ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain

We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we describe a novel deep network architecture for computing compact temporal content hashes (TCHs) from audio-visual streams with durations of minutes or hours. Our TCHs are sensitive to accidental or malicious content modification (tampering) but invariant to the codec used to encode the video. This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, Estonia and Norway participated.Comment: Accepted to CVPR Blockchain Workshop 201

Morphological evolution of the mammalian jaw adductor complex

The evolution of the mammalian jaw during the transition from non-mammalian synapsids to crown mammals is a key event in vertebrate history and characterised by the gradual reduction of its individual bones into a single element and the concomitant transformation of the jaw joint and its incorporation into the middle ear complex. This osteological transformation is accompanied by a rearrangement and modification of the jaw adductor musculature, which is thought to have allowed the evolution of a more-efficient masticatory system in comparison to the plesiomorphic synapsid condition. While osteological characters relating to this transition are well documented in the fossil record, the exact arrangement and modifications of the individual adductor muscles during the cynodont–mammaliaform transition have been debated for nearly a century. We review the existing knowledge about the musculoskeletal evolution of the mammalian jaw adductor complex and evaluate previous hypotheses in the light of recently documented fossils that represent new specimens of existing species, which are of central importance to the mammalian origins debate. By employing computed tomography (CT) and digital reconstruction techniques to create three-dimensional models of the jaw adductor musculature in a number of representative non-mammalian cynodonts and mammaliaforms, we provide an updated perspective on mammalian jaw muscle evolution. As an emerging consensus, current evidence suggests that the mammal-like division of the jaw adductor musculature (into deep and superficial components of the m. masseter, the m. temporalis and the m. pterygoideus) was completed in Eucynodontia. The arrangement of the jaw adductor musculature in a mammalian fashion, with the m. pterygoideus group inserting on the dentary was completed in basal Mammaliaformes as suggested by the muscle reconstruction of Morganucodon oehleri. Consequently, transformation of the jaw adductor musculature from the ancestral (‘reptilian’) to the mammalian condition must have preceded the emergence of Mammalia and the full formation of the mammalian jaw joint. This suggests that the modification of the jaw adductor system played a pivotal role in the functional morphology and biomechanical stability of the jaw joint

Diseño, implementación y optimización del sistema de compresión de imágenes sobre el ordenador de a bordo del proyecto de nanosátelite Eye-Sat

Eye-Sat es un Proyecto de nano satélites, dirigido por el CNES (Centre National d’Etudes Spatiales) y desarrollado principalmente por estudiantes de varias escuelas de ingeniería del territorio francés. El objetivo de este pequeño telescopio no solo radica en la oportunidad de realizar la demostración de distintos dispositivos tecnológicos, sino que también tiene como misión la adquisición de fotografías en la bandas de color e infrarrojo de la vía Láctea, así como el estudio de la intensidad y polarización de la luz Zodiacal. Los requerimientos de la misión exigen el desarrollo de un algoritmo de compresión de imágenes sin pérdidas para las imágenes “Color Filter Array” CFA (Bayer) e infrarrojas adquiridas por el satélite. Como miembro de la comisión consultativa para los sistemas espaciales, CNES ha seleccionado el estándar CCSDS-123.0-B como algoritmo base para cumplir los requerimientos de la misión. A este algoritmo se le añadirán modificaciones o mejoras, adaptadas a las imágenes tipo, con el fin de mejorar las prestaciones de compresión y de complejidad. La implementación y la optimización del algoritmo será desarrollada sobre la plataforma Xilinx Zynq® All Programmable SoC, el cual incluye una FPGA y un Dual-core ARM® Cortex™-A9 processor with NEONTM DSP/FPU Engine

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations. The goal of this work is to combine the main advantages of the two: to follow a big picture view, while providing a particular theory and its implementation. In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which it should be easier to integrate additional required functionality. In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world, learning the influence of one's own actions on the world, model-based reinforcement learning, hierarchical planning and plan execution, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations with the following properties: 1) they are increasingly more abstract, but can retain details when needed, and 2) they are easy to manipulate in their local and symbolic-like form, thus also allowing one to observe the learning process at each level of abstraction. On all levels of the system, the representation of the data can be interpreted in both a symbolic and a sub-symbolic manner. This enables the architecture to learn efficiently using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl

Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences

Results: We present an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. We combine accurate unsupervised processes with an intuitive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc

Facilitating forensic examinations of multi-user computer environments through session-to-session analysis of internet history

This paper proposes a new approach to the forensic investigation of Internet history artefacts by aggregating the history from a recovered device into sessions and comparing those sessions to other sessions to determine whether they are one-time events or form a repetitive or habitual pattern. We describe two approaches for performing the session aggregation: fixed-length sessions and variable-length sessions. We also describe an approach for identifying repetitive pattern of life behaviour and show how such patterns can be extracted and represented as binary strings. Using the Jaccard similarity coefficient, a session-to-session comparison can be performed and the sessions can be analysed to determine to what extent a particular session is similar to any other session in the Internet history, and thus is highly likely to correspond to the same user. Experiments have been conducted using two sets of test data, where multiple users have access to the same computer. By identifying patterns of Internet usage that are unique to each user, our approach exhibits a high success rate in attributing particular sessions of the Internet history to the correct user. This can provide considerable help to a forensic investigator trying to establish which user was using the computer when a web-related crime was committed