20,025 research outputs found
Discarding Impossible Events from Statistical Language Models
Colloque avec actes et comité de lecture. internationale.International audienceThis paper describes a method for detectingvimpossible bigrams using a vocabulary of V elements. The idea is to discard all the ungrammatical events to expect an improvement of the language model. We extract the impossible bigrams by using automatic rules. The biclass associations which are ungrammatical are detected and all the corresponding bigrams are analyzed and set as possible or impossible events, we also decided to manage for each of the retrieved rule an exception list
Conservation of information and the foundations of quantum mechanics
We review a recent approach to the foundations of quantum mechanics inspired
by quantum information theory. The approach is based on a general framework,
which allows one to address a large class of physical theories which share
basic information-theoretic features. We first illustrate two very primitive
features, expressed by the axioms of causality and purity-preservation, which
are satisfied by both classical and quantum theory. We then discuss the axiom
of purification, which expresses a strong version of the Conservation of
Information and captures the core of a vast number of protocols in quantum
information. Purification is a highly non-classical feature and leads directly
to the emergence of entanglement at the purely conceptual level, without any
reference to the superposition principle. Supplemented by a few additional
requirements, satisfied by classical and quantum theory, it provides a complete
axiomatic characterization of quantum theory for finite dimensional systems.Comment: 11 pages, contribution to the Proceedings of the 3rd International
Conference on New Frontiers in Physics, July 28-August 6 2014, Orthodox
Academy of Crete, Kolymbari, Cret
A categorical semantics for causal structure
We present a categorical construction for modelling causal structures within
a general class of process theories that include the theory of classical
probabilistic processes as well as quantum theory. Unlike prior constructions
within categorical quantum mechanics, the objects of this theory encode
fine-grained causal relationships between subsystems and give a new method for
expressing and deriving consequences for a broad class of causal structures. We
show that this framework enables one to define families of processes which are
consistent with arbitrary acyclic causal orderings. In particular, one can
define one-way signalling (a.k.a. semi-causal) processes, non-signalling
processes, and quantum -combs. Furthermore, our framework is general enough
to accommodate recently-proposed generalisations of classical and quantum
theory where processes only need to have a fixed causal ordering locally, but
globally allow indefinite causal ordering.
To illustrate this point, we show that certain processes of this kind, such
as the quantum switch, the process matrices of Oreshkov, Costa, and Brukner,
and a classical three-party example due to Baumeler, Feix, and Wolf are all
instances of a certain family of processes we refer to as in
the appropriate category of higher-order causal processes. After defining these
families of causal structures within our framework, we give derivations of
their operational behaviour using simple, diagrammatic axioms.Comment: Extended version of a LICS 2017 paper with the same titl
Making AI Meaningful Again
Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy
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Abstraction and context in concept representation
This paper develops the notion of abstraction in the context of the psychology of concepts, and discusses its relation to context dependence in knowledge representation. Three general approaches to modelling conceptual knowledge from the domain of cognitive psychology are discussed, which serve to illustrate a theoretical dimension of increasing levels of abstraction
Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems
This paper describes a comprehensive prototype of large-scale fault adaptive
embedded software developed for the proposed Fermilab BTeV high energy physics
experiment. Lightweight self-optimizing agents embedded within Level 1 of the
prototype are responsible for proactive and reactive monitoring and mitigation
based on specified layers of competence. The agents are self-protecting,
detecting cascading failures using a distributed approach. Adaptive,
reconfigurable, and mobile objects for reliablility are designed to be
self-configuring to adapt automatically to dynamically changing environments.
These objects provide a self-healing layer with the ability to discover,
diagnose, and react to discontinuities in real-time processing. A generic
modeling environment was developed to facilitate design and implementation of
hardware resource specifications, application data flow, and failure mitigation
strategies. Level 1 of the planned BTeV trigger system alone will consist of
2500 DSPs, so the number of components and intractable fault scenarios involved
make it impossible to design an `expert system' that applies traditional
centralized mitigative strategies based on rules capturing every possible
system state. Instead, a distributed reactive approach is implemented using the
tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th
Annual IEEE International Conference and Workshop on the Engineering of
Computer Based Systems (ECBS), Washington, DC, April, 200
Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval
Although more and more language pairs are covered by machine translation
services, there are still many pairs that lack translation resources.
Cross-language information retrieval (CLIR) is an application which needs
translation functionality of a relatively low level of sophistication since
current models for information retrieval (IR) are still based on a
bag-of-words. The Web provides a vast resource for the automatic construction
of parallel corpora which can be used to train statistical translation models
automatically. The resulting translation models can be embedded in several ways
in a retrieval model. In this paper, we will investigate the problem of
automatically mining parallel texts from the Web and different ways of
integrating the translation models within the retrieval process. Our
experiments on standard test collections for CLIR show that the Web-based
translation models can surpass commercial MT systems in CLIR tasks. These
results open the perspective of constructing a fully automatic query
translation device for CLIR at a very low cost.Comment: 37 page
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Logics of Imprecise Comparative Probability
This paper studies connections between two alternatives to the standard probability calculus for representing and reasoning about uncertainty: imprecise probability andcomparative probability. The goal is to identify complete logics for reasoning about uncertainty in a comparative probabilistic language whose semantics is given in terms of imprecise probability. Comparative probability operators are interpreted as quantifying over a set of probability measures. Modal and dynamic operators are added for reasoning about epistemic possibility and updating sets of probability measures
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