Evaluation and Optimization of Rendering Techniques for Autonomous Driving Simulation

In order to meet the demand for higher scene rendering quality from some autonomous driving teams (such as those focused on CV), we have decided to use an offline simulation industrial rendering framework instead of real-time rendering in our autonomous driving simulator. Our plan is to generate lower-quality scenes using a game engine, extract them, and then use an IQA algorithm to validate the improvement in scene quality achieved through offline rendering. The improved scenes will then be used for training

The Instantaneous Redshift Difference of Gravitationally Lensed Images: Theory and Observational Prospects

Due to the expansion of our Universe, the redshift of distant objects changes with time. Although the amplitude of this redshift drift is small, it will be measurable with a decade-long campaigns on the next generation of telescopes. Here we present an alternative view of the redshift drift which captures the expansion of the universe in single epoch observations of the multiple images of gravitationally lensed sources. Considering a sufficiently massive lens, with an associated time delay of order decades, simultaneous photons arriving at a detector would have been emitted decades earlier in one image compared to another, leading to an instantaneous redshift difference between the images. We also investigate the effect of peculiar velocities on the redshift difference in the observed images. Whilst still requiring the observational power of the next generation of telescopes and instruments, the advantage of such a single epoch detection over other redshift drift measurements is that it will be less susceptible to systematic effects that result from requiring instrument stability over decade-long campaigns.Comment: 6 pages, 5 figure

The Redshift Difference in Gravitational Lensed Systems: A Novel Probe of Cosmology

The exploration of the redshift drift, a direct measurement of cosmological expansion, is expected to take several decades of observation with stable, sensitive instruments. We introduced a new method to probe cosmology which bypasses the long-period observation by observing the redshift difference, an accumulation of the redshift drift, in multiple-image gravitational lens systems. With this, the photons observed in each image will have traversed through different paths between the source and the observer, and so the lensed images will show different redshifts when observed at the same instance. Here, we consider the impact of the underlying cosmology on the observed redshift difference in gravitational lens systems, generating synthetic data for realistic lens models and exploring the accuracy of determined cosmological parameters. We show that, whilst the redshift difference is sensitive to the densities of matter and dark energy within a universe, it is independent of the Hubble constant. Finally, we determine the observational considerations for using the redshift difference as a cosmological probe, finding that one thousand lensed sources are enough to make robust determinations of the underlying cosmological parameters. Upcoming cluster lens surveys, such as the Euclid, are expected to detect a sufficient number of such systems.Comment: 10 pages, 12 figures, 1 tabl

Effect of Laser Irradiation on sIg A and Mucosa Structure of Upper Respiratory Tract with Six-week Incremental Exercise

[Objective] Mucosal immune suppression, with chronic intensive exercise, can be associated with an increased risk of upper respiratory tract infections, which should be related to the deterioration of the nasal mucosa structure. This study aimed to observe the change of nasal mucosa structure with 6-week incremental exercise, and to explore the effect of low level laser irradiation on nasal mucosa structure and mucosal immune function. [Methods] 40 Sprague–Dawle rats, aged 8 weeks, were divided into 4 groups : Control, Exercise, Low power (4mw, 12.23 J/cm2) and High power laser (6mw, 18.34J/cm2) groups. Incremental treadmill exercise protocols: successive 6 weeks, 6 days/week, 30min /day. 10 m/min velocity during wk1, 20 m for wk2, with 5m/min/wk increment following weeks. The treatment of low level laser as following: He-Ne laser (0.19625 cm2 ), two irradiation point of nasal ala, 6-week duration, 6 days/wk, 2 times/day; 5min/time. Samples were taken pre and post 6-week exercise. Structure of mucosa of nose was observed by HE staining and sIgA tested by ELISA. [Results] 1) following changes occurred in Exercise group after 6-wk exercise: nasal mucosa was seriously damaged and cilia layer of free edge fell essentially off. And mucous degeneration, necrosis and inflammatory cell infiltration were observed. 2）compared with exercise group, significant improvement was found with laser treatment. 3) sIgA with different groups saw as Table 1. Table 1 sIgA changes after 6-wk exercise groups Control Exercise Low dose laser High dose laser sIgA(μg/ml) 52.92±6.69 50.20±4.76 70.77±4.24 73.71±3.91* * P\u3c0.05 [Conclusion] The long-term high-intensity exercise training would lead to destruction of nasal mucosa structure, and low energy laser irradiation had a beneficial effect on sIgA and nasal mucosa structure

Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation

End-to-end speech translation, a hot topic in recent years, aims to translate a segment of audio into a specific language with an end-to-end model. Conventional approaches employ multi-task learning and pre-training methods for this task, but they suffer from the huge gap between pre-training and fine-tuning. To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN) which bridges the gap by reusing all subnets in fine-tuning, keeping the roles of subnets consistent, and pre-training the attention module. Furthermore, we propose two simple but effective methods to guarantee the speech encoder outputs and the MT encoder inputs are consistent in terms of semantic representation and sequence length. Experimental results show that our model outperforms baselines 2.2 BLEU on a large benchmark dataset.Comment: AAAI202

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Open access articleSemantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable