124 research outputs found
Socializing Autonomous Units with the Reflexive Game Theory and Resonate-and-Fire neurons
In this study the concept of reflexia is applied to modeling behavior of
autonomous units. The relationship between reflexia, on the one hand, and
mirror neuron system and perception of emotions, on the other hand, is
introduced. The main method of using reflexia in a group of autonomous units is
Reflexive Game Theory (RGT). To embody RGT in a group of autonomous agents a
communication system is employed. This communication system uses frequency
domain multiplexing by means of Izhikevich's resonate-and-fire neural models.
The result of socialization of autonomous units by means of RGT and
communication system is illustrated in several examples.Comment: 10 pages, 15 figure
Modeling multistage decision processes with Reflexive Game Theory
This paper introduces application of Reflexive Game Theory to the matter of
multistage decision making processes. The idea behind is that each decision
making session has certain parameters like "when the session is taking place",
"who are the group members to make decision", "how group members influence on
each other", etc. This study illustrates the consecutive or sequential decision
making process, which consist of two stages. During the stage 1 decisions about
the parameters of the ultimate decision making are made. Then stage 2 is
implementation of Ultimate decision making itself. Since during stage 1 there
can be multiple decision sessions. In such a case it takes more than two
sessions to make ultimate (final) decision. Therefore the overall process of
ultimate decision making becomes multistage decision making process consisting
of consecutive decision making sessions.Comment: 8 pages, 5 figure
An Application of the EM-algorithm to Approximate Empirical Distributions of Financial Indices with the Gaussian Mixtures
In this study I briefly illustrate application of the Gaussian mixtures to
approximate empirical distributions of financial indices (DAX, Dow Jones,
Nikkei, RTSI, S&P 500). The resulting distributions illustrate very high
quality of approximation as evaluated by Kolmogorov-Smirnov test. This implies
further study of application of the Gaussian mixtures to approximate empirical
distributions of financial indices.Comment: 3 pages, 5 figure
The Inverse Task of the Reflexive Game Theory: Theoretical Matters, Practical Applications and Relationship with Other Issues
The Reflexive Game Theory (RGT) has been recently proposed by Vladimir
Lefebvre to model behavior of individuals in groups. The goal of this study is
to introduce the Inverse task. We consider methods of solution together with
practical applications. We present a brief overview of the RGT for easy
understanding of the problem. We also develop the schematic representation of
the RGT inference algorithms to create the basis for soft- and hardware
solutions of the RGT tasks. We propose a unified hierarchy of schemas to
represent humans and robots. This hierarchy is considered as a unified
framework to solve the entire spectrum of the RGT tasks. We conclude by
illustrating how this framework can be applied for modeling of mixed groups of
humans and robots. All together this provides the exhaustive solution of the
Inverse task and clearly illustrates its role and relationships with other
issues considered in the RGT.Comment: 27 pages, 6 figures, 3 table
Sparsifying and Down-scaling Networks to Increase Robustness to Distortions
It has been shown that perfectly trained networks exhibit drastic reduction
in performance when presented with distorted images. Streaming Network (STNet)
is a novel architecture capable of robust classification of the distorted
images while been trained on undistorted images. The distortion robustness is
enabled by means of sparse input and isolated parallel streams with decoupled
weights. Recent results prove STNet is robust to 20 types of noise and
distortions. STNet exhibits state-of-the-art performance for classification of
low light images, while being of much smaller size when other networks. In this
paper, we construct STNets by using scaled versions (number of filters in each
layer is reduced by factor of n) of popular networks like VGG16, ResNet50 and
MobileNetV2 as parallel streams. These new STNets are tested on several
datasets. Our results indicate that more efficient (less FLOPS), new STNets
exhibit higher or equal accuracy in comparison with original networks.
Considering a diversity of datasets and networks used for tests, we conclude
that a new type of STNets is an efficient tool for robust classification of
distorted images.Comment: 6 pages, 6 figure
A General Framework for Development of the Cortex-like Visual Object Recognition System: Waves of Spikes, Predictive Coding and Universal Dictionary of Features
This study is focused on the development of the cortex-like visual object
recognition system. We propose a general framework, which consists of three
hierarchical levels (modules). These modules functionally correspond to the V1,
V4 and IT areas. Both bottom-up and top-down connections between the
hierarchical levels V4 and IT are employed. The higher the degree of matching
between the input and the preferred stimulus, the shorter the response time of
the neuron. Therefore information about a single stimulus is distributed in
time and is transmitted by the waves of spikes. The reciprocal connections and
waves of spikes implement predictive coding: an initial hypothesis is generated
on the basis of information delivered by the first wave of spikes and is tested
with the information carried by the consecutive waves. The development is
considered as extraction and accumulation of features in V4 and objects in IT.
Once stored a feature can be disposed, if rarely activated. This cause update
of feature repository. Consequently, objects in IT are also updated. This
illustrates the growing process and dynamical change of topological structures
of V4, IT and connections between these areas
Streaming Networks: Increase Noise Robustness and Filter Diversity via Hard-wired and Input-induced Sparsity
The CNNs have achieved a state-of-the-art performance in many applications.
Recent studies illustrate that CNN's recognition accuracy drops drastically if
images are noise corrupted. We focus on the problem of robust recognition
accuracy of noise-corrupted images. We introduce a novel network architecture
called Streaming Networks. Each stream is taking a certain intensity slice of
the original image as an input, and stream parameters are trained
independently. We use network capacity, hard-wired and input-induced sparsity
as the dimensions for experiments. The results indicate that only the presence
of both hard-wired and input-induces sparsity enables robust noisy image
recognition. Streaming Nets is the only architecture which has both types of
sparsity and exhibits higher robustness to noise. Finally, to illustrate
increase in filter diversity we illustrate that a distribution of filter
weights of the first conv layer gradually approaches uniform distribution as
the degree of hard-wired and domain-induced sparsity and capacities increases.Comment: 17 pages, 37 figures. arXiv admin note: text overlap with
arXiv:1910.1110
Applications of the Streaming Networks
Most recently Streaming Networks (STnets) have been introduced as a mechanism
of robust noise-corrupted images classification. STnets is a family of
convolutional neural networks, which consists of multiple neural networks
(streams), which have different inputs and their outputs are concatenated and
fed into a single joint classifier. The original paper has illustrated how
STnets can successfully classify images from Cifar10, EuroSat and UCmerced
datasets, when images were corrupted with various levels of random zero noise.
In this paper, we demonstrate that STnets are capable of high accuracy
classification of images corrupted with Gaussian noise, fog, snow, etc.
(Cifar10 corrupted dataset) and low light images (subset of Carvana dataset).
We also introduce a new type of STnets called Hybrid STnets. Thus, we
illustrate that STnets is a universal tool of image classification when
original training dataset is corrupted with noise or other transformations,
which lead to information loss from original images.Comment: 4 pages, 6 figure
Ultrafast magneto-photocurrents in GaAs: Separation of surface and bulk contributions
We induce ultrafast magneto-photocurrents in a GaAs crystal employing
interband excitation with femtosecond laser pulses at room temperature and
non-invasively separate surface and bulk contributions to the overall current
response. The separation between the different symmetry contributions is
achieved by measuring the simultaneously emitted terahertz radiation for
different sample orientations. Excitation intensity and photon energy
dependences of the magneto-photocurrents for linearly and circularly polarized
excitations reveal an involvement of different microscopic origins, one of
which we believe is the inverse Spin-Hall effect. Our experiments are important
for a better understanding of the complex momentum-space carrier dynamics in
magnetic fields
Optical activity in chiral stacks of 2D semiconductors
We show that the stacks of two-dimensional semiconductor crystals with the
chiral packing exhibit optical activity and circular dichroism. We develop a
microscopic theory of these phenomena in the spectral range of exciton
transitions which takes into account the spin-dependent hopping of excitons
between the layers in the stack and the interlayer coupling of excitons via
electromagnetic field. For the stacks of realistic two-dimensional
semiconductors such as transition metal dichalcogenides, we calculate the
rotation and ellipticity angles of radiation transmitted through such
structures. The angles are resonantly enhanced at the frequencies of both
bright and dark exciton modes in the stack. We also study the photoluminescence
of chiral stacks and show that it is circularly polarized.Comment: 10 pages, 6 figure
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