Search Results (13,286)

Recent advances in graph neural networks have transformed structural pattern learning in domains ranging from social network analysis to biomolecular modeling. Nevertheless, practical deployments in mission-critical scenarios such as binary code similarity detection face two fundamental obstacles: first, the inherent noise in graph construction processes exemplified by incomplete control flow edges during binary function recovery; second, the substantial distribution discrepancies caused by cross-architecture instruction set variations. Conventional GNN architectures demonstrate severe performance degradation under such low signal-to-noise ratio conditions and cross-domain operational environments, particularly in security-sensitive vulnerability identification tasks where feature instability or domain shifts could trigger critical false judgments. To address these challenges, we propose GBsim, a novel approach that combines graph neural networks with natural language processing. GBsim employs a cross-architecture language model to transform binary functions into semantic graphs, leverages a multilayer GCN for structural feature extraction, and employs a Transformer layer to integrate semantic information, generates robust cross-architecture embeddings that maintain high performance despite significant distribution shifts. Extensive experiments on a large-scale cross-architecture dataset show that GBsim achieves an MRR of 0.901 and a Recall@1 of 0.831, outperforming state-of-the-art methods. In real-world vulnerability detection tasks, GBsim achieves an average recall rate of 81.3% on a 1-day vulnerability dataset, demonstrating its practical effectiveness in identifying security threats and outperforming existing methods by 2.1%. This performance advantage stems from GBsim’s ability to maximize information preservation across architectural boundaries, enhancing model robustness in the presence of noise and distribution shifts. Full article

The POVM theorem is a central result in Bohmian mechanics, grounding the measurement formalism of standard quantum mechanics in a statistical analysis based on the quantum equilibrium hypothesis (the Born rule for Bohmian particle positions). It states that the outcome statistics of an experiment are described by a positive operator-valued measure (POVM) acting on the Hilbert space of the measured system. In light of recent debates about the scope and status of this result, we provide a systematic presentation of the POVM theorem and its underlying assumptions with a focus on their conceptual foundations and physical justifications. We conclude with a brief discussion of the scope of the POVM theorem—especially the sense in which it does (and does not) place limits on what is “measurable” in Bohmian mechanics. Full article

We address the scientific “time” concept in the context of more general relaxation processes toward the Wärmetod of thermodynamic equilibrium. More specifically, we sketch a construction of a conceptual ladder of chemical reaction steps that can rigorously bridge a description from the microscopic domain of molecular quantum chemistry to the macroscopic materials domain of Gibbsian thermodynamics. This conceptual reformulation follows the pioneering work of Kenichi Fukui (Nobel 1981) in rigorously formulating the intrinsic reaction coordinate (IRC) pathway for controlled description of non-equilibrium passages between reactant and product equilibrium states of an overall material transformation. Elementary chemical reaction steps are thereby identified as the logical building-blocks of an integrated mathematical framework that seamlessly spans the gulf between classical (pre-1925) and quantal (post-1925) scientific conceptions and encompasses both static and dynamic aspects of material change. All modern chemical reaction rate studies build on the apparent infallibility of quantum-chemical solutions of Schrödinger’s wave equation and its Dirac-type relativistic corrections. This infallibility may now be properly accepted as an added“inductive law” of Gibbsian chemical thermodynamics, the only component of 19th-century physics that passed intact through the revolutionary quantum upheavals of 1925. Full article

In this work, we propose a wavelet-based framework for estimating the derivatives of a density function in the setting of continuous, stationary, and ergodic processes. Our primary focus is the derivation of the integrated mean square error (IMSE) over compact subsets of ${\mathbb{R}}^d$, which provides a quantitative measure of the estimation accuracy. In addition, a uniform convergence rate and normality are established. To establish the asymptotic behavior of the proposed estimators, we adopt a martingale approach that accommodates the ergodic nature of the underlying processes. Importantly, beyond ergodicity, our analysis does not require additional assumptions regarding the data. By demonstrating that the wavelet methodology remains valid under these weaker dependence conditions, we extend earlier results originally developed in the context of independent observations. Full article

This paper presents a quantum method to tackle optimization challenges. Departing from the typical applications of quantum theory in particle physics, we demonstrate our approach using the elastica problem as a concrete example. The elastica, a classic variational problem extensively studied by mathematicians, serves as an ideal test case. Within quantum theory, our central innovation lies in the way we handle boundary conditions by combining forward and backward propagating wave solutions, a concept inspired by the superposition of forward and backward time-traveling particle waves in quantum mechanics. This approach not only provides a novel solution method for the elastica problem but also opens new pathways for applying quantum mathematical techniques to classical optimization challenges in other domains. Full article

Chromatic quantum contextuality is a criterion of quantum nonclassicality based on (hyper)graph coloring constraints. If a quantum hypergraph requires more colors than the number of outcomes per maximal observable (context), it lacks a classical realization with n-uniform outcomes per context. Consequently, it cannot represent a “completable” noncontextual set of coexisting n-ary outcomes per maximal observable. This result serves as a chromatic analogue of the Kochen-Specker theorem. We present an explicit example of a four-colorable quantum logic in dimension three. Furthermore, chromatic contextuality suggests a novel restriction on classical truth values, thereby excluding two-valued measures that cannot be extended to n-ary colorings. Using this framework, we establish new bounds for the house, pentagon, and pentagram hypergraphs, refining previous constraints. Full article

One of the most important problems in agroecology is designing eco-friendly strategies to minimize the propagation of phytopathogens and pests. In this paper, we explore some strategies based on the modification of the plantation configuration together with percolation theory to prevent the propagation of phytopathogens and pests that move over nearest neighbor plants, such as the case of Phytophthora zoospores or pest mites. The percolation threshold is determined for well-mixed and intercropping plantations modeled in nearest neighbor square lattices. Our main result is that the best agroecology strategy consists of designing polyculture plantations to raise the net production yield. Full article

In the past, measures of the “Earth-likeness” of exoplanets have been qualitative, considering an abiotic Earth, or requiring discretionary choices of what parameters make a planet Earth-like. With the advent of high-resolution exoplanet spectroscopy, there is a growing need for a method of quantifying the Earth-likeness of a planet that addresses these issues while making use of the data available from modern telescope missions. In this work, we introduce an informational–entropic metric that makes use of the spectrum of an exoplanet to directly quantify how Earth-like the planet is. To illustrate our method, we generate simulated transmission spectra of a series of Earth-like and super-Earth exoplanets, as well as an exoJupiter and several gas giant exoplanets. As a proof of concept, we demonstrate the ability of the information metric to evaluate how similar a planet is to Earth, making it a powerful tool in the search for a candidate Earth 2.0. Full article

Even though Kemeny’s constant was first discovered in Markov chains and expressed by Kemeny in terms of mean first passage times on a graph, it can also be expressed using the pseudo-inverse of the Laplacian matrix representing the graph, which facilitates the calculation of a sharp upper bound of Kemeny’s constant. We show that for certain classes of graphs, a previously found bound is tight, which generalises previous results for bipartite and (generalised) windmill graphs. Moreover, we show numerically that for real-world networks, this bound can be used to find good numerical approximations for Kemeny’s constant. For certain graphs consisting of up to 100 K nodes, we find a speedup of a factor 30, depending on the accuracy of the approximation that can be achieved. For networks consisting of over 500 K nodes, the approximation can be used to estimate values for the Kemeny constant, where exact calculation is no longer feasible within reasonable computation time. Full article

Modeling the evolution of a system using stochastic dynamics typically implies increasing subjective uncertainty in the adopted state of the system and its environment as time progresses, and stochastic entropy production has been developed as a measure of this change. In some situations, the evolution of stochastic entropy production can be described using an Itô process, but mathematical difficulties can emerge if diffusion in the system phase space happens to be restricted to a subspace of a lower dimension. This situation can arise if there are constants of the motion, for example, or more generally when there are functions of the coordinates that evolve without noise. More simply, difficulties can emerge if there are more coordinates than there are independent noises. We show how the problem of computing the stochastic entropy production in such a situation can be overcome. We illustrate the approach using a simple case of diffusion on an ellipse. We go on to consider an open three-level quantum system modeled within a framework of Markovian quantum state diffusion. We show how a nonequilibrium stationary state of the system, with a constant mean rate of stochastic entropy production, can be established under suitable environmental couplings. Full article

Identifying key nodes in networks is a fundamental problem in network science. This study proposes a quantum deep reinforcement learning (QDRL) framework that integrates reinforcement learning with a variational quantum graph neural network, effectively identifying distributed influential nodes while preserving the network’s fundamental topological properties. By leveraging principles of quantum computing, our method is designed to reduce model parameters and computational complexity compared to traditional neural networks. Trained on small networks, it demonstrated strong generalization across diverse scenarios. We compared the proposed algorithm with some classical node ranking and network dismantling algorithms on various synthetical and empirical networks. The results suggest that the proposed algorithm outperforms existing baseline methods. Moreover, in synthetic networks based on Erdős–Rényi and Watts–Strogatz models, QDRL demonstrated its capability to alleviate the issue of localization in network information propagation and node influence ranking. Our research provides insights into addressing fundamental problems in complex networks using quantum machine learning, demonstrating the potential of quantum approaches for network analysis tasks. Full article

Small, forested catchments are prototypes of terrestrial ecosystems and have been studied in several disciplines of environmental science over several decades. Time series of water and matter fluxes and nutrient concentrations from these systems exhibit a bewildering diversity of spatiotemporal patterns, indicating the intricate nature of processes acting on a large range of time scales. Nonlinear dynamics is an obvious framework to investigate catchment time series. We analyzed selected long-term data from three headwater catchments in the Bramke valley, Harz mountains, Lower Saxony in Germany at common biweekly resolution for the period 1991 to 2023. For every time series, we performed gap filling, detrending, and removal of the annual cycle using singular system analysis (SSA), and then calculated metrics based on ordinal pattern statistics: the permutation entropy, permutation complexity, and Fisher information, as well as their generalized versions (q-entropy and α-entropy). Further, the position of each variable in Tarnopolski diagrams is displayed and compared to reference stochastic processes, like fractional Brownian motion, fractional Gaussian noise, and β noise. Still another way of distinguishing deterministic chaos and structured noise, and quantifying the latter, is provided by the complexity from ordinal pattern positioned slopes (COPPS). We also constructed horizontal visibility graphs and estimated the exponent of the decay of the degree distribution. Taken together, the analyses create a characterization of the dynamics of these systems which can be scrutinized for universality, either across variables or between the three geographically very close catchments. Full article

Aiming at the problem of reduced positioning accuracy of unmanned swarm navigation systems due to dynamic abrupt noise in a complex electromagnetic environment, this paper proposes an adaptive Kalman filtering positioning and calibration method based on dynamic mutation perception and collaborative correction. This method optimizes the performance of Kalman filtering by monitoring the mutation of acceleration and velocity in real time, designing a dynamic threshold detection mechanism, adaptively adjusting the covariance matrix, and using multidimensional scaling analysis to calculate the similarity of trajectories and collaboratively correct the current state. The experiment uses simulation and real scene data and compares algorithms such as the traditional extended Kalman filter to verify the effectiveness of the proposed method, providing an effective solution for the collaborative positioning of an unmanned swarm under complex electromagnetic interference. Full article

Natural Language Processing (NLP) stands as a forefront of artificial intelligence research, empowering computational systems to comprehend and process human language as used in everyday contexts. Language models (LMs) underpin this field, striving to capture the intricacies of linguistic structure and semantics by assigning probabilities to sequences of words. The trend towards large language models (LLMs) has shown significant performance improvements with increasing model size. However, the deployment of LLMs on resource-limited devices such as mobile and edge devices remains a challenge. This issue is particularly pronounced in languages other than English, including Korean, where pre-trained models are relatively scarce. Addressing this gap, we introduce a novel lightweight pre-trained Korean language model that leverages knowledge distillation and low-rank factorization techniques. Our approach distills knowledge from a 432 MB (approximately 110 M parameters) teacher model into student models of substantially reduced sizes (e.g., 53 MB ≈ 14 M parameters, 35 MB ≈ 13 M parameters, 30 MB ≈ 11 M parameters, and 18 MB ≈ 4 M parameters). The smaller student models further employ low-rank factorization to minimize the parameter count within the Transformer’s feed-forward network (FFN) and embedding layer. We evaluate the efficacy of our lightweight model across six established Korean NLP tasks. Notably, our most compact model, KR-ELECTRA-Small-KD, attains over 97.387% of the teacher model’s performance despite an 8.15× reduction in size. Remarkably, on the NSMC sentiment classification benchmark, KR-ELECTRA-Small-KD surpasses the teacher model with an accuracy of 89.720%. These findings underscore the potential of our model as an efficient solution for NLP applications in resource-constrained settings. Full article

This article reviews results from the literature illustrating the bottlenecks and tradeoffs of integrated sensing and communication (ISAC) through the lens of information theory, thus offering a distinct perspective compared to recent works that focus on signal processing, wireless communications, or other related overviews. Different models and scenarios are considered and compared. For example, scenarios where radar sensing is performed at the communication and radar transmitter (mono-static ISAC) and scenarios where the radar receiver differs from the radar transmitter (called bi-static radar). Similarly, we discuss ISAC bottlenecks and tradeoffs both in slowly-varying environments where the main sensing target is described by a single parameter and accordingly, sensing performance is described by detection error probabilities, as well as in fast-varying environments, where the sensing targets are described by vectors and thus vector-valued performance measures such as average distortions like mean-squared errors are used to determine sensing performances. This overview article further also considers limitations and opportunities in network ISAC environments, such as collaborative or interactive sensing, and the influence of secrecy and privacy requirements on ISAC systems, a line of research that has received growing interest over the last few years. For all these scenarios, we provide and discuss precise models and their limitations and provide either bounds or full characterizations of the fundamental information-theoretic performance limits of these systems. Further extensions as well as important open research directions are also discussed. Full article