arXiv subjects

Yi Zheng

Publications and source records attributed to Yi Zheng.

At least 73 records · Page 4Linked to original sources

arXiv2025

Silicon Nitride Microresonator Raman Lasers

Silicon nitride (SiN) has emerged as a promising platform for integrated nonlinear photonics because of its low propagation loss, wide transparency window, and CMOS compatibility. Nonlinear processes arising from photon-electron interactions, such as Kerr frequency comb generation and second harmonic generation, have been extensively explored. In contrast, photon-phonon interaction-based nonlinearities, such as stimulated Raman scattering, remain largely unexplored in this integrated platform, despite their potential for broadband frequency conversion. Here, we demonstrate efficient Raman lasing in ultra-high-Q SiN microresonators by harnessing the strong intracavity field enhancement and engineering the optical mode to overlap with the Raman-active silica cladding. Through dispersion engineering and waveguide geometry optimization, we suppress competing Kerr nonlinearities while enhancing Raman gain, achieving lasing with sub-2 mW thresholds. We further investigate the trade-off between optical confinement and quality factor, revealing its impact on the overall nonlinear efficiency. Moreover, we also demonstrate broadband tunability of the Raman shift exceeding 120 inverse centimeters, enabled by the wide Raman gain spectrum of silica, offering new flexibility in designing integrated tunable Raman lasers. These results position SiN as a viable platform for chip-scale Raman lasers, expanding the nonlinear optics toolbox of the SiN platform and enabling compact, power-efficient light sources for applications in spectroscopy, optical communications, and quantum photonics.

physics.optics↗

arXiv2025

Optimizing Large Model Training through Overlapped Activation Recomputation

Large model training often uses recomputation to alleviate memory pressure and pipelines to exploit the parallelism of data, tensors, and devices. However, existing recomputation approaches may incur high overhead when training real-world models, as they are executed on demand in the critical training path. In this paper, we present Lynx, a new recomputation framework to reduce overhead by overlapping recomputation with communication in training pipelines. To reduce the large search space for recomputation strategies, we propose a heuristic-based recomputation scheduling algorithm, which is based on the observation that there are identical structures in large DNN models so that we can apply the same scheduling policy to all such structures. Additionally, we propose a recomputation-aware model partitioning method to balance each stage's execution time for improved training throughput. Our comprehensive evaluation using GPT models with 1.3B-23B parameters shows that Lynx outperforms existing recomputation approaches by up to 1.37x.

cs.DC↗

arXiv2025

Ultrawhite structural starch film for sustainable cooling

Reducing human reliance on high-electricity-consuming cooling technologies like air conditioning is crucial for reshaping the global energy paradigm. Through utilizing natural starch gelatinization, freezedrying and densification processes, we fabricated an ultrawhite cooling starch film with an ultrahigh solar reflectance of 0.96 and strong infrared emittance of 0.94. The porous structure of the cooling starch film, systematically controlled by the mechanical pressing processing, allows for effective scattering of solar radiation while emitting strongly during the atmospheric transparency window, thereby contributing to high-efficiency daytime radiative cooling capacity. Furthermore, the cooling starch film exhibits excellent mechanical tensile strength, measuring at up to 38.5 megapascals, which is more than twice the strength of natural wood. The ultrawhite radiative cooling starch film holds significant promise for optimizing cooling energy usage, especially in hot and arid climates.

physics.app-ph↗

arXiv2025

Flexible Tuning of Asymmetric Near-field Radiative Thermal Transistor by Utilizing Distinct Phase Change Materials

Phase change materials (PCMs) play a pivotal role in the development of advanced thermal devices due to their reversible phase transitions, which drastically modify their thermal and optical properties. In this study, we present an effective dynamic thermal transistor with an asymmetric design that employs distinct PCMs, vanadium dioxide (VO2) and germanium antimony telluride (GST), on either side of the gate terminal, which is the center of the control unit of the near-field thermal transistor. This asymmetry introduces unique thermal modulation capabilities, taking control of thermal radiation in the near-field regime. VO2 transitions from an insulating to a metallic state, while GST undergoes a reversible switch between amorphous (aGST) and crystalline (cGST) phases, each inducing substantial changes in thermal transport properties. By strategically combining these materials, the transistor exhibits enhanced functionality, dynamically switching between states of absorbing and releasing heat by tuning the temperature of gate. This gate terminal not only enables active and efficient thermal management but also provides effective opportunities for manipulating heat flow in radiative thermal circuits. Our findings highlight the potential of such asymmetrically structured thermal transistors in advancing applications across microelectronics, high-speed data processing, and sustainable energy systems, where precise and responsive thermal control is critical for performance and efficiency.

cond-mat.mtrl-sci↗

arXiv2025

Improving Retrieval-Augmented Deep Assertion Generation via Joint Training

Unit testing attempts to validate the correctness of basic units of the software system under test and has a crucial role in software development and testing. Very recent work proposes a retrieve-and-edit approach to generate unit test oracles, i.e., assertions. Despite being promising, it is still far from perfect due to some limitations, such as splitting assertion retrieval and generation into two separate components without benefiting each other. In this paper, we propose AG-RAG, a retrieval-augmented automated assertion generation approach that leverages external codebases and joint training to address various technical limitations of prior work. Inspired by the plastic surgery hypothesis, AG-RAG attempts to combine relevant unit tests and advanced pre-trained language models (PLMs) with retrieval-augmented fine-tuning. AG-RAG builds a dense retriever to search for relevant test-assert pairs (TAPs) with semantic matching and a retrieval-augmented generator to synthesize accurate assertions with the focal-test and retrieved TAPs as input. Besides, AG-RAG leverages a code-aware language model CodeT5 as the cornerstone to facilitate both assertion retrieval and generation tasks. Furthermore, the retriever is optimized in conjunction with the generator as a whole pipeline with a joint training strategy. This unified design fully adapts both components specifically for retrieving more useful TAPs, thereby generating accurate assertions. We extensively evaluate AG-RAG against six state-of-the-art AG approaches on two benchmarks and three metrics. Experimental results show that AG-RAG significantly outperforms previous AG approaches on all benchmarks and metrics, e.g., improving the most recent baseline EditAS by 20.82% and 26.98% in terms of accuracy. AG-RAG also correctly generates 1739 and 2866 unique assertions that all baselines fail to generate, 3.45X and 9.20X more than EditAS.

cs.SE↗

arXiv2025

Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models

Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been proposed to automatically generate test assertions, with recent integration-based approaches considered state-of-the-art. Despite being promising, such integration-based approaches face several limitations, including reliance on lexical matching for assertion retrieval and a limited training corpus for assertion generation. This paper proposes a novel retrieval-augmented deep assertion generation approach, namely RetriGen, based on a hybrid retriever and a pre-trained language model (PLM)-based generator. Given a focal-test, RetriGen first builds a hybrid assertion retriever to search for the most relevant Test-Assert Pair from external codebases. The retrieval process considers lexical similarity and semantical similarity via a token-based and an embedding-based retriever, respectively. RetriGen then treats assertion generation as a sequence-to-sequence task and designs a PLM-based assertion generator to predict a correct assertion. We conduct extensive experiments to evaluate RetriGen against six state-of-the-art approaches across two large-scale datasets and two metrics. The results demonstrate that RetriGen achieves 57.66% accuracy and 73.24% CodeBLEU, outperforming all baselines with average improvements of 50.66% and 14.14%, respectively.

cs.SE↗

arXiv2025

Comprehensive Analysis of Thermal Dissipation in Lithium-Ion Battery Packs

Effective thermal management is critical for lithium-ion battery packs' safe and efficient operations, particularly in applications such as drones, where compact designs and varying airflow conditions present unique challenges. This study investigates the thermal performance of a 16-cell lithium-ion battery pack by optimizing cooling airflow configurations and integrating phase change materials (PCMs) for enhanced heat dissipation. Seven geometric configurations were evaluated under airflow speeds ranging from 0 to 15 m/s, reflecting the operational conditions of civilian drones. A comprehensive 3D simulation approach was used to analyze the effects of inlet and outlet configurations, airflow dynamics, and PCM phase transition behavior. Results indicate that the trapezoidal (wide-base) configuration, paired with a 5-inlet and 1-outlet setup, achieves the most balanced performance, effectively maintaining optimal operating temperatures across low and high-speed airflow conditions. PCM integration further stabilized thermal behavior, with phase change durations extending to 12.5 min under tested conditions. These findings highlight the importance of geometric optimization and material integration in advancing compact and reliable thermal management systems for energy-dense battery packs. This study provides a foundation for designing efficient cooling strategies tailored to lightweight applications such as drones and portable energy storage systems.

eess.SY↗

arXiv2025

Studying baryon acoustic oscillations using photometric redshifts from the DESI Legacy Imaging survey DR9

Context.The Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Survey DR9 (DR9 hereafter), with its extensive dataset of galaxy locations and photometric redshifts, presents an opportunity to study baryon acoustic oscillations (BAOs) in the region covered by the ongoing spectroscopic survey with DESI. Aims. We aim to investigate differences between different parts of the DR9 footprint. Furthermore, we want to measure the BAO scale for luminous red galaxies within them. Our selected redshift range of 0.6 to 0.8 corresponds to the bin in which a tension between DESI Y1 and eBOSS was found. Methods. We calculated the anisotropic two-point correlation function in a modified binning scheme to detect the BAOs in DR9 data. We then used template fits based on simulations to measure the BAO scale in the imaging data. Results. Our analysis reveals the expected correlation function shape in most of the footprint areas, showing a BAO scale consistent with Planck's observations. Aside from identified mask-related data issues in the southern region of the South Galactic Cap, we find a notable variance between the different footprints. Conclusions. We find that this variance is consistent with the difference between the DESI Y1 and eBOSS data, and it supports the argument that that tension is caused by sample variance. Additionally, we also uncovered systematic biases not previously accounted for in photometric BAO studies. We emphasize the necessity of adjusting for the systematic shift in the BAO scale associated with typical photometric redshift uncertainties to ensure accurate measurements.

astro-ph.CO↗

arXiv2025

Waste Animal Bone-derived Calcium Phosphate Particles with High Solar Reflectance

Highly reflective Calcium Phosphate (CAP) nanoparticles have been obtained from waste chicken and porcine bones. Chicken and pork bones have been processed and calcined at temperatures between 600°C and 1200°C to remove organic material and resulting in CAP bio-ceramic compounds with high reflectance. The reflectivity of the materials in the solar wavelength region is on par with chemically synthesized CAP. The high reflectivity, consistently over 90%, as well as the size distribution and packing density of the nanoparticles obtained in these early bone studies make a strong case for pursuing this avenue to obtain pigment for high solar reflectivity applications, such as passive daytime radiative cooling. The results presented indicate a viable path toward a cost-effective and eco-friendly source of highly reflective cooling pigments. By sourcing calcium phosphates from animal bones, there is also the potential to divert large quantities of bone waste generated by the meat industry from landfills, further contributing toward sustainability and energy reduction efforts in the construction industry and beyond.

eess.SY↗

arXiv2025

Global Semantic-Guided Sub-image Feature Weight Allocation in High-Resolution Large Vision-Language Models

As the demand for high-resolution image processing in Large Vision-Language Models (LVLMs) grows, sub-image partitioning has become a popular approach for mitigating visual information loss associated with fixed-resolution processing. However, existing partitioning methods uniformly process sub-images, resulting in suboptimal image understanding. In this work, we reveal that the sub-images with higher semantic relevance to the entire image encapsulate richer visual information for preserving the model's visual understanding ability. Therefore, we propose the Global Semantic-guided Weight Allocator (GSWA) module, which dynamically allocates weights to sub-images based on their relative information density, emulating human visual attention mechanisms. This approach enables the model to focus on more informative regions, overcoming the limitations of uniform treatment. We integrate GSWA into the InternVL2-2B framework to create SleighVL, a lightweight yet high-performing model. Extensive experiments demonstrate that SleighVL outperforms models with comparable parameters and remains competitive with larger models. Our work provides a promising direction for more efficient and contextually aware high-resolution image processing in LVLMs, advancing multimodal system development.

cs.CV↗

arXiv2025

Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have achieved remarkable success in a wide range of multimodal tasks by integrating pre-trained vision encoders and large language models. However, current LVLMs primarily rely on visual features extracted from the final layers of the vision encoder, overlooking the complementary information available in shallower layers. While recent approaches have explored the use of multilayer visual features in LVLMs, they tend to be task-agnostic and fail to examine the dependencies of hierarchical visual features on specific tasks. To address these gaps, we systematically investigate the contributions of visual features from different encoder layers using 18 benchmarks spanning 6 task categories. Our findings reveal that multilayer features provide complementary strengths with varying task dependencies, and uniform fusion leads to suboptimal performance. Building on these insights, we propose the instruction-guided vision aggregator, a module that dynamically integrates multi-layer visual features based on textual instructions, without increasing the number of visual tokens. Extensive evaluations demonstrate the superior performance of our method. Additionally, an in-depth analysis of the aggregator's behavior highlights the dominance of mid-to-high-level features in semantic-rich tasks and the critical role of low-level features in fine-grained perception.

cs.CV↗

arXiv2025

FASP: Fast and Accurate Structured Pruning of Large Language Models

The rapid increase in the size of large language models (LLMs) has significantly escalated their computational and memory demands, posing challenges for efficient deployment, especially on resource-constrained devices. Structured pruning has emerged as an effective model compression method that can reduce these demands while preserving performance. In this paper, we introduce FASP (Fast and Accurate Structured Pruning), a novel structured pruning framework for LLMs that emphasizes both speed and accuracy. FASP employs a distinctive pruning structure that interlinks sequential layers, allowing for the removal of columns in one layer while simultaneously eliminating corresponding rows in the preceding layer without incurring additional performance loss. The pruning metric, inspired by Wanda, is computationally efficient and effectively selects components to prune. Additionally, we propose a restoration mechanism that enhances model fidelity by adjusting the remaining weights post-pruning. We evaluate FASP on the OPT and LLaMA model families, demonstrating superior performance in terms of perplexity and accuracy on downstream tasks compared to state-of-the-art methods. Our approach achieves significant speed-ups, pruning models such as OPT-125M in 17 seconds and LLaMA-30B in 15 minutes on a single NVIDIA RTX 4090 GPU, making it a highly practical solution for optimizing LLMs.

cs.LG↗

arXiv2025

Enhancing Lifelong Multi-Agent Path Finding with Cache Mechanism

Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial in autonomous warehouse operations. Lifelong MAPF (L-MAPF), where agents are continuously reassigned new targets upon completing their current tasks, offers a more realistic approximation of real-world warehouse scenarios. While cache storage systems can enhance efficiency and reduce operational costs, existing approaches primarily rely on expectations and mathematical models, often without adequately addressing the challenges of multi-robot planning and execution. In this paper, we introduce a novel mechanism called Lifelong MAPF with Cache Mechanism (L-MAPF-CM), which integrates high-level cache storage with low-level path planning. We have involved a new type of map grid called cache for temporary item storage. Additionally, we involved a task assigner (TA) with a locking mechanism to bridge the gap between the new cache grid and L-MAPF algorithm. The TA dynamically allocates target locations to agents based on their status in various scenarios. We evaluated L-MAPF-CM using different cache replacement policies and task distributions. L-MAPF-CM has demonstrated performance improvements particularly with high cache hit rates and smooth traffic conditions.

cs.RO↗

arXiv2024

Theory of the monochromatic advanced-wave picture and applications in biphoton optics

Klyshko's advanced-wave picture (AWP) is mainly interpreted by replacing the nonlinear crystal producing biphotons via spontaneous parametric down-conversion (SPDC) by a mirror in quantum imaging protocols with thin crystals, where the biphotons are perfectly correlated in position at the crystal. To better explain the biphoton spatial states produced by arbitrary crystals and pump beams, we develop a formal theory of AWP with monochromatic lights that the conditional wave function of one photon is calculated by propagation, multiplication, and another propagation. The case of more general photon postselection or no detection and the inclusion of polarization are studied. Then, we explain the form of the biphoton state from SPDC with a bulk crystal and its free-space propagation. By treating the biphoton wave function as an impulse response function of a classical optical setup, we analyze quantum imaging with undetected photons and quantum holography with polarization entanglement, where properties like the spatial resolution can be concisely deduced. This method can be employed to design nonlinear materials or novel quantum imaging techniques. Finally, we discuss Klyshko's original proposal beyond monochromatic lights with the Hong-Ou-Mandel effect as an example.

quant-ph↗

arXiv2024

Effect of top metallic contacts on energy conversion performances for near-field thermophotovoltaics

The design of metallic contact grids on the front side of thermophotovoltaic cells is critical since it can cause significant optical and electrical resistive losses, particularly in the near field. However, from the theoretical point of view, this effect has been either discarded or studied by means of extremely simplified models like the shadowing methods, that consist in simply ignoring the fraction of the semiconductor surface covered by metal. Our study, based on a rigorous three-body theoretical framework and implemented using the scattering matrix approach with the Fourier modal method augmented with adaptive spatial resolution, provides deeper insight into the influence of the front metal contact grid. This approach allows direct access to the radiative power absorbed by the semiconductor, enabling the proposal of an alternative definition for the thermophotovoltaic cell efficiency. By modeling this grid as a metallic grating, we demonstrate its significant impact on the net radiative power absorbed by the cell and, consequently, on the generated electrical power. Our analysis reveals behaviors differing substantially from those predicted by previous simplistic approaches.

physics.optics↗

arXiv2024

Exploring Thouless Pumping in the Generalized Creutz Model: A Graphical Method and Modulation Schemes

Thouless pumping with nontrivial topological phases provides a powerful means for the manipulation of matter waves in one-dimensional lattice systems. The band topology is revealed by the quantization of pumped charge. In the context of Thouless pumping, we present a graphical representation for the topological phases characterized by the Chern number of an effective two-dimensional band. We illustrate how the two topological phases with distinct Zak phase is connected in the pumping process. Such a visual depiction exhibits typical patterns that is directly related to a linking number and to the Chern number, allowing for the construction of Thouless pumping schemes in a practical way. As a demonstration, we present a generalized Creutz model with tunable Peierls phase, inter-leg imbalance and diagonal hopping. Various modulation schemes for Thouless pumping are studied, focusing on their graphical representations in Bloch space, as well as the quantized pumping phenomenon in real space.

cond-mat.quant-gas↗

arXiv2024

Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment

We study the dynamic pricing and replenishment problems under inconsistent decision frequencies. Different from the traditional demand assumption, the discreteness of demand and the parameter within the Poisson distribution as a function of price introduce complexity into analyzing the problem property. We demonstrate the concavity of the single-period profit function with respect to product price and inventory within their respective domains. The demand model is enhanced by integrating a decision tree-based machine learning approach, trained on comprehensive market data. Employing a two-timescale stochastic approximation scheme, we address the discrepancies in decision frequencies between pricing and replenishment, ensuring convergence to local optimum. We further refine our methodology by incorporating deep reinforcement learning (DRL) techniques and propose a fast-slow dual-agent DRL algorithm. In this approach, two agents handle pricing and inventory and are updated on different scales. Numerical results from both single and multiple products scenarios validate the effectiveness of our methods.

cs.LG↗

arXiv2024

Enhancement of 3D Gaussian Splatting using Raw Mesh for Photorealistic Recreation of Architectures

The photorealistic reconstruction and rendering of architectural scenes have extensive applications in industries such as film, games, and transportation. It also plays an important role in urban planning, architectural design, and the city's promotion, especially in protecting historical and cultural relics. The 3D Gaussian Splatting, due to better performance over NeRF, has become a mainstream technology in 3D reconstruction. Its only input is a set of images but it relies heavily on geometric parameters computed by the SfM process. At the same time, there is an existing abundance of raw 3D models, that could inform the structural perception of certain buildings but cannot be applied. In this paper, we propose a straightforward method to harness these raw 3D models to guide 3D Gaussians in capturing the basic shape of the building and improve the visual quality of textures and details when photos are captured non-systematically. This exploration opens up new possibilities for improving the effectiveness of 3D reconstruction techniques in the field of architectural design.

cs.CV↗