IA Scholar Query: Global Optimization of 0-1 Hyperbolic Programs.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgMon, 21 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Packing densities of Delzant and semitoric polygons
https://scholar.archive.org/work/6frmsb7ueng3tpjfojnuv6vnla
Exploiting the relationship between 4-dimensional toric and semitoric integrable systems with Delzant and semitoric polygons, respectively, we develop techniques to compute certain equivariant packing densities and equivariant capacities of these systems by working exclusively with the polygons. This expands on results of Pelayo and Pelayo-Schmidt. We compute the densities of several important examples and we also use our techniques to solve the equivariant semitoric perfect packing problem, i.e. we list all semitoric polygons for which the associated semitoric system admits an equivariant packing which fills all but a set of measure zero of the manifold. This paper also serves as a concise and accessible introduction to Delzant and semitoric polygons in dimension four.Yu Du, Gabriel Kosmacher, Yichen Liu, Jeff Massman, Joseph Palmer, Timothy Thieme, Jerry Wu, Zheyu Zhangwork_6frmsb7ueng3tpjfojnuv6vnlaMon, 21 Nov 2022 00:00:00 GMTGRaM-X: A new GPU-accelerated dynamical spacetime GRMHD code for Exascale computing with the Einstein Toolkit
https://scholar.archive.org/work/z5yefcx3r5cpra7s5qoktj6evu
We present GRaM-X (General Relativistic accelerated Magnetohydrodynamics on AMReX), a new GPU-accelerated dynamical-spacetime general relativistic magnetohydrodynamics (GRMHD) code which extends the GRMHD capability of Einstein Toolkit to GPU-based exascale systems. GRaM-X supports 3D adaptive mesh refinement (AMR) on GPUs via a new AMR driver for the Einstein Toolkit called CarpetX which in turn leverages AMReX, an AMR library developed for use by the United States DOE's Exascale Computing Project (ECP). We use the Z4c formalism to evolve the equations of GR and the Valencia formulation to evolve the equations of GRMHD. GRaM-X supports both analytic as well as tabulated equations of state. We implement TVD and WENO reconstruction methods as well as the HLLE Riemann solver. We test the accuracy of the code using a range of tests on static spacetime, e.g. 1D MHD shocktubes, the 2D magnetic rotor and a cylindrical explosion, as well as on dynamical spacetimes, i.e. the oscillations of a 3D TOV star. We find excellent agreement with analytic results and results of other codes reported in literature. We also perform scaling tests and find that GRaM-X shows a weak scaling efficiency of ∼ 40-50% on 2304 nodes (13824 NVIDIA V100 GPUs) with respect to single-node performance on OLCF's supercomputer Summit.Swapnil Shankar, Philipp Mösta, Steven R. Brandt, Roland Haas, Erik Schnetter, Yannick de Graafwork_z5yefcx3r5cpra7s5qoktj6evuMon, 21 Nov 2022 00:00:00 GMTDark matter halos and scaling relations of extremely massive spiral galaxies from extended HI rotation curves
https://scholar.archive.org/work/4scmqi2lurbebo6x7tutxzpwvy
We present new and archival atomic hydrogen () observations of of the most massive spiral galaxies in the local Universe (M_⋆>10^11 M_⊙). From 3D kinematic modeling of the datacubes, we derive extended rotation curves, and from these, we estimate masses of the dark matter halos and specific angular momenta of the discs. We confirm that massive spiral galaxies lie at the upper ends of the Tully-Fisher relation (mass vs velocity, M ∝ V^4) and Fall relation (specific angular momentum vs mass, j ∝ M^0.6), in both stellar and baryonic forms, with no significant deviations from single power laws. We study the connections between baryons and dark matter through the stellar (and baryon)-to-halo ratios of mass f_M≡ M_⋆/M_h and specific angular momentum f_j≡ j_⋆/j_h and f_j,bar≡ j_bar/j_h. Combining our sample with others from the literature for less massive disc-dominated galaxies, we find that f_M rises monotonically with M_⋆ and M_h (instead of the inverted-U shaped f_M for spheroid-dominated galaxies), while f_j and f_j,bar are essentially constant near unity over four decades in mass. Our results indicate that disc galaxies constitute a self-similar population of objects closely linked to the self-similarity of their dark halos. This picture is reminiscent of early analytical models of galaxy formation wherein discs grow by relatively smooth and gradual inflow, isolated from disruptive events such as major mergers and strong AGN feedback, in contrast to the more chaotic growth of spheroids.Enrico M. Di Teodoro, Lorenzo Posti, S. Michael Fall, Patrick M. Ogle, Thomas Jarrett, Philip N. Appleton, Michelle E. Cluver, Martha P. Haynes, Ute Lisenfeldwork_4scmqi2lurbebo6x7tutxzpwvyMon, 21 Nov 2022 00:00:00 GMTTorsion in 1-cusped Picard modular groups
https://scholar.archive.org/work/yun346mekvbm3gw7ungfwmf4d4
We present a systematic effective method to construct coarse fundamental domains for the action of the Picard modular groups PU(2,1,𝒪_d) where 𝒪_d has class number one, i.e. d=1,2,3,7,11,19,43,67,163. The computations can be performed quickly up to the value d=19. As an application of this method, we classify conjugacy classes of torsion elements, deduce short presentations for the groups, and construct neat subgroups of small index.Martin Deraux, Mengmeng Xuwork_yun346mekvbm3gw7ungfwmf4d4Mon, 21 Nov 2022 00:00:00 GMTDepletion of fossil fuel reserves and projections of CO_2 concentration in the Earth atmosphere
https://scholar.archive.org/work/jvbf2etanfagfnelmyjgjxrxdu
The paper has been suggested by two observations: 1) the atmospheric CO_2 growth rate is smaller than that ascribed to the emission of fossil fuels combustion, 2) the fossil fuel reserves are finite. The first observation has lead the way to a simple kinetic mode, based on the balance of 1) land/ocean CO_2 absorption and 2) CO_2 anthropogenic emission limited solely by depletion of the present day fossil-fuel reserves, in a business-as-usual scenario. The second observation has suggested to extrapolate past CO_2 emissions by fossil fuel combustion in the future years up to 2200 CE, by constraining emissions to the physical limits of reserves availability. The Meixner curve (hyperbolic secant distribution) has been used to model the pathway of resource exploitation for the three main classes of fossil fuels, crude oil, natural gas and coal. The kinetic model, driven by the extrapolated emissions, has been employed to project the CO_2 atmospheric concentration due to fossil fuel combustion close to the zero-reserve epoch. The result is just the output of simple models tuned on well-known experimental data. Error analysis of literature data provides the method robustness and the relevant uncertainty band. Contribution of other greenhouse gases like methane and nitrous oxide has been neglected, since their emissions cannot be projected with the paper methodology (they do not derive from fossil reserves). Notwithstanding this limitation, paper results clearly demonstrate that some of the IPCC projections of the CO_2 concentration are largely overestimated if compared to the physical limits of fossil fuel exploitation.Daniele Mazza, Enrico Canutowork_jvbf2etanfagfnelmyjgjxrxduSun, 20 Nov 2022 00:00:00 GMTHyperbolic Sliced-Wasserstein via Geodesic and Horospherical Projections
https://scholar.archive.org/work/5d76dyxq2nf63hwuianrnyo2oe
It has been shown beneficial for many types of data which present an underlying hierarchical structure to be embedded in hyperbolic spaces. Consequently, many tools of machine learning were extended to such spaces, but only few discrepancies to compare probability distributions defined over those spaces exist. Among the possible candidates, optimal transport distances are well defined on such Riemannian manifolds and enjoy strong theoretical properties, but suffer from high computational cost. On Euclidean spaces, sliced-Wasserstein distances, which leverage a closed-form of the Wasserstein distance in one dimension, are more computationally efficient, but are not readily available on hyperbolic spaces. In this work, we propose to derive novel hyperbolic sliced-Wasserstein discrepancies. These constructions use projections on the underlying geodesics either along horospheres or geodesics. We study and compare them on different tasks where hyperbolic representations are relevant, such as sampling or image classification.Clément Bonet, Laetitia Chapel, Lucas Drumetz, Nicolas Courtywork_5d76dyxq2nf63hwuianrnyo2oeFri, 18 Nov 2022 00:00:00 GMTAnomaly detection for multivariate time series in water distribution IoT systems
https://scholar.archive.org/work/dvlgviwkmbdv3etf7azmyg36fy
Water is a common good and a limited and strategic resource that needs to be protected and used in a sustainable way. Water resources are under unprecedented stress levels because of the combination of demographic evolution, the trend of increasing urbanisation, and climate change. These conditions force water operators to manage the water demand sustainably and efficiently, and identify and manage water leakages properly. In the last few years, ICT greatly improved and matured, producing novel data-based methodologies, new devices, and smart Decision Support Systems to help improve water resource management in man-made or natural environments, especially regarding monitoring and reporting quality, quantity, reuse of water, extreme events, and leakages. Some water utilities have already put into practise new digital technologies, such as smart sensors and actuators, drones and robots, and advanced data analytics models that provide detailed data, allowing deeper insights into water availability, quality, and use. In particular, many water distribution networks are continuously monitored through installed sensors that measure hydraulic parameters and water quality parameters. This continuous monitoring allows the collection of raw data to be used in multiple engineering applications, with flow rate and pressure data being the most widely used time series by water utilities. Performing leakage control, detecting changes in customer water demand or manoeuvring in the network can be summarised as physical anomaly detection. To perform this task, the time series of flow rate and pressure need to be analysed and passed through an anomaly detection model. The aim of this work is to create an anomaly detection model based on semi-supervised methods of deep learning to discover both sensor anomalies, caused by malfunctions and failures of the devices, and physical anomalies.Irene Ferfoglia, Luca Bortolussi, Stefano Alberto Russo, Francesca Zanellowork_dvlgviwkmbdv3etf7azmyg36fyThu, 17 Nov 2022 00:00:00 GMTTransfer Learning for Electricity Price Forecasting
https://scholar.archive.org/work/v2anmyzztvgx5nhg3dcievv6wi
Electricity price forecasting is an essential task in all the deregulated markets of the world. The accurate prediction of the day-ahead electricity prices is an active research field and available data from various markets can be used as an input for forecasting. A collection of models have been proposed for this task, but the fundamental question on how to use the available big data is often neglected. In this paper, we propose to use transfer learning as a tool for utilizing information from other electricity price markets for forecasting. We pre-train a neural network model on source markets and finally do a fine-tuning for the target market. Moreover, we test different ways to use the rich input data from various electricity price markets. Our experiments on four different day-ahead markets indicate that transfer learning improves the electricity price forecasting performance in a statistically significant manner. Furthermore, we compare our results with stateof-the-art methods in a rolling window scheme to demonstrate the performance of the transfer learning approach.Salih Gunduz, Umut Ugurlu, Ilkay Oksuzwork_v2anmyzztvgx5nhg3dcievv6wiThu, 17 Nov 2022 00:00:00 GMTNIMBLE: MCMC, Particle Filtering, and Programmable Hierarchical Modeling
https://scholar.archive.org/work/dl4jzqpnbvczfcjffeetap6dmy
NIMBLE is an R package for hierarchical statistical modeling (aka graphical modeling). It enables writing general models along with methods such as Markov chain Monte Carlo (MCMC), particle filtering (aka sequential Monte Carlo), and other general methods.NIMBLE Development Teamwork_dl4jzqpnbvczfcjffeetap6dmyWed, 16 Nov 2022 00:00:00 GMTInstability for rank one factors of product actions
https://scholar.archive.org/work/4y5kifa7ajfgdezuke274wjyv4
We provide a counterexample to a standard interpretation of the Katok-Spatzier conjecture, and pose questions which may serve as reasonable replacements.Kurt Vinhagework_4y5kifa7ajfgdezuke274wjyv4Wed, 16 Nov 2022 00:00:00 GMTDr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics
https://scholar.archive.org/work/o4q5lwow3nc5nmpl5rhwtm754m
The symbolic AI community is increasingly trying to embrace machine learning in neuro-symbolic architectures, yet is still struggling due to cultural barriers. To break the barrier, this rather opinionated personal memo attempts to explain and rectify the conventions in Statistics, Machine Learning, and Deep Learning from the viewpoint of outsiders. It provides a step-by-step protocol for designing a machine learning system that satisfies a minimum theoretical guarantee necessary for being taken seriously by the symbolic AI community, i.e., it discusses "in what condition we can stop worrying and accept statistical machine learning." Unlike most textbooks which are written for students trying to specialize in Stat/ML/DL and willing to accept jargons, this memo is written for experienced symbolic researchers that hear a lot of buzz but are still uncertain and skeptical. Information on Stat/ML/DL is currently too scattered or too noisy to invest in. This memo prioritizes compactness, citations to old papers (many in early 20th century), and concepts that resonate well with symbolic paradigms in order to offer time savings. It prioritizes general mathematical modeling and does not discuss any specific function approximator, such as neural networks (NNs), SVMs, decision trees, etc. Finally, it is open to corrections. Consider this memo as something similar to a blog post taking the form of a paper on Arxiv.Masataro Asaiwork_o4q5lwow3nc5nmpl5rhwtm754mWed, 16 Nov 2022 00:00:00 GMTGraph Filters for Signal Processing and Machine Learning on Graphs
https://scholar.archive.org/work/sk2yvoq4fzbkphzw43ibpjtp5u
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article serves the dual purpose of providing a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations between signal processing, machine learning, and application domains.Elvin Isufi, Fernando Gama, David I. Shuman, Santiago Segarrawork_sk2yvoq4fzbkphzw43ibpjtp5uWed, 16 Nov 2022 00:00:00 GMTUnbalanced Optimal Transport, from Theory to Numerics
https://scholar.archive.org/work/f75h4kix25gdlmndmdnn4ovthe
Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare in a geometrically faithful way point clouds and more generally probability distributions. The wide adoption of OT into existing data analysis and machine learning pipelines is however plagued by several shortcomings. This includes its lack of robustness to outliers, its high computational costs, the need for a large number of samples in high dimension and the difficulty to handle data in distinct spaces. In this review, we detail several recently proposed approaches to mitigate these issues. We insist in particular on unbalanced OT, which compares arbitrary positive measures, not restricted to probability distributions (i.e. their total mass can vary). This generalization of OT makes it robust to outliers and missing data. The second workhorse of modern computational OT is entropic regularization, which leads to scalable algorithms while lowering the sample complexity in high dimension. The last point presented in this review is the Gromov-Wasserstein (GW) distance, which extends OT to cope with distributions belonging to different metric spaces. The main motivation for this review is to explain how unbalanced OT, entropic regularization and GW can work hand-in-hand to turn OT into efficient geometric loss functions for data sciences.Thibault Séjourné, Gabriel Peyré, François-Xavier Vialardwork_f75h4kix25gdlmndmdnn4ovtheWed, 16 Nov 2022 00:00:00 GMTAutomated patient-robot assignment for a robotic rehabilitation gym: a simplified simulation model
https://scholar.archive.org/work/3oyy6uh7dneudfw7wtczmw3tfy
Background A robotic rehabilitation gym can be defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, we propose an automated system that could dynamically assign patients to different robots within a session in order to optimize rehabilitation outcome. Methods As a first step toward implementing a practical patient-robot assignment system, we present a simplified mathematical model of a robotic rehabilitation gym. Mixed-integer nonlinear programming algorithms are used to find effective assignment and training solutions for multiple evaluation scenarios involving different numbers of patients and robots (5 patients and 5 robots, 6 patients and 5 robots, 5 patients and 7 robots), different training durations (7 or 12 time steps) and different complexity levels (whether different patients have different skill acquisition curves, whether robots have exit times associated with them). In all cases, the goal is to maximize total skill gain across all patients and skills within a session. Results Analyses of variance across different scenarios show that disjunctive and time-indexed optimization models significantly outperform two baseline schedules: staying on one robot throughout a session and switching robots halfway through a session. The disjunctive model results in higher skill gain than the time-indexed model in the given scenarios, and the optimization duration increases as the number of patients, robots and time steps increases. Additionally, we discuss how different model simplifications (e.g., perfectly known and predictable patient skill level) could be addressed in the future and how such software may eventually be used in practice. Conclusions Though it involves unrealistically simple scenarios, our study shows that intelligently moving patients between different rehabilitation robots can improve overall skill acquisition in a multi-patient multi-robot environment. While robotic rehabilitation gyms are not yet commonplace in clinical practice, prototypes of them already exist, and our study presents a way to use intelligent decision support to potentially enable more efficient delivery of technologically aided rehabilitation.Benjamin A. Miller, Bikranta Adhikari, Chao Jiang, Vesna D. Novakwork_3oyy6uh7dneudfw7wtczmw3tfyWed, 16 Nov 2022 00:00:00 GMTEndemic Oscillations for SARS-CoV-2 Omicron – A SIRS model analysis
https://scholar.archive.org/work/wzt7vmgk3bdw3oiuogtq6tao4e
The SIRS model with constant vaccination and immunity waning rates is well known to show a transition from a disease-free to an endemic equilibrium as the basic reproduction number r_0 is raised above threshold. It is shown that this model maps to Hethcote's classic endemic model originally published in 1973. In this way one obtains unifying formulas for a whole class of models showing endemic bifurcation. In particular, if the vaccination rate is smaller than the recovery rate and r_- < r_0 < r_+ for certain upper and lower bounds r_±, then trajectories spiral into the endemic equilibrium via damped infection waves. Latest data of the SARS-CoV-2 Omicron variant suggest that according to this simplified model continuous vaccination programs will not be capable to escape the oscillating endemic phase. However, in view of the strong damping factors predicted by the model, in reality these oscillations will certainly be overruled by time-dependent contact behaviors.Florian Nillwork_wzt7vmgk3bdw3oiuogtq6tao4eWed, 16 Nov 2022 00:00:00 GMTCharacterizing 4-string contact interaction using machine learning
https://scholar.archive.org/work/qwyyhmqkdvccxka2jvruonat4u
The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning. We obtain Strebel quadratic differentials on 4-punctured spheres as a neural network by performing unsupervised learning with a custom-built loss function. This allows us to solve for local coordinates and compute their associated mapping radii numerically. We also train a neural network distinguishing vertex from Feynman region. As a check, 4-tachyon contact term in the tachyon potential is computed and a good agreement with the results in the literature is observed. We argue that our algorithm is manifestly independent of number of punctures and scaling it to characterize the geometry of n-string contact interaction is feasible.Harold Erbin, Atakan Hilmi Fıratwork_qwyyhmqkdvccxka2jvruonat4uWed, 16 Nov 2022 00:00:00 GMTPhysics-Informed Machine Learning: A Survey on Problems, Methods and Applications
https://scholar.archive.org/work/a6ifx4g5tnd37bipbzvdixutke
Recent advances of data-driven machine learning have revolutionized fields like computer vision, reinforcement learning, and many scientific and engineering domains. In many real-world and scientific problems, systems that generate data are governed by physical laws. Recent work shows that it provides potential benefits for machine learning models by incorporating the physical prior and collected data, which makes the intersection of machine learning and physics become a prevailing paradigm. In this survey, we present this learning paradigm called Physics-Informed Machine Learning (PIML) which is to build a model that leverages empirical data and available physical prior knowledge to improve performance on a set of tasks that involve a physical mechanism. We systematically review the recent development of physics-informed machine learning from three perspectives of machine learning tasks, representation of physical prior, and methods for incorporating physical prior. We also propose several important open research problems based on the current trends in the field. We argue that encoding different forms of physical prior into model architectures, optimizers, inference algorithms, and significant domain-specific applications like inverse engineering design and robotic control is far from fully being explored in the field of physics-informed machine learning. We believe that this study will encourage researchers in the machine learning community to actively participate in the interdisciplinary research of physics-informed machine learning.Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhuwork_a6ifx4g5tnd37bipbzvdixutkeTue, 15 Nov 2022 00:00:00 GMTDesign and training of deep reinforcement learning agents
https://scholar.archive.org/work/v4bnexxtdbgkvdgyu4jub7gulm
Deep reinforcement learning is a field of research at the intersection of reinforcement learning and deep learning. On one side, the problem that researchers address is the one of reinforcement learning: to act efficiently. A large number of algorithms were developed decades ago in this field to update value functions and policies, explore, and plan. On the other side, deep learning methods provide powerful function approximators to address the problem of representing functions such as policies, value functions, and models. The combination of ideas from these two fields offers exciting new perspectives. However, building successful deep reinforcement learning experiments is particularly difficult due to the large number of elements that must be combined and adjusted appropriately. This thesis proposes a broad overview of the organization of these elements around three main axes: agent design, environment design, and infrastructure design. Arguably, the success of deep reinforcement learning research is due to the tremendous amount of effort that went into each of them, both from a scientific and engineering perspective, and their diffusion via open source repositories. For each of these three axes, a dedicated part of the thesis describes a number of related works that were carried out during the doctoral research. The first part, devoted to the design of agents, presents two works. The first one addresses the problem of applying discrete action methods to large multidimensional action spaces. A general method called action branching is proposed, and its effectiveness is demonstrated with a novel agent, named BDQ, applied to discretized continuous action spaces. The second work deals with the problem of maximizing the utility of a single transition when learning to achieve a large number of goals. In particular, it focuses on learning to reach spatial locations in games and proposes a new method called Q-map to do so efficiently. An exploration mechanism based on this method is then used to demonstrate the effect [...]Fabio Pardo, Petar Kormushev, Andrew Davison, Dyson Technology Limited (Firm)work_v4bnexxtdbgkvdgyu4jub7gulmTue, 15 Nov 2022 00:00:00 GMTSignature Methods in Machine Learning
https://scholar.archive.org/work/flra4olwvvc5jiu6yv47y62xrq
Signature-based techniques give mathematical insight into the interactions between complex streams of evolving data. These insights can be quite naturally translated into numerical approaches to understanding streamed data, and perhaps because of their mathematical precision, have proved useful in analysing streamed data in situations where the data is irregular, and not stationary, and the dimension of the data and the sample sizes are both moderate. Understanding streamed multi-modal data is exponential: a word in n letters from an alphabet of size d can be any one of d^n messages. Signatures remove the exponential amount of noise that arises from sampling irregularity, but an exponential amount of information still remain. This survey aims to stay in the domain where that exponential scaling can be managed directly. Scalability issues are an important challenge in many problems but would require another survey article and further ideas. This survey describes a range of contexts where the data sets are small enough to remove the possibility of massive machine learning, and the existence of small sets of context free and principled features can be used effectively. The mathematical nature of the tools can make their use intimidating to non-mathematicians. The examples presented in this article are intended to bridge this communication gap and provide tractable working examples drawn from the machine learning context. Notebooks are available online for several of these examples. This survey builds on the earlier paper of Ilya Chevryev and Andrey Kormilitzin which had broadly similar aims at an earlier point in the development of this machinery. This article illustrates how the theoretical insights offered by signatures are simply realised in the analysis of application data in a way that is largely agnostic to the data type.Terry Lyons, Andrew D. McLeodwork_flra4olwvvc5jiu6yv47y62xrqTue, 15 Nov 2022 00:00:00 GMTGestationally dependent immune organization at the maternal-fetal interface
https://scholar.archive.org/work/kz5rkctlefcqtiuisdhcnqrtmu
The immune system and placenta have a dynamic relationship across gestation to accommodate fetal growth and development. High-resolution characterization of this maternal-fetal interface is necessary to better understand the immunology of pregnancy and its complications. We developed a single-cell framework to simultaneously immuno-phenotype circulating, endovascular, and tissue-resident cells at the maternal-fetal interface throughout gestation, discriminating maternal and fetal contributions. Our data reveal distinct immune profiles across the endovascular and tissue compartments with tractable dynamics throughout gestation that respond to a systemic immune challenge in a gestationally dependent manner. We uncover a significant role for the innate immune system where phagocytes and neutrophils drive temporal organization of the placenta through remarkably diverse populations, including PD-L1+ subsets having compartmental and early gestational bias. Our approach and accompanying datasets provide a resource for additional investigations into gestational immunology and evoke a more significant role for the innate immune system in establishing the microenvironment of early pregnancy.Amber R. Moore, Nora Vivanco Gonzalez, Katherine A. Plummer, Olivia R. Mitchel, Harleen Kaur, Moises Rivera, Brian Collica, Mako Goldston, Ferda Filiz, Michael Angelo, Theo D. Palmer, Sean C. Bendallwork_kz5rkctlefcqtiuisdhcnqrtmuTue, 15 Nov 2022 00:00:00 GMT