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.
We grow single crystals of MnPd$_5$P and
Mn(Pt$_{1-x}$Pd$_x$)$_5$P by adding Mn into (Pt$_{1-x}$Pd$_{x}$)-P based melts. All compounds in the family adopt the layered anti-CeCoIn$_5$ structure with
space group P4/mmm, and EDS and XRD results indicate that MnPt$_5$P and
MnPd$_5$P form a solid solution. The antiferromagnetic region
makes up a bubble that persists to x $\approx$ 0.009 for T $\approx$ 150 K,
with all samples x $<$ 0.009 recovering their initial ferromagnetic state with
further cooling to base temperature. Over the same low x range we find a
non-monotonic change in the room temperature unit cell volume, further
suggesting that pure MnPt$_5$P is close to an instability. The
Curie temperature increases rapidly with x, rising from T$_C$ $\approx$ 197 K
at x = 0.013 to a maximum of T$_C$ $\approx$ 312 K for x $\approx$ 0.62, and
then falls back to T$_C$ $\approx$ 295 K for pure MnPd$_5$P (x = 1). Given that
Pt and Pd are isoelectronic, this work raises questions as to the origin of the
extreme sensitivity of the magnetic ground state in MnPt$_5$P upon introducing
Pd.
We report the growth and characterization of MnPd$_5$P, a ferromagnet with
T$_C$ $\approx$ 295 K, and conduct a substitutional study with its
antiferromagnetic analogue MnPt$_5$P. We grow single crystals of MnPd$_5$P and
Mn(Pt$_{1-x}$Pd$_x$)$_5$P by adding Mn into (Pt$_{1-x}$Pd$_{x}$)-P based melts.
All compounds in the family adopt the layered anti-CeCoIn$_5$ structure with
space group P4/mmm, and EDS and XRD results indicate that MnPt$_5$P and
MnPd$_5$P form a solid solution. Based on magnetization and resistance data, we
construct a T-x phase diagram for Mn(Pt$_{1-x}$Pd$_x$)$_5$P and demonstrate the
antiferromagnetic order found in MnPt$_5$P is extraordinarily sensitive to Pd
substitution. At low Pd fractions (x $<$ 0.010), the single antiferromagnetic
transition in pure MnPt$_5$P splits into a higher temperature ferromagnetic
transition followed on cooling by a lower temperature ferromagnetic to
antiferromagnetic transition and then by a re-entrant antiferromagnetic to
ferromagnetic transition at lower temperatures. The antiferromagnetic region
makes up a bubble that persists to x $\approx$ 0.009 for T $\approx$ 150 K,
with all samples x $<$ 0.009 recovering their initial ferromagnetic state with
further cooling to base temperature. Over the same low x range we find a
non-monotonic change in the room temperature unit cell volume, further
suggesting that pure MnPt$_5$P is close to an instability. Once x $>$ 0.010,
Mn(Pt$_{1-x}$Pd$_x$)$_5$P undergoes a single ferromagnetic transition. The
Curie temperature increases rapidly with x, rising from T$_C$ $\approx$ 197 K
at x = 0.013 to a maximum of T$_C$ $\approx$ 312 K for x $\approx$ 0.62, and
then falls back to T$_C$ $\approx$ 295 K for pure MnPd$_5$P (x = 1). Given that
Pt and Pd are isoelectronic, this work raises questions as to the origin of the
extreme sensitivity of the magnetic ground state in MnPt$_5$P upon introducing
Pd.
Authors: Tyler J. Slade, Ranuri S. Dissanayaka Mudiyanselage, Nao Furukawa, Tanner R. Smith, Juan Schmidt, Lin-Lin Wang, Chang-Jong Kang, Kaya Wei, Zhixue Shu, Tai Kong, Ryan Baumbach, Gabriel Kotliar, Sergey L. Budko, Weiwei Xie, Paul C. Canfield.
But at the same time there is growing concerns about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. Liability regimes will therefore play a key role in ensuring basic protection for victims using or interacting with these systems. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties. Specifically, we address the cases of cleaning robots, delivery drones and robots in education.
New emerging technologies powered by Artificial Intelligence (AI) have the
potential to disruptively transform our societies for the better. In
particular, data-driven learning approaches (i.e., Machine Learning (ML)) have
been a true revolution in the advancement of multiple technologies in various
application domains. But at the same time there is growing concerns about
certain intrinsic characteristics of these methodologies that carry potential
risks to both safety and fundamental rights. Although there are mechanisms in
the adoption process to minimize these risks (e.g., safety regulations), these
do not exclude the possibility of harm occurring, and if this happens, victims
should be able to seek compensation. Liability regimes will therefore play a
key role in ensuring basic protection for victims using or interacting with
these systems. However, the same characteristics that make AI systems
inherently risky, such as lack of causality, opacity, unpredictability or their
self and continuous learning capabilities, lead to considerable difficulties
when it comes to proving causation. This paper presents three case studies, as
well as the methodology to reach them, that illustrate these difficulties.
Specifically, we address the cases of cleaning robots, delivery drones and
robots in education. The outcome of the proposed analysis suggests the need to
revise liability regimes to alleviate the burden of proof on victims in cases
involving AI technologies.
Authors: David Fernández Llorca, Vicky Charisi, Ronan Hamon, Ignacio Sánchez, Emilia Gómez.
The option P2O
will have neutrinos from a 90 KW beam at Protvino to be detected at the ORCA
detector, the Upgraded P2O will have neutrinos from the upgraded 450 KW beam to
be detected at the ORCA detector and the option P2SO will have neutrinos from a
450 KW beam to be detected at the upgraded Super-ORCA detector. All these
options will have a baseline around 2595 km.
In this paper, we study the capability of different long-baseline experiment
options at the KM3NeT facility i.e., P2O, Upgraded P2O and P2SO to probe the
light sterile neutrino and compare their sensitivity with DUNE. The option P2O
will have neutrinos from a 90 KW beam at Protvino to be detected at the ORCA
detector, the Upgraded P2O will have neutrinos from the upgraded 450 KW beam to
be detected at the ORCA detector and the option P2SO will have neutrinos from a
450 KW beam to be detected at the upgraded Super-ORCA detector. All these
options will have a baseline around 2595 km. Our results show that the
experiments at the KM3NeT (DUNE) would be more sensitive if the value of
$\Delta m^2_{41}$ is around 10 (1) eV$^2$. Our results also show that the role
of near detector is very important for the study of sterile neutrinos and
addition of near detector improves the sensitivity as compared to only far
detector for 3+1 scenario. Among the three options at KM3NeT, the sensitivity
of P2O and updated P2O is limited and sensitivity of P2SO is either comparable
or better than DUNE.
Authors: Dinesh Kumar Singha, Monojit Ghosh, Rudra Majhi, Rukmani Mohanta.
We propose quantum control protocols for the high-fidelity preparation of target states in systems with Autler-Townes splitting. We investigate an approximated three-level system obtained from a four-level one by adiabatically eliminating a state that does not participate in the evolution.
We propose quantum control protocols for the high-fidelity preparation of
target states in systems with Autler-Townes splitting. We investigate an
approximated three-level system obtained from a four-level one by adiabatically
eliminating a state that does not participate in the evolution. In our work we
use linear, arctan, and Roland-Cerf functions for transferring population
between two eigenstates of the system obtaining a high fidelity for long
evolution times. Additionally, in order to overcome the restriction given by
the lifetimes of the experimental setup, we propose an accelerated adiabatic
evolution with a shortcut to adiabaticity protocol, which allows us to reach
fidelities close to one but much faster.
Authors: Michele Delvecchio, Teodora Kirova, Ennio Arimondo, Donatella Ciampini, Sandro Wimberger.
This makes the need for development of lightweight
models all the more imminent. One of the key features of
our method is that there is no need of finetuning after training the model. Both the training and pruning process is completed simultaneously.
In recent years, most of the deep learning solutions are targeted to be
deployed in mobile devices. This makes the need for development of lightweight
models all the more imminent. Another solution is to optimize and prune regular
deep learning models. In this paper, we tackle the problem of CNN model pruning
with the help of Self-Similarity Matrix (SSM) computed from the 2D CNN filters.
We propose two novel algorithms to rank and prune redundant filters which
contribute similar activation maps to the output. One of the key features of
our method is that there is no need of finetuning after training the model.
Both the training and pruning process is completed simultaneously. We benchmark
our method on two of the most popular CNN models - ResNet and VGG and record
their performance on the CIFAR-10 dataset.
Authors: S Rakshith, Jayesh Rajkumar Vachhani, Sourabh Vasant Gothe, Rishabh Khurana.
There are various trajectory planners for mobile manipulators. It is often challenging to compare their performance under similar circumstances due to differences in hardware, dissimilarity of tasks and objectives, as well as uncertainties in measurements and operating environments.
There are various trajectory planners for mobile manipulators. It is often
challenging to compare their performance under similar circumstances due to
differences in hardware, dissimilarity of tasks and objectives, as well as
uncertainties in measurements and operating environments. In this paper, we
propose a simulation framework to evaluate the performance of the local
trajectory planners to generate smooth, and dynamically and kinematically
feasible trajectories for mobile manipulators in the same environment. We focus
on local planners as they are key components that provide smooth trajectories
while carrying a load, react to dynamic obstacles, and avoid collisions. We
evaluate two prominent local trajectory planners, Dynamic-Window Approach (DWA)
and Time Elastic Band (TEB) using the metrics that we introduce. Moreover, our
software solution is applicable to any other local planners used in the Robot
Operating System (ROS) framework, without additional programming effort.
Authors: Sevag Tafnakaji, Hadi Hajieghrary, Quentin Teixeira, Yasemin Bekiroglu.
Quantum metrology is supposed to significantly improve the precision of
parameter estimation by utilizing suitable quantum resources. However, the
predicted precision can be severely distorted by realistic noises. As a demonstration, we numerically apply it to the problem of
frequency estimation under several typical Markovian noise channels. Furthermore, we conduct a proof-of-principle experiment in nuclear magnetic
resonance system to verify the effectiveness of the proposed scheme. The
research here is helpful for current quantum platforms to harness the power of
quantum metrology in realistic noise environments.
Quantum metrology is supposed to significantly improve the precision of
parameter estimation by utilizing suitable quantum resources. However, the
predicted precision can be severely distorted by realistic noises. Here, we
propose a control-enhanced quantum metrology scheme to defend against these
noises for improving the metrology performance. Our scheme can automatically
alter the parameter encoding dynamics with adjustable controls, thus leading to
optimal resultant states that are less sensitive to the noises under
consideration. As a demonstration, we numerically apply it to the problem of
frequency estimation under several typical Markovian noise channels. Through
comparing our control-enhanced scheme with the standard scheme and the
ancilla-assisted scheme, we show that our scheme performs better and can
improve the estimation precision up to around one order of magnitude.
Furthermore, we conduct a proof-of-principle experiment in nuclear magnetic
resonance system to verify the effectiveness of the proposed scheme. The
research here is helpful for current quantum platforms to harness the power of
quantum metrology in realistic noise environments.
Authors: Yue Zhai, Xiaodong Yang, Kai Tang, Xinyue Long, Xinfang Nie, Tao Xin, Dawei Lu, Jun Li.
Yet, quantitative treatments of this aspect of exoplanet studies remain generally under-explored. We circumscribe probable rocky exoplanet compositions based on a population analysis of stellar chemical abundances from the Hypatia and GALAH catalogues. Furthermore, stellar Mg/Si gives a first-order indication of mantle mineralogy, with high-Mg/Si stars leading to weaker, ferropericlase-rich mantles, and low-Mg/Si stars leading to mechanically stronger mantles. The element Na, which modulates crustal buoyancy and mantle clinopyroxene fraction, is affected by devolatilization the most. These differences likely lead to different evolutionary pathways among rocky exoplanets in the solar neighbourhood.
Rocky planet compositions regulate planetary evolution by affecting core
sizes, mantle properties, and melting behaviours. Yet, quantitative treatments
of this aspect of exoplanet studies remain generally under-explored. We attempt
to constrain the range of potential bulk terrestrial exoplanet compositions in
the solar neighbourhood (<200 pc). We circumscribe probable rocky exoplanet
compositions based on a population analysis of stellar chemical abundances from
the Hypatia and GALAH catalogues. We apply a devolatilization model to simulate
compositions of hypothetical, terrestrial-type exoplanets in the habitable
zones around Sun-like stars, considering elements O, S, Na, Si, Mg, Fe, Ni, Ca,
and Al. We further apply core-mantle differentiation by assuming constant
oxygen fugacity, and model the consequent mantle mineralogy with a Gibbs energy
minimisation algorithm. We report statistics on several compositional
parameters and propose a reference set of (21) representative planet
compositions for using as end-member compositions in imminent modelling and
experimental studies. We find a strong correlation between stellar Fe/Mg and
metallic core sizes, which can vary from 18 to 35 wt%. Furthermore, stellar
Mg/Si gives a first-order indication of mantle mineralogy, with high-Mg/Si
stars leading to weaker, ferropericlase-rich mantles, and low-Mg/Si stars
leading to mechanically stronger mantles. The element Na, which modulates
crustal buoyancy and mantle clinopyroxene fraction, is affected by
devolatilization the most. While we find that planetary mantles mostly consist
of Fe/Mg-silicates, core sizes and relative abundances of common minerals can
nevertheless vary significantly among exoplanets. These differences likely lead
to different evolutionary pathways among rocky exoplanets in the solar
neighbourhood.
Authors: Rob J. Spaargaren, Haiyang S. Wang, Stephen J Mojzsis, Maxim D Ballmer, Paul J Tackley.
We consider a setting where a firm sells a product over a
horizon of $T$ time steps. Our results reveal the surprising
phase transitions of the optimal regret with respect to $M$. We also show
that there exists an upper limit on how much the optimal regret can deteriorate
when $M$ grows large. Finally, we conduct extensive numerical experiments to
show the benefit of LEAP over other heuristic methods for this problem.
Motivated by the prevalence of ``price protection guarantee", which allows a
customer who purchased a product in the past to receive a refund from the
seller during the so-called price protection period (typically defined as a
certain time window after the purchase date) in case the seller decides to
lower the price, we study the impact of such policy on the design of online
learning algorithm for data-driven dynamic pricing with initially unknown
customer demand. We consider a setting where a firm sells a product over a
horizon of $T$ time steps. For this setting, we characterize how the value of
$M$, the length of price protection period, can affect the optimal regret of
the learning process. We show that the optimal regret is
$\tilde{\Theta}(\sqrt{T}+\min\{M,\,T^{2/3}\})$ by first establishing a
fundamental impossible regime with novel regret lower bound instances. Then, we
propose LEAP, a phased exploration type algorithm for \underline{L}earning and
\underline{EA}rning under \underline{P}rice Protection to match this lower
bound up to logarithmic factors or even doubly logarithmic factors (when there
are only two prices available to the seller). Our results reveal the surprising
phase transitions of the optimal regret with respect to $M$. Specifically, when
$M$ is not too large, the optimal regret has no major difference when compared
to that of the classic setting with no price protection guarantee. We also show
that there exists an upper limit on how much the optimal regret can deteriorate
when $M$ grows large. Finally, we conduct extensive numerical experiments to
show the benefit of LEAP over other heuristic methods for this problem.
Authors: Qing Feng, Ruihao Zhu, Stefanus Jasin.
Joint entity and relation extraction has been a core task in the field of information extraction. In addition, we learn the instance-level representation of relational triples via contrastive learning. Experimental results show that our proposed method achieves the state of the art on five widely used benchmarks.
Joint entity and relation extraction has been a core task in the field of
information extraction. Recent approaches usually consider the extraction of
relational triples from a stereoscopic perspective, either learning a
relation-specific tagger or separate classifiers for each relation type.
However, they still suffer from error propagation, relation redundancy and lack
of high-level connections between triples. To address these issues, we propose
a novel query-based approach to construct instance-level representations for
relational triples. By metric-based comparison between query embeddings and
token embeddings, we can extract all types of triples in one step, thus
eliminating the error propagation problem. In addition, we learn the
instance-level representation of relational triples via contrastive learning.
In this way, relational triples can not only enclose rich class-level semantics
but also access to high-order global connections. Experimental results show
that our proposed method achieves the state of the art on five widely used
benchmarks.
Authors: Zeqi Tan, Yongliang Shen, Xuming Hu, Wenqi Zhang, Xiaoxia Cheng, Weiming Lu, Yueting Zhuang.