5 research outputs found
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl
Gallium-free micromechanical sample preparation from ECAPed alluminium
Focused ion beam scanning electron microscopes (FIB-SEM) enable high precision site-specific material removal with practically no restriction on sample composition. Depending on the ion source (e.g. Ga+, Xe+), the rate of material removal differs significantly. In general, the design of Xe+ source allows using high ion beam currents that can be up to a few µA while maintaining beam quality and performance. However, the most relevant feature of Xe ions for this study is their non-metallic and inert nature which prevents any chemical interaction with the target material and formation of unwanted metallic compounds that alter the original properties of the sample that is being analyzed.
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On Expressing and Monitoring Oscillatory Dynamics
To express temporal properties of dense-time real-valued signals, the Signal
Temporal Logic (STL) has been defined by Maler et al. The work presented a
monitoring algorithm deciding the satisfiability of STL formulae on finite
discrete samples of continuous signals. The logic has been used to express and
analyse biological systems, but it is not expressive enough to sufficiently
distinguish oscillatory properties important in biology. In this paper we
define the extended logic STL* in which STL is augmented with a signal-value
freezing operator allowing us to express (and distinguish) detailed properties
of biological oscillations. The logic is supported by a monitoring algorithm
prototyped in Matlab. The monitoring procedure of STL* is evaluated on a
biologically-relevant case study.Comment: In Proceedings HSB 2012, arXiv:1208.315
Multi-center machine learning in imaging psychiatry : A meta-model approach
One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues. Moreover, in the case of very large samples, the computational complexity might become too large. The solution to this problem could be distributed learning. In this paper we investigated the possibility to create a meta-model by combining support vector machines (SVM) classifiers trained on the local datasets, without the need for sharing medical images or any other personal data. Validation was done in a 4-center setup comprising of 480 first-episode schizophrenia patients and healthy controls in total. We built SVM models to separate patients from controls based on three different kinds of imaging features derived from structural MRI scans, and compared models built on the joint multicenter data to the meta-models. The results showed that the combined meta-model had high similarity to the model built on all data pooled together and comparable classification performance on all three imaging features. Both similarity and performance was superior to that of the local models. We conclude that combining models is thus a viable alternative that facilitates data sharing and creating bigger and more informative models
LNCS
We present Mixed-time Signal Temporal Logic (STL−MX), a specification formalism which extends STL by capturing the discrete/ continuous time duality found in many cyber-physical systems (CPS), as well as mixed-signal electronic designs. In STL−MX, properties of components with continuous dynamics are expressed in STL, while specifications of components with discrete dynamics are written in LTL. To combine the two layers, we evaluate formulas on two traces, discrete- and continuous-time, and introduce two interface operators that map signals, properties and their satisfaction signals across the two time domains. We show that STL-mx has the expressive power of STL supplemented with an implicit T-periodic clock signal. We develop and implement an algorithm for monitoring STL-mx formulas and illustrate the approach using a mixed-signal example