Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Regulation of Blood Oxygenation Level Dependent Response: Primary Effect or Epiphenomenon?

In the last decade, several studies indicated that neuronal activity can be volitionally modulated using real-time fMRI (rtfMRI) based neurofeedback. Human participants through rtfMRI paradigms can learn to regulate the blood oxygenation level dependent (BOLD) response in several localized cortical and subcortical regions (for extended reviews see Caria et al., 2012; Weiskopf, 2012; Sulzer et al., 2013). Increasing evidence also indicated that strengthening or weakening specific BOLD activity using rtfMRI training leads to significant changes in cognitive, emotional and motor behavior (Caria et al., 2012; Weiskopf, 2012; Sulzer et al., 2013). These findings suggested that rtfMRI might represent an important novel approach in cognitive neuroscience by potentially providing indications of cause-and-effect relationships between brain and behavior, and also in clinical applications (Subramanian et al., 2011; Linden et al., 2012; Ruiz et al., 2013; Sitaram et al., 2014). Although rtfMRI studies extend and enrich a large body of literature demonstrating operant learning of neuronal oscillations, skepticism still exists on the validity and reliability of studies showing learned control of the BOLD response. In particular, it is still debated whether this phenomenon is a primary effect of learning or it just an epiphenomenon resulting, for instance, from repeated execution of some mental processes. Here, I will discuss specific psychophysiological and neurophysiological mechanisms presumably underlying learned regulation of the BOLD response to attempt to clarify its nature.
Shallow water modeling of Antarctic Bottom Water crossing the equator

[1] The dynamics of abyssal equator-crossing flows are examined by studying simplified models of the flow in the equatorial region in the context of reduced-gravity shallow water theory. A simple “frictional geostrophic” model for one-layer cross-equatorial flow is described, in which geostrophy is replaced at the equator by frictional flow down the pressure gradient. This model is compared via numerical simulations to the one-layer reduced-gravity shallow water model for flow over realistic equatorial Atlantic Ocean bottom topography. It is argued that nonlinear advection is important at key locations where it permits the current to flow against a pressure gradient, a mechanism absent in the frictional geostrophic model and one of the reasons this model predicts less cross-equatorial flow than the shallow water model under similar conditions. Simulations of the shallow water model with an annually varying mass source reproduce the correct amplitude of observed time variability of cross-equatorial flow. The time evolution of volume transport across specific locations suggests that mass is stored in an equatorial basin, which can reduce the amplitude of time dependence of fluid actually proceeding into the Northern Hemisphere as compared to the amount entering the equatorial basin. Observed time series of temperature data at the equator are shown to be consistent with this hypothesis.
A Supply-and-Demand Model for On-Demand Streaming in Networked Virtual Environments

In a large scale networked virtual environment, users may request various multimedia data in streaming. Although adopting a peer-to-peer (P2P) architecture can make the transmission of on-demand streaming more efficient, most existing research assume that the peers have infinite buffer to cache the entire content and supply the data from there. In this paper, we consider the practical case in which the peers have limited buffer space to cache only a portion of the multimedia content. Due to limited buffer space, peers need effective chunk selection strategies to fetch and discard chunks. We propose a supply-and-demand strategy to estimate the usability of chunks. Peers then select the chunks with a higher usability to fetch and discard the chunks that are useless. Simulation results show that the supply-and-demand strategy selects the chunks accurately and enhances the continuity of playing the stream.
Double Verification Secret Sharing Mechanism Based on Adaptive Pixel Pair Matching

Verifiability is essential for the secret sharing approach, which allows the involved participants to detect cheaters during the secret retrieval process. In this article, we propose a double verification secret sharing (DVSS) mechanism that can not only prevent fraudulent participants but also satisfy the requirements of secret payload, camouflage, image fidelity and lossless revealed secret. DVSS offers double verification process to enhance the cheater detectability; experimental results reveal that the designed scheme can share larger secret capacity and retain superior image quality than the related secret sharing methods.
Design and Calibration of a Fast 3D Scanning LADAR

A fast 3D scanning laser radar is designed and calibrated. A set of mechanical equipment driven by a step motor is designed to rotate the scanning plane of a 2D laser range finder to realize 3D scanning. A micro-controller system is embedded for the scanning control and synchronization with the host computer. Measurement data are acquired by a self-developed high-speed data transmission board. Besides the inherent measurement error of the 2D laser range finder, the error of the resulting 3D LADAR system is mainly caused by two reasons: 1) the unknown offset between the axis of the pitching motion and the actual origin of the 2D laser range finder; 2) the initial attitude error. Based on the system measurement model, a least sum of squared error method is adopted to calibrate the system. The experimental results show that the resulting system has high scanning speed, high data accuracy and therefore is a cost effective means to acquire high quality 3D data
Variational Shape Model for Lip Postures Recognition Using GA

A system for feature extraction and recognition of lip postures is presented, by constructing a shape model of the inner and outer contours of the lips, whose parameters are controlled by a canonical genetic algorithm. Features of each posture are the model parameters which better fit to the analyzed image. A Bayesian classifier is used to classify, under the assumption that there is equiprobability between the classes, obtaining results up to 82.4% of accurate classification
Escalation of Commiement in Software Projects: An Examination of Two Theories

Escalation of commitment is common in many software projects. It stands for the situation where managers decide to continue investing in or supporting a prior decision despite new evidence suggesting the original outcome expectation will be missed. Escalation of commitment is generally considered to be irrational. Past literature has proposed several theories to explain the behaviour. Two commonly used interpretations are self-justification and the framing effect. While both theories have been found effective in causing the escalation of commitment, their relative effect is less studied. The purpose of this study is to further investigate the primary factor that causes the escalation of commitment in software project related decisions. An experiment was designed to examine whether the escalation of commitment exists in different decision contingencies and which theories play a more important role in the escalation. One hundred and sixty two subjects participated in the experiment. The results indicate that both self-justification and problem framing have effects on commitment escalation in software projects but the effect of self-justification is stronger. Significant interaction effect is also found. A commitment is more likely to escalate if the problem is framed positively.
Anisotropic Triangulation of Parametric Surfaces via Close Packing of Ellipsoids

This paper describes a new computational method of fully automated anisotropic triangulation of a trimmed parametric surface. Given as input: (1) a domain geometry and (2) a 3×3 tensor field that specifies a desired anisotropic node-spacing, this new approach first packs ellipsoids closely in the domain by defining proximity-based interacting forces among the ellipsoids and finding a force-balancing configuration using dynamic simulation. The centers of the ellipsoids are then connected by anisotropic Delaunay triangulation for a complete mesh topology. Since a specified tensor field controls the directions and the lengths of the ellipsoids' principal axes, the method generates a graded anisotropic mesh whose elements conform precisely to the given tensor field.
Analysis of Myc-Induced Histone Modifications on Target Chromatin

The c-myc proto-oncogene is induced by mitogens and is a central regulator of cell growth and differentiation. The c-myc product, Myc, is a transcription factor that binds a multitude of genomic sites, estimated to be over 10–15% of all promoter regions. Target promoters generally pre-exist in an active or poised chromatin state that is further modified by Myc, contributing to fine transcriptional regulation (activation or repression) of the afferent gene. Among other mechanisms, Myc recruits histone acetyl-transferases to target chromatin and locally promotes hyper-acetylation of multiple lysines on histones H3 and H4, although the identity and combination of the modified lysines is unknown. Whether Myc dynamically regulates other histone modifications (or marks) at its binding sites also remains to be addressed. Here, we used quantitative chromatin immunoprecipitation (qChIP) to profile a total of 24 lysine-acetylation and -methylation marks modulated by Myc at target promoters in a human B-cell line with a regulatable c-myc transgene. Myc binding promoted acetylation of multiple lysines, primarily of H3K9, H3K14, H3K18, H4K5 and H4K12, but significantly also of H4K8, H4K91 and H2AK5. Dimethylation of H3K79 was also selectively induced at target promoters. A majority of target promoters showed co-induction of multiple marks - in various combinations - correlating with recruitment of the two HATs tested (Tip60 and HBO1), incorporation of the histone variant H2A.Z and transcriptional activation. Based on this and previous findings, we surmise that Myc recruits the Tip60/p400 complex to achieve a coordinated histone acetylation/exchange reaction at activated promoters. Our data are also consistent with the additive and redundant role of multiple acetylation events in transcriptional activation.
NOVEL NEURAL LEARNING STRATEGIES FOR HIGH PERFORMANCE VOWEL CLASSIFICATION

Feedforward neural network performances depend on finding optimal learning parameters. Among them, the learning rate is critical for both the convergence speed and the value of the learning error. This paper introduces a new method for learning rate adaptation, namely the “Anti–Oscillatory Dynamic Method”. Its performances a e evaluated on a vowel classification task, with various cost functions. A comparison with other strategies proves that our solution outperforms Moody’s “Search then Converge” technique.
Image processing for autonomous rendezvous in the OLEV mission

In this paper are described the image processing methods to be used by the OLEV mission during autonomous rendezvous operations. OLEV is a commercial mission aimed to extend the operational life of geostationary telecommunication satellites by supplying them control, navigation and guidance services. The OLEV satellite is brought to the vicinity (~2 km) of the client satellite by application of dedicated ground tracking techniques. At that point, the imaging system is activated, providing to the control system angular position and distance to the client satellite during the rendezvous operation. The images provided by the active rendezvous camera are transmitted to ground, where they are processed to detect and track the client satellite shape, extracting angular position and distance information. Shape detection is performed using novel morphological gray-scale reconstruction filters and automatic image thresholding techniques.
Statistical Model of Total Target Registration Error in Image-Guided Surgery

In a paired-point rigid registration, target registration error (TRE) is deemed to be the most important quality metric. TRE usually cannot be directly measured, and thus many TRE estimation algorithms have been proposed. However, target localization errors (TLEs) in two spaces are not considered in the definition of TRE. In this paper, we propose a new type of evaluation metric that is referred to as total TRE (TTRE) at a given target point with TLE incorporated. Statistics including mean, root mean square (rms), and covariance matrix of TTRE are derived without making any assumption of the TLE magnitude. TTRE and fiducial registration error (FRE) are proved to be uncorrelated when an ideal weighting scheme is adopted in solving the registration problem. The proposed error model is validated through extensive experiments. In the first experiment with random fiducials and targets, in 90% of the test cases, there shows no difference between the predicted and simulated TTRE statistics when six fiducials are used. In the second experiment of deep-brain stimulation surgery, the mean value of CC(TTRE,FRE) being $8.9246\times 10^{-4}\pm 0.0389 $ was observed, which indicates that TTRE and FRE are uncorrelated. In the third experiment of surgical tool-tip tracking, the mean and standard deviation of percentage differences between predicted and simulated TTRE rms values are $2.22\%\pm 0.77\%$ for the planar tool and $2.62\%\pm 0.59\%$ for the textral tool. In summary, our proposed algorithm can well model the TTRE metric. Note to Practitioners—This paper was motivated by considering the existence of target localization error in two spaces to be registered in a rigid point-based registration. Given fiducial localization error (FLE) and TLE probability distributions, the proposed TTRE model gives the covariance matrix and root mean square of TTRE at a given target point. The possible applications of the proposed TTRE model in an image-guided surgery (IGS) include: 1) estimation of surgical tool-tip tracking error distribution when pivot calibration uncertainty is considered; 2) accuracy evaluation of an image-to-patient registration; 3) updating of a preoperative surgical plan on a computed tomography/MRI image to consider the registration error at a given target point; and 4) estimation of real-time tool-tip error in touching a desired “true” surgical target, such as a tumor during surgery. To use the proposed error estimation algorithm, FLE and TLE in two spaces to be registered have to be known or estimated in advance to a registration process. The proposed error model is validated through extensive simulations.
Performance Enhancement of Downlink Multiuser DS-CDMA Detectors Using Processing by Independent Component Analysis

In this paper, the attention is focused on the generation of the received digital signal and its linear detection over a synchronous multiuser direct sequence code division multiple access (DS-CDMA) downlink system considering various spreading codes, additive white Gaussian noise (AWGN) and introducing varying signal-to-noise ratios (SNRs) and users' power distributions. The aim of the paper is to test the efficiency of implementing an independent component analysis (ICA) based estimator of the transmitted symbols preceded by principal component analysis (PCA) pre-processing of the received data to reduce dimension and decrease the noise effects. The noisy ICA algorithm is used either as: (1) a pre-processor prior to conventional detection: The idea here is to estimate the mixing matrix that contains the basic vectors and fading terms using the separating matrix and PCA subspace or as (2) a post-processor attached to a linear DS-CDMA receiver. Numerical simulations indicate that the first schema could constitute a powerful tool for channel and symbols estimations and has the advantage that it doesn't necessitate any prior knowledge of the users' codes or paths delays and strengths except providing a short pilot sequence (presenting 1% to 4% of the transmitted symbols of the user of interest) that is used to give a good enough initial guess of the demising matrix columns and so force the ICA iteration to be in the wanted user subspace. The second schema shows performance comparable to or superior than the exact blind detectors at relatively high SNR values
Decision-Making under Subjective Risk : Toward a General Theory of Pessimism ∗

The primary objective of this paper is to develop a framework in which a decisionmaker may have subjective beliefs about the “riskiness” of prospects, even though the risk structure of these prospects is objectively specified. Put differently, we investigate preferences over risky alternatives by postulating that such preferences arise from more basic preferences that act on the subjective transformations of these prospects. This allows deriving a theory of preferences over lotteries with distorted probabilities and provides information about the structure of such distortions. In particular, we are able to formulate a behavioral trait such as “pessimism” in the context of risk (independently of any sort of utility representation) as a particular manifestation of the uncertainty aversion phenomenon. Our framework also provides a strong connection between the notions of aversion to ambiguity and risk which are regarded as distinct traits in decision theory. JEL Classification: D11, D81.
Relationship between body weight and the increment in serum brain-derived neurotrophic factor after oral glucose challenge in men with obesity and metabolic syndrome

Abstract Brain-derived neurotrophic factor (BDNF) plays a role in energy homeostasis. However, the postprandial BDNF change has not been well investigated. We hypothesized that the BDNF increment after oral glucose challenge is associated with body weight. To address this possibility, man adults with obesity in conjunction with metabolic syndrome were compared with normal weight controls at baseline in the initial cross-sectional protocol. The obese subjects then underwent a 12-week program for body-weight reduction in the prospective protocol. The area under the curve (AUC) of serum BDNF was recorded during a 75 g oral glucose tolerant test and the BDNF AUC index was defined as [(AUC of BDNF) − (fasting BDNF*2 hours)]/(fasting BDNF*2 hours). A total of 25 controls and 36 obese subjects completed the study assessments. In the cross-sectional protocol, the BDNF AUC index was significantly higher in the obese subjects than in the controls (9.0 ± 16.5% vs. − 8.0 ± 22.5%, P = 0.001). After weight reduction (from 97.0 ± 12.5 kg to 88.6 ± 12.9 kg, P < 0.001), the percentage change of body weight was significantly associated with the BDNF AUC index after the study (95% CI between 0.21 and 1.82, P = 0.015). Using 6% weight reduction as a cut-off value, a larger weight reduction was able to reliably predict a negative BDNF AUC index. In conclusion, a high BDNF AUC index was observed for obese men in this study, whereas the index value significantly decreased after body-weight reduction. These findings suggest that postprandial BDNF increment may be associated with obesity.
Exploring Aesthetic Ideals of Gameplay

This paper describes a theoretical exploration of aesthetics ideals of gameplay. Starting from observations about the game artifact, several gameplay properties that can affect the aesthetical experience are identified, e.g. tempting challenges, cohesion, and gamer interaction. These properties are then used to describe several aesthetical ideals of gameplay, e.g. emergence, reenactment, meditative, and camaraderie. The properties and ideals provide concepts for how games attribute aesthetical value to gameplay design and how they distinguish their own preferences from inherent qualities of a game artifact.
Ieee 6th Workshop on Signal Processing Advances in Wireless Communications Maximum Snr Prefiltering for Mimo Systems

In this paper, we derive a class of FIR prefilters for MIMO systems that maximize the signal-to-noise ratio (S N R) defined as the sum of SNRs for the output branches with uncorrelated noise. This definition is motivated by an analysis of the pairwise error probability. The maximum-SNR (MSNR) class of prefilters includes decision feedback equalization (D E) and channel shortening equalization (CSE) prefilters. The feedback taps are completely suppressed by the CSE prefilter and not suppressed at all by the D E prefilter. The MSNR class includes hybrid prefilters which are a combination of CSE and DFE prefilters. Hybrid pre-filters allow middle ground for suppressing feedback taps which is desirable for equalization using decision feedback sequence estimation (DFSE). We show simulation results for joint DFSE dc-modulation of two 8-PSK modulated cochannel EDGE users.using a single antenna receiver. We find that a hybrid prefilter performs best for this example.
Toddlers on touchscreens: immediate effects of gaming and physical activity on cognitive flexibility of 2.5-year-olds in the US

ABSTRACT Interactive technologies have become a common play medium for young children; it is not unusual for toddlers to play games on a touchscreen device in lieu of games in the yard. Here, we compared the immediate effects of physical and touchscreen play on 2.5-year-olds’ cognitive flexibility, a key aspect of executive function. For nine minutes, toddlers engaged in touchscreen play or physical play; a third group drew and colored (control group). Next, a sorting task measured cognitive flexibility. The physical-play group outperformed the other two groups. Compared to the control group, toddlers’ cognitive flexibility benefited from physical play, whereas touchscreen play yielded no significant effect. Interestingly, toddlers who played the touchscreen game in a socially interactive way outperformed those who treated gaming as solitary play. Together, the results bear practical implications on whether and how to introduce young children to interactive technologies for play.
Phylogenetic analysis of mammalian maximal oxygen consumption during exercise

SUMMARY We compiled published values of mammalian maximum oxygen consumption during exercise () and supplemented these data with new measurements of for the largest rodent (capybara), 20 species of smaller-bodied rodents, two species of weasels and one small marsupial. Many of the new data were obtained with running-wheel respirometers instead of the treadmill systems used in most previous measurements of mammalian . We used both conventional and phylogenetically informed allometric regression models to analyze of 77 ‘species’ (including subspecies or separate populations within species) in relation to body size, phylogeny, diet and measurement method. Both body mass and allometrically mass-corrected showed highly significant phylogenetic signals (i.e. related species tended to resemble each other). The Akaike information criterion corrected for sample size was used to compare 27 candidate models predicting (all of which included body mass). In addition to mass, the two best-fitting models (cumulative Akaike weight=0.93) included dummy variables coding for three species previously shown to have high (pronghorn, horse and a bat), and incorporated a transformation of the phylogenetic branch lengths under an Ornstein–Uhlenbeck model of residual variation (thus indicating phylogenetic signal in the residuals). We found no statistical difference between wheel- and treadmill-elicited values, and diet had no predictive ability for . Averaged across all models, the allometric scaling exponent was 0.839, with 95% confidence limits of 0.795 and 0.883, which does not provide support for a scaling exponent of 0.67, 0.75 or unity.