Representations of wreath products of algebras

Filtrations of modules over wreath products of algebras are studied and corresponding multiplicity formulas are given in terms of Littlewood–Richardson coefficients. An example relevant to Jantzen filtrations in Schur algebras is presented

Kleshchev's decomposition numbers and branching coefficients in the Fock space

10.1090/S0002-9947-07-04202-XTransactions of the American Mathematical Society36031179-119

Evolutionary L∞ identification and model reduction for robust control

An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a 'worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L�¢���� error bound than existing methods in the literature do

Detection of zeptojoule microwave pulses using electrothermal feedback in proximity-induced Josephson junctions

We experimentally investigate and utilize electrothermal feedback in a microwave nanobolometer based on a normal-metal (\mbox{Au}_{x}\mbox{Pd}_{1-x}) nanowire with proximity-induced superconductivity. The feedback couples the temperature and the electrical degrees of freedom in the nanowire, which both absorbs the incoming microwave radiation, and transduces the temperature change into a radio-frequency electrical signal. We tune the feedback in situ and access both positive and negative feedback regimes with rich nonlinear dynamics. In particular, strong positive feedback leads to the emergence of two metastable electron temperature states in the millikelvin range. We use these states for efficient threshold detection of coherent 8.4 GHz microwave pulses containing approximately 200 photons on average, corresponding to 1.1 \mbox{ zJ} \approx 7.0 \mbox{ meV} of energy

Elicitation of Preference Structure in Engineering Design

Engineering design processes, which inherently involve multiple, often conflicting criteria, can be broadly classified into synthesis and analysis processes. Multiple Criteria Decision Making addresses synthesis and analysis processes through multiple objective optimisation to generate sets of efficient design solutions (i.e. on Pareto surfaces) and multiple attribute decision making to analyse and select the most preferred design solution(s). MCDM, therefore, has been widely used in all fields of engineering design; for example it has been applied to such diverse areas as naval battle ships criteria analysis/selection and product appearance design. Given a list of design alternatives with multiple conflicting criteria, preferences often determine the final selection of a particular set of design alternative(s). Preferences may also be used to drive the design/design optimisation processes. Various methods have been proposed to model preference structure, for example simple weights, multiple attribute utility theory, pairwise comparison, etc. Preference structure is often non-linear, discontinuous and complex. An Artificial Neural Network (ANN) learning-based preference elicitation method is presented in this paper. ANNs efficiently model the non-linearity, complexity and discontinuity nature of any given preference structure. A case study is presented to illustrate the learning-based approach to preference structure elicitation.

Identifying Retweetable Tweets with a Personalized Global Classifier

In this paper we present a method to identify tweets that a user may find interesting enough to retweet. The method is based on a global, but personalized classifier, which is trained on data from several users, represented in terms of user-specific features. Thus, the method is trained on a sufficient volume of data, while also being able to make personalized decisions, i.e., the same post received by two different users may lead to different classification decisions. Experimenting with a collection of approx.\ 130K tweets received by 122 journalists, we train a logistic regression classifier, using a wide variety of features: the content of each tweet, its novelty, its text similarity to tweets previously posted or retweeted by the recipient or sender of the tweet, the network influence of the author and sender, and their past interactions. Our system obtains F1 approx. 0.9 using only 10 features and 5K training instances.Comment: This is a long paper version of the extended abstract titled "A Personalized Global Filter To Predict Retweets", of the same authors, which was published in the 25th ACM UMAP conference in Bratislava, Slovakia, in July 201