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This paper presents the principal challenges and opportunities associated with computational biomechanics research. The underlying cognitive control involved in the process of human motion is inherently complex, dynamic, multidimensional, and highly non-linear. The dynamics produced by the internal and external forces and the body's ability to react to them is biomechanics. Complex and non-rigid bodies, needs a lot of computing power and systems to execute however, in the absence of adequate resources, one may rely on new technology, machine learning tools and model order reduction approaches. It is also believed that machine learning approaches can enable us to embrace this complexity, if we could use three arms of ML i.e. predictive modeling, classification, and dimensionality reduction. Biomechanics, since it deals with motion and mobility come with a huge set of data over time. Using computational (Computer Solvers), Numerical approaches (MOR) and technological advances (Wearable sensors), can let us develop computationally inexpensive frameworks for biomechanics focused studies dealing with a huge amount of data. A lot of misunderstanding arises because of extensive data, standardization of the tools to process this, database for the material property definitions, validation and verification of biomechanical models and analytical tools to model various phenomena using computational and modelling techniques. Study of biomechanics through computational simulations can improve the prevention and treatment of diseases, predict the injury to reduce the risk and hence can strengthen pivotal sectors like sports and lifestyle. This is why we choose to present all those challenges and problems associated with biomechanical simulation with complex geometries fail so as to help improve, analysis, performance and design for better lifestyle., Comment: 9 pages, 5 figures, 1 table, and conference paper

This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI -- AI that can perform a wide variety of tasks -- with a focus on understanding and managing its risks. A diverse group of 75 AI experts contributed to this report, including an international Expert Advisory Panel nominated by 30 countries, the EU, and the UN. Led by the Chair, these independent experts collectively had full discretion over the report's content., Comment: Available under the open government license at https://www.gov.uk/government/publications/international-scientific-report-on-the-safety-of-advanced-ai

Guitar Pickups have been in production for nearly 100 years, and the question of how exactly one pickup is tonally superior to another is still subject to a high level of debate. This paper is the first in a set demystifying the production of guitar pickups and introducing a level of scientific procedure to the conversation. Previous studies have analysed commercial off-the-shelf pickups, but these differ from each other in multiple ways. The novelty of this study is that dedicated experimental pickups were created, which vary only one parameter at a time in order to allow scientific study. The most fundamental qualities of a single-coil pickup are investigated: in this paper, number of turns and gauge of wire. A set of single-coil stratocaster-style pickups were created, with the number of turns of wire varied across the commercially available range (5000-12000 turns), and this was done for two widely used wire gauges (42 and 44 AWG). A frequency response analyser was used to obtain impedance across a frequency range. It is shown that resonant frequency decreases exponentially with number of turns, while the magnitude of the resonant peak increases linearly with number of turns. The wire gauge used has a significant impact on both parameters, with the thicker wire giving higher resonant frequencies and higher magnitudes than the thinner wire for the same number of turns. These impact the sound associated with the pickup: the resonant frequency is linked to the perceived tone of the pickup, and the magnitude to the output amplitude and hence 'gain.' Increasing the number of turns will give a higher output pickup with a darker tone, and thicker wire gives louder outputs and brighter tones - consistent with what can be observed in commercial pickups., Comment: 13 pages, 10 figures, to be submitted to Journal AES

This article examines public exposure to and perceptions of deepfakes based on insights from a nationally representative survey of 1403 UK adults. The survey is one of the first of its kind since recent improvements in deepfake technology and widespread adoption of political deepfakes. The findings reveal three key insights. First, on average, 15% of people report exposure to harmful deepfakes, including deepfake pornography, deepfake frauds/scams and other potentially harmful deepfakes such as those that spread health/religious misinformation/propaganda. In terms of common targets, exposure to deepfakes featuring celebrities was 50.2%, whereas those featuring politicians was 34.1%. And 5.7% of respondents recall exposure to a selection of high profile political deepfakes in the UK. Second, while exposure to harmful deepfakes was relatively low, awareness of and fears about deepfakes were high (and women were significantly more likely to report experiencing such fears than men). As with fears, general concerns about the spread of deepfakes were also high; 90.4% of the respondents were either very concerned or somewhat concerned about this issue. Most respondents (at least 91.8%) were concerned that deepfakes could add to online child sexual abuse material, increase distrust in information and manipulate public opinion. Third, while awareness about deepfakes was high, usage of deepfake tools was relatively low (8%). Most respondents were not confident about their detection abilities and were trustful of audiovisual content online. Our work highlights how the problem of deepfakes has become embedded in public consciousness in just a few years; it also highlights the need for media literacy programmes and other policy interventions to address the spread of harmful deepfakes.

Large language models can now generate political messages as persuasive as those written by humans, raising concerns about how far this persuasiveness may continue to increase with model size. Here, we generate 720 persuasive messages on 10 U.S. political issues from 24 language models spanning several orders of magnitude in size. We then deploy these messages in a large-scale randomized survey experiment (N = 25,982) to estimate the persuasive capability of each model. Our findings are twofold. First, we find evidence of a log scaling law: model persuasiveness is characterized by sharply diminishing returns, such that current frontier models are barely more persuasive than models smaller in size by an order of magnitude or more. Second, mere task completion (coherence, staying on topic) appears to account for larger models' persuasive advantage. These findings suggest that further scaling model size will not much increase the persuasiveness of static LLM-generated messages., Comment: 16 pages, 4 figures

Online harms, such as hate speech, trolling and self-harm promotion, continue to be widespread. While some work suggests women are disproportionately affected, other studies find mixed evidence for gender differences in experiences with content of this kind. Using a nationally representative survey of UK adults (N=1992), we examine exposure to a variety of harms, fears surrounding being targeted, the psychological impact of online experiences, the use of safety tools to protect against harm, and comfort with various forms of online participation across men and women. We find that while men and women see harmful content online to a roughly similar extent, women are more at risk than men of being targeted by harms including online misogyny, cyberstalking and cyberflashing. Women are significantly more fearful of being targeted by harms overall, and report greater negative psychological impact as a result of particular experiences. Perhaps in an attempt to mitigate risk, women report higher use of a range of safety tools and less comfort with several forms of online participation, with just 23% of women comfortable expressing political views online compared to 40% of men. We also find direct associations between fears surrounding harms and comfort with online behaviours. For example, fear of being trolled significantly decreases comfort expressing opinions, and fear of being targeted by misogyny significantly decreases comfort sharing photos. Our results are important because with much public discourse happening online, we must ensure all members of society feel safe and able to participate in online spaces.

There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central government, and measuring their potential for AI-driven automation. We estimate that UK central government conducts approximately one billion citizen-facing transactions per year in the provision of around 400 services, of which approximately 143 million are complex repetitive transactions. We estimate that 84% of these complex transactions are highly automatable, representing a huge potential opportunity: saving even an average of just one minute per complex transaction would save the equivalent of approximately 1,200 person-years of work every year. We also develop a model to estimate the volume of transactions a government service undertakes, providing a way for government to avoid conducting time consuming transaction volume measurements. Finally, we find that there is high turnover in the types of services government provide, meaning that automation efforts should focus on general procedures rather than services themselves which are likely to evolve over time. Overall, our work presents a novel perspective on the structure and functioning of modern government, and how it might evolve in the age of artificial intelligence.

Generative Artificial Intelligence (AI) has shown tremendous prospects in all aspects of technology, including design. However, due to its heavy demand on resources, it is usually trained on large computing infrastructure and often made available as a cloud-based service. In this position paper, we consider the potential, challenges, and promising approaches for generative AI for design on the edge, i.e., in resource-constrained settings where memory, compute, energy (battery) and network connectivity may be limited. Adapting generative AI for such settings involves overcoming significant hurdles, primarily in how to streamline complex models to function efficiently in low-resource environments. This necessitates innovative approaches in model compression, efficient algorithmic design, and perhaps even leveraging edge computing. The objective is to harness the power of generative AI in creating bespoke solutions for design problems, such as medical interventions, farm equipment maintenance, and educational material design, tailored to the unique constraints and needs of remote areas. These efforts could democratize access to advanced technology and foster sustainable development, ensuring universal accessibility and environmental consideration of AI-driven design benefits., Comment: Accepted for the Artificial Intelligence for Design Problems bridge program at AAAI 2024

Who sets the policy agenda? In this paper, we explore the roles of policy actors in agenda setting by studying their relative influence in policy-related discussions. Our approach builds on ``nodality'' \textemdash a concept in political science that determines the capacity of an actor to share information and to be at the centre of information networks. We propose a novel methodology that quantifies the nodality of all individual actors in any conversation by analysing a comprehensive set of their centrality measures in the related information network. We combine this with the analysis of the activity time-series, of the related conversation (or topic), to demonstrate how nodality scores relate to the capacity to drive topic-related activity. Here we analyse policy-related discussions on X (previously Twitter) and quantify the nodality of two sets of actors in the UK political system \textemdash Members of Parliament (MPs) and accredited journalists - on four policy topics: The Russia-Ukraine War, the Cost-of-Living Crisis, Brexit and COVID-19. Our results show that the capacity to influence the activity related to a topic is significantly and positively associated with nodality. In particular, we identify two dimensions of nodality that drive the capacity to influence topic-related activity. The first is ``active nodality", which reflects the level of topic-related engagement an individual actor has on the platform. The second dimension is ``inherent nodality" which is entirely independent of the platform and reflects the actor's institutional position (such as an MP in a front-bench role, or a journalist's position at a prominent media outlet)., Comment: 22 pages, 6 figures, 5 tables

Online harms, such as hate speech, misinformation, harassment and self-harm promotion, continue to be widespread. While some work suggests that women are disproportionately affected by such harms, other studies find little evidence for gender differences in overall exposure. Here, we present preliminary results from a large, nationally representative survey of UK adults (N = 2000). We asked about exposure to 15 specific harms, along with fears surrounding exposure and comfort engaging in certain online behaviours. While men and women report seeing online harms to a roughly equal extent overall, we find that women are significantly more fearful of experiencing every type of harm that we asked about, and are significantly less comfortable partaking in several online behaviours. Strikingly, just 24% of women report being comfortable expressing political opinions online compared with almost 40% of men, with similar overall proportions for challenging certain content. Our work suggests that women may suffer an additional psychological burden in response to the proliferation of harmful online content, doing more 'safety work' to protect themselves. With much public discourse happening online, gender inequality in public voice is likely to be perpetuated if women feel too fearful to participate. Our results are important because to establish greater equality in society, we must take measures to ensure all members feel safe and able to participate in the online space.

User facing 'platform safety technology' encompasses an array of tools offered by platforms to help people protect themselves from harm, for example allowing people to report content and unfollow or block other users. These tools are an increasingly important part of online safety: in the UK, legislation has made it a requirement for large platforms to offer them. However, little is known about user engagement with such tools. We present findings from a nationally representative survey of UK adults covering their awareness of and experiences with seven common safety technologies. We show that experience of online harms is widespread, with 67% of people having seen what they perceived as harmful content online; 26% of people have also had at least one piece of content removed by content moderation. Use of safety technologies is also high, with more than 80\% of people having used at least one. Awareness of specific tools is varied, with people more likely to be aware of 'post-hoc' safety tools, such as reporting, than preventative measures. However, satisfaction with safety technologies is generally low. People who have previously seen online harms are more likely to use safety tools, implying a 'learning the hard way' route to engagement. Those higher in digital literacy are also more likely to use some of these tools, raising concerns about the accessibility of these technologies to all users. Additionally, women are more likely to engage in particular types of online 'safety work'. We discuss the implications of our results for those seeking a safer online environment.

The spread of misinformation is a pressing global problem that has elicited a range of responses from researchers, policymakers, civil society and industry. Over the past decade, these stakeholders have developed many interventions to tackle misinformation that vary across factors such as which effects of misinformation they hope to target, at what stage in the misinformation lifecycle they are aimed at, and who they are implemented by. These interventions also differ in how effective they are at reducing susceptibility to (and curbing the spread of) misinformation. In recent years, a vast amount of scholarly work on misinformation has become available, which extends across multiple disciplines and methodologies. It has become increasingly difficult to comprehensively map all of the available interventions, assess their efficacy, and understand the challenges, opportunities and tradeoffs associated with using them. Few papers have systematically assessed and compared the various interventions, which has led to a lack of understanding in civic and policymaking discourses. With this in mind, we develop a new hierarchical framework for understanding interventions against misinformation online. The framework comprises three key elements: Interventions that Prepare people to be less susceptible; Interventions that Curb the spread and effects of misinformation; and Interventions that Respond to misinformation. We outline how different interventions are thought to work, categorise them, and summarise the available evidence on their efficacy; offering researchers, policymakers and practitioners working to combat online misinformation both an analytical framework that they can use to understand and evaluate different interventions (and which could be extended to address new interventions that we do not describe here) and a summary of the range of interventions that have been proposed to date.

Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI., Comment: 35 pages with references and appendix, 3 tables, 2 figures

Radical right influencers routinely use social media to spread highly divisive, disruptive and anti-democratic messages. Assessing and countering the challenge that such content poses is crucial for ensuring that online spaces remain open, safe and accessible. Previous work has paid little attention to understanding factors associated with radical right content that goes viral. We investigate this issue with a new dataset ROT which provides insight into the content, engagement and followership of a set of 35 radical right influencers. It includes over 50,000 original entries and over 40 million retweets, quotes, replies and mentions. We use a multilevel model to measure engagement with tweets, which are nested in each influencer. We show that it is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors, including the number of followers each influencer has, the type of content (original posts, quotes and replies), the length and toxicity of content, and whether influencers request retweets. We make ROT available for other researchers to use.

A wide range of natural and industrial processes involve heat and mass transport in porous media. In some important cases the transported substance may undergo phase change, e.g. from liquid to solid and vice versa in the case of freezing and thawing of soils. The predictive modelling of such phenomena faces physical (multiple physical processes taking place) and mathematical (evolving interface with step change of properties) challenges. In this work, we develop and test a non-local approach based on bond-based peridynamics which addresses the challenges successfully. Our formulation allows for predicting the location of the interface between phases, and for calculating the temperature and pressure distributions within the saturated porous medium under the conditions of pressure driven water flow. The formulation is verified against existing analytical solutions for 1D problems, as well as finite element transient solutions for 2D problems. The agreement found by the verification exercise demonstrates the accuracy of the proposed methodology. The detailed coupled description of heat and hydraulic processes can be considered as a critical step towards a thermo-hydro-mechanical model, which will allow, for example, description of the hydrological behaviour of permafrost soils and the frost heave phenomenon., Comment: 29 pages, 11 figures

A direct formulation of linear elasticity of cell complexes based on discrete exterior calculus is presented. The primary unknown are displacements, represented by primal vector-valued 0-cochain. Displacement differences and internal forces are represented by primal vector-valued 1-cochain and dual vector-valued 2-cochain, respectively. The macroscopic constitutive relation is enforced at primal 0-cells with the help of musical isomorphisms mapping cochains to smooth fields and vice versa. The balance of linear momentum is established at primal 0-cells. The governing equations are solved as a Laplace equation with a non-local and non-diagonal material Hodge star. Numerical simulations of several classical problems with analytic solutions are presented to validate the formulation. Good agreement with known solutions is obtained. The formulation provides a method to calculate the relations between displacement differences and internal forces for any lattice structure, when the structure is required to follow a prescribed macroscopic elastic behaviour. This is also the first and critical step in developing formulations for dissipative processes in cell complexes.

A formulation of elliptic boundary value problems is used to develop the first discrete exterior calculus (DEC) library for massively parallel computations with 3D domains. This can be used for steady-state analysis of any physical process driven by the gradient of a scalar quantity, e.g. temperature, concentration, pressure or electric potential, and is easily extendable to transient analysis. In addition to offering this library to the community, we demonstrate one important benefit from the DEC formulation: effortless introduction of strong heterogeneities and discontinuities. These are typical for real materials, but challenging for widely used domain discretization schemes, such as finite elements. Specifically, we demonstrate the efficiency of the method for calculating the evolution of thermal conductivity of a solid with a growing crack population. Future development of the library will deal with transient problems, and more importantly with processes driven by gradients of vector quantities.

Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of Brain. Intracranial compartments such as blood, brain and CSF are incompressible in nature. Therefore, if a volume imbalance in one of the aforenoted compartments is observed, the other reaches out to maintain net change to zero. Whereas, CSF has higher compliance over long term. However, if the CSF flow is obstructed in the ventricles, this compliance may get exhausted early. Brain tumor on the other hand poses a similar challenge towards destabilization of CSF flow by compressing any section of ventricles thereby ensuing obstruction. To avoid invasive procedures to study effects of tumor on CSF flow, numerical-based methods such as Finite element modeling (FEM) are used which provide excellent description of underlying pathological interaction. A 3D fluid-structure interaction (FSI) model is developed to study the effect of tumor growth on the flow of cerebrospinal fluid in ventricle system. The FSI model encapsulates all the physiological parameters which may be necessary in analyzing intraventricular CSF flow behavior. Findings of the model show that brain tumor affects CSF flow parameters by deforming the walls of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow velocity and considerable deformation on the walls of ventricles.

Online government petitions represent a new data-rich mode of political participation. This work examines the thus far understudied dynamics of sharing petitions on social media in order to garner signatures and, ultimately, a government response. Using 20 months of Twitter data comprising over 1 million tweets linking to a petition, we perform analyses of networks constructed of petitions and supporters on Twitter, revealing implicit social dynamics therein. We find that Twitter users do not exclusively share petitions on one issue nor do they share exclusively popular petitions. Among the over 240,000 Twitter users, we find latent support groups, with the most central users primarily being politically active "average" individuals. Twitter as a platform for sharing government petitions, thus, appears to hold potential to foster the creation of and coordination among a new form of latent support interest groups online., Comment: Presented at IPP2016 Conference, Oxford, UK. http://blogs.oii.ox.ac.uk/ipp-conference/2016.html

Detecting online hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific model weak points. It also risks overestimating generalisable model performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable more targeted diagnostic insights, we introduce HateCheck, a suite of functional tests for hate speech detection models. We specify 29 model functionalities motivated by a review of previous research and a series of interviews with civil society stakeholders. We craft test cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck's utility, we test near-state-of-the-art transformer models as well as two popular commercial models, revealing critical model weaknesses., Comment: Accepted at ACL 2021 (Main Conference)

The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic. It has also raised concerns about the spread of hateful language and prejudice online, especially hostility directed against East Asia. In this paper we report on the creation of a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. The classifier achieves an F1 score of 0.83 across all four classes. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook. The classifier can be implemented by other researchers, assisting with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice online during this global pandemic., Comment: 12 pages