137 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Online Algorithms with Randomly Infused Advice

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    We introduce a novel method for the rigorous quantitative evaluation of online algorithms that relaxes the "radical worst-case" perspective of classic competitive analysis. In contrast to prior work, our method, referred to as randomly infused advice (RIA), does not make any assumptions about the input sequence and does not rely on the development of designated online algorithms. Rather, it can be applied to existing online randomized algorithms, introducing a means to evaluate their performance in scenarios that lie outside the radical worst-case regime. More concretely, an online algorithm ALG with RIA benefits from pieces of advice generated by an omniscient but not entirely reliable oracle. The crux of the new method is that the advice is provided to ALG by writing it into the buffer ? from which ALG normally reads its random bits, hence allowing us to augment it through a very simple and non-intrusive interface. The (un)reliability of the oracle is captured via a parameter 0 ? ? ? 1 that determines the probability (per round) that the advice is successfully infused by the oracle; if the advice is not infused, which occurs with probability 1 - ?, then the buffer ? contains fresh random bits (as in the classic online setting). The applicability of the new RIA method is demonstrated by applying it to three extensively studied online problems: paging, uniform metrical task systems, and online set cover. For these problems, we establish new upper bounds on the competitive ratio of classic online algorithms that improve as the infusion parameter ? increases. These are complemented with (often tight) lower bounds on the competitive ratio of online algorithms with RIA for the three problems

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Constraint-based simulation of virtual crowds

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    Central to simulating pedestrian crowds is their motion and behaviour. It is required to understand how pedestrians move to simulate and predict scenarios with crowds of people. Pedestrian behaviours enhance the range of motions people can demonstrate, resulting in greater variety, believability, and accuracy. Models with complex computations and motion have difficulty in being extended with additional behaviours. This is because the structure of these models are not designed in a way that is generally compatible with collision avoidance behaviours. To address this issue, this thesis will research a possible pedestrian model that can simulate collision response with a wide range of additional behaviours. The model will do so by using constraints, a limit on the velocity of a person's movement. The proposed model will use constraints as the core computation. By describing behaviours in terms of constraints, these behaviours can be combined with the proposed model. Pedestrian simulations strike a balance between model complexity and runtime speed. Some models focus entirely on the complexity and accuracy of people, while other models focus on creating believable yet lightweight and performant simulations. Believable crowds look realistic to human observation, but do not match up to numerical analysis under scrutiny. The larger the population, and the more complex the motion of people, the slower the simulation will run. One route for improving performance of software is by using Graphical Processing Units (GPUs). GPUs are devices with theoretical performance that far outperforms equivalent multi-core CPUs. Research literature tends to focus on either the accuracy, or the performance optimisations of pedestrian crowd simulations. This suggests that there is opportunity to create more accurate models that run relatively quickly. Real time is a useful measure of model runtime. A simulation that runs in real time can be interactive and respond live to user input. By increasing the performance of the model, larger and more complex models can be simulated. This in turn increases the range of applications the model can represent. This thesis will develop a performant pedestrian simulation that runs in real time. It will explore how suitable the model is for GPU acceleration, and what performance gains can be obtained by implementing the model on the GPU

    Digital solutions for self-monitoring physical health and wellbeing during pregnancy

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    Perinatal disorders were among the top ten causes of global burden of disease in 2019. Better access to perinatal healthcare would help to reduce preventable morbidity. The increase in access to and use of smartphones presents a unique opportunity to transform and improve how women monitor their own health during pregnancy. This thesis aims to investigate the quality and usage of currently available pregnancy digital health tools for self-monitoring and to validate a newly developed, custom-built pregnancy self-monitoring tool. In Chapter 2, the most popular, commercially available pregnancy apps and their monitoring tools were evaluated for their quality by conducting a pregnancy app scoping review. In Chapters 3 and 4, pregnant women and healthcare professionals were surveyed and interviewed to better understand their usage of and attitudes towards digital health, as well as their thoughts about two hypothetical app features (a direct patient-to-healthcare professional communication tool and a novel body measurement tool). In Chapter 5, we test the performance of a first generation, custom-built body measurement tool (which we called BMT-1) by comparing the digital measurements extracted from photos taken on smartphones to physical measurements taken with measuring tape. The performance of BMT-1 was also assessed on a longitudinal set of digitally constructed pregnancy models. Collectively, the findings from Chapters 2, 3 and 4 provide evidence that there is both opportunity and scope for the development of new digital health tools to support and enhance the quality of care during pregnancy. The results from Chapter 5 indicate that BMT-1 successfully extracted body measurements from both photos and digitally constructed pregnancy models, though would require refinement before it could be launched. To finalise, in Chapter 6, I outline how these findings could help to guide the design, development and implementation of new pregnancy digital health tools

    Efficient Algorithms and Hardness Results for the Weighted k-Server Problem

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    Caching Connections in Matchings

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    Motivated by the desire to utilize a limited number of configurable optical switches by recent advances in Software Defined Networks (SDNs), we define an online problem which we call the Caching in Matchings problem. This problem has a natural combinatorial structure and therefore may find additional applications in theory and practice. In the Caching in Matchings problem our cache consists of kk matchings of connections between servers that form a bipartite graph. To cache a connection we insert it into one of the kk matchings possibly evicting at most two other connections from this matching. This problem resembles the problem known as Connection Caching, where we also cache connections but our only restriction is that they form a graph with bounded degree kk. Our results show a somewhat surprising qualitative separation between the problems: The competitive ratio of any online algorithm for caching in matchings must depend on the size of the graph. Specifically, we give a deterministic O(nk)O(nk) competitive and randomized O(nlogk)O(n \log k) competitive algorithms for caching in matchings, where nn is the number of servers and kk is the number of matchings. We also show that the competitive ratio of any deterministic algorithm is Ω(max(nk,k))\Omega(\max(\frac{n}{k},k)) and of any randomized algorithm is Ω(lognk2logklogk)\Omega(\log \frac{n}{k^2 \log k} \cdot \log k). In particular, the lower bound for randomized algorithms is Ω(logn)\Omega(\log n) regardless of kk, and can be as high as Ω(log2n)\Omega(\log^2 n) if k=n1/3k=n^{1/3}, for example. We also show that if we allow the algorithm to use at least 2k12k-1 matchings compared to kk used by the optimum then we match the competitive ratios of connection catching which are independent of nn. Interestingly, we also show that even a single extra matching for the algorithm allows to get substantially better bounds

    Efficient Algorithms and Hardness Results for the Weighted kk-Server Problem

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    In this paper, we study the weighted kk-server problem on the uniform metric in both the offline and online settings. We start with the offline setting. In contrast to the (unweighted) kk-server problem which has a polynomial-time solution using min-cost flows, there are strong computational lower bounds for the weighted kk-server problem, even on the uniform metric. Specifically, we show that assuming the unique games conjecture, there are no polynomial-time algorithms with a sub-polynomial approximation factor, even if we use cc-resource augmentation for c<2c < 2. Furthermore, if we consider the natural LP relaxation of the problem, then obtaining a bounded integrality gap requires us to use at least \ell resource augmentation, where \ell is the number of distinct server weights. We complement these results by obtaining a constant-approximation algorithm via LP rounding, with a resource augmentation of (2+ϵ)(2+\epsilon)\ell for any constant ϵ>0\epsilon > 0. In the online setting, an exp(k)\exp(k) lower bound is known for the competitive ratio of any randomized algorithm for the weighted kk-server problem on the uniform metric. In contrast, we show that 22\ell-resource augmentation can bring the competitive ratio down by an exponential factor to only O(2log)O(\ell^2 \log \ell). Our online algorithm uses the two-stage approach of first obtaining a fractional solution using the online primal-dual framework, and then rounding it online.Comment: This paper will appear in the proceedings of APPROX 202

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Online Paging with Heterogeneous Cache Slots

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