Method Exploration of Self-adaptive Entity Matching in Map Fusion

AbstractEntity matching is a crucial and hard technology in map fusion. Current methods still exists some deficiencies, such as matching efficiency is not high, low degree of automation and poor universality, these methods can not meet the matching needs of large data integration, therefore, the urgent need to develop more effective and intelligent methods. This paper analyzed present research situation and existing problems of entity matching, illustrated the necessity of developing self-adaptive entity matching, pointed out urgent research contents and key issues that need to be resolved urgently in self-adaptive entity matching, provided preliminary research scheme of implementing self-adaptive entity matching, finally, introduced characteristics and advantages of self-adaptive entity matching method presented in this paper

Weighted Networks: Applications from Power grid construction to crowd control

Since their discovery in the 1950\u27s by Erdos and Renyi, network theory (the study of objects and their associations) has blossomed into a full-fledged branch of mathematics. Due to the network\u27s flexibility, diverse scientific problems can be reformulated as networks and studied using a common set of tools. I define a network G = (V,E) composed of two parts: (i) the set of objects V, called nodes, and (ii) set of relationships (associations) E, called links, that connect objects in V. We can extend the classic network of nodes and links by describing the intensity of these associations with weights. More formally, weighted networks augment the classic network with a function f(e) from links to the real line, uncovering powerful ways to model real-world applications. This thesis studies new ways to construct robust micro powergrids, mine people\u27s perceptions of causality on a social network, and proposes a new way to analyze crowdsourcing all in the context of the weighted network model. The current state of Earth\u27s ecosystem and intensifying climate calls on scientists to find new ways to harvest clean affordable energy. A microgrid, or neighborhood-scale powergrid built using renewable energy sources attached to personal homes, suggest one way to ameliorate this energy crisis. We can study the stability (robustness) of such a small-scale system with weighted networks. A novel use of weighted networks and percolation theory guides the safe and efficient construction of power lines (links, E) connecting a small set of houses (nodes, V) to one another and weights each power line by the distance between houses. This new look at the robustness of microgrid structures calls into question the efficacy of the traditional utility. The next study uses the twitter social network to compare and contrast causal language from everyday conversation. Collecting a set of 1 million tweets, we find a set of words (unigrams), parts of speech, named entities, and sentiment signal the use of informal causal language. Breaking a problem difficult for a computer to solve into many parts and distributing these tasks to a group of humans to solve is called Crowdsourcing. My final project asks volunteers to \u27reply\u27 to questions asked of them and \u27supply\u27 novel questions for others to answer. I model this \u27reply and supply\u27 framework as a dynamic weighted network, proposing new theories about this network\u27s behavior and how to steer it toward worthy goals. This thesis demonstrates novel uses of, enhances the current scientific literature on, and presents novel methodology for, weighted networks

X-band synthetic aperture radar methods

Spaceborne Synthetic Aperture Radars (SARs), operating at L-band and above, offer microwave observations of the Earth at very high spatial resolution in almost all-weather conditions. Nevertheless, precipitating clouds can significantly affect the signal backscattered from the ground surface in both amplitude and phase, especially at X band and beyond. This evidence has been assessed by numerous recent efforts analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions at X band. This sensitivity can be exploited to detect and quantify precipitations from SARs at the spatial resolution of a few hundred meters, a very appealing feature considering the current resolution of precipitation products from space. Forward models of SAR response in the presence of precipitation have been developed for analyzing SAR signature sensitivity and developing rainfall retrieval algorithms. Precipitation retrieval algorithms from SARs have also been proposed on a semi-empirical basis. This chapter will review experimental evidences, modelling approaches, retrieval methods and recent applications of X-band SAR data to rainfall estimation

Reinforcement Learning for Mutation Operator Selection in Automated Program Repair

Automated program repair techniques aim to aid software developers with the challenging task of fixing bugs. In heuristic-based program repair, a search space of program variants is created by applying mutation operations on the source code to find potential patches for bugs. Most commonly, every selection of a mutation operator during search is performed uniformly at random. The inefficiency of this critical step in the search creates many variants that do not compile or break intended functionality, wasting considerable resources as a result. In this paper, we address this issue and propose a reinforcement learning-based approach to optimise the selection of mutation operators in heuristic-based program repair. Our solution is programming language, granularity-level, and search strategy agnostic and allows for easy augmentation into existing heuristic-based repair tools. We conduct extensive experimentation on four operator selection techniques, two reward types, two credit assignment strategies, two integration methods, and three sets of mutation operators using 22,300 independent repair attempts. We evaluate our approach on 353 real-world bugs from the Defects4J benchmark. Results show that the epsilon-greedy multi-armed bandit algorithm with average credit assignment is best for mutation operator selection. Our approach exhibits a 17.3% improvement upon the baseline, by generating patches for 9 additional bugs for a total of 61 patched bugs in the Defects4J benchmark

Factors affecting the mesothelioma detection rate within national and international epidemiological studies: insights from Scottish linked cancer registry-mortality data

ICD-9 code 163 (malignant neoplasm of pleura) listed as underlying cause of death detected only 40% of Scottish mesothelioma cases (all body sites) from the cancer registry in 1981–1999. This is lower than both the previously published 55% figure, derived from UK mesothelioma register data 1986–1991, which is based on any mention of mesothelioma on death certificates, cross-referenced to cancer registry data, and the 44% figure derived from Scottish mortality data 1981–1999, which captured any mention of mesothelioma on the death certificate. Detection from cancer registry data increased to 75% under ICD-10 in Scotland, confirming earlier predictions of the benefit of ICD-10's more specific mesothelioma codes. Including the accidental poisoning codes E866.4 (ICD-9) and X49 (ICD-10), covering poisoning by ‘unspecified' and ‘other' causes, which appear to have been used as coding surrogates for mesothelioma when asbestos exposure was explicitly mentioned in deaths suggestive of a mesothelioma, and which are recorded as the underlying cause of death in 4–7% of mesotheliomas, may improve the mesothelioma detection rate in future epidemiological studies

Hospital admissions and deaths relating to deliberate self-harm and accidents within 5 years of a cancer diagnosis: a national study in Scotland, UK

The risk of suicide in cancer patients has been reported as elevated in several countries. These patients are exposed to many medicines that may confuse or provide a means for harm, potentially also increasing their risk from accidents. Ratios of observed/expected numbers of hospital admission and death events relating to deliberate self-harm (DSH) and accidents were calculated in the 5 years from a cancer diagnosis in Scotland 1981–1995, compared to the matched general population. The relative risk (RR) of suicide was 1.51 (95% confidence interval (CI): 1.29–1.76). The RR of hospital admissions for DSH was not significantly increased, suggesting a strong suicidal intent in DSH acts in cancer patients. Accidental poisonings and all other accidents were both increased (RR death=3.69, 95% CI: 2.10–6.00; and 1.58, 95% CI: 1.48–1.69, respectively) (RR hospital admissions=1.32, 95% CI: 1.19–1.47; and 1.55, 95% CI: 1.53–1.57, respectively). The association of only certain tumour types (e.g. respiratory) with suicide and accidental poisoning, and a broad range of tumour types with an elevated risk of all other accidents, suggests accidental poisoning categories may be a common destination for code shifting of some DSH events. A previous history of DSH or accidents, significantly increased the RR of suicide or fatal accidents, respectively (RR suicide=14.86 (95% CI: 4.69–34.97) vs 1.16 (95% CI: 0.84–1.55)) (RR accidental death=3.37 (95% CI: 2.53–4.41) vs 1.29 (95% CI: 1.12–1.49)). Within 5 years of a cancer diagnosis, Scottish patients are at increased RR of suicide and fatal accidents, and increased RR of hospital admissions for accidents. Some of these accidents, particularly accidental poisonings, may contain hidden deliberate acts. Previous DSH or accidents are potential markers for those most at risk, in whom to target interventional techniques