IEBins: Iterative Elastic Bins for Monocular Depth Estimation

Monocular depth estimation (MDE) is a fundamental topic of geometric computer vision and a core technique for many downstream applications. Recently, several methods reframe the MDE as a classification-regression problem where a linear combination of probabilistic distribution and bin centers is used to predict depth. In this paper, we propose a novel concept of iterative elastic bins (IEBins) for the classification-regression-based MDE. The proposed IEBins aims to search for high-quality depth by progressively optimizing the search range, which involves multiple stages and each stage performs a finer-grained depth search in the target bin on top of its previous stage. To alleviate the possible error accumulation during the iterative process, we utilize a novel elastic target bin to replace the original target bin, the width of which is adjusted elastically based on the depth uncertainty. Furthermore, we develop a dedicated framework composed of a feature extractor and an iterative optimizer that has powerful temporal context modeling capabilities benefiting from the GRU-based architecture. Extensive experiments on the KITTI, NYU-Depth-v2 and SUN RGB-D datasets demonstrate that the proposed method surpasses prior state-of-the-art competitors. The source code is publicly available at https://github.com/ShuweiShao/IEBins.Comment: Accepted by NeurIPS 202

NENet: Monocular Depth Estimation via Neural Ensembles

Depth estimation is getting a widespread popularity in the computer vision community, and it is still quite difficult to recover an accurate depth map using only one single RGB image. In this work, we observe a phenomenon that existing methods tend to exhibit asymmetric errors, which might open up a new direction for accurate and robust depth estimation. We carefully investigate into the phenomenon, and construct a two-level ensemble scheme, NENet, to integrate multiple predictions from diverse base predictors. The NENet forms a more reliable depth estimator, which substantially boosts the performance over base predictors. Notably, this is the first attempt to introduce ensemble learning and evaluate its utility for monocular depth estimation to the best of our knowledge. Extensive experiments demonstrate that the proposed NENet achieves better results than previous state-of-the-art approaches on the NYU-Depth-v2 and KITTI datasets. In particular, our method improves previous state-of-the-art methods from 0.365 to 0.349 on the metric RMSE on the NYU dataset. To validate the generalizability across cameras, we directly apply the models trained on the NYU dataset to the SUN RGB-D dataset without any fine-tuning, and achieve the superior results, which indicate its strong generalizability. The source code and trained models will be publicly available upon the acceptance

An EEG-based brain-computer interface for gait training

This work presents an electroencephalography (EEG)-based Brain-computer Interface (BCI) that decodes cerebral activities to control a lower-limb gait training exoskeleton. Motor imagery (MI) of flexion and extension of both legs was distinguished from the EEG correlates. We executed experiments with 5 able-bodied individuals under a realistic rehabilitation scenario. The Power Spectral Density (PSD) of the signals was extracted with sliding windows to train a linear discriminate analysis (LDA) classifier. An average classification accuracy of 0.67±0.07 and AUC of 0.77±0.06 were obtained in online recordings, which confirmed the feasibility of decoding these signals to control the gait trainer. In addition, discriminative feature analysis was conducted to show the modulations during the mental tasks. This study can be further implemented with different feedback modalities to enhance the user performance

The Escherichia coli NarL receiver domain regulates transcription through promoter specific functions

BACKGROUND: The Escherichia coli response regulator NarL controls transcription of genes involved in nitrate respiration during anaerobiosis. NarL consists of two domains joined by a linker that wraps around the interdomain interface. Phosphorylation of the NarL N-terminal receiver domain (RD) releases the, otherwise sequestered, C-terminal output domain (OD) that subsequently binds specific DNA promoter sites to repress or activate gene expression. The aim of this study is to investigate the extent to which the NarL OD and RD function independently to regulate transcription, and the affect of the linker on OD function. RESULTS: NarL OD constructs containing different linker segments were examined for their ability to repress frdA-lacZ or activate narG-lacZ reporter fusion genes. These in vivo expression assays revealed that the NarL OD, in the absence or presence of linker helix α6, constitutively repressed frdA-lacZ expression regardless of nitrate availability. However, the presence of the linker loop α5-α6 reversed this repression and also showed impaired DNA binding in vitro. The OD alone could not activate narG-lacZ expression; this activity required the presence of the NarL RD. A footprint assay demonstrated that the NarL OD only partially bound recognition sites at the narG promoter, and the binding affinity was increased by the presence of the phosphorylated RD. Analytical ultracentrifugation used to examine domain oligomerization showed that the NarL RD forms dimers in solution while the OD is monomeric. CONCLUSIONS: The NarL RD operates as an on-off switch to occlude or release the OD in a nitrate-responsive manner, but has additional roles to directly stimulate transcription at promoters for which the OD lacks independent function. One such role of the RD is to enhance the DNA binding affinity of the OD to target promoter sites. The data also imply that NarL phosphorylation results in RD dimerization and in the separation of the entire linker region from the OD. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12866-015-0502-9) contains supplementary material, which is available to authorized users

Deep Convolutional Neural Network for EEG-Based Motor Decoding

Brain–machine interfaces (BMIs) have been applied as a pattern recognition system for neuromodulation and neurorehabilitation. Decoding brain signals (e.g., EEG) with high accuracy is a prerequisite to building a reliable and practical BMI. This study presents a deep convolutional neural network (CNN) for EEG-based motor decoding. Both upper-limb and lower-limb motor imagery were detected from this end-to-end learning with four datasets. An average classification accuracy of 93.36 ± 1.68% was yielded on the four datasets. We compared the proposed approach with two other models, i.e., multilayer perceptron and the state-of-the-art framework with common spatial patterns and support vector machine. We observed that the performance of the CNN-based framework was significantly better than the other two models. Feature visualization was further conducted to evaluate the discriminative channels employed for the decoding. We showed the feasibility of the proposed architecture to decode motor imagery from raw EEG data without manually designed features. With the advances in the fields of computer vision and speech recognition, deep learning can not only boost the EEG decoding performance but also help us gain more insight from the data, which may further broaden the knowledge of neuroscience for brain mapping

Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors

Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on the detection of upper-limb movement intention, while the fewer study has investigated the lower-limb movement intention decoding. This study presents a BMI to decode the self-paced lower-limb movement intention, with 10 healthy subjects participating in the experiment. We varied four influence factors including the movement type (dorsiflexion or plantar flexion), the limb side (left or right leg), the processing method (time-series analysis based on MRCP, i.e., movement-related cortical potential or frequency-domain estimation based on SMR, i.e., sensory motor rhythm) and the frequency band (e.g., delta, theta, mu, beta and MRCP band at [0.1 1] Hz), to estimate both single-trial and sample-based performance. Feature analysis was then conducted to show the discriminant power (DP) and brain modulations. The average detection latency was 0.334 +/- 0.216 s in single-trial basis across all conditions. An average area under the curve (AUC) of 91.0 +/- 3.5% and 68.2 +/- 4.6% was obtained for the MRCP-based and SMR-based method in the classification, respectively. The best performance was yielded from plantar flexion with left leg using time-series analysis on the MRCP band. The feature analysis indicated a cross-subject consistency of DP with the MRCP-based method and subject-specific variance of DP with the SMR-based method. The results presented here might be further exploited in a rehabilitation scenario. The comprehensive factor analysis might be used to shed light on the design of an effective brain switch to trigger external robotic devices

Interval versus external fixation for the treatment of pelvic fractures: a comparative study

Purpose: This retrospective study evaluated the efficacy and safety of internal fixation (IF) in the treatment of pelvic fractures (PF). Methods: A total of 263 unstable PF patients were treated from February 2009 to April 2015. Patients were divided into two groups according to type of fixation used to treat their PF: 136 cases received IF surgery (IF group); and, 127 cases received external fixation (EF) surgery (EF group). Postoperative follow-ups were conducted to record the clinical data, perioperative clinical indicators, Matta scores for fracture displacements, Majeed scores for hip functions and postoperative complications. Results: Operation time, blood loss, the total length of the wound, postoperative fever rate, hospitalization time and complication rate for the IF group were significantly decreased in comparison with the EF group, while the ratings of pain, working and sitting ability and Matta and Majeed scores of the IF group were significantly higher than those of the EF group. Conclusion: IF was found to be associated with shorter operation times, less blood loss and better postoperative rehabilitation in comparison with EF, suggesting that it is an effective therapy for the treatment of unstable PF and will lead to restoration of normal pelvis functions