All papers

This paper presents a dataset, benchmark, and coarse-to-fine learning scheme to detect tiny oriented objects in imagery.

This paper presents a dataset, benchmark, and coarse-to-fine learning scheme to detect tiny oriented objects in imagery.

This paper presents a dataset, benchmark, and coarse-to-fine learning scheme to detect tiny oriented objects in imagery.

This position paper proposes multi-agent reinforcement learning and mechanism design to reform academic peer review processes.

This position paper proposes multi-agent reinforcement learning and mechanism design to reform academic peer review processes.

This position paper proposes multi-agent reinforcement learning and mechanism design to reform academic peer review processes.

This position paper proposes multi-agent reinforcement learning and mechanism design to reform academic peer review processes.

This paper presents an agentic framework to compile large multi-modal requirement documents into runnable software systems using test-driven workflows.

This paper presents an internet of things voting system using fingerprint authentication on a microcontroller with cloud monitoring.

This paper presents a multiconductor transmission line model for two-dimensional lossy configurations based on mode reciprocity.

This paper presents a framework and a dataset to generate holistic full-body co-speech gestures from audio and masked movement priors.

This paper presents a framework and a dataset to generate holistic full-body co-speech gestures from audio and masked movement priors.

This paper introduces frequency assignment techniques using partially overlapped channels to improve spectral efficiency in integrated access and backhaul networks.

This paper surveys foundation models, agent systems, datasets, and computational tools applied to materials science discovery.

This paper surveys foundation models, agent systems, datasets, and computational tools applied to materials science discovery.

This paper surveys foundation models, agent systems, datasets, and computational tools applied to materials science discovery.

This paper surveys foundation models, agent systems, datasets, and computational tools applied to materials science discovery.

The authors present a lightweight convolutional network architecture for classifying multiple ophthalmic diseases from imbalanced retinal fundus images.

This paper presents an optimization method to separate crossing white matter fiber orientations in diffusion magnetic resonance imaging.

The authors propose an interpretable histopathology classification architecture evaluated across multiple hospital sites with verifiable decision records.

The paper develops a joint Bayesian reconstruction method with uncertainty quantification for multi-contrast X-ray computed tomography from sparse data.

This work develops a smooth-to-hard spectral regularization penalty with convergence stationarity guarantees for solving general linear inverse problems.

The paper introduces a framework to learn adaptive metric updates for proximal splitting algorithms while retaining convergence guarantees.

The authors introduce a multi-platform benchmark to measure the performance and tool efficacy of code-optimizing agents on high-performance computing kernels.

The study introduces a per-symbol static memory attribution method to analyze SRAM usage between FreeRTOS-C and Embassy-Rust on microcontrollers.

The study introduces a per-symbol static memory attribution method to analyze SRAM usage between FreeRTOS-C and Embassy-Rust on microcontrollers.

The paper shows that providing whole selected files to coding agents outperforms prompt compression and retrieval under fixed token budgets.

The paper presents an automated framework that revises open-source contribution guidelines by extracting and integrating evidence from code repositories.

The paper presents an automated framework that revises open-source contribution guidelines by extracting and integrating evidence from code repositories.

The authors propose a method to generate realistic software engineering benchmarks at scale by inverting historical bug fixes into issues.

This case study explores strategies for using automated coding agents to transform external software dependencies into local reimplementations.

The paper examines how different repository instruction categories in agent files affect token efficiency and automated code generation quality.

This empirical study evaluates how quantum circuit compilation and transpilation differences alter program execution behavior across cloud quantum hardware providers.

This empirical study evaluates how quantum circuit compilation and transpilation differences alter program execution behavior across cloud quantum hardware providers.

The paper presents a knowledge-augmented framework and benchmark to help automated code review account for historical software design decisions.

The authors analyze causal internal pathways in vision-language-action models to identify where visual and instruction features affect predicted actions.

This work uses block-sparse featurizers to extract interpretable representations for steering robot foundation model behavior and shaping training data.

The paper presents an absolute vehicle heading estimation framework that optimizes a skylight polarization model using polarimetric camera measurements.

The authors present a physical robotic framework for non-invasive automated testing to detect data loss in Android mobile applications.

The authors present a physical robotic framework for non-invasive automated testing to detect data loss in Android mobile applications.

The authors present a physical robotic framework for non-invasive automated testing to detect data loss in Android mobile applications.

This technical note compares two observer-based control schemes on a pendulum system to evaluate chattering attenuation under nonlinear friction.

This paper introduces an uncertainty-aware method to select effective gaits for damaged legged robots during continuous reset-free operation.

The authors present an adaptive mission planning method using ergodic search to maximize information gathering for solar-powered autonomous vessels.

The paper trains a reinforcement learning policy to recover vehicle stability beyond normal handling limits using learned drifting maneuvers.

Formulates a periodic optimal control problem to optimize speed trajectories and balance energy consumption with information gain in mobile robots.

Develops a constructive control barrier function approach using distance bubbles to enforce safe navigation in second-order dynamical systems.

Introduces an augmented Lagrangian trajectory optimization method using gradient flow to compute feasible motions for contact-constrained robots.

Develops an adaptive trajectory tracking controller for uncertain autonomous underwater vehicles operating with saturated thruster inputs.

Analyzes the geometric and transport conditions under which robots can use reversing wind headings to locate odour sources.

Introduces an agentic framework that extracts spatial cues from passive human videos to guide mobile robot manipulation via executable code policies.

Formulates an optimization-based control method to suppress slip-induced falls in remotely operated motorcycles during cornering on slippery surfaces.

Proposes a generative motion and impedance optimization framework combining reinforcement learning priors with model-based refinement for contact-rich tasks.

Presents a data alignment framework that converts human egocentric videos into controller-compatible training demonstrations for humanoid robots.

Formalizes a decision-theoretic framework and measurement standard for scheduling slow module invocations in dual-system vision-language-action models.

Introduces a benchmark evaluating few-shot generalization and knowledge transfer across related task families in dexterous robotic manipulation.

Introduces a hybrid LiDAR-inertial neural SLAM system combining geometric estimation with implicit neural mapping for real-time onboard navigation.

Proposes an agentic framework using procedural assistance memory to guide proactive robots in assisting users during daily routines.

Demonstrates closed-loop autonomous navigation of microbots using real-time object detection and magnetic steering along complex trajectories.

Proposes a model predictive path integral controller that samples execution-time initial states to mitigate real-world robotic planning delays.