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.
Extended Reality (XR) includes Virtual Reality (VR), Augmented Reality (AR)
and Mixed Reality (MR). XR is an emerging technology that simulates a realistic
environment for users. Thus, performance optimization plays an
essential role in many industry-standard XR applications. Our analysis
identified 14 types of performance bugs, including 12 types of bugs related to
UE settings issues and two types of CPP source code-related issues. To further
assist developers in detecting performance bugs based on the identified bug
patterns, we also developed a static analyzer, UEPerfAnalyzer, that can detect
performance bugs in both configuration files and source code.
Extended Reality (XR) includes Virtual Reality (VR), Augmented Reality (AR)
and Mixed Reality (MR). XR is an emerging technology that simulates a realistic
environment for users. XR techniques have provided revolutionary user
experiences in various application scenarios (e.g., training, education,
product/architecture design, gaming, remote conference/tour, etc.). Due to the
high computational cost of rendering real-time animation in limited-resource
devices and constant interaction with user activity, XR applications often face
performance bottlenecks, and these bottlenecks create a negative impact on the
user experience of XR software. Thus, performance optimization plays an
essential role in many industry-standard XR applications. Even though
identifying performance bottlenecks in traditional software (e.g., desktop
applications) is a widely explored topic, those approaches cannot be directly
applied within XR software due to the different nature of XR applications.
Moreover, XR applications developed in different frameworks such as Unity and
Unreal Engine show different performance bottleneck patterns and thus,
bottleneck patterns of Unity projects can't be applied for Unreal Engine
(UE)-based XR projects. To fill the knowledge gap for XR performance
optimizations of Unreal Engine-based XR projects, we present the first
empirical study on performance optimizations from seven UE XR projects, 78 UE
XR discussion issues and three sources of UE documentation. Our analysis
identified 14 types of performance bugs, including 12 types of bugs related to
UE settings issues and two types of CPP source code-related issues. To further
assist developers in detecting performance bugs based on the identified bug
patterns, we also developed a static analyzer, UEPerfAnalyzer, that can detect
performance bugs in both configuration files and source code.
Authors: Jason Hogan, Aaron Salo, Dhia Elhaq Rzig, Foyzul Hassan, Bruce Maxim.
First, we provide an analytical characterization for the optimal compression strategy for data with binary labels. We further show the improvements of our formulations over the information-bottleneck methods in classification performance.
We formulate the problem of performing optimal data compression under the
constraints that compressed data can be used for accurate classification in
machine learning. We show that this translates to a problem of minimizing the
mutual information between data and its compressed version under the constraint
on error probability of classification is small when using the compressed data
for machine learning. We then provide analytical and computational methods to
characterize the optimal trade-off between data compression and classification
error probability. First, we provide an analytical characterization for the
optimal compression strategy for data with binary labels. Second, for data with
multiple labels, we formulate a set of convex optimization problems to
characterize the optimal tradeoff, from which the optimal trade-off between the
classification error and compression efficiency can be obtained by numerically
solving the formulated optimization problems. We further show the improvements
of our formulations over the information-bottleneck methods in classification
performance.
Authors: Jingchao Gao, Ao Tang, Weiyu Xu.
This paper takes a closer look at these four tasks. One hypothesis is that
U-shaped scaling occurs when a task comprises a ''true task'' and a
''distractor task''. Medium-size models can do the distractor task, which hurts
performance, while only large-enough models can ignore the distractor task and
do the true task. The existence of U-shaped scaling implies that inverse
scaling may not hold for larger models.
Although scaling language models improves performance on a range of tasks,
there are apparently some scenarios where scaling hurts performance. For
instance, the Inverse Scaling Prize Round 1 identified four ''inverse scaling''
tasks, for which performance gets worse for larger models. These tasks were
evaluated on models of up to 280B parameters, trained up to 500 zettaFLOPs of
compute.
This paper takes a closer look at these four tasks. We evaluate models of up
to 540B parameters, trained on five times more compute than those evaluated in
the Inverse Scaling Prize. With this increased range of model sizes and
training compute, three out of the four tasks exhibit what we call ''U-shaped
scaling'' -- performance decreases up to a certain model size, and then
increases again up to the largest model evaluated. One hypothesis is that
U-shaped scaling occurs when a task comprises a ''true task'' and a
''distractor task''. Medium-size models can do the distractor task, which hurts
performance, while only large-enough models can ignore the distractor task and
do the true task. The existence of U-shaped scaling implies that inverse
scaling may not hold for larger models.
Second, we evaluate the inverse scaling tasks using chain-of-thought (CoT)
prompting, in addition to basic prompting without CoT. With CoT prompting, all
four tasks show either U-shaped scaling or positive scaling, achieving perfect
solve rates on two tasks and several sub-tasks. This suggests that the term
"inverse scaling task" is under-specified -- a given task may be inverse
scaling for one prompt but positive or U-shaped scaling for a different prompt.
Authors: Jason Wei, Yi Tay, Quoc V. Le.
The extraordinarily bright gamma-ray burst GRB 221009A was observed by a large number of observatories, from radio frequencies to gamma-rays. Gamma rays at these energies are expected to be absorbed by pair-production events on background photons when travelling intergalactic distances. We reconsider this scenario and account for astrophysical uncertainties due to poorly known magnetic fields and background photon densities.
The extraordinarily bright gamma-ray burst GRB 221009A was observed by a
large number of observatories, from radio frequencies to gamma-rays. Of
particular interest are the reported observations of photon-like air showers of
very high energy: an 18 TeV event in LHAASO and a 251 TeV event at Carpet-2.
Gamma rays at these energies are expected to be absorbed by pair-production
events on background photons when travelling intergalactic distances. Several
works have sought to explain the observations of these events, assuming they
originate from GRB 221009A, by invoking axion-like particles (ALPs). We
reconsider this scenario and account for astrophysical uncertainties due to
poorly known magnetic fields and background photon densities. We find that,
robustly, the ALP scenario cannot simultaneously account for an 18 TeV and a
251 TeV photon from GRB 221009A.
Authors: Pierluca Carenza, M. C. David Marsh.
We consider the normal approximation of Kabanov-Skorohod integrals on a
general Poisson space.
We consider the normal approximation of Kabanov-Skorohod integrals on a
general Poisson space. Our bounds are for the Wasserstein and the Kolmogorov
distance and involve only difference operators of the integrand of the
Kabanov-Skorohod integral. The proofs rely on the Malliavin-Stein method and,
in particular, on multiple applications of integration by parts formulae. As
examples, we study some linear statistics of point processes that can be
constructed by Poisson embeddings and functionals related to Pareto optimal
points of a Poisson process.
Authors: Günter Last, Ilya Molchanov, Matthias Schulte.
We have computed the Kretschmann invariant of the metric to study the singularities and verify that it reduces to general relativity's Kretschmann invariant as $\alpha\rightarrow0$.
In this article, we have examined the existence of a static spherically
symmetric solution in the Scalar Tensor Vector Gravity (STVG) and investigated
its horizon distances to develop boundary limitations for our test particle. We
have computed the Kretschmann invariant of the metric to study the
singularities and verify that it reduces to general relativity's Kretschmann
invariant as $\alpha\rightarrow0$. Further, we investigated the orbital motion
of a time-like and light-like test particle around the static solution by
developing an effective potential and the radius of the innermost stable
circular orbit (ISCO).
Authors: Devansh Shukla, Abhay Menon A, Kamlesh Pathak.
The inclusion of NICER data in our analyses results in
stiffened EOS posterior because of the massive pulsar PSR J0740+6620. We give
results at nuclear saturation density for the nuclear incompressibility, the
symmetry energy and its slope, as well as the nucleon effective mass, from our
analysis of those observational data.
The observations of optical and near-infrared counterparts of binary neutron
star mergers not only enrich our knowledge about the abundance of heavy
elements in the Universe, or help reveal the remnant object just after the
merger as generally known, but also can effectively constrain dense nuclear
matter properties and the equation of state (EOS) in the interior of the
merging stars. Following the relativistic mean-field description of nuclear
matter, we perform the Bayesian inference of the EOS and the nuclear matter
properties using the first multi-messenger event GW170817/AT2017gfo, together
with the NICER mass-radius measurements of pulsars. The kilonova is described
by a radiation-transfer model with the dynamical ejecta, and light curves
connect with the EOS through the quasi-universal relations between the ejecta
properties (the ejected mass, velocity, opacity or electron fraction) and
binary parameters (the mass ratio and reduced tidal deformability). It is found
that the posterior distributions of the reduced tidal deformability from the
AT2017gfo analysis display a bimodal structure, with the first peak enhanced by
the GW170817 data, leading to slightly softened posterior EOSs, while the
second peak cannot be achieved by a nuclear EOS with saturation properties in
their empirical ranges. The inclusion of NICER data in our analyses results in
stiffened EOS posterior because of the massive pulsar PSR J0740+6620. We give
results at nuclear saturation density for the nuclear incompressibility, the
symmetry energy and its slope, as well as the nucleon effective mass, from our
analysis of those observational data.
Authors: Zhenyu Zhu, Ang Li, Tong liu.
Our extensive experiments have demonstrated that SAP-DETR achieves 1.4 times convergency speed with competitive performance. Under the standard training scheme, SAP-DETR stably promotes the SOTA approaches by 1.0 AP. Based on ResNet-DC-101, SAP-DETR achieves 46.9 AP.
Recently, the dominant DETR-based approaches apply central-concept spatial
prior to accelerate Transformer detector convergency. These methods gradually
refine the reference points to the center of target objects and imbue object
queries with the updated central reference information for spatially
conditional attention. However, centralizing reference points may severely
deteriorate queries' saliency and confuse detectors due to the indiscriminative
spatial prior. To bridge the gap between the reference points of salient
queries and Transformer detectors, we propose SAlient Point-based DETR
(SAP-DETR) by treating object detection as a transformation from salient points
to instance objects. In SAP-DETR, we explicitly initialize a query-specific
reference point for each object query, gradually aggregate them into an
instance object, and then predict the distance from each side of the bounding
box to these points. By rapidly attending to query-specific reference region
and other conditional extreme regions from the image features, SAP-DETR can
effectively bridge the gap between the salient point and the query-based
Transformer detector with a significant convergency speed. Our extensive
experiments have demonstrated that SAP-DETR achieves 1.4 times convergency
speed with competitive performance. Under the standard training scheme,
SAP-DETR stably promotes the SOTA approaches by 1.0 AP. Based on ResNet-DC-101,
SAP-DETR achieves 46.9 AP.
Authors: Yang Liu, Yao Zhang, Yixin Wang, Yang Zhang, Jiang Tian, Zhongchao Shi, Jianping Fan, Zhiqiang He.
Reliable measurement of dependence between variables is essential in many
applications of statistics and machine learning. We support these claims through some
preliminary results using simulated data.
Reliable measurement of dependence between variables is essential in many
applications of statistics and machine learning. Current approaches for
dependence estimation, especially density-based approaches, lack in precision,
robustness and/or interpretability (in terms of the type of dependence being
estimated). We propose a two-step approach for dependence quantification
between random variables: 1) We first decompose the probability density
functions (PDF) of the variables involved in terms of multiple local moments of
uncertainty that systematically and precisely identify the different regions of
the PDF (with special emphasis on the tail-regions). 2) We then compute an
optimal transport map to measure the geometric similarity between the
corresponding sets of decomposed local uncertainty moments of the variables.
Dependence is then determined by the degree of one-to-one correspondence
between the respective uncertainty moments of the variables in the optimal
transport map. We utilize a recently introduced Gaussian reproducing kernel
Hilbert space (RKHS) based framework for multi-moment uncertainty decomposition
of the variables. Being based on the Gaussian RKHS, our approach is robust
towards outliers and monotone transformations of data, while the multiple
moments of uncertainty provide high resolution and interpretability of the type
of dependence being quantified. We support these claims through some
preliminary results using simulated data.
Authors: Rishabh Singh, Jose C. Principe.
We study truthful mechanisms for welfare maximization in online bipartite matching. In our (multi-parameter) setting, every buyer is associated with a (possibly private) desired set of items, and has a private value for being assigned an item in her desired set. This poses a significant challenge to the design of truthful mechanisms, due to the ability of buyers to strategize over future rounds.
We study truthful mechanisms for welfare maximization in online bipartite
matching. In our (multi-parameter) setting, every buyer is associated with a
(possibly private) desired set of items, and has a private value for being
assigned an item in her desired set. Unlike most online matching settings,
where agents arrive online, in our setting the items arrive online in an
adversarial order while the buyers are present for the entire duration of the
process. This poses a significant challenge to the design of truthful
mechanisms, due to the ability of buyers to strategize over future rounds. We
provide an almost full picture of the competitive ratios in different
scenarios, including myopic vs. non-myopic agents, tardy vs. prompt payments,
and private vs. public desired sets. Among other results, we identify the
frontier for which the celebrated $e/(e-1)$ competitive ratio for the
vertex-weighted online matching of Karp, Vazirani and Vazirani extends to
truthful agents and online items.
Authors: Michal Feldman, Federico Fusco, Stefano Leonardi, Simon Mauras, Rebecca Reiffenhäuser.