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.
The implementation is
discussed, demonstrated and provided as open-source software.
Efficient integrators with sensitivity propagation are an essential
ingredient for the numerical solution of optimal control problems. This paper
gives an overview on the acados integrators, their Python interface and
presents a workflow that allows using them with their sensitivities within a
nonlinear programming (NLP) solver interfaced by CasADi. The implementation is
discussed, demonstrated and provided as open-source software. The computation
times of the proposed integrator and its sensitivity computation are compared
to the native CasADi collocation integrator, CVODES and IDAS on different
examples. A speedup of one order of magnitude for simulation and of up to three
orders of magnitude for the forward sensitivity propagation is shown for an
airborne wind energy system model.
Authors: Jonathan Frey, Jochem De Schutter, Moritz Diehl.
We obtain text embeddings using Sentence-BERT applied to titles and abstracts. We obtain citation and inventor embeddings through TransE that is trained using the corresponding knowledge graphs. We apply the proposed patent retrieval method to a set of patents corresponding to a product family and an inventor's portfolio.
Patent retrieval influences several applications within engineering design
research, education, and practice as well as applications that concern
innovation, intellectual property, and knowledge management etc. In this
article, we propose a method to retrieve patents relevant to an initial set of
patents, by synthesizing state-of-the-art techniques among natural language
processing and knowledge graph embedding. Our method involves a patent
embedding that captures text, citation, and inventor information, which
individually represent different facets of knowledge communicated through a
patent document. We obtain text embeddings using Sentence-BERT applied to
titles and abstracts. We obtain citation and inventor embeddings through TransE
that is trained using the corresponding knowledge graphs. We identify using a
classification task that the concatenation of text, citation, and inventor
embeddings offers a plausible representation of a patent. While the proposed
patent embedding could be used to associate a pair of patents, we observe using
a recall task that multiple initial patents could be associated with a target
patent using mean cosine similarity, which could then be utilized to rank all
target patents and retrieve the most relevant ones. We apply the proposed
patent retrieval method to a set of patents corresponding to a product family
and an inventor's portfolio.
Authors: L Siddharth, Guangtong Li, Jianxi Luo.
Following the removal of axial confinement the momentum distribution of a
Tonks-Girardeau gas approaches that of a system of noninteracting spinless
fermions in the initial harmonic trap. This phenomenon, called dynamical
fermionization, has been experimentally confirmed in the case of the
Lieb-Liniger model and theoretically predicted in the case of multicomponent
systems at zero temperature. In the case of the Gaudin-Yang model we check numerically our
analytical predictions using the results obtained from a nonequilibrium
generalization of Lenard's formula describing the time evolution of the
field-field correlators.
Following the removal of axial confinement the momentum distribution of a
Tonks-Girardeau gas approaches that of a system of noninteracting spinless
fermions in the initial harmonic trap. This phenomenon, called dynamical
fermionization, has been experimentally confirmed in the case of the
Lieb-Liniger model and theoretically predicted in the case of multicomponent
systems at zero temperature. We prove analytically that for all spinor gases
with strong repulsive contact interactions at finite temperature the momentum
distribution after release from the trap asymptotically approaches that of a
system of spinless fermions at the same temperature but with a renormalized
chemical potential which depends on the number of components of the spinor
system. In the case of the Gaudin-Yang model we check numerically our
analytical predictions using the results obtained from a nonequilibrium
generalization of Lenard's formula describing the time evolution of the
field-field correlators.
Authors: Ovidiu I. Patu.
The resolvents of the discrete Dirichlet/Neumann Laplacians are embedded into the continuum using natural discretization and embedding operators. Norm resolvent convergence to their continuous counterparts is proven with a quadratic rate in the mesh size.
We extend recent results on discrete approximations of the Laplacian in
$\mathbf{R}^d$ with norm resolvent convergence to the corresponding results for
Dirichlet and Neumann Laplacians on a half-space. The resolvents of the
discrete Dirichlet/Neumann Laplacians are embedded into the continuum using
natural discretization and embedding operators. Norm resolvent convergence to
their continuous counterparts is proven with a quadratic rate in the mesh size.
These results generalize with a limited rate to also include operators with a
real, bounded, and H\"older continuous potential, as well as certain functions
of the Dirichlet/Neumann Laplacians, including any positive real power.
Authors: Horia Cornean, Henrik Garde, Arne Jensen.
The state space for solutions of the compressible Euler equations with a
general equation of state is examined. An arbitrary equation of state is
allowed, subject only to the physical requirements of thermodynamics.
The state space for solutions of the compressible Euler equations with a
general equation of state is examined. An arbitrary equation of state is
allowed, subject only to the physical requirements of thermodynamics. An
invariant region of the resulting Euler system is identified and the convexity
property of this region is justified by using only very minimal thermodynamical
assumptions. Finally, we show how an invariant-region-preserving (IRP) limiter
can be constructed for use in high order finite-volume type schemes to solve
the compressible Euler equations with a general constitutive relation.
Authors: Hailiang Liu, Ferdinand Thein.
The collective properties of final state hadrons produced in the high statistics $_{44}^{96}$Ru+$_{44}^{96}$Ru and $_{40}^{96}$Zr+$_{40}^{96}$Zr collisions at $\sqrt{s_\mathrm{NN}} = 200~\mathrm{GeV}$ are found to be significantly different. Such differences were argued to be precise probes of the difference in nucleon distribution in the isobar nuclei. We investigate the $J/\psi$ production in the isobar collision via a relativistic transport approach. The charmonium production provides an independent probe to study the nucleon distribution in the isobar system.
The collective properties of final state hadrons produced in the high
statistics $_{44}^{96}$Ru+$_{44}^{96}$Ru and $_{40}^{96}$Zr+$_{40}^{96}$Zr
collisions at $\sqrt{s_\mathrm{NN}} = 200~\mathrm{GeV}$ are found to be
significantly different. Such differences were argued to be precise probes of
the difference in nucleon distribution in the isobar nuclei. We investigate the
$J/\psi$ production in the isobar collision via a relativistic transport
approach. By comparing the isobar systems according to equal centrality bin and
equal multiplicity bin, we find that the yield ratio of $J/\psi$ is sensitive
to the differences in both the number of binary collisions and the medium
evolution. Besides, the elliptic flow $v_2$ of $J/\psi$ is qualitatively
different from the light hadrons, and the ratio between Ru+Ru and Zr+Zr
collisions is sensitive to the medium evolution. The charmonium production
provides an independent probe to study the nucleon distribution in the isobar
system.
Authors: Jiaxing Zhao, Shuzhe Shi.
Then a three-step inter-graph and intra-graph message
passing is performed to learn the context-dependent representation of the
proposals and query objects. These object representations are used to score the
proposals to generate object localization. The proposed method significantly
outperforms the baselines on four public datasets.
This paper presents a framework for jointly grounding objects that follow
certain semantic relationship constraints given in a scene graph. A typical
natural scene contains several objects, often exhibiting visual relationships
of varied complexities between them. These inter-object relationships provide
strong contextual cues toward improving grounding performance compared to a
traditional object query-only-based localization task. A scene graph is an
efficient and structured way to represent all the objects and their semantic
relationships in the image. In an attempt towards bridging these two modalities
representing scenes and utilizing contextual information for improving object
localization, we rigorously study the problem of grounding scene graphs on
natural images. To this end, we propose a novel graph neural network-based
approach referred to as Visio-Lingual Message PAssing Graph Neural Network
(VL-MPAG Net). In VL-MPAG Net, we first construct a directed graph with object
proposals as nodes and an edge between a pair of nodes representing a plausible
relation between them. Then a three-step inter-graph and intra-graph message
passing is performed to learn the context-dependent representation of the
proposals and query objects. These object representations are used to score the
proposals to generate object localization. The proposed method significantly
outperforms the baselines on four public datasets.
Authors: Aditay Tripathi, Anand Mishra, Anirban Chakraborty.
In fact, in the preconditioner, we reuse the original system matrix thus reducing computational burden. Numerical results corroborate the theoretical analysis and attest of the efficacy of the proposed preconditioning technique on both canonical and realistic scenarios.
We present a Calder\'on preconditioning scheme for the symmetric formulation
of the forward electroencephalographic (EEG) problem that cures both the dense
discretization and the high-contrast breakdown. Unlike existing Calder\'on
schemes presented for the EEG problem, it is refinement-free, that is, the
electrostatic integral operators are not discretized with basis functions
defined on the barycentrically-refined dual mesh. In fact, in the
preconditioner, we reuse the original system matrix thus reducing computational
burden. Moreover, the proposed formulation gives rise to a symmetric,
positive-definite system of linear equations, which allows the application of
the conjugate gradient method, an iterative method that exhibits a smaller
computational cost compared to other Krylov subspace methods applicable to
non-symmetric problems. Numerical results corroborate the theoretical analysis
and attest of the efficacy of the proposed preconditioning technique on both
canonical and realistic scenarios.
Authors: Viviana Giunzioni, John E. Ortiz G., Adrien Merlini, Simon B. Adrian, Francesco P. Andriulli.
The goal of this work is to localize sound sources in visual scenes with a
self-supervised approach. We propose a simple yet effective approach by slightly modifying
the contrastive loss with a negative margin. Extensive experimental results
show that our approach gives on-par or better performance than the
state-of-the-art methods. Furthermore, we demonstrate that the introduction of
a negative margin to existing methods results in a consistent improvement in
performance.
The goal of this work is to localize sound sources in visual scenes with a
self-supervised approach. Contrastive learning in the context of sound source
localization leverages the natural correspondence between audio and visual
signals where the audio-visual pairs from the same source are assumed as
positive, while randomly selected pairs are negatives. However, this approach
brings in noisy correspondences; for example, positive audio and visual pair
signals that may be unrelated to each other, or negative pairs that may contain
semantically similar samples to the positive one. Our key contribution in this
work is to show that using a less strict decision boundary in contrastive
learning can alleviate the effect of noisy correspondences in sound source
localization. We propose a simple yet effective approach by slightly modifying
the contrastive loss with a negative margin. Extensive experimental results
show that our approach gives on-par or better performance than the
state-of-the-art methods. Furthermore, we demonstrate that the introduction of
a negative margin to existing methods results in a consistent improvement in
performance.
Authors: Sooyoung Park, Arda Senocak, Joon Son Chung.
To the best of the author's knowledge, these are the first high dimensional examples of this type.
In this note we construct augmentations of Chekanov-Eliashberg algebras of
certain high dimensional Legendrian submanifolds that are not induced by exact
Lagrangian fillings. The obstructions to the existence of exact Lagrangian
fillings that we use are Seidel's isomorphism and the injectivity of a certain
algebraic map between the corresponding augmentation varieties proven by Gao
and Rutherford. To the best of the author's knowledge, these are the first high
dimensional examples of this type.
Authors: Roman Golovko.