42,196 research outputs found

    Unsupervised segmentation of irradiation\unicode{x2010}induced order\unicode{x2010}disorder phase transitions in electron microscopy

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    We present a method for the unsupervised segmentation of electron microscopy images, which are powerful descriptors of materials and chemical systems. Images are oversegmented into overlapping chips, and similarity graphs are generated from embeddings extracted from a domain\unicode{x2010}pretrained convolutional neural network (CNN). The Louvain method for community detection is then applied to perform segmentation. The graph representation provides an intuitive way of presenting the relationship between chips and communities. We demonstrate our method to track irradiation\unicode{x2010}induced amorphous fronts in thin films used for catalysis and electronics. This method has potential for "on\unicode{x2010}the\unicode{x2010}fly" segmentation to guide emerging automated electron microscopes.Comment: 7 pages, 3 figures. Accepted to Machine Learning and the Physical Sciences Workshop, NeurIPS 202

    Automated Calculation of Thermal Rate Coefficients using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning

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    We propose a methodology for fully automated calculation of thermal rate coefficients of gas phase chemical reactions, which is based on combining the ring polymer molecular dynamics (RPMD) with the machine-learning interatomic potentials actively learning on-the-fly. Based on the original computational procedure implemented in the RPMDrate code, our methodology gradually and automatically constructs the potential energy surfaces (PESs) from scratch with the data set points being selected and accumulated during the RPMDrate simulation. Such an approach ensures that our final machine-learning model provides reliable description of the PES which avoids artifacts during exploration of the phase space by RPMD trajectories. We tested our methodology on two representative thermally activated chemical reactions studied recently by RPMDrate at temperatures within the interval of 300--1000~K. The corresponding PESs were generated by fitting to only a few thousands automatically generated structures (less than 5000) while the RPMD rate coefficients retained the deviation from the reference values within the typical convergence error of RPMDrate. In future, we plan to apply our methodology to chemical reactions which proceed via complex-formation thus providing a completely general tool for calculating RPMD thermal rate coefficients for any polyatomic gas phase chemical reaction

    Surveillance by Algorithm: The NSA, Computerized Intelligence Collection, and Human Rights

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    ISIS’s cultivation of social media has reinforced states’ interest in using automated surveillance. However, automated surveillance using artificial intelligence (“machine learning”) techniques has also sharpened privacy concerns that have been acute since Edward Snowden’s disclosures. This Article examines machine-based surveillance by the NSA and other intelligence agencies through the prism of international human rights. Two camps have clashed on the human rights implications of machine surveillance abroad. The state-centric camp argues that human rights agreements like the International Covenant on Civil and Political Rights (ICCPR) do not apply extraterritorially. Moreover, the state-centric camp insists, machine surveillance is inherently unintrusive, like a dog seeing a human step out of the shower. Surveillance critics respond that machine and human access to data are equivalent invasions of privacy and legal protections must be equal for individuals within a state’s borders and nonnationals overseas. In a controversial recent decision, Schrems v. Data Protection Commissioner, the European Court of Justice appeared to side with surveillance’s critics. This Article argues that both the state-centric and critical positions are flawed. This Article agrees with surveillance critics that the ICCPR applies extraterritorially. Machine access to data can cause both ontological harm, stemming from individuals’ loss of spontaneity, and consequential harm, stemming from errors that machines compound in databases such as no-fly lists. However, the Schrems decision went too far by failing to acknowledge that human rights law provides states with a measure of deference in confronting threats such as ISIS. Deference on overseas surveillance is particularly appropriate given U.N. Security Council resolutions urging states to deny terrorists safe havens. But deference cannot be absolute. To provide appropriate safeguards, this Article recommends that machine searches abroad be tailored to compelling state purposes, scientifically validated, and subject to independent review

    Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing

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    Computation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document. This limits the applicability of quality metrics in applications such as hyperparameter optimization of image processing algorithms that operate on-the-fly on unseen documents. This work proposes the use of surrogate models to learn the behavior of a given document quality metric on existing datasets where ground truth images are available. The trained surrogate model can later be used to predict the metric value on previously unseen document images without requiring access to ground truth images. The surrogate model is empirically evaluated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets

    Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms

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    Machine learning techniques have successfully been used to extract structural information such as the crystal space group from powder X-ray diffractograms. However, training directly on simulated diffractograms from databases such as the ICSD is challenging due to its limited size, class-inhomogeneity, and bias toward certain structure types. We propose an alternative approach of generating synthetic crystals with random coordinates by using the symmetry operations of each space group. Based on this approach, we demonstrate online training of deep ResNet-like models on up to a few million unique on-the-fly generated synthetic diffractograms per hour. For our chosen task of space group classification, we achieved a test accuracy of 79.9% on unseen ICSD structure types from most space groups. This surpasses the 56.1% accuracy of the current state-of-the-art approach of training on ICSD crystals directly. Our results demonstrate that synthetically generated crystals can be used to extract structural information from ICSD powder diffractograms, which makes it possible to apply very large state-of-the-art machine learning models in the area of powder X-ray diffraction. We further show first steps toward applying our methodology to experimental data, where automated XRD data analysis is crucial, especially in high-throughput settings. While we focused on the prediction of the space group, our approach has the potential to be extended to related tasks in the future

    The historical development and basis of human factors guidelines for automated systems in aeronautical operations

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    In order to derive general design guidelines for automated systems a study was conducted on the utilization and acceptance of existing automated systems as currently employed in several commercial fields. Four principal study area were investigated by means of structured interviews, and in some cases questionnaires. The study areas were aviation, a both scheduled airline and general commercial aviation; process control and factory applications; office automation; and automation in the power industry. The results of over eighty structured interviews were analyzed and responses categoried as various human factors issues for use by both designers and users of automated equipment. These guidelines address such items as general physical features of automated equipment; personnel orientation, acceptance, and training; and both personnel and system reliability

    Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

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    In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA, all software processes in the system start execution subjected to a conventional ML detector for fast classification. If a piece of software receives a borderline classification, it is subjected to further analysis via more performance expensive and more accurate DL methods, via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays to the execution of software subjected to deep learning analysis as a way to "buy time" for DL analysis and to rate-limit the impact of possible malware in the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and 877 commonly used benign software samples from various categories for the Windows OS. Our results show that the false positive rate for conventional ML methods can reach 20%, and for modern DL methods it is usually below 6%. However, the classification time for DL can be 100X longer than conventional ML methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the percentage of software subjected to DL analysis was approximately 40% on average. Further, the application of delays in software subjected to ML reduced the detection time by approximately 10%. Finally, we found and discussed a discrepancy between the detection accuracy offline (analysis after all traces are collected) and on-the-fly (analysis in tandem with trace collection). Our insights show that conventional ML and modern DL-based malware detectors in isolation cannot meet the needs of efficient and effective malware detection: high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure
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