269 research outputs found
A Comparative Research on Competitiveness of Information Industry of China vs. Korea
This paper explores the competitiveness of information industry of China and Korea by means of comparative research based on the analysis of statistic data and the definition of items denoting the competitiveness. Consequently, we analyze the competitive and complementary relation of information industry of China vs. Korea, and put forward a co-operation project of China-Korea information industry ultimately
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
This paper presents a novel framework for simultaneously implementing
localization and segmentation, which are two of the most important vision-based
tasks for robotics. While the goals and techniques used for them were
considered to be different previously, we show that by making use of the
intermediate results of the two modules, their performance can be enhanced at
the same time. Our framework is able to handle both the instantaneous motion
and long-term changes of instances in localization with the help of the
segmentation result, which also benefits from the refined 3D pose information.
We conduct experiments on various datasets, and prove that our framework works
effectively on improving the precision and robustness of the two tasks and
outperforms existing localization and segmentation algorithms.Comment: 7 pages, 5 figures.This work has been accepted by ICRA 2019. The demo
video can be found at https://youtu.be/Bkt53dAehj
Reinforcement Learning from Statistical Feedback: the Journey from AB Testing to ANT Testing
Reinforcement Learning from Human Feedback (RLHF) has played a crucial role
in the success of large models such as ChatGPT. RLHF is a reinforcement
learning framework which combines human feedback to improve learning
effectiveness and performance. However, obtaining preferences feedback manually
is quite expensive in commercial applications. Some statistical commercial
indicators are usually more valuable and always ignored in RLHF. There exists a
gap between commercial target and model training. In our research, we will
attempt to fill this gap with statistical business feedback instead of human
feedback, using AB testing which is a well-established statistical method.
Reinforcement Learning from Statistical Feedback (RLSF) based on AB testing is
proposed. Statistical inference methods are used to obtain preferences for
training the reward network, which fine-tunes the pre-trained model in
reinforcement learning framework, achieving greater business value.
Furthermore, we extend AB testing with double selections at a single time-point
to ANT testing with multiple selections at different feedback time points.
Moreover, we design numerical experiences to validate the effectiveness of our
algorithm framework
Safety-Assured Speculative Planning with Adaptive Prediction
Recently significant progress has been made in vehicle prediction and
planning algorithms for autonomous driving. However, it remains quite
challenging for an autonomous vehicle to plan its trajectory in complex
scenarios when it is difficult to accurately predict its surrounding vehicles'
behaviors and trajectories. In this work, to maximize performance while
ensuring safety, we propose a novel speculative planning framework based on a
prediction-planning interface that quantifies both the behavior-level and
trajectory-level uncertainties of surrounding vehicles. Our framework leverages
recent prediction algorithms that can provide one or more possible behaviors
and trajectories of the surrounding vehicles with probability estimation. It
adapts those predictions based on the latest system states and traffic
environment, and conducts planning to maximize the expected reward of the ego
vehicle by considering the probabilistic predictions of all scenarios and
ensure system safety by ruling out actions that may be unsafe in worst case. We
demonstrate the effectiveness of our approach in improving system performance
and ensuring system safety over other baseline methods, via extensive
simulations in SUMO on a challenging multi-lane highway lane-changing case
study
Single-photon-driven high-order sideband transitions in an ultrastrongly coupled circuit quantum electrodynamics system
We report the experimental observation of high-order sideband transitions at
the single-photon level in a quantum circuit system of a flux qubit
ultrastrongly coupled to a coplanar waveguide resonator. With the coupling
strength reaching 10% of the resonator's fundamental frequency, we obtain clear
signatures of higher-order red and first-order blue-sideband transitions, which
are mainly due to the ultrastrong Rabi coupling. Our observation advances the
understanding of ultrastrongly-coupled systems and paves the way to study
high-order processes in the quantum Rabi model at the single-photon level.Comment: Accepted in Physical Review A. 12 pages, 6 figure
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