208,644 research outputs found
Error Bounds for Piecewise Smooth and Switching Regression
The paper deals with regression problems, in which the nonsmooth target is
assumed to switch between different operating modes. Specifically, piecewise
smooth (PWS) regression considers target functions switching deterministically
via a partition of the input space, while switching regression considers
arbitrary switching laws. The paper derives generalization error bounds in
these two settings by following the approach based on Rademacher complexities.
For PWS regression, our derivation involves a chaining argument and a
decomposition of the covering numbers of PWS classes in terms of the ones of
their component functions and the capacity of the classifier partitioning the
input space. This yields error bounds with a radical dependency on the number
of modes. For switching regression, the decomposition can be performed directly
at the level of the Rademacher complexities, which yields bounds with a linear
dependency on the number of modes. By using once more chaining and a
decomposition at the level of covering numbers, we show how to recover a
radical dependency. Examples of applications are given in particular for PWS
and swichting regression with linear and kernel-based component functions.Comment: This work has been submitted to the IEEE for possible publication.
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Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies
Robots are increasingly entering uncertain and unstructured environments.
Within these, robots are bound to face unexpected external disturbances like
accidental human or tool collisions. Robots must develop the capacity to
respond to unexpected events. That is not only identifying the sudden anomaly,
but also deciding how to handle it. In this work, we contribute a recovery
policy that allows a robot to recovery from various anomalous scenarios across
different tasks and conditions in a consistent and robust fashion. The system
organizes tasks as a sequence of nodes composed of internal modules such as
motion generation and introspection. When an introspection module flags an
anomaly, the recovery strategy is triggered and reverts the task execution by
selecting a target node as a function of a state dependency chart. The new
skill allows the robot to overcome the effects of the external disturbance and
conclude the task. Our system recovers from accidental human and tool
collisions in a number of tasks. Of particular importance is the fact that we
test the robustness of the recovery system by triggering anomalies at each node
in the task graph showing robust recovery everywhere in the task. We also
trigger multiple and repeated anomalies at each of the nodes of the task
showing that the recovery system can consistently recover anywhere in the
presence of strong and pervasive anomalous conditions. Robust recovery systems
will be key enablers for long-term autonomy in robot systems. Supplemental info
including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl
Identifying high-impact sub-structures for convolution kernels in document-level sentiment classification
Convolution kernels support the modeling of complex syntactic information in machine-learning tasks. However, such models are highly sensitive to the type and size of syntactic structure used. It is therefore an important challenge to automatically identify high impact sub-structures relevant to a given task. In this paper we present a systematic study investigating (combinations of) sequence and convolution kernels using different types of substructures in document-level sentiment classification. We show that minimal sub-structures extracted from constituency and dependency trees guided by a polarity lexicon show 1.45 point absolute improvement in accuracy over a bag-of-words classifier on a widely used sentiment corpus
Detecting and Refactoring Operational Smells within the Domain Name System
The Domain Name System (DNS) is one of the most important components of the
Internet infrastructure. DNS relies on a delegation-based architecture, where
resolution of names to their IP addresses requires resolving the names of the
servers responsible for those names. The recursive structures of the inter
dependencies that exist between name servers associated with each zone are
called dependency graphs. System administrators' operational decisions have far
reaching effects on the DNSs qualities. They need to be soundly made to create
a balance between the availability, security and resilience of the system. We
utilize dependency graphs to identify, detect and catalogue operational bad
smells. Our method deals with smells on a high-level of abstraction using a
consistent taxonomy and reusable vocabulary, defined by a DNS Operational
Model. The method will be used to build a diagnostic advisory tool that will
detect configuration changes that might decrease the robustness or security
posture of domain names before they become into production.Comment: In Proceedings GaM 2015, arXiv:1504.0244
Pattern Reification as the Basis for Description-Driven Systems
One of the main factors driving object-oriented software development for
information systems is the requirement for systems to be tolerant to change. To
address this issue in designing systems, this paper proposes a pattern-based,
object-oriented, description-driven system (DDS) architecture as an extension
to the standard UML four-layer meta-model. A DDS architecture is proposed in
which aspects of both static and dynamic systems behavior can be captured via
descriptive models and meta-models. The proposed architecture embodies four
main elements - firstly, the adoption of a multi-layered meta-modeling
architecture and reflective meta-level architecture, secondly the
identification of four data modeling relationships that can be made explicit
such that they can be modified dynamically, thirdly the identification of five
design patterns which have emerged from practice and have proved essential in
providing reusable building blocks for data management, and fourthly the
encoding of the structural properties of the five design patterns by means of
one fundamental pattern, the Graph pattern. A practical example of this
philosophy, the CRISTAL project, is used to demonstrate the use of
description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure
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