Miracle-dz/UniFAR-FTUs huggingface dataset:
Are EME Indicators of Vulnerability to Financial Crises Decoupling from Global Factors?

This paper assesses the extent to which common factors underlie indicators of vulnerability to financial crises in emerging market economies and whether this link is changing over time. We use a Bayesian dynamic common factor model to estimate their common component in a sample of up to 41 countries including both developed as well as emerging economies. This permits us to interpret the component in common to both of them as a global factor. We introduce time-variation into the model to investigate whether indicators are decoupling from global factors over time. While decoupling can be observed in a few cases, the exposure to global factors in most countries tends to fluctuate around the mean. Broadly speaking then, the answer is no.
Synchronous Machine Modeling for Low-Frequency Harmonic Studies

Power quality, and harmonics in particular, is a major issue in any isolated power system due to extensive use of non-linear power electronic devices. The harmonic problem in an isolated system is even more magnified because of the absence of an external grid, which is a basic characteristic of all isolated systems (like that of a ship). Harmonic studies are, therefore, conducted on a fairly regular basis to analyze and rectify the problems associated with harmonics. In order to conduct such studies in any electrical system, it is important to accurately model its components. As there is no transmission network or "system equivalent," the generator(s) represents a major portion of an isolated power system. Within this paper, appropriate models of synchronous generators for low-frequency harmonic studies are presented. These models are developed using detailed "dq0" representations of the synchronous machine.
The neural processing of hierarchical structure in music and speech at different timescales

Music, like speech, is a complex auditory signal that contains structures at multiple timescales, and as such is a potentially powerful entry point into the question of how the brain integrates complex streams of information. Using an experimental design modeled after previous studies that used scrambled versions of a spoken story (Lerner et al., 2011) and a silent movie (Hasson et al., 2008), we investigate whether listeners perceive hierarchical structure in music beyond short (~6 s) time windows and whether there is cortical overlap between music and language processing at multiple timescales. Experienced pianists were presented with an extended musical excerpt scrambled at multiple timescales—by measure, phrase, and section—while measuring brain activity with functional magnetic resonance imaging (fMRI). The reliability of evoked activity, as quantified by inter-subject correlation of the fMRI responses, was measured. We found that response reliability depended systematically on musical structure coherence, revealing a topographically organized hierarchy of processing timescales. Early auditory areas (at the bottom of the hierarchy) responded reliably in all conditions. For brain areas at the top of the hierarchy, the original (unscrambled) excerpt evoked more reliable responses than any of the scrambled excerpts, indicating that these brain areas process long-timescale musical structures, on the order of minutes. The topography of processing timescales was analogous with that reported previously for speech, but the timescale gradients for music and speech overlapped with one another only partially, suggesting that temporally analogous structures—words/measures, sentences/musical phrases, paragraph/sections—are processed separately.
Restraining integrated electric propulsion system power fluctuation using hybrid energy storage system

As the ship capacity increasing, large power load fluctuation caused a series of conditions including torque ripple and the voltage fluctuation of ship power grid, and frequently power load fluctuation can also reduce the fuel utilization rate. This paper studies the method about hybrid energy storage system to stabilize the power fluctuation of integrated electric propulsion system and the integrated power system dynamic simulation model is established. Combined with the battery and super capacitor, and considered the system power volatility and hybrid energy storage charge and discharge capacity, hybrid energy storage capacity configuration is optimized using Particle Swarm Optimization (PSO) algorithm. Meanwhile, The active parallel hybrid energy storage system (APHESS), which consist of battery, super capacitor, and the two charge and discharge controller system, is used to control energy storage unit having fast and accurate power throughput. The simulation results show that the energy management strategy, which is proposed in this paper, can better play the complementary advantages of the hybrid energy storages, has a better effect to eliminate the fluctuation of power system and the best economic performance.
How Contemporary Human Reproductive Behaviors Influence the Role of Fertility-Related Genes: The Example of the P53 Gene

Studies on human fertility genes have identified numerous risk/protective alleles involved in the occurrence of reproductive system diseases causing infertility or subfertility. Investigations we carried out in populations at natural fertility seem to suggest that the clinical relevance that some fertility genes are now acquiring depends on their interaction with contemporary reproductive behaviors (birth control, delayed childbearing, and spacing birth order, among others). In recent years, a new physiological role in human fertility regulation has emerged for the tumor- suppressor p53 gene (P53), and the P53 Arg72Pro polymorphism has been associated with recurrent implantation failure in humans. To lend support to our previous observations, we examined the impact of Arg72Pro polymorphism on fertility in two samples of Italian women not selected for impaired fertility but collected from populations with different (premodern and modern) reproductive behaviors. Among the women at near-natural fertility (n = 98), the P53 genotypes were not associated with different reproductive efficiency, whereas among those with modern reproductive behaviors (n = 68), the P53 genotypes were associated with different mean numbers of children [Pro/Pro = 0.75<Pro/Arg = 1.7<Arg/Arg = 2, (p = 0.056)] and a significant negative relationship between the number of children and P53 Pro allele frequencies (p = 0.028) was observed. These results are consistent with those of clinical studies reporting an association between the P53 Pro allele and recurrent implantation failure. By combining these findings with previous ones, we suggest here that some common variants of fertility genes may have become “detrimental” following exposure to modern reproductive patterns and might therefore be associated with reduced reproductive success. Set within an evolutionary framework, this change could lead to the selection of a set of gene variants fitter to current reproductive behaviors as the shift to later child-bearing age in developed countries.
Hierarchical Shape Description of MR Brain Images Based on Active Contour Models and Multi-Scale Differential Invariants

We have developed a novel hierarchical shape description tool, combining two recently developed and powerful techniques in image processing: differential invariants in scale space, and active contour models. We employ this tool for data-driven quantitative shape studies of Magnetic Resonance (MR) images of the brain at multiple scales. Extracting a shape at different levels of scale yields a multi-scale shape stack which can be used to detect shape changes over scale. The shape stack is obtained by performing a shape focusing process as a series of implicit segmentation steps for decreasing scales. Implicit segmentation is carried out using the well-known model of active contours. Starting from an initial active contour, a series of shape optimization processes is performed, where the energy function of the active contour model is regularized with respect to scale. The presented multi-scale shape stack shows promising results on extracting and characterizing complex shapes in MR brain images at a large set of scales.
EDGE DETECTION OF MULTISPECTRAL IMAGES USING NONPARAMETRIC LOCAL DENSITY ESTIMATION

Detection of edges in multispectral images has been a challenging task in the research community over the past few years. In this work, a novel vector-based approach is adopted for edge detection in multichannel remotely sensed images. The discontinuity between homogeneous regions in the image is detected using the image density value estimated at the mean vector of the sliding window. The proposed algorithm is nonparametric, computationally simple to implement, providing us with dimensionality reduction in the multivariate feature space.
Mining candidates for adverse drug interactions in electronic patient records

Electronic patient records provide a valuable source of information for detecting adverse drug events. In this paper, we explore two different but complementary approaches to extracting useful information from electronic patient records with the goal of identifying candidate drugs, or combinations of drugs, to be further investigated for suspected adverse drug events. We propose a novel filter-and-refine approach that combines sequential pattern mining and disproportionality analysis. The proposed method is expected to identify groups of possibly interacting drugs suspected for causing certain adverse drug events. We perform an empirical investigation of the proposed method using a subset of the Stockholm electronic patient record corpus. The data used in this study consists of all diagnoses and medications for a group of patients diagnoses with at least one heart related diagnosis during the period 2008--2010. The study shows that the method indeed is able to detect combinations of drugs that occur more frequently for patients with cardiovascular diseases than for patients in a control group, providing opportunities for finding candidate drugs that cause adverse drug effects through interaction.
A Case-Based Model of Probability and Pricing Judgments: Biases in Buying and Selling Uncertainty

We integrate a case-based model of probability judgment with prospect theory to explore asset pricing under uncertainty. Research within the “heuristics and biases” tradition suggests that probability judgments respond primarily to case-specific evidence and disregard aggregate characteristics of the class to which the case belongs, resulting in predictable biases. The dual-system framework presented here distinguishes heuristic assessments of value and evidence strength from deliberative assessments that incorporate prior odds and likelihood ratios following Bayes' rule. Hypotheses are derived regarding the relative sensitivity of judged probabilities, buying prices, and selling prices to case-versus class-based evidence. We test these hypotheses using a simulated stock market in which participants can learn from experience and have incentives for accuracy. Valuation of uncertain assets is found to be largely case based even in this economic setting; however, consistent with the framework's predictions, distinct patterns of miscalibration are found for buying prices, selling prices, and probability judgments. This paper was accepted by Brad Barber, Teck Ho, and Terrance Odean, special issue editors.
Prop-hanging control of a thrust vector vehicle with hybrid Nonlinear Dynamic Inversion method

The prop-hanging control of the fixed-wing aircraft can enable the vehicle to improve the cruise performance and hover ability. The prop-hanging control of the thrust vector vehicle would be a challenging problem, for the reason that it is a system with properties of inherent instability, coupling, non-linearity and non-minimum phase characteristics. To deal with these complex problems, a hybrid Nonlinear Dynamic Inversion (NDI) method is proposed in this paper. Inspired by NDI, Incremental NDI and angular acceleration feedback control methods, the hybrid NDI method includes three parts: feedforward control, proportional control and logical integral control, which can enable the system to have a rapid response in the transient phase and a satisfactory performance in the steady state. A numerical simulation is conducted to test the performance and the robustness of the proposed control method, and a Hardware-in-the-loop simulation is also conducted to check the practicability and effectiveness in the actual application. Simulation results show that the proposed control method can effectively improve system robustness and is practical.
A State-Space Model for National Football League Scores

Abstract This article develops a predictive model for National Football League (NFL) game scores using data from the period 1988–1993. The parameters of primary interest—measures of team strength—are expected to vary over time. Our model accounts for this source of variability by modeling football outcomes using a state-space model that assumes team strength parameters follow a first-order autoregressive process. Two sources of variation in team strengths are addressed in our model; week-to-week changes in team strength due to injuries and other random factors, and season-to-season changes resulting from changes in personnel and other longer-term factors. Our model also incorporates a home-field advantage while allowing for the possibility that the magnitude of the advantage may vary across teams. The aim of the analysis is to obtain plausible inferences concerning team strengths and other model parameters, and to predict future game outcomes. Iterative simulation is used to obtain samples from the joint .
Estimating the Impact of State Policies and Institutions with Mixed-Level Data

Researchers are often interested in the effects of state policies and institutions on individual behavior or other outcomes in sub-state-level observational units, such as election results in state legislative districts. In this article, we examine the issue of clustered data in state and local politics research and the analytical problems it can cause. Standard estimation methods applied in most regression models do not properly account for the clustering of observations within states, leading analysts to overstate the statistical significance of coefficient estimates, especially of state-level factors. We discuss the theory behind two approaches for dealing with clustering—clustered standard errors and multilevel modeling—and argue that calculating clustered standard errors is a more straightforward and practical approach, especially when working with large datasets or many cross-level interactions. We demonstrate the relevance of this topic by replicating a recent study of the effects of state post-registration laws on voter turnout (Wolfinger, Highton, and Mullin 2005).
Comparing Data Processing Frameworks for Scalable Clustering

Recent advances in the development of data parallel plat- forms have provided a significant thrust to the development of scalable data mining algorithms for analyzing massive data sets that are now commonplace. Scalable clustering is a com- mon data mining task that finds consequential applications in astronomy, biology, social network analysis and commercial domains. The variety of platforms available for implementa- tion of algorithmic solutions poses a dilemma of choice for researchers and practitioners, alike. This paper presents a case study for comparison of two open source data parallel frameworks for implementation of a scal- able clustering algorithm. Ascension of Map-Reduce frame- work to the top slot in less than a decade is attributed to its ease of programming, robustness and capability to perform automatic load balancing. Recently proposed Nephele-PACT framework is a serious contender, because of its design that offers optimized execution of complex data flows, prevalent in Big Data Analytics. Both frameworks use HDFS as the un- derlying distributed file system, hence the comparison is con- fined to the execution engines only. Experimentation on real and synthetic data reveals the better scalability of Nephele- PACT framework.
Classiﬁcation Methods Based on Formal Concept Analysis

Formal Concept Analysis (FCA) provides mathematical models for many domains of computer science, such as classiﬁcation, categorization, text mining, knowledge management, software development, bioinformatics, etc. These models are based on the mathematical properties of concept lattices. The complexity of generating a concept lattice puts a constraint to the applicability of software systems. In this paper we report on some attempts to evaluate simple FCA-based classiﬁcation algorithms. We present an experimental study of several benchmark datasets using FCA-based approaches. We discuss diﬃculties we encountered and make some suggestions concerning concept-based classiﬁcation algorithms.
Efficiently Retrieving Images that We Perceived as Similar

Despite growing interest in using sparse coding based methods for image classification and retrieval, progress in this direction has been limited by the high computational cost for generating each image's sparse representation. To overcome this problem, we leverage sparsity-based dictionary learning and hash-based feature selection to build a novel unsupervised way to efficiently pick out a query image's most important high-level features; the selected set of features effectively pinpoint to which group of images we would visually perceived the query as similar. Moreover, the method is adaptive to the retrieval database presented at the moment. The preliminary results based on L1 feature map show the method's efficiency and accuracy from the visual cognitive perspective.
Intermittency, synthetic turbulence and wavelet structure functions

Some techniques for the study of intermittency by means of wavelet transforms, are presented on an example of synthetic turbulent signal. Several features of the turbulent field, that cannot be probed looking at standard structure function scaling, become accessible in this way. The concept of a directly measurable intermittency scale, distinct from the scale of the fluctuations, is introduced. A method for optimizing the analyzing wavelets, which exploits this concept, and allows to minimize non-local contributions in scale to wavelet correlations, is described. The transition from a wavelet to a Fourier transform based description of an intermittent random field, and the possibility of using Fourier correlations to measure intermittency are discussed. Important limitations in the ability of structure functions to give a local in scale description of intermittent random fields, are observed.
Incubators vs Zombies: Fault-Tolerant, Short, Thin and Lanky Spanners for Doubling Metrics

Recently Elkin and Solomon gave a construction of spanners for doubling metrics that has constant maximum degree, hop-diameter O(log n) and lightness O(log n) (i.e., weight O(log n)w(MST). This resolves a long standing conjecture proposed by Arya et al. in a seminal STOC 1995 paper. However, Elkin and Solomon's spanner construction is extremely complicated; we offer a simple alternative construction that is very intuitive and is based on the standard technique of net tree with cross edges. Indeed, our approach can be readily applied to our previous construction of k-fault tolerant spanners (ICALP 2012) to achieve k-fault tolerance, maximum degree O(k^2), hop-diameter O(log n) and lightness O(k^3 log n).
Back to the future: a hybrid approach to transparent sharing of video games over the internet in real time

This paper presents a collaboration transparency system that allows for distributed sharing of legacy collocated video games in real time without modifying the original games. Our system has attracted two million registered players since it was launched in late 2008. As the system went beyond a research prototype and reached a large number of real users, we observed performance issues that were mainly caused by unpredicted networking latencies over the public Internet and opportunistic distinction of roles such as active players and passive observers. To address these issues, we propose a novel hybrid consistency control approach that combines the merits of optimistic methods, which help achieve interactivity, and pessimistic methods, which guarantee consistency. As a result, the system can tolerate network round-trip time up to 230 ms, improving the latest work by 43.7%. In addition, the system can support over 1,000 simultaneous observers when a video game is played over the Internet.
Functional conservation of Nematostella Wnts in canonical and noncanonical Wnt-signaling

Summary Cnidarians surprise by the completeness of Wnt gene subfamilies (11) expressed in an overlapping pattern along the anterior-posterior axis. While the functional conservation of canonical Wnt-signaling components in cnidarian gastrulation and organizer formation is evident, a role of Nematostella Wnts in noncanonical Wnt-signaling has not been shown so far. In Xenopus, noncanonical Wnt-5a/Ror2 and Wnt-11 (PCP) signaling are distinguishable by different morphant phenotypes. They differ in PAPC regulation, cell polarization, cell protrusion formation, and the so far not reported reorientation of the microtubules. Based on these readouts, we investigated the evolutionary conservation of Wnt-11 and Wnt-5a function in rescue experiments with Nematostella orthologs and Xenopus morphants. Our results revealed that NvWnt-5 and -11 exhibited distinct noncanonical Wnt activities by disturbing convergent extension movements. However, NvWnt-5 rescued XWnt-11 and NvWnt-11 specifically XWnt-5a depleted embryos. This unexpected ‘inverse’ activity suggests that specific structures in Wnt ligands are important for receptor complex recognition in Wnt-signaling. Although we can only speculate on the identity of the underlying recognition motifs, it is likely that these crucial structural features have already been established in the common ancestor of cnidarians and vertebrates and were conserved throughout metazoan evolution.
One of the major problems in genomic field is to perform sequence comparison on DNA and protein sequences

One of the major problems in genomic field is to perform sequence comparison on DNA and protein sequences. Executing sequence comparison on the DNA and protein data is a computationally intensive task. Sequence comparison is the basic step for all algorithms in protein sequences similarity. Parallel computing is an attractive solution to provide the computational power needed to speedup the lengthy process of the sequence comparison. Our main research is to enhance the protein sequence algorithm using dynamic programming method. In our approach, we parallelize the dynamic programming algorithm using multithreaded program to perform the sequence comparison and also developed a distributed protein database among many PCs using Remote Method Interface (RMI). As a result, we showed how different sizes of protein sequences data and computation of scoring matrix of these protein sequence on different number of processors affected the processing time and speed, as oppose to sequential processing.