2,853 research outputs found

    A clinical decision support system for detecting and mitigating potentially inappropriate medications

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    Background: Medication errors are a leading cause of preventable harm to patients. In older adults, the impact of ageing on the therapeutic effectiveness and safety of drugs is a significant concern, especially for those over 65. Consequently, certain medications called Potentially Inappropriate Medications (PIMs) can be dangerous in the elderly and should be avoided. Tackling PIMs by health professionals and patients can be time-consuming and error-prone, as the criteria underlying the definition of PIMs are complex and subject to frequent updates. Moreover, the criteria are not available in a representation that health systems can interpret and reason with directly. Objectives: This thesis aims to demonstrate the feasibility of using an ontology/rule-based approach in a clinical knowledge base to identify potentially inappropriate medication(PIM). In addition, how constraint solvers can be used effectively to suggest alternative medications and administration schedules to solve or minimise PIM undesirable side effects. Methodology: To address these objectives, we propose a novel integrated approach using formal rules to represent the PIMs criteria and inference engines to perform the reasoning presented in the context of a Clinical Decision Support System (CDSS). The approach aims to detect, solve, or minimise undesirable side-effects of PIMs through an ontology (knowledge base) and inference engines incorporating multiple reasoning approaches. Contributions: The main contribution lies in the framework to formalise PIMs, including the steps required to define guideline requisites to create inference rules to detect and propose alternative drugs to inappropriate medications. No formalisation of the selected guideline (Beers Criteria) can be found in the literature, and hence, this thesis provides a novel ontology for it. Moreover, our process of minimising undesirable side effects offers a novel approach that enhances and optimises the drug rescheduling process, providing a more accurate way to minimise the effect of drug interactions in clinical practice

    Classical and quantum algorithms for scaling problems

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    This thesis is concerned with scaling problems, which have a plethora of connections to different areas of mathematics, physics and computer science. Although many structural aspects of these problems are understood by now, we only know how to solve them efficiently in special cases.We give new algorithms for non-commutative scaling problems with complexity guarantees that match the prior state of the art. To this end, we extend the well-known (self-concordance based) interior-point method (IPM) framework to Riemannian manifolds, motivated by its success in the commutative setting. Moreover, the IPM framework does not obviously suffer from the same obstructions to efficiency as previous methods. It also yields the first high-precision algorithms for other natural geometric problems in non-positive curvature.For the (commutative) problems of matrix scaling and balancing, we show that quantum algorithms can outperform the (already very efficient) state-of-the-art classical algorithms. Their time complexity can be sublinear in the input size; in certain parameter regimes they are also optimal, whereas in others we show no quantum speedup over the classical methods is possible. Along the way, we provide improvements over the long-standing state of the art for searching for all marked elements in a list, and computing the sum of a list of numbers.We identify a new application in the context of tensor networks for quantum many-body physics. We define a computable canonical form for uniform projected entangled pair states (as the solution to a scaling problem), circumventing previously known undecidability results. We also show, by characterizing the invariant polynomials, that the canonical form is determined by evaluating the tensor network contractions on networks of bounded size

    Algorithms and complexity for approximately counting hypergraph colourings and related problems

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    The past decade has witnessed advancements in designing efficient algorithms for approximating the number of solutions to constraint satisfaction problems (CSPs), especially in the local lemma regime. However, the phase transition for the computational tractability is not known. This thesis is dedicated to the prototypical problem of this kind of CSPs, the hypergraph colouring. Parameterised by the number of colours q, the arity of each hyperedge k, and the vertex maximum degree Δ, this problem falls into the regime of Lovász local lemma when Δ ≲ qᵏ. In prior, however, fast approximate counting algorithms exist when Δ ≲ qᵏ/³, and there is no known inapproximability result. In pursuit of this, our contribution is two-folded, stated as follows. • When q, k ≥ 4 are evens and Δ ≥ 5·qᵏ/², approximating the number of hypergraph colourings is NP-hard. • When the input hypergraph is linear and Δ ≲ qᵏ/², a fast approximate counting algorithm does exist

    Rules, frequency, and predictability in morphological generalization: behavioral and computational evidence from the German plural system

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    Morphological generalization, or the task of mapping an unknown word (such as a novel noun Raun) to an inflected form (such as the plural Rauns), has historically proven a contested topic within computational linguistics and cognitive science, e.g. within the past tense debate (Rumelhart and McClelland, 1986; Pinker and Prince, 1988; Seidenberg and Plaut, 2014). Marcus et al. (1995) identified German plural inflection as a key challenge domain to evaluate two competing accounts of morphological generalization: a rule generation view focused on linguistic features of input words, and a type frequency view focused on the distribution of output inflected forms, thought to reflect more domain-general cognitive processes. More recent behavioral and computational research developments support a new view based on predictability, which integrates both input and output distributions. My research uses these methodological innovations to revisit a core dispute of the past tense debate: how do German speakers generalize plural inflection, and can computational learners generalize similarly? This dissertation evaluates the rule generation, type frequency, and predictability accounts of morphological generalization in a series of behavioral and computational experiments with the stimuli developed by Marcus et al.. I assess predictions for three aspects of German plural generalization: distribution of infrequent plural classes, influence of grammatical gender, and within-item variability. Overall, I find that speaker behavior is best characterized as frequency-matching to a phonologically-conditioned lexical distribution. This result does not support the rule generation view, and qualifies the predictability view: speakers use some, but not all available information to reduce uncertainty in morphological generalization. Neural and symbolic model predictions are typically overconfident relative to speakers; simple Bayesian models show somewhat higher speaker-like variability and accuracy. All computational models are outperformed by a static phonologically-conditioned lexical baseline, suggesting these models have not learned the selective feature preferences that inform speaker generalization

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    The opinions of science and mathematics teachers about beliefs, practices, and implementation of meaningful learning in Israel. A case study of Arab middle school(s)

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    Wydział Studiów EdukacyjnychWiele badań pokazuje, że przekonania nauczycieli dotyczące nauczania i uczenia się silnie oddziałują na ich praktykę zawodową. Celem tej pracy było zbadanie przekonań i praktyk nauczycieli przedmiotów ścisłych i matematyki w arabskich szkołach średnich w Izraelu w obliczy wdrażania nowej reformy edukacyjnej w tym kraju, silnie osadzonej na koncepcji meaningful learning. Zgodnie z tą koncepcją, uczniowie powinni być aktywni i zaangażowani w proces rozwiązywania problemów, którego rdzeniem jest szeroko ujmowany dialog pomiędzy uczestnikami procesu uczenia się. W badaniach wykorzystano strategię badań jakościowych. Prowadzono obserwacje w klasie, częściowo ustrukturyzowane wywiady oraz analizy dokumentów (m.in. planów lekcji, testów, arkuszy roboczych) i notatek terenowych. Uczestnikami badania było dwudziestu nauczycieli z trzech szkół średnich w społeczeństwie arabskim. Uzyskane dane pozwoliły zarysować obraz przekonań tych nauczycieli na temat meaningful learning oraz zidentyfikować sytuacje, które nauczyciele postrzegają jako realizację tej koncepcji. Praca kończy się rekomendacjami dotyczącymi dalszych etapów wdrażania reformy edukacji w Izraelu.The introduction of a new reform potentially challenges teachers’ beliefs and practices about teaching. This case study explores these challenges in the context of a new reform in Israel, where major educational reform has been undertaken. A considerable body of research, alternatively, advocates that teachers’ beliefs about teaching and learning affect their teaching practices and many aspects of their professional work. These beliefs and practices influence many factors on the contextual and teacher levels. Thus, this study aimed to investigate and understand Arab middle school science and mathematics teachers’ beliefs, practices, and implementation of meaningful learning in Israel. The resulting data served to construct a background picture regarding teachers’ beliefs on meaningful learning, classroom practices, and identifying situations that teachers perceived as the implementation of meaningful learning. The study found also that curricular demands, teacher perceptions of their students, pressures of time, assessment, crowded classrooms, lack of resources, workload, and inadequate teacher understanding of the components of meaningful learning inhibited student- centered instruction. Thus, along with the reformation of teachers, there should also be a reformation in the context of the learning atmosphere and infrastructures in tune with the new reform’s intentions

    Agnostic proper learning of monotone functions: beyond the black-box correction barrier

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    We give the first agnostic, efficient, proper learning algorithm for monotone Boolean functions. Given 2O~(n/ε)2^{\tilde{O}(\sqrt{n}/\varepsilon)} uniformly random examples of an unknown function f:{±1}n{±1}f:\{\pm 1\}^n \rightarrow \{\pm 1\}, our algorithm outputs a hypothesis g:{±1}n{±1}g:\{\pm 1\}^n \rightarrow \{\pm 1\} that is monotone and (opt+ε)(\mathrm{opt} + \varepsilon)-close to ff, where opt\mathrm{opt} is the distance from ff to the closest monotone function. The running time of the algorithm (and consequently the size and evaluation time of the hypothesis) is also 2O~(n/ε)2^{\tilde{O}(\sqrt{n}/\varepsilon)}, nearly matching the lower bound of Blais et al (RANDOM '15). We also give an algorithm for estimating up to additive error ε\varepsilon the distance of an unknown function ff to monotone using a run-time of 2O~(n/ε)2^{\tilde{O}(\sqrt{n}/\varepsilon)}. Previously, for both of these problems, sample-efficient algorithms were known, but these algorithms were not run-time efficient. Our work thus closes this gap in our knowledge between the run-time and sample complexity. This work builds upon the improper learning algorithm of Bshouty and Tamon (JACM '96) and the proper semiagnostic learning algorithm of Lange, Rubinfeld, and Vasilyan (FOCS '22), which obtains a non-monotone Boolean-valued hypothesis, then ``corrects'' it to monotone using query-efficient local computation algorithms on graphs. This black-box correction approach can achieve no error better than 2opt+ε2\mathrm{opt} + \varepsilon information-theoretically; we bypass this barrier by a) augmenting the improper learner with a convex optimization step, and b) learning and correcting a real-valued function before rounding its values to Boolean. Our real-valued correction algorithm solves the ``poset sorting'' problem of [LRV22] for functions over general posets with non-Boolean labels

    Two Source Extractors for Asymptotically Optimal Entropy, and (Many) More

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    A long line of work in the past two decades or so established close connections between several different pseudorandom objects and applications. These connections essentially show that an asymptotically optimal construction of one central object will lead to asymptotically optimal solutions to all the others. However, despite considerable effort, previous works can get close but still lack one final step to achieve truly asymptotically optimal constructions. In this paper we provide the last missing link, thus simultaneously achieving explicit, asymptotically optimal constructions and solutions for various well studied extractors and applications, that have been the subjects of long lines of research. Our results include: Asymptotically optimal seeded non-malleable extractors, which in turn give two source extractors for asymptotically optimal min-entropy of O(logn)O(\log n), explicit constructions of KK-Ramsey graphs on NN vertices with K=logO(1)NK=\log^{O(1)} N, and truly optimal privacy amplification protocols with an active adversary. Two source non-malleable extractors and affine non-malleable extractors for some linear min-entropy with exponentially small error, which in turn give the first explicit construction of non-malleable codes against 22-split state tampering and affine tampering with constant rate and \emph{exponentially} small error. Explicit extractors for affine sources, sumset sources, interleaved sources, and small space sources that achieve asymptotically optimal min-entropy of O(logn)O(\log n) or 2s+O(logn)2s+O(\log n) (for space ss sources). An explicit function that requires strongly linear read once branching programs of size 2nO(logn)2^{n-O(\log n)}, which is optimal up to the constant in O()O(\cdot). Previously, even for standard read once branching programs, the best known size lower bound for an explicit function is 2nO(log2n)2^{n-O(\log^2 n)}.Comment: Fixed some minor error
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