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Specification of human brain regions with orthogonal gradients of WNT and SHH in organoids reveals patterning variations across cell lines

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Cell Stem Cell 2025年第6期32卷 970-989.e11页

作者： Scuderi, Soraya Kang, Tae-Yun Jourdon, Alexandre Nelson, Alex Yang, Liang Wu, Feinan Anderson, George M. Mariani, Jessica Tomasini, Livia Sarangi, Vivekananda Abyzov, Alexej Levchenko, Andre Vaccarino, Flora M. Program in Neurodevelopment and Regeneration Yale University New Haven 06520 CT United States Child Study Center Yale University New Haven 06520 CT United States Systems Biology Institute Yale University West Haven 06516 CT United States Department of Biomedical Engineering Yale University New Haven 06520 CT United States Department of Health Sciences Research Mayo Clinic Rochester 55905 MN United States Department of Neuroscience Yale University New Haven 06520 CT United States Yale Stem Cell Center Yale University New Haven 06520 CT United States

The repertoire of neurons and their progenitors depends on their location along the antero-posterior and dorso-ventral axes of the neural tube. To model these axes, we designed the Dual Orthogonal-Morphogen Assisted Patterning System (Duo-MAPS) diffusion device to expose spheres of induced pluripotent stem cells (iPSCs) to concomitant orthogonal gradients of a posteriorizing and a ventralizing morphogen, activating WNT and SHH signaling, respectively. Comparison with single-cell transcriptomes from the fetal human brain revealed that Duo-MAPS-patterned organoids generated an extensive diversity of neuronal lineages from the forebrain, midbrain, and hindbrain. WNT and SHH crosstalk translated into early patterns of gene expression programs associated with the generation of specific brain lineages with distinct functional networks. Human iPSC lines showed substantial interindividual and line-to-line variations in their response to morphogens, highlighting that genetic and epigenetic variations may influence regional specification. Morphogen gradients promise to be a key approach to model the brain in its entirety. © 2025 Elsevier Inc.

关键词： brain development gradients human iPSC morphogens organoids patterning SHH WNT

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On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control

arXiv

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arXiv 2021年

作者： Bedi, Amrit Singh Parayil, Anjaly Zhang, Junyu Wang, Mengdi Koppel, Alec University of Maryland College ParkMD United States Microsoft Research India Department of Industrial Systems Engineering and Management National University of Singapore Singapore119077 Singapore Department of Electrical Engineering Center for Statistics and Machine Learning Princeton University Deepmind PrincetonNJ08544 United States JP Morgan AI Research 383 Madison Ave New YorkNY10017 United States

Reinforcement learning is a framework for interactive decision-making with incentives sequentially revealed across time without a system dynamics model. Due to its scaling to continuous spaces, we focus on policy search where one iteratively improves a parameterized policy with stochastic policy gradient (PG) updates. In tabular Markov Decision Problems (MDPs), under persistent exploration and suitable parameterization, global optimality may be obtained. By contrast, in continuous space, the non-convexity poses a pathological challenge as evidenced by existing convergence results being mostly limited to stationarity or arbitrary local extrema. To close this gap, we step towards persistent exploration in continuous space through policy parameterizations defined by distributions of heavier tails defined by tail-index parameter α, which increases the likelihood of jumping in state space. Doing so invalidates smoothness conditions of the score function common to PG. Thus, we establish how the convergence rate to stationarity depends on the policy’s tail index α, a Hölder continuity parameter, integrability conditions, and an exploration tolerance parameter introduced here for the first time. Further, we characterize the dependence of the set of local maxima on the tail index through an exit and transition time analysis of a suitably defined Markov chain, identifying that policies associated with Lévy Processes of a heavier tail converge to wider peaks. This phenomenon yields improved stability to perturbations in supervised learning, which we corroborate also manifests in improved performance of policy search, especially when myopic and farsighted incentives are misaligned. Copyright © 2021, The Authors. All rights reserved.

关键词： Parameterization

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The internet of federated things (IoFT): A vision for the future and in-depth survey of data-driven approaches for federated learning

arXiv

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arXiv 2021年

作者： Al Kontar, Raed Shi, Naichen Yue, Xubo Chung, Seokhyun Byon, Eunshin Chowdhury, Mosharaf Jin, Judy Kontar, Wissam Masoud, Neda Noueihed, Maher Okwudire, Chinedum E. Raskutti, Garvesh Saigal, Romesh Singh, Karandeep Ye, Zhisheng Department of Industrial & Operations Engineering University of Michigan Ann Arbor48105 United States Electrical Engineering & Computer Science University of Michigan Ann Arbor48105 United States Civil & Environmental Engineering University of Wisconsin Madison Madison53715 United States Civil & Environmental Engineering University of Michigan Ann Arbor48105 United States Industrial Engineering American University of Beirut Beirut1107 2020 Lebanon Mechanical Engineering University of Michigan Ann Arbor48105 United States Statistics University of Wisconsin Madison Madison53715 United States Learning Health Sciences University of Michigan Ann Arbor48105 United States Industrial Systems Engineering & Management National University of Singapore Singapore119077 Singapore

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the "cloud" will be substituted by the "crowd" where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing. © 2021, CC BY.

关键词： Internet of things

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programmable threshold sensing in wireless devices using Ising dynamics

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Nature Electronics 2025年 1-8页

作者： Nicolas Casilli Hussein M. E. Hussein Ryan Tetro Luca Colombo Matteo Rinaldi Cristian Cassella Seunghwi Kim Andrea Alù Philip X.-L. Feng Institute for Nano Systems Innovation (NanoSI) Department of Electrical and Computer Engineering Northeastern University Boston MA USA Photonics Initiative Advanced Science Research Center City University of New York New York City NY USA Physics Program Graduate Center City University of New York New York City NY USA Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

Ising machines—comprising dissipatively coupled nodes capable of emulating the behaviour of ferromagnetic spins—can form analogue computing engines that surpass the sequential processing constraints of von Neumann architectures. However, the incorporation of Ising dynamics into radio-frequency wireless technologies remains limited, especially in terms of their potential to enhance wireless sensing capabilities. Here we report a passive wireless sensor that uses Ising dynamics to accurately implement threshold sensing. The device correlates the occurrence of violations in a sensed parameter with transitions in the coupling state of two parametric oscillators acting as Ising spins. As a result, the accuracy of the device is unaffected by distortions in its input and output signals due to multipath and is less prone to clutter caused by co-site interference. We illustrate the potential of the approach in temperature threshold sensing using a microfabricated lithium niobate microelectromechanical temperature sensor to couple two radio-frequency parametric oscillators, and show that such a system allows the sensor threshold to be wirelessly reprogrammed.

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Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve

arXiv

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arXiv 2021年

作者： Sicilia, Anthony Zhao, Xingchen Hwang, Seong Jae Intelligent Systems Program University of Pittsburgh 210 S. Bouquet Street PittsburghPA15260 United States Department of Electrical & Computer Engineering Northeastern University 360 Huntington Ave BostonMA02115 United States Department of Artificial Intelligence Yonsei University Korea Republic of

Theoretically, domain adaptation is a well-researched problem. Further, this theory has been well-used in practice. In particular, we note the bound on target error given by Ben-David et al. (2010) and the well-known domain-aligning algorithm based on this work using Domain Adversarial Neural Networks (DANN) presented by Ganin and Lempitsky (2015). Recently, multiple variants of DANN have been proposed for the related problem of domain generalization, but without much discussion of the original motivating bound. In this paper, we investigate the validity of DANN in domain generalization from this perspective. We investigate conditions under which application of DANN makes sense and further consider DANN as a dynamic process during training. Our investigation suggests that the application of DANN to domain generalization may not be as straightforward as it seems. To address this, we design an algorithmic extension to DANN in the domain generalization case. Our experimentation validates both theory and algorithm. © 2021, CC BY.

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Spatial-nonlocality-induced non-Markovian electromagnetically induced transparency in a single giant atom

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Physical Review A 2022年第4期106卷 043710-043710页

作者： Y. T. Zhu S. Xue R. B. Wu W. L. Li Z. H. Peng M. Jiang Department of Automation Shanghai Jiao Tong University Shanghai 200240 People's Republic of China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 People's Republic of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 People's Republic of China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing 100084 People's Republic of China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 People's Republic of China School of Electronics and Information Engineering Soochow University Suzhou 215006 People's Republic of China

The nonlocal waveguide coupling of giant atoms can bring up novel physics and applications. In this paper, we find unconventional electromagnetically induced transparency in giant atoms that has not been reported so far. We present a consistent theory for describing the unconventional EIT which includes a real-space scattering method and a time-delayed master equation. Our analysis shows this phenomenon is a full quantum effect led by a quantized interaction between the atom and the waveguide. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom. This phenomenon results from the inherent nonlocality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time.

关键词： Quantum optics with artificial atoms Scattering theory Quantum master equation

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High-precision chemical quantum sensing in flowing monodisperse microdroplets

arXiv

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arXiv 2024年

作者： Sarkar, Adrisha Jones, Zachary Parashar, Madhur Druga, Emanuel Akkiraju, Amala Conti, Sophie Krishnamoorthi, Pranav Nachuri, Srisai Aman, Parker Hashemi, Mohammad Nunn, Nicholas Torelli, Marco Gilbert, Benjamin Wilson, Kevin R. Shenderova, Olga Tanjore, Deepti Ajoy, Ashok Department of Chemistry University of California Berkeley BerkeleyCA94720 United States Chemical Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems and Engineering Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Adamas Nanotechnologies Inc. RaleighNC27617 United States Energy Geoscience Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States CIFAR Azrieli Global Scholars Program 661 University Ave TorontoONM5G 1M1 Canada

We report on a novel flow-based method for high-precision chemical detection that integrates quantum sensing with droplet microfluidics. We deploy nanodiamond (ND) particles hosting fluorescent nitrogen vacancy (NV) defect centers as quantum sensors in rapidly flowing, monodisperse, picoliter-volume microdroplets containing analyte molecules. ND motion within these microcompartments facilitates close sensor-analyte interaction and mitigates particle heterogeneity. Microdroplet flow rates are rapid (upto 4cm/s) and with minimal drift. Pairing this controlled flow with microwave control of NV electronic spins, we introduce a new noise-suppressed mode of Optically Detected Magnetic Resonance (ODMR) that is sensitive to chemical analytes while resilient against experimental variations, achieving detection of analyte-induced signals at an unprecedented level of a few hundredths of a percent of the ND fluorescence. We demonstrate its application to detecting paramagnetic ions in droplets with simultaneously low limit-of-detection and low analyte volumes, in a manner significantly better than existing technologies. This is combined with exceptional measurement stability over >103s and across hundreds of thousands of droplets, while utilizing minimal sensor volumes and incurring low ND costs ( Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical detection

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A locational marginal pricing mechanism for uncertainty management based on improved multi-ellipsoidal uncertainty set

arXiv

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arXiv 2020年

作者： Zhao, Zongzheng Liu, Yixin Guo, Li Bai, Linquan Wang, Chengshan Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin300072 China Department of Systems Engineering and Engineering Management University of North Carolina at Charlotte CharlotteNC28223 United States

Large-scale integration of renewable energy sources (RES) brings huge challenges to the power system. A cost-effective reserve deployment and uncertainty pricing mechanism are critical to deal with the uncertainty and variability of RES. To this end, this paper proposes a novel locational marginal pricing mechanism in day-ahead market for managing uncertainties from RES. Firstly, an improved multi-ellipsoidal uncertainty set (IMEUS) considering the temporal correlation and conditional correlation of wind power forecast is formulated to better capture the uncertainty of wind power. The dimension of each ellipsoidal subset is optimized based on a comprehensive evaluation index to reduce the invalid region without large loss of modeling accuracy, so as to reduce the conservatism. Then, an IMEUS-based robust unit commitment (RUC) model and a robust economic dispatch (RED) model are established for the day-ahead market clearing. Both the reserve cost and ramping constraints are considered in the overall dispatch process. Furthermore, based on the Langrangian function of the RED model, a new locational marginal pricing mechanism is developed. The uncertainty locational marginal price (ULMP) is introduced to charge the RES for its uncertainties and reward the generators who provide reserve to mitigate uncertainties. The new pricing mechanism can provide effective price signals to incentivize the uncertainty management in the day-ahead market. Finally, the effectiveness of the proposed methods is verified via numerous simulations on the PJM 5-bus system and IEEE 118-bus system. © 2020, CC BY-NC-ND.

关键词： Electric load dispatching

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1 -Norm Based Central Point Analysis for Asymmetric Radial Data

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Annals of Data Science 2018年第3期5卷 469-486页

作者： An, Qi Fang, Shu-Cherng Nie, Tiantian Jiang, Shan Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States Department of Systems Engineering and Engineering Management University of North Carolina at Charlotte CharlotteNC United States

Multivariate asymmetric radial data clouds with irregularly positioned "spokes" and "clutters" are commonly seen in real life applications. In identifying the spoke directions of such data, a key initial step is to locate a central point from which each spoke extends and diverges. In this technical note, we propose a novel method that features a preselection procedure to screen out candidate points that have sufficiently many data points in the vicinity and identifies the central point by solving an 1-norm constrained discrete optimization program. Extensive numerical experiments show that the proposed method is capable of providing central points with superior accuracy and robustness compared with other known methods and is computationally efficient for implementation. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Principal component analysis

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Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

arXiv

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arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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