231 papers
The paper proposes DFPF-Net, a novel remote sensing change detection framework that integrates a pyramid vision transformer with a progressive fusion module and a dynamic change focus mechanism to effectively mitigate both global pseudo-changes and local noise, thereby outperforming mainstream methods across multiple datasets.
The paper introduces M2Diff, a multi-modality multi-task diffusion model that separately processes MRI and low-dose PET scans to extract and hierarchically fuse modality-specific features, thereby significantly improving the fidelity of standard-dose PET reconstruction for both healthy and Alzheimer's disease populations.
This paper proposes a two-stage probabilistic framework that first reconstructs incomplete satellite fire observations using various deep learning models and then predicts wildfire spread, effectively bridging the domain gap caused by partial observability to restore prediction accuracy to near-clean-input levels.
This paper demonstrates that neural audio codecs achieve optimal adversarial robustness in speech recognition at intermediate residual vector quantization depths, which effectively balance the suppression of adversarial perturbations with the preservation of speech content, outperforming traditional compression defenses.
This paper presents a policy-aware, cross-layer auditing methodology that successfully identifies Starlink service tiers and throttling states by analyzing distinct signatures in goodput, RTT, and an internal-to-user ratio derived from aligned terminal telemetry and host-side probes, without requiring operator visibility.
The paper proposes Universal Speech Content Factorization (USCF), a simple and invertible linear method that extracts low-rank, speaker-independent speech representations to enable competitive zero-shot voice conversion and efficient training of timbre-prompted text-to-speech models using minimal target speaker data.
This paper proposes a neural network-based tuning method for the parameters of a finite state model predictive controller in a five-phase induction motor drive, with its effectiveness validated through experimental step tests.
The paper introduces ZACH-ViT, a compact Vision Transformer that eliminates positional embeddings and the [CLS] token to achieve permutation-invariant processing, demonstrating that this architecture is particularly effective for few-shot medical imaging tasks with weak spatial priors while remaining competitive on datasets with stronger anatomical structures.
The paper proposes LexiSafe, a theoretically grounded offline safe reinforcement learning framework that employs lexicographic prioritization to strictly enforce safety constraints while optimizing task performance, offering improved guarantees and empirical results over existing methods for safety-critical cyber-physical systems.
The paper introduces PanSubNet, an interpretable deep learning framework that accurately predicts clinically relevant basal-like and classical molecular subtypes of pancreatic ductal adenocarcinoma directly from routine H&E-stained histology slides, offering a cost-effective and rapid alternative to traditional RNA-seq-based methods for precision oncology.
This paper presents and quantitatively evaluates an embedded EEG platform based on an ESP32-S3 and ADS1299 that achieves real-time, closed-loop SSVEP decoding with high measurement integrity, demonstrating 99.17% online accuracy and 27.66 bits/min information transfer rate entirely on-device.
This paper proposes a safety-guaranteed and optimal learning framework for autonomous systems that utilizes Weighted Signal Temporal Logic (WSTL) with structural pruning and log-transform techniques to efficiently solve preference-based learning problems as Mixed-Integer Linear Programs, validated through experiments in robotic navigation and Formula 1 racing.
This paper presents a framework for analyzing and synthesizing discrete-time optimization algorithms that guarantee certified exponential convergence and robustness against time-varying delays and unstable channel dynamics by utilizing Zames-Falb multipliers and linear matrix inequalities.
This paper proposes a fast, deterministic tensor completion method that leverages standard linear algebra to recover tensors from fiberwise observations along a single mode, offering efficient recovery guarantees without relying on random sampling assumptions.
This paper presents a customized, dependency-free C-based second-order cone programming solver for embedded real-time guidance and control applications that utilizes a predictor-corrector primal-dual interior-point method with homogeneous embedding to efficiently handle quadratic objectives without sparsity loss, supported by an automated code generation tool that outperforms existing solvers on typical problem scales.
This paper proposes a deterministic diffusion-based framework for controlling the probability density of nonlinear control-affine systems by leveraging a forward noise-excitation process and a reverse denoising feedback law to steer state distributions toward desired targets, with theoretical guarantees for drift-free and linear time-invariant dynamics.
This paper proposes a few-shot image fusion framework that leverages a Granular Ball Pixel Computation algorithm to generate adaptive, confidence-aware incomplete priors, enabling a lightweight neural network to learn effective fusion rules from minimal data without requiring real fused images for supervision.
This paper demonstrates that four recent deep speech denoising models are vulnerable to psychoacoustically hidden adversarial noise, which can render their output unintelligible while remaining imperceptible to human listeners, thereby highlighting critical safety concerns for their deployment in high-stakes applications.
This paper proposes a novel targeted exploration strategy for uncertain linear time-invariant systems with energy-bounded, non-stochastic disturbances, utilizing a semidefinite program to guarantee parameter estimation accuracy based on spectral content conditions that account for initial parametric uncertainty.
This paper proposes a novel method that leverages Large Language Models to evolve interpretable control policies represented as standard Python programs, offering a transparent and verifiable alternative to black-box neural network controllers for dynamical systems.