A strategic study of establishing the industrial needs in pattern design and technology in Mainland China

2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Attributes and action recognition based on convolutional neural networks and spatial pyramid VLAD encoding

© Springer International Publishing AG 2017.Determination of human attributes and recognition of actions in still images are two related and challenging tasks in computer vision, which often appear in fine-grained domains where the distinctions between the different categories are very small. Deep Convolutional Neural Network (CNN) models have demonstrated their remarkable representational learning capability through various examples. However, the successes are very limited for attributes and action recognition as the potential of CNNs to acquire both of the global and local information of an image remains largely unexplored. This paper proposes to tackle the problem with an encoding of a spatial pyramid Vector of Locally Aggregated Descriptors (VLAD) on top of CNN features. With region proposals generated by Edgeboxes, a compact and efficient representation of an image is thus produced for subsequent prediction of attributes and classification of actions. The proposed scheme is validated with competitive results on two benchmark datasets: 90.4% mean Average Precision (mAP) on the Berkeley Attributes of People dataset and 88.5% mAP on the Stanford 40 action dataset

Erlang Code Evolution Control

During the software lifecycle, a program can evolve several times for different reasons such as the optimisation of a bottle-neck, the refactoring of an obscure function, etc. These code changes often involve several functions or modules, so it can be difficult to know whether the correct behaviour of the previous releases has been preserved in the new release. Most developers rely on a previously defined test suite to check this behaviour preservation. We propose here an alternative approach to automatically obtain a test suite that specifically focusses on comparing the old and new versions of the code. Our test case generation is directed by a sophisticated combination of several already existing tools such as TypEr, CutEr, and PropEr; and other ideas such as allowing the programmer to chose an expression of interest that must preserve the behaviour, or the recording of the sequences of values to which this expression is evaluated. All the presented work has been implemented in an open-source tool that is publicly available on GitHub.Comment: Pre-proceedings paper presented at the 27th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur, Belgium, 10-12 October 2017 (arXiv:1708.07854

Comment on "Conjectures on exact solution of three-dimensional (3D) simple orthorhombic Ising lattices" [arXiv:0705.1045]

It is shown that a recent article by Z.-D. Zhang [arXiv:0705.1045] is in error and violates well-known theorems.Comment: LaTeX, 3 pages, no figures, submitted to Philosophical Magazine. Expanded versio

Vehicle Re-identification in Still Images: Application of Semi-supervised Learning and Re-ranking

Vehicle re-identification (re-ID), namely, finding exactly the same vehicle from a large number of vehicle images, remains a great challenge in computer vision. Most existing vehicle re-ID approaches follow a fully supervised learning methodology, in which sufficient labeled training data is required. However, this limits their scalability to realistic applications, due to the high cost of data labeling. In this paper, we adopted a Generative Adversarial Network (GAN) to generate unlabeled samples and enlarge the training set. A semi supervised learning scheme with the Convolutional Neural Networks (CNN) was proposed accordingly, which assigns a uniform label distribution to the unlabeled images to regularize the supervised model and improve the performance of the vehicle re-ID system. Besides, an improved re-ranking method based on the Jaccard distance and k-reciprocal nearest neighbors is proposed to optimize the initial rank list. Extensive experiments over the benchmark datasets VeR1-776, VehicleID and VehicleReID have demonstrated that the proposed method outperforms the state-of-the-art approaches for vehicle re-ID

Tagging Scientific Publications using Wikipedia and Natural Language Processing Tools. Comparison on the ArXiv Dataset

In this work, we compare two simple methods of tagging scientific publications with labels reflecting their content. As a first source of labels Wikipedia is employed, second label set is constructed from the noun phrases occurring in the analyzed corpus. We examine the statistical properties and the effectiveness of both approaches on the dataset consisting of abstracts from 0.7 million of scientific documents deposited in the ArXiv preprint collection. We believe that obtained tags can be later on applied as useful document features in various machine learning tasks (document similarity, clustering, topic modelling, etc.)

A surface plasmon enhanced FLIM-FRET imaging approach based on Au nanoparticles

In this report we have demonstrated a fluorescence resonant energy transfer (FRET)-fluorescence lifetime imaging microscopy (FLIM) combined approach to study the intracellular pathway of gold nanoparticles. The detected energy transfer between gold nanorods (GNRs) and green fluorescence protein (GFP) labeled Hela cell earlyendosomes and the in-depth lifetime distribution analysis on the transfer process suggest an endocytotic uptake process of GNRs. Furthermore, the FRET-FLIM method profits from a surface plasmon enhanced energy transfer mechanism when taking into consideration of GNRs and two photon excitation, and is effective in biological imaging, sensing, and even in single molecular tracing in both in vivo and in vitro studies

Magnetars and pulsars: a missing link

There is growing evidence that soft gamma-ray repeaters (SGRs) and anomalous X-ray pulsars (AXPs) are isolated neutron stars with superstrong magnetic fields, i.e., magnetars, marking them a distinguished species from the conventional species of spindown-powered isolated neutron stars, i.e., radio pulsars. The current arguments in favor of the magnetar interpretation of SGR/AXP phenomenology will be outlined, and the two energy sources in magnetars, i.e. a magnetic dissipation energy and a spindown energy, will be reviewed. I will then discuss a missing link between magnetars and pulsars, i.e., lack of the observational evidence of the spindown-powered behaviors in known magnetars. Some recent theoretical efforts in studying such behaviors will be reviewed along with some predictions testable in the near future.Comment: Invited talk at the Sixth Pacific Rim Conference on Stellar Astrophysics, a tribute to Helmut A. Abt, July 11-17, 2002, Xi'an. To appear in the proceedings (eds. K. S. Cheng, K. C. Leung & T. P. Li

Response of bacterioplankton community structures to hydrological conditions and anthropogenic pollution in contrasting subtropical environments

Bacterioplankton community structures under contrasting subtropical marine environments (Hong Kong waters) were analyzed using 16S rRNA gene denaturing gradient gel electrophoresis (DGGE) and subsequent sequencing of predominant bands for samples collected bimonthly from 2004 to 2006 at five stations. Generally, bacterial abundance was significantly higher in the summer than in the winter. The general seasonal variations of the bacterial community structure, as indicated by cluster analysis of the DGGE pattern, were best correlated with temperature at most stations, except for the station close to a sewage discharge outfall, which was best explained by pollution-indicating parameters (e.g. biochemical oxygen demand). Anthropogenic pollutions appear to have affected the presence and the intensity of DGGE bands at the stations receiving discharge of primarily treated sewage. The relative abundance of major bacterial species, calculated by the relative intensity of DGGE bands after PCR amplification, also indicated the effects of hydrological or seasonal variations and sewage discharges. For the first time, a systematic molecular fingerprinting analysis of the bacterioplankton community composition was carried out along the environmental and pollution gradient in a subtropical marine environment, and it suggests that hydrological conditions and anthropogenic pollutions altered the total bacterial community as well as the dominant bacterial groups. © 2009 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.published_or_final_versio