High-level Model Research Articles

First-Principles Models of Polymorphism of Pharmaceuticals: Maximizing the Accuracy-to-Cost Ratio.

Accuracy and sophistication of in silico models of structure, internal dynamics, and cohesion of molecular materials at finite temperatures increase over time. Applicability limits of ab initio polymorph ranking that would be feasible at reasonable costs currently represent crystals of moderately sized molecules (less than 20 nonhydrogen atoms) and simple unit cells (containing rather only one symmetry-irreducible molecule). Extending the applicability range of the underlying first-principles methods to larger systems with a real-life significance, and enabling to perform such computations in a high-throughput regime represent additional challenges to be tackled in computational chemistry. This work presents a novel composite method that combines the computational efficiency of density-functional tight-binding (DFTB) methods with the accuracy of density-functional theory (DFT). Being rooted in the quasi-harmonic approximation, it uses a cheap method to perform all of the costly scans of how static and dynamic characteristics of the crystal vary with respect to its volume. Such data are subsequently corrected to agree with a higher-level model, which must be evaluated only at a single volume of the crystal. It thus enables predictions of structural, cohesive, and thermodynamic properties of complex molecular materials, such as pharmaceuticals or organic semiconductors, at a fraction of the original computational cost. As the composite model retains the solid physical background, it suffers from a minimum accuracy deterioration compared to the full treatment with the costly approach. The novel methodology is demonstrated to provide consistent results for the structural and thermodynamic properties of real-life molecular crystals and their polymorph ranking.

CUQIpy: I. Computational uncertainty quantification for inverse problems in Python

This paper introduces CUQIpy, a versatile open-source Python package for computational uncertainty quantification (UQ) in inverse problems, presented as Part I of a two-part series. CUQIpy employs a Bayesian framework, integrating prior knowledge with observed data to produce posterior probability distributions that characterize the uncertainty in computed solutions to inverse problems. The package offers a high-level modeling framework with concise syntax, allowing users to easily specify their inverse problems, prior information, and statistical assumptions. CUQIpy supports a range of efficient sampling strategies and is designed to handle large-scale problems. Notably, the automatic sampler selection feature analyzes the problem structure and chooses a suitable sampler without user intervention, streamlining the process. With a selection of probability distributions, test problems, computational methods, and visualization tools, CUQIpy serves as a powerful, flexible, and adaptable tool for UQ in a wide selection of inverse problems. Part II of the series focuses on the use of CUQIpy for UQ in inverse problems with partial differential equations.

High-Level Defence Model against Routing Attacks on the Internet-of-Things


 
 
 
 This paper is part of the doctoral work that aims to answer the following research question: “To what extent can an intelligent security model effectively defend against routing attacks in RPL-based Internet of Things (IoT) with a demonstration of less network resource consumption, high detection rate, and minimal false negatives?” To answer this question, this paper proposes a high-level conceptual framework to defend the IoT against routing attacks. In recent works, mitigation techniques have been proposed to act against routing attacks, however conceptual defence or mitigation framework is not presented as a set of steps to follow to develop an effective and robust intelligent security model. This paper aims to present a high-level conceptual defence framework against routing attacks; specifically, sinkhole, rank, DIS-Flooding, and worst parent. The four mentioned routing attacks are capable of disturbing IoT network functions and operations, and consuming network resources such as memory and power.
 
 
 
 

Deep Reinforcement Learning-Based Hierarchical Energy Control Strategy of a Platoon of Connected Hybrid Electric Vehicles Through Cloud Platform

Due to the features of strong non-linearity and hybrid driving with multi-power sources, a novel deep reinforcement learning(DRL)-based hierarchical energy control architecture is presented for a group of connected hybrid electric vehicles (HEVs) in the platoon through cloud platform. Firstly, a higher-level model predictive control (MPC) law is designed to determine the desired acceleration of connected HEVs in a platoon. Secondly,a reward function with the change of state of charge (SOC) and instantaneous fuel consumption as independent variables is constructed, and the expert knowledge-based deep deterministic policy gradient (DDPG) method with prioritized experience replay (PER) is used for designing the lower-level energy management strategy of connected HEVs platooning. Then, to illustrate the rationality of decision-making by the presented DDPG strategy, the influence of state variables such as vehicle speed, desired acceleration and battery SOC on the agent action values of presented DDPG method is discussed. Finally, the test results show that the proposed control scheme can reasonably allocate the engine and motor powers in real time, and finally achieve the safe and energy-saving driving of connected HEVs in the platoon.

Bridging the gap between high-level quantum chemical methods and deep learning models

Supervised deep learning (DL) models are becoming ubiquitous in computational chemistry because they can efficiently learn complex input-output relationships and predict chemical properties at a cost significantly lower than methods based on quantum mechanics. The central challenge in many DL applications is the need to invest considerable computational resources in generating large ( N>1×105 ) training sets such that the resulting DL model can be generalized reliably to unseen systems. The lack of better alternatives has encouraged the use of low-cost and relatively inaccurate density-functional theory (DFT) methods to generate training data, leading to DL models that lack accuracy and reliability. In this article, we describe a robust and easily implemented approach based on property-specific atom-centered potentials (ACPs) that resolves this central challenge in DL model development. ACPs are one-electron potentials that are applied in combination with a computationally inexpensive but inaccurate quantum mechanical method (e.g. double-ζ DFT) and fitted against relatively few high-level data ( N≈1×103 – 1×104 ), possibly obtained from the literature. The resulting ACP-corrected methods retain the low cost of the double-ζ DFT approach, while generating high-level-quality data in unseen systems for the specific property for which they were designed. With this approach, we demonstrate that ACPs can be used as an intermediate method between high-level approaches and DL model development, enabling the calculation of large and accurate DL training sets for the chemical property of interest. We demonstrate the effectiveness of the proposed approach by predicting bond dissociation enthalpies, reaction barrier heights, and reaction energies with chemical accuracy at a computational cost lower than the DFT methods routinely used for DL training data set generation.

A perceptual approach to address complex water management issues in lowland permeable catchments

Water quality management is a pressing global concern, and an increasingly complex issue due to climate and land-use change, legacy pollution, and the persistent release of well-known and emerging contaminants from diffuse and point sources. The increasing availability of high-frequency monitoring data is leading existing, often heuristic approaches, to be judged inadequate. Water managers frequently rely on simple qualitative and/or quantitative approaches for decision-making, but a lack of tangible improvement in freshwater quality outcomes is demanding new and innovative approaches that rely more on physical process understanding, rather than precedent. In this study, we drew upon local geological, hydrogeological, and hydrological data to infer a high-level perceptual model of surface/groundwater interactions in a chalk stream in Dorset, UK. We used the perceptual model to interrogate spatial and temporal trends in historical water quality data and to construct reach-scale nutrient mass balances. Through novel representation with Sankey diagrams, the perceptual model highlighted the relative importance of different hydrological features. Surface/groundwater interactions were found to occur predominantly by spring flow. We demonstrate that river flow accretion was dominated by the Chalk aquifer despite only occupying ca. 15 % of the surface bedrock area, and that spring sources, whilst vital to dilute treated sewage inputs in baseflow conditions, were also major sources of legacy nitrate. Nutrient mass balances showed that sewage treatment works contributed ca. 13 % to soluble reactive phosphorus load, with groundwater accounting for ca. 48 %. Thus, a determinand often associated with point-source pollution was shown to be diffuse dominated in this river. The study demonstrated how a multi-disciplinary approach to water management, based on a comprehensive perceptual modelling approach, could identify hitherto unknown sources and relative contributors to freshwater pollution and allow flow and load apportionment studies to provide useful decision-support to manage nutrient pollution. The novel application of perceptual modelling tools, such as the Sankey diagram, allows different source attributions to be presented in an accessible manner, and can be readily transferred to other study areas.

Comparative analysis of approaches for automated compliance checking of construction data

While the domain of Automated Compliance Checking (ACC) has gained track, the construction industry has been flooded with different approaches. This paper studies these different approaches for use in compliance checking of construction data. The approaches are compared by defining constraints for the same set of five requirements, each of a different category, stemming from the Flemish building regulation on accessibility. Eight approaches have been selected for comparison: two IFC-based approaches (Solibri Model Checker and the upcoming buildingSMART standard IDS), two general data standards and their accompanying schema definition languages (JSON Schema and XSD), and four Linked Data approaches (OWL, SWRL, SPARQL, and SHACL). Besides the pure functional analysis, the relative uptake and support in tooling are also considered. While XML/XSD and JSON/JSON Schema and the Linked Data approaches are in essence domain-independent, only the latter has an extra layer for agreeing on high-level data modeling (and thus data validation) patterns in the construction domain with the EN17632-1:2022 standard. SHACL is considered the most adept method from the Linked Data approaches since it is fully standardized for both inputs and outputs and was developed for validation use cases.

On the two-layer high-level Green-Naghdi model in a general form

On the two-layer high-level Green-Naghdi model in a general form

A Systems-Thinking Model of Data Management and Use in US Archaeology

OverviewArchaeology in the United States is caught in a “curation crisis” (Childs 1995; Childs and Warner 2019; Marquardt et al. 1982; SAA Advisory Committee on Curation 2003; Trimble and Marino 2003) and a “digital data crisis” (or “deluge”) more specifically (Bevan 2015; Clarke 2015; Kansa and Kansa 2021; Katsianis et al. 2022; Kersel 2015; McManamon et al. 2017:239–240; Rivers Cofield et al. 2024). Recent estimates suggest that, collectively, over 1.4 billion dollars are spent annually to support archaeological work that is mandated by federal law (SRI Foundation 2020). Although substantial efforts are underway to generate and provide mechanisms for managing, curating, and sharing the resultant digital data, we suggest that a critical step that has yet to be taken is to describe and visualize the components, connections, and causal dynamics of the US digital data system as it currently functions. Here, we specifically apply a “systems thinking” approach to produce such a high-level model of this system. We argue that understanding and visualizing this system will help us all “think bigger” (Heilen and Manney 2023); identify sources of knowledge, opportunities for critical analysis, collaboration, and capacity building; and increase much-needed archaeological digital literacy (Kansa and Kansa 2022). We conceptualize this as bringing “equilibrium” to the system, and in this article, we make several suggestions on how to bring this about. These insights can enable practitioners to better understand their roles in and contributions to the overall system and to evaluate efforts to improve data sharing, management, and curation practices not only within their organizations and departments but beyond.

Population genetic simulation: Benchmarking frameworks for non-standard models of natural selection.

Population genetic simulation has emerged as a common tool for investigating increasingly complex evolutionary and demographic models. Software capable of handling high-level model complexity has recently been developed, and the advancement of tree sequence recording now allows simulations to merge the efficiency and genealogical insight of coalescent simulations with the flexibility of forward simulations. However, frameworks utilizing these features have not yet been compared and benchmarked. Here, we evaluate various simulation workflows using the coalescent simulator msprime and the forward simulator SLiM, to assess resource efficiency and determine an optimal simulation framework. Three aspects were evaluated: (1) the burn-in, to establish an equilibrium level of neutral diversity in the population; (2) the forward simulation, in which temporally fluctuating selection is acting; and (3) the final computation of summary statistics. We provide typical memory and computation time requirements for each step. We find that the fastest framework, a combination of coalescent and forward simulation with tree sequence recording, increases simulation speed by over twenty times compared to classical forward simulations without tree sequence recording, although it does require six times more memory. Overall, using efficient simulation workflows can lead to a substantial improvement when modelling complex evolutionary scenarios-although the optimal framework ultimately depends on the available computational resources.

High-level aftereffects reveal the role of statistical features in visual shape encoding

Visual shape perception is central to many everyday tasks, from object recognition to grasping and handling tools.1,2,3,4,5,6,7,8,9,10 Yet how shape is encoded in the visual system remains poorly understood. Here, we probed shape representations using visual aftereffects-perceptual distortions that occur following extended exposure to a stimulus.11,12,13,14,15,16,17 Such effects are thought to be caused by adaptation in neural populations that encode both simple, low-level stimulus characteristics17,18,19,20 and more abstract, high-level object features.21,22,23 To tease these two contributions apart, we used machine-learning methods to synthesize novel shapes in a multidimensional shape space, derived from a large database of natural shapes.24 Stimuli were carefully selected such that low-level and high-level adaptation models made distinct predictions about the shapes that observers would perceive following adaptation. We found that adaptation along vector trajectories in the high-level shape space predicted shape aftereffects better than simple low-level processes. Our findings reveal the central role of high-level statistical features in the visual representation of shape. The findings also hint that human vision is attuned to the distribution of shapes experienced in the natural environment.

A novel teacher–student hierarchical approach for learning primitive information

In this study, we focus on the challenge of robot motion generation, which is critical for improving the ability of robots to perform downstream tasks. To this end, inspired by the idea of variational inference, we propose a novel teacher–student hierarchical approach for learning continuous primitive spaces to generate robot motions. We first learn a variational auto-encoder (VAE) based on Gated Recurrent Unit (GRU) from demonstrations, the encoder is used as a teacher model to encode trajectories to primitive information, and the decoder aims to decode latent information to joint trajectories. To learn a primitive action space for the high-level model, we propose a novel learning method to train a student model. We utilize the trained teacher to encode trajectories to labels, and use the Kullback–Leibler (KL) divergence and the reconstruction errors between the label and the predicted primitive information as the loss to train a student model. We then utilize trained student models to guide high-level policy learning. We evaluate our approach on three different robot datasets. Experiments demonstrate that our method obtains the ability to generate new motion and discover common information across tasks, it also can be used to accelerate downstream task learning.

Network dynamics of positive energy districts: a coevolutionary business ecosystem analysis

IntroductionAmid the rising interest in sustainable urban development, Positive Energy Districts (PEDs) have become a focus of research. This study examines the dynamic processes that influence the development and scalability of PEDs from a co-evolutionary business ecosystem perspective.MethodsTo delve into the dynamics of Positive Energy Districts, we applied the business ecosystem framework to a real-world case study, namely the Hunziker Areal. Our research methodology involved the development and validation of a high-level conceptual model. This was achieved through workshops and guided interviews with experts engaged in pilot and research projects related to PEDs.ResultsThe study highlights the significance of employing a systemic approach to evaluate the potential of PEDs in enhancing housing sustainability while creating value for diverse stakeholders. Through the utilization of causal loop diagrams, key feedback loops explaining the diffusion of PEDs are identified. Moreover, the study reveals varying perceptions of PED utility among stakeholders, who assess the impact using different Key Performance Indicators (KPIs) such as CO2 target achievement and well-being. Key factors influencing technology adoption, such as low prosumer electricity unit cost, are also identified.DiscussionOur findings further shed light on crucial aspects affecting value capture and the attractiveness of the ecosystem to investors. Additionally, the study underscores the critical role of supportive policies and regulations in facilitating the diffusion and scalability of Positive Energy Districts.

Goal-Conditioned Hierarchical Reinforcement Learning With High-Level Model Approximation.

Hierarchical reinforcement learning (HRL) exhibits remarkable potential in addressing large-scale and long-horizon complex tasks. However, a fundamental challenge, which arises from the inherently entangled nature of hierarchical policies, has not been understood well, consequently compromising the training stability and exploration efficiency of HRL. In this article, we propose a novel HRL algorithm, high-level model approximation (HLMA), presenting both theoretical foundations and practical implementations. In HLMA, a Planner constructs an innovative high-level dynamic model to predict the k -step transition of the Controller in a subtask. This allows for the estimation of the evolving performance of the Controller. At low level, we leverage the initial state of each subtask, transforming absolute states into relative deviations by a designed operator as Controller input. This approach facilitates the reuse of subtask domain knowledge, enhancing data efficiency. With this designed structure, we establish the local convergence of each component within HLMA and subsequently derive regret bounds to ensure global convergence. Abundant experiments conducted on complex locomotion and navigation tasks demonstrate that HLMA surpasses other state-of-the-art single-level RL and HRL algorithms in terms of sample efficiency and asymptotic performance. In addition, thorough ablation studies validate the effectiveness of each component of HLMA.

Intact high-level visual functions in congenital rod-monochromacy.

High-level visual functions such as reading and face recognition rely on global processes, which are often insensitive to high spatial frequencies. However, it is unknown whether a sharp cone signal is necessary for the development of these skills or whether a blurry rod signal is sufficient. CNGA3/B3-achromatopsia is a congenital disease stemming from cone dysfunction, leading to rod-only vision characterized by nystagmus, impaired acuity, and complete color blindness. We tested reading and face recognition in CNGA3/B3-achromatopsia patients (ACHM) to determine whether a rod signal is sufficient for these skills to develop. We tested 10 ACHM and 10 controls in three experiments under dark and light conditions. Initially, we evaluated acuity along the eccentricity axis. Later, we tested reading speed and upright/inverted face matching accuracy while tracking participants' eye movements. Given that ACHM patients' acuity under light conditions resembled that of controls under dark conditions, we selected these conditions for comparison. Remarkably, ACHM reading speed, face recognition abilities, and susceptibility to face inversion were not inferior to those of controls. Additionally, ACHM patients exhibited similar eye movements to controls, focusing their attention on specific areas of words and faces that indicate expertise. Despite the evident low-level limitations, ACHM patients demonstrated notable high-level visual skills, suggesting that rod-only vision is sufficient for the development of proficient reading and face recognition. These findings not only corroborate empirical evidence for high-level vision models but also enrich the discussion regarding the reasons for high-level deficits observed in individuals who have gained sight late in life.

An empirical study on the construction of sample branches in colleges and universities in promoting the development of students’ subjectivity

Abstract The development of student subjectivity constitutes a crucial dimension in achieving the comprehensive growth of students. This study examines the role of constructing model branches within higher education institutions and explores their influence on fostering student subjectivity. To this end, an enhanced K-means clustering algorithm, augmented by a particle swarm optimization technique, is employed to process the data of college students, thereby facilitating the development of model branches in these institutions. Additionally, the initial warning feature matrix within the collaborative filtering recommendation algorithm is applied to quantitatively assess student characteristic factors in the establishment of model branches, enabling the creation of an early warning system. The effect of model branch construction on the evolution of student subjectivity is subsequently analyzed through Gaussian process regression and partial least squares regression methods. The findings indicate that the extent of model branch development significantly impacts student subjectivity. Specifically, the average development level of student subjectivity in institutions with high-level model branches surpasses that in institutions lacking such structures by 17.255 points. Thus, the establishment of high-level model branches in colleges and universities emerges as a vital strategy to enhance student subjectivity and promote balanced and holistic student development.

Deep Analysis of Visual Product Reviews

With the recent shift toward the digital world, online customers are increasing rapidly. To fulfill the market demand, competitive market strategies are introduced in the e-commerce industry. Analyzing customer feedback is becoming one of the integral parts of this market strategy, and indispensable to a service provider. In recent days, it can be noticed that customers upload purchased product images with their review scores. In this article, we undertake the task of analyzing such visual reviews, which is very new of its kind. In the past, the researchers worked on analyzing language feedback. However, here, we do not take any assistance from linguistic reviews that may be absent, since a recent trend can be observed where customers prefer to quickly upload visual feedback through a smartphone instead of typing language feedback. We propose a hierarchical architecture, where the higher-level model engages in product categorization, and the lower-level model pays attention to predicting the review score from a customer-provided product image. We generated a database by procuring real visual product reviews, which was quite challenging. Our architecture obtained some promising results by performing extensive experiments on the employed database. The proposed hierarchical architecture attained a 26.26% more accuracy than the single-level best comparable architecture.

“The issue of weight is a personal responsibility” – Demands for performing in gendered karate in the Western neoliberal society

Even though its roots can be traced to an ancient past, karate was born as a Japanese martial art around a century ago. It went through a sportivization process both by being spread from East to West and by becoming a combat sport. In this dual process, karate in Western society has retained many of its traditional practices, loyal to what is understood as its Japanese roots, at the same time that it became an Olympic sport (though only briefly), the apex of the sportive world. Then, karate mixes within itself a complex identity, combining elements of a martial art and a combat sport in a manner of a selective tradition (Williams, 1977). Besides that, karate can be described as a gender binary environment, where the original and hegemonic masculine ethos prevails. Women are allowed to take part in the martial culture, but not without cost. They participated in the Olympic setting as much as men, having the same number of categories for fighting, for example. Notwithstanding, this equality does not necessarily mean fair inclusion and equity. In this paper, we explore how a select group of women who were members of a karate Olympic national squad dealt with the embodied experience of pain and suffering, both somatically and sociologically, from training sessions and competitions. We expect to provide some evidence of how karate is adapted to the Western model of elite sport; how women are prepared to supposedly address demands of non-discriminatory policies in sport; how women thrive and survive in the Western neoliberal high-level sport model and, despite their highlighted performances, are not duly acknowledged by male peers neither receive training taking into account female specifics, such as menstruation. We researched the Spanish women’s Olympic karate squad in their preparation for the Tokyo 2020 (2021) Olympic Games. We conducted an ethnographic project with auto-ethnographic notes from the first author since full completion of the original plan was not possible given the advent of COVID-19. We focus mainly on semi-structured interviews conducted with the team while considering the experience of the first author of the article, who has been a karate practitioner for several years, to question, corroborate, and deepen understanding of data collected. We reflect on the somewhat precarious situation of women in the hyper-masculine environment of karate and the conditions that need to be in place to ensure their full and valued participation. We found that karateka women face several challenges to conquer their space in karate, often negotiating in a way of resisting adversities, and giving in, in order to retain hard-won privileges. Therefore, while women fighters can represent a subversion of structures, they still face patriarchal diktats.

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Learning in Cities from Within and Across Cities: A Scoping Review

Abstract Cities evolve rapidly while providing both opportunities and posing challenges. To cope with the emerging behaviours of cities, contextual innovations and development are essential. Driving innovations through the learning of contextual knowledge in cities is crucial. In this study, we explore relevant studies to identify the frameworks for human-centric innovations in cities that consider learning from within and/or across cities. We analyse how learning in cities has been addressed in those studies and find that even though some aspects of learning in cities have been studied, a comprehensive framework for how cities can learn as an innovation ecosystem is missing. Based on the findings of a scoping review and insights from the theories of the Triple, Quadruple and Quintuple helices of innovation, we present a high-level conceptual model for cities as innovation ecosystems. The model is aimed to support sustainable human-centric development through the understanding of city learning through multilevel interactions and feedback.