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 10464 publications
Silent Data Corruption by 10× Test Escapes Threatens Reliable Computing
Rama Govindaraju
Eric Liu
Subhasish Mitra
Mike Fuller
IEEE (2025) (to appear)
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Summary:
Silent Data Corruption by 10x Test Escapes Threatens Reliable Computing" highlights a critical issue: manufacturing defects, dubbed "test escapes," are evading current testing methods at an alarming rate, ten times higher than industry targets. These defects lead to Silent Data Corruption (SDC), where applications produce incorrect outputs without error indications, costing companies significantly in debugging, data recovery, and service disruptions. The paper proposes a three-pronged approach: quick diagnosis of defective chips directly from system-level behaviors, in-field detection using advanced testing and error detection techniques like CASP, and new, rigorous test experiments to validate these solutions and improve manufacturing testing practices.
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Fast electronic structure quantum simulation by spectrum amplification
Guang Hao Low
Robbie King
Dominic Berry
Qiushi Han
Albert Eugene DePrince III
Alec White
Rolando Somma
arXiv:2502.15882 (2025)
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The most advanced techniques using fault-tolerant quantum computers to estimate the ground-state energy of a chemical Hamiltonian involve compression of the Coulomb operator through tensor factorizations, enabling efficient block-encodings of the Hamiltonian. A natural challenge of these methods is the degree to which block-encoding costs can be reduced. We address this challenge through the technique of spectrum amplification, which magnifies the spectrum of the low-energy states of Hamiltonians that can be expressed as sums of squares. Spectrum amplification enables estimating ground-state energies with significantly improved cost scaling in the block encoding normalization factor $\Lambda$ to just $\sqrt{2\Lambda E_{\text{gap}}}$, where $E_{\text{gap}} \ll \Lambda$ is the lowest energy of the sum-of-squares Hamiltonian. To achieve this, we show that sum-of-squares representations of the electronic structure Hamiltonian are efficiently computable by a family of classical simulation techniques that approximate the ground-state energy from below. In order to further optimize, we also develop a novel factorization that provides a trade-off between the two leading Coulomb integral factorization schemes-- namely, double factorization and tensor hypercontraction-- that when combined with spectrum amplification yields a factor of 4 to 195 speedup over the state of the art in ground-state energy estimation for models of Iron-Sulfur complexes and a CO$_{2}$-fixation catalyst.
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An Empirical Study of Time of Day Breakpoints in Traffic Light Plans
Eliav Buchnik
Tom Kalvari
Jack Haddad
Dan Karliner
Danny Veikherman
Shai Ferster
Ori Rottenstreich
2025
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Fixed time strategy is a common approach in signal traffic control in which signal plans are simple and periodic, enjoying easy implementation without detection mechanisms. A traffic light is associated with several daily plans, each applied to several consecutive hours. Time-of-day breakpoints (TODs) refer to the times over the day in which the plan is changed. TODs are often selected based on traffic, aiming to divide the day into groups of consecutive hours with similar traffic characteristics within each group of hours. We present a methodology to study time-of-day breakpoints in practice. We use this methodology to estimate and analyze time-of-day breakpoints in the city of Rio de Janeiro, Brazil based on traffic properties derived from traffic trajectories. Our study examines over 900 of the city intersections. We refer to properties such as the number of daily plans and the times by which plans start. We also provide traffic-aware insights on the potential improvement in the selection of TODs and identify key intersections where adjusting TODs could reduce average delay times. We identify potential improvements in over 8% of the examined intersections. These findings provide valuable insights for traffic engineers seeking to optimize signal timing.
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Virtual hand representation in Head-Mounted Displays (HMDs) offers immersive and intuitive interactions in Virtual Reality (VR). However, current hand tracking algorithms are prone to errors, which can disrupt the user experience and hinder task performance. This paper presents a novel method for providing users with visual feedback when the quality of hand tracking decreases. Our approach employs a notification modal that warns users of potential failures. We identified three common hand tracking failure scenarios and evaluated the effectiveness of our method in two distinct VR tasks: object manipulation and complex assembly tasks. Results show that our early warning system reduces task completion time, lowers hand-tracking failures by up to 83%, decreases errors, improves system usability, and reduces cognitive load. This work contributes to the development of more robust and user-friendly VR HMD applications by enhancing hand tracking reliability, usability, and workload.
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Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
Fei Wang
The Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) (2025) (to appear)
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Retrieval-Augmented Generation (RAG), while effective in integrating external knowledge to address the limitations of large language models (LLMs), can be undermined by imperfect retrieval, which may introduce irrelevant, misleading, or even malicious information. Despite its importance, previous studies have rarely explored the behavior of RAG through joint analysis on how errors from imperfect retrieval attribute and propagate, and how potential conflicts arise between the LLMs' internal knowledge and external sources. We find that imperfect retrieval augmentation might be inevitable and quite harmful, through controlled analysis under realistic conditions. We identify the knowledge conflicts between LLM-internal and external knowledge from retrieval as a bottleneck to overcome in the post-retrieval stage of RAG. To render LLMs resilient to imperfect retrieval, we propose Astute RAG, a novel RAG approach that adaptively elicits essential information from LLMs' internal knowledge, iteratively consolidates internal and external knowledge with source-awareness, and finalizes the answer according to information reliability. Our experiments using Gemini and Claude demonstrate that Astute RAG significantly outperforms previous robustness-enhanced RAG methods. Notably, Astute RAG is the only approach that matches or exceeds the performance of LLMs without RAG under worst-case scenarios. Further analysis reveals that Astute RAG effectively resolves knowledge conflicts, improving the reliability and trustworthiness of RAG systems.
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Linear Elastic Caching via Ski Rental
Todd Lipcon
The biennial Conference on Innovative Data Systems Research (2025)
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In this work we study the Linear Elastic Caching problem, where the goal is to minimize the total cost of a cache inclusive of not just its misses, but also its memory footprint integrated over time. We demonstrate a theoretical connection to the classic ski rental problem and propose a practical algorithm that combines online caching algorithms with ski rental policies. We also introduce a lightweight machine learning-based algorithm for ski rental that is optimized for production workloads and is easy to integrate within existing database systems. Evaluations on both production workloads in Google Spanner and publicly available traces show that the proposed elastic caching approach can significantly reduce the total cache cost compared to traditional fixed-size cache policies.
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Augmenting LLMs with context leads to improved performance across many applications. Despite much research on Retrieval Augmented Generation (RAG) systems, an open question is whether errors arise because LLMs fail to utilize the context from retrieval or the context itself is insufficient to answer the query. To shed light on this, we develop a new notion of sufficient context, along with a way to classify instances that have enough information to answer the query. We then use sufficient context to analyze several models and datasets. By stratifying errors based on context sufficiency, we find that proprietary LLMs (Gemini, GPT, Claude) excel at answering queries when the context is sufficient, but often output incorrect answers instead of abstaining when the context is not. On the other hand, open-source LLMs (Llama, Mistral, Gemma) hallucinate or abstain often, even with sufficient context. We further categorize cases when the context is useful, and improves accuracy, even though it does not fully answer the query and the model errs without the context. Building on our findings, we explore ways to reduce hallucinations in RAG systems, including a new selective generation method that leverages sufficient context information for guided abstention. Our method improves the fraction of correct answers among times where the model responds by 2--10% for Gemini, GPT, and Gemma.
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Capturing Real-World Habitual Sleep Patterns with a Novel User-centric Algorithm to Pre-Process Fitbit Data in the All of Us Research Program: Retrospective observational longitudinal study
Hiral Master
Jeffrey Annis
Karla Gleichauf
Lide Han
Peyton Coleman
Kelsie Full
Neil Zheng
Doug Ruderfer
Logan Schneider
Evan Brittain
Journal of Medical Internet Research (2025)
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Background:
Commercial wearables such as Fitbit quantify sleep metrics using fixed calendar times as default measurement periods, which may not adequately account for individual variations in sleep patterns. To address this limitation, experts in sleep medicine and wearable technology developed a user-centric algorithm designed to more accurately reflect actual sleep behaviors and improve the validity of wearable-derived sleep metrics.
Objective:
This study aims to describe the development of a new user-centric algorithm, compare its performance with the default calendar-relative algorithm, and provide a practical guide for analyzing All of Us Fitbit sleep data on a cloud-based platform.
Methods:
The default and user-centric algorithms were implemented to preprocess and compute sleep metrics related to schedule, duration, and disturbances using high-resolution Fitbit sleep data from 8563 participants (median age 58.1 years, 6002/8341, 71.96%, female) in the All of Us Research Program (version 7 Controlled Tier). Variations in typical sleep patterns were calculated by examining the differences in the mean number of primary sleep logs classified by each algorithm. Linear mixed-effects models were used to compare differences in sleep metrics across quartiles of variation in typical sleep patterns.
Results:
Out of 8,452,630 total sleep logs collected over a median of 4.2 years of Fitbit monitoring, 401,777 (4.75%) nonprimary sleep logs identified by the default algorithm were reclassified as primary sleep by the user-centric algorithm. Variation in typical sleep patterns ranged from –0.08 to 1. Among participants with the greatest variation in typical sleep patterns, the user-centric algorithm identified significantly more total sleep time (by 17.6 minutes; P<.001), more wake after sleep onset (by 13.9 minutes; P<.001), and lower sleep efficiency (by 2.0%; P<.001), on average. Differences in sleep stage metrics between the 2 algorithms were modest.
Conclusions:
The user-centric algorithm captures the natural variability in sleep schedules, providing an alternative approach to preprocess and evaluate sleep metrics related to schedule, duration, and disturbances. A publicly available R package facilitates the implementation of this algorithm for clinical and translational research.
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Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques
Shashank Kapoor
Aman Raj
IEEE Compsac 2025 (2025)
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Large Language Models (LLMs) are revolutionizing many areas of AI, but their substantial resource requirements limit their deployment on mobile and edge devices. This survey paper provides a comprehensive overview of techniques for compressing LLMs to enable efficient inference in resource-constrained environments. We examine three primary approaches: knowledge distillation, model quantization and model pruning. For each technique, we discuss the underlying principles, present different forms, and provide examples of successful applications. We also briefly discuss complementary techniques like mixture-of-experts and early exit strategies and highlight the promising future directions. We aim to provide a valuable resource for both researchers and practitioners seeking to optimize LLMs for edge deployment. To the best of our knowledge, this is the first paper that provides a focused survey of LLM compression techniques from the lens of resource-constrained environments.
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Ransomware over Modern Web Browsers: A Novel Strain and A New Defense Mechanism
Harun Oz
Ahmet Aris
Leonardo Babun
Selcuk Uluagac
Abbas Acar
ACM Transactions on the Web (2025)
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Ransomware is an increasingly prevalent form of malware targeting end-users, governments, and businesses. As it has evolved,
adversaries added new capabilities to their arsenal. Throughout the ransomware evolution, the adversaries propose a next-generation
browser-based ransomware, RøB, that performs its malicious actions via emerging web technologies, File System Access API (FSA) and
WebAssembly (Wasm). RøB uses this API through the victims’ browsers; hence, it does not require the victims to download and install
malicious binaries. We performed extensive evaluations with 3 different OSs, 23 file formats, 29 distinct directories, 5 cloud providers,
and 4 antivirus solutions. Our evaluations show that RøB can encrypt various types of files in the local and cloud-integrated directories,
external storage devices, and network-shared folders of victims. Our experiments also reveal that popular cloud solutions, Box
Individual and Apple iCloud can be severely affected by RøB. Moreover, we conducted tests with commercial antivirus software such
as AVG, Avast, Kaspersky, Malware Bytes that perform sensitive directory and suspicious behavior monitoring against ransomware.
We verified that RøB can evade these antivirus software and encrypt victim files. Moreover, existing ransomware detection solutions
in the literature also cannot be a remedy against RøB due to its distinct features. Therefore, in this paper, we also propose broguard,
a new detection system for RøB-like attacks. broguard monitors the web applications that use the FSA API via function hooking and
uses a machine learning classifier to detect RøB-like attacks in real-time without any file loss. Performance evaluations of broguard
on a comprehensive dataset show that broguard can detect RøB-like browser-based ransomware attacks with over 99% accuracy and
minimal overhead.
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This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the subjective evaluation processes of speech and audio codecs and discusses the limitations of purely data-driven approaches, which often require inefficiently large architectures to match the performance of model-based methods. The study presents hybrid systems as a viable solution, offering significant improvements to the performance of conventional codecs through meticulously chosen design enhancements. Specifically, it introduces a neural network-based signal enhancer designed to post-process existing codecs’ output, along with the autoencoder-based end-to-end models and LPCNet—hybrid systems that combine linear predictive coding (LPC) with neural networks. Furthermore, the paper delves into predictive models operating within custom feature spaces (TF-Codec) or predefined transform domains (MDCTNet) and examines the use of psychoacoustically calibrated loss functions to train end-to-end neural audio codecs. Through these investigations, the paper demonstrates the potential of hybrid systems to advance the field of speech and audio coding by bridging the gap between traditional model-based approaches and modern data-driven techniques.
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Society-Centric Product Innovation in the Era of Customer Obsession
International Journal of Science and Research Archive (IJSRA), Volume 14 - Issue 1 (2025)
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This article provides a comprehensive analysis of the evolving landscape of innovation in the technology sector, with a focus on the intersection of technological progress and social responsibility. The article explores key challenges facing the industry, including public trust erosion, digital privacy concerns, and the impact of automation on workforce dynamics. It investigates responsible innovation frameworks' emergence and implementation across various organizations, highlighting the transformation from traditional development approaches to more society-centric models. The article demonstrates how companies balance innovation speed with social responsibility, incorporate ethical considerations into their development processes, and address digital disparities across different demographics. By examining how companies balance the pace of innovation with ethical responsibilities, integrate social considerations into their processes, and address digital inequities across diverse demographics, the article underscores the transformative potential of these frameworks. Through insights into cross-functional teams, impact assessment tools, and stakeholder engagement strategies, it demonstrates how responsible innovation drives both sustainable business value and societal progress.
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PageFlex: Flexible and Efficient User-space Delegation of Linux Paging Policies with eBPF
Kan Wu
Zhiyuan Guo
Suli Yang
Rajath Shashidhara
Wei Xu
Alex Snoeren
Kim Keeton
2025
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To increase platform memory efficiency, hyperscalers like Google and Meta transparently demote “cold” application data to cheaper cost-per-byte memory tiers like compressed memory and NVMe SSDs. These systems rely on standard kernel paging policies and mechanisms to maximize the achievable memory savings without hurting application performance. Although the literature promises better policies, implementing and deploying them within the Linux kernel
is challenging. Delegating policies and mechanisms to user space, through userfaultfd or library-based approaches, incurs overheads and may require modifying application code. We present PageFlex, a framework for delegating Linux paging policies to user space with minimal overhead and full compatibility with existing real-world deployments. PageFlex uses eBPF to delegate policy decisions while providing low-overhead access to in-kernel memory state and access information, thus balancing flexibility and performance. Additionally, PageFlex supports different paging strategies for distinct memory regions and application phases. We show that
PageFlex can delegate existing kernel-based policies with little (< 1%) application slowdown, effectively realizing the benefits of state-of-the-art policies like Hyperbolic caching and Leap prefetching, and unlocking application-specific benefits through region- and phase-aware policy specialization.
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We revisit the fundamental question of formally defining what constitutes a reconstruction attack. While often clear from the context, our exploration reveals that a precise definition is much more nuanced than it appears, to the extent that a single all-encompassing definition may not exist. Thus, we employ a different strategy and aim to "sandwich" the concept of reconstruction attacks by addressing two complementing questions: (i) What conditions guarantee that a given system is protected against such attacks? (ii) Under what circumstances does a given attack clearly indicate that a system is not protected? More specifically,
* We introduce a new definitional paradigm -- Narcissus Resiliency -- to formulate a security definition for protection against reconstruction attacks. This paradigm has a self-referential nature that enables it to circumvent shortcomings of previously studied notions of security. Furthermore, as a side-effect, we demonstrate that Narcissus resiliency captures as special cases multiple well-studied concepts including differential privacy and other security notions of one-way functions and encryption schemes.
* We formulate a link between reconstruction attacks and Kolmogorov complexity. This allows us to put forward a criterion for evaluating when such attacks are convincingly successful.
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Browser fingerprinting is an online tracking technique that is being increasingly adopted for profiling and ad targeting purposes. While prior work has analyzed the prevalence and impact of browser fingerprinting on the Web, they have traditionally relied on large-scale automated crawls. Naturally, these cannot replicate real-human interactions, e.g., solve CAPTCHAs, evade bot detectors, or operate behind login pages and paywalls. This prompts the question as to whether or not the fingerprinting ecosystem is appreciably different in real-world browsing sessions. In this paper, we begin to address this question by designing and conducting a user study aimed at collecting actual telemetry data from real browsing sessions of 30 users.
We find that almost half of the fingerprinting websites identified from real user browsing sessions are missed by equivalent automated crawls. This is mainly due to the inability of automated crawls to identify and visit authentication pages, being blocked by bot detectors, and/or failing to perform user interactions that specifically trigger browser fingerprinting scripts. We also find new fingerprinting vectors that are consistently present in fingerprinting scripts captured by real user browsing sessions yet missing from automated crawls. Finally, we assess the feasibility of collecting fingerprinting training data in a privacy-preserving way. We conclude that private models built on real user browsing sessions can detect browser fingerprinting more effectively than models trained on automated crawls alone, while simultaneously providing strong privacy guarantees to users.
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