35 research outputs found
Institutional factors governing the deployment of remote experiments: lessons from the rexnet project
Remote labs offer many unique advantages to
students as they provide opportunities to access experiments
and learning scenarios that would be otherwise unavailable.
At the same time, however, these opportunities introduce
real challenges to the institutions hosting the remote labs.
This paper draws on the experiences of the REXNET
project consortium to expose a number of these issues as a
means of furthering the debate on the value of remote labs
and the best practices in deploying them. The paper
presents a brief outline of the various types of remote lab
scenarios that might be deployed. It then describes the key
human and technological actors that have an interest in or
are intrinsic to a remote lab instance, with a description of
the role of each actor and their interest. Some relationships
between these various actors are then discussed with some
factors that might influence those relationships. Finally
some general issues are briefly described
Predictions from Lattice QCD
In the past year, we calculated with lattice QCD three quantities that were
unknown or poorly known. They are the dependence of the form factor in
semileptonic decay, the decay constant of the meson, and the
mass of the meson. In this talk, we summarize these calculations, with
emphasis on their (subsequent) confirmation by experiments.Comment: v1: talk given at the International Conference on QCD and Hadronic
Physics, Beijing, June 16-20, 2005; v2: poster presented at the XXIIIrd
International Symposium on Lattice Field Theory, Dublin, July 25-3
Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in which,
contrary to standard multiclass classification, an instance can be associated
with several class labels simultaneously. In this chapter, we advocate a
rule-based approach to multi-label classification. Rule learning algorithms are
often employed when one is not only interested in accurate predictions, but
also requires an interpretable theory that can be understood, analyzed, and
qualitatively evaluated by domain experts. Ideally, by revealing patterns and
regularities contained in the data, a rule-based theory yields new insights in
the application domain. Recently, several authors have started to investigate
how rule-based models can be used for modeling multi-label data. Discussing
this task in detail, we highlight some of the problems that make rule learning
considerably more challenging for MLC than for conventional classification.
While mainly focusing on our own previous work, we also provide a short
overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models
in Computer Vision and Machine Learning. The Springer Series on Challenges in
Machine Learning. Springer (2018). See
http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further
informatio
Should causal models always be Markovian? The case of multi-causal forks in medicine
The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second position by considering the multi-causal forks, which are widespread in contemporary medicine (Section 2). A non-Markovian causal model for such forks is introduced and shown to be mathematically tractable (Sections 6, 7, and 8). The paper also gives a general discussion of the controversy about the Markov condition (Section 1), and of the related controversy about probabilistic causality (Sections 3, 4, and 5
GEOVISUALIZATION FOR SMART VIDEO SURVEILLANCE
Nowadays with the emergence of smart cities and the creation of new sensors capable to connect to the network,
it is not only possible to monitor the entire infrastructure of a city, including roads, bridges, rail/subways, airports,
communications, water, power, but also to optimize its resources, plan its preventive maintenance and monitor security
aspects while maximizing services for its citizens. In particular, the security aspect is one of the most important issues
due to the need to ensure the safety of people. However, if we want to have a good security system, it is necessary to
take into account the way that we are going to present the information. In order to show the amount of information
generated by sensing devices in real time in an understandable way, several visualization techniques are proposed for
both local (involves sensing devices in a separated way) and global visualization (involves sensing devices as a whole).
Taking into consideration that the information is produced and transmitted from a geographic location, the integration
of a Geographic Information System to manage and visualize the behavior of data becomes very relevant. With the
purpose of facilitating the decision-making process in a security system, we have integrated the visualization techniques
and the Geographic Information System to produce a smart security system, based on a cloud computing architecture,
to show relevant information about a set of monitored areas with video cameras