StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks

Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that leverages a deep neural network to detect occupancy from stereo images directly. Instead of learning the point cloud correspondence from the stereo data, our approach extracts the compact obstacle distribution based on volumetric representations. In addition, we prune the computation of safety irrelevant spaces in a coarse-to-fine manner based on octrees generated by the decoder. As a result, we achieve real-time performance on the onboard computer (NVIDIA Jetson TX2). Our approach detects obstacles accurately in the range of 32 meters and achieves better IoU (Intersection over Union) and CD (Chamfer Distance) scores with only 2% of the computation cost of the state-of-the-art stereo model. Furthermore, we validate our method's robustness and real-world feasibility through autonomous navigation experiments with a real robot. Hence, our work contributes toward closing the gap between the stereo-based system in robot perception and state-of-the-art stereo models in computer vision. To counter the scarcity of high-quality real-world indoor stereo datasets, we collect a 1.36 hours stereo dataset with a Jackal robot which is used to fine-tune our model. The dataset, the code, and more visualizations are available at https://lhy.xyz/stereovoxelnet

All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management

During the deployment of deep neural networks (DNNs) on edge devices, many research efforts are devoted to the limited hardware resource. However, little attention is paid to the influence of dynamic power management. As edge devices typically only have a budget of energy with batteries (rather than almost unlimited energy support on servers or workstations), their dynamic power management often changes the execution frequency as in the widely-used dynamic voltage and frequency scaling (DVFS) technique. This leads to highly unstable inference speed performance, especially for computation-intensive DNN models, which can harm user experience and waste hardware resources. We firstly identify this problem and then propose All-in-One, a highly representative pruning framework to work with dynamic power management using DVFS. The framework can use only one set of model weights and soft masks (together with other auxiliary parameters of negligible storage) to represent multiple models of various pruning ratios. By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i.e., keeping the difference in speed performance under various execution frequencies as small as possible. Our experiments demonstrate that our method not only achieves high accuracy for multiple models of different pruning ratios, but also reduces their variance of inference latency for various frequencies, with minimal memory consumption of only one model and one soft mask

Association Analysis and Identification of ZmHKT1;5 Variation With Salt-Stress Tolerance

The high-affinity potassium transporter (HKT) genes are essential for plant salt stress tolerance. However, there were limited studies on HKTs in maize (Zea mays), and it is basically unknown whether natural sequence variations in these genes are associated with the phenotypic variability of salt tolerance. Here, the characterization of ZmHKT1;5 was reported. Under salt stress, ZmHKT1;5 expression increased strongly in salt-tolerant inbred lines, which accompanied a better-balanced Na+/K+ ratio and preferable plant growth. The association between sequence variations in ZmHKT1;5 and salt tolerance was evaluated in a diverse population comprising 54 maize varieties from different maize production regions of China. Two SNPs (A134G and A511G) in the coding region of ZmHKT1;5 were significantly associated with different salt tolerance levels in maize varieties. In addition, the favorable allele of ZmHKT1; 5 identified in salt tolerant maize varieties effectively endowed plant salt tolerance. Transgenic tobacco plants of overexpressing the favorable allele displayed enhanced tolerance to salt stress better than overexpressing the wild type ZmHKT1;5. Our research showed that ZmHKT1;5 expression could effectively enhance salt tolerance by maintaining an optimal Na+/K+ balance and increasing the antioxidant activity that keeps reactive oxygen species (ROS) at a low accumulation level. Especially, the two SNPs in ZmHKT1;5 might be related with new amino acid residues to confer salt tolerance in maize.Key Message: Two SNPs of ZmHKT1;5 related with salt tolerance were identified by association analysis. Overexpressing ZmHKT1;5 in tobaccos showed that the SNPs might enhance its ability to regulating Na+/K+ homeostasis

Topological Susceptibility under Gradient Flow

We study the impact of the Gradient Flow on the topology in various models of lattice field theory. The topological susceptibility $\chi_{\rm t}$ is measured directly, and by the slab method, which is based on the topological content of sub-volumes ("slabs") and estimates $\chi_{\rm t}$ even when the system remains trapped in a fixed topological sector. The results obtained by both methods are essentially consistent, but the impact of the Gradient Flow on the characteristic quantity of the slab method seems to be different in 2-flavour QCD and in the 2d O(3) model. In the latter model, we further address the question whether or not the Gradient Flow leads to a finite continuum limit of the topological susceptibility (rescaled by the correlation length squared, $\xi^{2}$). This ongoing study is based on direct measurements of $\chi_{\rm t}$ in $L \times L$ lattices, at $L/\xi \simeq 6$.Comment: 8 pages, LaTex, 5 figures, talk presented at the 35th International Symposium on Lattice Field Theory, June 18-24, 2017, Granada, Spai

/UV Synergistic Aging of Polyester Polyurethane Film Modified by Composite UV Absorber

The pure polyester polyurethane (TPU) film and the modified TPU (M-TPU) film containing 2.0 wt.% inorganic UV absorbers mixture (nano-ZnO/CeO2 with weight ratio of 3 : 2) and 0.5 wt.% organic UV absorbers mixture (UV-531/UV-327 with weight ratio of 1 : 1) were prepared by spin-coating technique. The accelerated aging tests of the films exposed to constant UV radiation of 400 ± 20 µW/cm2 (313 nm) with an ozone atmosphere of 100 ± 2 ppm were carried out by using a self-designed aging equipment at ambient temperature and relative humidity of 20%. The aging resistance properties of the films were evaluated by UV-Vis spectra, Fourier transform infrared spectra (FT-IR), photooxidation index, and carbonyl index analysis. The results show that the composite UV absorber has better protection for TPU system, which reduces distinctly the degradation of TPU film. O3/UV aging of the films increases with incremental exposure time. PI and CI of TPU and M-TPU films increase with increasing exposure time, respectively. PI and CI of M-TPU films are much lower than that of TPU film after the same time of exposure, respectively. Distinct synergistic aging effect exists between ozone aging and UV aging when PI and CI are used as evaluation index, respectively. Of course, the formula of these additives needs further improvement for industrial application