19,523 research outputs found
Commentary: Both/And: A Response to De(fence)/Defense
In this paper we introduce non-dualism and begin by answering the questions posed by the editors of this journal. We address the theme of de(fence) and propose a paradigmatic shift. For many years, art teachers have advocated tirelessly in defense of the field, fighting for funding and legitimacy in an educational landscape that prioritizes other subjects. While the reaction to fight is appropriate, art reveals another way. It aids us in our task of living in the liminal, and it gives us the chance to suspend our judgments and forego meaning in favor of experience. Art can help us transition from the dual mind to a non-dualistic awareness. When we experience art as it is, we stop seeing differences and start to see connections
How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change
Direct visual localization has recently enjoyed a resurgence in popularity
with the increasing availability of cheap mobile computing power. The
competitive accuracy and robustness of these algorithms compared to
state-of-the-art feature-based methods, as well as their natural ability to
yield dense maps, makes them an appealing choice for a variety of mobile
robotics applications. However, direct methods remain brittle in the face of
appearance change due to their underlying assumption of photometric
consistency, which is commonly violated in practice. In this paper, we propose
to mitigate this problem by training deep convolutional encoder-decoder models
to transform images of a scene such that they correspond to a previously-seen
canonical appearance. We validate our method in multiple environments and
illumination conditions using high-fidelity synthetic RGB-D datasets, and
integrate the trained models into a direct visual localization pipeline,
yielding improvements in visual odometry (VO) accuracy through time-varying
illumination conditions, as well as improved metric relocalization performance
under illumination change, where conventional methods normally fail. We further
provide a preliminary investigation of transfer learning from synthetic to real
environments in a localization context. An open-source implementation of our
method using PyTorch is available at https://github.com/utiasSTARS/cat-net.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the
IEEE International Conference on Robotics and Automation (ICRA'18), Brisbane,
Australia, May 21-25, 201
What Ways Can We Use Big Data to Offer More Personalized and Tailored HR Services to our Employees?
Big data analytics—analytic techniques operating on big data—is continuing to disrupt the way decision-making is occurring. Instead of relying on intuition, decisions are made based on statistical analysis, emerging technologies and massive amounts of current and historical data. Predictive analytics, which will be featured in much of the research below, is a type of big data analytics that predicts an outcome by correlating the relationships of various factors. These predictions can be made utilizing a variety of organized structured data and disorganized unstructured data (i.e. social media posts, surveys, etc.
The application of regional-scale geochemical data in defining the extent of aeolian sediments : the Late Pleistocene loess and coversand deposits of East Anglia, UK
The ‘European Coversand Sheet’ is a discontinuous ‘sheet’ of aeolian
(windblown) loess and coversand that extends through eastern and
southern England, across the English Channel into northern France,
Belgium and the Netherlands (Kasse, 1997; Antoine et al., 2003). Whilst
some of the earlier aeolian sediments date from the Middle
Pleistocene, most correspond to the Late Pleistocene Weichselian /
Devensian and earliest Holocene stages. East Anglia contains
considerable accumulations of aeolian sediment. Although several
valuable studies have attempted to determine the spatial extent of
aeolian material (e.g. Catt, 1977, 1985), defining their margins has
proved largely difficult because aeolian material is highly susceptible to
reworking and removal by various natural and anthropogenic agents.
Within this study, we use regional‐scale geochemical data from
soils to reconstruct the extent of aeolian sediments in East Anglia. A
specific geochemical signature, defined by elevated concentrations of
Hafnium (Hf) and Zirconium (Zr), is strongly characteristic of soils
developed on aeolian deposits within the United States, China, Europe
and New Zealand (Taylor et al., 1983). The data suggests that the
approach is sufficiently sensitive to identify a residual aeolian
component within soils even where deposits may be thin and unmappable
by conventional methods, or if the material has been largely
eroded
Computation in generalised probabilistic theories
From the existence of an efficient quantum algorithm for factoring, it is
likely that quantum computation is intrinsically more powerful than classical
computation. At present, the best upper bound known for the power of quantum
computation is that BQP is in AWPP. This work investigates limits on
computational power that are imposed by physical principles. To this end, we
define a circuit-based model of computation in a class of operationally-defined
theories more general than quantum theory, and ask: what is the minimal set of
physical assumptions under which the above inclusion still holds? We show that
given only an assumption of tomographic locality (roughly, that multipartite
states can be characterised by local measurements), efficient computations are
contained in AWPP. This inclusion still holds even without assuming a basic
notion of causality (where the notion is, roughly, that probabilities for
outcomes cannot depend on future measurement choices). Following Aaronson, we
extend the computational model by allowing post-selection on measurement
outcomes. Aaronson showed that the corresponding quantum complexity class is
equal to PP. Given only the assumption of tomographic locality, the inclusion
in PP still holds for post-selected computation in general theories. Thus in a
world with post-selection, quantum theory is optimal for computation in the
space of all general theories. We then consider if relativised complexity
results can be obtained for general theories. It is not clear how to define a
sensible notion of an oracle in the general framework that reduces to the
standard notion in the quantum case. Nevertheless, it is possible to define
computation relative to a `classical oracle'. Then, we show there exists a
classical oracle relative to which efficient computation in any theory
satisfying the causality assumption and tomographic locality does not include
NP.Comment: 14+9 pages. Comments welcom
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