Miracle-dz/UniFAR-FTUs huggingface dataset:
A ratio test for the accurate automatic P — wave onset detection

In this paper a new nonlinear transformation of the raw data based on the notion of the length of seismogram and a ratio test statistic for the accurate automatic P — wave onset detection problem are proposed. The basic characteristic of the proposed statistic is that it makes no assumptions for the properties of the noise, apart from very loose stationarity requirements. Based on this statistic, we propose an algorithm for the accurate estimation of the arrival times. From a series of experiments on real signals we have conducted, the proposed picking method seems to outperform well known in the literature methods.
Effects of Cursor Orientation and Required Precision on Positioning Movements on Computer Screens

Arrowhead cursors used within graphic-user interfaces include implicit directional cues that may not be compatible with desired axis of motion. In addition, arrowhead cursors may not afford the best cues for location, and such effects may be exacerbated when there is a greater need for precise cursor placement. To address the impact of the cursor's orientation on its positioning, 12 participants were required to move cursors (pointing arrows) leftward or rightward to targets (small, medium, or large) on a computer screen. Response latencies were influenced by compatibilities between movement direction and cursor shape but only when moderate levels of precision were required. Rightward movements were slower and less accurate, and movements were slower and less efficient when arrows pointed in compatible directions. Implicit directional cues only elicited compatibility effects with moderate precision requirements. Arrowhead cursors compatible with direction of motion led to slower cursor movements and less efficient cursor trajectories. Where response initiation is important, compatible arrowheads are beneficial, but if speed of cursor placement is an issue, an orientation neutral cursor with area effect might be preferable, or a more direct interface (e.g., touch-sensitive screen) might be more appropriate.
A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials

It has been recognized that in order to drive Intelligent Tutoring Systems (ITSs) into mainstream use by the teaching community, it is essential to support teachers through the entire ITS process: Design, Development, Deployment, Reflection and Adaptation. Although research has been done on supporting teachers through design to deployment of ITSs, there is surprisingly little discussion about support for teachers' Reflection - the ability to draw conclusions from ITS usage, and Adaptation - adapting the content to better meet the needs of students. We describe our work on developing analysis tools and methodologies that support reflection and adaptation by teachers. The work was done in the context of helping teachers understand student's behavior in Adaptive Tutorials by post-analysis of the system's data-logs. We used a hybrid solution - part of the data-mining effort is teacher driven and part is automated. We tested our approach by comparing the results of expert analysis of two Adaptive Tutorials with and without an automated Refinement Suggestion Tool, and found it to be a useful teacher's aid. By using this tool, teachers act as 'action researchers', confirming or disproving their hypotheses about the best way to use ITS technology.
Robust Top-k Query Evaluation in Wireless Sensor Networks

In many applications of sensor networks including environmental monitoring and security surveillance, a large volume of sensed data generated by sensors is needed to be either collected at the base station or aggregated within the network to respond to user queries. However, due to the high failure rate of wireless communication, robust query processing is very important for some critical queries in sensor networks by dealing with the messages loss in sensor networks. In this paper, we propose a novel localized and adaptive algorithm for robust top-$k$ query evaluation in wireless sensor networks, which strives the right tradeoff between the energy consumption and the accuracy of obtained results. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithm on real datasets. The experimental results show that the proposed algorithm is energy-efficient when meeting the specified result accuracy requirement.
Quo vadis, deficit? How high the tax level will be when the economic cycle reverses?

The economic recession dampened tax revenues, causing deterioration – partly temporary, partly permanent – in the general government balance. The fiscal position can be assessed realistically if we can determine the level of revenue and deficit in the medium run. In order to do this, we prepare estimates of the trends of the macroeconomic variables determining tax bases and of the elasticity between tax revenues and tax bases. Trends in macroeconomic variables can be determined in three ways. Results from the macroeconometric model are more reliable and consistent. The simple time series method (ECB) is acceptable if it relies on prior estimation – e.g. one derived from the model – of the trends of macroeconomic variables. The Multivariate Hodrick-Prescott filter method (MVHP) only requires exogenously given potential GDP, and is thus suitable for simulation and for determining the uncertainty surrounding the estimate. Our model-based results show that the deficit for 2010 would be 2.8% lower if – over the medium term – there were convergence with the potential GDP forecast by the model. From 2011 this negative cyclical component will diminish by an annual 0.4-0.5%. If potential GDP is 1% lower, from 2011 tax revenues would approach a lower medium-term tax level 0.28-0.29% faster based on the MVHP method. If potential GDP is 1% higher, convergence to a higher tax level would be 0.30-0.31% slower.
NUMA-optimized In-memory and Semi-external-memory Parameterized Clustering

K-means is one of the most influential and utilized machine learning algorithms today. Its computation limits the performance and scalability of many statistical analysis and machine learning tasks. We rethink k-means in terms of modern architectures to develop a novel parallelization scheme that delays and minimizes synchronization barriers. We utilize this to develop two modules k$||$means$_T$, and SEM-k$||$means$_T^{RC}$. k$||$means$_T$ is optimized for NUMA architectures and radically improves the performance for datasets that fit in memory. SEM-k$||$means$_T^{RC}$ improves k-means' scalability on a memory budget using semi-external memory, SSDs and a minimalistic triangle inequality pruning algorithm. SEM-k$||$means$_T^{RC}$ scales to billions of points on a single machine, using a fraction of the resources that distributed in-memory systems require. We show k$||$means$_T$ outperforms commercial products like H$_2$O, Dato and Spark's MLlib by up to an order of magnitude on $O(10^7)$ point datasets. Furthermore, SEM-k$||$means$_T^{RC}$ effortlessly clusters $\geq O(10^8)$ point datasets while outperforming distributed in-memory frameworks.
Does Risk Aversion Vary During the Year? Evidence from Bid-Ask Spreads

We document previously undiscovered seasonal patterns in bid-ask spreads which are economically and statistically significant. After controlling for well-known conditional effects on spreads, such as risk, liquidity, and asymmetric information effects, we find that spreads based on individual dealer quotes are on average 20 basis points wider during the fall and winter relative to their unconditional average of 180 basis points. Furthermore, inside spreads are about 20 basis points narrower during these periods when spreads based on individual quotes are wider. We posit that these opposing patterns in quoted spreads and inside spreads are the logical outcome of seasonally changing risk aversion among a subset of market makers. When we consider periods of high market volatility, we find that the economic and statistical significance of the seasonal patterns in spreads intensify, as they should if they arise for reasons related to risk aversion. Our findings are robust to a wide range of tests and specification changes. Independent of the cause, researchers studying spreads need to be mindful of the strong seasonal patterns in individual dealer spreads and inside spreads, as does any market participant who has discretion in the timing of his trades.
Endoxifen, a Secondary Metabolite of Tamoxifen, and 4-OH-Tamoxifen Induce Similar Changes in Global Gene Expression Patterns in MCF-7 Breast Cancer Cells

We recently demonstrated that endoxifen (4-hydroxy-N-desmethyl-tamoxifen), a pharmacogenetically regulated metabolite of tamoxifen, is equipotent to 4-hydroxy-tamoxifen (4-OH-Tam) with respect to estrogen receptor binding and inhibition of 17β-estradiol (E2)-induced cell proliferation. Endoxifen was also found to be more abundant in human plasma than 4-OH-Tam, and its formation has been shown to be primarily catalyzed by cytochrome P450 2D6 (CYP2D6). Here, we report studies evaluating the effects of endoxifen, 4-OH-Tam, and E2 on gene expression in MCF-7 cells using Affymetrix U133A GeneChip Arrays (Santa Clara, CA). We detected 4062 genes that were E2-regulated (1924 induced; 2138 suppressed), and the ratio of E2-induced versus E2-suppressed genes was consistent regardless of the cutoff value. In the presence of E2, 2444 and 2390 genes were affected by 4-OH-Tam and endoxifen, respectively, when no minimal -fold change cutoff was implemented. The majority of genes regulated by the tamoxifen metabolites were also E2-responsive (74.4 and 73.3%, respectively). Endoxifen and 4-OH-Tam had overlapping effects on 1365 E2-sensitive genes, whose -fold effects between these metabolites were highly correlated (R2 = 0.99). A significant correlation was also found between the -fold effects of 249 E2-insensitive genes coregulated by both metabolites (R2 = 0.99). Hierarchical clustering analysis demonstrated similar gene regulation patterns between these metabolites, which were distinct from E2 or vehicle treatment patterns. Using real time-polymerase chain reaction, we validated the gene expression patterns of five genes that were differentially regulated by endoxifen and 4-OH-Tam. We conclude that endoxifen and 4-OH-Tam have similar effects on global gene expression patterns in MCF-7 cells and that the majority of the affected genes are estrogen-regulated genes.
Modulation of Immune Complex–induced Inflammation In Vivo by the Coordinate Expression of Activation and Inhibitory Fc Receptors

Autoantibodies and immune complexes are major pathogenic factors in autoimmune injury, responsible for initiation of the inflammatory cascade and its resulting tissue damage. This activation results from the interaction of immunoglobulin (Ig)G Fc receptors containing an activation motif (ITAM) with immune complexes (ICs) and cytotoxic autoantibodies which initiates and propagates an inflammatory response. In vitro, this pathway can be interrupted by coligation to FcγRIIB, an IgG Fc receptor containing an inhibitory motif (ITIM). In this report, we describe the in vivo consequences of FcγRII deficiency in the inflammatory response using a mouse model of IC alveolitis. At subthreshold concentrations of ICs that fail to elicit inflammatory responses in wild-type mice, FcγRII-deficient mice developed robust inflammatory responses characterized by increased hemorrhage, edema, and neutrophil infiltration. Bronchoalveolar fluids from FcγRII−/− stimulated mice contain higher levels of tumor necrosis factor and chemotactic activity, suggesting that FcγRII deficiency lowers the threshold of IC stimulation of resident cells such as the alveolar macrophage. In contrast, complement- and complement receptor–deficient mice develop normal inflammatory responses to suprathreshold levels of ICs, while FcRγ−/− mice are completely protected from inflammatory injury. An inhibitory role for FcγRII on macrophages is demonstrated by analysis of FcγRII−/− macrophages which show greater phagocytic and calcium flux responses upon FcγRIII engagement. These data reveal contrasting roles for the cellular receptors for IgG on inflammatory cells, providing a regulatory mechanism for setting thresholds for IC sensitivity based on the ratio of ITIM to ITAM FcγR expression. Exploiting the FcγRII inhibitory pathway could thus provide a new therapeutic approach for modulating antibody-triggered inflammation.
DIAMOND: A graphical interface toolbox for comparative modal analysis and damage identification

This paper introduces a new toolbox of graphical-interface software algorithms for the numerical simulation of vibration tests, analysis of modal data, finite element model correlation, and the comparison of both linear and nonlinear damage identification techniques. This toolbox is unique because it contains several different vibration-based damage identification algorithms, categorized as those which use only measured response and sensor location information (non-model-based techniques) and those which use finite element model correlation (model-based techniques). Another unique feature of this toolbox is the wide range of algorithms for experimental modal analysis. The toolbox also contains a unique capability that utilizes the measured coherence functions and Monte Carlo analysis to perform statistical uncertainty analysis on the modal parameters and damage identification results. This paper contains a detailed description of the various modal analysis, damage identification, and model correlation capabilities of toolbox, and also shows a sample application which uses the toolbox to analyze the statistical uncertainties on the results of a series of modal tests performed on a highway bridge.
Modern Perspectives on Stabilization Policies

The present paper describes recent research on two central themes of Keynes' General Theory: (i) the social waste associated with recessions, and (ii) the effectiveness of fiscal policy as a stabilization tool. The paper also discusses some evidence on the extent to which fiscal policy has been used as a stabilizing tool in industrial economies over the past two decades.
Identifying Sources of Configurality in Three Face Processing Tasks

Participants performed three feature-complete face processing tasks involving detection of changes in: (1) feature size and (2) feature identity in successive matching tasks, and (3) feature orientation. In each experiment, information in the top (eyes) and bottom (mouths) parts of faces were manipulated. All tasks were performed with upright and inverted faces. Data were analyzed first using group-based analysis of signal detection measures (sensitivity and bias), and second using analysis of multidimensional measures of sensitivity and bias along with probit regression models in order to draw inferences about independence and separability as defined within general recognition theory (Ashby and Townsend, 1986). The results highlighted different patterns of perceptual and decisional influences across tasks and orientations. There was evidence of orientation specific configural effects (violations of perceptual independence, perceptual seperability and decisional separabilty) in the Feature Orientation Task. For the Feature Identity Task there were orientation specific performance effects and there was evidence of configural effects (violations of decisional separability) in both orientations. Decisional effects are consistent with previous research (Wenger and Ingvalson, 2002, 2003; Richler et al., 2008; Cornes et al., 2011). Crucially, the probit analysis revealed violations of perceptual independence that remain undetected by marginal analysis.
Isolectin B4-Positive and -Negative Nociceptors Are Functionally Distinct

Small-diameter sensory neurons that are primarily nociceptors can be divided neurochemically into two populations: isolectin B4 (IB4)-positive nonpeptidergic neurons, and IB4-negative peptidergic neurons. It has been shown that IB4-positive neurons depend on glial-derived neurotrophic factor (GDNF), whereas IB4-negative neurons depend on NGF for survival during postnatal development (Molliver et al., 1997). Furthermore, these two populations of nociceptors terminate in distinct regions of the superficial spinal cord. To date, however, no evidence exists that indicates whether these two groups of nociceptors have distinct functional roles in the process of nociception (Snider and McMahon, 1998). To search for functional differences, we performed whole-cell voltage and current-clamp recordings on acutely isolated adult mouse dorsal root ganglion neurons that were labeled with fluorescent IB4. We found that IB4-positive neurons have longer-duration action potentials, higher densities of TTX-resistant sodium currents, and smaller noxious heat-activated currents than IB4-negative neurons. Furthermore, we show that NGF, but not GDNF, directly increases the number of neurons that respond to noxious heat. The different electrophysiological properties expressed by IB4-positive and -negative small neurons, including their different heat sensitivities, indicates that they may relay distinct aspects of noxious stimuli both acutely and after injury in vivo.
2 5 Ja n 20 02 Precision Optical Measurements and Fundamental Physical Constants

The electron and proton are involved in various phenomena of different fields of physics. As a result, fundamental physical constants related to their properties (such as electron/proton charge (e), the electron and proton masses (me and mp), the Rydberg constant (Ry), the fine structure constant (α) etc), can be traced in various basic equations of physics of atoms, molecules, solid state, nuclei and particles etc. Until recently most accurate measurements came from radio frequency experiments only, which supplied us with precise values of most of the fundamental constants and accurate tests of the quantum theory of simple atoms (bound state QED). The optical measurements delivered to us a value for the Rydberg constant, but were not accurate enough to provide any competitive test of the QED. During the last decade the status of the optical measurements changed dramatically because of
Developing a new automated tool for detecting and monitoring dust and sand storms using MODIS and meteosat SEVIRI-MSG data

In the last three decades, significant advances have been made in mapping and monitoring environmental changes from Earth Observation satellites across a range of spatial and temporal scales. For example, with the actual Earth observation satellites, we can observe large areas rather than sparse points and provide unique information about properties of the surface or shallow layers of the earth. Indeed, the extent of many environmental and meteorological events such as vegetation stress, fogs, sandstorms and droughts cannot be captured by ground measurements alone making remote sensing an indispensable tool in environmental monitoring. The harsh nature of sub-African region makes ground-based monitoring and mapping of their local environment difficult and expensive. However, in contrast with their appearing harsh nature, arid desert areas tend to be fragile ecosystems where little climate perturbations may cause tremendous changes in their landscapes. Additionally, due to their low precipitation rates, arid regions are the world's major source of atmospheric dust that has an impact on local, regional and global climate. This paper will present an overview of current tools used for dust and sandstorm monitoring over bright-reflecting surfaces. An overview of current and potential applications of remote sensing in sub-African countries as well as the challenges that may be faced will also be presented.
Common Spatial Pattern Patches -an Optimized Filter Ensemble for Adaptive Bcis

The use of an ensemble of local Common Spatial Patterns (CSP) patches (CSPP) is proposed, which can be considered as a compromise between Laplacians and CSP: CSPP reaches a robust performance with less training data than CSP, while being superior to Laplacian filtering. This property is shown to be particularly useful for the co-adaptive calibration design and is demonstrated in combination with on-line adaptation, on off-line calibration data of 80 naïve users.
VALIDITY OF MEASURING MUSCLE ACTIVATION LEVELS BY ISOKINETIC TESTING

The study aimed at testing the possibility of using an isokinetic dynamometer to measure muscle activation level (AL). The AL measured with an isokinetic dynamometer and calculated as a ratio between the maximum isometric and eccentric torques was compared against the AL obtained by the double interpolated twitch method. Eighteen male students with different levels of training participated in the study. The average ALs measured by the isokinetic dynamometer (AL_ISOK) at knee angles of 55° and 60°, as well as the average AL calculated from the maximum eccentric torque independently of a knee angle at angular velocities of 60°/s (100.10±13.25%, 92.43±11.82% and 84.33±9.29%, respectively) and 120°/s (99.93±11.93%, 94.61±13.79% and 88.52±14.07%, respectively) did not differ significantly from the average ALs measured by the double interpolated twitch method (AL_TW=89.50±7.42%). Significant correlations were found between AL_TW and several AL_ISOK at the angular velocity of 120°/s. However, the correlations were negative and low to medium (from –.478 to –.609). It was concluded that the AL_ISOK were not comparable to the AL_TW and therefore seemed not suitable for the activation level measurement, although the negative correlations between the two methods still leave this possibility.
Learning temporal sequences using dual‐weight neurons

Abstract This paper considers the use of neural networks (NN's) in learning temporal sequence recognition and reproduction for which the sequence degree is unknown. This approach uses the output ambiguity to train the network without the need to assume or construct a separate model for the input sequence degree. First we introduce a primitive network called the DNN, comprising a plurality of dual‐weight (DN) neurons. Each neuron is linked to other neurons by a long‐term excitatory weight and a short‐term inhibitory weight. Fast learning is made possible by employing a two‐pass training rule to encode the temporal distance between two arbitrary pattern occurrences. The resulting DNN is then extended into a more generalized model, namely the DNN2. By incorporating the two‐pass rule and a self‐organizing algorithm, the DNN2 can achieve autonomous temporal sequence recognition and reproduction. Using training efficiency and hardware complexity criteria, the DNN networks are also contrasted with the work of Wang and Yuwono (1995).
Domain-Specific Modeling for Robotics: From Language Construction to Ready-made Controllers and End-user Applications

We present an approach and tools implemented for model-driven development of robotics using Domain-specific Modeling (DSM). Special attention is paid to the automatic generation of robot control logic, and facilitation of the validation and certification of software components. We describe the approach using practical examples on how to construct and use domain-specific languages for modeling (1) topological properties of a robot arm, (2) robot controllers, (3) robot actions and activities as well as (4) generating and verifying end-user applications from the models. Additionally, we describe on how to build DSM tools for visual debugging and how to include generation of documents in a model-level debugging scenario. Using fast code generators and adaptive run-time systems our tool chain supports generation, incremental update and execution of native code for various hardware platforms and operating systems "on hot". Our approach is applicable to both industrial and human-like robots and is verified by developing practical robot applications.