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 ionization loss can only be measured in the
depleted layer of the detector. The thickness of the depleted layer in a flat
semiconductor detector can be smoothly regulated by the value of the bias
voltage of the detector. Ionization loss
spectra should be different for channeling and nonchanneling particles, and
both fractions can be determined. The application of a Si surface-barrier
detector-target is considered.
A new method for the experimental study of ionization loss of relativistic
negatively charged particles moving in a crystal in the channeling regime using
a semiconductor surface-barrier detector with smoothly tunable thickness of the
depleted layer is proposed. The ionization loss can only be measured in the
depleted layer of the detector. The thickness of the depleted layer in a flat
semiconductor detector can be smoothly regulated by the value of the bias
voltage of the detector. Therefore, the energy distribution of the ionization
loss of relativistic particles which cross the detector and move in the
channeling regime in the detector crystal can be measured along the path of the
particles at variation of the bias voltage of the detector. Ionization loss
spectra should be different for channeling and nonchanneling particles, and
both fractions can be determined. The application of a Si surface-barrier
detector-target is considered. Measurements with such a detector would make it
possible to study: the energy distribution of ionization loss of channeling
negatively and positively charged particles; spatial distribution of ionization
loss as a function of the path length of channeling particles; the dechanneling
length of negatively charged particles; and to clear up the role of
rechanneling of the particles in the crystal. Comparison of experimental data
with calculations can help to develop a description of the dynamics of motion
of negatively charged particles channeling in a crystal. A better understanding
of the dechanneling length properties can be useful in the production of
positrons and other particles such as neutrons by an electron beam in crystals,
and in the development of crystalline undulators, and at a crystal-based
extraction of electron beams from a synchrotron.
Authors: A. V. Shchagin, G. Kube, S. A. Strokov, W. Lauth.
We present a polynomial-time algorithm with an approximation ratio of $13/8 = 1.625$ improving upon an earlier $5/3$-approximation. We further introduce, as key ingredients, the technique of repeated simultaneous contractions and provide improved lower bounds for instances that cannot be contracted.
We consider the matching augmentation problem (MAP), where a matching of a
graph needs to be extended into a $2$-edge-connected spanning subgraph by
adding the minimum number of edges to it. We present a polynomial-time
algorithm with an approximation ratio of $13/8 = 1.625$ improving upon an
earlier $5/3$-approximation. The improvement builds on a new
$\alpha$-approximation preserving reduction for any $\alpha\geq 3/2$ from
arbitrary MAP instances to well-structured instances that do not contain
certain forbidden structures like parallel edges, small separators, and
contractible subgraphs. We further introduce, as key ingredients, the technique
of repeated simultaneous contractions and provide improved lower bounds for
instances that cannot be contracted.
Authors: Mohit Garg, Felix Hommelsheim, Nicole Megow.
Flares and coronal mass ejections are powered by magnetic energy stored in
coronal electric currents. (2022). We find similar
photospheric imprints in a simple model of a non-potential AR with known
currents. Both of these hypotheses are testable with
non-potential coronal field extrapolations.
Flares and coronal mass ejections are powered by magnetic energy stored in
coronal electric currents. Here, we explore the nature of coronal currents in
observed and model active region (ARs) by studying manifestations of these
currents in photospheric vector magnetograms. We employ Gauss's separation
method, recently introduced to the solar physics literature, to partition the
photospheric field into three distinct components, each arising from a separate
source: (i) currents passing through the photosphere, (ii) currents flowing
below it, and (iii) currents flowing above it. We refer to component (iii) as
the photospheric imprint of coronal currents. In both AR 10930 and AR 11158,
photospheric imprints exhibit large-scale, spatially coherent structures along
these regions' central, sheared polarity inversion lines (PILs) that are
consistent with coronal currents flowing horizontally above these PILs, similar
to recent findings in AR 12673 by Schuck et al. (2022). We find similar
photospheric imprints in a simple model of a non-potential AR with known
currents. We find that flare-associated changes in photospheric imprints in AR
11158 accord with earlier reports that near-PIL fields become more horizontal,
consistent with the "implosion" scenario. We hypothesize that this evolution
effectively shortens, in an overall sense, current-carrying coronal fields,
leading to decreased inductive energy (DIE) in the coronal field. We further
hypothesize that, in the hours prior to flares, parts of the coronal field
slowly expand, in a process we deem coronal inflation (CI) -- essentially, the
inverse of the implosion process. Both of these hypotheses are testable with
non-potential coronal field extrapolations.
Authors: Brian T. Welsch.
To evaluate the quality of the selected instruction, we evaluate the zero-shot performance of another LLM following the selected instruction. We conduct extensive qualitative and quantitative analyses to explore the performance of APE. Please check out our webpage at https://sites.google.com/view/automatic-prompt-engineer.
By conditioning on natural language instructions, large language models
(LLMs) have displayed impressive capabilities as general-purpose computers.
However, task performance depends significantly on the quality of the prompt
used to steer the model, and most effective prompts have been handcrafted by
humans. Inspired by classical program synthesis and the human approach to
prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic
instruction generation and selection. In our method, we treat the instruction
as the "program," optimized by searching over a pool of instruction candidates
proposed by an LLM in order to maximize a chosen score function. To evaluate
the quality of the selected instruction, we evaluate the zero-shot performance
of another LLM following the selected instruction. Experiments on 24 NLP tasks
show that our automatically generated instructions outperform the prior LLM
baseline by a large margin and achieve better or comparable performance to the
instructions generated by human annotators on 19/24 tasks. We conduct extensive
qualitative and quantitative analyses to explore the performance of APE. We
show that APE-engineered prompts can be applied to steer models toward
truthfulness and/or informativeness, as well as to improve few-shot learning
performance by simply prepending them to standard in-context learning prompts.
Please check out our webpage at
https://sites.google.com/view/automatic-prompt-engineer.
Authors: Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, Jimmy Ba.
In
the diffusion limit of infinite-velocity propagation we recover the results for
the heterogeneous diffusion process. We also analyze the finite-velocity heterogeneous
diffusion process in presence of stochastic Poissonian resetting. We show that
the system reaches a non-equilibrium stationary state. The transition to this
non-equilibrium steady state is analysed in terms of the large deviation
function.
We analyze diffusion processes with finite propagation speed in a
non-homogeneous medium in terms of the heterogeneous telegrapher's equation. In
the diffusion limit of infinite-velocity propagation we recover the results for
the heterogeneous diffusion process. The heterogeneous telegrapher's process
exhibits a rich variety of diffusion regimes including hyperdiffusion,
ballistic motion, superdiffusion, normal diffusion and subdiffusion, and
different crossover dynamics characteristic for complex systems in which
anomalous diffusion is observed. The anomalous diffusion exponent in the short
time limit is twice the exponent in the long time limit, in accordance to the
crossover dynamics from ballistic diffusion to normal diffusion in the standard
telegrapher's process. We also analyze the finite-velocity heterogeneous
diffusion process in presence of stochastic Poissonian resetting. We show that
the system reaches a non-equilibrium stationary state. The transition to this
non-equilibrium steady state is analysed in terms of the large deviation
function.
Authors: Trifce Sandev, Ljupco Kocarev, Ralf Metzler, Aleksei Chechkin.
Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of 19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies' effectiveness and make adjustments quickly.
We present a near-real-time global gridded daily CO$_2$ emissions dataset
(GRACED) throughout 2021. GRACED provides gridded CO$_2$ emissions at a
0.1degree*0.1degree spatial resolution and 1-day temporal resolution from
cement production and fossil fuel combustion over seven sectors, including
industry, power, residential consumption, ground transportation, international
aviation, domestic aviation, and international shipping. GRACED is prepared
from a near-real-time daily national CO$_2$ emissions estimates (Carbon
Monitor), multi-source spatial activity data emissions and satellite NO$_2$
data for time variations of those spatial activity data. GRACED provides the
most timely overview of emissions distribution changes, which enables more
accurate and timely identification of when and where fossil CO$_2$ emissions
have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level
two-sigma uncertainty of value of 19.9% in 2021, indicating the reliability of
GRACED was not sacrificed for the sake of higher spatiotemporal resolution that
GRACED provides. Continuing to update GRACED in a timely manner could help
policymakers monitor energy and climate policies' effectiveness and make
adjustments quickly.
Authors: Xinyu Dou, Jinpyo Hong, Philippe Ciais, Frédéric Chevallier, Feifan Yan, Ying Yu, Yifan Hu, Da Huo, Yun Sun, Yilong Wang, Steven J. Davis, Monica Crippa, Greet Janssens-Maenhout, Diego Guizzardi, Efisio Solazzo, Xiaojuan Lin, Xuanren Song, Biqing Zhu, Duo Cui, Piyu Ke, Hengqi Wang, Wenwen Zhou, Xia Huang, Zhu Deng, Zhu Liu.
The difference set of an outcome in an auction is the set of types that the
auction mechanism maps to the outcome.
The difference set of an outcome in an auction is the set of types that the
auction mechanism maps to the outcome. We give a complete characterization of
the geometry of the difference sets that can appear for a dominant strategy
incentive compatible multi-unit auction showing that they correspond to regular
subdivisions of the unit cube. This observation is then used to construct
mechanisms that are robust in the sense that the set of items allocated to a
player does change only slightly when the player's reported type is changed
slightly.
Authors: Michael Joswig, Max Klimm, Sylvain Spitz.
Three right-handed neutrinos are introduced to cancel the gauge anomaly. This effective theory is realized in three renormalizable contexts with heavy fermion singlets, scalar doublets and fermion doublets.
We extend the $SU(3)_c \times SU(2)_L \times U(1)_Y$ standard model by a
$U(1)_{Y'}$ gauge symmetry. Three right-handed neutrinos are introduced to
cancel the gauge anomaly. One Higgs singlet is responsible for spontaneously
breaking the $U(1)_{Y'}$ symmetry while the standard model Higgs doublet does
not carry any $U(1)_{Y'}$ charges. The down-type quarks, up-type quarks,
charged leptons and neutral neutrinos obtain their Dirac masses through four
types of dimension-5 operators constructed by the fermion doublets and singlets
with the Higgs doublet and singlet. This effective theory is realized in three
renormalizable contexts with heavy fermion singlets, scalar doublets and
fermion doublets. The heavy fermion singlets and doublets for generating the
neutrino masses also accommodate a successful Dirac leptogenesis to explain the
baryon asymmetry in the universe.
Authors: Su-Ping Chen, Pei-Hong Gu.
We study the fixed-parameter tractability of the following fundamental
problem: given two directed graphs $\vec H$ and $\vec G$, count the number of
copies of $\vec H$ in $\vec G$. The standard setting, where the tractability is
well understood, uses only $|\vec H|$ as a parameter. In this paper we take a
step forward, and adopt as a parameter $|\vec H|+d(\vec G)$, where $d(\vec G)$
is the maximum outdegree of $|\vec G|$.
We study the fixed-parameter tractability of the following fundamental
problem: given two directed graphs $\vec H$ and $\vec G$, count the number of
copies of $\vec H$ in $\vec G$. The standard setting, where the tractability is
well understood, uses only $|\vec H|$ as a parameter. In this paper we take a
step forward, and adopt as a parameter $|\vec H|+d(\vec G)$, where $d(\vec G)$
is the maximum outdegree of $|\vec G|$. Under this parameterization, we
completely characterize the fixed-parameter tractability of the problem in both
its non-induced and induced versions through two novel structural parameters,
the fractional cover number $\rho^*$ and the source number $\alpha_s$. On the
one hand we give algorithms with running time $f(|\vec H|,d(\vec G)) \cdot
|\vec G|^{\rho^*\!(\vec H)+O(1)}$ and $f(|\vec H|,d(\vec G)) \cdot |\vec
G|^{\alpha_s(\vec H)+O(1)}$ for counting respectively the copies and induced
copies of $\vec H$ in $\vec G$; on the other hand we show that, unless the
Exponential Time Hypothesis fails, for any class $\vec C$ of directed graphs
the (induced) counting problem is fixed-parameter tractable if and only if
$\rho^*(\vec C)$ ($\alpha_s(\vec C)$) is bounded. These results explain how the
orientation of the pattern can make counting easy or hard, and prove that a
classic algorithm by Chiba and Nishizeki and its extensions (Chiba, Nishizeki
SICOMP 85; Bressan Algorithmica 21) are optimal unless ETH fails.
Authors: Marco Bressan, Matthias Lanzinger, Marc Roth.
We consider unsupervised learning methods for characterizing the disordered microscopic structure of supercooled liquids and glasses. Overall, our results indicate that glassy binary mixtures have a broad spectrum of structural features.
We consider unsupervised learning methods for characterizing the disordered
microscopic structure of supercooled liquids and glasses. Specifically, we
perform dimensionality reduction of smooth structural descriptors that describe
radial and bond-orientational correlations, and assess the ability of the
method to grasp the essential structural features of glassy binary mixtures. In
several cases, a few collective variables account for the bulk of the
structural fluctuations within the first coordination shell and also display a
clear connection with the fluctuations of particle mobility. Fine-grained
descriptors that characterize the radial dependence of bond-orientational order
better capture the structural fluctuations relevant for particle mobility, but
are also more difficult to parametrize and to interpret. We also find that
principal component analysis of bond-orientational order parameters provides
identical results to neural network autoencoders, while having the advantage of
being easily interpretable. Overall, our results indicate that glassy binary
mixtures have a broad spectrum of structural features. In the temperature range
we investigate, some mixtures display well-defined locally favored structures,
which are reflected in bimodal distributions of the structural variables
identified by dimensionality reduction.
Authors: Daniele Coslovich, Robert L. Jack, Joris Paret.