Miracle-dz/UniFAR-FTUs huggingface dataset:
Dopaminergic Medication Modulates Learning from Feedback and Error-Related Negativity in Parkinson’s Disease: A Pilot Study

Dopamine systems mediate key aspects of reward learning. Parkinson’s disease (PD) represents a valuable model to study reward mechanisms because both the disease process and the anti-Parkinson medications influence dopamine neurotransmission. The aim of this pilot study was to investigate whether the level of levodopa differently modulates learning from positive and negative feedback and its electrophysiological correlate, the error related negativity (ERN), in PD. Ten PD patients and ten healthy participants performed a two-stage reinforcement learning task. In the Learning Phase, they had to learn the correct stimulus within a stimulus pair on the basis of a probabilistic positive or negative feedback. Three sets of stimulus pairs were used. In the Testing Phase, the participants were tested with novel combinations of the stimuli previously experienced to evaluate whether they learned more from positive or negative feedback. PD patients performed the task both ON- and OFF-levodopa in two separate sessions while they remained on stable therapy with dopamine agonists. The electroencephalogram (EEG) was recorded during the task. PD patients were less accurate in negative than positive learning both OFF- and ON-levodopa. In the OFF-levodopa state they were less accurate than controls in negative learning. PD patients had a smaller ERN amplitude OFF- than ON-levodopa only in negative learning. In the OFF-levodopa state they had a smaller ERN amplitude than controls in negative learning. We hypothesize that high tonic dopaminergic stimulation due to the dopamine agonist medication, combined to the low level of phasic dopamine due to the OFF-levodopa state, could prevent phasic “dopamine dips” indicated by the ERN needed for learning from negative feedback.
Performance of Tcp/ip over Abr 1

The Available Bit Rate (ABR) service has been developed to support data applications over Asynchronous Transfer Mode (ATM). It is hence interesting to study the performance of reliable data transport protocols like TCP over ABR. We study the eeect of running large unidirectional le transfer applications on TCP over ABR with an explicit rate algorithm (ERICA) implemented at the ATM switches. The study shows that it is possible to get maximum TCP throughput when there are enough buuers at the switches and the sources are rate-limited by ABR. However, when the number of buuers is smaller, there is a large reduction in throughput even though the Cell Loss Ratio (CLR) is very small and the sources are window-limited by TCP. The primary reason for the large throughput degradation is the TCP timer granularity (which aaects the timeout interval) and retransmission of duplicate packets which are discarded at the destination. We show that switch buuers should not be dimensioned based on the ABR Source parameter Transient Buuer Exposure (TBE). When ABR capacity is varied, we nd that CLR exhibits high variance and is not a good indicator of TCP performance. Further, larger buuers and a larger number of TCP sources increase TCP throughput. Even when buuers are small, dropping of End of Message (EOM) cells should be avoided. This avoids merging of packets at the destination AAL5 and improves fairness.
Implementation of the DKSS Algorithm for Multiplication of Large Numbers

The Schönhage-Strassen algorithm (SSA) is the de-facto standard for multiplication of large integers. For N-bit numbers it has a time bound of O(N log N log log N). De, Kurur, Saha and Saptharishi (DKSS) presented an asymptotically faster algorithm with a better time bound of N• log N• 2O(log* N). For this paper, a simplified DKSS multiplication was implemented. Assuming a sensible upper limit on the input size, some required constants could be precomputed. This allowed to simplify the algorithm to save some complexity and run-time. Still, run-time is about 30 times larger than SSA, while memory requirements are about 2.3 times higher than SSA. A possible crossover point is estimated to be out of reach even if we utilized the whole universe for computer memory.
Hourglass stabilization and the virtual element method

In this paper, we establish the connections between the virtual element method (VEM) and the hourglass control techniques that have been developed since the early 1980s to stabilize underintegrated C0 Lagrange finite element methods. In the VEM, the bilinear form is decomposed into two parts: a consistent term that reproduces a given polynomial space and a correction term that provides stability. The essential ingredients of C0 ‐continuous VEMs on polygonal and polyhedral meshes are described, which reveals that the variational approach adopted in the VEM affords a generalized and robust means to stabilize underintegrated finite elements. We focus on the heat conduction (Poisson) equation and present a virtual element approach for the isoparametric four‐node quadrilateral and eight‐node hexahedral elements. In addition, we show quantitative comparisons of the consistency and stabilization matrices in the VEM with those in the hourglass control method of Belytschko and coworkers. Numerical examples in two and three dimensions are presented for different stabilization parameters, which reveals that the method satisfies the patch test and delivers optimal rates of convergence in the L2 norm and the H1 seminorm for Poisson problems on quadrilateral, hexahedral, and arbitrary polygonal meshes. Copyright © 2015 John Wiley & Sons, Ltd.
Emotion related structures in large image databases

We introduce two large databases consisting of 750 000 and 1.2 million thumbnail-sized images, labeled with emotion-related keywords. The smaller database consists of images from Matton Images, an image provider. The larger database consists of web images that were indexed by the crawler of the image search engine Picsearch. The images in the Picsearch database belong to one of 98 emotion related categories and contain meta-data in the form of secondary keywords, the originating website and some view statistics. We use two psycho-physics related feature vectors based on the emotional impact of color combinations, the standard RGB-histogram and two SIFT-related descriptors to characterize the visual properties of the images. These features are then used in two-class classification experiments to explore the discrimination properties of emotion-related categories. The clustering software and the classifiers are available in the public domain, and the same standard configurations are used in all experiments. Our findings show that for emotional categories, descriptors based on global image statistics (global histograms) perform better than local image descriptors (bag-of-words models). This indicates that content-based indexing and retrieval using emotion-based approaches are fundamentally different from the dominant object-recognition based approaches for which SIFT-related features are the standard descriptors.
Trajectories in religious development among African American young adults

Religiosity and spirituality have been an important component within the African American culture throughout U.S. history. Previous research has documented the importance of religion to African Americans, particularly in terms of coping with the negative experiences they face in the U.S. While many studies have focused on the positive impact of religiosity and spirituality on African American’s mental health, fewer studies have addressed change in African American’s religiosity over time, especially during the period when they transition from adolescence into young adulthood. Adolescence is a crucial developmental transition and can disclose a tremendous amount of knowledge about religious socialization and change in the life course. Utilizing data from the Family and Community Health Study (FACHS), a longitudinal study that examines African American families, this study extends the current state of the literature by examining and identifying multiple trajectories in African American adolescents’ religious development. Overall, there was a significant decline in religiosity during both adolescence and young adulthood. There was also individual variability in the change in religiosity during both developmental periods. Parental religiosity and deviant peer affiliation continued to have a significant impact on African American religiosity during both adolescence and young adulthood. Other sociocultural factors that predicted longterm growth, decline, or stability in their religiosity were also examined. Finally, implications of these findings as well as future directions for research on these relationships are discussed.
Evolutionary-fuzzy prediction for strategic QoS routing in broadband networks

We present an application of evolutionary-fuzzy prediction in inter-domain routing of broadband network connections with quality of services requirements in the case of an integrated ATM and SDH networking architecture. The higher-layer nature of inter-domain routing requires us to review the whole routing process in order to maximize performance at low decision making cost, a clear case for fuzzy-set logic based algorithms. In order to probabilistically avoid shortage of resources, besides effectively computing the feasible paths for an incoming connection request, under uncertainty, these paths should be compared in terms of the negative impact in resource supply which may be caused by other connections. To do this, besides enriching the protocol with vectors of per-class shadow cost metrics, quantized using fuzzy-set theory, we used a subset-interactive autoregressive time-series predictor which is based on fuzzy measures to evaluate path costs under supply metrics that change over time. Moreover, as the connections we are dealing with are semi-permanent, we must take into account the connections lifetime. The complete system is part of the MISA (Management of Integrated SDH and ATM) network architecture and will be implemented and evaluated in the global broadband connection service of the pan-European test-bed.
Using the uncertainty handling CMA-ES for finding robust optima

Algorithms that search for robust optima often evaluate the effective fitness (robust fitness) based on stochastic approximation schemes. In this setting, finding robust optima can be recast as an optimization problem with an/a uncertain/noisy objective function. This paper studies whether state-of-the-art uncertainty handling techniques, proposed in the context of optimizing noisy objective functions, can be applied for finding robust optima. In this paper, the UH-CMA-ES is modified to handle approximations of the effective fitness. This modified approach, named RO-UH-CMA-ES, is evaluated empirically and compared to other schemes that aim to find robust optima. The experiments on multiple benchmark problems show that the RO-UH-CMA-ES yields comparable results for multi-modal problems and it outperforms other schemes on unimodal problems.
Distributed frequency resource control for intercell interference control in heterogeneous networks

In heterogeneous cellular networks (HCN) which consists of macrocells and numerous picocells, efficient interference management schemes between macrocells and picocells are so crucial to the overall system performance. We propose a dynamic cooperative silencing control (DCS) scheme for intercell interference control (ICIC). It is a low-complex, low-feedback and distributed algorithm using only strongly interfered neighboring users' information. The system simulation shows that the system performance and in particular the cell-edge throughput is significantly increased with the proposed silencing scheme. It offers 420% and 190% higher average spectral efficiency and edge-user spectral efficiency in compared to macrocell only case, respectively.
Quasi ‐ Experimental Evaluation Study of the Serious Game “ Hazard Recognition ” Simulation in the Oil & Gas Industry

Within the oil and gas community Shell is the leading company when it comes to safety, as a result for this ongoing quest to better safety Shell laid down a request at Delft University of Technology in 2009 for the development of the serious game Hazard Recognition, adding a new training literacy to their existing methods. Because Hazard Recognition is an unique game, the learning efficacy is thus far not understood. In order to understand the learning efficacy of Hazard Recognition, this study researched the ability of the game to achieve its learning efficacy and the possible underlying factors with its players. By the use of an quasi experimental set up, within the Comprehensive Evaluation Framework of Mayer et al. (2012) data has been gathered within 4 workshops within Shell, Accenture and TU Delft with 90 participants. It can be concluded that Hazard Recognition improves players' skills and knowledge on hazard detection and assessment, and it facilitates significant learning efficacy in this topic. 1 1 INTRODUCTION Hazards are an integral part of the environment oil & gas professionals operate in every day. This prominence has made the oil & gas industry a branch with a high acknowledgement of danger, consequence and costly risk. Because of this acknowledgement, the industry has put much effort in building safety institutions throughout the whole industry to make the workplace safe. Within the oil & gas community Shell is the leading company when it comes to casualty and accident prevention (Shell, 2013). As result of this constant quest for better safety training and procedures, Shell has laid down a request with Delft University of Technology in 2009 for the development of the serious gaming simulation Hazard Recognition, adding a new literacy to their existing set of training methods (I. S. Mayer et al., 2009). Hazard Recognition's learning objective is " to identify, assess and manage site hazards effectively ". These objectives are to be achieved by simulating Shell's training worksite Simwell in Schoonebeek in a 3D virtual environment and exposing players to worksite hazards (Shell, 2009). Serious games are widespread in business environments and their use has grown dramatically, not only because many companies' fascination for the use of serious As part of this growing interest there have been developed various simulations that share similarities with Hazard Recognition (HARC & Haut, n. Hazard Recognition cannot be regarded as a novelty in either its technique, subject …
Wireless sensor networks based on compressed sensing

The data collected through high densely distributed wireless sensor networks is immense. The asymmetry between the data acquisition and information processing makes a great challenge to the restriction of energy and computation consumption of the sensor nodes, and it limits the application of wireless sensor networks. However, the recent works show that compressed sensing can break through this limitation of asymmetry. Compressed sensing is an emerging theory that is based on the fact that a signal can be recovered through a relatively small number of random projections which contain most of its salient information. In this paper, we introduce the background of compressive sensing, and then applications of compressed sensing in wireless sensor networks are presented.
Teaching Naked Techniques: Leveraging Research on Learning to Improve the Effectiveness of Teaching

• Pedagogical research and the learning sciences suggest strategies for designing classes and courses in ways that can improve student learning. • Teaching Naked Techniques provides a step-by step guide to thinking of teaching as a design problem. • The distinct value that faculty members add to instruction is in designing experiences that motivate students to do the work. • TNT also provides a how, where, when, and why of educational technology. Teaching has never mattered more. There is a well-established connection between student success and the choices faculty make regarding their teaching (Sullivan & McConnell, 2017). With student success re-emerging in discussions of higher education’s purpose, we need to heed this connection. Fortunately, there has never been more science to guide us as we work to improve student learning. A convergence of educational research, brain, behavioral and neuro-science, and cognitive psychology have given us tremendous insight into how students learn. Instructional practice should be built on a research-based foundation and should evolve as a result of new insights. In addition, technology has moved us from a world in which content was relatively scarce, but generally available from reliable sources like encyclopedias and books, to the Internet, where content is abundant but often unreliable. Second, technology continues to change some of our fundamental assumptions around social proximity. Today’s students have profoundly different expectations for a range of concepts that relate to teaching and learning, and many of these changes are a result of their relationships with technology. Teaching Naked: How Moving Technology Out of Your College Classroom Will Improve Student Learning (Bowen, 2012) focused on the implications of these technological changes for higher education. It suggested that while content will continue to be an essential feature of teaching and thinking, technology has also created a world where more and more learning will happen after graduation. If we become too focused on “professing” content, then our value will continue to decrease in a world of increasing access to content. The Internet contains a much broader selection of lectures, demonstrations, animations, and examples, on more Te aching naked Techniques:
Power-Delay Efficient Overlap-Based Charge-Sharing Free Pseudo-Dynamic D Flip-Flops

Clock overlap is an important issue in the design of sequential circuits and is typically avoided. In this paper we present a new power-efficient edge-triggered D flip-flop in which we have benefited from the overlap period of the clock signal. The design procedure of the proposed D flip-flop is presented. The performance of the flip-flop is compared with several state of the art flip-flops in a shift register and a pipeline adder in 0.18¿m CMOS technology. The proposed flip-flop has the lowest power-delay-product and consumes less area compared to others.
Teacher personality: a review of psychological research and guidelines for a more comprehensive theory in educational psychology

ABSTRACT The current review aims to demonstrate that findings from personality theories can help educational psychology craft a more thorough explanation of the role of teacher personality in the educational process. This topic seemed to have been inadvertently omitted. The following five groups of studies in psychology and related fields (classified based on their research objectives) are critically analysed: studies of teacher typologies, studies of teachers’ desirable and undesirable features, studies of teachers’ professional behaviours and their influence on students, studies of teachers’ professional identity and studies of teacher personality within the framework of personality theories (particularly within the Five-Factor Model of personality). Arguments in support of personality theories, and methodological dilemmas in assessing teachers’ personalities involving data collection and research design are also discussed. The article suggests that those findings on teachers’ personalities acquired in the context of personality theories might currently serve as the best starting point for a more comprehensive psychological theory of teacher personality in educational psychology – particularly when considered alongside current knowledge regarding small social group management and aspects of learning and development theories. Thus, the current review might serve as the basis for a separate field within school psychology focused on the psychological aspects of the pedagogical profession.
The Near-Infrared Camera and Multi-Object Spectrometer Ultra Deep Field: Observations, Data Reduction, and Galaxy Photometry

This paper describes the observations and data reduction techniques for the version 2.0 images and catalog of the Near-Infrared Camera and Multi-Object Spectrometer Ultra Deep Field (NICMOS UDF) Treasury program. All sources discussed in this paper are based on detections in the combined NICMOS F110W and F160W bands only. The NICMOS images are drizzled to 0. ″09 pixel-1 and aligned to the Advanced Camera for Surveys UDF F850LP image, which was rebinned to the same pixel scale. These form the NICMOS version 2.0 UDF images. The catalog sources are chosen with a conservative detection limit to avoid the inclusion of numerous spurious sources. The catalog contains 1293 objects in the 144″ × 144″ NICMOS subfield of the UDF. The 5 σ signal-to-noise ratio level is an average 0. ″6 diameter aperture AB magnitude of 27.7 at 1.1 and 1.6 μm. The catalog sources, listed in order of right ascension, satisfy a minimum signal-to-noise ratio criterion of 1.4 σ in at least seven contiguous pixels of the combined F110W and F160W image.
A statistical framework for protein quantitation in bottom-up MS-based proteomics

MOTIVATION Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. RESULTS We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. AVAILABILITY The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).
Impact in participatory health research: what can we learn from research on participatory evaluation?

Abstract Participatory Health Research is a collective term adopted globally for participatory action research in a health context. As an approach to research, it challenges current ways used within the health sciences to measure research impact as research, learning and action are integrated throughout the research process and dependent on context and participation. The literature on participatory evaluation is explored to see if it can offer some insights into how best to articulate impact. Similar debates are taking place particularly concerning the relationship between degree of participation and impact, how impact should be defined and the role of wider social forces on the evaluation process. A focus on core values, such as social justice, differentiates transformative evaluation from more pragmatic participatory evaluation, but this is poorly conceptualised in evaluation models.
How can acoustic-to-articulatory maps be constrained?

The objective of the presentation is to examine issues in constraining acoustic-to-articulatory maps by means of facial data and other a priori knowledge regarding speech production. Constraints that are considered are the insertion of data on lip opening, spread and protrusion, as well as other facial data together with constraints on the vocal tract length. A priori knowledge that has been taken into account concerns the deformation and speed of deformation of the vocal tract as well as phonetic rules regarding vowel-typical tract shapes. Inverse maps that have been tested are formant-to-area and formant-to-parametric sagittal profile maps as well as audio/visual-to-electromagnetic coil trajectory maps. The results obtained while mapping audio-only data compared to audio combined with other data are discussed.