Stability of negative ionization fronts: regularization by electric screening?

We recently have proposed that a reduced interfacial model for streamer propagation is able to explain spontaneous branching. Such models require regularization. In the present paper we investigate how transversal Fourier modes of a planar ionization front are regularized by the electric screening length. For a fixed value of the electric field ahead of the front we calculate the dispersion relation numerically. These results guide the derivation of analytical asymptotes for arbitrary fields: for small wave-vector k, the growth rate s(k) grows linearly with k, for large k, it saturates at some positive plateau value. We give a physical interpretation of these results.Comment: 11 pages, 2 figure

Spontaneous Branching of Anode-Directed Streamers between Planar Electrodes

Non-ionized media subject to strong fields can become locally ionized by penetration of finger-shaped streamers. We study negative streamers between planar electrodes in a simple deterministic continuum approximation. We observe that for sufficiently large fields, the streamer tip can split. This happens close to Firsov's limit of `ideal conductivity'. Qualitatively the tip splitting is due to a Laplacian instability quite like in viscous fingering. For future quantitative analytical progress, our stability analysis of planar fronts identifies the screening length as a regularization mechanism.Comment: 4 pages, 6 figures, submitted to PRL on Nov. 16, 2001, revised version of March 10, 200

Artificial Intelligence in Historical Document Analysis:Pattern recognition and machine learning techniques in the study of ancient manuscripts with a focus on the Dead Sea Scrolls

The Ph.D. thesis investigates the potential of artificial intelligence (AI) in analyzing ancient historical manuscripts, focusing on the Dead Sea Scrolls (DSS) images. The research employs several computer vision, pattern recognition, and machine learning techniques to address writer identification and dating challenges. An initial study highlights the successful application of character shape features, achieving high accuracy in identifying multiple authors within the DSS collection.After recognizing the crucial role of binarization (extracting ink traces from the background materials) for accurate writer identification, the thesis introduces BiNet, an artificial deep neural network. BiNet utilizes multispectral images and outperforms traditional models in binarizing highly degraded ancient manuscripts, facilitating improved calculation of textural and allographic features. Building upon the success of BiNet, the study identifies multiple authors for the Great Isaiah Scroll, one of the longest scrolls in the DSS collection. The quantitative findings propose a hypothesis contrary to established assumptions about the scroll's authorship.With the success of writer identification, the thesis employs support vector regression and a self-organizing time map for broader time period classification. Enoch, a Bayesian regression-based model for date prediction, integrates AI with radiocarbon dating, presenting a pioneering technique for estimating manuscript dates. The interdisciplinary fusion of AI with historical research enhances our understanding of writers' identities, the dating of ancient manuscripts, and the contextualization of historical narratives. The thesis advances methodologies for analyzing ancient manuscripts, contributing to improved interpretations of the past and laying the foundation for further interdisciplinary exploration in historical document analysis

Artificial Intelligence in Historical Document Analysis:Pattern recognition and machine learning techniques in the study of ancient manuscripts with a focus on the Dead Sea Scrolls

The Ph.D. thesis investigates the potential of artificial intelligence (AI) in analyzing ancient historical manuscripts, focusing on the Dead Sea Scrolls (DSS) images. The research employs several computer vision, pattern recognition, and machine learning techniques to address writer identification and dating challenges. An initial study highlights the successful application of character shape features, achieving high accuracy in identifying multiple authors within the DSS collection.After recognizing the crucial role of binarization (extracting ink traces from the background materials) for accurate writer identification, the thesis introduces BiNet, an artificial deep neural network. BiNet utilizes multispectral images and outperforms traditional models in binarizing highly degraded ancient manuscripts, facilitating improved calculation of textural and allographic features. Building upon the success of BiNet, the study identifies multiple authors for the Great Isaiah Scroll, one of the longest scrolls in the DSS collection. The quantitative findings propose a hypothesis contrary to established assumptions about the scroll's authorship.With the success of writer identification, the thesis employs support vector regression and a self-organizing time map for broader time period classification. Enoch, a Bayesian regression-based model for date prediction, integrates AI with radiocarbon dating, presenting a pioneering technique for estimating manuscript dates. The interdisciplinary fusion of AI with historical research enhances our understanding of writers' identities, the dating of ancient manuscripts, and the contextualization of historical narratives. The thesis advances methodologies for analyzing ancient manuscripts, contributing to improved interpretations of the past and laying the foundation for further interdisciplinary exploration in historical document analysis

Propagation and Structure of Planar Streamer Fronts

Streamers often constitute the first stage of dielectric breakdown in strong electric fields: a nonlinear ionization wave transforms a non-ionized medium into a weakly ionized nonequilibrium plasma. New understanding of this old phenomenon can be gained through modern concepts of (interfacial) pattern formation. As a first step towards an effective interface description, we determine the front width, solve the selection problem for planar fronts and calculate their properties. Our results are in good agreement with many features of recent three-dimensional numerical simulations. In the present long paper, you find the physics of the model and the interfacial approach further explained. As a first ingredient of this approach, we here analyze planar fronts, their profile and velocity. We encounter a selection problem, recall some knowledge about such problems and apply it to planar streamer fronts. We make analytical predictions on the selected front profile and velocity and confirm them numerically. (abbreviated abstract)Comment: 23 pages, revtex, 14 ps file

Streamer Propagation as a Pattern Formation Problem: Planar Fronts

Streamers often constitute the first stage of dielectric breakdown in strong electric fields: a nonlinear ionization wave transforms a non-ionized medium into a weakly ionized nonequilibrium plasma. New understanding of this old phenomenon can be gained through modern concepts of (interfacial) pattern formation. As a first step towards an effective interface description, we determine the front width, solve the selection problem for planar fronts and calculate their properties. Our results are in good agreement with many features of recent three-dimensional numerical simulations.Comment: 4 pages, revtex, 3 ps file

Oil Spill Segmentation using Deep Encoder-Decoder models

Crude oil is an integral component of the modern world economy. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unavoidable. Even though oil spills are in and themselves difficult to clean up, the first and foremost challenge is to detect spills. In this research, the authors test the feasibility of deep encoder-decoder models that can be trained effectively to detect oil spills. The work compares the results from several segmentation models on high dimensional satellite Synthetic Aperture Radar (SAR) image data. Multiple combinations of models are used in running the experiments. The best-performing model is the one with the ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over Union (IoU) of 64.868% and a class IoU of 61.549% for the "oil spill" class when compared with the current benchmark model, which achieved a mean IoU of 65.05% and a class IoU of 53.38% for the "oil spill" class.Comment: 10 pages, 8 figures, 4 table

Oil Spill Segmentation using Deep Encoder-Decoder models

Crude oil is an integral component of the modern world economy. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unavoidable. Even though oil spills are in and themselves difficult to clean up, the first and foremost challenge is to detect spills. In this research, the authors test the feasibility of deep encoder-decoder models that can be trained effectively to detect oil spills. The work compares the results from several segmentation models on high dimensional satellite Synthetic Aperture Radar (SAR) image data. Multiple combinations of models are used in running the experiments. The best-performing model is the one with the ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over Union (IoU) of 64.868% and a class IoU of 61.549% for the "oil spill" class when compared with the current benchmark model, which achieved a mean IoU of 65.05% and a class IoU of 53.38% for the "oil spill" class

Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)

The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. This study takes an innovative approach to palaeography-the study of ancient handwriting-as a new entry point to access this scribal culture. One of the problems of palaeography is to determine writer identity or difference when the writing style is near uniform. This is exemplified by the Great Isaiah Scroll (1QIsaa). To this end, we use pattern recognition and artificial intelligence techniques to innovate the palaeography of the scrolls and to pioneer the microlevel of individual scribes to open access to the Bible's ancient scribal culture. We report new evidence for a breaking point in the series of columns in this scroll. Without prior assumption of writer identity, based on point clouds of the reduced-dimensionality feature-space, we found that columns from the first and second halves of the manuscript ended up in two distinct zones of such scatter plots, notably for a range of digital palaeography tools, each addressing very different featural aspects of the script samples. In a secondary, independent, analysis, now assuming writer difference and using yet another independent feature method and several different types of statistical testing, a switching point was found in the column series. A clear phase transition is apparent in columns 27-29. We also demonstrated a difference in distance variances such that the variance is higher in the second part of the manuscript. Given the statistically significant differences between the two halves, a tertiary, post-hoc analysis was performed using visual inspection of character heatmaps and of the most discriminative Fraglet sets in the script. Demonstrating that two main scribes, each showing different writing patterns, were responsible for the Great Isaiah Scroll, this study sheds new light on the Bible's ancient scribal culture by providing new, tangible evidence that ancient biblical texts were not copied by a single scribe only but that multiple scribes, while carefully mirroring another scribe's writing style, could closely collaborate on one particular manuscript