4,520 research outputs found
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde
Machine Learning For In-Region Location Verification In Wireless Networks
In-region location verification (IRLV) aims at verifying whether a user is
inside a region of interest (ROI). In wireless networks, IRLV can exploit the
features of the channel between the user and a set of trusted access points. In
practice, the channel feature statistics is not available and we resort to
machine learning (ML) solutions for IRLV. We first show that solutions based on
either neural networks (NNs) or support vector machines (SVMs) and typical loss
functions are Neyman-Pearson (N-P)-optimal at learning convergence for
sufficiently complex learning machines and large training datasets . Indeed,
for finite training, ML solutions are more accurate than the N-P test based on
estimated channel statistics. Then, as estimating channel features outside the
ROI may be difficult, we consider one-class classifiers, namely auto-encoders
NNs and one-class SVMs, which however are not equivalent to the generalized
likelihood ratio test (GLRT), typically replacing the N-P test in the one-class
problem. Numerical results support the results in realistic wireless networks,
with channel models including path-loss, shadowing, and fading
Are Children Decision-Makers Within the Household?
Children are seldom accounted for in household behavioural models. They are usually assumed to have neither the capacity nor the power to influence the household decision process. The literature on collective models has so far incorporated children through the "caring preferences" of their parents or has treated them as household public goods [Bourguignon (1999); Blundell et al. (2005)]. This paper seeks to determine whether children of a certain age are decision-makers. We focus on the decision-making process within households composed of two adults and one child of at least 16 years of age. We first summarize the main restrictions that have been proposed to test the collective model in the context of multiple decision-makers [Chiappori and Ekeland (2006)]. We also show how a minimal number of decision-makers can be inferred from parametric constraints. Second, we apply these tests on data drawn from a series of U.K. Family Expenditure Surveys. Our results show clear evidence that it may be incorrect to assume that daughters and children aged between 16 and 21 are not full members influencing the household decision-making process.intra-household allocation, collective household models, children, demand analysis, Pareto efficiency, rank tests
Rule Of Thumb: Deep derotation for improved fingertip detection
We investigate a novel global orientation regression approach for articulated
objects using a deep convolutional neural network. This is integrated with an
in-plane image derotation scheme, DeROT, to tackle the problem of per-frame
fingertip detection in depth images. The method reduces the complexity of
learning in the space of articulated poses which is demonstrated by using two
distinct state-of-the-art learning based hand pose estimation methods applied
to fingertip detection. Significant classification improvements are shown over
the baseline implementation. Our framework involves no tracking, kinematic
constraints or explicit prior model of the articulated object in hand. To
support our approach we also describe a new pipeline for high accuracy magnetic
annotation and labeling of objects imaged by a depth camera.Comment: To be published in proceedings of BMVC 201
Corruption and Democracy
What is the impact of democracy on corruption? In most models, analysts assume a negative relationship, with more democracy leading to less corruption. But recent theoretical developments and case evidence support an inverted U relationship between corruption and democracy. By drawing on a panel data set covering a large number of countries between 1996 and 2003, substantial empirical support is found for an inverted U relationship between democracy and corruption. The turning point in corruption occurs rather early in the life of new democracies and at rather low per capita incomes.corruption, electoral democracy, consolidated democracy, rule of law, government effectiveness
Proximal methods for structured group features and correlation matrix nearness
Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: junio de 2014Optimization is ubiquitous in real life as many of the strategies followed both by nature and
by humans aim to minimize a certain cost, or maximize a certain benefit. More specifically,
numerous strategies in engineering are designed according to a minimization problem, although
usually the problems tackled are convex with a di erentiable objective function, since these
problems have no local minima and they can be solved with gradient-based techniques. Nevertheless,
many interesting problems are not di erentiable, such as, for instance, projection problems
or problems based on non-smooth norms. An approach to deal with them can be found in
the theory of Proximal Methods (PMs), which are based on iterative local minimizations using
the Proximity Operator (ProxOp) of the terms that compose the objective function.
This thesis begins with a general introduction and a brief motivation of the work done. The state
of the art in PMs is thoroughly reviewed, defining the basic concepts from the very beginning
and describing the main algorithms, as far as possible, in a simple and self-contained way.
After that, the PMs are employed in the field of supervised regression, where regularized models
play a prominent role. In particular, some classical linear sparse models are reviewed and unified
under the point of view of regularization, namely the Lasso, the Elastic–Network, the Group
Lasso and the Group Elastic–Network. All these models are trained by minimizing an error
term plus a regularization term, and thus they fit nicely in the domain of PMs, as the structure of
the problem can be exploited by minimizing alternatively the di erent expressions that compose
the objective function, in particular using the Fast Iterative Shrinkage–Thresholding Algorithm
(FISTA). As a real-world application, it is shown how these models can be used to forecast wind
energy, where they yield both good predictions in terms of the error and, more importantly,
valuable information about the structure and distribution of the relevant features.
Following with the regularized learning approach, a new regularizer is proposed, called the
Group Total Variation, which is a group extension of the classical Total Variation regularizer
and thus it imposes constancy over groups of features. In order to deal with it, an approach to
compute its ProxOp is derived. Moreover, it is shown that this regularizer can be used directly
to clean noisy multidimensional signals (such as colour images) or to define a new linear model,
the Group Fused Lasso (GFL), which can be then trained using FISTA. It is also exemplified
how this model, when applied to regression problems, is able to provide solutions that identify
the underlying problem structure. As an additional result of this thesis, a public software
implementation of the GFL model is provided.
The PMs are also applied to the Nearest Correlation Matrix problem under observation uncertainty.
The original problem consists in finding the correlation matrix which is nearest to the
true empirical one. Some variants introduce weights to adapt the confidence given to each entry
of the matrix; with a more general perspective, in this thesis the problem is explored directly
considering uncertainty on the observations, which is formalized as a set of intervals where the
measured matrices lie. Two di erent variants are defined under this framework: a robust approach
called the Robust Nearest Correlation Matrix (which aims to minimize the worst-case
scenario) and an exploratory approach, the Exploratory Nearest Correlation Matrix (which focuses
on the best-case scenario). It is shown how both optimization problems can be solved
using the Douglas–Rachford PM with a suitable splitting of the objective functions.
The thesis ends with a brief overall discussion and pointers to further work.La optimización está presente en todas las facetas de la vida, de hecho muchas de las estrategias
tanto de la naturaleza como del ser humano pretenden minimizar un cierto coste, o maximizar
un cierto beneficio. En concreto, multitud de estrategias en ingeniería se diseñan según problemas
de minimización, que habitualmente son problemas convexos con una función objetivo
diferenciable, puesto que en ese caso no hay mínimos locales y los problemas pueden resolverse
mediante técnicas basadas en gradiente. Sin embargo, hay muchos problemas interesantes que
no son diferenciables, como por ejemplo problemas de proyección o basados en normas no suaves.
Una aproximación para abordar estos problemas son los Métodos Proximales (PMs), que
se basan en minimizaciones locales iterativas utilizando el Operador de Proximidad (ProxOp)
de los términos de la función objetivo.
La tesis comienza con una introducción general y una breve motivación del trabajo hecho. Se
revisa en profundidad el estado del arte en PMs, definiendo los conceptos básicos y describiendo
los algoritmos principales, dentro de lo posible, de forma simple y auto-contenida.
Tras ello, se emplean los PMs en el campo de la regresión supervisada, donde los modelos regularizados
tienen un papel prominente. En particular, se revisan y unifican bajo esta perspectiva
de regularización algunos modelos lineales dispersos clásicos, a saber, Lasso, Elastic–Network,
Lasso Grupal y Elastic–Network Grupal. Todos estos modelos se entrenan minimizando un término
de error y uno de regularización, y por tanto encajan perfectamente en el dominio de los
PMs, ya que la estructura del problema puede ser aprovechada minimizando alternativamente las
diferentes expresiones que componen la función objetivo, en particular mediante el Algoritmo
Fast Iterative Shrinkage–Thresholding (FISTA). Como aplicación al mundo real, se muestra que
estos modelos pueden utilizarse para predecir energía eólica, donde proporcionan tanto buenos
resultados en términos del error como información valiosa sobre la estructura y distribución de
las características relevantes.
Siguiendo con esta aproximación, se propone un nuevo regularizador, llamado Variación Total
Grupal, que es una extensión grupal del regularizador clásico de Variación Total y que por
tanto induce constancia sobre grupos de características. Para aplicarlo, se desarrolla una aproximación
para calcular su ProxOp. Además, se muestra que este regularizador puede utilizarse
directamente para limpiar señales multidimensionales ruidosas (como imágenes a color) o para
definir un nuevo modelo lineal, el Fused Lasso Grupal (GFL), que se entrena con FISTA. Se
ilustra cómo este modelo, cuando se aplica a problemas de regresión, es capaz de proporcionar
soluciones que identifican la estructura subyacente del problema. Como resultado adicional de
esta tesis, se publica una implementación software del modelo GFL.
Asimismo, se aplican los PMs al problema de Matriz de Correlación Próxima (NCM) bajo incertidumbre.
El problema original consiste en encontrar la matriz de correlación más cercana a
la empírica verdadera. Algunas variantes introducen pesos para ajustar la confianza que se da a
cada entrada de la matriz; con un carácter más general, en esta tesis se explora el problema considerando
incertidumbre en las observaciones, que se formaliza como un conjunto de intervalos
en el que se encuentran las matrices medidas. Bajo este marco se definen dos variantes: una
aproximación robusta llamada NCM Robusta (que minimiza el caso peor) y una exploratoria,
NCM Exploratoria (que se centra en el caso mejor). Ambos problemas de optimización pueden
resolverse con el PM de Douglas–Rachford y una partición adecuada de las funciones objetivo.
La tesis concluye con una discusión global y referencias a trabajo futur
Continuous-variable quantum neural networks
We introduce a general method for building neural networks on quantum
computers. The quantum neural network is a variational quantum circuit built in
the continuous-variable (CV) architecture, which encodes quantum information in
continuous degrees of freedom such as the amplitudes of the electromagnetic
field. This circuit contains a layered structure of continuously parameterized
gates which is universal for CV quantum computation. Affine transformations and
nonlinear activation functions, two key elements in neural networks, are
enacted in the quantum network using Gaussian and non-Gaussian gates,
respectively. The non-Gaussian gates provide both the nonlinearity and the
universality of the model. Due to the structure of the CV model, the CV quantum
neural network can encode highly nonlinear transformations while remaining
completely unitary. We show how a classical network can be embedded into the
quantum formalism and propose quantum versions of various specialized model
such as convolutional, recurrent, and residual networks. Finally, we present
numerous modeling experiments built with the Strawberry Fields software
library. These experiments, including a classifier for fraud detection, a
network which generates Tetris images, and a hybrid classical-quantum
autoencoder, demonstrate the capability and adaptability of CV quantum neural
networks
Tax Decentralisation and local Government size
The aim of this paper is to re-examine the relationship between fiscal federalism and the size of local governments. Traditionally, the empirical studies have focused on the different accountability power of grants and local taxes, concluding that the former encourages the growth and the latter contributes to contain local public spending. Yet, the existing literature is more silent about the possibility that different types of tax autonomy may still have differential impacts on the expansion of the local public sector. The paper addresses this issue by introducing a new testable hypothesis - the “Tax Separation Hypothesis” (TSH) - according to which tax decentralisation organised on tax bases used only by local governments would favour most the containment of local public expenditures, while that organised on tax base sharing (i.e. piggybacking mechanisms) is not expected to have a significant impact on the local government size. Using an unbalanced panel data set of OECD countries, we adopt the novel approach of disentangling the impact of local taxes - on income, property, and goods and services - on the size of the local public sector. In particular, property taxes only - mostly based on a “tax separation” scheme - seem to have a negative impact on the size of local government. Instead, both income taxes and general taxes on goods and services – often shared with central governments – have uncertain impacts on the size of local governments (and more frequently positive). We conclude that tax decentralisation is a necessary condition to contain local public expenditures, yet it is not sufficient, as a tax separation scheme would in fact be required.Fiscal decentralisation; Tax sharing; Tax separation; Property taxes; Local government size.
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