Reflectance measurement of two-dimensional photonic crystal nanocavities with embedded quantum dots

The spectra of two-dimensional photonic crystal slab nanocavities with embedded InAs quantum dots are measured by photoluminescence and reflectance. In comparing the spectra taken by these two different methods, consistency with the nanocavities' resonant wavelengths is found. Furthermore, it is shown that the reflectance method can measure both active and passive cavities. Q-factors of nanocavities, whose resonant wavelengths range from 1280 to 1620 nm, are measured by the reflectance method in cross polarization. Experimentally, Q-factors decrease for longer wavelengths and the intensity, reflected by the nanocavities on resonance, becomes minimal around 1370 nm. The trend of the Q-factors is explained by the change of the slab thickness relative to the resonant wavelength, showing a good agreement between theory and experiment. The trend of reflected intensity by the nanocavities on resonance can be understood as effects that originate from the PC slab and the underlying air cladding thickness. In addition to three dimensional finite-difference time-domain calculations, an analytical model is introduced that is able to reproduce the wavelength dependence of the reflected intensity observed in the experiment.Comment: 24 pages, 7 figures, corrected+full versio

Shielding effectiveness of original and modified building materials

This contribution deals with the determination of the shielding effectiveness of building materials used for office, factory and government buildings. Besides the examination of standard materials, measurements were also performed on modified materials, e.g. ferro concrete with enhanced shielding effectiveness due to a changed mixture or structure of the reinforcement. The measurements of original and modified materials were carried out in a fully anechoic room (FAR) according to IEEE 299-1997 from 80 MHz up to 10 GHz

XAI-Explainable artificial intelligence

Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications

Finite-temperature hole dynamics in the t-J model: Exact results for high dimensions

We discuss the dynamics of a single hole in the t-J model at finite temperature, in the limit of large spatial dimensions. The problem is shown to yield a simple and physically transparent solution, that exemplifies the continuous thermal evolution of the underlying string picture from the T=0 string-pinned limit through to the paramagnetic phase.Comment: 6 pages, including 2 figure

Inference of Temporally Varying Bayesian Networks

When analysing gene expression time series data an often overlooked but crucial aspect of the model is that the regulatory network structure may change over time. Whilst some approaches have addressed this problem previously in the literature, many are not well suited to the sequential nature of the data. Here we present a method that allows us to infer regulatory network structures that may vary between time points, utilising a set of hidden states that describe the network structure at a given time point. To model the distribution of the hidden states we have applied the Hierarchical Dirichlet Process Hideen Markov Model, a nonparametric extension of the traditional Hidden Markov Model, that does not require us to fix the number of hidden states in advance. We apply our method to exisiting microarray expression data as well as demonstrating is efficacy on simulated test data

Tell me more?: the effects of mental model soundness on personalizing an intelligent agent

What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agent's personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system's reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system's reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user's intentions

Local bifurcations in differential equations with state-dependent delay

This is the author accepted manuscript. The final version is available from AIP Publishing via the DOI in this record.A common task when analysing dynamical systems is the determination of normal forms near local bifurcations of equilibria. As most of these normal forms have been classified and analysed, finding which particular class of normal form one encounters in a numerical bifurcation study guides follow-up computations. This paper builds on normal form algorithms for equilibria of delay differential equations with constant delay that were developed and implemented in DDE-Biftool recently. We show how one can extend these methods to delay-differential equations with state-dependent delay (sd-DDEs). Since higher degrees of regularity of local center manifolds are still open for sd-DDEs, we give an independent (still only partial) argument which phenomena from the truncated normal must persist in the full sd-DDE. In particular, we show that all invariant manifolds with a sufficient degree of normal hyperbolicity predicted by the normal form exist also in the full sd-DDEJ.S. gratefully acknowledges the financial support of the EPSRC via grants EP/N023544/1 and EP/N014391/1. J.S. has also received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement number 643073

Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

Principles of Explanatory Debugging to personalize interactive machine learning

How can end users efficiently influence the predictions that machine learning systems make on their behalf? This paper presents Explanatory Debugging, an approach in which the system explains to users how it made each of its predictions, and the user then explains any necessary corrections back to the learning system. We present the principles underlying this approach and a prototype instantiating it. An empirical evaluation shows that Explanatory Debugging increased participants' understanding of the learning system by 52% and allowed participants to correct its mistakes up to twice as efficiently as participants using a traditional learning system