207 research outputs found
Quantum Synchronizable Codes From Quadratic Residue Codes and Their Supercodes
Quantum synchronizable codes are quantum error-correcting codes designed to
correct the effects of both quantum noise and block synchronization errors.
While it is known that quantum synchronizable codes can be constructed from
cyclic codes that satisfy special properties, only a few classes of cyclic
codes have been proved to give promising quantum synchronizable codes. In this
paper, using quadratic residue codes and their supercodes, we give a simple
construction for quantum synchronizable codes whose synchronization
capabilities attain the upper bound. The method is applicable to cyclic codes
of prime length
Information-Coupled Turbo Codes for LTE Systems
We propose a new class of information-coupled (IC) Turbo codes to improve the
transport block (TB) error rate performance for long-term evolution (LTE)
systems, while keeping the hybrid automatic repeat request protocol and the
Turbo decoder for each code block (CB) unchanged. In the proposed codes, every
two consecutive CBs in a TB are coupled together by sharing a few common
information bits. We propose a feed-forward and feed-back decoding scheme and a
windowed (WD) decoding scheme for decoding the whole TB by exploiting the
coupled information between CBs. Both decoding schemes achieve a considerable
signal-to-noise-ratio (SNR) gain compared to the LTE Turbo codes. We construct
the extrinsic information transfer (EXIT) functions for the LTE Turbo codes and
our proposed IC Turbo codes from the EXIT functions of underlying convolutional
codes. An SNR gain upper bound of our proposed codes over the LTE Turbo codes
is derived and calculated by the constructed EXIT charts. Numerical results
show that the proposed codes achieve an SNR gain of 0.25 dB to 0.72 dB for
various code parameters at a TB error rate level of , which complies
with the derived SNR gain upper bound.Comment: 13 pages, 12 figure
A Comprehensive Study and Comparison of the Robustness of 3D Object Detectors Against Adversarial Attacks
Recent years have witnessed significant advancements in deep learning-based
3D object detection, leading to its widespread adoption in numerous
applications. As 3D object detectors become increasingly crucial for
security-critical tasks, it is imperative to understand their robustness
against adversarial attacks. This paper presents the first comprehensive
evaluation and analysis of the robustness of LiDAR-based 3D detectors under
adversarial attacks. Specifically, we extend three distinct adversarial attacks
to the 3D object detection task, benchmarking the robustness of
state-of-the-art LiDAR-based 3D object detectors against attacks on the KITTI
and Waymo datasets. We further analyze the relationship between robustness and
detector properties. Additionally, we explore the transferability of
cross-model, cross-task, and cross-data attacks. Thorough experiments on
defensive strategies for 3D detectors are conducted, demonstrating that simple
transformations like flipping provide little help in improving robustness when
the applied transformation strategy is exposed to attackers. Finally, we
propose balanced adversarial focal training, based on conventional adversarial
training, to strike a balance between accuracy and robustness. Our findings
will facilitate investigations into understanding and defending against
adversarial attacks on LiDAR-based 3D object detectors, thus advancing the
field. The source code is publicly available at
\url{https://github.com/Eaphan/Robust3DOD}.Comment: 30 pages, 14 figure
MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents
Significant advancements have occurred in the application of Large Language
Models (LLMs) for various tasks and social simulations. Despite this, their
capacities to coordinate within task-oriented social contexts are
under-explored. Such capabilities are crucial if LLMs are to effectively mimic
human-like social behavior and produce meaningful results. To bridge this gap,
we introduce collaborative generative agents, endowing LLM-based Agents with
consistent behavior patterns and task-solving abilities. We situate these
agents in a simulated job fair environment as a case study to scrutinize their
coordination skills. We propose a novel framework that equips collaborative
generative agents with human-like reasoning abilities and specialized skills.
Our evaluation demonstrates that these agents show promising performance.
However, we also uncover limitations that hinder their effectiveness in more
complex coordination tasks. Our work provides valuable insights into the role
and evolution of LLMs in task-oriented social simulations
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