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Lattice Design for Multiple Topologically Protected Edge Modes

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Lattice Design for Multiple Topologically Protected Edge Mod...

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Gyunghun Kim Joseph Suh Dayeong Lee Namkyoo Park Sunkyu Yu Department of Electrical and Computer Engineering Intelligent Wave Systems Laboratory Seoul National University Seoul Korea Department of Electrical and Computer Engineering Photonic Systems Laboratory Seoul National University Seoul Korea

We propose the lattice design that allows multiple topologically protected edge modes. The scattering between these modes, which is linear, energy preserving, and robust against local disorders, is discussed in terms ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Lattices Scattering Signal processing Lasers and electrooptics Laser modes Electro-optical waveguides

Sustainable Mining in the Era of Artificial Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 1-4页

作者： Long Chen Yuting Xie Yutong Wang Shirong Ge Fei-Yue Wang IEEE the State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of SciencesBeijing 100190China the National Laboratory of Pattern Recognition at the Institute of Automation and Waytous Ltd.China the School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275GuangdongChina the School of Mechanical Electronic and Information Engineering China University of Mining and Technology BeijingBejing 100083China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread ***-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

关键词： Sustainable mining consequences

Data-driven building energy efficiency prediction using physics-informed neural networks

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Data-driven building energy efficiency prediction using phys...

Conference on Technologies for Sustainability (Sustech)

作者： Vasilis Michalakopoulos Sotiris Pelekis Giorgos Kormpakis Vagelis Karakolis Spiros Mouzakitis Dimitris Askounis Decision Support Systems Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece

ISBN: (数字)9798350394344

ISBN: (纸本)9798350394351

The analytical prediction of building energy performance in residential buildings based on the heat losses of its individual envelope components is a challenging task. It is worth noting that this field is still in its infancy, with relatively limited research conducted in this specific area to date, especially when it comes for data-driven approaches. In this paper we introduce a novel physics-informed neural network model for addressing this problem. Through the employment of unexposed datasets that encompass general building information, audited characteristics, and heating energy consumption, we feed the deep learning model with general building information, while the model’s output consists of the structural components and several thermal properties that are in fact the basic elements of an energy performance certificate (EPC). On top of this neural network, a function, based on physics equations, calculates the energy consumption of the building based on heat losses and enhances the loss function of the deep learning model. This methodology is tested on a real case study for 256 buildings located in Riga, Latvia. Our investigation comes up with promising results in terms of prediction accuracy, paving the way for automated, and data-driven energy efficiency performance prediction based on basic properties of the building, contrary to exhaustive energy efficiency audits led by humans, which are the current status quo.

关键词： Heating systems Deep learning Energy consumption Buildings Neural networks Mathematical models Energy efficiency

A Decision Support Tool for Aggregation and Prioritisation of Energy Efficiency Investments in Buildings

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A Decision Support Tool for Aggregation and Prioritisation o...

International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Ioanna Andreoulaki Daniela Stoian Aikaterini Papapostolou Andriana Stavrakaki Vangelis Marinakis Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Reducing buildings' energy consumption through energy efficiency interventions has become a key priority to mitigate climate change. To this end, stakeholders involved in the implementation and financing of energy efficiency projects in the building sector could benefit from the development of specific methodologies and user-friendly tools that can assist them by supporting them in managing large datasets and by simplifying decision making processes. In this context, the aim of this paper is to propose a decision-making tool for triggering investments in energy efficiency. The tool's workflow consists of three layers: project selection (layer 1) - narrowing down the list of potential energy efficiency projects according to investor preferences, aggregation (layer 2) - leveraging an optimisation algorithm to create clusters of similar projects, and finally, prioritisation (layer 3) - applying the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method to rank the clusters. The tool brings significant added value to stakeholders involved in financing energy efficiency interventions, offering standardisation and automation of procedures.

关键词： Potential energy Sensitivity analysis Navigation Buildings Energy efficiency MCDM Stakeholders Reliability Optimization Investment

BERTtime Stories: Investigating the Role of Synthetic Story Data in Language Pre-training

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arXiv 2024年

作者： Theodoropoulos, Nikitas Filandrianos, Giorgos Lyberatos, Vassilis Lymperaiou, Maria Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

We describe our contribution to the Strict and Strict-Small tracks of the 2nd iteration of the BabyLM Challenge. The shared task is centered around efficient pre-training given data constraints motivated by human development. In response, we study the effect of synthetic story data in language pre-training using TinyStories: a recently introduced dataset of short stories. Initially, we train GPT-Neo models on subsets of TinyStories, while varying the amount of available data. We find that, even with access to less than 100M words, the models are able to generate high-quality, original completions to a given story, and acquire substantial linguistic knowledge. To measure the effect of synthetic story data, we train LTG-BERT encoder models on a combined dataset of: a subset of TinyStories, story completions generated by GPT-Neo, and a subset of the BabyLM dataset. Our experimentation reveals that synthetic data can occasionally offer modest gains, but overall have a negative influence on linguistic understanding. Our work offers an initial study on synthesizing story data in low resource settings and underscores their potential for augmentation in data-constrained language modeling. We publicly release our models and implementation on our GitHub 1 © 2024, CC BY-NC-ND.

关键词： Data assimilation

Enhancing adversarial robustness in Natural Language Inference using explanations

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arXiv 2024年

作者： Koulakos, Alexandros Lymperaiou, Maria Filandrianos, Giorgos Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

The surge of state-of-the-art transformer-based models has undoubtedly pushed the limits of NLP model performance, excelling in a variety of tasks. We cast the spotlight on the underexplored task of Natural Language Inference (NLI), since models trained on popular well-suited datasets are susceptible to adversarial attacks, allowing subtle input interventions to mislead the model. In this work, we validate the usage of natural language explanation as a model-agnostic defence strategy through extensive experimentation: only by fine-tuning a classifier on the explanation rather than premise-hypothesis inputs, robustness under various adversarial attacks is achieved in comparison to explanation-free baselines. Moreover, since there is no standard strategy for testing the semantic validity of the generated explanations, we research the correlation of widely used language generation metrics with human perception, in order for them to serve as a proxy towards robust NLI models. Our approach is resource-efficient and reproducible without significant computational limitations. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models

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arXiv 2024年

作者： Mitsouras, Ilias Tsonis, Eleftherios Tzouveli, Paraskevi Voulodimos, Athanasios Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

Diffusion models have demonstrated remarkable performance in text-to-image synthesis, producing realistic and high resolution images that faithfully adhere to the corresponding text-prompts. Despite their great success, they still fall behind in sketch-to-image synthesis tasks, where in addition to text-prompts, the spatial layout of the generated images has to closely follow the outlines of certain reference sketches. Employing an MLP latent edge predictor to guide the spatial layout of the synthesized image by predicting edge maps at each denoising step has been recently proposed. Despite yielding promising results, the pixel-wise operation of the MLP does not take into account the spatial layout as a whole, and demands numerous denoising iterations to produce satisfactory images, leading to time inefficiency. To this end, we introduce U-Sketch, a framework featuring a U-Net type latent edge predictor, which is capable of efficiently capturing both local and global features, as well as spatial correlations between pixels. Moreover, we propose the addition of a sketch simplification network that offers the user the choice of preprocessing and simplifying input sketches for enhanced outputs. The experimental results, corroborated by user feedback, demonstrate that our proposed U-Net latent edge predictor leads to more realistic results, that are better aligned with the spatial outlines of the reference sketches, while drastically reducing the number of required denoising steps and, consequently, the overall execution time. © 2024, CC BY.

关键词： Pixels

Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives

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arXiv 2024年

作者： Mastromichalakis, Orfeas Menis Liartis, Jason Stamou, Giorgos Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece

Explainable Artificial Intelligence (XAI) has emerged as a critical area of research aimed at enhancing the transparency and interpretability of AI systems. Counterfactual Explanations (CFEs) offer valuable insights into the decision-making processes of machine learning algorithms by exploring alternative scenarios where certain factors differ. Despite the growing popularity of CFEs in the XAI community, existing literature often overlooks the diverse needs and objectives of users across different applications and domains, leading to a lack of tailored explanations that adequately address the different use cases. In this paper, we advocate for a nuanced understanding of CFEs, recognizing the variability in desired properties based on user objectives and target applications. We identify three primary user objectives and explore the desired characteristics of CFEs in each case. By addressing these differences, we aim to design more effective and tailored explanations that meet the specific needs of users, thereby enhancing collaboration with AI systems. © 2024, CC BY-NC-ND.

关键词： Decision making

Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

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computers, Materials & Continua 2022年第9期72卷 5939-5954页

作者： Shaher Alshammrei Sahbi Boubaker Lioua Kolsi Department of Mechanical Engineering College of EngineeringUniversity of Ha’ilHa’il2440Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Laboratory of Metrology and Energy Systems National Engineering SchoolEnergy Engineering DepartmentUniversity of MonastirMonastir5000Tunisia

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and *** this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra *** achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and ***,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target *** its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following *** this aim,the MR was equipped with an ultrasonic sensor used as obstacle *** an obstacle is found,the MR updates its diagraph by excluding the corresponding ***,Dijkstra algorithm runs on the modified *** procedure is repeated until reaching the target *** verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 *** obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking *** study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

关键词： Mobile robot(MR) STEAM path planning obstacle avoidance improved dijkstra algorithm