DIAGaRa: An Incremental Algorithm for Inferring Implicative Rules from Examples

An approach is proposed for inferring implicative logical rules from examples. The concept of a good diagnostic test for a given set of positive examples lies in the basis of this approach. The process of inferring good diagnostic tests is considered as a process of inductive common sense reasoning. The incremental approach to learning algorithms is implemented in an algorithm DIAGaRa for inferring implicative rules from examples.

Utilizing encoding in scalable linear optics quantum computing

We present a scheme which offers a significant reduction in the resources required to implement linear optics quantum computing. The scheme is a variation of the proposal of Knill, Laflamme and Milburn, and makes use of an incremental approach to the error encoding to boost probability of success.

MASTER DATA MANAGEMENT MATURITY AND TECHNOLOGY ASSESSMENT - From theory to practice, case Ineo Oy

After decades of mergers and acquisitions and successive technology trends such as CRM, ERP and DW, the data in enterprise systems is scattered and inconsistent. Global organizations face the challenge of addressing local uses of shared business entities, such as customer and material, and at the same time have a consistent, unique, and consolidate view of financial indicators. In addition, current enterprise systems do not accommodate the pace of organizational changes and immense efforts are required to maintain data. When it comes to systems integration, ERPs are considered “closed” and expensive. Data structures are complex and the “out-of-the-box” integration options offered are not based on industry standards. Therefore expensive and time-consuming projects are undertaken in order to have required data flowing according to business processes needs. Master Data Management (MDM) emerges as one discipline focused on ensuring long-term data consistency. Presented as a technology-enabled business discipline, it emphasizes business process and governance to model and maintain the data related to key business entities. There are immense technical and organizational challenges to accomplish the “single version of the truth” MDM mantra. Adding one central repository of master data might prove unfeasible in a few scenarios, thus an incremental approach is recommended, starting from areas most critically affected by data issues. This research aims at understanding the current literature on MDM and contrasting it with views from professionals. The data collected from interviews revealed details on the complexities of data structures and data management practices in global organizations, reinforcing the call for more in-depth research on organizational aspects of MDM. The most difficult piece of master data to manage is the “local” part, the attributes related to the sourcing and storing of materials in one particular warehouse in The Netherlands or a complex set of pricing rules for a subsidiary of a customer in Brazil. From a practical perspective, this research evaluates one MDM solution under development at a Finnish IT solution-provider. By means of applying an existing assessment method, the research attempts at providing the company with one possible tool to evaluate its product from a vendor-agnostics perspective.

A hipótese do crescimento rápido: novos argumentos

The hypothesis of fast growth. New arguments. After the 1981-2003 quasi-stagnation, the Brazilian economy signals that it is back to fast growth. The aborted growth spurts in this period accumulated an expansion potential that is now emerging. The growth frustration was particularly significant in the case of the growth surge beginning in 1999, as an outcome of the large 1999 depreciation of the real and the substantial 1990s increase of productivity. Now this repressed growth is being spontaneously liberated. It may be additionally liberated by adequate economic policies. Improvements in macroeconomic policy and the implementation of mini micro reforms are consistent with this incremental approach.

A physically based trunk soft tissue modeling for scoliosis surgery planning systems

One of the major concerns of scoliotic patients undergoing spinal correction surgery is the trunk's external appearance after the surgery. This paper presents a novel incremental approach for simulating postoperative trunk shape in scoliosis surgery. Preoperative and postoperative trunk shapes data were obtained using three-dimensional medical imaging techniques for seven patients with adolescent idiopathic scoliosis. Results of qualitative and quantitative evaluations, based on the comparison of the simulated and actual postoperative trunk surfaces, showed an adequate accuracy of the method. Our approach provides a candidate simulation tool to be used in a clinical environment for the surgery planning process.

Reconciling the Irreconcilable? Sovereign Debt Restructuring and the Principles of International Law. A Law and Economics Perspective

This Thesis focuses on the principles of international law relevant to the resolution of legal disputes arising from sovereign insolvency conflicts. It attempts to contribute to the “incremental” approach literature by identifying principles, justifying their application in litigation and assessing whether they may help to reconcile the trade-offs prevalent in that context. For that purpose, this Thesis distinguishes between two different types of principles. First, it investigates the “Principles of Public International Law” (henceforth, “PIL principles”). Said category refers to norms of the law of nations which can be considered functionally and structurally similar to domestic constitutional principles (i.e., that can be regarded as “optimization” or “prima facie” requirements). This Thesis underscores the PIL principles protecting the interests of the creditors and citizens as well as the “public interest”, arguing that decision makers face a trade-off between these principles in the context of restructurings. Secondly, this Thesis inquires into the “general principles of domestic law” (henceforth, “GPDs”) which can be applied in sovereign debt restructuring. Two GPDs are identified: a “stay” on litigation and a “cram down” on dissenting creditors’ claims. Although both principles have been identified by the prior literature, this work advances a small but significant “twist” in the methodology used for that purpose: it relies exclusively on functional and comparative analysis. Moreover, this work justifies the application of said GPDs for two jurisdictions: New York and Germany. Finally, it posits that those GPDs can help to mitigate the trade-offs between PIL principles, thus reconciling the interests at stake.

Decentralized systems for the protection and portability of personal data

The General Data Protection Regulation (GDPR) has been designed to help promote a view in favor of the interests of individuals instead of large corporations. However, there is the need of more dedicated technologies that can help companies comply with GDPR while enabling people to exercise their rights. We argue that such a dedicated solution must address two main issues: the need for more transparency towards individuals regarding the management of their personal information and their often hindered ability to access and make interoperable personal data in a way that the exercise of one's rights would result in straightforward. We aim to provide a system that helps to push personal data management towards the individual's control, i.e., a personal information management system (PIMS). By using distributed storage and decentralized computing networks to control online services, users' personal information could be shifted towards those directly concerned, i.e., the data subjects. The use of Distributed Ledger Technologies (DLTs) and Decentralized File Storage (DFS) as an implementation of decentralized systems is of paramount importance in this case. The structure of this dissertation follows an incremental approach to describing a set of decentralized systems and models that revolves around personal data and their subjects. Each chapter of this dissertation builds up the previous one and discusses the technical implementation of a system and its relation with the corresponding regulations. We refer to the EU regulatory framework, including GDPR, eIDAS, and Data Governance Act, to build our final system architecture's functional and non-functional drivers. In our PIMS design, personal data is kept in a Personal Data Space (PDS) consisting of encrypted personal data referring to the subject stored in a DFS. On top of that, a network of authorization servers acts as a data intermediary to provide access to potential data recipients through smart contracts.

Methodological approach to the first and second lactate threshold in incremental cardiopulmonary exercise testing

Determination of an 'anaerobic threshold' plays an important role in the appreciation of an incremental cardiopulmonary exercise test and describes prominent changes of blood lactate accumulation with increasing workload. Two lactate thresholds are discerned during cardiopulmonary exercise testing and used for physical fitness estimation or training prescription. A multitude of different terms are, however, found in the literature describing the two thresholds. Furthermore, the term 'anaerobic threshold' is synonymously used for both, the 'first' and the 'second' lactate threshold, bearing a great potential of confusion. The aim of this review is therefore to order terms, present threshold concepts, and describe methods for lactate threshold determination using a three-phase model with reference to the historical and physiological background to facilitate the practical application of the term 'anaerobic threshold'.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

An incremental tri-partite approach to ontology learning

In this paper we present a new approach to ontology learning. Its basis lies in a dynamic and iterative view of knowledge acquisition for ontologies. The Abraxas approach is founded on three resources, a set of texts, a set of learning patterns and a set of ontological triples, each of which must remain in equilibrium. As events occur which disturb this equilibrium various actions are triggered to re-establish a balance between the resources. Such events include acquisition of a further text from external resources such as the Web or the addition of ontological triples to the ontology. We develop the concept of a knowledge gap between the coverage of an ontology and the corpus of texts as a measure triggering actions. We present an overview of the algorithm and its functionalities.

Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm

We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton`s method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The analysis of transient and steady-state performances at each individual node within the network is developed by using a weighted spatial-temporal energy conservation relation and confirmed by computer simulations. The simulation results also verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance with lower computational and memory cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, as compared to a distributed recursive-least-squares (RLS) based method.

O método de lançamento incremental. Conceitos base e uma aplicação prática

Ao longo desta dissertação, é abordada a temática das obras de arte, focando-se um processo construtivo em particular, que é o Método de Lançamento Incremental. Começa-se por um enquadramento geral da temática das obras de arte, sendo feita a sua descrição, e faz-se uma síntese histórica dos materiais utilizados nas mesmas. De seguida, são apresentados os tipos de tabuleiros existentes e as tipologias estruturais das obras de arte. São mencionados ainda os processos e equipamentos construtivos que são utilizados na sua construção. É, de seguida, feita uma abordagem mais profunda ao processo construtivo alvo desta dissertação, nomeadamente questões de índole prática e de dimensionamento. É feita ainda uma aplicação prática, sendo feito um Estudo Prévio de uma solução para uma obra de arte executada com este processo construtivo. Termina-se indicando aspetos importantes na monitorização das obras de arte executadas pelo processo construtivo alvo desta dissertação, sendo ainda apresentadas as conclusões a que se chegou no final da mesma e possíveis desenvolvimentos futuros.

Incremental compilation and deployment for OutSystems Platform

OutSystems Platform is used to develop, deploy, and maintain enterprise web an mobile web applications. Applications are developed through a visual domain specific language, in an integrated development environment, and compiled to a standard stack of web technologies. In the platform’s core, there is a compiler and a deployment service that transform the visual model into a running web application. As applications grow, compilation and deployment times increase as well, impacting the developer’s productivity. In the previous model, a full application was the only compilation and deployment unit. When the developer published an application, even if he only changed a very small aspect of it, the application would be fully compiled and deployed. Our goal is to reduce compilation and deployment times for the most common use case, in which the developer performs small changes to an application before compiling and deploying it. We modified the OutSystems Platform to support a new incremental compilation and deployment model that reuses previous computations as much as possible in order to improve performance. In our approach, the full application is broken down into smaller compilation and deployment units, increasing what can be cached and reused. We also observed that this finer model would benefit from a parallel execution model. Hereby, we created a task driven Scheduler that executes compilation and deployment tasks in parallel. Our benchmarks show a substantial improvement of the compilation and deployment process times for the aforementioned development scenario.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.