Miracle-dz/UniFAR-FTUs huggingface dataset:
Exchange Rates, Macroeconomic Fundamentals and Risk Aversion

This paper proposes a theoretical model for determining the exchange rate based on the interaction between international bond markets with different maturities. The model accommodates the presence of risk premia between short- and long-term bonds. The difference in risk premium between international bond markets produces imbalances between their yields and is responsible for the differences in equilibrium between the future spot exchange rate and the corresponding forward price. These departures from the expectations hypothesis of the international term structure of interest rates lead to unintended effects on the efficacy of monetary policy in open economies. The existence of imbalances in the risk premium between countries can be considered by monetary authorities as an alternative tool for conducting monetary policy and boosting real output.
Visual Aided GPS Navigation for Autonomous Mobile Robots

Navigation based on GPS data has been the most commonly used methodology for the autonomous run of a mobile robot in indoor and outdoor environments. However, the reading of the GPS receivers fluctuates over a considerable range esp. in countries like India where there is a dearth of GPS signals and locally available correction table. So the commonly received value by GPS receiver can be accurate over a range of 10-15mts which is inadequate if the test results require accuracy. This paper introduces a technique for the development of a visual aided GPS navigation system for a mobile robot in which we have predefined visual landmarks for the various important landmarks which robot has to visit. In our approach, we have mounted a stereovision camera on the robot platform for image acquisition, real time object recognition, detection and local features extraction from images using Scale-Invariant Feature Transform (SIFT). Robust software based on the Open Computer Vision Library (OpenCV) has been developed for handling images from a stereovision camera and determine local heading direction. Following the description of the navigation technique, real world experiments demonstrate the functionality of the presented approach thereby substantiating the validity of the algorithm. The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org
Vision based control of aerial robotic vehicles using the port Hamiltonian framework

This paper investigates the formulation of sensor based control for aerial robotic vehicles based on the port Hamiltonian framework. The paper considers the particular case of vision based control and develops a model for an “infinite dimensional” visual energy port that uses optical flow in the image plane as a ‘velocity’ in the port Hamiltonian formalism.
Active learning approach to detecting standing dead trees from ALS point clouds combined with aerial infrared imagery

Due to their role in certain essential forest processes, dead trees are an interesting object of study within the environmental and forest sciences. This paper describes an active learning-based approach to detecting individual standing dead trees, known as snags, from ALS point clouds and aerial color infrared imagery. We first segment individual trees within the 3D point cloud and subsequently find an approximate bounding polygon for each tree within the image. We utilize these polygons to extract features based on the pixel intensity values in the visible and infrared bands, which forms the basis for classifying the associated trees as either dead or living. We define a two-step scheme of selecting a small subset of training examples from a large initially unlabeled set of objects. In the first step, a greedy approximation of the kernelized feature matrix is conducted, yielding a smaller pool of the most representative objects. We then perform active learning on this moderate-sized pool, using expected error reduction as the basic method. We explore how the use of semi-supervised classifiers with minimum entropy regularizers can benefit the learning process. Based on validation with reference data manually labeled on images from the Bavarian Forest National Park, our method attains an overall accuracy of up to 89% with less than 100 training examples, which corresponds to 10% of the pre-selected data pool.
Energy Structure of Optimal Positional Strategies in Mean Payoff Games

This work studies structural aspects concerning optimal positional strategies in Mean Payoff Games, it is a contribution to understanding the relationship between optimal positional strategies in MPGs and small energy-progress measures in reweighted energy games. In particular, it is observed an energy decomposition theorem which describes the whole space of all optimal positional strategies in terms of so-called extremal small energy-progress measures. This points out what we called the "energy structure" of the space of all optimal positional strategies in MPGs, as it allows for a disjoint-set decomposition (of that space) which is expressed in terms of small energy-progress measures in reweighted energy games.
Generalized Belief Propagation

Belief propagation (BP) was only supposed to work for treelike networks but works surprisingly well in many applications involving networks with loops, including turbo codes. However, there has been little understanding of the algorithm or the nature of the solutions it finds for general graphs. We show that BP can only converge to a stationary point of an approximate free energy, known as the Bethe free energy in statistical physics. This result characterizes BP fixed-points and makes connections with variational approaches to approximate inference. More importantly, our analysis lets us build on the progress made in statistical physics since Bethe's approximation was introduced in 1935. Kikuchi and others have shown how to construct more accurate free energy approximations, of which Bethe's approximation is the simplest. Exploiting the insights from our analysis, we derive generalized belief propagation (GBP) versions of these Kikuchi approximations. These new message passing algorithms can be significantly more accurate than ordinary BP, at an adjustable increase in complexity. We illustrate such a new GBP algorithm on a grid Markov network and show that it gives much more accurate marginal probabilities than those found using ordinary BP.
Weaving Through Weather on a Danish Caravan Site

The aim of this article is to study embodied experiences and negotiations with everyday weather on a Danish caravan site. I present a contrast to representations of certain types of weather as something you travel away from to seek out the sun. This is done by introducing a sensuous and embodied focus to mundane weather that is also encountered on holiday. Drawing on conceptions of embodiment, the everyday and sensuous, I argue that experiences of weather are relational and multisensuous. This article has three aims. First, to nuance discourses on weather by researching people’s embodied and sensuous negotiations of weather. Second, to broaden understandings of holiday weather beyond that of sunny destinations. And third, proposing the concept of “weathering” to inform the relatively neglected role of everyday weather in tourism research. In this article, my aim is to discuss forms of somatic, sensuous, everyday routines, and movements cultivated and performed with weather.
The Effect of Ignoring Statistical Interactions in Regression Analyses Conducted in Epidemiologic Studies: An Example with Survival Analysis Using Cox Proportional Hazards Regression Model

Objective To demonstrate the adverse impact of ignoring statistical interactions in regression models used in epidemiologic studies. Study design and setting Based on different scenarios that involved known values for coefficient of the interaction term in Cox regression models we generated 1000 samples of size 600 each. The simulated samples and a real life data set from the Cameron County Hispanic Cohort were used to evaluate the effect of ignoring statistical interactions in these models. Results Compared to correctly specified Cox regression models with interaction terms, misspecified models without interaction terms resulted in up to 8.95 fold bias in estimated regression coefficients. Whereas when data were generated from a perfect additive Cox proportional hazards regression model the inclusion of the interaction between the two covariates resulted in only 2% estimated bias in main effect regression coefficients estimates, but did not alter the main findings of no significant interactions. Conclusions When the effects are synergic, the failure to account for an interaction effect could lead to bias and misinterpretation of the results, and in some instances to incorrect policy decisions. Best practices in regression analysis must include identification of interactions, including for analysis of data from epidemiologic studies.
Rescue of Cyclic AMP Mediated Long Term Potentiation Impairment in the Hippocampus of Mecp2 Knockout (Mecp2-/y) Mice by Rolipram

Rett syndrome (RTT) patients experience learning difficulties and memory loss. Analogous deficits of hippocampal plasticity are reported in mouse models of RTT. To elucidate the underlying pathophysiology, we studied long term potentiation (LTP) at the CA3 to CA1 synapses in the hippocampus in acute brain slices from WT and Mecp2-/y mice, by either activating cAMP dependent pathway or using high frequency stimulation, by means of patch clamp. We have observed that, the NMDA channel current characteristics remain unchanged in the Mecp2-/y mice. The adenylyl cyclase (AC) agonist forskolin evoked a long lasting potentiation of evoked EPSCs in WT CA1 neurons, but only minimally enhanced the EPSCs in the Mecp2-/y mice. This weaker potentiation in Mecp2-/y mice was ameliorated by application of phosphodiesterase 4 inhibitor rolipram. The hyperpolarization activated cyclic nucleotide gated channel current (Ih) was potentiated to similar extent by forskolin in both phenotypes. Multiple tetanus induced cAMP-dependent plasticity was also impaired in the Mecp2-/y mice, and was also partially rescued by rolipram. Western blot analysis of CA region of Mecp2-/y mice hippocampus revealed more than twofold up-regulation of protein kinase A (PKA) regulatory subunits, while the expression of the catalytic subunit remained unchanged. We hypothesize that the overexpressed PKA regulatory subunits buffer cAMP and restrict the PKA mediated phosphorylation of target proteins necessary for LTP. Blocking the degradation of cAMP, thereby saturating the regulatory subunits alleviated this defect.
Effect of Motion-Gesture Recognizer Error Pattern on User Workload and Behavior

Bi-level thresholding is a motion gesture recognition technique that mediates between false positives, and false negatives by using two threshold levels: a tighter threshold that limits false positives and recognition errors, and a looser threshold that prevents repeated errors (false negatives) by analyzing movements in sequence. In this paper, we examine the effects of bi-level thresholding on the workload and acceptance of end-users. Using a wizard-of-Oz recognizer, we hold recognition rates constant and adjust for fixed versus bi-level thresholding. Given identical recognition rates, we show that systems using bi-level thresholding result in significant lower workload scores on the NASA-TLX and accelerometer variance. Overall, these results argue for the viability of bi-level thresholding as an effective technique for balancing between false positives, recognition errors and false negatives.
A framework for automatic generation of security controller

This paper concerns the study, the development and the synthesis of mechanisms for guaranteeing the security of complex systems, i.e. systems composed of several interacting components. A complex system under analysis is described as an open system, i.e. a system in which an unspecified component (a component whose behaviour is not fixed in advance) interacts with the known part of the system. Within this formal approach, we propose techniques that aim at synthesize controller programs able to guarantee that, for all possible behaviours of the unspecified component, the system should work properly, e.g. it should be able to satisfy a certain property. For performing this task, we first need to identify the set of necessary and sufficient conditions that the unspecified component has to satisfy in order to ensure that the whole system is secure. Hence, by exploiting the satisfiability procedures for temporal logic, we automatically synthesize an appropriate controller program that forces the unspecified component to meet these conditions. This will ensure the security of the whole system. In particular, we contribute within the area of the enforcement of security properties by proposing a flexible and automated framework that goes beyond the definition of how a system should behave to work properly. Indeed, while the majority of the related work focuses on the definition of monitoring mechanisms, we also address the synthesis problem. Moreover, we describe a tool for the synthesis of secure systems which is able to generate appropriate controller programs. This tool is also able to translate the synthesized controller programs into the ConSpec language. ConSpec programs can be actually deployed for enforcing security policies on mobile Java applications by using the run‐time framework developed in the ambit of the European Project S3MS. Copyright © 2010 John Wiley & Sons, Ltd.
Conflict Competence Among Undergraduates: Exploring the Impact of Fear of Negative Evaluation

According to a study by the Bureau of Labor Statistics, workplace conflict costs employers up to $359 billion annually (as cited by CPP Global, 2008). Considering this cost, teaching the current generation of college students to effectively manage conflict is a charge for colleges and universities who aim to prepare students to successfully enter the workforce after graduation. Current methods of conflict resolution training, however, lack consistency and show varying levels of success. Developing effective tools depends on a clear understanding of barriers students face in learning to effectively resolve conflicts. In looking to examine these barriers, this study explored whether Fear of Negative Evaluation (FNE) correlates with avoidance among undergraduate students across the United States. While the correlation between FNE and conflict avoidance was only found to be moderate, correlations to other styles of conflict resolution demonstrate that FNE may play some moderating role in the conflict resolution styles that students most use. The difference in findings between LGBTQ+ participants and other participants with similarly high BFNE-S scores demonstrates that other factors besides FNE must also influence conflict resolution style. These findings strengthen the call for a better understanding of the multiple factors that impact conflict competence.
Evidence that TRPM8 Is an Androgen-Dependent Ca2+ Channel Required for the Survival of Prostate Cancer Cells

The Ca2+-permeable channel TRPM8 is thought to play an important role in the pathophysiology of prostate cancer. We have investigated the intracellular location of TRPM8 and its role as a Ca2+-permeable channel in an androgen-responsive and an androgen-insensitive prostate cancer cell line. We report evidence from immunofluorescence experiments that in the androgen-responsive LNCaP cell line, the TRPM8 protein is expressed in the endoplasmic reticulum and plasma membrane, acts as a Ca2+-permeable channel (assessed using Fura-2 to measure increases in the cytoplasmic Ca2+ concentration) in each of these membranes, and is regulated by androgen. Although TRPM8 was detected in the androgen-insensitive PC-3 cell line, no evidence was obtained for regulation of its expression by androgen. The results of experiments using LNCaP cells, the TRPM8 antagonist capsazepine, and small interference RNA targeted to TRPM8 indicate that TRPM8 is required for cell survival. These results indicate that TRPM8 is an important determinator of Ca2+ homeostasis in prostate epithelial cells and may be a potential target for the action of drugs in the management of prostate cancer.
Evolving Neural Network Structure by Indirect Encoding Based on BQPSO

This paper proposes a novel algorithm of neural network structure evolve. First, the algorithm designs an indirect encoding schema representing the structure of neural network, use joint seed representing the existence of connection in neural network. Then, creating and evolving the coordinates of the joint seed using Binary Quantum-behaved Particle Swarm Optimization (BQPSO), evolving the value of the joint seed using nine-palace evolving rule, by separately evolve the coordinates and value of the joint seed, the growing and pruning of the network structure is achieved. The experimental results show that the algorithm has stable complexity when dealing with different scales of neural network. By the proposed indirect encoding schema and separated coordinates and value evolving, the algorithm solves the problem of geometrically growing structure-evolving complexity successfully.
Big data parallelism: Challenges in different computational paradigms

Developers are engaged themselves in processing big data for different computational environments especially in different information systems, biological expression preparations and visual and graphical modelling. Digital Elevation Models (DEMs) in Geographic Information Systems (GIS) is one such information systems where in memory computation faces a lot challenges to manipulate and visualize the data. Scalable distributed framework broadly exhibit two design characteristics: (i) they are using memory scalability in such a manner that the amount of memory required by each process decreases as the number of processes used to solve a given problem instance increases, and (ii) they exploit coarse grain parallelism in the sense that they structure their computations into a sequence of local computation followed by communication phases in which the local computations take a non-trivial amount of time and often involve a non-trivial subset of the process' memory. In this paper we will discuss about big data, data science, different models available in the parallel paradigms, the pros and cons and the probable way out to work with high dimensional data.
Saliency detection based on an edge-preserving filter

How to detect visual salient regions is a challenging problem in computer vision. Recently, saliency detection methods that use boundaries or convex hulls under Bayesian framework have attracted lots of attention. Although these methods achieve state-of-the-art results, there still exist some limitations, e.g., the background will get highlighted when the initial convex hulls are not good enough. This paper presents a new algorithm that retains the advantages of such saliency maps while overcoming their shortcomings. First, the initial convex hull is improved by the image matting model which can be efficiently solved by an edge-preserving filter. Second, a more accurate prior map can be obtained by the improved convex hull. Third, the final convex hull is further refined by an edge-preserving filter to compute the observation likelihood. Finally, the Bayesian framework is employed to compute the saliency map. Extensive experiments compared with state-of-the-art saliency detection algorithms demonstrate the effectiveness of our method.
Harmonic Frequency Estimation Based on Modified-MUSIC Algorithm in Power System

This paper presents a new harmonics frequency estimation method. Unlike the conventional harmonic frequen- cy estimation method (fast Fourier transform), the new algorithm is based on spectrum analysis techniques often used to estimate the direction of angle, the most popular is the multiple signal classification(MUSIC) algorithm The drawbacks of MUSIC algorithm are concluded. Improved-MUSIC approximation algorithm is introduced and compared with FFT based on algorithm for harmonic frequency estimation. Theoretical analysis and simulations show this algorithm is a su- per-resolution algorithm with small data length.
Influence of input features in perceptual tempo induction

A total of 4 algorithms have been submitted. They consist in a set of Matlab functions and a binary file compiled under the Windows operating system. In accordance with the functional framework for rhythm description systems proposed in [2], the algorithms consist in three main processing blocks: feature list creation from audio, periodicity function computation and parsing. Low-level features are computed on a frame-by-frame basis. Algorithms account for 4 different sets of features. Algorithm0 uses 13 features: the magnitudenormalized derivative of the energy in the 8 frequency bands proposed by Dixon et al. [1] (note that Dixon et al. [1] use the derivative, normalizing by the magnitude yields significant improvements) and the magnitudenormalized derivative of 5 spectral features (the mean of the spectral peaks and the spectrum geometric mean, kurtosis, low-frequency energy ratio, mean and skewness). Algorithm1 uses 8 features: Algorithm0 energy features. Algorithm2 uses 9 spectral features: the magnitude-normalized derivative of the the spectral peaks mean, harmonic centroid and harmonic deviation and the spectrum flatness, geometric mean, kurtosis, low-frequency energy ratio, mean and skewness. Algorithm3 uses 13 features: the derivative of the MFCCs. For more details on the relevance of different feature sets, see [3]. All algorithms implement the autocorrelation as periodicity function. Parsing the periodicity function and inferring the most salient tempo (T1, and the meter as a byproduct) is done similarly as in [1]: prominent peaks are collected from each periodicity function. The algorithm then considers all pairs of peaks as possible beat/measure combinations, and computes the fit of all periodicity peaks to each hypothesis, using a weighted sum, where the weights represent the likelihood of each metrical unit appearing as a strong periodicity, given the meter [1]. The second most salient tempo (T2) is chosen as the periodicity (differing from T1) with the highest weight. The normalised relative salience/strength of T1 (ST1) is computed from T1 and T2 weights. The phases of T1 and T2 (P1 and P2) are computed by correlation of pulse trains with feature lists.
Study of Regular and Irregular States in Generic Systems

In this work we semiclassically analyzed the high lying eigenstates of a mixed type Hamiltonian system. For the regular states we employ the Einstein-Brillouin-Keller quantization, while for the chaotic states, following the principle of uniform semiclassical condensation, we obtain a prediction for their wavefunction autocorrelation function.