Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and validation of the Polish experimental short version of the Young Schema Questionnaire (YSQ-ES-PL) for the assessment of early maladaptive schemas

The Young Schema Questionnaire (YSQ) measures the intensity of early maladaptive schemas connected with symptoms of mental disorders, particularly personality disorders. We conducted a procedure of shortening the Polish version of the YSQ, analogous to the original one, and determined the psychometric properties of the instrument thus developed by performing a reliability and validity analysis ( n = 1.073). In the second part of the study we tested the factor structure of the YSQ using confirmatory factor analysis in the sample from Study 1 and an independent group ( n = 898). We reduced the number of items in the questionnaire from 232 to 90 – leaving five items in each of the 18 scales corresponding to specific schemas. We obtained adequate and high internal consistency coefficients for each subscale and for the whole instrument. The overall measure of schemas was positively associated with beliefs characteristic for all personality disorders measured by the Personality Beliefs Questionnaire (PBQ). The intensity of the schemas (except the Self-Sacrifice scale) significantly differentiated participants from the clinical ( n = 31) and nonclinical groups ( n = 1,042). We also confirmed the theoretical factor structure of the instrument, although the weakness of some measures of fit suggests the need for further research. The obtained results support the use of the experimental short Polish version of the Young Schema Questionnaire as a measure of early maladaptive schemas.
Characterization of Classes of Singular Linear Differential-Algebraic Equations

We study linear, possibly over- or under-determined, differentialalgebraic equations that have the same solution behavior as linear differential-algebraic equations with well-dened strangeness index. In particular, we give three different characterizations for differentialalgebraic equations, namely by means of solution spaces, canonical forms, and derivative arrays. We distinguish two levels of generalization, where the more restrictive case contains an additional assumption on the structure of the set of consistent inhomogeneities.
FDG-PET and Neuropsychiatric Symptoms among Cognitively Normal Elderly Persons: The Mayo Clinic Study of Aging

One of the key research agenda of the field of aging is investigation of presymptomatic Alzheimer’s disease (AD). Furthermore, abnormalities in brain glucose metabolism (as measured by FDG-PET) have been reported among cognitively normal elderly persons. However, little is known about the association of FDG-PET abnormalities with neuropsychiatric symptoms (NPS) in a population-based setting. Thus, we conducted a cross-sectional study derived from the ongoing population-based Mayo Clinic Study of Aging in order to examine the association between brain glucose metabolism and NPS among cognitively normal (CN) persons aged > 70 years. Participants underwent FDG-PET and completed the Neuropsychiatric Inventory Questionnaire (NPI-Q), Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI). Cognitive classification was made by an expert consensus panel. We conducted multivariable logistic regression analyses to compute odds ratios (OR) and 95% confidence intervals after adjusting for age, sex, and education. For continuous variables, we used linear regression and Spearman rank-order correlations. Of 668 CN participants (median 78.1 years, 55.4% males), 205 had an abnormal FDG-PET (i.e., standardized uptake value ratio < 1.32 in AD-related regions). Abnormal FDG-PET was associated with depression as measured by NPI-Q (OR = 2.12; 1.23–3.64); the point estimate was further elevated for APOE ɛ4 carriers (OR = 2.59; 1.00–6.69), though marginally significant. Additionally, we observed a significant association between abnormal FDG-PET and depressive and anxiety symptoms when treated as continuous measures. These findings indicate that NPS, even in community-based samples, can be an important additional tool to the biomarker-based investigation of presymptomatic AD.
Differential-Detection Aided Large-Scale Generalized Spatial Modulation is Capable of Operating in High-Mobility Millimeter-Wave Channels

A large-scale differential-detection aided generalized spatial modulation (GSM) system is proposed, which relies on a novel Gram–Schmidt basis set and an adaptive low-complexity detector, and is evidently suitable for high-mobility millimeter-wave (mmWave) channels. We consider nonstationary time-varying mmWave channels and assume that the beam-angles remain relatively fixed, while the channel coefficients vary rapidly. In this scenario, it is a challenging task to find the accurate estimates of channel coefficients for digital beamforming, which becomes an even more severe problem, as the numbers of subarrays and subcarriers increase. Our analog-beamforming-aided nonsquare differentially detected scheme achieves a higher transmission rate than the conventional coherent multiple-input multiple-output schemes because the pilot overhead and the complex-valued feedback are eliminated. Our simulation results following the IEEE 802.11ad specifications show that the performance of our proposed nonsquare differential GSM improved upon increasing the number of subarrays, where the maximum transmission rate of 16 b/s/Hz was considered.
Parser Evaluation and the BNC: Evaluating 4 constituency parsers with 3 metrics

We evaluate discriminative parse reranking and parser self-training on a new English test set using four versions of the Charniak parser and a variety of parser evaluation metrics. The new test set consists of 1,000 hand-corrected British National Corpus parse trees. We directly evaluate parser output using both the Parseval and the Leaf Ancestor metrics. We also convert the hand-corrected and parser output phrase structure trees to dependency trees using a state-of-the-art functional tag labeller and constituent-to-dependency conversion tool, and then calculate label accuracy, unlabelled attachment and labelled attachment scores over the dependency structures. We find that reranking leads to a performance improvement on the new test set (albeit a modest one). We find that self-training using BNC data leads to significantly better results. However, it is not clear how effective self-training is when the training material comes from the North American News Corpus.
Improvisation in the learning organization: a defense of the infra-ordinary

Purpose This paper aims to describe the hidden presence of improvisation in organizations. The authors explore this presence through George Perec’s notion of the infra-ordinary applied to the study of the learning organization and its paradoxes. Design/methodology/approach Most studies of paradox and improvisation are qualitative and inductive. In this conceptual paper, the authors offer a conceptual debate aiming to redirect conceptual attention on studies belonging to the domains of learning, improvisation and paradox. Findings The authors defend the thesis that improvisation is an example of a paradoxical practice that belongs to the domain of infra-ordinary rather than, as has been habitually assumed in extant research, the extraordinary. Research limitations/implications The study draws research attention to the potential of the infra-ordinary in the domains of paradox, improvisation and learning. Practical implications For practice, the study shows that improvisation can be a relatively trivial organizational practice as people try to solve problems in their everyday lives. Social implications Most organizations depend upon the capacity of their members to solve problems as these emerge. Yet, organization theory has failed to consider this dimension. As a result, organizations may be unintentionally harming their capacity to learn and adapt to environments by assuming that improvisation is extra-ordinary. Originality/value The study of paradox and improvisation from an infra-ordinary perspective has not been explicitly attempted.
Syntax and Semantics of a Multi-Level Modeling Language

The domain specific modeling and simulation language ML-Rules makes it possible to describe cell biological systems at different levels of organization. A model is formed by attributed and dynamically nested species, with reactions that are constrained by functions on attributes. In this paper, we extend ML-Rules to also support constraints using functions on multi-sets of species, i.e., solutions. Further, we present the formal syntax and semantics of ML-Rules, we define its stochastic simulator and we illustrate its expressiveness based on a model of the cell cycle and proliferation.
Improving the Tracking Capability of Adaptive Filters via Convex Combination

As is well known, Hessian-based adaptive filters (such as the recursive-least squares algorithm (RLS) for supervised adaptive filtering, or the Shalvi-Weinstein algorithm (SWA) for blind equalization) converge much faster than gradient-based algorithms [such as the least-mean-squares algorithm (LMS) or the constant-modulus algorithm (CMA)]. However, when the problem is tracking a time-variant filter, the issue is not so clear-cut: there are environments for which each family presents better performance. Given this, we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability. We show the potential of this combination and provide a unified theoretical model for the steady-state excess mean-square error for convex combinations of gradient- and Hessian-based algorithms, assuming a random-walk model for the parameter variations. The proposed model is valid for algorithms of the same or different families, and for supervised (LMS and RLS) or blind (CMA and SWA) algorithms.
Analysis and status quo of smartphone-based indoor localization systems

Over the past decade, indoor localization has been a topic of interest for both the academic and industrial communities. The need for location estimation, fueled mainly by inaccuracies of GPS indoors, has been addressed by specifically designed systems achieving a high localization accuracy but with a high deployment cost. Lately, dedicated systems are being replaced by smartphones through intelligent use of the built-in sensors. For instance, an accelerometer that can detect user activity can be combined with a wireless radio that captures wireless signal strength information to locate a user. With the advent of such technology, a myriad of systems have been proposed in the literature presenting an indefinite picture to a reader as to which systems actually work and which do not given practical considerations. This article takes up the goal of surveying state-of-the-art smartphone based indoor localization systems with critical analysis on their properties such as accuracies across various sensor designs, energy consumption and computational cost, user satisfaction and so on, thereby providing a status quo of such systems.
Discriminating between elderly and young using a fractal dimension analysis of centre of pressure

The aim of this project was to evaluate the use of a new analysis technique, fractal dimension analysis, for quantification of quiet stance centre of pressure (COP). By using a fractal dimension analysis of COP, it might be possible to gain more information about control during quiet stance than traditional analyses have previously allowed. The current project considered a group of young healthy participants and a group of elderly healthy participants to compare traditional measures of COP against a fractal dimension analysis of COP. Results indicated that both types of analyses are able to distinguish between eyes open and eyes closed in the elderly group. However, the fractal dimension analysis more accurately detected differences between the participant groups when standing with their eyes closed. Based on these results it is suggested that fractal dimension analysis is more informative about posture control than traditional measures. It is suggested that a fractal dimension type of analysis can be incorporated into clinical testing to identify patients with pathologies.
Vibrational Modal Frequencies and Shapes of Two-Span Continuous Timber Flooring Systems

Based on classic vibrational bending theory on beams, this paper provides comprehensive analytical formulae for dynamic characteristics of two equal span continuous timber flooring systems, including frequency equations, modal frequencies, and modal shapes. Four practical boundary conditions are considered for end supports, including free, sliding, pinned, and fixed boundaries, and a total of sixteen combinations of flooring systems are created. The deductions of analytical formulae are also expanded to two unequal span continuous flooring systems with pinned end supports, and empirical equations for obtaining the fundamental frequency are proposed. The acquired analytical equations for vibrational characteristics can be applied for practical design of two-span continuous flooring systems. Two practical design examples are provided as well.
Murder at the Dinner Table: Family Narratives of Forensic Mental Health Professionals*

Abstract Stemming from work on emergency professionals directly affected by trauma exposure, attention has turned to the impact of work-related trauma on their families, including media and public scrutiny, trauma contagion, marital discord, and overprotective parenting. More recently, colleagues in forensic mental health are speaking anecdotally not only about the personal impact of exposure to violence, but also the impact on their families. This study uses a narrative approach to elicit stories from adult children of forensic psychiatrists to explore the extent of exposure to disturbing material, the impact of exposure, and mechanisms employed by parents to mitigate risk and exposure.
The Careers of Immigrants

I use a unique linked employer employee panel covering all wage earners in the private sector in Portugal to shed new light on the careers of immigrants. During the first ten years in the country immigrants close one third of the initial immigrant-native wage gap. I show that one third of this wage catch-up is accounted for by firm heterogeneity: Immigrants remain in the same occupations but get jobs with better paying _rms. Over time immigrants move to larger, more productive firms and with a higher share of native workers. These patterns are similar for all the recent immigrants irrespective of their origin and in particular of whether their mother tongue is the host country's language. Motivated by these new stylized facts, I suggest an economic assimilation mechanism which highlights imperfect information about immigrant productivity. I build an employer learning model with firm heterogeneity and complementarities between worker and firm type. The initial uncertainty over immigrants' productivity prevents them from getting access to the best jobs. Over time, productivity is revealed and immigrants obtain better firm matches. I derive predictions on the immigrant wage distributions over time, on their mobility patterns and on the productivity distribution of firms they are matched with. The predictions of the model are in line with the data and are not trivially derived from competing explanations.
Image denoising using ridgelet transform in a collaborative filtering framework

Redundancy within a natural image has been widely exploited for enhancing the sparsity in representation to achieve better restoration. The BM3D is the current state-of-the-art denoising method. It combines the transform-domain and the non-local means spatial-domain algorithms and is named as “collaborative filtering”. The discrete wavelet transform is shown to be the best performing transform in the collaborative filtering framework for Gaussian noise removal from the natural images. The BM3D is based on the assumption of homogeneity. However, in case of non-homogeneous textured image, there are structures such as lines, curves, etc. which still need to be exploited carefully. Due to the inherent limitations of the wavelets in representing the line singularity, the ridgelet transform was proposed a decade ago. In this paper, we explore the use of ridgelet transform in collaborative filtering framework and show that the BM3D performs better with the ridgelet transform than with the wavelet transform for the denoising textured image.
The Importance of Household Preferences: Using Revealed Preference Data to Develop a Contingent Valuation Experiment to Value Asthma

Valuation of morbidity associated with childhood asthma is signi…cant both to policy and to non-market valuation methodologies. Our results show that household perceptions and beliefs, such as belief in one’s ability to predict and control asthma attacks, and relative perceptions of the overall burden asthma places on a family, have a larger impact on valuation than traditional measures of asthma severity. More generally, our approach can be applied to other chronic illnesses as well, such as diabetes or chronic pain.
Reaching Families through Social Media: Training Extension Professionals to Implement Technology in Their Work

Cooperative Extension professionals have a long tradition of helping improve the lives of the families they serve by sharing research-based information. More than ever, families are getting their information online, creating a need for Extension professionals to deliver content via technology. This article describes a training designed to teach Extension professionals ways to increase their reach to families through the use of technology in their work. Extension professionals attended an 8-hour, face-to-face training in which they completed a pre, post, and follow-up survey. Results from the training indicated that this training was effective in changing attitudes about the usefulness of technology and increasing their use of social media to reach families.
Detection of Alzheimer's amyloid in transgenic mice using magnetic resonance microimaging

The presence of amyloid‐β (Aβ) plaques in the brain is a hallmark pathological feature of Alzheimer's disease (AD). Transgenic mice overexpressing mutant amyloid precursor protein (APP), or both mutant APP and presenilin‐1 (APP/PS1), develop Aβ plaques similar to those in AD patients, and have been proposed as animal models in which to test experimental therapeutic approaches for the clearance of Aβ. However, at present there is no in vivo whole‐brain imaging method to detect Aβ plaques in mice or men. A novel method is presented to detect Aβ plaques in the brains of transgenic mice by magnetic resonance microimaging (μMRI). This method uses Aβ1‐40 peptide, known for its high binding affinity to Aβ, magnetically labeled with either gadolinium (Gd) or monocrystalline iron oxide nanoparticles (MION). Intraarterial injection of magnetically labeled Aβ1‐40, with mannitol to transiently open the blood–brain barrier (BBB), enabled the detection of many Aβ plaques. Furthermore, the numerical density of Aβ plaques detected by μMRI and by immunohistochemistry showed excellent correlation. This approach provides an in vivo method to detect Aβ in AD transgenic mice, and suggests that diagnostic MRI methods to detect Aβ in AD patients may ultimately be feasible. Magn Reson Med 50:293–302, 2003. © 2003 Wiley‐Liss, Inc.
Adjustment of foreign EQ-5D-3L utilities can increase their transferability

Background Foreign utilities of the EQ-5D-3L (3-level version of the EuroQol-5 Dimension of health questionnaire) are not readily transferrable to economic evaluations conducted from a national perspective. It has been advised to avoid transferring mean utilities from one country to another without adjusting them; yet no such method exists. Purpose The present study aimed to develop a method for adjusting mean utilities to increase their transferability from one country to another. Methods Seven datasets containing EQ-5D-3L answers were valued using value sets from four countries: the UK, the Netherlands, Germany, and Spain. Hereby, seven mean utility values were obtained for each country. This allowed for three pairwise comparisons: 1) UK mean values vs Dutch mean values; 2) UK mean values vs German mean values; and 3) UK mean values vs Spanish mean values. For each of these three comparisons, a regression model was fitted using the mean UK utilities as the dependent variable and the other country’s mean utilities as the independent variable. The coefficients from the three regression models were validated using results from a published article containing mean utilities obtained by valuing the EQ-5D-3L data using all four value sets. Results The findings suggested that adjustment of foreign utilities may increase transferability between countries where value sets are not comparable. It was possible to adjust the mean utilities valued by the Dutch and German value sets to make them reflect mean UK utilities as there were substantial differences between these value sets. Transferability of the Spanish mean utility values was not improved as the Spanish and UK value sets are sufficiently similar. Conclusion It is feasible to adjust foreign mean utilities of the EQ-5D-3L to make them reflect national preferences for health.
A parallel algorithm for chimera grid with implicit hole cutting method

The chimera grid methods have been widely used in the simulation of flow over complex configurations and unsteady moving boundary process. Lee and Baeder presented the implicit hole cutting (IHC) method, which improves the practicability and robustness of chimera grid method. But the excessive time consumption of this method restricts the scalability of parallelism. In this article, based on the parallel implementation of IHC method with structured multi-block grid, the factors which restrict the performance and efficiency are analyzed. Cartesian auxiliary grid is introduced to reduce the communication and computing cost. Finally, test cases are presented to demonstrate the effectiveness of this algorithm, and the calculation and data communication are reduced on the premise of maintaining accuracy.
An initial approach to distributed adaptive fault-handling in networked systems

We present a distributed adaptive fault-handling algorithm applied in networked systems. The probabilistic approach that we use makes the proposed method capable of adaptively detect and localize network faults by the use of simple end-to-end test transactions. Our method operates in a fully distributed manner, such that each network element detects faults using locally extracted information as input. This allows for a fast autonomous adaption to local network conditions in real-time, with significantly reduced need for manual configuration of algorithm parameters. Initial results from a small synthetically generated network indicate that satisfactory algorithm performance can be achieved, with respect to the number of detected and localized faults, detection time and false alarm rate.
Using EM-trained string-edit distances for approximate matching of acoustic morphemes

Our research concerns spoken language understanding within the domain of automated telecommunication services. In the recent papers we presented a new methodology for training of statistical language models for recognition and understanding of utterances from large corpora of phone sequences obtained as the output of a task-independent ASR-system. The advantage of this strategy compared to the traditional word-based strategy is that we don’t have to manually transcribe large amounts of data in order to extract acoustic morphemes to train the classifier. Since the baseline strategy suffered high False Rejection Rates caused by finding no acoustic morphemes in the test data, we describe in this paper how approximate matching can be incorporated in the Bayes-classifier to reduce FRR. The experiments are evaluated for “How May I Help You? ”-task.