11,765 research outputs found

    CARLA+: An Evolution of the CARLA Simulator for Complex Environment Using a Probabilistic Graphical Model

    Get PDF
    In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers

    Evaluation Methodologies in Software Protection Research

    Full text link
    Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 572 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks

    Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames

    Full text link
    Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning. Recent slot-based neural networks that learn about objects in a self-supervised manner have made exciting progress in this direction. However, they typically fall short at adequately capturing spatial symmetries present in the visual world, which leads to sample inefficiency, such as when entangling object appearance and pose. In this paper, we present a simple yet highly effective method for incorporating spatial symmetries via slot-centric reference frames. We incorporate equivariance to per-object pose transformations into the attention and generation mechanism of Slot Attention by translating, scaling, and rotating position encodings. These changes result in little computational overhead, are easy to implement, and can result in large gains in terms of data efficiency and overall improvements to object discovery. We evaluate our method on a wide range of synthetic object discovery benchmarks namely CLEVR, Tetrominoes, CLEVRTex, Objects Room and MultiShapeNet, and show promising improvements on the challenging real-world Waymo Open dataset.Comment: Accepted at ICML 2023. Project page: https://invariantsa.github.io

    Technology for Low Resolution Space Based RSO Detection and Characterisation

    Get PDF
    Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment

    An American Knightmare: Joker, Fandom, and Malicious Movie Meaning-Making

    Get PDF
    This monograph concerns the long-standing communication problem of how individuals can identify and resist the influence of unethical public speakers. Scholarship on the issue of what Socrates & Plato called the “Evil Lover” – i.e., the ill-intended rhetor – began with the Greek philosophers, but has carried into [post]Modern anxieties. For instance, the study of Nazi propaganda machines, and the rhetoric of Hitler himself, rejuvenated interest in the study of speech and communication in the U.S. and Europe. Whereas unscrupulous sophists used lectures and legal forums, and Hitler used a microphone, contemporary Evil Lovers primarily draw on new, internet-related tools to share their malicious influence. These new tools of influence are both more far-reaching and more subtle than the traditional practices of listening to a designated speaker appearing at an overtly political event. Rhetorician Ashley Hinck has recently noted the ways that popular culture – communication about texts which are commonly accessible and shared – are now significant sites through which citizens learn moral and political values. Accordingly, the talk of internet influencers who interpret popular texts for other fans has the potential to constitute strong persuasive power regarding ethics and civic responsibility. The present work identifies and responds to a particular case example of popular culture text that has been recently, and frequently, leveraged in moral and civic discourses: Todd Phillips’ Joker. Specifically, this study takes a hermeneutic approach to understanding responses, especially those explicitly invoking political ideology, to Joker as a method of examining civic meaning-making. A special emphasis is placed on the online film criticisms of Joker from white nationalist movie fans, who clearly exemplify ways that media responses can be leveraged by unethical speakers (i.e., Evil Lovers) and subtly diffused. The study conveys that these racist movie fans can embed values related to “trolling,” incelism, and xenophobia into otherwise seemingly innocuous talk about film. While the sharing of such speech does not immediately mean its positive reception, this kind of communication yet constitutes a new and understudied attack on democratic values such as justice and equity. The case of white nationalist movie fan film criticism therefore reflects a particular brand of communicative strategy for contemporary Evil Lovers in communicating unethical messages under the covert guise of mundane movie talk

    Optimal neighbourhood selection in structural equation models

    Full text link
    We study the optimal sample complexity of neighbourhood selection in linear structural equation models, and compare this to best subset selection (BSS) for linear models under general design. We show by example that -- even when the structure is \emph{unknown} -- the existence of underlying structure can reduce the sample complexity of neighbourhood selection. This result is complicated by the possibility of path cancellation, which we study in detail, and show that improvements are still possible in the presence of path cancellation. Finally, we support these theoretical observations with experiments. The proof introduces a modified BSS estimator, called klBSS, and compares its performance to BSS. The analysis of klBSS may also be of independent interest since it applies to arbitrary structured models, not necessarily those induced by a structural equation model. Our results have implications for structure learning in graphical models, which often relies on neighbourhood selection as a subroutine

    Behavioural ecology of the greater bilby (Macrotis lagotis) and conservation tool development in a semi-wild sanctuary

    Full text link
    Conservation translocations are becoming an increasingly necessary tool to stem the decline of threatened species globally. The greater bilby (Macrotis lagotis) is a nationally threatened species in Australia. While bilby translocations are expected to contribute to the species’ persistence, the scarcity of information on their behaviour and ecology prevents informed-management. By intensively studying a population of bilbies both prior to, and following reintroduction, and subsequent reinforcements to a fenced sanctuary, I aimed to (1) advance knowledge of bilby behaviour and examine behaviours potentially relevant to fitness (i.e. survival and breeding success), (2) improve ecological knowledge of bilbies within understudied (temperate) climates, and (3) use this knowledge to suggest and develop effective tools for their conservation. Chapter 1 describes the current state of research in applied conservation research, and how increased behavioural data could help address some of the current knowledge gaps for bilby conservation. In Chapter 2, I examined patterns in bilby resource selection, finding that selection changed between seasons and years due to the environmental conditions and resources available. I also found that resource requirements are likely to be behavioural-state dependent and sex-specific. In Chapter 3, I constructed social networks to examine nocturnal proximity of bilbies and concurrent burrow sharing and found that associations were non-random. Expanding on this, in Chapter 4, I found that burrow sharing was likely to help describe breeding strategies, as males strongly avoided other males, and mixed-sex dyads exhibited kin-avoidance when mate choice was more limited. In Chapter 5, I developed a test to screen personality traits in bilbies, finding links between male response to handling and relative breeding success post-release. Lastly, in Chapter 6, I described a method to collect detailed movement data on the bilby, and discussed some of the practical and animal welfare constraints for its application. My thesis provides new insights into the behavioural ecology of the bilby with potential implications for future management of the species. With further translocations necessary for long-term persistence of the bilby, this research is highly relevant to current and future management of this ecologically important species, with potential applications to other similarly at-risk species

    Modified Theories of Gravity and Cosmological Applications

    Get PDF
    This reprint focuses on recent aspects of gravitational theory and cosmology. It contains subjects of particular interest for modified gravity theories and applications to cosmology, special attention is given to Einstein–Gauss–Bonnet, f(R)-gravity, anisotropic inflation, extra dimension theories of gravity, black holes, dark energy, Palatini gravity, anisotropic spacetime, Einstein–Finsler gravity, off-diagonal cosmological solutions, Hawking-temperature and scalar-tensor-vector theories

    Exploring QCD matter in extreme conditions with Machine Learning

    Full text link
    In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure

    I am Robot, Your Health Adviser for Older Adults: Do You Trust My Advice?

    Get PDF
    Artificial intelligence and robotic solutions are seeing rapid development for use across multiple occupations and sectors, including health and social care. As robots grow more prominent in our work and home environments, whether people would favour them in receiving useful advice becomes a pressing question. In the context of human–robot interaction (HRI), little is known about people’s advice-taking behaviour and trust in the advice of robots. To this aim, we conducted an experimental study with older adults to measure their trust and compliance with robot-based advice in health-related situations. In our experiment, older adults were instructed by a fictional human dispenser to ask a humanoid robot for advice on certain vitamins and over-the-counter supplements supplied by the dispenser. In the first experimented condition, the robot would give only information-type advice, i.e., neutral informative advice on the supplements given by the human. In the second condition, the robot would give recommendation-type advice, i.e., advice in favour of more supplements than those suggested initially by the human. We measured the trust of the participants in the type of robot-based advice, anticipating that they would be more trusting of information-type advice. Moreover, we measured the compliance with the advice, for participants who received robot-based recommendations, and a closer proxy of the actual use of robot health advisers in home environments or facilities in the foreseeable future. Our findings indicated that older adults continued to trust the robot regardless of the type of advice received, highlighting a type of protective role of robot-based recommendations on their trust. We also found that higher trust in the robot resulted in higher compliance with its advice. The results underpinned the likeliness of older adults welcoming a robot at their homes or health facilities
    • …
    corecore