7,793 research outputs found
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
Higher plant proteins of cyanobacterial origin : are they or are they not preferentially targeted to chloroplasts?
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
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
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
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