Desynchronization and inhibition of Kuramoto oscillators by scalar mean-field feedback

Motivated by neuroscience applications, and in particular by the deep brain stimulation treatment for Parkinson’s disease, we have recently derived a simplified model of an interconnected neuronal population under the effect of its mean-field proportional feedback. In this paper, we rely on that model to propose conditions under which proportional mean-field feedback achieves either oscillation inhibition or desynchronization. More precisely, we show that for small natural frequencies, this scalar control signal induces an inhibition of the collective oscillation. For the closed-loop system, this situation corresponds to a fixed point which is shown to be almost globally asymptotically stable in the fictitious case of zero natural frequencies and all-to-all coupling and feedback. In the case of an odd number of oscillators, this property is shown to be robust to small natural frequencies and heterogencities in both the coupling and feedback topology. On the contrary, for large natural frequencies, we show that scalar proportional mean-field feedback is able to induce desynchronization. After having recalled a formal definition for desynchronization, we show how it can be induced in a network of originally synchronized oscillators.     

Static Coarse Grain Task Scheduling with Cache Optimization Using OpenMP

Effective use of cache memory is getting more important with increasing gap between the processor speed and memory access speed. Also, use of multigrain parallelism is getting more important to improve effective performance beyond the limitation of loop iteration level parallelism. Considering these factors, this paper proposes a coarse grain task static scheduling scheme considering cache optimization. The proposed scheme schedules coarse grain tasks to threads so that shared data among coarse grain tasks can be passed via cache after task and data decomposition considering cache size at compile time. It is implemented on OSCAR Fortran multigrain parallelizing compiler and evaluated on Sun Ultra80 four-processor SMP workstation using Swim and Tomcatv from the SPEC fp 95. As the results, the proposed scheme gives us 4.56 times speedup for Swim and 2.37 times on 4 processors for Tomcatv respectively against the Sun Forte HPC Ver. 6 update 1 loop parallelizing compiler.     

A smoothed particle hydrodynamics and coarse‐grained molecular dynamics hybrid technique for modelling elastic particles and breakable capsules under various flow conditions

In this work, a hybrid numerical approach, which combines elements of SPH and coarse‐grained molecular dynamics, is used to investigate the effect of various flow conditions to deformable and breakable shell‐structures such as capsules, vesicles or biological cells. Copyright © 2014 John Wiley & Sons, Ltd.     

VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams

Data Mining and Knowledge Discovery - The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that...     

Skew Gaussian processes for classification

Machine Learning - Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have...     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

A smartphone-based fall detection system

Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a smartphone-based fall detection system that monitors the movements of patients, recognizes a fall, and automatically sends a request for help to the caregivers. To reduce the problem of false alarms, the system includes novel techniques for the recognition of those activities of daily living that could be erroneously mis-detected as falls (such as sitting on a sofa or lying on a bed). To limit the intrusiveness of the system, a small external sensing unit can also be used for the acquisition of movement data.     

Oxidative post-treatments for enhanced corrosion resistance of aluminium electrodeposited from ionic liquids

In this paper we report the results of a study on the corrosion properties of carbon steel (UNI Fe360B) coated with a thin aluminium layer electrodeposited from ionic liquids and subsequently post-treated in an oxidizing environment. Two different types of treatments were investigated: thermal oxidation in air and electrochemical anodization. We found that heat treatments provided only a modest improvement of the anticorrosion properties while the electrochemical treatment produced a significant improvement.     

The morphological properties of PP coextruded tape fabrics

In the field of self‐reinforced composites many researchers have focused their attention on the coextruded tapes composed of polypropylene core and PP/PE copolymer skin. Two similar commercial fabrics (P and T) have been compared in respect of their peel resistance. For both materials, peel resistance has a periodic trend that regularly follows fabric weave style. T has demonstrated an average peel resistance and a well‐bonded area slightly greater than P. Skin/core interfacial properties have been investigated and a crosscheck between differential scanning calorimetry (DSC) and Raman spectroscopy has been adopted to understand the influence of skin structure on consolidated laminate. DSC curves exhibit three melting peaks during first heating for both fabrics, corresponding to copolymer, skin/core interface, and core melting. After consolidation at 140°C stretching‐induced superstructure and PP crystallinity degree are preserved. The presence of PP/PE copolymer + PE blend only in fabric P has been pointed out and PE content has been calculated. POLYM. ENG. SCI., 56:727–734, 2016. © 2016 Society of Plastics Engineers