Papers made digestable
Our architecture simplifies the obstacle-perception
problem to that of place-dependent change detection. While we use the method with VT&R, it
can be generalized to suit arbitrary path-following applications.
Visual Teach and Repeat 3 (VT&R3), a generalization of stereo VT&R, achieves
long-term autonomous path-following using topometric mapping and localization
from a single rich sensor stream. In this paper, we improve the capabilities of
a LiDAR implementation of VT&R3 to reliably detect and avoid obstacles in
changing environments. Our architecture simplifies the obstacle-perception
problem to that of place-dependent change detection. We then extend the
behaviour of generic sample-based motion planners to better suit the
teach-and-repeat problem structure by introducing a new edge-cost metric paired
with a curvilinear planning space. The resulting planner generates naturally
smooth paths that avoid local obstacles while minimizing lateral path deviation
to best exploit prior terrain knowledge. While we use the method with VT&R, it
can be generalized to suit arbitrary path-following applications. Experimental
results from online run-time analysis, unit testing, and qualitative
experiments on a differential drive robot show the promise of the technique for
reliable long-term autonomous operation in complex unstructured environments.
Authors: Jordy Sehn, Yuchen Wu, Timothy D. Barfoot.
The statistical and design considerations that pertain to
dose optimization are discussed. The sample size savings range from 16.6% to 27.3%,
depending on the design and scenario, with a mean savings of 22.1%.
The traditional more-is-better dose selection paradigm, developed based on
cytotoxic chemotherapeutics, is often problematic When applied to the
development of novel molecularly targeted agents (e.g., kinase inhibitors,
monoclonal antibodies, and antibody-drug conjugates). The US Food and Drug
Administration (FDA) initiated Project Optimus to reform the dose optimization
and dose selection paradigm in oncology drug development and call for more
attention to benefit-risk consideration.
We systematically investigated the operating characteristics of the seamless
phase 2-3 design as a strategy for dose optimization, where in stage 1
(corresponding to phase 2) patients are randomized to multiple doses, with or
without a control; and in stage 2 (corresponding to phase 3) the efficacy of
the selected optimal dose is evaluated with a randomized concurrent control or
historical control. Depending on whether the concurrent control is included and
the type of endpoints used in stages 1 and 2, we describe four types of
seamless phase 2-3 dose-optimization designs, which are suitable for different
clinical settings. The statistical and design considerations that pertain to
dose optimization are discussed. Simulation shows that dose optimization phase
2-3 designs are able to control the familywise type I error rates and yield
appropriate statistical power with substantially smaller sample size than the
conventional approach. The sample size savings range from 16.6% to 27.3%,
depending on the design and scenario, with a mean savings of 22.1%. Due to the
interim dose selection, the phase 2-3 dose-optimization design is logistically
and operationally more challenging, and should be carefully planned and
implemented to ensure trial integrity.
Authors: Liyun Jiang, Ying Yuan.
We significantly improve performance using properties of the posterior
in our active learning scheme and for the definition of the GP prior. In
particular we account for the expected dynamical range of the posterior in
different dimensionalities. We test our model against a number of synthetic and
cosmological examples.
We present the GPry algorithm for fast Bayesian inference of general
(non-Gaussian) posteriors with a moderate number of parameters. GPry does not
need any pre-training, special hardware such as GPUs, and is intended as a
drop-in replacement for traditional Monte Carlo methods for Bayesian inference.
Our algorithm is based on generating a Gaussian Process surrogate model of the
log-posterior, aided by a Support Vector Machine classifier that excludes
extreme or non-finite values. An active learning scheme allows us to reduce the
number of required posterior evaluations by two orders of magnitude compared to
traditional Monte Carlo inference. Our algorithm allows for parallel
evaluations of the posterior at optimal locations, further reducing wall-clock
times. We significantly improve performance using properties of the posterior
in our active learning scheme and for the definition of the GP prior. In
particular we account for the expected dynamical range of the posterior in
different dimensionalities. We test our model against a number of synthetic and
cosmological examples. GPry outperforms traditional Monte Carlo methods when
the evaluation time of the likelihood (or the calculation of theoretical
observables) is of the order of seconds; for evaluation times of over a minute
it can perform inference in days that would take months using traditional
methods. GPry is distributed as an open source Python package (pip install
gpry) and can also be found at https://github.com/jonaselgammal/GPry.
Authors: Jonas El Gammal, Nils Schöneberg, Jesús Torrado, Christian Fidler.
We consider the fundamental scheduling problem of minimizing the sum of
weighted completion times on a single machine in the non-clairvoyant setting. However, to the best of our knowledge, this concept has never been considered
for the total completion time objective in the non-clairvoyant model. This implies
a performance guarantee of $(1+3\sqrt{3})\approx 6.197$ for the deterministic
algorithm and of $\approx 3.032$ for the randomized version.
We consider the fundamental scheduling problem of minimizing the sum of
weighted completion times on a single machine in the non-clairvoyant setting.
While no non-preemptive algorithm is constant competitive, Motwani, Phillips,
and Torng (SODA '93) proved that the simple preemptive round robin procedure is
$2$-competitive and that no better competitive ratio is possible, initiating a
long line of research focused on preemptive algorithms for generalized variants
of the problem. As an alternative model, Shmoys, Wein, and Williamson (FOCS
'91) introduced kill-and-restart schedules, where running jobs may be killed
and restarted from scratch later, and analyzed then for the makespan objective.
However, to the best of our knowledge, this concept has never been considered
for the total completion time objective in the non-clairvoyant model.
We contribute to both models: First we give for any $b > 1$ a tight analysis
for the natural $b$-scaling kill-and-restart strategy for scheduling jobs
without release dates, as well as for a randomized variant of it. This implies
a performance guarantee of $(1+3\sqrt{3})\approx 6.197$ for the deterministic
algorithm and of $\approx 3.032$ for the randomized version. Second, we show
that the preemptive Weighted Shortest Elapsed Time First (WSETF) rule is
$2$-competitive for jobs released in an online fashion over time, matching the
lower bound by Motwani et al. Using this result as well as the competitiveness
of round robin for multiple machines, we prove performance guarantees of
adaptions of the $b$-scaling algorithm to online release dates and unweighted
jobs on identical parallel machines.
Authors: Sven Jäger, Guillaume Sagnol, Daniel Schmidt genannt Waldschmidt, Philipp Warode.
Frozen pretrained models have become a viable alternative to the
pretraining-then-finetuning paradigm for transfer learning. With this work, we hope to
bring greater attention to this promising path of freezing pretrained image
models.
Frozen pretrained models have become a viable alternative to the
pretraining-then-finetuning paradigm for transfer learning. However, with
frozen models there are relatively few parameters available for adapting to
downstream tasks, which is problematic in computer vision where tasks vary
significantly in input/output format and the type of information that is of
value. In this paper, we present a study of frozen pretrained models when
applied to diverse and representative computer vision tasks, including object
detection, semantic segmentation and video action recognition. From this
empirical analysis, our work answers the questions of what pretraining task
fits best with this frozen setting, how to make the frozen setting more
flexible to various downstream tasks, and the effect of larger model sizes. We
additionally examine the upper bound of performance using a giant frozen
pretrained model with 3 billion parameters (SwinV2-G) and find that it reaches
competitive performance on a varied set of major benchmarks with only one
shared frozen base network: 60.0 box mAP and 52.2 mask mAP on COCO object
detection test-dev, 57.6 val mIoU on ADE20K semantic segmentation, and 81.7
top-1 accuracy on Kinetics-400 action recognition. With this work, we hope to
bring greater attention to this promising path of freezing pretrained image
models.
Authors: Yutong Lin, Ze Liu, Zheng Zhang, Han Hu, Nanning Zheng, Stephen Lin, Yue Cao.
Regression on observational data can fail to capture a causal relationship in
the presence of unobserved confounding. A common
assumption is the independence of causal mechanisms, which relies on
concentration phenomena in high dimensions. We then use tools from random matrix theory to derive an adapted,
consistent estimator.
Regression on observational data can fail to capture a causal relationship in
the presence of unobserved confounding. Confounding strength measures this
mismatch, but estimating it requires itself additional assumptions. A common
assumption is the independence of causal mechanisms, which relies on
concentration phenomena in high dimensions. While high dimensions enable the
estimation of confounding strength, they also necessitate adapted estimators.
In this paper, we derive the asymptotic behavior of the confounding strength
estimator by Janzing and Sch\"olkopf (2018) and show that it is generally not
consistent. We then use tools from random matrix theory to derive an adapted,
consistent estimator.
Authors: Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg.
The availability of Martian atmospheric data provided by several Martian missions broadened the opportunity to investigate and study the conditions of the Martian ionosphere. The bagged regression trees method performed best out of all the evaluated methods.
The availability of Martian atmospheric data provided by several Martian
missions broadened the opportunity to investigate and study the conditions of
the Martian ionosphere. As such, ionospheric models play a crucial part in
improving our understanding of ionospheric behavior in response to different
spatial, temporal, and space weather conditions. This work represents an
initial attempt to construct an electron density prediction model of the
Martian ionosphere using machine learning. The model targets the ionosphere at
solar zenith ranging from 70 to 90 degrees, and as such only utilizes
observations from the Mars Global Surveyor mission. The performance of
different machine learning methods was compared in terms of root mean square
error, coefficient of determination, and mean absolute error. The bagged
regression trees method performed best out of all the evaluated methods.
Furthermore, the optimized bagged regression trees model outperformed other
Martian ionosphere models from the literature (MIRI and NeMars) in finding the
peak electron density value, and the peak density height in terms of
root-mean-square error and mean absolute error.
Authors: Abdollah Masoud Darya, Noora Alameri, Muhammad Mubasshir Shaikh, Ilias Fernini.
As a consequence of galaxy clustering, close galaxies observed on the plane
of the sky should be spatially correlated with a probability inversely
proportional to their angular separation. Depending on the depth of the
survey, however, such angular correlation is reduced by chance projections. In
this work, we implement a deep learning model to distinguish between apparent
and real angular neighbours by solving a classification task. We adopt a graph
neural network architecture to tie together the photometry, the spectroscopy
and the spatial information between neighbouring galaxies. We train and
validate the algorithm on the data of the VIPERS galaxy survey, for which
SED-fitting based photometric redshifts are also available. When objects
for which no physical companion can be identified are excluded, all photometric
redshifts' quality metrics improve significantly, confirming that their
estimates were of lower quality.
As a consequence of galaxy clustering, close galaxies observed on the plane
of the sky should be spatially correlated with a probability inversely
proportional to their angular separation. In principle, this information can be
used to improve photometric redshift estimates when spectroscopic redshifts are
available for some of the neighbouring objects. Depending on the depth of the
survey, however, such angular correlation is reduced by chance projections. In
this work, we implement a deep learning model to distinguish between apparent
and real angular neighbours by solving a classification task. We adopt a graph
neural network architecture to tie together the photometry, the spectroscopy
and the spatial information between neighbouring galaxies. We train and
validate the algorithm on the data of the VIPERS galaxy survey, for which
SED-fitting based photometric redshifts are also available. The model yields a
confidence level for a pair of galaxies to be real angular neighbours, enabling
us to disentangle chance superpositions in a probabilistic way. When objects
for which no physical companion can be identified are excluded, all photometric
redshifts' quality metrics improve significantly, confirming that their
estimates were of lower quality. For our typical test configuration, the
algorithm identifies a subset containing ~75% of high-quality photometric
redshifts, for which the dispersion is reduced by as much as 50% (from 0.08 to
0.04), while the fraction of outliers reduces from 3% to 0.8%. Moreover, we
show that the spectroscopic redshift of the angular neighbour with the highest
detection probability provides an excellent estimate of the redshift of the
target galaxy, comparable or even better than the corresponding template
fitting estimate.
Authors: F. Tosone, M. S. Cagliari, L. Guzzo, B. R. Granett, A. Crespi.
We consider a two-dimensional Hamiltonian which couples the Berry-Keating Hamiltonian to the number operator on the half-line via a unitary transformation. The Riemann zeta function appears at the boundary of the resulting confined wave function and vanishes as a result of the imposed boundary condition.
We construct a formally self-adjoint Hamiltonian whose eigenvalues correspond
to the nontrivial zeros of the Riemann zeta function. We consider a
two-dimensional Hamiltonian which couples the Berry-Keating Hamiltonian to the
number operator on the half-line via a unitary transformation. We demonstrate
that the unitary operator, which is composed of squeeze (dilation) operators
and an exponential of the number operator, confines the eigenfunction of the
Hamiltonian to one dimension as the squeezing parameter tends towards infinity.
The Riemann zeta function appears at the boundary of the resulting confined
wave function and vanishes as a result of the imposed boundary condition. If
the formal argument presented here can be made more rigorous, particularly if
it can be shown rigorously that the Hamiltonian remains self-adjoint under the
imposed boundary condition, then our approach has the potential to imply that
the Riemann hypothesis is true.
Authors: Enderalp Yakaboylu.
However, the complex
current pathways within such nanostructures are difficult to disentangle using
conventional experimental methods. Here, we measure the conductivity of a
technologically relevant Ru/Co bilayer nanostructure in a contact-free fashion
using THz time-domain spectroscopy.
Many modern spintronic technologies, such as spin valves, spin Hall
applications, and spintronic THz emitters, are based on electrons crossing
buried internal interfaces within metallic nanostructures. However, the complex
current pathways within such nanostructures are difficult to disentangle using
conventional experimental methods. Here, we measure the conductivity of a
technologically relevant Ru/Co bilayer nanostructure in a contact-free fashion
using THz time-domain spectroscopy. By applying an effective resistor network
to the data, we resolve the complex current pathways within the nanostructure
and determine the degree of electronic transparency of the internal interface
between the Ru and Co nanolayers.
Authors: Nicolas S. Beermann, Savio Fabretti, Karsten Rott, Hassan A. Hafez, Günter Reiss, Dmitry Turchinovich.
Our study utilizes archival Hubble Space Telescope data obtained with the Advanced Camera for Surveys using multiple filters (GO-10246). In this study, we investigate the companion population of the ONC with a double point-spread function (PSF) fitting algorithm sensitive to separations larger than 10au (0.025") using empirical PSF models. We find the companion frequency in the ONC is consistent with the Galactic field population, likely from high transient stellar density states, and a probability of 0.002 that it is consistent with that of Taurus. We also find the ONC mass ratio distribution is consistent with the field and Taurus, potentially indicative of its primordial nature, a direct outcome of the star formation process.
We present updated results constraining multiplicity demographics for the
stellar population of the Orion Nebula Cluster (ONC, a high-mass, high-density
star-forming region), across primary masses 0.08-0.7M$_{\odot}$. Our study
utilizes archival Hubble Space Telescope data obtained with the Advanced Camera
for Surveys using multiple filters (GO-10246). Previous multiplicity surveys in
low-mass, low-density associations like Taurus identify an excess of companions
to low-mass stars roughly twice that of the Galactic field and find the mass
ratio distribution consistent with the field. Previously, we found the
companion frequency to low-mass stars in the ONC is consistent with the
Galactic field over mass ratios=0.6-1.0 and projected separations=30-160au,
without placing constraints on the mass ratio distribution. In this study, we
investigate the companion population of the ONC with a double point-spread
function (PSF) fitting algorithm sensitive to separations larger than 10au
(0.025") using empirical PSF models. We identified 44 companions (14 new), and
with a Bayesian analysis, estimate the companion frequency to low-mass stars in
the ONC =0.13$^{+0.05}_{-0.03}$ and the power law fit index to the mass ratio
distribution =2.08$^{+1.03}_{-0.85}$ over all mass ratios and projected
separations of 10-200au. We find the companion frequency in the ONC is
consistent with the Galactic field population, likely from high transient
stellar density states, and a probability of 0.002 that it is consistent with
that of Taurus. We also find the ONC mass ratio distribution is consistent with
the field and Taurus, potentially indicative of its primordial nature, a direct
outcome of the star formation process.
Authors: Matthew De Furio, Christopher Liu, Michael R. Meyer, Megan Reiter, Adam Kraus, Trent Dupuy, John Monnier.
The properties of the $X(3872)$ and its spin partner, the $X(4014)$, are
studied both in vacuum and at finite temperature. By incorporating the thermal spectral functions of open charm
mesons, the calculation is extended to finite temperature. By applying heavy-quark flavor symmetry, the properties of their bottom
counterparts in the axial-vector and tensor channels are also predicted. All
the dynamically generated states show a decreasing mass and acquire an
increasing decay width with temperature, following the trend observed in their
meson constituents.
The properties of the $X(3872)$ and its spin partner, the $X(4014)$, are
studied both in vacuum and at finite temperature. Using an effective hadron
theory based on the hidden-gauge Lagrangian, the $X(3872)$ is dynamically
generated from the $s$-wave rescattering of a pair of pseudoscalar and vector
charm mesons. By incorporating the thermal spectral functions of open charm
mesons, the calculation is extended to finite temperature. Similarly, the
properties of the $X(4014)$ are obtained out of the scattering of charm vector
mesons. By applying heavy-quark flavor symmetry, the properties of their bottom
counterparts in the axial-vector and tensor channels are also predicted. All
the dynamically generated states show a decreasing mass and acquire an
increasing decay width with temperature, following the trend observed in their
meson constituents. These results are relevant in relativistic heavy-ion
collisions at high energies, in analyses of the collective medium formed after
hadronization or in femtoscopic studies, and can be tested in lattice-QCD
calculations exploring the melting of heavy mesons at finite temperature.
Authors: Gloria Montaña, Angels Ramos, Laura Tolos, Juan M. Torres-Rincon.
We investigate the second-order nonlinear electronic thermal transport induced by temperature gradient. We develop the quantum kinetic theory framework to describe thermal transport in presence of a temperature gradient. We employ the developed theory to study the thermal response in tilted massive Dirac systems. We show that besides the different scattering time dependence, the various current contributions have distinct temperature dependence in the low temperature limit.
We investigate the second-order nonlinear electronic thermal transport
induced by temperature gradient. We develop the quantum kinetic theory
framework to describe thermal transport in presence of a temperature gradient.
Using this, we predict an intrinsic scattering time independent nonlinear
thermal current in addition to the known extrinsic nonlinear Drude and Berry
curvature dipole contributions. We show that the intrinsic thermal current is
determined by the band geometric quantities and is non-zero only in systems
where both the space inversion and time-reversal symmetries are broken. We
employ the developed theory to study the thermal response in tilted massive
Dirac systems. We show that besides the different scattering time dependence,
the various current contributions have distinct temperature dependence in the
low temperature limit. Our systematic and comprehensive theory for nonlinear
thermal transport paves the way for future theoretical and experimental studies
on intrinsic thermal responses.
Authors: Harsh Varshney, Kamal Das, Pankaj Bhalla, Amit Agarwal.
It is well known that blazars can show variability on a wide range of time
scales. Specifically, these
sources are PKS 0454$-$234, S5 0716+714, OJ 014, PG 1553+113, and PKS
2155$-$304.
It is well known that blazars can show variability on a wide range of time
scales. This behavior can include periodicity in their $\gamma$-ray emission,
whose clear detection remains an ongoing challenge, partly due to the inherent
stochasticity of the processes involved and also the lack of adequately-well
sampled light curves. We report on a systematic search for periodicity in a
sample of 24 blazars, using twelve well-established methods applied to
Fermi-LAT data. The sample comprises the most promising candidates selected
from a previous study, extending the light curves from nine to twelve years and
broadening the energy range analyzed from $>$1 GeV to $>$0.1 GeV. These
improvements allow us to build a sample of blazars that display a period
detected with global significance $\gtrsim3\,\sigma$. Specifically, these
sources are PKS 0454$-$234, S5 0716+714, OJ 014, PG 1553+113, and PKS
2155$-$304. Periodic $\gamma$-ray emission may be an indication of modulation
of the jet power, particle composition, or geometry but most likely originates
in the accretion disk, possibly indicating the presence of a supermassive black
hole binary system.
Authors: P. Peñil, M. Ajello, S. Buson, A. Domínguez, J. R. Westernacher-Schneider, J. Zrake.
In this survey paper we discuss some recent results and related open questions in additive combinatorics, in particular, questions about sumsets in finite abelian groups.
In this survey paper we discuss some recent results and related open
questions in additive combinatorics, in particular, questions about sumsets in
finite abelian groups.
Authors: Bela Bajnok.