51,645 research outputs found
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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
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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
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