Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychosocial Working Conditions and Cognitive Complaints among Swedish Employees

Background Cognitive complaints involving problems with concentration, memory, decision-making and thinking are relatively common in the work force. The sensitivity of both subjective and objective cognitive functioning to common psychiatric conditions, stress levels and to cognitive load makes it plausible that psychosocial working conditions play a role in cognitive complaints. Thus, this study aimed to test the associations between psychosocial work factors and cognitive complaints in nationally representative samples of the Swedish work force. Cross-sectional (n = 9751) and prospective (n = 3644; two time points two years apart) sequential multiple regression analyses were run, adjusting for general confounders, depressive- and sleeping problems. Additional prospective analyses were run adjusting for baseline cognitive complaints. Cross-sectional results High quantitative demands, information and communication technology (ICT) demands, underqualification and conflicts were positively associated with cognitive complaints, while social support, good resources at work and overqualification were negatively associated with cognitive complaints in all models. Skill discretion and decision authority were weakly associated with cognitive complaints. Conflicts were more strongly associated with cognitive complaints in women than in men, after adjustment for general confounders. Prospective results Quantitative job demands, ICT demands and underqualification were positively associated with future cognitive complaints in all models, including when adjusted for baseline cognitive complaints. Decision authority was weakly positively associated with future cognitive complaints, only after adjustment for depressive- and sleeping problems respectively. Social support was negatively associated with future cognitive complaints after adjustment for general confounders and baseline cognitive complaints. Skill discretion and resources were negatively associated with future cognitive complaints after adjustment for general confounders. The associations between quantitative demands and future cognitive complaints were stronger in women. Discussion/Conclusions The findings indicate that psychosocial working conditions should be taken into account when considering cognitive complaints among employees.
Theories of accounting ethics and their dissemination

Volume 1: Part 1. Principles and Responsibilities Part 2. Code of Professional Conduct Part 3. Accounting and Regulating Matters Volume 2: Part 1. Some Basic Notions about Accounting Ethics Part 2. Ethical Theories Part 3. Economics and Accounting Ethics Part 4. Education of Accounting Ethics Volume 3: Part 1. Independence Issues Part 2. Ethical Reasoning by CPAs Part 3. International Comparisons Part 4. Ethics and Accounting Education Volume 4: Part 1. Historical Crises and Early Warning Signals Part 2. Enron and its Aftermath Part 3. Where Do We Go From Here?
Respiratory pattern of diaphragmatic breathing and pilates breathing in COPD subjects

BACKGROUND: Diaphragmatic breathing (DB) is widely used in pulmonary rehabilitation (PR) of patients with chronic obstructive pulmonary disease (COPD), however it has been little studied in the scientific literature. The Pilates breathing (PB) method has also been used in the rehabilitation area and has been little studied in the scientific literature and in COPD. OBJECTIVES: To compare ventilatory parameters during DB and PB in COPD patients and healthy adults. METHOD: Fifteen COPD patients (COPD group) and fifteen healthy patients (healthy group) performed three types of respiration: natural breathing (NB), DB, and PB, with the respiratory pattern being analyzed by respiratory inductive plethysmography. The parameters of time, volume, and thoracoabdominal coordination were evaluated. After the Shapiro-Wilk normality test, ANOVA was applied followed by Tukey's test (intragroup analysis) and Student's t-test (intergroup analysis; p<0.05). RESULTS: DB promoted increase in respiratory volumes, times, and SpO2 as well as decrease in respiratory rate in both groups. PB increased respiratory volumes in healthy group, with no additional benefits of respiratory pattern in the COPD group. With respect to thoracoabdominal coordination, both groups presented higher asynchrony during DB, with a greater increase in the healthy group. CONCLUSIONS: DB showed positive effects such as increase in lung volumes, respiratory motion, and SpO2 and reduction in respiratory rate. Although there were no changes in volume and time measurements during PB in COPD, this breathing pattern increased volumes in the healthy subjects and increased oxygenation in both groups. In this context, the acute benefits of DB are emphasized as a supporting treatment in respiratory rehabilitation programs.
Analysis of multiband astronomical images using multiscale tools

Many regions of the sky are currently being observed with a high number of spectral bands with various instruments and this trend shall develop considerably in the coming years. Even with few wavelength-bands, it is not easy to match the objects identified in each individual image, or to get consistent measurements and classify them spectrally. A detection based on fusion images is proposed. Different algorithms are tested for building such an image. The best results in terms of object detection are obtained from those involving a deconvolution with a wavelet approach. Another way to sort the pixels of astronomical images into a coherent set of physical sources is to classify them following some basic assumptions. The main difficulty comes from the fact that astronomical sources fill a multidimensional continuum of spectral classes leading to a significant color classes increase with the number of bands. The pixels spectral behaviour is assumed here to be a superimposition of several pure elements. The resulting mixing categories define a set of intermediate classes, and the application of the matching pursuit algorithm allows then to get class percentages, providing very promising results for the analysis of multi-wavelength astronomical images.
Learning about actions in infancy without a rationality principle

Between 6 and 9 months of age, infants begin to differentiate between the actions of others that are “rational” with respect to goals and those that are not. According to the teleological stance theory, this behavior is underpinned by an innate, naive rationality principle; according to a statistical learning account, experience alone is sufficient to explain this behavior. We present a recurrent neural network implementation of statistical learning that incorporates relevant pre-experimental experience that serves to shape expectations about goal-directed action. This model replicates the looking-time patterns of infants at different points in developmental time, and it demonstrates that a rationality principle is not necessary to account for the extant data.
Predicting the Power Output of Distributed Renewable Energy Resources within a Broad Geographical Region

In recent years, estimating the power output of inherently intermittent and potentially distributed renewable energy sources has become a major scientific and societal concern. In this paper, we provide an algorithmic framework, along with an interactive web-based tool, to enable short-to-middle term forecasts of photovoltaic (PV) systems and wind generators output. Importantly, we propose a generic PV output estimation method, the backbone of which is a solar irradiance approximation model that incorporates free-to-use, readily available meteorological data coming from online weather stations. The model utilizes non-linear approximation components for turning cloud-coverage into radiation forecasts, such as an MLP neural network with one hidden layer. We present a thorough evaluation of the proposed techniques, and show that they can be successfully employed within a broad geographical region (the Mediterranean belt) and come with specific performance guarantees. Crucially, our methods do not rely on complex and expensive weather models and data, and our web-based tool can be of immediate use to the community as a simulation data acquisition platform.
Real-time GDP Growth Forecasts

The authors forecast current-quarter real GDP growth using monthly data that would have been available to an analyst in real time. They demonstrate that using real-time data is of major importance both when estimating GDP forecasting models and when evaluating their performance. Moreover, the authors show that the out-of-sample forecasting performance of their model is comparable or superior to that of the Blue-Chip consensus forecast provided that more than one month of current-quarter data are available
On-line Tree Size Prediction using Incremental Models Computer Science Technical Report 11-01

It is often useful to know, in advance, how much effort will be required to solve a search problem. Current techniques for estimating the number of nodes that a bounded depth-first search will expand require copious amounts of off-line training and only make predictions in domains wi th integer-valued heuristic estimates. We present a new technique for estimating the number of nodes that a bounded d epth-first search will expand. Our technique has two main benefits over previous approaches: 1) it will work in dom ains with real-valued heuristic estimates and 2) it can be trained on-line during an iteration of search. We show that our technique can predict as well as previous approaches when trained off-line. We also show that our new a pproach can be used on-line to accurately predict the number of nodes that an IDA* style search will encounter on su bsequent iterations. We demonstrate the usefulness of this technique by controlling an IDA* search: using knowl edge learned on previous iterations to set the bound for subsequent iterations. While our technique has more overhe ad t an previous methods for controlling IDA*, it can give more robust performance by accurately doubling the amount o f search effort between iterations.
Using image registration to reconstruct spatiotemporal electrical activity in cardiac optical mapping studies

Optical mapping using membrane potential-sensitive fluorescent dyes provides a means of recording electrical activation on the heart surface with high spatial resolution. However, in order to interpret these data, it is necessary to correct for artifact introduced by heart wall motion. This paper describes a novel image registration technique that enables electrical activity on the heart surface to be reconstructed in the absence of pharmacological uncouplers commonly used to minimize heart wall motion. Image registration substantially reduced motion artifact and enabled activation times and action potential duration to be reconstructed accurately with respect to well-defined material points on the heart surface.
Semantic Multi-modal Analysis , Structuring , and Visualization for Candid Personal Interaction Videos

Videos are rich in multimedia content and semantics, which should be used by video browsers to better present the audio-visual information to the viewer. Ubiquitous video players allow for content to be scanned linearly, rarely providing summaries or methods for searching. Through analysis of audio and video tracks, it is possible to extract text transcripts from audio, displayed text from video, and higher-level semantics through speaker identification and scene analysis. External data sources, when available, can be used to cross-reference the video content and impose a structure for organization. Various research tools have addressed video summarization and browsing using one or more of these modalities; however, most of them assume edited videos as input. We focus our research on genres in personal interaction videos and collections of such videos in their unedited form. We present and verify formal models for their structure, and develop methods for their automatic analysis, summarization and indexing. We specify the characteristic semantic components of three related genres of candidly captured videos: formal instructions or lectures, student team project presentations, and discussions. For each genre, we design and validate a separate multi-modal approach to the segmentation and structuring of their content. We develop novel user interfaces to support browsing and searching the multi-modal video information, and introduce the tool in a classroom environment with ≈160 students per semester. UI elements are designed according to the underlying video structure to address video browsing in a structured multi-modal space. These user interfaces include image/video browsers, audio/video segmentation browsers, and text/filtered ASR transcript browsers. Through several user studies, we evaluate and refine our indexing methods, browser interface, and the tools usefulness in the classroom. We propose a core/module methodology to analysis, structure, and visualization of personal interaction videos. Analysis, structure, and visualization techniques in the core are common to all genres. Modular features are characteristic to video genres, and are applied selectively. Structure of interactions in each video is derived from the combination of the resulting audio, visual, and textual features. We expect that the framework can be applied to genres not covered here with the addition or replacement of few characteristic modules.
Foreground rejection for parallax removal in video sequence stitching

In image stitching, parallax is key challenge that leads to inaccurate registration and ghosting effect of objects in the result. A novel foreground rejection method is proposed in this paper to remove parallax in video sequence. Firstly, the global motion is estimated between two frames using SIFT feature matching. The logical OR operators are then applied to the pixels that have high displaced frame difference for foreground extraction. There are two sets of foreground calculated from the near frames and far frames. Voting scheme is applied in the way that only the near-frame foreground appears inside the area of far-frame foreground that is considered as the actual foreground. The extracted foreground at this stage is mostly the edges of objects. Therefore, the foreground rejection method proposed in this paper aims to provide better motion vector estimation and ghosting effect reduction. The removal of the major parts of the high difference foreground is sufficient; hence the simple two-stage rendering is applied. The implemented results indicate that more accurate registration is achieved by including our proposed foreground rejection method. Moreover, the background of the stitched video sequence does not have the visible ghosting effect.
ON PROPERTIES OF THE GENERALIZED MAJORIZATION

C ‡ Abstract. In this paper, a complete solution of a problem involving generalized majorization of partitions is given: for two pairs of partitions (d,a) and (c,b) necessary and sufficient conditions for the existence of a partition g that is majorized by both pairs is determined. The obtained conditions are explicit, the solution is constructive and it uses novel techniques and indices. Although the problem is motivated by the applications in matrix pencil completions problems, all results are purely combinatorial and they give a new perspective on comparison of partitions.
Notice of RetractionImpact of the number of generations on the fitness value and the time required to find the optimal solution in standard GA applications

Genetic Algorithms (GA) are search and optimization algorithms and as such are used for minimizing or maximizing a given function and if possible finding its most suitable solution. They can be used for finding a solution to problems that are difficult to solve with traditional optimization techniques, including problems that are not well defined or difficult to be mathematically modeled, such as the traveling salesman problem and the 2D packing problem. The data we obtain from the applications that implement the GA are visually presented as a graph from which we can see the progress of the GA's fitness value minimization over the generations, the smallest fitness value, the generation of its occurrence and the solution itself. Within this visualization we also made an analysis on the impact of the number of generations on the fitness value and the time required for finding the optimal solution.
Efficient ID-based authentication and key agreement protocols for the session initiation protocol

In a widely deployed VoIP system tens of thousands of clients compete for the SIP proxy server's authentication service. SIP protocol implementations have to meet certain QoS and security requirements. In this study new ID-based protocols are proposed for the SIP authentication and key agreement protocols. These protocols minimize the use of expensive pairing functions but still resist notable attacks. The security of the proposed protocols are analyzed and demonstrated with security proofs based on the BJM security model. Finally, the performance overhead of the proposed protocols are compared to ID-based SIP authentication and key agreement protocols given in the literature.
A Maximum Margin Approach for Semisupervised Ordinal Regression Clustering

Ordinal regression (OR) is generally defined as the task where the input samples are ranked on an ordinal scale. OR has found a wide variety of applications, and a great deal of work has been done on it. However, most of the existing work focuses on supervised/semisupervised OR classification, and the semisupervised OR clustering problems have not been explicitly addressed. In real-world OR applications, labeling a large number of training samples is usually time-consuming and costly, and instead, a set of unlabeled samples can be utilized to set up the OR model. Moreover, although the sample labels are unavailable, we can sometimes get the relative ranking information of the unlabeled samples. This sample ranking information can be utilized to refine the OR model. Hence, how to build an OR model on the unlabeled samples and incorporate the sample ranking information into the process of improving the clustering accuracy remains a key challenge for OR applications. In this paper, we consider the semisupervised OR clustering problems with sampleranking constraints, which give the relative ranking information of the unlabeled samples, and put forward a maximum margin approach for semisupervised OR clustering (M2SORC). On one hand, M2SORC seeks a set of parallel hyperplanes to partition the unlabeled samples into clusters. On the other hand, a loss function is put forward to incorporate the sample ranking information into the clustering process. As a result, the optimization function of M2SORC is formulated to maximize the margins of the closest neighboring clusters and meanwhile minimize the loss associated with the sample-ranking constraints. Extensive experiments on OR data sets show that the proposed M2SORC method outperforms the traditional semisupervised clustering methods considered.
Class specific centralized dictionary learning based kernel collaborative representation for fine-grained image classification

Classification algorithms based sparse coding have formed a mature system for visual recognition. Recent studies suggest collaborative representation is a much more effective method for classification, compared with sparse representation, the objective function of collaborative representation is constrained by ℓ2-norm. Traditional collaborative representation based classification always uses a set of training samples to construct a dictionary directly, which causes high residual error and thus reduces the correct rate of classification. To handle the problem, we propose an innovative method, which integrates centralized image coding and class specific dictionary learning algorithm with collaborative representation based classification together, namely class specific centralized dictionary learning based collaborative representation (CSCDL-CRC). Meanwhile, kernel method can obtain nonlinear information between data points through mapping feature space to kernel space, especially when it is applied to image classification. We extended our proposed CSCDL-CRC to the kernel space to improve the classification performance. We make plenty of experiments on three frequently-used fine-grained image datasets, including Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset, Oxford 102-Flowers dataset and Stanford Dogs dataset, to validate the effectiveness of the proposed approach.
Reduction of power field radiation for PLC applications

In this article, we present our investigations on the radiation of the power-line communication (PLC) system. Power line communication or power line carrier is a technology for carrying data on a conductor usually dedicated to electric power transmission. At high frequency, the power lines, whose are dimensioned and adjusted for electrical energy transfer, not to carry high frequency (HF) data signal, act then as radiating antennas.
Understanding and creating CPD for and with teachers : the development and implementation of a model for CPD

The current socio-political climate is one in which there is an increased performance management culture in schools. Evidence suggests continuing professional development should improve teachers’ practice so that pupils’ learning is enhanced. This Systematic Review focuses on exploring the range of continuing professional development available to teachers, in particular considering what helps support teachers to continually develop their practice. A meta-ethnography approach was used to systematically analyse seven studies which met the pre-determined criteria to be included in this paper. Adopting Noblit and Hare’s (1988) seven step approach synthesis, and consequent mapping of these studies, indicated three third order constructs; intersubjectivity, reflection, and common goal. Synthesis suggests these are necessary for professional learning to occur for teachers. The review led to the development of a model with three overlapping constructs needed for such professional learning to occur. It was concluded that in order to explore these three constructs and the link to professional learning, further empirical research was needed. Implications for applied educational psychologists include application of this model to explore and develop effective professional learning with teachers.
Concurrent Separation Logic with Weak Updates

Concurrent Separation Logic (CSL) provides a simple but powerful technique for reasoning about shared-memory concurrent programs. Unfortunately , CSL and separation logic can only support " strong updates, " where mutation to a memory location is safe only if there is a strong memory separation. This severely limits the applicability of CSL since most statically typed languages (e.g., ML, Java, C#) support only " weak updates, " where there is a single global " memory typing " context (which should not be separated) to enforce type-preserving memory updates. In this paper, we attempt to develop a single framework that can support both strong and weak updates. To this end, we present a semantic model of weak updates and then incorporate it into CSL. Our model treats a specification of weak-update memory as a recursively defined predicate and takes the fixed point of the predicate as the global memory-typing context. We show how this model can be smoothly incorporated into CSL by lifting memory assertions and separating conjunction to parametrize over the memory-typing context. Our extension of CSL is significant since it is the first program logic that can successfully support modular verification of concurrent programs with general ML-style mutable references.