Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes

Information-theoretic principles for learning and acting have been proposed to solve particular classes of Markov Decision Problems. Mathematically, such approaches are governed by a variational free energy principle and allow solving MDP planning problems with information-processing constraints expressed in terms of a Kullback-Leibler divergence with respect to a reference distribution. Here we consider a generalization of such MDP planners by taking model uncertainty into account. As model uncertainty can also be formalized as an information-processing constraint, we can derive a unified solution from a single generalized variational principle. We provide a generalized value iteration scheme together with a convergence proof. As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning. We demonstrate the benefits of this approach in a grid world simulation.Comment: 16 pages, 3 figure

Size matters: cardinality-constrained clustering and outlier detection via conic optimization

Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outlier cluster and performs cardinality-constrainedK-means clustering on the residual dataset, treating the cluster cardinalities as a given input. We cast this problem as a mixed-integer linear program (MILP) that admits tractable semidefinite and linear programming relaxations. We propose deterministic rounding schemes thattransform the relaxed solutions to feasible solutions for the MILP. We also prove that these solutions areoptimal in the MILP if a cluster separation condition holds

An iterative decision-making scheme for Markov decision processes and its application to self-adaptive systems

Software is often governed by and thus adapts to phenomena that occur at runtime. Unlike traditional decision problems, where a decision-making model is determined for reasoning, the adaptation logic of such software is concerned with empirical data and is subject to practical constraints. We present an Iterative Decision-Making Scheme (IDMS) that infers both point and interval estimates for the undetermined transition probabilities in a Markov Decision Process (MDP) based on sampled data, and iteratively computes a confidently optimal scheduler from a given finite subset of schedulers. The most important feature of IDMS is the flexibility for adjusting the criterion of confident optimality and the sample size within the iteration, leading to a tradeoff between accuracy, data usage and computational overhead. We apply IDMS to an existing self-adaptation framework Rainbow and conduct a case study using a Rainbow system to demonstrate the flexibility of IDMS

A whole-cell biosensor for the detection of gold

Geochemical exploration for gold (Au) is becoming increasingly important to the mining industry. Current processes for Au analyses require sampling materials to be taken from often remote localities. Samples are then transported to a laboratory equipped with suitable analytical facilities, such as Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) or Instrumental Neutron Activation Analysis (INAA). Determining the concentration of Au in samples may take several weeks, leading to long delays in exploration campaigns. Hence, a method for the on-site analysis of Au, such as a biosensor, will greatly benefit the exploration industry. The golTSB genes from Salmonella enterica serovar typhimurium are selectively induced by Au(I/III)-complexes. In the present study, the golTSB operon with a reporter gene, lacZ, was introduced into Escherichia coli. The induction of golTSB::lacZ with Au(I/III)-complexes was tested using a colorimetric β-galactosidase and an electrochemical assay. Measurements of the β-galactosidase activity for concentrations of both Au(I)- and Au(III)-complexes ranging from 0.1 to 5 µM (equivalent to 20 to 1000 ng g⁻¹ or parts-per-billion (ppb)) were accurately quantified. When testing the ability of the biosensor to detect Au(I/III)-complexes(aq) in the presence of other metal ions (Ag(I), Cu(II), Fe(III), Ni(II), Co(II), Zn, As(III), Pb(II), Sb(III) or Bi(III)), cross-reactivity was observed, i.e. the amount of Au measured was either under- or over-estimated. To assess if the biosensor would work with natural samples, soils with different physiochemical properties were spiked with Au-complexes. Subsequently, a selective extraction using 1 M thiosulfate was applied to extract the Au. The results showed that Au could be measured in these extracts with the same accuracy as ICP-MS (P<0.05). This demonstrates that by combining selective extraction with the biosensor system the concentration of Au can be accurately measured, down to a quantification limit of 20 ppb (0.1 µM) and a detection limit of 2 ppb (0.01 µM).Carla M. Zammit, Davide Quaranta, Shane Gibson, Anita J. Zaitouna, Christine Ta, Joël Brugger, Rebecca Y. Lai, Gregor Grass, Frank Reit

Measurement of the Higgs boson coupling properties in the H → ZZ* → 4 decay channel at √s = 13 TeV with the ATLAS detector

The coupling properties of the Higgs boson are studied in the four-lepton (e, μ) decay channel using 36.1 fb−1 of pp collision data from the LHC at a centre-of-mass energy of 13 TeV collected by the ATLAS detector. Cross sections are measured for the main production modes in several exclusive regions of the Higgs boson production phase space and are interpreted in terms of coupling modifiers. The inclusive cross section times branching ratio for H → ZZ∗ decay and for a Higgs boson absolute rapidity below 2.5 is measured to be 1. 73 − 0.23 + 0.24 (stat.) − 0.08 + 0.10 (exp.) ± 0.04(th.) pb compared to the Standard Model prediction of 1.34±0.09 pb. In addition, the tensor structure of the Higgs boson couplings is studied using an effective Lagrangian approach for the description of interactions beyond the Standard Model. Constraints are placed on the non-Standard-Model CP-even and CP-odd couplings to Z bosons and on the CP-odd coupling to gluons

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments