Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detection. A novel illumination-aware weighting mechanism is present to accurately depict illumination condition of a scene. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy. Putting all of the pieces together, we present a powerful framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset

Hertz-level Measurement of the 40Ca+ 4s 2S1/2-3d 2D5/2 Clock Transition Frequency With Respect to the SI Second through GPS

We report a frequency measurement of the clock transition of a single ^40Ca^+ ion trapped and laser cooled in a miniature ring Paul trap with 10^-15 level uncertainty. In the measurement, we used an optical frequency comb referenced to a Hydrogen maser, which was calibrated to the SI second through the Global Positioning System (GPS). Two rounds of measurements were taken in May and June 2011, respectively. The frequency was measured to be 411 042 129 776 393.0(1.6) Hz with a fractional uncertainty of 3.9{\times}10^-15 in a total averaging time of > 2{\times}10^6 s within 32 days

Facial Expression Decoding based on fMRI Brain Signal

The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions

Global dynamics of advection-dominated accretion flows with magnetically driven outflow

We study the global dynamics of advection-dominated accretion flows (ADAFs) with magnetically driven outflows. A fraction of gases in the accretion flow is accelerated into the outflows, which leads to decreasing of the mass accretion rate in the accretion flow towards the black hole. We find that the r-dependent mass accretion rate is close to a power-law one, m_dot r^s, as assumed in the advection-dominated inflow-outflow solution (ADIOS), in the outer region of the ADAF, while it deviates significantly from the power-law r-dependent accretion rate in the inner region of the ADAF. It is found that the structure of the ADAF is significantly changed in the presence of the outflows. The temperatures of the ions and electrons in the ADAF decreases in the presence of outflows, as a fraction of gravitational power released in the ADAF is tapped to accelerate the outflows.Comment: 9 pages, 7 figures, accepted for publication in MNRA

Stability analysis of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this paper, the problem of stability analysis for a class of impulsive stochastic Cohen–Grossberg neural networks with mixed delays is considered. The mixed time delays comprise both the time-varying and infinite distributed delays. By employing a combination of the M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and exponential p-stability of the equilibrium point for the addressed impulsive stochastic Cohen–Grossberg neural network with mixed delays. The proposed method, which does not make use of the Lyapunov functional, is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and/or distributed delays. We then extend our main results to the case where the parameters contain interval uncertainties. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. An example is given to show the effectiveness of the obtained results.This work was supported by the Natural Science Foundation of CQ CSTC under grant 2007BB0430, the Scientific Research Fund of Chongqing Municipal Education Commission under Grant KJ070401, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany

Electric field and temperature scaling of polarization reversal in silicon doped hafnium oxide ferroelectric thin films

HfO2-based binary lead-free ferroelectrics show promising properties for non-volatile memory applications, providing that their polarization reversal behavior is fully understood. In this work, temperature-dependent polarization hysteresis measured over a wide applied field range has been investigated for Si-doped HfO2 ferroelectric thin films. Our study indicates that in the low and medium electric field regimes (E < twofold coercive field, 2E(c)), the reversal process is dominated by the thermal activation on domain wall motion and domain nucleation; while in the high-field regime (E > 2E(c)), a non-equilibrium nucleation-limited-switching mechanism dominates the reversal process. The optimum field for ferroelectric random access memory (FeRAM) applications was determined to be around 2.0 MV/cm, which translates into a 2.0 V potential applied across the 10 nm thick films

Genetic characterization of H1N2 influenza a virus isolated from sick pigs in Southern China in 2010

In China H3N2 and H1N1 swine influenza viruses have been circulating for many years. In January 2010, before swine were infected with foot and mouth disease in Guangdong, some pigs have shown flu-like symptoms: cough, sneeze, runny nose and fever. We collected the nasopharyngeal swab of all sick pigs as much as possible. One subtype H1N2 influenza viruses were isolated from the pig population. The complete genome of one isolate, designated A/swine/Guangdong/1/2010(H1N2), was sequenced and compared with sequences available in GenBank. The nucleotide sequences of all eight viral RNA segments were determined, and then phylogenetic analysis was performed using the neighbor-joining method. HA, NP, M and NS were shown to be closely to swine origin. PB2 and PA were close to avian origin, but NA and PB1were close to human origin. It is a result of a multiple reassortment event. In conclusion, our finding provides further evidence about the interspecies transmission of avian influenza viruses to pigs and emphasizes the importance of reinforcing swine influenza virus (SIV) surveillance, especially before the emergence of highly pathogenic FMDs in pigs in Guangdong

Calcium Ions Stimulate the Hyperphosphorylation of Tau by Activating Microsomal Prostaglandin E Synthase 1

Alzheimer’s disease (AD) is reportedly associated with the accumulation of calcium ions (Ca2+), and this accumulation is responsible for the phosphorylation of tau. Although several lines of evidence demonstrate the above phenomenon, the inherent mechanisms remain unknown. Using APP/PS1 Tg mice and neuroblastoma (N)2a cells as in vivo and in vitro experimental models, we observed that Ca2+ stimulated the phosphorylation of tau by activating microsomal PGE synthase 1 (mPGES1) in a prostaglandin (PG) E2-dependent EP receptor-activating manner. Specifically, the highly accumulated Ca2+ stimulated the expression of mPGES1 and the synthesis of PGE2. Treatment with the inhibitor of Ca2+ transporter, NMDAR, attenuated the expression of mPGES1 and the production of PGE2 were attenuated in S(+)-ketamine-treated APP/PS1 Tg mice. Elevated levels of PGE2 were responsible for the hyperphosphorylation of tau in an EP-1-, EP-2-, and EP-3-dependent but not EP4-dependent cyclin-dependent kinase (Cdk) 5-activating manner. Reciprocally, the knockdown of the expression of mPGES1 ameliorated the expected cognitive decline by inhibiting the phosphorylation of tau in APP/PS1 Tg mice. Moreover, CDK5 was found to be located downstream of EP1-3 to regulate the phosphorylation of tau though the cleavage of p35 to p25. Finally, the phosphorylation of tau by Ca2+ contributed to the cognitive decline of APP/PS1 Tg mice