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DronePick: Object Picking and Delivery Teleoperation with the Drone Controlled by a Wearable Tactile Display

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DronePick: Object Picking and Delivery Teleoperation with th...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Roman Ibrahimov Evgeny Tsykunov Vladimir Shirokun Andrey Somov Dzmitry Tsetserukou Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology Moscow Russian Federation Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russian Federation

ISBN: (纸本)9781728126234

We report on the teleoperation system DronePick which provides remote object picking and delivery by a human- controlled quadcopter. The main novelty of the proposed system is that the human user continuously gets the visual and haptic feedback for accurate teleoperation. DronePick consists of a quadcopter equipped with a magnetic grabber, a tactile glove with finger motion tracking sensor, hand tracking system, and the Virtual Reality (VR) application. The human operator teleoperates the quadcopter by changing the position of the hand. The proposed vibrotactile patterns representing the location of the remote object relative to the quadcopter are delivered to the glove. It helps the operator to determine when the quadcopter is right above the object. When the “pick” command is sent by clasping the hand in the glove, the quadcopter decreases its altitude and the magnetic grabber attaches the target object. The whole scenario is in parallel simulated in VR. The air flow from the quadcopter and the relative positions of VR objects help the operator to determine the exact position of the delivered object to be picked. The experiments showed that the vibrotactile patterns were recognized by the users at the high recognition rates: the average 99% recognition rate and the average 2.36s recognition time. The real-life implementation of DronePick featuring object picking and delivering to the human was developed and tested.

关键词： drone honey bees Gloves remote operation Picking Human Touch Variable reluctance Obstetric Delivery commands air flow

Corvus A: A low-mass, isolated galaxy at 3.5 Mpc

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arXiv 2024年

作者： Jones, Michael G. Sand, David J. Mutlu-Pakdil, Burçin Fielder, Catherine E. Crnojević, Denija Bennet, Paul Spekkens, Kristine Donnerstein, Richard Doliva-Dolinsky, Amandine Karunakaran, Ananthan Strader, Jay Zaritsky, Dennis Steward Observatory University of Arizona 933 North Cherry Avenue Rm. N204 TucsonAZ85721-0065 United States Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States University of Tampa 401 West Kennedy Boulevard TampaFL33606 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Physics and Space Science Royal Military College of Canada P.O. Box 17000 Station Forces Kingston ONK7K 7B4 Canada Department of Physics Engineering Physics and Astronomy Queen’s University KingstonONK7L 3N6 Canada Department of Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Dunlap Institute for Astronomy and Astrophysics University of Toronto TorontoONM5S 3H4 Canada Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States

We report the discovery of Corvus A, a low-mass, gas-rich galaxy at a distance of approximately 3.5 Mpc, identified in DR10 of the Dark Energy Camera Legacy Imaging Survey during the initial phase of our ongoing SEmi-Automated Machine LEarning Search for Semi-resolved galaxies (SEAMLESS). Jansky Very Large Array observations of Corvus A detect H i line emission at a radial velocity of 523 ± 2 km s−1. Magellan/Megacam imaging reveals an irregular and complex stellar population with both young and old stars. We detect UV emission in Neil Gehrels Swift observations, indicative of recent star formation. However, there are no signs of H ii regions in Hα imaging from Steward Observatory’s Kuiper telescope. Based on the Megacam color magnitude diagram we measure the distance to Corvus A via the tip-of-the-red-giant-branch standard candle as 3.48 ± 0.24 Mpc. This makes Corvus A remarkably isolated, with no known galaxy within ∼1 Mpc. Based on this distance, we estimate the H i and stellar mass of Corvus A to be log MHI/M. = 6.59 and log M∗/M. = 6.0. Although there are some signs of rotation, the H i distribution of Corvus A appears to be close to face-on, analogous to that of Leo T, and we therefore do not attempt to infer a dynamical mass from its H i line width. Higher resolution synthesis imaging is required to confirm this morphology and to draw robust conclusions from its gas kinematics. © 2024, CC BY.

关键词： Galaxies

Constructions of batch codes via finite geometry

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arXiv 2019年

作者： Polyanskii, Nikita Vorobyev, Ilya Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow127051 Russia Advanced Combinatorics and Complex Networks Lab Moscow Institute of Physics and Technology Dolgoprudny141701 Russia

A primitive k-batch code encodes a string x of length n into string y of length N, such that each multiset of k symbols from x has k mutually disjoint recovering sets from y. We develop new explicit and random coding constructions of linear primitive batch codes based on finite geometry. In some parameter regimes, our proposed codes have lower redundancy than previously known batch codes. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

A New Algorithm for Two-Stage Group Testing

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arXiv 2019年

作者： Vorobyev, Ilya Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow127051 Russia Advanced Combinatorics and Complex Networks Lab Moscow Institute of Physics and Technology Dolgoprudny141701 Russia

Group testing is a well-known search problem that consists in detecting of s defective members of a set of t samples by carrying out tests on properly chosen subsets of samples. In classical group testing the goal is to find all defective elements by using the minimal possible number of tests in the worst case. In this work, two-stage group testing is considered. Using the hypergraph approach we design a new search algorithm, which allows improving the known results for fixed s and t → ∞. For the case s = 2 this algorithm achieves information-theoretic lower bound 2 log2 t(1 + o(1)) on the number of tests in the worst case. Also, the problem of finding m out of s defectives is considered. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Empirical study of extreme overfitting points of neural networks

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arXiv 2019年

作者： Merkulov, Daniil Oseledets, Ivan Skolkovo Institute of Science and Technology Center for Computational and Data-Intensive Science and Engineering Moscow Russia Moscow Institute of Physics and Technology Moscow Russia Institute of Numerical Mathematics Russian Academy of Sciences Moscow Russia

In this paper we propose a method of obtaining points of extreme overfitting - parameters of modern neural networks, at which they demonstrate close to 100 % training accuracy, simultaneously with almost zero accuracy on the test sample. Despite the widespread opinion that the overwhelming majority of critical points of the loss function of a neural network have equally good generalizing ability, such points have a huge generalization error. The paper studies the properties of such points and their location on the surface of the loss function of modern neural networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Integrated information as a common signature of dynamical and information-processing complexity

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arXiv 2021年

作者： Mediano, Pedro A.M. Rosas, Fernando E. Farah, Juan Carlos Shanahan, Murray Bor, Daniel Barrett, Adam B. Department of Psychology University of Cambridge CambridgeCB2 3EB United Kingdom Centre for Psychedelic Research Department of Brain Science Imperial College London LondonSW7 2DD United Kingdom Data Science Institute Imperial College London LondonSW7 2AZ United Kingdom Centre for Complexity Science Imperial College London LondonSW7 2AZ United Kingdom School of Engineering École Polytechnique Fédérale de Lausanne LausanneCH-1015 Switzerland Department of Computing Imperial College London LondonSW7 2RH United Kingdom Sackler Center for Consciousness Science Department of Informatics University of Sussex BrightonBN1 9RH United Kingdom The Data Intensive Science Centre Department of Physics and Astronomy University of Sussex BrightonBN1 9QH United Kingdom

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress. Nonetheless, given the shared theoretical goals between both approaches, it is reasonable to conjecture the existence of underlying common signatures that capture interesting behaviour in both dynamical and information-processing systems. Here we argue that a pragmatic use of Integrated Information Theory (IIT), originally conceived in theoretical neuroscience, can provide a potential unifying framework to study complexity in general multivariate systems. Furthermore, by leveraging metrics put forward by the integrated information decomposition (ΦID) framework, our results reveal that integrated information can effectively capture surprisingly heterogeneous signatures of complexity - including metastability and criticality in networks of coupled oscillators as well as distributed computation and emergent stable particles in cellular automata - without relying on idiosyncratic, ad-hoc criteria. These results show how an agnostic use of IIT can provide important steps towards bridging the gap between informational and dynamical approaches to complex systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Information theory

Time- and memory-efficient representation of complex mesoscale potentials

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JOURNAL OF COMPUTATIONAL PHYSICS 2017年 343卷 110-114页

作者： Drozdov, Grigory Ostanin, Igor Oseledets, Ivan Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia

We apply the modern technique of approximation of multivariate functions - tensor train cross approximation - to the problem of the description of physical interactions between complex-shaped bodies in a context of computational nanomechanics. In this note we showcase one particular example - van der Waals interactions between two cylindrical bodies - relevant to modeling of carbon nanotube systems. The potential is viewed as a tensor ( multidimensional table) which is represented in compact form with the help of tensor train decomposition. The described approach offers a universal solution for the description of van der Waals interactions between complex-shaped nanostructures and can be used within the framework of such systems of mesoscale modeling as recently emerged mesoscopic distinct element method (MDEM). (C) 2017 Elsevier Inc. All rights reserved.

关键词： Mesoscale modeling Tensor train cross approximation

Pavo: Discovery of a star-forming dwarf galaxy just outside the Local Group

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arXiv 2023年

作者： Jones, Michael G. Mutlu-Pakdil, Burçin Sand, David J. Donnerstein, Richard Crnojević, Denija Bennet, Paul Fielder, Catherine E. Karunakaran, Ananthan Spekkens, Kristine Strader, Jay Urquhart, Ryan Zaritsky, Dennis Steward Observatory University of Arizona 933 North Cherry Avenue Rm. N204 TucsonAZ85721-0065 United States Department of Physics and Astronomy Dartmouth College HanoverNH03755 United States University of Tampa 401 West Kennedy Boulevard TampaFL33606 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Dunlap Institute for Astronomy and Astrophysics University of Toronto TorontoONM5S 3H4 Canada Department of Physics and Space Science Royal Military College of Canada Station Forces P.O. Box 17000 KingstonONK7K 7B4 Canada Department of Physics Engineering Physics and Astronomy Queen’s University KingstonONK7L 3N6 Canada Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States

We report the discovery of Pavo, a faint (MV = −10.0), star-forming, irregular, and extremely isolated dwarf galaxy at D ≈ 2 Mpc. Pavo was identified in Dark Energy Camera Legacy Survey imaging via a novel approach that combines low surface brightness galaxy search algorithms and machine learning candidate classifications. Follow-up imaging with the Inamori-Magellan Areal Camera & Spectrograph on the 6.5 m Magellan Baade telescope revealed a color–magnitude diagram (CMD) with an old stellar population, in addition to the young population that dominates the integrated light, and a tip-of-the-red-giant-branch distance estimate of (formula presented) Mpc. The blue population of stars in the CMD is consistent with the youngest stars having formed no later than 150 Myr ago. We also detected no Hα emission with SOAR telescope imaging, suggesting we may be witnessing a temporary low in Pavo’s star formation. We estimate the total stellar mass of Pavo to be log M∗/M☉ = 5.6 ± 0.2 and measure an upper limit on its H i gas mass of 1.0 × 106 M☉ based on the HIPASS survey. Given these properties, Pavo’s closest analog is Leo P (D = 1.6 Mpc), previously the only known isolated, star-forming, Local Volume dwarf galaxy in this mass range. However, Pavo appears to be even more isolated, with no other known galaxy residing within over 600 kpc. As surveys and search techniques continue to improve, we anticipate an entire population of analogous objects being detected just outside the Local Group. © 2023, CC BY.

关键词： Cameras

Iterative joint image demosaicking and denoising using a residual denoising network

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arXiv 2018年

作者： Kokkinos, Filippos Lefkimmiatis, Stamatios Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology

Modern digital cameras rely on the sequential execution of separate image processing steps to produce realistic images. The first two steps are usually related to denoising and demosaicking where the former aims to reduce noise from the sensor and the latter converts a series of light intensity readings to color images. Modern approaches try to jointly solve these problems, i.e. joint denoising-demosaicking which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest are perturbed by noise. While there are several machine learning systems that have been recently introduced to solve this problem, the majority of them relies on generic network architectures which do not explicitly take into account the physical image model. In this work we propose a novel algorithm which is inspired by powerful classical image regularization methods, large-scale optimization, and deep learning techniques. Consequently, our derived iterative optimization algorithm, which involves a trainable denoising network, has a transparent and clear interpretation compared to other black-box data driven approaches. Our extensive experimentation line demonstrates that our proposed method outperforms any previous approaches for both noisy and noisefree data across many different datasets. This improvement in reconstruction quality is attributed to the rigorous derivation of an iterative solution and the principled way we design our denoising network architecture, which as a result requires fewer trainable parameters than the current state-of-the-art solution and furthermore can be efficiently trained by using a significantly smaller number of training data than existing deep demosaicking networks. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture