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

Trypanosomatids are common and diverse parasites of Drosophila

SUMMARYDrosophila melanogasteris an important model system of immunity and parasite resistance, yet most studies use parasites that do not naturally infect this organism. We have studied trypanosomatids in natural populations to assess the prevalence and diversity of these gut parasites. We collected several species ofDrosophilafrom Europe and surveyed them for trypanosomatids using conserved primers for two genes. We have used the conserved GAPDH sequence to construct a phylogenetic tree and the highly variable spliced leader RNA to assay genetic diversity. All 5 of the species that we examined were infected, and the average prevalence ranged from 1 to 6%. There are several different groups of trypanosomatids, related to other monoxenous Trypanosomatidae. These may represent new trypanosomatid species and were found in different species of EuropeanDrosophilafrom different geographical locations. The detection of a little studied natural pathogen inD. melanogasterand related species provides new opportunities for research into both theDrosophilaimmune response and the evolution of hosts and parasites.</jats:p

Engineering of spin-lattice relaxation dynamics by digital growth of diluted magnetic semiconductor CdMnTe

The technological concept of "digital alloying" offered by molecular-beam epitaxy is demonstrated to be a very effective tool for tailoring static and dynamic magnetic properties of diluted magnetic semiconductors. Compared to common "disordered alloys" with the same Mn concentration, the spin-lattice relaxation dynamics of magnetic Mn ions has been accelerated by an order of magnitude in (Cd,Mn)Te digital alloys, without any noticeable change in the giant Zeeman spin splitting of excitonic states, i.e. without effect on the static magnetization. The strong sensitivity of the magnetization dynamics to clustering of the Mn ions opens a new degree of freedom for spin engineering.Comment: 9 pages, 3 figure

Model-guided design of ligand-regulated RNAi for programmable control of gene expression

Progress in constructing biological networks will rely on the development of more advanced components that can be predictably modified to yield optimal system performance. We have engineered an RNA-based platform, which we call an shRNA switch, that provides for integrated ligand control of RNA interference (RNAi) by modular coupling of an aptamer, competing strand, and small hairpin (sh) RNA stem into a single component that links ligand concentration and target gene expression levels. A combined experimental and mathematical modelling approach identified multiple tuning strategies and moves towards a predictable framework for the forward design of shRNA switches. The utility of our platform is highlighted by the demonstration of fine-tuning, multi-input control, and model-guided design of shRNA switches with an optimized dynamic range. Thus, shRNA switches can serve as an advanced component for the construction of complex biological systems and offer a controlled means of activating RNAi in disease therapeutics

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

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

Electric field control of magnetization dynamics in ZnMnSe/ZnBeSe diluted-magnetic-semiconductor heterostructures

We show that the magnetization dynamics in diluted magnetic semiconductors can be controlled separately from the static magnetization by means of an electric field. The spin-lattice relaxation (SLR) time of magnetic Mn2+ ions was tuned by two orders of magnitude by a gate voltage applied to n-type modulation-doped (Zn,Mn)Se/(Zn,Be)Se quantum wells. The effect is based on providing an additional channel for SLR by a two-dimensional electron gas (2DEG). The static magnetization responsible for the giant Zeeman spin splitting of excitons was not influenced by the 2DEG density

Creating excitonic entanglement in quantum dots through the optical Stark effect

We show that two initially non-resonant quantum dots may be brought into resonance by the application of a single detuned laser. This allows for control of the inter-dot interactions and the generation of highly entangled excitonic states on the picosecond timescale. Along with arbitrary single qubit manipulations, this system would be sufficient for the demonstration of a prototype excitonic quantum computer.Comment: 4 pages, 3 figures; published version, figure 3 improved, corrections to RWA derive