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.
End-to-End automatic speech recognition (ASR) models aim to learn a
generalised speech representation to perform recognition. In this domain there
is little research to analyse internal representation dependencies and their
relationship to modelling approaches. It was found that specific
neural representations within the transformer layers exhibit correlated
behaviour which impacts recognition performance.
End-to-End automatic speech recognition (ASR) models aim to learn a
generalised speech representation to perform recognition. In this domain there
is little research to analyse internal representation dependencies and their
relationship to modelling approaches. This paper investigates cross-domain
language model dependencies within transformer architectures using SVCCA and
uses these insights to exploit modelling approaches. It was found that specific
neural representations within the transformer layers exhibit correlated
behaviour which impacts recognition performance.
Altogether, this work provides analysis of the modelling approaches affecting
contextual dependencies and ASR performance, and can be used to create or adapt
better performing End-to-End ASR models and also for downstream tasks.
Authors: Anna Ollerenshaw, Md Asif Jalal, Thomas Hain.
Virtual Reality (VR) is an emerging technique that provides a unique real-time experience for users. However, testing VR applications is challenging due to their nature which necessitates physical interactivity, and their reliance on hardware systems. Despite the recent advancements in VR technology and its usage scenarios, we still know little about VR application testing. To fill up this knowledge gap, we performed an empirical study on 97 open-source VR applications including 28 industrial projects. Finally, through manual analysis of 220 test cases from four VR applications and 281 test cases from four non-VR applications, we identified that VR applications require specific categories of test cases to ensure VR application quality attributes.
Virtual Reality (VR) is an emerging technique that provides a unique
real-time experience for users. VR technologies have provided revolutionary
user experiences in various scenarios (e.g., training, education,
product/architecture design, gaming, remote conference/tour, etc.). However,
testing VR applications is challenging due to their nature which necessitates
physical interactivity, and their reliance on hardware systems. Despite the
recent advancements in VR technology and its usage scenarios, we still know
little about VR application testing. To fill up this knowledge gap, we
performed an empirical study on 97 open-source VR applications including 28
industrial projects. Our analysis identified that 74.2% of the VR projects
evaluated did not have any tests, and for the VR projects that did, the median
functional-method to test-method ratio was low in comparison to other project
categories. Moreover, we uncovered tool support issues concerning the
measurement of VR code coverage, and the code coverage and assertion density
results we were able to generate were also relatively low, as they respectively
had averages of 15.63% and 17.69%. Finally, through manual analysis of 220 test
cases from four VR applications and 281 test cases from four non-VR
applications, we identified that VR applications require specific categories of
test cases to ensure VR application quality attributes. We believe that our
findings constitute a call to action for the VR development community to
improve testing aspects and provide directions for software engineering
researchers to develop advanced techniques for automatic test case generation
and test quality analysis for VR applications.
Authors: Dhia Elhaq Rzig, Nafees Iqbal, Isabella Attisano, Xue Qin, Foyzul Hassan.
Reinforcement learning in partially observable domains is challenging due to
the lack of observable state information. Thankfully, learning offline in a
simulator with such state information is often possible. Our approach can leverage the
fully-observable policy for exploration and parts of the domain that are fully
observable while still being able to learn under partial observability. On six
robotics domains, our method outperforms pure imitation, pure reinforcement
learning, the sequential or parallel combination of both types, and a recent
state-of-the-art method in the same setting. A successful policy transfer to a
physical robot in a manipulation task from pixels shows our approach's
practicality in learning interesting policies under partial observability.
Reinforcement learning in partially observable domains is challenging due to
the lack of observable state information. Thankfully, learning offline in a
simulator with such state information is often possible. In particular, we
propose a method for partially observable reinforcement learning that uses a
fully observable policy (which we call a state expert) during offline training
to improve online performance. Based on Soft Actor-Critic (SAC), our agent
balances performing actions similar to the state expert and getting high
returns under partial observability. Our approach can leverage the
fully-observable policy for exploration and parts of the domain that are fully
observable while still being able to learn under partial observability. On six
robotics domains, our method outperforms pure imitation, pure reinforcement
learning, the sequential or parallel combination of both types, and a recent
state-of-the-art method in the same setting. A successful policy transfer to a
physical robot in a manipulation task from pixels shows our approach's
practicality in learning interesting policies under partial observability.
Authors: Hai Nguyen, Andrea Baisero, Dian Wang, Christopher Amato, Robert Platt.
Let $Q$ be a bipartite quiver with vertex set $Q_0$ such that the number of arrows between any two source and sink vertices is constant. As a key step in our approach, we first solve the polytopal problem for semi-invariants of $Q$ and its flag-extensions. Specifically, let $Q_{\beta}$ be the flag-extension of $Q$ obtained by attaching a flag $\mathcal{F}(x)$ of length $\beta(x)-1$ at every vertex $x$ of $Q$, and let $\widetilde{\beta}$ be the extension of $\beta$ to $Q_{\beta}$ that takes values $1, \ldots, \beta(x)$ along the vertices of the flag $\mathcal{F}(x)$ for every vertex $x$ of $Q$. For an integral weight $\widetilde{\sigma}$ of $Q_{\beta}$, let $K_{\widetilde{\sigma}}$ be the dimension of the space of semi-invariants of weight $\widetilde{\sigma}$ on the representation space of $\widetilde{\beta}$-dimensional complex representations of $Q_{\beta}$. We show that $K_{\widetilde{\sigma}}$ can be expressed as the number of lattice points of a certain hive-type polytope.
Let $Q$ be a bipartite quiver with vertex set $Q_0$ such that the number of
arrows between any two source and sink vertices is constant. Let
$\beta=(\beta(x))_{x \in Q_0}$ be a dimension vector of $Q$ with positive
integer coordinates, and let $\Delta(Q, \beta)$ be the moment cone associated
to $(Q, \beta)$. We show that the membership problem for $\Delta(Q, \beta)$ can
be solved in strongly polynomial time.
As a key step in our approach, we first solve the polytopal problem for
semi-invariants of $Q$ and its flag-extensions. Specifically, let $Q_{\beta}$
be the flag-extension of $Q$ obtained by attaching a flag $\mathcal{F}(x)$ of
length $\beta(x)-1$ at every vertex $x$ of $Q$, and let $\widetilde{\beta}$ be
the extension of $\beta$ to $Q_{\beta}$ that takes values $1, \ldots, \beta(x)$
along the vertices of the flag $\mathcal{F}(x)$ for every vertex $x$ of $Q$.
For an integral weight $\widetilde{\sigma}$ of $Q_{\beta}$, let
$K_{\widetilde{\sigma}}$ be the dimension of the space of semi-invariants of
weight $\widetilde{\sigma}$ on the representation space of
$\widetilde{\beta}$-dimensional complex representations of $Q_{\beta}$.
We show that $K_{\widetilde{\sigma}}$ can be expressed as the number of
lattice points of a certain hive-type polytope. This polytopal description
together with Derksen-Weyman's Saturation Theorem for quiver semi-invariants
allows us to use Tardos's algorithm to solve the membership problem for
$\Delta(Q,\beta)$ in strongly polynomial time. In particular, this yields a
strongly polynomial time algorithm for solving the generic semi-stability
problem for representations of $Q$ and $Q_\beta$.
Authors: Calin Chindris, Brett Collins, Daniel Kline.
An asymptotic
expansion of the respective eigenfunction as $\varepsilon\rightarrow 0$ is also
obtained.
In this paper we consider the two-dimensional Schr\"odinger operator with an
attractive potential which is a multiple of the characteristic function of an
unbounded strip-shaped region, whose thickness is varying and is determined by
the function $\mathbb{R}\ni x \mapsto d+\varepsilon f(x)$, where $d > 0$ is a
constant, $\varepsilon > 0$ is a small parameter, and $f$ is a compactly
supported continuous function. We prove that if $\int_{\mathbb{R}} f
\,\mathsf{d} x > 0$, then the respective Schr\"odinger operator has a unique
simple eigenvalue below the threshold of the essential spectrum for all
sufficiently small $\varepsilon >0$ and we obtain the asymptotic expansion of
this eigenvalue in the regime $\varepsilon\rightarrow 0$. An asymptotic
expansion of the respective eigenfunction as $\varepsilon\rightarrow 0$ is also
obtained. In the case that $\int_{\mathbb{R}} f \,\mathsf{d} x < 0$ we prove
that the discrete spectrum is empty for all sufficiently small $\varepsilon >
0$. In the critical case $\int_{\mathbb{R}} f \,\mathsf{d} x = 0$, we derive a
sufficient condition for the existence of a unique bound state for all
sufficiently small $\varepsilon > 0$.
Authors: Pavel Exner, Sylwia Kondej, Vladimir Lotoreichik.
The weights involved are arbitrary nonnegative sequences and may differ in the domain and codomain spaces. Simplifications of these formulas are derived in the case of these operators acting on power-weighted $\ell^\infty$.
For a large class of operators acting between weighted $\ell^\infty$ spaces,
exact formulas are given for their norms and the norms of their restrictions to
the cones of nonnegative sequences; nonnegative, nonincreasing sequences; and
nonnegative, nondecreasing sequences. The weights involved are arbitrary
nonnegative sequences and may differ in the domain and codomain spaces. The
results are applied to the Ces\'aro and Copson operators, giving their norms
and their distances to the identity operator on the whole space and on the
cones. Simplifications of these formulas are derived in the case of these
operators acting on power-weighted $\ell^\infty$. As an application, best
constants are given for inequalities relating the weighted $\ell^\infty$ norms
of the Ces\'aro and Copson operators both for general weights and for power
weights.
Authors: Sorina Barza, Bizuneh Minda Demissie, Gord Sinnamon.
This extends Szarek's optimal Khinchin inequality (1976) which
corresponds to $q=1$.
Ball's celebrated cube slicing (1986) asserts that among hyperplane sections
of the cube in $\mathbb{R}^n$, the central section orthogonal to
$(1,1,0,\dots,0)$ has the greatest volume. We show that the same continues to
hold for slicing $\ell_p$ balls when $p > 10^{15}$, as well as that the same
hyperplane minimizes the volume of projections of $\ell_q$ balls for $1 < q < 1
+ 10^{-12}$. This extends Szarek's optimal Khinchin inequality (1976) which
corresponds to $q=1$. These results thus address the resilience of the
Ball--Szarek hyperplane in the ranges $2 < p < \infty$ and $1 < q < 2$, where
analysis of the extremizers has been elusive since the works of Koldobsky
(1998), Barthe--Naor (2002) and Oleszkiewicz (2003).
Authors: Alexandros Eskenazis, Piotr Nayar, Tomasz Tkocz.
We consider Schr\"{o}dinger equations with real quadratic Hamiltonians, for which the Wigner distribution of the solution at a given time equals, up to a linear coordinate transformation, the Wigner distribution of the initial condition.
We consider Schr\"{o}dinger equations with real quadratic Hamiltonians, for
which the Wigner distribution of the solution at a given time equals, up to a
linear coordinate transformation, the Wigner distribution of the initial
condition. Based on Hardy's uncertainty principle for the joint time-frequency
representation, we prove a uniqueness result for such Schr\"{o}dinger
equations, where the solution cannot have strong decay at two distinct times.
This approach reproduces known, sharp results for the free Schr\"{o}dinger
equation and the harmonic oscillator, and we also present an explicit scheme
for quadratic systems based on positive definite matrices.
Authors: Helge Knutsen.
A diffusion auction is a market to sell commodities over a social network,
where the challenge is to incentivize existing buyers to invite their neighbors
in the network to join the market. We observe that strategic agents may gain an unfair advantage in existing
mechanisms through such attacks.
A diffusion auction is a market to sell commodities over a social network,
where the challenge is to incentivize existing buyers to invite their neighbors
in the network to join the market. Existing mechanisms have been designed to
solve the challenge in various settings, aiming at desirable properties such as
non-deficiency, incentive compatibility and social welfare maximization. Since
the mechanisms are employed in dynamic networks with ever-changing structures,
buyers could easily generate fake nodes in the network to manipulate the
mechanisms for their own benefits, which is commonly known as the Sybil attack.
We observe that strategic agents may gain an unfair advantage in existing
mechanisms through such attacks. To resist this potential attack, we propose
two diffusion auction mechanisms, the Sybil tax mechanism (STM) and the Sybil
cluster mechanism (SCM), to achieve both Sybil-proofness and incentive
compatibility in the single-item setting. Our proposal provides the first
mechanisms to protect the interests of buyers against Sybil attacks with a mild
sacrifice of social welfare and revenue.
Authors: Hongyin Chen, Xiaotie Deng, Ying Wang, Yue Wu, Dengji Zhao.
We introduce a novel approach to reveal ordering fluctuations in sheared dense suspensions, using line scanning in a combined rheometer and laser scanning confocal microscope. We validate the technique with a moderately dense suspension, observing modest shear-induced ordering and a nearly linear flow profile. Higher applied stress produces shear thickening with large fluctuations in boundary stress which we find are accompanied by dramatic fluctuations in suspension flow speeds.
We introduce a novel approach to reveal ordering fluctuations in sheared
dense suspensions, using line scanning in a combined rheometer and laser
scanning confocal microscope. We validate the technique with a moderately dense
suspension, observing modest shear-induced ordering and a nearly linear flow
profile. At high concentration ($\phi = 0.55$) and applied stress just below
shear thickening, we report ordering fluctuations with high temporal
resolution, and directly measure a decrease in order with distance from the
suspension's bottom boundary as well as a direct correlation between order and
particle concentration. Higher applied stress produces shear thickening with
large fluctuations in boundary stress which we find are accompanied by dramatic
fluctuations in suspension flow speeds. The peak flow rates are independent of
distance from the suspension boundary, indicating that they likely arise from
transient jamming that creates solid-like aggregates of particles moving
together, but only briefly because the high speed fluctuations are interspersed
with regions flowing much more slowly, suggesting that shear thickening
suspensions possess complex internal structural dynamics, even in relatively
simple geometries.
Authors: Joia M. Miller, Daniel L. Blair, Jeffrey S. Urbach.