arXiv subjects

Guohui Li

Publications and source records attributed to Guohui Li.

14 recordsLinked to original sources

arXiv2025

Femtosecond low-threshold all-optical switching enabled by giant broadband optical nonlinearity from heteroatom doping

Ultrafast all-optical switching (AOS) is pivotal for advancing integrated photonic devices, from high-speed photonic information processing to next generation all-optical computing and communication networks. However, conventional nonlinear materials suffer from sluggish response time, high power threshold, weak and narrow-bandwidth optical nonlinearities, critically limiting their viability. Here, we report a heteroatom engineering strategy to overcome these limitations by designing zero-dimensional nitrogen-doped carbon quantum dots (N-CQDs) with nonlinear optical performance far exceeding the state-of-the-art. Leveraging spatial self-phase modulation (SSPM) and ultrafast pump-probe technique, we first demonstrate an all-in-one AOS platform, where femtosecond laser pulses serve dual roles as control and signal beams. The AOS simultaneously realizes ultrafast response time (520 fs), ultralow threshold energy (2.2 Wcm-2), and giant nonlinear refraction indexes (10-5 cm2/W) in the wide spectral range (400-1064 nm), yielding performance surpassing state-of-the-art nonlinear carbon materials (i.e. carbon nanotube) by orders of magnitude. Spectroscopic and bandgap analyses attribute these exotic performances to enhanced n-pi interaction enabled by nitrogen doping, which amplifies nonlinear polarization dynamics. Crucially, ultrafast fluorescence spectroscopy reveals a large two-photon absorption cross-section of the N-CQDs, challenging the conventional cognition that broadband SSPM necessitates single-photon excitation. This discovery unveils a multi-channel AOS rooted in synergistic single-photon and two-photon processes.. This work demonstrates a new paradigm for achieving ultrafast, broadband, and energy-efficient AOS by heteroatom doping engineering.

physics.optics↗

arXiv2024

CMAL: A Novel Cross-Modal Associative Learning Framework for Vision-Language Pre-Training

With the flourishing of social media platforms, vision-language pre-training (VLP) recently has received great attention and many remarkable progresses have been achieved. The success of VLP largely benefits from the information complementation and enhancement between different modalities. However, most of recent studies focus on cross-modal contrastive learning (CMCL) to promote image-text alignment by pulling embeddings of positive sample pairs together while pushing those of negative pairs apart, which ignores the natural asymmetry property between different modalities and requires large-scale image-text corpus to achieve arduous progress. To mitigate this predicament, we propose CMAL, a Cross-Modal Associative Learning framework with anchor points detection and cross-modal associative learning for VLP. Specifically, we first respectively embed visual objects and textual tokens into separate hypersphere spaces to learn intra-modal hidden features, and then design a cross-modal associative prompt layer to perform anchor point masking and swap feature filling for constructing a hybrid cross-modal associative prompt. Afterwards, we exploit a unified semantic encoder to learn their cross-modal interactive features for context adaptation. Finally, we design an associative mapping classification layer to learn potential associative mappings between modalities at anchor points, within which we develop a fresh self-supervised associative mapping classification task to boost CMAL's performance. Experimental results verify the effectiveness of CMAL, showing that it achieves competitive performance against previous CMCL-based methods on four common downstream vision-and-language tasks, with significantly fewer corpus. Especially, CMAL obtains new state-of-the-art results on SNLI-VE and REC (testA).

cs.CV↗

arXiv2024

Tailoring 4H-SiC Surface Electronic States by Atomic-Layer Deposition for Ideal Peta-Ohm Resistors

High resolution resistors capable of detecting minuscule currents are vital for advanced sensors, but existing off-shelf models struggle with inconsistent resistance under varying voltages. The underlying physics of this issue is rooted in unstable surface charges and intrinsic inhomogeneity of surface potential caused by spontaneous polarization (SP) in commercial semi-insulating silicon carbide (SiC) devices. In this work, we found that coating SiC surfaces with an ultrathin zinc oxide layer immobilizes the dangling surface charges in place and balances the natural electric field of the material, ensuring stable resistance even at extreme voltages up to 1000 V. The resulting SiC resistor maintains a record-high resistance of one peta-ohm (10^15 {\Omega}) with negligible voltage fluctuations, outperforming conventional options. Additionally, these devices can switch states when exposed to light or heat, making them dual-purpose tools for ultra-sensitive measurements and sensors. This breakthrough combines high stability, scalability for mass production, and multifunctionality, opening doors to next-generation precision technologies in fields like quantum sensing and environmental monitoring.

physics.app-ph↗

arXiv2024

EventLens: Leveraging Event-Aware Pretraining and Cross-modal Linking Enhances Visual Commonsense Reasoning

Visual Commonsense Reasoning (VCR) is a cognitive task, challenging models to answer visual questions requiring human commonsense, and to provide rationales explaining why the answers are correct. With emergence of Large Language Models (LLMs), it is natural and imperative to explore their applicability to VCR. However, VCR task demands more external knowledge to tackle its challenging questions, necessitating special designs to activate LLMs' commonsense reasoning abilities. Also, most existing Multimodal LLMs adopted an abstraction of entire input image, which makes it difficult to comprehend VCR's unique co-reference tags between image regions and text, posing challenges for fine-grained alignment. To address these issues, we propose EventLens that leverages Event-Aware Pretraining and Cross-modal Linking and EnhanceS VCR. First, by emulating the cognitive process of human reasoning, an Event-Aware Pretraining auxiliary task is introduced to better activate LLM's global comprehension of intricate scenarios. Second, during fine-tuning, we further utilize reference tags to bridge RoI features with texts, while preserving both modality semantics. Finally, we use instruct-style prompts to narrow the gap between pretraining and fine-tuning, and task-specific adapters to better integrate LLM's inherent knowledge with new commonsense. Experimental results show the effectiveness of our proposed auxiliary task and fine-grained linking strategy.

cs.CV↗

arXiv2024

DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation

Learning precise representations of users and items to fit observed interaction data is the fundamental task of collaborative filtering. Existing studies usually infer entangled representations to fit such interaction data, neglecting to model the diverse matching relationships between users and items behind their interactions, leading to limited performance and weak interpretability. To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data. Specifically, we first implement the disentangling concept by unifying an attention-aware dual disentanglement and disentangled variational autoencoder to infer the disentangled latent representations of users and items. Further, to encourage the correspondence and independence of disentangled representations of users and items, we design a neighborhood-enhanced representation constraint with a customized contrastive mechanism to improve the representation quality. Extensive experiments on three real-world benchmarks show that our proposed model significantly outperforms several recent state-of-the-art baselines. Further empirical experimental results also illustrate the interpretability of the disentangled representations learned by DualVAE.

cs.IR↗

arXiv2023

LGMRec: Local and Global Graph Learning for Multimodal Recommendation

The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal features or modal-related graph structure to learn user local interests. Nevertheless, these approaches encounter two limitations: (1) Shared updates of user ID embeddings result in the consequential coupling between collaboration and multimodal signals; (2) Lack of exploration into robust global user interests to alleviate the sparse interaction problems faced by local interest modeling. To address these issues, we propose a novel Local and Global Graph Learning-guided Multimodal Recommender (LGMRec), which jointly models local and global user interests. Specifically, we present a local graph embedding module to independently learn collaborative-related and modality-related embeddings of users and items with local topological relations. Moreover, a global hypergraph embedding module is designed to capture global user and item embeddings by modeling insightful global dependency relations. The global embeddings acquired within the hypergraph embedding space can then be combined with two decoupled local embeddings to improve the accuracy and robustness of recommendations. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our LGMRec over various state-of-the-art recommendation baselines, showcasing its effectiveness in modeling both local and global user interests.

cs.IR↗

arXiv2021

Degradation Mechanism of Perovskite under High Charge Carrier Density Condition

Extensive studies have focused on degradation of perovskite at low charge carrier density (<10^16 cm^-3), but few have surveyed the degradation mechanism at high charge carrier density (~10^18 cm^-3). Here, we investigate the degradation mechanisms of perovskite under high charge carrier conditions. Unlike the observations in previous works, we find that MAPbI3 degradation starts at surface defects and progressing from the surface defects towards neighboring regions under high charge carrier density condition. By using PbI2 passivation, the defect-initiated degradation is significantly suppressed and the nanoplatelet degrades in a layer-by-layer way, enabling the MAPbI3 laser sustain for 4500 s (2.7*10^7 pulses), which is almost 3 times longer than that of the nanoplatelet laser without passivation. Meanwhile, the PbI2 passivated MAPbI3 nanoplatelet laser with the nanoplatelet cavity displaying a maximum quality factor up to ~7800, the highest reported for all MAPbI3 nanoplatelet cavities. Furthermore, a high stability MAPbI3 nanoplatelet laser that can last for 8500 s (5.1*10^7 pulses) is demonstrated based on a dual passivation strategy, by retarding the defect-initiated degradation and surface-initiated degradation, simultaneously. This work provides in-depth insights for understanding the degradation of perovskite at high charge carrier density.

physics.optics↗

arXiv2021

High-responsivity, High-detectivity Photomultiplication Organic Photodetector Realized by a Metal-Insulator-Semiconductor Tunneling Junction

Organic photodetectors (OPDs) possess bright prospects in applications of medical imaging and wearable electronics due to the advantages such as low cost, good biocompatibility, and good flexibility. Photomultiplication OPDs (PM-OPDs) enabled by the trap-assisted carrier tunneling injection effect exhibit external quantum efficiencies far greater than unity, thus the acquired responsivities are extremely high. However, the reported PM-OPDs with high responsivity performances are all accompanied by high dark currents due to the introduction of carrier traps, which inevitably results in inferior detectivities. In this work, we modify a P3HT:PCBM donor-rich PM-OPD by introducing an atomically thin Al2O3 interfacial layer through the ALD technique, obtaining a high responsivity of 8294 A/W and high detectivity of 6.76*10^14 Jones, simultaneously, both of which are among the highest reported for bulk heterojunction PM-OPDs. Ascribed to the introduction of the atomically thin Al2O3 layer, the metal-insulator-semiconductor (MIS) tunneling junction is formed, which brings forward a suppressed dark current along with an increased amounts of holes tunneling under forward bias. Meanwhile, the weak light detection limit of the modified PM-OPD within the linear response range reaches the level of nW/cm2. Based on the proposed PM-OPD, a proof-of-concept image sensor with 26*26 pixels is demonstrated, which can respond to both ultraviolet light and visible light. The PM-OPD based sensor arrays can find broad applications for medical imaging, wearable electronics, etc.

physics.app-ph↗

arXiv2020

A Light Heterogeneous Graph Collaborative Filtering Model using Textual Information

Due to the development of graph neural networks, graph-based representation learning methods have made great progress in recommender systems. However, data sparsity is still a challenging problem that most graph-based recommendation methods are confronted with. Recent works try to address this problem by utilizing side information. In this paper, we exploit the relevant and easily accessible textual information by advanced natural language processing (NLP) models and propose a light RGCN-based (RGCN, relational graph convolutional network) collaborative filtering method on heterogeneous graphs. Specifically, to incorporate rich textual knowledge, we utilize a pre-trained NLP model to initialize the embeddings of text nodes. Afterward, by performing a simplified RGCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can be adjusted with textual knowledge, which effectively alleviates the negative effects of data sparsity. Moreover, the matching function used by most graph-based representation learning methods is the inner product, which is not appropriate for the obtained embeddings that contain complex semantics. We design a predictive network that combines graph-based representation learning with neural matching function learning, and demonstrate that this architecture can bring a significant performance improvement. Extensive experiments are conducted on three publicly available datasets, and the results verify the superior performance of our method over several baselines.

cs.IR↗

arXiv2020

A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation

Due to its nature of learning from dynamic interactions and planning for long-run performance, reinforcement learning (RL) recently has received much attention in interactive recommender systems (IRSs). IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient. Moreover, data sparsity is another challenging problem that most IRSs are confronted with. While the textual information like reviews and descriptions is less sensitive to sparsity, existing RL-based recommendation methods either neglect or are not suitable for incorporating textual information. To address these two problems, in this paper, we propose a Text-based Deep Deterministic Policy Gradient framework (TDDPG-Rec) for IRSs. Specifically, we leverage textual information to map items and users into a feature space, which greatly alleviates the sparsity problem. Moreover, we design an effective method to construct an action candidate set. By the policy vector dynamically learned from TDDPG-Rec that expresses the user's preference, we can select actions from the candidate set effectively. Through experiments on three public datasets, we demonstrate that TDDPG-Rec achieves state-of-the-art performance over several baselines in a time-efficient manner.

cs.IR↗

arXiv2018

Dust concentration vision measurement based on moment of inertia in gray level-rank co-occurrence matrix

To improve the accuracy of existing dust concentration measurements, a dust concentration measurement based on Moment of inertia in Gray level-Rank Co-occurrence Matrix (GRCM), which is from the dust image sample measured by a machine vision system is proposed in this paper. Firstly, a Polynomial computational model between dust Concentration and Moment of inertia (PCM) is established by experimental methods and fitting methods. Then computing methods for GRCM and its Moment of inertia are constructed by theoretical and mathematical analysis methods. And then developing an on-line dust concentration vision measurement experimental system, the cement dust concentration measurement in a cement production workshop is taken as a practice example with the system and the PCM measurement. The results show that measurement error is within 9%, and the measurement range is 0.5-1000 mg/m3. Finally, comparing with the filter membrane weighing measurement, light scattering measurement and laser measurement, the proposed PCM measurement has advantages on error and cost, which can be provided a valuable reference for the dust concentration vision measurements.

cs.CV↗

arXiv2015

Beam Collimation Using an Anisotropic Metamaterial Slab without Any Nanometer-sized Aperture

Plasmonic beam collimation effect has been thoroughly investigated based on the well-known nanometer-scale bull's eye structure formed by complex and high-cost fabrication processes. In this work, we report our effort for attaining beam collimation using an anisotropic metamaterial (AMM) slab that consists of a stack of alternating metal/dielectric layers and an integrated top metal grating. The results show that AMM slab allows creating the beam collimation effect similar to that of the bull's eye structure, an enabling technology for practical application due to its simple architecture and cost benefits. The excitation of surface plasmons at the AMM/air interface is derived. The structure of the AMM slab and its impact on beaming performance were analyzed using the effective medium theory and Finite Element Method.

physics.optics↗

arXiv2012

Optical Curtain Effect: Extraordinary Optical Transmission Enhanced by Antireflection

In this paper, we employ an antireflective coating which comprises of inverted pi shaped metallic grooves to manipulate the behaviour of a TM-polarized plane wave transmitted through a periodic nanoslit array. At normal incidence, such scheme can not only retain the optical curtain effect in the output region, but also generate the extraordinary transmission of light through the nanoslits with the total transmission efficiency as high as 90%. Besides, we show that the spatially invariant field distribution in the output region as well as the field distribution of resonant modes around the inverted pi shaped grooves can be reproduced immaculately when the system is excited by an array of point sources beneath the inverted pi shaped grooves. In further, we investigate the influence of center-groove and side-corners of the inverted pi shaped grooves on suppressing the reflection of light, respectively. Based on our work, it shows promising potential in applications of enhancing the extraction efficiency as well as controlling the beaming pattern of light emitting diodes.

physics.optics↗

arXiv2006

Dual-Topology Hamiltonian-Replica-Exchange Overlap Histogramming Method to Calculate Relative Free Energy Difference in Rough Energy Landscape

A novel overlap histogramming method based on Dual-Topology Hamiltonian-Replica-Exchange simulation technique is presented to efficiently calculate relative free energy difference in rough energy landscape, in which multiple conformers coexist and are separated by large energy barriers. The proposed method is based on the realization that both DT-HERM exchange efficiency and confidence of free energy determination in overlap histogramming method depend on the same criteria: neighboring states' energy derivative distribution overlap. In this paper, we demonstrate this new methodology by calculating free energy difference between amino acids: Leucine and Asparagine, which is an identified chanllenging system for free energy simulations.

physics.chem-ph↗