Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust centralized fusion Kalman predictors with multiplicative noises, uncertain noise variances, and missing measurements

In this paper, the robust centralized fusion Kalman prediction problem is considered for linear discrete-time multisensor systems with multiplicative noises, uncertain noise variances and missing measurements. By introducing two fictitious noises, the system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case centralized fusion system with the conservative upper bounds of the noise variances, we present robust centralized fusion time-varying Kalman predictor, and guarantee its actual prediction error variance to have the corresponding minimal upper bounds for all admissible noise variance uncertainties. By use of the Lyapunov equation approach, we prove its robustness. The accuracy relation among the robust local and centralized fusion Kalman predictor is given. Simulation results show the effectiveness and correctness of the proposed results.
An ontological approach to model and query multimodal concurrent linguistic annotations

This paper focuses on the representation and querying of knowledge-based multimodal data. This work stands in the OTIM project which aims at processing multimodal annotation of a large conversational French speech corpus. Within OTIM, we aim at providing linguists with a unique framework to encode and manipulate numerous linguistic domains (from prosody to gesture). Linguists commonly use Typed Feature Structures (TFS) to provide an uniform view of multimodal annotations but such a representation cannot be used within an applicative framework. Moreover TFS expressibility is limited to hierarchical and constituency relations and does not suit to any linguistic domain that needs for example to represent temporal relations. To overcome these limits, we propose an ontological approach based on Description logics (DL) for the description of linguistic knowledge and we provide an applicative framework based on OWL DL (Ontology Web Language) and the query language SPARQL.
True Online Temporal-Difference Learning

The temporal-difference methods TD($\lambda$) and Sarsa($\lambda$) form a core part of modern reinforcement learning. Their appeal comes from their good performance, low computational cost, and their simple interpretation, given by their forward view. Recently, new versions of these methods were introduced, called true online TD($\lambda$) and true online Sarsa($\lambda$), respectively (van Seijen & Sutton, 2014). These new versions maintain an exact equivalence with the forward view at all times, whereas the traditional versions only approximate it for small step-sizes. We hypothesize that these true online methods not only have better theoretical properties, but also dominate the regular methods empirically. In this article, we put this hypothesis to the test by performing an extensive empirical comparison. Specifically, we compare the performance of true online TD($\lambda$)/Sarsa($\lambda$) with regular TD($\lambda$)/Sarsa($\lambda$) on random MRPs, a real-world myoelectric prosthetic arm, and a domain from the Arcade Learning Environment. We use linear function approximation with tabular, binary, and non-binary features. Our results suggest that the true online methods indeed dominate the regular methods. Across all domains/representations the learning speed of the true online methods are often better, but never worse than that of the regular methods. An additional advantage is that no choice between traces has to be made for the true online methods. Besides the empirical results, we provide an in-depth analysis of the theory behind true online temporal-difference learning. In addition, we show that new true online temporal-difference methods can be derived by making changes to the online forward view and then rewriting the update equations.
An Optimized Pipelined-Subranging ADC Architecture

This paper reports an optimized pipelined-subranging ADC architecture that features new design techniques, such as, multiple MDACs, multiple open-loop residue amplifiers, relative comparison method and closed-loop circular resistive interpolation network. Multiple MDACs and multiple residue amplifier relaxes the linearity requirement down to the level that can be readily handled by the open-loop structure for fast settling while maintaining low power consumption. Relative comparison method helps to suppress the gain error caused by the inaccurate open-loop gain. The trade-off between speed and accuracy is broken by the circular resistive interpolation network. The new ADC architecture is successfully verified in design of a 12bit 100Msps pipelined-subranging ADC in commercial 0.35¿m CMOS technology with the following specifications achieved: 2Vpp differential input range, ±0.6LSB DNL, 70dB SFDR, 62dB SNDR, ± 2% FS gain error, power dissipation of 520mW and a die size of 9mm2
Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm

In this paper the sequential prediction problem with expert advice is considered for the case where losses of experts suffered at each step cannot be bounded in advance. We present some modification of Kalai and Vempala algorithm of following the perturbed leader where weights depend on past losses of the experts. New notions of a volume and a scaled fluctuation of a game are introduced. We present a probabilistic algorithm protected from unrestrictedly large one-step losses. This algorithm has the optimal performance in the case when the scaled fluctuations of one-step losses of experts of the pool tend to zero.
Study on the Constitutive Behavior and Damage Evolution in the FPZ of WST Specimens Using the Vic-2D and Strain Gauges

The Vic-2D is an innovate system that uses a non-invasive method called Digital Image Correlation (DIC) technique, which provides a contour map of strains of an entire specimen surface subject to mechanical tests, based on gray value digitalized images. The paper presents the results of experimental investigations of the constitutive behavior and damage evolution in the fracture process zone (FPZ) of concrete using the Vic-2D, in comparison with the traditional strain gauge method. The model tests were performed with notched concrete specimens via the Wedge Splitting Test (WST) method. During the tests, stress-strain curves and evolution of the damage variable were determined and analyzed at multiple levels. The results provide the achievements obtained and some limitations with both two methods.
A New Approach to Treating Breast Cancer Combining Tumor Removal and Intraoperative Radiotherapy: Is it Viable?

How does radiotherapy work? The aim of radiation is to kill tumor cells selectively without damaging normal cells. It works by generating free radicals, which cause singleor double-stranded breaks in cellular DNA. This affects tumor cells more than normal cells because tumor cells are less able to repair DNA damage than normal cells, and are more frequently in the radiosensitive part of the cell cycle.
Nonlinear Stability Analysis of Concrete-Filled Fiber-Reinforced Polymer-Tube Columns: Experimental and Theoretical Investigation

Few studies have been conducted on the buckling modes of failure of concrete-filled fiber-reinforced polymer (FRP)-tube (CFFT) columns. This paper seeks to fill this research gap by presenting both experimental results and theoretical analysis for buckling responses of CFFT columns. The effect of three parameters (FRP tube thickness, concrete compressive strength, and slenderness ratio) and the parameters’ interaction on the buckling behavior were investigated. The experimental program consisted of testing 22 circular CFFT columns with a total height ranging from 305 to 1520 mm (12 to 60 in.) and an internal tube diameter of 152 mm (6 in.). The experimental results showed that uniaxial compressive strength of CFFTs was reduced by 13-23% as the slenderness ratio increased from 4 to 20, depending on the concrete compressive strength and the thickness of FRP tubes. Both the axial strength and stiffness of slender columns were increased as a result of the confining effect of the FRP tubes. The enhancement of the axial strength of the slender column was more pronounced for lower-strength concrete than for higher-strength concrete. Increasing the thickness of the glass-FRP tubes significantly improved the ultimate load capacity of the tested specimens. Predicted slenderness limit was slightly affected by both the concrete compressive strength and FRP tube thickness. A simplified formula for the limit slenderness ratio was proposed for the design of CFFT columns.
Characterization and reduction of motion artifacts in photoplethysmographic signals from a wrist-worn device

Methods for analyzing various motion artifacts in photoplethysmography (PPG) signals, recorded by a wristworn device are reported. The analysis looks both at intrinsic PPG signal properties, through standard deviation, skew and kurtosis, but also at its relationship to five possible motion reference signals, through the correlation coefficient and mutual information. The investigated references include the X, Y, Z accelerometer axes, acceleration magnitude and a different wavelength PPG channel. The latter showed higher correlation to the input PPG signal during movement. A wavelet based motion artifact reduction algorithm is described. The algorithm improves the visibility of the heart rate (HR) components in the frequency domain representation of the signals.
Using Hybrid MCDM Model for Enhancing the Participation of Teacher in Recreational Sports

Recreational sports have become the standard way for improving individual health and quality of life. If teachers could participate more in recreational sports, it would promote healthier lifestyle, relieve stress, and encourage greater numbers into education-related careers. This study was designed to evaluate the influential factors and relative weights of personal preference and external environment by applying a hybrid MCDM model combining DEMATEL with ANP methods. The purpose of this study is to demonstrate how the MCDM model can be applied to recreation sports and find the important factors that can enhance recreational sports participation and select activities that can enhance educators’ ability. In the results, the external environment was determined to be the most important dimension. In the seven evaluation criteria, “psychology” was the most important factor to be considered when planning the recreational sports program, followed by “entertainment” condition.
Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking

This paper is concerned with the problem of top-K ranking from pairwise comparisons. Given a collection of n items and a few pairwise comparisons across them, one wishes to identify the set of K items that receive the highest ranks. To tackle this problem, we adopt the logistic parametric model - the Bradley-Terry-Luce model, where each item is assigned a latent preference score, and where the outcome of each pairwise comparison depends solely on the relative scores of the two items involved. Recent works have made significant progress towards characterizing the performance (e.g. the mean square error for estimating the scores) of several classical methods, including the spectral method and the maximum likelihood estimator (MLE). However, where they stand regarding top-K ranking remains unsettled. We demonstrate that under a natural random sampling model, the spectral method alone, or the regularized MLE alone, is minimax optimal in terms of the sample complexity - the number of paired comparisons needed to ensure exact top-K identification, for the fixed dynamic range regime. This is accomplished via optimal control of the entrywise error of the score estimates. We complement our theoretical studies by numerical experiments, confirming that both methods yield low entrywise errors for estimating the underlying scores. Our theory is established via a novel leave-one-out trick, which proves effective for analyzing both iterative and non-iterative procedures. Along the way, we derive an elementary eigenvector perturbation bound for probability transition matrices, which parallels the Davis-Kahan Θ theorem for symmetric matrices. This also allows us to close the gap between the l 2 error upper bound for the spectral method and the minimax lower limit.
Climate-carbon cycle feedback analysis: Results from the C

Eleven coupled climate–carbon cycle models used a common protocol to study the coupling between climate change and the carbon cycle. The models were forced by historical emissions and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 anthropogenic emissions of CO2 for the 1850–2100 time period. For each model, two simulations were performed in order to isolate the impact of climate change on the land and ocean carbon cycle, and therefore the climate feedback on the atmospheric CO2 concentration growth rate. There was unanimous agreement among the models that future climate change will reduce the efficiency of the earth system to absorb the anthropogenic carbon perturbation. A larger fraction of anthropogenic CO2 will stay airborne if climate change is accounted for. By the end of the twenty-first century, this additional CO2 varied between 20 and 200 ppm for the two extreme models, the majority of the models lying between 50 and 100 ppm. The higher CO2 levels led to an additional climate warming ranging between 0.1° and 1.5°C. All models simulated a negative sensitivity for both the land and the ocean carbon cycle to future climate. However, there was still a large uncertainty on the magnitude of these sensitivities. Eight models attributed most of the changes to the land, while three attributed it to the ocean. Also, a majority of the models located the reduction of land carbon uptake in the Tropics. However, the attribution of the land sensitivity to changes in net primary productivity versus changes in respiration is still subject to debate; no consensus emerged among the models.
Estimation of blur and noise parameters in remote sensing

In this paper we propose a new algorithm to estimate the parameters of the noise related to the sensor and the impulse response of the optical system, from a blurred and noisy satellite or aerial image. The noise is supposed to be white, Gaussian and stationary. The blurring kernel has a parametric form and is modeled in such a way as to take into account the physics of the system (the atmosphere, the optics and the sensor). The observed scene is described by a fractal model, taking into account the scale invariance properties of natural images. The estimation is performed automatically by maximizing a marginalized likelihood, which is achieved by a deterministic algorithm whose complexity is limited to O (N), where N is the number of pixels.
Motion Parallax Representation for Indirect Augmented Reality

Indirect augmented reality (IAR) presents pre-generated augmented images for achieving high-quality geometric and photometric registration between pre-captured images and virtual objects. Meanwhile, IAR causes spatial inconsistency between the real world and presented images when users move from the location where the real scene was captured. This paper describes a novel way to address the spatial inconsistency; namely, enabling viewpoint change in IAR. The key idea of this study is to employ an image-based rendering technique using pre-captured multi-view omnidirectional images to provide free-viewpoint navigation. For a pilot study, we have developed an IAR system representing a motion parallax effect using an optical-flow-based camera motion estimation.
Support Vector Machines for DS-UWB Channel Equalisation

We propose a novel equalization scheme for the direct sequence (DS) based ultra wideband (UWB) indoor system, utilizing support vector machine (SVMs). SVMs are effective learning technique which gains more consideration in pattern classification. In this work, a bank of SV classifiers are trained by received signal chips in order to extract the subsequent corresponding symbols. Simulation results confirm the superb efficiency of the proposed technique for many scenarios of transmission, especially the performance in Light-of-Sight cases where only few pilot blocks are used for training.
Linear Sifting Of Decision Diagrams

We propose a new algorithm, called linear sifting, for theoptimization of decision diagrams that combines the efficiency of sifting and the power of linear transformations. We show that the new algorithm is applicable to large examples, and that inmany cases it leads to substantiallymore compact diagrams when compared to simple variablereordering. We show inwhat sense linear transformationscomplement variable reordering, and we discuss applications of the new technique to synthesis and verification.
Hypercorrection of high con fi dence errors in children

Three experiments investigated whether the hypercorrection effect e the finding that errors committed with high confidence are easier, rather than more difficult, to correct than are errors committed with low confidence e occurs in grade school children as it does in young adults. All three experiments showed that Grade 3e6 children hypercorrected high confidence errors and the children also claimed that they ‘knew those answers all along. ’ Experiment 2 included two second-guess tasks following error commission, one in which the children attempted to choose the correct answer from six options and the other in which they tried to generate a correct second response. Neither provided evidence that children actually knew high confidence corrections all along. Experiment 3, however, showed that the children had some preferential partial knowledge insofar as they needed fewer hints to guess the correct answers to high confidence than to low confidence errors. 2011 Published by Elsevier Ltd. A number of recent studies have investigated a phenomenon known as the hypercorrection effect (Butterfield & Metcalfe, 2001), in which errors endorsed with higher confidence are more likely to be corrected on a final test than are errors endorsed with lower confidence. This effect has been shown to occur when corrective feedback, in which the correct answer is provided, is given to all errors (Butler, Fazio and Marsh, 2010; Butler & Roediger, 2007; 2008; Butterfield & Mangels, 2003; Butterfield & Metcalfe, 2006; Eich, Stern, & Metcalfe, submitted for publication; Fazio & Marsh, 2009, 2010; Kulhavy & Stock, 1989; Kulhavy, Yekovich & Dyer, 1976; Marsh, 2011; Metcalfe, Butterfield, Habeck, & Stern, submitted for publication; Metcalfe & Finn, 2011; Sitzman & Rhodes, 2010). In the standard paradigm used to investigate this phenomenon (see, e.g., Butterfield & Metcalfe, 2001) participants are asked to generate the answers to general information questions and rate their confidence in the correctness of each answer they produced. They are then given the correct answer. In contrast to theoretical expectations, which suggest that responses in which one is highly confident should be particularly difficult to correct, the high confidence errors are more likely to be corrected on the retest than are the errors endorsed with lower confidence, even retest at a considerable delay (Butler et al., 2010; Butterfield & alfe), bridgid.finn@wustl.edu
cell Stimulation , Lysis , and Separation in Microdevices

Quantitative data on the dynamics of cell signaling induced by different stimuli requires large sets of self-consistent and dynamic measures of protein activities, concentrations, and states of modification. A typical process flow in these experiments starts with the addition of stimuli to cells (cytokines or growth factors) under controlled conditions of concentration, time, and temperature, followed at various intervals by cell lysis and the preparation of extracts. Microfluidic systems offer the potential to do laborious assays in a reproducible and automated fashion [1].
Influence of Trans-Boundary Air Pollution on the Urban Atmosphere in Fukuoka, Japan

To understand the influence of trans-boundary air pollution on the air quality of Fukuoka, the mass concentration and chemical composition of fine particulate matter (PM) were observed at urban (Fukuoka) and rural (Fukue Island) sites in the northern Kyushu area in Japan. Chemical composition was measured using an aerosol mass spectrometer. Organic aerosol (OA) data were analyzed by the positive matrix factorization (PMF) method. Sulfate and low-volatile oxygenated OA (LV-OOA) were dominant for all of the PM2.5 mass variations on Fukue Island, where the trans-boundary air pollution is dominant in the winter-spring season. In Fukuoka, however, sulfate accounted for the largest fraction of total chemical species under high PM2.5 mass concentrations (>35 µg·m−3), while organics and nitrate made up a large fraction under low PM2.5 (<10 µg·m−3). Under the high PM2.5 condition, LV-OOA was also dominant. This indicates that high PM2.5 mass concentrations were attributed to the long-range transport of air pollution. Although the trans-boundary air pollution prevails in the winter-spring season, high sulfate concentrations were observed in the summer of 2012. In addition to the volcanic activities and photochemical reactions, long-range, trans-boundary air pollutions are influential factors not only in winter-spring but also in summer.
A Hierarchical Game Approach to Inter-Operator Spectrum Sharing

In this paper, we address the problem of spectrum sharing where wireless (competitive) operators coexist in the same frequency band. First, we model this problem as a strategic non-cooperative game where operators simultaneously share the spectrum according to the Nash Equilibrium (N.E). Given a set of channel realizations, several Nash equilibria exist which render the outcome of the game unpredictable. Second, the inter-operator spectrum sharing problem is reformulated as a hierarchical power allocation game [14], where one of the operators (i.e., primary) poses as a leader and the other operator (i.e., secondary) as a follower. Using backward induction [12], the Stackelberg equilibrium (S.E) is reached where the best response of the secondary operator is taken into account upon maximizing the primary operator's payoff. It turns out that the Stackelberg approach yields better payoffs for operators compared to the classical greedy water-filling approach. Furthermore, to reach pareto-efficient boundaries, the spectrum sharing problem is formulated as a repeated game, where players interact over a longer period of time and learning from each other's strategies. Numerical results provide a comparison between the non-cooperative, hierarchical and centralized approach.
The hnRNP C proteins contain a nuclear retention sequence that can override nuclear export signals

Nascent pre-mRNAs associate with the abundant heterogeneous nuclear RNP (hnRNP) proteins and remain associated with them throughout the time they are in the nucleus. The hnRNP proteins can be divided into two groups according to their nucleocytoplasmic transport properties. One group is completely restricted to the nucleus in interphase cells, whereas the other group, although primarily nuclear at steady state, shuttles between the nucleus and the cytoplasm. Nuclear export of the shuttling hnRNP proteins is mediated by nuclear export signals (NESs). Mounting evidence indicates that NES-bearing hnRNP proteins are mediators of mRNA export. The hnRNP C proteins are representative of the nonshuttling group of hnRNP proteins. Here we show that hnRNP C proteins are restricted to the nucleus not because they lack an NES, but because they bear a nuclear retention sequence (NRS) that is capable of overriding NESs. The NRS comprises approximately 78 amino acids and is largely within the auxiliary domain of hnRNP C1. We suggest that the removal of NRS-containing hnRNP proteins from pre- mRNA/mRNA is required for mRNA export from the nucleus and is an essential step in the pathway of gene expression.