О связи T-матриц, возникающих в осесимметричной задаче рассеяния света сфероидом

The relations between the T-matrices emerging when solving the problem of light scattering by a spheroid by applying the expansions of the electro-magnetic fields in the employing spheroidal and spherical bases are found. The behavior of the obtained relations is numerically studied, and it is noted that in a wide range of the task parameter values the calculation of the spheroidal T-matrix and its corresponding transformation is the only practical way to derive the spherical T-matrix often used in applications.

Dynamics of an artificial recurrent neural network for the problem of modeling a cognitive function

The purpose of this work is to build an artificial recurrent neural network whose activity models a cognitive function relating to the comparison of two vibrotactile stimuli coming with a delay and to analyze dynamic mechanisms underlying its work. Methods of the work are machine learning, analysis of spatiotemporal dynamics and phase space. Results. Activity of the trained recurrent neural network models a cognitive function of the comparison of two stimuli with a delay. Model neurons exhibit mixed selectivity during the course of the task. In the multidimensional activity, the components are found each of which depends on a certain task parameter. Conclusion. The training of the artificial neural network to perform the funciton analogous to the experimentally observed process is accompanied by the emergence of dynamic properties of model neurons which are similar to those found in the experiment.

Learning Personalized Attribute Preference via Multi-Task AUC Optimization

Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators. However, the consensus might fail in settings, especially when a wide spectrum of annotators with different interests and comprehension about the attribute words are involved. In this paper, we develop a novel multi-task method to understand and predict personalized attribute annotations. Regarding the attribute preference learning for each annotator as a specific task, we first propose a multi-level task parameter decomposition to capture the evolution from a highly popular opinion of the mass to highly personalized choices that are special for each person. Meanwhile, for personalized learning methods, ranking prediction is much more important than accurate classification. This motivates us to employ an Area Under ROC Curve (AUC) based loss function to improve our model. On top of the AUC-based loss, we propose an efficient method to evaluate the loss and gradients. Theoretically, we propose a novel closed-form solution for one of our non-convex subproblem, which leads to provable convergence behaviors. Furthermore, we also provide a generalization bound to guarantee a reasonable performance. Finally, empirical analysis consistently speaks to the efficacy of our proposed method.

A Task Parameter Inference Framework for Real-Time Embedded Systems

While recent studies addressed security attacks in real-time embedded systems, most of them assumed prior knowledge of parameters of periodic tasks, which is not realistic under many environments. In this paper, we address how to infer task parameters, from restricted information obtained by simple system monitoring. To this end, we first develop static properties that are independent of inference results and therefore applied only once in the beginning. We further develop dynamic properties each of which can tighten inference results by feeding an update of the inference results obtained by other properties. Our simulation results demonstrate that the proposed inference framework infers task parameters for RM (Rate Monotonic) with reasonable tightness; the ratio of exactly inferred task periods is 95.3% and 65.6%, respectively with low and high task set use. The results also discover that the inference performance varies with the monitoring interval length and the task set use.

Effects of anterior cingulate cortex lesions on a continuous performance task for mice

Background: Important tools in the study of prefrontal cortical -dependent executive functions are cross-species behavioural tasks with translational validity. A widely used test of executive function and attention in humans is the continuous performance task. Optimal performance in variations of this task is associated with activity along the medial wall of the prefrontal cortex, including the anterior cingulate cortex, for its essential components such as response control, target detection and processing of false alarm errors. Methods: We assess the validity of a recently developed rodent touchscreen continuous performance task that is analogous to typical human continuous performance task procedures. Here, we evaluate the performance of mice with quinolinic acid -induced lesions centred on the anterior cingulate cortex in the rodent touchscreen continuous performance task following a range of task parameter manipulations designed to challenge attention and impulse control. Results: Lesioned mice showed a disinhibited response profile expressed as a decreased response criterion and increased false alarm rates. Anterior cingulate cortex lesions also resulted in a milder increase in inter-trial interval responses and hit rate. Lesions did not affect discriminative sensitivity d′. The disinhibited behaviour of anterior cingulate cortex -lesioned animals was stable and not affected by the manipulation of variable task parameter manipulations designed to increase task difficulty. The results are in general agreement with human studies implicating the anterior cingulate cortex in the processing of inappropriate responses. Conclusion: We conclude that the rodent touchscreen continuous performance task may be useful for studying prefrontal cortex function in mice and has the capability of providing meaningful links between animal and human cognitive tasks.

Adaptive Group Sparse Multi-task Learning via Trace Lasso

In multi-task learning (MTL), tasks are learned jointly so that information among related tasks is shared and utilized to help improve generalization for each individual task. A major challenge in MTL is how to selectively choose what to share among tasks. Ideally, only related tasks should share information with each other. In this paper, we propose a new MTL method that can adaptively group correlated tasks into clusters and share information among the correlated tasks only. Our method is based on the assumption that each task parameter is a linear combination of other tasks' and the coefficients of the linear combination are active only if there is relatedness between the two tasks. Through introducing trace Lasso penalty on these coefficients, our method is able to adaptively select the subset of coefficients with respect to the tasks that are correlated to the task. Our model frees the process of determining task clustering structure as used in the literature. Efficient optimization methods based on alternating direction method of multipliers (ADMM) is developed to solve the problem. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of our method in terms of clustering related tasks and generalization performance.