417 research outputs found
Identification of second-order kernels in aerodynamics
Volterra series is one of the powerful system identification methods for representing the nonlinear dynamic system behavior. The methods of step response and impulse response are commonly applied to a discrete aerodynamic Computational Fluid Dynamic (CFD) to identify the first- and second-order Volterra kernels. A critical problem, however, is the difficulty of identifying the second-order Volterra kernels correctly in CFD-based method. In this paper the second-order Volterra kernel function is expanded in terms of Chebyshev functions to reduce the size of the problem and the accuracy of the identification is also improved based on a third-order reduced model of Volterra series
SCFSAP controls organ size by targeting PPD proteins for degradation in Arabidopsis thaliana
Control of organ size by cell proliferation and growth is a fundamental process, but the mechanisms that determine the final size of organs are largely elusive in plants. We have previously revealed that the ubiquitin receptor DA1 regulates organ size by repressing cell proliferation in Arabidopsis. Here we report that a mutant allele of STERILE APETALA (SAP) suppresses the da1-1 mutant phenotype. We show that SAP is an F-box protein that forms part of a SKP1/Cullin/F-box E3 ubiquitin ligase complex and controls organ size by promoting the proliferation of meristemoid cells. Genetic analyses suggest that SAP may act in the same pathway with PEAPOD1 and PEAPOD2, which are negative regulators of meristemoid proliferation, to control organ size, but does so independently of DA1. Further results reveal that SAP physically associates with PEAPOD1 and PEAPOD2, and targets them for degradation. These findings define a molecular mechanism by which SAP and PEAPOD control organ size
CA2: Cyber Attacks Analytics
The VAST Challenge 2020 Mini-Challenge 1 requires participants to identify
the responsible white hat groups behind a fictional Internet outage. To address
this task, we have created a visual analytics system named CA2: Cyber Attacks
Analytics. This system is designed to efficiently compare and match subgraphs
within an extensive graph containing anonymized profiles. Additionally, we
showcase an iterative workflow that utilizes our system's capabilities to
pinpoint the responsible group.Comment: IEEE Conference on Visual Analytics Science and Technology (VAST)
Challenge Workshop 202
Finetuning Offline World Models in the Real World
Reinforcement Learning (RL) is notoriously data-inefficient, which makes
training on a real robot difficult. While model-based RL algorithms (world
models) improve data-efficiency to some extent, they still require hours or
days of interaction to learn skills. Recently, offline RL has been proposed as
a framework for training RL policies on pre-existing datasets without any
online interaction. However, constraining an algorithm to a fixed dataset
induces a state-action distribution shift between training and inference, and
limits its applicability to new tasks. In this work, we seek to get the best of
both worlds: we consider the problem of pretraining a world model with offline
data collected on a real robot, and then finetuning the model on online data
collected by planning with the learned model. To mitigate extrapolation errors
during online interaction, we propose to regularize the planner at test-time by
balancing estimated returns and (epistemic) model uncertainty. We evaluate our
method on a variety of visuo-motor control tasks in simulation and on a real
robot, and find that our method enables few-shot finetuning to seen and unseen
tasks even when offline data is limited. Videos, code, and data are available
at https://yunhaifeng.com/FOWM .Comment: CoRL 2023 Oral; Project website: https://yunhaifeng.com/FOW
Color Maker: a Mixed-Initiative Approach to Creating Accessible Color Maps
Quantitative data is frequently represented using color, yet designing
effective color mappings is a challenging task, requiring one to balance
perceptual standards with personal color preference. Current design tools
either overwhelm novices with complexity or offer limited customization
options. We present ColorMaker, a mixed-initiative approach for creating
colormaps. ColorMaker combines fluid user interaction with real-time
optimization to generate smooth, continuous color ramps. Users specify their
loose color preferences while leaving the algorithm to generate precise color
sequences, meeting both designer needs and established guidelines. ColorMaker
can create new colormaps, including designs accessible for people with
color-vision deficiencies, starting from scratch or with only partial input,
thus supporting ideation and iterative refinement. We show that our approach
can generate designs with similar or superior perceptual characteristics to
standard colormaps. A user study demonstrates how designers of varying skill
levels can use this tool to create custom, high-quality colormaps. ColorMaker
is available at https://colormaker.orgComment: To appear at the ACM CHI '24 Conference on Human Factors in Computing
System
Angle-Uniform Parallel Coordinates
We present angle-uniform parallel coordinates, a data-independent technique
that deforms the image plane of parallel coordinates so that the angles of
linear relationships between two variables are linearly mapped along the
horizontal axis of the parallel coordinates plot. Despite being a common method
for visualizing multidimensional data, parallel coordinates are ineffective for
revealing positive correlations since the associated parallel coordinates
points of such structures may be located at infinity in the image plane and the
asymmetric encoding of negative and positive correlations may lead to
unreliable estimations. To address this issue, we introduce a transformation
that bounds all points horizontally using an angle-uniform mapping and shrinks
them vertically in a structure-preserving fashion; polygonal lines become
smooth curves and a symmetric representation of data correlations is achieved.
We further propose a combined subsampling and density visualization approach to
reduce visual clutter caused by overdrawing. Our method enables accurate visual
pattern interpretation of data correlations, and its data-independent nature
makes it applicable to all multidimensional datasets. The usefulness of our
method is demonstrated using examples of synthetic and real-world datasets.Comment: Computational Visual Media, 202
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