Miracle-dz/UniFAR-FTUs huggingface dataset:
Maturity-Onset Diabetes of the Young

SamenvattingDe 30-jarige Corrie de Vreugd bezoekt de huisarts. Zij denkt dat ze diabetes heeft en vraagt of de huisarts haar bloedsuiker wil prikken. De Vreugd heeft de laatste weken last van dorst, ook ’s nachts, en moet veel plassen. Ze is wat afgevallen. Desondanks is de BMI 31. In de familie komt geen diabetes mellitus voor. Corrie de Vreugd heeft een blanco voorgeschiedenis. De huisarts twijfelt over het type diabetes mellitus.
FINDING STRONGLY CONNECTED COMPONENTS IN PARALLEL

This paper presents the method of a parallel implementation of Tarjan’s algorithm that solves an important problem of detection of strongly connected components in a directed graph. A theoretical synthesis of the corresponding scheme of the algorithm is done with the means of systems of modified algorithmic algebras developed by V.M. Glushkov (SAA-M). Theoretical estimations of the speedup of the algorithm are obtained. These estimations have been checked up and confirmed in the experiment.
Analysis of modulation characteristics of widely tunable sampled-grating distributed reflector (SGDBR) lasers based on transmission line laser model

The combined laser model based on transmission line laser mode (TLLM) is extended to investigate intensity modulation (IM) response and frequency modulation (FM) response of SGDBR lasers in this paper. While carrier-depended refraction index, spontaneous emission noise and their coupling are included in the consequent dynamic frequency shift and intensity fluctuation can reflect dynamics of SGDBR lasers in more real circumstances. Simulation results indicate that direct modulation bandwidth increases as bias current increases in IM. Frequency chirp in small signal IM and the fluctuation of output optical power in FM are also discussed.
1,2-Dimethylhydrazine-induced alterations in protein kinase C activity in the rat preneoplastic colon.

Recently, a number of studies in experimental animals and humans have suggested that alterations in the activity of protein kinase C (PKC) may be involved in the malignant transformation process. To determine whether such alterations in this kinase were present before the development of 1,2-dimethylhydrazine (DMH)-induced colon cancers, rats were given s.c. injections of this procarcinogen (20 mg/kg body weight/week) or diluent for 10 or 15 weeks. Animals were sacrificed after these time periods and colonic epithelium was harvested from each group. The activity and distribution of PKC in the cytosolic and membrane fractions of these preparations as well as 1,2-diacylglycerol mass and phosphoinositide turnover were then examined and compared in the presence and absence of 10 nM 1,25-dihydroxycholecalciferol, an agent which has previously been found to influence these biochemical parameters in the normal rat colonic epithelium. The results of these studies demonstrate that: (a) the percentage of PKC activity in the membrane fraction was significantly greater in DMH-treated animals compared to their control counterparts at 10 and 15 weeks; (b) the total PKC activity was similar at 10 weeks, but markedly reduced in the colonic mucosa of the DMH-treated group at 15 weeks; (c) 1,2-diacylglycerol mass and phosphoinositide turnover were increased in the colonic mucosa of rats administered this carcinogen at both time points; and (d) in control, but not in DMH-treated animals, in vitro addition of 1,25-dihydroxycholecalciferol increased PKC activity, 1,2-diacylglycerol mass and phosphoinositide turnover at each of the times studied. Based on these findings, it would appear that alterations in PKC activity may play a role in the malignant transformation process of the colon in animals administered DMH.
Conditioning properties of the stationary distribution for a Markov chain

Let T be an irreducible stochastic matrix with stationary vector πT . The condi- tioning of π T under perturbation of T is discussed by providing an attainable upper bound on the absolute value of the derivative of each entry in πT with respect to a given perturbation matrix. Connections are made with an existing condition number for π T , and the results are applied to the class of Markov chains arising from a random walk on a tree.
Extracting Low-Dimensional Latent Structure from Time Series in the Presence of Delays

Noisy, high-dimensional time series observations can often be described by a set of low-dimensional latent variables. Commonly used methods to extract these latent variables typically assume instantaneous relationships between the latent and observed variables. In many physical systems, changes in the latent variables manifest as changes in the observed variables after time delays. Techniques that do not account for these delays can recover a larger number of latent variables than are present in the system, thereby making the latent representation more difficult to interpret. In this work, we introduce a novel probabilistic technique, time-delay gaussian-process factor analysis (TD-GPFA), that performs dimensionality reduction in the presence of a different time delay between each pair of latent and observed variables. We demonstrate how using a gaussian process to model the evolution of each latent variable allows us to tractably learn these delays over a continuous domain. Additionally, we show how TD-GPFA combines temporal smoothing and dimensionality reduction into a common probabilistic framework. We present an expectation/conditional maximization either (ECME) algorithm to learn the model parameters. Our simulations demonstrate that when time delays are present, TD-GPFA is able to correctly identify these delays and recover the latent space. We then applied TD-GPFA to the activity of tens of neurons recorded simultaneously in the macaque motor cortex during a reaching task. TD-GPFA is able to better describe the neural activity using a more parsimonious latent space than GPFA, a method that has been used to interpret motor cortex data but does not account for time delays. More broadly, TD-GPFA can help to unravel the mechanisms underlying high-dimensional time series data by taking into account physical delays in the system.
Read leveling for flash storage systems

Due to its several attractive benefits such as shock resistance, energy efficiency, and space-efficient form factor, flash memory is now applied to a wide range of electronics. Typically, since write requests are harmful to the health of flash memory, some flash-based storage devices tend to be deployed for read-intensive applications recently. However, as the technology node keeps going, read disturbance becomes a worsening problem in flash memory. Even under a pure read workload, flash memory often needs to refresh disturbed data, which brings about additionalwrite and erase operations. In this work, we propose a new design direction, read leveling, that aims at distributing read-hot data over different flash blocks. Thus, all read operations could be issued to different blocks as evenly as possible, so as to minimize the interference between read-hot data and other valid data on the same block and avoid refreshing cost. A series of experiments were conducted to prove the effectiveness of the proposed concept, and the results are very encouraging.
An XMM-Newton study of the X-ray binary MXB 1659-298 and the discovery of narrow X-ray absorption lines

We report the discovery of narrow X-ray absorption lines from the low-mass X-ray binary MXB 1659- 298 during an XMM-Newton observation in 2001 February. The 7.1 hr orbital cycle is clearly evident with narrow X-ray eclipses preceded by intense dipping activity. A sinusoid-like OM B-band modulation with a peak-to-peak modulation of0.5 mag and a minimum coincident with the X-ray eclipse is visible. EPIC and RGS spectra reveal the presence of narrow resonant absorption features identied with O viii 1s-2p, 1s-3p and 1s-4p, Ne x 1s-2p, Fe xxv 1s-2p, and Fe xxvi 1s-2p transitions, together with a broad Fe emission feature at 6:47 +0:18 0:14 keV. The equivalent widths of the Fe absorption features show no obvious dependence on orbital phase, even during dipping intervals. The equivalent widths of the other features are consistent with having the same values during persistent and dipping intervals. Previously, the only X-ray binaries known to exhibit narrow X-ray absorption lines were two superluminal jet sources and it had been suggested that these features are related to the jet formation mechanism. This now appears unlikely, and instead their presence may be related to the viewing angle of the system. The MXB 1659-298 0.6{12 keV continuum is modeled using absorbed cuto power-law and blackbody components. During dips the blackbody is more strongly absorbed than the power-law. The spectral shape of the 3.6% of 0:5 10 keV emission that remains during eclipses is consistent with that during non-dipping intervals.
THE INTERNAL STRUCTURE OF NOUN PHRASES

The aim of this paper is to describe the internal syntax of Hungarian noun phrases in terms of their immediate constituent structure. The external syntax of NPs, i.e. their distribution in larger phrases and sentences will be mentioned only in passing – for a more detailed discussion, see Kenesei (1985). Section 1 deals with the lower bar-levels, and Section 2 describes the possessive constructions: the results are summarized as context-free rule schemata in Section 3.
An environmental adaptive control system of a wheel type mobile robot for the rough terrain movement

The transportation using wheels is one of the most popular transportation mechanisms for mobile robots because a wheel type mobile system has high energy efficiency, simple mechanism and well investigated control system. However, the wheel type mobile robots have the difficulty in the rough terrain movement. In this research, we propose an environmental adaptive control system for a wheel type mobile robot for the rough terrain movement. This proposal system recognized the traveling environment and switched the adaptable controllers, and showed the better performance than only one controller.
Collaborative magic lens graph exploration

We present the design and implementation of a prototype consisting of several mobile devices that allow for multi user exploration of dynamically visualised graphs of large data sets in a mixed reality environment. Tablet devices are used to represent nodes and placed on a table as augmented reality tracking targets. From these nodes a graph is dynamically loaded and visualised in mixed reality space. Multiple users can interact with the graph through further mobile devices acting as magic lenses. We explore different interaction methods for basic graph exploration tasks based on the previous research in interactive graph exploration.
Scalable Power Management Using Multilevel Reinforcement Learning for Multiprocessors

Dynamic power management has become an imperative design factor to attain the energy efficiency in modern systems. Among various power management schemes, learning-based policies that are adaptive to different environments and applications have demonstrated superior performance to other approaches. However, they suffer the scalability problem for multiprocessors due to the increasing number of cores in a system. In this article, we propose a scalable and effective online policy called MultiLevel Reinforcement Learning (MLRL). By exploiting the hierarchical paradigm, the time complexity of MLRL is O(n lg n) for n cores and the convergence rate is greatly raised by compressing redundant searching space. Some advanced techniques, such as the function approximation and the action selection scheme, are included to enhance the generality and stability of the proposed policy. By simulating on the SPLASH-2 benchmarks, MLRL runs 53% faster and outperforms the state-of-the-art work with 13.6% energy saving and 2.7% latency penalty on average. The generality and the scalability of MLRL are also validated through extensive simulations.
Variational and linearly-implicit integrators, with applications

We show that symplectic and linearly-implicit integrators proposed by [Zhang and Skeel, 1997] are variational linearizations of Newmark methods. When used in conjunction with penalty methods (i.e., methods that replace constraints by stiff potentials), these integrators permit coarse time-stepping of holonomically constrained mechanical systems and bypass the resolution of nonlinear systems. Although penalty methods are widely employed, an explicit link to Lagrange multiplier approaches appears to be lacking; such a link is now provided (in the context of two-scale flow convergence [Tao, Owhadi and Marsden, 2010]). The variational formulation also allows efficient simulations of mechanical systems on Lie groups.
How Do Auditors Order Their Tasks, and How Does Task Ordering Affect Performance?

I examine how the subjectivity of task criteria influences auditors’ ordering and performance of audit tasks under time pressure. Tasks with more objective criteria provide little flexibility in how well they can be completed (i.e. they are either performed correctly or incorrectly). On the other hand, tasks with more subjective criteria have a wider range of performance levels that satisfy the “letter” of the criteria, but not necessarily the “spirit. ” I predict and find that under high time pressure, auditors tend to prioritize completion of an objective task over a subjective task, resulting in lower performance on the subjective task relative to when time pressure is low. By decreasing performance only on subjective tasks, auditors can address all of the criteria for both tasks if only in letter, rather than in spirit. I predict that this effect will be mitigated by informing auditors of heightened inherent risk related to the subjective task. While I find that auditors have an increased likelihood of performing the subjective task first when this task has higher inherent risk, I do not find differences in performance on either task relative to when inherent risk for both tasks was the same.
It's more than you can tell : The implicit side of helping

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Diversity Networks: Neural Network Compression Using Determinantal Point Processes

We introduce Divnet, a flexible technique for learning networks with diverse neurons. Divnet models neuronal diversity by placing a Determinantal Point Process (DPP) over neurons in a given layer. It uses this DPP to select a subset of diverse neurons and subsequently fuses the redundant neurons into the selected ones. Compared with previous approaches, Divnet offers a more principled, flexible technique for capturing neuronal diversity and thus implicitly enforcing regularization. This enables effective auto-tuning of network architecture and leads to smaller network sizes without hurting performance. Moreover, through its focus on diversity and neuron fusing, Divnet remains compatible with other procedures that seek to reduce memory footprints of networks. We present experimental results to corroborate our claims: for pruning neural networks, Divnet is seen to be notably superior to competing approaches.
Accelerating convergence in cartesian genetic programming by using a new genetic operator

Genetic programming algorithms seek to find interpretable and good solutions for problems which are difficult to solve analytically. For example, we plan to use this paradigm to develop a car accident severity prediction model for new occupant safety functions. This complex problem will suffer from the major disadvantage of genetic programming, which is its high demand for computational effort to find good solutions. A main reason for this demand is a low rate of convergence. In this paper, we introduce a new genetic operator called forking to accelerate the rate of convergence. Our idea is to interpret individuals dynamically as centers of local Gaussian distributions and allow a sampling process in these distributions when populations get too homogeneous. We demonstrate this operator by extending the Cartesian Genetic Programming algorithm and show that on our examples convergence is accelerated by over 50% on average. We finish this paper with giving hints about parameterization of the forking operator for other problems.
Teaching of grasp/graspless manipulation for industrial robots by human demonstration

In this paper, we propose a simple teaching method for industrial robots by human demonstration. The method can be divided into two parts: the teaching phase and the planning phase. In the teaching phase, a human operator demonstrates a manipulation of an object, and two cameras recognize the path of the object by observing markers attached on the object. In the planning phase, a motion planner generates a sequence of robot operations to play back the demonstrated path by pick-and-place and pushing. The proposed method incorporates automated camera calibration required for human demonstration, which enables labor-saving teaching and compensates the absolute positional error of industrial robots.
Gesundheitliche Auswirkungen befristeter Verträge in Deutschland und Spanien

Empfohlene Zitierung / Suggested Citation: Gash, V. , Mertens, A. , & Romeu Gordo, L. (2008). Gesundheitliche Auswirkungen befristeter Verträge in Deutschland und Spanien. In K. -S. Rehberg (Hrsg.), Die Natur der Gesellschaft: Verhandlungen des 33. Kongresses der Deutschen Gesellschaft für Soziologie in Kassel 2006. Teilbd. 1 u. 2 (S. 4418-4428). Frankfurt am Main: Campus Verl. https://nbnresolving.org/urn:nbn:de:0168-ssoar-154879
Multi-Agent Based Disaster Management System: A Review

Disasters, like earthquakes, floods, etc. can cause large number of deaths, injured and homeless. Appropriate responses are needed in the form of allocating resources to handle the effects of disasters. Multi-agent disaster system has been extensively used in the different tasks of decentralized disaster problem solving such as communication among agents, collective decision making, cooperation, collaborative planning in large scale that deals with uncertainty and conflicting information during disaster management . In this paper, we analyze the current disaster management and response systems, taking in consideration of domain requirements, agent-based design methodology and systems’ comparative view.
Effects of different subanaesthetic doses of (S)-ketamine on psychopathology and binocular depth inversion in man

The role of the N-methyl-D-aspartate (NMDA) neurotransmitter system in relation to psychoses is not completely understood, but represent a challenge in neurobiological research. The psychotic states induced by NMDA antagonists such as phencyclidine and ketamine have been described as being most similar to schizophrenia and the NMDA system has been implicated in the pathogenesis of schizophrenia. Binocular depth inversion, an illusion of visual perception, has been shown to be impaired in psychotic and psychotomimetic states in healthy and schizophrenic subjects. In this study, pictures of natural and artificial objects were presented stereoscopically to 12 healthy male volunteers and depth perception assessed using an operationalized method. The effects of the psychotomimetic S-enantiomer of the anaesthetic ketamine in two different subanaesthetic doses were compared with those of a placebo. In spite of dose dependence and grave subjective and significant objective psychopathology, no significant impairment of binocular depth perception was found with (S)-ketamine. Implications related to memory function, perceptogenesis and ‘bottom-up’ processing in ketamine model psychosis and schizophrenia are discussed.
What is the Best Strategy “Evidence-Based Practice” to Teach Literacy Skills for Students with Multiple Disabilities? A Systematic Review

A systematic of literature was carried out on peer-reviewed journals published from 2000 to 2015 to help indetermining the best strategy of evidence-based practice that can be applied in teaching literacy skills amongstudents with multiple disabilities. A total of 12 studies were reviewed, some of which included science andmathematics skills alongside literacy skills. The articles were evaluated according to the methodological processesused in carrying out the studies. Generally, all the strategies were found to be evidence based practices, which can beused to teach students with multiple disabilities. The systematic instruction and self-directed learning emerged as themost popular peer teaching and technology. Due to lack of enough studies that majored specifically on the studentswho suffered from more than one type of disability, other forms of severe disabilities like autism and intellectualdisability, which are considered under the umbrella of multiple disabilities, were included in the systematic review.Additionally, in the process of defining certain target responses to demonstrate learning, with discrete responsesbeing common, the type of feedback and systematic prompting that was commonly used was time delay, whilestimulus fading was the least used component. One-to-one instruction and massed trials were the most commonlyused formats for teaching. Though this is not a proof that systematic instruction is the best strategy, it is a suggestionthat it is applicable in several situations and preferred by many researchers. Other strategies have also equally givenpositive responses and are thus effective in teaching literacy among students with multiple disabilities.
Murder, She Wrote

Enhanced sensitivity to information of negative (compared to positive) valence has an adaptive value, for example, by expediting the correct choice of avoidance behavior. However, previous evidence for such enhanced sensitivity has been inconclusive. Here we report a clear advantage for negative over positive words in categorizing them as emotional. In 3 experiments, participants classified briefly presented (33 ms or 22 ms) masked words as emotional or neutral. Categorization accuracy and valence-detection sensitivity were both higher for negative than for positive words. The results were not due to differences between emotion categories in either lexical frequency, extremeness of valence ratings, or arousal. These results conclusively establish enhanced sensitivity for negative over positive words, supporting the hypothesis that negative stimuli enjoy preferential access to perceptual processing.
Error Bound and Convergence Analysis of Matrix Splitting Algorithms for the Affine Variational Inequality Problem

Consider the affine variational inequality problem. It is shown that the distance to the solution set from a feasible point near the solution set can be bounded by the norm of a natural residual at that point. This bound is then used to prove linear convergence of a matrix splitting algorithm for solving the symmetric case of the problem. This latter result improves upon a recent result of Luo and Tseng that further assumes the problem to be monotone.
Scene Dynamics Estimation for Parameter Adjustment of Gaussian Mixture Models

The scene dynamics can provide useful statistical information for adjusting parameters of Gaussian mixture models (GMMs) in video surveillance. The contributions of this paper are twofold. First, an adaptive scene dynamics estimation approach is proposed. Second, we propose a scene-dynamics based method to adjust two types of GMMs' parameters, i.e., the learning rates and number of Gaussian components. For the learning rates, the scene dynamics are integrated into different kinds of pixel-type feedback schemes to control different kinds of learning rates. Experimental results demonstrate that the proposed method can effectively improve the performance of GMMs in surveillance scenes with complex dynamic backgrounds.