Evaluating Explanation Methods for Vision-and-Language Navigation

The ability to navigate robots with natural language instructions in an unknown environment is a crucial step for achieving embodied artificial intelligence (AI). With the improving performance of deep neural models proposed in the field of vision-and-language navigation (VLN), it is equally interesting to know what information the models utilize for their decision-making in the navigation tasks. To understand the inner workings of deep neural models, various explanation methods have been developed for promoting explainable AI (XAI). But they are mostly applied to deep neural models for image or text classification tasks and little work has been done in explaining deep neural models for VLN tasks. In this paper, we address these problems by building quantitative benchmarks to evaluate explanation methods for VLN models in terms of faithfulness. We propose a new erasure-based evaluation pipeline to measure the step-wise textual explanation in the sequential decision-making setting. We evaluate several explanation methods for two representative VLN models on two popular VLN datasets and reveal valuable findings through our experiments.Comment: Accepted by ECAI 202

Dual sub-swarm interaction QPSO algorithm based on different correlation coefficients

A novel quantum-behaved particle swarm optimization (QPSO) algorithm, the dual sub-swarm interaction QPSO algorithm based on different correlation coefficients (DCC-QPSO), is proposed by constructing master-slave sub-swarms with different potential well centres. In the novel algorithm, the master sub-swarm and the slave sub-swarm have different functinons during the evolutionary process through separate information processing strategies. The master subswarm is conducive to maintaining population diversity and enhancing the global search ability of particles. The slave sub-swarm accelerates the convergence rate and strengthens the particles’ local searching ability. With the critical information contained in the search space and results of the basic QPSO algorithm, this new algorithm avoids the rapid disappearance of swarm diversity and enhances searching ability through collaboration between sub-swarms. Experimental results on six test functions show that DCC-QPSO outperforms the traditional QPSO algorithm regarding optimization of multimodal functions, with enhancement in both convergence speed and precision

HVDetFusion: A Simple and Robust Camera-Radar Fusion Framework

In the field of autonomous driving, 3D object detection is a very important perception module. Although the current SOTA algorithm combines Camera and Lidar sensors, limited by the high price of Lidar, the current mainstream landing schemes are pure Camera sensors or Camera+Radar sensors. In this study, we propose a new detection algorithm called HVDetFusion, which is a multi-modal detection algorithm that not only supports pure camera data as input for detection, but also can perform fusion input of radar data and camera data. The camera stream does not depend on the input of Radar data, thus addressing the downside of previous methods. In the pure camera stream, we modify the framework of Bevdet4D for better perception and more efficient inference, and this stream has the whole 3D detection output. Further, to incorporate the benefits of Radar signals, we use the prior information of different object positions to filter the false positive information of the original radar data, according to the positioning information and radial velocity information recorded by the radar sensors to supplement and fuse the BEV features generated by the original camera data, and the effect is further improved in the process of fusion training. Finally, HVDetFusion achieves the new state-of-the-art 67.4\% NDS on the challenging nuScenes test set among all camera-radar 3D object detectors. The code is available at https://github.com/HVXLab/HVDetFusio

A Calculation Method of X-Ray Emitted Intensity in Multi-Layer Films by Monte Carlo Simulation

A calculation method of X-ray emitted intensity in multi-layer films is proposed in this paper. The method is based on the work developed by us: (1) a simplified physical model of electron scattering and Monte Carlo evaluations in a single medium and in multi-layer media and (2) the theories and the formulae for excitation, absorption and fluorescence of characteristic X-rays. The intensity ratio of X-rays for the known thickness films, Au/Cu/Si and Cr/Ni/Si, were calculated at 20, 25 and 30 keV. Calculated results are compared with experimental values of electron microprobe analysis for the multi-layer film specimens, and the correspondence is excellent. The work lays foundations for X-ray quantitative microanalysis of multi-layer specimens

Betanodavirus non-structural protein B1: A novel anti-necrotic death factor that modulates cell death in early replication cycle in fish cells

AbstractThe functions of the Betanodavirus non-structural protein B1 is still unknown. We examined B1 expression patterns and investigated novel cell death regulatory functions for this viral protein following RGNNV infection in fish cells. The B1 gene (336 nt) was cloned from the redspotted grouper nervous necrosis virus (RGNNV) genome. B1 mRNA was rapidly expressed in the fish cells from viral RNA3 at 12 h post-infection (p.i.). At the protein level, expression was low at 12 h p.i., and then increased rapidly between 24 h and 72 h p.i. In RGNNV-infected, B1-containing fish cells, over expression of RGNNV B1 reduced Annexin-V positive cells by 50% and 65% at 48 h and 72 h p.i., respectively, and decreased loss of mitochondrial membrane potential (MMP) by 20% and 70% at 48 h and 72 h p.i., respectively. Finally, B1 knockdown during RGNNV infection using anti-sense RNA increased necrotic cell death and reduced cell viability during the early replication cycle (24 h p.i.). Our results suggest that B1 is an early expression protein that has an anti-necrotic cell death function which reduces the MMP loss and enhances viral host cell viability. This finding provides new insights into RNA viral pathogenesis and disease control