195 research outputs found
Astronomical interferometry using continuous variable quantum teleportation
We propose a method to build an astronomical interferometer using continuous
variable quantum teleportation to overcome the transmission loss between
distant telescopes. The scheme relies on two-mode squeezed states shared by
distant telescopes as entanglement resources, which are distributed using
continuous variable quantum repeaters. We find the optimal measurement on the
teleported states, which uses beam-splitters and photon-number-resolved
detection. Compared to prior proposals relying on discrete states, our scheme
has the advantages of using linear optics to implement the scheme without
wasting stellar photons and making use of multiphoton events, which are
regarded as noise in previous discrete schemes.Comment: 15 pages, 7 figure
Simplified calculation method for transverse seismic response of aqueducts considering fluid-structure interaction
Aqueduct is the key structure in water conveyance engineering, which may be damaged during earthquake. Although numerous water conveyance designs have been built, the current state of researches on aqueduct aseismic design is inadequate. In this paper, based on the fluid-structure interaction dynamics and response spectra analysis, a simplified analysis method was proposed to evaluate the transverse seismic response of aqueducts, and the simplified calculating results were compared with the results of the nonlinear finite element calculation of fluid-structure interaction and experimental results. The results showed that the simplified analysis method put forward in this paper could be used to evaluate the transverse seismic response of aqueducts. In the condition that the pier height is less than 40 m, the first-order lateral vibration mode of the aqueduct has a higher model contribution rate; the simplified calculation method can achieve extremely high accuracy. The simplified calculation precision decreases as the height increases when the pier height exceeds 40 m
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
We investigate safe multi-agent reinforcement learning, where agents seek to
collectively maximize an aggregate sum of local objectives while satisfying
their own safety constraints. The objective and constraints are described by
{\it general utilities}, i.e., nonlinear functions of the long-term
state-action occupancy measure, which encompass broader decision-making goals
such as risk, exploration, or imitations. The exponential growth of the
state-action space size with the number of agents presents challenges for
global observability, further exacerbated by the global coupling arising from
agents' safety constraints. To tackle this issue, we propose a primal-dual
method utilizing shadow reward and -hop neighbor truncation under a
form of correlation decay property, where is the communication radius.
In the exact setting, our algorithm converges to a first-order stationary point
(FOSP) at the rate of . In the sample-based
setting, we demonstrate that, with high probability, our algorithm requires
samples to achieve an
-FOSP with an approximation error of ,
where . Finally, we demonstrate the effectiveness of our model
through extensive numerical experiments.Comment: 50 page
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?
Few-shot learning aims to train models that can be generalized to novel
classes with only a few samples. Recently, a line of works are proposed to
enhance few-shot learning with accessible semantic information from class
names. However, these works focus on improving existing modules such as visual
prototypes and feature extractors of the standard few-shot learning framework.
This limits the full potential use of semantic information. In this paper, we
propose a novel few-shot learning framework that uses pre-trained language
models based on contrastive learning. To address the challenge of alignment
between visual features and textual embeddings obtained from text-based
pre-trained language model, we carefully design the textual branch of our
framework and introduce a metric module to generalize the cosine similarity.
For better transferability, we let the metric module adapt to different
few-shot tasks and adopt MAML to train the model via bi-level optimization.
Moreover, we conduct extensive experiments on multiple benchmarks to
demonstrate the effectiveness of our method
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