Hiroki Sayama

#RENCI researchers Anirban Mandal + Cong W. are listed as authors on a paper being presented at 2 PM ET in Room B311 at #SC24. https://lnkd.in/guG7Np4E #LLM #anomalydetection #computationalworkflows #computationalefficiency

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#Mathematics #highcited ✨ [High Cited Paper] The paper titled “#Dynamic Behavior Analysis and Synchronization of Memristor-Coupled Heterogeneous #Discrete #NeuralNetworks” has been viewed by 2700, cited by 41. Read at https://buff.ly/3M1JMUx #dynamical_systems #MDPIOpenAccess #ComSciMath_Mdpi

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This #specialissue "Algorithms for Virtual and Augmented Environments" published 5 high-quality papers, and is viewed by 7573! Guest Editors: Dr. Simone Fontana, Dr. Silvia Corchs and Dr. Aurora Saibene 🔗We invite you to read and share: https://lnkd.in/ePYCftxs 👉Thank the Guest Editors' efforts on this Special Issue! MDPI Università degli Studi di Milano-Bicocca #artificialintelligencealgorithms #virtualreality #augmentedreality

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Three articles you may have missed 1: Our Guiding Principles (blank) 2: When is ISI not ISI? https://buff.ly/2yUQXvm 3: Detecting AI generated papers: Two recent articles https://buff.ly/4dqZ2ql

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This article on Phys org discusses a study on the tunable quantum anomalous Hall effects (QAHE) in van der Waals heterostructures, particularly focusing on materials composed of Bi and MnBi₂Te₄ monolayers. Researchers, using first-principles calculations, have demonstrated that by manipulating the magnetization orientation through strain, magnetic fields, or twisting, the magnetic and topological properties of these materials can be significantly altered. This tunability allows for the realization of QAHE with controllable Chern numbers, offering potential advancements in topotronic devices. For a detailed exploration, you can read the full article on Phys org: https://lnkd.in/e8zH6qEw

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Electrical & Computer Engineering Assoc. Prof. Jia (Kevin) Liu leads a team of researchers that received a $1.2M grant from NSF to develop a real-time, crowd-sourced geospatial digital twin to enhance how vital information is delivered to mobile devices. https://lnkd.in/gF9P8gZp Geospatial digital twins allow for real-time updates and interactions, making them invaluable for applications such as monitoring, maintenance and emergency response. Traditionally, data for GDTs has been collected through automated systems like distributed sensor devices, satellites and drones. “These methods have limitations, especially when it comes to updating data quickly and covering hard-to-reach areas,” said Liu. To overcome these challenges, the team will develop a novel approach that involves the community through “human-in-the-loop” strategies. This means using crowd-sourced data, where people provide real-time updates to digital models. This method not only promises to enhance the accuracy and timeliness of the information, but also to allow discovery of new information.

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At a recent lab meeting, Jayson Lynch summarized current progress in improving algorithmic performance. Attached are a few slides from his presentation. To learn more about progress in different types of algorithms, visit our Algorithm Wiki: a comprehensive overview of different Algorithms and their development at https://lnkd.in/dE-KSK3z For more of an introduction, read this short overview of some of the key terms and trends: https://lnkd.in/dm_n5Zua

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Applied Affective Computing https://bit.ly/3naKZQw provides State-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches to affective computing systems and social signal processing. Authors: Leimin Tian, Sharon Oviatt, Michal Muszynski, Brent Chamberlain, & Jennifer Healey. #AI #AffectiveComputing #MachineLearning #SocialRobots #BuiltEnvironments #Multimodal #DataCollection #ReinforcementLearning #Synthesizing #Natural and #Believable #EmotionalExpressions ACM, Association for Computing Machinery

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Joint work with Dhrumil Patel and Patrick Coles now published in Quantum - the open journal for quantum science: https://lnkd.in/g7vUGDjn Popular Summary: Many real-world problems in science and industry can be expressed as optimization problems, which involve finding the best solution while meeting specific constraints. Among these, a special class of optimization problems called semidefinite programming holds significance. They are widely used to model or approximate problems arising in various fields such as operations research, combinatorial optimization, control theory, and quantum information theory. For solving these programs, quantum algorithms have been proven to provide a quadratic speedup over classical algorithms. However, these quantum algorithms are not well-suited for current quantum devices, which are noisy and limited in their capabilities. In this work, we propose three quantum algorithms designed to run on these noisy devices. Our algorithms are hybrid quantum-classical algorithms that have a classical computer available for optimization, only calling a quantum computer for tasks that are not efficiently solvable by it. We rigorously analyze the performance of one of our algorithms, quantifying how rapidly it converges to the optimal value. Finally, to demonstrate their practicality, we numerically simulate our quantum algorithms for problems like MaxCut, a prominent graph theoretic problem. Our simulations showcase their effectiveness even in the presence of noise.

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📚 Attention Physics and Astronomy Researchers and Educators📚 By 2050, @SOA_2050 envisions a #STEMM ecosystem rooted in equity, inclusion, and scientific excellence. Learn how AIP's #DEIBA Strategic Plan and #TEAMUPTogether Initiative support this meaningful initiative. https://lnkd.in/eeBDSRa9

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A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models (George Mason University, July 2024) Paper: https://lnkd.in/gYTCnZ3Z Abstract: "Mechanistic interpretability (MI) is an emerging sub-field of interpretability that seeks to understand a neural network model by reverse-engineering its internal computations. Recently, MI has garnered significant attention for interpreting transformer-based language models (LMs), resulting in many novel insights yet introducing new challenges. However, there has not been work that comprehensively reviews these insights and challenges, particularly as a guide for newcomers to this field. To fill this gap, we present a comprehensive survey outlining fundamental objects of study in MI, techniques that have been used for its investigation, approaches for evaluating MI results, and significant findings and applications stemming from the use of MI to understand LMs. In particular, we present a roadmap for beginners to navigate the field and leverage MI for their benefit. Finally, we also identify current gaps in the field and discuss potential future directions."

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Our new #MachineLearning series benefits from exceptionally high standards and innovations in peer review. IOP Publishing ensures fairness and transparency with double-anonymous and transparent peer review across all our peer reviewed journals. Learn more➡️https://ow.ly/FvRh50STB0f #OpenAccess #AI

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