726 research outputs found
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
Imitation learning is an effective approach for autonomous systems to acquire
control policies when an explicit reward function is unavailable, using
supervision provided as demonstrations from an expert, typically a human
operator. However, standard imitation learning methods assume that the agent
receives examples of observation-action tuples that could be provided, for
instance, to a supervised learning algorithm. This stands in contrast to how
humans and animals imitate: we observe another person performing some behavior
and then figure out which actions will realize that behavior, compensating for
changes in viewpoint, surroundings, object positions and types, and other
factors. We term this kind of imitation learning "imitation-from-observation,"
and propose an imitation learning method based on video prediction with context
translation and deep reinforcement learning. This lifts the assumption in
imitation learning that the demonstration should consist of observations in the
same environment configuration, and enables a variety of interesting
applications, including learning robotic skills that involve tool use simply by
observing videos of human tool use. Our experimental results show the
effectiveness of our approach in learning a wide range of real-world robotic
tasks modeled after common household chores from videos of a human
demonstrator, including sweeping, ladling almonds, pushing objects as well as a
number of tasks in simulation.Comment: Accepted at ICRA 2018, Brisbane. YuXuan Liu and Abhishek Gupta had
equal contributio
Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities
With recent advances in artificial intelligence (AI) and robotics, unmanned
vehicle swarms have received great attention from both academia and industry
due to their potential to provide services that are difficult and dangerous to
perform by humans. However, learning and coordinating movements and actions for
a large number of unmanned vehicles in complex and dynamic environments
introduce significant challenges to conventional AI methods. Generative AI
(GAI), with its capabilities in complex data feature extraction,
transformation, and enhancement, offers great potential in solving these
challenges of unmanned vehicle swarms. For that, this paper aims to provide a
comprehensive survey on applications, challenges, and opportunities of GAI in
unmanned vehicle swarms. Specifically, we first present an overview of unmanned
vehicles and unmanned vehicle swarms as well as their use cases and existing
issues. Then, an in-depth background of various GAI techniques together with
their capabilities in enhancing unmanned vehicle swarms are provided. After
that, we present a comprehensive review on the applications and challenges of
GAI in unmanned vehicle swarms with various insights and discussions. Finally,
we highlight open issues of GAI in unmanned vehicle swarms and discuss
potential research directions.Comment: 23 page
Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis
Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide
range of applications and attracted increasing attention in the field of
intelligent transportation systems because of its versatility and
effectiveness. As an emerging force in the revolutionary trend of deep
learning, Siamese networks shine in UAV-based object tracking with their
promising balance of accuracy, robustness, and speed. Thanks to the development
of embedded processors and the gradual optimization of deep neural networks,
Siamese trackers receive extensive research and realize preliminary
combinations with UAVs. However, due to the UAV's limited onboard computational
resources and the complex real-world circumstances, aerial tracking with
Siamese networks still faces severe obstacles in many aspects. To further
explore the deployment of Siamese networks in UAV-based tracking, this work
presents a comprehensive review of leading-edge Siamese trackers, along with an
exhaustive UAV-specific analysis based on the evaluation using a typical UAV
onboard processor. Then, the onboard tests are conducted to validate the
feasibility and efficacy of representative Siamese trackers in real-world UAV
deployment. Furthermore, to better promote the development of the tracking
community, this work analyzes the limitations of existing Siamese trackers and
conducts additional experiments represented by low-illumination evaluations. In
the end, prospects for the development of Siamese tracking for UAV-based
intelligent transportation systems are deeply discussed. The unified framework
of leading-edge Siamese trackers, i.e., code library, and the results of their
experimental evaluations are available at
https://github.com/vision4robotics/SiameseTracking4UAV
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Recent advances in artificial intelligence (AI) have significantly
intensified research in the geoscience and remote sensing (RS) field. AI
algorithms, especially deep learning-based ones, have been developed and
applied widely to RS data analysis. The successful application of AI covers
almost all aspects of Earth observation (EO) missions, from low-level vision
tasks like super-resolution, denoising and inpainting, to high-level vision
tasks like scene classification, object detection and semantic segmentation.
While AI techniques enable researchers to observe and understand the Earth more
accurately, the vulnerability and uncertainty of AI models deserve further
attention, considering that many geoscience and RS tasks are highly
safety-critical. This paper reviews the current development of AI security in
the geoscience and RS field, covering the following five important aspects:
adversarial attack, backdoor attack, federated learning, uncertainty and
explainability. Moreover, the potential opportunities and trends are discussed
to provide insights for future research. To the best of the authors' knowledge,
this paper is the first attempt to provide a systematic review of AI
security-related research in the geoscience and RS community. Available code
and datasets are also listed in the paper to move this vibrant field of
research forward
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