检索结果-内蒙古大学图书馆

Formal Development of Safe Automated Driving using Differential Dynamic Logic

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Selvaraj, Yuvaraj Ahrendt, Wolfgang Fabian, Martin Zenseact Gothenburg417 56 Sweden Department of Electrical Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden

The challenges in providing convincing arguments for safe and correct behavior of automated driving (AD) systems have so far hindered their widespread commercial deployment. Conventional development approaches such as testing and simulation are limited by non-exhaustive analysis, and can thus not guarantee correctness in all possible scenarios. Formal methods is an approach to provide mathematical proofs of correctness, using a model of a system, that could be used to give the necessary arguments. This paper investigates the use of differential dynamic logic and the deductive verification tool KeYmaera X in the development of an AD feature. Specifically, formal models and safety proofs of different design variants of a Decision & Control module for an in-lane AD feature are presented. In doing so, the assumptions and invariant conditions necessary to guarantee safety are identified, and the paper shows how such an analysis helps during the development process in requirement refinement and formulation of the operational design domain. Furthermore, it is shown how the performance of the different models is formally analyzed exhaustively, in all their allowed behaviors. © 2022, CC BY-SA.

关键词： Safety factor

Personalised Medicine: Establishing predictive machine learning models for drug responses in patient derived cell culture

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Abdel-Rehim, Abbi Orhobor, Oghenejokpeme Griffiths, Gareth Soldatova, Larisa King, Ross D. Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge United Kingdom The National Institute of Agricultural Botany Cambridge United Kingdom ValiRx Plc Nottingham United Kingdom Department of Computing University of London London United Kingdom Department of Biology and Biological Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden The Alan Turing Institute London United Kingdom

The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for patients with tumours presenting well-defined mutations. However, the field is still in its infancy, and personalised treatments are far from being standard of care. Personalised medicine is often associated with the utilisation of omics data. Yet, implementation of multi-omics data has proven difficult, due to the variety and scale of the information within the data, as well as the complexity behind the myriad of interactions taking place within the cell. An alternative approach to precision medicine is to employ a function-based profile of the cell. This involves screening a range of drugs against patient derived cells (or derivative organoids and xenograft models). Here we demonstrate a proof-of-concept, where a collection of drug screens against a highly diverse set of patient-derived cell lines, are leveraged to identify putative treatment options for a "new patient". We show that this methodology is highly efficient in ranking the drugs according to their activity towards the target cells. We argue that this approach offers great potential, as activities can be efficiently imputed from various subsets of the drug treated cell lines that do not necessarily originate from the same tissue type. © 2024, CC BY-NC-ND.

关键词： Personalized medicine

A Formal-Methods Approach to Provide Evidence in Automated-Driving Safety Cases

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Krook, Jonas Selvaraj, Yuvaraj Ahrendt, Wolfgang Fabian, Martin Zenseact Gothenburg417 56 Sweden The Department of Electrical Engineering Chalmers University of Technology Gothenburg412 96 Sweden The Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden

The safety of automated driving systems must be justified by convincing arguments and supported by compelling evidence to persuade certification agencies, regulatory entities, and the general public to allow the systems on public roads. This persuasion is typically facilitated by compiling the arguments and the compelling evidence into a safety case. Reviews and testing, two common approaches to ensure correctness of automotive systems cannot explore the typically infinite set of possible behaviours. In contrast, formal methods are exhaustive methods that can provide mathematical proofs of correctness of models, and they can be used to prove that formalizations of functional safety requirements are fulfilled by formal models of system components. This paper shows how formal methods can provide evidence for the correct break-down of the functional safety requirements onto the components that are part of feedback loops, and how this evidence fits into the argument of the safety case. If a proof is obtained, the formal models are used as requirements on the components. This structure of the safety argumentation can be used to alleviate the need for reviews and tests to ensure that the break-down is correct, thereby saving effort both in data collection and verification time. © 2022, CC BY.

关键词： Formal methods

Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Truong, Giang Le, Huu Parra, Alvaro Gilani, Syed Zulqarnain Islam, Syed M.S. Suter, David School of Science Edith Cowan University Australia Department of Electrical Engineering Chalmers University of Technology Sweden School of Computer Science The University of Adelaide Australia

With current trends in sensors (cheaper, more volume of data) and applications (increasing affordability for new tasks, new ideas in what 3D data could be useful for);there is corresponding increasing interest in the ability to automatically, reliably, and cheaply, register together individual point clouds. The volume of data to handle, and still elusive need to have the registration occur fully reliably and fully automatically, mean there is a need to innovate further. One largely untapped area of innovation is that of exploiting the semantic information of the points in question. Points on a tree should match points on a tree, for example, and not points on car. Moreover, such a natural restriction is clearly human-like - a human would generally quickly eliminate candidate regions for matching based on semantics. Employing semantic information is not only efficient but natural. It is also timely - due to the recent advances in semantic classification capabilities. This paper advances this theme by demonstrating that state of the art registration techniques, in particular ones that rely on "preservation of length under rigid motion" as an underlying matching consistency constraint, can be augmented with semantic information. Semantic identity is of course also preserved under rigid-motion, but also under wider motions present in a scene. We demonstrate that not only the potential obstacle of cost of semantic segmentation, and the potential obstacle of unreliability of semantic segmentation;are both no impediment to achieving both speed and accuracy in fully automatic registration of large scale point clouds. In particular, we take a state of the art approach, that uses an (approximate) maximum clique heuristic (a clique on edges encapsulating matches that preserve length), to yield a version further enhanced with exploitation of semantic information. In essence, cheaply and reliably (for the purposes required) pruning the data that has to be dealt with. © 2022, CC0

关键词： Semantics

An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhang, Shiliang Computer Science and Engineering Chalmers University of Technology Sweden

Electrical vehicle (EV) raises to promote an eco-sustainable society. Nevertheless, the "range anxiety" of EV hinders its wider acceptance among customers. This paper proposes a novel solution to range anxiety based on a federated-learning model, which is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks. Specifically, the new approach extends the federated-learning structure with two components: anomaly detection and sharing policy. The first component identifies preventing factors in model learning, while the second component offers guidelines for information sharing amongst vehicle networks when the sharing is necessary to preserve learning efficiency. The two components collaborate to enhance learning robustness against data heterogeneities in networks. Numerical experiments are conducted, and the results show that compared with considered solutions, the proposed approach could provide higher accuracy of battery-consumption estimation for vehicles under heterogeneous data distributions, without increasing the time complexity or transmitting raw data among vehicle networks. © 2021, CC BY-NC-ND.

关键词： Anomaly detection

Efficient evaluation of the error probability for pilot-assisted finite-blocklength transmission

学校读者我要写书评

暂无评论

Efficient evaluation of the error probability for pilot-assi...

Asilomar Conference on Signals, Systems & computers

作者： A. Oguz Kislal Alejandro Lancho Giuseppe Durisi Erik G. Ström Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA

We propose a numerically efficient method for evaluating the random-coding union bound with parameter $s$ on the error probability achievable in the finite-blocklength regime by a pilot-assisted transmission scheme employing Gaussian code-books and operating over a memoryless block-fading channel. Our method relies on the saddlepoint approximation, which, differently from previous results reported for similar scenarios, is performed with respect to the number of fading blocks (a.k.a. diversity branches) spanned by each codeword, instead of the number of channel uses per block. This different approach avoids a costly numerical averaging of the error probability over the realizations of the fading process and of its pilot-based estimate at the receiver and yields a significant reduction of the number of channel realizations required to estimate the error probability via Monte-Carlo simulation. For example, in a single-input single-output scenario, when four or more diversity branches are available, an error probability of 10 -4 can be estimated accurately using our method by using less than 3000 samples. In contrast, the conventional saddlepoint approach requires around 10 6 samples.

关键词： Fading channels computers Monte Carlo methods Upper bound Error probability Symbols Channel estimation

Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Kolloff, Christopher Olsson, Simon Chalmers University of Technology Department for Computer Science and Engineering Rännvägen 6 Gothenburg412 58 Sweden

Machine learning (ML) has emerged as a pervasive tool in science, engineering, and beyond. Its success has also led to several synergies with molecular dynamics (MD) simulations, which we use to identify and characterize the major metastable states of molecular systems. Typically, we aim to determine the relative stabilities of these states and how rapidly they interchange. This information allows mechanistic descriptions of molecular mechanisms, enables a quantitative comparison with experiments, and facilitates their rational design. ML impacts all aspects of MD simulations - from analyzing the data and accelerating sampling to defining more efficient or more accurate simulation models. This chapter focuses on three fundamental problems in MD simulations: accurately parameterizing coarse-grained force fields, sampling thermodynamically stable states, and analyzing the exchange kinetics between those states. In addition, we outline several state-of-the-art neural network architectures and show how they are combined with physics-motivated learning objectives to solve MD-specific problems. Finally, we highlight open questions and challenges in the field and give some perspective on future developments. Copyright © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

Enhancing Functional and Extra Motor Abilities: A Focus Group Study on the Re-Design of an Extra-Robotic Finger

学校读者我要写书评

暂无评论

Enhancing Functional and Extra Motor Abilities: A Focus Grou...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sjoerd Hendriks Basma Hasanen Naqash Afzal Irfan Hussain Mohammad Obaid Department of Computer Science and Engineering Interaction Design Unit Chalmers University of Technology Gothenburg Sweden Department of Mechanical & Nuclear Engineering Khalifa University Abu Dhabi United Arab Emirates Center for Autonomous Robotic Systems Khalifa University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

The utilization of extra-robotic limbs to regain and extend motor abilities poses challenges. Nonetheless, the evidence suggesting the extension of these abilities without functional interference holds promise for the advancement of extra-robotic fingers or limbs. To gain insights into design improvement from diverse perspectives, we conducted a focus group study utilizing a prototype supernumerary robotic finger (SRF). The main purpose of the study is to gain knowledge on the current prototype, and then use the outcomes in a future co-designing process of SRF having the potential to be used as an assistive and augmented tool. Fifteen healthy subjects with an engineering background participated in the current study. We analyzed the collected data through a thematic analysis process to generate key design recommendations. These recommendations contain suggestions for designing for personal and social acceptance, adaptability, and multi-modal interaction. The results serve as a subset of future subsequent studies to be carried out with other stakeholders of the prototype, including patients, clinicians, and designers. Future work focuses on using the recommendations in a co-design process to inform the prototyping of SRF technologies supporting and extending motor abilities in upper limbs.

关键词： Fingers Prototypes Interference Motors Stakeholders Robots

Personalized Public Policy Analysis in Social sciences Using Causal-Graphical Normalizing Flows

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Balgi, Sourabh Peña, Jose M. Daoud, Adel Linköping University Sweden Linköping University Sweden The Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE). Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ACE and CACE. However, much work remains before traditional estimation methods can be used for counterfactual inference, and for the benefit of Personalized Public Policy Analysis (P3A) in the social sciences. While doctors rely on personalized medicine to tailor treatments to patients in laboratory settings (relatively closed systems), P3A draws inspiration from such tailoring but adapts it for open social systems. In this article, we develop a method for counterfactual inference that we name causal-Graphical Normalizing Flow (c-GNF), facilitating P3A. A major advantage of c-GNF is that it suits the open system in which P3A is conducted. First, we show how c-GNF captures the underlying SCM without making any assumption about functional forms. This capturing capability is enabled by the deep neural networks that model the underlying SCM via observational data likelihood maximization using gradient descent. Second, we propose a novel dequantization trick to deal with discrete variables, which is a limitation of normalizing flows in general. Third, we demonstrate in experiments that c-GNF performs on-par with IPW and RWR in terms of bias and variance for estimating the ACE, when the true functional forms are known, and better when they are unknown. Fourth and most importantly, we conduct counterfactual inference with c-GNFs, demonstrating promising empirical performance. Because IPW and RWR, like other traditional methods, lack the capability of counterfactual inference, c-GNFs will likely play a major role in tailoring personalized treatment, facilitati

关键词： Behavioral research