Miracle-dz/UniFAR-FTUs huggingface dataset:
The visual N1 component as an index of a discrimination process.

Many previous studies have demonstrated that the visual N1 component is larger for attended-location stimuli than for unattended-location stimuli. This difference is observed typically only for tasks involving a discrimination of the attended-location stimuli, suggesting that the N1 wave reflects a discrimination process that is applied to the attended location. The present study tested this hypothesis by examining the N1 component elicited by attended stimuli under conditions that either required or did not require the subject to perform a discrimination. Specifically, the N1 elicited by foveal stimuli during choice-reaction time (RT) tasks was compared with the N1 elicited by identical stimuli during simple-RT tasks. In three experiments, a larger posterior N1 was observed in choice-RT tasks than in simple-RT tasks, even when several potential confounds were eliminated (e.g., arousal and motor preparation). This N1 discrimination effect was observed even when no motor response was required and was present for both color- and form-based discriminations. Moreover, this discrimination effect was equally large for easy and difficult discriminations, arguing against a simple resource-based explanation of the present results. Instead, the results of this study are consistent with the hypothesis that the visual N1 component reflects the operation of a discrimination process within the focus of attention.
Measurement of Unbound Drug Exposure in Brain: Modeling of pH Partitioning Explains Diverging Results between the Brain Slice and Brain Homogenate Methods

Currently used methodology for determining unbound drug exposure in brain combines measurement of the total drug concentration in the whole brain in vivo with estimation of brain tissue binding from one of two available in vitro methods: equilibrium dialysis of brain homogenate and the brain slice uptake method. This study of 56 compounds compares the fraction of unbound drug in brain (fu,brain), determined using the brain homogenate method, with the unbound volume of distribution in brain (Vu,brain), determined using the brain slice method. Discrepancies were frequent and were primarily related to drug pH partitioning, attributable to the preservation of cellular structures in the slice that are absent in the homogenate. A mathematical model for pH partitioning into acidic intracellular compartments was derived to predict the slice Vu,brain from measurements of fu,brain and drug pKa. This model allowed prediction of Vu,brain from fu,brain within a 2.2-fold error range for 95% of the drugs compared with a 4.5-fold error range using the brain homogenate fu,brain method alone. The greatest discrepancies between the methods occurred with compounds that are actively transported into brain cells, including gabapentin, metformin, and prototypic organic cation transporter substrates. It was concluded that intrabrain drug distribution is governed by several diverse mechanisms in addition to nonspecific binding and that the slice method is therefore more reliable than the homogenate method. As an alternative, predictions of Vu,brain can be made from homogenate fu,brain using the pH partition model presented, although this model does not take into consideration possible active brain cell uptake.
What Readers want to Experience - An Approach to Quantify Conversational Maxims with Preferences for Reading Behaviour

Searching information on web pages is a tedious task for users as web servers provide complete web pages and do not tailor their content to the user's current information need. This leaves an enormous amount of workload for the user and influences his emotional attitude towards the whole task even if a search engine has filtered pages that are relevant to a user query. In this paper, we propose an approach to adapt the response to queries to user preferences for his reading experience in order to leverage the problem of information overload. With these preferences, it is possible to select the most preferred content from a web page. In our view, the preferences are a quantitative way to express conversational maxims. We present our experimental approach to learn these preferences from annotated browsing sessions and introduce a decision strategy for the selection of content on the basis of the learned preferences.
Estimation Over Deterministic Multiaccess Channels

We study the problem of communicating sensor readings over a Gaussian multiaccess (MAC) channel. We focus on the scenario that each sensor observes a single random variable, and transmits it using certain signaling in a shared channel. The objective is the design of channel waveforms (i.e., signal constellation) to facilitate the estimation of field parameters from the channel output. We propose a new approach named Histogram-Delivering Multiple Access (HDMA). In case of symmetric channel gains, it is shown that the HDMA is asymptotically optimal in the limit of large number of sensors. In particular, we show that the HDMA together with a variant of the maximum-likelihood estimator achieves the Cramer-Rao lower bound asymptotically. We then compare the performance of HDMA with other approaches that allocate orthogonal channels to sensors such as TDMA.
'Bend it Like Beckham': Identity, Socialization and Assimilation

We first develop a model of identity formation resulting from the interaction of cultural transmission and socialization inside the family, peer effects and social interactions, and identity choice. We then put the model to data using the UK Fourth National Survey of Ethnic Minorities. We show that the main determinants of ethnic identity include past racial harassment experiences, language spoken at home and with friends, quality of housing, and structure of the family. Most importantly, we find that, consistently with our theoretical analysis, identity and socialization to an ethnic minority are, other things equal, more intense in mixed neighbourhood than in segregated neighbourhoods. We argue that this last result has important and up-to-now unnoticed implications for integration and assimilation policies.
Coevolutionary genetic variation in the legume‐rhizobium transcriptome

Coevolutionary change requires reciprocal selection between interacting species, where the partner genotypes that are favoured in one species depend on the genetic composition of the interacting species. Coevolutionary genetic variation is manifested as genotype × genotype (G × G) interactions for fitness in interspecific interactions. Although quantitative genetic approaches have revealed abundant evidence for G × G interactions in symbioses, the molecular basis of this variation remains unclear. Here we study the molecular basis of G × G interactions in a model legume‐rhizobium mutualism using gene expression microarrays. We find that, like quantitative traits such as fitness, variation in the symbiotic transcriptome may be partitioned into additive and interactive genetic components. Our results suggest that plant genetic variation had the largest influence on nodule gene expression and that plant genotype and the plant genotype × rhizobium genotype interaction determine global shifts in rhizobium gene expression that in turn feedback to influence plant fitness benefits. Moreover, the transcriptomic variation we uncover implicates regulatory changes in both species as drivers of symbiotic gene expression variation. Our study is the first to partition genetic variation in a symbiotic transcriptome and illuminates potential molecular routes of coevolutionary change.
Textural Feature Selection based Mutual Information

— In this paper we propose a method using the mutual information criterion to evaluate a set of candidate features (or parameters) and to select an informative subset of the most pertinent features for textural classification. Then, we evaluate its efficiency compared to the case when we use the whole set of features as well as to the most known method in the field of the reduction of parameters: the Principal Component Analysis (PCA). Our method is based on Maximal-Relevance-Minimal-Redundancy (MRMR) algorithm [1] [2]. The experimental tests of this study correspond to six classes of textures extracted from the Brodatz album. The parameters are extracted by using the co-occurrence matrices method and the classifier used is a multilayer neuronal network. The results of our study prove that MRMR algorithm is more performing than both the whole set of features and PCA.
Slow dynamics, dynamic heterogeneities, and fragility of supercooled liquids confined in random media

Using molecular dynamics simulations, we study the slow dynamics of supercooled liquids confined in a random matrix of immobile obstacles. We study the dynamical crossover from glass-like to Lorentz-gas-like behavior in terms of the density correlation function, the mean square displacement, the nonlinear dynamic susceptibility, the non-Gaussian parameter, and the fragility. We find the cooperative and spatially heterogeneous dynamics to be suppressed as the obstacle density increases, leading to a more Arrhenius-like behavior in the temperature dependence of the relaxation time. Our findings are qualitatively consistent with the results of recent experimental and numerical studies for various classes of spatially heterogeneous systems. We also investigate the dependence of the dynamics of mobile particles on the protocol used to generate the random matrix. A re-entrant transition from the arrested phase to the liquid phase as the mobile particle density increases is observed for a class of protocols. This re-entrance is explained in terms of the distribution of the volume of the voids that are available to the mobile particles.
Travelling plateaus for a hyperbolic Keller–Segel system with attraction and repulsion: existence and branching instabilities

How can repulsive and attractive forces, acting on a conservative system, create stable travelling patterns or branching instabilities? We have proposed to study this question in the framework of the hyperbolic Keller–Segel system with logistic sensitivity. This is a model system motivated by experiments on cell communities auto-organization, a field which is also called socio-biology. We continue earlier modelling work, where we have shown numerically that branching patterns arise for this system and we have analysed this instability by formal asymptotics for small diffusivity of the chemo-repellent. Here we are interested in the more general situation, where the diffusivities of both the chemo-attractant and the chemo-repellent are positive. To do so, we develop an appropriate functional analysis framework. We apply our method to two cases. Firstly we analyse steady states. Secondly we analyse travelling waves when neglecting the degradation coefficient of the chemo-repellent; the unique wave speed appears through a singularity cancellation which is the main theoretical difficulty. This shows that in different situations the cell density takes the shape of a plateau. The existence of steady states and travelling plateaus are a symptom of how rich the system is and why branching instabilities can occur. Numerical tests show that large plateaus may split into smaller ones, which remain stable.
Improvement of Bandwidth Efficient Video and Live Streaming in Mobile using AWS Cloud

The new method has been proposed a novel idea for mobile video streaming and live streaming in the clouds. The user request for videos over the mobiles through wireless links this wireless links capacity cannot be corporate with the traffic demand. As gap between traffic demand and link capacity, with link conditions, result poor quality service and sending data on this channel result in buffering time. Mobile traffic is serious concern for mobile network operator to provide the qos to mobile user, this video traffic exceeds the video bandwidth capacity of cellular network, to address the delay, packet lose video traffic. This paper propose a new framework, bandwidth efficient mobile video and live streaming using Amazon web services(AWS) elastic computing cloud (ec2) called as AWS Cloud, to demonstrate video performance and cloud server for live streaming and adaptive video streaming in cloud. which has two main parts: adaptive mobile live video streaming (LVS) and adaptive video streaming (AVS). LVS and AVS construct a private agent to provide video streaming services efficiently for every mobile user. For a given user, LVS-AVS lets an agent adaptively adjust user streaming flow with a scalable video coding technique based on the feedback of link quality. Sharing the video based on url path of video in cloud for social networking. The framework shows that the private agents in the clouds can effectively provide the adaptive streaming for both live visuals and video in cloud. keywords: Live streaming, Adaptive video streaming, cloud computing, AWS, ec2.
Self-Adaptation to Device Distribution Changes

A key problem when coordinating the behaviour of devices in situated networks (e.g., pervasive computing, smart cities, Internet of Things, wireless sensor networks) is adaptation to changes impacting network topology, density, and heterogeneity. Computational goals for such systems are often expressed in terms of geometric properties of the continuous environment in which the devices are situated, and the results of resilient computations should depend primarily on that continuous environment, rather than the particulars of how devices happen to be distributed through it. In this paper, we identify a new property of distributed algorithms, eventual consistency, which guarantees that computation self-stabilizes to a final state that approximates a predictable limit as the density and speed of devices increases. We then identify a large class of programs that are eventually consistent, building on prior results on the field calculus computational model to identify a class of self-stabilizing programs. Finally, we confirm through simulation of pervasive network scenarios that eventually consistent programs from this class can provide resilient behavior where programs that are only self-stabilizing fail badly.
Severe MgADP Inhibition of Bacillus subtilis F1-ATPase Is Not Due to the Absence of Nucleotide Binding to the Noncatalytic Nucleotide Binding Sites

F1-ATPase from Bacillus subtilis (BF1) is severely suppressed by the MgADP inhibition. Here, we have tested if this is due to the loss of nucleotide binding to the noncatalytic site that is required for the activation. Measurements with a tryptophan mutant of BF1 indicated that the noncatalytic sites could bind ATP normally. Furthermore, the mutant BF1 that cannot bind ATP to the noncatalytic sites showed much lower ATPase activity. It was concluded that the cause of strong MgADP inhibition of BF1 is not the weak nucleotide binding to the noncatalytic sites but the other steps required for the activation.
Development of a Mobile Multispectral Imaging Platform for Precise Field Phenotyping

Phenotyping in field experiments is challenging due to interactions between plants and effects from biotic and abiotic factors which increase complexity in plant development. In such environments, visual or destructive measurements are considered the limiting factor and novel approaches are necessary. Remote multispectral imaging is a powerful method that has shown significant potential to estimate crop physiology. However, precise measurements of phenotypic differences between crop varieties in field experiments require exclusion of the disturbances caused by wind and varying sunlight. A mobile and closed multispectral imaging system was developed to study canopies in field experiments. This system shuts out wind and sunlight to ensure the highest possible precision and accuracy. Multispectral images were acquired in an experiment with four different wheat varieties, two different nitrogen levels, replicated on two different soil types at four different dates from 15 May (BBCH 13) to 18 June (BBCH 41 to 57). The images were analyzed and derived vegetation coverage and Normalized Difference Vegetation index (NDVI) were used to assess varietal differences. The results showed potentials for differentiating between the varieties using both vegetation coverage and NDVI, especially at the early growth stages. The perspectives of high-precision and high-throughput imaging for field phenotyping are discussed including the potentials of measuring varietal differences via spectral imaging in comparison to other simpler technologies such as spectral reflectance and RGB imaging.
A Differential Evolution Based Adaptive Neural Network Pitch Controller for a Doubly Fed Wind Turbine Generator System

Extraction of maximum energy from wind and transferring it to the grid with high efficiency are challenging problems. To this end, this study proposes a smart pitch controller for a wind turbine-doubly fed induction generator system using a Differential Evolution (DE) based adaptive neural network. The nominal weights for the back-propagation neural network controller are obtained from input-output training data generated by DE optimization method. These weights are then adaptively updated in time domain depending on the variation of the system outputs. The adaptive control strategy has been tested through simulation of complete system dynamics comprising of the turbine-generator system and its various components. It has been observed that the DE based smart pitch controller is able to achieve efficient energy transfer to the grid and at the same time provide a good damping profile. Locally collected wind data was used in the testing phase.
Correction of RT–qPCR data for genomic DNA-derived signals with ValidPrime

Genomic DNA (gDNA) contamination is an inherent problem during RNA purification that can lead to non-specific amplification and aberrant results in reverse transcription quantitative PCR (RT—qPCR). Currently, there is no alternative to RT(−) controls to evaluate the impact of the gDNA background on RT–PCR data. We propose a novel method (ValidPrime) that is more accurate than traditional RT(−) controls to test qPCR assays with respect to their sensitivity toward gDNA. ValidPrime measures the gDNA contribution using an optimized gDNA-specific ValidPrime assay (VPA) and gDNA reference sample(s). The VPA, targeting a non-transcribed locus, is used to measure the gDNA contents in RT(+) samples and the gDNA reference is used to normalize for GOI-specific differences in gDNA sensitivity. We demonstrate that the RNA-derived component of the signal can be accurately estimated and deduced from the total signal. ValidPrime corrects with high precision for both exogenous (spiked) and endogenous gDNA, contributing ∼60% of the total signal, whereas substantially reducing the number of required qPCR control reactions. In conclusion, ValidPrime offers a cost-efficient alternative to RT(−) controls and accurately corrects for signals derived from gDNA in RT–qPCR.
Deconstructing the Subprime Debacle Using New Indices of Underwriting Quality and Economic Conditions: A First Look

We document that technical progress in originating and pricing mortgages has enabled a trend since 1979 toward more relaxed credit standards on mortgage lending, which is reflected in rising foreclosure rates. We then decompose annual variation in mortgage performance measured by share of loans entering foreclosure into a part due to economic conditions and a part due to underwriting changes. The decomposition provides natural metrics or indices of national underwriting quality and economic conditions. The results suggest that the recent subprime debacle can be attributed about equally to each factor. The deterioration since 1990 was marked by two periods. In the first, during the 1990s, there was a lowering of observable credit standards, like loan to value ratios. It was deliberate and related to the use of credit scores and the development of more sophisticated underwriting systems. The negative effects of eroding loan quality on foreclosures were to some extent masked by strong local and national economic conditions during this period. In the second period, after 2002, there was little change in observable loan characteristics like loan to value or credit history. This second period is associated with the rise of subprime and Alt-A markets but also with subprime and other "non-agency" securitization. Securitization induced moral hazard and a deterioration in underwriting standards that was not easily observed by investors in the securities.
A MULTILEVEL FAST MULTIPOLE METHOD WITH SPHERICAL HARMONICS EXPANSION OF THE k-SPACE INTEGRALS

A novel diagonalized multilevel fast multipole method (MLFMM) for time harmonic Maxwell's equations is proposed which realizes considerable memory savings without compromising accuracy and computational speed. The improvement is achieved by expanding the k-space integrals over the unit sphere in spherical harmonics. Since the spherical harmonics expansion is carried out for cartesian vector components of the expansion functions, only very few expansion coefficients are required. Aggregations, plane wave translations, and disaggregations are performed using the k-space samples of a numerical quadrature rule. However, the incoming plane waves on the finest MLFMM level are expanded in spherical harmonics again. Thus, due to the orthonormality of spherical harmonics the testing integrals for the individual testing functions are simplified into series over products of spherical harmonics expansion coefficients.
Unsupervised Discovery of Coordinate Terms for Multiple Aspects from Search Engine Query Logs

A method is described for discovering coordinate terms, such as "Honda'' and "Nissan,'' for a given term, such as "Toyota,'' as well as their common topic terms, from the query logs of a Web search engine. Coordinate terms are good candidates for use in making comparisons. A HITS-based algorithm is applied to a bipartite graph between coordinate term candidates and co-occurrence patterns to identify coordinate and topic terms. Spectral analysis is used to distinguish coordinate terms corresponding to different aspects of the search term. As a result, we can discover terms related to the terms in a search engine query that reflect the needs and interests of the user.
A Model-based Approach to Self-Protection in SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems, which are widely used in monitoring and controlling critical infrastructure sectors, are highly vulnerable to cyber attacks. Current security solutions can protect SCADA systems from known cyber assaults, but most solutions require human intervention. This paper applies autonomic computing technology to monitor SCADA system performance, and proactively estimate upcoming attacks for a given system model of a physical infrastructure. We also present the feasibility of intrusion detection systems for known and unknown attack detection. A dynamic intrusion response system is designed to evaluate recommended responses, and appropriate responses are executed to influence attack impacts. We used a case study of a water storage tank to develop an attack that modifies Modbus messages transmitted between slaves and masters. Experimental results show that, with little or no human intervention, the proposed approach enhances the security of the SCADA system, reduces protection time delays, and maintains water storage tank performance.
A Flexible OFDM System Simulation Model with BER Performance Test

This work aims at developing flexible simulation model for orthogonal frequency division multiplexing (OFDM). In this model, the system parameters such as modulation scheme symbol order, or the number of subcarriers can be changed easily according to specific OFDM system implementation. This system can serve as a key tool for researchers in the area of OFDM systems. In this work, the proposed model is used to conduct different experiments to examine the performance of OFDM system. For instance, the Bit Error Rates (BER) of OFDM system under different noise conditions have been computed. In