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 10132 publications
BigLake: BigQuery’s Evolution toward a Multi-Cloud Lakehouse
Justin Levandoski
Garrett Casto
Mingge Deng
Rushabh Desai
Thibaud Hottelier
Amir Hormati
Jeff Johnson
Dawid Kurzyniec
Prem Ramanathan
Gaurav Saxena
Vidya Shanmugam
Yuri Volobuev
SIGMOD (2024)
Preview abstract
BigQuery’s cloud-native disaggregated architecture has allowed Google Cloud to evolve the system to meet several customer needs across the analytics and AI/ML workload spectrum. A key customer requirement for BigQuery centers around the unification of data lake and enterprise data warehousing workloads. This approach combines: (1) the need for core data management primitives, e.g., security, governance, common runtime metadata, performance acceleration, ACID transactions, provided by an enterprise data warehouses coupled with (2) harnessing the flexibility of the open source format and analytics ecosystem along with new workload types such as AI/ML over unstructured data on object storage. In addition, there is a strong requirement to support BigQuery as a multi-cloud offering given cloud customers are opting for a multi-cloud footprint by default.
This paper describes BigLake, an evolution of BigQuery toward a multi-cloud lakehouse to address these customer requirements in novel ways. We describe three main innovations in this space. We first present BigLake tables, making open-source table formats (e.g., Apache Parquet, Iceberg) first class citizens, providing fine-grained governance enforcement and performance acceleration over these formats to BigQuery and other open-source analytics engines. Next, we cover the design and implementation of BigLake Object tables that allow BigQuery to integrate AI/ML for inferencing and processing over unstructured data. Finally, we present Omni, a platform for deploying BigQuery on non-GCP clouds, focusing on the infrastructure and operational innovations we made to provide an enterprise lakehouse product regardless of the cloud provider hosting the data.
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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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DOCCI: Descriptions of Connected and Contrasting Images
Garrett Tanzer
Jaemin Cho
Su Wang
Sunayana Rane
Zack Berger
Zarana Parekh
(2024)
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Despite recent advancements, text-to-image (T2I) models still exhibit critical limitations, such as errors in understanding spatial relationships, object counting, text rendering, and more. One challenge in overcoming these failure modes is the lack of resources; the majority of existing image-text datasets provide only brief captions that do not offer sufficient detail to discrepancies between images and their descriptions. To advance the development of T2I models further, we introduce \textbf{Descriptions of Connected and Contrasting Images (DOCCI)}, a dataset of 15k images taken by a single person with detailed human-annotated descriptions in English. We meticulously annotated detailed and coherent descriptions, averaging 136 words, which sufficiently differentiate images from related or similar ones. We intentionally curated images that showcase a diverse range of visual properties, including entities with their attributes, various orientations, and lighting effects, many of which are related to each other. We thoroughly analyze the quality and characteristics of the image-description pairs, and assess the performance of the latest T2I and I2T models. The experimental results indicate that the current state-of-the-art T2I models still struggle with the aforementioned challenges, and even the SOTA models have not fully addressed them. DOCCI is publicly available, and we believe that this dataset will be a valuable benchmark for vision-language research.
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SQL Has Problems. We Can Fix Them: Pipe Syntax In SQL
Shannon Bales
Matthew Brown
Jean-Daniel Browne
Brandon Dolphin
Romit Kudtarkar
Andrey Litvinov
Jingchi Ma
John Morcos
Michael Shen
David Wilhite
Xi Wu
Lulan Yu
Proc. VLDB Endow. (2024), pp. 4051-4063 (to appear)
Preview abstract
SQL has been extremely successful as the de facto standard language for working with data. Virtually all mainstream database-like systems use SQL as their primary query language. But SQL is an old language with significant design problems, making it difficult to learn, difficult to use, and difficult to extend. Many have observed these challenges with SQL, and proposed solutions involving new languages. New language adoption is a significant obstacle for users, and none of the potential replacements have been successful enough to displace SQL.
In GoogleSQL, we’ve taken a different approach - solving SQL’s problems by extending SQL. Inspired by a pattern that works well in other modern data languages, we added piped data flow syntax to SQL. The results are transformative - SQL becomes a flexible language that’s easier to learn, use and extend, while still leveraging the existing SQL ecosystem and existing userbase. Improving SQL from within allows incrementally adopting new features, without migrations and without learning a new language, making this a more productive approach to improve on standard SQL.
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Shorts vs. Regular Videos on YouTube: A Comparative Analysis of User Engagement and Content Creation Trends
Caroline Violot
Tugrulcan Elmais
Mathias Humbert
ACM Web Science Conference 2024 (WEBSCI24) (2024)
Preview abstract
YouTube introduced the Shorts video format in 2021, allowing users to upload short videos that are prominently displayed on its website and app. Despite having such a large visual footprint, there are no studies to date that have looked at the impact Shorts introduction had on the production and consumption of content on YouTube.
This paper presents the first comparative analysis of YouTube Shorts versus regular videos with respect to user engagement (i.e., views, likes, and comments), content creation frequency and video categories. We collected a dataset containing information about 70k channels that posted at least one Short, and we analyzed the metadata of all the videos (9.9M Shorts and 6.9M regular videos) they uploaded between January 2021 and December 2022, spanning a two-year period including the introduction of Shorts. Our longitudinal analysis shows that content creators consistently increased the frequency of Shorts production over this period, especially for newly-created channels, which surpassed that of regular videos. We also observe that Shorts target mostly entertainment categories, while regular videos cover a wide variety of categories. In general, Shorts attract more views and likes per view than regular videos, but attract less comments per view. However, Shorts do not outperform regular videos in the education and political categories as much as they do in other categories.
Our study contributes to understanding social media dynamics, to quantifying the spread of short-form content, and to motivating future research on its impact on society.
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Comparative analysis of genAI features in Business Intelligence Platforms
Aqsa Fulara
International Journal of Computer Trends and Technology, Volume 72 Issue 4, 95-101, April 2024 (2024)
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The study is a comparative analysis of generative AI capabilities and their applications in BI plaforms. The rapid advancement here has opened new frontiers for data driven decision making and insights generation. However, integration in BI tools is largely unexplored in academia. The findings reveal significant variations in approach taken by different BI tools for similar genAI tasks.
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Advances in deep learning systems have allowed large models to match or surpass human accuracy on a number of skills such as image classification, basic programming, and standardized test taking. As the performance of the most capable models begin to saturate on tasks where humans already achieve high accuracy, it becomes necessary to benchmark models on increasingly complex abilities. One such task is forecasting the future outcome of events. In this work we describe experiments using a novel dataset of real world events and associated human predictions, an evaluation metric to measure forecasting ability, and the accuracy of a number of different LLM based forecasting designs on the provided dataset. Additionally, we analyze the performance of the LLM forecasters against human predictions and find that models still struggle to make accurate predictions about the future. Our follow-up experiments indicate this is likely due to models' tendency to guess that most events are unlikely to occur (which tends to be true for many prediction datasets, but does not reflect actual forecasting abilities). We reflect on next steps for developing a systematic and reliable approach to studying LLM forecasting.
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Augmentations vs Algorithms: What Works in Self-Supervised Learning
Warren Morningstar
Alex Bijamov
Chris Duvarney
Luke Friedman
Neha Kalibhat
Philip Mansfield
Renan Rojas-Gomez
Karan Singhal
Bradley Green
Sushant Prakash
Arxiv (2024) (to appear)
Preview abstract
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL). While the recent literature in this space leaves the impression that the pretraining algorithm is of critical importance to performance, understanding its effect is complicated by the difficulty in making objective and direct comparisons between methods. We propose a new framework which unifies many seemingly disparate SSL methods into a single shared template. Using this framework, we identify aspects in which methods differ and observe that in addition to changing the pretraining algorithm, many works also use new data augmentations or more powerful model architectures. We compare several popular SSL methods using our framework and find that many algorithmic additions, such as prediction networks or new losses, have a minor impact on downstream task performance (often less than 1%), while enhanced augmentation techniques offer more significant performance improvements (2−4%). Our findings challenge the premise that SSL is being driven primarily by algorithmic improvements, and suggest instead a bitter lesson for SSL: that augmentation diversity and data / model scale are more critical contributors to recent advances in self-supervised learning.
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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
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Given a training data-set $\mathcal{S}$, and a reference data-set $\mathcal{T}$, we design a simple and efficient algorithm to reweigh the loss function such that the limiting distribution of the neural network weights that result from training on $\mathcal{S}$ approaches the limiting distribution that would have resulted by training on $\mathcal{T}$. Such reweighing can be used to correct for Train-Test distribution shift, when we don't have access to the labels of $\mathcal{T}$. It can also be used to perform (soft) multi-criteria optimization on neural nets, when we have access to the labels of $\mathcal{T}$, but $\mathcal{S}$ and $\mathcal{T}$ have few common points.
As a motivating application, we train a graph neural net to recognize small molecule binders to MNK2 (a MAP Kinase, responsible for cell signaling) which are non-binders to MNK1 (a very similar protein), even in the absence of training data common to both data-sets. We are able to tune the reweighing parameters so that overall change in holdout loss is negligible, but the selectivity, i.e., the fraction of top 100 MNK2 binders that are MNK1 non-binders, increases from 54\% to 95\%, as a result of our reweighing.
We expect the algorithm to be applicable in other settings as well, since we prove that when the metric entropy of the input data-sets is bounded, our random sampling based greedy algorithm outputs a close to optimal reweighing, i.e., the two invariant distributions of network weights will be provably close in total variation distance.
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Website Data Transparency in the Browser
Sebastian Zimmeck
Daniel Goldelman
Owen Kaplan
Logan Brown
Justin Casler
Judeley Jean-Charles
Joe Champeau
24th Privacy Enhancing Technologies Symposium (PETS 2024), PETS (to appear)
Preview abstract
Data collection by websites and their integrated third parties is often not transparent. We design privacy interfaces for the browser to help people understand who is collecting which data from them. In a proof of concept browser extension, Privacy Pioneer, we implement a privacy popup, a privacy history interface, and a watchlist to notify people when their data is collected. For detecting location data collection, we develop a machine learning model based on TinyBERT, which reaches an average F1 score of 0.94. We supplement our model with deterministic methods to detect trackers, collection of personal data, and other monetization techniques. In a usability study with 100 participants 82% found Privacy Pioneer easy to understand and 90% found it useful indicating the value of privacy interfaces directly integrated in the browser.
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This paper presents a Multifunctional wearable
sensing system that integrates flexible Laser-Induced-Graphene
(LIG) based sensors and an Open-Source Analog Front-End
(AFE) chip. The LIG sensors are fabricated on polyimide (PI)
Flexible Printed Circuit Board (FPCB) through CO2 infrared
laser direct-write method. The LIG sensors provide repeatable
high-precision temperature sensing, humidity measurement, and
strain detection capabilities. The temperature sensing charac-
terization shows the resistive LIG sensor has a sensitivity of
-0.0493 %/°C, the linear fit R-square factors ≥ 0.9973 across -40
°C to 125 °C. The capacitive humidity sensor achieves a 23.6
times capacitance at 95% relative humidity (RH) compared to
the value observed in a dry environment. Our proposed AFE
chip contains a hybrid folded-cascode Operational Amplifier
(OPAMP) and a Successive Approximation Register Analog-
to-Digital Converter (SAR ADC). Designed using open-source
analog flow and fabricated in GF180 OpenPDK, the AFE chip
serves as a flexible and universal readout platform, adaptable for
various sensing applications. A real-time demonstration of finger
bending detection is performed to validate the functionality.
The multifunctional sensing capability provide by the wearable
system is attractive for personal healthcare application. This
work underscores the integration of the LIG sensors and the
AFE chip, developed using open-source tools which facilitate
rapid and affordable prototyping for a multifunctional flexible
wearable sensing system.
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Background. Wildfire research uses ensemble methods to analyze fire behaviors and assess
uncertainties. Nonetheless, current research methods are either confined to simple models
or complex simulations with limits. Modern computing tools could allow for efficient, high-
fidelity ensemble simulations. Aims. This study proposes a high-fidelity ensemble wildfire
simulation framework for studying wildfire behavior, ML tasks, fire-risk assessment, and
uncertainty analysis. Methods. In this research, we present a simulation framework that
integrates the Swirl-Fire large-eddy simulation tool for wildfire predictions with the Vizier
optimization platform for automated run-time management of ensemble simulations and
large-scale batch processing. All simulations are executed on tensor-processing units to
enhance computational efficiency. Key results. A dataset of 117 simulations is created,
each with 1.35 billion mesh points. The simulations are compared to existing experimental
data and show good agreement in terms of fire rate of spread. Computations are done for
fire acceleration, mean rate of spread, and fireline intensity. Conclusions. Strong coupling
between these 2 parameters are observed for the fire spread and intermittency. A critical
Froude number that delineates fires from plume-driven to convection-driven is identified and
confirmed with literature observations. Implications. The ensemble simulation framework
is efficient in facilitating parametric wildfire studies.
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We describe a quantum algorithm for the Planted Noisy kXOR problem (also known as sparse Learning Parity with Noise) that achieves a nearly quartic (4th power) speedup over the best known classical algorithm while also only using logarithmically many qubits. Our work generalizes and simplifies prior work of Hastings, by building on his quantum algorithm for the Tensor Principal Component Analysis (PCA) problem. We achieve our quantum speedup using a general framework based on the Kikuchi Method (recovering the quartic speedup for Tensor PCA), and we anticipate it will yield similar speedups for further planted inference problems. These speedups rely on the fact that planted inference problems naturally instantiate the Guided Sparse Hamiltonian problem. Since the Planted Noisy kXOR problem has been used as a component of certain cryptographic constructions, our work suggests that some of these are susceptible to super-quadratic quantum attacks.
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Chain-of-Table: Evolves Tables in the LLM Reasoning Chain for Table Understanding
Zilong Wang
Hao Zhang
Chun-Liang Li
Jingbo Shang
ICLR (2024)
Preview abstract
Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices.
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