Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models

Deep convolutional neural networks have recently achieved great success on image aesthetics assessment task. In this paper, we propose an efficient method which takes the global, local and scene-aware information of images into consideration and exploits the composite features extracted from corresponding pretrained deep learning models to classify the derived features with support vector machine. Contrary to popular methods that require fine-tuning or training a new model from scratch, our training-free method directly takes the deep features generated by off-the-shelf models for image classification and scene recognition. Also, we analyzed the factors that could influence the performance from two aspects: the architecture of the deep neural network and the contribution of local and scene-aware information. It turns out that deep residual network could produce more aesthetics-aware image representation and composite features lead to the improvement of overall performance. Experiments on common large-scale aesthetics assessment benchmarks demonstrate that our method outperforms the state-of-the-art results in photo aesthetics assessment.Comment: Accepted by ICIP 201

Compression via Compressive Sensing : A Low-Power Framework for the Telemonitoring of Multi-Channel Physiological Signals

Telehealth and wearable equipment can deliver personal healthcare and necessary treatment remotely. One major challenge is transmitting large amount of biosignals through wireless networks. The limited battery life calls for low-power data compressors. Compressive Sensing (CS) has proved to be a low-power compressor. In this study, we apply CS on the compression of multichannel biosignals. We firstly develop an efficient CS algorithm from the Block Sparse Bayesian Learning (BSBL) framework. It is based on a combination of the block sparse model and multiple measurement vector model. Experiments on real-life Fetal ECGs showed that the proposed algorithm has high fidelity and efficiency. Implemented in hardware, the proposed algorithm was compared to a Discrete Wavelet Transform (DWT) based algorithm, verifying the proposed one has low power consumption and occupies less computational resources.Comment: 2013 International Workshop on Biomedical and Health Informatic

Intermediate coherent-phase(PB) states of radiation fields and their nonclassical properties

Intermediate states interpolating coherent states and Pegg-Barnett phase states are investigated using the ladder operator approach. These states reduce to coherent and Pegg-Barnett phase states in two different limits. Statistical and squeezing properties are studied in detail.Comment: 9 pages, 3 EPS figures, use epsf.sty. Accepted for publication in Phys.Lett.

A Study of the Supply-side Reform of Clinical Legal Education: From the Perspective of Fostering Outstanding Legal Talents

In this thriving 21st century, all segments of the society especially the market have been posing stricter requirements for legal graduates. Responsively, the 2.0 initiative of Fostering Outstanding Legal Talents has new tasks for the clinical legal education. As a response, this essay will explore in three possible ways that may bring the quality of legal talents to an outstanding level based on the theory of supply-side structural reform and the existing problems of clinical legal education. They are enrichment of the ways of legal evaluation, curriculum framework reform and advancement of practical skills of teaching faculty