27,016 research outputs found
Conceptual Spaces in Object-Oriented Framework
The aim of this paper is to show that the middle level of
mental representations in a conceptual spaces framework is consistent
with the OOP paradigm. We argue that conceptual spaces framework
together with vague prototype theory of categorization appears to be
the most suitable solution for modeling the cognitive apparatus of
humans, and that the OOP paradigm can be easily and intuitively
reconciled with this framework. First, we show that the prototypebased
OOP approach is consistent with Gärdenfors’ model in terms
of structural coherence. Second, we argue that the product of cloning
process in a prototype-based model is in line with the structure of
categories in Gärdenfors’ proposal. Finally, in order to make the fuzzy
object-oriented model consistent with conceptual space, we
demonstrate how to define membership function in a more cognitive
manner, i.e. in terms of similarity to prototype
A functional quantum programming language
We introduce the language QML, a functional language for quantum computations
on finite types. Its design is guided by its categorical semantics: QML
programs are interpreted by morphisms in the category FQC of finite quantum
computations, which provides a constructive semantics of irreversible quantum
computations realisable as quantum gates. QML integrates reversible and
irreversible quantum computations in one language, using first order strict
linear logic to make weakenings explicit. Strict programs are free from
decoherence and hence preserve superpositions and entanglement - which is
essential for quantum parallelism.Comment: 15 pages. Final version, to appear in Logic in Computer Science 200
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
We develop a new Low-level, First-order Probabilistic Programming Language
(LF-PPL) suited for models containing a mix of continuous, discrete, and/or
piecewise-continuous variables. The key success of this language and its
compilation scheme is in its ability to automatically distinguish parameters
the density function is discontinuous with respect to, while further providing
runtime checks for boundary crossings. This enables the introduction of new
inference engines that are able to exploit gradient information, while
remaining efficient for models which are not everywhere differentiable. We
demonstrate this ability by incorporating a discontinuous Hamiltonian Monte
Carlo (DHMC) inference engine that is able to deliver automated and efficient
inference for non-differentiable models. Our system is backed up by a
mathematical formalism that ensures that any model expressed in this language
has a density with measure zero discontinuities to maintain the validity of the
inference engine.Comment: Published in the proceedings of the 22nd International Conference on
Artificial Intelligence and Statistics (AISTATS
QPCF: higher order languages and quantum circuits
qPCF is a paradigmatic quantum programming language that ex- tends PCF with
quantum circuits and a quantum co-processor. Quantum circuits are treated as
classical data that can be duplicated and manipulated in flexible ways by means
of a dependent type system. The co-processor is essentially a standard QRAM
device, albeit we avoid to store permanently quantum states in between two
co-processor's calls. Despite its quantum features, qPCF retains the classic
programming approach of PCF. We introduce qPCF syntax, typing rules, and its
operational semantics. We prove fundamental properties of the system, such as
Preservation and Progress Theorems. Moreover, we provide some higher-order
examples of circuit encoding
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