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Chaudhary, Shivam (author), Pandey, Pankaj (author), Miyapuram, Krishna Prasad (author), Lomas, J.D. (author)In the modern world, it is easy to get lost in thought, partly because of the vast knowledge available at our fingertips via smartphones that divide our cognitive resources and partly because of our intrinsic thoughts. In this work, we aim to find the differences in the neural signatures of mind-wandering and meditation that are common across...conference paper 2022
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Ganguly, Amlan (author), Abadal, Sergi (author), Thakkar, Ishan (author), Enright Jerger, Natalie (author), Riedel, Marc (author), Babaie, M. (author), Balasubramonian, Rajeev (author), Sebastian, Abu (author), Pasricha, Sudeep (author), Taskin, Baris (author)The computing world is witnessing a proverbial Cambrian explosion of emerging paradigms propelled by applications, such as artificial intelligence, big data, and cybersecurity. The recent advances in technology to store digital data inside a deoxyribonucleic acid (DNA) strand, manipulate quantum bits (qubits), perform logical operations with...journal article 2022
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Pérez-Dattari, Rodrigo (author), Ferreira de Brito, B.F. (author), de Groot, O.M. (author), Kober, J. (author), Alonso-Mora, J. (author)The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current autonomous navigation systems mainly rely on geometric information and hard-coded rules to induce safe and socially compliant behaviors. Yet, in unstructured urban...journal article 2022
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Yang, Ximei (author), Guendel, Ronny (author), Yarovoy, Alexander (author), Fioranelli, F. (author)Human activities classification in assisted living is one of the emerging applications of radar. The conventional analysis considers micro-Doppler signatures as the chosen input for feature extraction or deep learning classification algorithms, or, less frequently, other radar data formats such as the range-time, the range-Doppler, or the...conference paper 2022
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Xing, Xuejun (author), Guo, Jianwei (author), Nan, L. (author), Gu, Qingyi (author), Zhang, Xiaopeng (author), Yan, Dong Ming (author)The point pair feature (PPF) is widely used in industrial applications for estimating 6D poses of known objects from unrecognized point clouds. The key to the success of PPF matching is to establish correct 3D correspondences between the object and the scene, i.e., finding as many valid similar point pairs as possible. Thus, a set of...journal article 2022
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Xiao, C. (author), Lin, H.X. (author), Leeuwenburgh, O. (author), Heemink, A.W. (author)History matching can play a key role in improving geological characterization and reducing the uncertainty of reservoir model predictions. Application of reservoir history matching is restricted by the huge computational cost by amongst others the many runs of the full model. Surrogate models with a reduced complexity are therefore used to...journal article 2022
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Castillo, J.M. (author), Arif, M. (author), Starmans, M.P.A. (author), Niessen, W.J. (author), Bangma, C.H. (author), Schoots, Ivo G. (author), Veenland, J.F. (author)The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning-and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods,...journal article 2022
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Jeon, Jaemin (author), Kim, Jaeyong (author), Lee, Jong Jun (author), Shin, D. (author), Kim, Yoon Young (author)This study presents a new modeling technique to estimate the stiffness matrix of a thin-walled beam-joint structure using deep learning. When thin-walled beams meet at joints, significant sectional deformations occur, such as warping and distortion. These deformations should be considered in the one-dimensional beam analysis, but it is...journal article 2022
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Cuperman, Rafael (author), Jansen, K.M.B. (author), Ciszewski, M.G. (author)Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we...journal article 2022
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Mostafavi, F. (author), Tahsildoost, Mohammad (author), Zomorodian, Zahra Sadat (author), Shahrestani, Seyed Shayan (author)Purpose: In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design. Design/methodology/approach: A methodology using an image-based deep learning model called pix2pix is proposed...journal article 2022
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Mody, Prerak (author), Chaves-de-Plaza, Nicolas F. (author), Hildebrandt, K.A. (author), Staring, M. (author)Bayesian Neural Nets (BNN) are increasingly used for robust organ auto-contouring. Uncertainty heatmaps extracted from BNNs have been shown to correspond to inaccurate regions. To help speed up the mandatory quality assessment (QA) of contours in radiotherapy, these heatmaps could be used as stimuli to direct visual attention of clinicians to...conference paper 2022
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Li, Fang (author), Li, Xueyuan (author), Liu, Qi (author), Li, Z. (author)Pedestrian detection is an important branch of computer vision, and it has important applications in the fields of autonomous driving, artificial intelligence and video surveillance.With the rapid development of deep learning and the proposal of large-scale datasets, pedestrian detection has reached a new stage and achieves better performance...journal article 2022
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Rezaeezade, A. (author), Perin, G. (author), Picek, S. (author)Profiling side-channel analysis allows evaluators to estimate the worst-case security of a target. When security evaluations relax the assumptions about the adversary’s knowledge, profiling models may easily be sub-optimal due to the inability to extract the most informative points of interest from the side-channel measurements. When used for...conference paper 2022
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Smirnova, Alisa (author), Yang, J. (author), Yang, Dingqi (author), Cudre-Mauroux, Philippe (author)Noisy labels represent one of the key issues in supervised machine learning. Existing work for label noise reduction mainly takes a probabilistic approach that infers true labels from data distributions in low-level feature spaces. Such an approach is not only limited by its capability to learn high-quality data representations, but also by...journal article 2022
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Shengren, H. (author), Salazar, Edgar Mauricio (author), Vergara Barrios, P.P. (author), Palensky, P. (author)Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems’ operational cost and technical constraints (e.g, generation-demand...conference paper 2022
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Mohammadpourfard, Mostafa (author), Weng, Yang (author), Khalili, Abdullah (author), Genc, Istemihan (author), Shefaei, A. (author), Mohammadi-Ivatloo, Behnam (author)The expansion of power systems over large geographical areas renders centralized processing inefficient. Therefore, the distributed operation is increasingly adopted. This work introduces a new type of attack against distributed state estimation of power systems, which operates on inter-area boundary buses. We show that the developed attack...journal article 2022
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Merkx, D.G.M. (author), Scholten, Sebastiaan (author), Frank, Stefan L. (author), Ernestus, Mirjam (author), Scharenborg, O.E. (author)Many computational models of speech recognition assume that the set of target words is already given. This implies that these models learn to recognise speech in a biologically unrealistic manner, i.e. with prior lexical knowledge and explicit supervision. In contrast, visually grounded speech models learn to recognise speech without prior...journal article 2022
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Applis, L.H. (author), Panichella, A. (author), van Deursen, A. (author)Metamorphic testing is a well-established testing technique that has been successfully applied in various domains, including testing deep learning models to assess their robustness against data noise or malicious input. Currently, metamorphic testing approaches for machine learning (ML) models focused on image processing and object recognition...conference paper 2021
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Li, G. (author), Knoop, V.L. (author), van Lint, J.W.C. (author)Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. Recently, deep learning approach, as a data-driven alternative to traffic flow model-based data assimilation and prediction methods, has become popular in this domain. Many of these deep learning...journal article 2021
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Ahishakiye, Emmanuel (author), van Gijzen, M.B. (author), Tumwiine, Julius (author), Wario, Ruth (author), Obungoloch, Johnes (author)Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Deep learning and its applications in medical imaging, especially in image reconstruction have received considerable attention in the literature in recent years. This study reviews records obtained...review 2021