modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort,requiring an accurate description of the electronic processes and t...
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modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort,requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic *** demonstrate the development and application of an integrated machine learning model that describes on the same footing structural,energetic,and functional properties of barium titanate(BaTiO_(3)),a prototypical *** model uses ab initio calculations as a reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio *** predictions allow us to assess the microscopic mechanism of the ferroelectric *** presence of an order-disorder transition for the Ti off-centered states is the main driver of the ferroelectric transition,even though the coupling between symmetry breaking and cell distortions determines the presence of intermediate,partly-ordered ***,we thoroughly probe the static and dynamical behavior of BaTiO_(3)across its phase diagram without the need to introduce a coarse-grained description of the ferroelectric ***,we apply the polarization model to calculate the dielectric response properties of the material in a full ab initio manner,again reproducing the correct qualitative experimental behavior.
There is a need for systematic characterization of complex networked systems involving friendly forces and opponent forces to understand the adversary opportunities and capabilities to cause harm and develop counter-s...
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Many computational studies on hotspot microfluidic cooling devices found in the literature rely on simplified assumptions and conventions that do not capture the full complexity of the conjugate thermal problem, such ...
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Cyber attackers' evolving skills cause it challenging to secure the network. Thus it is paramount to characterize adversarial strategies and estimate the attacker's capability. Furthermore, estimating the adve...
Cyber attackers' evolving skills cause it challenging to secure the network. Thus it is paramount to characterize adversarial strategies and estimate the attacker's capability. Furthermore, estimating the adversarial capability can aid the cyber defender when deciding to place deceptive elements in the network. In this paper, we address the problem of characterizing adversarial strategies and develop a suite of metrics that quantify the opportunity and capability of the adversary. Using these metrics, the cyber defender can estimate the attacker's capability. In our simulation, we incorporated the developed metrics to estimate adversary capabilities based on the attacker's aggression, knowledge, and stealthiness level. To minimize the adversarial impact, we consider placing decoy nodes as deceptive elements in the network and measure the effectiveness of having decoy nodes. Our experimental evaluation suggests that placing decoy nodes in the network can effectively increase the attacker's resource usage and decrease the win percentage.
Owing to the interesting physical characteristics of perovskites, herein, we investigate RbZnX3 (X = Cl, Br) halide perovskites for sustainable green energy applications. All the given structures were made relaxed and...
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Cyber attackers' evolving skills cause it challenging to secure the network. Thus it is paramount to characterize adversarial strategies and estimate the attacker's capability. Furthermore, estimating the adve...
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ISBN:
(纸本)9781665476621
Cyber attackers' evolving skills cause it challenging to secure the network. Thus it is paramount to characterize adversarial strategies and estimate the attacker's capability. Furthermore, estimating the adversarial capability can aid the cyber defender when deciding to place deceptive elements in the network. In this paper, we address the problem of characterizing adversarial strategies and develop a suite of metrics that quantify the opportunity and capability of the adversary. Using these metrics, the cyber defender can estimate the attacker's capability. In our simulation, we incorporated the developed metrics to estimate adversary capabilities based on the attacker's aggression, knowledge, and stealthiness level. To minimize the adversarial impact, we consider placing decoy nodes as deceptive elements in the network and measure the effectiveness of having decoy nodes. Our experimental evaluation suggests that placing decoy nodes in the network can effectively increase the attacker's resource usage and decrease the win percentage.
The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urba...
The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.
Today cyber adversaries utilize advanced techniques to victimize target assets. To tackle the adversaries, it is of utmost importance to understand potential techniques they may use to exploit network vulnerabilities....
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Today cyber adversaries utilize advanced techniques to victimize target assets. To tackle the adversaries, it is of utmost importance to understand potential techniques they may use to exploit network vulnerabilities. Attack graph has always been a crucial tool for network vulnerability analysis. However, the current state-of-the-art attack graph can not predict adversarial techniques. To overcome the gap, we utilize the MITRE ATT&CK matrix in this work and map the techniques with the attack graph node descriptions. We first formulate a comprehensive dataset from ATT&CK consisting of all the adversarial strategies, subtechniques, associated tactics, and mitigation for the enterprise network. We then capture the attack graph node descriptions and apply the term frequency-inverse document frequency (TF-IDF) algorithm to map the attack techniques with the available node descriptions. Next, we generate the cosine similarity to determine an adversary’s top methods to attack a network. We then map those techniques with the associated tactics and mitigation strategies as enumerated in the ATT&CK matrix. Finally, we illustrate the analysis using a networked system’s attack graph. This proposed method would help identify and validate adversarial techniques and guide in selecting mitigation mechanisms for security enhancement.
We present a method to reconstruct the dielectric susceptibility (scattering potential) of an inhomogeneous scattering medium, based on the solution to the inverse scattering problem with internal sources. We consider...
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We present a method to reconstruct the dielectric susceptibility (scattering potential) of an inhomogeneous scattering medium, based on the solution to the inverse scattering problem with internal sources. We consider a scalar model of light propagation in the medium. We employ the theory of reproducing kernel Hilbert spaces, together with regularization to recover the susceptibility of two- and three-dimensional scattering media. Numerical examples illustrate the effectiveness of the proposed reconstruction method.
In the recommendation systems (RSs), it is imperative to incorporate the hidden contextual meaning of users’ provided ratings in the similarity computation. To draw such contextual meanings, existing models use the f...
In the recommendation systems (RSs), it is imperative to incorporate the hidden contextual meaning of users’ provided ratings in the similarity computation. To draw such contextual meanings, existing models use the fixed categorization of accessible ratings. However, due to the excessive variation in similarly co-rated item pairs, they produce ambiguous contextual meanings that yield inconsistent results for the user pairs similarities. Therefore, to deal with this problem, this paper proposes an adaptive divisional categorization (ADC)-based RS, namely ADC@
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