10,695 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea
ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Automatically discovering composable abstractions from raw perceptual data is
a long-standing challenge in machine learning. Recent slot-based neural
networks that learn about objects in a self-supervised manner have made
exciting progress in this direction. However, they typically fall short at
adequately capturing spatial symmetries present in the visual world, which
leads to sample inefficiency, such as when entangling object appearance and
pose. In this paper, we present a simple yet highly effective method for
incorporating spatial symmetries via slot-centric reference frames. We
incorporate equivariance to per-object pose transformations into the attention
and generation mechanism of Slot Attention by translating, scaling, and
rotating position encodings. These changes result in little computational
overhead, are easy to implement, and can result in large gains in terms of data
efficiency and overall improvements to object discovery. We evaluate our method
on a wide range of synthetic object discovery benchmarks namely CLEVR,
Tetrominoes, CLEVRTex, Objects Room and MultiShapeNet, and show promising
improvements on the challenging real-world Waymo Open dataset.Comment: Accepted at ICML 2023. Project page: https://invariantsa.github.io
On the Use of YouTube, Digital Games, Argument Maps, and Digital Feedback in Teaching Philosophy
We give an overview of the methodological possibilities of some important digital tools for teaching philosophy. Several didactically applicable methods have evolved in digital culture, including their implicit methodologies, theories about how these methods may be used. These methodologies are already applied by philosophers today and have their benefits and justifications in philosophy classes as well. They can help to solve known problems of philosophy education. We discuss problems of incomprehensibility and their possible solutions through digital explanations in pod- and videocasts such as YouTube; problems of interaction, motivation, and immersion that digital games and gamification may solve; problems of the complexity of philosophical content and digital concept- and argument-maps to deal with these; problems of implicitness and the possibility to make implicit things in philosophy class explicit through indirect feedback tools
Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning
Object-centric learning (OCL) aspires general and compositional understanding
of scenes by representing a scene as a collection of object-centric
representations. OCL has also been extended to multi-view image and video
datasets to apply various data-driven inductive biases by utilizing geometric
or temporal information in the multi-image data. Single-view images carry less
information about how to disentangle a given scene than videos or multi-view
images do. Hence, owing to the difficulty of applying inductive biases, OCL for
single-view images remains challenging, resulting in inconsistent learning of
object-centric representation. To this end, we introduce a novel OCL framework
for single-view images, SLot Attention via SHepherding (SLASH), which consists
of two simple-yet-effective modules on top of Slot Attention. The new modules,
Attention Refining Kernel (ARK) and Intermediate Point Predictor and Encoder
(IPPE), respectively, prevent slots from being distracted by the background
noise and indicate locations for slots to focus on to facilitate learning of
object-centric representation. We also propose a weak semi-supervision approach
for OCL, whilst our proposed framework can be used without any assistant
annotation during the inference. Experiments show that our proposed method
enables consistent learning of object-centric representation and achieves
strong performance across four datasets. Code is available at
\url{https://github.com/object-understanding/SLASH}
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