Complex oxide systems have attracted considerable attention because of their fascinating properties, including the magnetic ordering at the conducting interface between two band insulators, such as LaAlO3 and SrTiO3. However, the manipulation of the spin degree of freedom at the LaAlO3/SrTiO3 heterointerface has remained elusive. Here, we have fabricated hybrid magnetic tunnel junctions consisting of Co and LaAlO3/SrTiO3 ferromagnets with the insertion of a Ti layer in between, which clearly exhibit magnetic switching and the tunnelling magnetoresistance effect below 10 K. The magnitude and sign of the tunnelling magnetoresistance are strongly dependent on the direction of the rotational magnetic field parallel to the LaAlO3/SrTiO3 plane, which is attributed to a strong Rashba-type spin-orbit coupling in the LaAlO3/SrTiO3 heterostructure. Our study provides a further support for the existence of the macroscopic ferromagnetism at LaAlO3/SrTiO3 heterointerfaces and opens a novel route to realize interfacial spintronics devices.

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This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected.

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Abstract. One of the most important steps in text processing and information retrieval is stemming -reducing of words to stems expressing their base meaning, e.g., bake, baked, bakes, baking → bak-. We suggest an unsupervised method of recognition such inflection patterns automatically, with no a priori information on the given language, basing exclusively on a list of words extracted from a large text. For a given word list V we construct two sets of strings: stems S and endings E, such that each word from V is a concatenation of a stem from S and ending from E. To select an optimal model, we minimize the total number of elements in S and E. Though such a simplistic model does not reflect many phenomena of real natural language morphology, it shows surprisingly promising results on different European languages. In addition to practical value, we believe that this can also shed light on the nature of human language.