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1. Diffusion models in protein structure and docking

   https://doi.org/10.1002/wcms.1711  

   Yim, Jason ; Stärk, Hannes ; Corso, Gabriele ; Jing, Bowen ; Barzilay, Regina ; Jaakkola, Tommi S. ( April 2024 , WIREs Computational Molecular Science)

   Generative AI is rapidly transforming the frontier of research in computational structural biology. Indeed, recent successes have substantially advanced protein design and drug discovery. One of the key methodologies underlying these advances is diffusion models (DM). Diffusion models originated in computer vision, rapidly taking over image generation and offering superior quality and performance. These models were subsequently extended and modified for uses in other areas including computational structural biology. DMs are well equipped to model high dimensional, geometric data while exploiting key strengths of deep learning. In structural biology, for example, they have achieved state‐of‐the‐art results on protein 3D structure generation and small molecule docking. This review covers the basics of diffusion models, associated modeling choices regarding molecular representations, generation capabilities, prevailing heuristics, as well as key limitations and forthcoming refinements. We also provide best practices around evaluation procedures to help establish rigorous benchmarking and evaluation. The review is intended to provide a fresh view into the state‐of‐the‐art as well as highlight its potentials and current challenges of recent generative techniques in computational structural biology.

   This article is categorized under:

   Data Science > Artificial Intelligence/Machine Learning

   Structure and Mechanism > Molecular Structures

   Software > Molecular Modeling

    

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2. Memory-Based Computing for Energy-Efficient AI: Grand Challenges

   https://doi.org/10.1109/VLSI-SoC57769.2023.10321880  

   Karimzadeh, Foroozan ; Imani, Mohsen ; Asgari, Bahar ; Cao, Ningyuan ; Lin, Yingyan ; Fang, Yan ( October 2023 , IEEE)

   The remarkable progress in artificial intelligence (AI) has ushered in a new era characterized by models with billions of parameters, enabling extraordinary capabilities across diverse domains. However, these achievements come at a significant cost in terms of memory and energy consumption. The growing demand for computational resources raises grand challenges for the sustainable development of energy-efficient AI systems. This paper delves into the paradigm of memory-based computing as a promising avenue to address these challenges. By capitalizing on the inherent characteristics of memory and its efficient utilization, memory-based computing offers a novel approach to enhance AI performance while reducing the associated energy costs. Our paper systematically analyzes the multifaceted aspects of this paradigm, highlighting its potential benefits and outlining the challenges it poses. Through an exploration of various methodologies, architectures, and algorithms, we elucidate the intricate interplay between memory utilization, computational efficiency, and AI model complexity. Furthermore, we review the evolving area of hardware and software solutions for memory-based computing, underscoring their implications for achieving energy-efficient AI systems. As AI continues its rapid evolution, identifying the key challenges and insights presented in this paper serve as a foundational guide for researchers striving to navigate the complex field of memory-based computing and its pivotal role in shaping the future of energy-efficient AI. 

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3. Questions and controversies in the study of time-varying functional connectivity in resting fMRI

   https://doi.org/10.1162/netn_a_00116  

   Lurie, Daniel J. ; Kessler, Daniel ; Bassett, Danielle S. ; Betzel, Richard F. ; Breakspear, Michael ; Kheilholz, Shella ; Kucyi, Aaron ; Liégeois, Raphaël ; Lindquist, Martin A. ; McIntosh, Anthony Randal ; et al ( January 2020 , Network Neuroscience)

   The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience. 

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4. Emerging rare-earth doped material platforms for quantum nanophotonics

   https://doi.org/10.1515/nanoph-2019-0185  

   Zhong, Tian ; Goldner, Philippe ( September 2019 , Nanophotonics)

   null (Ed.)

   Abstract Rare-earth dopants are arguably one of the most studied optical centers in solids, with applications spanning from laser optoelectronics, biosensing, lighting to displays. Nevertheless, harnessing rare-earth dopants’ extraordinary coherence properties for quantum information technologies is a relatively new endeavor, and has been rapidly advancing in recent years. Leveraging the state-of-the-art photonic technologies, on-chip rare-earth quantum devices functioning as quantum memories, single photon sources and transducers have emerged, often with potential performances unrivaled by other solid-state quantum technologies. These existing quantum devices, however, nearly exclusively rely on macroscopic bulk materials as substrates, which may limit future scalability and functionalities of such quantum systems. Thus, the development of new platforms beyond single crystal bulk materials has become an interesting approach. In this review article, we summarize the latest progress towards nanoscale, low-dimensional rare-earth doped materials for enabling next generation rare-earth quantum devices. Different platforms with a variety of synthesis methods are surveyed. Their key metrics measured to date are presented and compared. Special attention is placed on the connection between the topology of each platform to its target device applications. Lastly, an outlook for near term prospects of these platforms are given, with a hope to spur broader interests in rare-earth doped materials as a promising candidate for quantum information technologies. 

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5. Detecting Cyber Threats in Non-English Hacker Forums: An Adversarial Cross-Lingual Knowledge Transfer Approach

   Ebrahimi, M ; Samtani, S ; Chai, Y ; Chen, H. ( May 2020 , IEEE Symposium on Security and Privacy (IEEE S&P), Deep Learning and Security Workshop (DLS))

   The regularity of devastating cyber-attacks has made cybersecurity a grand societal challenge. Many cybersecurity professionals are closely examining the international Dark Web to proactively pinpoint potential cyber threats. Despite its potential, the Dark Web contains hundreds of thousands of non-English posts. While machine translation is the prevailing approach to process non-English text, applying MT on hacker forum text results in mistranslations. In this study, we draw upon Long-Short Term Memory (LSTM), Cross-Lingual Knowledge Transfer (CLKT), and Generative Adversarial Networks (GANs) principles to design a novel Adversarial CLKT (A-CLKT) approach. A-CLKT operates on untranslated text to retain the original semantics of the language and leverages the collective knowledge about cyber threats across languages to create a language invariant representation without any manual feature engineering or external resources. Three experiments demonstrate how A-CLKT outperforms state-of-the-art machine learning, deep learning, and CLKT algorithms in identifying cyber-threats in French and Russian forums. 

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