Let G and H be simple graphs and let S subset of V(G). A set S is called a non-local resolving set of G if each pair of non-adjacent vertices of G is distinguished by at least one vertex of S. The nonlocal metric dimension of G, denoted by dim(nl)(G), is the minimum cardinality of such a set. This invariant is complementary to the classical metric dimension and to the local metric dimension: instead of distinguishing all vertex pairs or only adjacent vertex pairs, it isolates the information needed to distinguish non-adjacent pairs. This distinction is especially relevant for graph products in which the construction itself creates new non-adjacencies. In this paper, we study the nonlocal metric dimension of hierarchical product graphs. We first give a general lower bound for dim(nl)(G(U) (sic) H) in terms of dim(nl)(H), and then discuss exact values for several families, including K-n(U) (sic) H, K-1,K-m(U) (sic) P-n, and P-n(U) (sic) K-1,K-m.
This paper introduces an interactive music system with quantum musical agents that communicate by teleporting quantum states to one another. Human performers interact in real time with agents whose melodic and rhythmic behaviours are encoded as quantum states using Single Qubit Probability Amplitude Modulation (SQPAM) and structured through Quantum Phase Estimation (QPE). Up to three agents are combined within a single quantum circuit, with directed communication via quantum teleportation. We are interested in supporting ambiguous, transformative interactions reminiscent of free Jazz improvisation. Therefore, rather than treating noise and decoherence as limitations, the system embraces NISQ-era constraints as creative affordances, framing agent communication as quantum whispers, that is, deliberate, musically expressive imperfections in state transfer. We provide demonstrations and analyses based on melodic correlation, pitch-set distance, and state fidelity, where a continuum between imitation and divergence can be observed. We developed a tunable interpretation method to assess how agents reinterpret teleported states. This work positions teleportation as a promising interaction mechanism for agent-based quantum computer music and outlines future directions toward distributed ensembles connected via the Quantum Internet.
This paper addresses the stability problem of cyber-physical DC microgrids under false data injection (FDI) attacks. A distributed adaptive resilient control method is proposed to ensure that the control objectives of proportional current sharing and average voltage balance are achieved in DC microgrids. The designed control strategy generates an adaptive resilience compensation term based on the residual between the estimate states of a well-designed estimator and the actual states, which does not require all the states of the distributed generations and any information of FDI attacks. The simulations for DC microgrids validate the effectiveness of the proposed method.
Recent branch-based MaxIS solvers improve solution quality through recursive search and targeted branching [Hespe D, Lamm S, Schorr C. Targeted branching for the maximum independent set problem. 2021], but their runtime can grow rapidly on large IoT conflict graphs. This paper proposes KernelGreedyMaxIS, a deterministic polynomial-time framework for scalable independent-set computation. The method combines lightweight degree-based kernelization with minimum-degree greedy inference, separating locally justified reductions from heuristic selection. This design improves reproducibility, avoids random seeds and training data, and provides predictable runtime behavior. Theoretical analysis proves reduction correctness, feasibility, deterministic termination, and $ O(n<^>{2}log n) $ O(n2logn) worst-case complexity. Experiments show competitive solution quality with fast runtime on benchmark and real-world graphs.
We propose a low-carbon automotive-component logistics network optimization method based on the improved particle swarm algorithm (IPSO) to reduce delivery times and operational costs while lowering carbon footprints. Accounting for key variables such as transportation distance, load capacity and transport modes, a multi-objective optimization model of the cost-optimal green automotive-parts logistics network is established and grouping-control evolution strategy, opposition-based search and mutation-crossover strategies are introduced in particle swarm algorithm (PSO) to obtain a global optimal solution. Experimental results show that the proposed low-carbon logistics optimization network achieves lower costs, times and carbon emissions compared to the traditional networks.
An network that is closely interconnected by smaller units, forms the structural core in systems of parallel computing, integrating units like processors and their communication links for effective data exchange. Its performance is primarily governed by parameters such as wirelength, dilation, bandwidth and minimum cutwidth. The arrangement of these configurations in an optimal setup remains a key challenge, where the study of graph embedding offers an effective framework for enhancing parallel algorithm performance and overall system efficiency. This study employes an embedding function, which maps the guest graph, the family of (K-p - C-p)(n )onto suitable host structures.
We present a polynomial-time algorithm for minimum vertex cover achieving an approximation ratio strictly less than 2 for any finite undirected graph with at least one edge. The algorithm reduces the problem to a minimum weighted vertex cover on a degree-1 auxiliary graph using weights 1/d(v) , solves it optimally via Cauchy-Schwarz-balanced selection, and projects the solution back to a valid cover. Correctness and the strict sub-2 ratio are rigorously proved. Runtime is O(|V|+|E|), confirming practical scalability. [GRAPHICS]
This study addresses the critical challenge of balancing robust feature representation and computational efficiency in loop closure detection for visual simultaneous localization and mapping (SLAM) systems under sudden illumination variations, as well as the issue of temporal misalignment between image sequence matching and real-world scenes. To tackle these problems, we propose an illumination-adaptive, hierarchical, and lightweight framework for robust loop closure detection in Visual SLAM. Its core architecture comprises three synergistic modules: a Swin-Tiny Transformer-based feature extraction module, which enhances illumination robustness by leveraging mid-level structural features integrated with sliding window self-attention; a dimension-reduced network-based vector locally aggregated descriptor generation module, designed to optimize both matching speed and storage overhead while preserving discriminative power; and a motion-constrained Fast Dynamic Time Warping (FastDTW) sequence matching module that improves temporal alignment between image sequences and physical scenes by incorporating robot motion parameters. Extensive evaluations on the New College and City Centre datasets-characterized by significant illumination variations-demonstrate that the proposed framework achieves precision rates of 80.6% and 78.3% at 50% recall, respectively, with a per-frame processing delay of only 18 ms. Compared with Oriented FAST and Rotated BRIEF (ORB)-SLAM3, our framework yields a 22% improvement in precision, validating its superiority in balancing robustness, efficiency, and lightweight performance for complex illumination scenarios.
Surface electromyography (sEMG) sensors are widely used in human-computer interaction, yet the failure of a single sensor can compromise system usability. We propose a methodological framework for implementing a fail-safe mechanism in multi-sensor sEMG systems. Using arm sEMG recordings of rock-paper-scissors gestures, we extracted hand-crafted features and quantified class separability via the maximum Fisher discriminant ratio (FDR). A multi-layer perceptron validated our approach, consistent with prior findings and physiological evidence. Systematic sensor ablations and FDR analysis produced a ranking of crucial versus replaceable sensors. This ranking informs robust device design, sensor redundancy, and reliability in clinical and practical applications.
We model the fungal mycelium as a time-dependent simplicial complex. We prove that this representation is unique up to homeomorphism, establishing topology as a faithful invariant of mycelial architecture. Within this framework, logical inference emerges as cytoplasmic flow, when derivations correspond to directed paths, yielding monotonic reasoning in tree-like regions and non-monotonic, defeasible inference in loopy zones. According to this interpretation, any consistent system encoding arithmetic and exhibiting logical completeness cannot collapse to a discrete set of points but must contain non-bounding 1-cycles. Consequently, such systems inherently support undecidable propositions and non-monotonic dynamics.
This work explores the practical exploitation of ReRAM technology, addressing erratic switching cell performance by leveraging comprehensive memory access methods aligned with prognostics and health management (PHM) principles. The paper highlights the operational characteristics of memristive devices while rigorously reviewing their non-ideal aspects through experimental measurements. A novel resistance-to-data mapping strategy is introduced to formulate PHM-enhanced REA D/WRITE methods, improving ReRAM cell reliability and endurance. The practical development of a ReRAM control unit (ReMCU) is presented with design alternatives that aim to significantly advance the practical exploitation of ReRAM technology across emerging applications, from nonvolatile memory chips to neuromorphic/edge computing.
Spiking Neural P systems are a class of distributed and parallel models of computation inspired by the way biological neurons communicate through spikes. In this paper, we introduce and investigate a variant, called Target-Indicated Spiking Neural P systems with Mute Rules in which neurons are labeled and every spiking rule that uses a marked spike explicitly specifies the unique target neuron to which the marked spike is transferred. The computation of such a system generates a trace language, defined as the set of words obtained by recording, at each step, the label of the neuron holding the marked spike.
Large-scale Discrete Element simulations are highly compute-intensive, and achieving energy-efficient performance remains a major challenge for next-generation exascale systems. This work evaluates the performance and energy characteristics of DEM kernels on multinode systems using MESA-PD within waLBerla and P4IRS, a portable framework optimized for CPUs and GPUs. While MESA-PD efficiently supports CPU simulations, it lacks GPU portability. Integrating P4IRS into waLBerla enables GPU-accelerated and portable DEM execution. Using a settling-spheres benchmark, we study performance and energy consumption across different systems, achieving up to 90% weak-scaling efficiency on 16 CPU nodes and demonstrating scalability to 256 GPUs.
Performance analysis in single-threaded, virtual-machine-based event-driven systems remains difficult due to abstraction layers separating applications, runtimes, and the operating system. Conventional profilers are effective for deterministic, multi-threaded systems but cannot capture the complex asynchronous interactions in environments such as Python, Node.js, Deno, or Lua. We introduce a runtime-level instrumentation technique that operates inside the virtual machine to capture event identifiers and contextual data, enabling post-hoc reconstruction across kernel, runtime, and user-space layers. Our Node Compass prototype demonstrates this approach, producing fine-grained, multilayer traces with minimal overhead and enabling precise bottleneck detection, thereby advancing observability in asynchronous runtimes.
We study one-dimensional binary Probabilistic Cellular Automaton (PCA) that interpolate between Wolfram's classical rules 23, 77, 178 and 232. These rules are the only ones that satisfy two criteria: (i) in the case of a majority in the neighbourhood states, the central site takes either the majority state or the opposite and (ii) if the neighbourhood states are tied, the central site either changes its current state or keeps it. The PCA is defined by two Bernoulli random variables with parameters p,r is an element of[0,1] , and we analytically solve small-size cases by using a Markov process formulation.
The inherent vulnerabilities and distributed nature of IoT systems necessitate IDS that balance accuracy with computational efficiency. This study proposes a lightweight, distributed IDS leveraging ensemble deep learning to address IoT security challenges. By integrating multiple lightweight models (DNN, CNN, GRU) via various ensemble strategies, the framework enhances detection capabilities while minimizing resource overhead. Evaluated on the CICIDS2017 and Edge-IIoT datasets, the ensemble approach outperforms single-model solutions, specifically stacking and boosting in rare threat detection. This work demonstrates the viability of decentralized, resource-aware IDS deployments, offering a scalable solution for securing dynamic IoT ecosystems amidst evolving cyber threats.
