Miracle-dz/UniFAR-FTUs huggingface dataset:
Thought Games : Advancements in Gaming with a Brain-Computer Interface from a Game Design Perspective

In this paper we look at modern advancements in BrainComputer Interface research in order to reflect the possibilities at hand for using the brain as an input method for modern video games. This paper used literature on game design in order to identify core requirements for creating ‘good’ games. Existing BCI games described in literature were then compared to these requirements to determine to what extend the requirements are currently being satisfied. This revealed that a favourable direction for achieving real-time gaming with a BCI is to focus on games that utilise the controls as means to an end rather than as the goal itself. Furthermore a balance between players feeling in control and allowing for mistakes to be made – the so called paradox of control – should become a primary focus in research related to BCI gaming.
Wind profiles generated with a scanning cloud radar

Since summer 2005 IMK operates the novel cloud radar MIRA36-S. One special feature of this 35.5 GHz radar is its scanning capability, which allows antenna motion between 0◦ and 360◦ in azimuth and between -45 ◦ and +45◦ relative to the local zenith. One benefit of this feature is the possibility to derive profiles of the wind vector from PPI scans. From the algorithmic point of view the procedure is identical to that for precipitation radars. However, some differences between cloud and precipitation radars require some further investigations to get the best results:
Adaptive neuro-fuzzy inference system for speckle noise reduction in SAR images

An adaptive neuro-fuzzy inference system (ANFIS) based method is proposed for speckle noise reduction in synthetic aperture radar (SAR) images. Before using active RADAR (radio detection and ranging) and SAR imageries, the very first step is to reduce the effect of speckle noise. Reduction of speckle noise is one of the most important processes to increase the quality of radar coherent images. Filtering is the common method which is used to reduce the speckle noise. For this purpose, two ANFISs are trained and outputs of these systems are converted to one output through a mean calculator in this work. Performance of the proposed method is compared with performances of state-of-the-art methods in the literature for speckle noise reduction. Results are presented by filtered images and a table.
The Spiros imaging software for the Integral SPI spectrometer

A key tool in the package of software available for the analysis of data from the SPI spectrometer of Integral is the SPIROS system developed at the University of Birmingham. Although intended primarily for the analysis of point sources and for the extraction of spectral information, SPIROS has many additional capabilities. The software is described with particular emphasis on the most widely used modes of operation and on the relationship to other imaging and data reduction techniques.
A statistical proxy for sulphuric acid concentration

Abstract. Gaseous sulphuric acid is a key precursor for new particle formation in the atmosphere. Previous experimental studies have confirmed a strong correlation between the number concentrations of freshly formed particles and the ambient concentrations of sulphuric acid. This study evaluates a body of experimental gas phase sulphuric acid concentrations, as measured by Chemical Ionization Mass Spectrometry (CIMS) during six intensive measurement campaigns and one long-term observational period. The campaign datasets were measured in Hyytiala, Finland, in 2003 and 2007, in San Pietro Capofiume, Italy, in 2009, in Melpitz, Germany, in 2008, in Atlanta, Georgia, USA, in 2002, and in Niwot Ridge, Colorado, USA, in 2007. The long term data were obtained in Hohenpeissenberg, Germany, during 1998 to 2000. The measured time series were used to construct proximity measures ("proxies") for sulphuric acid concentration by using statistical analysis methods. The objective of this study is to find a proxy for sulfuric acid that is valid in as many different atmospheric environments as possible. Our most accurate and universal formulation of the sulphuric acid concentration proxy uses global solar radiation, SO 2 concentration, condensation sink and relative humidity as predictor variables, yielding a correlation measure ( R ) of 0.87 between observed concentration and the proxy predictions. Interestingly, the role of the condensation sink in the proxy was only minor, since similarly accurate proxies could be constructed with global solar radiation and SO 2 concentration alone. This could be attributed to SO 2 being an indicator for anthropogenic pollution, including particulate and gaseous emissions which represent sinks for the OH radical that, in turn, is needed for the formation of sulphuric acid.
Structural and sequencing analysis of local target DNA recognition by MLV integrase

Target-site selection by retroviral integrase (IN) proteins profoundly affects viral pathogenesis. We describe the solution nuclear magnetic resonance structure of the Moloney murine leukemia virus IN (M-MLV) C-terminal domain (CTD) and a structural homology model of the catalytic core domain (CCD). In solution, the isolated MLV IN CTD adopts an SH3 domain fold flanked by a C-terminal unstructured tail. We generated a concordant MLV IN CCD structural model using SWISS-MODEL, MMM-tree and I-TASSER. Using the X-ray crystal structure of the prototype foamy virus IN target capture complex together with our MLV domain structures, residues within the CCD α2 helical region and the CTD β1-β2 loop were predicted to bind target DNA. The role of these residues was analyzed in vivo through point mutants and motif interchanges. Viable viruses with substitutions at the IN CCD α2 helical region and the CTD β1-β2 loop were tested for effects on integration target site selection. Next-generation sequencing and analysis of integration target sequences indicate that the CCD α2 helical region, in particular P187, interacts with the sequences distal to the scissile bonds whereas the CTD β1-β2 loop binds to residues proximal to it. These findings validate our structural model and disclose IN-DNA interactions relevant to target site selection.
A NEW MOTOR SPEED MEASUREMENT ALGORITHM BASED ON ACCURATE SLOT HARMONIC SPECTRAL ANALYSIS

In this paper a new induction motor speed method based on Chirp-Z Transform and zero padding of the supply current is presented. The algorithm has been implemented on a microprocessor. A virtual instrument has been built to control and to monitor on line motor speed. The motor speed method has been tested on a 7.5 kW induction motor driven by a pulse width modulation voltage source inverter for different load and supply frequency conditions. The proposed method has been compared to a FFT based one implemented on the same microprocessor. Thanks to a shorter sampling time window errors related to not stationary current signal are reduced. Then the achieved goal is an increased accuracy due to shorter sampling time window and better spectral resolution. Moreover the processing time has been reduced increasing algorithm speed response.
Distributed implementation of linear network operators using graph filters

A signal in a network (graph) can be defined as a vector whose elements represent the value of a given magnitude at the different nodes. A linear network (graph) operator is then a linear transformation whose input and output are graph signals. This paper investigates how to use graph filters to implement generic network linear operators in a distributed manner, so that nodes only need to exchange a finite number of messages with their neighbors. The schemes are designed within the framework of shift-invariant graph filters, which are polynomials of the so-called graph-shift operator. The graph-shift operator is a matrix that accounts for the topology of the network, and the filter coefficients - which are the same across nodes - are the coefficients of that polynomial. First, we identify conditions under which the linear operator can be computed exactly. Then, we provide approximate designs for cases where perfect implementation is unfeasible. Setups where each node is allowed to use a different set of filter coefficients are briefly discussed. Finally, we apply this framework to the problem of finite-time consensus and analyze the graph-filter approximation performance for general linear graph operators.
Classification with Gaussians and Convex Loss

This paper considers binary classification algorithms generated from Tikhonov regularization schemes associated with general convex loss functions and varying Gaussian kernels. Our main goal is to provide fast convergence rates for the excess misclassification error. Allowing varying Gaussian kernels in the algorithms improves learning rates measured by regularization error and sample error. Special structures of Gaussian kernels enable us to construct, by a nice approximation scheme with a Fourier analysis technique, uniformly bounded regularizing functions achieving polynomial decays of the regularization error under a Sobolev smoothness condition. The sample error is estimated by using a projection operator and a tight bound for the covering numbers of reproducing kernel Hilbert spaces generated by Gaussian kernels. The convexity of the general loss function plays a very important role in our analysis.
Isotone Equilibrium in Games of Incomplete Information

An isotone pure strategy equilibrium exists in any game of incomplete information in which (1) each player i's action set is a finite sublattice of multi-dimensional Euclidean space, (2) types are multidimensional and atomless, and each player's interim expected payoff function satisfies two "non-primitive conditions" whenever others adopt isotone pure strategies: (3) single-crossing in own action and type and (4) quasisupermodularity in own action. Similarly, given that (134) and (2') types are multi-dimensional (with atoms) an isotone mixed strategy equilibrium exists. Conditions (34) are satisfied in supermodular and log-supermodular games given affiliated types, and in games with independent types in which each player's ex post payoff satisfies (a) supermodularity in own action and (b) non-decreasing differences in own action and type. These results also extend to games with a continuum action space when each player's ex post payoff is also continuous in his and others' actions.
Doxorubicin and NRG-1/erbB4-Deficiency Affect Gene Expression Profile: Involving Protein Homeostasis in Mouse

The accumulating evidence demonstrates the essential role of neuregulin-1 signaling in the adult heart, and, moreover, indicates that an impaired neuregulin signaling exacerbates the doxorubicin-mediated cardiac toxicity. Despite this strong data, the specific cardiomyocyte targets of the active erbB2/erbB4 heterodimer remain unknown. In this paper, we examined pathways involved in cardiomyocyte damage as a result of the cardiac sensitization to anthracycline toxicity in the ventricular muscle-specific erbB4 knockout mouse. We performed morphological analyses to evaluate the ventricular remodeling and employed a cDNA microarray to assess the characteristic gene expression profile, verified data by real-time RT-PCR, and then grouped into functional categories and pathways. We confirm the upregulation of genes related to the classical signature of a hypertrophic response, implicating an erbB2-dependent mechanism in doxorubicin-treated erbB4-KO hearts. Our results indicate the remarkable downregulation of IGF-I/PI-3′ kinase pathway and extends our current knowledge by uncovering an altered ubiquitin-proteasome system leading to cardiomyocyte autophagic vacuolization.
L-isoleucine-supplemented Oral Rehydration Solution in the Treatment of Acute Diarrhoea in Children: A Randomized Controlled Trial

Antimicrobial peptides represent an important component of the innate immune defenses of living organisms, including humans. They are broad-spectrum surface-acting agents secreted by the epithelial cells of the body in response to infection. Recently, L-isoleucine and its analogues have been found to induce antimicrobial peptides. The objectives of the study were to examine if addition of L-isoleucine to oral rehydration salts (ORS) solution would reduce stool output and/or duration of acute diarrhoea in children and induce antimicrobial peptides in intestine. This double-blind randomized controlled trial was conducted at the Dhaka Hospital of ICDDR,B. Fifty male children, aged 6-36 months, with acute diarrhoea and some dehydration, attending the hospital, were included in the study. Twenty-five children received L-isoleucine (2 g/L)-added ORS (study), and 25 received ORS without L-isoleucine (control). Stool weight, ORS intake, and duration of diarrhoea were the primary outcomes. There was a trend in reduction in mean±standard deviation (SD) daily stool output (g) of children in the L-isoleucine group from day 2 but it was significant on day 3 (388±261 vs 653±446; the difference between mean [95% confidence interval (CI) (-)265 (−509, −20); p=0.035]. Although the cumulative stool output from day 1 to day 3 reduced by 26% in the isoleucine group, it was not significant. Also, there was a trend in reduction in the mean±SD intake of ORS solution (mL) in the L-isoleucine group but it was significant only on day 1 (410±169 vs 564±301), the difference between mean (95% CI) (-)154 (-288, −18); p=0.04. The duration (hours) of diarrhoea was similar in both the groups. A gradual increase in stool concentrations of ß-defensin 2 and 3 was noted but they were not significantly different between the groups. L-isoleucine-supplemented ORS might be beneficial in reducing stool output and ORS intake in children with acute watery diarrhoea. A further study is warranted to substantiate the therapeutic effect of L-isoleucine.
Multivariate GARCH models : Inference and Evaluation

This paper presents a preliminary evaluation, on exchange rates data, of the main multivariate GARCH models in the literature. It shows how the Dynamic Conditional Correlation model of Engle(2001) outperforms the other models and sets to use it in the estimation of US-Bond data. Results with respect to the latter are controversial: the model proved to be extremely hard to estimate but the non-converged estimates seemed to allow the model to fit the data quite accurately. I also suggest how to carry out the estimation of a large scale GARCH: by a modified version of the DCC and by proposing the H-GARCH and the very fast estimation methodology associated with it. Empirical results w.r. t. the H-GARCH suggest that smoothed realizations are a better proxy, for the realization of the variance covariance matrix, than the simple outer-product of the residuals.
MRI Brain Image Enhancement Using Filtering Techniques

Magnetic Resonance Image is one of the best technologies currently being used for diagnosing brain cancer at advanced stages. This paper proposes a novel approach for the MRI image enhancement, which is based on the Modified Tracking Algorithm, Histogram Equalization and Center Weighted Median (CWM) filter. This method consists of two approaches. The first approach is applying the modified tracking algorithm to remove the film artifacts, labels and skull region and then applying the Histogram Equalization and Center Weighted Median (CWM) filter techniques separately to enhance the images. Brain tumor is one of the major causes for the increase in Mortality among people. A tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. This year 2012, an estimated 22,910 adults (12,630 men and 10,280 women) in the United States will be diagnosed with primary malignant tumors of the brain and spinal cord. It is estimated that 13,700 adults (7,720 men and 5,980 women) will die from this disease this year. Brain tumors are the tenth most common cause of cancer death in women. About 4,000 children and teens will be diagnosed with a brain or central nervous system tumor this year. More than half of these are in children younger than 15. About 20% to 40% of patients with other types of cancer will have it spread to the brain. The most common primary cancers that spread to the brain are lung, breast, unknown primary, melanoma, and colon cancer (10). The tumor is divided in to two categories: Primary brain tumor and secondary tumor. A primary malignant brain tumor is one that originates in the brain itself although these tumors often shed cancerous cells to other sites in the central nervous system and spread to other parts of the body. A secondary or metastatic brain tumor occurs when cancer cells spread to the brain from a primary cancer in another part of the body. Detection of brain tumor requires high-resolution brain MRI. Most Medical Imaging Studies and detection conducted using MRI, Positron Emission Tomography (PET) and Computed tomography (CT) Scan. Now a days MRI systems are very important in medical image analysis. The MRI image shows the clear distinction between the tissues, bones and fluid, so it makes easy to distinguish the tumor part from the image. In order to find the tumor part efficiently the MRI image should be enhanced properly.
Building Efficient Framework on Visual Object Recognition Project Proposal

The problem of recognizing generic object categories is one of most active areas in computer vision and pattern recognition. The problem lies in the center of visual object recognition challenge. The task is trying to automatically recognition genetic object in natural scene. The problem statement: It views whole image as one item, aim at extracting the most impressive Region of Interests (ROI) to be classified. It is challenging due to: large data set of various numbers of classes, even under the same class, the shape and appearance may vary at different levels, in addition, real world images naturally contain irrelevant background, and object occlusions and large viewpoint changes may also result in the obstacle. How to handle this problem efficiently and obtain promising recognition rate is still in progress, and the improvement on this work will make contribution to the fundamental computer vision applications.
A practical scheme for non-interactive verifiable secret sharing

This paper presents an extremely efficient, non-interactive protocol for verifiable secret sharing. Verifiable secret sharing (VSS) is a way of bequeathing information to a set of processors such that a quorum of processors is needed to access the information. VSS is a fundamental tool of cryptography and distributed computing. Seemingly difficult problems such as secret bidding, fair voting, leader election, and flipping a fair coin have simple one-round reductions to VSS. There is a constant-round reduction from Byzantine Agreement to non-interactive VSS. Non-interactive VSS provides asynchronous networks with a constant-round simulation of simultaneous broadcast networks whenever even a bare majority of processors are good. VSS is constantly repeated in the simulation of fault-free protocols by faulty systems. As verifiable secret sharing is a bottleneck for so many results, it is essential to find efficient solutions.
Polarization diversity for overlapping cellular networks

This paper investigates the impact of the polarization on the link probability of transmission and the throughput in the context of the cellular network architectures. A channel link model is proposed in the speciﬁc case of dual-polarized, Rayleigh-faded communication links. The per-node throughput is analyzed and the gain achieved by using two distinct polarization modes is discussed. It appears that, for uniform random placement of the nodes in a scenario where two cellular networks are deployed on a same location, the dual-polarized channel is an eﬃcient diversity technique. Dans ce papier, nous nous int´eressons `a l’´evaluation du d´ebit d’un r´eseau cellulaire bi-polaire. Aﬁn de d´ecrire de mani`ere g´en´eralis´ee l’impact de la polarisation sur les performances d’un r´eseau, nous d´erivons une expression compl`ete de la probabilit´e de transmission sur un lien donn´e, dans le cas d’une transmission bi-polaire et sujette `a un ´evanouissement de Rayleigh. Le d´ebit sur un lien donn´e est analys´e et le gain obtenu en utilisant deux modes de polarisation distincts mis en ´evidence. Nos r´esultats montrent que, pour un placement r´egulier des noeuds dans un sc´enario o`u deux r´eseaux cellulaires sont co-localis´es, un canal de communication bi-polaire oﬀre une diversit´e importante.
A station-based concept for teaching the neurological examination: A prospective quasi-experimental study

Background: The neurological examination is considered to be complex and contributes to the phenomenon of “neurophobia”. It is traditionally taught in small groups by residents (“traditional concept”), making the learning success partially dependent on the resident’s level of clinical training, didactic education and personal motivation. Aim of this study was to examine the effects of a newly developed concept (“station concept”) for teaching the neurological examination on achieving an improved and more equal transfer of knowledge and practical skills. Methods: A prospective quasi-experimental design was used to compare the traditional concept with the newly developed station concept, in which the teaching content was divided in eight subdivisions (stations) with one resident being assigned to one station. The primary endpoints of the study were the differences in students’ self-assessments of learning success in the different subdomains of the neurological examination, and secondary analyses focused on evaluation results of students and residents. Results: 144 students and 28 residents participated in the traditional concept (summer semester 2012) and 151 students and 28 residents in the station concept (winter semester 2012/13). In the station-concept students’ self-assessment significantly improved in the domains “Motor System”, “Coordination” and “Mental Status” compared to the traditional concept. Students’ evaluation showed significant improvement in five out of eight points. Fifty percent of residents rated the new approach superior to the traditional approach, ten percent as inferior. Conclusion: The station concept improved students’ self-assessed learning success as well as evaluation results while simultaneously achieving high acceptance in residents.
Mining rare event classes in noisy EEG by over sampling techniques

Mining is processing data to obtain interesting pattern or knowledge. Noisy EEG can be received on some abnormal state of brain activities. These signals can be logged in data sheets and the samples are taken to identify the rare events. The sampling technique here we used is SMOTE (Synthetic Minority Over-sampling Technique). An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of “normal” patterns with only a small percentage of “abnormal” or “interesting” patterns. It is also the case that the cost of misclassifying an abnormal (interesting) pattern as a normal pattern is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class.
On verifying game designs and playing strategies using reinforcement learning

In this paper we elaborate on the application of reinforcement learning to the design of a new strategy game. We deal with playability and learning issues, attempting to use intelligently generated self-playing sequences to determine playability of various initial board configurations. The machine's a priori knowledge about the game is restricted to the rules only, so, the initially encouraging and intuitive results suggest that this design verification strategy may be useful to a board range of design problems.