406 research outputs found

    Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning

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    The paper introduces the application of information geometry to describe the ground states of Ising models by utilizing parity-check matrices of cyclic and quasi-cyclic codes on toric and spherical topologies. The approach establishes a connection between machine learning and error-correcting coding. This proposed approach has implications for the development of new embedding methods based on trapping sets. Statistical physics and number geometry applied for optimize error-correcting codes, leading to these embedding and sparse factorization methods. The paper establishes a direct connection between DNN architecture and error-correcting coding by demonstrating how state-of-the-art architectures (ChordMixer, Mega, Mega-chunk, CDIL, ...) from the long-range arena can be equivalent to of block and convolutional LDPC codes (Cage-graph, Repeat Accumulate). QC codes correspond to certain types of chemical elements, with the carbon element being represented by the mixed automorphism Shu-Lin-Fossorier QC-LDPC code. The connections between Belief Propagation and the Permanent, Bethe-Permanent, Nishimori Temperature, and Bethe-Hessian Matrix are elaborated upon in detail. The Quantum Approximate Optimization Algorithm (QAOA) used in the Sherrington-Kirkpatrick Ising model can be seen as analogous to the back-propagation loss function landscape in training DNNs. This similarity creates a comparable problem with TS pseudo-codeword, resembling the belief propagation method. Additionally, the layer depth in QAOA correlates to the number of decoding belief propagation iterations in the Wiberg decoding tree. Overall, this work has the potential to advance multiple fields, from Information Theory, DNN architecture design (sparse and structured prior graph topology), efficient hardware design for Quantum and Classical DPU/TPU (graph, quantize and shift register architect.) to Materials Science and beyond.Comment: 71 pages, 42 Figures, 1 Table, 1 Appendix. arXiv admin note: text overlap with arXiv:2109.08184 by other author

    Land Use and Land Cover Mapping in a Changing World

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    It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classiïŹcation systems

    METROPOLITAN ENCHANTMENT AND DISENCHANTMENT. METROPOLITAN ANTHROPOLOGY FOR THE CONTEMPORARY LIVING MAP CONSTRUCTION

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    We can no longer interpret the contemporary metropolis as we did in the last century. The thought of civil economy regarding the contemporary Metropolis conflicts more or less radically with the merely acquisitive dimension of the behaviour of its citizens. What is needed is therefore a new capacity for imagining the economic-productive future of the city: hybrid social enterprises, economically sustainable, structured and capable of using technologies, could be a solution for producing value and distributing it fairly and inclusively. Metropolitan Urbanity is another issue to establish. Metropolis needs new spaces where inclusion can occur, and where a repository of the imagery can be recreated. What is the ontology behind the technique of metropolitan planning and management, its vision and its symbols? Competitiveness, speed, and meritocracy are political words, not technical ones. Metropolitan Urbanity is the characteristic of a polis that expresses itself in its public places. Today, however, public places are private ones that are destined for public use. The Common Good has always had a space of representation in the city, which was the public space. Today, the Green-Grey Infrastructure is the metropolitan city's monument that communicates a value for future generations and must therefore be recognised and imagined; it is the production of the metropolitan symbolic imagery, the new magic of the city

    A Tutorial on the Tracking, Telemetry, and Command (TT&C) for Space Missions

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    This paper presents a tutorial on the Tracking, Telemetry, and Command (TT&C) for spacecraft and satellite missions. In particular, it provides a thorough summary of the design of the TT&C, starting from elementary system aspects and going down to the details of the on-board TT&C subsystem design, its units, and the physical layer. The paper is then complemented with a description of emerging TT&C techniques and technologies, the standardization framework, and practical examples of actual spacecraft design of European space missions. The here-presented tutorial is thought for professionals (also in other telecommunication engineering fields) willing to face the challenges and state-of-the-art of the TT&C, and know more about this fundamental function that allows us to control and monitor our spacecraft on a daily basis

    Signal classification at discrete frequencies using machine learning

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    Incidents such as the 2018 shut down of Gatwick Airport due to a small Unmanned Aerial System (UAS) airfield incursion, have shown that we don’t have routine and consistent detection and classification methods in place to recognise unwanted signals in an airspace. Today, incidents of this nature are taking place around the world regularly. The first stage in mitigating a threat is to know whether a threat is present. This thesis focuses on the detection and classification of Global Navigation Satellite Systems (GNSS) jamming radio frequency (RF) signal types and small commercially available UAS RF signals using machine learning for early warning systems. RF signals can be computationally heavy and sometimes sensitive to collect. With neural networks requiring a lot of information to train from scratch, the thesis explores the use of transfer learning from the object detection field to lessen this burden by using graphical representations of the signal in the frequency and time domain. The thesis shows that utilising the benefits of transfer learning with both supervised and unsupervised learning and graphical signal representations, can provide high accuracy detection and classification, down to the fidelity of whether a small UAS is flying or stationary. By treating the classification of RF signals as an image classification problem, this thesis has shown that transfer learning through CNN feature extraction reduces the need for large datasets while still providing high accuracy results. CNN feature extraction and transfer learning was also shown to improve accuracy as a precursor to unsupervised learning but at a cost of time, while raw images provided a good overall solution for timely clustering. Lastly the thesis has shown that the implementation of machine learning models using a raspberry pi and software defined radio (SDR) provides a viable option for low cost early warning systems

    Cyber-Human Systems, Space Technologies, and Threats

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    CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp

    Proceedings of the 29th International Symposium on Analytical and Environmental Problems

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    Socio-ecological sustainability of cotton farming systems in central India

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    Agricultural land covers 38% of the total world’s land surface area. These are man made ecosystems which provide Ecosystem Services of food, fibre and fuel to human society. With the world population predicted to reach 8.9 billion by 2050, one of the most important challenges the world is facing today is to increase its agricultural production in ways that is sustainable. To work towards a more sustainable world, 17 Sustainable Development Goals (SDGs) have been adopted by the world leaders. There have been many studies looking at the ecology of food production but not on cotton production. Cotton is the most important fibre in the world: it is also the most polluting cash crop in the world. India is responsible for 26% of global cotton production of which more than 95% is genetically modified Bt-cotton. As well as being a major conventional producer, India is also the largest country producer of organic cotton. Despite this leading role, India has one of the lowest yields per hectare in the worldwhich is attributable to challenges in soil fertility and inadequate plant protection.Focusing on the impact of agricultural management on biodiversity is essential to ensure that cotton productivity is ecologically sustainable in the long-term. In this study, the functional biodiversity above and below ground was evaluated on plot-scale and farm-scale systems using bio-indicators to evaluate the potential ecological sustainability of four cotton farming systems (CFS) practiced in India: conventional; Bt conventional; organic and biodynamic. The long-term comparison study showed thatBt-cotton had no further significant effect on the above and below ground biota in comparison to the non Bt-conventional cotton systems. Both organic systems showeda significant higher biodiversity in comparison to both conventional systems. In the above ground diversity, the predator: pest ratio was higher in both organic systems. In the below ground diversity, the earthworm biomass and abundance were higher in both organic systems. The fungi Trichoderma sp. was significantly more abundant in Biodynamic systems in comparison to other systems. The aim of this thesis was to assess the socio-ecological sustainability of cotton farming in Central India. To evaluate the socio-ecological sustainability, this study assessed farm-scale systems using working cotton farms (12 farms: 6 pairs of Bt conventional and organic systems) by modifying an FAO model to develop a context based assessment tool. The study showed that conventional management hadnegative effects on the above and below ground functional biodiversity on the plot scale and farm-scale cotton systems. On the farms, socio-economic indicators showed that organic systems were significantly more sustainable in comparison to conventional systems, however, there is still need improvement for both farming systems. Adding ecological empirical data to the framework didn’t make a difference in determining which of the two systems were the most sustainable. However, integrated the ecological indicators facilitated insightful understanding of farmers management choices and highlighted the contextual problem that farmers face while growing cotton in Central India

    Use of Bacteroides thetaiotaomicron derived extracellular vesicles as vaccine delivery vehicles for mucosal vaccines against respiratory pathogens

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    Most infectious pathogens enter the body via mucosal sites, yet very few mucosal vaccines have been licensed. Major hurdles in mucosal vaccine development, such as stability, mucosal barriers and immune tolerance, hinder delivery of antigens to mucosa-immune cells. New vaccine delivery systems are therefore needed to provide broad and long-term protection against respiratory viruses. To address these issues Bacteroides thetaiotaomicron (Bt), a human commensal gut bacterium, has been engineered to export into their bacterial extracellular vesicles (BEVs) Yersinia pestis, influenza virus (IV), or SARS-CoV-2 antigens. In addition, two means of decorating Bt BEVs with antigens have also been explored, using highly expressed vitamin B12 receptors and chemical conjugation. Pre-clinical studies using non-human primates (Y. pestis) or murine models (IAV and SARS-CoV-2) were used to determine the immunogenicity of Bt BEV vaccines and their ability to induce protective immune responses. Native Bt BEVs displayed inherent adjuvanticity after intranasal administration, as shown by their ability to elicit mobilisation of immune cells and the development of organised lymphoid structures in the upper and lower respiratory tract. BEV vaccines were safe with no signs of any adverse effects in immunised animals. Bt BEVs vaccine formulations induced antigen-specific local and systemic humoral (IgA/IgG) and cellular (IFN- and/or TNF- producing CD4/8 T cells) immune responses. For Y. pestis BEV vaccines correlates of protection were obtained from serum antibody mediated neutralisation and cytotoxicity assays using live plague bacteria. BEV-IAV vaccines provided heterotypic protection against a lethal dose of H1N1 IAV. Initial pre-clinical studies of SARS-CoV-2 BEV vaccines showed them to be capable of inducing low levels of antigen-specific mucosal and systemic IgA and IgG antibodies. Additional studies to optimise antigen expression, dose and frequency refinements are needed. The results obtained showed that Bt BEV could be used as a platform to produce mucosal vaccines against respiratory pathogens
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