Miracle-dz/UniFAR-FTUs huggingface dataset:
On the diversity of compact objects within supernova remnants – I. A parametric model for magnetic field evolution

A wealth of X-ray and radio observations has revealed in the past decade a growing diversity of neutron stars (NSs) with properties spanning orders of magnitude in magnetic field strength and ages, and with emission processes explained by a range of mechanisms dictating their radiation properties. However, serious difficulties exist with the magneto-dipole model of isolated NS fields and their inferred ages, such as a large range of observed braking indices (n, with values often <3) and a mismatch between the NS and associated supernova remnant (SNR) ages. This problem arises primarily from the assumptions of a constant magnetic field with n = 3, and an initial spin period that is much smaller than the observed current period. It has been suggested that a solution to this problem involves magnetic field evolution, with some NSs having magnetic fields buried within the crust by accretion of fall-back supernova material following their birth. In this work, we explore a parametric phenomenological model for magnetic field growth that generalizes previous suggested field evolution functions, and apply it to a variety of NSs with both secure SNR associations and known ages. We explore the flexibility of the model by recovering the results of previous work on buried magnetic fields in young NSs. Our model fits suggest that apparently disparate classes of NSs may be related to one another through the time evolution of the magnetic field.
Model predictive control for energy-efficient buildings: An airport terminal building study

Heating, ventilation, and air conditioning (HVAC) systems in commercial buildings consume a large proportion of energy in the world. One possible way to reduce the cost is to optimize HVAC operation through predictive control methods. Such approaches rely on dynamic models to meet comfort constraints and to minimize energy usage. In this paper, we employ two different model types: an resistance-capacitance (R-C) network model and an artificial neural network (ANN) model, in order to model thermal dynamics of an airport terminal building. The R-C model serves as a control model and its parameters are identified using a gray box identification technique. The ANN model is built in recursive form and works as a simulator to provide system feedback to the controller. We implement a model predictive control (MPC) with two supervisory scenarios on such a simulation environment to evaluate their energy- saving potential. Simulation results during cooling season show 5% to 18% of daily energy savings can be achieved when the proposed MPC is applied to the building.
Bayesian Network Structure Estimation Based on the Bayesian/MDL Criteria When Both Discrete and Continuous Variables Are Present

We consider estimation of Bayesian network structures given a finite number of examples when both discrete and continuous random variables are present in a Bayesian network. It is not hard to estimate Bayesian network structures based on the MDL/Bayesian criteria if each variable takes a finite value. On the other hand, because continuous data contain infinite precisions, its posterior probability cannot be evaluated in a well defined manner. We extend the notion of the MDL/Bayesian criteria in the most general setting in terms of Radon-Nikodym derivatives, and propose a method to estimate Bayesian network structures without assuming each variable to be either discrete or continuous.
Fitted spectral hashing

Spectral hashing (SpH) is an efficient and simple binary hashing method, which assumes that data are sampled from a multidimensional uniform distribution. However, this assumption is too restrictive in practice. In this paper we propose an improved method, Fitted Spectral Hashing, to relax this distribution assumption. Our work is based on the fact that one-dimensional data of any distribution could be mapped to a uniform distribution without changing the local neighbor relations among data items. We have found that this mapping on each PCA direction has certain regular pattern, and could fit data well by S-Curve function, Sigmoid function. With more parameters Fourier function also fit data well. Thus with Sigmoid function and Fourier function, we propose two binary hashing methods. Experiments show that our methods are efficient and outperform state-of-the-art methods.
Is there a difference? Exchange rate nonlinearities in European agri-food (versus total) exports to the US

Each time the Euro starts appreciating, a discussion on how painful this might hit European exporters arises in media, making politician and economists work out the ways to mitigate possible shocks. Still, in his recent study, Verheyen (2013a) using aggregated European exports to the US as an example, showed, that in the long run exports react on exchange rate changes in a nonlinear way. Particularly his analysis revealed, that a positive impact on trade during the Euro depreciation seem to outweigh the losses caused by its appreciations. In this paper, I test whether this holds true for agri-food exports as well. To address this question, I apply a partial sum decomposition approach and the NARDL framework of Shin et al. (2013) to aggregated agri-food exports as well as to total exports of eleven European countries to the US, which is currently the major partner of the EU in agri-food trade. The outcomes suggest, that the exchange rate nonlinearities are even more pronounced in agri-food than in total exports. Despite the ongoing discussion regarding the nocent effect of a strong national currency on exports, the estimation results suggest that European agri-food exporters have found their way to cope with such negative effects. European exporters seem to benefit more from Euro depreciation, than its appreciation harm them. I interpret this finding as a sign of pricing strategies application (e.g., pricing-to-market) to the European agri-food exports.
Random symmetric matrices on Clifford algebras

We consider Brownian motions and other processes (Ornstein-Uhlenbeck processes, spherical Brownian motions) on various sets of symmetric matrices constructed from algebra structures, and look at their associated spectral measure processes. This leads to the identification of the multiplicity of the eigenvalues, together with the identification of the spectral measures. For Clifford algebras, we thus recover Bott's periodicity.
Applications of an Web information mining model to data mining and information retrieval tasks

We have developed a model to mine information in applications involving graph analysis. We demonstrate the model characteristics using a Web warehouse, where nodes represent Web pages and edges represent hyperlinks. In this paper we apply the model to data mining and information retrieval tasks. Related to data mining, we present views for clustering Web nodes and for finding frequent itemsets for association rule mining. Related to information retrieval, we present views for performing a simple query, for clustering the query results and an attempt to improve the quality of the ranking. The use of the model with these purposes demonstrates its modularity, flexibility, applicability and broadness in graph problems.
PAM: A novel performance/power aware meta-scheduler for multi-core systems

Sharing resources such as caches and main memory bandwidth in multi-core systems requires a more sophisticated scheduling scheme. PAM is a low-overhead, user-level meta-scheduler which does not require any hardware or software changes. In particular, it operates by detecting resource congestions and providing guidelines to the standard system scheduler by limiting the assignment of processes to subsets of available cores. PAM contains a cache model that it uses to predict the impact of new schedules. PAM can be used to improve the system along three dimensions: performance, power, and energy consumption (and any combination of these three). On our prototype, we show individual benchmarks can improve by up to 33% and the overall system performance can be improved by as much as 14%.
Incident duration model on urban freeways using Ordered Probit Model

Effective traffic incident management requires a full understanding of the various corresponding properties to accurately estimate incident durations and to benefit decision makings of reducing the impact of non-recurring incident relevant congestions. This paper incorporates discrete choice theory in incident duration prediction. Ordered Probit Model is employed to forecast the incident duration. All 62941 incident records from Beijing Transportation Management Bureau are used for model establishment and other 8000 records for validation. The correct estimation ratio of the model is 69.11%, which shows the reliability of the model is rather satisfactory.
Local dimensionality reduction for multiple instance learning

Multiple instance learning involves labeling bags (sets of instances) rather than individual instances. Positive bags contain both true positive and false positive instances, leading to label ambiguity, while negative bags consist of only true negative instances. Since labels for individual instances are not known, a direct application of existing discriminant analysis or dimensionality reduction methods often yields an undesirable projection direction due to this label ambiguity in positive bags. In this paper we present a citation local Fisher discriminant analysis (CLFDA) where we incorporate both citation and reference information into local Fisher discriminant analysis, in order to detect false positive instances whose corresponding labels are corrected to be negative. To our best knowledge, CLFDA is the first attempt in supervised dimensionality reduction for multiple instance learning. Numerical experiments on several benchmark datasets confirm that CLFDA outperforms existing methods in the task of multiple instance learning.
American Federalism: A View from the States

societies. These weaknesses of the consociational model have not been overcome, nor has the suggestion that stability may be due to other factors, such as the attitudes of the citizens or the socioeconomic system, been given much attention. Despite these objections, Democracies is a very readable and interesting synthetic work, which reveals the author's easy command of a broad range of material and his capacity to build an interesting and thought-provoking argument upon it. It can be recommended as a valuable addition to anyone's bookshelf.
Theoretical Framework for the Optimization of Microphone Array Configuration for Humanoid Robot Audition

An important aspect of a humanoid robot is audition. Previous work has presented robot systems capable of sound localization and source segregation based on microphone arrays with various configurations. However, no theoretical framework for the design of these arrays has been presented. In the current paper, a design framework is proposed based on a novel array quality measure. The measure is based on the effective rank of a matrix composed of the generalized head related transfer functions (GHRTFs) that account for microphone positions other than the ears. The measure is shown to be theoretically related to standard array performance measures such as beamforming robustness and DOA estimation accuracy. Then, the measure is applied to produce sample designs of microphone arrays. Their performance is investigated numerically, verifying the advantages of array design based on the proposed theoretical framework.
Bayes Network Analysis of Trade Effects of Currency Unions and Free Trade Agreements

We investigate the effects of trade and currency unions on trade within the reduced form gravity model. The database is a standard database in this field for around 200 countries over the last fifty years. The statistical model we use is a Bayes network from the machine learning and the artificial intelligence research which generalizes many standard econometric models. The emphasis in this paper is on a simplified specification of models with discrete and continuous variables and on simultaneous equations time series models. The results indicate that trade and currency unions have substantially lower effects on trade than derived in previous empirical work which we reproduce as special cases of our statistical framework.
Selection Bias and Information Bias in Clinical Research

The internal validity of an epidemiological study can be affected by random error and systematic error. Random error reflects a problem of precision in assessing a given exposure-disease relationship and can be reduced by increasing the sample size. On the other hand, systematic error or bias reflects a problem of validity of the study and arises because of any error resulting from methods used by the investigator when recruiting individuals for the study, from factors affecting the study participation (selection bias) or from systematic distortions when collecting information about exposures and outcomes (information bias). Another important factor which may affect the internal validity of a clinical study is confounding. In this article, we focus on two categories of bias: selection bias and information bias. Confounding will be described in a future article of this series.
On optimum data vector for InSAR interferometric phase estimation

In this paper, a new method of the interferometric phase estimation based on the model of the optimal weighted joint data (OWJD) vector is introduced. The method performs the optimization on the received radar echo vector by using the construction of the OWJD vector and all coherence information available, i.e., the processing based on the model of the OWJD vector can not only adaptively coregister the SAR images, but also accurately determine the dimensions of the noise subspace, which is independent of the magnitude and direction of the coregistration error. Theoretical analysis and experimental results show that the method can provide the accurate estimation of the interferometric phase even if the coregistration error is close to one pixel.
Power Struggle in the Food Chain? Lessons from Empirical Studies on Power Influences in Chains and Marketing Channels

Power plays a key role in any chain relationship. In the past large manufacturers such as Unilever and Nestle were the most dominant players in the chain. However, as a result of consolidation among retailers, power has shifted towards retailers. Nowadays such retailers as Wal-Mart, Carrefour, and Tesco are seen as powerful gatekeepers controlling the access to the end consumer. Moreover, branded manufacturers and large multinational retailers become increasingly dependent on fewer and larger suppliers who can deliver high quality products at competitive prices. Since many decades there is discussion going on about the role of power in chains and marketing channels. In order to understand how power and its interactions with other constructs in the chain can be used beneficially for management of the whole chain, it is of great importance to investigate the influences and effects of power. Some of the phenomena which are said to be influenced by power include performance, satisfaction, commitment, relationship quality, conflict, compliance, problem solving, chain integration and collaboration. In this context, the aim of our work is to analyze and systemize existing empirical studies, which measure the influences of power on other phenomena, and discuss the strengths and weaknesses of existing conceptualizations of power, in order to add clarity to the existing turmoil in the literature and contribute to the understanding of the role of power in chains and marketing channels for managerial purposes.
PRICE DAMPING AND PRICE INSULATING EFFECTS OF WHEAT EXPORT RESTRICTIONS IN KAZAKHSTAN, RUSSIA, AND UKRAINE

This study analyzes the domestic price effects of export controls for all 3 KRU countries during the 2007/08 as well as the 2010/11 commodity price peaks. We develop two indicators to measure the strength of the export controls’ price damping and price insulating effect within a non-linear long-run price transmission model. Our analysis comprises 11 cases of export controls, distinguishing regional price effects within Russia. We observe heterogeneity in the damping and insulating effects of the export controls among the KRU and among the regions of Russia. Our model identifies the strongest domestic price effects during the export ban in North Caucasus (Russia), which were transmitted to Central, Black Earth and Volga regions by wheat flows from North Caucasus. For Ukraine the strongest price effects are observed during the export tax system 2011. The price effects identified for the 2006/7, 2007/8 and 2010/11 export quota systems are comparable to those observed for Central, Black Earth and Volga region. Contrary, our results do not identify any price effects of the export ban in Russia on Ural and West Siberia. We also do not find price decreasing effects during the export ban in Kazakhstan and the export tax system in Russia 2007/8. Concluding, the effectiveness of export controls in the KRU to dampen and decouple domestic wheat prices from world market price developments is generally rather limited.
Poorer Cognitive Performance in Patients with Essential Tremor-Parkinson’s Disease vs. Patients with Parkinson’s Disease

Background Patients with essential tremor (ET) seem to be at increased risk of developing Parkinson’s disease (PD). Surprisingly, little has been written about this clinical entity, ET-PD. Cognitive dysfunction is a well-known feature of PD, and can also be an issue in patients with ET. Whether the presence of the combined diagnosis, ET-PD, is associated with additive cognitive effects as compared with PD has not been studied. Methods Thirty ET-PD patients and 53 age-matched PD patients were enrolled in a clinical-epidemiological study. Two cognitive screens, the Telephone Interview for Cognitive Status (TICS, score = 0–41) and Folstein Mini-Mental State Examination (MMSE; range 0–30), were administered. Results The MMSE score was lower in ET-PD than PD [26.5 ± 3.1 (median 28.0) vs. 28.4 ± 2.2 (median 29.0), p = 0.001]. The TICS score was lower in ET-PD than PD [31.7 ± 3.9 (32.0) vs. 35.0 ± 2.0 (35.0), p < 0.001]. Subscores of these tests that related to orientation (p < 0.001), language (p < 0.001), and working memory (p = 0.001) were lower in ET-PD than PD, whereas the delayed memory subscore was only marginally lower in ET-PD than PD (p = 0.06), and the two groups did not differ with respect to the motor/construction subscore (p = 0.22). Both global cognitive scores were inversely correlated with disease duration (for MMSE score, Spearman’s r = −0.46, p < 0.001; for TICS score, Spearman’s r = −0.53, p < 0.001). Conclusion The combined diagnosis, ET-PD, seemed to be associated with additive cognitive effects as compared with PD alone.
A Novel Method on Disturbance Analysis and Feed-Forward Compensation in Permanent Magnet Linear Motor System

Linear motor system has been developed to achieve high speed and high precision motion control in linear motion system, since rotary type motor has backlash and hysteresis due to mechanical parts like chain, belt or screw. However, paradoxically, linear motor system becomes more sensitive to the external disturbance, because it doesn't have such transmission mechanism. Many studies have been focused on detent force which have dominant and position dependant non-linear properties among the external disturbances. But the solutions suggested by them are mostly complicated and dificult to apply due to the non-linear property of detent force. In this paper, a novel method to analyze the disturbance magnitude related to the harmonics order is introduced. It is based on a disturbance observer that is famous for its simple but robust and powerful scheme to eliminate disturbance. Nevertheless, from the implementation perspective, there exists a limitation with direct feedback of the disturbance observer output. It is because the bandwidth of disturbance observer is limited by measurement noise, while the disturbance gets higher frequency with the speed due to the position dependant property of detent force. To resolve this problem, feed-forward controller with a function of position is suggested. Through experimental results, the suggested method was verified to have much better performance compared to direct feedback of disturbance observer.
A Case of Nonuremic Calciphylaxis in a Caucasian Woman

We report a case of nonuremic calcific arteriolopathy (NUCA) in an 82-year-old Caucasian woman from rural Australia. The patient had no history of kidney disease or dialysis. NUCA is rare disease suspected on cutaneous and clinical features and diagnosed by characteristic findings on skin biopsy and vasculature imaging. Calcification induced microvascular occlusion in the absence of renal failure may not be immediately apparent. Clinical suspicion and appropriate investigations are essential for making a diagnosis. A diagnosis of NUCA may be missed given the rarity of the disease, and dermatologists and patients alike would benefit from a greater awareness of this disease.
Patterns of Ancestry, Signatures of Natural Selection, and Genetic Association with Stature in Western African Pygmies

African Pygmy groups show a distinctive pattern of phenotypic variation, including short stature, which is thought to reflect past adaptation to a tropical environment. Here, we analyze Illumina 1M SNP array data in three Western Pygmy populations from Cameroon and three neighboring Bantu-speaking agricultural populations with whom they have admixed. We infer genome-wide ancestry, scan for signals of positive selection, and perform targeted genetic association with measured height variation. We identify multiple regions throughout the genome that may have played a role in adaptive evolution, many of which contain loci with roles in growth hormone, insulin, and insulin-like growth factor signaling pathways, as well as immunity and neuroendocrine signaling involved in reproduction and metabolism. The most striking results are found on chromosome 3, which harbors a cluster of selection and association signals between approximately 45 and 60 Mb. This region also includes the positional candidate genes DOCK3, which is known to be associated with height variation in Europeans, and CISH, a negative regulator of cytokine signaling known to inhibit growth hormone-stimulated STAT5 signaling. Finally, pathway analysis for genes near the strongest signals of association with height indicates enrichment for loci involved in insulin and insulin-like growth factor signaling.
Neuro-Fuzzy (NF) based relay selection and resource allocation for cooperative networks

This paper focuses on a joint relay selection and resource allocation algorithm for an Amplify-and-Forward (AF) cooperative network. In a multiuser scenario, optimal relay selection and power assignment for the heterogeneous, i.e., real time (RT) and non-real time (NRT), users are a combinational problem. We propose a Neuro-Fuzzy (NF) based optimal relay selection algorithm for selecting the best relay and then allocate resources suboptimally to the RT and NRT users on priority basis. Performance evaluations reveal that the proposed joint scheme has low complexity and better outage behavior as compared to the conventional schemes.