14,666 research outputs found
Ludics and its Applications to natural Language Semantics
Proofs, in Ludics, have an interpretation provided by their counter-proofs,
that is the objects they interact with. We follow the same idea by proposing
that sentence meanings are given by the counter-meanings they are opposed to in
a dialectical interaction. The conception is at the intersection of a
proof-theoretic and a game-theoretic accounts of semantics, but it enlarges
them by allowing to deal with possibly infinite processes
Microwave responses of the western North Atlantic
Features and objects in the Western North Atlantic Ocean - the Eastern Seaboard of the United States - are observed from Earth orbit by passive microwaves. The intensities of their radiated flux signatures are measured and displayed in color as a microwave flux image. The features of flux emitting objects such as the course of the Gulf Stream and the occurrence of cold eddies near the Gulf Stream are identified by contoured patterns of relative flux intensities. The flux signatures of ships and their wakes are displayed and discussed. Metal data buoys and aircraft are detected. Signal to clutter ratios and probabilities of detection are computed from their measured irradiances. Theoretical models and the range equations that explain passive microwave detection using the irradiances of natural sources are summarized
A feasible algorithm for typing in Elementary Affine Logic
We give a new type inference algorithm for typing lambda-terms in Elementary
Affine Logic (EAL), which is motivated by applications to complexity and
optimal reduction. Following previous references on this topic, the variant of
EAL type system we consider (denoted EAL*) is a variant without sharing and
without polymorphism. Our algorithm improves over the ones already known in
that it offers a better complexity bound: if a simple type derivation for the
term t is given our algorithm performs EAL* type inference in polynomial time.Comment: 20 page
Gaussian process model based predictive control
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark
Adaptive, cautious, predictive control with Gaussian process priors
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example
A Logical Product Approach to Zonotope Intersection
We define and study a new abstract domain which is a fine-grained combination
of zonotopes with polyhedric domains such as the interval, octagon, linear
templates or polyhedron domain. While abstract transfer functions are still
rather inexpensive and accurate even for interpreting non-linear computations,
we are able to also interpret tests (i.e. intersections) efficiently. This
fixes a known drawback of zonotopic methods, as used for reachability analysis
for hybrid sys- tems as well as for invariant generation in abstract
interpretation: intersection of zonotopes are not always zonotopes, and there
is not even a best zonotopic over-approximation of the intersection. We
describe some examples and an im- plementation of our method in the APRON
library, and discuss some further in- teresting combinations of zonotopes with
non-linear or non-convex domains such as quadratic templates and maxplus
polyhedra
Gaussian Process priors with uncertain inputs? Application to multiple-step ahead time series forecasting
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form y t = f(Yt-1 ,..., Yt-L ), the prediction of y at time t + k is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction
Superheated Droplet Detectors as CDM Detectors: The SIMPLE Experiment
Superheated Droplet Detectors (SDDs) are becoming commonplace in neutron
personnel dosimetry. Their total insensitivity to minimum ionizing radiation
(while responsive to nuclear recoils of energies ~ few keV), together with
their low cost, ease of production, and operation at room temperature and 1 atm
makes them ideal for Cold Dark Matter (CDM) searches. SDD's are optimal for the
exploration of the spin-dependent neutralino coupling due to their high
fluorine content. The status of SIMPLE (Superheated Instrument for Massive
ParticLe Experiments) is presented. Under realistic background considerations,
we expect an improvement in the present Cold Dark Matter sensitivity of 2-3
orders of magnitude after ~1 kg-y of data acquisition.Comment: 6 pages, including 4 figures. To appear in the Proceedings of the
Intl. Workshop on the Identification of Dark Matter (Sheffield, Sept. 96
Lazy Abstraction-Based Controller Synthesis
We present lazy abstraction-based controller synthesis (ABCS) for
continuous-time nonlinear dynamical systems against reach-avoid and safety
specifications. State-of-the-art multi-layered ABCS pre-computes multiple
finite-state abstractions of varying granularity and applies reactive synthesis
to the coarsest abstraction whenever feasible, but adaptively considers finer
abstractions when necessary. Lazy ABCS improves this technique by constructing
abstractions on demand. Our insight is that the abstract transition relation
only needs to be locally computed for a small set of frontier states at the
precision currently required by the synthesis algorithm. We show that lazy ABCS
can significantly outperform previous multi-layered ABCS algorithms: on
standard benchmarks, lazy ABCS is more than 4 times faster
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