Miracle-dz/UniFAR-FTUs huggingface dataset:
Factor Matrix Nuclear Norm Minimization for Low-Rank Tensor Completion

Most existing low-n-rank minimization algorithms for tensor completion suffer from high computational cost due to involving multiple singular value decompositions (SVDs) at each iteration. To address this issue, we propose a novel factor matrix rank minimization method for tensor completion problems. Based on the CANDECOMP/PARAFAC (CP) decomposition, we first formulate a factor matrix rank minimization model by deducing the relation between the rank of each factor matrix and the mode-n rank of a tensor. Then, we introduce a tractable relaxation of our rank function, which leads to a convex combination problem of much smaller scale matrix nuclear norm minimization. Finally, we develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the proposed problem. The experimental results on both synthetic and real-world data validate the effectiveness of our approach. Moreover, our method is significantly faster than the state-of-the-art approaches and scales well to handle large datasets.
On the Demand for Corporate Property Insurance

Since changes in the firm-specific or unsystematic risks faced by a corporation have no effect on firm value, corporate insurance purchases might seem unwarranted. However, more than 57 percent of insurance premiums are paid by businesses. This apparent contradiction has motivated researchers to suggest factors other than simple risk reduction that create corporate incentives to purchase insurance. This article tests the practical validity of most of the analytic arguments regarding corporate demand for insurance. In general, the empirical evidence from corporate property insurance purchases is consistent with the various theoretical arguments regarding corporate demand for insurance. The results suggest insurance helps to reduce various agency costs associated with stakeholder conflicts, provides real services, and reduces taxes. Finally, the less risky nature of regulated industries compared with unregulated industries is believed to lessen the various corporate incentives to purchase property insurance.
Parallelizing Active Memory Ants with MapReduce for Clustering Financial Time Series Data

Clustering financial time series data is a computationally intensive problem. Ant brood clustering (ABC) is a meta-heuristic algorithm inspired by how ants sort the broods in their nest according to their shape and size. In this paper, ants are incorporated with short term memory to avoid redundant random walks, a drawback experienced in original ABC. This algorithm, called ABC-INTE (Ant Brood Clustering with Intelligent Ants), is applied to cluster financial time series data using MapReduce programming model. Our algorithm employs alternate number of mappers over multiple MapReduce iterations to exploit data parallelism and indirect communication among mappers. We evaluate the clustering quality, by using the sum of squares for the mean intra-cluster distance (MICD) to measure the intra-cluster distance and sum of squares of the inter-clusters distance (SSICD) to measure the inter-cluster distance. Our algorithm achieves a value of 475.47 for the ratio SSICD/SS(MICD), which outperforms both sequential implementation and the MapReduce implementation with multiple mappers but no multiple MapReduce iterations.
Framingham Risk Score for Prediction of Cardiovascular Diseases: A Population-Based Study from Southern Europe

Background The question about what risk function should be used in primary prevention remains unanswered. The Framingham Study proposed a new algorithm based on three key ideas: use of the four risk factors with the most weight (cholesterol, blood pressure, diabetes and smoking), prediction of overall cardiovascular diseases and incorporating the concept of vascular age. The objective of this study was to apply this new function in a cohort of the general non Anglo-Saxon population, with a 10-year follow-up to determine its validity. Methods The cohort was studied in 1992-94 and again in 2004-06. The sample comprised 959 randomly-selected persons, aged 30-74 years, who were representative of the population of Albacete, Spain. At the first examination cycle, needed data for the new function were collected and at the second examination, data on all events were recorded during the follow-up period. Discrimination was studied with ROC curves. Comparisons of prediction models and reality in tertiles (Hosmer-Lemeshow) were performed, and the individual survival functions were calculated. Results The mean risks for women and men, respectively, were 11.3% and 19.7% and the areas under the ROC curve were 0.789 (95%CI, 0.716-0.863) and 0.780 (95%CI, 0.713-0.847) (P<0.001, both). Cardiovascular disease events occurred in the top risk tertiles. Of note were the negative predictive values in both sexes, and a good specificity in women (85.6%) and sensitivity in men (79.1%) when their risk for cardiovascular disease was high. This model overestimates the risk in older women and in middle-aged men. The cumulative probability of individual survival by tertiles was significant in both sexes (P<0.001). Conclusions The results support the proposal for “reclassification” of Framingham. This study, with a few exceptions, passed the test of discrimination and calibration in a random sample of the general population from southern Europe.
Restriction of the moment map to certain non-Lagrangian submanifolds

Consider a Hamiltonian torus action on a connected symplectic manifold M for which the associated moment map Phi is proper in some sense. Let Q be a closed submanifold of M. We show that under certain local conditions on Q one has Phi(Q)=Phi(M). We apply this result in the special case that Q arises as the fixed point set of some involution on M which is not necessarily antisymplectic.
Translation Analysis and Translation Automation

We argue that the concept of translation analysis provides a suitable foundation for a new generation of translation support tools. We show that pre-existing translations can be analyzed into a structured translation memory and describe our TransSearch bilingual concordancing system, which allows translators to harness such a memory. We claim that translation analyzers can help detect translation errors in draft translations and we present the results of an experiment on the detection of deceptive cognates conducted as part of our TransCheck project. Finally, we claim that translation analysis can facilitate the speech-to-text transcription of dictated translations and introduce our new TransTalk project.
Serum 25-hydroxyvitamin D levels do not correlate with asthma severity in a case-controlled study of children and adolescents

Abstract Background: There is no consensus on the association between vitamin D and asthma. Objective: To determine the relationship between 25-hydroxyvitamin D [25(OH)D] levels and asthma symptom severity in children and adolescents. Methods: A retrospective, case-control study of 263 subjects of ages 2–19 years with asthma who were compared to 284 non-asthmatic controls of similar ages. Subjects were excluded if they had diseases of calcium or vitamin D metabolism or were receiving calcium or vitamin D supplementation. Serum 25(OH)D was measured in all subjects. Asthma symptom severity, usually stratified into 6 steps, was stratified into five steps [1–5] based on the number and dose of controller medications used as outlined by the National Heart, Lung, and Blood Institute’s guidelines. Mean 25(OH)D values were compared between the asthmatic patients and controls, as well as among the five steps of asthma symptom severity. Results were adjusted for age, sex, BMI, race and severity of asthma symptoms. Results: There was no difference in 25(OH)D between asthmatic patients and controls (28.64±10.09 vs. 28.42±11.47, p=1.0). However, there was a significant difference in 25(OH)D between obese and non-obese asthmatic patients (23.33±7.67 vs. 30.16±10.20, p<0.0001), as well as obese and non-obese controls (24.56±9.90 vs. 29.50±11.66, p=0.003). Mean 25(OH)D levels did not vary significantly among the five steps of asthma symptom severity. Conclusions: There were no differences in mean 25(OH)D levels between asthmatic patients and controls. Mean 25(OH)D level was significantly lower in both the obese asthmatic patients and obese controls. Asthma severity had no relationship to mean 25(OH)D levels.
Modelling Patterns for Deep Web Wrapper Generation

Interfaces of web information systems are highly heterogeneous. Additionally to schema heterogeneity they diﬀer at the presentation layer. Web interface wrappers need to understand these interfaces in order to enable interoperation among web information systems. In contrast to the general scenario it has been observed that inside of application domains (e.g. air travel) hetergeneity is limited. More in detail web interfaces share a limited common vocabulary and use a small set of layout variants. Thus we propose the existence of web interface patterns which are characterized by these two aspects: the used vocabulary on the one hand and the common layout of pages on the other. These patterns can be derived from a domain model which is structured into an ontological model and a layout model. The paper introduces metamodels for ontological and layout models and describes a model driven approach to generate patterns from a sample set of web interfaces. We use a clustering algorithm to identify correspondences between model instances. This pattern approach allows for the generation of wrappers of deep web sources of a speciﬁc domain.
Using Genetic Algorithm for Overcurrent Relay Coordination in Industrial Power System

In this paper a modified genetic algorithm is used to perform the IDMT overcurrent relay coordination applied in an industrial plant radial power distribution system for quickly getting the result of the best coordination comparing with the computer-aided software of traditional method. The result shows that it is fast and adoptable compared to the traditional method by verifying with an existing power system settings.
Classification of plant leaves using Morphological features and Zernike moments

Plants are an integral part of our ecosystems. Their identification and classification has always been a matter of interest for the botanists as well as for the laymen. With around 3 million recognized plant species, only a tiny part of the plants is known. The leaves of the plant carry a lot of information about the plant species. These features are extracted and are used as a basis for an automated identification and classification. The advancement in image processing has made this a quick and easy process. In the proposed work, Morphological features and Zernike moments are used to identify and classify the plant leaves. The features extracted are independent of leaf growth and image translation, rotation and scaling and are studied to develop an approach that produces the best classification algorithm. First, the developed algorithms are used to classify a training set of images. Then, a testing set of images is used for verifying the classification algorithms. The output displays the leaf classified into its particular class.
fLexiCoGraph: creating and managing curated graph-based lexicographical data

We present the conceptual foundations and basic features of fLexiCoGraph, a generic software package for creating and presenting curated human-oriented lexicographical resources that are roughly modeled according to Měchura’s (2016) idea of graph-augmented trees. The system is currently under development and will be made accessible as open source software. As a sample use case we discuss an existing online database of loanwords borrowed from German into other languages which is based on a growing number of language-specific loanword dictionaries (Lehnwortportal Deutsch). The paper outlines the conceptual foundations of fLexiCoGraph’s hybrid graph/XML data model. To establish a database, XML-based resources may be imported or even input manually. An additional graph database layer is then constructed from these XML source documents in a freely configurable, but automated way; subsequently, the resulting graph can be manipulated and enlarged through a visual user interface in such a way that keeps the relationship to the source document information explicit at all times. We sketch the tooling support for different kinds of graph-level editing processes, including mechanisms for dealing with updated XML source documents and coping with duplicate or inconsistent information, and briefly discuss the browser interface for end users.
Novel modified energy detection spectrum sensing technique for FM wireless microphone signals

Spectrum Sensing is an important functionality of Cognitive Radio (CR). Accuracy and speed of estimation are the key indicators to select the appropriate spectrum sensing technique. In this paper, we focus on spectrum sensing of narrowband wireless microphone (WM) signals using the modified periodogram and Welch method for different window types. It is well-known that the power of the WM signal is highly concentrated in the frequency domain, particularly under silence. Due to this property, spectrum sensing can be performed by what we call modified energy detection (MED) which applies of two techniques. First, a proposed window is used to reduce the height of the side-lobes or spectral leakage, which is called modified periodogram method. This increases the difference between the signal PSD peak and the noise level resulting in better detection. Second, Welch's method is used, which takes the average of the modified periodograms using overlapped windows that decreases the noise variance compared to single periodogram estimation. The performance of the proposed spectrum sensing technique is demonstrated by computer simulations.
A smart-gentry based software system for secret program execution

Currently generic executable programs can only be encrypted during transmission and storage. To execute the program itself and the data it operates on must be decrypted. If the execution system is not trusted or compromised, both the program code and data are endangered. Recent advances in homomorphic cryptography show how additions and multiplications can be executed in encrypted space, i.e. without decrypting the information, the arithmetic operations themselves are not encrypted. To date, a universal implementation of a homomorphic system, capable of executing arbitrary programs and allowing for practical experiences is still missing. In this paper we present the first method to compute a non-linear arbitrary secret program on an untrusted resource using fully homomorphic encrypted circuits. We use our own implementation of the Smart-Gentry crypto-system as a foundation and define a processor architecture which is capable of executing encrypted programs on encrypted data. Unlike other approaches, such as static one-pass boolean circuit simulations, our system supports read and write memory access, dynamic parameters and non-linear programs, that render branch-decisions at runtime and cannot be represented in a circuit with hard-wired in-circuit parameters and data. Our implementation comprises the runtime environment for an encrypted program and an assembler to generate the encrypted machine code. The system represents a first step to show the capabilities of homomorphic encryption in software and system architecture.
Atomic architectural component recovery for program understanding and evolution

Component recovery and remodularization is a means to get back control on large and complex legacy systems suffering from ad-hoc changes by recovering logical components and restructuring the physical components accordingly to decrease coupling among components and increase cohesion of components. This thesis is on unifying, quantitatively and qualitatively evaluating, improving, and integrating automatic and semi-automatic methods for component recovery.
Multidimensional wellness promotion in the health and fitness industry

ABSTRACT Current trends in the United States indicate an increase in sedentary behaviors, obesity, stress and poor diet, contributing to heightened rates of chronic illness and mortality. These trends illustrate a need for prioritizing prevention and wellness promotion, and conceptualizing health as a multidimensional construct. The exercise and fitness industry is uniquely positioned to support individuals in establishing healthy lifestyle trends that address multiple domains of wellness. This research study utilized health and fitness professional survey data to assess relationships between the frequency of addressing each of the five primary domains of wellness (physical, social, emotional, intellectual and spiritual), and a number of demographic variables. Relationships between the frequency of addressing domains of wellness and all demographic variables (e.g. physical wellness by industry role) were examined using Pearson Chi Square Tests of Independence. Results indicate differences in the frequency that unique dimensions of wellness were addressed with clients, as well as differences based on industry role and gender. Implications are discussed, including challenges associated with a consensus organizational definition of wellness, and variability in training and education requirements of fitness professionals, that may impact the promotion of wellness domains beyond the traditional physical focus.
A Dynamic Spatial Panel Data Approach to the German Wage Curve

A wave curve is a decreasing function of wages on the regional unemployment rate. Most empirical studies on the wave curve ignore possible spatial interaction effects between the regions which are the primary units of research. This paper reconsiders the western German wage curve with a special focus on the geography of labour markets. Spillovers between regions are taken into account. The paper tests whether the unemployment rate in the larger surrounding region also affects wages. In addition, agglomeration effects and effects of local monopsony are assessed. The main data base is a random sample of 974,179 employees observed over the period 1980-2004 and covering 326 NUTS3 units (districts). This rich data set is used to estimate a dynamic wage curve according to the two-step approach of Bell et al. (2002). In the first step one controls for individual heterogeneity and in the second step one allows for spatial effects of unemployment across regions on wages. We check the sensitivity of this wage elasticity to various spatial weight matrices as well as allowing for the endogeneity of unemployment. We also estimate the wage elasticity for various population groups.
A sampling-based approach to scalable constraint satisfaction in linear sampled-data systems - Part I: Computation

Sampled-data (SD) systems, which are composed of both discrete- and continuous-time components, are arguably one of the most common classes of cyberphysical systems in practice; most modern controllers are implemented on digital platforms while the plant dynamics that are being controlled evolve continuously in time. As with all cyberphysical systems, ensuring hard constraint satisfaction is key in the safe operation of SD systems. A powerful analytical tool for guaranteeing such constraint satisfaction is the viability kernel: the set of all initial conditions for which a safety-preserving control law (that is, a control law that satisfies all input and state constraints) exists. In this paper we present a novel sampling-based algorithm that tightly approximates the viability kernel for high-dimensional sampled-data linear time-invariant (LTI) systems. Unlike prior work in this area, our algorithm formally handles both the discrete and continuous characteristics of SD systems. We prove the correctness and convergence of our approximation technique, provide discussions on heuristic methods to optimally bias the sampling process, and demonstrate the results on a twelve-dimensional flight envelope protection problem.
Noise Reduction in BOLD-Based fMRI Using Component Analysis

Principle Component Analysis (PCA) and Independent Component Analysis (ICA) were used to decompose the fMRI time series signal and separate the BOLD signal change from the structured and random noise. Rather than using component analysis to identify spatial patterns of activation and noise, the approach we took was to identify PCA or ICA components contributing primarily to the noise. These noise components were identified using an unsupervised algorithm that examines the Fourier decomposition of each component time series. Noise components were then removed before subsequent reconstruction of the time series data. The BOLD contrast sensitivity (CS(BOLD)), defined as the ability to detect a BOLD signal change in the presence of physiological and scanner noise, was then calculated for all voxels. There was an increase in CS(BOLD) values of activated voxels after noise reduction as a result of decreased image-to-image variability in the time series of each voxel. A comparison of PCA and ICA revealed significant differences in their treatment of both structured and random noise. ICA proved better for isolation and removal of structured noise, while PCA was superior for isolation and removal of random noise. This provides a framework for using and evaluating component analysis techniques for noise reduction in fMRI.
Where's My Drink? Enabling Peripheral Real World Interactions While Using HMDs

Head Mounted Displays (HMDs) allow users to experience virtual reality with a great level of immersion. However, even simple physical tasks like drinking a beverage can be difficult and awkward while in a virtual reality experience. We explore mixed reality renderings that selectively incorporate the physical world into the virtual world for interactions with physical objects. We conducted a user study comparing four rendering techniques that balances immersion in a virtual world with ease of interaction with the physical world. Finally, we discuss the pros and cons of each approach, suggesting guidelines for future rendering techniques that bring physical objects into virtual reality.
Large-Scale USN Middleware Based on Context-Aware

This paper designs LS-ware (Large-Scale USN middleware) in order to collect and save large-scale sensing data and to analyze collected data and run status sensing function. LS-ware not only provides USN middlewarepsilas basic function such as Meta information, quality process, or status management, but also makes large-scale sensing data light-weighted and provides a suitable event processing feature. LS-ware develops and utilizes a four level scheduling algorithm for continuous large-scale sensing data collection. In order to manage sensing data more effectively, it parses data packet structure, extracts information, and looses datapsilas weight. It recognizes an event from light weighted sensing data, and sends the status information to a client.
The potential for a great earthquake along the southernmost Ryukyu subduction zone

Interseismic GPS data along the Hualien‐Suao coast (NE Taiwan) shows a pattern of strain accumulation that is consistent with a potential future large shallow earthquake along the southernmost Ryukyu subduction zone. The measured shortening rate parallel to the Ryukyu Trench is 80 mm/yr, about twice of the shortening rate perpendicular to the Ryukyu Trench. We invert for slip‐deficit rates and the geometric configuration of the plate interface. Our preferred fault model dips 10° northward and extends about 70 km from the Ryukyu Trench to a depth of 13 km. The slip‐deficit rate exhibits a left‐lateral motion of 78 mm/yr and a normal motion of 36 mm/yr on a 290°‐trending fault. The slip rate budget of the southernmost Ryukyu subduction zone is close to the plate convergence rate, suggesting the plate interface is fully locked. Assessments of seismic hazard in this region need to consider the potential threat from Mw 7.5∼8.7 tsunami earthquakes generated by shallow ruptures.