arXiv subjects

Ziyu He

Publications and source records attributed to Ziyu He.

6 recordsLinked to original sources

arXiv2026

Mid-infrared single-pixel imaging via two-photon optical encoding

Mid-infrared (MIR) imaging offers powerful capabilities for label-free chemical analysis, yet its practical deployment remains hindered by the high cost and cryogenic complexity of conventional cameras. Two-photon absorption (TPA) provides a promising route to room-temperature MIR detection, but existing TPA imagers based on raster scanning or array detectors are constrained by slow acquisition speed or limited detection sensitivity. Here we present a scanning-free MIR single-pixel imaging approach based on non-degenerate TPA in a silicon detector. The involved spatial encoding is realized by a near-infrared structured pump with a resolution of 7 $\mu$m, thus allowing high-fidelity MIR optical modulation through the phase-matching-free nonlinear interaction. Consequently, the spatially modulated TPA response is intrinsically integrated in the single-element photodetector, which favors computational reconstruction of the impinging MIR image by correlating measured intensities and predetermined patterns. Notably, the use of advanced algorithms of compressed sensing and deep learning facilitate image recovery under sub-Nyquist sampling with a compression ratio of 10\% and photon-starved illumination with an incident light flux of 0.5 pJ/pulse. Furthermore, a multispectral imaging over 2.5-3.8 $\mu$m is manifested for chemical discrimination of plastic films. The presented architecture would offer a broadband and sensitive alternative for MIR imaging in various fields ranging from biomedical diagnostics to material inspection.

physics.optics↗

arXiv2026

Mid-infrared temporal ghost imaging via two-photon structured encoding

Temporal ghost imaging (TGI) enables ultrafast signal reconstruction beyond electronic bandwidth limits. Extending this concept to the mid-infrared (MIR) regime through nonlinear frequency conversion offers new opportunities for high-fidelity temporal detection, but remains constrained by stringent phase-matching condition, limited spectral coverage, and intricate optical alignment. Here, we propose and demonstrate a broadband MIR TGI system based on non-degenerate two-photon absorption. A temporally encoded near-infrared pump transfers structured modulation onto a MIR signal directly at a silicon detector, which facilitates concurrent modulation and detection without external nonlinear crystals. The reconstructed temporal waveforms exceed the detector bandwidth by more than fortyfold, achieve a detection sensitivity of 0.05 pJ/pulse, allow compressed sensing with 80\% fewer measurements, and support broadband operation across 2.5-3.8 $\mu$m. This compact, alignment-free, and room-temperature system establishes a practical route for fast and sensitive MIR time-domain analysis, holding great promise for applications in time-resolved molecular spectroscopy, high-precision infrared ranging, and high-speed free-space communication.

physics.optics↗

arXiv2026

Mid-infrared single-photon sub-pixel temporal ghost imaging

Temporal ghost imaging (TGI) enables ultrafast temporal signal recovery using slow detectors, offering a promising route for high-speed mid-infrared (MIR) detection. However, conventional schemes remain limited in temporal resolution by the modulation bandwidth or pattern timescale, and are mostly confined to structured illumination. Here, we demonstrated a high-resolution MIR single-photon computational TGI system, which integrated nonlinear structured detection with sub-pixel temporal shifting. A pre-programmed near-infrared pump serves as a temporally optical gate to drive sum-frequency generation in a nonlinear crystal. Consequently, MIR waveforms at 3.4 $\mu$m were upconverted, and captured by a room-temperature silicon detector. We realized sub-pixel operation by fractional-bin temporal stepping of the gate and multi-shot fusion via pseudo-inverse reconstruction. The sub-pixel shifting strategy decouples the achievable resolution from modulation speed, enabling 40 ps temporal precision at a driving rate of only 3.125 Gbps. This performance surpasses both detector jitter and pattern-rate limits, while maintaining single-photon sensitivity. The presented paradigm establishes a versatile route for ultrafast MIR waveform reconstruction, opening new opportunities in high-resolution infrared sensing and quantum photonics.

physics.optics↗

arXiv2023

Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models

Rank models play a key role in industrial recommender systems, advertising, and search engines. Existing works utilize semantic tags and user-item interaction behaviors, e.g., clicks, views, etc., to predict the user interest and the item hidden representation for estimating the user-item preference score. However, these behavior-tag-based models encounter great challenges and reduced effectiveness when user-item interaction activities are insufficient, which we called "the long-tail ranking problem". Existing rank models ignore this problem, but its common and important because any user or item can be long-tailed once they are not consistently active for a short period. In this paper, we propose a novel neighbor enhancement structure to help train the representation of the target user or item. It takes advantage of similar neighbors (static or dynamic similarity) with multi-level attention operations balancing the weights of different neighbors. Experiments on the well-known public dataset MovieLens 1M demonstrate the efficiency of the method over the baseline behavior-tag-based model with an absolute CTR AUC gain of 0.0259 on the long-tail user dataset.

cs.IR↗

arXiv2018

Convolutional Sequence to Sequence Non-intrusive Load Monitoring

A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partially overlapped output sequences of the network are averaged to produce the final output of the model. We apply the proposed model to the REDD dataset and compare it with the convolutional sequence to point model in the literature. Results show that the proposed model is able to give satisfactory disaggregation performance for appliances with varied characteristics.

stat.ML↗

arXiv2018

Short-term Load Forecasting with Deep Residual Networks

We present in this paper a model for forecasting short-term power loads based on deep residual networks. The proposed model is able to integrate domain knowledge and researchers' understanding of the task by virtue of different neural network building blocks. Specifically, a modified deep residual network is formulated to improve the forecast results. Further, a two-stage ensemble strategy is used to enhance the generalization capability of the proposed model. We also apply the proposed model to probabilistic load forecasting using Monte Carlo dropout. Three public datasets are used to prove the effectiveness of the proposed model. Multiple test cases and comparison with existing models show that the proposed model is able to provide accurate load forecasting results and has high generalization capability.

stat.ML↗