Miracle-dz/UniFAR-FTUs huggingface dataset:
Die Bedeutung einer Einzelfallstudie erworbener Prosopagnosie für das Verständnis der Gesichtererkennung

7 1. Einleitung . 9 JQs Geschichte . 10 2. Hintergründe der Kognitiven Neuropsychologie . 12 Geschichte der Kognitiven Neuropsychologie . 12 Vorannahmen der Kognitiven Neuropsychologie . 15 Einzelfallstudien in der Kognitiven Neuropsychologie . 20 Doppeldissoziationen Definition . 21 Doppeldissoziationen– Rechtfertigung der Inferenz auf (kognitive) Modularität . 22 4. Schädel-Hirn-Traumata (SHT) und neuropsychologische Rehabilitation . 26 5. Theorie der Gesichtererkennung . 30 I. Kognitives Modul des Gesichtererkennens und Prosopagnosie als Krankheitsbild . 30 I.1 1986: Das Jahr kognitiver Modelle der Gesichtererkennung . 31 I.2 Konfigurale Verarbeitung von Gesichtern und Inversionseffekt . 33 I.3 Dissoziation zwischen Gesichtsund Objekterkennung: Kognitive Hinweise . 37 I.4 Dissoziationen zwischen Gesichtsinformationen? Kognitive Hinweise . 39 I.4. 1 Dissoziation von Geschlechtserkennen und Identitätserkennen . 39 I.4. 2. Dissoziation von Identitätserkennen und Emotionserkennen . 46 II. Modell der neurologischen Modularität: Fokus auf Identitätserkennen . 52 6. Fragestellung . 57 7. Durchführung der Messungen und Auswertung . 58 Stichprobe . 58 Auswertungsmethoden . 59 MRT . 65 8. Ergebnisse . 66 I. Neuropsychologische Testungen. 66 I.1. Gruppe: Kognitive Basisfunktionen . 66 I.2. Gruppe: Gesichterspezifische Testungen . 76 II. MRT-Aufnahmen . 85 9. Diskussion . 87 10. Zusammenfassung und Ausblick . 106 11. Danksagung . 108 12. Abkürzungsverzeichnis . 109 13. Abbildungsverzeichnis . 111 Lebenslauf . 113 Eidesstattliche Versicherung . 114 Literaturverzeichnis . 115
APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR FLOOD FORECASTING

In hydrology, as in a number of diverse fields, there has been an increasing use of Artificial Neural Networks (ANN) as black-box simplified models. This is mainly justified by their ability to model complex non-linear patterns; in addition they can self-adjust and produce a consistent response when ‘trained’ using observed outputs. This paper utilises various types of ANNs in an attempt to assess the relative performance of existing models. Ali Efenti, a subcatchment of the river Pinios (Greece), is examined and the results support the hypothesis that ANNs can produce qualitative forecasts. A 7-hour ahead forecast in particular proves to be of fairly high precision, especially when an error prediction technique is introduced to the ANN models.
Robotic tactile recognition of pseudorandom encoded objects

This paper discusses an original model-based method for blind robotic tactile recognition of three-dimensional objects. Conveniently shaped geometric symbols representing terms of a pseudorandom array (PRA) are embossed on object surfaces. Symbols recovered by tactile probing are recognized using a neural network and then clustered in a PRA window that contains enough information to fully identify the absolute coordinates of the recovered window within the encoding PRA. By knowing how different object models were mapped to the PRA, it is possible to unambiguously identify the object face and the exact position of the recovered symbols on the face.
Integrated navigation system using camera and gimbaled laser scanner for indoor and outdoor autonomous flight of UAVs

This paper describes an integrated navigation sensor module, including a camera, a laser scanner, and an inertial sensor, for unmanned aerial vehicles (UAVs) to fly both indoors and outdoors. The camera and the gimbaled laser sensor work in a complementary manner to extract feature points from the environment around the vehicle. The features are processed using an online extended Kalman filter (EKF) in simultaneous localization and mapping (SLAM) algorithm to estimate the navigational states of the vehicle. In this paper, a new method is proposed for calibrating a camera and a gimbaled laser sensor. This calibration method uses a simple visual marker to calibrate the camera and the laser scanner with each other. We also propose a real-time navigation algorithm based on the EKF SLAM algorithm, which is suitable for our camera-laser sensor package. The algorithm merges image features with laser range data for state estimation. Finally, these sensors and algorithms are implemented on our octo-rotor UAV platform and the result shows that our onboard navigation module can provide a real-time three-dimensional navigation solution without any assumptions or prior information on the surroundings.
One versus many: Capturing the use of multiple emotion regulation strategies in response to an emotion-eliciting stimulus

The past decade and a half has witnessed a renewed interest in the study of affective processes. James Gross’ process model of emotion regulation has provided a theoretical framework for this approach. This model stipulates that individuals have a repertoire of emotion regulation strategies they use in order to modify their affect and/or the situations eliciting such affect. However, empirical investigations of the use of emotion regulation strategies have largely oversimplified this model by assuming that individuals use only one regulation strategy to manage the affect elicited by a given emotion-eliciting stimulus or situation. This is problematic because it has resulted in a limited understanding of the complex process by which individuals select and implement regulation strategies. In this brief report, we present findings suggesting that people spontaneously use multiple emotion regulation strategies in response to a brief disgust-eliciting film clip. We discuss implications for future empirical work on emotion regulation strategies.
Management of multiple congested conditions in unbundled operation of a power system

This paper studies congestion management based on congestion pricing as may be done by an independent power system operator. Four main concepts are discussed: congestion pricing can lead to the same solution as an optimal power flow; pricing need not have cost information available; good estimates of nonlinear cost coefficients are necessary; and pricing for congestion management is separable from pricing for the purpose of transmission network revenue reconciliation. Emphasis is on the determination of sufficient information by observation of the behavior of the market to permit optimum pricing without explicit communication among parties, and without generators ever being explicitly concerned about the relief of flow congestion. Results from simulated experiments are presented.
Collaborative Access Control

As the growth of network, there comes a need for collaboration over network. Cooperation between different parties involves operations on shared objects. Security is one of the most important aspects of the system. It must be well managed under the collaboration. This introduces the access control for collaborative environment in which the traditional access control cannot fulfill the requirement. In this article, three access control models for collaborative environment are introduced and compared using several evaluating criteria. The three models are the representative examples of the directions of collaborative access control. A prediction is made at the end of the article pointing out the main trend.
Relations between popularity and prosocial behavior in middle school and high school Chinese adolescents

The concurrent and longitudinal associations between popularity, likeability, and prosocial behavior were evaluated in this three-year study of middle school and high school Chinese adolescents. The initial sample included 766 middle school (mean age = 13.3 years) and 668 high school participants (mean age = 16.6 years); there were 880 (399 girls) middle school and 841 (450 girls) adolescents who participated in at least one year of data collection. Significant positive associations between popularity, prosociality, and academic achievement were found. Both popularity and likeability concurrently predicted significant unique variance in prosocial behavior after controlling for academic achievement; longitudinal cross-lagged analyses revealed bi-directional associations between popularity and prosocial behavior such that popularity positively predicted subsequent prosocial behavior and prosocial behavior predicted subsequent popularity. Cross-cultural research on popularity may profitably focus on variation in prosocial behavior as the relative salience of coercive and prosocial control strategies may vary across cultures.
The role of inflammation and macrophage accumulation in the development of obesity-induced type 2 diabetes mellitus and the possible therapeutic effects of long-chain n-3 PUFA

The WHO estimate that >1×106 deaths in Europe annually can be attributed to diseases related to excess body weight, and with the rising global obesity levels this death rate is set to drastically increase. Obesity plays a central role in the metabolic syndrome, a state of insulin resistance that predisposes patients to the development of CVD and type 2 diabetes mellitus. Obesity is associated with low-grade chronic inflammation characterised by inflamed adipose tissue with increased macrophage infiltration. This inflammation is now widely believed to be the key link between obesity and development of insulin resistance. In recent years it has been established that activation of pro-inflammatory pathways can cross talk with insulin signalling pathways via a number of mechanisms including (a) down-regulation of insulin signalling pathway proteins (e.g. GLUT4 and insulin receptor substrate (IRS)-1), (b) serine phosphorylation of IRS-1 blocking its tyrosine phosphorylation in response to insulin and (c) induction of cytokine signalling molecules that sterically hinder insulin signalling by blocking coupling of the insulin receptor to IRS-1. Long-chain (LC) n-3 PUFA regulate gene expression (a) through transcription factors such as PPAR and NF-κB and (b) via eicosanoid production, reducing pro-inflammatory cytokine production from many different cells including the macrophage. LC n-3 PUFA may therefore offer a useful anti-inflammatory strategy to decrease obesity-induced insulin resistance, which will be examined in the present review.
Multiplex-PCR assay for detection of Salmonella typhimurium and Salmonella enteritidis in poultry feedstuffs.

Multiplex polymerase chain reaction (M-PCR), the concurrent amplification of two or more polymerase chain reaction (PCR) products in the same reaction tube, can be applied to the rapid detection of pathogenic microorganisms in animal feedstuffs. Poultry feedstuffs has been implicated as important established source of infection to specific Salmonella serovars. Poultry feedstuffs can be an origin for transfer bacteria. In this study, 30 feedstuffs samples were collected from feed stocks of broiler chicken farms in Kermanshah province in the west of Iran. In order to isolate Salmonella, conventional cultural methods including pre-enrichment, enrichment and selective agar plating tests were performed. To confirm the identification of isolated colonies as Salmonella. and determining as typhimurium and Enteritidisserovars, a M-PCR assay, using three pairs of primers were employed, S141 and S139 for InvA gene, specific for the genus of Salmonella spp. Fli15 and Tym for FliC gene, specific for Typhimurium serovar and Sef167 and Sef478 for sefA gene, specific for Enteritidisserovar. M-PCR results indicated that 3.3% of samples were contaminated with Salmonella spp. and confirmed that all contaminated samples belong to Salmonella typhimurium serovar and no contamination with Salmonella enteritidis has been detected. In conclusion, it is recommended that M-PCR method can be used as a viable alternative to traditional cultural methods for detection of poultry feedstuffs contamination by Salmonella serovars.
Fast distributed consensus with Chebyshev polynomials

Global observation of the environment is a key component in sensor networks and multi-robot systems. Distributed consensus algorithms make all the nodes in the network to achieve a common perception by local interactions between direct neighbors. The convergence rate of these algorithms depends on the network connectivity, which is related to the second largest eigenvalue of the weighted adjacency matrix of the communication graph. When the connectivity is small, a large number of communication rounds is required to achieve the consensus. In this paper we present a new distributed consensus algorithm which uses the properties of Chebyshev polynomials to significantly increase the convergence rate. The algorithm is expressed in the form of a linear iteration and, at each step, the nodes only require to transmit their current state to their neighbors. The difference with respect to previous approaches is that our algorithm is based on a second order difference equation. We provide the analytical expression of the convergence rate and we study in which conditions it is faster than computing the powers of the weighted matrix. This improvement reduces the number of messages between nodes, saving both power and time to the networked system. We evaluate our algorithm in a simulated environment showing the benefits of our approach.
Collaborative Anti-jamming in Cognitive Radio Networks Using Minimax-Q Learning

Cognitive radio is an efficient technique for realization of dynamic spectrum access. Since in the cognitive radio network (CRN) environment, the secondary users (SUs) are susceptible to the random jammers, the security issue of the SU's channel access becomes crucial for the CRN framework. The rapidly varying spectrum dynamics of CRN along with the jammer's actions leads to challenging scenario. Stochastic zero-sum game and Markov decision process (MDP) are generally used to model the scenario concerned. To learn the channel dynamics and the jammer's strategy the SUs use reinforcement learning (RL) algorithms, like Minimax-Q learning. In this paper, we have proposed the multi-agent multi-band collaborative anti-jamming among the SUs to combat single jammer using the Minimax-Q learning algorithm. The SUs collaborate via sharing the policies or episodes. Here, we have shown that the sharing of the learned policies or episodes enhances the learning probability of SUs about the jammer's strategies but reward reduces as the cost of communication increases. Simulation results show improvement in learning probability of SU by using collaborative anti-jamming using Minimax-Q learning over single SU fighting the jammer scenario.
On ladder structures and linear phase conditions for bi-orthogonal filter banks

Some results for multidimensional m-band bi-orthogonal filter banks are presented. In the first part of the paper the authors introduce the ladder structure as a method for the design and implementation of the aforementioned filter banks. The second part of the paper focuses upon enforcing linear phase (LP) conditions in the context of ladder structures. The formulation of the LP conditions is done with the help of an algebraically oriented method for describing multidimensional filter banks. It is shown that in this notation, ladder structures allow a natural formulation of LP conditions. <<ETX>>
Tools for IndoWordNet Development

WordNet is an essential resource in Natural language processing. A lexical database like WordNet has a variety of practical applications like machine translation, information retrieval and many more. The creation of a comprehensive WordNet requires many hands and minds working collaboratively. In addition WordNet creation and maintenance demands creation of a wide range of software tools. All this is possible with help from some funding agency. Department of Information Technology, Ministry of Communications and Information Technology, Government of India, New Delhi is one such funding agency who has been a driving force behind the ongoing research work of WordNet development in India. Currently the IndoWordNet group in India is working towards the development of a multilingual WordNet which includes 16 Indian languages. Two more have been added recently. Many tools have been developed to assist in the WordNet development process. Based on the multilingual WordNet work being carried out by the IndoWordNet family need is felt for more tools for WordNet. This paper discusses the various existing WordNet tools from different dimensions. We also propose a tool to augment the building of IndoWordNet. The key features for this new tool being proposed are also covered.
A Geodesic Active Contour Framework for Finding Glass

This paper addresses the problem of finding objects made of glass (or other transparent materials) in images. Since the appearance of glass objects depends for the most part on what lies behind them, we propose to use binary criteria ("are these two regions made of the same material? ") rather than unary ones ("is this glass? ") to guide the segmentation process. Concretely, we combine two complementary measures of affinity between regions made of the same material and discrepancy between regions made of different ones into a single objective function, and use the geodesic active contour framework to minimize this function over pixel labels. The proposed approach has been implemented, and qualitative and quantitative experimental results are presented.
Almost the same stories: narrative patterns in EU treaty referendums

ABSTRACT The article analyses narratives that favoured or opposed European integration in three referendum debates held during the constitutional process of the 2000s: in France and the Netherlands in 2005 (Constitutional Treaty) as well as the first vote in Ireland on the Lisbon Treaty in 2008. In all debates examined, speakers used similar narratives of European integration. While most narrative patterns have transnational character, however, they are also remarkably country-specific. They depend on the specific cultural context and the social positions of the speakers articulating them.
Modelling Inflation Dynamics : A Critical Survey of Recent Research

In recent years, a broad academic consensus has arisen around the use of rational expectations sticky-price models to capture inflation dynamics. These models are seen as providing an empirically reasonable characterization of observed inflation behavior once suitable measures of the output gap are chosen; and, moreover, are perceived to be robust to the Lucas critique in a way that earlier econometric models of inflation are not. We review the principal conclusions of this literature concerning: 1) the ability of these models to fit the data; 2) the importance of rational forward-looking expectations in price setting; and 3) the appropriate measure of inflationary pressures. We argue that existing rational expectations sticky-price models fail to provide a useful empirical description of the inflation process, especially relative to traditional econometric Phillips curves of the sort commonly employed for policy analysis and forecasting. ∗jeremy.b. rudd@frb.gov. ∗∗karl.whelan@centralbank.ie. An earlier draft of this paper was prepared for the FRB/JMCB Conference “Quantitative Evidence on Price Determination. ” The views expressed are our own and do not necessarily reflect the views of the Board of Governors, the staff of the Federal Reserve System, or the Central Bank of Ireland. We thank Anne McGuinness for excellent research assistance. We are also grateful to our conference discussants, Laurence Ball and Bennett McCallum, for a number of useful comments.
A Non-Binary Associative Memory with Exponential Pattern Retrieval Capacity and Iterative Learning: Extended Results

We consider the problem of neural association for a network of non-binary neurons. Here, the task is to first memorize a set of patterns using a network of neurons whose states assume values from a finite number of integer levels. Later, the same network should be able to recall previously memorized patterns from their noisy versions. Prior work in this area consider storing a finite number of purely random patterns, and have shown that the pattern retrieval capacities (maximum number of patterns that can be memorized) scale only linearly with the number of neurons in the network. In our formulation of the problem, we concentrate on exploiting redundancy and internal structure of the patterns in order to improve the pattern retrieval capacity. Our first result shows that if the given patterns have a suitable linear-algebraic structure, i.e. comprise a sub-space of the set of all possible patterns, then the pattern retrieval capacity is in fact exponential in terms of the number of neurons. The second result extends the previous finding to cases where the patterns have weak minor components, i.e. the smallest eigenvalues of the correlation matrix tend toward zero. We will use these minor components (or the basis vectors of the pattern null space) to both increase the pattern retrieval capacity and error correction capabilities. An iterative algorithm is proposed for the learning phase, and two simple neural update algorithms are presented for the recall phase. Using analytical results and simulations, we show that the proposed methods can tolerate a fair amount of errors in the input while being able to memorize an exponentially large number of patterns.
Adolescent gambling in Great Britain

This article briefly overviews some of the latest research into adolescent gambling and looks at the recent 2005 Gambling Act in relation to adolescent gambling measures. The new Gambling Act came into force on 1 September 2007, and replaces existing law on gambling in England, Scotland and Wales. Based on the most recent findings and the fact that adolescents can still legally gamble on fruit machines in seaside arcades and family leisure centres, it is argued that adolescent gambling is still a cause for concern in Great Britain, particularly as problem gambling among adolescents is four to five times more prevalent than adult problem rates (i.e. 3.5% of adolescents compared to 0.6% of adults).
Multiple roots of estimating functions

Estimating functions can have multiple roots. In such cases, the statistician must choose among the roots to estimate the parameter. Standard asymptotic theory shows that in a wide variety of cases, there exists a unique consistent root, and that this root will lie asymptotically close to other consistent (possibly inefficient) estimators for the parameter. For this reason, attention has largely focused on the problem of selecting this root and determining its approximate asymptotic distribution. In this paper, however, we concentrate on the exact distribution of the roots as a random set. In particular, we propose the use of higher‐order root intensity functions as a tool for examining the properties of the roots and determining their most problematic features. The use of root intensity functions of first and second order is illustrated by application to the score function for the Cauchy location model.
CS 533 Computer Networks Term Project : Prefetching on WWW – its past and future

This paper summarizes the various research efforts on prefetching schemes in WWW. We try to classify and rate the previous methods and results on this topic and point out the potential works. We will proceed from several perspectives: questions addressed in the past, methods that have been used, advantages and limitations, comparison of different results, open questions, new changes in Internet environments, modifications needed in protocol or algorithm for successful prefetching, prefetching effects and future directions. keywords: prefetching, WWW, cache, latency.