185 research outputs found
A privacy-preserving, decentralized and functional Bitcoin e-voting protocol
Bitcoin, as a decentralized digital currency, has caused extensive research
interest. There are many studies based on related protocols on Bitcoin,
Bitcoin-based voting protocols also received attention in related literature.
In this paper, we propose a Bitcoin-based decentralized privacy-preserving
voting mechanism. It is assumed that there are n voters and m candidates. The
candidate who obtains t ballots can get x Bitcoins from each voter, namely nx
Bitcoins in total. We use a shuffling mechanism to protect voter's voting
privacy, at the same time, decentralized threshold signatures were used to
guarantee security and assign voting rights. The protocol can achieve
correctness, decentralization and privacy-preservings. By contrast with other
schemes, our protocol has a smaller number of transactions and can achieve a
more functional voting method.Comment: 5 pages;3 figures;Smartworld 201
The Limit Order Book Recreation Model (LOBRM): An Extended Analysis
The limit order book (LOB) depicts the fine-grained demand and supply
relationship for financial assets and is widely used in market microstructure
studies. Nevertheless, the availability and high cost of LOB data restrict its
wider application. The LOB recreation model (LOBRM) was recently proposed to
bridge this gap by synthesizing the LOB from trades and quotes (TAQ) data.
However, in the original LOBRM study, there were two limitations: (1)
experiments were conducted on a relatively small dataset containing only one
day of LOB data; and (2) the training and testing were performed in a
non-chronological fashion, which essentially re-frames the task as
interpolation and potentially introduces lookahead bias. In this study, we
extend the research on LOBRM and further validate its use in real-world
application scenarios. We first advance the workflow of LOBRM by (1) adding a
time-weighted z-score standardization for the LOB and (2) substituting the
ordinary differential equation kernel with an exponential decay kernel to lower
computation complexity. Experiments are conducted on the extended LOBSTER
dataset in a chronological fashion, as it would be used in a real-world
application. We find that (1) LOBRM with decay kernel is superior to
traditional non-linear models, and module ensembling is effective; (2)
prediction accuracy is negatively related to the volatility of order volumes
resting in the LOB; (3) the proposed sparse encoding method for TAQ exhibits
good generalization ability and can facilitate manifold tasks; and (4) the
influence of stochastic drift on prediction accuracy can be alleviated by
increasing historical samples.Comment: 16 pages, preprint accepted for publication in the European
Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECML-PKDD 2021
VLPrompt: Vision-Language Prompting for Panoptic Scene Graph Generation
Panoptic Scene Graph Generation (PSG) aims at achieving a comprehensive image
understanding by simultaneously segmenting objects and predicting relations
among objects. However, the long-tail problem among relations leads to
unsatisfactory results in real-world applications. Prior methods predominantly
rely on vision information or utilize limited language information, such as
object or relation names, thereby overlooking the utility of language
information. Leveraging the recent progress in Large Language Models (LLMs), we
propose to use language information to assist relation prediction, particularly
for rare relations. To this end, we propose the Vision-Language Prompting
(VLPrompt) model, which acquires vision information from images and language
information from LLMs. Then, through a prompter network based on attention
mechanism, it achieves precise relation prediction. Our extensive experiments
show that VLPrompt significantly outperforms previous state-of-the-art methods
on the PSG dataset, proving the effectiveness of incorporating language
information and alleviating the long-tail problem of relations.Comment: 19 pages, 9 figure
HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation
Panoptic Scene Graph generation (PSG) is a recently proposed task in image
scene understanding that aims to segment the image and extract triplets of
subjects, objects and their relations to build a scene graph. This task is
particularly challenging for two reasons. First, it suffers from a long-tail
problem in its relation categories, making naive biased methods more inclined
to high-frequency relations. Existing unbiased methods tackle the long-tail
problem by data/loss rebalancing to favor low-frequency relations. Second, a
subject-object pair can have two or more semantically overlapping relations.
While existing methods favor one over the other, our proposed HiLo framework
lets different network branches specialize on low and high frequency relations,
enforce their consistency and fuse the results. To the best of our knowledge we
are the first to propose an explicitly unbiased PSG method. In extensive
experiments we show that our HiLo framework achieves state-of-the-art results
on the PSG task. We also apply our method to the Scene Graph Generation task
that predicts boxes instead of masks and see improvements over all baseline
methods
Text Promptable Surgical Instrument Segmentation with Vision-Language Models
In this paper, we propose a novel text promptable surgical instrument segmentation approach to overcome challenges associated with diversity and differentiation of surgical instruments in minimally invasive surgeries. We redefine the task as text promptable, thereby enabling a more nuanced comprehension of surgical instruments and adaptability to new instrument types. Inspired by recent advancements in vision-language models, we leverage pretrained image and text encoders as our model backbone and design a text promptable mask decoder consisting of attention- and convolution-based prompting schemes for surgical instrument segmentation prediction. Our model leverages multiple text prompts for each surgical instrument through a new mixture of prompts mechanism, resulting in enhanced segmentation performance. Additionally, we introduce a hard instrument area reinforcement module to improve image feature comprehension and segmentation precision. Extensive experiments on several surgical instrument segmentation datasets demonstrate our model's superior performance and promising generalization capability. To our knowledge, this is the first implementation of a promptable approach to surgical instrument segmentation, offering significant potential for practical application in the field of robotic-assisted surgery. Code is available at https://github.com/franciszzj/TP-SIS
Frequency-astigmatism asymmetric nonlinear conversion of structured light lasers
Nonlinear optics of structured light has recently delivered intriguing
fundamental physical phenomena in light-matter interactions and advanced
applications from classical imaging to quantum informatics. The mutual
interaction between spin, orbital angular momentum (OAM) and wavelength is
extensively studied in such cases. In this work, we go beyond only considering
OAM and wavelength by taking the nonlinear frequency conversion and transverse
mode astigmatism conversion as two building blocks and investigating how single
modes and complicated multiplexed modes evolve after them. In particular, We
found a generalized law of nonlinear conversion structured light from
experiments and theories, that the converted modes are highly related to the
sequence of these two blocks, obeying an inherent (non)commutative rule in
which. This effect not only creates extended structured laser modes but serve
as new rules in nonlinear structured light manipulation
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