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arXiv 2025年

作者： Mehta, Neil Gonçalves, Inês Montagna, Alberto Fleury, Mathis Caetano, Gustavo Esteves, Inês Vourvopoulos, Athanasios Grover, Pulkit Figueiredo, Patrícia Institute for Systems and Robotics Department of Bioengineering Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Electrical and Computer Engineering Department Carnegie Mellon University United States

Simultaneous EEG-fMRI recordings are increasingly used to investigate brain activity by leveraging the complementary high spatial and high temporal resolution of fMRI and EEG signals respectively. It remains unclear, however, to what degree these two imaging modalities capture shared information about neural activity. Here, we investigate whether it is possible to predict both task-evoked and spontaneous fMRI signals of motor brain networks from EEG time-varying spectral power using interpretable models trained for individual subjects with Sparse Group Lasso regularization. Critically, we test the trained models on data acquired from each subject on a different day and obtain statistical validation by comparison with appropriate null models as well as the conventional EEG sensorimotor rhythm. We find significant prediction results in most subjects, although less frequently for resting-state compared to task-based conditions. Furthermore, we interpret the model learned parameters to understand representations of EEG-fMRI coupling in terms of predictive EEG channels, frequencies, and haemodynamic delays. In conclusion, our work provides evidence of the ability to predict fMRI motor brain activity from EEG recordings alone across different days, in both task-evoked and spontaneous conditions, with statistical significance in individual subjects. These results present great potential for translation to EEG neurofeedback applications. © 2025, CC BY.

关键词： Image coding

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Methods for Verifying the Security of Available IoT Devices

Methods for Verifying the Security of Available IoT Devices

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International Symposium on Intelligent systems and informatics (SISY)

作者： Jan Francisti Zoltán Balogh György Molnár Kopják József Sebestyén Gergely Enikő Nagy Department of Informatics Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Institute of Cyberphysical Systems Óbuda University Budapest Hungary

In present, the field of Internet of Things (IoT) is experiencing significant development, as evidenced by the everincreasing number of connected devices equipped with various sensors and controllers. These devices have become an integral part of smart homes, offering numerous benefits. Paradoxically, their growing popularity also brings an increased risk of vulnerabilities that threaten the security and privacy of users. This paper focuses on analysing common IoT devices used in smart homes to identify weaknesses and propose measures for improving their protection. This will involve evaluating security tests and providing recommendations for securing the devices and safeguarding their users. The proposed solutions and recommendations will assist users in better understanding potential threats and protecting their devices from unauthorized access and the leakage of sensitive information. This research approach contributes to enhancing the security and privacy of IoT device users in smart homes.

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Phenotypic landscape of an invasive fungal pathogen reveals its unique biology

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Cell 2025年

作者： Michael J. Boucher Sanjita Banerjee Meenakshi B. Joshi Angela L. Wei Matthew J. Nalley Manning Y. Huang Susan Lei Massimiliano Ciranni Andrew Condon Andreas Langen Thomas D. Goddard Ippolito Caradonna Alexi I. Goranov Christina M. Homer Yasaman Mortensen Sarah Petnic Morgann C. Reilly Ying Xiong Katherine J. Susa Vito Paolo Pastore Hiten D. Madhani Department of Biochemistry and Biophysics University of California San Francisco San Francisco CA 94158 USA Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa 16145 Genoa Italy Department of Pharmaceutical Chemistry University of California San Francisco San Francisco CA 94158 USA

Cryptococcus neoformans is the most common cause of fungal meningitis and the top-ranking WHO fungal priority pathogen. Only distantly related to model fungi, C. neoformans is also a powerful experimental system for exploring conserved eukaryotic mechanisms lost from specialist model yeast lineages. To decipher its biology globally, we constructed 4,328 gene deletions and measured—with exceptional precision—the fitness of each mutant under 141 diverse growth-limiting in vitro conditions and during murine infection. We defined functional modules by clustering genes based on their phenotypic signatures. In-depth studies leveraged these data in two ways. First, we defined and investigated new components of key signaling pathways, which revealed metazoan-like cellular machinery not present in model yeasts. Second, we identified environmental adaptation mechanisms repurposed to promote mammalian virulence by C. neoformans , which lacks a known animal reservoir. Our work provides an unprecedented resource for deciphering a deadly human pathogen.

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Model predictive control of a vehicle platoon

Model predictive control of a vehicle platoon

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IEEE International Conference on System of systems engineering (SoSE)

作者： Simone Graffione Chiara Bersani Roberto Sacile Enrico Zero DIBRIS- Department on Informatics Bioengineering Robotics and System Engineering University of Genova Genova Italy

ISBN: (数字)9781728180502

ISBN: (纸本)9781728180519

The objective of this paper is to develop a model predictive control (MPC) approach to control the inter vehicular distance and the speed in a vehicle platoon in order to improve road safety and to minimize fuel consumption. It is assumed to have a multicast topology communication system among vehicles. The dynamic model of each single car considers the tractive force, its aerodynamic, and the rolling resistance of the vehicles. The control law is dedicated to manage the longitudinal spacing of the platoon according to leader's behavior. The objective is to minimize the tractive force to be applied to each vehicle in order to maintain a constant distance among vehicles according to position and speed reference values. The non-linear cinematic model has been linearized around its previous state value. The centralized controller regulates vehicle accelerations or decelerations over a time horizon. The objective is to optimize the cost function, which minimizes the square deviations of positions, speeds, and tractive forces, as well as the predefined inter vehicular distances in the platoon. The control model has been tested in two case studies that impose, respectively, constant and time varying speed reference values. The results demonstrate the effectiveness of the proposed approach with special relevance to the constant speed case.

关键词： vehicle platoon longitudinal control MPC control autonomous vehicle LONGITUDINAL CONTROL Autonomous Vehicles Traction force vehicle Model predictive control Predictive control Cost functions deceleration Rolling resistance Road safety

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Proposal of an Architecture to support High Quality Automatic Data Collection in the context of Multi-Centric Studies

Proposal of an Architecture to support High Quality Automati...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Sara Mora Elena Lazarova Mauro Giacomini Department of Informatics Bioengineering Robotics and System Engineering (DIBRIS) University of Genoa Genoa Italy

ISBN: (数字)9781728152004

ISBN: (纸本)9781728152011

The increasing ease of people to move from one place to another and the rapid emergence of multi-centric clinical trials make necessary an extensive multilayer integration of data from different health areas so that it is possible to minimize the need for human intervention. The idea behind this work is to exploit the information contextualization properties guaranteed by the Clinical Document Architecture Release 2.0 (CDA R2) together with the skills of Machine Learning so that it is possible to highlight values out of the therapeutic range or outside the range which generally data belong to. The proposed architecture it is composed by three elements designed for the purpose of supporting the automatic transfer of high-quality data from one system to another and to point out any outliers. The architecture supports the creation of a large, well-structured and well-contextualized database for multi-centric clinical studies.

关键词： Databases Hospitals Standards Machine learning Support vector machines Clinical trials Vocabulary

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Cancer mutational signatures identification with sparse dictionary learning 1

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14th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2017

作者： Tozzo, Veronica Barla, Annalisa Department of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa16146 Italy

ISBN: (数字)9783030141608

ISBN: (纸本)9783030141592

Somatic DNA mutations are a characteristic of cancerous cells, being usually key in the origin and development of cancer. In the last few years, somatic mutations have been studied in order to understand which processes or conditions may generate them, with the purpose of developing prevention and treatment strategies. In this work we propose a novel sparse regularised method that aims at extracting mutational signatures from somatic mutations. We developed a pipeline that extracts the dataset from raw data and performs the analysis returning the signatures and their relative usage frequencies. A thorough comparison between our method and the state of the art procedure reveals that our pipeline can be used alternatively without losing information and possibly gaining more interpretability and precision. © 2019, Springer Nature Switzerland AG.

关键词： Pipelines

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Application of graph-curvature features in computer-aided diagnosis for histopathological image identification of gastric cancer

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Intelligent Medicine 2024年第3期4卷 141-152页

作者： Ruilin He Chen Li Xinyi Yang Jinzhu Yang Tao Jiang Marcin Grzegorzek Hongzan Sun Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110016China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110016China School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China Institute for Medical Informatics University of LuebeckLuebeckGermany Department of Knowledge Engineering University of Economics in KatowiceKatowicePoland Shengjing Hospital of China Medical University ShenyangLiaoning 110136China

Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some *** study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological *** The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for ***,the U-net was applied to segment the ***,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was *** third step was to extract the graph-curvature features of the histopathological image according to the MST ***,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be *** During the experiment,we used various methods for *** the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were *** found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological *** the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern *** the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and *** comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.

关键词： Gastric cancer Graph-curvature feature Image identification

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Compact Belief State Representation for Task Planning

Compact Belief State Representation for Task Planning

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Evgenii Safronov Michele Colledanchise Lorenzo Natale Istituto Italiano di Tecnologia Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering Università di Genova Genova Italy

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Task planning in a probabilistic belief space generates complex and robust execution policies in domains affected by state uncertainty. The performance of a task planner relies on the belief space representation of the world. However, such representation becomes easily intractable as the number of variables and execution time grow. To address this problem, we developed a novel belief space representation based on the Cartesian product and union operations over belief substates. These two operations and single variable assignment nodes form And-Or directed acyclic graph of Belief States (AOBSs). We show how to apply actions with probabilistic outcomes and how to measure the probability of conditions holding true over belief states. We evaluated AOBSs performance in simulated forward state space exploration. We compared the size of AOBSs with the size of Binary Decision Diagrams (BDDs) that were previously used to represent belief state. We show that AOBSs representation more compact than a full belief state and it scales better than BDDs for most of the cases.

关键词： Task analysis Planning Probabilistic logic Boolean functions Robots Binary decision diagrams

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Compact belief state representation for task planning

arXiv

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arXiv 2020年

作者： Safronov, Evgenii Colledanchise, Michele Natale, Lorenzo Istituto Italiano di Tecnologia Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering Università di Genova Genova Italy

关键词： Directed graphs

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Task planning with belief behavior trees

arXiv

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arXiv 2020年

In this paper, we propose Belief Behavior Trees (BBTs), an extension to Behavior Trees (BTs) that allows to automatically create a policy that controls a robot in partially observable environments. We extend the semantic of BTs to account for the uncertainty that affects both the conditions and action nodes of the BT. The tree gets synthesized following a planning strategy for BTs proposed recently: from a set of goal conditions we iteratively select a goal and find the action, or in general the subtree, that satisfies it. Such action may have preconditions that do not hold. For those preconditions, we find an action or subtree in the same fashion. We extend this approach by including, in the planner, actions that have the purpose to reduce the uncertainty that affects the value of a condition node in the BT (for example, turning on the lights to have better lighting conditions). We demonstrate that BBTs allows task planning with non-deterministic outcomes for actions. We provide experimental validation of our approach in a real robotic scenario and – for sake of reproducibility – in a simulated one. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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