143 research outputs found
Accelerated materials language processing enabled by GPT
Materials language processing (MLP) is one of the key facilitators of
materials science research, as it enables the extraction of structured
information from massive materials science literature. Prior works suggested
high-performance MLP models for text classification, named entity recognition
(NER), and extractive question answering (QA), which require complex model
architecture, exhaustive fine-tuning and a large number of human-labelled
datasets. In this study, we develop generative pretrained transformer
(GPT)-enabled pipelines where the complex architectures of prior MLP models are
replaced with strategic designs of prompt engineering. First, we develop a
GPT-enabled document classification method for screening relevant documents,
achieving comparable accuracy and reliability compared to prior models, with
only small dataset. Secondly, for NER task, we design an entity-centric
prompts, and learning few-shot of them improved the performance on most of
entities in three open datasets. Finally, we develop an GPT-enabled extractive
QA model, which provides improved performance and shows the possibility of
automatically correcting annotations. While our findings confirm the potential
of GPT-enabled MLP models as well as their value in terms of reliability and
practicability, our scientific methods and systematic approach are applicable
to any materials science domain to accelerate the information extraction of
scientific literature
MatGD: Materials Graph Digitizer
We have developed MatGD (Material Graph Digitizer), which is a tool for
digitizing a data line from scientific graphs. The algorithm behind the tool
consists of four steps: (1) identifying graphs within subfigures, (2)
separating axes and data sections, (3) discerning the data lines by eliminating
irrelevant graph objects and matching with the legend, and (4) data extraction
and saving. From the 62,534 papers in the areas of batteries, catalysis, and
MOFs, 501,045 figures were mined. Remarkably, our tool showcased performance
with over 99% accuracy in legend marker and text detection. Moreover, its
capability for data line separation stood at 66%, which is much higher compared
to other existing figure mining tools. We believe that this tool will be
integral to collecting both past and future data from publications, and these
data can be used to train various machine learning models that can enhance
material predictions and new materials discovery.Comment: 23 pages, 4 figure
Invariant subspaces for operators whose spectra are Carathéodory regions
AbstractIn this paper it is shown that if an operator T satisfies ‖p(T)‖⩽‖p‖σ(T) for every polynomial p and the polynomially convex hull of σ(T) is a Carathéodory region whose accessible boundary points lie in rectifiable Jordan arcs on its boundary, then T has a nontrivial invariant subspace. As a corollary, it is also shown that if T is a hyponormal operator and the outer boundary of σ(T) has at most finitely many prime ends corresponding to singular points on ∂D and has a tangent at almost every point on each Jordan arc, then T has a nontrivial invariant subspace
ARM MOTIONS FOR DIFFERENT TARGET POSITIONS DURING TAEKWONDO ROUNDHOUSE KICKS
The purpose of this study was to investigate arm motions for five different target positions during Taekwondo roundhouse kicks. Nine Taekwondo experts performed roundhouse kicks at a target. A 3D motion analysis was conducted. One-way repeated ANOVA was used to compare the arm motion among five conditions. This study reveals that a higher kick needs the increased vertical separation of the right and left arm (elbow and wrist) in release phase. For a longer kick at Body level, elbows should be more vertically apart and wrists should be more horizontally apart in the release phase. Both attackers and counter attackers in Taekwondo athletes can use the arm swing characteristics at different target heights and distances
Online Hyperparameter Meta-Learning with Hypergradient Distillation
Many gradient-based meta-learning methods assume a set of parameters that do
not participate in inner-optimization, which can be considered as
hyperparameters. Although such hyperparameters can be optimized using the
existing gradient-based hyperparameter optimization (HO) methods, they suffer
from the following issues. Unrolled differentiation methods do not scale well
to high-dimensional hyperparameters or horizon length, Implicit Function
Theorem (IFT) based methods are restrictive for online optimization, and short
horizon approximations suffer from short horizon bias. In this work, we propose
a novel HO method that can overcome these limitations, by approximating the
second-order term with knowledge distillation. Specifically, we parameterize a
single Jacobian-vector product (JVP) for each HO step and minimize the distance
from the true second-order term. Our method allows online optimization and also
is scalable to the hyperparameter dimension and the horizon length. We
demonstrate the effectiveness of our method on two different meta-learning
methods and three benchmark datasets
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
The discovery of the disentanglement properties of the latent space in GANs
motivated a lot of research to find the semantically meaningful directions on
it. In this paper, we suggest that the disentanglement property is closely
related to the geometry of the latent space. In this regard, we propose an
unsupervised method for finding the semantic-factorizing directions on the
intermediate latent space of GANs based on the local geometry. Intuitively, our
proposed method, called Local Basis, finds the principal variation of the
latent space in the neighborhood of the base latent variable. Experimental
results show that the local principal variation corresponds to the semantic
factorization and traversing along it provides strong robustness to image
traversal. Moreover, we suggest an explanation for the limited success in
finding the global traversal directions in the latent space, especially W-space
of StyleGAN2. We show that W-space is warped globally by comparing the local
geometry, discovered from Local Basis, through the metric on Grassmannian
Manifold. The global warpage implies that the latent space is not well-aligned
globally and therefore the global traversal directions are bound to show
limited success on it.Comment: 23 pages, 19 figure
Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback
Diffusion models have recently shown remarkable success in high-quality image
generation. Sometimes, however, a pre-trained diffusion model exhibits partial
misalignment in the sense that the model can generate good images, but it
sometimes outputs undesirable images. If so, we simply need to prevent the
generation of the bad images, and we call this task censoring. In this work, we
present censored generation with a pre-trained diffusion model using a reward
model trained on minimal human feedback. We show that censoring can be
accomplished with extreme human feedback efficiency and that labels generated
with a mere few minutes of human feedback are sufficient. Code available at:
https://github.com/tetrzim/diffusion-human-feedback.Comment: Published in NeurIPS 202
C-ITS Environment Modeling and Attack Modeling
As technology advances, cities are evolving into smart cities, with the
ability to process large amounts of data and the increasing complexity and
diversification of various elements within urban areas. Among the core systems
of a smart city is the Cooperative-Intelligent Transport Systems (C-ITS). C-ITS
is a system where vehicles provide real-time information to drivers about
surrounding traffic conditions, sudden stops, falling objects, and other
accident risks through roadside base stations. It consists of road
infrastructure, C-ITS centers, and vehicle terminals. However, as smart cities
integrate many elements through networks and electronic control, they are
susceptible to cybersecurity issues. In the case of cybersecurity problems in
C-ITS, there is a significant risk of safety issues arising. This technical
document aims to model the C-ITS environment and the services it provides, with
the purpose of identifying the attack surface where security incidents could
occur in a smart city environment. Subsequently, based on the identified attack
surface, the document aims to construct attack scenarios and their respective
stages. The document provides a description of the concept of C-ITS, followed
by the description of the C-ITS environment model, service model, and attack
scenario model defined by us.Comment: in Korean Language, 14 Figures, 15 Page
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