Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information

In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects. Our approach is based on the value of information, a parameterized, information-theoretic criterion that measures the change in costs associated with changes in information. Optimizing the value of information yields a deterministic annealing style of clustering with many benefits. For instance, investigators avoid needing to a priori specify the number of clusters, as the partitions naturally undergo phase changes, during the annealing process, whereby the number of clusters changes in a data-driven fashion. The global-best partition can also often be identified.Comment: Submitted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP

An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits

In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards. High amounts of policy information are associated with exploration-dominant searches of the space and yield high rewards. Low amounts of policy information favor the exploitation of existing knowledge. Information, in this criterion, is quantified by a parameter that can be varied during search. We demonstrate that a simulated-annealing-like update of this parameter, with a sufficiently fast cooling schedule, leads to an optimal regret that is logarithmic with respect to the number of episodes.Comment: Entrop

Reduction of Markov Chains using a Value-of-Information-Based Approach

In this paper, we propose an approach to obtain reduced-order models of Markov chains. Our approach is composed of two information-theoretic processes. The first is a means of comparing pairs of stationary chains on different state spaces, which is done via the negative Kullback-Leibler divergence defined on a model joint space. Model reduction is achieved by solving a value-of-information criterion with respect to this divergence. Optimizing the criterion leads to a probabilistic partitioning of the states in the high-order Markov chain. A single free parameter that emerges through the optimization process dictates both the partition uncertainty and the number of state groups. We provide a data-driven means of choosing the `optimal' value of this free parameter, which sidesteps needing to a priori know the number of state groups in an arbitrary chain.Comment: Submitted to Entrop

Value of information in the binary case and confusion matrix

The simplest Bayesian system used to illustrate ideas of probability theory is a coin and a boolean utility function. To illustrate ideas of hypothesis testing, estimation or optimal control, one needs to use at least two coins and a confusion matrix accounting for the utilities of four possible outcomes. Here we use such a system to illustrate the main ideas of Stratonovich’s value of information (VoI) theory in the context of a financial time-series forecast. We demonstrate how VoI can provide a theoretical upper bound on the accuracy of the forecasts facilitating the analysis and optimization of models

Properties of iterative Monte Carlo single histogram reweighting

We present iterative Monte Carlo algorithm for which the temperature variable is attracted by a critical point. The algorithm combines techniques of single histogram reweighting and linear filtering. The 2d Ising model of ferromagnet is studied numerically as an illustration. In that case, the iterations uncovered stationary regime with invariant probability distribution function of temperature which is peaked nearly the pseudocritical temperature of specific heat. The sequence of generated temperatures is analyzed in terms of stochastic autoregressive model. The error of histogram reweighting can be better understood within the suggested model. The presented model yields a simple relation, connecting variance of pseudocritical temperature and parameter of linear filtering.Comment: 3 figure

Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation

Representation learning is currently a very hot topic in modern machine learning, mostly due to the great success of the deep learning methods. In particular low-dimensional representation which discriminates classes can not only enhance the classification procedure, but also make it faster, while contrary to the high-dimensional embeddings can be efficiently used for visual based exploratory data analysis. In this paper we propose Maximum Entropy Linear Manifold (MELM), a multidimensional generalization of Multithreshold Entropy Linear Classifier model which is able to find a low-dimensional linear data projection maximizing discriminativeness of projected classes. As a result we obtain a linear embedding which can be used for classification, class aware dimensionality reduction and data visualization. MELM provides highly discriminative 2D projections of the data which can be used as a method for constructing robust classifiers. We provide both empirical evaluation as well as some interesting theoretical properties of our objective function such us scale and affine transformation invariance, connections with PCA and bounding of the expected balanced accuracy error.Comment: submitted to ECMLPKDD 201

Year 2000 Performance Report

The mandate of the ST/CV-OP unit consists in the operation and maintenance of the cooling systems and air conditioning installations for the PS accelerator complex, the SPS and LEP machines as well as the heating plants and all CERN pumping stations. This paper intends to provide the performance report related to the last twelve months of activity of the operation unit. The role of the Computed Aided Maintenance and the evolution of a set of performance indicators during last three years will also be presented. A brief analysis of the data and a comment related to opportunity represented by the LEP-LHC transition will follow. In addition the author will try to give in figures a more specific idea of the operation environment, how this function evolves in numbers and which are, in his opinion, the improvement axes and the eventual risks

The LHC experiments as seen from the Technical Sector

Since the beginning of the collaboration between the ST division and the LHC experiments, already in 1998, the technical sector has provided different structures for the support of the experiments, aiming to coordinate all the activities, which traditionally belong in the technical sector's mandate, like civil engineering and structures, cooling and ventilation, cranes and transport, electricity, gas, etc. A picture of the last year's activity, mainly concentrated on the ATLAS and CMS experiments, shows how the synergies between project managers, staff involved and group structures can strongly improve the service level in the technical domain. This closer collaboration has facilitated the development of further ties linked to the competence available in the groups, and of great interest to the experiments. The steady rise in demand confirms that the choice, made by the experiments, confirms that the technical sector support is a real need in this are

The ALMA Early Science View of FUor/EXor objects. III. The Slow and Wide Outflow of V883 Ori

We present Atacama Large Millimeter/ sub-millimeter Array (ALMA) observations of V883 Ori, an FU Ori object. We describe the molecular outflow and envelope of the system based on the $^{12}$CO and $^{13}$CO emissions, which together trace a bipolar molecular outflow. The C$^{18}$O emission traces the rotational motion of the circumstellar disk. From the $^{12}$CO blue-shifted emission, we estimate a wide opening angle of $\sim$ 150$^{^{\circ}}$ for the outflow cavities. Also, we find that the outflow is very slow (characteristic velocity of only 0.65 km~s$^{-1}$), which is unique for an FU Ori object. We calculate the kinematic properties of the outflow in the standard manner using the $^{12}$CO and $^{13}$CO emissions. In addition, we present a P Cygni profile observed in the high-resolution optical spectrum, evidence of a wind driven by the accretion and being the cause for the particular morphology of the outflows. We discuss the implications of our findings and the rise of these slow outflows during and/or after the formation of a rotationally supported disk.Comment: 12 pages, 7 figures, 2 tables. Accepte