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We report of a high yield method to form nanopore molecular ensembles junctions containing ~40,000 molecules, in which the semimetal bismuth (Bi) is a top contact. Conductance histograms of these junctions are double-peaked (bi-modal), a behavior that is typical for single molecule junctions but not expected for junctions with thousands of molecules. This unique observation is shown to result from a new form of quantum interference that is inter-molecular in nature, which occurs in these junctions since the very long coherence length of the electrons in Bi enables them to probe large ensembles of molecules while tunneling through the junctions. Under such conditions, each molecule within the ensembles becomes an interference path that modifies via its tunneling phase the electronic structure of the entire junction. This new form of quantum interference holds a great promise for robust novel conductance effects in practical molecular junctions. Quantum interference effect in the conductance of single molecule junctions has been attracting intensive interest in recent years. Here, Li and Selzer show the presence of intermolecular quantum interference over 40,000 molecules in a molecular ensemble junction with bismuth as the top electrode. [ABSTRACT FROM AUTHOR]

Action recognition is usually a central problem for many practical applications, such as video annotations, video surveillance and human computer interaction. Most action recognition approaches are based on localized spatio-temporal features that can vary significantly when the viewpoint changes. However, their performance rapidly drops when the viewpoints of the training and testing data are different. In this paper, we propose a transfer learning framework for view-invariant action recognition by the way of sharing image stitching feature among different views. Experimental results on multi-view action recognition IXMAS dataset demonstrate that our method produces remarkably good results and outperforms baseline methods. [ABSTRACT FROM AUTHOR]

The knowledge of where a person is looking is useful in human computer interaction as well as human behavior analysis. Headpose estimation from low resolution images is still a challenge problem due to noisy feature representation for low resolution images. In this paper, we investigate transfer learning technique to conquer the weakness of the apperance-based feature of humans head-pose when their relative locations to far-field cameras are different. We evaluate our methods on public datasets which prove the efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a simple method is presented for multi-user space-time channel estimation in Time Division-Synchronized Code Division Multiple Access (TD-SCDMA) systems. The method is based on a specific midamble assignment strategy, which results in a cyclic Toeplitz midamble-matrix in the linear equation of the received data vectors. A Fast Fourier Transform (FFT)-based algorithm is used to obtain the estimate of the uplink multi-user space-time channels. Furthermore, the estimated space-time channel is applied to the identification of multi-paths for each user, and Direction Of Arrival (DOA) estimation for each path is carried out by using the extracted spatial signature vector. Aside from the simplicity in computation, the proposed direction of arrival estimation method can effectively resolve multi-paths regardless of the correlation and angle separations of the multi-paths. [ABSTRACT FROM AUTHOR]