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Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure

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arXiv 2022年

作者： Yu, Christina Lee Airoldi, Edoardo M. Borgs, Christian Chayes, Jennifer T. School of Operations Research and Information Engineering Cornell University IthacaNY United States Department of Statistics Operations and Data Science Fox School of Business Temple University PhiladelphiaPA United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA United States Departments of Electrical Engineering and Computer Science Statistics Mathematics The School of Information UC Berkeley BerkeleyCA United States

Randomized experiments are widely used to estimate the causal effects of a proposed treatment in many areas of science, from medicine and healthcare to the physical and biological sciences, from the social sciences to engineering, to public policy and to the technology industry at large. Here, we consider situations where classical methods for estimating the total treatment effect on a target population are considerably biased due to confounding network effects, i.e., the fact that the treatment of an individual may impact their neighbors’ outcomes, an issue referred to as network interference or as non-individualized treatment response. A key challenge in these situations, is that the network is often unknown, and difficult, or costly, to measure. In this paper, we characterize the limitations in estimating the total treatment effect without knowledge of the network that drives interference, assuming a potential outcomes model with heterogeneous additive network effects. This model encompasses a broad class of network interference sources, including spillover, peer effects, and contagion. Within this framework, we show that, surprisingly, given access to average historical baseline measurements prior to the experiment, we can develop a simple estimator and efficient randomized design that outputs an unbiased estimate with low variance. Our solution does not require knowledge of the underlying network structure, and it comes with statistical guarantees for a broad class of models. We believe our results are poised to impact current randomized experimentation strategies due to its ease of interpretation and implementation, alongside its provable theoretical insights under heterogeneous network effects. Copyright © 2022, The Authors. All rights reserved.

关键词： Heterogeneous networks

Machine Learning Approach to the Floquet-Lindbladian Problem

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arXiv 2022年

作者： Volokitin, V. Meyerov, I. Denisov, Sergey Department of Mathematical Software and Supercomputing Technologies Lobachevsky University Nizhny Novgorod603950 Russia Department of Computer Science Oslo Metropolitan University OsloN-0130 Norway NordSTAR – Nordic Center for Sustainable and Trustworthy AI Research OsloN-0166 Norway Department of Applied Mathematics Lobachevsky University Nizhny Novgorod603950 Russia

Similar to its classical version, quantum Markovian evolution can be either time-discrete or time-continuous. Discrete quantum Markovian evolution is usually modeled with completely-positive trace-preserving maps while time-continuous evolution is often specified with superoperators referred to as "Lindbladians". Here we address the following question: Being given a quantum map, can we find a Lindbladian which generates an evolution identical – when monitored at discrete instances of time – to the one induced by the map? It was demonstrated that the problem of getting the answer to this question can be reduced to an NP-complete (in the dimension N of the Hilbert space the evolution takes place in) problem. We approach this question from a different perspective by considering a variety of Machine Learning (ML) methods and trying to estimate their potential ability to give the correct answer. Complimentary, we use the performance of different ML methods as a tool to check the hypothesis that the answer to the question is encoded in spectral properties of the so-called Choi matrix, which can be constructed from the given quantum map. As a test bed, we use two single-qubit models for which the answer can be obtained by using the reduction procedure. The outcome of our experiment is that, for a given map, the property of being generated by a time-independent Lindbladian is encoded both in the eigenvalues and the eigenstates of the corresponding Choi matrix. Copyright © 2022, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

Unraveling the mysteries of wormhole formation in Rastall-Rainbow gravity: A comprehensive study using the embedding approach

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arXiv 2024年

作者： Errehymy, Abdelghani Banerjee, Ayan Donmez, Orhan Daoud, Mohammed Nisar, Kottakkaran Sooppy Abdel-Aty, Abdel-Haleem Astrophysics Research Centre School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Private Bag X54001 Durban4000 South Africa Astrophysics and Cosmology Research Unit School of Mathematics Statistics and Computer Science University of KwaZulu–Natal Private Bag X54001 Durban4000 South Africa College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Department of Physics Faculty of Sciences Ibn Tofail University P.O. Box 133 Kenitra14000 Morocco Abdus Salam International Centre for Theoretical Physics Miramare Trieste34151 Italy Department of Mathematics College of Science and Humanities in Alkharj Prince Sattam bin Abdulaziz University Al Kharj Saudi Arabia Department of Physics College of Sciences University of Bisha P.O. Box 344 Bisha61922 Saudi Arabia

The present work looks for the possible existence of static and spherically symmetric wormhole geometries in Rastall-Rainbow gravity. Since, the Rastall-Rainbow gravity model has been constructed with the combination of Rastall theory and the gravity’s rainbow formalism. Taking advantage of the Karmarkar condition for embedding class one metrics, we solve the modified field equations analytically that describe wormholes for specific choice of redshift function. For specific parameter ranges, the solution represents a traversable wormhole that exhibits the violation of null energy condition and consequently the weak energy condition also. Furthermore, we focus on the wormhole stability via adiabatic sound velocity analysis. This model establishes a strong connection between two model parameters, namely, the Rastall parameters and the Rainbow functions, and how it affects the wormhole solution. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

Improving the Estimation of Coronary Artery Disease by Classification Machine Learning Algorithm

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Improving the Estimation of Coronary Artery Disease by Class...

International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Hamzeh Ghorbani Alla Krasnikova Parvin Ghorbani Simin Ghorbani Harutyun S. Hovhannisyan Arsen Minasyan Natali Minasian Mehdi Ahmadi Alvar Harutyun Stepanyan Mohammazreza Azodinia Faculty of General Medicine University of Traditional Medicine of Armenia (UTMA) Yerevan Armenia Department of Cardiology Faculty of Medicine Yerevan State medical university Yerevan Armenia Department of Cardiology Faculty of Medicine Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Department of Nursing and Midwifery Faculty of Nursing Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Department of Internal Disease Propaedeutics Yerevan State Medical University Yerevan Armenia Faculty of General Medicine Yaroslavl State Medical University Yaroslavl Russia Department of Computer Engineering Faculty of Engineering Shahid Chamran University Ahwaz Iran Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary

This research paper aims to predict coronary artery disease (CAD) using data from 350 patients collected at one of the hospitals in Armenia. CAD is a critical parameter which can have a significant impact on patients' life and survival. The study considers several input variables, including level of cholesterol (LOC), patient's age (PA), type of chest pain (TCP), number of arteries blocked (NAB), sex (S), and family history (FH), to make accurate predictions. To achieve this crucial task of CAD prediction, the researchers employed three powerful classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). Among these, the Random Forest algorithm stands out for its robustness and numerous advantages, including high accuracy, ability to handle outliers effectively, provision of feature importance insights, and reduced risk of overfitting. The research findings presented in this article demonstrate the impressive performance of the Random Forest algorithm, showcasing an accuracy value of 0.95 and a precision value of 0.94. These results indicate the model's ability to make precise and reliable predictions, essential when dealing with a life-or-death parameter like CAD. By conducting a comparative analysis based on statistical parameters, the researchers establish that Random Forest outperforms both SVM and LR. Thus, the conclusion drawn from the study suggests that the ranking of the algorithms based on their performance is as follows: RF > SVM > LR.

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End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities

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arXiv 2022年

作者： Srinivasan, Muralikrishnan Song, Jinxiang Grabowski, Alexander Szczerba, Krzysztof Iversen, Holger K. Schmidt, Mikkel N. Zibar, Darko Schröder, Jochen Larsson, Anders Häger, Christian Wymeersch, Henk The Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden Chalmers University of Technology GothenburgSE-41296 Sweden OpenLight Photonics Mountain ViewCA94087 United States The Department of Photonic Engineering Technical University of Denmark 2800 Kgs Lyngby Denmark The Department of Applied Mathematics and Computer Science Technical University of Denmark. 2800 Kgs Lyngby Denmark

Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh and time-varying conditions, thus requiring adaptive and flexible designs of the communication chain. Such designs can be built based on mathematical models (model-based design) or learned from data (machine learning (ML) based design). Various ML techniques have recently come to the forefront, replacing individual components in the transmitters and receivers with deep neural networks. Beyond such component-wise learning, end-to-end (E2E) autoencoder approaches can reach the ultimate performance through co-optimizing entire parameterized transmitters and receivers. This tutorial paper aims to provide an overview of ML for VCSEL-based OIs, with a focus on E2E approaches, dealing specifically with the unique challenges facing VCSELs, such as the wide temperature variations and complex models. © 2022, CC BY.

关键词： Optical communication

A Robust Slot Filling Model based on LSTM and CRF for IoT Voice Interaction

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A Robust Slot Filling Model based on LSTM and CRF for IoT Vo...

IEEE Globecom Workshops

作者： Mourad Jbene Smail Tigani Rachid Saadane Abdellah Chehri Laboratory Engineering system SIRC/LAGeS-EHTP Hassania School of Public Works Casablanca Morocco Engineering Unit Euro-Med Research Center Euro-Med University Fes 51 Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston CANADA

ISBN: (纸本)9781665459761

The Internet Of Things (IoT) is an emerging paradigm representing a network of infrastructure connecting different types of devices in different places. Recently, IoT-based Conversational systems gained more attention, providing users with a new human-computer interaction mode. These new systems allow users to interact with the IoT ecosystem through a virtual assistant. They also heavily rely on Natural Language Processing (NLP) techniques for intelligent and efficient communication. Understanding the user’s needs is the first step toward a more humanized IoT interaction system. This can be achieved using Natural language understanding (NLU) modules. Slot Filling is one of the core sub-tasks in NLU. It is an active research area focusing on extracting attribute values from the user’s utterance. In this study, we propose an LSTM-based model for slot filling. The model benefits from contextual embeddings extracted from the BERT transformer model, the attention mechanism, and the conditional random field (CRF) that is known to model strong dependence among adjacent tags in the output sequence. Experiments show that our model achieved competitive results on two widely used benchmark datasets.

关键词： Virtual assistants Semantics Focusing Transformer cores Transformers Filling Natural language processing

Adaptive Intelligent System for Monitoring and Control the Parameters of Pipelines Electrochemical Corrosion Protection

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Adaptive Intelligent System for Monitoring and Control the P...

2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Khussanov, Zhakhongir Prokhorov, Oleksandr Prokhorov, Valeriy Tevyashev, Andriy Turdybekova, Dilfuza Shatalov, Oleksii 'Structural And Biochemical Materials' M. Auezov South Kazakhstan University Regional Laboratory Of Engineering Shymkent Kazakhstan National Aerospace University 'Kharkiv Aviation Institute' Department Of Computer Science And Information Technologies Kharkiv Ukraine Natioanal University Of Radio Electronics Problematic Scientifically Adv. Lab. For Radio Monitoring And Proc. Of Radio Technical Information Kharkiv Ukraine National University Of Radio Electronics Department Of Applied Mathematics Kharkiv Ukraine M. Auezov South Kazakhstan University Shymkent Kazakhstan National University Of Radio Electronics Department Of Artificial Intelligence Kharkiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The paper is dedicated to the creation of adaptive intelligent system that provides control and management of parameters of cathodic protection stations, taking into account changes in external conditions in some sections of pipelines. Multicriteria task of optimization of operation modes of cathodic protection stations is considered, as optimization is performed both by the criterion of optimality of protective potential distribution (uniformity of protective total potential distribution along the pipeline length) and by the criterion of minimum total protective current of stations. The system provides increase of reliability of electrochemical protection system as a whole and accordingly prevents possible emergencies at pipeline system, as well as reduces costs of current repair of pipelines, due to reliability and uninterrupted protection. © 2022 IEEE.

关键词： Adaptive control systems

A new approach to dealing with interval data in the TOPSIS method

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Procedia computer Science 2022年 207卷 4545-4555页

作者： Aleksandra Kaczyńska Neeraj Gandotra Wojciech Sałabun Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Żołnierska 49 71-210 Szczecin Poland Yogananda School of AI Computers and Data Science Shoolini University Solan 173229 HP India

Dealing with uncertainty is a significant direction of contemporary research on decision support systems. Despite many sophisticated methods of dealing with uncertain data, such as fuzzy sets and their numerous generalizations, methods based on interval numbers are still very popular. This is because interval data are much less complex and easier to process, and they are also easier to determine. However, there is a research gap regarding whether such a simple approach can adequately model uncertainty. This paper proposes a new approach to handling interval data in the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on the cartesian product of boundaries. This approach is compared with the most popular extension proposed by Jahanshahloo. In this work, two study cases taken from the literature were analyzed. Finally, the results of both approaches were compared, and conclusions were drawn.

关键词： TOPSIS interval data decision-making MCDA

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

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arXiv 2022年

作者： Goswami, Somdatta Li, David S. Rego, Bruno V. Latorre, Marcos Humphrey, Jay D. Karniadakis, George Em Division of Applied Mathematics Brown University ProvidenceRI United States Department of Biomedical Engineering Yale University New HavenCT United States Center for Research and Innovation in Bioengineering Valencia Polytechnic University Valencia Spain School of Engineering Brown University ProvidenceRI United States

Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta resulting from compromised wall composition, structure, and function, which can lead to life-threatening dissection or rupture. Several genetic mutations and predisposing factors that contribute to TAA have been studied in mouse models to characterize specific changes in aortic microstructure and material properties that result from a wide range of mechanobiological insults. By contrast, assessments of TAA progression in vivo are largely limited to measurements of aneurysm size and growth rate. It has been shown, however, that aortic geometry alone is not sufficient to predict the patient-specific progression of TAA but computational modeling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating insults. In this work, we present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify contributing factors for TAA by using synthetic finite element-based datasets of aortic growth and remodeling (G&R) resulting from prescribed mechanobiological insults. For training data, we investigate multiple types of TAA risk factors and spatial distributions within a computationally efficient constrained mixture model to generate axial-azimuthal maps of aortic dilatation and distensibility. The trained network is then capable of predicting the initial distribution and extent of the insult from a given set of dilatation and distensibility information, which in turn can be used to determine subsequent aortic geometry and mechanical properties. Two DeepONet frameworks are proposed, one trained on sparse information and one on full-field grayscale images, to gain insight into a preferred neural operator-based approach. Performance of the surrogate models is evaluated through multiple simulations carried out on insult distributions varying from fusiform (analytically defined) to complex (randomly generated). We show that thi

关键词： Blood vessels