Differences in spatial memory recognition due to cognitive style

Field independence refers to the ability to perceive details from the surrounding context as a whole and to represent the environment by relying on an internal reference frame. Conversely, field dependence individuals tend to focus their attention on single environmental features analysing them individually. This cognitive style affects several visuo-spatial abilities including spatial memory. This study assesses both the effect of field independence and field dependence on performance displayed on virtual environments of different complexity. Forty young healthy individuals took part in this study. Participants performed the Embedded Figures Test for field independence or dependence assessment and a new spatial memory recognition test. The spatial memory recognition test demanded to memorize a green box location in a virtual room picture. Thereafter, during ten trials participants had to decide if a green box was located in the same position as in the sample picture. Five of the pictures were correct. The information available in the virtual room was manipulated. Hence, two different experimental conditions were tested: a virtual room containing all landmarks and a virtual room with only two cues. Accuracy and reaction time were registered. Analyses demonstrated that higher field independent individuals were related to better spatial memory performance in two landmarks condition and were faster in all landmark condition. In addition, men and women did not differ in their performance. These results suggested that cognitive style affects spatial memory performance and this phenomenon is modulated by environment complexity. This does not affect accuracy but time spent. Moreover, field dependent individuals are unable to organize the navigational field by relying on internal reference frames when few landmarks are available, and this causes them to commit more errors

Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models

© 2018 Esmaili et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background Transport injuries commonly result in significant disease burden, leading to physical disability, mental health deterioration and reduced quality of life. Analyzing the patterns of healthcare service utilization after transport injuries can provide an insight into the health of the affected parties, allow improved health system resource planning, and provide a baseline against which any future system-level interventions can be evaluated. Therefore, this research aims to use time series of service utilization provided by a compensation agency to identify groups of claimants with similar utilization patterns, describe such patterns, and characterize the groups in terms of demographic, accident type and injury type. Methods To achieve this aim, we have proposed an analytical framework that utilizes latent variables to describe the utilization patterns over time and group the claimants into clusters based on their service utilization time series. To perform the clustering without dismissing the temporal dimension of the time series, we have used a well-established statistical approach known as the mixture of hidden Markov models (MHMM). Ensuing the clustering, we have applied multinomial logistic regression to provide a description of the clusters against demographic, injury and accident covariates. Results We have tested our model with data on psychology service utilization from one of the main compensation agencies for transport accidents in Australia, and found that three clear clusters of service utilization can be evinced from the data. These three clusters correspond to claimants who have tended to use the services 1) only briefly after the accident; 2) for an intermediate period of time and in moderate amounts; and 3) for a sustained period of time, and intensely. The size of these clusters is approximately 67%, 27% and 6% of the number of claimants, respectively. The multinomial logistic regression analysis has showed that claimants who were 30 to 60-year-old at the time of accident, were witnesses, and who suffered a soft tissue injury were more likely to be part of the intermediate cluster than the majority cluster. Conversely, claimants who suffered more severe injuries such as a brain head injury or anon-limb fracture injury and who started their service utilization later were more likely to be part of the sustained cluster

A Hardware Generator of Multi-Point Distributed Random Numbers for Monte Carlo Simulation

Monte Carlo simulation of weak approximation of stochastic differential equations constitutes an intensive computational task. In applications such as finance, for instance, to achieve "real time" execution, as often required, one needs highly efficient implementations of the multi-point distributed random number generator underlying the simulations. In this paper, a fast and flexible dedicated hardware solution on a field programmable gate array is presented. A comparative performance analysis between a software-only and the poposed hardware solution demonstrated that the hardware solution is bottleneck-free, retains the flexibility of the software solution and significantly increases the computational efficiency. Moreover, simulations in Applications wuch as economics insurance, physics, population dynamics, epidemiology, structural mechanics, checmistry and biotechnology can benefit from the obtained speedups

ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems

Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.54 BLEU points, and also a marked improvement over a state-of-the-art system.Comment: Accepted at NAACL-HLT 201

Core-periphery or decentralized? Topological shifts of specialized information on Twitter

In this paper we investigate shifts in Twitter network topology resulting from the type of information being shared. We identified communities matching areas of agricultural expertise and measured the core-periphery centralization of network formations resulting from users sharing generic versus specialized information. We found that centralization increases when specialized information is shared and that the network adopts decentralized formations as conversations become more generic. The results are consistent with classical diffusion models positing that specialized information comes with greater centralization, but they also show that users favor decentralized formations, which can foster community cohesion, when spreading specialized information is secondary

ReWE: Regressing word embeddings for regularization of neural machine translation systems

Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value). Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.54 BLEU points, and also a marked improvement over a state-of-the-art system

Modeling variability of the lactation curves of cows in automated milking systems

Historically, cow selection criteria were developed for conventional milking systems that have regular milking intervals (MI). However, in automatic milking systems (AMS), there is variability in MI within and between cows. These sources of variability provide an opportunity to identify cows with high daily milk yield (DY) and long MI. An extended MI (longer than 16 h in pasture-based systems) has a negative effect on DY. Cows that tolerate extended MI and maintain high DY can be considered more efficient than cows with low DY and long MI, or with high DY but short MI, thereby improving robotic system use. Knowledge of the behavior and parameters of lactation curves of cows in AMS could help farmers to identify cows with a specific lactational phenotype. The objective of this study was to identify individual cows with high DY and long MI within herds, which could reflect increased tolerance to milk accumulation under AMS. A database containing records for 773,483 milking events for one year (July 2016–June 2017) from 4 pasture-based AMS farms was used. Lactation curves within each herd were fitted using several mixed models including fixed effects for the parameters of the lactation curve and random cow effects. Predicted curves of average DY according to parity (multiparous and primiparous) were obtained. The best linear unbiased prediction of the random cow effect allowed us to categorize lactations as having either high or low milk production. The median MI of each lactation was then used to categorize cows as having either short or long MI. Daily yield at the peak of lactation, days to peak and 305-d cumulative milk production were used to compare the effect of DY and MI categories, as well as the DY × MI interaction. Milk production by multiparous and primiparous cows with high DY and long MI was between 35 and 45% higher than that of the low DY and short MI. From all lactations analyzed, the incidence of animals with high DY and long MI across farms was 7.5%. We have identified and quantified a new, AMS-specific, phenotype (the combination of a relatively higher DY with relatively longer MI) with potential to increase use of AMS units. Identifying more efficient animals should help generate new approaches for differential management and for selecting cows in AMS.Fil: Masía, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Lyons, N. A.. Intensive Livestock Industries; AustraliaFil: Piccardi, Mónica Belén. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Área de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Balzarini, Monica Graciela. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Departamento de Desarrollo Rural. Área de Estadística y Biometría; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Hovey, R. C.. University of California at Davis; Estados UnidosFil: Garcia, S. C.. University of Sydney; Australi

Leveraging Discourse Rewards for Document-Level Neural Machine Translation

Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the individual sentences in the document needs to retain aspects of the discourse at document level. However, document-level translation models are usually not trained to explicitly ensure discourse quality. Therefore, in this paper we propose a training approach that explicitly optimizes two established discourse metrics, lexical cohesion (LC) and coherence (COH), by using a reinforcement learning objective. Experiments over four different language pairs and three translation domains have shown that our training approach has been able to achieve more cohesive and coherent document translations than other competitive approaches, yet without compromising the faithfulness to the reference translation. In the case of the Zh-En language pair, our method has achieved an improvement of 2.46 percentage points (pp) in LC and 1.17 pp in COH over the runner-up, while at the same time improving 0.63 pp in BLEU score and 0.47 pp in F_BERT.Comment: Accepted at COLING 202

Saffron: A multitask neuroprotective agent for retinal degenerative diseases

Both age related macular degeneration (AMD) and light induced retinal damage share the common major role played by oxidative stress in the induction/progression of degenerative events. Light damaged (LD) rats have been widely used as a convenient model to gain insight into the mechanisms of degenerative disease, to enucleate relevant steps and to test neuroprotectants. Among them, saffron has been shown to ameliorate degenerative processes and to regulate many genes and protective pathways. Saffron has been also tested in AMD patients. We extended our analysis to a possible additional effect regulated by saffron and compared in AMD patients a pure antioxidant treatment (Lutein/zeaxanthin) with saffron treatment. Methods: Animal model. Sprague-Dawley (SD) adult rats, raised at 5 lux, were exposed to 1000 lux for 24 h and then either immediately sacrificed or placed back at 5 lux for 7 days recovery period. A group of animals was treated with saffron. We performed in the animal model: (1) SDS-PAGE analysis; (2) Western Blotting (3) Enzyme activity assay (4) Immunolabelling; in AMD patients: a longitudinal open-label study 29 (+/- 5) months in two groups of patients: lutein/zeaxanthin (19) and saffron (23) treated. Visual function was tested every 8 months by ERG recordings in addition to clinical examination. Results: Enzymatic activity of MMP-3 is reduced in LD saffron treated retinas and is comparable to control as it is MMP-3 expression. LD treated retinas do not present "rosettes" and microglia activation and migration is highly reduced. Visual function remains stable in saffron treated AMD patients while deteriorates in the lutein/zeaxanthin group. Conclusion: Our results provide evidence of an additional way of action of saffron treatment confirming the complex nature of neuroprotective activities of its chemical components. Accordingly, long term follow-up in AMD patients reveals an added value of saffron supplementation treatment compared to classical antioxidant protocol

The Cosmic-Ray Proton and Helium Spectra measured with the CAPRICE98 balloon experiment

A new measurement of the primary cosmic-ray proton and helium fluxes from 3 to 350 GeV was carried out by the balloon-borne CAPRICE experiment in 1998. This experimental setup combines different detector techniques and has excellent particle discrimination capabilities allowing clear particle identification. Our experiment has the capability to determine accurately detector selection efficiencies and systematic errors associated with them. Furthermore, it can check for the first time the energy determined by the magnet spectrometer by using the Cherenkov angle measured by the RICH detector well above 20 GeV/n. The analysis of the primary proton and helium components is described here and the results are compared with other recent measurements using other magnet spectrometers. The observed energy spectra at the top of the atmosphere can be represented by (1.27+-0.09)x10^4 E^(-2.75+-0.02) particles (m^2 GeV sr s)^-1, where E is the kinetic energy, for protons between 20 and 350 GeV and (4.8+-0.8)x10^2 E^(-2.67+-0.06) particles (m^2 GeV nucleon^-1 sr s)^-1, where E is the kinetic energy per nucleon, for helium nuclei between 15 and 150 GeV nucleon^-1.Comment: To be published on Astroparticle Physics (44 pages, 13 figures, 5 tables