arXiv subjects

Jin Niu

Publications and source records attributed to Jin Niu.

7 recordsLinked to original sources

arXiv2026

Multiscale carrier-envelope phase characterization of 2-μm pulses delivered by a 200-kHz optical parametric amplifier

Light fields with a central wavelength of 2 um are very well suited for strong-field-driven charge carrier control: Their photon energy lies far below the band gap of many materials, while their oscillation period remains significantly shorter than the coherence time of charge carrier oscillations. The resulting potential for field-driven charge carrier control is contingent on the reproducibility of the field structure of such ultrashort laser pulses. Here, we present a compact 200-kHz laser system that delivers ultrashort pulses with a duration of less than 20 fs in the spectral range around 2 um and with a pulse energy of 25 uJ. The electric field structure of the 2-um pulses is characterized in detail. In particular, the carrier-envelope phase (CEP) is measured over a wide range of timescales, from microseconds to hours. Passive stabilization due to difference frequency generation results in a root mean square value of carrier-envelope phase noise of less than 70 mrad over all measured time scales. The applicability of the pulses is demonstrated by measuring CEP-dependent high-order harmonic spectra with energies of up to 160 eV.

physics.optics↗

arXiv2026

Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning

In the context of global urbanization and motorization, traffic congestion has become a significant issue, severely affecting the quality of life, environment, and economy. This paper puts forward a single-agent reinforcement learning (RL)-based regional traffic signal control (TSC) model. Different from multi - agent systems, this model can coordinate traffic signals across a large area, with the goals of alleviating regional traffic congestion and minimizing the total travel time. The TSC environment is precisely defined through specific state space, action space, and reward functions. The state space consists of the current congestion state, which is represented by the queue lengths of each link, and the current signal phase scheme of intersections. The action space is designed to select an intersection first and then adjust its phase split. Two reward functions are meticulously crafted. One focuses on alleviating congestion and the other aims to minimize the total travel time while considering the congestion level. The experiments are carried out with the SUMO traffic simulation software. The performance of the TSC model is evaluated by comparing it with a base case where no signal-timing adjustments are made. The results show that the model can effectively control congestion. For example, the queuing length is significantly reduced in the scenarios tested. Moreover, when the reward is set to both alleviate congestion and minimize the total travel time, the average travel time is remarkably decreased, which indicates that the model can effectively improve traffic conditions. This research provides a new approach for large-scale regional traffic signal control and offers valuable insights for future urban traffic management.

cs.LG↗

arXiv2026

Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations

Traffic congestion, primarily driven by intersection queuing, significantly impacts urban living standards, safety, environmental quality, and economic efficiency. While Traffic Signal Control (TSC) systems hold potential for congestion mitigation, traditional optimization models often fail to capture real-world traffic complexity and dynamics. This study introduces a novel single-agent reinforcement learning (RL) framework for regional adaptive TSC, circumventing the coordination complexities inherent in multi-agent systems through a centralized decision-making paradigm. The model employs an adjacency matrix to unify the encoding of road network topology, real-time queue states derived from probe vehicle data, and current signal timing parameters. Leveraging the efficient learning capabilities of the DreamerV3 world model, the agent learns control policies where actions sequentially select intersections and adjust their signal phase splits to regulate traffic inflow/outflow, analogous to a feedback control system. Reward design prioritizes queue dissipation, directly linking congestion metrics (queue length) to control actions. Simulation experiments conducted in SUMO demonstrate the model's effectiveness: under inference scenarios with multi-level (10%, 20%, 30%) Origin-Destination (OD) demand fluctuations, the framework exhibits robust anti-fluctuation capability and significantly reduces queue lengths. This work establishes a new paradigm for intelligent traffic control compatible with probe vehicle technology. Future research will focus on enhancing practical applicability by incorporating stochastic OD demand fluctuations during training and exploring regional optimization mechanisms for contingency events.

cs.LG↗

arXiv2025

Subpixel correction of diffraction pattern shifts in ptychography via automatic differentiation

Ptychography, a coherent diffraction imaging technique, has become an indispensable tool in materials characterization, biological imaging, and nanostructure analysis due to its capability for high-resolution, lensless reconstruction of complex-valued images. In typical workflows, raw diffraction patterns are commonly cropped to isolate the valid central region before reconstruction. However, if the crop is misaligned from the diffraction pattern's zero-order, reconstruction may suffer from slower convergence, phase wrapping, and reduced image fidelity. These issues are further exacerbated in experimental configurations involving reflective geometries or broadband illumination, where incorrect cropping introduces systematic preprocessing errors that compromise the entire ptychographic inversion. To address this challenge, we present an approach based on automatic differentiation (AD), where the cropping shift is treated as an optimizable parameter within the reconstruction framework. By integrating shift correction into the backpropagation loop, our method simultaneously refines the object, probe, and shift positions without requiring manual tuning. Simulation results demonstrate that, even with initial offsets ranging up to 5 pixels, the proposed method achieves subpixel correction, with an average deviation below 0.5 pixels. Experiments in the extreme ultraviolet (EUV) regime further validate the method's robustness and effectiveness. This AD-based strategy enhances the automation and robustness of ptychographic reconstructions, and is adaptable to diverse experimental conditions.

physics.optics↗

arXiv2024

Multi-color Wavefront Sensor using Talbot effect for High-order Harmonic Generation

We present a novel method for multi-color wavefront measurement of high-order harmonic generation beams using the Talbot effect, validated both theoretically and experimentally for the first time. Each harmonic maintains a unique wavefront and produces an independent set of self-images along the optical axis.We achieved the wavefronts reconstruction of three harmonics in a single measurement scan, expanding the spectrally-resolved capability of the conventional Talbot effect wavefront sensor. This breakthrough introduces a novel tool for studying the multi-color wavefront in high-order harmonic generation, unlocking the potential to investigate spatiotemporal ultrafast nonlinear dynamics in attosecond pulse formation on a shot-by-shot basis.

physics.optics↗

arXiv2023

Longitudinal Position and Cancer Risk in the United States Revisited

Background: The debate over daylight saving time has surged, with interests in the effects of sunlight exposure on health. \commentnj{Prior studies simulated daylight saving time and standard time conditions by analyzing different locations within time zones and neighboring areas across time zone borders. Methods: We analyzed cancer incidence rates from various longitudinal positions within time zones and at time zone borders in the contiguous United States. Using data from State Cancer Profiles (2016-2020), we analyzed total cancer of 19 types and specific rates for eight cancers, adjusted for age and includes all demographics. Log-linear regression is used to replicate a previous study, and spatial regression models are employed to explore discontinuities at borders. Results: Cancer rate differences lack statistical significance within time zones and near borders for total cancer and most individual cancers. Exceptions included breast, prostate, and liver \& bile duct cancers, which exhibited significant relationships with relative position at the 95\% significance level. Breast and liver and bile duct cancers saw decreases, while prostate cancer incidence increased from west to east within time zones. Conclusions: Relative position does not have a significant impact on cancer incidence, hence cancer development in general. Isolated exceptions may warrant further investigation as more data becomes available. Impact: Our findings challenge prior research, revealing numerous inconsistencies. These disparities urge a reconsideration of the potential disparities in human health associated with daylight saving time and standard time. They offer insights contribute to the ongoing discussion surrounding the retention or abandonment of DST.

stat.AP↗

arXiv2020

Hybrid Interest Modeling for Long-tailed Users

User behavior modeling is a key technique for recommender systems. However, most methods focus on head users with large-scale interactions and hence suffer from data sparsity issues. Several solutions integrate side information such as demographic features and product reviews, another is to transfer knowledge from other rich data sources. We argue that current methods are limited by the strict privacy policy and have low scalability in real-world applications and few works consider the behavioral characteristics behind long-tailed users. In this work, we propose the Hybrid Interest Modeling (HIM) network to hybrid both personalized interest and semi-personalized interest in learning long-tailed users' preferences in the recommendation. To achieve this, we first design the User Behavior Pyramid (UBP) module to capture the fine-grained personalized interest of high confidence from sparse even noisy positive feedbacks. Moreover, the individual interaction is too sparse and not enough for modeling user interest adequately, we design the User Behavior Clustering (UBC) module to learn latent user interest groups with self-supervised learning mechanism novelly, which capture coarse-grained semi-personalized interest from group-item interaction data. Extensive experiments on both public and industrial datasets verify the superiority of HIM compared with the state-of-the-art baselines.

cs.IR↗