280 research outputs found
Improving the Knowledge Gradient Algorithm
The knowledge gradient (KG) algorithm is a popular policy for the best arm
identification (BAI) problem. It is built on the simple idea of always choosing
the measurement that yields the greatest expected one-step improvement in the
estimate of the best mean of the arms. In this research, we show that this
policy has limitations, causing the algorithm not asymptotically optimal. We
next provide a remedy for it, by following the manner of one-step look ahead of
KG, but instead choosing the measurement that yields the greatest one-step
improvement in the probability of selecting the best arm. The new policy is
called improved knowledge gradient (iKG). iKG can be shown to be asymptotically
optimal. In addition, we show that compared to KG, it is easier to extend iKG
to variant problems of BAI, with the -good arm identification and
feasible arm identification as two examples. The superior performances of iKG
on these problems are further demonstrated using numerical examples.Comment: 32 pages, 42 figure
A robust modulation classification method using convolutional neural networks
Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized
Overview of Sensing Attacks on Autonomous Vehicle Technologies and Impact on Traffic Flow
While perception systems in Connected and Autonomous Vehicles (CAVs), which
encompass both communication technologies and advanced sensors, promise to
significantly reduce human driving errors, they also expose CAVs to various
cyberattacks. These include both communication and sensing attacks, which
potentially jeopardize not only individual vehicles but also overall traffic
safety and efficiency. While much research has focused on communication
attacks, sensing attacks, which are equally critical, have garnered less
attention. To address this gap, this study offers a comprehensive review of
potential sensing attacks and their impact on target vehicles, focusing on
commonly deployed sensors in CAVs such as cameras, LiDAR, Radar, ultrasonic
sensors, and GPS. Based on this review, we discuss the feasibility of
integrating hardware-in-the-loop experiments with microscopic traffic
simulations. We also design baseline scenarios to analyze the macro-level
impact of sensing attacks on traffic flow. This study aims to bridge the
research gap between individual vehicle sensing attacks and broader macroscopic
impacts, thereby laying the foundation for future systemic understanding and
mitigation
AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
Multi-task learning (MTL) aims at enhancing the performance and efficiency of
machine learning models by training them on multiple tasks simultaneously.
However, MTL research faces two challenges: 1) modeling the relationships
between tasks to effectively share knowledge between them, and 2) jointly
learning task-specific and shared knowledge. In this paper, we present a novel
model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges.
AdaTT is a deep fusion network built with task specific and optional shared
fusion units at multiple levels. By leveraging a residual mechanism and gating
mechanism for task-to-task fusion, these units adaptively learn shared
knowledge and task specific knowledge. To evaluate the performance of AdaTT, we
conduct experiments on a public benchmark and an industrial recommendation
dataset using various task groups. Results demonstrate AdaTT can significantly
outperform existing state-of-the-art baselines
Flexible Robotic Scanning Device for Intraoperative Endomicroscopy in MIS
Optical biopsy methods such as probe-based confocal endomicroscopy can provide intraoperative real-time assessment of tumour margins, including during minimally invasive surgery with flexible endoscopes or robotic platforms. Mosaics can be produced by translating the probe across the target, but it remains difficult to scan over a large field-of-view with a flexible endomicroscope. In this paper, we have developed a novel flexible scanning device for intraoperative endomicroscopy in MIS. A Schott leached imaging bundle was integrated into the device and enables the approach, via a flexible path, to deep and narrow spaces in the human body that otherwise would not accessible. The proposed device uses a gear-based flexible concentric tube scanning mechanism to facilitate large field-of-view mosaicing. Experimental results show that the device is able to scan different surface trajectories (e.g. a spiral pattern over a hemi-spherical surface). Results from lens tissue paper and porcine liver tissue are demonstrated, illustrating a viable scanning approach for endomicroscopy in MIS
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
As a key component to intuitive cognition and reasoning solutions in human
intelligence, causal knowledge provides great potential for reinforcement
learning (RL) agents' interpretability towards decision-making by helping
reduce the searching space. However, there is still a considerable gap in
discovering and incorporating causality into RL, which hinders the rapid
development of causal RL. In this paper, we consider explicitly modeling the
generation process of states with the causal graphical model, based on which we
augment the policy. We formulate the causal structure updating into the RL
interaction process with active intervention learning of the environment. To
optimize the derived objective, we propose a framework with theoretical
performance guarantees that alternates between two steps: using interventions
for causal structure learning during exploration and using the learned causal
structure for policy guidance during exploitation. Due to the lack of public
benchmarks that allow direct intervention in the state space, we design the
root cause localization task in our simulated fault alarm environment and then
empirically show the effectiveness and robustness of the proposed method
against state-of-the-art baselines. Theoretical analysis shows that our
performance improvement attributes to the virtuous cycle of causal-guided
policy learning and causal structure learning, which aligns with our
experimental results
Tunable topological phase transition in soft Rayleigh beam system with imperfect interfaces
Acoustic metamaterials, particularly the topological insulators, exhibit
exceptional wave characteristics that have sparked considerable research
interest. The study of imperfect interfaces affect is of significant importance
for the modeling of wave propagation behavior in topological insulators. This
paper models a soft Rayleigh beam system with imperfect interfaces, and
investigates its topological phase transition process tuned by mechanical
loadings. The model reveals that the topological phase transition process can
be observed by modifying the distance between imperfect interfaces in the
system. When a uniaxial stretch is applied, the topological phase transition
points for longitudinal waves decrease within a limited frequency range, while
they increase within a larger frequency scope for transverse waves. Enhancing
the rigidity of the imperfect interfaces also enables shifting of the
topological phase transition point within a broader frequency range for
longitudinal waves and a confined range for transverse waves. The transition of
topologically protected interface modes in the transmission performance of a
twenty-cell system is verified, which include altering frequencies, switching
from interface mode to edge mode. Overall, this study provides a new approach
and guideline for controlling topological phase transition in composite and
soft phononic crystal systems.Comment: 39 pages,8 figure
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