Symmetric Pattern Research Articles

Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

Efficient Simultaneous Second Harmonic Generation and Dispersive Wave Generation in Lithium Niobate Thin Film

AbstractLithium niobate thin film (LNTF) is a promising platform for ultra‐low loss nonlinear integrated photonics. Here, the simultaneous generation of second harmonic wave (SHW) and dispersive wave (DW) are demonstrated in a single LNTF under the pump of a femtosecond pulse laser, with a conversion efficiency exceeding 25%. The second harmonic generation (SHG) uses the modal phase matching mechanism based on the second‐order nonlinear effect, while the DW generation is based on the perturbations of soliton dynamics caused by self‐phase modulation and higher‐order dispersion. Notably, significant and symmetrical SHW and DW patterns are observed, which exhibit strong spatial dispersion properties. A comprehensive analysis of the phase‐matching conditions are conducted for SHG and DW generation and provide a clear elucidation of the spectral properties of different regions of the emitted light patterns. Additionally, the evolution of the pump light in LNTF is thoroughly investigated, and the solutions of the generalized Schrödinger equation are in good agreement with these experimental results. This work sheds new light on the rich physics of nonlinear optical interactions on LNTF, and by utilizing the synergistic effect of second‐order and third‐order nonlinear effects, this study anticipates achieving efficient and high energy on‐chip broadband frequency conversion and supercontinuum generation across octaves.

Clinical and neurophysiological aspects of peripheral neuropathy in patients with myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) are a heterogeneous group of clonal disorders, manifesting multiple clinical autoimmune inflammatory phenomena, including rarely peripheral neuropathy. Twenty-four patients diagnosed with MDS and 29 healthy subjects were enrolled in this prospective study in a 5-year period. Every subject was assessed by symptoms questionnaire and clinical neurological examination followed by nerve conduction studies, quantitative sensory testing and skin biopsy. Peripheral neuropathy was diagnosed in 12 subjects (50%). Our study indicated that peripheral neuropathy involving large and small nerve fibres, with a symmetrical length-dependent pattern, is not uncommon between patients with MDS.

FLSAD: Defending Backdoor Attacks in Federated Learning via Self-Attention Distillation

Federated Learning (FL), as a distributed machine learning framework, can effectively learn symmetric and asymmetric patterns from large-scale participants. However, FL is susceptible to malicious backdoor attacks through attackers injecting triggers into the backdoored model, resulting in backdoor samples being misclassified as target classes. Due to the stealthy nature of backdoor attacks in FL, it is difficult for users to discover the symmetric and asymmetric backdoor properties. Currently, backdoor defense methods in FL cause model performance degradation while reducing backdoors. In addition, some methods will assume the existence of clean samples, which does not match the realistic scenarios. To address such issues, we propose FLSAD, an effective backdoor defense method in FL via self-attention distillation. FLSAD can recover the triggers using an entropy maximization estimator. Based on the recovered triggers, we leverage the self-attention distillation to eliminate the backdoor. Compared with the baseline backdoor defense methods, FLSAD can reduce the success rates of different state-of-the-art backdoor attacks to 2% on four real-world datasets through extensive evaluation.

Evaluation of the cross sections and photoelectron angular distribution parameters following atomic photoionization under extreme conditions

In this manuscript, we report on a theoretical study of the atomic structures, cross sections, and photoelectron angular distribution parameters following the atomic photoionization under extreme conditions. To achieve this goal, a relativistic approach using the Dirac-Coulomb Hamiltonian within the framework of relativistic configuration interaction, taking advantage of independent particle basis wave functions, is proposed. To describe the interaction of charged particles in the ideal and non-ideal plasmas, the Debye potential and the pseudopotential are implemented, the latter being derived from a progressive resolution of the Bogolyubov chain equations. Both bound and continuous state wave functions, essential for a comprehensive understanding of quantum systems, are determined from the modified local central potential, which is obtained in a self-consistent manner to represent the electronic shielding effect on the nuclear potential. The photoionization processes are evaluated using the relativistic distorted wave approach, which is consistent with the principles of relativistic Dirac theory and thus provides an accurate description of the dynamics. As a test desk, the present method is applied to the evaluation of the energies, ionization potentials, wave functions, cross sections, and photoelectron angular distribution parameters, using the single photon ionization of the Li-like Fe XXIV ions as the basis for analysis. Our results demonstrate that the plasma environment effect not only decreases the ionization potentials and increases the cross sections but also affects the photoelectron angular distribution parameters across different shells, leading to a more balanced and symmetrical photoelectron distribution pattern. A detailed comparison is made between our results, and the available well-established theoretical predictions and experimental data of the unshielded case in the literature shows a good agreement. The present work provides novel insights and theoretical models that not only help us to better understand the fundamental properties of the complex systems but are also beneficial for innovative applications in the study of astrophysical and laboratory plasmas.

Two-step Fourier single-pixel imaging for secure and efficient hidden information transmission

In the rapidly evolving field of optical information security, single-pixel imaging (SPI) has emerged as a promising technique for hidden information transmission. However, traditional SPI methods face significant challenges, including the need for excessive modulation patterns and the vulnerability of encrypted information during transmission. Furthermore, the field lacks efficient methods to reconstruct both plaintext and ciphertext images from the same set of single-pixel measurements. Here, we propose a novel and efficient encryption strategy for Fourier single-pixel imaging (FSPI) that addresses these critical challenges. Our approach integrates two key innovations: a two-step Fourier-total variation conjugate gradient descent (F-TVCGD) method and a dual-key decryption mechanism. The F-TVCGD method significantly reduces the number of modulation patterns required for image reconstruction, enhancing efficiency and minimizing data redundancy. Our dual-key mechanism enables the reconstruction of both plaintext and ciphertext images from a single set of single-pixel measurements using different decryption keys, significantly enhancing security without compromising efficiency. The incorporation of Fourier symmetric patterns improves the convergence robustness of the symmetric gradient descent (SGD) algorithm, leading to superior performance under challenging conditions such as sparse sampling and noise attacks. Numerical simulations and optical experiments validate our method's improvements in both accuracy and security compared to traditional approaches. Our findings demonstrate that the proposed F-TVCGD and SGD strategies effectively address the challenges of excessive modulation patterns and information vulnerability in FSPI.

Variable Presence of an Evolutionarily New Brain Structure is Related to Trait Impulsivity.

Impulsivity is a multidimensional construct reflecting poor constraint over one's behaviors. Clinical psychology research identifies separable impulsivity dimensions that are each unique transdiagnostic indicators for psychopathology. Yet, despite this apparent clinical importance, the shared and unique neuroanatomical correlates of these factors remain largely unknown. Concomitantly, neuroimaging research identifies variably present human brain structures implicated in cognition and disorder: the folds (sulci) of the cerebral cortex located in the latest developing and most evolutionarily expanded hominoid-specific association cortices. We tethered these two fields to test whether variability in one such structure in anterior cingulate cortex (ACC)-the paracingulate sulcus (PCGS)-was related to individual differences in trait impulsivity. 120 adult participants with internalizing or externalizing psychopathology completed a magnetic resonance imaging scan and the Three-Factor Impulsivity Index. Using precision imaging techniques, we manually identified the PCGS, when present, and acquired quantitative folding metrics (PCGS length and ACC local gyrification index). Neuroanatomical-behavioral analyses revealed that participants with leftward or symmetrical PCGS patterns had greater severity of Lack of Follow Through (LFT)-which captures inattention and lack of perseverance-than those with rightward asymmetry. Neuroanatomical-functional analyses identified that the PCGS co-localized with a focal locus found in a neuroimaging meta-analysis on a feature underlying LFT. Both quantitative folding metrics did not relate to any impulsivity dimension. This study advances understanding of the neuroanatomical correlates of impulsivity and establishes the notion that the topographical organization of distinct, hominoid-specific cortical expanses underlie separable impulsivity dimensions with robust, transdiagnostic implications for psychopathology.

Optimal Curve Fitting for Serial Chain in Six-Crease Origami Unit

Abstract Origami-inspired structures have been widely used in aerospace and robotics for three-dimensional (3D) symmetrical configurations using crease-symmetrical origami basic patterns. These patterns offer advantages in repeatable and systematic modeling and mass production. However, few studies have focused on 3D non-symmetrical structures using symmetrical origami basic patterns due to their structure complexity, limiting their application. Therefore, we aim to analyze the folding behavior in 3D non-symmetrical structures using a 6-crease symmetry origami base pattern. To achieve this goal, we first focus on behavior in a two-dimensional (2D) plane. This paper presents a scheme for the behavior of origami units with an optimal curve-fitting algorithm. The curve can be any 2D space curve. The fitting curve, constructed by numerical analysis and an optimal approaching scheme, can satisfy error requirements and retain foldable origami unit features. The paper verifies the feasibility of the curve-fitting scheme by presenting two curve examples, including a quadratic curve and a sin wave function. The results show that the fitting error is reduced by 99% when no boundary conditions are applied. This research provides valuable insights into understanding origami unit kinematic optimization through forward and inverse kinematics. It offers potential applications in the engineering design of foldable structures and precision origami-inspired mechanism, thereby opening avenues for further exploration of complex origami structures and their applications in emerging technologies.

Quantifying Drosophila melanogaster Eye Phenotypes: A Computational Approach Integrating ilastik and Flynotyper.

The Drosophila melanogaster compound eye is a well-structured and comprehensive array of around 800 ommatidia, exhibiting a symmetrical and hexagonal pattern. This regularity and ease of observation make the Drosophila eye system a powerful tool to model various human neurodegenerative diseases. However, ways of quantifying abnormal phenotypes, such as manual ranking of eye severity scores, have limitations, especially when ranking weak alterations in eye morphology. To overcome these limitations, computational approaches have been developed such as Flynotyper. The use of a ring light allows for better qualitative images accessing the intactness of individual ommatidia. However, these images cannot be analyzed by Flynotyper directly due to shadows on ommatidia introduced by the ring light. Here, we describe an unbiased way to quantify rough eye phenotypes observed in Drosophila disease models by combining two software, ilastik and Flynotyper. By preprocessing the images with ilastik, successful quantification of the rough eye phenotype can be achieved with Flynotyper.

Compact Substrate Integrated Waveguide Cubical Dielectric Resonator Antenna for fifth-generation applications

A comprehensive analysis of a wideband Cubical Dielectric Resonator Antenna is presented, specifically designed for 5G-NR wireless communication applications, focusing on the N257, and N258 frequency bands. This antenna exhibits symmetrical radiation patterns, a wider bandwidth, improved efficiency, and substantial gain characteristics. The design employs a low-loss substrate-integrated waveguide structure to enhance gain while reducing cross-polarization effects. This is accomplished through an innovative approach of establishing boundary conditions using copper wire stitching for vias, superseding the conventional soldering iron method, which guarantees minimal radiation leakage, compact dimensions, and reduced losses. The perforated dielectric resonator antenna possesses higher mode excitations, and implementing metallic strips on two resonator surfaces facilitates the simultaneous excitation of fundamental and higher-order modes, thereby yielding a broadband response. The overall dimensions of the antenna are 15.29 × 8.42 × 3.8 mm3(1.3×0.7×0.3)λ03, which facilitates a measured impedance bandwidth of 8.2 GHz, spanning from 24.4 to 32.6 GHz (28.7%), accompanied by a high gain of 8.1 dBi and efficiency levels reaching up to 94%. The prototype is constructed utilizing the standard printed circuit board technique, and simulation outcomes are derived using the High-Frequency Structure Simulator (HFSS).

Cascaded capsule twin attentional dilated convolutional network for malicious URL detection

Malware is one of the most popular cyber-attacks, and it is becoming more common on the network every day. In contrast to benign transmission, which typically exhibits symmetrical patterns, malware communication often shows asymmetrical behaviours, making detection a complex challenge. Fortunately, malware can be distinguished and identified for actual activities utilizing a variety of artificial intelligence methods. However, insufficient work has been allocated to the problem of handling high-dimensional and huge data. This paper proposes a novel deep learning-based approach to identify malicious Uniform Resource Locators (URLs) specifically designed to handle the challenges posed by large-scale and complex data. Initially, input data is sourced from a comprehensive Kaggle dataset, which includes diverse and large-scale URL samples. The URLs are then transformed into vector representations using a Vector Embedding Module, which employs a character-level word embedding technique to capture intricate patterns within the URLs. To further refine the data, the Chaotic Kookaburra Efficient-Bo Network (CKEBO-Net) is applied to extract the most significant features from these vectors, effectively reducing the dimensionality and computational burden. Subsequently, the Cascaded Capsule Twin Attentional Dilated Convolutional Network (C2TA_DiCN) model is introduced to classify and identify malicious URLs with high precision. This model leverages the unique strengths of capsule networks and attentional mechanisms, enhancing its capability to capture subtle dependencies within the data. Furthermore, the Lyrebird Meta-heuristic Optimization (LMO) algorithm is used to fine-tune the model parameters appropriately, ensuring that the training process is efficient and robust. The proposed approach is implemented using Python and rigorously evaluated on the Kaggle dataset. Simulation results demonstrate that the proposed method significantly outperforms existing models, achieving a malicious URL detection accuracy of 99.7%.

Torque-driven superhydrophobic cylinders swim in circles

A superhydrophobic cylinder is a circular cylinder with a boundary pattern of alternating shear-free menisci and solid portions. This article derives analytical solutions for the torque-driven Stokes flow around a superhydrophobic cylinder that is free to move, or ‘swim’, subject to the constraint that it is free of net force. The mathematical approach is a natural generalization of one used to solve for transverse shear flow over a superhydrophobic surface comprising a periodic array of no-shear slots (Crowdy 2011 Phys. Fluids 23 (doi: 10.1063/1.3605575 )). First, the torque-driven Stokes flow around a superhydrophobic cylinder with two unequal no-shear menisci and two equal solid portions is solved in detail. It is then shown how that analysis generalizes, in a natural way, to a cylinder with M no-shear menisci between M solid portions with full exposition given of the case of M = 3 equal solid portions. A further generalization to flow around a superhydrophobic cylinder with an N -fold rotationally symmetric patterning with M menisci in each 2 π / N sector is made. Torque-driven superhydrophobic cylinders with a no-shear pattern devoid of any rotational symmetry are shown to swim on circular trajectories with formulas for the radius and cylinder angular velocity on these trajectories derived here as functions of the no-shear pattern geometry. The M = 1 case of a ‘Janus’ superhydrophobic cylinder having one meniscus and one solid portion can be solved completely explicitly.

Radiation Pattern Analysis and Phase Shifter for Base Station Mobile Communication Circular Array Antenna at 900MHz Frequency by Using 4NEC2X Software

Background: This paper presents the utilization of phase shifters to manipulate and control the radiation patterns in Uniform Circular Array (UCA) antennas. UCAs, known for their 360-degree coverage and symmetrical radiation patterns, are enhanced by incorporating phase shifters, enabling dynamic beam steering and pattern shaping. Materials and Methods: A uniform circular antenna array operating at 900 GHz frequency has been intended for use in base stations using 4NEC2X simulation software. Results: A single-element dipole antenna having a 2.14 dBi gain is used as a base for constructing a uniform 0.5 λ spacing circular array. The number of elements gradually increased from 2 to 12, raising the gain to 12.3 dBi. HPBW decreased both in vertical and horizontal plans from 80o to 60o and 360o to 20o respectively this implies that the circular array antenna's directivity is enhanced. The eight-element array beam scanning was performed from 0o to 360o without any distortion in radiation pattern, gain, and directivity, contradictory with the linear array. Conclusions: The studied case and simulation output illustrate the impact of the element numbers and phase shifter configurations on properties of the radiation pattern of the uniform circular array can be obtained when increasing the number of elements, the gain increases too and scanning the main beam without any change and distortion

Graphene-Based Dual-Band Metasurface Absorber with High Frequency Ratio.

In this paper, we propose a novel dual-band metasurface absorber with a high frequency ratio based on graphene. By carefully designing a centrally symmetrical graphene pattern and positioning it on a glass medium, while utilizing ITO as a ground, the metasurface absorber achieves remarkable high frequency ratio microwave absorption. Specifically, this metasurface absorber exhibits two distinct resonance points at 3.7 GHz and 14 GHz, with an impressive frequency ratio over 3.5. It achieves over 90% absorption efficiency in the frequency ranges of 3.5-4.5 GHz and 13.5-14.5 GHz, highlighting its capability to effectively absorb microwaves across widely spaced frequency bands. Furthermore, the metasurface absorber demonstrates optical transparency and polarization insensitivity, adding to its versatility and potential applications. The measured results of the fabricated prototype validate its design and potential for practical use.

Walking on different inclines affects gait symmetry differently in the anterior-posterior and vertical directions: implication for future sensorimotor training

A symmetric gait pattern in humans reflects near-identical movement in bilateral limbs during walking. However, little is known about how gait symmetry changes on different inclines. This study aimed to address this knowledge gap using the central pattern generator and internal model hypotheses. Eighteen healthy young adults underwent five 2-minute walking trials (inclines of +15%, +8%, 0%, −8%, and −15%). Dependent variables included step time, step length, step width, maximum heel clearance, time to peaks of maximum heel clearance, their corresponding coefficients of variation (CV), and respective symmetry indices (SI). Significant differences were observed in SI of step length (p = .022), step length variability (p < .001), step width variability (p =.001), maximum heel clearance (p < .001), and maximum heel clearance variability (p = .049). Compared to level walking, walking at −8% and −15% inclines increased SI of step length (p = .011, p = .039 respectively) but decreased SI of maximum heel clearance (p = .025, p = .019 respectively). These observations suggested that incline walking affected gait symmetry differently, possibly due to varied internal models used in locomotion. Downhill walking improved vertical gait symmetry but reduced anterior-posterior symmetry compared to level walking. Downhill walking may be a preferable rehabilitation protocol for enhancing gait symmetry, as it activates internal model controls. Even slight downhill inclines could increase active control loading, beneficial for the elderly and those with impaired gait.

Fast and Efficient Drone Path Planning Using Riemannian Manifold in Indoor Environment

This paper introduces an innovative dual-path planning algorithm rooted in a topological three-dimensional Riemannian manifold (T3DRM) to optimize drone navigation in complex environments. It seamlessly integrates strategies for both discrete and continuous obstacles, employing spherical navigation for the former and hyperbolic paths for the latter. Serving as a transformative tool, the T3DRM facilitates efficient path planning by transitioning between discrete and continuous domains. In uncertain environments with unpredictable obstacle positions, our methodology categorizes these positions as discrete or continuous based on their distribution patterns. Discrete obstacles exhibit random distributions, while continuous obstacles display symmetrical patterns with continuity. Leveraging topological metrics, the T3DRM efficiently classifies these patterns for effective path planning. The findings of this research demonstrate the efficiency of path planning based on classified obstacle positions, enabling swift and efficient drone navigation. This research introduces a pioneering application of a T3DRM, accelerating drone navigation in uncertain environments through a dual approach that simultaneously transforms navigation in primal and dual domains. By enabling spherical and hyperbolic navigation concurrently, the T3DRM offers a comprehensive solution to discrete and continuous path planning challenges. The proposed approach can be used for various indoor applications, especially for warehouse management, surveillance and security, navigation in complex structures, indoor farming, site inspection, healthcare facilities, etc.

Ab initio calculations of anomalous seniority breaking in the πg9/2 shell for the N = 50 isotones

We performed ab initio valence-space in-medium similarity renormalization group (VS-IMSRG) calculations based on chiral two-nucleon and three-nucleon interactions to investigate the anomalous seniority breaking in the neutron number N=50 isotones: 92Mo, 94Ru, 96Pd, and 98Cd. Our calculations well reproduced the measured low-lying spectra and electromagnetic E2 transitions in these nuclei, supporting partial seniority conservation in the first πg9/2 shell. Recent experiments have revealed that, compared to the symmetric patterns predicted under the conserved seniority symmetry, the 41+→21+E2 transition strength in 94Ru is significantly enhanced and that in 96Pd is suppressed. In contrast, the 61+→41+ and 81+→61+ transitions exhibit the opposite trend. We found that this anomalous asymmetry is sensitive to subtle seniority breaking effects, providing a stringent test for state-of-the-art nucleon-nucleon interactions and nuclear models. We analyzed the anomalous asymmetry using VS-IMSRG calculations across various valence spaces. Our ab initio results suggest that core excitations of both proton and neutron across the Z=50 shell are ascribed to the observed anomalous seniority breaking in the N=50 isotones.

A Unified Hardware Design for Multiplication, Division, and Square Roots Using Binary Logarithms

Multiplication, division, and square root operations introduce significant challenges in digital signal processing (DSP) systems, traditionally requiring multiple operations that increase execution time and hardware complexity. This study presents a novel approach that leverages binary logarithms to perform these operations using only addition, subtraction, and shifts, enabling a unified hardware implementation—a marked departure from conventional methods that handle these operations separately. The proposed design, involving logarithm and antilogarithm calculations, exhibits an algebraically symmetrical pattern that further optimizes the processing flow. Additionally, this study introduces innovative log-domain correction terms specifically designed to minimize computation errors—a critical improvement over existing methods that often struggle with precision. Compared to standard hardware implementations, the proposed design significantly reduces hardware resource utilization and power consumption while maintaining high operational frequency.

Acoustic wave propagation in depth-evolving sound-speed field using the lattice Boltzmann method

This study investigates the propagation of sound waves within deep-sea low-sound-speed channels using the lattice Boltzmann method, with a key focus on the influence of depth-dependent sound speed on wave propagation. The depth-variable sound speed condition is realized through the incorporation of an external force proportional to the density gradient. After the model verification, investigations into the two-dimensional spreading of sound sources reveal that the depth-dependent sound speed curves the wave propagation. When source depths differing from the low-sound-speed channel, wave paths deviate due to contrasting speeds above and below. When the sound source is situated within the low-sound-speed channel, waves exhibit converging patterns. The simulations also detail the total reflection behavior of sound waves. When the incident angle falls exceeds the critical angle, the waves remain intact within the low-sound-speed channel, thereby enabling the preservation of high amplitude acoustic signals even at remote locations. The subsequent simulations of sound wave propagation around obstacles demonstrate that the low-sound-speed channel also exhibits better signal transmission capabilities in the presence of obstacles. In a uniform sound speed environment, acoustic wave propagation around a submarine exhibits a symmetric pattern. By contrast, under depth-evolving speed conditions, submarines operating at various depths manifest distinct propagation characteristics, such as asymmetric wave propagation during shallow diving, as well as wave attenuation or even silencing when cruising within low-sound-speed channels. These findings underscore the profound implications of depth-evolving sound speed on underwater acoustic signal detection and transmission.

The White Matter Fiber Tract Deforms Most in the Perpendicular Direction During In Vivo Volunteer Impacts.

White matter (WM) tract-related strains are increasingly used to quantify brain mechanical responses, but their dynamics in live human brains during invivo impact conditions remain largely unknown. Existing research primarily looked into the normal strain along the WM fiber tracts (i.e., tract-oriented normal strain), but it is rarely the case that the fiber tract only endures tract-oriented normal strain during impacts. In this study, we aim to extend the invivo measurement of WM fiber deformation by quantifying the normal strain perpendicular to the fiber tract (i.e., tract-perpendicular normal strain) and the shear strain along and perpendicular to the fiber tract (i.e., tract-oriented shear strain and tract-perpendicular shear strain, respectively). To achieve this, we combine the three-dimensional strain tensor from the tagged magnetic resonance imaging with the diffusion tensor imaging (DTI) from an open-access dataset, including 44 volunteer impacts under two head loading modes, i.e., neck rotations (N = 30) and neck extensions (N = 14). The strain tensor is rotated to the coordinate system with one axis aligned with DTI-revealed fiber orientation, and then four tract-related strain measures are calculated. The results show that tract-perpendicular normal strain peaks are the largest among the four strain types (p < 0.05, Friedman's test). The distribution of tract-related strains is affected by the head loading mode, of which laterally symmetric patterns with respect to the midsagittal plane are noted under neck extensions, but not under neck rotations. Our study presents a comprehensive invivo strain quantification toward a multifaceted understanding of WM dynamics. We find that the WM fiber tract deforms most in the perpendicular direction, illuminating new fundamentals of brain mechanics. The reported strain images can be used to evaluate the fidelity of computational head models, especially those intended to predict fiber deformation under noninjurious conditions.