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.
Sort By
1 - 15 of 10129 publications
Geographical accessibility to emergency obstetric care in urban Nigeria using closer-to-reality travel time estimates
Aduragbemi Banke-Thomas
Kerry L. M. Wong
Tope Olubodun
Peter M. Macharia
Narayanan Sundararajan
Yash Shah
Mansi Kansal
Swapnil Vispute
Olakunmi Ogunyemi
Uchenna Gwacham-Anisiobi
Jia Wang
Ibukun-Oluwa Omolade Abejirinde
Prestige Tatenda Makanga
Ngozi Azodoh
Charles Nzelu, PhD
Charlotte Stanton
Bosede B. Afolabi
Lenka Beňová
Lancet Global Health (2024)
Preview abstract
Background
Better accessibility of emergency obstetric care (CEmOC) facilities can significantly reduce maternal and perinatal deaths. However, pregnant women living in urban settings face additional complex challenges travelling to facilities. We estimated geographical accessibility and coverage to the nearest, second nearest, and third nearest public and private CEmOC facilities in the 15 largest Nigerian cities.
Methods
We mapped city boundaries, verified and geocoded functional CEmOC facilities, and assembled population distribution for women of childbearing age (WoCBA). We used Google Maps Platform’s internal Directions Application Programming Interface (API) to derive driving times to public, private, or either facility-type. Median travel time (MTT) and percentage of WoCBA able to reach care were summarised for eight traffic scenarios (peak and non-peak hours on weekdays and weekends) by city and within-city (wards) under different travel time thresholds (<15, <30, <60 min).
Findings
City-level MTT to the nearest CEmOC facility ranged from 18min (Maiduguri) to 46min (Kaduna). Within cities, MTT varied by location, with informal settlements and peripheral areas being the worst off. The percentages of WoCBA within 60min to their nearest public CEmOC were nearly universal; whilst the percentages of WoCBA within 30min reach to their nearest public CEmOC were between 33% in Aba to over 95% in Ilorin and Maiduguri. During peak traffic times, the median number of public CEmOC facilities reachable by WoCBA under 30min was zero in eight of 15 cities.
Interpretation
This approach provides more context-specific, finer, and policy-relevant evidence to support improving CEmOC service accessibility in urban Africa.
View details
Auto-bidding and Auctions in Online Advertising: A Survey
Ashwinkumar Badanidiyuru Varadaraja
Christopher Liaw
Haihao (Sean) Lu
Andres Perlroth
Georgios Piliouras
Ariel Schvartzman
Kelly Spendlove
Hanrui Zhang
Mingfei Zhao
ACM SIGecom Exchanges, 22 (2024)
Preview abstract
In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opportunities that have arisen as markets embrace this autobidding and cover a range of topics in this area, including bidding algorithms, equilibrium analysis and efficiency of common auction formats, and optimal auction design.
View details
Data Exchange Markets via Utility Balancing
Aditya Bhaskara
Sungjin Im
Kamesh Munagala
Govind S. Sankar
WebConf (2024)
Preview abstract
This paper explores the design of a balanced data-sharing marketplace for entities with heterogeneous datasets and machine learning models that they seek to refine using data from other agents. The goal of the marketplace is to encourage participation for data sharing in the presence of such heterogeneity. Our market design approach for data sharing focuses on interim utility balance, where participants contribute and receive equitable utility from refinement of their models. We present such a market model for which we study computational complexity, solution existence, and approximation algorithms for welfare maximization and core stability. We finally support our theoretical insights with simulations on a mean estimation task inspired by road traffic delay estimation.
View details
Rapid initial state preparation for the quantum simulation of strongly correlated molecules and materials
Dominic Berry
Yu Tong
Alec White
Tae In Kim
Lin Lin
Seunghoon Lee
Garnet Chan
arXiv:2409.11748 (2024)
Preview abstract
Studies on quantum algorithms for ground state energy estimation often assume perfect ground state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here we address that problem in two ways: by faster preparation of matrix product state (MPS) approximations, and more efficient filtering of the prepared state to find the ground state energy. We show how to achieve unitary synthesis with a Toffoli complexity about $7 \times$ lower than that in prior work, and use that to derive a more efficient MPS preparation method. For filtering we present two different approaches: sampling and binary search. For both we use the theory of window functions to avoid large phase errors and minimise the complexity. We find that the binary search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about 0.003. Finally, we estimate the total resources to perform ground state energy estimation of FeMoco and Iron cluster systems by estimating ground state overlap on an MPS initial state through extrapolation. With a modest bond dimension of 4000 we estimate a 0.96 overlap squared value producing total resources of $7.5 \times 10^{10}$ Toffoli gates; validating naive estimates where we assume perfect ground state overlap. These extrapolations allay practical concerns of exponential overlap decay in challenging-to-compute chemical systems.
View details
Connecting Language Technologies with Rich, Diverse Data Sources Covering Thousands of Languages
Sebastian Ruder
Julia Kreutzer
Clara Rivera
Ishank Saxena
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Preview abstract
Contrary to common belief, there are rich and diverse data sources available for many thousands of languages, which can be used to develop technologies for these languages. In this paper, we provide an overview of some of the major online data sources, the types of data that they provide access to, potential applications of this data, and the number of languages that they cover. Even this covers only a small fraction of the data that exists; for example, printed books are published in many languages but few online aggregators exist.
View details
Knowledge Distillation with Perturbed Loss: From a Vanilla Teacher to a Proxy Teacher
Rongzhi Zhang
Chao Zhang
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), ACM, pp. 4278 - 4289
Preview abstract
Knowledge distillation is a popular technique to transfer knowledge from a large teacher model to a small student model. Typically, the student learns to imitate the teacher by minimizing the KL divergence of its output distribution with the teacher's output distribution. In this work, we argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution. Therefore, forcing the student to blindly imitate the unreliable teacher output distribution leads to inferior performance. To this end, we propose a novel knowledge distillation objective PTLoss by first representing the vanilla KL-based distillation loss function via a Maclaurin series and then perturbing the leading-order terms in this series. This perturbed loss implicitly transforms the original teacher into a proxy teacher with a distribution closer to the ground truth distribution. We establish the theoretical connection between this "distribution closeness'' and the student model generalizability, which enables us to select the PTLoss's perturbation coefficients in a principled way. Extensive experiments on six public benchmark datasets demonstrate the effectiveness of PTLoss with teachers of different scales.
View details
Wear's my Data? Understanding the Cross-Device Runtime Permission Model in Wearables
Doguhan Yeke
Muhammad Ibrahim
Habiba Farukh
Abdullah Imran
Antonio Bianchi
Z. Berkay Celik
IEEE Symposium on Security and Privacy (2024) (to appear)
Preview abstract
Wearable devices are becoming increasingly important, helping us stay healthy and connected. There are a variety
of app-based wearable platforms that can be used to manage
these devices. The apps on wearable devices often work with a
companion app on users’ smartphones. The wearable device and
the smartphone typically use two separate permission models
that work synchronously to protect sensitive data. However, this
design creates an opaque view of the management of permission-
protected data, resulting in over-privileged data access without
the user’s explicit consent. In this paper, we performed the first
systematic analysis of the interaction between the Android and
Wear OS permission models. Our analysis is two-fold. First,
through taint analysis, we showed that cross-device flows of
permission-protected data happen in the wild, demonstrating
that 28 apps (out of the 150 we studied) on Google Play
have sensitive data flows between the wearable app and its
companion app. We found that these data flows occur without
the users’ explicit consent, introducing the risk of violating
user expectations. Second, we conducted an in-lab user study
to assess users’ understanding of permissions when subject to
cross-device communication (n = 63). We found that 66.7% of
the users are unaware of the possibility of cross-device sensitive
data flows, which impairs their understanding of permissions in
the context of wearable devices and puts their sensitive data at
risk. We also showed that users are vulnerable to a new class of
attacks that we call cross-device permission phishing attacks on
wearable devices. Lastly, we performed a preliminary study on
other watch platforms (i.e., Apple’s watchOS, Fitbit, Garmin
OS) and found that all these platforms suffer from similar
privacy issues. As countermeasures for the potential privacy
violations in cross-device apps, we suggest improvements in the
system prompts and the permission model to enable users to
make better-informed decisions, as well as on app markets to
identify malicious cross-device data flows.
View details
CALRec: Contrastive Alignment of Generative LLMs For Sequential Recommendation
Keyi Yu
18th ACM Conference on Recommender Systems (RecSys 2024) (2024) (to appear)
Preview abstract
Personalized recommendation requires understanding both the candidate items and user preferences. Traditional collaborative filtering approaches rely on embedding users and items in the same representation space while recent efforts formulate the problem into sequential user activity modeling and future activity prediction tasks. Some of the most recent efforts leverage autoregressive large language models to directly generate the recommendation. This work proposes CALRec, a sequential recommendation framework aligning the generative task based on PaLM-2 LLM with contrastive learning tasks for user/item understanding. To leverage the strong generalization capabilities of the state-of-the-art pretrained LLMs, our input consists of pure texts following differentiable text templates for user inputs and item inputs. We propose novel ways of combining generative loss and contrastive losses in multi-category joint continuous pretraining, followed by domain-specific finetuning. During training, the LLM backbone trains in a two-tower fashion to comprehend users’ consecutive behaviors and descriptions of individual items. Our model outperforms many state-of-the-art baselines significantly especially in ranking tasks. Our systematic ablation study reveals that (i) multi-category pretraining and domain-adaptation finetuning are both important and deliver better performance when combined, and (ii) contrastive alignment further improves the quality among many categories of the Amazon review dataset.
View details
Preview abstract
We present an approach to modeling an image-space prior on scene motion. Our prior is learned from a collection of motion trajectories extracted from real video sequences depicting natural, oscillatory dynamics such as trees, flowers, candles, and clothes swaying in the wind. We model this dense, long-term motion prior in the Fourier domain:given a single image, our trained model uses a frequency-coordinated diffusion sampling process to predict a spectral volume, which can be converted into a motion texture that spans an entire video. Along with an image-based rendering module, these trajectories can be used for a number of downstream applications, such as turning still images into seamlessly looping videos, or allowing users to realistically interact with objects in real pictures by interpreting the spectral volumes as image-space modal bases, which approximate object dynamics.
View details
Preview abstract
This is an invited OFC 2024 conference workshop talk regarding a new type of lower-power datacenter optics design choice: linear pluggable optics. In this talk I will discuss the fundamental performance constraints facing linear pluggable optics and their implications on DCN and ML use cases
View details
Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns
Ariel Goldstein
Avigail Grinstein-Dabush
Haocheng Wang
Zhuoqiao Hong
Bobbi Aubrey
Samuel A. Nastase
Zaid Zada
Eric Ham
Harshvardhan Gazula
Eliav Buchnik
Werner Doyle
Sasha Devore
Patricia Dugan
Roi Reichart
Daniel Friedman
Orrin Devinsky
Adeen Flinker
Uri Hasson
Nature Communications (2024)
Preview abstract
Contextual embeddings, derived from deep language models (DLMs), provide
a continuous vectorial representation of language. This embedding space
differs fundamentally from the symbolic representations posited by traditional
psycholinguistics. We hypothesize that language areas in the human brain,
similar to DLMs, rely on a continuous embedding space to represent language.
To test this hypothesis, we densely record the neural activity patterns in the
inferior frontal gyrus (IFG) of three participants using dense intracranial arrays
while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derive a continuous vectorial representation
for each word (i.e., a brain embedding) in each patient. We demonstrate that
brain embeddings in the IFG and the DLM contextual embedding space have
common geometric patterns using stringent zero-shot mapping. The common
geometric patterns allow us to predict the brain embedding of a given left-out
word in IFG based solely on its geometrical relationship to other nonoverlapping words in the podcast. Furthermore, we show that contextual
embeddings better capture the geometry of IFG embeddings than static word
embeddings. The continuous brain embedding space exposes a vector-based
neural code for natural language processing in the human brain.
View details
Securing the AI Software Supply Chain
Isaac Hepworth
Kara Olive
Kingshuk Dasgupta
Michael Le
Mark Lodato
Mihai Maruseac
Sarah Meiklejohn
Shamik Chaudhuri
Tehila Minkus
Google, Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043 (2024)
Preview abstract
As AI-powered features gain traction in software applications, we see many of the same problems we’ve faced with traditional software—but at an accelerated pace. The threat landscape continues to expand as AI is further integrated into everyday products, so we can expect more attacks. Given the expense of building models, there is a clear need for supply chain solutions.
This paper explains our approach to securing our AI supply chain using provenance information and provides guidance for other organizations. Although there are differences between traditional and AI development processes and risks, we can build on our work over the past decade using Binary Authorization for Borg (BAB), Supply-chain Levels for Software Artifacts (SLSA), and next-generation cryptographic signing solutions via Sigstore, and adapt these to the AI supply chain without reinventing the wheel. Depending on internal processes and platforms, each organization’s approach to AI supply chain security will look different, but the focus should be on areas where it can be improved in a relatively short time.
Readers should note that the first part of this paper provides a broad overview of “Development lifecycles for traditional and AI software”. Then we delve specifically into AI supply chain risks, and explain our approach to securing our AI supply chain using provenance information. More advanced practitioners may prefer to go directly to the sections on “AI supply chain risks,” “Controls for AI supply chain security,” or even the “Guidance for practitioners” section at the end of the paper, which can be adapted to the needs of any organization.
View details
Sharing is leaking: blocking transient-execution attacks with core-gapped confidential VMs
Charly Castes
29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4 (ASPLOS '24) (2024)
Preview abstract
Confidential VMs on platforms such as Intel TDX, AMD SEV and Arm CCA promise greater security for cloud users against even a hypervisor-level attacker, but this promise has been shattered by repeated transient-execution vulnerabilities and CPU bugs. At the root of this problem lies the need to multiplex CPU cores with all their complex microarchitectural state among distrusting entities, with an untrusted hypervisor in control of the multiplexing.
We propose core-gapped confidential VMs, a set of software-only modifications that ensure that no distrusting code shares a core, thus removing all same-core side-channels and transient-execution vulnerabilities from the guest’s TCB. We present an Arm-based prototype along with a performance evaluation showing that, not only does core-gapping offer performance competitive with non-confidential VMs, the greater locality achieved by avoiding shared cores can even improve performance for CPU-intensive workloads.
View details
Characterizing a Memory Allocator at Warehouse Scale
Zhuangzhuang Zhou
Nilay Vaish
Patrick Xia
Christina Delimitrou
Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3, Association for Computing Machinery, La Jolla, CA, USA (2024), 192–206
Preview abstract
Memory allocation constitutes a substantial component of warehouse-scale computation. Optimizing the memory allocator not only reduces the datacenter tax, but also improves application performance, leading to significant cost savings.
We present the first comprehensive characterization study of TCMalloc, a warehouse-scale memory allocator used in our production fleet. Our characterization reveals a profound diversity in the memory allocation patterns, allocated object sizes and lifetimes, for large-scale datacenter workloads, as well as in their performance on heterogeneous hardware platforms. Based on these insights, we redesign TCMalloc for warehouse-scale environments. Specifically, we propose optimizations for each level of its cache hierarchy that include usage-based dynamic sizing of allocator caches, leveraging hardware topology to mitigate inter-core communication overhead, and improving allocation packing algorithms based on statistical data. We evaluate these design choices using benchmarks and fleet-wide A/B experiments in our production fleet, resulting in a 1.4% improvement in throughput and a 3.4% reduction in RAM usage for the entire fleet. At our scale, even a single percent CPU or memory improvement translates to significant savings in server costs.
View details
Preview abstract
2022 marked the 50th anniversary of memory safety vulnerabilities, first reported by Anderson et al. Half a century later, we are still dealing with memory safety bugs despite substantial investments to improve memory unsafe languages.
Like others', Google’s data and internal vulnerability research show that memory safety bugs are widespread and one of the leading causes of vulnerabilities in memory-unsafe codebases. Those vulnerabilities endanger end users, our industry, and the broader society.
At Google, we have decades of experience addressing, at scale, large classes of vulnerabilities that were once similarly prevalent as memory safety issues. Based on this experience we expect that high assurance memory safety can only be achieved via a Secure-by-Design approach centered around comprehensive adoption of languages with rigorous memory safety guarantees.
We see no realistic path for an evolution of C++ into a language with rigorous memory safety guarantees that include temporal safety. As a consequence, we are considering a gradual transition of C++ code at Google towards other languages that are memory safe.
Given the large volume of pre-existing C++, we believe it is nonetheless necessary to improve the safety of C++ to the extent practicable. We are considering transitioning to a safer C++ subset, augmented with hardware security features like MTE.
View details
