Local Rules Research Articles

Dynamical phase transitions in graph cellular automata.

Discrete dynamical systems can exhibit complex behavior from the iterative application of straightforward local rules. A famous class of examples comes from cellular automata whose global dynamics are notoriously challenging to analyze. To address this, we relax the regular connectivity grid of cellular automata to a random graph, which gives the class of graph cellular automata. Using the dynamical cavity method and its backtracking version, we show that this relaxation allows us to derive asymptotically exact analytical results on the global dynamics of these systems on sparse random graphs. Concretely, we showcase the results on a specific subclass of graph cellular automata with "conforming nonconformist" update rules, which exhibit dynamics akin to opinion formation. Such rules update a node's state according to the majority within their own neighborhood. In cases where the majority leads only by a small margin over the minority, nodes may exhibit nonconformist behavior. Instead of following the majority, they either maintain their own state, switch it, or follow the minority. For configurations with different initial biases towards one state we identify sharp dynamical phase transitions in terms of the convergence speed and attractor types. From the perspective of opinion dynamics this answers when consensus will emerge and when two opinions coexist almost indefinitely.

Urban Sustainability Development in Morocco, a Review

During the last decades, Morocco has recorded substantial urbanization and faced challenges related to urban sprawl and encroachment on fertile lands. This paper reviews several studies assessing urban sustainability development in 27 Moroccan urban areas using the UN’s Sustainable Development Goal (SDG) indicator 11.3.1, i.e., the ratio of Land Consumption Rate (LCR) to Population Growth Rate (PGR). Among the 27 urban areas reviewed, analysis of SDG 11.3.1 data revealed that only 7 exhibited trends towards sustainable urban development, while the remaining 20 showed a divergence from the sustainability objectives. We analyze the studies, conducted between 2020 and 2023, describing in depth the relationship between LCRs and PGRs, and provide a comprehensive summary of the SDG 11.3.1 outcome, discussing along the way methodological variations, and introducing a new concept for the evaluation of urban land consumption. The review reveals disparate regional urban development trends, emphasizing the need for local land tenure rules. Additionally, the study discusses how the SDG 11.3.1 reacts under the influence of several parameters such as land characteristics, climate, and geographic location. While the study acknowledges the limitations of SDG 11.3.1, it found that the indicator provides valuable insights into trends and comparisons, and should offer assistance to stakeholders, urban planners, and decision makers in their pursuit of sustainable development. However, these identified limitations underscore the need for a more comprehensive and multidimensional urban sustainability development indicator, capable of better addressing the complexities of the urban environment.

Interpretable Machine Learning for Finding Intermediate-mass Black Holes

Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine-learning (ML) models trained on GC simulations can in principle predict IMBH host candidates based on observable features. This approach has two limitations: first, an accurate ML model is expected to be a black box due to complexity; second, despite our efforts to simulate GCs realistically, the simulation physics or initial conditions may fail to reflect reality fully. Therefore our training data may be biased, leading to a failure in generalization to observational data. Both the first issue—explainability/interpretability—and the second—out of distribution generalization and fairness—are active areas of research in ML. Here we employ techniques from these fields to address them: we use the anchors method to explain an Extreme Gradient Boosting (XGBoost) classifier; we also independently train a natively interpretable model using Certifiably Optimal RulE ListS (CORELS). The resulting model has a clear physical meaning, but loses some performance with respect to XGBoost. We evaluate potential candidates in real data based not only on classifier predictions but also on their similarity to the training data, measured by the likelihood of a kernel density estimation model. This measures the realism of our simulated data and mitigates the risk that our models may produce biased predictions by working in extrapolation. We apply our classifiers to real GCs, obtaining a predicted classification, a measure of the confidence of the prediction, an out-of-distribution flag, a local rule explaining the prediction of XGBoost, and a global rule from CORELS.

Multiscale computational study to predict the irradiation-induced change in engineering properties of fusion reactor materials

In this study, we address the impact of irradiation conditions in a tokamak on the engineering properties of materials, leading to potential degradation of in-vessel components over their lifecycle. Our approach involves a predictive model for irradiation-induced damage, employing a multiscale computational framework. This framework integrates various simulation techniques, including Monte Carlo-based neutronics (OpenMC), dislocation dynamics (DD) using MoDELib, and finite element analysis (FEA) with Code_Aster. This integration offers a versatile solver capable of analysing tokamak components exposed to different irradiation doses and temperature conditions.To showcase the utility of this multiscale computational framework, we present a case study focused on tungsten monoblock designs. We assess the failure probabilities of these designs at different stages of their lifecycle. Neutron heating and damage energy values are obtained from OpenMC neutronics simulations. The neutron heating values serve as volumetric heat sources for the FEA thermal simulation. We calculate the displacement per atom (dpa) across the monoblock at various full power days (day 0, day 100, and day 1000) using the damage energy values. The irradiation-induced defect densities, dependent on temperature and dpa, are inputs to DD microstructural simulations performed on the representative volume element (RVE) using MoDELib. This allows us to obtain the yield stress of the material. Subsequently, the thermal fields from the FEA thermal simulation, along with the dpa and temperature-dependent yield stress from the DD simulation, are implemented for FEA mechanical simulations.To evaluate the failure probability of the monoblock designs at different stages of their lifecycle, we conduct an SDC-IC assessment, incorporating a plastic flow localization rule within the current framework. This comprehensive approach provides insights into the thermo-mechanical behaviour of in-vessel components subjected to neutron irradiation, offering a predictive capability for assessing their performance over time.

Decomposition-based multi-objective approach for a green hybrid flowshop rescheduling problem with consistent sublots

Rescheduling in a hybrid flowshop holds significant importance in modern industries that face uncertain events. Moreover, real-world manufacturing scenarios often utilise lot streaming to enhance market competitiveness. In light of escalating energy demands and their consequential environmental impacts, contemporary manufacturing companies are placing a heightened emphasis on energy efficiency. This study addressed a green hybrid flowshop rescheduling problem with consistent sublots (GHFRP_CS) in the context of urgent lot insertion. Initially, we establish an optimisation model aimed at minimising the makespan, total energy consumption, and system stability. To tackle this NP-hard multi-objective optimization problem, we develop a constructive heuristic generating promising solutions based on lot split, sequence, and local search rules. Further improvement is achieved through a multi-objective discrete artificial bee colony algorithm (MDABC). MDABC decomposes the problem into sub-problems, initiating solutions with the constructive heuristic and refining them through employed bee, onlooker bee, and scout bee phases. Computational experiments compare MDABC with other multi-objective evolutionary algorithms (MOEAs) on small- and large-scale problems. Results demonstrate MDABC's superiority, achieving fourfold accuracy and efficiency enhancement for small-scale instances and sixfold improvement for large-scale problems at low cost compared to other MOEAs.

LR-XFL: Logical Reasoning-Based Explainable Federated Learning

Federated learning (FL) is an emerging approach for training machine learning models collaboratively while preserving data privacy. The need for privacy protection makes it difficult for FL models to achieve global transparency and explainability. To address this limitation, we incorporate logic-based explanations into FL by proposing the Logical Reasoning-based eXplainable Federated Learning (LR-XFL) approach. Under LR-XFL, FL clients create local logic rules based on their local data and send them, along with model updates, to the FL server. The FL server connects the local logic rules through a proper logical connector that is derived based on properties of client data, without requiring access to the raw data. In addition, the server also aggregates the local model updates with weight values determined by the quality of the clients’ local data as reflected by their uploaded logic rules. The results show that LR-XFL outperforms the most relevant baseline by 1.19%, 5.81% and 5.41% in terms of classification accuracy, rule accuracy and rule fidelity, respectively. The explicit rule evaluation and expression under LR-XFL enable human experts to validate and correct the rules on the server side, hence improving the global FL model’s robustness to errors. It has the potential to enhance the transparency of FL models for areas like healthcare and finance where both data privacy and explainability are important.

Sleep-Like Unsupervised Replay Improves Performance When Data Are Limited or Unbalanced (Student Abstract)

The performance of artificial neural networks (ANNs) degrades when training data are limited or imbalanced. In contrast, the human brain can learn quickly from just a few examples. Here, we investigated the role of sleep in improving the performance of ANNs trained with limited data on the MNIST and Fashion MNIST datasets. Sleep was implemented as an unsupervised phase with local Hebbian type learning rules. We found a significant boost in accuracy after the sleep phase for models trained with limited data in the range of 0.5-10% of total MNIST or Fashion MNIST datasets. When more than 10% of the total data was used, sleep alone had a slight negative impact on performance, but this was remedied by fine-tuning on the original data. This study sheds light on a potential synaptic weight dynamics strategy employed by the brain during sleep to enhance memory performance when training data are limited or imbalanced.

Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.

Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.

On the Local Cache Update Rules in Streaming Federated Learning

On the Local Cache Update Rules in Streaming Federated Learning

Learning Stiffness Tensors in Self-Activated Solids via a Local Rule.

Mechanical metamaterials are often designed with particular properties for specific load-bearing functions. Alternatively, this study aims to create a class of active lattice metamaterials, dubbed self-activated solids, that can learn desired stiffness tensors from the elastic deformations they experienced, a crucial feature to improve the performance, efficiency, and functionality of materials. Artificial adaptive matters that combine sensory, control, and actuation elements can offer appealing solutions. However, challenges still remain: The designs will rely on accurate off-line and global computations, as well as intricate coordination among individual elements. Here, a simple online and local learning strategy is initiated based on contrastive Hebbian learning to gradually guide self-activated solids to possess sought-after stiffness tensors autonomously and reversibly. During learning, the bond stiffness of the active lattice varies depending only on its local strain. The numerical tests show that the self-activated solid can not only achieve the desired bulk, shear, and coupling moduli but also manifest uni-mode and bi-mode extremal materials by itself after experiencing the corresponding elastic deformations. Further, the self-activated solid can also achieve the desired time-varying moduli when exposed to temporally different loads. The design is applicable to any lattice geometries and is resistant to damage and instabilities. The material design approach and the physical learning strategy suggested can benefit the design of autonomous materials, physical learning machines, and adaptiverobots.

Abnormal Linear Dichroism in Anisotropic Van der Waals Crystal CrOCl Revealed by Phase‐Dependent Polarized Raman Scattering

AbstractWithin angle‐resolved polarized Raman spectroscopy (ARPRS) experiments, ensuring accurate configurations is crucial for obtaining reliable results, especially when dealing with anisotropic materials like CrOCl with anisotropic crystal structure and phonon properties. However, comprehensive understanding of phase‐dependent ARPRS and the calibration of phase angle on anisotropic phonon modes from a crystallographic perspective are still lacking. Herein, detailed investigation on phase‐dependent ARPRS and looked into the anisotropic photo‐phonon interaction in CrOCl through in situ ARPRS is conducted. The Raman tensors and crystal orientation are acquired under two typical configurations. Phase‐dependent ARPRS are thoroughly analyzed for the calibration of analyzer deviation. The anomalous anisotropic response in phase‐dependent ARPRS is closely related to the intrinsic linear dichroism (LD) effect in CrOCl, which is further confirmed by angle‐resolved polarization absorption. Moreover, the strong localized rules for various parallel energy bands of CrOCl have generated anomalous LD conversion effect near 470 nm, which expands the potential application of CrOCl in polarization‐wavelength selected devices. This findings can offer in‐depth comprehension on the anisotropic phonon physics of CrOCl and a feasible approach to utilize phase‐dependent ARPRS for probing the undiscovered abundant photo‐induced effects in low‐symmetry 2D materials.

The role of heterogeneity in autonomous perimeter defense problems

When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant.

Flocking Behavior of Boids Driven by Hyperchaotic MACM System

In the present work a detailed study is presented, on the design, programming, and investigation of the behavior of flocking (Movement type flock), through the model of BOIDS, for its acronym in English "Bird Oid Object" (Object type bird), which was devised by Craig Reynolds in 1986. This complex flocking behavior that occurs arises from the interaction of simple local rules, in which complexity and sensitivity to initial conditions are present. A measure of chaotic compound will be introduced to the algorithm by means of a new four-dimensional autonomous hyperchaotic system based on the 3D Méndez-Arellano-Cruz-Martínez (MACM) system. The measures proposed herein, therefore, may have the potential to predict, control, and exemplify the behavior of group intelligence study systems that occur in nature, allowing the implementation of these systems in groups of robots through the implementation of hyperchaotic trajectories in the future, to obtain greater speed and efficiency, obstacle and collisions avoidance in their flights.

Generalisation of a synchronous solution of the parity problem on cyclic configurations over a non-circulant graph

The parity problem is a classical binary benchmark for addressing the computational ability and limitations of automata networks. It refers to conceiving a local rule to allow deciding whether the number of 1-states in the nodes of an arbitrary network is an odd or even number, without global access to the nodes. In its standard formulation, the automata network has an odd number of nodes whose states, arranged as a cyclic configuration, should converge to a fixed point of all 0s, if the initial configuration has an even number of 1s, or to a fixed point of all 1s, otherwise. It has been shown that a local rule alone is able to solve the problem in this formulation, including a synchronous solution we have recently shown, totally based on the local parity rule of the cellular automata elementary space (number 150), with a certain pattern of connection between the nodes. Here, we generalise this solution, showing how to construct many others, combining rule 150 with rules 170 and 240, which are the local shifts of the same space, in such a way that the original solution is just one among countless possibilities. Such solutions may have different convergence times for specific configurations, but are equivalent in the context of all configurations of a given size; also, various solutions form symmetric pairs, in terms of the actual rules they used. The solutions were evaluated computationally and presented here without their formal proofs, but empirical evidences strongly suggest that they can be obtained by the same kind of technique we used in the solution exclusively with rule 150.

Connecting microscopic and mesoscopic mechanics in model structural glasses.

We present a novel formalism to characterize elastic heterogeneities in amorphous solids. In particular, we derive high-order strain-energy expansions for pairwise energies under athermal quasistatic dynamics. We then use the presented formalism to study the statistical properties of pairwise expansion coefficients and their link with the statistics of soft, quasilocalized modes, for a wide range of formation histories in both two- and three-dimensional systems. We further exploit the presented framework to access local yield stress maps by performing a non-linear stress-strain expansion within a cavity embedded in a frozen matrix. We show that our "bond micromechanics" compare well with the original "frozen matrix" method, with the caveat of overestimating large stress activations. We additionally show how local yield rules can be used as an input for a scalar elasto-plastic model (EPM) to predict the stress response of materials ranging from ductile to brittle. Finally, we highlight some of the limits of simple mesoscale models in capturing the aging dynamics of post-yielding systems. Intriguingly, we observe subdiffusive and diffusive shear band growths for particle-based simulations and EPMs, respectively.

Inspection policy optimization for hierarchical multistate systems under uncertain mission scenarios: A risk-averse perspective

Most engineered systems intend to perform missions with a pre-specified target success probability to reduce undesirable failure risks. Before executing the next mission, inspection activities are conducted across various physical levels for assessing the probability of mission success. However, due to the randomness of a system’s degradation behavior and the presence of measurement errors, inspection results inevitably contain uncertainty. Meanwhile, mission durations and acceptable system states may also be uncertain, due to uncontrollable factors, such as random operating environments and mission demands. In such a circumstance, it is of great significance to identify the optimal multilevel inspection policy to answer, as great confident as possible, the question that the system can complete the next mission with a target mission success probability. This paper develops a novel metric to gauge the effectiveness of a multilevel inspection policy to assess if the system can complete the next mission with a pre-specified target success probability from a risk-averse perspective, based on which an optimization method is proposed to seek an inspection policy under uncertain scenarios with the aim of minimizing the maximum regret of the proposed metric. A stochastic fractal search algorithm, along with two tailored local search rules, is designed to efficiently resolve the resulting optimization problem. Two cases, including a three-component system and a rocket fueling mechanism’s control system, are used to illustrate the efficacy of the proposed approach, which is capable of effectively identifying the risk of mission failures by inspection policies.

Development of a New Bioequivalent Omeprazole Product.

Background and Objectives: The enteric form of omeprazole is one of the most commonly prescribed medications. Similarly to Europe, Kazakhstan relies on the localization of pharmaceutical drug production as one of its primary strategies to ensure that its population has access to affordable and good-quality medicines. This study comprehensively describes the technologically available development of bioequivalent delayed-release omeprazole. Materials and Methods: Various regimes and technological parameters were tested on laboratory- and production-scale equipment to establish a technical process where a functional and gastro-protective layer is essential. According to the ICH guidance on stability testing and Kazakhstan local rules, stability studies were conducted under conditions appropriate for climate zone II. The comparison of the rate and extent of absorption with subsequent assessment of the bioequivalence of the generic and reference drugs after a single dose of each drug at a dose of 40 mg was performed. Results: The quantitative and qualitative composition and technology of producing a new generic enteric form of omeprazole in capsules were developed and implemented at the manufacturing site of solid forms. Dissolution profiles in media with pH 1.2 and 6.8 were proven. During the accelerated six-month and long-term twelve-month studies, the developed formulation in both packaging materials at each control point passed the average weight and mass uniformity test, dissolution test, acid-resistance stage test, buffer stage test, impurity assay, and microbiological purity test and met all the specification criteria. A bioequivalence study in 24 healthy volunteers compared against the innovative drug showed the bioequivalency of the new generic system. The obtained values from the test and reference products were 1321 ± 249.0 ng/mL and 1274 ± 233 ng/mL for Cmax, 4521 ± 841 ng·h /mL and 4371 ± 695 ng·h /mL for AUC0-t, and 4636 ± 814 ng·h /mL and 4502 ± 640 ng·h /mL for AUC0-∞. Conclusions: Using affordable technologies, a bioequivalent generic delayed-release formulation of 20 and 40 mg omeprazole has been developed.

Mode entanglement in fermionic and bosonic Harmonium

Mode entanglement in many-body quantum systems is an active area of research. It provides crucial insight into the suitability of many-body systems for quantum information processing tasks. Local super-selection rules must be taken into account when assessing the amount of physically accessible entanglement. This requires amending well-established entanglement measures by incorporating local parity and local particle number constraints. In this paper, we report on mode entanglement present in the analytically solvable system of N-Harmonium. To the knowledge of the authors, this is the first analytic study of the physically accessible mode and mode-mode entanglement of an interacting many-body system in a continuous state space. We find that super-selection rules dramatically reduce the amount of physically accessible entanglement, which vanishes entirely in some cases. Our results strongly suggest the need to re-evaluate intra and inter-mode entanglement in other fermionic and bosonic systems.

Management Logistics Maritime: A Literature Review

Using a systematic literature review as the primary research approach, the purpose of this study is to provide an overview of the research trends connected to management of maritime logistics in both Asia and Indonesia. The literature review was carried out with the help of Leximancer, which is a text analytics software, in order to determine the major ideas and recurring topics that were addressed in the research that were chosen. A form of research known as a systematic literature review was utilized for this particular investigation. According to the findings of the comprehensive evaluation of the relevant literature, researchers in Asia and Indonesia follow comparable lines of enquiry in the field of maritime logistics management research. The studies concentrate their attention particularly on topics like the management of supply chains, the optimization of transportation, and the promotion of sustainability in maritime logistics. In spite of the fact that the study tendencies in Asia and Indonesia are quite comparable to one another, certain studies in Indonesia place a distinct emphasis on particular ideas like society, the environment, and marine development. Taking into account its archipelagic character, local rules, and vital position in international trade, these ideas are reflective of the particular challenges and opportunities that Indonesia has when it comes to the management of its maritime logistics. To summarize, Asian research on logistics and maritime management focuses on risks, performance, globalization, environmental sustainability, and green practices. Therefore, Asian trends can be utilized by Indonesian scholars in their studies of maritime logistics management.

Three-phase multi-criteria ranking considering three-way decision framework and criterion fuzzy concept

The criterion fuzzy concept refers to a fuzzy set that represents the decision-maker's subjective preference for each criterion within the universe of criteria. Addressing the challenge of ranking all alternatives based on a given criterion fuzzy concept is a novel research direction in the field of fuzzy multi-criteria ranking issues. This paper proposes a three-phase approach for multi-criteria ranking in fuzzy environments, which combines the criterion fuzzy concept and three-way decision thinking. The proposed approach not only analyzes the decision-making characteristics of all alternatives but also facilitates their ranking. During the first phase, a qualitative classification method based on the criterion fuzzy concept and ideal solutions is defined, which divides all alternatives into three independent decision sub-regions. During the second phase, by analyzing the priority relationships among the alternatives within every sub-region, three local ranking rules for alternatives are proposed to determine the ranking of alternatives in each classification region. During the third phase, the semantic relations among three classification regions are considered to give an overall ranking of all alternatives. Finally, combined with two existing quantitative ranking indicators, multiple data sets are employed to verify the feasibility and superiority of the proposed three-phase multi-criteria ranking approach.