Spatial Reasoning for Few-Shot Object Detection

Although modern object detectors rely heavily on a significant amount of training data, humans can easily detect novel objects using a few training examples. The mechanism of the human visual system is to interpret spatial relationships among various objects and this process enables us to exploit contextual information by considering the co-occurrence of objects. Thus, we propose a spatial reasoning framework that detects novel objects with only a few training examples in a context. We infer geometric relatedness between novel and base RoIs (Region-of-Interests) to enhance the feature representation of novel categories using an object detector well trained on base categories. We employ a graph convolutional network as the RoIs and their relatedness are defined as nodes and edges, respectively. Furthermore, we present spatial data augmentation to overcome the few-shot environment where all objects and bounding boxes in an image are resized randomly. Using the PASCAL VOC and MS COCO datasets, we demonstrate that the proposed method significantly outperforms the state-of-the-art methods and verify its efficacy through extensive ablation studies.Comment: Pattern Recognition, Vol.120, 202

Prediction of Giant Spin Motive Force due to Rashba Spin-Orbit Coupling

Magnetization dynamics in a ferromagnet can induce a spin-dependent electric field through spin motive force. Spin current generated by the spin-dependent electric field can in turn modify the magnetization dynamics through spin-transfer torque. While this feedback effect is usually weak and thus ignored, we predict that in Rashba spin-orbit coupling systems with large Rashba parameter $\alpha_{\rm R}$, the coupling generates the spin-dependent electric field [\pm(\alpha_{\rm R}m_e/e\hbar) (\vhat{z}\times \partial \vec{m}/\partial t)], which can be large enough to modify the magnetization dynamics significantly. This effect should be relevant for device applications based on ultrathin magnetic layers with strong Rashba spin-orbit coupling.Comment: 4+ pages, 2 figure

Inactivation of Medial Prefrontal Cortex Impairs Time Interval Discrimination in Rats

Several lines of evidence suggest the involvement of prefrontal cortex in time interval estimation. The underlying neural processes are poorly understood, however, in part because of the paucity of physiological studies. The goal of this study was to establish an interval timing task for physiological recordings in rats, and test the requirement of intact medial prefrontal cortex (mPFC) for performing the task. We established a temporal bisection procedure using six different time intervals ranging from 3018 to 4784 ms that needed to be discriminated as either long or short. Bilateral infusions of muscimol (GABAA receptor agonist) into the mPFC significantly impaired animal's performance in this task, even when the animals were required to discriminate between only the longest and shortest time intervals. These results show the requirement of intact mPFC in rats for time interval discrimination in the range of a few seconds

Electroactive shape memory performance of polyurethane composite having homogeneously dispersed and covalently crosslinked carbon nanotubes

The electroactive shape memory of carbon nanotube-filled polyurethane composites, prepared by conventional blending, in situ and cross-linking polymerization, is studied in terms of the dispersion of the tubes The covalently bonded tubes are homogeneously dispersed within the polyurethane by introducing carboxyl groups on the sidewall of the tubes and selecting a cross-linking polymerization method The resultant composites, which have 92% shape retention and 95% shape recovery, are expected to be used as preferential materials in various actuatorsArticleCARBON. 48(5):1598-1603 (2010)journal articl

Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals

Recent advances in brain-computer interface (BCI) technology, particularly based on generative adversarial networks (GAN), have shown great promise for improving decoding performance for BCI. Within the realm of Brain-Computer Interfaces (BCI), GANs find application in addressing many areas. They serve as a valuable tool for data augmentation, which can solve the challenge of limited data availability, and synthesis, effectively expanding the dataset and creating novel data formats, thus enhancing the robustness and adaptability of BCI systems. Research in speech-related paradigms has significantly expanded, with a critical impact on the advancement of assistive technologies and communication support for individuals with speech impairments. In this study, GANs were investigated, particularly for the BCI field, and applied to generate text from EEG signals. The GANs could generalize all subjects and decode unseen words, indicating its ability to capture underlying speech patterns consistent across different individuals. The method has practical applications in neural signal-based speech recognition systems and communication aids for individuals with speech difficulties.Comment: 5 pages, 2 figure