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 10822 publications
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For many practical applications of quantum computing, the slowest and most costly steps involve coherently accessing classical data. We help address this challenge by applying mass production techniques, which can sometimes allow us to perform operations many times in parallel for a cost that is comparable to a single execution[1-3]. We combine existing mass-production results with modern approaches for loading classical data using ``quantum read-only memory.'' We show that quantum mass production techniques offer no benefit when we consider a cost model that focuses purely on the number of non-Clifford gates. However, analyzing the constant factors in a more nuanced cost model, we find that it may be possible to obtain a reduction in cost of an order or magnitude or more for a variety reasonably-sized fault-tolerant quantum algorithms. We present several applications of quantum mass-production techniques beyond naive parallelization, including a strategy for reducing the cost of serial calls to the same data loading step.
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mmMUSE: An mmWave-based Motion-resilient Universal Speech Enhancement System
Chenming He
Yanyong Zhang
Kai Wang
Dequan Wang
Lingyu Wang
the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), ACM (2026) (to appear)
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Voice-based smart systems can greatly enhance user experiences by allowing higher-quality interactions through better voice perception. Speech enhancement can benefit such systems by isolating noise from speech. Recently, integrating millimeter-wave (mmWave) with audio for speech perception has gained increasing attention due to microphones' limitations in noisy environments. However, mmWave-based vocal extraction is severely affected by motion, which disperses vocal signals across ranges and introduces distortions. In this paper, we propose an mmWave-based motion-resilient universal speech enhancement system called mmMUSE, which fuses mmWave and audio signals. To mitigate motion interference, we develop a Doppler-based method for motion-robust vocal signal extraction. Moreover, by introducing the Vocal-Noise-Ratio metric to assess the prominence of vocal signals from mmWave, we achieve real-time voice activity detection that gains 3.81 dB of SISDR in noisy speeches. Additionally, we design a two-stage complex-valued network that includes an attention-based fusion network for cross-modal complementing and a time-frequency masking network for correcting amplitude and phase of speech to isolate noises.
Using mmWave and audio datasets from 46 participants, mmMUSE outperforms the state-of-the-art speech enhancement models, achieving an average SISDR improvement of 3.12 dB. Additionally, mmMUSE achieves SISDR improvements of 16.51 dB, 17.93 dB, 14.93 dB, and 18.95 dB in controlled environments involving intense noise, extensive motion, multiple speakers, and various obstructive materials, respectively. Finally, we evaluate mmMUSE in real-world scenarios including running, public spaces, and driving, maintaining a word error rate (WER) below 10%.
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AI coding assistants are rapidly becoming integral to modern software development. A key challenge in this space is the continual need to migrate and modernize codebases in response to evolving software ecosystems. Traditionally, such migrations have relied on rule-based systems and human intervention. With the advent of powerful large language models (LLMs), AI-driven agentic frameworks offer a promising alternative—but their effectiveness remains underexplored. In this paper, we introduce FreshBrew, a novel benchmark for evaluating AI-based agentic frameworks on project-level Java migrations. We benchmark several such frameworks, powered by state-of-the-art LLMs, and compare their performance against established rule-based tools. Our evaluation of AI agents on this benchmark of 228 repositories shows that the top-performing model, Gemini 2.5 Flash, can successfully migrate 56.5% of projects to JDK 17. Our empirical analysis reveals novel insights into the critical strengths and limitations of current agentic approaches, offering actionable insights into their real-world applicability. By releasing FreshBrew publicly upon acceptance, we aim to facilitate rigorous, reproducible evaluation and catalyze progress in AI-driven codebase modernization.
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A growing body of research has demonstrated that the behavior of large language models can be effectively controlled at inference time by directly modifying their internal states, either through vector additions to their activations or through updates to their weight matrices. These techniques, while powerful, are often guided by empirical heuristics, such as deriving steering vectors from the average activations of contrastive prompts. This work provides a theoretical foundation for these interventions, explaining how they emerge from the fundamental computations of the transformer architecture. Building on the recent finding that a prompt's influence can be mathematically mapped to implicit weight updates (Dherin et al., 2025), we generalize this theory to deep, multi-block transformers. We show how the information contained in any chunk of a user prompt is represented and composed internally through weight vectors and weight matrices. We then derive a principled method for condensing this information into token-independent thought vectors and thought matrices. These constructs provide a theoretical explanation for existing vector- and matrix-based model editing techniques and offer a direct, computationally-grounded method for transmuting textual input into reusable weight updates.
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On the relationship of speed limit and CO2 emissions in urban traffic
Tamás Tettamanti
Balázs Varga
Ori Rottenstreich
Transportation Research Interdisciplinary Perspectives, 32 (2025)
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The paper analyzes the relationship between urban speed limits and vehicle emissions. There is an ongoing trend of reducing speed limits from to for the sake of increasing road safety. However, the impact of this policy on emissions is still unclear. It can be mixed depending on the proportion of dynamic and steady-state driving. While cruising emissions are higher at lower speeds, lower speeds entail less acceleration in urban traffic. Based on our investigation, one network topology feature (road length) and two traffic-related parameters (traffic volume and turning ratio) have been suggested for analysis being the most relevant to affect vehicle emission. Their correlation with potential emission reduction was evaluated using high-fidelity traffic simulation based on traffic scenarios validated with real traffic data. Random forest regression was used to support the optimal selection of zones for speed limit reduction. Traffic simulations on large urban networks prove that emission reductions of over 10% can be achieved in the case of a well-chosen speed limit policy.
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PROTECT: A Framework to Foster Digital Resilience for Youth Navigating Technology-Facilitated Abuse
Diana Freed
Natalie Bazarova
Dan Cosley
Patrick Gage Kelley
Social Sciences Journal, 14(6) (2025)
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Youth are increasingly exposed to a broad range of technology-facilitated abuse that challenges their safety and well-being. Building on previous work that examined youth help-seeking behaviors, coping strategies, threats they encounter, and the social support systems around them, we articulate a framework— called PROTECT—Problem recognition, Reaching out, Organizing support, Training, Engaging experts, Continuous support, and Tackling safety measures—which integrates existing models of support, help-seeking, and digital skills to offer a high-level, structured approach to adults who serve as a support system to youth navigate technology-facilitated abuse. The framework unpacks social and contextual dynamics that influence help-seeking behaviors, providing a foundation for educators, advocates, health professionals, developers and other adult stakeholders to design and develop trauma-informed, timely interventions to promote resilience.
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The Cost of Consistency: Submodular Maximization with Constant Recourse
Paul Duetting
Federico Fusco
Ashkan Norouzi Fard
Ola Svensson
Proceedings of the 57th Annual ACM Symposium on Theory of Computing (2025), 1406–1417
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In this work, we study online submodular maximization and how the requirement of maintaining a stable solution impacts the approximation. In particular, we seek bounds on the best-possible
approximation ratio that is attainable when the algorithm is allowed to make, at most, a constant number of updates per step. We show a tight information-theoretic bound of $2/3$ for general monotone submodular functions and an improved (also tight) bound of $3/4$ for coverage functions. Since both these bounds are attained by non poly-time algorithms, we also give a poly-time randomized algorithm that achieves a $0.51$-approximation. Combined with an
information-theoretic hardness of $1/2$ for deterministic algorithms from prior work, our work thus shows a separation between deterministic and randomized algorithms, both information theoretically and for poly-time algorithms.
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I know what I don't know: improving model cascades through confidence tuning
Stephan Rabanser
Nathalie Rauschmayr
Petra Poklukar
Congchao Wang
2025
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Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these challenges, local smaller models are often deployed alongside larger models, relying on routing and deferral mechanisms to offload complex tasks. However, existing approaches inadequately balance the capabilities of these models, often resulting in unnecessary deferrals or sub-optimal resource usage. In this work we introduce a novel loss function called Gatekeeper for calibrating smaller models in cascade setups. Our approach fine-tunes the smaller model to confidently handle tasks it can perform correctly while deferring complex tasks to the larger model. Moreover, it incorporates a mechanism for managing the trade-off between model performance and deferral accuracy, and is broadly applicable across various tasks and domains without any architectural changes. We evaluated our method on encoder-only, decoder-only, and encoder-decoder architectures. Experiments across image classification, language modeling, and vision-language tasks show that our approach substantially improves deferral performance.
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We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly (over T rounds) posts prices that depend on the observable
dimensional context and receives binary purchase feedback. Unlike prior work assuming homogeneous buyer types, in our setting the buyer's valuation type is drawn from an unknown distribution with finite support K*. We develop a contextual pricing algorithm based on Optimistic Posterior Sampling with regret K* sqrt(dT), which we prove to be tight in d, T up to logarithmic terms. Finally, we refine our analysis for the non-contextual pricing case, proposing a variance-aware Zooming algorithm that achieves the optimal dependence on K*.
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A Call to Action: Advancing the Conversation Around Neurodivergent Education-Employment Transitions
Dannie Lynn Fountain
Vicki Baker
Kevin Danley
Closing the Gap (2025)
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Neurodiversity is still largely stigmatized and excluded from DEIB frameworks and related organizational initiatives, despite the increased recognition regarding the benefits of neuroinclusion within the education and corporate spheres. We seek to address this knowledge-to-practice gap through the creation of the Neurodiversity Engagement Framework. By highlighting supports needed for neurodivergent individuals, and those that support them, the framework helps neurodivergent individuals navigate within and across higher education and industry contexts. Informed by an interdisciplinary review of literature from higher education, industry, and corporate leadership contexts, the Neurodiversity Engagement Framework brings to light prevailing challenges within practices and policies, serving as a guide for the creation of a more supportive foundation for neurodiverse individuals to thrive. In this manuscript, readers are encouraged to consider the myriad of impacts that neurodiversity has on higher education and industry experiences and the ways that organizations can be more proactive in their support of this growing population. To conclude, we offer a roadmap for future research and practice to further elucidate ways academic and corporation leaders and policymakers can effectively support neurodivergent individuals.
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The JPEG XL Image Codec: History, Features, Coding Tools, Design Rationale, and Future
Jon Sneyers
Luca Versari
Zoltan Szabadka
Amnon Cohen-Tidhar
Moritz Firsching
Evgenii Kliuchnikov
Tal Lev-Ami
Eric Portis
Thomas Richter
WATANABE Osamu
arxiv (2025) (to appear)
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This article provides an extensive overview of the JPEG XL codec, describing its features and coding tools,
the design rationale behind it, as well as its performance, history and potential future. It can be used as a
companion document to the standard (ISO/IEC 18181), or as a standalone article to better understand the
codec, either at a high level or in considerable technical detail.
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AI and Generative AI Transforming Disaster Management: A Survey of Damage Assessment and Response Techniques
Aman Raj
IEEE Compsac 2025 (2025)
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Natural disasters, including earthquakes, wildfires and cyclones, bear a huge risk on human lives as well as infrastructure assets. An effective response to disaster depends on the ability to rapidly and efficiently assess the intensity of damage. Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) presents a breakthrough solution, capable of combining knowledge from multiple types and sources of data, simulating realistic scenarios of disaster, and identifying emerging trends at a speed previously unimaginable. In this paper, we present a comprehensive review on the prospects of AI and GenAI in damage assessment for various natural disasters, highlighting both its strengths and limitations. We talk about its application to multimodal data such as text, image, video, and audio, and also cover major issues of data privacy, security, and ethical use of the technology during crises. The paper also recognizes the threat of Generative AI misuse, in the form of dissemination of misinformation and for adversarial attacks. Finally, we outline avenues of future research, emphasizing the need for secure, reliable, and ethical Generative AI systems for disaster management in general. We believe that this work represents the first comprehensive survey of Gen-AI techniques being used in the field of Disaster Assessment and Response.
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TRACT: Regression-Aware Fine-tuning Meets Chain-of-Thought Reasoning for LLM-as-a-Judge
Cheng-Han Chiang
Hung-yi Lee
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics (2025)
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The LLM-as-a-judge paradigm uses large language models (LLMs) for automated text evaluation, where a numerical assessment is assigned by an LLM to the input text following scoring rubrics. Existing methods for LLM-as-a-judge use cross-entropy (CE) loss for fine-tuning, which neglects the numeric nature of score prediction. Recent work addresses numerical prediction limitations of LLM fine-tuning through regression-aware fine-tuning, which, however, does not consider chain-of-thought (CoT) reasoning for score prediction. In this paper, we introduce TRACT (Two-stage Regression-Aware fine-tuning with CoT), a method combining CoT reasoning with regression-aware training. TRACT consists of two stages: first, seed LLM is fine-tuned to generate CoTs, which serve as supervision for the second stage fine-tuning. The training objective of TRACT combines the CE loss for learning the CoT reasoning capabilities, and the regression-aware loss for the score prediction. Experiments across four LLM-as-a-judge datasets and two LLMs show that TRACT significantly outperforms existing methods. Extensive ablation studies validate the importance of each component in TRACT.
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Scalability of Generative AI Models: Challenges and Opportunities in Large-Scale Data Generation and Training
International Journal of Computer Science and Information Technology Research (IJCSITR) (2025)
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Scalability of Generative AI Models: Challenges and Opportunities in Large-Scale Data Generation and Training
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Differentially Private Insights into AI Use
Daogao Liu
Pritish Kamath
Alexander Knop
Adam Sealfon
Da Yu
Chiyuan Zhang
Conference on Language Modeling (COLM) 2025 (2025)
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We introduce Urania, a novel framework for generating insights about LLM chatbot interactions with rigorous differential privacy (DP) guarantees. The framework employs a private clustering mechanism and innovative keyword extraction methods, including frequency-based, TF-IDF-based, and LLM-guided approaches. By leveraging DP tools such as clustering, partition selection, and histogram-based summarization, Urania provides end-to-end privacy protection. Our evaluation assesses lexical and semantic content preservation, pair similarity, and LLM-based metrics, benchmarking against a non-private method inspired by CLIO (Tamkin et al., 2024). Moreover, we develop a simple empirical privacy evaluation that demonstrates the enhanced robustness of our DP pipeline. The results show the framework’s ability to extract meaningful conversational insights while maintaining stringent user privacy, effectively balancing data utility with privacy preservation.
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