59 research outputs found
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
The availability of a large amount of electronic health records (EHR)
provides huge opportunities to improve health care service by mining these
data. One important application is clinical endpoint prediction, which aims to
predict whether a disease, a symptom or an abnormal lab test will happen in the
future according to patients' history records. This paper develops deep
learning techniques for clinical endpoint prediction, which are effective in
many practical applications. However, the problem is very challenging since
patients' history records contain multiple heterogeneous temporal events such
as lab tests, diagnosis, and drug administrations. The visiting patterns of
different types of events vary significantly, and there exist complex nonlinear
relationships between different events. In this paper, we propose a novel model
for learning the joint representation of heterogeneous temporal events. The
model adds a new gate to control the visiting rates of different events which
effectively models the irregular patterns of different events and their
nonlinear correlations. Experiment results with real-world clinical data on the
tasks of predicting death and abnormal lab tests prove the effectiveness of our
proposed approach over competitive baselines.Comment: 8 pages, this paper has been accepted by AAAI 201
Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition
Facial Expression Recognition (FER) in the wild is an extremely challenging
task. Recently, some Vision Transformers (ViT) have been explored for FER, but
most of them perform inferiorly compared to Convolutional Neural Networks
(CNN). This is mainly because the new proposed modules are difficult to
converge well from scratch due to lacking inductive bias and easy to focus on
the occlusion and noisy areas. TransFER, a representative transformer-based
method for FER, alleviates this with multi-branch attention dropping but brings
excessive computations. On the contrary, we present two attentive pooling (AP)
modules to pool noisy features directly. The AP modules include Attentive Patch
Pooling (APP) and Attentive Token Pooling (ATP). They aim to guide the model to
emphasize the most discriminative features while reducing the impacts of less
relevant features. The proposed APP is employed to select the most informative
patches on CNN features, and ATP discards unimportant tokens in ViT. Being
simple to implement and without learnable parameters, the APP and ATP
intuitively reduce the computational cost while boosting the performance by
ONLY pursuing the most discriminative features. Qualitative results demonstrate
the motivations and effectiveness of our attentive poolings. Besides,
quantitative results on six in-the-wild datasets outperform other
state-of-the-art methods.Comment: Codes will be public on https://github.com/youqingxiaozhua/APVi
Flame behavior from an opening at different elevations on the facade wall of a fire compartment
Identifying and Overcoming Challenges in High School CubeSat Programs
This paper analyzes the various problems high school CubeSat organizations face, as well as their potential solutions. We conducted a case study of various high school CubeSat organizations from around the United States, interviewing them about their mission goals, organizational structure, funding sources, and other relevant information. We found that the three most common significant problems faced by these CubeSat teams were a lack of student training, turnover, and time commitment constraints. By comparison, a lack of funding and access to mentors were not expressed as the most significant problems for any of the groups. Teams shared their solution to training issues, turnover, and time commitment constraints, which included the utilization of kits, satellite simulators, and a more hands-on student training approach. In the future, we hope to expand the scope of this study to procure enough data to conduct a meaningful statistical analysis
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