1,007 research outputs found
A Topological Study of Chaotic Iterations. Application to Hash Functions
International audienceChaotic iterations, a tool formerly used in distributed computing, has recently revealed various interesting properties of disorder leading to its use in the computer science security field. In this paper, a comprehensive study of its topological behavior is proposed. It is stated that, in addition to being chaotic as defined in the Devaney's formulation, this tool possesses the property of topological mixing. Additionally, its level of sensibility, expansivity, and topological entropy are evaluated. All of these properties lead to a complete unpredictable behavior for the chaotic iterations. As it only manipulates binary digits or integers, we show that it is possible to use it to produce truly chaotic computer programs. As an application example, a truly chaotic hash function is proposed in two versions. In the second version, an artificial neural network is used, which can be stated as chaotic according to Devaney
Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification
[Abriged] Astronomical Wide Field Imaging performed with new large format CCD
detectors poses data reduction problems of unprecedented scale which are
difficult to deal with traditional interactive tools. We present here NExt
(Neural Extractor): a new Neural Network (NN) based package capable to detect
objects and to perform both deblending and star/galaxy classification in an
automatic way. Traditionally, in astronomical images, objects are first
discriminated from the noisy background by searching for sets of connected
pixels having brightnesses above a given threshold and then they are classified
as stars or as galaxies through diagnostic diagrams having variables choosen
accordingly to the astronomer's taste and experience. In the extraction step,
assuming that images are well sampled, NExt requires only the simplest a priori
definition of "what an object is" (id est, it keeps all structures composed by
more than one pixels) and performs the detection via an unsupervised NN
approaching detection as a clustering problem which has been thoroughly studied
in the artificial intelligence literature. In order to obtain an objective and
reliable classification, instead of using an arbitrarily defined set of
features, we use a NN to select the most significant features among the large
number of measured ones, and then we use their selected features to perform the
classification task. In order to optimise the performances of the system we
implemented and tested several different models of NN. The comparison of the
NExt performances with those of the best detection and classification package
known to the authors (SExtractor) shows that NExt is at least as effective as
the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at
http://www.na.astro.it/~andreon/listapub.htm
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
Neural 3D Video Synthesis
We propose a novel approach for 3D video synthesis that is able to represent
multi-view video recordings of a dynamic real-world scene in a compact, yet
expressive representation that enables high-quality view synthesis and motion
interpolation. Our approach takes the high quality and compactness of static
neural radiance fields in a new direction: to a model-free, dynamic setting. At
the core of our approach is a novel time-conditioned neural radiance fields
that represents scene dynamics using a set of compact latent codes. To exploit
the fact that changes between adjacent frames of a video are typically small
and locally consistent, we propose two novel strategies for efficient training
of our neural network: 1) An efficient hierarchical training scheme, and 2) an
importance sampling strategy that selects the next rays for training based on
the temporal variation of the input videos. In combination, these two
strategies significantly boost the training speed, lead to fast convergence of
the training process, and enable high quality results. Our learned
representation is highly compact and able to represent a 10 second 30 FPS
multi-view video recording by 18 cameras with a model size of just 28MB. We
demonstrate that our method can render high-fidelity wide-angle novel views at
over 1K resolution, even for highly complex and dynamic scenes. We perform an
extensive qualitative and quantitative evaluation that shows that our approach
outperforms the current state of the art. We include additional video and
information at: https://neural-3d-video.github.io/Comment: Project website: https://neural-3d-video.github.io
On-the-Go Reflectance Transformation Imaging with Ordinary Smartphones
Reflectance Transformation Imaging (RTI) is a popular technique that allows the recovery of per-pixel reflectance information by capturing an object under different light conditions. This can be later used to reveal surface details and interactively relight the subject. Such process, however, typically requires dedicated hardware setups to recover the light direction from multiple locations, making the process tedious when performed outside the lab.
We propose a novel RTI method that can be carried out by recording videos with two ordinary smartphones. The flash led-light of one device is used to illuminate the subject while the other captures the reflectance. Since the led is mounted close to the camera lenses, we can infer the light direction for thousands of images by freely moving the illuminating device while observing a fiducial marker surrounding the subject. To deal with such amount of data, we propose a neural relighting model that reconstructs object appearance for arbitrary light directions from extremely compact reflectance distribution data compressed via Principal Components Analysis (PCA). Experiments shows that the proposed technique can be easily performed on the field with a resulting RTI model that can outperform state-of-the-art approaches involving dedicated hardware setups
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