394 research outputs found
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
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