Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10128 publications
Preview abstract
We propose a neural network model that can separate target speech sources from interfering sources at different angular regions using two microphones. The model is trained with simulated room impulse responses (RIRs) using omni-directional microphones without needing to collect real RIRs. By relying on specific angular regions and multiple room simulations, the model utilizes consistent time difference of arrival (TDOA) cues, or what we call delay contrast, to separate target and interference sources while remaining robust in various reverberation environments. We demonstrate the model is not only generalizable to a commercially available device with a slightly different microphone geometry, but also outperforms our previous work which uses one additional microphone on the same device. The model runs in real-time on-device and is suitable for low-latency streaming applications such as telephony and video conferencing.
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A product manager’s specific role varies from one company to the next. Still, all product managers balance many aspects of their job, including customers’ needs, a vision for new products, and the project team. So what tools and strategies are needed to create a successful career as a product manager? What are the “5 Things You Need To Create A Successful Career As A Product Manager”? Authority Magazine speaks with Aqsa Fulara, a product manager at Google to answer these questions with stories and insights from her experiences.
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Drug Design on Quantum Computers
Raffaele Santagati
Alán Aspuru-Guzik
Matthias Degroote
Leticia Gonzalez
Elica Kyoseva
Nikolaj Moll
Markus Oppel
Robert Parrish
Michael Streif
Christofer Tautermann
Horst Weiss
Nathan Wiebe
Clemens Utschig-Utschig
Nature Physics (2024)
Preview abstract
The promised industrial applications of quantum computers often rest on their anticipated ability to perform accurate, efficient quantum chemical calculations. Computational drug discovery relies on accurate predictions of how candidate drugs interact with their targets in a cellular environment involving several thousands of atoms at finite temperatures. Although quantum computers are still far from being used as daily tools in the pharmaceutical industry, here we explore the challenges and opportunities of applying quantum computers to drug design. We discuss where these could transform industrial research and identify the substantial further developments needed to reach this goal.
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DORSal: Diffusion for Object-centric Representations of Scenes et al.
Allan Jabri
Emiel Hoogeboom
Thomas Kipf
International Conference on Learning Representations (2024)
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Recent progress in 3D scene understanding enables scalable learning of representations across large datasets of diverse scenes. As a consequence, generalization to unseen scenes and objects, rendering novel views from just a single or a handful of input images, and controllable scene generation that supports editing, is now possible. However, training jointly on a large number of scenes typically compromises rendering quality when compared to single-scene optimized models such as NeRFs. In this paper, we leverage recent progress in diffusion models to equip 3D scene representation learning models with the ability to render high-fidelity novel views, while retaining benefits such as object-level scene editing to a large degree. In particular, we propose DORSal, which adapts a video diffusion architecture for 3D scene generation conditioned on frozen object-centric slot-based representations of scenes. On both complex synthetic multi-object scenes and on the real-world large-scale Street View dataset, we show that DORSal enables scalable neural rendering of 3D scenes with object-level editing and improves upon existing approaches.
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Seeking in Cycles: How Users Leverage Personal Information Ecosystems to Find Mental Health Information
Ashlee Milton
Fernando Maestre
Rebecca Umbach
Stevie Chancellor
Proceedings of the CHI Conference on Human Factors in Computing Systems (2024)
Preview abstract
Information is crucial to how people understand their mental health and well-being, and many turn to online sources found through search engines and social media. We present the findings from an interview study (n = 17) of participants who use online platforms to seek information about their mental illnesses. We found that participants leveraged multiple platforms in a cyclical process for finding information from their personal information ecosystems, driven by the adoption of new information and uncertainty surrounding the credibility of information. Concerns about privacy, fueled by perceptions of stigma and platform design, also influenced their information-seeking decisions. Our work proposes theoretical implications for social computing and information retrieval on information seeking in users' personal information ecosystems. We also offer design implications to support users in navigating their personal information ecosystems to find mental health information.
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Socio-spatial equity analysis of relative wealth index and emergency obstetric care accessibility in urban Nigeria
Kerry L. M. Wong
Aduragbemi Banke-Thomas
Tope Olubodun
Peter M. Macharia
Charlotte Stanton
Narayanan Sundararajan
Yash Shah
Mansi Kansal
Swapnil Vispute
Olakunmi Ogunyemi
Uchenna Gwacham-Anisiobi
Jia Wang
Ibukun-Oluwa Omolade Abejirinde
Prestige Tatenda Makanga
Bosede B. Afolabi
Lenka Beňová
Communications Medicine, 4 (2024), pp. 34
Preview abstract
Background
Better geographical accessibility to comprehensive emergency obstetric care (CEmOC) facilities can significantly improve pregnancy outcomes. However, with other factors, such as affordability critical for care access, it is important to explore accessibility across groups. We assessed CEmOC geographical accessibility by wealth status in the 15 most-populated Nigerian cities.
Methods
We mapped city boundaries, verified and geocoded functional CEmOC facilities, and assembled population distribution for women of childbearing age and Meta’s Relative Wealth Index (RWI). We used the Google Maps Platform’s internal Directions Application Programming Interface to obtain driving times to public and private facilities. City-level median travel time (MTT) and number of CEmOC facilities reachable within 60 min were summarised for peak and non-peak hours per wealth quintile. The correlation between RWI and MTT to the nearest public CEmOC was calculated.
Results
We show that MTT to the nearest public CEmOC facility is lowest in the wealthiest 20% in all cities, with the largest difference in MTT between the wealthiest 20% and least wealthy 20% seen in Onitsha (26 vs 81 min) and the smallest in Warri (20 vs 30 min). Similarly, the average number of public CEmOC facilities reachable within 60 min varies (11 among the wealthiest 20% and six among the least wealthy in Kano). In five cities, zero facilities are reachable under 60 min for the least wealthy 20%. Those who live in the suburbs particularly have poor accessibility to CEmOC facilities.
Conclusions
Our findings show that the least wealthy mostly have poor accessibility to care. Interventions addressing CEmOC geographical accessibility targeting poor people are needed to address inequities in urban settings.
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Preview abstract
Browser fingerprinting is often associated with cross-site user tracking, a practice that many browsers (e.g., Safari, Brave, Edge, Firefox and Chrome) want to block. However, less is publicly known about its uses to enhance online safety, where it can provide an additional security layer against service abuses (e.g., in combination with CAPTCHAs) or during user authentication. To the best of our knowledge, no fingerprinting defenses deployed thus far consider this important distinction when blocking fingerprinting attempts, so they might negatively affect website functionality and security.
To address this issue we make three main contributions. First, we propose and evaluate a novel machine learning-based method to automatically identify authentication pages (i.e. sign-in and sign-up pages). Our algorithm -- which relies on a hybrid unsupervised/supervised approach -- achieves 96-98% precision and recall on a large, manually-labelled dataset of 10,000 popular sites. Second, we compare our algorithm with other methods from prior works on the same dataset, showing that it significantly outperforms all of them (+83% F1-score). Third, we quantify the prevalence of fingerprinting scripts across sign-in and sign-up pages (9.2%) versus those executed on other pages (8.9%); while the rates of fingerprinting are similar, home pages and authentication pages differ in the third-party scripts they include and how often these scripts are labeled as tracking. We also highlight the substantial differences in fingerprinting behavior on login and sign-up pages.
Our work sheds light on the complicated reality that fingerprinting is used to both protect user security and invade user privacy, and that this dual nature must be considered by fingerprinting mitigations.
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See Through Vehicles: Fully Occluded Vehicle Detection with Millimeter Wave Radar
Chenming He
Chengzhen Meng
Chunwang He
Beibei Wang
Yubo Yan
Yanyong Zhang
MobiCom 2024: The 30th Annual International Conference On Mobile Computing And Networking
Preview abstract
A crucial task in autonomous driving is to continuously detect nearby vehicles. Problems thus arise when a vehicle is occluded and becomes “unseeable”, which may lead to accidents. In this study, we develop mmOVD, a system that can detect fully occluded vehicles by involving millimeter-wave radars to capture the ground-reflected signals passing beneath the blocking vehicle’s chassis. The foremost challenge here is coping with ghost points caused by frequent multi-path reflections, which highly resemble the true points. We devise a set of features that can efficiently distinguish the ghost points by exploiting the neighbor points’ spatial and velocity distributions. We also design a cumulative clustering algorithm to effectively aggregate the unstable ground reflected radar points over consecutive frames to derive the bounding boxes of the vehicles.
We have evaluated mmOVD in both controlled environments and real-world environments. In an underground garage and two campus roads, we conducted controlled experiments in 56 scenes with 8 vehicles, including a minibus and a motorcycle. Our system accurately detects occluded vehicles for the first time, with a 91.1% F1 score for occluded vehicle detection and a 100% success rate for occlusion event detection. More importantly, we drove 324km on crowded roads at a speed up to 70km per hour and show we could achieve an occlusion detection success rate of 92% and a low false alarm rate of 4% with only 10% of the training data in complex real-world environments.
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Density-based User Representation through Gaussian Process Regression for Multi-interest Personalized Retrieval
Haolun Wu
Xue Liu
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS-24), Vancouver (2024)
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Personalized recommendation systems are increasingly essential in our information-rich society, aiding users in navigating the expansive online realm. However, accurately modeling the diverse and dynamic interests of the users remains a formidable challenge. Existing user modeling methods, like Single-point User Representation (SUR) and Multi-point User Representation (MUR), have their limitations in terms of accuracy, diversity, computation cost, and adaptability. To overcome these challenges, we introduce a novel model, the Density-based User Representation (DUR), leveraging Gaussian Process Regression (GPR), which has not been extensively explored in multi-interest recommendation and retrieval. Our approach inherently captures user interest dynamics without manual tuning, provides uncertainty-awareness, and is more efficient than point-based representation methods. This paper outlines the development and implementation of GPR4DUR, details its evaluation protocols, and presents extensive analysis demonstrating its effectiveness and efficiency. Experiments on real-world offline datasets confirm our method’s adaptability and efficiency. Further online experiments simulating user behavior illuminate the benefits of our method in the exploration-exploitation trade-off by effectively utilizing model uncertainty.
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Measuring Developer Experience with a Longitudinal Survey
Jessica Lin
Jill Dicker
IEEE Software (2024)
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At Google, we’ve been running a quarterly large-scale survey with developers since 2018. In this article, we will discuss how we run EngSat, some of our key learnings over the past 6 years, and how we’ve evolved our approach to meet new needs and challenges.
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Preview abstract
Most of our interactions with digital content currently occur inside 2D screens, however moving from that format to immersive setups brings a paradigm shift. From content inside the screen to users inside the content. This change requires a revisit to how we blend the analog and the digital and how we transfer content between the two modes. Perhaps it even asks for new guidelines too. While different solutions appear in the space, the dynamic range only seems to widen. We can start to see what works and what does not work so well, in an empirical or ethnographic approach, beyond laboratory studies. But if we want to accelerate adoption we need to further the understanding on how current tasks can be improved. How this new form of interaction can increase their productivity. In this paper we focus on analyzing and converging what we think works, and envisioning how this new set of immersive devices and interactions can enable productivity beyond already existing tools.
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Pathfinder: High-Resolution Control-Flow Attacks with Conditional Branch Predictor
Andrew Kwong
Archit Agarwal
Christina Garman
Daniel Genkin
Dean Tullsen
Deian Stefan
Hosein Yavarzadeh
Max Christman
Mohammadkazem Taram
International Conference on Architectural Support for Programming Languages and Operating Systems, ACM (2024)
Preview abstract
This paper presents novel attack primitives that provide adversaries with the ability to read and write the path history register (PHR) and the prediction history tables (PHTs) of the conditional branch predictor in modern Intel CPUs. These primitives enable us to recover the recent control flow (the last 194 taken branches) and, in most cases, a nearly unlimited control flow history of any victim program. Additionally, we present a tool that transforms the PHR into an unambiguous control flow graph, encompassing the complete history of every branch. This work provides case studies demonstrating the practical impact of novel reading and writing/poisoning primitives. It includes examples of poisoning AES to obtain intermediate values and consequently recover the secret AES key, as well as recovering a secret image by capturing the complete control flow of libjpeg routines. Furthermore, we demonstrate that these attack primitives are effective across virtually all protection boundaries and remain functional in the presence of all recent control-flow mitigations from Intel.
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Preview abstract
With the increase in the number of privacy regulations, small development teams are forced to make privacy decisions on their own. In this paper, we conduct a mixed-method survey study, including statistical and qualitative analysis, to evaluate the privacy perceptions, practices, and knowledge of members involved in various phases of the Software Development Life Cycle (SDLC). Our survey includes 362 participants from 23 countries, encompassing roles such as product managers, developers, and testers. Our results show diverse definitions of privacy across SDLC roles, emphasizing the need for a holistic privacy approach throughout SDLC. We find that software teams, regardless of their region, are less familiar with privacy concepts (such as anonymization), relying on self-teaching and forums. Most participants are more familiar with GDPR and HIPAA than other regulations, with multi-jurisdictional compliance being their primary concern. Our results advocate the need for role-dependent solutions to address the privacy challenges, and we highlight research directions and educational takeaways to help improve privacy-aware SDLC.
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Stable quantum-correlated many-body states through engineered dissipation
Xiao Mi
Alexios Michailidis
Sara Shabani
Jerome Lloyd
Rajeev Acharya
Igor Aleiner
Trond Andersen
Markus Ansmann
Frank Arute
Kunal Arya
Juan Atalaya
Gina Bortoli
Alexandre Bourassa
Leon Brill
Michael Broughton
Bob Buckley
Tim Burger
Nicholas Bushnell
Jimmy Chen
Benjamin Chiaro
Desmond Chik
Charina Chou
Josh Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Alejo Grajales Dau
Dripto Debroy
Agustin Di Paolo
ILYA Drozdov
Andrew Dunsworth
Lara Faoro
Edward Farhi
Reza Fatemi
Vinicius Ferreira
Ebrahim Forati
Brooks Foxen
Élie Genois
William Giang
Dar Gilboa
Raja Gosula
Steve Habegger
Michael Hamilton
Monica Hansen
Sean Harrington
Paula Heu
Markus Hoffmann
Trent Huang
Ashley Huff
Bill Huggins
Sergei Isakov
Justin Iveland
Cody Jones
Pavol Juhas
Kostyantyn Kechedzhi
Marika Kieferova
Alexei Kitaev
Andrey Klots
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Dave Landhuis
Pavel Laptev
Kim Ming Lau
Lily Laws
Joonho Lee
Kenny Lee
Yuri Lensky
Alexander Lill
Wayne Liu
Orion Martin
Amanda Mieszala
Shirin Montazeri
Alexis Morvan
Ramis Movassagh
Wojtek Mruczkiewicz
Charles Neill
Ani Nersisyan
Michael Newman
JiunHow Ng
Murray Ich Nguyen
Tom O'Brien
Alex Opremcak
Andre Petukhov
Rebecca Potter
Leonid Pryadko
Charles Rocque
Negar Saei
Kannan Sankaragomathi
Henry Schurkus
Christopher Schuster
Mike Shearn
Aaron Shorter
Noah Shutty
Vladimir Shvarts
Jindra Skruzny
Clarke Smith
Rolando Somma
George Sterling
Doug Strain
Marco Szalay
Alfredo Torres
Guifre Vidal
Cheng Xing
Jamie Yao
Ping Yeh
Juhwan Yoo
Grayson Young
Yaxing Zhang
Ningfeng Zhu
Jeremy Hilton
Anthony Megrant
Yu Chen
Vadim Smelyanskiy
Dmitry Abanin
Science, 383 (2024), pp. 1332-1337
Preview abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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MarkovGen: Structured Prediction for Efficient Text-to-Image Generation
Sadeep Jayasumana
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2024)
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Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts. However, this quality comes at significant computational cost: nearly all of these models are iterative and require running sampling multiple times with large models. This iterative process is needed to ensure that different regions of the image are not only aligned with the text prompt, but also compatible with each other. In this work, we propose a light-weight approach to achieving this compatibility between different regions of an image, using a Markov Random Field (MRF) model. We demonstrate the effectiveness of this method on top of the latent token-based Muse text-to-image model. The MRF richly encodes the compatibility among image tokens at different spatial locations to improve quality and significantly reduce the required number of Muse sampling steps. Inference with the MRF is significantly cheaper, and its parameters can be quickly learned through back-propagation by modeling MRF inference as a differentiable neural-network layer. Our full model, MarkovGen, uses this proposed MRF model to both speed up Muse by 1.5X and produce higher quality images by decreasing undesirable image artifacts.
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