83 research outputs found
Breaking the Blockage for Big Data Transmission: Gigabit Road Communication in Autonomous Vehicles
Recently, the spectrum band beyond 60 GHz has attracted attention with the growth of traffic demand. Previous studies assumed that these bands are not suitable for vehicle communications due to the short range and high rate of blockage. However, it also means that there is no existing service or regulation designed for these bands, which makes this area free to apply. Therefore, in this article, we draw a potential map of THz vehicle transmission for autonomous vehicles to break the blockage of short-range and unstable links. First, we give a brief overview of possible waveforms followed by the specific channel at 0.1-1 THz. Then we propose an autonomous relay algorithm called ATLR for the gigabit-level communication in the high-speed road environment. Finally, we discuss how the THz transmission helps relieve the interference problem and provide extra data to support various instructions in autonomous vehicles
Causal Knowledge Transfer from Task Affinity
Recent developments in deep representation models through counterfactual
balancing have led to a promising framework for estimating Individual Treatment
Effects (ITEs) that are essential to causal inference in the Neyman-Rubin
potential outcomes framework. While Randomized Control Trials are vital to
understanding causal effects, they are sometimes infeasible, costly, or
unethical to conduct. Motivated by these potential obstacles to data
acquisition, we focus on transferring the causal knowledge acquired in prior
experiments to new scenarios for which only limited data is available. To this
end, we first observe that the absolute values of ITEs are invariant under the
action of the symmetric group on the labels of treatments. Given this
invariance, we propose a symmetrized task distance for calculating the
similarity of a target scenario with those encountered before. The
aforementioned task distance is then used to transfer causal knowledge from the
closest of all the available previously learned tasks to the target scenario.
We provide upper bounds on the counterfactual loss and ITE error of the target
task indicating the transferability of causal knowledge. Empirical studies are
provided for various real-world, semi-synthetic, and synthetic datasets
demonstrating that the proposed symmetrized task distance is strongly related
to the estimation of the counterfactual loss. Numerical results indicate that
transferring causal knowledge reduces the amount of required data by up to 95%
when compared to training from scratch. These results reveal the promise of our
method when applied to important albeit challenging real-world scenarios such
as transferring the knowledge of treatment effects (e.g., medicine, social
policy, personal training, etc.) studied on a population to other groups absent
in the study
Few-Shot Continual Learning for Conditional Generative Adversarial Networks
In few-shot continual learning for generative models, a target mode must be
learned with limited samples without adversely affecting the previously learned
modes. In this paper, we propose a new continual learning approach for
conditional generative adversarial networks (cGAN) based on a new mode-affinity
measure for generative modeling. Our measure is entirely based on the cGAN's
discriminator and can identify the existing modes that are most similar to the
target. Subsequently, we expand the continual learning model by including the
target mode using a weighted label derived from those of the closest modes. To
prevent catastrophic forgetting, we first generate labeled data samples using
the cGAN's generator, and then train the cGAN model for the target mode while
memory replaying with the generated data. Our experimental results demonstrate
the efficacy of our approach in improving the generation performance over the
baselines and the state-of-the-art approaches for various standard datasets
while utilizing fewer training samples
Robust Reinforcement Learning through Efficient Adversarial Herding
Although reinforcement learning (RL) is considered the gold standard for
policy design, it may not always provide a robust solution in various
scenarios. This can result in severe performance degradation when the
environment is exposed to potential disturbances. Adversarial training using a
two-player max-min game has been proven effective in enhancing the robustness
of RL agents. In this work, we extend the two-player game by introducing an
adversarial herd, which involves a group of adversaries, in order to address
() the difficulty of the inner optimization problem, and
() the potential over pessimism caused by the selection of a
candidate adversary set that may include unlikely scenarios. We first prove
that adversarial herds can efficiently approximate the inner optimization
problem. Then we address the second issue by replacing the worst-case
performance in the inner optimization with the average performance over the
worst- adversaries. We evaluate the proposed method on multiple MuJoCo
environments. Experimental results demonstrate that our approach consistently
generates more robust policies
Bubbling solutions for an anisotropic Emden–Fowler equation
Calculus of Variations and Partial Differential Equations, à paraîtr
Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document
Visual relation extraction (VRE) aims to extract relations between entities
from visuallyrich documents. Existing methods usually predict relations for
each entity pair independently based on entity features but ignore the global
structure information, i.e., dependencies between entity pairs. The absence of
global structure information may make the model struggle to learn long-range
relations and easily predict conflicted results. To alleviate such limitations,
we propose a GlObal Structure knowledgeguided relation Extraction (GOSE)
framework, which captures dependencies between entity pairs in an iterative
manner. Given a scanned image of the document, GOSE firstly generates
preliminary relation predictions on entity pairs. Secondly, it mines global
structure knowledge based on prediction results of the previous iteration and
further incorporates global structure knowledge into entity representations.
This "generate-capture-incorporate" schema is performed multiple times so that
entity representations and global structure knowledge can mutually reinforce
each other. Extensive experiments show that GOSE not only outperforms previous
methods on the standard fine-tuning setting but also shows promising
superiority in cross-lingual learning; even yields stronger data-efficient
performance in the low-resource setting.Comment: Work in progres
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