Hasil untuk "Dynamic and structural geology"

P.L. Hancock

Nanbing Li, Weijie Peng, Jin Luo et al.

Functional verification plays a central role in ensuring the correctness of modern integrated circuit designs, where constrained-random verification is widely adopted to generate diverse stimuli under high-level constraints. In industrial verification environments, constraint solving increasingly involves dynamic data structures whose shape and content are determined at runtime, causing the sets of variables and constraint instances to evolve across solver invocations, which in turn leads to substantial overhead when nested and high-dimensional structures repeatedly expand across solves. We formalize this class of problems as the Dynamic Data Structure Constraint Satisfaction Problem (D2SCSP),which captures the interaction between dynamic data structure expansion and constraint evaluation. We propose a dependency-guided problem partitioning framework combined with an incremental encoding and constraint activation mechanism, enabling reuse of solver state and encodings across multiple solves. The framework is integrated into an industrial SystemVerilog verification flow and implemented in the commercial simulator VeriSim. Experimental results on industrial benchmarks demonstrate significant performance improvements, achieving an average speedup of 24.80x over a baseline and 1.72x over a state-of-the-art commercial simulator, highlighting the practicality of the approach for real-world verification workflows.

ramin khoshchehreh mohammadi, Mehrdad Hosseini Shakib, mahmood khodam et al.

With the expansion of innovative technologies, the banking industry has faced profound transformations. Artificial intelligence, as one of the most significant of these technologies, has the potential to transform the nature of banking services; however, its impact on social banking, particularly in cooperative banks, has received less attention. This research aims to investigate the impact of artificial intelligence functions on the performance of social banking in Iranian cooperative banks, utilizing a system dynamics approach. The study adopts a mixed approach (qualitative-quantitative). In the qualitative section, key variables were identified using an expert panel, and in the quantitative section, a system dynamics model was developed using Vensim software. The stock-flow model simulated the relationships between main variables, including sustainable development, bank reputation, unpredictable liquidity, non-performing loans, and artificial intelligence infrastructure, over 10 years (2021-2031). The results of the sensitivity analysis and scenario development demonstrated that strategic investments in artificial intelligence infrastructure, enhanced data protection protocols, and improved financial transparency contribute significantly to an enhanced bank reputation, substantially reduce unpredictable liquidity fluctuations, and notably decrease non-performing loans, thereby supporting sustainable banking operations. Model validation tests, including boundary conditions tests, structural tests, uncertainty tests, and integration tests, confirmed the accuracy of the relationships. This model can serve as a tool for decision-making and policy-making regarding the application of artificial intelligence in the country's social banking system.

D.-G. Lee, E. Y. Kwon, J. Kam et al.

<p>Marine phytoplankton play a crucial role in the ocean's food web, marine ecosystems, and the carbon cycle. Their responses to external forcing vary across phytoplankton species, and phytoplankton community shifts can have important implications for their roles in the Earth's system. Here, we find that phytoplankton communities in the Subpolar North Atlantic shift toward smaller species under greenhouse warming that are not easily recovered even under CO<span class="inline-formula">₂</span> removal scenarios. Despite negative CO<span class="inline-formula">₂</span> emissions, the persistent collapse of larger-celled diatom populations and the shift toward smaller phytoplankton communities is a consequence of lower surface nutrient availability following the slowdown of the Atlantic Meridional Overturning Circulation (AMOC). This weakening of AMOC and associated nutrient transport exhibits delayed recovery. Depleting nutrients disrupts trophic dynamics by altering primary limiting nutrient components, contributing to the continued decrease in diatoms and an increase in smaller phytoplankton. Consequently, the downsizing of the phytoplankton community indicates a large reduction in the ocean's biological carbon export capacity.</p>

Janis Norden, Elisa Oostwal, Michael Chappell et al.

The successful application of modern machine learning for time series classification is often hampered by limitations in quality and quantity of available training data. To overcome these limitations, available domain expert knowledge in the form of parametrised mechanistic dynamical models can be used whenever it is available and time series observations may be represented as an element from a given class of parametrised dynamical models. This makes the learning process interpretable and allows the modeller to deal with sparsely and irregularly sampled data in a natural way. However, the internal processes of a dynamical model are often only partially observed. This can lead to ambiguity regarding which particular model realization best explains a given time series observation. This problem is well-known in the literature, and a dynamical model with this issue is referred to as structurally unidentifiable. Training a classifier that incorporates knowledge about a structurally unidentifiable dynamical model can negatively influence classification performance. To address this issue, we employ structural identifiability analysis to explicitly relate parameter configurations that are associated with identical system outputs. Using the derived relations in classifier training, we demonstrate that this method significantly improves the classifier's ability to generalize to unseen data on a number of example models from the biomedical domain. This effect is especially pronounced when the number of training instances is limited. Our results demonstrate the importance of accounting for structural identifiability, a topic that has received relatively little attention from the machine learning community.

Peijun Zhu, Ning Yang, Baoliang Tian et al.

Mixture-of-Experts (MoE) Large Language Models (LLMs) face a trilemma of load imbalance, parameter redundancy, and communication overhead. We introduce a unified framework based on dynamic expert clustering and structured compression to address these issues cohesively. Our method employs an online clustering procedure that periodically regroups experts using a fused metric of parameter and activation similarity, which stabilizes expert utilization. To our knowledge, this is one of the first frameworks to leverage the semantic embedding capability of the router to dynamically reconfigure the model's architecture during training for substantial efficiency gains. Within each cluster, we decompose expert weights into a shared base matrix and extremely low-rank residual adapters, achieving up to fivefold parameter reduction per group while preserving specialization. This structure enables a two-stage hierarchical routing strategy: tokens are first assigned to a cluster, then to specific experts within it, drastically reducing the routing search space and the volume of all-to-all communication. Furthermore, a heterogeneous precision scheme, which stores shared bases in FP16 and residual factors in INT4, coupled with dynamic offloading of inactive clusters, reduces peak memory consumption to levels comparable to dense models. Evaluated on GLUE and WikiText-103, our framework matches the quality of standard MoE models while reducing total parameters by approximately 80%, improving throughput by 10% to 20%, and lowering expert load variance by a factor of over three. Our work demonstrates that structural reorganization is a principled path toward scalable, efficient, and memory-effective MoE LLMs. Code is available at https://github.com/szdtzpj/Breaking_the_moe_trilemma

Minghao Fu, Biwei Huang, Zijian Li et al.

Understanding climate dynamics requires going beyond correlations in observational data to uncover their underlying causal process. Latent drivers, such as atmospheric processes, play a critical role in temporal dynamics, while direct causal influences also exist among geographically proximate observed variables. Traditional Causal Representation Learning (CRL) typically focuses on latent factors but overlooks such observable-to-observable causal relations, limiting its applicability to climate analysis. In this paper, we introduce a unified framework that jointly uncovers (i) causal relations among observed variables and (ii) latent driving forces together with their interactions. We establish conditions under which both the hidden dynamic processes and the causal structure among observed variables are simultaneously identifiable from time-series data. Remarkably, our guarantees hold even in the nonparametric setting, leveraging contextual information to recover latent variables and causal relations. Building on these insights, we propose CaDRe (Causal Discovery and Representation learning), a time-series generative model with structural constraints that integrates CRL and causal discovery. Experiments on synthetic datasets validate our theoretical results. On real-world climate datasets, CaDRe not only delivers competitive forecasting accuracy but also recovers visualized causal graphs aligned with domain expertise, thereby offering interpretable insights into climate systems.

Chenghao Wang, Kaushik Venkatesh Krishnamurthy, Shreyansh Pitroda et al.

Multi-modal ground-aerial robots have been extensively studied, with a significant challenge lying in the integration of conflicting requirements across different modes of operation. The Husky robot family, developed at Northeastern University, and specifically the Husky v.2 discussed in this study, addresses this challenge by incorporating posture manipulation and thrust vectoring into multi-modal locomotion through structure repurposing. This quadrupedal robot features leg structures that can be repurposed for dynamic legged locomotion and flight. In this paper, we present the hardware design of the robot and report primary results on dynamic quadrupedal legged locomotion and hovering.

Mansoureh Zarezadeh

The increased complexity of contemporary organizations necessitates adapting analysis and decision-making models as cognitive and analytical tools to cope with this complexity. Systemic thinking and its methodologies offer a way to overcome these complications to a desirable degree. Strategies are enacted in the organization when they are operationalized, as the diverse viewpoints of its stakeholders often hinder the practical attainment of the organization's strategic objectives. Hence, the researcher selected soft systems methodology (SSM) as one of the prevalent systems thinking methodologies to address this challenge and to achieve a relative alignment among the stakeholders' interests. Given that intervention in the organization is the primary prerequisite to resolving organizational problems with this methodology, the Iran Statistics Center (ISC) was chosen as a case study. At the onset of the intervention to enhance the processes of implementing the strategies of ISC with SSM, the main steps of operationalizing the strategies were elicited in the planning department, and then from the steps to devise an operational plan to increase employees molivation the motivation of employeesThe operational plan, developed through stakeholder involvement and consideration of diverse perspectives, facilitated the formulation of task strategies with a focus on executability. This approach aimed to bridge the gap between the strategic and operational layers within ISC. Additionally, the development of evaluation indicators enabled the monitoring of strategy execution within ISC. Besides developing a strategic and operational plan, this research also had other outcomes, such as organizational learning by using SSM. Through the education and facilitation of the researcher and the department staff, they became empowered to develop an operational plan for other strategies.

Carolyn Nuyen, David Schmidt

Episodic tremor and slip (ETS) is well-documented along the entire length of the Cascadia subduction zone. We explore how the occurrence of ETS varies at the southernmost edge of the subduction zone, where geometric complexity and a slab window likely alter conditions along the plate interface. This work uses tremor and GNSS time series data to identify nineteen of the largest ETS events in southern Cascadia between 2016.5-2022 and document source properties for events approaching the slab edge. Distributed slip models for these events show that cumulative fault slip along the megathrust reaches a maximum near 40.5° N latitude and that large ETS events accommodate up to 85% of plate convergence at this location. However, ETS fault slip and tremor terminate near 40° N latitude, some 50 km before the southern lateral edge of the subducting plate. After considering a range of explanations, we propose that the complex geometry and progressive heating of the subducting plate modifies ETS behavior and does not allow seismic slip to occur along the plate interface in southernmost Cascadia below 35 km depth.

Konrad Majewski, Michał Pilipczuk, Anna Zych-Pawlewicz

We design a randomized data structure that, for a fully dynamic graph $G$ updated by edge insertions and deletions and integers $k, d$ fixed upon initialization, maintains the answer to the Split Completion problem: whether one can add $k$ edges to $G$ to obtain a split graph. The data structure can be initialized on an edgeless $n$-vertex graph in time $n \cdot (k d \cdot \log n)^{\mathcal{O}(1)}$, and the amortized time complexity of an update is $5^k \cdot (k d \cdot \log n)^{\mathcal{O}(1)}$. The answer provided by the data structure is correct with probability $1-\mathcal{O}(n^{-d})$.

Paul M. J. Van den Hof, Shengling Shi, Stefanie J. M. Fonken et al.

When estimating a single subsystem (module) in a linear dynamic network with a prediction error method, a data-informativity condition needs to be satisfied for arriving at a consistent module estimate. This concerns a condition on input signals in the constructed, possibly MIMO (multiple input multiple output) predictor model being persistently exciting, which is typically guaranteed if the input spectrum is positive definite for a sufficient number of frequencies. Generically, the condition can be formulated as a path-based condition on the graph of the network model. The current condition has two elements of possible conservatism: (a) rather than focussing on the full MIMO model, one would like to be able to focus on consistently estimating the target module only, and (b) structural information, such as structural zero elements in the interconnection structure or known subsystems, should be taken into account. In this paper relaxed conditions for data-informativity are derived addressing these two issues, leading to relaxed path-based conditions on the network graph. This leads to experimental conditions that are less strict, i.e. require a smaller number of external excitation signals. Additionally, the new expressions for data-informativity in identification are shown to be closely related to earlier derived conditions for (generic) single module identifiability.

Qiming Chen

A series of laboratory and large-scale field model footing tests were conducted to assess the modulus and stress distribution behavior of a clayey soil foundation, both with/without geogrid reinforcement, deviating from the conventional approach of evaluating the strength performance, such as bearing capacity. The modulus was evaluated at three settlement ratios of <i>s</i>/<i>B</i> = 1, 3, and 5%, while the stress distribution angle <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfenced separators="|"><mrow><mi>α</mi></mrow></mfenced></mrow></semantics></math></inline-formula> was estimated at three applied surface pressures of 234 kPa, 468 kPa, and 936 kPa. The results indicated a stiffer load-settlement response when geogrid reinforcement was included. The modulus of reinforced clayey soil remained nearly constant for test sections with the same reinforced ratio, with the modulus improvement increasing as the reinforced ratio (<i>R</i>_r) increased. The modulus improvement also increased with the settlement ratio (<i>s</i>/<i>B</i>). These results demonstrated that the stress distribution improvement decreased as the surface pressure increased. Generally, both the modulus and stress distribution improvement exhibited an increase with an increase in the tensile modulus of the geogrid. While laboratory model tests consistently provided a higher improvement in the modulus than large-scale field model tests in this study due to a higher reinforced ratio, the stress distribution improvement was similar for both.

Silvia Vettori, Emilio Di Lorenzo, Bart Peeters et al.

Digital technologies can be used to gather accurate information about the behavior of structural components for improving systems design, as well as for enabling advanced Structural Health Monitoring strategies. New avenues for achieving automated and continuous structural assessment are opened up via development of virtualization approaches delivering so-called Digital Twins, i.e., digital mirrored representations of physical. In this framework, the main motivation of this work stems from the existing challenges in the implementation and deployment of a real-time predictive framework for virtualization of dynamic systems. Kalman-based filters are usually employed in this context to address the task of joint input-state prediction in structural dynamics. A Gaussian Process Latent Force Model (GPLFM) approach is exploited in this work to construct flexible data-driven a priori models for the unknown inputs, which are then coupled with a mechanistic model of the structural component under study for input-state estimation. The use of GP regression for this task overcomes the limitations of the conventional random-walk model, thus limiting the necessity of offline user-dependent calibration of this type of data assimilation methods. This paper proposes the use of alternative covariance functions for GP regression in structural dynamics. A theoretical analysis of the GPLFMs linked to the investigated covariance functions is offered. The outcome of this study provides insights into the applicability of each covariance type for GP-based input-state estimation. The proposed framework is validated via an illustrative simulated example, namely a 3 Degrees of Freedom system subjected to an array of different loading scenarios. Additionally, the performance of the method is experimentally assessed on the task of joint input-state estimation during testing of a 3D-printed scaled wind turbine blade.

Rafał Abram, Dariusz Chrobak

Atom arrangement plays a critical role in determining material properties. It is, therefore, essential for materials science and engineering to identify and characterize distinct atom configurations. Currently, crystal structures can be determined either by its static properties or by quantifying its structural evolution. Here we show how to classify an atom into phase solely by its speed dynamics. We model silicon crystals at different phase transition points and use a single atom speed trajectory to demonstrate that crystal-structure-independent Maxwell distribution of speed is generated by crystal-structure-dependent atom dynamics. As the classification accuracy of the method increases with trajectory length, we show that subtle difference in local atomic structures can be identified using sufficiently long trajectories. Thanks to symbolization of atom dynamics, the method is computationally efficient and suitable for an analysis of large datasets on the fly.

Marion Jaud, Laurent Geoffroy, François Chauvet et al.

Shaohua Fan, Shuyang Zhang, Xiao Wang et al.

Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there have been many studies on structure learning with various types of data, the structure learning on the dynamic graph has not been explored yet, and thus we study the learning problem of node feature generation mechanism on such ubiquitous dynamic graph data. In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features. These two kinds of relationships form a DAG, which could effectively characterize the feature generation process in a concise way. To learn such a DAG, we cast the learning problem as a continuous score-based optimization problem, which consists of a differentiable score function to measure the validity of the learned DAGs and a smooth acyclicity constraint to ensure the acyclicity of the learned DAGs. These two components are translated into an unconstraint augmented Lagrangian objective which could be minimized by mature continuous optimization techniques. The resulting algorithm, named GraphNOTEARS, outperforms baselines on simulated data across a wide range of settings that may encounter in real-world applications. We also apply the proposed approach on two dynamic graphs constructed from the real-world Yelp dataset, demonstrating our method could learn the connections between node features, which conforms with the domain knowledge.

Xin Jiang, Wei Li, Shijie Wang et al.

A remarkable earthquake struck Yutian, China on June 26th, 2020. Here, we use Sentinel-1 images to investigate the deformation induced by this event. We invert the InSAR observations using a two-step approach: a nonlinear inversion to constrain fault geometries with uniform slip based on the rectangular plane dislocation in an elastic half-space, followed by a linear inversion to retrieve the slip distribution on the fault plane. The results show that the maximum LOS displacement is 22.6 ​cm, and the fault accessed to the ruptured characteristics of normal faults with the minor left-lateral strike-slip component. The fault model indicates a 210° strike. The main rupture zone concentrates in the depth of 5–15 ​km, and the fault slip peaks at 0.89 ​m at the depth of 9 ​km. Then, we calculate the variation of the static Coulomb stress based on the optimal fault model, the results suggest that the Coulomb stress of the Altyn Tagh fault and other neighboring faults has increased and more attention should be paid to possible seismic risks.

J. Stadmark, C. Jesus-Rydin, D. J. Conley

<p>Sex-disaggregated data on the success rates of applications to the individual grants at the European Research Council and selected national funding agencies show similar outcomes for women and men. There are large differences in success rates between countries and in all countries with applicants to the European Research Council men are applying disproportionally more (and women less) compared to the demography of the researchers in the higher education sectors in the respective countries. Therefore, the proportion of women funded is even lower than their representation in the fields of Natural Science and Engineering and Technology. Some contributing factors are discussed and the question on how the current and future success rates could be interpreted is raised.</p>