67 research outputs found
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Perceptual Decision Making of Humans and Deep Learning Machines: aBehavioral Study
Human visual perception system is a key issue of the cognitive researches. It is also an inspiring prototype of thecutting-edge artificial intelligence researches - deep learning. It is interesting to investigate the behaviors of humans and deeplearning machines on vision tasks. In this paper, we focus on the perceptual decision making and object recognition on distortedimages. We found that in a wide range of distortion levels, the recognition rates of human subjects are smoothly increased alongwith the decreases of distortions. Although the deep learning machines perform obviously worse than human subjects, theirrecognition rates vary with the similar trends. It indicates that the deep learning machines make a good simulation to the humanbeing on the perceptual decision making tasks
Large Language Models for Telecom: The Next Big Thing?
The evolution of generative artificial intelligence (GenAI) constitutes a
turning point in reshaping the future of technology in different aspects.
Wireless networks in particular, with the blooming of self-evolving networks,
represent a rich field for exploiting GenAI and reaping several benefits that
can fundamentally change the way how wireless networks are designed and
operated nowadays. To be specific, large language models (LLMs), a subfield of
GenAI, are envisioned to open up a new era of autonomous wireless networks, in
which a multimodal large model trained over various Telecom data, can be
fine-tuned to perform several downstream tasks, eliminating the need for
dedicated AI models for each task and paving the way for the realization of
artificial general intelligence (AGI)-empowered wireless networks. In this
article, we aim to unfold the opportunities that can be reaped from integrating
LLMs into the Telecom domain. In particular, we aim to put a forward-looking
vision on a new realm of possibilities and applications of LLMs in future
wireless networks, defining directions for designing, training, testing, and
deploying Telecom LLMs, and reveal insights on the associated theoretical and
practical challenges
Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures
In this work, we study the problem of semantic communication and inference,
in which a student agent (i.e. mobile device) queries a teacher agent (i.e.
cloud sever) to generate higher-order data semantics living in a simplicial
complex. Specifically, the teacher first maps its data into a k-order
simplicial complex and learns its high-order correlations. For effective
communication and inference, the teacher seeks minimally sufficient and
invariant semantic structures prior to conveying information. These minimal
simplicial structures are found via judiciously removing simplices selected by
the Hodge Laplacians without compromising the inference query accuracy.
Subsequently, the student locally runs its own set of queries based on a masked
simplicial convolutional autoencoder (SCAE) leveraging both local and remote
teacher's knowledge. Numerical results corroborate the effectiveness of the
proposed approach in terms of improving inference query accuracy under
different channel conditions and simplicial structures. Experiments on a
coauthorship dataset show that removing simplices by ranking the Laplacian
values yields a 85% reduction in payload size without sacrificing accuracy.
Joint semantic communication and inference by masked SCAE improves query
accuracy by 25% compared to local student based query and 15% compared to
remote teacher based query. Finally, incorporating channel semantics is shown
to effectively improve inference accuracy, notably at low SNR values
Review about evaluation methods of recoverable reserves of deep water drive gas reservoirs in China
It has been widely accepted that China is one of the biggest natural gas consumers. Related to the imports of LNG, China stands in a very uncomfortable situation. Most domestic gas reservoirs fall within deep water drive gas reservoirs inordinately, which has entered the production depletion stage. Accurate estimation of SEC recoverable reserves of deep water drive gas reservoirs is of great significance for gas consumption planning and peak shaving. The existing calculation methods of recoverable reserves mainly consist of static methods and dynamic methods. In the early stage of exploration and development, the volumetric method has often been utilized to calculate the recoverable reserves. With the continuous development of gas reservoirs, the main methods for evaluation are dynamic methods, including the successive subtraction of production method, water drive curve method, prediction model method, attenuation curve method, improved virtual curve method, and material balance method for deep gas reservoirs
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
Simultaneous Localization and Mapping (SLAM) is moving towards a robust
perception age. However, LiDAR- and visual- SLAM may easily fail in adverse
conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D
Radar, thermal camera and IMU can work robustly. But only a few literature can
be found. A major reason is the lack of related datasets, which seriously
hinders the research. Even though some datasets are proposed based on 4D radar
in past four years, they are mainly designed for object detection, rather than
SLAM. Furthermore, they normally do not include thermal camera. Therefore, in
this paper, NTU4DRadLM is presented to meet this requirement. The main
characteristics are: 1) It is the only dataset that simultaneously includes all
6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS.
2) Specifically designed for SLAM tasks, which provides fine-tuned ground truth
odometry and intentionally formulated loop closures. 3) Considered both
low-speed robot platform and fast-speed unmanned vehicle platform. 4) Covered
structured, unstructured and semi-structured environments. 5) Considered both
middle- and large- scale outdoor environments, i.e., the 6 trajectories range
from 246m to 6.95km. 6) Comprehensively evaluated three types of SLAM
algorithms. Totally, the dataset is around 17.6km, 85mins, 50GB and it will be
accessible from this link: https://github.com/junzhang2016/NTU4DRadLMComment: 2023 IEEE International Intelligent Transportation Systems Conference
(ITSC 2023
Aerial base stations with opportunistic links for next generation emergency communications
Rapidly deployable and reliable mission-critical communication networks are fundamental requirements to guarantee the successful operations of public safety officers during disaster recovery and crisis management preparedness. The ABSOLUTE project focused on designing, prototyping, and demonstrating a high-capacity IP mobile data network with low latency and large coverage suitable for many forms of multimedia delivery including public safety scenarios. The ABSOLUTE project combines aerial, terrestrial, and satellites communication networks for providing a robust standalone system able to deliver resilience communication systems. This article focuses on describing the main outcomes of the ABSOLUTE project in terms of network and system architecture, regulations, and implementation of aerial base stations, portable land mobile units, satellite backhauling, S-MIM satellite messaging, and multimode user equipments
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