234 research outputs found
Quantum Circuits of AES with a Low-depth Linear Layer and a New Structure
In recent years quantum computing has developed rapidly. The security threat posed by quantum computing to cryptography makes it necessary to better evaluate the resource cost of attacking algorithms, some of which require quantum implementations of the attacked cryptographic building blocks. In this paper we manage to optimize quantum circuits of AES in several aspects. Firstly, based on de Brugière \textit{et al.}\u27s greedy algorithm, we propose an improved depth-oriented algorithm for synthesizing low-depth CNOT circuits with no ancilla qubits. Our algorithm finds a CNOT circuit of AES MixColumns with depth 10, which breaks a recent record of depth 16. In addition, our algorithm gives low-depth CNOT circuits for many MDS matrices and matrices used in block ciphers studied in related work. Secondly, we present a new structure named compressed pipeline structure to synthesize quantum circuits of AES, which can be used for constructing quantum oracles employed in quantum attacks based on Grover and Simon\u27s algorithms. When the number of ancilla qubits required by the round function and its inverse is not very large, our structure will have a better trade-off of - cost. We then give detailed quantum circuits of AES-128 under the guidance of our structure and make some comparisons with other circuits. Finally, our encryption circuit and key schedule circuit have their own application scenarios. The Encryption oracle used in Simon\u27s algorithm built with the former will have smaller depth. For example, we can construct an AES-128 Encryption oracle with -depth 33, while the previous best result is 60. A small variant of the latter, along with our method to make an Sbox input-invariant, can avoid the allocation of extra ancilla qubits for storing key words in the shallowed pipeline structure. Based on this, we achieve a quantum circuit of AES-128 with the lowest - cost 130720 to date
Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks
Predicting vehicle trajectories is crucial for ensuring automated vehicle
operation efficiency and safety, particularly on congested multi-lane highways.
In such dynamic environments, a vehicle's motion is determined by its
historical behaviors as well as interactions with surrounding vehicles. These
intricate interactions arise from unpredictable motion patterns, leading to a
wide range of driving behaviors that warrant in-depth investigation. This study
presents the Graph-based Interaction-aware Multi-modal Trajectory Prediction
(GIMTP) framework, designed to probabilistically predict future vehicle
trajectories by effectively capturing these interactions. Within this
framework, vehicles' motions are conceptualized as nodes in a time-varying
graph, and the traffic interactions are represented by a dynamic adjacency
matrix. To holistically capture both spatial and temporal dependencies embedded
in this dynamic adjacency matrix, the methodology incorporates the Diffusion
Graph Convolutional Network (DGCN), thereby providing a graph embedding of both
historical states and future states. Furthermore, we employ a driving
intention-specific feature fusion, enabling the adaptive integration of
historical and future embeddings for enhanced intention recognition and
trajectory prediction. This model gives two-dimensional predictions for each
mode of longitudinal and lateral driving behaviors and offers probabilistic
future paths with corresponding probabilities, addressing the challenges of
complex vehicle interactions and multi-modality of driving behaviors.
Validation using real-world trajectory datasets demonstrates the efficiency and
potential
VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images
The precise combination of image sensor and micro-lens array enables lenslet
light field cameras to record both angular and spatial information of incoming
light, therefore, one can calculate disparity and depth from light field
images. In turn, 3D models of the recorded objects can be recovered, which is a
great advantage over other imaging system. However, reflective and texture-less
areas in light field images have complicated conditions, making it hard to
correctly calculate disparity with existing algorithms. To tackle this problem,
we introduce a novel end-to-end network VommaNet to retrieve multi-scale
features from reflective and texture-less regions for accurate disparity
estimation. Meanwhile, our network has achieved similar or better performance
in other regions for both synthetic light field images and real-world data
compared to the state-of-the-art algorithms. Currently, we achieve the best
score for mean squared error (MSE) on HCI 4D Light Field Benchmark
Geometric instability of graph neural networks on large graphs
We analyse the geometric instability of embeddings produced by graph neural
networks (GNNs). Existing methods are only applicable for small graphs and lack
context in the graph domain. We propose a simple, efficient and graph-native
Graph Gram Index (GGI) to measure such instability which is invariant to
permutation, orthogonal transformation, translation and order of evaluation.
This allows us to study the varying instability behaviour of GNN embeddings on
large graphs for both node classification and link prediction
A Driving Risk Surrogate and Its Application in Car-Following Scenario at Expressway
Traffic safety is important in reducing death and building a harmonious
society. In addition to studies of accident incidences, the perception of
driving risk is significant in guiding the implementation of appropriate
driving countermeasures. Risk assessment can be conducted in real-time for
traffic safety due to the rapid development of communication technology and
computing capabilities. This paper aims at the problems of difficult
calibration and inconsistent thresholds in the existing risk assessment
methods. It proposes a risk assessment model based on the potential field to
quantify the driving risk of vehicles. Firstly, virtual energy is proposed as
an attribute considering vehicle sizes and velocity. Secondly, the driving risk
surrogate(DRS) is proposed based on potential field theory to describe the risk
degree of vehicles. Risk factors are quantified by establishing submodels,
including an interactive vehicle risk surrogate, a restrictions risk surrogate,
and a speed risk surrogate. To unify the risk threshold, acceleration for
implementation guidance is derived from the risk field strength. Finally, a
naturalistic driving dataset in Nanjing, China, is selected, and 3063 pairs of
following naturalistic trajectories are screened out. Based on that, the
proposed model and other models use for comparisons are calibrated through the
improved particle optimization algorithm. Simulations prove that the proposed
model performs better than other algorithms in risk perception and response,
car-following trajectory, and velocity estimation. In addition, the proposed
model exhibits better car-following ability than existing car-following models
A Framework with Improved Heuristics to Optimize Low-Latency Implementations of Linear Layers
In recent years, lightweight cryptography has been a hot field in symmetric cryptography. One of the most crucial problems is to find low-latency implementations of linear layers. The current main heuristic search methods include the Boyar-Peralta (BP) algorithm with depth limit and the backward search. In this paper we firstly propose two improved BP algorithms with depth limit mainly by minimizing the Euclidean norm of the new distance vector instead of maximizing it in the tie-breaking process of the BP algorithm. They can significantly increase the potential for finding better results. Furthermore, we give a new framework that combines forward search with backward search to expand the search space of implementations, where the forward search is one of the two improved BP algorithms. In the new framework, we make a minor adjustment of the priority of rules in the backward search process to enable the exploration of a significantly larger search space. As results, we find better results for the most of matrices studied in previous works. For example, we find an implementation of AES MixColumns of depth 3 with 99 XOR gates, which represents a substantial reduction of 3 XOR gates compared to the existing record of 102 XOR gates
A novel adaptive function-dual Kalman filtering strategy for online battery model parameters and state of charge co-estimation.
This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal resistance-polarization circuit model is constructed on the basis of the Thevenin equivalent circuit to characterize the difference in internal resistance between charge and discharge. The extended Kalman filter is improved through adding an adaptive noise tracking algorithm and the Kalman gain in the unscented Kalman filter algorithm is improved by introducing a dynamic equation. In addition, for benignization of outliers of the two above mentioned algorithms, a new dual Kalman algorithm is proposed in this paper by adding a transfer function and through weighted mutation. The model and algorithm accuracy is verified through working condition experiments. The result shows that: the errors of the three algorithms are all maintained within 0.8% during the initial period and middle stages of the discharge; the maximum error of the improved extension of Kalman algorithm is over 1.5%, that of improved unscented Kalman increases to 5%, and the error of the new dual Kalman algorithm is still within 0.4% during the latter period of the discharge. This indicates that the accuracy and robustness of the new dual Kalman algorithm is better than those of traditional algorithm
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