90 research outputs found
Comparing experimental sensitivities of Dark Matter experiments
The complementarity of dark matter experiments can be evinced from comparing their sensitivities to a given model. This thesis presents an introduction to dark matter, an overview of its detection methods, the model-dependent methods for comparing collider searches with dark matter indirect detection searches, and approaches comparing dark matter experiments in the weak dark mediator – quark coupling regime. The corresponding code for Monte Carlo simulation and an analysis framework for a mono-jet search at colliders is also available as part of this thesis: https://github.com/Boyu622/MCsetup_monojet.gitNo embargoAcademic Major: Physic
AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation
The robotics community is increasingly interested in autonomous aerial
transportation. Unmanned aerial vehicles with suspended payloads have
advantages over other systems, including mechanical simplicity and agility, but
pose great challenges in planning and control. To realize fully autonomous
aerial transportation, this paper presents a systematic solution to address
these difficulties. First, we present a real-time planning method that
generates smooth trajectories considering the time-varying shape and non-linear
dynamics of the system, ensuring whole-body safety and dynamic feasibility.
Additionally, an adaptive NMPC with a hierarchical disturbance compensation
strategy is designed to overcome unknown external perturbations and inaccurate
model parameters. Extensive experiments show that our method is capable of
generating high-quality trajectories online, even in highly constrained
environments, and tracking aggressive flight trajectories accurately, even
under significant uncertainty. We plan to release our code to benefit the
community.Comment: Accepted by IEEE Robotics and Automation Letter
Enhancing Deep Knowledge Tracing with Auxiliary Tasks
Knowledge tracing (KT) is the problem of predicting students' future
performance based on their historical interactions with intelligent tutoring
systems. Recent studies have applied multiple types of deep neural networks to
solve the KT problem. However, there are two important factors in real-world
educational data that are not well represented. First, most existing works
augment input representations with the co-occurrence matrix of questions and
knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday
terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly
integrate such intrinsic relations into the final response prediction task.
Second, the individualized historical performance of students has not been well
captured. In this paper, we proposed \emph{AT-DKT} to improve the prediction
performance of the original deep knowledge tracing model with two auxiliary
learning tasks, i.e., \emph{question tagging (QT) prediction task} and
\emph{individualized prior knowledge (IK) prediction task}. Specifically, the
QT task helps learn better question representations by predicting whether
questions contain specific KCs. The IK task captures students' global
historical performance by progressively predicting student-level prior
knowledge that is hidden in students' historical learning interactions. We
conduct comprehensive experiments on three real-world educational datasets and
compare the proposed approach to both deep sequential KT models and
non-sequential models. Experimental results show that \emph{AT-DKT} outperforms
all sequential models with more than 0.9\% improvements of AUC for all
datasets, and is almost the second best compared to non-sequential models.
Furthermore, we conduct both ablation studies and quantitative analysis to show
the effectiveness of auxiliary tasks and the superior prediction outcomes of
\emph{AT-DKT}.Comment: Accepted at WWW'23: The 2023 ACM Web Conference, 202
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