Influences of leakage currents on the transport properties and photoelectric effects in heterojunctions composed of colossal magnetoresistance manganites and Nb-doped titanates

The effects of leakage currents were investigated for Pr 0.7Sr 0.3MnO 3/Nb-SrTiO 3 heterojunctions. It was found that small amounts of leakage currents could cause pronounced detriment to the rectifying properties but had very limited impacts on the barrier heights determined from the forward currents. Significant open circuit voltages V OC were observed when the highly rectified junctions were illuminated by a visible light with a wavelength of 532 nm. For the less rectified junctions, the leakage currents reduced V OC severely and resulted in an anomalous temperature dependence of V OC. Theories for semiconductor contacts were employed in order to discuss these results. © 2012 American Institute of Physics.published_or_final_versio

Electric currents induced step-like resistive jumps and negative differential resistance in thin films of Nd0.7Sr0.3MnO3

Electric-currents-induced emergent phenomena were found in microbridges of Nd 0.7Sr 0.3MnO 3. After the samples were processed by currents of high densities, a second metal-insulator transition appeared at low temperatures. This resistance peak was very sensitive to weak currents. More salient features were the step-like resistance jumps. At temperatures near these resistance steps, negative differential resistance was observed. Interfacial effects related to electrodes could be ruled out. These effects might be due to current-enhanced inhomogeneity. © 2012 American Institute of Physics.published_or_final_versio

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments. Code and supplementary material are available at http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201

First Principles Study on the Electronic Structure and Interface Stability of Hybrid Silicene/Fluorosilicene Nanoribbons

© 2015 Macmillan Publishers Limited. The interface stability of hybrid silicene/fluorosilicene nanoribbons (SFNRs) has been investigated by using density functional theory calculations, where fluorosilicene is the fully fluorinated silicene. It is found that the diffusion of F atoms at the zigzag and armchair interfaces of SFNRs is endothermic, and the corresponding minimum energy barriers are respectively 1.66 and 1.56 eV, which are remarkably higher than the minimum diffusion energy barrier of one F atom and two F atoms on pristine silicene 1.00 and 1.29 eV, respectively. Therefore, the thermal stability of SFNRs can be significantly enhanced by increasing the F diffusion barriers through silicene/fluorosilicene interface engineering. In addition, the electronic and magnetic properties of SFNRs are also investigated. It is found that the armchair SFNRs are nonmagnetic semiconductors, and the band gap of armchair SFNRs presents oscillatory behavior when the width of silicene part changing. For the zigzag SFNRs, the antiferromagnetic semiconducting state is the most stable one. This work provides fundamental insights for the applications of SFNRs in electronic devices

Density functional theory study on the electronic properties and stability of silicene/silicane nanoribbons

© The Royal Society of Chemistry 2015. The thermal stability of silicene/silicane nanoribbons (SSNRs) has been investigated by using density functional theory calculations, where silicane is the fully hydrogenated silicene. It was found that the minimum energy barriers for the diffusion of hydrogen atoms at the zigzag and armchair interfaces of SSNRs are 1.54 and 1.47 eV, respectively, while the diffusion of H atoms at both interfaces is always endothermic. Meanwhile, the minimum diffusion energy barriers of one H atom and two H atoms on pristine silicene are 0.73 and 0.87 eV, respectively. Therefore, the thermal stability of SSNRs can be significantly enhanced by increasing the hydrogen diffusion barriers through silicene/silicane interface engineering. In addition, the zigzag SSNR remains metallic, whereas the armchair SSNR is semiconducting. However, the silicene nanoribbons part-determine the metallic or semiconducting behaviour in the SSNRs. This work provides fundamental insights for the applications of SSNRs in electronic devices. This journal i

Age-related enhancement of the slow outward calcium-activated potassium current in hippocampal CA1 pyramidal neurons in vitro

Aging is associated with learning deficits and a decrease in neuronal excitability, reflected by an enhanced post-burst afterhyperpolarization (AHP), in CA1 hippocampal pyramidal neurons. To identify the current(s) underlying the AHP altered in aging neurons, whole-cell voltage-clamp recording experiments were performed in hippocampal slices from young and aging rabbits. Similar to previous reports, aging neurons were found to rest at more hyperpolarized potentials and have larger AHPs than young neurons. Given that compounds that reduce the slow outward calcium-activated potassium current (sI(AHP)), a major constituent of the AHP, also facilitate learning in aging animals, the sI(AHP) was pharmacologically isolated and characterized. Aging neurons were found to have an enhanced sI(AHP), the amplitude of which was significantly correlated to the amplitude of the AHP (r = 0.63; p < 0.001). Thus, an enhanced sI(AHP) contributes to the enhanced AHP in aging. No differences were found in the membrane resistance, capacitance, or kinetic and voltage-dependent properties of the sI(AHP). Because enhanced AHP in aging neurons has been hypothesized to be secondary to an enhanced Ca2+ influx via the voltage-gated L-type Ca2+ channels, we further examined the sI(AHP) in the presence of an L-type Ca2+ channel blocker, nimodipine (10 μM). Nimodipine caused quantitatively greater reductions in the sIAHP in aging neurons than in young neurons; however, the residual sIAHP was still significantly larger in aging neurons than in young neurons. Our data, in conjunction with previous studies showing a correlation between the AHP and learning, suggest that the enhancement of the sIAHP in aging is a mechanism that contributes to age-related learning deficits

Long-Term Visual Object Tracking Benchmark

We propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long-term tracking.Comment: ACCV 2018 (Oral

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.This research is supported by the Center forDynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

Long-term Tracking in the Wild: A Benchmark

We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining standardized evaluations on large sets of diverse videos. However, these works have focused exclusively on sequences that are just tens of seconds in length and in which the target is always visible. Consequently, most researchers have designed methods tailored to this "short-term" scenario, which is poorly representative of practitioners' needs. Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance. The OxUvA dataset is much larger than the object tracking datasets of recent years: it comprises 366 sequences spanning 14 hours of video. We assess the performance of several algorithms, considering both the ability to locate the target and to determine whether it is present or absent. Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used "in the wild". The project website is http://oxuva.netComment: To appear at ECCV 201