Highly Efficient Regression for Scalable Person Re-Identification

Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available. This work proposes a truly scalable solution to re-id by addressing both problems. Specifically, a Highly Efficient Regression (HER) model is formulated by embedding the Fisher's criterion to a ridge regression model for very fast re-id model learning with scalable memory/storage usage. Importantly, this new HER model supports faster than real-time incremental model updates therefore making real-time active learning feasible in re-id with human-in-the-loop. Extensive experiments show that such a simple and fast model not only outperforms notably the state-of-the-art re-id methods, but also is more scalable to large data with additional benefits to active learning for reducing human labelling effort in re-id deployment

FPS-SFT: A Multi-dimensional Sparse Fourier Transform Based on the Fourier Projection-slice Theorem

We propose a multi-dimensional (M-D) sparse Fourier transform inspired by the idea of the Fourier projection-slice theorem, called FPS-SFT. FPS-SFT extracts samples along lines (1-dimensional slices from an M-D data cube), which are parameterized by random slopes and offsets. The discrete Fourier transform (DFT) along those lines represents projections of M-D DFT of the M-D data onto those lines. The M-D sinusoids that are contained in the signal can be reconstructed from the DFT along lines with a low sample and computational complexity provided that the signal is sparse in the frequency domain and the lines are appropriately designed. The performance of FPS-SFT is demonstrated both theoretically and numerically. A sparse image reconstruction application is illustrated, which shows the capability of the FPS-SFT in solving practical problems

Tensor Discriminant Analysis for View-based Object Recognition

In this paper, we use a general M th order tensor dis-criminant analysis approach [11] for view based object recognition. This method is an extension of the 2D im-age coding technique [10] to general M th order tensors for discriminant analysis, and has good convergence prop-erty. We demonstrate the performance advantages of this approach over existing techniques using experiments on the COIL-100 and the ETH-80 datasets. Specifically, our ex-perimental results on ETH-80 show the particular strength of this tensor discriminant analysis method when only a small number of training samples with big intra-class vari-ation are available. 1

Imaging molecular orbitals with laser-induced electron tunneling spectroscopy

Photoelectron spectroscopy in intense laser fields has proven to be a powerful tool for providing detailed insights into molecular structure. The ionizing molecular orbital, however, has not been reconstructed from the photoelectron spectra, mainly due to the fact that its phase information can be hardly extracted. In this work, we propose a method to retrieve the phase information of the ionizing molecular orbital with laser-induced electron tunneling spectroscopy. By analyzing the interference pattern in the photoelectron spectrum, the weighted coefficients and the relative phases of the constituent atomic orbitals for a molecular orbital can be extracted. With this information we reconstruct the highest occupied molecular orbital of N$_2$. Our work provides a reliable and general approach for imaging of molecular orbitals with the photoelectron spectroscopy.Comment: 6 pages, 4 figures, including Supplementary Material

Understanding the tribological impacts of alkali element on lubrication of binary borate melt

Melt lubricants have been regarded as an effective class to deliver lubrication on moving mechanical contacts at extreme temperatures. Among the elementary constituents, alkali elements play a critical role in governing the physical-chemical characteristics of the lubricant despite the obscurity regarding their intrinsic roles on the rubbing interfaces. The present study attempts to unfold the effects of sodium on the tribological responses of mating steel pair under borate melt lubrication. It has been found that the involvement of Na inspires a total reversal in lubricating potentials of the lone B2O3melt manifested by remarkable friction reduction, wear inhibition and prolonged load-bearing capacity. These exceptional performances are attributed to the accretion of nanothin Na layers on the contact interfaces. The interfacial occurrences are interpreted from a physico-chemistry perspective while the influences of surface microstructure are also discussed in detail. Multiple characterizations are employed to thoroughly examine the sliding interfaces in multi-dimensions including Scanning Electron Microscopy (SEM), Scanning Transmission Electron Microscopy (STEM) and Atomic Force Microscopy (AFM). In addition, chemical fingerprints of relevant elements are determined by Energy Dispersive Spectroscopy (EDS) and Electron Loss Energy Spectroscopy (EELS)