91 research outputs found
An Examination of the Effect of Multiple Supervisors on Flight Trainees\u27 Performance
One of the major roles of the flight supervisory control during flight training is that a trainee is guided to stay within acceptable range of flight performance parameters. Under the supervisory control during flight training, however, a trainee may perceive psychological stress. Literature suggests that when pilots are monitored by flight supervisor during flight training, their perception of psychological stress may increase and thus, result in increasing flight performance. This work proposes to examine whether the presence of flight supervisors has an effect on trainee’s performance. This study will further find the number of flight supervisors needed for pilot flight training. An empirical result of the present research study is expected to provide practical implications as to how differently trainees will perform under the supervision of single supervisor versus multiple supervisors. The present work will statistically verify one research hypotheses. The hypothesis is that flight trainee’s performance would increase under the condition of multiple supervisors compared with single supervisor. Independent variables include the presence of supervisor (1 vs. 3) and scenario order presented with participants. Dependent variables include three types of flight performance (performance on skill, scanning, and task load). A within-subject two-way ANOVA with counterbalancing mixed design will be conducted to statistically verify the research hypothesis. A sample size of this research study is 32 participants
Effective Discriminative Feature Selection with Non-trivial Solutions
Feature selection and feature transformation, the two main ways to reduce
dimensionality, are often presented separately. In this paper, a feature
selection method is proposed by combining the popular transformation based
dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity
regularization. We impose row sparsity on the transformation matrix of LDA
through -norm regularization to achieve feature selection, and
the resultant formulation optimizes for selecting the most discriminative
features and removing the redundant ones simultaneously. The formulation is
extended to the -norm regularized case: which is more likely to
offer better sparsity when . Thus the formulation is a better
approximation to the feature selection problem. An efficient algorithm is
developed to solve the -norm based optimization problem and it is
proved that the algorithm converges when . Systematical experiments
are conducted to understand the work of the proposed method. Promising
experimental results on various types of real-world data sets demonstrate the
effectiveness of our algorithm
Profile-based Maximum Penalised Likelihood Trajectory Estimation from Space-borne LOS Measurements
Estimating the boost-phase trajectory of a ballistic missile using line of sight measurements from space-borne passive sensors is an important issue in missile defense. A well-known difficulty of this issue is the poor-observability of the target motion. A profile-based maximum penalised likelihood estimator is presented, which is expected to work in poor-observability scenarios. Firstly, a more adaptable boost-phase profile is proposed by introducing unknown parameters. Then, the estimator is given based on the Bayesian paradigm. After that, a special penalty for box constraint is constructed based on a mixed distribution. Numerical results for some typical scenarios and sensitivity with respect to a priori information are reported to show that the proposed estimator is promising
SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval
To address 3D object retrieval, substantial efforts have been made to
generate highly discriminative descriptors of 3D objects represented by a
single modality, e.g., voxels, point clouds or multi-view images. It is
promising to leverage the complementary information from multi-modality
representations of 3D objects to further improve retrieval performance.
However, multi-modality 3D object retrieval is rarely developed and analyzed on
large-scale datasets. In this paper, we propose self-and-cross attention based
aggregation of point cloud and multi-view images (SCA-PVNet) for 3D object
retrieval. With deep features extracted from point clouds and multi-view
images, we design two types of feature aggregation modules, namely the
In-Modality Aggregation Module (IMAM) and the Cross-Modality Aggregation Module
(CMAM), for effective feature fusion. IMAM leverages a self-attention mechanism
to aggregate multi-view features while CMAM exploits a cross-attention
mechanism to interact point cloud features with multi-view features. The final
descriptor of a 3D object for object retrieval can be obtained via
concatenating the aggregated features from both modules. Extensive experiments
and analysis are conducted on three datasets, ranging from small to large
scale, to show the superiority of the proposed SCA-PVNet over the
state-of-the-art methods
Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering
The main challenge in video question answering (VideoQA) is to capture and
understand the complex spatial and temporal relations between objects based on
given questions. Existing graph-based methods for VideoQA usually ignore
keywords in questions and employ a simple graph to aggregate features without
considering relative relations between objects, which may lead to inferior
performance. In this paper, we propose a Keyword-aware Relative Spatio-Temporal
(KRST) graph network for VideoQA. First, to make question features aware of
keywords, we employ an attention mechanism to assign high weights to keywords
during question encoding. The keyword-aware question features are then used to
guide video graph construction. Second, because relations are relative, we
integrate the relative relation modeling to better capture the spatio-temporal
dynamics among object nodes. Moreover, we disentangle the spatio-temporal
reasoning into an object-level spatial graph and a frame-level temporal graph,
which reduces the impact of spatial and temporal relation reasoning on each
other. Extensive experiments on the TGIF-QA, MSVD-QA and MSRVTT-QA datasets
demonstrate the superiority of our KRST over multiple state-of-the-art methods.Comment: under revie
A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023
In this technical report, we present our findings from a study conducted on
the EPIC-KITCHENS-100 Unsupervised Domain Adaptation task for Action
Recognition. Our research focuses on the innovative application of a
differentiable logic loss in the training to leverage the co-occurrence
relations between verb and noun, as well as the pre-trained Large Language
Models (LLMs) to generate the logic rules for the adaptation to unseen action
labels. Specifically, the model's predictions are treated as the truth
assignment of a co-occurrence logic formula to compute the logic loss, which
measures the consistency between the predictions and the logic constraints. By
using the verb-noun co-occurrence matrix generated from the dataset, we observe
a moderate improvement in model performance compared to our baseline framework.
To further enhance the model's adaptability to novel action labels, we
experiment with rules generated using GPT-3.5, which leads to a slight decrease
in performance. These findings shed light on the potential and challenges of
incorporating differentiable logic and LLMs for knowledge extraction in
unsupervised domain adaptation for action recognition. Our final submission
(entitled `NS-LLM') achieved the first place in terms of top-1 action
recognition accuracy.Comment: Technical report submitted to CVPR 2023 EPIC-Kitchens challenge
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