changes

Most adaptive filtering algorithms are designed with smooth changes in mind. For instance, the Kalman filter (KF) usually assumes a random walk model for the state, where the state noise is simply a multivalue Gaussian random variable. However, in practice changes can be abrupt. In this letter, we consider the case where the state noise of the KF is impulsive, corresponding to a probability density function (PDF) given by the sum of two Gaussians. This will result in a PDF for the state that is given by a sum of Gaussians, corresponding to an implementation of a Gaussian sum filter, with multiple KFs contributing to the resulting output. Simulation and theoretical considerations show that in steady state only one filter is operating, while several filters are important at transitions. The operation of the algorithm is then similar to optimally detecting sudden changes, and restarting the KF at those moments, resulting in an close optimal step size varying algorithm. Simulations show improved performance of the proposed algorithm when compared to state of the art algorithms.
A Hybrid Method for Blind Estimation of Frequency Dependent Reverberation Time Using Speech Signals

Reverberation time is an important room acoustical parameter that can be used to identify the acoustic environment, predict speech intelligibility and model the late reverberation for binaural rendering, etc. Several blind estimation algorithms of reverberation time have been proposed by analyzing recorded speech signals. Unfortunately, the estimation accuracy for the frequency dependent reverberation time is lower than for the full-band reverberation time due to the lower signal energy in sub-band filters. This study presents a novel approach for the blind estimation of reverberation time in the full frequency range. The maximum likelihood method is applied for the estimation of the reverberation time from low- to mid-frequencies, and the reverberation time from mid- to high-frequencies is predicted by our proposed model based on the analysis of the reverberation time calculated from room impulse responses in different rooms. The proposed method is validated by two experiments and shows a good performance.
Robot Learning by Demonstration with local Gaussian process regression

In recent years there was a tremendous progress in robotic systems, and however also increased expectations: A robot should be easy to program and reliable in task execution. Learning from Demonstration (LfD) offers a very promising alternative to classical engineering approaches. LfD is a very natural way for humans to interact with robots and will be an essential part of future service robots. In this work we first review heteroscedastic Gaussian processes and show how these can be used to encode a task. We then introduce a new Gaussian process regression model that clusters the input space into smaller subsets similar to the work in [11]. In the next step we show how these approaches fit into the Learning by Demonstration framework of [2], [3]. At the end we present an experiment on a real robot arm that shows how all these approaches interact.
Detection of aberrant hippocampal mossy fiber connections: Ex vivo mesoscale diffusion MRI and microtractography with histological validation in a patient with uncontrolled temporal lobe epilepsy

Understanding the neurobiology and functional connectivity of hippocampal structures is essential for improving the treatment of mesial temporal lobe epilepsy. At the macroscale, in vivo MRI often reveals hippocampal atrophy and decreased fractional anisotropy, whereas at the microscopic scale, there frequently is evidence of neuronal loss and gliosis. Mossy fiber sprouting in the dentate gyrus (DG), with evidence of glutamatergic synapses in the stratum moleculare (SM) putatively originating from granule cell neurons, may also be observed. This aberrant connection between the DG and SM could produce a reverberant excitatory circuit. However, this hypothesis cannot easily be evaluated using macroscopic or microscopic techniques. We here demonstrate that the ex vivo mesoscopic MRI of surgically excised hippocampi can bridge the explanatory and analytical gap between the macro‐ and microscopic scale. Specifically, diffusion‐ and T2‐weighted MRI can be integrated to visualize a cytoarchitecture that is akin to immunohistochemistry. An appropriate spatial resolution to discern individual cell layers can then be established. Processing of diffusion tensor images using tractography detects extra‐ and intrahippocampal connections, hence providing a unique systems view of the hippocampus and its connected regions. Here, this approach suggests that there is indeed an aberrant connection between the DG and SM, supporting the sprouting hypothesis of a reverberant excitatory network. Mesoscopic ex vivo MR imaging hence provides an exciting new avenue to study hippocampi from treatment‐resistant patients and allows exploration of existing hypotheses, as well as the development of new treatment strategies based on these novel insights. Hum Brain Mapp 37:780–795, 2016. © 2015 Wiley Periodicals, Inc.
Massive and Widespread Organelle Genomic Expansion in the Green Algal Genus Dunaliella

The mitochondrial genomes of chlamydomonadalean green algae are renowned for their highly reduced and conserved gene repertoires, which are almost fixed at 12 genes across the entire lineage. The sizes of these genomes, however, are much more variable, with some species having small, compact mitochondrial DNAs (mtDNAs) and others having expanded ones. Earlier work demonstrated that the halophilic genus Dunaliella contains extremely inflated organelle genomes, but to date the mtDNA of only one isolate has been explored. Here, by surveying mtDNA architecture across the Chlamydomonadales, we show that various Dunaliella species have undergone massive levels of mitochondrial genomic expansion, harboring the most inflated, intron-dense mtDNAs available from chlorophyte green algae. The same also appears to be true for their plastid genomes, which are potentially among the largest of all plastid-containing eukaryotes. Genetic divergence data are used to investigate the underlying causes of such extreme organelle genomic architectures, and ultimately reveal order-of-magnitude differences in mitochondrial versus plastid mutation rates within Dunaliella.
Proteomic analysis of plasma membrane vesicles isolated from the rat renal cortex

Polarized epithelial cells are responsible for the vectorial transport of solutes and have a key role in maintaining body fluid and electrolyte homeostasis. Such cells contain structurally and functionally distinct plasma membrane domains. Brush border and basolateral membranes of renal and intestinal epithelial cells can be separated using a number of different separation techniques, which allow their different transport functions and receptor expressions to be studied. In this communication, we report a proteomic analysis of these two membrane segments, apical and basolateral, obtained from the rat renal cortex isolated by two different methods: differential centrifugation and free‐flow electrophoresis. The study was aimed at assessing the nature of the major proteins isolated by these two separation techniques. Two analytical strategies were used: separation by sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) at the protein level or by cation‐exchange high‐performance liquid chromatography (HPLC) after proteolysis (i.e., at the peptide level). Proteolytic peptides derived from the proteins present in gel pieces or from HPLC fractions after proteolysis were sequenced by on‐line liquid chromatography‐tandem mass spectrometry (LC‐MS/MS). Several hundred proteins were identified in each membrane section. In addition to proteins known to be located at the apical and basolateral membranes, several novel proteins were also identified. In particular, a number of proteins with putative roles in signal transduction were identified in both membranes. To our knowledge, this is the first reported study to try and characterize the membrane proteome of polarized epithelial cells and to provide a data set of the most abundant proteins present in renal proximal tubule cell membranes. ∁
9 PET Imaging of Gliomas

Noninvasive imaging methods, including positron emission tomography (PET), have become essential for diagnosis and staging of gliomas, and monitoring of treatment response. The utility of these techniques have been found to be highly dependent on tumor grade. According to the World Health Organization (WHO) classification of tumors (Kleihues and Sobin 2000), gliomas are classified into 3 main histological types: astrocytoma, oligodendroglioma and glioblastoma. These histological types are further classified on the basis of anaplasia and degree of malignancy as: grade I, noninvasive glioma (pilocytic astrocytoma); grade II, less-invasive glioma (astrocytomas and oligodendrogliomas); grade III, invasive glioma (analplastic astrocytoma/oligodendrogliomas); and grade IV, highly invasive glioma (glioblastoma, or GBM). Low-grade gliomas (grade I and II) typically affect younger patients. Grade I glioma is the most common form of glioma in children and is less frequent in adults (Burkhard et al. 2003) while grade II gliomas are common in adults (mean age of onset is 40 years) (Hagerstrand et al. 2008). Median survival for low-grade glioma is varied but prognosis and treatment require regular follow ups. Low-grade gliomas grow slowly or stabilize spontaneously and with surgical resection, median survival can be 20 years or more (Burkhard et al. 2003). For high-grade gliomas, the mean age of onset is 40 years for grade III glioma and 61 years for GBM (Ohgaki and Kleihues 2005). GBM is the most malignant and most common glioma, accounting for 45% 50% of all adult gliomas. Median survival for grade III glioma is 2-3 years and for GBM is 1 year (Chen 2007). For optimal disease prognosis, treatment and follow up, one should be able to delineate the tumor lesion and most importantly, differentiate benign lesions from neoplastic lesions, low-grade from high grade tumors, and tumor progression from therapy induced necrosis. As will be discussed later, efforts are also being directed toward defining early imaging predictors of response to therapy. Conventional imaging with magnetic resonance imaging (MRI) provides excellent anatomical definition of brain tumors. MRI is highly sensitive in identifying lesions, mass effect, edema, hemorrhage, necrosis and signs of increased intracranial pressure (Chen 2007). Pathologic changes are characterized on MRI by increased water content (edema) and blood-brain barrier (BBB) disruption, visualized as contrast enhancement (Grosu et al. 2002). Most tumors (low-grade or high-grade) have prolonged T1 and T2 relaxation times and thus
A Variational Approach to Edge Detection

The problem of detecting intensity changes in images is canonical in vision. Edge detection operators are typically designed to optimally estimate first or second derivative over some (usually small) support. Other criteria such as output signal to noise ratio or bandwidth have also been argued for. This paper describes an attempt to formulate a set of edge detection criteria that capture as directly as possible the desirable properties of the detector. Variational techniques are used to find a solution over the space of all possible functions. The first criterion is that the detector have low probability of error i.e. failing to mark edges or falsely marking non-edges. The second is that the marked points should be as close as possible to the centre of the true edge. The third criterion is that there should be low probability of more than one response to a single edge. The third criterion is claimed to be new, and it became necessary when an operator designed using the first two criteria was found to have excessive multiple responses. The edge model that will be considered here is a one-dimensional step edge in white Gaussian noise although the same technique has been applied to an extended impulse or ridge profile. The result is a one dimensional operator that approximates the first derivative of a Gaussian. Its extension to two dimensions is also discussed.
Second fusion algorithm of satellite images by à trous-Curvelet transform

For the multi-spectral and panchromatic image on the same scene, the second fusion algorithm by a trous-curvelet transform is constructed. Above all, the first fusion image can be acquired by using the panchromatic image to substitute the intensity component of the multi-spectral image. Then the non-negative matrix factorization (NMF) is carried out between the first fusion image and the original multi-spectral image which followed by a trous-curvelet transform on the first feature component of each image based on the theory of the maximum variance. For decomposition coefficients, the new algorithm based on weighted regional similarity is introduced. Finally, the ultimate fusion image can be acquired through inverse NMF on the new first feature component. Experimental results show that the new method can get better effect on maintaining spectral information and enhancing space quality than other methods such as wavelet transform.
Phlebotonic and Compression Stocking Therapy in Venous Edema Management: An Overview of Recent Advances with a Focus on Cyclo 3 Fort® and Progressive Compression Stockings

Venous edema of the limb presents several challenges to clinicians, particularly with regard to its definition, pathophysiology and measurement, as well as those relating to its management. Compression therapy forms the cornerstone of standard care for thromboprophylaxis and management of lower limb venous and lymphatic disorders. Venoactive drugs, of plant or synthetic origin, have also been shown to be effective and safe in the management of venous edema and/or chronic venous disease-related symptoms. This article, based on a symposium held at the XXV World Congress of the International Union of Angiology on 2–5 July 2012 (Prague, Czech Republic), provides an overview of some of the recent developments in the use of phlebotonic and compression therapy in the management of patients with lower limb venous edema, with a focus on Cyclo 3 Fort® and progressive compression stockings.
Image Analysis for Microelectronic Retinal Prosthesis

By way of extracellular, stimulating electrodes, a microelectronic retinal prosthesis aims to render discrete, luminous spots-so-called phosphenes-in the visual field, thereby providing a phosphene image (PI) as a rudimentary remediation of profound blindness. As part thereof, a digital camera, or some other photosensitive array, captures frames, frames are analyzed, and phosphenes are actuated accordingly by way of modulated charge injections. Here, we present a method that allows the assessment of image analysis schemes for integration with a prosthetic device, that is, the means of converting the captured image (high resolution) to modulated charge injections (low resolution). We use the mutual-information function to quantify the amount of information conveyed to the PI observer (device implantee), while accounting for the statistics of visual stimuli. We demonstrate an effective scheme involving overlapping, Gaussian kernels, and discuss extensions of the method to account for short-term visual memory in observers, and their perceptual errors of omission and commission.
GPU-Accelerated Generation of Correctly Rounded Elementary Functions

The IEEE 754-2008 standard recommends the correct rounding of some elementary functions. This requires solving the Table Maker’s Dilemma (TMD), which implies a huge amount of CPU computation time. In this article, we consider accelerating such computations, namely the Lefèvre algorithm on graphics processing units (GPUs), which are massively parallel architectures with a partial single instruction, multiple data execution. We first propose an analysis of the Lefèvre hard-to-round argument search using the concept of continued fractions. We then propose a new parallel search algorithm that is much more efficient on GPUs thanks to its more regular control flow. We also present an efficient hybrid CPU-GPU deployment of the generation of the polynomial approximations required in the Lefèvre algorithm. In the end, we manage to obtain overall speedups up to 53.4 × on one GPU over a sequential CPU execution and up to 7.1 × over a hex-core CPU, which enable a much faster solution of the TMD for the double-precision format.
Putting Order in the Impossible

The class of visual illusions called ‘impossible figures’ (illusory spatial interpretations of pictures) is analyzed in order to introduce an ordering into the great variety of such figures. Such an ordering facilitates reference, unifies terminology, and establishes a conceptual framework for further investigations of the subject, making easier the choice and systematic generation of various types of figures (for example, in systematic psychological experiments). First, the notion of ‘impossible figure’ is defined and certain other related classes of figures (so-called ‘likely’ and ‘unlikely’ figures) are distinguished. Second, the fundamental ‘impossibility sources’ are identified as elementary ‘building blocks’ of all impossible figures. Finally, two broad classes of impossible figures, multibars (or ‘impossible polygons’) and striped figures, are briefly described.
Performance evaluation framework for sustainable freight transportation systems

Sustainability in freight transportation demands for a balance among economic, environmental, and social aspects in shipping commodities throughout a supply chain. The performance evaluation of sustainable freight transportation (SFT) systems is of prime importance as it facilitates perpetual and objective assessment for advancements towards sustainability targets. In this study, we propose a comprehensive framework to assess the sustainability performance of freight transportation systems. We pinpoint critical success factors (CSFs) affecting the performance of SFT by carrying out an extensive literature review and conducting a Delphi study to seek experts’ opinions from industry as well as academia. Further, a comprehensive hierarchical framework is developed to establish inter-relationships among these CSFs followed by prioritisation of these CSFs. The application of the proposed framework is illustrated through a case example. This framework would also serve as a reference for decision-makers in selecting the most sustainable freight transportation system.
Inversion Modeling of Dam-Zoning Elasticity Modulus for Heightened Concrete Dam Using ICS-IPSO Algorithm

A new approach was developed for the inversion modeling of dam-zoning elasticity modulus for heightened concrete dam, with old and new concrete zones. The proposed inversion modeling procedure takes advantage of the improved cuckoo search (ICS) algorithm and improved particle swarm optimization (IPSO) algorithm to adjust the mechanical parameters, which are used as input. An objective function is constructed based on the horizontal displacement increment by using the finite element method (FEM) and statistical analysis of the prototype monitoring data. One ideal arch dam model and one actual heightened concrete dam were taken as examples. The proposed method was used to implement the optimal selection of the dam-zoning elasticity modulus. The inversion analysis results indicate that the mechanical parameters identification method for heightened concrete gravity dams proposed in this article is accurate and has a fast convergence rate. Consequently, it can be applied as a reliable model to identify the dam-zoning elasticity modulus in practical engineering applications.
Design of multi-channel filter bank using ABC optimized fractional derivative constraints

This paper presents an optimized algorithm for the design of M-Channel cosine modulated (CM) quadrature mirror filter (QMF) bank. For this purpose, objective function is formulated in term of polyphse components of the prototype filter of filter bank subjecting optimized fractional derivative constraint using artificial bee colony (ABC) algorithm. Polyphase component is optimized by minimizing the objective function, which is formulated as sum of mean square error of passband error and stopband error. Fractional derivative constraints are optimized using peak reconstruction error as an objective function. The proposed method is examined by several performance parameters of QMF bank which show superiority of proposed method.
Neuro-Calibration of a Camera Using Particle Swarm Optimization

In this paper, a particle swarm optimization (PSO) based camera calibration approach is presented to determine the external and internal calibration parameters from the knowledge of a given set of points in object space. First, the image formation model for a pinhole camera is formulated in terms of a feed-forward neural network (NN) and then this neural network is trained using particle swarm optimization. The effect of noise and number of control points are studied in the estimation of calibration parameters. Results from our extensive study are presented to demonstrate the excellent performance of the proposed technique in terms of convergence, accuracy, and robustness.