358 research outputs found
On the Rebrightenings of Classical Novae during the Early Phase
We report on the spectral evolution of 6 classical novae, V1186 Sco, V2540
Oph, V4745 Sgr, V5113 Sgr, V458 Vul, and V378 Ser, based on the low-resolution
spectra obtained at the Fujii-Bisei Observatory and the Bisei Astronomical
Observatory, Japan. In the light curves, these 6 novae show several
rebrightenings during the early phase lasting ~10 days after the first maximum
in fast novae, and ~100 days in slow novae. The early spectra of all of these
novae had emission lines with a P-Cygni profile at the maximum brightness. The
absorption component of the P-Cygni profiles then disappeared after the
maximum, and reappeared when the novae brightened to the next maximum. We
suggest that the re-appearance of the absorption component at the rebrightening
is attributable to re-expansion of the photosphere after it once shifts
sufficiently inside. From the light curves, we found that the time intervals of
the rebrightenings of these 6 novae show a similar systematic trend, which is
applicable to all types of novae: fast and slow, and Fe II type and hybrid
type. Moreover, we note the difference between the spectra at the
rebrightenings during the early phase and at the rebrightening in V2362 Cyg,
and at the oscillation during the transition phase in V1494 Aql, which means
difference of the physical mechanism of the rebrightening during the early
phase and the later oscillations.Comment: 11 pages, 15 figures, accepted for publication in PAS
Non-linear Evolution of Matter Power Spectrum in Modified Theory of Gravity
We present a formalism to calculate the non-linear matter power spectrum in
modified gravity models that explain the late-time acceleration of the Universe
without dark energy. Any successful modified gravity models should contain a
mechanism to recover General Relativity (GR) on small scales in order to avoid
the stringent constrains on deviations from GR at solar system scales. Based on
our formalism, the quasi non-linear power spectrum in the
Dvali-Gabadadze-Porratti (DGP) braneworld models and gravity models are
derived by taking into account the mechanism to recover GR properly. We also
extrapolate our predictions to fully non-linear scales using the Parametrized
Post Friedmann (PPF) framework. In gravity models, the predicted
non-linear power spectrum is shown to reproduce N-body results. We find that
the mechanism to recover GR suppresses the difference between the modified
gravity models and dark energy models with the same expansion history, but the
difference remains large at weakly non-linear regime in these models. Our
formalism is applicable to a wide variety of modified gravity models and it is
ready to use once consistent models for modified gravity are developed.Comment: 25 pages, 8 figures, comparison to N-body simulations in DGP added,
published in PR
Runner re-identification from single-view video in the open-world setting
In many sports, player re-identification is crucial for automatic video
processing and analysis. However, most of the current studies on player
re-identification in multi- or single-view sports videos focus on
re-identification in the closed-world setting using labeled image dataset, and
player re-identification in the open-world setting for automatic video analysis
is not well developed. In this paper, we propose a runner re-identification
system that directly processes single-view video to address the open-world
setting. In the open-world setting, we cannot use labeled dataset and have to
process video directly. The proposed system automatically processes raw video
as input to identify runners, and it can identify runners even when they are
framed out multiple times. For the automatic processing, we first detect the
runners in the video using the pre-trained YOLOv8 and the fine-tuned
EfficientNet. We then track the runners using ByteTrack and detect their shoes
with the fine-tuned YOLOv8. Finally, we extract the image features of the
runners using an unsupervised method using the gated recurrent unit autoencoder
model. To improve the accuracy of runner re-identification, we use dynamic
features of running sequence images. We evaluated the system on a running
practice video dataset and showed that the proposed method identified runners
with higher accuracy than one of the state-of-the-art models in unsupervised
re-identification. We also showed that our unsupervised running dynamic feature
extractor was effective for runner re-identification. Our runner
re-identification system can be useful for the automatic analysis of running
videos.Comment: 18 pages, 8 figure
Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from a Monocular Camera and IMUs
Automatic evaluating systems are fundamental issues in sports technologies.
In many sports, such as figure skating, automated evaluating methods based on
pose estimation have been proposed. However, previous studies have evaluated
skaters' skills in 2D analysis. In this paper, we propose an automatic edge
error judgment system with a monocular smartphone camera and inertial sensors,
which enable us to analyze 3D motions. Edge error is one of the most
significant scoring items and is challenging to automatically judge due to its
3D motion. The results show that the model using 3D joint position coordinates
estimated from the monocular camera as the input feature had the highest
accuracy at 83% for unknown skaters' data. We also analyzed the detailed motion
analysis for edge error judgment. These results indicate that the monocular
camera can be used to judge edge errors automatically. We will provide the
figure skating single Lutz jump dataset, including pre-processed videos and
labels, at https://github.com/ryota-takedalab/JudgeAI-LutzEdge
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling
Extracting the rules of real-world multi-agent behaviors is a current
challenge in various scientific and engineering fields. Biological agents
independently have limited observation and mechanical constraints; however,
most of the conventional data-driven models ignore such assumptions, resulting
in lack of biological plausibility and model interpretability for behavioral
analyses. Here we propose sequential generative models with partial observation
and mechanical constraints in a decentralized manner, which can model agents'
cognition and body dynamics, and predict biologically plausible behaviors. We
formulate this as a decentralized multi-agent imitation-learning problem,
leveraging binary partial observation and decentralized policy models based on
hierarchical variational recurrent neural networks with physical and
biomechanical penalties. Using real-world basketball and soccer datasets, we
show the effectiveness of our method in terms of the constraint violations,
long-term trajectory prediction, and partial observation. Our approach can be
used as a multi-agent simulator to generate realistic trajectories using
real-world data.Comment: 17 pages with 7 figures and 4 tables, accepted in Neural Network
Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
Analysis of invasive sports such as soccer is challenging because the game
situation changes continuously in time and space, and multiple agents
individually recognize the game situation and make decisions. Previous studies
using deep reinforcement learning have often considered teams as a single agent
and valued the teams and players who hold the ball in each discrete event. Then
it was challenging to value the actions of multiple players, including players
far from the ball, in a spatiotemporally continuous state space. In this paper,
we propose a method of valuing possible actions for on- and off-ball soccer
players in a single holistic framework based on multi-agent deep reinforcement
learning. We consider a discrete action space in a continuous state space that
mimics that of Google research football and leverages supervised learning for
actions in reinforcement learning. In the experiment, we analyzed the
relationships with conventional indicators, season goals, and game ratings by
experts, and showed the effectiveness of the proposed method. Our approach can
assess how multiple players move continuously throughout the game, which is
difficult to be discretized or labeled but vital for teamwork, scouting, and
fan engagement.Comment: 12 pages, 4 figure
Spectral Evolution of the Unusual Slow Nova V5558 Sgr
We report on the spectral evolution of the enigmatic, very slow nova V5558
Sgr, based on the low-resolution spectra obtained at the Fujii-Bisei
Observatory and the Bisei Astronomical Observatory, Japan during a period of
2007 April 6 to 2008 May 3. V5558 Sgr shows a pre-maximum halt and then several
flare-like rebrightenings, which is similar to another very slow nova V723 Cas.
In our observations, the spectral type of V5558 Sgr evolved from the He/N type
toward the Fe II type during the pre-maximum halt, and then toward the He/N
type again. This course of spectral transition was observed for the first time
in the long history of the nova research. In the rebrightening stage after the
initial brightness maximum, we could identify many emission lines accompanied
by a stronger absorption component of the P-Cygni profile at the brightness
maxima. We found that the velocity of the P-Cygni absorption component measured
from the emission peak decreased at the brightness maxima. Furthermore, we
compared the spectra of V5558 Sgr with V723 Cas, and other novae which
exhibited several rebrightenings during the early phase.Comment: 8 pages, 7figures, accepted for publication in PAS
Estimation of control area in badminton doubles with pose information from top and back view drone videos
The application of visual tracking to the performance analysis of sports
players in dynamic competitions is vital for effective coaching. In doubles
matches, coordinated positioning is crucial for maintaining control of the
court and minimizing opponents' scoring opportunities. The analysis of such
teamwork plays a vital role in understanding the dynamics of the game. However,
previous studies have primarily focused on analyzing and assessing singles
players without considering occlusion in broadcast videos. These studies have
relied on discrete representations, which involve the analysis and
representation of specific actions (e.g., strokes) or events that occur during
the game while overlooking the meaningful spatial distribution. In this work,
we present the first annotated drone dataset from top and back views in
badminton doubles and propose a framework to estimate the control area
probability map, which can be used to evaluate teamwork performance. We present
an efficient framework of deep neural networks that enables the calculation of
full probability surfaces. This framework utilizes the embedding of a Gaussian
mixture map of players' positions and employs graph convolution on their poses.
In the experiment, we verify our approach by comparing various baselines and
discovering the correlations between the score and control area. Additionally,
we propose a practical application for assessing optimal positioning to provide
instructions during a game. Our approach offers both visual and quantitative
evaluations of players' movements, thereby providing valuable insights into
doubles teamwork. The dataset and related project code is available at
https://github.com/Ning-D/Drone_BD_ControlAreaComment: 15 pages, 10 figures, to appear in Multimedia Tools and Application
Predictive World Models from Real-World Partial Observations
Cognitive scientists believe adaptable intelligent agents like humans perform
reasoning through learned causal mental simulations of agents and environments.
The problem of learning such simulations is called predictive world modeling.
Recently, reinforcement learning (RL) agents leveraging world models have
achieved SOTA performance in game environments. However, understanding how to
apply the world modeling approach in complex real-world environments relevant
to mobile robots remains an open question. In this paper, we present a
framework for learning a probabilistic predictive world model for real-world
road environments. We implement the model using a hierarchical VAE (HVAE)
capable of predicting a diverse set of fully observed plausible worlds from
accumulated sensor observations. While prior HVAE methods require complete
states as ground truth for learning, we present a novel sequential training
method to allow HVAEs to learn to predict complete states from partially
observed states only. We experimentally demonstrate accurate spatial structure
prediction of deterministic regions achieving 96.21 IoU, and close the gap to
perfect prediction by 62% for stochastic regions using the best prediction. By
extending HVAEs to cases where complete ground truth states do not exist, we
facilitate continual learning of spatial prediction as a step towards realizing
explainable and comprehensive predictive world models for real-world mobile
robotics applications. Code is available at
https://github.com/robin-karlsson0/predictive-world-models.Comment: Accepted for IEEE MOST 202
3次元再構成を用いた非骨性距踵骨癒合症の形態学的分析
Background: Resection of talocalcaneal coalitions has generally involved osseous coalitions. We attempted to evaluate the morphology of nonosseous talocalcaneal coalitions. This study aimed to investigate if the calcaneal articular surface area of feet with talocalcaneal coalitions is different than that of normal feet. Methods: Twenty nonosseous talocalcaneal coalition cases with analyzable computed tomography (CT) scans were compared to 20 control cases. Three-dimensional models of the talus and calcaneus were constructed, and the surface areas of the posterior facet (SPF), whole talocalcaneal joint of the calcaneus (SWJ), and coalition site (SCS) of each 3D-CT model were measured. "Calibrated" values of the 2 groups were created to adjust for relative size of the tali and then compared. The preoperative and postoperative AOFAS Ankle-Hindfoot scale was calculated for 9 cases that had undergone single coalition resection. Results: The calibrated SPF and SWJ were significantly greater in the coalition group than in the control group (40% and 12%, respectively). No significant difference was detected between the calibrated (SWJ - SCS) value of the coalition group and the calibrated SWJ value of the control group. The AOFAS scale was improved postoperatively in all 9 cases analyzed. Conclusion: The calcaneal articular surface of nonosseous talocalcaneal coalition feet in our series was larger than that of the normal feet. This study indicates that the total calcaneal articular surface after coalition resection may be comparable to the calcaneal articular surface of normal feet. We suggest that the indication for coalition resection be reconsidered for nonosseous coalition. Level of evidence: Level III, retrospective comparative study.博士(医学)・甲第815号・令和4年3月15日© The Author(s) 2021. Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0
License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without
further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/
open-access-at-sage)
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