arXiv subjects

Yiming Sun

Publications and source records attributed to Yiming Sun.

At least 55 records · Page 3Linked to original sources

arXiv2025

Heterogeneous-free narrow linewidth semiconductor laser with optical injection locking

Narrow linewidth lasers are indispensable for coherent optical systems, including communications, metrology, and sensing. Although compact semiconductor lasers with narrow linewidths and low noise have been demonstrated, their spectral purity typically relies on hybrid or heterogeneous external cavity feedback. Here, we present a theoretical and experimental demonstration of a heterogeneous free optical injection locking (HF OIL) semiconductor laser. By integrating a topological interface state extended (TISE) laser with a micro ring resonator (MRR) on an AlGaInAs multiple quantum well platform,we achieve monolithic photon injection and phase locking, thereby reducing the optical linewidth. We fabricated and characterized a 1550 nm sidewall HF OIL laser, achieving stable single mode operation over a broad current range (65 to 300 mA) and a side mode suppression ratio (SMSR) over 50 dB. Under injection locking, the devices Voigt fitted linewidth narrowed from over 1.7 MHz (free running) to 4.2 kHz, representing a three order of magnitude improvement over conventional distributed feedback lasers. The intrinsic linewidth of 1.4 kHz is measured by correlated delayed self-heterodyne frequency noise power spectrum density (FN PSD) method. Moreover, the HF OIL laser demonstrated high phase stability and the ability to transition from a random phased to a phase locked state. These results underscore the potential of HF-OIL lasers in advancing coherent optical communications and phase encoders in quantum key distribution (QKD) systems.

physics.optics↗

arXiv2024

ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model

Visual object tracking aims to locate a targeted object in a video sequence based on an initial bounding box. Recently, Vision-Language~(VL) trackers have proposed to utilize additional natural language descriptions to enhance versatility in various applications. However, VL trackers are still inferior to State-of-The-Art (SoTA) visual trackers in terms of tracking performance. We found that this inferiority primarily results from their heavy reliance on manual textual annotations, which include the frequent provision of ambiguous language descriptions. In this paper, we propose ChatTracker to leverage the wealth of world knowledge in the Multimodal Large Language Model (MLLM) to generate high-quality language descriptions and enhance tracking performance. To this end, we propose a novel reflection-based prompt optimization module to iteratively refine the ambiguous and inaccurate descriptions of the target with tracking feedback. To further utilize semantic information produced by MLLM, a simple yet effective VL tracking framework is proposed and can be easily integrated as a plug-and-play module to boost the performance of both VL and visual trackers. Experimental results show that our proposed ChatTracker achieves a performance comparable to existing methods.

cs.CV↗

arXiv2024

Integrating Object Detection Modality into Visual Language Model for Enhanced Autonomous Driving Agent

In this paper, we propose a novel framework for enhancing visual comprehension in autonomous driving systems by integrating visual language models (VLMs) with additional visual perception module specialised in object detection. We extend the Llama-Adapter architecture by incorporating a YOLOS-based detection network alongside the CLIP perception network, addressing limitations in object detection and localisation. Our approach introduces camera ID-separators to improve multi-view processing, crucial for comprehensive environmental awareness. Experiments on the DriveLM visual question answering challenge demonstrate significant improvements over baseline models, with enhanced performance in ChatGPT scores, BLEU scores, and CIDEr metrics, indicating closeness of model answer to ground truth. Our method represents a promising step towards more capable and interpretable autonomous driving systems. Possible safety enhancement enabled by detection modality is also discussed.

cs.CV↗

arXiv2024

Dynamic Brightness Adaptation for Robust Multi-modal Image Fusion

Infrared and visible image fusion aim to integrate modality strengths for visually enhanced, informative images. Visible imaging in real-world scenarios is susceptible to dynamic environmental brightness fluctuations, leading to texture degradation. Existing fusion methods lack robustness against such brightness perturbations, significantly compromising the visual fidelity of the fused imagery. To address this challenge, we propose the Brightness Adaptive multimodal dynamic fusion framework (BA-Fusion), which achieves robust image fusion despite dynamic brightness fluctuations. Specifically, we introduce a Brightness Adaptive Gate (BAG) module, which is designed to dynamically select features from brightness-related channels for normalization, while preserving brightness-independent structural information within the source images. Furthermore, we propose a brightness consistency loss function to optimize the BAG module. The entire framework is tuned via alternating training strategies. Extensive experiments validate that our method surpasses state-of-the-art methods in preserving multi-modal image information and visual fidelity, while exhibiting remarkable robustness across varying brightness levels. Our code is available: https://github.com/SunYM2020/BA-Fusion.

cs.CV↗

arXiv2024

Learning Multimodal Cues of Children's Uncertainty

Understanding uncertainty plays a critical role in achieving common ground (Clark et al.,1983). This is especially important for multimodal AI systems that collaborate with users to solve a problem or guide the user through a challenging concept. In this work, for the first time, we present a dataset annotated in collaboration with developmental and cognitive psychologists for the purpose of studying nonverbal cues of uncertainty. We then present an analysis of the data, studying different roles of uncertainty and its relationship with task difficulty and performance. Lastly, we present a multimodal machine learning model that can predict uncertainty given a real-time video clip of a participant, which we find improves upon a baseline multimodal transformer model. This work informs research on cognitive coordination between human-human and human-AI and has broad implications for gesture understanding and generation. The anonymized version of our data and code will be publicly available upon the completion of the required consent forms and data sheets.

cs.CL↗

arXiv2024

Widely Tunable Photonic Filter Based on Equivalent Chirped Four-Phase-Shifted Sampled Bragg Gratings

We have developed an integrated dual-band photonic filter (PF) utilizing equivalent chirped four-phase-shifted sidewall-sampled Bragg gratings (4PS-SBG) on a silicon-on-insulator (SOI) platform. Using the reconstruction equivalent-chirp technique, we designed linearly chirped 4PS Bragg gratings with two π-phase shifts (π-PS) positioned at 1/3 and 2/3 of the grating cavity, introducing two passbands in the +1st order channel. Leveraging the significant thermo-optic effect of silicon, dual-band tuning is achieved through integrated micro-heaters (MHs) on the chip surface. By varying the injection currents from 0 to 85 mA into the MHs, the device demonstrates continuous and wide-range optical frequency division (OFD) performance, with the frequency interval between the two passbands adjustable from 52.1 GHz to 439.5 GHz. Four notable frequency division setups at 100 GHz, 200 GHz, 300 GHz, and 400 GHz were demonstrated using a 100 GHz, 1535 nm semiconductor passive mode-locked laser as the light source.

physics.optics↗

arXiv2024

Dual-band Photonic Filters with Wide Tunable Range Using Chirped Sampled Gratings

We have developed a photonic filter featuring dual independently tunable passbands. Employing the reconstruction equivalent-chirp technique, we designed linearly chirped sampled Bragg gratings with two equivalent phase shifts positioned at 1/3 and 2/3 of the cavity, thus introducing two passbands in the +1st channel. Leveraging the significant thermo-optic effect of silicon, dual-band tuning is achieved via micro-heaters integrated on the chip surface. By tuning the injection currents ranging from 0 to 35 mA into the micro-heaters, the filter exhibits a wide range of dual-wavelength filtering performance, with the frequency interval between the two passbands adjustable from 37.2 GHz to 186.1 GHz.

physics.optics↗

arXiv2024

One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models

Active learning (AL) for multiple target models aims to reduce labeled data querying while effectively training multiple models concurrently. Existing AL algorithms often rely on iterative model training, which can be computationally expensive, particularly for deep models. In this paper, we propose a one-shot AL method to address this challenge, which performs all label queries without repeated model training. Specifically, we extract different representations of the same dataset using distinct network backbones, and actively learn the linear prediction layer on each representation via an $\ell_p$-regression formulation. The regression problems are solved approximately by sampling and reweighting the unlabeled instances based on their maximum Lewis weights across the representations. An upper bound on the number of samples needed is provided with a rigorous analysis for $p\in [1, +\infty)$. Experimental results on 11 benchmarks show that our one-shot approach achieves competitive performances with the state-of-the-art AL methods for multiple target models.

cs.LG↗

arXiv2024

Continuous Phase Modulation Technology Based on Grating Period Interval for High Grating Coupling Efficiency and Precise Wavelength Control

A novel grating modulation technique for laser arrays is proposed and demonstrated. This method modifies the initial phase within each grating period, applying a total phase shift that increments in an arithmetic progression, ensuring equal channel spacing across the array. Despite the varying phase shifts, the device maintains coupling efficiency comparable to traditional uniform grating structures. Furthermore, the continuous phase modulation enhances the stability of the lasing wavelength of the primary mode, reducing sensitivity to fabrication errors. This improved tolerance to manufacturing inaccuracies represents a significant technological advancement, making this approach highly promising for applications requiring precise and stable wavelength control.

physics.optics↗

arXiv2024

Transfer Learning with Clinical Concept Embeddings from Large Language Models

Knowledge sharing is crucial in healthcare, especially when leveraging data from multiple clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer learning can facilitate cross-site knowledge transfer, but a major challenge is heterogeneity in clinical concepts across different sites. Large Language Models (LLMs) show significant potential of capturing the semantic meaning of clinical concepts and reducing heterogeneity. This study analyzed electronic health records from two large healthcare systems to assess the impact of semantic embeddings from LLMs on local, shared, and transfer learning models. Results indicate that domain-specific LLMs, such as Med-BERT, consistently outperform in local and direct transfer scenarios, while generic models like OpenAI embeddings require fine-tuning for optimal performance. However, excessive tuning of models with biomedical embeddings may reduce effectiveness, emphasizing the need for balance. This study highlights the importance of domain-specific embeddings and careful model tuning for effective knowledge transfer in healthcare.

cs.CL↗

arXiv2024

Narrow Linewidth Distributed Feedback Lasers Utilizing Distributed Phase Shift

This study proposes and experimentally demonstrates a distributed feedback (DFB) laser with a distributed phase shift (DPS) region at the center of the DFB cavity. By modeling the field intensity distribution in the cavity and the output spectrum, the DPS region length and phase shift values have been optimized. Experimental comparisons with lasers using traditional π-phase shifts confirm that DFB lasers with optimized DPS lengths and larger phase shifts (up to 15π) achieve stable single longitudinal mode operation over a broader current range, with lower threshold current, higher power slope efficiency, and a higher side mode suppression ratio (SMSR). Furthermore, the minimum optical linewidth is reduced significantly, from 1.3 MHz to 220 kHz.

physics.optics↗

arXiv2024

A Recent Survey of Heterogeneous Transfer Learning

The application of transfer learning, leveraging knowledge from source domains to enhance model performance in a target domain, has significantly grown, supporting diverse real-world applications. Its success often relies on shared knowledge between domains, typically required in these methodologies. Commonly, methods assume identical feature and label spaces in both domains, known as homogeneous transfer learning. However, this is often impractical as source and target domains usually differ in these spaces, making precise data matching challenging and costly. Consequently, heterogeneous transfer learning (HTL), which addresses these disparities, has become a vital strategy in various tasks. In this paper, we offer an extensive review of over 60 HTL methods, covering both data-based and model-based approaches. We describe the key assumptions and algorithms of these methods and systematically categorize them into instance-based, feature representation-based, parameter regularization, and parameter tuning techniques. Additionally, we explore applications in natural language processing, computer vision, multimodal learning, and biomedicine, aiming to deepen understanding and stimulate further research in these areas. Our paper includes recent advancements in HTL, such as the introduction of transformer-based models and multimodal learning techniques, ensuring the review captures the latest developments in the field. We identify key limitations in current HTL studies and offer systematic guidance for future research, highlighting areas needing further exploration and suggesting potential directions for advancing the field.

cs.LG↗

arXiv2024

Free-form Grid Structure Form Finding based on Machine Learning and Multi-objective Optimisation

Free-form structural forms are widely used to design spatial structures for their irregular spatial morphology. Current free-form form-finding methods cannot adequately meet the material properties, structural requirements or construction conditions, which brings the deviation between the initial 3D geometric design model and the constructed free-form structure. Thus, the main focus of this paper is to improve the rationality of free-form morphology considering multiple objectives in line with the characteristics and constraints of material. In this paper, glued laminated timber is selected as a case. Firstly, machine learning is adopted based on the predictive capability. By selecting a free-form timber grid structure and following the principles of NURBS, the free-form structure is simplified into free-form curves. The transformer is selected to train and predict the curvatures of the curves considering the material characteristics. After predicting the curvatures, the curves are transformed into vectors consisting of control points, weights, and knot vectors. To ensure the constructability and robustness of the structure, minimising the mass of the structure, stress and strain energy are the optimisation objectives. Two parameters (weight and the z-coordinate of the control points) of the free-from morphology are extracted as the variables of the free-form morphology to conduct the optimisation. The evaluation algorithm was selected as the optimal tool due to its capability to optimise multiple parameters. While optimising the two variables, the mechanical performance evaluation indexes such as the maximum displacement in the z-direction are demonstrated in the 60th step. The optimisation results for structure mass, stress and strain energy after 60 steps show the tendency of oscillation convergence, which indicates the efficiency of the proposal multi-objective optimisation.

cs.LG↗

arXiv2024

Narrow Linewidth Laser Based on Extended Topological Interface States in One-Dimensional Photonic Crystals

Recent advances in topological one-dimensional photonic crystal concepts have enabled the development of robust light-emitting devices by incorporating a topological interface state (TIS) at the cavity center. In this study, we theoretically and experimentally demonstrate a one-dimensional TIS-extended photonic crystal (1D-TISE-PC) structure. By integrating a linearly dispersive zero-index one-dimensional photonic crystal structure with a four-phase shift sampled grating, photons propagate along the cavity without phase differences, enhancing the robustness to material variations and extending the TIS. Our findings indicate that extending the TIS promotes a more uniform photon distribution along the laser cavity and mitigates the spatial hole burning (SHB) effect. We fabricated and characterized a 1550 nm sidewall 1D-TISE-PC semiconductor laser, achieving stable single-mode operation across a wide current range from 60 to 420 mA, with a side-mode suppression ratio of 50 dB. The 1D-TISE-PC structure exhibited a linewidth narrowing effect to approximately 150 kHz Lorentzian linewidth. Utilizing reconstruction equivalent-chirp technology for the 4PS sampled grating enabled precise wavelength control in 1D-TISE-PC laser arrays, achieving a wavelength spacing of 0.796 nm +- 0.003 nm. We show that the TIS still exists in the TISE cavity and topological protection is preserved. Its mode extension characteristics mitigate the SHB so narrows the linewidth. We argue that the design simplicity and improvement of the fabrication tolerance make this architecture suitable for high-power and narrow-linewidth semiconductor lasers development.

physics.optics↗

arXiv2024

Online Transfer Learning for RSV Case Detection

Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications. However, utilizing this technique for classification tasks with sequential data often faces challenges, primarily attributed to the scarcity of class labels. To address this challenge, we introduce Multi-Source Adaptive Weighting (MSAW), an online multi-source transfer learning method. MSAW integrates a dynamic weighting mechanism into an ensemble framework, enabling automatic adjustment of weights based on the relevance and contribution of each source (representing historical knowledge) and target model (learning from newly acquired data). We demonstrate the effectiveness of MSAW by applying it to detect Respiratory Syncytial Virus cases within Emergency Department visits, utilizing multiple years of electronic health records from the University of Pittsburgh Medical Center. Our method demonstrates performance improvements over many baselines, including refining pre-trained models with online learning as well as three static weighting approaches, showing MSAW's capacity to integrate historical knowledge with progressively accumulated new data. This study indicates the potential of online transfer learning in healthcare, particularly for developing machine learning models that dynamically adapt to evolving situations where new data is incrementally accumulated.

cs.LG↗

arXiv2023

Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected from 13 ERs may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm, the Single-DANN algorithm, and three baseline methods. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge. Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informative for developing a prediction model to identify COVID-19 patients who are very likely to revisit an ER within 7 days after discharge.

cs.LG↗

arXiv2023

Using the Uniqueness of Global Identifiers to Determine the Provenance of Python Software Source Code

We consider the problem of identifying the provenance of free/open source software (FOSS) and specifically the need of identifying where reused source code has been copied from. We propose a lightweight approach to solve the problem based on software identifiers-such as the names of variables, classes, and functions chosen by programmers. The proposed approach is able to efficiently narrow down to a small set of candidate origin products, to be further analyzed with more expensive techniques to make a final provenance determination.By analyzing the PyPI (Python Packaging Index) open source ecosystem we find that globally defined identifiers are very distinct. Across PyPI's 244 K packages we found 11.2 M different global identifiers (classes and method/function names-with only 0.6% of identifiers shared among the two types of entities); 76% of identifiers were used only in one package, and 93% in at most 3. Randomly selecting 3 non-frequent global identifiers from an input product is enough to narrow down its origins to a maximum of 3 products within 89% of the cases.We validate the proposed approach by mapping Debian source packages implemented in Python to the corresponding PyPI packages; this approach uses at most five trials, where each trial uses three randomly chosen global identifiers from a randomly chosen python file of the subject software package, then ranks results using a popularity index and requires to inspect only the top result. In our experiments, this method is effective at finding the true origin of a project with a recall of 0.9 and precision of 0.77.

cs.SE↗

arXiv2023

Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion

Infrared and visible image fusion aims to integrate comprehensive information from multiple sources to achieve superior performances on various practical tasks, such as detection, over that of a single modality. However, most existing methods directly combined the texture details and object contrast of different modalities, ignoring the dynamic changes in reality, which diminishes the visible texture in good lighting conditions and the infrared contrast in low lighting conditions. To fill this gap, we propose a dynamic image fusion framework with a multi-modal gated mixture of local-to-global experts, termed MoE-Fusion, to dynamically extract effective and comprehensive information from the respective modalities. Our model consists of a Mixture of Local Experts (MoLE) and a Mixture of Global Experts (MoGE) guided by a multi-modal gate. The MoLE performs specialized learning of multi-modal local features, prompting the fused images to retain the local information in a sample-adaptive manner, while the MoGE focuses on the global information that complements the fused image with overall texture detail and contrast. Extensive experiments show that our MoE-Fusion outperforms state-of-the-art methods in preserving multi-modal image texture and contrast through the local-to-global dynamic learning paradigm, and also achieves superior performance on detection tasks. Our code will be available: https://github.com/SunYM2020/MoE-Fusion.

cs.CV↗