Integrating Learning from Examples into the Search for Diagnostic Policies

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO* algorithm to solve this MDP. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on five benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on todays desktop computers

PDFS: Practical Data Feed Service for Smart Contracts

Smart contracts are a new paradigm that emerged with the rise of the blockchain technology. They allow untrusting parties to arrange agreements. These agreements are encoded as a programming language code and deployed on a blockchain platform, where all participants execute them and maintain their state. Smart contracts are promising since they are automated and decentralized, thus limiting the involvement of third trusted parties, and can contain monetary transfers. Due to these features, many people believe that smart contracts will revolutionize the way we think of distributed applications, information sharing, financial services, and infrastructures. To release the potential of smart contracts, it is necessary to connect the contracts with the outside world, such that they can understand and use information from other infrastructures. For instance, smart contracts would greatly benefit when they have access to web content. However, there are many challenges associated with realizing such a system, and despite the existence of many proposals, no solution is secure, provides easily-parsable data, introduces small overheads, and is easy to deploy. In this paper we propose PDFS, a practical system for data feeds that combines the advantages of the previous schemes and introduces new functionalities. PDFS extends content providers by including new features for data transparency and consistency validations. This combination provides multiple benefits like content which is easy to parse and efficient authenticity verification without breaking natural trust chains. PDFS keeps content providers auditable, mitigates their malicious activities (like data modification or censorship), and allows them to create a new business model. We show how PDFS is integrated with existing web services, report on a PDFS implementation and present results from conducted case studies and experiments.Comment: Blockchain; Smart Contracts; Data Authentication; Ethereu

Multigraded Castelnuovo-Mumford Regularity

We develop a multigraded variant of Castelnuovo-Mumford regularity. Motivated by toric geometry, we work with modules over a polynomial ring graded by a finitely generated abelian group. As in the standard graded case, our definition of multigraded regularity involves the vanishing of graded components of local cohomology. We establish the key properties of regularity: its connection with the minimal generators of a module and its behavior in exact sequences. For an ideal sheaf on a simplicial toric variety X, we prove that its multigraded regularity bounds the equations that cut out the associated subvariety. We also provide a criterion for testing if an ample line bundle on X gives a projectively normal embedding.Comment: 30 pages, 5 figure

Optical orientation and alignment of excitons in direct and indirect band gap (In,Al)As/AlAs quantum dots with type-I band alignment

The spin structure and spin dynamics of excitons in an ensemble of (In,Al)As/AlAs quantum dots (QDs) with type-I band alignment, containing both direct and indirect band gap dots, are studied. Time-resolved and spectral selective techniques are used to distinguish between the direct and indirect QDs. The exciton fine structure is studied by means of optical alignment and optical orientation techniques in magnetic fields applied in the Faraday or Voigt geometries. A drastic difference in emission polarization is found for the excitons in the direct QDs involving a $\Gamma$-valley electron and the excitons in the indirect QDs contributed by an $X$-valley electron. We show that in the direct QDs the exciton spin dynamics is controlled by the anisotropic exchange splitting, while in the indirect QDs it is determined by the hyperfine interaction with nuclear field fluctuations. The anisotropic exchange splitting is determined for the direct QD excitons and compared with model calculations

Optical orientation and alignment of excitons in direct and indirect band gap (In,Al)As/AlAs quantum dots with type-I band alignment

The spin structure and spin dynamics of excitons in an ensemble of (In,Al)As/AlAs quantum dots (QDs) with type-I band alignment, containing both direct and indirect band gap dots, are studied. Time-resolved and spectral selective techniques are used to distinguish between the direct and indirect QDs. The exciton fine structure is studied by means of optical alignment and optical orientation techniques in magnetic fields applied in the Faraday or Voigt geometries. A drastic difference in emission polarization is found for the excitons in the direct QDs involving a $\Gamma$-valley electron and the excitons in the indirect QDs contributed by an $X$-valley electron. We show that in the direct QDs the exciton spin dynamics is controlled by the anisotropic exchange splitting, while in the indirect QDs it is determined by the hyperfine interaction with nuclear field fluctuations. The anisotropic exchange splitting is determined for the direct QD excitons and compared with model calculations