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 10193 publications
PreFix: Optimizing the Performance of Heap-Intensive Applications
Chaitanya Mamatha Ananda
Rajiv Gupta
Han Shen
CGO 2025: International Symposium on Code Generation and Optimization, Las Vegas, NV, USA (to appear)
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Analyses of heap-intensive applications show that a small fraction of heap objects account for the majority of heap accesses and data cache misses. Prior works like HDS and HALO have shown that allocating hot objects in separate memory regions can improve spatial locality leading to better application performance. However, these techniques are constrained in two primary ways, limiting their gains. First, these techniques have Imperfect Separation, polluting the hot memory region with several cold objects. Second, reordering of objects across allocations is not possible as the original object allocation order is preserved. This paper presents a novel technique that achieves near perfect separation of hot objects via a new context mechanism that efficiently identifies hot objects with high precision. This technique, named PreFix, is based upon Preallocating memory for a Fixed small number of hot objects. The program, guided by profiles, is instrumented to compute context information derived from
dynamic object identifiers, that precisely identifies hot object allocations that are then placed at predetermined locations in the preallocated memory. The preallocated memory region for hot objects provides the flexibility to reorder objects across allocations and allows colocation of objects that are part of a hot data stream (HDS), improving spatial locality. The runtime overhead of identifying hot objects is not significant as this optimization is only focused on a small number of static hot allocation sites and dynamic hot objects. While there is an increase in the program’s memory foot-print, it is manageable and can be controlled by limiting the size of the preallocated memory. In addition, PreFix incorporates an object recycling optimization that reuses the same preallocated space to store different objects whose lifetimes are not expected to overlap. Our experiments with 13 heap-intensive applications yields reductions in execution times ranging from 2.77% to 74%. On average PreFix reduces execution time by 21.7% compared to 7.3% by HDS and 14% by HALO. This is due to PreFix’s precision in hot object identification, hot object colocation, and low runtime overhead.
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Storage on Android has evolved significantly over the years, with each new Android version introducing changes aimed at enhancing usability, security, and privacy. While these updates typically help with restricting app access to storage through various mechanisms, they may occasionally introduce new complexities and vulnerabilities. A prime example is the introduction of scoped storage in Android 10, which fundamentally changed how apps interact with files. While intended to enhance user privacy by limiting broad access to shared storage, scoped storage has also presented developers with new challenges and potential vulnerabilities to address. However, despite its significance for user privacy and app functionality, no systematic studies have been performed to study Android’s scoped storage at depth from a security perspective. In this paper, we present the first systematic security analysis of the scoped storage mechanism. To this end, we design and implement a testing tool, named ScopeVerif, that relies on differential analysis to uncover security issues and implementation inconsistencies in Android’s storage. Specifically, ScopeVerif takes a list of security properties and checks if there are any file operations that violate any security properties defined in the official Android documentation. Additionally, we conduct a comprehensive analysis across different Android versions as well as a cross-OEM analysis to identify discrepancies in different implementations and their security implications. Our study identifies both known and unknown issues of scoped storage. Our cross-version analysis highlights undocumented changes as well as partially fixed security loopholes across versions. Additionally, we discovered several vulnerabilities in scoped storage implementations by different OEMs. These vulnerabilities stem from deviations from the documented and correct behavior, which potentially poses security risks. The affected OEMs and Google have acknowledged our findings and offered us bug bounties in response.
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We study the existence of almost fair and near-optimal solutions to a routing problem as defined in the seminal work of Rosenthal. We focus on the setting where multiple alternative routes are available for each potential request (which corresponds to a potential user of the network). This model captures a collection of diverse applications such as packet routing in communication networks, routing in road networks with multiple alternative routes, and the economics of transportation of goods.
Our recommended routes have provable guarantees in terms of both the total cost and fairness concepts such as approximate envy-freeness. We employ and appropriately combine tools from algorithmic game theory and fair division. Our results apply on two distinct models: the splittable case where the request is split among the selected paths (e.g., routing a fleet of trucks) and the unsplittable case where the request is assigned to one of its designated paths (e.g., a single user request). Finally, we conduct an empirical analysis to test the performance of our approach against simpler baselines using the real world road network of New York City.
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Databases in the Era of Memory-Centric Computing
Anastasia Ailamaki
Lawrence Benson
Helena Caminal
Jana Gičeva
Eric Seldar
Lisa Wu Wills
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The increasing disparity between processor core counts and memory bandwidth, coupled with the rising cost and underutilization of memory, introduces a performance and cost Memory Wall and presents a significant challenge to the scalability of database systems. We argue that current processor-centric designs are unsustainable, and we advocate for a shift towards memory-centric computing, where disaggregated memory pools enable cost-effective scaling and robust performance. Database systems are uniquely positioned to leverage memory-centric systems because of their intrinsic data-centric nature. We demonstrate how memory-centric database operations can be realized with current hardware, paving the way for more efficient and scalable data management in the cloud.
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The problem of contract design addresses the challenge of moral hazard in principle-agent setups. The agent exerts costly efforts that produce a random outcome with an associated reward for the principal. Moral hazard refers to the tension that the principal cannot observe the agent’s effort level hence needs to incentivize the agent only through rewarding the realized effort outcome, i.e., the contract. Bayesian contract design studies the principal’s design problem of an optimal contract when facing an unknown agent characterized by a private Bayesian type. In its most general form, the agent’s type is inherently “multi-parameter” and can arbitrarily affect both the agent’s productivity and effort costs. In contrast, a natural single-parameter setting of much recent interest simplifies the agent’s type to a single value that describes the agent’s cost per unit of effort, whereas agents’ efforts are assumed to be equally
productive.
The main result of this paper is an almost approximation-preserving polynomial-time reduction from the most general multi-parameter Bayesian contract design (BCD) to single-parameter BCD. That is, for any multi-parameter BCD instance I^M, we construct a single-parameter instance I^S such that any β-approximate contract (resp. menu of contracts) of I^S can in turn be converted to a (β − ϵ)-approximate contract (resp. menu of contracts) of I^M. The reduction is in time polynomial in the input size and log(1/ϵ); moreover, when β = 1 (i.e., the given single-parameter solution is exactly optimal), the dependence on 1/ϵ can be removed, leading to a polynomial-time exact reduction. This efficient reduction is somewhat surprising because in the closely related problem of Bayesian mechanism design, a polynomial-time reduction from multi-parameter to single-parameter setting is believed to not exist. Our result demonstrates the intrinsic difficulty of addressing moral hazard in Bayesian contract design, regardless of being single-parameter or multi-parameter.
As byproducts, our reduction answers two open questions in recent literature of algorithmic contract design: (a) it implies that optimal contract design in single-parameter BCD is not in APX unless P=NP even when the agent’s type distribution is regular, answering the open question of [3] in the negative; (b) it implies that the principal’s (order-wise) tight utility gap between using a menu of contracts and a single contract is Θ(n) where n is the number of actions, answering the major open question of [27] for the single-parameter case.
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Today’s smartphone interactions are typically designed with one primary preset, accompanied by customization settings that can be manually adjusted. To promote the creation of contextually aware experiences, researchers have highlighted the factors that influence mobile device usage in the ability-based design framework. This paper expands upon existing frameworks and contributes to an empirical understanding of smartphone accessibility. Through a 10-day longitudinal diary study and video interview with 24 individuals who do and do not identify as having a disability, the research also illustrates the reactions of reattempt, adaptation, and avoidance, which were used in response to a lack of smartphone accessibility. Despite experiencing scenarios where accessibility settings could be leveraged, 20 out of 24 participants did not use accessibility settings on their smartphone. A total of 12 out of 24 participants tried accessibility settings on their smartphones, however identifying accessibility was not for them. This work highlights the need to shift current design practices to better serve the accessibility community.
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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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Gemini & Physical World: Large Language Models Can Estimate the Intensity of Earthquake Shaking from Multi-Modal Social Media Posts
Marc Stogaitis
Tajinder Gadh
Richard Allen
Alexei Barski
Robert Bosch
Patrick Robertson
Youngmin Cho
Nivetha Thiruverahan
Aman Raj
Geophysical Journal International (2025), ggae436
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This paper presents a novel approach for estimating the ground shaking intensity using real-time social media data and CCTV footage. Employing the Gemini 1.5 Pro’s (Reid et al. 2024) model, a multi-modal language model, we demonstrate the ability to extract relevant information from unstructured data utilizing generative AI and natural language processing. The model’s output, in the form of Modified Mercalli Intensity (MMI) values, align well with independent observational data. Furthermore, our results suggest that beyond its advanced visual and auditory understanding abilities, Gemini appears to utilize additional sources of knowledge, including a simplified understanding of the general relationship between earthquake magnitude, distance, and MMI intensity, which it presumably acquired during its training, in its reasoning and decision-making processes. These findings raise intriguing questions about the extent of Gemini's general understanding of the physical world and its phenomena. Gemini’s ability to generate results consistent with established scientific knowledge highlights the potential of LLMs like Gemini in augmenting our understanding of complex physical phenomena such as earthquakes. More specifically, the results of this study highlight the potential of LLMs like Gemini to revolutionize citizen seismology by enabling rapid, effective, and flexible analysis of crowdsourced data from eyewitness accounts for assessing earthquake impact and providing crisis situational awareness. This approach holds a great promise for improving early warning systems, disaster response, and overall resilience in earthquake-prone regions. This study provides a significant step toward harnessing the power of social media and AI for earthquake disaster mitigation.
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WindowMirror is a framework for using XR headsets in productivity scenarios. The toolkit provides users with a simulated, extended screen real-estate. It allows users to interact with multiple desktop applications in real-time within a XR environment. Our architecture has two main modules: one a Unity package and a Python backend, which makes it easy to use and extend. WindowMirror supports traditional desktop interaction methods such as mouse, keyboard, and hand tracking. Furthermore, it features a Cylindrical Window Layout, an emerging design pattern which is particularly effective for single-user, egocentric perspectives. The introduction of WindowMirror aims to set a foundation for future research in XR screen-focused productivity scenarios.
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BEYOND THE CODE: AI REGULATIONS AS THE SECRET COMPASS OF ENGINEERING MANAGERS
Proceedings of the American Society for Engineering Management 2024 International Annual Conference (2024)
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Technology is a product of society. As technology evolves, the norms governing it have to mature for enabling its proper use within the society. The interest in Artificial Intelligence (AI) has surged following the introduction of chatGPT. Firms, both large and small, are competing to develop new products and solutions involving AI. Amidst these developments, leading corporations such as Google and Microsoft have proactively committed to upholding responsible innovation in AI development. Governments worldwide are responding with the creation of guidelines and regulations in the field. Notably, in March 2024, the United Nations General Assembly (UNGA) adopted landmark regulation on AI.
At the heart of these developments in AI are engineering managers who leverage technical advances to build products and services that create value. To effectively harness AI for human benefit, engineering managers must be aware of these evolving regulations governing AI. Some regulations such as Digital Markets Act (DMA) and General Data Protection Regulations (GDPR) have far reaching consequences for organizations globally. Having a working knowledge of these statutory requirements will enable engineering managers to identify the opportunities and constraints in leveraging AI technology while building products and services. It will allow them to make informed decisions about data collection methods, model training processes, the deployment of AI systems and metrics for their evaluation. At scale, it can become a competitive advantage for the firms they work in, as explored through real-world examples in this paper.
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Efficient Language Model Architectures for Differentially Private Federated Learning
Yanxiang Zhang
Privacy Regulation and Protection in Machine Learning Workshop at ICLR 2024 (2024) (to appear)
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Cross-device federated learning (FL) is a technique that trains a model on data distributed across typically millions of edge devices without data ever leaving the devices.
SGD is the standard client optimizer for on device training in cross-device FL, favored for its memory and computational efficiency.
However, in centralized training of neural language models, adaptive optimizers are preferred as they offer improved stability and performance.
In light of this, we ask if language models can be modified such that they can be efficiently trained with SGD client optimizers and answer this affirmatively.
We propose a scale-invariant \emph{Coupled Input Forget Gate} (SI CIFG) recurrent network by modifying the sigmoid and tanh activations in the recurrent cell
and show that this new model converges faster and achieves better utility than the standard CIFG recurrent model in cross-device FL in large scale experiments.
We further show that the proposed scale invariant modification also helps in federated learning of larger transformer models.
Finally, we demonstrate the scale invariant modification is also compatible with other non-adaptive algorithms.
Particularly, our results suggest an improved privacy utility trade-off in federated learning with differential privacy.
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Open Se Cura: First Silicon Results of an Auditable and Transparent Hardware Root of Trust System using Open EDA in 16-nm
Guanchen Tao
Ming-Hung Chen
Bangfei Pan
Kai Yick
Dennis Sylvester
Mehdi Saligane
IEEE Solid-State Circuits Magazine, 16(2024), pp. 58-66
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Hardware root of trust (HRoT) is essential for IoT devices as it provides critical user data protection. However, each novel use case significantly lengthens the development time for an HRoT system. Furthermore, most HRoT solutions are proprietary, and users lack permission to inspect and audit such systems [1] , [2] . This article introduces Open Se Cura, which is an open source framework designed to expedite the implementation of secure and transparent HRoT systems. The platform grants designers the flexibility to choose their preferred electronic design automation (EDA) tools. They can opt for proprietary EDA solutions or select from open source alternatives, including OpenROAD [3] , [4] , using the OpenFASOC framework [5] , [6] . Additionally, the platform supports the use of open source process design kits (PDKs) to present a transparent and auditable approach to hardware–software co-design. This approach enables fast and trustworthy HRoT system implementation and is openly available to reproduce its results and security efficacy [7] . The extended version of the Open Se Cura reference design is showcased through FPGA emulation and its 22-nm ASIC implementation. We finally present the first measurement results of a 16-nm silicon implementation of selected components from OpenTitan, the security RoT hardware building block of Open Se Cura. This work was integrated using OpenFASOC’s modular flow, which allows one to call for open tools, such as OpenROAD, for physical design and closed tools for the missing open source EDA in 16 nm.
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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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Generalizing Tree-Level Sap Flow Across the European Continent
Ralf Loritz
Chen Huan Wu
Daniel Klotz
Martin Gauch
Frederik Kratzert
Maoya Bassiouni
Geophysical Research Letters (2024)
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Sap flow offers key insights about transpiration dynamics and forest-climate interactions. Accurately simulating sap flow remains challenging due to measurement uncertainties and interactions between global and local environmental controls. Addressing these complexities, this study leveraged Long Short-Term Memory networks (LSTMs) with SAPFLUXNET to predict hourly tree-level sap flow across Europe. We built models with diverse training sets to assess performance under previously unseen conditions. The average Kling-Gupta Efficiency was 0.77 for models trained on 50% of time series across all forest stands, and 0.52 for models trained on 50% of the forest stands. Continental models not only matched but surpassed the performance of specialized and baselines for all genera and forest types, showcasing the capacity of LSTMs to effectively generalize across tree genera, climates, and forest ecosystems given minimal inputs. This study underscores the potential of LSTMs in generalizing state-dependent ecohydrological processes and bridging tree level measurements to continental scales.
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Connected TV (CTV) devices blend characteristics of digital desktop and mobile devices--such as the option to log in and the ability to access a broad range of online content--and linear TV--such as a living room experience that can be shared by multiple members of a household. This blended viewing experience requires the development of measurement methods that are adapted to this novel environment. For other devices, ad measurement and planning have an established history of being guided by the ground truth of panels composed of people who share their device behavior. A CTV panel-only measurement solution for reach is not practical due to the panel size that would be needed to accurately measure smaller digital campaigns. Instead, we generalize the existing approach used to measure reach for other devices that combines panel data with other data sources (e.g., ad server logs, publisher-provided self-reported demographic data, survey data) to account for co-viewing. This paper describes data from a CTV panel and shows how this data can be used to effectively measure the aggregate co-viewing rate and fit demographic models that account for co-viewing behavior. Special considerations include data filtering, weighting at the panelist and household levels to ensure representativeness, and measurement uncertainty.
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