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6th International Workshop on Artificial Intelligence and Cognition, AIC 2018

作者： Banaee, Hadi Schaffernicht, Erik Loutfi, Amy Centre for Applied Autonomous Sensor Systems School of Science and Technology Örebro University ÖrebroSE-701 82 Sweden

This paper discusses the task of creating semantic representations to describe numerical observations using conceptual spaces. The theory of conceptual spaces is considered as a semantic representation to conceptualise the perceived numerical information and to infer linguistic descriptions. We propose a data-driven approach to construct conceptual spaces from numerical data automatically. First, the elements of a conceptual space are derived based on a set of numerical observations in order to semantically represent the concepts of a given data set. This data-driven conceptual space is then employed for the task of semantic inference, in order to linguistically describe unknown perceived observations. © 2019 CEUR-WS. All rights reserved.

关键词： Semantics

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A machine learning-based approach to detect threats in bio-cyber DNA storage systems

arXiv

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arXiv 2020年

作者： Tavella, Federico Giaretta, Alberto Conti, Mauro Balasubramaniam, Sasitharan Department of Mathematics University of Padua Padua Italy Department of Science and Technology Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Waterford Institute of Technology Ireland

Data storage is one of the main computing issues of this century. Not only storage devices are converging to strict physical limits, but also the amount of data generated by users is growing at an unbelievable rate. To face these challenges, data centres grew constantly over the past decades. However, this growth comes with a price, particularly from the environmental point of view. Among various promising media, DNA is one of the most fascinating candidate. In our previous work, we have proposed an automated archival architecture which uses bioengineered bacteria to store and retrieve data, previously encoded into DNA. This storage technique is one example of how biological media can deliver power-efficient storing solutions. The similarities between these biological media and classical ones can also be a drawback, as malicious parties might replicate traditional attacks on the former archival system, using biological instruments and techniques. In this paper, first we analyse the main characteristics of our storage system and the different types of attacks that could be executed on it. Then, aiming at identifying on-going attacks, we propose and evaluate detection techniques, which rely on traditional metrics and machine learning algorithms. We identify and adapt two suitable metrics for this purpose, namely generalized entropy and information distance. Moreover, our trained models achieve an AUROC over 0.99 and AUPRC over 0.91. Copyright © 2020, The Authors. All rights reserved.

关键词： DNA

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Lifted Model Checking for Relational MDPs

arXiv

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arXiv 2021年

作者： Yang, Wen-Chi Raskin, Jean-François de Raedt, Luc Department of Computer Science KU Leuven Celestijnenlaan 200a - box 2402 Leuven3001 Belgium Université libre de Bruxelles Campus de la Plaine BruxellesCP212 - 1050 Belgium Centre for Applied Autonomous Sensor Systems Örebro University Sweden

Probabilistic model checking has been developed for verifying systems that have stochastic and nondeterministic behavior. Given a probabilistic system, a probabilistic model checker takes a property and checks whether or not the property holds in that system. For this reason, probabilistic model checking provide rigorous guarantees. So far, however, probabilistic model checking has focused on propositional models where a state is represented by a symbol. On the other hand, it is commonly required to make relational abstractions in planning and reinforcement learning. Various frameworks handle relational domains, for instance, STRIPS planning and relational Markov Decision Processes. Using propositional model checking in relational settings requires one to ground the model, which leads to the well known state explosion problem and intractability. We present pCTL-REBEL, a lifted model checking approach for verifying pCTL properties of relational MDPs. It extends REBEL, a relational model-based reinforcement learning technique, toward relational pCTL model checking. PCTL-REBEL is lifted, which means that rather than grounding, the model exploits symmetries to reason about a group of objects as a whole at the relational level. Theoretically, we show that pCTL model checking is decidable for relational MDPs that have a possibly infinite domain, provided that the states have a bounded size. Practically, we contribute algorithms and an implementation of lifted relational model checking, and we show that the lifted approach improves the scalability of the model checking approach. © 2021, CC BY.

关键词： Model checking

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Multi-Object Rearrangement with Monte Carlo Tree Search: A Case Study on Planar Nonprehensile Sorting

Multi-Object Rearrangement with Monte Carlo Tree Search: A C...

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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Haoran Song Joshua A. Haustein Weihao Yuan Kaiyu Hang Michael Yu Wang Danica Kragic Johannes A. Stork Robotics Institute Hong Kong University of Science and Technology Hong Kong China Centre for Autonomous Systems EECS KTH Royal Institute of Technology Stockholm Sweden Yale University New Haven Connecticut USA Centre for Applied Autonomous Sensor Systems Orebro University Orebro Sweden

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated and real-world sorting tasks. We observe that the algorithm is capable of reliably sorting large numbers of convex and non-convex objects, as well as convex objects in the presence of immovable obstacles.

关键词： Monte Carlo methods Search problems Planning Reliability Task analysis Intelligent robots Sorting

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L-Alanine Supported Autogenous Eruption Combustion Synthesis of Ni/NiO@RuO₂ Heterostructure for Electrochemical Glucose and pH sensor

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ECS sensors Plus 2023年第3期2卷 034601-034601页

作者： Padmanathan, N. Sasikumar, R. Thayanithi, V. Razeeb, Kafil M. Micro-Nano Systems Centre Tyndall National Institute University College Cork Dyke Parade Lee Maltings Cork T12 R5CP Ireland Department of Physics Karpagam Academy of Higher Education Tamilnadu Coimbatore 641021 India Department of Chemistry PSG Institute of Technology and Applied Research Tamilnadu Coimbatore 641062 India Department of Physics Nandha Engineering College (Autonomous) Tamilnadu Erode 638052 India

Safety and quality control are important for long-term storage and preservation of food. Glucose and food pH are the two most common markers for evaluating food quality. Herein, we constructed a Ni/NiO@RuO₂ heterostructure-based two-way sensor via a novel eruption combustion pattern (ECP) using non-conventional amino acid as a propellant. This approach has the unique points of interests of in situ doping of oxides and the formation of heterojunctions, providing well-developed pores and high surface areas to enhance the material performance. The Ni/NiO@RuO₂ heterostructures have been tested as a bi-functional catalyst for glucose and pH sensing. The sensor exhibits a fast response time of <0.1 ± 0.02 s, a sensitivity of 641.95 ± 0.5 μA mM⁻¹ cm⁻² towards glucose with a 0.4 ± 0.08 μM detection limit and a linear response of 0.1 to 5 mM. As a pH sensor, it exhibits an acceptable sensitivity of −41.6 mV pH⁻¹ with a response time of <50 s over a pH range of 2-12. Moreover, this bi-functional sensor based on Ni/NiO@RuO₂ performs well when applied to a selection of beverage samples. This study provides a new scalable and low-cost approach to fabricating hetero-oxide nanostructures with controllable heterojunctions for various sensor applications. © 2023 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited

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OS03-08 Comprehensive chemical and toxicological screening of e-waste plastic chemicals

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Toxicology Letters 2024年 399卷 S66-S66页

作者： A. Alijagic F. Södergren Seilitz A. Bredberg A. Hakonen M. Larsson V. Sjöberg O. Kotlyar A. Persson N. Scherbak D. Repsilber A. Kärrman T. Wang E. Särndahl M. Engwall Örebro University Man-Technology-Environment Research Center Örebro Sweden Örebro University Inflammatory Response and Infection Susceptibility Centre Örebro Sweden Örebro University School of Medical Sciences Faculty of Medicine and Health Örebro Sweden Research Institutes of Sweden Gothenburg Sweden Sensor Visions AB Gothenburg Sweden Örebro University Centre for Applied Autonomous Sensor Systems Örebro Sweden Linköping University Department of Physics Chemistry and Biology Linköping Sweden

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Fast Multi-grid Methods for Minimizing Curvature Energies

arXiv

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arXiv 2022年

作者： Zhang, Zhenwei Chen, Ke Tang, Ke Duan, Yuping Center for Applied Mathematics Tianjin University Tianjin300072 China Liverpool Centre of Mathematics for Healthcare Centre for Mathematical Imaging Techniques Department of Mathematical Sciences University of Liverpool LiverpoolL69 7ZL United Kingdom The Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

The geometric high-order regularization methods such as mean curvature and Gaussian curvature, have been intensively studied during the last decades due to their abilities in preserving geometric properties including image edges, corners, and contrast. However, the dilemma between restoration quality and computational efficiency is an essential roadblock for high-order methods. In this paper, we propose fast multi-grid algorithms for minimizing both mean curvature and Gaussian curvature energy functionals without sacrificing accuracy for efficiency. Unlike the existing approaches based on operator splitting and the Augmented Lagrangian method (ALM), no artificial parameters are introduced in our formulation, which guarantees the robustness of the proposed algorithm. Meanwhile, we adopt the domain decomposition method to promote parallel computing and use the fine-to-coarse structure to accelerate convergence. Numerical experiments are presented on image denoising, CT, and MRI reconstruction problems to demonstrate the superiority of our method in preserving geometric structures and fine details. The proposed method is also shown effective in dealing with large-scale image processing problems by recovering an image of size 1024×1024 within 40s, while the ALM method requires around 200s. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps 24

Binding in hippocampal-entorhinal circuits enables compositi...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Christopher J. Kymn Sonia Mazelet Anthony Thomas Denis Kleyko E. Paxon Frady Friedrich T. Sommer Bruno A. Olshausen Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Department of Electrical and Computer Engineering UC Davis Davis Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden and Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Intel Labs Santa Clara Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Intel Labs Santa Clara Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Herbert Wertheim School of Optometry & Vision Science UC Berkeley Berkeley

ISBN: (纸本)9798331314385

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions. Code is available at https://***/SoniaMaz8/Hippocampal_enthorinal_circuit

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A Unified Decision-Theoretic Model for Information Gathering and Communication Planning

A Unified Decision-Theoretic Model for Information Gathering...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jennifer Renoux Tiago S. Veiga Pedro U. Lima Matthijs T. J. Spaan Center of Applied Autonomous Sensor Systems Orebro University Sweden Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Institute for Systems and Robotics Instituto Superior Tecnico University of Lisbon Portugal Department of Software Technology Delft University of Technology The Netherlands

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

We consider the problem of communication planning for human-machine cooperation in stochastic and partially observable environments. Partially Observable Markov Decision Processes with Information Rewards (POMDPs-IR) form a powerful framework for information-gathering tasks in such environments. We propose an extension of the POMDP-IR model, called a Communicating POMDP-IR (com-POMDP-IR), that allows an agent to proactively plan its communication actions by using an approximation of the human's beliefs. We experimentally demonstrate the capability of our com-POMDPIR agent to limit its communication to relevant information and its robustness to lost messages.

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Scene learning, recognition and similarity detection in a fuzzy ontology via human examples 4

Scene learning, recognition and similarity detection in a fu...

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4th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2017

作者： Buoncompagni, Luca Mastrogiovanni, Fulvio Saffiotti, Alessandro Department of Informatics Bioengineering Robotics and Systems Engineering Italy Centre for Applied Autonomous Sensor Systems Orebro University Sweden

This paper introduces a Fuzzy Logic framework for scene learning, recognition and similarity detection, where scenes are taught via human examples. The framework allows a robot to: (i) deal with the intrinsic vagueness associated with determining spatial relations among objects;(ii) infer similarities and dissimilarities in a set of scenes, and represent them in a hierarchical structure represented in a Fuzzy ontology. In this paper, we briefly formalize our approach and we provide a few use cases by way of illustration. Nevertheless, we discuss how the framework can be used in real-world scenarios. © 2018 CEUR-WS. All rights reserved.

关键词： Fuzzy logic

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