Octal Bent Generalized Boolean Functions

In this paper we characterize (octal) bent generalized Boolean functions defined on \BBZ_2^n with values in \BBZ_8. Moreover, we propose several constructions of such generalized bent functions for both $n$ even and $n$ odd

Skin Admittance Measurement for Emotion Recognition: A Study over Frequency Sweep

The electrodermal activity (EDA) is a reliable physiological signal for monitoring the sympathetic nervous system. Several studies have demonstrated that EDA can be a source of effective markers for the assessment of emotional states in humans. There are two main methods for measuring EDA: endosomatic (internal electrical source) and exosomatic (external electrical source). Even though the exosomatic approach is the most widely used, differences between alternating current (AC) and direct current (DC) methods and their implication in the emotional assessment field have not yet been deeply investigated. This paper aims at investigating how the admittance contribution of EDA, studied at different frequency sources, affects the EDA statistical power in inferring on the subject?s arousing level (neutral or aroused). To this extent, 40 healthy subjects underwent visual affective elicitations, including neutral and arousing levels, while EDA was gathered through DC and AC sources from 0 to 1 kHz. Results concern the accuracy of an automatic, EDA feature-based arousal recognition system for each frequency source. We show how the frequency of the external electrical source affects the accuracy of arousal recognition. This suggests a role of skin susceptance in the study of affective stimuli through electrodermal response

Accelerating Monte Carlo simulations with an NVIDIA® graphics processor

Modern graphics cards, commonly used in desktop computers, have evolved beyond a simple interface between processor and display to incorporate sophisticated calculation engines that can be applied to general purpose computing. The Monte Carlo algorithm for modelling photon transport in turbid media has been implemented on an NVIDIA® 8800gt graphics card using the CUDA toolkit. The Monte Carlo method relies on following the trajectory of millions of photons through the sample, often taking hours or days to complete. The graphics-processor implementation, processing roughly 110 million scattering events per second, was found to run more than 70 times faster than a similar, single-threaded implementation on a 2.67 GHz desktop computer

Decomposing generalized bent and hyperbent functions

In this paper we introduce generalized hyperbent functions from $F_{2^n}$ to $Z_{2^k}$, and investigate decompositions of generalized (hyper)bent functions. We show that generalized (hyper)bent functions from $F_{2^n}$ to $Z_{2^k}$ consist of components which are generalized (hyper)bent functions from $F_{2^n}$ to $Z_{2^{k^\prime}}$ for some $k^\prime < k$. For odd $n$, we show that the Boolean functions associated to a generalized bent function form an affine space of semibent functions. This complements a recent result for even $n$, where the associated Boolean functions are bent.Comment: 24 page

An Appropriate English-learning Activity for Japanese University Students - A Case Study of Shinshu University Students -

Article信州大学教育学部紀要. 76: 63-71 (1992)departmental bulletin pape

Minutes of the General Assembly

Minutes of the General Assembl

Deep learning applied to fish otolith images

This thesis is concerned with classification and regression using deep learning applied to fish otolith images. Otoliths (earstones) are calcified structures in the inner ear of vertebrates, and are used, for instance, in fish stock assessment and fish age determination. We use convolutional neural networks – a class of deep learning models - on two specific problems: discrimination between Northeast Arctic Cod and Norwegian Coastal Cod, and age determination of Greenland halibut. In relation to classification and regression, we are also concerned with the usage of cross-validation procedures such as k*l-fold cross-validation, to obtain reliable test results. We obtain test results for all available data, and we argue for the usage of cross-validation on the bases of variations in test results. Furthermore, feature relevance attribution methods are discussed and compared, which aims at explaining outputs from deep learning models by attributing relevance scores to the input. These comparisons are conducted using image input heatmaps produced by methods such as gradient saliency maps, guided backpropagation, and integrated gradients, along with two proposed variations of those techniques