Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Supervised Clustering Algorithm Based on Min-Max Modular Network with Gaussian-Zero-Crossing Functions

In this paper, we show that the shape, size and location of the receptive field around each instance are different and decided by the distribution of training data in a min-max modular network with Gaussian-zero-crossing functions. Based on this property, we propose a new supervised clustering algorithm which has the following features: First, the incremental clustering ability, which means the number of clusters need not to be predefined, it can grow up automatically, also, the training data need not to be processed iteratively; Second, attaching more importance to border instances than non-border instances, which guarantees the good generalization performance and training data reduction ratio; Third, outlier removal ability, which removes noise instances from training data; Last, cluster combination ability, which reduces the number of clusters further. Experiments on an artificial problem and several real-world applications demonstrate these attractive features of our new clustering algorithm.
FuRIA: An Inverse Solution Based Feature Extraction Algorithm Using Fuzzy Set Theory for Brain–Computer Interfaces

This paper presents FuRIA, a trainable feature extraction algorithm for noninvasive brain-computer interfaces (BCI). FuRIA is based on inverse solutions and on the new concepts of fuzzy region of interest (ROI) and fuzzy frequency band. FuRIA can automatically identify the relevant ROI and frequency bands for the discrimination of mental states, even for multiclass BCI. Once identified, the activity in these ROI and frequency bands can be used as features for any classifier. The evaluations of FuRIA showed that the extracted features were interpretable and can lead to high classification accuracies.
Cloud-user security based on RSA and MD5 algorithm for resource attestation and sharing in java environment

The increased degree of connectivity and the increasing amount of data has led many providers and in particular data centers to employ larger infrastructures with dynamic load and access balancing. This lead to the demand of cloud computing. But there are some security concerns when we handle and share data in the cloud computing environment. In this paper we propose a new cloud computing environment where we approach a trusted cloud environment which is controlled by both the client and the cloud environment admin. Our approach is mainly divided into two parts. First part is controlled by the normal user which gets permission by the cloud environment for performing operation and for loading data. Second part shows a secure trusted computing for the cloud, if the admin of the cloud want to read and update the data then it take permission from the client environment. This provides a way to hide the data and normal user and can protect their data from the cloud provider. This provides a two way security protocol which helps both the cloud and the normal user. For the above concept we apply RSA and MD 5 algorithm. When the cloud user upload the data in the cloud environment, the data is uploaded in encrypted form using RSA algorithm and the cloud admin can decrypt using their own private key. For updating the data in the cloud environment admin request the user for a secure key. Cloud user sends a secure key with a message digest tag for updating data. If any outsiders perform a change in the key, the tag bit is also changed indicating the key is not secure and correct.
Design and Implementation of a User-Friendly Graphical Data Presentation Component for Monitoring Applications

Graphical data presentation plays a very important role for lots of monitoring applications. Nowadays there are many environments for implementing it like MSChart in Microsoft VB, JFrameChart in Java and Waveform Graphs in LabVIEW. However, they are not always adequate and flexible enough to design powerful software. In this paper an ActiveX component for graphical data display is designed and implemented. Its data model, curve model and other key functions have been illustrated in detail. The component can be applied to multiple monitoring applications for both desktop and web applications. Results show that the component is flexible and reliable. And it shortens the system development cycle remarkably.
Apoptotic Force and Tissue Dynamics During Drosophila Embryogenesis

www.sciencemag. org (this information is current as of November 5, 2008 ): The following resources related to this article are available online at http://www.sciencemag. org/cgi/content/full/321/5896/1683 version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag. org/cgi/content/full/321/5896/1683/DC1 can be found at: Supporting Online Material found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag. org/cgi/content/full/321/5896/1683#related-content http://www.sciencemag. org/cgi/content/full/321/5896/1683#otherarticles , 10 of which can be accessed for free: cites 21 articles This article http://www.sciencemag. org/cgi/collection/development Development : subject collections This article appears in the following http://www.sciencemag. org/about/permissions.dtl in whole or in part can be found at: this article permission to reproduce of this article or about obtaining reprints Information about obtaining
Context Sensing and Feature Discovery for Improving Classifications

We propose context sensing as features for improved accuracy in classifications in our ongoing research. In many applications, features extracted from purposed sensors may not be enough for classification tasks accurately. Context features can help to discriminate classes in such cases. To address the problem, we first present how context can be used as features in classifications. Further, we present a case study on energy appliance identification from aggregated power usage for a household using context sensing and features.
Bronchial Smooth Muscle Cells of Asthmatics Promote Angiogenesis through Elevated Secretion of CXC-Chemokines (ENA-78, GRO-α, and IL-8)

Background Airway wall remodelling is a key pathology of asthma. It includes thickening of the airway wall, hypertrophy and hyperplasia of bronchial smooth muscle cells (BSMC), as well as an increased vascularity of the sub-epithelial cell layer. BSMC are known to be the effector cells of bronchoconstriction, but they are increasingly recognized as an important source of inflammatory mediators and angiogenic factors. Objective To compare the angiogenic potential of BSMC of asthmatic and non-asthmatic patients and to identify asthma-specific angiogenic factors. Methods Primary BSMC were isolated from human airway tissue of asthmatic and non-asthmatic patients. Conditioned medium (CM) collected from BSMC isolates was tested for angiogenic capacity using the endothelial cell (EC)-spheroid in vitro angiogenesis assay. Angiogenic factors in CM were quantified using a human angiogenesis antibody array and enzyme linked immunosorbent assay. Results Induction of sprout outgrowth from EC-spheroids by CM of BSMC obtained from asthma patients was increased compared with CM of control BSMC (twofold, p < 0.001). Levels of ENA-78, GRO-α and IL-8 were significantly elevated in CM of BSMC from asthma patients (p < 0.05 vs. non-asthmatic patients). SB 265610, a competitive antagonist of chemokine (CXC-motif) receptor 2 (CXCR2), attenuated the increased sprout outgrowth induced by CM of asthma patient-derived BSMC. Conclusions BSMC isolated from asthma patients exhibit increased angiogenic potential. This effect is mediated through the CXCR2 ligands (ENA78, GRO-α and IL-8) produced by BSMC. Implications CXCR2 ligands may play a decisive role in directing the neovascularization in the sub-epithelial cell layers of the lungs of asthma patients. Counteracting the CXCR2-mediated neovascularization by pharmaceutical compounds may represent a novel strategy to reduce airway remodelling in asthma.
A wideband circularly polarized stacked patch antenna array and feed system

A new circularly polarized dual fed stacked patch antenna was proposed. In the antenna design, air gaps in between the substrates and the ground plane were used to maintain the gain of the antenna. Annular gap was also used to compensate the inductance on the feeding probes. As a result, bandwidth of 16.7%, gain of 8 dB and axial ratio less than 2.2 dB were achieved. An 8×8 antenna array was simulated together with associated feed systems and a gain of 24 dB was achieved across the band.
Robust mobile robot velocity estimation using redundant number of optical mice

This paper presents the robust velocity estimation of an omnidirectional mobile robot using a polygonal array of optical mice that are installed at the bottom of a mobile robot. First, the velocity kinematics from a mobile robot to an array of optical mice is derived as an overdetermined linear system. Second, the velocity of a mobile robot is estimated by the least squares solution, which is shown to the same as the simple average of the velocity measurements from optical mice. Third, the redundant number of optical mice is shown to contribute to the robustness of the proposed velocity estimation method against both measurement noises and partial malfunctions. Finally, experimental results are given to demonstrate the validity and performance of the proposed mobile robot velocity estimation scheme.
Noradrenergic Stimulation Enhances Human Action Monitoring

Noradrenergic neurotransmission has been associated with the modulation of higher cognitive functions mediated by the prefrontal cortex. In the present study, the impact of noradrenergic stimulation on the human action-monitoring system, as indexed by event-related brain potentials, was examined. After the administration of a placebo or the selective α2-adrenoceptor antagonist yohimbine, which stimulates firing in the locus ceruleus and noradrenaline release, electroencephalograpic recordings were obtained from healthy volunteers performing a letter flanker task. Yohimbine led to an increase in the amplitude of the error-related negativity in conjunction with a significant reduction of action errors. Reaction times were unchanged, and the drug did not modify the N2 in congruent versus incongruent trials, a measure of preresponse conflict, or posterror adjustments as measured by posterror slowing of reaction time. The present findings suggest that the locus ceruleus-noradrenaline system exerts a rather specific effect on human action monitoring.
Distributed Storage in Wireless Sensor Networks with Network Coding

As part of the Wireless and Sensor Systems (WiSe): Real Time Enterprise project we provide a description of our ongoing research in the area of wireless sensor networks. In general, wireless sensor networks are prone to node failures, unreliable transmission links, interference and buffer overflows. In our project we intend to set up a reliable multi-hop communication for such harsh environments, where the main focus is on data persistence. In order to achieve these goals data redundancy in the network is required. The approach of adding redundancy in wireless networks corresponds to distributed networked storage which is relevant for wireless sensor networks where node failures are not uncommon. For this purpose we consider network coding to store the sensed data in a robust and efficient way, while at the same time minimizing communication costs in the network. Network coding is a technique invented to achieve efficient multicast communication by allowing coding inside the network which means that packets are mixed at the intermediate nodes. This can be viewed as applying encoding operations on the node’s received data from the incoming channels whereas the resulting encoded information is sent over the outgoing channel. At the sink nodes the multicast information is retrieved by decoding the received information. Hence by using distributed storage with network coding we aim to sense the data at the highest possible rates and to reconstruct the information by querying any arbitrary subset nodes of K storage nodes in a network of N storage nodes, where K ≤ N . Note that storage nodes can operate as relay nodes as well. Furthermore we require the datacollector to reconstruct all stored data at the K storage nodes at once with high probability, as long as the sensing rates are inside the network-layer capacity region. A possible application of distributed information storage in wireless sensor networks is environment monitoring and data persistence in a logistical environment.
Cancer Stem Cells: Dynamic Entities in an Ever-Evolving Paradigm

The cancer stem cell (CSC) hypothesis postulates that there is a hierarchy of cellular differentiation within cancers and that the bulk population of tumor cells is derived from a relatively small population of multi-potent neoplastic stem-like cells (CSCs). This tumor-initiating cell population plays an important role in maintaining tumor growth through their unlimited self-renewal, therapeutic resistance, and capacity to propagate tumors through asymmetric cell division. Recent findings from multiple laboratories show that cancer progenitor cells have the capacity to de-differentiate and acquire a stem-like phenotype in response to either genetic manipulation or environmental cues. These findings suggest that CSCs and relatively differentiated progenitors coexist in dynamic equilibrium and are subject to bidirectional conversion. In this review, we discuss emerging concepts regarding the stem-like phenotype, its acquisition by cancer progenitor cells, and the molecular mechanisms involved. Understanding the dynamic equilibrium between CSCs and cancer progenitor cells is critical for the development of novel therapeutic strategies that focus on depleting tumors of their tumor-propagating cell population.
Nanotechnology based Effective Design Approach for Code Converter Circuits using QCA

scheme for reducing the number of cells in Quantum dot Cellular Automata circuit is presented. In this paper, an effective approach to analysis and design of code converter circuit using quantum dot cellular automata is explored in nanotechnology. This paper three input majority gate is the fundamental component of the QCA circuit implementation. It provides a considerable reduction in hardware cost compared to the other accessible methods. All other type of combinational circuits like adder, subtractor, decoder, and encoder etc can be constructed by the three input majority gate circuit only. The proposed code converter circuit is designed and replicated using quantum dot cellular automata designer tool for the four input levels. Finally, these proposed QCA circuits are compared with the other established and conservative circuits in terms of area and cell counts.
Dynamic Protocol Switching Scheme for Data Query in Wireless Sensor Networks

The dynamic routing layer based data query system is introduced to handle data query with different route selection mechanism. The route selection is done with reference to the data query and network density factors. A centralized authority manages the route selection operation. In this case all the query values are redirected to the centralized authority. The central authority initiates the route selection and query distribution operations. The centralized dynamic query selection mechanism makes delay and computation overhead for data query processing with high node density environment. The proposed system is designed to handle the data query operations with dynamic routing layer mechanism in a decentralized manner. The sensor nodes select the routing protocol with reference to the data query and network density information. The self-adaptive data query system is also enhanced to handle join query and event query values.
Kurtosis and Skewness of Density Histograms on Inspiratory and Expiratory CT Scans in Smokers

The aim of this study is to evaluate the relationship between lung function and kurtosis or skewness of lung density histograms on computed tomography (CT) in smokers. Forty-six smokers (age range 46−81 years), enrolled in the Lung Tissue Research Consortium, underwent pulmonary function tests (PFT) and chest CT at full inspiration and full expiration. On both inspiratory and expiratory scans, kurtosis and skewness of the density histograms were automatically measured by open-source software. Correlations between CT measurements and lung function were evaluated by the linear regression analysis. Although no significant correlations were found between inspiratory kurtosis or skewness and PFT results, expiratory kurtosis significantly correlated with the following: the percentage of predicted value of forced expiratory volume in the first second (FEV1), the ratio of FEV1 to forced vital capacity (FVC), and the ratio of residual volume (RV) to total lung capacity (TLC) (FEV1%predicted, R = −0.581, p < 0.001; FEV1/FVC, R = −0.612, p < 0.001; RV/TLC, R = 0.613, p < 0.001, respectively). Similarly, expiratory skewness showed significant correlations with PFT results (FEV1%predicted, R = −0.584, p < 0.001; FEV1/FVC, R = −0.619, p < 0.001; RV/TLC, R = 0.585, p < 0.001, respectively). Also, the expiratory/inspiratory (E/I) ratios of kurtosis and skewness significantly correlated with FEV1%predicted (p < 0.001), FEV1/FVC (p < 0.001), RV/TLC (p < 0.001), and the percentage of predicted value of diffusing capacity for carbon monoxide (kurtosis E/I ratio, p = 0.001; skewness E/I ratio, p = 0.03, respectively). We conclude therefore that expiratory values and the E/I ratios of kurtosis and skewness of CT densitometry reflect airflow limitation and air-trapping. Higher kurtosis or skewness on expiratory CT scan indicates more severe conditions in smokers.
Aspekte der Genetik, Epigenetik und Gen-Umwelt-Interaktion bei suizidalem Verhalten

Zusammenfassung Suizidalem Verhalten liegt ein komplexes Zusammenspiel von vielen Faktoren zugrunde. Es wirken unterschiedliche Risiken zusammen, wie psychiatrische Erkrankungen, Persönlichkeitsparameter (z. B. Aggression, Impulsivität, Neurotizismus), schwere Kindheitstraumata, chronische Erkrankungen, soziale Faktoren (z. B. Arbeitslosigkeit, fehlendes soziales Netz, traumatische Lebensereignisse), kognitive Flexibilität sowie neurobiologische und genetische Faktoren. Dabei fungieren die letzteren im Sinne prädisponierender Risikofaktoren in Wechselwirkung mit Umweltfaktoren. Im Folgenden sollen die konsistentesten genetischen Befunde zusammengefasst werden. Insbesondere wird auf die familiäre Häufung suizidalen Verhaltens sowie auf die geschätzte Heritabilität von ca. 40–55% eingegangen. Darüber hinaus werden Kandidatengenstudien insbesondere aus dem Feld des serotonergen Systems vorgestellt, neuere genomweite Assoziationsstudien diskutiert sowie Gen-Umwelt-Analysen und epigenetische Ansätze beleuchtet.
GPGPU-based data parallel region growing algorithm for cell nuclei detection

Nowadays microscopic analysis of tissue samples is done more and more by using digital imagery and special immunodiagnostic software. These are typically specific applications developed for one distinct field, but some subroutines are commonly repeated, for example several applications contain steps that can detect cell nuclei in a sample image. The aim of our research is developing a new data parallel algorithm that can be implemented even in a GPGPU environment and that is capable of counting hematoxylin eosin (HE) stained cell nuclei and of identifying their exact locations and sizes (using a variation of the region growing method). Our presentation contains the detailed description of the algorithm, the peculiarity of the CUDA implementation, and the evaluation of the created application (regarding its accuracy and the decrease in the execution time).
Adaptive RBFNN versus self-tuning controller: An experimental comparative study

In this paper a comparative study evaluates two different techniques: neural adaptive control and self tuning (ST) control. The neural adaptive control is based on a new hybrid learning technique using an adaptive learning rate for the on-line learning of a Gaussian Radial Basis Function Neural Network (RBFNN) type. In the self tuning structure the control parameters are updated from a pole placement design via the estimation of the process model. A selective forgetting factor method is applied to both control schemes: in the RBFNN to on-line estimate the second layer weights and in the ST to estimate the process parameters. These two techniques are applied to a laboratory-scaled pilot plant, an airstream heating (PT 326), in order to evaluate their performance. Experimental results show the effectiveness of both the proposed methods for set-point tracking and disturbance rejection.
Interference Mitigation in Multicell LTE Systems: Performance over Correlated Fading Channels

We consider a multicell multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiple-access (OFDMA) scenario conforming to the 3GPP long term evolution (LTE) recommendation. Scheduling policies proposed for interference mitigation in LTE rely on a pseudo-random permutation of the subcarriers (interference randomization) or on a centrally coordinated reuse of the OFDMA bandwidth (interference coordination). For both the approaches, this paper investigates the performance of a multiple antenna communication over a Rayleigh fading multipath channels. An analytical model is derived to assess the average bit error probability for frequency-selective (correlated) channels with either stationary or nonstationary (due to subcarrier permutation) multicell interference. Simulation results corroborate the proposed analytical model for different traffic conditions and propagation scenarios.
Carboxylic ionophores enhance the cytotoxic potency of ligand- and antibody-delivered ricin A chain

The highly selective cytotoxicity of site-directed ricin A chain conjugates can be potentiated by membrane-active carboxylic ionophores. The combined use of the two agents results in much faster inactivation of ribosomes and subsequent cell death and lysis. The potency of A chain cytotoxins is correspondingly increased by several orders of magnitude and cells that sparsely express the target antigen or receptor can be killed.
GAQPR and its application in discovering frequent structures in time series

A new genetic algorithm based on the quantum probability representation (GAQPR) is proposed, in which each individual evolves independently; a new crossover operator is designed to integrate searching processes of multiple individuals into a more efficient global searching process; a new mutation operator is also proposed. The algorithm is used to discover frequent structures in time series, experiment results show that the GAQPR is efficient for the complex multi-peak searching problem.