Reviewerly/exHarmony huggingface dataset:
Longest Common Prefix Arrays for Succinct k-Spectra

The k-spectrum of a string is the set of all distinct substrings of length k occurring in the string. K-spectra have many applications in bioinformatics including pseudoalignment and genome assembly. The Spectral Burrows-Wheeler Transform (SBWT) has been recently introduced as an algorithmic tool to efficiently represent and query these objects. The longest common prefix ( $$\textit{LCP}$$ ) array for a k-spectrum is an array of length n that stores the length of the longest common prefix of adjacent k-mers as they occur in lexicographical order. The $$\textit{LCP}$$ array has at least two important applications, namely to accelerate pseudoalignment algorithms using the SBWT and to allow simulation of variable-order de Bruijn graphs within the SBWT framework. In this paper we explore algorithms to compute the $$\textit{LCP}$$ array efficiently from the SBWT representation of the k-spectrum. Starting with a straightforward O(nk) time algorithm, we describe algorithms that are efficient in both theory and practice. We show that the $$\textit{LCP}$$ array can be computed in optimal O(n) time, where n is the length of the SBWT of the spectrum. In practical genomics scenarios, we show that this theoretically optimal algorithm is indeed practical, but is often outperformed on smaller values of k by an asymptotically suboptimal algorithm that interacts better with the CPU cache. Our algorithms share some features with both classical Burrows-Wheeler inversion algorithms and LCP array construction algorithms for suffix arrays. Our C++ implementations of these algorithms are available at https://github.com/jnalanko/kmer-lcs .
Engineering a Textbook Approach to Index Massive String Dictionaries

We study the problem of engineering space-time efficient indexes that support membership and lexicographic (rank) queries on very large static dictionaries of strings. Our solution is based on a very simple approach that consists of decoupling string storage and string indexing by means of a blockwise compression of the sorted dictionary strings (to be stored in external memory) and a succinct implementation of a Patricia trie (to be stored in internal memory) built on the first string of each block. Our experimental evaluation on two new datasets, which are at least one order of magnitude larger than the ones used in the literature, shows that (i) the state-of-the-art compressed string dictionaries (such as FST, PDT, CoCo-trie) do not provide significant benefits if used in an indexing setting compared to Patricia tries, and (ii) our two-level approach enables the indexing of 3.5 billion strings taking 273 GB in less than 200 MB of internal memory, which is available on any commodity machine, while still guaranteeing comparable or faster query performance than those offered by array-based solutions used in modern storage systems, such as RocksDB, thus possibly influencing their future designs.
Optimal Wheeler Language Recognition

A Wheeler automaton is a finite state automaton whose states admit a total Wheeler order, reflecting the co-lexicographic order of the strings labeling source-to-node paths). A Wheeler language is a regular language admitting an accepting Wheeler automaton. Wheeler languages admit efficient and elegant solutions to hard problems such as automata compression and regular expression matching, therefore deciding whether a regular language is Wheeler is relevant in applications requiring efficient solutions to those problems. In this paper, we show that it is possible to decide whether a DFA with n states and m transitions recognizes a Wheeler language in O(mn) time. This is a significant improvement over the running time $$O(n^{13} + m\log n)$$ of the previous polynomial-time algorithm (Alanko et al. Information and Computation 2021). A proof-of-concept implementation of this algorithm is available in a public repository. We complement this upper bound with a conditional matching lower bound stating that, unless the strong exponential time hypothesis (SETH) fails, the problem cannot be solved in strongly subquadratic time. The same problem is known to be PSPACE-complete when the input is an NFA (D’Agostino et al. Theoretical Computer Science 2023). Together with that result, our paper essentially closes the algorithmic problem of Wheeler language recognition.
Toward a Robust Sensor Fusion Step for 3D Object Detection on Corrupted Data

Multimodal sensor fusion methods for 3D object detection have been revolutionizing the autonomous driving research field. Nevertheless, most of these methods heavily rely on dense LiDAR data and accurately calibrated sensors which is often not the case in real-world scenarios. Data from LiDAR and cameras often come misaligned due to the miscalibration, decalibration, or different frequencies of the sensors. Additionally, some parts of the LiDAR data may be occluded and parts of the data may be missing due to hardware malfunction or weather conditions. This work presents a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fusion step</i> that addresses data corruptions and makes sensor fusion for 3D object detection more robust. Through extensive experiments, we demonstrate that our method performs on par with state-of-the-art approaches on normal data and outperforms them on misaligned data.
VINet: Lightweight, scalable, and heterogeneous cooperative perception for 3D object detection

Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) has emerged to significantly advance the perception of automated driving. However, current cooperative object detection methods mainly focus on ego-vehicle efficiency without considering the practical issues of system-wide costs. In this paper, we introduce VINet, a unified deep learning-based CP network for scalable, lightweight, and heterogeneous cooperative 3D object detection. VINet is the first CP method designed from the standpoint of large-scale system-level implementation and can be divided into three main phases: (1) Global Pre-Processing and Lightweight Feature Extraction which prepare the data into global style and extract features for cooperation in a lightweight manner; (2) Two-Stream Fusion which fuses the features from scalable and heterogeneous perception nodes; and (3) Central Feature Backbone and 3D Detection Head which further process the fused features and generate cooperative detection results. An open-source data experimental platform is designed and developed for CP dataset acquisition and model evaluation. The experimental analysis shows that VINet can reduce 84% system-level computational cost and 94% system-level communication cost while improving the 3D detection accuracy.
Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm

The conformal antenna is a precision device installed on the wing of an aircraft, and its components are distributed on a curved surface. Quality detection is required after assembly. In solving the path planning problem for conformal antenna surface detection, the traditional genetic algorithm faces problems such as slow convergence and easily falling into a local optimal solution. To solve this problem, an improved genetic algorithm combining the historical optimal population (CHOP-IGA) is proposed. First, the algorithm uses the probability-based four-nearest-neighbor method to construct an initial population. Subsequently, the probabilities of the crossover and mutation operators are dynamically adjusted. Next, the algorithm applies the crossover and mutation operators to the population and performs mutation operations on each individual of the historical optimal population. Then, the fitness value is calculated and the next generation of individuals is selected from the parent, offspring, and historical optimal populations according to the elite selection strategy. Finally, the current best fitness is checked to determine whether updating the historical optimal population is necessary. When the termination condition is satisfied, the algorithm outputs the optimal result. Experiments showed that the algorithm satisfactorily solved the path planning problem for conformal antenna surface detection, with a 48.44% improvement in detection efficiency.
Deep Reinforcement Learning for Flipper Control of Tracked Robots in Urban Rescuing Environments

Tracked robots equipped with flippers and LiDAR sensors have been widely used in urban search and rescue. Achieving autonomous flipper control is important in enhancing the intelligent operation of tracked robots within complex urban rescuing environments. While existing methods mainly rely on the heavy work of manual modeling, this paper proposes a novel Deep Reinforcement Learning (DRL) approach named ICM-D3QN for autonomous flipper control in complex urban rescuing terrains. Specifically, ICM-D3QN comprises three modules: a feature extraction and fusion module for extracting and integrating robot and environment state features, a curiosity module for enhancing the efficiency of flipper action exploration, and a deep Q-Learning control module for learning robot-control policy. In addition, a specific reward function is designed, considering both safety and passing smoothness. Furthermore, simulation environments are constructed using the Pymunk and Gazebo physics engine for training and testing. The learned policy is then directly transferred to our self-designed tracked robot in a real-world environment for quantitative analysis. The consistently high performance of the proposed approach validates its superiority over hand-crafted control models and state-of-the-art DRL strategies for crossing complex terrains.
A study on dynamic group signature scheme with threshold traceability for blockchain

Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all participants. However, single-signature applications in transactions can lead to user identification issues due to the reuse of public keys. To address this issue, group signatures can be used, where the same group public key is used to verify signatures from group members to provide anonymity to users. However, in dynamic groups where membership may change, an attack can occur where a user who has left the group can disguise themselves as a group member by leaking a partial key. This problem cannot be traced back to the partial key leaker. In this paper, we propose assigning different partial keys to group members to trace partial key leakers and partially alleviate the damage caused by partial key leaks. Exist schemes have shown that arbitrary tracing issues occurred when a single administrator had exclusive key generation and tracing authority. This paper proposes a group signature scheme that solves the synchronization problem by involving a threshold number of TMs while preventing arbitrary tracing by distributing authority among multiple TMs.
Application of a Hyperspectral Remote Sensing Model for the Inversion of Nickel Content in Urban Soil

Hyperspectral remote sensing technology can provide a rapid and nondestructive method for soil nickel (Ni) content detection. In order to select a high-effective method for estimating the soil Ni content using a hyperspectral remote sensing technique, 88 soil samples were collected in Urumqi, northwest China, to obtain Ni contents and related hyperspectral data. At first, 12 spectral transformations were used for the original spectral data. Then, Pearson’s correlation coefficient analysis (PCC) and the CARS method were used for selecting important wavelengths. Finally, partial least squares regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) models were used to establish the hyperspectral inversion models of the Ni content in the soil using the important wavelengths. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual prediction deviation (RPD) were selected to evaluate the inversion effects of the models. The results indicated that using the PCC and CARS method for the original and transformed wavebands can effectively improve the correlations between the spectral data and Ni content of the soil in the study area. The random forest regression model, based on the first-order differentiation of the reciprocal (RTFD–RFR), was more stable and had the best inversion effects, with the highest predictive ability (R2 = 0.866, RMSE = 1.321, MAE = 0.986, RPD = 2.210) for determining the Ni content in the soil. The RTFD–RFR methods can be used as a means of the inversion of the Ni content in urban soil. The results of the study can provide a technical support for the hyperspectral estimation of the Ni content of urban soil.
Providing prediction reliability through deep neural networks for recommender systems

Deep learning-based recommendation approaches have shown significant improvement in the accuracy of recommender systems (RSs). However, beyond accuracy, reliability measures are gaining attention to evaluate the validity of predictions and enhance user satisfaction. Such measures can ensure that the recommended items are high-scoring items with high reliability. To integrate the native concept of reliability into a deep learning model, this paper proposes a deep neural network-based recommendation framework with prediction reliability. This framework filters out unreliable prediction ratings according to a pre-defined reliability threshold, ensuring the credibility and reliability of top-N recommendation. The proposed framework relies solely on user ratings for reliability, making it highly generalizable and scalable. Additionally, we design a data pre-processing method to address the issue of uneven distribution of ratings before model training, which effectively improves the effectiveness and fairness. The experiments on four benchmark datasets demonstrate that the proposed scheme is superior to other comparison methods in evaluation metrics. Furthermore, our framework performs better on sparse datasets than on dense datasets, indicating its ability to make strong predictions even with insufficient information.
Demonstration of a switched CV-QKD network

Abstract A quantum channel is a physical media able to carry quantum signals. Quantum key distribution (QKD) requires direct quantum channels between every pair of prepare-and-measure modules. This requirement heavily compromises the scalability of networks of directly connected QKD modules. A way to avoid this problem is to introduce switches that can dynamically reconfigure the set of connections. The reconfiguration of a quantum channel implies that the modules using it can adapt to the new channel and peer. The maturity and flexibility of continuous-variable QKD (CV-QKD) qualifies it as a strong contender for integration into optical communication networks. Here we present the implementation of a switched CV-QKD network embedded in the Madrid quantum testbed. The optical switching of the quantum paths significantly reduces the amount of required QKD modules and facilitates the scalability of the network. This demonstration highlights the flexibility and ease of integration of this emerging technology.
Evanescent coupling of nonlinear integrated cavities for all-optical reservoir computing

Abstract We consider theoretically a network of evanescently coupled optical microcavities to implement a space-multiplexed optical neural network in an integrated nanophotonic circuit. Nonlinear photonic network integrations based on evanescent coupling ensure a highly dense integration, reducing the chip footprint by several orders of magnitude compared to commonly used designs based on long waveguide connections while allowing the processing of optical signals with bandwidth in a practical range. Different nonlinear effects inherent to such microcavities are studied for realizing an all-optical autonomous computing substrate based on the reservoir computing concept, and their contribution to computing performance is demonstrated. We provide an in-depth analysis of the impact of basic microcavity parameters on the computational metrics of the system, namely, the dimensionality and the consistency. Importantly, we find that differences between frequencies and bandwidths of supermodes formed by the evanescent coupling are the determining factor of the reservoir’s dimensionality and scalability. The network’s dimensionality can be improved with frequency-shifting nonlinear effects such as the Kerr effect, while two-photon absorption has the opposite effect. Finally, we demonstrate in simulation that the proposed reservoir is capable of solving the Mackey–Glass prediction and the optical signal recovery tasks at gigahertz timescale.
Decoding Eye Blink and Related EEG Activity in Realistic Working Environments

Accurately evaluating cognitive load during work-related tasks in complex real-world environments is challenging, leading researchers to investigate the use of eye blinking as a fundamental pacing mechanism for segmenting EEG data and understanding the neural mechanisms associated with cognitive workload. Yet, little is known about the temporal dynamics of eye blinks and related visual processing in relation to the representation of task-specific information. Therefore, we analyzed EEG responses from two experiments involving simulated driving (re-active and pro-active) with three levels of task load for each, as well as operating a steam engine (active vs. passive), to decode the temporal dynamics of eye blink activity and the subsequent neural activity that follows blinking. As a result, we successfully decoded the binary representation of difficulty levels for pro-active driving using multivariate pattern analysis. However, the decoding level varied for different re-active driving conditions, which could be attributed to the required level of alertness. Furthermore, our study revealed that it was possible to decode both driving types as well as steam engine operating conditions, with the most significant decoding activity observed approximately 200 ms after a blink. Additionally, our findings suggest that eye blinks have considerable potential for decoding various cognitive states that may not be discernible through neural activity, particularly near the peak of the blink. The findings demonstrate the potential of blink-related measures alongside EEG data to decode cognitive states during complex tasks, with implications for improving evaluations of cognitive and behavioral states during tasks, such as driving and operating machinery.
State Estimation of Electromagnetic Scanning Micro-Mirrors Using Unscented Kalman Filtering Method<sup>*</sup>

A nonsmooth dynamic filtering strategy with the frame of unscented Kalman filter (UKF) is proposed for state estimation of electromagnetic scanning micromirrors (ESM) contaminated by random noise. In this method, a stochastic nonsmooth sandwich state-space model with rate-dependent hysteresis is developed to depict the characteristics of the ESM disturbed by random noise. Then, the UKF scheme for the state estimation of ESM system is developed. Afterward, the convergence is provided. Subsequently, a simulated example is illustrated to evaluate the behavior of the proposed filtering method. Finally, experimental results to the state estimation of an ESM are presented.
S2H-GNN: Learning Soft to Hard Feature Matching with Sparsified Graph Neural Network

In this work, we introduce a soft-to-hard network (S2H-GNN) for precise local feature matching between image pairs. The core of S2H-GNN is a weighted context aggregation algorithm that enables nodes to receive information from their neighbors in order to form a more global understanding. S2HGNN leverages a sparsified graph neural network to enhance the features, consisting of the following modules alternately: (1) Inducing Point Initial Module that uses cosine correlation to identify highly scored keypoints as Inducing points. (2) Message Passing Module that integrates the features of original keypoints into those of Inducing points, which are further leveraged to exchange messages through self attention and cross attention. (3) Attention Upsample Module that propagates the feature of Inducing points to that of original keypoints, obtaining discriminative feature representation. Additionally, we propose an assignment layer to obtain a soft assignment matrix, which views feature matching as a graphy matching problem. Finally, we introduce a novel gap loss to guide S2H-GNN in learning a discriminative cognition of matching and non-matching keypoints, ultimately obtaining the hard assignment matrix. Experimental results demonstrate that S2H-GNN achieves comparable or higher performance in image matching and homography estimation tasks.
Bounded-DWA: An Efficient Local Planner for Ackermann-driven Vehicles on Sandy Terrain

We present a new dynamic window approach (DWA) for mobile vehicles equipped with Ackermann steering geometry that adheres to Ackermann kinematic constraints. By integrating these constraints with the sampling window in DWA, we can further reduce and bound the sampling range and enhance the efficiency of the DWA when a mobile vehicle moves on sandy terrain. Furthermore, we improve the evaluation function to optimize the selected trajectory. Our algorithm is successfully validated in ROS and Gazebo through comparison with other existing local planner such as the original DWA and TEB algorithms. We also successfully deploy our Bounded-DWA in the application of coverage path planning, where tree-planting robots traverse on sandy terrain.
Vision-Based Real-Time Object Detection and Voice Alert for Blind Assistance System

Blindness is a very common and unendurable disability among many disabilities. About 285 million people are visually impaired in the world, as per the WHO. Visually impaired people often face various challenges in their daily lives, particularly when it comes to navigating unfamiliar locations and recognizing unfamiliar objects in their surroundings. State-of-the-art computer vision technologies can be used for assisting visually impaired people. Real-time object detection is a difficult operation as it requires more computing power to recognize objects in real time. SSD (Single Shot multi-box Detection) is a quicker detection method that is suitable for real-time object detection. A lightweight network model is required as the prototype device is tested with a Raspberry Pi microprocessor. Therefore, MobileNetV2 is incorporated with the SSD model to increase the accuracy level of detecting real-time household objects. The developed portable prototype comprises Raspberry Pi 3 model B, a USB camera, earphones, a distance sensor, and rechargeable batteries. The objects captured by the camera are classified by the object detection model and the audio feedback is given to the user through the earphones.