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 10203 publications
Beyond Touchscreens: Designing for Co-Occurring Accessibility Needs
Melissa Barnhart Wantland
Mai Kobori
Universal Access in Human-Computer Interaction, Springer-Verlag (2025) (to appear)
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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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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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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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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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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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Circadian rhythm of heart rate and activity: a cross-sectional study
Maryam Khalid
Logan Schneider
Aravind Natarajan
Conor Heneghan
Karla Gleichauf
Chronobiology International (2025)
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ABSTRACT
Background: Circadian rhythms are commonly observed in a number of physiological processes. Consumer wearable devices have made it possible to obtain continuous time series data from a large number of individuals. We study circadian rhythms from measurements of heart rate, movement, and sleep, from a cohort of nearly 20,000 participants over the course of 30 days.
Methods: Participation was restricted to Fitbit users of age 21 years or older residing in the United States or Canada. Participants were enrolled through a recruitment banner shown on the Fitbit App. The advertisement was shown to 531,359 Fitbit users, and 23,239 enrolled in the program. Of these, we obtained heart rate data from 19,350 participants. We obtain the underlying circadian rhythm from time series heart rate by modeling the circadian rhythm as a sum over the first two Fourier harmonics. The first Fourier harmonic accounts for the 24-hour rhythmicity, while the second harmonic accounts for non-sinusoidal perturbations.
Findings: We observe a circadian rhythm in both heart rate and acceleration. From the diurnal modulation, we obtain the following circadian parameters: (i) amplitude of modulation, (ii) bathyphase, (iii) acrophase, (iv) non-sinusoidal fraction, and (v) fraction of day when the heart rate is greater than the mean. The amplitude, bathyphase, and acrophase depend on sex, and decrease with age. The waketime on average, follows the bathyphase by 2.4 hours. In most individuals, the circadian rhythm of heart rate lags the circadian rhythm of activity.
Interpretation: Circadian metrics for heart rate and activity can be reliably obtained from commercially available wearable devices. Distributions of circadian metrics can be valuable tools for individual-level interpretation.
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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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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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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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InstructPipe: Building Visual Programming Pipelines with Human Instructions using LLMs in Visual Blocks
Zhongyi Zhou
Jing Jin
Xiuxiu Yuan
Jun Jiang
Jingtao Zhou
Yiyi Huang
Kristen Wright
Jason Mayes
Mark Sherwood
Alex Olwal
Ram Iyengar
Na Li
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI), ACM, pp. 23
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Visual programming provides beginner-level programmers with a coding-free experience to build their customized pipelines. Existing systems require users to build a pipeline entirely from scratch, implying that novice users need to set up and link appropriate nodes all by themselves, starting from a blank workspace. We present InstructPipe, an AI assistant that enables users to start prototyping machine learning (ML) pipelines with text instructions. We designed two LLM modules and a code interpreter to execute our solution. LLM modules generate pseudocode of a target pipeline, and the interpreter renders a pipeline in the node-graph editor for further human-AI collaboration. Technical evaluations reveal that InstructPipe reduces user interactions by 81.1% compared to traditional methods. Our user study (N=16) showed that InstructPipe empowers novice users to streamline their workflow in creating desired ML pipelines, reduce their learning curve, and spark innovative ideas with open-ended commands.
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Did You Misclick? Reversing 5-Point Satisfaction Scales Causes Unintended Responses
Mario Callegaro
CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems
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When fielding satisfaction questions, survey platforms offer the option to randomly reverse the response options. In this paper, we provide evidence that the use of this option leads to biased results. In Study 1, we show that reversing vertically oriented response options leads to significantly lower satisfaction ratings – from 90 to 82 percent in our case. Study 2 had survey respondents verify their response and found that on a reversed scale, the very-dissatisfied option was selected unintentionally in about half of the cases. The cause, shown by Study 3, is that survey respondents expect the positive option at the top and do not always pay sufficient attention to the question, combined with the similar spelling of satisfied and dissatisfied. To prevent unintentional responses from biasing the results, we recommend keeping the positive option at the top in vertically-oriented scales with visually-similar endpoint labels.
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Middle-mile logistics describes the problem of routing shipments through a network of hubs while respecting deadlines upon arrival. We consider that the hubs are linked by predefined lines, to which we have to assign vehicles. A very challenging aspect of the problem comes from the finite capacity of the vehicles: allocating a shipment to a given vehicle might block another one from using the same vehicle.
Typical exact solution methods, based on a multicommodity-flow formulation, scale poorly with the problem size and real-world instances become quickly intractable. Instead, we turn to reinforcement learning (RL) by rephrasing the middle-mile problem as a multi-objective Markov decision process, where the state is a graph: the lines (edges) between the hubs and the parcels (nodes). At each round, we assign one shipment to a vehicle or decide that it stays in the same hub. The key ingredients of our proposed method are the extraction of small feature graphs from the state and the combination of graph neural networks (GNN) with model-free RL.
We use the PPO (proximal policy optimization) algorithm, which maintains both an actor and a critic, while being able to cope with a varying number of actions depending on the state. We compare linear functions and GraphNet (a particular kind of GNN) to approximate the policy and value functions. GNNs can deliver up to 40% more shipments than a linear function and both approaches scale well with the number of shipments per truck.
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Think before you speak: Training language models with pause tokens
Sachin Goyal
Ziwei Ji
Aditya Menon
Vaishnavh Nagarajan
International Conference on Learning Representations (ICLR) (2024)
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The present-day language model generates its response by producing a series of tokens in immediate succession: the $K+1$th token is an outcome of manipulating exactly $K$ hidden values in each layer corresponding to each of the $K$ previous tokens. Is it possible to somehow allow the model to manipulate more hidden values before committing to an answer? If yes, would this help? We explore these questions by training models with learnable \textit{pause} tokens. Besides feeding the usual prefix to the model, our idea is to feed the model with an additional sequence of pause tokens. On these tokens, the model's output is ignored all the way until the last pause token, where we begin extracting the answer. We explore this idea of ``delayed answering'' in a 1B model, where we consider both pre-training and/or fine-tuning with pause tokens. We find that while merely finetuning a standard model is not very helpful, pause-pretrained models shows promise on some downstream tasks such as GSM (reasoning) and Squad, CommonSenseQA and Lambada (question-answering tasks). We also conduct various ablations to explore the effect of the number of pause tokens. While our work takes a preliminary exploration in delayed computations for language models by focusing on a 1B model, we hope it inspires future work that can make this idea practically feasible without pre-training and for models trained with other pretraining objectives and other sizes.
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Specialized Large multi-modal models (LMMs) have exhibited remarkable performance across numerous tasks, however, generalist LMMs suffer from performance degradation when training with a large collection of tasks. Recent research suggests Mixture of Experts (MoE) Models help instruction tuning, however, for LMMs of parameter size around O(50-100B), the prohibitive cost of replicating and storing the expert models severely limits the number of experts we can use.
We propose Omni-SMoLA that softly mixes many multimodal low rank experts to large models without introducing significant new parameter count compared to conventional MoE models. The core idea is that the large model provides a foundational backbone and different lightweight experts learn specialized knowledge residually. Extensive experiments demonstrate that the SMoLA approach helps improve the generalist performance across a broad range of visual question answering and captioning tasks, achieving a new state-of-the-art generalist performance that matches or outperforms single specialized LMM baselines.
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Optimal Mechanisms for a Value Maximizer: The Futility of Screening Targets
Proceedings of the 25th ACM Conference on Economics and Computation (EC) (2024)
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Motivated by the increased adoption of autobidding algorithms in internet advertising markets, we study the design of optimal mechanisms for selling items to a value-maximizing buyer with a return-on-spend constraint. The buyer's values and target ratio in the return-on-spend constraint are private. We restrict attention to deterministic sequential screening mechanisms that can be implemented as a menu of prices paid for purchasing an item or not. The main result of this paper is to provide a characterization of an optimal mechanism. Surprisingly, we show that the optimal mechanism does not require target screening, i.e., offering a single pair of prices is optimal for the seller. The optimal mechanism is a subsidized posted price that provides a subsidy to the buyer to encourage participation and then charges a fixed unit price for each item sold. The seller's problem is a challenging non-linear mechanism design problem, and a key technical contribution of our work is to provide a novel approach to analyze non-linear pricing contracts.
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