Spherical Transformer: Adapting Spherical Signal to CNNs

Convolutional neural networks (CNNs) have been widely used in various vision tasks, e.g. image classification, semantic segmentation, etc. Unfortunately, standard 2D CNNs are not well suited for spherical signals such as panorama images or spherical projections, as the sphere is an unstructured grid. In this paper, we present Spherical Transformer which can transform spherical signals into vectors that can be directly processed by standard CNNs such that many well-designed CNNs architectures can be reused across tasks and datasets by pretraining. To this end, the proposed method first uses locally structured sampling methods such as HEALPix to construct a transformer grid by using the information of spherical points and its adjacent points, and then transforms the spherical signals to the vectors through the grid. By building the Spherical Transformer module, we can use multiple CNN architectures directly. We evaluate our approach on the tasks of spherical MNIST recognition, 3D object classification and omnidirectional image semantic segmentation. For 3D object classification, we further propose a rendering-based projection method to improve the performance and a rotational-equivariant model to improve the anti-rotation ability. Experimental results on three tasks show that our approach achieves superior performance over state-of-the-art methods

Robot Protection in the Hazardous Environments

Rescue missions for chemical, biological, radiological, nuclear, and explosive (CBRNE) incidents are highly risky and sometimes it is impossible for rescuers to perform, while these accidents vary dramatically in features and protection requirements. The purpose of this chapter is to present several protection approaches for rescue robots in the hazardous conditions. And four types of rescue robots are presented, respectively. First, design factors and challenges of the rescue robots are analyzed and indicated for these accidents. Then the rescue robots with protective modification are presented, respectively, meeting individual hazardous requirements. And finally several tests are conducted to validate the effectiveness of these modified robots. It is clear that these well-designed robots can work efficiently for the CBRNE response activities

Fast Hybrid Cascade for Voxel-based 3D Object Classification

Voxel-based 3D object classification has been frequently studied in recent years. The previous methods often directly convert the classic 2D convolution into a 3D form applied to an object with binary voxel representation. In this paper, we investigate the reason why binary voxel representation is not very suitable for 3D convolution and how to simultaneously improve the performance both in accuracy and speed. We show that by giving each voxel a signed distance value, the accuracy will gain about 30% promotion compared with binary voxel representation using a two-layer fully connected network. We then propose a fast fully connected and convolution hybrid cascade network for voxel-based 3D object classification. This threestage cascade network can divide 3D models into three categories: easy, moderate and hard. Consequently, the mean inference time (0.3ms) can speedup about 5x and 2x compared with the state-of-the-art point cloud and voxel based methods respectively, while achieving the highest accuracy in the latter category of methods (92%). Experiments with ModelNet andMNIST verify the performance of the proposed hybrid cascade network

Exploring Straighter Trajectories of Flow Matching with Diffusion Guidance

Flow matching as a paradigm of generative model achieves notable success across various domains. However, existing methods use either multi-round training or knowledge within minibatches, posing challenges in finding a favorable coupling strategy for straight trajectories. To address this issue, we propose a novel approach, Straighter trajectories of Flow Matching (StraightFM). It straightens trajectories with the coupling strategy guided by diffusion model from entire distribution level. First, we propose a coupling strategy to straighten trajectories, creating couplings between image and noise samples under diffusion model guidance. Second, StraightFM also integrates real data to enhance training, employing a neural network to parameterize another coupling process from images to noise samples. StraightFM is jointly optimized with couplings from above two mutually complementary directions, resulting in straighter trajectories and enabling both one-step and few-step generation. Extensive experiments demonstrate that StraightFM yields high quality samples with fewer step. StraightFM generates visually appealing images with a lower FID among diffusion and traditional flow matching methods within 5 sampling steps when trained on pixel space. In the latent space (i.e., Latent Diffusion), StraightFM achieves a lower KID value compared to existing methods on the CelebA-HQ 256 dataset in fewer than 10 sampling steps

Chansu inhibits the expression of cortactin in colon cancer cell lines in vitro and in vivo

Background: Chansu is a transitional Chinese medicine that has been used for centuries as therapy for inflammation, anaesthesia and arrhythmia in China and other Asian countries. Recently, it has also been used for anti-cancer purposes. We have previously shown that Chansu has a huge pro-apoptotic potential on colon cancer cells, but to date the detailed mechanism of this action is not well understood. Methods: One of the major components of Chansu, Cinobufagin (CBF) was used to treat cancer cells. The expressions of levels of cortactin, an important factor in tumour progression and cancer invasion, were assessed in in vitro and in vivo experiments. Additional analyses were performed in subcellular protein fractions and immune-fluorescent staining was used to define cortactin protein expression and the changes of location in CBF-treated cells. Results: CBF strongly inhibited the expression of cortactin in HCT116 cells. There were reductions of both mRNA transcription and protein synthesis, which were more significant in the absence of oxygen in vitro. In addition, nuclear translocation of cortactin was observed in HCT116 cells post CBF exposure but not in the negative control, indicating that CBF is likely to interrupt co-localisation of cortactin to cytoskeletal proteins. Most importantly, CBF could diminish the expression of cortactin in human HCT116 xenograft tumours in nude mouse in vivo. Conclusions: CBF inhibits cortactin expression and nuclear translocation in colon cancer cells in vitro and in mouse models bearing human colon tumour in vivo, suggesting it might disrupt actin-regulated cell movement. Thus, CBF or Chansu could be developed as an effective anti-cancer therapy to stop local invasion and metastasis

The mechanisms of Chansu in inducing efficient apoptosis in colon cancer cells

Chansu is one of the most widely used traditional Chinese medicines in China, Japan, and other Southeast Asian countries primarily for antipain, anti-inflammation, and recently anticancer. Over 10 recipes and remedies contained Chansu, which are easily available in pharmacies and hospitals, but the mechanisms of action were not clearly articulated. In the present study, Cinobufagin (CBF), the major compound of Chansu, was employed as a surrogate marker to determine its ability in inducing cancer cell death. As expected, CBF has significant cancer-killing capacity for a range of cancers, but such ability differs markedly. Colon and prostate cancers are more sensitive than skin and lung cancers. Interestingly, cancer cells die through apoptotic pathway either being biphasic caspase- 3-dependent (HCT116) or independent (HT29). Multipathway analysis reveals that CBF-induced apoptosis is likely modulated by the hypoxia-inducing factor-1 alpha subunit (HIF-

Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment. It transforms a tensor program written for a single device into an equivalent distributed program that is capable of scaling up to thousands of devices with no user configuration. Rhino firstly works on a semantically independent intermediate representation of tensor programs, which facilitates its generalization to unprecedented applications. Additionally, it implements a task-oriented controller and a distributed runtime for optimal performance. Rhino explores on a complete and systematic parallelization strategy space that comprises all the paradigms commonly employed in deep learning (DL), in addition to strided partitioning and pipeline parallelism on non-linear models. Aiming to efficiently search for a near-optimal parallel execution plan, our analysis of production clusters reveals general heuristics to speed up the strategy search. On top of it, two optimization levels are designed to offer users flexible trade-offs between the search time and strategy quality. Our experiments demonstrate that Rhino can not only re-discover the expert-crafted strategies of classic, research and production DL models, but also identify novel parallelization strategies which surpass existing systems for novel models