Miracle-dz/UniFAR-FTUs huggingface dataset:
A presentation language for GIS cadastral data

This paper introduces a presentation language for GIS. This language is designed to be used a standalone tool, or in conjunction with a query and manipulation functions. The proposed language is based on an objet-oriented data model and on concepts used in the object-oriented query language proposed standard OQL (Object Query Language). The language is part of the SPRING environment, a GIS system which integrates the diverse types of spatial data (cadastral maps, thematic maps, digital terrain models and images).
Prevalence and clinical significance of accidental findings in electron-beam tomographic scans for coronary artery calcification.

AIMS To analyse the prevalence, and diagnostic and therapeutic consequences, of accidental findings in electron-beam tomographic scans performed for evaluation of coronary artery calcification. METHODS AND RESULTS A total of 1812 consecutive patients with known or suspected coronary artery disease underwent electron-beam tomography. In 583 (32%) of the patients, i.v. contrast was also administered for non-invasive coronary angiography. A total of 2055 non-coronary pathological findings were observed in 953 (53%) of the patients. The prevalence of extra-cardiac disease, as shown in native scans and contrast studies, was assessed separately. In 583 (32%) patients, cardiac structures or the pericardium were affected, in 423 (23%) aortic disease was found. Lung disease was found in 357 (20%), and pathology of other organs in 273 patients (15%). The most frequent findings were aortic calcium in 423 (23%) patients and heart valve calcification in 317 patients (17%). Malignant disease could be detected in three patients. Further diagnostic investigations were done in 191 (11%) patients, 141 (74%) of which concerned the heart. In 22 (1.2%) patients, specific therapy was initiated following electron-beam tomographic findings. CONCLUSION Accidental non-coronary pathology is a frequent finding in electron-beam tomographic calcium scanning, and often requires diagnostic or therapeutic action. Profound knowledge of the radiological differential diagnosis of the thoracic organs is necessary for reporting electron-beam tomographic scans, in order to avoid misdiagnosis and to receive a high quality interpretation.
Respiratory motion correction in positron emission tomography

Background: Accounting for respiratory motion in PET is often accomplished using retrospective phase binning. Due to the inefﬁcient use of the acquired data, this technique necessitates protocol adjustments in the form of either longer scan times or higher injected activities to produce diagnostic quality images. Purpose: To efﬁciently correct for respiratory motion in PET imaging by developing an interpretable and unsupervised deep learning technique called F LOW N ET -PET. Methods: The network was trained to predict the optical ﬂow between two PET frames from different breathing amplitude ranges. The trained model aligns different retrospectively-gated PET images, providing a ﬁnal image with similar counting statistics as a non-gated image, but without the blurring effects. F LOW N ET -PET was applied to anthropomorphic digital phantom data, which provided the possibility to design robust metrics to quantify the corrections. When comparing the predicted optical ﬂows to the ground truths, the median absolute error was found to be smaller than the pixel and slice widths. The improvements were illustrated by comparing against images without motion and computing the intersection over union (IoU) of the tumors as well as the enclosed activity and coefﬁcient of variation (CoV) within the no-motion tumor volume before and after the corrections were applied. Results: The average relative improvements provided by the network were 64%, 89%, and 75% for the IoU, total activity, and CoV, respectively. F LOW N ET -PET achieved similar results as the conventional retrospective phase binning approach, but only required one sixth of the scan duration. Conclusions: F LOW N ET -PET accurately predicts interpretable optical ﬂows that correct for the respiratory motion found in PET data. By using unsupervised learning, this method can be readily transferred to clinical data as well as other imaging modalities. The code and data have been made publicly available (https://github.com/teaghan/FlowNet PET).
Microstructural evolution of ZrO_2–HfO_2 nanolaminate structures grown by atomic layer deposition

Zirconia–hafnia (ZrO_2–HfO_2) nanolaminate structures were grown using the atomic layer deposition (ALD) technique with different stacking sequences and layer thickness layer thicknesses. The microstructural evolution and surface roughness were compared with those of single-layer ZrO_2 or HfO_2 films using transmission electron microscopy and atomic force microscopy. Thin single-layer ALD-ZrO_2 films were polycrystalline and composed of the tetragonal ZrO_2 phase as-deposited, whereas thicker (>14 nm) films were composed mainly of the monoclinic phase. HfO_2 films were amorphous as-deposited and crystallized into primarily monoclinic during subsequent anneals at temperatures over 500 °C. All the nanolaminate structures having individual layer thicknesses greater than approximately 2 nm were crystalline (mixture of tetragonal and monoclinic phases) independent of layer sequence and also exhibited a layer-to-layer epitaxy relationship within each grain. However, the identity of the starting layer determined the final grain size and surface roughness of the nanolaminates. A qualitative model for the observed microstructure evolution of the laminate films is proposed.
Ranking, Boosting, and Model Adaptation

We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaR ank, which has been shown to be empirically optimal for a widely used information retrieval measure. The algorithm is based on boosted regression trees, although the ideas apply to any weak learners, and it is significantly fast er in both train and test phases than the state of the art, for comparable accuracy. We also show how to find the optimal linear combination for any two ran kers, and we use this method to solve the line search problem exactly during boosting. In addition, we show that starting with a previously tra ined model, and boosting using its residuals, furnishes an effective techn ique for model adaptation, and we give results for a particularly pressing prob lem in Web Search - training rankers for markets for which only small amounts of labeled data are available, given a ranker trained on much more data from a larger market.
Cognitive Effects of Mindfulness Training: Results of a Pilot Study Based on a Theory Driven Approach

The present paper reports a pilot study which tested cognitive effects of mindfulness practice in a theory-driven approach. Thirty-four fifth graders received either a mindfulness training which was based on the mindfulness-based stress reduction approach (experimental group), a concentration training (active control group), or no treatment (passive control group). Based on the operational definition of mindfulness by Bishop et al. (2004), effects on sustained attention, cognitive flexibility, cognitive inhibition, and data-driven as opposed to schema-based information processing were predicted. These abilities were assessed in a pre-post design by means of a vigilance test, a reversible figures test, the Wisconsin Card Sorting Test, a Stroop test, a visual search task, and a recognition task of prototypical faces. Results suggest that the mindfulness training specifically improved cognitive inhibition and data-driven information processing.
Embedding considering dependencies between pixels

This paper introduces a steganographic algorithm that generates a stego image based on a model describing plausible values for the pixels of that image. The model is established by analyzing a number of realizations of the cover image. The main contribution of the approach is that the model describes dependencies on adjacent pixels. Consequently, embedding according to the suggested approach considers image structures. The general approach includes parameters that influence the achievable security of embedding. Extensive practical tests using selected steganalytical methods illustrate the improvements of security compared to additive steganography and point out reasonable settings for the parameters.
Surface characterization of novel hydrophobic UV‐vurable siloxane‐modified methacrylate/boehmite nanocomposites

Novel UV-photopolymerizable siloxane-modified methacrylate/Boehmite nanocomposites were experimented as possible protective coatings for different substrates. To this aim, unfilled and Boehmite-filled suspensions were applied and photocured on a glass substrate. A subsequent detailed experimental characterization was performed in order to evaluate the effect of the presence of Boehmite nanoparticles on the surface properties of the coatings and on their durability characteristics. In particular, transparency, glass transition temperature, scratch, and surface hardness, contact angle, and color change were measured on the coatings applied on glass support before and after QUV accelerated weathering cycles. POLYM. COMPOS. , 00:000‐000, 2013. a 2013 Society of Plastics Engineers
Tag Integrated Multi-Label Music Style Classification with Hypergraph

Automatic music style classification is an important, but challenging problem in music information retrieval. It has a number of applications, such as indexing of and searching in musical databases. Traditional music style classification approaches usually assume that each piece of music has a unique style and they make use of the music contents to construct a classifier for classifying each piece into its unique style. However, in reality, a piece may match more than one, even several different styles. Also, in this modern Web 2.0 era, it is easy to get a hold of additional, indirect information (e.g., music tags) about music. This paper proposes a multi-label music style classification approach, called Hypergraph integrated Support Vector Machine (HiSVM), which can integrate both music contents and music tags for automatic music style classification. Experimental results based on a real world data set are presented to demonstrate the effectiveness of the method.
Fast Bootstrapping and Permutation Testing for Assessing Reproducibility and Interpretability of Multivariate fMRI Decoding Models

Multivariate decoding models are increasingly being applied to functional magnetic imaging (fMRI) data to interpret the distributed neural activity in the human brain. These models are typically formulated to optimize an objective function that maximizes decoding accuracy. For decoding models trained on full-brain data, this can result in multiple models that yield the same classification accuracy, though some may be more reproducible than others—i.e. small changes to the training set may result in very different voxels being selected. This issue of reproducibility can be partially controlled by regularizing the decoding model. Regularization, along with the cross-validation used to estimate decoding accuracy, typically requires retraining many (often on the order of thousands) of related decoding models. In this paper we describe an approach that uses a combination of bootstrapping and permutation testing to construct both a measure of cross-validated prediction accuracy and model reproducibility of the learned brain maps. This requires re-training our classification method on many re-sampled versions of the fMRI data. Given the size of fMRI datasets, this is normally a time-consuming process. Our approach leverages an algorithm called fast simultaneous training of generalized linear models (FaSTGLZ) to create a family of classifiers in the space of accuracy vs. reproducibility. The convex hull of this family of classifiers can be used to identify a subset of Pareto optimal classifiers, with a single-optimal classifier selectable based on the relative cost of accuracy vs. reproducibility. We demonstrate our approach using full-brain analysis of elastic-net classifiers trained to discriminate stimulus type in an auditory and visual oddball event-related fMRI design. Our approach and results argue for a computational approach to fMRI decoding models in which the value of the interpretation of the decoding model ultimately depends upon optimizing a joint space of accuracy and reproducibility.
Downlink resource allocation for data traffic in heterogenous cellular CDMA networks

In this paper, using the dynamic pricing platform, a novel framework for downlink resource allocation in heterogeneous cellular CDMA networks is proposed. For each user, we define a utility that is a function of channel status and delay condition of that individual user as well as network load status. The network utility is then defined as the summation of the users' achieved utilities. We solve downlink resource allocation problem through maximization of total network utility. This approach results in a suboptimal base-station assignment scheme which-unlike previous work-is network optimal instead of cell optimal. We then show that optimal base-station assignment is a multidimensional multiple-choice Knapsack problem (MMKP). Since MMKP is NP-Hard a polynomial-time suboptimal heuristic algorithm is then employed to develop an efficient base-station assignment.
Error Recovery in Shared Memory Multiprocessors Using Private Caches

The problem of recovering from processor transient faults in shared memory multiprocessor systems is examined. A user-transparent checkpointing and recovery scheme using private caches is presented. Processes can recover from errors due to faulty processors by restarting from the checkpointed computation state. Implementation techniques using checkpoint identifiers and recovery stacks are examined as a means of reducing performance degradation in processor utilization during normal execution. This cache-based checkpointing technique prevents rollback propagation, provides rapid recovery, and can be integrated into standard cache coherence protocols. An analytical model is used to estimate the relative performance of the scheme during normal execution. Extensions to take error latency into account are presented. >
Understanding the mTOR signaling pathway via mathematical modeling

The mechanistic target of rapamycin (mTOR) is a central regulatory pathway that integrates a variety of environmental cues to control cellular growth and homeostasis by intricate molecular feedbacks. In spite of extensive knowledge about its components, the molecular understanding of how these function together in space and time remains poor and there is a need for Systems Biology approaches to perform systematic analyses. In this work, we review the recent progress how the combined efforts of mathematical models and quantitative experiments shed new light on our understanding of the mTOR signaling pathway. In particular, we discuss the modeling concepts applied in mTOR signaling, the role of multiple feedbacks and the crosstalk mechanisms of mTOR with other signaling pathways. We also discuss the contribution of principles from information and network theory that have been successfully applied in dissecting design principles of the mTOR signaling network. We finally propose to classify the mTOR models in terms of the time scale and network complexity, and outline the importance of the classification toward the development of highly comprehensive and predictive models. WIREs Syst Biol Med 2017, 9:e1379. doi: 10.1002/wsbm.1379
Detailed design of fretting fatigue apparatus and tests on 7050-T7451 Al alloy

The aim of this work was to design a fretting apparatus and to carry out a series of tests to evaluate the effect of a mean bulk load on the fretting fatigue life of an aeronautical Al 7050-T7451 alloy. The device was mounted on a servohydralic test system. The configuration chosen to carry out the experimental program was of cylindrical pads pressed against flat tensile specimens. A model was proposed to compute the apparatus equivalent stiffness, which controls the magnitude of the fretting load. The tests were designed so that all relevant parameters, apart from the mean bulk load (always applied before the contact loads), were kept constant. This essentially means that the equivalent shear stress amplitude computed at a material point, as defined in many multiaxial stress based high cycle fatigue models, was identical from test to test although the registered fretting lives varied greatly. These results pose a great challenge to a number of fretting lifing methodologies available in the literature.
Joint Distributions based on DFB and Gaussian Mixtures for Evaluation of Style Similarity among Paintings

In this paper, we study the ability of joint statistical information of directional subbands in evaluating style similarity among Chinese ink paintings by employing the Gaussian mixture models with different mixture components. The optimal number of mixture components can be automatically learned from the training features by pruning the mixture models. Two types of Gaussian mixture models are built on two different sets of features: one is based on the high-order statistical moments of directional subbands; the other one is based on the parameters of generalized Gaussian density (GGD) of the marginal distributions of directional subbands. The experimental results show that the accuracy of the model based on the parameters of GGD is better than that of the model based on the high-order statistical moments
Taming the Runaway Problem of Inflationary Landscapes

A wide variety of vacua, and their cosmological realization, may provide an explanation for the apparently anthropic choices of some parameters of particle physics and cosmology. If the probability on various parameters is weighted by volume, a flat potential for slow-roll inflation is also naturally understood, since the flatter the potential the larger the volume of the sub-universe. However, such inflationary landscapes have a serious problem, predicting an environment that makes it exponentially hard for observers to exist and giving an exponentially small probability for a moderate universe like ours. A general solution to this problem is proposed, and is illustrated in the context of inflaton decay and leptogenesis, leading to an upper bound on the reheating temperature in our sub-universe. In a particular scenario of chaotic inflation and non-thermal leptogenesis, predictions can be made for the size of CP violating phases, the rate of neutrinoless double beta decay and, in the case of theories with gauge-mediated weak scale supersymmetry, for the fundamental scale of supersymmetry breaking.
Precomputation-based guarding for dynamic and leakage power reduction

We present a precomputation-based guarding technique to reduce both dynamic and static power consumptions in CMOS VLSI circuits. More precisely, a high threshold sleep transistor is placed in series with some portions of the circuit. Based on the input values of the circuit, the sleep transistor is turned on and off, thus, saving both dynamic and static power. We show how to apply this technique to a number of common arithmetic blocks, including comparators, adders and multipliers. Finally, dynamic guarding and sleep transistor activity reduction techniques for improving the performance of the method are presented. Experimental results show 81% reduction in the power consumption of data path modules of a commercial VLIW processor can be achieved using our techniques. This is 20% higher than what has been achieved by previous methods.
RPF: a quality assessment tool for protein NMR structures

We describe the RPF web server, a quality assessment tool for protein NMR structures. The RPF server measures the ‘goodness-of-fit’ of the 3D structure with NMR chemical shift and unassigned NOESY data, and calculates a discrimination power (DP) score, which estimates the differences between the fits of the query structures and random coil structures to these experimental data. The DP-score is an accuracy predictor of the query structure. The RPF server also maps local structure quality measures onto the 3D structure using an online molecular viewer, and onto the NMR spectra, allowing refinement of the structure and/or NOESY peak list data. The RPF server is available at: http://nmr.cabm. rutgers.edu/rpf.
Hybrid modeling for the evaluation of risk perception by air-traffic controllers

We present a mathematical framework for the estimation of the level of risk perceived by Air-Traffic Controllers (ATCo) in a given traffic situation. We first derive a hybrid model to represent trajectory prediction performed by ATCo. The hybrid framework makes it possible to have a description of the aircraft evolution, including flight plans seen as a succession of legs with constant parameters, very close to the description used by ATCo. Using this model, we consider a worst case approach to compute a cost related to the worst possible predicted situation and the time before this situation can occur. In order to compute the risk function, we reformulate the optimization problem using impulse differential inclusions and a viability approach. We illustrate our approach on a simple traffic situation for which human-in-the-loop experimentations provide validation data, and we provide numerical result for more complex traffic situations.
Synthetic Aperture Radar ( SAR ) ImagesProcessing : A Review

The Synthetic Aperture Radar (SAR) images are important application of optical satellite images because of its ability to operate in any weather conditions. This has applications in agriculture, geology, ecology, hydrology, oceanography and military, etc. With the improvement of SAR technology, the resolution of the images has increased. These images are corrupted by a strong noise, called Speckle noise. This noise degrades the overall quality of the image. So it is important to remove the speckle noise and preserve all features in SAR images with the suitable technique. This paper presents the various important techniques which are used to remove speckle noise from SAR images and each technique has their own advantages and limitations. After the study of different techniques we will choose suitable technique for despeckling.
Beating the Multiplicative Weights Update Algorithm

Multiplicative weights update algorithms have been used extensively in designing iterative algorithms for many computational tasks. The core idea is to maintain a distribution over a set of experts and update this distribution in an online fashion based on the parameters of the underlying optimization problem. In this report, we study the behavior of a special MWU algorithm used for generating a global coin flip in the presence of an adversary that tampers the experts' advice. Specifically, we focus our attention on two adversarial strategies: (1) non-adaptive, in which the adversary chooses a fixed set of experts a priori and corrupts their advice in each round; and (2) adaptive, in which this set is chosen as the rounds of the algorithm progress. We formulate these adversarial strategies as being greedy in terms of trying to maximize the share of the corrupted experts in the final weighted advice the MWU computes and provide the underlying optimization problem that needs to be solved to achieve this goal. We provide empirical results to show that in the presence of either of the above adversaries, the MWU algorithm takes $\mathcal{O}(n)$ rounds in expectation to produce the desired output. This result compares well with the current state of the art of $\mathcal{O}(n^3)$ for the general Byzantine consensus problem. Finally, we briefly discuss the extension of these adversarial strategies for a general MWU algorithm and provide an outline for the framework in that setting.