Quantum memory: Write, read and reset

A model is presented for the quantum memory, the content of which is a pure quantum state. In this model, the fundamental operations of writing on, reading, and resetting the memory are performed through scattering from the memory. The requirement that the quantum memory must remain in a pure state after scattering implies that the scattering is of a special type, and only certain incident waves are admissible. An example, based on the Fermi pseudo-potential in one dimension, is used to demonstrate that the requirements on the scattering process are consistent and can be satisfied. This model is compared with the commonly used model for the quantum memory; the most important difference is that the spatial dimensions and interference play a central role in the present model.Comment: RevTeX4, 7 pages, no figure

Gait recognition under carrying condition : a static dynamic fusion method

When an individual carries an object, such as a briefcase, conventional gait recognition algorithms based on average silhouette/Gait Energy Image (GEI) do not always perform well as the object carried may have the potential of being mistakenly regarded as a part of the human body. To solve such a problem, in this paper, instead of directly applying GEI to represent the gait information, we propose a novel dynamic feature template for classification. Based on this extracted dynamic information and some static feature templates (i.e., head part and trunk part), we cast gait recognition on the large USF (University of South Florida) database by adopting a static/dynamic fusion strategy. For the experiments involving carrying condition covariate, significant improvements are achieved when compared with other classic algorithms

A multi-task learning CNN for image steganalysis

Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN

Mass formula of division algebras over global function fields

AbstractIn this paper we give two proofs of the mass formula for definite central division algebras over global function fields, due to Denert and Van Geel. The first proof is based on a calculation of Tamagawa measures. The second proof is based on analytic methods, in which we establish the relationship directly between the mass and the value of the associated zeta function at zero

Matrix factorization with rating completion : an enhanced SVD Model for collaborative filtering recommender systems

Collaborative filtering algorithms, such as matrix factorization techniques, are recently gaining momentum due to their promising performance on recommender systems. However, most collaborative filtering algorithms suffer from data sparsity. Active learning algorithms are effective in reducing the sparsity problem for recommender systems by requesting users to give ratings to some items when they enter the systems. In this paper, a new matrix factorization model, called Enhanced SVD (ESVD) is proposed, which incorporates the classic matrix factorization algorithms with ratings completion inspired by active learning. In addition, the connection between the prediction accuracy and the density of matrix is built to further explore its potentials. We also propose the Multi-layer ESVD, which learns the model iteratively to further improve the prediction accuracy. To handle the imbalanced data sets that contain far more users than items or more items than users, the Item-wise ESVD and User-wise ESVD are presented, respectively. The proposed methods are evaluated on the famous Netflix and Movielens data sets. Experimental results validate their effectiveness in terms of both accuracy and efficiency when compared with traditional matrix factorization methods and active learning methods

Theory and application of Fermi pseudo-potential in one dimension

The theory of interaction at one point is developed for the one-dimensional Schrodinger equation. In analog with the three-dimensional case, the resulting interaction is referred to as the Fermi pseudo-potential. The dominant feature of this one-dimensional problem comes from the fact that the real line becomes disconnected when one point is removed. The general interaction at one point is found to be the sum of three terms, the well-known delta-function potential and two Fermi pseudo-potentials, one odd under space reflection and the other even. The odd one gives the proper interpretation for the delta'(x) potential, while the even one is unexpected and more interesting. Among the many applications of these Fermi pseudo-potentials, the simplest one is described. It consists of a superposition of the delta-function potential and the even pseudo-potential applied to two-channel scattering. This simplest application leads to a model of the quantum memory, an essential component of any quantum computer.Comment: RevTeX4, 32 pages, no figure

Incremental updating feature extracion for camera identification

Sensor Pattern Noise (SPN) is an inherent fingerprint of imaging devices, which has been widely used in the tasks of digital camera identification, image classification and forgery detection. In our previous work, a feature extraction method based on PCA denoising concept was applied to extract a set of principal components from the original noise residual. However, this algorithm is inefficient when query cameras are continuously received. To solve this problem, we propose an extension based on Candid Covariance-free Incremental PCA (CCIPCA) and two modifications to incrementally update the feature extractor according to the received cameras. Experimental results show that the PCA and CCIPCA based features both outperform their original features on the ROC performance, and CCIPCA is more efficient on camera updating