arXiv subjects

Michael Lee

Publications and source records attributed to Michael Lee.

6 recordsLinked to original sources

arXiv2026

Align AI to Dynamic Human-AI Workflows

Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions. In this paper, we argue for a shift from static and emulative to interactive and complementary alignment, where preferences emerge through interaction and alignment is defined not by satisfying preferences alone. We first formalize this gap by contrasting existing alignment with a trajectory-level view in which human and model behavior co-evolve over time. Because these interaction dynamics have not been adequately captured within existing ML formulations, we ground this perspective in insights from an interdisciplinary workshop. We draw on lessons from social-science accounts of human-human collaboration and then argue that human-AI systems amplify these dynamics, introducing new asymmetries that make reasoning about uncertainty harder and introduce new coordination challenges. Based on these lessons and new challenges, we conclude by outlining a research agenda for developing AI systems that align with humans in interaction, requiring an interdisciplinary synthesis of machine learning and the social and decision sciences.

cs.AI↗

arXiv2026

Handling Scope Checks (Extended Version)

Metaprogramming and effect handlers interact in unexpected, and sometimes undesirable, ways. One example is scope extrusion: the generation of ill-scoped code. Scope extrusion can either be preemptively prevented, via static type systems, or retroactively detected, via dynamic checks. Static type systems exist in theory, but struggle with a range of implementation and usability problems in practice. In contrast, dynamic checks exist in practice (e.g. in MetaOCaml), but are understudied in theory. Designers of metalanguages are thus given little guidance regarding the design and implementation of checks. We present the first formal study of dynamic scope extrusion checks, introducing a calculus ($\lambda_{\langle\langle\text{op}\rangle\rangle}$) for describing and evaluating checks. Further, we introduce a novel dynamic check $\unicode{x2014}$ the "Cause-for-Concern" check $\unicode{x2014}$ which we prove correct, characterise without reference to its implementation, and argue combines the advantages of existing dynamic checks. Finally, we extend our framework with refined environment classifiers, which statically prevent scope extrusion, and compare their expressivity with the dynamic checks.

cs.PL↗

arXiv2025

DeepAf: One-Shot Spatiospectral Auto-Focus Model for Digital Pathology

While Whole Slide Imaging (WSI) scanners remain the gold standard for digitizing pathology samples, their high cost limits accessibility in many healthcare settings. Other low-cost solutions also face critical limitations: automated microscopes struggle with consistent focus across varying tissue morphology, traditional auto-focus methods require time-consuming focal stacks, and existing deep-learning approaches either need multiple input images or lack generalization capability across tissue types and staining protocols. We introduce a novel automated microscopic system powered by DeepAf, a novel auto-focus framework that uniquely combines spatial and spectral features through a hybrid architecture for single-shot focus prediction. The proposed network automatically regresses the distance to the optimal focal point using the extracted spatiospectral features and adjusts the control parameters for optimal image outcomes. Our system transforms conventional microscopes into efficient slide scanners, reducing focusing time by 80% compared to stack-based methods while achieving focus accuracy of 0.18 {\mu}m on the same-lab samples, matching the performance of dual-image methods (0.19 {\mu}m) with half the input requirements. DeepAf demonstrates robust cross-lab generalization with only 0.72% false focus predictions and 90% of predictions within the depth of field. Through an extensive clinical study of 536 brain tissue samples, our system achieves 0.90 AUC in cancer classification at 4x magnification, a significant achievement at lower magnification than typical 20x WSI scans. This results in a comprehensive hardware-software design enabling accessible, real-time digital pathology in resource-constrained settings while maintaining diagnostic accuracy.

cs.CV↗

arXiv2023

VGF: Value-Guided Fuzzing -- Fuzzing Hardware as Hardware

As the complexity of logic designs increase, new avenues for testing digital hardware becomes necessary. Fuzz Testing (fuzzing) has recently received attention as a potential candidate for input vector generation on hardware designs. Using this technique, a fuzzer is used to generate an input to a logic design. Using a simulation engine, the logic design is given the generated stimulus and some metric of feedback is given to the fuzzer to aid in the input mutation. However, much like software fuzzing, hardware fuzzing uses code coverage as a metric to find new possible fuzzing paths. Unfortunately, as we show in this work, this coverage metric falls short of generic on some hardware designs where designers have taken a more direct approach at expressing a particular microarchitecture, or implementation, of the desired hardware. With this work, we introduce a new coverage metric which employs not code coverage, but state coverage internal to a design. By observing changes in signals within the logic circuit under testing, we are able to explore the state space of the design and provide feedback to a fuzzer engine for input generation. Our approach, Value-Guided Fuzzing (VGF), provides a generic metric of coverage which can be applied to any design regardless of its implementation. In this paper, we introduce our state-based VGF metric as well as a sample implementation which can be used with any VPI, DPI, VHPI, or FLI compliant simulator, making it completely HDL agnostic. We demonstrate the generality of VGF and show how our sample implementation is capable of finding bugs considerably faster than previous approaches.

cs.AR↗

arXiv2020

Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects

The ability to determine what parts of objects and surfaces people touch as they go about their daily lives would be useful in understanding how the COVID-19 virus spreads. To determine whether a person has touched an object or surface using visual data, images, or videos, is a hard problem. Computer vision 3D reconstruction approaches project objects and the human body from the 2D image domain to 3D and perform 3D space intersection directly. However, this solution would not meet the accuracy requirement in applications due to projection error. Another standard approach is to train a neural network to infer touch actions from the collected visual data. This strategy would require significant amounts of training data to generalize over scale and viewpoint variations. A different approach to this problem is to identify whether a person has touched a defined object. In this work, we show that the solution to this problem can be straightforward. Specifically, we show that the contact between an object and a static surface can be identified by projecting the object onto the static surface through two different viewpoints and analyzing their 2D intersection. The object contacts the surface when the projected points are close to each other; we call this cross view projection consistency. Instead of doing 3D scene reconstruction or transfer learning from deep networks, a mapping from the surface in the two camera views to the surface space is the only requirement. For planar space, this mapping is the Homography transformation. This simple method can be easily adapted to real-life applications. In this paper, we apply our method to do office occupancy detection for studying the COVID-19 transmission pattern from an office desk in a meeting room using the contact information.

cs.CV↗

arXiv2011

Observation of Tunneling Current in Semiconducting Graphene p-n Junctions

We demonstrate a tunneling and rectification behavior in bilayer graphene. A stepped dielectric top gate creates a spatially modulated electric field, which opens the band gap in the graphene and produces an insulating region at the p-n interface. A current-voltage relationship exhibiting differential resistance peak at forward bias stems from the tunneling current through the insulating region at the p-n interface. The tunneling current reflects singularities in the density of states modified by the electric field. This work suggests that the effect of carrier charge tuning by external electric field in 2D semiconductors is analogously to that by impurity doping in 3D semiconductors.

cond-mat.mes-hall↗