302 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well

    Exercise therapy for tendinopathy: a mixed-methods evidence synthesis exploring feasibility, acceptability and effectiveness.

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    Tendons are cords of strong, flexible tissue that attach muscles to bones, allowing joints to move. Tendinopathy is a common condition that can affect any tendon in the body, causing pain and limiting function. Exercise is often used to treat tendinopathy. We examined over 500 research papers on exercise for tendinopathy. The most common tendons to be studied were the calf (Achilles), knee (patellar), elbow, and shoulder. Strengthening exercise was studied most often, especially in lower limb tendinopathy. Other types of exercise such as stretching, balance and aerobic activity were less common, but were used to some extent in the upper and lower limbs. We found that exercise therapy is safe and beneficial for the tendinopathies that have been studied to date. Exercise may be most beneficial when combined with another intervention such as injection or electrotherapy. Strengthening exercise may be most beneficial for lower limb tendinopathies. However, more research is needed on the type of strengthening and the dosage, such as how many exercises and how much resistance to use. Shoulder tendinopathies may benefit from exercise that targets joint flexibility and position more than strengthening. We also found that people who receive exercise therapy for tendinopathy are generally satisfied with the effect it has on their symptoms. Finally, we found that an individualised, person-centred approach to delivering exercise therapy is valued by people with tendinopathy. They also believe that the patient-healthcare provider relationship is important for promoting the confidence and motivation people need to continue with exercise programmes, especially when they complete them independently. Although we examined a lot of papers, many of the studies were low quality. This means there is still a need for high-quality studies to tell us how effective specific types of exercise are for specific tendinopathies. There is also a need for more studies on patients' and professionals' experiences of receiving or providing exercise for tendinopathy.This project is registered as www.osf.io/a8ewy/ (scoping review); PROSPERO CRD 42020168187 (efficacy reviews); https://osf.io/preprints/sportrxiv/y7sk6/ (efficacy review 1); https://osf.io/preprints/sportrxiv/eyxgk/ (efficacy review 2); https://osf.io/preprints/sportrxiv/mx5pv/ (efficacy review 3); PROSPERO CRD42020164641 (mixed method review)

    Randomized Control Trials in the Field of Development

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    In October 2019, Abhijit Banerjee, Esther Duflo, and Michael Kremer jointly won the 51st Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel "for their experimental approach to alleviating global poverty." But what is the exact scope of their experimental method, known as randomized control trials (RCTs)? Which sorts of questions are RCTs able to address and which do they fail to answer? This book provides answers to these questions, explaining how RCTs work, what they can achieve, why they sometimes fail, how they can be improved and why other methods are both useful and necessary. Chapters contributed by leading specialists in the field present a full and coherent picture of the main strengths and weaknesses of RCTs in the field of development. Looking beyond the epistemological, political, and ethical differences underlying many of the disagreements surrounding RCTs, it explores the implementation of RCTs on the ground, outside of their ideal theoretical conditions and reveals some unsuspected uses and effects, their disruptive potential, but also their political uses. The contributions uncover the implicit worldview that many RCTs draw on and disseminate, and probe the gap between the method's narrow scope and its success, while also proposing improvements and alternatives. This book warns against the potential dangers of their excessive use, arguing that the best use for RCTs is not necessarily that which immediately springs to mind, and offering opportunity to come to an informed and reasoned judgement on RCTs and what they can bring to development

    Charting Dark Matter interactions

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    The nature of Dark Matter (DM) is one of the most compelling problems in Fundamental Physics.It is a well established fact that the Standard Model (SM) of particle physics and General Relativity (GR) by themselves cannot explain astrophysical and cosmological data such as galactic rotation curves, the Cosmic Microwave Background (CMB) and the distribution of structures at large scales.These data indicate the existence of a new fluid, the DM, that is: 1) collisionless 2) cold 3) dominated by GR at large distances.Very few properties are known about the particles making up the DM.The two main ones are: i) the DM must interact weakly with SM particles, and ii) the DM must be stable on cosmological time scales.These two properties by themselves are too general to draw a clear picture of the Dark Sector (DS). In this Thesis we will try to assess some of its properties in light of current and future experiments.The most natural possibility is for the DM to interact with the weakest of the SM forces, the electroweak (EW) force. We completely characterize this kind of DM particles, called WIMPs.After computing their masses, set by EW annihilations, we study their phenomenology at future lepton colliders and at Direct Detection (DD) experiments. The lightest WIMPs are a perfect target for realistic future lepton colliders, while to probe the heaviest ones future Xenon DD experiments are needed.The second scenario we analyze is the case in which DM does not interact with any of the SM force mediators. In this case, the Effective Field Theory (EFT) approach is needed. We introduce a set of portal operators that have received little attention in the past. After describing a model-independent approach, we discuss bounds on the portals coming from high intensity experiments, like neutrino experiments at Fermilab (e.g. DUNE). These are competitive with respect to current constraints.The last possibility is the case in which even portals are absent. In this scenario, the clustering of both species during the Universe evolution can provide a window on the DM nature. We focus on models in which the DM has a long range self-interaction mediated by a light scalar.We study the evolution of inhomogeneities, and compare the predicted CMB anisotropies and galaxy power spectra with current and future data (like Euclid), setting strong bounds on the strength of the self-interaction.Finally we comment on how theoretical insights on DM stability can constrain DM model building

    3D Localization and Tracking Methods for Multi-Platform Radar Networks

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    Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algorithms for efficient fusion of information obtained through multiple receivers has attracted much attention. However, considerable challenges remain. This article provides an overview on recent unconstrained and constrained localization techniques as well as multitarget tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing methods are illustrated and explored in detail, one aimed at accomplishing localization tasks the other tracking functions. As to the former, assuming a MPRN with one transmitter and multiple receivers, the angular and range constrained estimator (ARCE) algorithm capitalizes on the knowledge of the transmitter antenna beamwidth. As to the latter, the scalable sum-product algorithm (SPA) based MTT technique is presented. Additionally, a solution to combine ARCE and SPA-based MTT is investigated in order to boost the accuracy of the overall surveillance system. Simulated experiments show the benefit of the combined algorithm in comparison with the conventional baseline SPA-based MTT and the stand-alone ARCE localization, in a 3D sensing scenario

    SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation

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    In this paper, we introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud to obtain a precise alignment with the model point cloud. Training our framework involves two operations: An SE(3) diffusion process and an SE(3) reverse process. The SE(3) diffusion process gradually perturbs the optimal rigid transformation of a pair of point clouds by continuously injecting noise (perturbation transformation). By contrast, the SE(3) reverse process focuses on learning a denoising network that refines the noisy transformation step-by-step, bringing it closer to the optimal transformation for accurate pose estimation. Unlike standard diffusion models used in linear Euclidean spaces, our diffusion model operates on the SE(3) manifold. This requires exploiting the linear Lie algebra se(3)\mathfrak{se}(3) associated with SE(3) to constrain the transformation transitions during the diffusion and reverse processes. Additionally, to effectively train our denoising network, we derive a registration-specific variational lower bound as the optimization objective for model learning. Furthermore, we show that our denoising network can be constructed with a surrogate registration model, making our approach applicable to different deep registration networks. Extensive experiments demonstrate that our diffusion registration framework presents outstanding pose estimation performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets.Comment: Accepted by NeurIPS-202

    Modelling and Solving the Single-Airport Slot Allocation Problem

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    Currently, there are about 200 overly congested airports where airport capacity does not suffice to accommodate airline demand. These airports play a critical role in the global air transport system since they concern 40% of global passenger demand and act as a bottleneck for the entire air transport system. This imbalance between airport capacity and airline demand leads to excessive delays, as well as multi-billion economic, and huge environmental and societal costs. Concurrently, the implementation of airport capacity expansion projects requires time, space and is subject to significant resistance from local communities. As a short to medium-term response, Airport Slot Allocation (ASA) has been used as the main demand management mechanism. The main goal of this thesis is to improve ASA decision-making through the proposition of models and algorithms that provide enhanced ASA decision support. In doing so, this thesis is organised into three distinct chapters that shed light on the following questions (Iā€“V), which remain untapped by the existing literature. In parentheses, we identify the chapters of this thesis that relate to each research question. I. How to improve the modelling of airline demand flexibility and the utility that each airline assigns to each available airport slot? (Chapters 2 and 4) II. How can one model the dynamic and endogenous adaptation of the airportā€™s landside and airside infrastructure to the characteristics of airline demand? (Chapter 2) III. How to consider operational delays in strategic ASA decision-making? (Chapter 3) IV. How to involve the pertinent stakeholders into the ASA decision-making process to select a commonly agreed schedule; and how can one reduce the inherent decision-complexity without compromising the quality and diversity of the schedules presented to the decision-makers? (Chapter 3) V. Given that the ASA process involves airlines (submitting requests for slots) and coordinators (assigning slots to requests based on a set of rules and priorities), how can one jointly consider the interactions between these two sides to improve ASA decision-making? (Chapter 4) With regards to research questions (I) and (II), the thesis proposes a Mixed Integer Programming (MIP) model that considers airlinesā€™ timing flexibility (research question I) and constraints that enable the dynamic and endogenous allocation of the airportā€™s resources (research question II). The proposed modelling variant addresses several additional problem characteristics and policy rules, and considers multiple efficiency objectives, while integrating all constraints that may affect airport slot scheduling decisions, including the asynchronous use of the different airport resources (runway, aprons, passenger terminal) and the endogenous consideration of the capabilities of the airportā€™s infrastructure to adapt to the airline demandā€™s characteristics and the aircraft/flight type associated with each request. The proposed model is integrated into a two-stage solution approach that considers all primary and several secondary policy rules of ASA. New combinatorial results and valid tightening inequalities that facilitate the solution of the problem are proposed and implemented. An extension of the above MIP model that considers the trade-offs among schedule displacement, maximum displacement, and the number of displaced requests, is integrated into a multi-objective solution framework. The proposed framework holistically considers the preferences of all ASA stakeholder groups (research question IV) concerning multiple performance metrics and models the operational delays associated with each airport schedule (research question III). The delays of each schedule/solution are macroscopically estimated, and a subtractive clustering algorithm and a parameter tuning routine reduce the inherent decision complexity by pruning non-dominated solutions without compromising the representativeness of the alternatives offered to the decision-makers (research question IV). Following the determination of the representative set, the expected delay estimates of each schedule are further refined by considering the whole airfieldā€™s operations, the landside, and the airside infrastructure. The representative schedules are ranked based on the preferences of all ASA stakeholder groups concerning each scheduleā€™s displacement-related and operational-delay performance. Finally, in considering the interactions between airlinesā€™ timing flexibility and utility, and the policy-based priorities assigned by the coordinator to each request (research question V), the thesis models the ASA problem as a two-sided matching game and provides guarantees on the stability of the proposed schedules. A Stable Airport Slot Allocation Model (SASAM) capitalises on the flexibility considerations introduced for addressing research question (I) through the exploitation of data submitted by the airlines during the ASA process and provides functions that proxy each requestā€™s value considering both the airlinesā€™ timing flexibility for each submitted request and the requestsā€™ prioritisation by the coordinators when considering the policy rules defining the ASA process. The thesis argues on the compliance of the proposed functions with the primary regulatory requirements of the ASA process and demonstrates their applicability for different types of slot requests. SASAM guarantees stability through sets of inequalities that prune allocations blocking the formation of stable schedules. A multi-objective Deferred-Acceptance (DA) algorithm guaranteeing the stability of each generated schedule is developed. The algorithm can generate all stable non-dominated points by considering the trade-off between the spilled airline and passenger demand and maximum displacement. The work conducted in this thesis addresses several problem characteristics and sheds light on their implications for ASA decision-making, hence having the potential to improve ASA decision-making. Our findings suggest that the consideration of airlinesā€™ timing flexibility (research question I) results in improved capacity utilisation and scheduling efficiency. The endogenous consideration of the ability of the airportā€™s infrastructure to adapt to the characteristics of airline demand (research question II) enables a more efficient representation of airport declared capacity that results in the scheduling of additional requests. The concurrent consideration of airlinesā€™ timing flexibility and the endogenous adaptation of airport resources to airline demand achieves an improved alignment between the airport infrastructure and the characteristics of airline demand, ergo proposing schedules of improved efficiency. The modelling and evaluation of the peak operational delays associated with the different airport schedules (research question III) provides allows the study of the implications of strategic ASA decision-making for operations and quantifies the impact of the airportā€™s declared capacity on each scheduleā€™s operational performance. In considering the preferences of the relevant ASA stakeholders (airlines, coordinators, airport, and air traffic authorities) concerning multiple operational and strategic ASA efficiency metrics (research question IV) the thesis assesses the impact of alternative preference considerations and indicates a commonly preferred schedule that balances the stakeholdersā€™ preferences. The proposition of representative subsets of alternative schedules reduces decision-complexity without significantly compromising the quality of the alternatives offered to the decision-making process (research question IV). The modelling of the ASA as a two-sided matching game (research question V), results in stable schedules consisting of request-to-slot assignments that provide no incentive to airlines and coordinators to reject or alter the proposed timings. Furthermore, the proposition of stable schedules results in more intensive use of airport capacity, while simultaneously improving scheduling efficiency. The models and algorithms developed as part of this thesis are tested using airline requests and airport capacity data from coordinated airports. Computational results that are relevant to the context of the considered airport instances provide evidence on the potential improvements for the current ASA process and facilitate data-driven policy and decision-making. In particular, with regards to the alignment of airline demand with the capabilities of the airportā€™s infrastructure (questions I and II), computational results report improved slot allocation efficiency and airport capacity utilisation, which for the considered airport instance translate to improvements ranging between 5-24% for various schedule performance metrics. In reducing the difficulty associated with the assessment of multiple ASA solutions by the stakeholders (question IV), instance-specific results suggest reductions to the number of alternative schedules by 87%, while maintaining the quality of the solutions presented to the stakeholders above 70% (expressed in relation to the initially considered set of schedules). Meanwhile, computational results suggest that the concurrent consideration of ASA stakeholdersā€™ preferences (research question IV) with regards to both operational (research question III) and strategic performance metrics leads to alternative airport slot scheduling solutions that inform on the trade-offs between the schedulesā€™ operational and strategic performance and the stakeholdersā€™ preferences. Concerning research question (V), the application of SASAM and the DA algorithm suggest improvements to the number of unaccommodated flights and passengers (13 and 40% improvements) at the expense of requests concerning fewer passengers and days of operations (increasing the number of rejected requests by 1.2% in relation to the total number of submitted requests). The research conducted in this thesis aids in the identification of limitations that should be addressed by future studies to further improve ASA decision-making. First, the thesis focuses on exact solution approaches that consider the landside and airside infrastructure of the airport and generate multiple schedules. The proposition of pre-processing techniques that identify the bottleneck of the airportā€™s capacity, i.e., landside and/or airside, can be used to reduce the size of the proposed formulations and improve the required computational times. Meanwhile, the development of multi-objective heuristic algorithms that consider several problem characteristics and generate multiple efficient schedules in reasonable computational times, could extend the capabilities of the models propositioned in this thesis and provide decision support for some of the worldā€™s most congested airports. Furthermore, the thesis models and evaluates the operational implications of strategic airport slot scheduling decisions. The explicit consideration of operational delays as an objective in ASA optimisation models and algorithms is an issue that merits investigation since it may further improve the operational performance of the generated schedules. In accordance with current practice, the models proposed in this work have considered deterministic capacity parameters. Perhaps, future research could propose formulations that consider stochastic representations of airport declared capacity and improve strategic ASA decision-making through the anticipation of operational uncertainty and weather-induced capacity reductions. Finally, in modelling airlinesā€™ utility for each submitted request and available time slot the thesis proposes time-dependent functions that utilise available data to approximate airlinesā€™ scheduling preferences. Future studies wishing to improve the accuracy of the proposed functions could utilise commercial data sources that provide route-specific information; or in cases that such data is unavailable, employ data mining and machine learning methodologies to extract airlinesā€™ time-dependent utility and preferences
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