arXiv subjects

Hexin Zhang

Publications and source records attributed to Hexin Zhang.

3 recordsLinked to original sources

arXiv2026

Iterative Inference-time Scaling with Adaptive Frequency Steering for Image Super-Resolution

Diffusion models have become a leading paradigm for image super-resolution (SR), but existing methods struggle to guarantee both the high-frequency perceptual quality and the low-frequency structural fidelity of generated images. Although inference-time scaling can theoretically improve this trade-off by allocating more computation, existing strategies remain suboptimal: reward-driven particle optimization often causes perceptual over-smoothing, while optimal-path search tends to lose structural consistency. To overcome these difficulties, we propose Iterative Diffusion Inference-Time Scaling with Adaptive Frequency Steering (IAFS), a training-free framework that jointly leverages iterative refinement and frequency-aware particle fusion. IAFS addresses the challenge of balancing perceptual quality and structural fidelity by progressively refining the generated image through iterative correction of structural deviations. Simultaneously, it ensures effective frequency fusion by adaptively integrating high-frequency perceptual cues with low-frequency structural information, allowing for a more accurate and balanced reconstruction across different image details. Extensive experiments across multiple diffusion-based SR models show that IAFS effectively resolves the perception-fidelity conflict, yielding consistently improved perceptual detail and structural accuracy, and outperforming existing inference-time scaling methods.

cs.CV↗

arXiv2024

Resolving the Valence of Iron Oxides by Resonant Photoemission Spectroscopy

Precisely determining the oxidation states of metal cations within variable-valence transition metal oxides remains a significant challenge, yet it is crucial for understanding and predicting the properties of these technologically important materials. Iron oxides, in particular, exhibit a remarkable diversity of electronic structures due to the variable valence states of iron (Fe2+ and Fe3+), however, quantitative analysis using conventional X-ray photoelectron spectroscopy (XPS) is challenging because of significant overlapping of the Fe2p spectra among different oxidation states. In this study, we leverage the intriguing case of Pt supported FeO2 phase of monolayer thickness (ML) as a model system and employ Resonant Photoemission Spectroscopy (ResPES) to directly quantify the cation valence states and compositional ratios in this complex Fe oxide. Our results reveal that this ultrathin FeO2 film (Pt-O-Fe-O), contrary to the +3 valence predicted by density functional theory (DFT), consists of an equal mixture of Fe2+ and Fe3+ cations, yielding an average valence of +2.5. Structurally, FeO2 is likely derived from the Fe3O4 sublattice, featuring an octahedral Fe layer (50% Fe3+ and 50% Fe2+) bonded to upper and lower oxygen layers.

cond-mat.mtrl-sci↗

arXiv2021

Efficiency at maximum power of thermoelectric heat engines with the symmetric semiconductor superlattice

Efficiency at maximum power (EMP) is a very important specification for a heat engine to evaluate the capacity of outputting adequate power with high efficiency. It has been proved theoretically that the limit EMP of thermoelectric heat engine can be achieved with the hypothetical boxcar-shaped electron transmission, which is realized here by the resonant tunneling in the one-dimensional symmetric InP/InSe superlattice. It is found with the transfer matrix method that a symmetric mode is robust that regardless of the periodicity, and the obtained boxcar-like electron transmission stems from the strong coupling between symmetric mode and Fabry-Pérot modes inside the allowed band. High uniformity of the boxcar-like transmission and the sharp drop of the transmission edge are both beneficial to the maximum power and the EMP, which are optimized by the bias voltage and the thicknesses of barrier and well. The maximum power and EMP are extracted with the help of machine learning technique, and more than 95% of their theoretical limits can both be achieved for smaller temperature difference, smaller barrier width and larger well width. We hope the obtain results could provide some basic guidance for the future designs of high EMP thermoelectric heat engines.

cond-mat.mes-hall↗