Miracle-dz/UniFAR-FTUs huggingface dataset:
Capture of visual attention interferes with multisensory speech processing

Attending to a conversation in a crowded scene requires selection of relevant information, while ignoring other distracting sensory input, such as speech signals from surrounding people. The neural mechanisms of how distracting stimuli influence the processing of attended speech are not well understood. In this high-density electroencephalography (EEG) study, we investigated how different types of speech and non-speech stimuli influence the processing of attended audiovisual speech. Participants were presented with three horizontally aligned speakers who produced syllables. The faces of the three speakers flickered at specific frequencies (19 Hz for flanking speakers and 25 Hz for the center speaker), which induced steady-state visual evoked potentials (SSVEP) in the EEG that served as a measure of visual attention. The participants' task was to detect an occasional audiovisual target syllable produced by the center speaker, while ignoring distracting signals originating from the two flanking speakers. In all experimental conditions the center speaker produced a bimodal audiovisual syllable. In three distraction conditions, which were contrasted with a no-distraction control condition, the flanking speakers either produced audiovisual speech, moved their lips, and produced acoustic noise, or moved their lips without producing an auditory signal. We observed behavioral interference in the reaction times (RTs) in particular when the flanking speakers produced naturalistic audiovisual speech. These effects were paralleled by enhanced 19 Hz SSVEP, indicative of a stimulus-driven capture of attention toward the interfering speakers. Our study provides evidence that non-relevant audiovisual speech signals serve as highly salient distractors, which capture attention in a stimulus-driven fashion.
Separation of mutation avoidance and antirecombination functions in an Escherichia coli mutS mutant

DNA mismatch repair in Escherichia coli has been shown to be involved in two distinct processes: mutation avoidance, which removes potential mutations arising as replication errors, and antirecombination which prevents recombination between related, but not identical (homeologous), DNA sequences. We show that cells with the mutSΔ800 mutation (which removes the C-terminal 53 amino acids of MutS) on a multicopy plasmid are proficient for mutation avoidance. In interspecies genetic crosses, however, recipients with the mutSΔ800 mutation show increased recombination by up to 280-fold relative to mutS+. The MutSΔ800 protein binds to O6-methylguanine mismatches but not to intrastrand platinated GG cross-links, explaining why dam bacteria with the mutSΔ800 mutation are resistant to cisplatin, but not MNNG, toxicity. The results indicate that the C-terminal end of MutS is necessary for antirecombination and cisplatin sensitization, but less significant for mutation avoidance. The inability of MutSΔ800 to form tetramers may indicate that these are the active form of MutS.
Motion Planning for Multiple Robots

Abstract. We study the motion-planning problem for pairs and triples of robots operating in a shared workspace containing n obstacles. A standard way to solve such problems is to view the collection of robots as one composite robot, whose number of degrees of freedom is d , the sum of the numbers of degrees of freedom of the individual robots. We show that it is sufficient to consider a constant number of robot systems whose number of degrees of freedom is at most d-1 for pairs of robots, and d-2 for triples. (The result for a pair assumes that the sum of the number of degrees of freedom of the robots constituting the pair reduces by at least one if the robots are required to stay in contact; for triples a similar assumption is made. Moreover, for triples we need to assume that a solution with positive clearance exists.) We use this to obtain an O(nd) time algorithm to solve the motion-planning problem for a pair of robots; this is one order of magnitude faster than what the standard method would give. For a triple of robots the running time becomes O(nd-1) , which is two orders of magnitude faster than the standard method. We also apply our method to the case of a collection of bounded-reach robots moving in a low-density environment. Here the running time of our algorithm becomes O(n log n) both for pairs and triples.
Collaborative Filtering with Localised Ranking

In recommendation systems, one is interested in the ranking of the predicted items as opposed to other losses such as the mean squared error. Although a variety of ways to evaluate rankings exist in the literature, here we focus on the Area Under the ROC Curve (AUC) as it widely used and has a strong theoretical underpinning. In practical recommendation, only items at the top of the ranked list are presented to the users. With this in mind we propose a class of objective functions which primarily represent a smooth surrogate for the real AUC, and in a special case we show how to prioritise the top of the list. This loss is differentiable and is optimised through a carefully designed stochastic gradient-descent-based algorithm which scales linearly with the size of the data. We mitigate sample bias present in the data by sampling observations according to a certain power-law based distribution. In addition, we provide computation results as to the efficacy of the proposed method using synthetic and real data.
Ageing and changes in lung mechanics

Due to increasing life expectancy and low fertility, the European Union (EU) is an ageing society. Currently, 16% of its population is aged >65 yrs compared with an estimated 7% for the entire world. Further ageing of the EU population is projected over the next two decades, as shown in figure 1⇓ for the UK, whose current population aged >65 yrs is identical to the EU average. By 2021, nearly 10% of the UK population is projected to be aged >75 yrs, with increased male survival reducing the current striking preponderance of females in the aged population. These demographic trends are important for the future patterns of healthcare and disease. Therefore, it is encouraging that three papers in the current issue of the European Respiratory Journal 1–3 address “normal” ageing of different aspects of airway function and study older subjects compared with many earlier studies. Fig. 1— UK population projections in millions up to 2021. ▪: females ≥65 yrs of age; •: males ≥65 yrs of age; ♦: females ≥75 yrs of age; ▴: males ≥75 yrs of age. Total UK population in 2001 was 60 million. Changes in pulmonary elastic and resistive properties, and in maximum expiratory flow with increasing age, were first described 40 yrs ago, admittedly by small cross-sectional studies of young adults versus elderly subjects. These studies established that the maximum size of the lungs (total lung capacity) did not change with age, but functional residual capacity (FRC) and residual volume (RV) both increased so that inspiratory capacity and vital capacity (VC) both declined 4. The increase in FRC was due to an increase in relaxation volume of the respiratory system, which arose from changes in the static recoil pressure of both the chest wall and the lungs. Static recoil pressure of the lungs ( P L) fell at all …
AR GIS on a Physical Map Based on Map Image Retrieval Using LLAH Tracking

This paper presents am ethod for retrieving ac orresponding map of a captured map image from a map database. Our method is inspired from LLAH based Document Image Retrieval (DIR). LLAH is a method for recognizing a point by using a LLAH feature composed of its neighbor points. Since Map Image Retrieval (MIR) is achieved by analyzing distribution of intersections, the LLAH feature is used in order to describe the distribution. In our method, registration and retrieval in LLAH based DIR are improved for reducing the computational costs of the retrieval. In addition, the LLAH features are updated while a camera is moving. Our improvements enable MIR to the case of strong camera tilting, occlusion and fewer intersections.
Orion: A Hybrid Hierarchical Control Plane of Software-Defined Networking for Large-Scale Networks

The decoupled architecture and the fine-grained flow control feature of SDN limit the scalability of SDN network. In order to address this problem, some studies construct the flat control plane architecture, other studies build the hierarchical control plane architecture to improve the scalability of SDN. However, the two kinds of structure still have unresolved issues: the flat control plane structure can not solve the super-linear computational complexity growth of the control plane when SDN network scales to large size, the centralized abstracted hierarchical control plane structure brings path stretch problem. To address the two issues, we propose Orion, a hybrid hierarchical control plane for large-scale networks. Orion can effectively reduce the computational complexity growth of SDN control plane from super-linear to linear. Meanwhile, we design an abstracted hierarchical routing method to solve the path stretch problem. Further, Orion is implemented to verify the feasibility of the hybrid hierarchical approach. Finally, we verify the effectiveness of Orion both from the theoretical and experimental aspects.
Do Frogs Communicate with Seismic Signals?

Male white-lipped frogs exhibit conspicuous behavioral responses to calling conspecific males that are nearby but out of view. Since the calls often are accompanied by strong seismic signals (thumps), and since the male white-lipped frog exhibits the most acute sensitivity to seismic stimuli yet observed in any animal, these animals may use seismic signals as well as auditory signals for intraspecific communication.
Optimal learning control for discrete-time nonlinear systems using generalized policy iteration based adaptive dynamic programming

In this paper, a novel generalized policy iteration algorithm is investigated to solve infinite horizon optimal control problems for discrete-time nonlinear systems. Two iteration indices are introduced in the generalized policy iteration algorithm, which iterate for policy improvement and policy evaluation, respectively. For the first time the properties of monotonicity, convergence and admissibility for the generalized policy iteration algorithm are analyzed to guarantee that the iterative performance index function converges to the optimum and the iterative control law stabilizes the control system. Finally, numerical results are presented to illustrate the performance of the developed method.
The Information Content of Interest Rate Futures Options

Option prices are being increasingly employed to extract market expectations and views about monetary policy. In this paper, Eurodollar options are monitored to examine the evolution of market sentiment over the possible future values of Eurodollar rates. Risk-neutral probability functions are employed to synopsize the information contained in the prices of Eurodollar futures options. Several common methods of estimating risk-neutral probability density functions are examined. A method based on a mixture of lognormals density is found to rank first and a method based on a Hermite polynomial approximation is found to rank second. Several standard summary statistics are also examined, namely volatility, skewness and kurtosis. The volatility measure is fairly robust across methods, while the skewness and kurtosis measure are model-sensitive. As a concrete example, the days surrounding the September 1998 Federal Market Open Committee are examined. *The author would like to thank David Watt, Paul Gilbert, Toni Gravelle, Peter Thurlow, and Mark Zelmer for their helpful comments. Special thanks goes to Michael Rockinger for data that enabled computer code testing. The views expressed in this paper are those of the author and should not be attributed to the Bank of Canada. Bank of Canada Banque du Canada
Dataflow Acceleration of scikit-learn Gaussian Process Regression

Big data revolution has sparked the widespread use of predictive data analytics based on sophisticated machine learning tasks. Fast data analysis have become very important, and this fact stresses software developers and computer architects to deliver more efficient design solutions able to address the increased performance requirements. Dataflow computing engines from Maxeler has been recently emerged as a promising way of performing high performance computation, utilizing FPGA devices. In this paper, we focus on exploiting Maxeler's dataflow computing for accelerating Gaussian Process Regression from scikit-learn Python library, one of the most computationally intensive and with poor scaling characteistics machine learning algorithm. Through extensive analysis over diverse datasets, we point out which NumPy and SciPy functions forms the major performance bottlenecks that should be implemented in a dataflow acceleration engine and then we discuss the mapping decisions that enable the generation of parameterized dataflow engines. Finally, we show that the proposed acceleration solution delivers significant speedups for the examined datasets, while it also reports good scalability in respect to increased dataset sizes.
Ownership Rights versus Access Rights Allocation to Critical Resources: An Empirical Study of the Economic Impact of Changes in Oil Governance

During the 1970s, many oil-producing countries gave National Oil Companies (NOC's) ownership rights to oil and gas resources. Following the success of Norway in managing its oil and gas resources, development institutions have tried to push oil-producing countries to change their oil governance. Over the past two decades, several countries have enacted laws that create a regulatory entity and establish the NOC only as a business entity. Thus, these NOCs now are only given access rights to explore and produce oil and gas like other international oil companies. Employing a difference-in-difference method, this paper aims to empirically investigate the impact of changes in oil governance, specifically of changes in allocation of ownership rights versus access rights, to aggregate domestic income. Using data from 35 countries in the period 1990-2012, our results suggest that a country which creates a separate regulatory entity and makes the NOC merely a business entity increases its aggregate domestic income by around 10%.
Response to Mease and Wyner, Evidence Contrary to the Statistical View of Boosting, JMLR 9:131-156, 2008

For such a simple algorithm, it is fascinating and remarkable what a rich diversity of interpretations, views, perspectives and explanations have emerged of AdaBoost. Originally, AdaBoost was proposed as a “boosting” algorithm in the technical sense of the word: given access to “weak” classifiers, just slightly better in performance than random guessing, and given sufficient data, a true boosting algorithm can provably produce a combined classifier with nearly perfect accuracy (Freund and Schapire, 1997). AdaBoost has this property, but it also has been shown to be deeply connected with a surprising range of other topics, such as game theory, on-line learning, linear programming, logistic regression and maximum entropy (Breiman, 1999; Collins et al., 2002; Demiriz et al., 2002; Freund and Schapire, 1996, 1997; Kivinen and Warmuth, 1999; Lebanon and Lafferty, 2002). As we discuss further below, AdaBoost can been seen as a method for maximizing the “margins” or confidences of the predictions made by its generated classifier (Schapire et al., 1998). The current paper by Mease and Wyner, of course, focuses on another perspective, the so-called statistical view of boosting. This interpretation, particularly as expounded by Friedman et al. (2000), focuses on the algorithm as a stagewise procedure for minimizing the exponential loss function, which is related to the loss minimized in logistic regression, and whose minimization can be viewed, in a certain sense, as providing estimates of the conditional probability of the label. Taken together, these myriad interpretations of AdaBoost form a robust theory of the algorithm that provides understanding from an extraordinary range of points of view in which each perspective tells us something unique about the algorithm. The statistical view, for instance, has been of tremendous value, allowing for the practical conversion of AdaBoost’s predictions into conditional probabilities, as well as the algorithm’s generalization and extension to many other loss functions and learning problems. Still, each perspective has its weaknesses, which are important to identify to keep our theory in touch with reality. The current paper is superb in exposing empirical phenomena that are apparently difficult to understand according to the statistical view. From a theoretical perspective, the statistical interpretation has other weaknesses. As discussed by Mease and Wyner, this interpretation does not explain AdaBoost’s observed tendency not to overfit, particularly in the absence of regularization
Estimate of solar radius from f-mode frequencies

Frequency and rotational splittings of the solar f- modes are estimated from the GONG data. Contrary to earlier observations the frequencies of f-modes are found to be close to the theoretically computed values for a standard solar model. The f-mode being essentially a surface mode is a valuable di- agnostic probe of the properties of the solar surface, and also provides an independent measure of solar radius. The estimated solar radius is found to be about 0.03% less than what is tra- ditionally used in construction of standard solar models. If this decreaseinsolarradiusisconrmedthenthecurrentsolarmod- els as well as inversion results will need to be revised. The ro- tational splittings of the f-modes yield an independent measure of the rotation rate near the solar surface, which is compared with other measurements.
Carrier-Mediated Cocaine Transport at the Blood-Brain Barrier as a Putative Mechanism in Addiction Liability

Background: The rate of entry of cocaine into the brain is a critical factor that influences neuronal plasticity and the development of cocaine addiction. Until now, passive diffusion has been considered the unique mechanism known by which cocaine crosses the blood-brain barrier. Methods: We reassessed mechanisms of transport of cocaine at the blood-brain barrier using a human cerebral capillary endothelial cell line (hCMEC/D3) and in situ mouse carotid perfusion. Results: Both in vivo and in vitro cocaine transport studies demonstrated the coexistence of a carrier-mediated process with passive diffusion. At pharmacological exposure level, passive diffusion of cocaine accounted for only 22.5% of the total cocaine influx in mice and 5.9% in hCMEC/D3 cells, whereas the carrier-mediated influx rate was 3.4 times greater than its passive diffusion rate in vivo. The functional identification of this carrier-mediated transport demonstrated the involvement of a proton antiporter that shared the properties of the previously characterized clonidine and nicotine transporter. The functionnal characterization suggests that the solute carrier (SLC) transporters Oct (Slc22a1-3), Mate (Slc47a1) and Octn (Slc22a4-5) are not involved in the cocaine transport in vivo and in vitro. Diphenhydramine, heroin, tramadol, cocaethylene, and norcocaine all strongly inhibited cocaine transport, unlike benzoylecgonine. Trans-stimulation studies indicated that diphenhydramine, nicotine, 3,4-methylenedioxyamphetamine (ecstasy) and the cathinone compound 3,4-methylenedioxypyrovalerone (MDPV) were also substrates of the cocaine transporter. Conclusions: Cocaine transport at the BBB involves a proton-antiporter flux that is quantitatively much more important than its passive diffusion. The molecular identification and characterization of this transporter will provide new tools to understand its role in addictive mechanisms.
Asymptotic form of the approach to equilibrium in reversible recombination reactions

The reversible reactions and are investigated. From the exact Langevin equations describing our model, we set up a systematic approximation scheme to compute the approach of the density of C particles to its equilibrium value. We show that for a sufficiently long time t, this approach takes the form of a power law , for any dimension d. The amplitude A is also computed exactly, but is expected to be model dependent. For uncorrelated initial conditions, the C density turns out to be a monotonic time function. The cases of correlated initial conditions and unequal diffusion constants are investigated as well. In the former, correlations may break the monotonicity of the density or in some special cases they may change the long time behaviour. For the latter, the power law remains valid, only the amplitude changes, even in the extreme case of immobile C particles. We also consider the case of segregated initial condition for which a reaction front is observed, and confirm that its width is governed by mean-field exponent in any dimension.
The Use of Neuroimaging Methodology in Counselling Psychology Research: Promises, Pitfalls, and Recommendations

The use of neuroimaging has become increasingly popular among applied psychological researchers. However, when implementing a neuroimaging study in counselling psychology, several conceptual and methodological issues that may threaten internal or external validity must be considered. Although neuroimaging methods hold promise as a tool for counselling psychologists to utilize as we move toward a greater emphasis on science-based practice, important limitations must be addressed at the conceptual (e.g., the reverse inference problem, the danger of neurorealism, and lack of ecological validity) and methodological (e.g., preprocessing, design of experimental tasks, multiple comparisons correction) levels. We discuss the advantages and limitations of the application of neuroimaging to the field of counselling psychology and provide recommendations for those who wish to conduct research in this area.
Overexpression of the Glyoxalase II Gene Leads to Enhanced Salinity Tolerance in Brassica Juncea

Engineering of salinity tolerance in agronomically important crop plants is required to increase their productivity by enabling them to grow in saline soils, which are otherwise left uncultivated. Since an increase in the enzymes of glyoxalase system has been shown to impart salinity tolerance in the model plant tobacco, we used the glyoxalase II gene for engineering salinity tolerance in an important oil yielding crop, Brassica juncea. The transgenic plants of B. juncea overexpressing the glyoxalase II gene showed higher salinity tolerance as compared to the untransformed control plants as observed by delayed senescence in leaf discs at 400 mM and 800 mM NaCl in T1 generation. The percentage of germination of the T2 transgenic seeds was higher at 150 mM and 200 mM NaCl as compared to the seeds of untransformed plants. This for the first time demonstrates the applicability of utilizing the glyoxalase II gene for enhanced salinity tolerance in an oilseed crop plant B. juncea.