165 research outputs found
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles
With one billion monthly viewers, and millions of users discussing and
sharing opinions, comments below YouTube videos are rich sources of data for
opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset,
a freely-available collections of more than 50,000 YouTube comments and
metadata below autonomous vehicle (AV)-related videos. We describe its creation
process, its content and data format, and discuss its possible usages.
Especially, we do a case study of the first self-driving car fatality to
evaluate the dataset, and show how we can use this dataset to better understand
public attitudes toward self-driving cars and public reactions to the accident.
Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on
Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018
Music Sequence Prediction with Mixture Hidden Markov Models
Recommendation systems that automatically generate personalized music
playlists for users have attracted tremendous attention in recent years.
Nowadays, most music recommendation systems rely on item-based or user-based
collaborative filtering or content-based approaches. In this paper, we propose
a novel mixture hidden Markov model (HMM) for music play sequence prediction.
We compare the mixture model with state-of-the-art methods and evaluate the
predictions quantitatively and qualitatively on a large-scale real-world
dataset in a Kaggle competition. Results show that our model significantly
outperforms traditional methods as well as other competitors. We conclude by
envisioning a next-generation music recommendation system that integrates our
model with recent advances in deep learning, computer vision, and speech
techniques, and has promising potential in both academia and industry.Comment: Accepted to the 4th International Conference on Artificial
Intelligence and Applications (AI 2018
Understanding and Improving Knowledge Distillation for Quantization-Aware Training of Large Transformer Encoders
Knowledge distillation (KD) has been a ubiquitous method for model
compression to strengthen the capability of a lightweight model with the
transferred knowledge from the teacher. In particular, KD has been employed in
quantization-aware training (QAT) of Transformer encoders like BERT to improve
the accuracy of the student model with the reduced-precision weight parameters.
However, little is understood about which of the various KD approaches best
fits the QAT of Transformers. In this work, we provide an in-depth analysis of
the mechanism of KD on attention recovery of quantized large Transformers. In
particular, we reveal that the previously adopted MSE loss on the attention
score is insufficient for recovering the self-attention information. Therefore,
we propose two KD methods; attention-map and attention-output losses.
Furthermore, we explore the unification of both losses to address
task-dependent preference between attention-map and output losses. The
experimental results on various Transformer encoder models demonstrate that the
proposed KD methods achieve state-of-the-art accuracy for QAT with sub-2-bit
weight quantization.Comment: EMNLP 2022 Main Track Long Pape
Token-Scaled Logit Distillation for Ternary Weight Generative Language Models
Generative Language Models (GLMs) have shown impressive performance in tasks
such as text generation, understanding, and reasoning. However, the large model
size poses challenges for practical deployment. To solve this problem,
Quantization-Aware Training (QAT) has become increasingly popular. However,
current QAT methods for generative models have resulted in a noticeable loss of
accuracy. To counteract this issue, we propose a novel knowledge distillation
method specifically designed for GLMs. Our method, called token-scaled logit
distillation, prevents overfitting and provides superior learning from the
teacher model and ground truth. This research marks the first evaluation of
ternary weight quantization-aware training of large-scale GLMs with less than
1.0 degradation in perplexity and no loss of accuracy in a reasoning task
PROBE3.0: A Systematic Framework for Design-Technology Pathfinding with Improved Design Enablement
We propose a systematic framework to conduct design-technology pathfinding
for PPAC in advanced nodes. Our goal is to provide configurable, scalable
generation of process design kit (PDK) and standard-cell library, spanning key
scaling boosters (backside PDN and buried power rail), to explore PPAC across
given technology and design parameters. We build on PROBE2.0, which addressed
only area and cost (AC), to include power and performance (PP) evaluations
through automated generation of full design enablements. We also improve the
use of artificial designs in the PPAC assessment of technology and design
configurations. We generate more realistic artificial designs by applying a
machine learning-based parameter tuning flow. We further employ
clustering-based cell width-regularized placements at the core of routability
assessment, enabling more realistic placement utilization and improved
experimental efficiency. We demonstrate PPAC evaluation across scaling boosters
and artificial designs in a predictive technology node.Comment: 14 pages, 17 figures, submitted to IEEE Trans. on CA
Strain sensitive flexible magnetoelectric ceramic nanocomposites
Advanced flexible electronics and soft robotics require the development and
implementation of flexible functional materials. Magnetoelectric (ME) oxide
materials can convert magnetic input into electric output and vice versa,
making them excellent candidates for advanced sensing, actuating, data storage,
and communication. However, their application has been limited to rigid devices
due to their brittle nature. Here, we report flexible ME oxide composite
(BaTiO3/CoFe2O4) thin film nanostructures that can be transferred onto a
stretchable substrate such as polydimethylsiloxane (PDMS). In contrast to rigid
bulk counterparts, these ceramic nanostructures display a flexible behavior and
exhibit reversibly tunable ME coupling via mechanical stretching. We believe
our study can open up new avenues for integrating ceramic ME composites into
flexible electronics and soft robotic devices
Automatic segmentation of cardiac structures for breast cancer radiotherapy
Background and purpose
We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy.
Material and methods
We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method.
Results
The Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance.
Conclusion
We developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials
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