Fiedler and Markham (1994) proved {\left( {\frac{{\det \hat H}}{k}} \right)^k} \geqslant \det H, where H = (H_{ij})_{i,j}^{n}_{=1} is a positive semidefinite matrix partitioned into n × n blocks with each block k × k and \hat H = \left( {tr{H_{ij}}} \right)_{i,j = 1}^n. We revisit this inequality mainly using some terminology from quantum information theory. Analogous results are included. For example, under the same condition, we prove \det \left( {{I_n} + \hat H} \right) \geqslant \det {\left( {{I_{nk}} + kH} \right)^{{1 \mathord{\left/ {\vphantom {1 k}} \right. \kern-\nulldelimiterspace} k}}}., Minghua Lin., and Obsahuje seznam literatury
A (finite) acyclic connected graph is called a tree. Let W be a finite nonempty set, and let H(W) be the set of all trees T with the property that W is the vertex set of T. We will find a one-to-one correspondence between H(W) and the set of all binary operations on W which satisfy a certain set of three axioms (stated in this note).
This paper proposes an immunity-based RBF training algorithm for nonlinear dynamic problems. Exploiting the locally-tuned structure of RBF network through immunological metaphor, a two-stage learning technique is proposed to configure RBF centers and widths in the hidden layer. Inspired by affinity maturation process of immune response, immune evolutionary mechanism (IEM) with memory operations is implemented in the learning stages to dynamically fine-tune the network performance. Experiment results also demonstrate that the algorithm has reached good performance with relatively low computational efforts in dynamic environments.
The paper deals with a new stochastic optimization model, named OMoGaS-SV (Optimization Modelling for Gas Seller-Stochastic Version), to assist companies dealing with gas retail commercialization. Stochasticity is due to the dependence of consumptions on temperature uncertainty. Due to nonlinearities present in the objective function, the model can be classified as an NLP mixed integer model, with the profit function depending on the number of contracts with the final consumers, the typology of such consumers and the cost supported to meet the final demand. Constraints related to a maximum daily gas consumption, to yearly maximum and minimum consumption in order to avoid penalties and to consumption profiles are included. The results obtained by the stochastic version give clear indication of the amount of losses that may appear in the gas seller's budget and are compared with the results obtained by the deterministic version (see Allevi et al. \cite{ABIV}).
Identifying a VoIP call as SPAM based on call characteristics is an important issue that has never been studied before. Most of the studies of VoIP SPAM impose the whole burden on the callee to judge SPAM calls. In other words, the accuracy of the identification process is totally based on the callee identifying the call as SPAM, which is questionable and not reliable. In this paper, a two-stage VoIP SPAM identification framework is introduced. The first stage is a pre-call identification process, which uses a set of parameters about the call that can be collected before allowing the call to go through. The second stage is a post-call identification process that uses other parameters that can be collected during/after the call. The first stage provides a pre-call evaluation score of the call, while the second stage further tunes this score. In the proposed framework, the decision of identifying VoIP SPAM calls is based on several uncertain parameters that represent meta-data of VoIP calls. These parameters include call duration, amount of exchanged information in each direction, and calling pattern. In this study, the potential set of parameters that can be used to identify VoIP SPAM are investigated. A set of rules is used in addition to any prior evaluation of the caller to provide the pre-call score. Then, a fuzzy-logic controller is developed to identify VoIP SPAM in the second stage. An augmented ongoing tuning strategy is adopted where callee feedback, if any, is taken into account to further tune the identification process. Simulation studies are carried out to demonstrate the effectiveness of the two-stage approach in identifying VoIP SPAM based on the proposed framework.
There are many inequalities measuring the deviation of the average of a function over an interval from a linear combination of values of the function and some of its derivatives. A general setting is given from which the desired inequalities are obtained using Hölder’s inequality. Moreover, sharpness of the constants is usually easy to prove by studying the equality cases of Hölder’s inequality. Comparison of averages, extension to weighted integrals and $n$-dimensional results are also given.