36 research outputs found
UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View
In the field of 3D object detection for autonomous driving, the sensor
portfolio including multi-modality and single-modality is diverse and complex.
Since the multi-modal methods have system complexity while the accuracy of
single-modal ones is relatively low, how to make a tradeoff between them is
difficult. In this work, we propose a universal cross-modality knowledge
distillation framework (UniDistill) to improve the performance of
single-modality detectors. Specifically, during training, UniDistill projects
the features of both the teacher and the student detector into Bird's-Eye-View
(BEV), which is a friendly representation for different modalities. Then, three
distillation losses are calculated to sparsely align the foreground features,
helping the student learn from the teacher without introducing additional cost
during inference. Taking advantage of the similar detection paradigm of
different detectors in BEV, UniDistill easily supports LiDAR-to-camera,
camera-to-LiDAR, fusion-to-LiDAR and fusion-to-camera distillation paths.
Furthermore, the three distillation losses can filter the effect of misaligned
background information and balance between objects of different sizes,
improving the distillation effectiveness. Extensive experiments on nuScenes
demonstrate that UniDistill effectively improves the mAP and NDS of student
detectors by 2.0%~3.2%
On Transforming Reinforcement Learning by Transformer: The Development Trajectory
Transformer, originally devised for natural language processing, has also
attested significant success in computer vision. Thanks to its super expressive
power, researchers are investigating ways to deploy transformers to
reinforcement learning (RL) and the transformer-based models have manifested
their potential in representative RL benchmarks. In this paper, we collect and
dissect recent advances on transforming RL by transformer (transformer-based RL
or TRL), in order to explore its development trajectory and future trend. We
group existing developments in two categories: architecture enhancement and
trajectory optimization, and examine the main applications of TRL in robotic
manipulation, text-based games, navigation and autonomous driving. For
architecture enhancement, these methods consider how to apply the powerful
transformer structure to RL problems under the traditional RL framework, which
model agents and environments much more precisely than deep RL methods, but
they are still limited by the inherent defects of traditional RL algorithms,
such as bootstrapping and "deadly triad". For trajectory optimization, these
methods treat RL problems as sequence modeling and train a joint state-action
model over entire trajectories under the behavior cloning framework, which are
able to extract policies from static datasets and fully use the long-sequence
modeling capability of the transformer. Given these advancements, extensions
and challenges in TRL are reviewed and proposals about future direction are
discussed. We hope that this survey can provide a detailed introduction to TRL
and motivate future research in this rapidly developing field.Comment: 26 page
ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning
Many existing autonomous driving paradigms involve a multi-stage discrete
pipeline of tasks. To better predict the control signals and enhance user
safety, an end-to-end approach that benefits from joint spatial-temporal
feature learning is desirable. While there are some pioneering works on
LiDAR-based input or implicit design, in this paper we formulate the problem in
an interpretable vision-based setting. In particular, we propose a
spatial-temporal feature learning scheme towards a set of more representative
features for perception, prediction and planning tasks simultaneously, which is
called ST-P3. Specifically, an egocentric-aligned accumulation technique is
proposed to preserve geometry information in 3D space before the bird's eye
view transformation for perception; a dual pathway modeling is devised to take
past motion variations into account for future prediction; a temporal-based
refinement unit is introduced to compensate for recognizing vision-based
elements for planning. To the best of our knowledge, we are the first to
systematically investigate each part of an interpretable end-to-end
vision-based autonomous driving system. We benchmark our approach against
previous state-of-the-arts on both open-loop nuScenes dataset as well as
closed-loop CARLA simulation. The results show the effectiveness of our method.
Source code, model and protocol details are made publicly available at
https://github.com/OpenPerceptionX/ST-P3.Comment: ECCV 202
The Forty-Sixth Euro Congress on Drug Synthesis and Analysis: Snapshot
The 46th EuroCongress on Drug Synthesis and Analysis (ECDSA-2017) was arranged within the celebration of the 65th Anniversary of the Faculty of Pharmacy at Comenius University in Bratislava, Slovakia from 5-8 September 2017 to get together specialists in medicinal chemistry, organic synthesis, pharmaceutical analysis, screening of bioactive compounds, pharmacology and drug formulations; promote the exchange of scientific results, methods and ideas; and encourage cooperation between researchers from all over the world. The topic of the conference, Drug Synthesis and Analysis, meant that the symposium welcomed all pharmacists and/or researchers (chemists, analysts, biologists) and students interested in scientific work dealing with investigations of biologically active compounds as potential drugs. The authors of this manuscript were plenary speakers and other participants of the symposium and members of their research teams. The following summary highlights the major points/topics of the meeting
Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population
Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P interaction  = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications
Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population.
Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications
Prompt-Tuning Decision Transformer with Preference Ranking
Prompt-tuning has emerged as a promising method for adapting pre-trained
models to downstream tasks or aligning with human preferences. Prompt learning
is widely used in NLP but has limited applicability to RL due to the complex
physical meaning and environment-specific information contained within RL
prompts. These factors require supervised learning to imitate the
demonstrations and may result in a loss of meaning after learning.
Additionally, directly extending prompt-tuning approaches to RL is challenging
because RL prompts guide agent behavior based on environmental modeling and
analysis, rather than filling in missing information, making it unlikely that
adjustments to the prompt format for downstream tasks, as in NLP, can yield
significant improvements. In this work, we propose the Prompt-Tuning DT
algorithm to address these challenges by using trajectory segments as prompts
to guide RL agents in acquiring environmental information and optimizing
prompts via black-box tuning to enhance their ability to contain more relevant
information, thereby enabling agents to make better decisions. Our approach
involves randomly sampling a Gaussian distribution to fine-tune the elements of
the prompt trajectory and using preference ranking function to find the
optimization direction, thereby providing more informative prompts and guiding
the agent towards specific preferences in the target environment. Extensive
experiments show that with only 0.03% of the parameters learned, Prompt-Tuning
DT achieves comparable or even better performance than full-model fine-tuning
in low-data scenarios. Our work contributes to the advancement of prompt-tuning
approaches in RL, providing a promising direction for optimizing large RL
agents for specific preference tasks.Comment: 18 page
The fate and oxidative stress of different sized SiO2 nanoparticles in zebrafish (Danio rerio) larvae
Nanoparticle such as silicon dioxide nanoparticles (nano-SiO2) are extensively produced and applied in society. Hence there is an increasing concern about their exposure and toxicity to human and wildlife. To understand the effects of sizes of NPs on their bioavailability and toxicity, zebrafish (Danio rerio) embryos (2 h post-fertilization, hpf) were exposed to 25, 50 and 100 mg/L of 15 or 30 nm nano-SiO2 for 5 days respectively. The results showed that SiO2 could be readily uptaken by zebrafish, and the accumulation of SiO2 was significantly higher in 15 nm treatments groups compared to 30 nm nano-SiO2 treated groups. Furthermore, exposure to 15 nm nano-SiO2 at the concentration of 100 mg/L resulted in more significant changes in reactive oxygen species (ROS) levels, perturbation of lipid peroxidative and antioxidant system than the same concentration of 30 nm nano-SiO2, indicating small sized nano-SiO2 evoked severer oxidative stress in zebrafish larvae. In addition, the more significant up-regulation of transcription of genes related to oxidative stress (e.g., nrf2 and sod1) in 15 nm nano-SiO2 at the 100 mg/L treatments groups provided more evidence for this speculation. Given the above, 15 nm nano-SiO2 were more likely to enter and accumulate in zebrafish larvae, thus causing more serious oxidative stress in vivo. These results may provide additional information on the fate and toxicities of different sizes of NPs. (C) 2019 Elsevier Ltd. All rights reserved
Downregulation of CyclophilinA/CD147 Axis Induces Cell Apoptosis and Inhibits Glioma Aggressiveness
Gliomas are the most common primary tumors in the brain with poor prognosis. Previous studies have detected high expression of Cyclophilin A (CyPA) and CD147, respectively, in glioma. However, the correlation between their expressions and glioma prognosis remains unclear. Here, we investigated the expression of CyPA and CD147 in different types of glioma and characterized their relationships with clinical features, prognosis, and cell proliferation. Results showed that CyPA and CD147 expressions were elevated in higher grade gliomas. Moreover, the knockdown of CyPA and CD147 by RNA interference significantly induced cell express apoptosis biomarkers such as Annexin V and inhibited proliferation biomarkers like EdU in glioma cells. In summary, our findings revealed that high expression of CyPA and CD147 correlated with glioma grades. Moreover, downregulation of the Cyclophilin A/CD147 axis induces cell apoptosis and inhibits glioma aggressiveness. Those indicating CyPA and CD147 could be used as both potential predictive biomarkers and a potential therapeutic target
Sensing-Based Dynamic Spectrum Sharing in Integrated Wireless Sensor and Cognitive Satellite Terrestrial Networks
This paper presents a cognitive satellite communication based wireless sensor network, which combines the wireless sensor network and the cognitive satellite terrestrial network. To address the conflict between the continuously increasing demand and the spectrum scarcity in the space network, the cognitive satellite terrestrial network becomes a promising candidate for future hybrid wireless networks. With the higher transmit capacity demand in satellite networks, explicit concerns on efficient resource allocation in the cognitive network have gained more attention. In this background, we propose a sensing-based dynamic spectrum sharing scheme for the cognitive satellite user, which is able to maximize the ergodic capacity of the satellite user with the interference of the primary terrestrial user below an acceptable average level. Firstly, the cognitive satellite user monitors the channel allocated to the terrestrial user through the wireless sensor network; then, it adjusts the transmit power based on the sensing results. If a terrestrial user is busy, the satellite user can access the channel with constrained power to avoid deteriorating the communication quality of the terrestrial user. Otherwise, if the terrestrial user is idle, the satellite user allocates the transmit power based on its benefit to enhance the capacity. Since the sensing-based dynamic spectrum sharing optimization problem can be modified into a nonlinear fraction programming problem in perfect/imperfect sensing conditions, respectively, we solve them by the Lagrange duality method. Computer simulations have shown that, compared with the opportunistic spectrum access, the proposed method can increase the channel capacity more than 20 % for P a v = 10 dB in a perfect sensing scenario. In an imperfect sensing scenario, P a v = 15 dB and Q a v = 5 dB, the optimal sensing time achieving the highest ergodic capacity is about 2.34 ms when the frame duration is 10 ms