The development of democratic systems is a crucial task as confirmed by its selection one the Millennium Sustainable Development Goals United Nations. In this article, we report on progress project that aims to address barriers, which information overload, achieving effective direct citizen participation in decision-making processes. main objectives are explore if application Natural Language Processing ( NLP ) and machine learning can improve citizens’ experience digital platforms. Taking...

Abstract Wildfires pose a significantly increasing hazard to global ecosystems due the climate crisis. Due its complex nature, there is an urgent need for innovative approaches wildfire prediction, such as machine learning. This research took unique approach, differentiating from classical supervised learning, and addressed gap in unsupervised prediction using autoencoders clustering techniques anomaly detection. Historical weather normalized difference vegetation index data sets of...

This paper discusses an approach to inertial parameter estimation for the case of cargo carrying spacecraft that is based on causal learning, i.e. learning from responses spacecraft, under actuation. Different configurations (inertial sets) are simulated different actuation profiles, in order produce optimised time-series clustering classifier can be used distinguish between them. The comprised finite sequences constant inputs applied order, typical actuators available. By system's across...

We explore the phenomenological implications on charged lepton flavor violating (LFV) processes from slepton mixing within Minimal Supersymmetric Standard Model (MSSM). work under model-independent hypothesis of general in sector, being parametrized by a complete set dimensionless ${\ensuremath{\delta}}_{ij}^{AB}$ ($A$, $B=L$, $R$; $i$, $j=1$, 2, 3, $i\ensuremath{\ne}j$) parameters. The present upper bounds most relevant LFV processes, together with requirement compatibility choice MSSM...

We explore the phenomenological implications on nonminimal flavor violating (NMFV) processes from squark mixing within minimal supersymmetric standard model (MSSM). work under model-independent hypothesis of general in sector, being parametrized by a complete set dimensionless ${\ensuremath{\delta}}_{ij}^{AB}$ ($A,B=L,R$; $i,j=u,c,t$ or $d,s,b$; $i\ensuremath{\ne}j$) parameters. The present upper bounds most relevant NMFV processes, together with requirement compatibility choice MSSM...

Research on automated social media rumour verification, the task of identifying veracity questionable information circulating media, has yielded neural models achieving high performance, with accuracy scores that often exceed 90%. However, none these studies focus real-world generalisability proposed approaches, is whether perform well datasets other than those which they were initially trained and tested. In this work we aim to fill gap by assessing top performing verification covering a...

Work on social media rumour verification utilises signals from posts, their propagation and users involved. Other lines of work target identifying fact-checking claims based information Wikipedia, or trustworthy news articles without considering context. However works combining the with external evidence wider web are lacking. To facilitate research in this direction, we release a novel dataset, PHEMEPlus, an extension PHEME benchmark, which contains conversations as well relevant for each...

Abstract Today’s conflicts are becoming increasingly complex, fluid, and fragmented, often involving a host of national international actors with multiple divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense dynamics, such the range parties evolution their political positions, distinction between relevant less in peace-making, or identification key issues interdependence. International peace efforts appear ill-equipped...

We calculate the one-loop corrections to Higgs boson masses within context of MSSM with Non-Minimal Flavor Violation in squark sector. take into account all relevant restrictions from BR(B -> X_s gamma), BR(B_s mu^+ mu^-) and ΔM_{B_s}. find sizable lightest mass that are considerably larger than expected ILC precision for acceptable values mixing parameters deltas. delta^{LR}_{ct} delta^{RL}_{ct} specially relevant, mainly at low tan beta.

In recent years participatory budgeting (PB) in Scotland has grown from a handful of community-led processes to movement supported by local and national government. This is epitomized an agreement between the Scottish Government Convention Local Authorities (COSLA) that at least 1% authority budgets will be subject PB. ongoing research paper explores challenges emerge this 'scaling up' or 'mainstreaming' across 32 authorities make up Scotland. The main objective evaluate use digital platform...

Social media and user-generated content (UGC) have become increasingly important features of journalistic work in a number different ways. However, the growth misinformation means that news organisations had devote more resources to determining its veracity publishing corrections if it is found be misleading. In this work, we present results interviews with eight members fact-checking teams from two organisations. Team described their processes challenges they currently face completing...

Participatory budgeting (PB) is already well established in Scotland the form of community led grant-making yet has recently transformed from a grass-roots activity to mainstream process or embedded 'policy instrument'. An integral part this turn use Consul digital platform as primary means citizen participation. Using mixed method approach, ongoing research paper explores how each 32 local authorities that make up utilise engage their citizens PB and they then sense citizens' contributions....

We study the phenomenological implications of sfermion flavour mixing in supersymmetry context Non-Minimal Flavour Violation (NMFV). general hypothesis, parametrizing squark and slepton mass matrices by a complete set delta^XY_ij (X,Y=L,R; i,j= t,c,u or b,s,d for squarks/1,2,3 sleptons). With respect to sector, we behaviour B-physics observables BR(B -> Xs gamma), BR(Bs mu+ mu-) delta M_B_s update constraints parameters coming from them. present one-loop corrections Higgs boson masses...

Miguel Arana-Catania, Elena Kochkina, Arkaitz Zubiaga, Maria Liakata, Robert Procter, Yulan He. Proceedings of the 2022 Conference North American Chapter Association for Computational Linguistics: Human Language Technologies. 2022.

Gas turbine superalloys experience hot corrosion, driven by factors including corrosive deposit flux, temperature, gas composition, and component material. The full mechanism still needs clarification research often focuses on laboratory work. As such, there is interest in causal discovery to confirm the significance of identify potential missing relationships or co-dependencies between these factors. algorithm Fast Causal Inference (FCI) has been trialled a small set data, with outputs...

Autonomous robotic arm manipulators have the potential to make planetary exploration and in-situ resource utilization missions more time efficient productive, as manipulator can handle objects itself perform goal-specific actions. We train a autonomously study of which it has no prior knowledge, such rocks. This is achieved using causal machine learning in simulated environment. Here, interacts with objects, classifies them based on differing factors. These are parameters, mass or friction...

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use machine learning techniques to help navigate review process. ML has previously been used reliably 'screen' articles for - that is, identify relevant based on reviewers' inclusion criteria. application subsequent stages a review, however, such as data extraction evidence mapping, is in its infancy. We therefore set out develop series tools...

In this work we demonstrate the possibility of estimating wind environment a UAV without specialised sensors, using only UAV's trajectory, applying causal machine learning approach. We implement curiosity method which combines times series classification and clustering with framework. analyse three distinct environments: constant wind, shear turbulence, explore different optimisation strategies for optimal manoeuvres to estimate conditions. The proposed approach can be used design...

Abstract The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use machine learning techniques to help navigate review process. Machine has previously been used reliably “screen” articles for – that is, identify relevant based on reviewers’ inclusion criteria. application subsequent stages a review, however, such as data extraction evidence mapping, is in its infancy. We, therefore, set out...

This paper presents a machine learning approach to estimate the inertial parameters of spacecraft in cases when those change during operations, e.g. multiple deployments payloads, unfolding appendages and booms, propellant consumption as well in-orbit servicing active debris removal operations. The uses time series clustering together with an optimised actuation sequence generated by reinforcement facilitate distinguishing among different parameter sets. performance proposed strategy is...

Autonomous operations of robots in unknown environments are challenging due to the lack knowledge dynamics interactions, such as objects' movability. This work introduces a novel Causal Reinforcement Learning approach enhancing robotics and applies it an urban search rescue (SAR) scenario. Our proposed machine learning architecture enables learn causal relationships between visual characteristics objects, texture shape, upon interaction, their movability, significantly improving...