79 research outputs found
Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator
In this paper, we introduce a novel approach to fine-grained cross-view
geo-localization. Our method aligns a warped ground image with a corresponding
GPS-tagged satellite image covering the same area using homography estimation.
We first employ a differentiable spherical transform, adhering to geometric
principles, to accurately align the perspective of the ground image with the
satellite map. This transformation effectively places ground and aerial images
in the same view and on the same plane, reducing the task to an image alignment
problem. To address challenges such as occlusion, small overlapping range, and
seasonal variations, we propose a robust correlation-aware homography estimator
to align similar parts of the transformed ground image with the satellite
image. Our method achieves sub-pixel resolution and meter-level GPS accuracy by
mapping the center point of the transformed ground image to the satellite image
using a homography matrix and determining the orientation of the ground camera
using a point above the central axis. Operating at a speed of 30 FPS, our
method outperforms state-of-the-art techniques, reducing the mean metric
localization error by 21.3% and 32.4% in same-area and cross-area
generalization tasks on the VIGOR benchmark, respectively, and by 34.4% on the
KITTI benchmark in same-area evaluation.Comment: 19 pages. Reducing the cross-view geo-localization problem to a 2D
image alignment problem by utilizing BEV transformation, and completing the
alignment process with a correlation-aware homography estimator. Code:
https://github.com/xlwangDev/HC-Ne
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
To retrieve more relevant, appropriate and useful documents given a query,
finding clues about that query through the text is crucial. Recent deep
learning models regard the task as a term-level matching problem, which seeks
exact or similar query patterns in the document. However, we argue that they
are inherently based on local interactions and do not generalise to ubiquitous,
non-consecutive contextual relationships. In this work, we propose a novel
relevance matching model based on graph neural networks to leverage the
document-level word relationships for ad-hoc retrieval. In addition to the
local interactions, we explicitly incorporate all contexts of a term through
the graph-of-word text format. Matching patterns can be revealed accordingly to
provide a more accurate relevance score. Our approach significantly outperforms
strong baselines on two ad-hoc benchmarks. We also experimentally compare our
model with BERT and show our advantages on long documents.Comment: To appear at AAAI 202
Integral Attack on the Full FUTURE Block Cipher
FUTURE is a recently proposed lightweight block cipher that achieved a remarkable hardware performance due to careful design decisions. FUTURE is an Advanced Encryption Standard (AES)-like Substitution-Permutation Network (SPN) with 10 rounds, whose round function consists of four components, i.e., SubCell, MixColumn, ShiftRow and AddRoundKey. Unlike AES, it is a 64-bit-size block cipher with a 128-bit secret key, and the state can be arranged into 16 cells. Therefore, the operations of FUTURE including its S-box is defined over . The previous studies have shown that the integral properties of 4-bit S-boxes are usually weaker than larger-size S-boxes, thus the number of rounds of FUTURE, i.e., 10 rounds only, might be too aggressive to provide enough resistance against integral cryptanalysis.
In this paper, we mount the integral cryptanalysis on FUTURE. With state-of-the-art detection techniques, we identify several integral distinguishers of 7 rounds of FUTURE. By extending this 7-round distinguisher by 3 forward rounds, we manage to recover all the 128 bits secret keys from the full FUTURE cipher without the full codebook for the first time. To further achieve better time complexity, we also present a key recovery attack on full FUTURE with full codebook. Both attacks have better time complexity than existing results
Inactivation of Myeloma Cancer Cells by Helium and Argon Plasma Jets: The Effect Comparison and the Key Reactive Species
In plasma cancer therapy, the inactivation of cancer cells under plasma treatment is closely related to the reactive oxygen and nitrogen species (RONS) induced by plasmas. Quantitative study on the plasma-induced RONS that related to cancer cells apoptosis is critical for advancing the research of plasma cancer therapy. In this paper, the effects of several reactive species on the inactivation of LP-1 myeloma cancer cells are comparatively studied with variable working gas composition, surrounding gas composition, and discharge power. The results show that helium plasma jet has a higher cell inactivation efficiency than argon plasma jet under the same discharge power. By comparing the concentration of aqueous phase reactive species and the cell inactivation efficiency under different working gases and discharge powers, it is demonstrated that the inactivation efficiency of LP-1 myeloma cancer cells is strongly correlated with the concentration of peroxynitrite (ONOOH/ONOO-). Published by AIP Publishing
Intracellular ROS Mediates Gas Plasma-Facilitated Cellular Transfection in 2D and 3D Cultures
This study reports the potential of cold atmospheric plasma (CAP) as a versatile tool for delivering oligonucleotides into mammalian cells. Compared to lipofection and electroporation methods, plasma transfection showed a better uptake efficiency and less cell death in the transfection of oligonucleotides. We demonstrated that the level of extracellular aqueous reactive oxygen species (ROS) produced by gas plasma is correlated with the uptake efficiency and that this is achieved through an increase of intracellular ROS levels and the resulting increase in cell membrane permeability. This finding was supported by the use of ROS scavengers, which reduced CAP-based uptake efficiency. In addition, we found that cold atmospheric plasma could transfer oligonucleotides such as siRNA and miRNA into cells even in 3D cultures, thus suggesting the potential for unique applications of CAP beyond those provided by standard transfection techniques. Together, our results suggest that cold plasma might provide an efficient technique for the delivery of siRNA and miRNA in 2D and 3D culture models
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