315 research outputs found
Poincar\'e invariance of spinning binary dynamics in the post-Minkowskian Hamiltonian approach
We initiate the construction of the global Poincar\'e algebra generators in
the context of the post-Minkowskian Hamiltonian formulation of gravitating
binary dynamics in isotropic coordinates that is partly inspired by scattering
amplitudes. At the first post-Minkowskian (1PM) order, we write down the
Hamiltonian in a form valid in an arbitrary inertial frame. Then we construct
the boost generator at the same order which uniquely solves all the equations
required by the Poincar\'e algebra. Our results are linear in Newton's constant
but exact in velocities and spins, including all spin multiple moments. We also
compute the generators of canonical transformations that proves the equivalence
between our new generators and the corresponding generators in the ADM
coordinates up to the second post-Newtonian (2PN) order.Comment: 33 pages; v2. references added, minor correction
Classical observables from partial wave amplitudes
We study the formalism of Kosower-Maybee-O'Connell (KMOC) to extract
classical impulse from quantum amplitude in the context of the partial wave
expansion of a 2-to-2 elastic scattering. We take two complementary approaches
to establish the connection. The first one takes advantage of Clebsch-Gordan
relations for the base amplitudes of the partial wave expansion. The second one
is a novel adaptation of the traditional saddle point approximation in the
semi-classical limit. In the former, an interference between the S-matrix and
its conjugate leads to a large degree of cancellation such that the saddle
point approximation to handle a rapidly oscillating integral is no longer
needed. As an example with a non-orbital angular momentum, we apply our methods
to the charge-monopole scattering problem in the probe limit and reproduce both
of the two angles characterizing the classical scattering. A spinor basis for
the partial wave expansion, a non-relativistic avatar of the spinor-helicity
variables, plays a crucial role throughout our computations.Comment: 48 pages, 3 figure
Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor
Despite recent progress in language models, generating constrained text for
specific domains remains a challenge, particularly when utilizing black-box
models that lack domain-specific knowledge. In this paper, we introduce ScoPE
(Score-based Progressive Editor) generation, a novel approach for controlled
text generation for black-box language models. We employ ScoPE to facilitate
text generation in the target domain by integrating it with language models
through a cascading approach. Trained to enhance the target domain score of the
edited text, ScoPE progressively edits intermediate output discrete tokens to
align with the target attributes throughout the auto-regressive generation
process of the language model. This iterative process guides subsequent steps
to produce desired output texts for the target domain. Our experimental results
on diverse controlled generations demonstrate that ScoPE effectively
facilitates controlled text generation for black-box language models in both
in-domain and out-of-domain conditions, which is challenging for existing
methods
Learning-based Uncertainty-aware Navigation in 3D Off-Road Terrains
This paper presents a safe, efficient, and agile ground vehicle navigation
algorithm for 3D off-road terrain environments. Off-road navigation is subject
to uncertain vehicle-terrain interactions caused by different terrain
conditions on top of 3D terrain topology. The existing works are limited to
adopt overly simplified vehicle-terrain models. The proposed algorithm learns
the terrain-induced uncertainties from driving data and encodes the learned
uncertainty distribution into the traversability cost for path evaluation. The
navigation path is then designed to optimize the uncertainty-aware
traversability cost, resulting in a safe and agile vehicle maneuver. Assuring
real-time execution, the algorithm is further implemented within parallel
computation architecture running on Graphics Processing Units (GPU).Comment: 6 pages, 6 figures, submitted to International Conference on Robotics
and Automation (ICRA 2023
Learning Terrain-Aware Kinodynamic Model for Autonomous Off-Road Rally Driving With Model Predictive Path Integral Control
High-speed autonomous driving in off-road environments has immense potential
for various applications, but it also presents challenges due to the complexity
of vehicle-terrain interactions. In such environments, it is crucial for the
vehicle to predict its motion and adjust its controls proactively in response
to environmental changes, such as variations in terrain elevation. To this end,
we propose a method for learning terrain-aware kinodynamic model which is
conditioned on both proprioceptive and exteroceptive information. The proposed
model generates reliable predictions of 6-degree-of-freedom motion and can even
estimate contact interactions without requiring ground truth force data during
training. This enables the design of a safe and robust model predictive
controller through appropriate cost function design which penalizes sampled
trajectories with unstable motion, unsafe interactions, and high levels of
uncertainty derived from the model. We demonstrate the effectiveness of our
approach through experiments on a simulated off-road track, showing that our
proposed model-controller pair outperforms the baseline and ensures robust
high-speed driving performance without control failure.Comment: Accepted to IEEE Robotics and Automation Letters (and ICRA 2024). Our
video can be found at https://youtu.be/VXf_prNQnJo Project page :
https://sites.google.com/view/terrainawarekinody
Offline-to-Online Knowledge Distillation for Video Instance Segmentation
In this paper, we present offline-to-online knowledge distillation (OOKD) for
video instance segmentation (VIS), which transfers a wealth of video knowledge
from an offline model to an online model for consistent prediction. Unlike
previous methods that having adopting either an online or offline model, our
single online model takes advantage of both models by distilling offline
knowledge. To transfer knowledge correctly, we propose query filtering and
association (QFA), which filters irrelevant queries to exact instances. Our KD
with QFA increases the robustness of feature matching by encoding
object-centric features from a single frame supplemented by long-range global
information. We also propose a simple data augmentation scheme for knowledge
distillation in the VIS task that fairly transfers the knowledge of all classes
into the online model. Extensive experiments show that our method significantly
improves the performance in video instance segmentation, especially for
challenging datasets including long, dynamic sequences. Our method also
achieves state-of-the-art performance on YTVIS-21, YTVIS-22, and OVIS datasets,
with mAP scores of 46.1%, 43.6%, and 31.1%, respectively
Finger-triggered portable PDMS suction cup for equipment-free microfluidic pumping
This study presents a finger-triggered portable polydimethylsiloxane suction cup that enables equipment-free microfluidic pumping. The key feature of this method is that its operation only involves a “pressing-and-releasing” action for the cup placed at the outlet of a microfluidic device, which transports the fluid at the inlet toward the outlet through a microchannel. This method is simple, but effective and powerful. The cup is portable and can easily be fabricated from a three-dimensional printed mold, used without any pre-treatment, reversibly bonded to microfluidic devices without leakage, and applied to various material-based microfluidic devices. The effect of the suction cup geometry and fabrication conditions on the pumping performance was investigated. Furthermore, we demonstrated the practical applications of the suction cup by conducting an equipment-free pumping of thermoplastic-based microfluidic devices and water-in-oil droplet generation.11Yscopu
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Investigating mutability and the plasmodium falciparum chloroquine resistance transporter in drug resistant malaria parasites
Malaria persists today as a significant burden for a large part of the world. However, over the past few decades, a concerted effort by governments, non-governmental organizations, researchers, and community health workers worldwide has yielded progress in reducing the deadly impact of this disease. Today, some of these gains are threatened by the rise of antimalarial drug resistance, a recurring problem that has impeded global malaria reduction efforts before. Research on Plasmodium falciprum resistance to the numerous antimalarial compounds used today and in the past has made significant progress on determining which specific mutations modulate drug susceptibility and to what degree they do so. To gain a comprehensive understanding of drug resistance, we need to elucidate how and why it arises.
Therefore, it is important to elucidate whether some malaria parasites acquire resistance-conferring mutations faster than others and why the native function of the genetic factors involved lend themselves to modulating drug resistance. For instance, resistance to multiple antimalarial therapies has repeatedly emerged in Southeast Asia. We investigated the long-held hypothesis that this was due to the ability of these parasites to mutate significantly faster than non-Southeast Asian strains. Elucidating whether this hypermutability phenotype accurately represents Southeast Asian parasite evolvability is important, as it can inform when resistance would be expected to next arise, particularly in the Greater Mekong Subregion in Southeast Asia.
Here, we have adapted a fluctuation assay to Plasmodium falciparum and determined that some contemporaneous Cambodian parasites exhibit a mild mutator, but not a hypermutator, phenotype. We also show that this is likely driven by mutations in DNA repair genes carried predominantly by multidrug resistant Southeast Asian parasites.
One of the most common genes in which drug resistance-conferring mutations occurs is the P. falciparum chloroquine resistance transporter (pfcrt). Mutations in pfcrt are associated with parasite susceptibility to many of the antimalarial compounds that have been used in a clinical setting to date. However, beyond its role in drug resistance, little is known about the native function of PfCRT. To facilitate the study of pfcrt, we have designed a zinc-finger nuclease (ZFN)-based gene engineering system that introduces a single double-strand break in intron 1 of pfcrt. Our ZFN strategy enables replacing nearly any endogenous pfcrt locus with a user-defined recombinant pfcrt allele. We show that our method of pfcrt allelic replacement is fast, efficient, and reliable.
We used this system to generate a unique mutant parasite encoding a pfcrt-L272F mutation, which enlarges the parasite digestive vacuole, the lysosome-like organelle used to catabolize host-derived hemoglobin for amino acid salvage. Our results provide clear evidence that PfCRT is associated with the terminal steps of hemoglobin degradation, overall parasite fitness, and the balance of osmolytes across the digestive vacuole membrane. Bringing clarity to the native function of PfCRT can reveal how and why this single genetic factor has been and continues to be involved in the resistance to many different antimalarial compounds
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