arXiv subjects

Xue Dong

Publications and source records attributed to Xue Dong.

At least 19 recordsLinked to original sources

arXiv2026

High-Energy Microresonator Soliton Generation

Kerr resonators generate stable frequency combs in a compact platform with applications in coherent communications, sensing, quantum information processing, and astrophysics. Ultrashort pulses can be generated, moreover, with a wavelength and repetition rate flexibility inaccessible by traditional mode-locked lasers, which is desirable for high peak-power applications including in biomedicine and materials processing. However, for these applications, single-pulse energies will need to be significantly improved beyond the fJ level characteristic of sources today. Here we describe and demonstrate a simple approach for increasing the pulse energy in anomalous dispersion Kerr microresonators. Through scaling laws based on a mean-field cavity model and supported by experimentally-accurate numerical simulations, we show that pulse energy scales strongly with output coupling if supported by sufficient drive power. In strongly over-coupled cavities, femtosecond pulses can be stabilized with pulse energy beyond the pJ level. With this theoretical framework, by pumping a 12 GHz Si3N4 cavity with 30% output coupling with 2.6-ps time-lens generated pulses, we observe stable 74-fs pulses with a record output pulse energy of 6 pJ. High energy Kerr resonators are anticipated to improve performance for current applications and compliment mode locked lasers for high peak power applications.

physics.optics↗

arXiv2026

Field-induced topological Hall effect and butterfly-shaped magnetoresistance in the centrosymmetric antiferromagnet EuAuAs

The coupling between magnetic and electronic degrees of freedom gives rise to a variety of intriguing transport phenomena. Among them, the topological Hall effect, originating from the real-space Berry phase associated with nontrivial magnetic textures, has attracted considerable attention. Here, we systematically investigate the magnetic and transport properties of antiferromagnet EuAuAs. Magnetic characterizations reveal antiferromagnetic transition at 5.7 K and 6.3 K for $H \parallel ab$ and $H \parallel c$, accompanied by metamagnetic transition and small hysteresis for $H \parallel ab$. Electrical transport measurements reveal a pronounced topological Hall effct in the antiferromagnetic state with $H \parallel ab$ and $I \parallel c$, which may be attributed to finite scalar spin chirality. Furthermore, the magnetoresistance exhibits butterfly-shaped hysteresis and strong angular dependence, which are likely associated with spin-dependent electron scattering, magnetic-domain evolution, and domain-wall pinning. Our results suggest that field-induced spin textures play an important role in the magnetotransport properties and provide insights into the interplay between magnetic textures and electronic transport in centrosymmetric antiferromagnets.

cond-mat.mtrl-sci↗

arXiv2026

Biocompatibility of Additively Manufactured Fe-AZ31 Biodegradable Composites for Craniofacial Implant Applications

Metallic plating systems composed of titanium and its alloys remain the standard treatment for craniofacial bony fixation but may require secondary removal due to infection, implant migration, or discomfort. Thus, biodegradable metallic implants may eliminate complications and secondary procedures while maintaining structural integrity. Our previous work demonstrated the fabrication of immiscible Fe-AZ31 composites via additive manufacturing with improved degradation kinetics over pure Iron. This study aimed to evaluate the in vitro and in vivo biocompatibility of Fe-AZ31 composites for potential craniofacial fixation applications. Pure iron (Fe), Mg alloy (AZ31) and Fe-AZ31 samples were fabricated for extract-based cytotoxicity testing using HFF-1 fibroblasts, L929 fibroblasts and hFOB osteoblasts. Metal extracts were prepared at a 3 cm^2/mL surface-to-volume ratio in complete media at 37C and cell viability was measured by live/dead assay after 24 and 72h exposure. For in vivo evaluation, Fe-AZ31, Fe, and Ti plates were implanted subcutaneously in wild type mice for 6 weeks, 3 and 6 months. Implant degradation, histologic response, hematology, and serum biochemistry were assessed. Fe-AZ31 extracts demonstrated >70% cell viability across all cell types at both timepoints with normal cell morphology and adhesion, whereas AZ31 extracts caused marked cytotoxicity associated with pronounced alkalization (pH 10). In vivo, Fe-AZ31 implants exhibited gradual surface corrosion accompanied by mild, transient inflammation and minimal capsule formation over time. No systemic toxicity was observed. Hematology and serum biochemistry remained within physiological limits. Additively manufactured Fe-AZ31 composites demonstrate acceptable biocompatibility and favorable tissue responses, supporting their development as resorbable metallic fixation devices for craniofacial reconstruction.

physics.med-ph↗

arXiv2025

Optical Computing with Spectrally Multiplexed Features in Complex Media

Artificial intelligence (AI) has rapidly evolved into a critical technology; however, electrical hardware struggles to keep pace with the exponential growth of AI models. Free space optical hardware provides alternative approaches for large-scale optical processing, and in-memory computing, with applications across diverse machine learning tasks. Here, we explore the use of broadband light scattering in free-space optical components, specifically complex media, which generate uncorrelated optical features at each wavelength. By treating individual wavelengths as independent predictors, we demonstrate improved classification accuracy through in-silico majority voting, along with the ability to estimate uncertainty without requiring access to the model's probability outputs. We further demonstrate that linearly combining multiwavelength features, akin to spectral shaping, enables us to tune output features with improved performance on classification tasks, potentially eliminating the need for multiple digital post-processing steps. These findings illustrate the spectral multiplexing or broadband advantage for free-space optical computing.

physics.optics↗

arXiv2025

A composition theory for upward planar orders

An upward planar order on an acyclic directed graph $G$ is a special linear extension of the edge poset of $G$ that satisfies the nesting condition. This order was introduced to combinatorially characterize upward plane graphs and progressive plane graphs (commonly known as plane string diagrams). In this paper, motivated by the theory of graphical calculus for monoidal categories, we establish a composition theory for upward planar orders. The main result is that the composition of upward planar orders is an upward planar order. This theory provides a practical method to calculate the upward planar order of a progressive plane graph or an upward plane graph.

math.CO↗

arXiv2025

Modality Reliability Guided Multimodal Recommendation

Multimodal recommendation faces an issue of the performance degradation that the uni-modal recommendation sometimes achieves the better performance. A possible reason is that the unreliable item modality data hurts the fusion result. Several existing studies have introduced weights for different modalities to reduce the contribution of the unreliable modality data in predicting the final user rating. However, they fail to provide appropriate supervisions for learning the modality weights, making the learned weights imprecise. Therefore, we propose a modality reliability guided multimodal recommendation framework that uniquely learns the modality weights supervised by the modality reliability. Considering that there is no explicit label provided for modality reliability, we resort to automatically identify it through the BPR recommendation objective. In particular, we define a modality reliability vector as the supervision label by the difference between modality-specific user ratings to positive and negative items, where a larger difference indicates a higher reliability of the modality as the BPR objective is better satisfied. Furthermore, to enhance the effectiveness of the supervision, we calculate the confidence level for the modality reliability vector, which dynamically adjusts the supervision strength and eliminates the harmful supervision. Extensive experiments on three real-world datasets show the effectiveness of the proposed method.

cs.IR↗

arXiv2024

Preview-based Category Contrastive Learning for Knowledge Distillation

Knowledge distillation is a mainstream algorithm in model compression by transferring knowledge from the larger model (teacher) to the smaller model (student) to improve the performance of student. Despite many efforts, existing methods mainly investigate the consistency between instance-level feature representation or prediction, which neglects the category-level information and the difficulty of each sample, leading to undesirable performance. To address these issues, we propose a novel preview-based category contrastive learning method for knowledge distillation (PCKD). It first distills the structural knowledge of both instance-level feature correspondence and the relation between instance features and category centers in a contrastive learning fashion, which can explicitly optimize the category representation and explore the distinct correlation between representations of instances and categories, contributing to discriminative category centers and better classification results. Besides, we introduce a novel preview strategy to dynamically determine how much the student should learn from each sample according to their difficulty. Different from existing methods that treat all samples equally and curriculum learning that simply filters out hard samples, our method assigns a small weight for hard instances as a preview to better guide the student training. Extensive experiments on several challenging datasets, including CIFAR-100 and ImageNet, demonstrate the superiority over state-of-the-art methods.

cs.CV↗

arXiv2024

Tele-Aloha: A Low-budget and High-authenticity Telepresence System Using Sparse RGB Cameras

In this paper, we present a low-budget and high-authenticity bidirectional telepresence system, Tele-Aloha, targeting peer-to-peer communication scenarios. Compared to previous systems, Tele-Aloha utilizes only four sparse RGB cameras, one consumer-grade GPU, and one autostereoscopic screen to achieve high-resolution (2048x2048), real-time (30 fps), low-latency (less than 150ms) and robust distant communication. As the core of Tele-Aloha, we propose an efficient novel view synthesis algorithm for upper-body. Firstly, we design a cascaded disparity estimator for obtaining a robust geometry cue. Additionally a neural rasterizer via Gaussian Splatting is introduced to project latent features onto target view and to decode them into a reduced resolution. Further, given the high-quality captured data, we leverage weighted blending mechanism to refine the decoded image into the final resolution of 2K. Exploiting world-leading autostereoscopic display and low-latency iris tracking, users are able to experience a strong three-dimensional sense even without any wearable head-mounted display device. Altogether, our telepresence system demonstrates the sense of co-presence in real-life experiments, inspiring the next generation of communication.

cs.CV↗

arXiv2024

Ultrafast Carrier Relaxation Dynamics in a Nodal-Line Semimetal PtSn$_4$

Topological Dirac nodal-line semimetals host topologically nontrivial electronic structure with nodal-line crossings around the Fermi level, which could affect the photocarrier dynamics and lead to novel relaxation mechanisms. Herein, by using time- and angle-resolved photoemission spectroscopy, we reveal the previously-inaccessible linear dispersions of the bulk conduction bands above the Fermi level in a Dirac nodal-line semimetal PtSn$_4$, as well as the momentum and temporal evolution of the gapless nodal lines. A surprisingly ultrafast relaxation dynamics within a few hundred femtoseconds is revealed for photoexcited carriers in the nodal line. Theoretical calculations suggest that such ultrafast carrier relaxation is attributed to the multichannel scatterings among the complex metallic bands of PtSn$_4$ via electron-phonon coupling. In addition, a unique dynamic relaxation mechanism contributed by the highly anisotropic Dirac nodal-line electronic structure is also identified. Our work provides a comprehensive understanding of the ultrafast carrier dynamics in a Dirac nodal-line semimetal.

cond-mat.mes-hall↗

arXiv2024

Prompt-based Multi-interest Learning Method for Sequential Recommendation

Multi-interest learning method for sequential recommendation aims to predict the next item according to user multi-faceted interests given the user historical interactions. Existing methods mainly consist of a multi-interest extractor that embeds the multiple user interests based on the user interactions, and a multi-interest aggregator that aggregates the learned multi-interest embeddings to derive the final user embedding, used for predicting the user rating to an item. Despite their effectiveness, existing methods have two key limitations: 1) they directly feed the user interactions into the multi-interest extractor and aggregator, while ignoring their different learning objectives, and 2) they merely consider the centrality of the user interactions to embed multiple interests of the user, while overlooking their dispersion. To tackle these limitations, we propose a prompt-based multi-interest learning method (PoMRec), where specific prompts are inserted into user interactions, making them adaptive to the extractor and aggregator. Moreover, we utilize both the mean and variance embeddings of user interactions to embed the user multiple interests for the comprehensively user interest learning. We conduct extensive experiments on three public datasets, and the results verify that our proposed PoMRec outperforms the state-of-the-art multi-interest learning methods.

cs.IR↗

arXiv2024

Quantum Optimal Control Theory for the Shaping of Flying Qubits

The control of flying qubits carried by itinerant photons is ubiquitous in quantum networks. Beside their logical states, the shape of flying qubits must also be tailored for high-efficiency information transmission. In this paper, we introduce quantum optimal control theory to the shaping of flying qubits. Building on the flying-qubit control model established in our previous work, we design objective functionals for the generation of shaped flying qubits under practical constraints on the emitters and couplers. Numerical simulations employing gradient-descent algorithms demonstrate that the optimized control can effectively mitigate unwanted level and photon leakage caused by these non-idealities. Notably, while coherent control offers limited shaping capacity with a fixed coupler, it can significantly enhance the shaping performance when combined with a tunable coupler that has restricted tunability. The proposed optimal control framework provides a systematic approach to achieving high-quality control of flying qubits using realistic quantum devices.

quant-ph↗

arXiv2023

Depth-multiplexing spectral domain OCT for full eye length imaging with a single modulation unit

Clinical measurement of a patient's axial eye length is emerging as a crucial approach to track progression and monitor management of myopia. However, the preferred method for such measurements is swept-source OCT, whose cost prohibits broad use, especially in lower-income communities. Spectral domain (SD) OCT is a more affordable option, but it has limited imaging depth range, so is not suitable for full eye length measurement. Depth-multiplexing (DM) techniques for SD-OCT provide a workaround by capturing images at multiple depths within the eye. However, these methods typically require multiple light modulation units or detectors for simultaneous imaging across depths, adding complexity and cost. In response, we propose a novel DM-SD-OCT approach that utilizes a single light modulation unit for depth encoding. We capture images at multiple depths within the eye simultaneously with a single line scan camera, then computationally demix the contributions from different depths. Here, we demonstrate acquisition and demixing of signals from three distinct depths within the eye and validate experimentally in human subjects. Our method thus offers a cost-effective solution for comprehensive eye length measurement in clinical myopia research.

physics.optics↗

arXiv2023

Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective

Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterating on EBR in production, we still identify some fundamental issues in existing methods. First, when dealing with large corpus of candidate items, EBR models often have difficulties in balancing the performance on distinguishing highly relevant items (positives) from both irrelevant ones (easy negatives) and from somewhat related yet not competitive ones (hard negatives). Also, we have little control in the diversity and fairness of the retrieval results because of the ``greedy'' nature of nearest vector search. These issues compromise the performance of EBR methods in large-scale industrial scenarios. This paper introduces a simple and proven-in-production solution to overcome these issues. The proposed solution takes a divide-and-conquer approach: the whole set of candidate items are divided into multiple clusters and we run EBR to retrieve relevant candidates from each cluster in parallel; top candidates from each cluster are then combined by some controllable merging strategies. This approach allows our EBR models to only concentrate on discriminating positives from mostly hard negatives. It also enables further improvement from a multi-tasking learning (MTL) perspective: retrieval problems within each cluster can be regarded as individual tasks; inspired by recent successes in prompting and prefix-tuning, we propose an efficient task adaption technique further boosting the retrieval performance within each cluster with negligible overheads.

cs.IR↗

arXiv2022

Latent Evolution Model for Change Point Detection in Time-varying Networks

Graph-based change point detection (CPD) play an irreplaceable role in discovering anomalous graphs in the time-varying network. While several techniques have been proposed to detect change points by identifying whether there is a significant difference between the target network and successive previous ones, they neglect the natural evolution of the network. In practice, real-world graphs such as social networks, traffic networks, and rating networks are constantly evolving over time. Considering this problem, we treat the problem as a prediction task and propose a novel CPD method for dynamic graphs via a latent evolution model. Our method focuses on learning the low-dimensional representations of networks and capturing the evolving patterns of these learned latent representations simultaneously. After having the evolving patterns, a prediction of the target network can be achieved. Then, we can detect the change points by comparing the prediction and the actual network by leveraging a trade-off strategy, which balances the importance between the prediction network and the normal graph pattern extracted from previous networks. Intensive experiments conducted on both synthetic and real-world datasets show the effectiveness and superiority of our model.

cs.SI↗

arXiv2022

Dual Preference Distribution Learning for Item Recommendation

Recommender systems can automatically recommend users with items that they probably like. The goal of them is to model the user-item interaction by effectively representing the users and items. Existing methods have primarily learned the user's preferences and item's features with vectorized embeddings, and modeled the user's general preferences to items by the interaction of them. In fact, users have their specific preferences to item attributes and different preferences are usually related. Therefore, exploring the fine-grained preferences as well as modeling the relationships among user's different preferences could improve the recommendation performance. Toward this end, we propose a dual preference distribution learning framework (DUPLE), which aims to jointly learn a general preference distribution and a specific preference distribution for a given user, where the former corresponds to the user's general preference to items and the latter refers to the user's specific preference to item attributes. Notably, the mean vector of each Gaussian distribution can capture the user's preferences, and the covariance matrix can learn their relationship. Moreover, we can summarize a preferred attribute profile for each user, depicting his/her preferred item attributes. We then can provide the explanation for each recommended item by checking the overlap between its attributes and the user's preferred attribute profile. Extensive quantitative and qualitative experiments on six public datasets demonstrate the effectiveness and explainability of the DUPLE method.

cs.IR↗

arXiv2022

120-fs single-pulse generation from stretched-pulse fiber Kerr resonators

Fiber Kerr resonators are simple driven resonators with desirable wavelength and repetition rate flexibility for generating ultrashort pulses for applications including telecommunications, biomedicine, and materials processing. However, fiber Kerr resonators to date often generate longer pulses and require more complicated techniques for generating single pulses than would be desirable for applications. Here we address these limits by demonstrating robust single-pulse performance with 120-fs pulse durations in fiber Kerr resonators based on stretched-pulse solitons. Through matching numerical and experimental studies, stretched-pulse soliton performance is found to strongly depend on the total cavity length, and the optimum length is found to depend on the drive, Raman scattering, and the total pulse stretching. By designing the cavity for this optimum with the described setup, stable stretched-pulse solitons with 120-fs duration are experimentally observed. In addition, soliton trapping is demonstrated with a pulsed drive source despite large intracavity breathing and single-pulse performance is observed. Robust with high performance single-pulse generation is a critical step toward useful femtosecond pulse generation.

physics.optics↗

arXiv2022

Flying-Qubit Control via a Three-level Atom with Tunable Waveguide Couplings

The control of flying qubits is at the core of quantum networks. As often carried by single-photon fields, the flying-qubit control involves not only their logical states but also their shapes. In this paper, we explore a variety of flying-qubit control problems using a three-level atom with time-varying tunable couplings to two input-output channels. It is shown that one can tune the couplings of a $Λ$-type atom to distribute a single photon into the two channels with arbitrary shapes, or use a $V$-type atom to catch an arbitrary-shape distributed single photon. The $Λ$-type atom can also be designed to transfer a flying qubit from one channel to the other, with both the central frequency and the photon shape being converted. With a $Ξ$-type atom, one can use the tunable coupling to shape a pair of correlated photons via cascaded emission. In all cases, analytical formulas are derived for the coupling functions to fulfil these control tasks, and their physical limitations are discussed as well. These results provide useful control protocols for high-fidelity quantum information transmission over complex quantum networks.

quant-ph↗

arXiv2021

Design and evaluation of a FPGA-ADC prototype for the PET detector based on LYSO Crystals and SiPM arrays

The aim of this study is to design and evaluate a simple free running Analog-Digital Converter (ADC) based on the Field Programmable Gate Array (FPGA) device to accomplish the energy and position readout of the silicon photomultiplier (SiPM) array for application as PET scanners. This simple FPGA-ADC based on a carry chain Time-Digital Converter (TDC) implemented on a Kintex-7 FPGA consists of only one off-chip resistor so it has greater advantages in improving system integration and reducing cost than commercial chips. In this paper, a FPGA-ADC based front-end electronics prototype is presented, and both the design principle and implementation considerations are discussed. Experiments were performed using an 8 x 8 (crystal size: 4 x 4 x 15 mm3 ) and a 12 x 12 (crystal size: 2.65 x2.65 x 15 mm3 ) segmented LYSO crystals coupled with an 8 x 8 SiPM (Jseries, from ON Semiconductor) array which is under 22Na point source excitation. Initial test results indicate that the energy resolution of the two detectors after correction is around 13.2% and 13.5 % at 511 keV, and the profiles of the flood histograms show a clear visualization of the discrete scintillator element. All measurements were carried out at room temperature (~25 degree), without additional cooling

physics.ins-det↗