594 research outputs found
CP Violation from Flavor Symmetry in a Lepton Quarticity Dark Matter Model
We propose a simple model where neutrinos are
predicted to be Dirac fermions. The smallness of their masses follows from a
type-I seesaw mechanism and the leptonic CP violating phase correlates with the
pattern of flavor symmetry breaking. The scheme naturally harbors
a WIMP dark matter candidate associated to the Dirac nature of neutrinos, in
that the same lepton number symmetry also ensures dark matter stability.Comment: 16 pages, 5 figures, Dark Matter Direct Detection Constraints
Updated, Conclusions Unchanged, Published Versio
Generalized Bottom-Tau unification, neutrino oscillations and dark matter: predictions from a lepton quarticity flavor approach
We propose an extension of the Standard Model with a Lepton Quarticity
symmetry correlating dark matter stability with the Dirac nature of neutrinos.
The flavor symmetry predicts (i) a generalized bottom-tau mass relation
involving all families, (ii) small neutrino masses are induced a la seesaw,
(iii) CP must be significantly violated in neutrino oscillations, (iv) the
atmospheric angle lies in the second octant, and (v) only the
normal neutrino mass ordering is realized.Comment: 13 pages, 3 figure
Seesaw roadmap to neutrino mass and dark matter
We describe the many pathways to generate Majorana and Dirac neutrino mass
through generalized dimension-5 operators a la Weinberg. The presence of new
scalars beyond the Standard Model Higgs doublet implies new possible field
contractions, which are required in the case of Dirac neutrinos. We also notice
that, in the Dirac neutrino case, the extra symmetries needed to ensure the
Dirac nature of neutrinos can also be made responsible for stability of dark
matter.Comment: 12 pages, 5 figures, published versio
Online Learning in Neural Machine Translation
[EN] High quality translations are in high demand these days. Although machine
translation offers acceptable performance, it is not sufficient in some cases and
human supervision is required. In order to ease the translation task of the human,
machine translation systems take part in this process. When a sentence in the
source language needs to be translated, it is fed to the system which outputs a
hypothesis translation. The human then, corrects this hypothesis (also known as
post-editing) in order to obtain a high quality translation. Being able to transfer
the knowledge that a human translator exhibit when post-editing a translation to
the machine translation system is a desirable feature, as it has been proven that a
more accurate machine translation system helps to increase the efficiency of the
post-editing process.
Because the post-editing scenario requires an already trained system, online
learning techniques are suited for this task. In this work, three online learning
algorithms have been proposed and applied to a neural machine translation sys-
tem in a post-editing scenario. They rely on the Passive-Aggressive online learn-
ing approach in which the model is updated after every sample in order to fulfil
a correctness criterion while remembering previously learned information. The
goal is to adapt and refine an already trained system with new samples on-the-
fly as the post-editing process takes place (hence, the update time must be kept
under control).
Moreover, these new algorithms are compared with well-stablished online
learning variants of the stochastic gradient descent algorithm. Results show im-
provements on the translation quality of the system after applying these algo-
rithms, reducing human effort in the post-editing process.[ES] La traducción de gran calidad está muy demandada en la actualidad. A pesar
de que la traducción automática ofrece unas prestaciones aceptables, en algunos
casos no es suficiente y es necesaria la supervisión humana. Para facilitar la tarea
de traducción del humano, los sistemas de traducción automática toman parte en
este proceso. Cuando una nueva oración en el idioma origen necesita ser tradu-
cida, esta se introduce en el sistema, el cual obtiene como salida una hipótesis de
traducción. El humano entonces, corrige esta hipótesis (también conocido como
post-editar) para obtener una traducción de mayor calidad. Ser capaz de transfe-
rir el conocimiento que el humano exhibe cuando realiza la tarea de post-edición
al sistema de traducción automática es una característica deseable puesto que se
ha demostrado que un sistema de traducción mas preciso ayuda a aumentar la
eficiencia del proceso de post-edición.
Debido a que el proceso de post-edición requiere un sistema ya entrenado, las
técnicas de aprendizaje en línea son las adecuadas para esta tarea. En este traba-
jo, se proponen tres algoritmos de aprendizaje en línea aplicados a un traductor
automático neuronal en un escenario de post-edición. Estos algoritmos se basan
en la aproximación en línea Passive-Aggressive en la cual el modelo se actualiza
después de cada muestra con el objetivo de cumplir un criterio de corrección a
la vez que manteniendo información previa aprendida. El objetivo es adaptar y
refinar un sistema ya entrenado con nuevas muestras al vuelo mientras el pro-
ceso de post-edición se lleva a cabo (por tanto, el tiempo de actualización debe
mantenerse bajo control).
Además, estos algoritmos se comparan con otras bien conocidas variantes en
línea del algoritmo de descenso por gradiente estocástico. Los resultados mues-
tran una mejora en la calidad de las traducciones después de aplicar estos algo-
ritmos, reduciendo así el esfuerzo humano en el proceso de post-edición.[CA] La traducció de gran qualitat es troba molt demanada en l’actualitat. Tot i
que la traducció automàtica oferix unes prestacions acceptables, en alguns casos
no és suficient i és necessària la supervisió humana. Per a facilitar la tasca de
traducció de l’humà, els sistemes de traducció automàtica prenen part en aquest
procés. Quan una nova oració en el llenguatge origen necessita ser traduïda,
esta s’introduïx en el sistema, el qual obté com a eixida una hipòtesi de traducció.
Llavors, l’humà corregix aquesta hipòtesi (també conegut com a post-editar) per a
obtindre una traducció de major qualitat. Ser capaços de transferir el coneixement
que l’ humà exhibix quan realitza la tasca de post-edició al sistema de traducció
automàtica és una característica desitjable ja que s’ha demostrat que un sistema
de traducció mes precís ajuda a augmentar l‘eficiència del procés de post-edició.
Pel fet que el procés de post-edició requerix un sistema ja entrenat, les tècniques
d’aprenentatge en línia són les adequades per aquesta tasca. En este treball,
es proposen tres algoritmes d’aprenentatge en línia aplicats a un traductor automàtic
neuronal en un escenari de post-edició. Estos algoritmes es basen en
l’aproximació en línia Passive-Aggressive en la qual el model s’actualitza després
de cada mostra amb l’objectiu de complir un criteri de correcció al mateix temps
que manté informació prèvia apresa. L’objectiu és adaptar i refinar un sistema ja
entrenat amb noves mostres al vol mentre el procés de post-edició es du a terme
(per tant, el temps d’actualització ha de mantenir-se controlat).
A més, estos algoritmes es comparen amb altres ben conegudes variants en
línia de l’algoritme de descens per gradient estocàstic. Els resultats mostren una
millora en la qualitat de les traduccions després d’aplicar estos algoritmes, reduint
així l’esforç humà en el procés de post-edició.Cebrián Chuliá, L. (2017). Aprendizaje en línea en traducción automática basada en redes neuronales. http://hdl.handle.net/10251/86299TFG
Learning Probabilistic Finite State Automata For Opponent Modelling
Artificial Intelligence (AI) is the branch of the Computer Science field that
tries to imbue intelligent behaviour in software systems. In the early years of
the field, those systems were limited to big computing units where researchers
built expert systems that exhibited some kind of intelligence. But with the
advent of different kinds of networks, which the more prominent of those is
the Internet, the field became interested in Distributed Artificial Intelligence
(DAI) as the normal move.
The field thus moved from monolithic software architectures for its AI sys-
tems to architectures where several pieces of software were trying to solve a
problem or had interests on their own. Those pieces of software were called
Agents and the architectures that allowed the interoperation of multiple agents
were called Multi-Agent Systems (MAS). The agents act as a metaphor that
tries to describe those software systems that are embodied in a given environ-
ment and that behave or react intelligently to events in the environment.
The AI mainstream was initially interested in systems that could be taught
to behave depending on the inputs perceived. However this rapidly showed
ineffective because the human or the expert acted as the knowledge bottleneck
for distilling useful and efficient rules. This was in best cases, in worst cases
the task of enumerating the rules was difficult or plainly not affordable. This
sparked the interest of another subfield, Machine Learning and its counter part
in a MAS, Distributed Machine Learning. If you can not code all the scenario
combinations, code within the agent the rules that allows it to learn from the
environment and the actions performed.
With this framework in mind, applications are endless. Agents can be used
to trade bonds or other financial derivatives without human intervention, or
they can be embedded in a robotics hardware and learn unseen map config-
uration in distant locations like distant planets. Agents are not restricted to
interactions with humans or the environment, they can also interact with other
agents themselves. For instance, agents can negotiate the quality of service of a channel before establishing a communication or they can share information
about the environment in a cooperative setting like robot soccer players.
But there are some shortcomings that emerge in a MAS architecture. The
one related to this thesis is that partitioning the task at hand into agents
usually entails that agents have less memory or computing power. It is not
economically feasible to replicate the big computing unit on each separate
agent in our system. Thus we can say that we should think about our agents as
computationally bounded , that is, they have a limited amount of computing
power to learn from the environment. This has serious implications on the
algorithms that are commonly used for learning in these settings.
The classical approach for learning in MAS system is to use some variation
of a Reinforcement Learning (RL) algorithm [BT96, SB98]. The main idea
around those algorithms is that the agent has to maintain a table with the per-
ceived value of each action/state pair and through multiple iterations obtain a
set of decision rules that allows to take the best action for a given environment.
This approach has several flaws when the current action depends on a single
observation seen in the past (for instance, a warning sign that a robot per-
ceives). Several techniques has been proposed to alleviate those shortcomings.
For instance to avoid the combinatorial explosion of states and actions, instead
of storing a table with the value of the pairs an approximating function like a
neural network can be used instead. And for events in the past, we can extend
the state definition of the environment creating dummy states that correspond
to the N-tuple (stateN, stateN−1, . . . , stateN−t
Reivindicación de la comanditaria por acciones ante el anteproyecto de ley de código mercantil
La sociedad comanditaria por acciones podría servir para facilitar la financiación de las
PYMES y Empresas familiares, junto a otras funciones, pero en Derecho español está
desaprovechada, debido a que la Ley de Sociedades de Capital la regula siguiendo el
modelo suizo-italiano, y no el franco-alemán, que es el que ha dado mejores resultados.
A este error se añaden otros en su concreta regulación. Además, la Ley del Mercado de
Valores no la regula como sociedad cotizada. El Anteproyecto de Ley de Código
Mercantil tampoco mejora su regulación
Dirac Neutrinos and Dark Matter Stability from Lepton Quarticity
We propose to relate dark matter stability to the possible Dirac nature of
neutrinos. The idea is illustrated in a simple scheme where small Dirac
neutrino masses arise from a type--I seesaw mechanism as a result of a
discrete lepton number symmetry. The latter implies the existence of a viable
WIMP dark matter candidate, whose stability arises from the same symmetry which
ensures the Diracness of neutrinos.Comment: 12 pages, 6 figures, Report N IFIC/16-4
CP Symmetries as Guiding Posts: revamping tri-bi-maximal Mixing. Part II
In this follow up of arXiv:1812.04663 we analyze the generalized CP
symmetries of the charged lepton mass matrix compatible with the complex
version of the Tri-Bi-Maximal (TBM) lepton mixing pattern. These symmetries are
used to `revamp' the simplest TBM \textit{Ansatz} in a systematic way. Our
generalized patterns share some of the attractive features of the original TBM
matrix and are consistent with current oscillation experiments. We also discuss
their phenomenological implications both for upcoming neutrino oscillation and
neutrinoless double beta decay experiments.Comment: 19 pages, 8 figures. Title change to match the first par
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