5,121 research outputs found
Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning
Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to
spontaneously set diverse goals to learn a set of skills. Despite the excellent
works proposed in various fields, reaching distant goals in temporally extended
tasks remains a challenge for GCRL. Current works tackled this problem by
leveraging planning algorithms to plan intermediate subgoals to augment GCRL.
Their methods need two crucial requirements: (i) a state representation space
to search valid subgoals, and (ii) a distance function to measure the
reachability of subgoals. However, they struggle to scale to high-dimensional
state space due to their non-compact representations. Moreover, they cannot
collect high-quality training data through standard GC policies, which results
in an inaccurate distance function. Both affect the efficiency and performance
of planning and policy learning. In the paper, we propose a goal-conditioned RL
algorithm combined with Disentanglement-based Reachability Planning (REPlan) to
solve temporally extended tasks. In REPlan, a Disentangled Representation
Module (DRM) is proposed to learn compact representations which disentangle
robot poses and object positions from high-dimensional observations in a
self-supervised manner. A simple REachability discrimination Module (REM) is
also designed to determine the temporal distance of subgoals. Moreover, REM
computes intrinsic bonuses to encourage the collection of novel states for
training. We evaluate our REPlan in three vision-based simulation tasks and one
real-world task. The experiments demonstrate that our REPlan significantly
outperforms the prior state-of-the-art methods in solving temporally extended
tasks.Comment: Accepted by 2023 RAL with ICR
RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models
The emergence of Neural Radiance Fields (NeRF) has promoted the development
of synthesized high-fidelity views of the intricate real world. However, it is
still a very demanding task to repaint the content in NeRF. In this paper, we
propose a novel framework that can take RGB images as input and alter the 3D
content in neural scenes. Our work leverages existing diffusion models to guide
changes in the designated 3D content. Specifically, we semantically select the
target object and a pre-trained diffusion model will guide the NeRF model to
generate new 3D objects, which can improve the editability, diversity, and
application range of NeRF. Experiment results show that our algorithm is
effective for editing 3D objects in NeRF under different text prompts,
including editing appearance, shape, and more. We validate our method on both
real-world datasets and synthetic-world datasets for these editing tasks.
Please visit https://repaintnerf.github.io for a better view of our results.Comment: IJCAI 2023 Accepted (Main Track
Spectroscopic and Photometric Observations of Unidentified Ultraviolate Variable Objects in GUVV-2 Catalog
An NUV-optical diagram made for sources from the secend Galaxy Evolution
Explorer (GALEX) Ultraviolet Variability (GUVV-2) Catalog provide us a method
to tentatively classify the unknown GUVV2 sources by their NUV-optical
magnitudes. On the purpose of testing the correctness and generality of the
method, we carry out a program on the spectroscopic observations of the
unidentified GUVV2 sources. The spectroscopic identification for these 37
sources are 19 type -A to -F stars, 10 type -G to -K stars and 7 M dwarf stars
together with an AGN. We also present the light curves in R-band for two RR
Lyrae star candidates selected from the NUV-optical diagram, both of which
perform cyclic variations. Combining there light curves and colors, we classify
them as RR Lyrae stars. To confirm the results, we shows a color-color diagram
for the 37 newly spectroscopically identified objects compared with the
previously identified ones, which manifests good consistence with our former
results, indicating that the ultroviolet variable sources can be initially
classified by their NUV/optical color-color diagram.Comment: 10 pages, 7 figure
Few-photon imaging at 1550 nm using a low-timing-jitter superconducting nanowire single-photon detector
We demonstrated a laser depth imaging system based on the time-correlated
single-photon counting technique, which was incorporated with a low-jitter
superconducting nanowire single-photon detector (SNSPD), operated at the
wavelength of 1550 nm. A sub-picosecond time-bin width was chosen for photon
counting, resulting in a discrete noise of less than one/two counts for each
time bin under indoor/outdoor daylight conditions, with a collection time of 50
ms. Because of the low-jitter SNSPD, the target signal histogram was
significantly distinguishable, even for a fairly low retro-reflected photon
flux. The depth information was determined directly by the highest bin counts,
instead of using any data fitting combined with complex algorithms. Millimeter
resolution depth imaging of a low-signature object was obtained, and more
accurate data than that produced by the traditional Gaussian fitting method was
generated. Combined with the intensity of the return photons, three-dimensional
reconstruction overlaid with reflectivity data was realized.Comment: 7 pages, 9 figure
Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion Modeling
Diffusion models excel at generating photo-realistic images but come with
significant computational costs in both training and sampling. While various
techniques address these computational challenges, a less-explored issue is
designing an efficient and adaptable network backbone for iterative refinement.
Current options like U-Net and Vision Transformer often rely on
resource-intensive deep networks and lack the flexibility needed for generating
images at variable resolutions or with a smaller network than used in training.
This study introduces LEGO bricks, which seamlessly integrate Local-feature
Enrichment and Global-content Orchestration. These bricks can be stacked to
create a test-time reconfigurable diffusion backbone, allowing selective
skipping of bricks to reduce sampling costs and generate higher-resolution
images than the training data. LEGO bricks enrich local regions with an MLP and
transform them using a Transformer block while maintaining a consistent
full-resolution image across all bricks. Experimental results demonstrate that
LEGO bricks enhance training efficiency, expedite convergence, and facilitate
variable-resolution image generation while maintaining strong generative
performance. Moreover, LEGO significantly reduces sampling time compared to
other methods, establishing it as a valuable enhancement for diffusion models
EFFECTS OF LONG-TERM TAI CHI EXERCISE ON BALANCE CONTROL IN OLDER ADULTS
This study assessed the static and dynamic balance control of older adults who have 10 years of Tai Chi exercise experience and compared their characteristics with their sedentary counterparts. The abilities were measured using methods: single-leg stance times with eyes open and closed; sway of center of pressure (COP) during static standing with eyes open/closed, and leaning the body in three specific directions. Compared with control group, 1) Tai Chi Group showed longer single-leg stance times with eyes open and closed, 2) slower sway velocity of COP in mediolateral and anterioposterior directions and shorter sway distance in both directions, and 3) shorter total, anterioposterior, and mediolateral routes and shorter time spent during the dynamic balance test. Long-term Tai Chi exercise improves the balance ability, especially the dynamic balance, of older adults
Efficient Characterizations of Multiphoton States with Ultra-thin Integrated Photonics
Metasurface enables the generation and manipulation of multiphoton
entanglement with flat optics, providing a more efficient platform for
large-scale photonic quantum information processing. Here, we show that a
single metasurface optical chip would allow more efficient characterizations of
multiphoton entangled states, such as shadow tomography, which generally
requires fast and complicated control of optical setups to perform projective
measurements in different bases, a demanding task using conventional optics.
The compact and stable device here allows implementations of general positive
observable value measures with a reduced sample complexity and significantly
alleviates the experimental complexity to implement shadow tomography.
Integrating self-learning and calibration algorithms, we observe notable
advantages in the reconstruction of multiphoton entanglement, including using
fewer measurements, having higher accuracy, and being robust against optical
loss. Our work unveils the feasibility of metasurface as a favorable integrated
optical device for efficient characterization of multiphoton entanglement, and
sheds light on scalable photonic quantum technologies with ultra-thin
integrated optics.Comment: 15 pages, 9 figure
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