The organic functional group effect on the electronic structure of graphene nano-ribbon: A first-principles study

We report a first-principles study of the electronic structure of functionalized graphene nano-ribbon (aGNRs-f) by organic functional group (CH2C6H5) and find that CH2C6H5 functionalized group does not produce any electronic states in the gap and the band gap is direct. By changing both the density of the organic functional group and the width of the aGNRs-f, a band gap tuning exhibits a fine three family behavior through the side effect. Meanwhile, the carriers at conduction band minimum and valence band maximum are located in both CH2C6H5 and aGNR regions when the density of the CH2C6H5 is big; while they distribute dominantly in aGNR conversely. The band gap modulation effects make the aGNRs-f good candidates with high quantum efficiency and much more wavelength choices range from 750 to 93924 nm both for lasers, light emitting diodes and photo detectors due to the direct band gap and small carrier effective masses.Comment: 20 pages, 5 figure

Attribute-Enhanced Face Recognition with Neural Tensor Fusion Networks

Deep learning has achieved great success in face recognition, however deep-learned features still have limited invariance to strong intra-personal variations such as large pose changes. It is observed that some facial attributes (e.g. eyebrow thickness, gender) are robust to such variations. We present the first work to systematically explore how the fusion of face recognition features (FRF) and facial attribute features (FAF) can enhance face recognition performance in various challenging scenarios. Despite the promise of FAF, we find that in practice existing fusion methods fail to leverage FAF to boost face recognition performance in some challenging scenarios. Thus, we develop a powerful tensor-based framework which formulates feature fusion as a tensor optimisation problem. It is nontrivial to directly optimise this tensor due to the large number of parameters to optimise. To solve this problem, we establish a theoretical equivalence between low-rank tensor optimisation and a two-stream gated neural network. This equivalence allows tractable learning using standard neural network optimisation tools, leading to accurate and stable optimisation. Experimental results show the fused feature works better than individual features, thus proving for the first time that facial attributes aid face recognition.We achieve state-of-the-art performance on three popular databases: MultiPIE (cross pose, lighting and expression), CASIA NIR-VIS2.0 (cross-modality environment) and LFW (uncontrolled environment)

Compact Planar Sparse Array Antenna with Optimum Element Dimensions for SATCOM Ground Terminals

A novel antenna array architecture for low-cost and compact SATCOM mobile terminal is presented. Based on equal-amplitude aperiodic phased array with fewer active chain numbers, it possesses advantages including lower weight, less cost, and higher power efficiency compared to conventional periodic phased arrays. It is implemented with printed patch antenna so that it guarantees compactness. The elements position and dimensions are jointly designed, with an effective sparse array synthesis strategy that takes actual patch antenna design constraint into consideration, to obtain a maximum array aperture efficiency. Executable and practical approach for variable dimension patch antenna designing, including defect substrate element and small scale array, is introduced and utilized to implement proposed sparse array. Full-wave simulation results demonstrate the advantages of proposed array antenna as well as the effectiveness of corresponding design approach

Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention

With a focus on abnormal events contained within untrimmed videos, there is increasing interest among researchers in video anomaly detection. Among different video anomaly detection scenarios, weakly-supervised video anomaly detection poses a significant challenge as it lacks frame-wise labels during the training stage, only relying on video-level labels as coarse supervision. Previous methods have made attempts to either learn discriminative features in an end-to-end manner or employ a twostage self-training strategy to generate snippet-level pseudo labels. However, both approaches have certain limitations. The former tends to overlook informative features at the snippet level, while the latter can be susceptible to noises. In this paper, we propose an Anomalous Attention mechanism for weakly-supervised anomaly detection to tackle the aforementioned problems. Our approach takes into account snippet-level encoded features without the supervision of pseudo labels. Specifically, our approach first generates snippet-level anomalous attention and then feeds it together with original anomaly scores into a Multi-branch Supervision Module. The module learns different areas of the video, including areas that are challenging to detect, and also assists the attention optimization. Experiments on benchmark datasets XDViolence and UCF-Crime verify the effectiveness of our method. Besides, thanks to the proposed snippet-level attention, we obtain a more precise anomaly localization

Torsion and accelerating expansion of the universe in quadratic gravitation

Several exact cosmological solutions of a metric-affine theory of gravity with two torsion functions are presented. These solutions give a essentially different explanation from the one in most of previous works to the cause of the accelerating cosmological expansion and the origin of the torsion of the spacetime. These solutions can be divided into two classes. The solutions in the first class define the critical points of a dynamical system representing an asymptotically stable de Sitter spacetime. The solutions in the second class have exact analytic expressions which have never been found in the literature. The acceleration equation of the universe in general relativity is only a special case of them. These solutions indicate that even in vacuum the spacetime can be endowed with torsion, which means that the torsion of the spacetime has an intrinsic nature and a geometric origin. In these solutions the acceleration of the cosmological expansion is due to either the scalar torsion or the pseudoscalar torsion function. Neither a cosmological constant nor dark energy is needed. It is the torsion of the spacetime that causes the accelerating expansion of the universe in vacuum. All the effects of the inflation, the acceleration and the phase transformation from deceleration to acceleration can be explained by these solutions. Furthermore, the energy and pressure of the matter without spin can produce the torsion of the spacetime and make the expansion of the universe decelerate as well as accelerate.Comment: 20 pages. arXiv admin note: text overlap with gr-qc/0604006, arXiv:1110.344