Miracle-dz/UniFAR-FTUs huggingface dataset:
DIVIDED WE VOTE…TURNOUT DECLINE IN ESTABLISHED DEMOCRACIES:EVIDENCE FROM COSTA RICA

Turnout has decreased in 23 out of 36 established democracies since 1945. Among all worldwide cases Costa Rica is a good laboratory for studying turnout oscillations. During three decades between 1958 and 1994, political participation average rates remained high in a comparative perspective. Following that trend, one would easily predict that individuals socialized during that period of time must replicate automatically their parents’ and grandparents’ levels of high political activism. However, turnout patterns in the country since 1998 do not fit very well in that story. Older voters are no longer voting at the rates they usually did and the younger are entering into politics during times of lower participation. The combination of these two factors can have big deleterious and long-lasting effects on turnout trends. Estimating the causal relationship between consecutive voting decisions has proven to be intrinsically difficult for social scientists. Drawing on an exceptionally rich and unique turnout dataset and a mixed-methods approach that includes longitudinal multivariate analysis, face-to-face interviews and geospatial statistics this contribution seeks to explain why in Costa Rica, currently the oldest and most stable democracy in Latin America, turnout has declined? And more importantly: what factors drive these changes? Individual characteristics, although still relevant in static theories regarding turnout determinants, have proved to be insufficient for understanding why individuals’ turnout behavior varies over time. Instead, I theorize that the partisan (group) identity that makes people turn out because they are loyal to the party in a context of conflict produces stronger motivation but it is activated selectively, and it only works in a context of “threat” or polarized deliberation. Nevertheless, these incentives may change over time, they may increase or decrease under certain circumstances impacting turnout. Essentially, the more polarized the electoral competition, the easier it will be for citizens to cast their vote. A polarized deliberation, the key factor in my causal story, creates the conditions for the activation of strong partisan identities that stimulate voters’ mobilization to the polls. Therefore, people vote more in a context of polarization. Conversely, if polarization declines, people will vote less.
Fast Evaluation of Time–Harmonic Maxwell's Equations Using the Reduced Basis Method

The reduced basis method (RBM) generates low- order models for the solution of parametrized partial differential equations to allow for efficient evaluation in many-query and real-time contexts. We show the theoretical framework in which the RBM is applied to Maxwell's equations and present numerical results for model reduction in frequency domain. Using rigorous error estimators, the RBM achieves low-order models under variation of material parameters and geometry. The RBM reduces model order by a factor of 50 to 100 and reduces compute time by a factor of 200 and more for numerical experiments using standard circuit elements.
Incremental learning for bootstrapping object classifier models

Many state of the art object classification applications require many data samples, whose collection is usually a very costly process. Performing initial model training with synthetic samples (from virtual reality tools) has been proposed as a possible solution, although the resulting classification models need to be adapted (fine-tuned) to real-world data afterwards. For this bootstrapping process, we propose to use an incremental learning algorithm from the cognitive robotics domain which is particularly suited for perceptual problems. We apply it to a pedestrian detection problem where a synthetic dataset is used for initial training, and two different real-world datasets for fine-tuning and evaluation. The proposed scheme greatly reduces the number of real-world samples required while maintaining high classification accuracy. We additionally demonstrate several innovative incremental learning schemes for object detection, the basic idea being that usually only very few background samples are actually similar to pedestrian samples. By suitable arrangement of incremental learning steps, we can keep classification models simple by representing only such “hard” background samples.
IQAS: Inference question answering system for handling temporal inference

One of the most crucial problems in any Natural Language Processing (NLP) task is the representation of time. This includes applications such as Information Retrieval techniques (IR), Information Extraction (IE) and Question/answering systems (QA). This paper deals with temporal information involving several forms of inference in Arabic language.
The Executive Control of Face Memory

Patients with frontal lobe damage and cognitively normal elderly individuals demonstrate increased susceptibility to false facial recognition. In this paper we review neuropsychological evidence consistent with the notion that the common functional impairment underlying face memory distortions in both subject populations is a context recollection/source monitoring deficit, coupled with excessive reliance on relatively preserved facial familiarity signals in recognition decisions. In particular, we suggest that due to the breakdown of strategic memory retrieval, monitoring, and decision operations, individuals with frontal lobe impairment caused by focal damage or age-related functional decline do not have a reliable mechanism for attributing the experience of familiarity to the correct context or source. Memory illusions are mostly apparent under conditions of uncertainty when the face cue does not directly elicit relevant identity-specific contextual information, leaving the source of familiarity unspecified or ambiguous. Based on these findings, we propose that remembering faces is a constructive process that requires dynamic interactions between temporal lobe memory systems that operate in an automatic or bottom-up fashion and frontal executive systems that provide strategic top-down control of recollection. Executive memory control functions implemented by prefrontal cortex play a critical role in suppressing false facial recognition and related source memory misattributions.
Reduced Variability of Ongoing and Evoked Cortical Activity Leads to Improved Behavioral Performance

Sensory responses of the brain are known to be highly variable, but the origin and functional relevance of this variability have long remained enigmatic. Using the variable foreperiod of a visual discrimination task to assess variability in the primate cerebral cortex, we report that visual evoked response variability is not only tied to variability in ongoing cortical activity, but also predicts mean response time. We used cortical local field potentials, simultaneously recorded from widespread cortical areas, to gauge both ongoing and visually evoked activity. Trial-to-trial variability of sensory evoked responses was strongly modulated by foreperiod duration and correlated both with the cortical variability before stimulus onset as well as with response times. In a separate set of experiments we probed the relation between small saccadic eye movements, foreperiod duration and manual response times. The rate of eye movements was modulated by foreperiod duration and eye position variability was positively correlated with response times. Our results indicate that when the time of a sensory stimulus is predictable, reduction in cortical variability before the stimulus can improve normal behavioral function that depends on the stimulus.
Locating where Transient Signals Travel in Inhomogeneous Media

Locating where transient signals travel between a source and receiver requires a final step that is needed after using a theory of diffraction such as the integral theorem of Helmholtz and Kirchhoff. Introduced here, the final step accounts for interference between adjacent apertures on a phase screen by adaptively adjusting their phase and amplitude, yielding a hierarchy of energy contributions to any desired window of signal travel time at the receiver. The method allows one to check errors in ray theory at finite wavelengths. Acoustic propagation at long distance in the oceanic waveguide (50-100 Hz, 0.05 s resolution) has significant deviations from ray theory. The boundary condition of zero pressure at the surface of the ocean appears to cause sound to travel in a nearly horizontal trajectory for a much greater distance near the surface than predicted by rays. The first Fresnel zone is an inappropriate scale to characterize where transient sounds travel near a ray path as assumed by a standard scattering theory. Instead, the Fresnel zone is too large by an order of magnitude for cases investigated here. Regions where sounds travel can have complicated structures defying a simple length scale. These results are applicable to the physics of underwater sound, optics, radio communication, radar, geophysics, and theories of wave-scattering.
Chinese Restaurant Process for cognate clustering: A threshold free approach

In this paper, we introduce a threshold free approach, motivated from Chinese Restaurant Process, for the purpose of cognate clustering. We show that our approach yields similar results to a linguistically motivated cognate clustering system known as LexStat. Our Chinese Restaurant Process system is fast and does not require any threshold and can be applied to any language family of the world.
Using 3D GNG-based reconstruction for 6DoF egomotion

Several recent works deal with 3D data in mobile robotic problems, e.g. mapping. Data come from any kind of sensor (time of flight cameras and 3D lasers) providing a huge amount of unorganized 3D data. In this paper we detail an efficient method to build complete 3D models from a Growing Neural Gas (GNG). We show that the use of GNG provides better results than other approaches. The GNG obtained is then applied to a sequence. From GNG structure, we propose to calculate planar patches and thus obtaining a fast method to compute the movement performed by a mobile robot by means of a 3D models registration algorithm. Final results of 3D mapping are also shown.
Domain-Driven Data Mining for IT Infrastructure Support

Support analytics (i.e., statistical analysis, modeling and mining of customer/operations support tickets data) is important in service industries. In this paper, we adopt a domain-driven data mining approach to support analytics with a focus on IT infrastructure Support (ITIS) services. We identify specific business questions and then propose algorithms for answering them. The questions are: (1) How to reduce the overall workload? (2) How to improve efforts spent in ticket processing? (3) How to improve compliance to service level agreements? We propose novel formalizations of these notions and propose rigorous statistics-based algorithms for these questions. The approach is domain-driven in the sense that the results produced are directly usable by and easy to understand for end-users having no expertise in data-mining, do not require any experimentation and often discover novel and non-obvious answers. All this helps in better acceptance among end-users and more active use of the results produced. The algorithms have been implemented and have produced satisfactory results on more than 25 real-life ITIS datasets, one of which we use for illustration.
Precision in Design Reusability using Software Agent based Design Triggers

software development methodologies for today's software developments have not been proven good enough to cope with the unconventional demands and rapid application development. There is a serious need for computer aided methodology which can cope with the challenging need of the today's requirements. Computer aided software agents playing important role in new age of software development. The software design phase development experience of software development lifecycle can be enhanced using design triggers agents. This can result in increased efficiency in overall software development process and can help in producing quick solutions resulting in reduced development cost and effort.
Standardisation and validation of micro-CT for the morphological characterisation of porous structures

In order to understand and to simulate the functional behaviour of porous structures during loading, quantitative knowledge of the relationship between the morphology and the mechanical behaviour is necessary. Therefore, the porous structures need to be quantified morphologically. In this research, the quantification will be performed by using micro-CT. However, to date, this technique encounters some problems. Optimal acquisition parameters still need to be found by 'trial and error', which is subjective and time consuming. Additionally, there is a lack of quantitative validation criteria. Both problems are tackled in this study, where, at this point, titanium bone scaffolds are assessed as proof of principle. By using a micro-CT simulator, theoretically optimal acquisition parameters are determined. These parameters are then used for the validation of micro-CT by means of metallographic slicing combined with optical microscopy. By matching the optical image with the micro-CT image of the same cross-section, validation criteria are defined. Based on three sliced parts of one Ti sample, a mean percentage in mismatch of 39.2 ± 7.8 % and in overlap of 75.6 ± 3.5 % with regards to the optical images is determined. To investigate whether these differences have a serious influence on the structural parameters, determined from the micro-CT images and the corresponding optical images, further image analysis is performed.
Leishmania spp. Proteome Data Sets: A Comprehensive Resource for Vaccine Development to Target Visceral Leishmaniasis

Visceral leishmaniasis is a neglected infectious disease caused primarily by Leishmania donovani and Leishmania infantum protozoan parasites. A significant number of infections take a fatal course. Drug therapy is available but still costly and parasites resistant to first line drugs are observed. Despite many years of trial no commercial vaccine is available to date. However, development of a cost effective, needle-independent vaccine remains a high priority. Reverse vaccinology has attracted much attention since the term has been coined and the approach tested by Rappuoli and colleagues. This in silico selection of antigens from genomic and proteomic data sets was also adapted to aim at developing an anti-Leishmania vaccine. Here, an analysis of the efforts is attempted and the challenges to be overcome by these endeavors are discussed. Strategies that led to successful identification of antigens will be illustrated. Furthermore, these efforts are viewed in the context of anticipated modes of action of effective anti-Leishmania immune responses to highlight possible advantages and shortcomings.
Pollen-tube tip growth requires a balance of lateral propagation and global inhibition of Rho-family GTPase activity

Rapid tip growth allows for efficient development of highly elongated cells (e.g. neuronal axons, fungal hyphae and pollen tubes) and requires an elaborate spatiotemporal regulation of the growing region. Here, we use the pollen tube as a model to investigate the mechanism regulating the growing region. ROPs (Rho-related GTPases from plants) are essential for pollen tip growth and display oscillatory activity changes in the apical plasma membrane (PM). By manipulating the ROP activity level, we showed that the PM distribution of ROP activity as an apical cap determines the tip growth region and that efficient tip growth requires an optimum level of the apical ROP1 activity. Excessive ROP activation induced the enlargement of the tip growth region, causing growth depolarization and reduced tube elongation. Time-lapse analysis suggests that the apical ROP1 cap is generated by lateral propagation of a localized ROP activity. Subcellular localization and gain- and loss-of-function analyses suggest that RhoGDI- and RhoGAP-mediated global inhibition limits the lateral propagation of apical ROP1 activity. We propose that the balance between the lateral propagation and the global inhibition maintains an optimal apical ROP1 cap and generates the apical ROP1 activity oscillation required for efficient pollen-tube elongation.
Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering

This paper studies the application of Kalman filtering as a post-processing method in numerical predictions of wind speed. Two limited-area atmospheric models have been employed, with different options/capabilities of horizontal resolution, to provide wind speed forecasts. The application of Kalman filter to these data leads to the elimination of any possible systematic errors, even in the lower resolution cases, contributing further to the significant reduction of the required CPU time. The potential of this method in wind power applications is also exploited. In particular, in the case of wind power prediction, the results obtained showed a remarkable improvement in the model forecasting skill.
Linear Doherty Power Amplifier With an Enhanced Back-Off Efficiency Mode for Handset Applications

This paper presents a linear Doherty power amplifier (PA) with enhanced back-off efficiency mode for handset applications. For linear Doherty operation, we analyze the gain modulation as well as the cancellation of the third-order intermodulation distortion in order to improve the linearity. A compact design method is also discussed to implement on a single chip for a handset. The proposed Doherty PA delivers good performance with regard to the third-generation (3G)/fourth-generation (4G) modulation signals. A switched-load power-mode PA is adopted in the proposed Doherty PA to enhance the efficiency in the low-power region with over 10-dB back-off. For demonstration purposes, the PA is implemented using an InGaP/GaAs heterojunction bipolar transistor and AlGaAs/InGaAs pseudomorphic high electron-mobility transistor process. The PA is tested at 1.85 GHz using both a long-term evolution signal with 16-quadrature amplitude modulation, 7.5-dB peak-to-average power ratio, and 10-MHz bandwidth (BW) and a wideband code division multiple access signal with 3.3-dB PAPR and 3.84-MHz BW. The proposed linear Doherty PA with an enhanced back-off efficiency mode delivers good performance in both the high- and low-power modes, implying that the dual-power-mode Doherty PA configuration can be a promising candidate for extending the battery life of handheld devices in 3G and 4G wireless communication systems.
Stability of softly switched multiregional dynamic output controllers with a static antiwindup filter: A discrete-time case

This paper addresses the problem of model-based global stability analysis of discrete-time Takagi-Sugeno multiregional dynamic output controllers with static antiwindup filters. The presented analyses are reduced to the problem of a feasibility study of the Linear Matrix Inequalities (LMIs), derived based on Lyapunov stability theory. Two sets of LMIs are considered candidate derived from the classical common quadratic Lyapunov function, which may in some cases be too conservative, and a fuzzy Lyapunov function candidate, which has been proven to significantly reduce the conservatism level, although at the cost of increasing the number of LMIs. Two numerical examples illustrate the main result.
Exploring social activeness and dynamic interest in community-based recommender system

Community-based recommender systems have attracted much research attention. Forming communities allows us to reduce data sparsity and focus on discovering the latent characteristics of communities instead of individuals. Previous work focused on how to detect the community using various algorithms. However, they failed to consider users' social attributes, such as social activeness and dynamic interest, which have strong correlations to users' preference and choice. Intuitively, people have different social activeness in a social network. Ratings from users with high activeness are more likely to be trustworthy. Temporal dynamic of interest is also significant to user's preference. In this paper, we propose a novel community-based framework. We first employ PLSA-based model incorporating social activeness and dynamic interest to discover communities. Then the state-of-the-art matrix factorization method is applied on each of the communities. The experiment results on two real world datasets validate the effectiveness of our method for improving recommendation performance.
RBP-Var: a database of functional variants involved in regulation mediated by RNA-binding proteins

Transcription factors bind to the genome by forming specific contacts with the primary DNA sequence; however, RNA-binding proteins (RBPs) have greater scope to achieve binding specificity through the RNA secondary structure. It has been revealed that single nucleotide variants (SNVs) that alter RNA structure, also known as RiboSNitches, exhibit 3-fold greater local structure changes than replicates of the same DNA sequence, demonstrated by the fact that depletion of RiboSNitches could result in the alteration of specific RNA shapes at thousands of sites, including 3′ UTRs, binding sites of microRNAs and RBPs. However, the network between SNVs and post-transcriptional regulation remains unclear. Here, we developed RBP-Var, a database freely available at http://www.rbp-var.biols. ac.cn/, which provides annotation of functional variants involved in post-transcriptional interaction and regulation. RBP-Var provides an easy-to-use web interface that allows users to rapidly find whether SNVs of interest can transform the secondary structure of RNA and identify RBPs whose binding may be subsequently disrupted. RBP-Var integrates DNA and RNA biology to understand how various genetic variants and post-transcriptional mechanisms cooperate to orchestrate gene expression. In summary, RBP-Var is useful in selecting candidate SNVs for further functional studies and exploring causal SNVs underlying human diseases.
CaSAPI : a system for credulous and sceptical argumentation

We present the CaSAPI system, implementing (a generalisation of) three existing computational mechanisms [8–10] for determining argumentatively whether potential beliefs can be deemed to be acceptable and, if so, for computing supports for them. These mechanisms are defined in terms of dialectical disputes amongst two fictional agents: a proponent agent, eager to determine the acceptability of the beliefs, and an opponent agent, trying to undermine the existence of an acceptable support for the beliefs, by finding attacks against it that the proponent needs to counter-attack in turn. The three mechanisms differ in the level of scepticism of the proponent agent and are defined for (flat) assumption-based argumentation frameworks [3]. Thus, they can serve as decision-making mechanisms for all instances of these frameworks. In this paper we show how they can be used for logic programming, legal reasoning, practical reasoning, and agent reasoning.