Resonant tunneling of interacting electrons in a one-dimensional wire

We consider the conductance of a one-dimensional wire interrupted by a double-barrier structure allowing for a resonant level. Using the electron-electron interaction strength as a small parameter, we are able to build a non-perturbative analytical theory of the conductance valid in a broad region of temperatures and for a variety of the barrier parameters. We find that the conductance may have a non-monotonic crossover dependence on temperature, specific for a resonant tunneling in an interacting electron system.Comment: 4 pages. 2 figure

Electron-positron annihilation into Dirac magnetic monopole and antimonopole: the string ambiguity and the discrete symmetries

We address the problem of string arbitrariness in the quantum field theory of Dirac magnetic monopoles. Different prescriptions are shown to yield different physical results. The constraints due to the discrete symmetries (C and P) are derived for the process of electron- positron annihilation into the monopole-antimonopole pair. In the case of the annihilation through the one-photon channel, the production of spin 0 monopoles is absolutely forbidden; spin 1/2 monopole and antimonopole should have the same helicities (or, equivalently, the monopole-antimonopole state should be p-wave $^1P_1$).Comment: 14 pages, revtex, 3 figure

Change detection in categorical evolving data streams

Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution. To cope with these issues, we propose a new unsupervised change detection method, called CDCStream (Change Detection in Categorical Data Streams), well suited for categorical data streams. The proposed method is able to detect changes in a batch incremental scenario. It is based on the two following characteristics: (i) a summarization strategy is proposed to compress the actual batch by extracting a descriptive summary and (ii) a new segmentation algorithm is proposed to highlight changes and issue warnings for a data stream. To evaluate our proposal we employ it in a learning task over real world data and we compare its results with state of the art methods. We also report qualitative evaluation in order to show the behavior of CDCStream