Papers made digestable
Interactive segmentation enables users to extract masks by providing simple
annotations to indicate the target, such as boxes, clicks, or scribbles. Among
these interaction formats, scribbles are the most flexible as they can be of
arbitrary shapes and sizes. This enables scribbles to provide more indications
of the target object. However, previous works mainly focus on click-based
configuration, and the scribble-based setting is rarely explored. In this work,
we attempt to formulate a standard protocol for scribble-based interactive
segmentation. Basically, we design diversified strategies to simulate scribbles
for training, propose a deterministic scribble generator for evaluation, and
construct a challenging benchmark. Besides, we build a strong framework
ScribbleSeg, consisting of a Prototype Adaption Module(PAM) and a Corrective
Refine Module (CRM), for the task. Extensive experiments show that ScribbleSeg
performs notably better than previous click-based methods. We hope this could
serve as a more powerful and general solution for interactive segmentation. Our
code will be made available.
Authors: Xi Chen, Yau Shing Jonathan Cheung, Ser-Nam Lim, Hengshuang Zhao.
Most protoplanetary discs are thought to undergo violent and frequent
accretion outbursts, during which the accretion rate and central luminosity are
elevated for several decades. This temporarily increases the disc temperature,
leading to the sublimation of ice species as snowlines move outwards. In this
paper, we investigate how an FUor-type accretion outburst alters the growth and
appearance of dust aggregates at different locations in protoplanetary discs.
We develop a model based on the Monte Carlo approach to simulate locally the
coagulation and fragmentation of icy dust particles and investigate different
designs for their structure and response to sublimation. Our main finding is
that the evolution of dust grains located between the quiescent and outburst
water snowlines is driven by significant changes in composition and porosity.
The time required for the dust population to recover from the outburst and
return to a coagulation/fragmentation equilibrium depends on the complex
interplay of coagulation physics and outburst properties, and can take up to
4500 yr at 5 au. Pebble-sized particles, the building blocks of planetesimals,
are either deprecated in water ice or completely destroyed, respectively
resulting in drier planetesimals or halting their formation altogether. When
accretion outbursts are frequent events, the dust can be far from collisional
equilibrium for a significant fraction of time, offering opportunities to track
past outbursts in discs at millimetre wavelengths. Our results highlight the
importance of including accretion outbursts in models of dust coagulation and
planet formation.
Authors: Adrien Houge, Sebastiaan Krijt.
We present Generative Semantic Segmentation (GSS), a generative learning
approach for semantic segmentation. Uniquely, we cast semantic segmentation as
an image-conditioned mask generation problem. This is achieved by replacing the
conventional per-pixel discriminative learning with a latent prior learning
process. Specifically, we model the variational posterior distribution of
latent variables given the segmentation mask. To that end, the segmentation
mask is expressed with a special type of image (dubbed as maskige). This
posterior distribution allows to generate segmentation masks unconditionally.
To achieve semantic segmentation on a given image, we further introduce a
conditioning network. It is optimized by minimizing the divergence between the
posterior distribution of maskige (i.e., segmentation masks) and the latent
prior distribution of input training images. Extensive experiments on standard
benchmarks show that our GSS can perform competitively to prior art
alternatives in the standard semantic segmentation setting, whilst achieving a
new state of the art in the more challenging cross-domain setting.
Authors: Jiaqi Chen, Jiachen Lu, Xiatian Zhu, Li Zhang.
The radial acceleration relation (RAR) of late-type galaxies relates their
dynamical acceleration, $g_\text{obs}$, to that sourced by baryons alone,
$g_\text{bar}$, across their rotation curves. Literature fits to the RAR have
fixed the galaxy parameters on which the relation depends -- distance,
inclination, luminosity and mass-to-light ratios -- to their maximum a priori
values with an uncorrelated Gaussian contribution to the uncertainties on
$g_\text{bar}$ and $g_\text{obs}$. In reality these are free parameters of the
fit, contributing systematic rather than statistical error. Assuming a range of
possible functional forms for the relation with or without intrinsic scatter
(motivated by Modified Newtonian Dynamics with or without the external field
effect), I use Hamiltonian Monte Carlo to perform the full joint inference of
RAR and galaxy parameters for the SPARC dataset. This reveals the intrinsic RAR
underlying that observed. I find an acceleration scale $a_0=(1.19 \pm 0.04 \,
\text{(stat)} \pm 0.09 \, \text{(sys)}) \: \times \: 10^{-10}$ m s$^{-2}$, an
intrinsic scatter $\sigma_\text{int}=(0.034 \pm 0.01 \, \text{(stat)} \pm 0.01
\, \text{(sys)})$ dex (assuming the SPARC error model is reliable) and weak
evidence for the external field effect. I make summary statistics of all my
analyses publicly available for future SPARC studies or applications of a
calibrated RAR, for example redshift-independent distance measurement.
Authors: Harry Desmond.
Assessing predictive models can be challenging. Modelers must navigate a wide
array of evaluation methodologies implemented with incompatible interfaces
across multiple packages which may give different or even contradictory
results, while ensuring that their chosen approach properly estimates the
performance of their model when generalizing to new observations. Assessing
models fit to spatial data can be particularly difficult, given that model
errors may exhibit spatial autocorrelation, model predictions are often
aggregated to multiple spatial scales by end users, and models are often tasked
with generalizing into spatial regions outside the boundaries of their initial
training data.
The waywiser package for the R language attempts to make assessing spatial
models easier by providing an ergonomic toolkit for model evaluation tasks,
with functions for multiple assessment methodologies sharing a unified
interface. Functions from waywiser share standardized argument names and
default values, making the user-facing interface simple and easy to learn.
These functions are additionally designed to be easy to integrate into a wide
variety of modeling workflows, accepting standard classes as inputs and
returning size- and type-stable outputs, ensuring that their results are of
consistent and predictable data types and dimensions. Additional features make
it particularly easy to use waywiser along packages and workflows in the
tidymodels ecosystem.
Authors: Michael J Mahoney.