The search for optimal finite-length binary block codes is a long-standing open problem for memoryless binary symmetric channels (BSCs) with the maximum likelihood decoding. A recent work studied the optimal codes amo...
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Systematic management of all phases of the lifecycle of an industrial control system, recently referred to as operational technology or OT for short, ensures that the system is up to date, compliant with regulations a...
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Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial f...
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We report a thin film Lead Zirconate Titanate (PZT) on Silicon-on-Insulator (SOI) based microcantilever for fluid dynamic viscosity (ηfluid) sensing using optical and electrical characterization schemes. The viscosit...
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Scan chain architecture is widely employed in modern VLSI design for test applications. However, it often leads to high power consumption during testing. The architecture experiences elevated simultaneous switching ac...
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This study introduces the Community Sentiment and Engagement Index (CSEI)., developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to ...
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This paper presents the analysis and simulation for a novel high-power density electric vehicle dc-dc converter. In this paper, a novel interleaved phase-shift half-bridge converter that mainly comprises two modules o...
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With increased power and prevalence of AI systems, it is ever more critical that AI systems are designed to serve all, i.e., people with diverse values and perspectives. However, aligning models to serve pluralistic h...
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With increased power and prevalence of AI systems, it is ever more critical that AI systems are designed to serve all, i.e., people with diverse values and perspectives. However, aligning models to serve pluralistic human values remains an open research question. In this piece, we propose a roadmap to pluralistic alignment, specifically using large language models as a test bed. We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses;2) Steerably pluralistic models that can steer to reflect certain perspectives;and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution. We also formalize and discuss three possible classes of pluralistic benchmarks: 1) Multi-objective benchmarks, 2) Tradeoff steerable benchmarks that incentivize models to steer to arbitrary trade-offs, and 3) Jury-pluralistic benchmarks that explicitly model diverse human ratings. We use this framework to argue that current alignment techniques may be fundamentally limited for pluralistic AI;indeed, we highlight empirical evidence, both from our own experiments and from other work, that standard alignment procedures might reduce distributional pluralism in models, motivating the need for further research on pluralistic alignment. Copyright 2024 by the author(s)
This study explores the relationship between quiz question performance and preceding quiz activities, aiming to predict the former through the lens of diverse machine learning models. Our research explores four distin...
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We present a wavefront shaping method that computes the optimal wavefront for random-access focusing through scattering in 3D, by using prior knowledge of the reconstructed 3D refractive index (RI), measured in epi-mo...
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