15,744 research outputs found
Source-independent quantum random number generation
Quantum random number generators can provide genuine randomness by appealing
to the fundamental principles of quantum mechanics. In general, a physical
generator contains two parts---a randomness source and its readout. The source
is essential to the quality of the resulting random numbers; hence, it needs to
be carefully calibrated and modeled to achieve information-theoretical provable
randomness. However, in practice, the source is a complicated physical system,
such as a light source or an atomic ensemble, and any deviations in the
real-life implementation from the theoretical model may affect the randomness
of the output. To close this gap, we propose a source-independent scheme for
quantum random number generation in which output randomness can be certified,
even when the source is uncharacterized and untrusted. In our randomness
analysis, we make no assumptions about the dimension of the source. For
instance, multiphoton emissions are allowed in optical implementations. Our
analysis takes into account the finite-key effect with the composable security
definition. In the limit of large data size, the length of the input random
seed is exponentially small compared to that of the output random bit. In
addition, by modifying a quantum key distribution system, we experimentally
demonstrate our scheme and achieve a randomness generation rate of over
bit/s.Comment: 11 pages, 7 figure
Transmission resonance in a composite plasmonic structure
The design, fabrication, and optical properties of a composite plasmonic
structure, a two-dimentional array of split-ring resonators inserted into
periodic square holes of a metal film, have been reported. A new type of
transmission resonance, which makes a significant difference from the
conventional peaks, has been suggested both theoretically and experimentally.
To understand this effect, a mechanism of ring- resonance induced dipole
emission is proposed.Comment: 14 pages, 4 figure
Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training
The Visual Question Answering (VQA) system offers a user-friendly interface
and enables human-computer interaction. However, VQA models commonly face the
challenge of language bias, resulting from the learned superficial correlation
between questions and answers. To address this issue, in this study, we present
a novel framework to reduce the language bias of the VQA for remote sensing
data (RSVQA). Specifically, we add an adversarial branch to the original VQA
framework. Based on the adversarial branch, we introduce two regularizers to
constrain the training process against language bias. Furthermore, to evaluate
the performance in terms of language bias, we propose a new metric that
combines standard accuracy with the performance drop when incorporating
question and random image information. Experimental results demonstrate the
effectiveness of our method. We believe that our method can shed light on
future work for reducing language bias on the RSVQA task
Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images
Aiming at answering questions based on the content of remotely sensed images,
visual question answering for remote sensing data (RSVQA) has attracted much
attention nowadays. However, previous works in RSVQA have focused little on the
robustness of RSVQA. As we aim to enhance the reliability of RSVQA models, how
to learn robust representations against new words and different question
templates with the same meaning is the key challenge. With the proposed
augmented dataset, we are able to obtain more questions in addition to the
original ones with the same meaning. To make better use of this information, in
this study, we propose a contrastive learning strategy for training robust
RSVQA models against diverse question templates and words. Experimental results
demonstrate that the proposed augmented dataset is effective in improving the
robustness of the RSVQA model. In addition, the contrastive learning strategy
performs well on the low resolution (LR) dataset.Comment: This paper was submitted to the JURSE 2023 conference on November 5,
202
- …