Search Results (20)

The Tree–Seed Algorithm (TSA) is a symmetry-driven metaheuristic algorithm that shows potential for complex optimization problems, but it suffers from local optimum entrapment and slow convergence. To address these limitations, we propose the ADTSA algorithm. First, ADTSA adopts a symmetry-driven dual-layer framework for seed generation, which promotes effective information exchange between subpopulations and accelerates convergence speed. In later iterations, ADTSA enhances the population’s exploitation ability through a population fusion mechanism, further improving the convergence speed. Moreover, we propose a historical optimal solution archiving and replacement mechanism, along with a t-distribution perturbation mechanism, to enhance the algorithm’s ability to escape local optima. ADTSA also strengthens population diversity and avoids local optima through convex lens symmetric reverse generation based on the optimal solution. With these mechanisms, ADTSA converges more effectively to the global optimum during the evolutionary process. Tests on the IEEE CEC 2014 benchmark functions showed that ADTSA outperformed several top-performing algorithms, such as LSHADE, JADE, LSHADE-RSP, and the latest TSA variants, and it also excelled in comparison with other optimization algorithms, including GWO, PSO, BOA, GA, and RSA, underscoring its robust performance across diverse testing scenarios. The proposed ADTSA’s applicability in solving complex constrained problems was also validated, with the results showing that ADTSA achieved the best solutions for these complex problems. Full article

Although some progress has been made in deep learning-based remote sensing image change detection, the complexity of scenes and the diversity of changes in remote sensing images lead to challenges related to background interference. For instance, remote sensing images typically contain numerous background regions, while the actual change regions constitute only a small proportion of the overall image. To address these challenges in remote sensing image change detection, this paper proposes a Dynamic Adaptive Context Attention Network (DACA-Net) based on an exchanging dual encoder–decoder (EDED) architecture. The core innovation of DACA-Net is the development of a novel Dynamic Adaptive Context Attention Module (DACAM), which learns attention weights and automatically adjusts the appropriate scale according to the features present in remote sensing images. By fusing multi-scale contextual features, DACAM effectively captures information regarding changes within these images. In addition, DACA-Net adopts an EDED architectural design, where the conventional convolutional modules in the EDED framework are replaced by DACAM modules. Unlike the original EDED architecture, DACAM modules are embedded after each encoder unit, enabling dynamic recalibration of T1/T2 features and cross-temporal information interaction. This design facilitates the capture of fine-grained change features at multiple scales. This architecture not only facilitates the extraction of discriminative features but also promotes a form of structural symmetry in the processing pipeline, contributing to more balanced and consistent feature representations. To validate the applicability of our proposed method in real-world scenarios, we constructed an Unmanned Aerial Vehicle (UAV) remote sensing dataset named the Guangxi Beihai Coast Nature Reserves (GBCNR). Extensive experiments conducted on three public datasets and our GBCNR dataset demonstrate that the proposed DACA-Net achieves strong performance across various evaluation metrics. For example, it attains an F1 score (F1) of 72.04% and a precision(P) of 66.59% on the GBCNR dataset, representing improvements of 3.94% and 4.72% over state-of-the-art methods such as semantic guidance and spatial localization network (SGSLN) and bi-temporal image Transformer (BIT), respectively. These results verify that the proposed network significantly enhances the ability to detect critical change regions and improves generalization performance. Full article

Oriented objects in RSI (Remote Sensing Imagery) typically present arbitrary rotations, extreme aspect ratios, multi-scale variations, and complex backgrounds. These factors often result in feature misalignment, representational ambiguity, and regression inconsistency, which significantly degrade detection performance. To address these issues, GAANet (Gaussian-Augmented Additive Network), a symmetry-driven framework for ODD (oriented object detection), is proposed. GAANet incorporates a symmetry-preserving mechanism into three critical components—feature extraction, representation modeling, and metric optimization—facilitating systematic improvements from structural representation to learning objectives. A CAX-ViT (Contextual Additive Exchange Vision Transformer) is developed to enhance multi-scale structural modeling by combining spatial–channel symmetric interactions with convolution–attention fusion. A GBBox (Gaussian Bounding Box) representation is employed, which implicitly encodes directional information through the invariance of the covariance matrix, thereby alleviating angular periodicity problems. Additionally, a GPIoU (Gaussian Product Intersection over Union) loss function is introduced to ensure geometric consistency between training objectives and the SkewIoU evaluation metric. GAANet achieved a 90.58% mAP on HRSC2016, 89.95% on UCAS-AOD, and 77.86% on the large-scale DOTA v1.0 dataset, outperforming mainstream methods across various benchmarks. In particular, GAANet showed a +3.27% mAP improvement over ${\mathrm{R}}^3$Det and a +4.68% gain over Oriented R-CNN on HRSC2016, demonstrating superior performance over representative baselines. Overall, GAANet establishes a closed-loop detection paradigm that integrates feature interaction, probabilistic modeling, and metric optimization under symmetry priors, offering both theoretical rigor and practical efficacy. Full article

In urban road networks, the integration of connected and autonomous vehicles (CAV) significantly influences traffic flow patterns, with the Fleet Composition—representing the positioning of these vehicles within convoys—being crucial in dictating the symmetry of information exchange amongst them. First, the vehicle composition of the mixed traffic flow is analyzed, and the mathematical analytical expressions of the random distribution characteristics of different types of vehicles are constructed. Second, we analyze the vehicle according to human characteristics in different situations. Then, consider the following characteristics and reaction times of manual drivers, establish a mixed traffic flow following model, and validate the established following. Finally, the basic graph model considering Fleet Composition is derived, and the effects of reaction time, Fleet Composition, driver following characteristics, and other parameters on road capacity under different penetration rates of CAV are analyzed by a Python and SUMO joint simulation. Finally, the characteristics of mixed traffic flow at intersections were analyzed. Full article

A theoretical account of development in mesocortical anatomy is derived from the free energy principle, operating in a neural field with both Hebbian and anti-Hebbian neural plasticity. An elementary structural unit is proposed, in which synaptic connections at mesoscale are arranged in paired patterns with mirror symmetry. Exchanges of synaptic flux in each pattern form coupled spatial eigenmodes, and the line of mirror reflection between the paired patterns operates as a Markov blanket, so that prediction errors in exchanges between the pairs are minimized. The theoretical analysis is then compared to the outcomes from a biological model of neocortical development, in which neuron precursors are selected by apoptosis for cell body and synaptic connections maximizing synchrony and also minimizing axonal length. It is shown that this model results in patterns of connection with the anticipated mirror symmetries, at micro-, meso- and inter-arial scales, among lateral connections, and in cortical depth. This explains the spatial organization and functional significance of neuron response preferences, and is compatible with the structural form of both columnar and noncolumnar cortex. Multi-way interactions of mirrored representations can provide a preliminary anatomically realistic model of cortical information processing. Full article

The mechanism of brain information processing unfolds within spatial and temporal domains inherently linked to the concept of space–time symmetry. Biological evolution, beginning with the prevalent molecular chirality, results in the handedness of human cognitive and psychological functions (the phenomena known as biochirality). The key element in the chain of chirality transfer from the downstream to upstream processes is the pyramidal neuron (PyrN) morphology–function paradigm (archetype). The most apparent landmark of PyrNs is the geometry of the cell soma. However, “why/how PyrN’s soma gains the shape of quasi-tetrahedral symmetry” has never been explicitly articulated. Resolving the above inquiry is only possible based on the broad-view assumption that encoding 3D space requires specific 3D geometry of the neuronal detector and corresponding network. Accordingly, our hypothesis states that if the primary function of PyrNs, at the organism level, is sensory space symmetry perception, then the pyramidal shape of soma is the best evolutionary-selected geometry to support sensory-motor coupling. The biological system’s non-equilibrium (NE) state is fundamentally linked to an asymmetric, non-racemic, steady state of molecular constituents. The chiral theory of pyramidal soma shape conceptually agrees that living systems have evolved as non-equilibrium systems that exchange energy with the environment. The molecular mechanism involved in developing PyrN’s soma is studied in detail. However, the crucial missing element—the reference to the fundamental link between molecular chirality and the function of spatial navigation—is the main obstacle to resolving the question in demand: why did PyrNs’ soma gain the shape of quasi-tetrahedral symmetry? Full article

Living organisms are active open systems far from thermodynamic equilibrium. The ability to behave actively corresponds to dynamical metastability: minor but supercritical internal or external effects may trigger major substantial actions such as gross mechanical motion, dissipating internally accumulated energy reserves. Gaining a selective advantage from the beneficial use of activity requires a consistent combination of sensual perception, memorised experience, statistical or causal prediction models, and the resulting favourable decisions on actions. This information processing chain originated from mere physical interaction processes prior to life, here denoted as structural information exchange. From there, the self-organised transition to symbolic information processing marks the beginning of life, evolving through the novel purposivity of trial-and-error feedback and the accumulation of symbolic information. The emergence of symbols and prediction models can be described as a ritualisation transition, a symmetry-breaking kinetic phase transition of the second kind previously known from behavioural biology. The related new symmetry is the neutrally stable arbitrariness, conventionality, or code invariance of symbols with respect to their meaning. The meaning of such symbols is given by the structural effect they ultimately unleash, directly or indirectly, by deciding on which actions to take. The early genetic code represents the first symbols. The genetically inherited symbolic information is the first prediction model for activities sufficient for survival under the condition of environmental continuity, sometimes understood as the “final causality” property of the model. Full article

Quantum Key Distribution is a secure method that implements cryptographic protocols. The applications of quantum key distribution technology have an important role: to enhance the security in communication systems. It is originally inspired by the physical concepts associated with quantum mechanics. It aims to enable a secure exchange of cryptographic keys between two parties through an unsecured quantum communication channel. This work proposes a secure half-duplex bidirectional quantum key distribution protocol. The security of the proposed protocol is proved against collective attacks by estimating the interception of any eavesdropper with high probability in both directions under the control of the two parties. A two-qubit state encodes two pieces of information; the first qubit represents the transmitted bit and the second qubit represents the basis used for measurement. The partial diffusion operator is used to encrypt the transmitted qubit state as an extra layer of security. The predefined symmetry transformations induced by unitary in conjunction with the asymmetrical two-qubit teleportation scheme retain the protocol’s secrecy. Compared to the previous protocols, the proposed protocol has better performance on qubit efficiency. Full article

In cosmetic surgery, bridging the anticipation gap between the patients and the physicians can be challenging if there lacks objective and transparent information exchange during the decision-making and surgical process. Among all factors, facial symmetry is the most important for assessing facial attractiveness. The aim of this work is to promote communications between the two parties by providing a quadruple of quantitative measurements: overall asymmetry index (oAI), asymmetry vector, classification, and confidence vector, using an artificial neural network classifier to model people’s perception acquired from visual questionnaires concerning facial asymmetry. The questionnaire results exhibit a Cronbach’s Alpha value of 0.94 and categorize the respondents’ perception of each stimulus face into perceived normal (PN), perceived asymmetrically normal (PAN), and perceived abnormal (PA) categories. The trained classifier yields an overall root mean squared error < 0.01, and its result shows that the oAI is, in general, proportional to the degree of perceived asymmetry. However, there exist faces that are difficult to classify as either PN or PAN or either PAN or PA with competing confidence values. In such cases, oAI alone is not sufficient to articulate facial asymmetry. Assisting surgeon–patient conversations with the proposed asymmetry quadruple is advised to avoid or to mitigate potential medical disputes. Full article

The thermal scattering law (TSL), i.e., S(α,β), represents the momentum and energy exchange phase space for a material. The incoherent and coherent components of the TSL correlate an atom’s trajectory with itself and/or with other atoms in the lattice structure. This structural information is especially important for low energies where the wavelength of neutrons is on the order of the lattice interatomic spacing. Both thermal neutron scattering as well as low energy resonance broadening involve processes where incoming neutron responses are lattice dependent. Traditionally, Doppler broadening for absorption resonances approximates these interactions by assuming a Maxwell–Boltzmann distribution for the neutron velocity. For high energies and high temperatures, this approximation is reasonable. However, for low temperatures or low energies, the lattice structure binding effects will influence the velocity distribution. Using the TSL to determine the Doppler broadening directly introduces the material structure into the calculation to most accurately capture the momentum and energy space. Typically, the TSL is derived assuming cubic lattice symmetry. This approximation collapses the directional lattice information, including the polarization vectors and associated energies, into an energy-dependent function called the density of states. The cubic approximation, while valid for highly symmetric and uniformly bonded materials, is insufficient to capture the true structure. In this work, generalized formulation for the exact, lattice-dependent TSL is implemented within the Full Law Analysis Scattering System Hub (FLASSH) using polarization vectors and associated energies as fundamental input. These capabilities are utilized to perform the generalized structure Doppler broadening analysis for UO₂. Full article

If there are two dependent positive real variables ${\mathfrak{x}}_1$ and ${\mathfrak{x}}_2$, and only ${\mathfrak{x}}_1$ is known, what is the probability that ${\mathfrak{x}}_2$ is larger versus smaller than ${\mathfrak{x}}_1$? There is no uniquely correct answer according to “frequentist” and “subjective Bayesian” definitions of probability. Here we derive the answer given the “objective Bayesian” definition developed by Jeffreys, Cox, and Jaynes. We declare the standard distance metric in one dimension, $d(A,B)\equiv |A-B|$, and the uniform prior distribution, as axioms. If neither variable is known, $P(x_2<x_1)=P(x_2>x_1)$. This appears obvious, since the state spaces $x_2<x_1$ and $x_2>x_1$ have equal size. However, if ${\mathfrak{x}}_1$ is known and ${\mathfrak{x}}_2$ unknown, there are infinitely more numbers in the space $x_2>{\mathfrak{x}}_1$ than $x_2<{\mathfrak{x}}_1$. Despite this asymmetry, we prove $P(x_2<{\mathfrak{x}}_1\mid {\mathfrak{x}}_1)=P(x_2>{\mathfrak{x}}_1\mid {\mathfrak{x}}_1)$, so that ${\mathfrak{x}}_1$ is the median of $p\left(x_2\right|{\mathfrak{x}}_1)$, and ${\mathfrak{x}}_1$ is statistically independent of ratio ${\mathfrak{x}}_2/{\mathfrak{x}}_1$. We present three proofs that apply to all members of a set of distributions. Each member is distinguished by the form of dependence between variables implicit within a statistical model (gamma, Gaussian, etc.), but all exhibit two symmetries in the joint distribution $p(x_1,x_2)$ that are required in the absence of prior information: exchangeability of variables, and non-informative priors over the marginal distributions $p\left(x_1\right)$ and $p\left(x_2\right)$. We relate our conclusion to physical models of prediction and intelligence, where the known ’sample’ could be the present internal energy within a sensor, and the unknown the energy in its external sensory cause or future motor effect. Full article

In this paper, we focus on Orthogonal Frequency Division Multiplexing (OFDM) transceivers where undersampling is employed by the receiver Analog/Digital Converter (ADC) when sparse information is exchanged. Several Fast Fourier Transform (FFT) symmetry properties are exploited to allow the substitution of specific input values by others that have already been sampled by the ADC. Several architectures have been proposed in the literature for efficient FFT implementations in terms of power, speed and hardware resources. The FFT input/output values, twiddle factors, etc., are complex numbers with their real and imaginary parts being represented using fixed point format. A tradeoff has to be made between rounding error and complexity. The optimal minimum FFT word length is investigated by combining the undersampling and the rounding error. A configurable new FFT architecture has been developed in hardware description language to test the error model with various FFT sizes, word lengths and Quadrature Amplitude Modulations (QAM). A system designer can take into account the sparseness of the input data and define the desired rounding and undersampling error relation. Τhe developed error model would then predict the required word length and ADC resolution with average Root Mean Square Error (RMSE) less than 1. Full article

An Entropic Dynamics of exchange rates is laid down to model the dynamics of foreign exchange rates, FX, and European Options on FX. The main objective is to represent an alternative framework to model dynamics. Entropic inference is an inductive inference framework equipped with proper tools to handle situations where incomplete information is available. Entropic Dynamics is an application of entropic inference, which is equipped with the entropic notion of time to model dynamics. The scale invariance is a symmetry of the dynamics of exchange rates, which is manifested in our formalism. To make the formalism manifestly invariant under this symmetry, we arrive at choosing the logarithm of the exchange rate as the proper variable to model. By taking into account the relevant information about the exchange rates, we derive the Geometric Brownian Motion, GBM, of the exchange rate, which is manifestly invariant under the scale transformation. Securities should be valued such that there is no arbitrage opportunity. To this end, we derive a risk-neutral measure to value European Options on FX. The resulting model is the celebrated Garman–Kohlhagen model. Full article

Driven by the vision of the Internet of Things (IoT), there has been a dramatic shift in mobile computing in recent years from centralized mobile cloud computing (MCC) to mobile edge computing (MEC). The main features of MECs are to promote mobile computing, network control, and storage to the edge of the network in order to achieve computationally intensive and latency-critical applications on resource-constrained mobile devices. Therefore, MEC is proposed to enable computing directly at the edge of the network, which can deliver new applications and services, especially for the IoT. In order to provide a highly flexible and reliable platform for the IoT, a MEC-based IoT platform (MIoT) is proposed in this study. Through the MIoT, the information asymmetrical symmetry between the consumer and producer can be reduced to a certain extent. Because of the IoT platform, fault tolerance is an important research topic. In order to deal with the impact of a faulty component, it is important to reach an agreement in the event of a failure before performing certain special tasks. For example, the initial time of all devices and the time stamp of all applications should be the same in a smart city before further processing. However, previous protocols for distributed computing were not sufficient for MIoT. Therefore, in this study, a new polynomial time and optimal algorithm is proposed to revisit the agreement problem. The algorithm makes all fault-free nodes decide on the same initial value with minimal rounds of message exchanges and tolerate the maximal number of allowable faulty components in the MIoT. Full article

Can two sites of positive electrostatic potential localized on the outer surfaces of two halogen atoms (and especially fluorine) in different molecular domains attract each other to form a non-covalent engagement? The answer, perhaps counterintuitive, is yes as shown here using the electronic structures and binding energies of the interactions for a series of 22 binary complexes formed between identical or different atomic domains in similar or related halogen-substituted molecules containing fluorine. These were obtained using various computational approaches, including density functional and ab initio first-principles theories with M06-2X, RHF, MP2 and CCSD(T). The physical chemistry of non-covalent bonding interactions in these complexes was explored using both Quantum Theory of Atoms in Molecules and Symmetry Adapted Perturbation Theories. The surface reactivity of the 17 monomers was examined using the Molecular Electrostatic Surface Potential approach. We have demonstrated inter alia that the dispersion term, the significance of which is not always appreciated, which emerges either from an energy decomposition analysis, or from a correlated calculation, plays a structure-determining role, although other contributions arising from electrostatic, exchange-repulsion and polarization effects are also important. The 0.0010 a.u. isodensity envelope, often used for mapping the electrostatic potential is found to provide incorrect information about the complete nature of the surface reactive sites on some of the isolated monomers, and can lead to a misinterpretation of the results obtained. Full article