arXiv subjects

Yunfeng Liu

Publications and source records attributed to Yunfeng Liu.

At least 19 recordsLinked to original sources

arXiv2026

Numerical Investigation of Mach 10 Kerosene-Fueled Oblique Detonation Waves at Different Flight Altitudes

The initiation and propagation mechanisms of kerosene-fueled oblique detonation waves are key issues in the development of oblique detonation wave engines. In this study, numerical simulations of kerosene-fueled oblique detonation at a flight Mach number of 10 were conducted using the two-dimensional conservative Euler equations and a second-order two-step global chemical reaction model. The objective was to compare wedge-induced initiation with bump-forced initiation. The results show that, at different flight altitudes, the oblique detonation flow field follows the \r{ho}L binary scaling law. Wedge-induced initiation requires highly precise matching of parameters such as the inlet-exit conditions, wedge angle and length, and equivalence ratio. In contrast, bump-forced initiation exploits the high total temperature and total pressure at the stagnation point and therefore does not require precise matching of these parameters. Therefore, the bump-forced initiation method provides reliable oblique detonation initiation and a stable oblique detonation flow field.

physics.flu-dyn↗

arXiv2026

VIABench: A Comprehensive Video Benchmark Collected from Blind Individuals for Visual Impairment Assistance

Visually impaired individuals (VIIs) encounter significant daily challenges due to limited access to visual information. Although Multimodal Large Language Models (MLLMs) have achieved impressive results on general vision and language tasks, their practical utility in real-world blind assistance still remains largely underexplored. To fill this gap, we introduce VIABench, a comprehensive video benchmark specifically designed to evaluate MLLMs in Visually Impaired Assistance scenarios using first-person videos recorded or shared by VIIs themselves. VIABench defines three core tasks, each targeting a distinct requirement in visual assistance. Proactive Reminder: Assesses the model's ability to interpret ongoing video content while proactively anticipating and verbally describing upcoming navigation-critical events; Visual Question Answering (VQA): Evaluates the model's capacity to answer user-posed questions about the environment or objects within the video; Vision-Guided Interaction: Tests context-aware reasoning to accomplish intentional interactions between user and environment. To ensure a robust and fair evaluation, we propose a rigorous benchmarking pipeline that supports both online (real-time) and offline settings. Our experiments demonstrate that current MLLMs still struggle to deliver comprehensive support for VIIs, especially in the Proactive Reminder task, which demands accurate anticipation and real-time responsiveness. We hope VIABench will drive future research toward developing customized MLLMs for real-world assistance, ultimately improving navigation and interaction experiences for visually impaired individuals. Code and data will be released at https://github.com/MCG-NJU/VIABench.

cs.CV↗

arXiv2025

Semantic Temporal Single-photon LiDAR

Temporal single-photon (TSP-) LiDAR presents a promising solution for imaging-free target recognition over long distances with reduced size, cost, and power consumption. However, existing TSP-LiDAR approaches are ineffective in handling open-set scenarios where unknown targets emerge, and they suffer significant performance degradation under low signal-to-noise ratio (SNR) and short acquisition times (fewer photons). Here, inspired by semantic communication, we propose a semantic TSP-LiDAR based on a self-updating semantic knowledge base (SKB), in which the target recognition processing of TSP-LiDAR is formulated as a semantic communication. The results, both simulation and experiment, demonstrate that our approach surpasses conventional methods, particularly under challenging conditions of low SNR and limited acquisition time. More importantly, our self-updating SKB mechanism can dynamically update the semantic features of newly encountered targets in the SKB, enabling continuous adaptation without the need for extensive retraining of the neural network. In fact, a recognition accuracy of 89% is achieved on nine types of unknown targets in real-world experiments, compared to 66% without the updating mechanism. These findings highlight the potential of our framework for adaptive and robust target recognition in complex and dynamic environments.

eess.IV↗

arXiv2025

Convective flux analysis on the propagation mechanism of oblique detonation waves

The aim of this study is to investigate the propagation mechanism of oblique detonation waves using the vector flux analysis method through numerical simulations. A two-dimensional numerical study is conducted on stoichiometric hydrogen-air oblique detonation waves based on the conservative Euler equations and a one-step global chemical reaction model. The wedge angle is 25°, with a freestream static temperature of 851.5 K, velocity of 2473.4 m/s, and pressure of 42.5 kPa. The motion mechanism of transverse waves is analyzed using the vector flux method. The results show that the oblique detonation front consists of three regions: an induction zone, an overdriven detonation zone, and a transverse-wave region. Under different activation energies, only either upward-propagating or downward-propagating transverse waves exist on the oblique detonation front; the two do not occur simultaneously. At low activation energy, downward-propagating transverse waves dominate, whereas at high activation energy, upward-propagating transverse waves appear.

physics.flu-dyn↗

arXiv2025

Quantitative Correlation between One-Dimensional Periodically Oscillating Detonation Waves and Two-Dimensional Regular Cellular Detonation Waves

The objective of this study is to establish a quantitative relationship between one-dimensional (1D) periodically oscillating detonation waves and two-dimensional (2D) regular cellular detonation waves. Numerical simulations were conducted for stoichiometric hydrogen-air detonation waves using the conservative Euler equations coupled with a single-step overall chemical reaction model. The evolution of both 1D periodically oscillating detonation waves and 2D regular cellular detonation waves was analyzed through flux-vector analysis. The results reveal that the period of forming a 2D detonation cell is equivalent to the period of the 1D periodically oscillating detonation wave. Furthermore, 2D regular cellular detonation waves exhibit two distinct time scales:(1) the collision period of triple-points, and (2) the ignition delay time of the heated gas behind the incident shock wave. These two time scales reach a balance for regular cellular detonation waves. The findings demonstrate that the ignition delay time is the key physical parameter for establishing a direct quantitative correlation between the two systems.

physics.flu-dyn↗

arXiv2025

Convective flux analysis on the instability of one-dimensional detonation

One-dimensional numerical simulations using the Euler equations and irreversible one-step Arrhenius kinetics are conducted to study the instability mechanism of a one-dimensional gaseous detonation. By increasing the activation energy, this study identifies the characteristics of stable detonation, periodic detonation, pulsating detonation, and detonation quenching. The key difference between this study and previous research is that it is the first quantitative analysis of convective flux, kinetic energy flux, and chemical reaction heat flux. These three fluxes undergo intensive change on the detonation front and the flow field at each time step depends on the algebra summation of them. The mechanisms of detonation instability, detonation reignition and detonation quenching process can be revealed quantitatively by analyzing these fluxes.

physics.flu-dyn↗

arXiv2024

BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to associate a text with a set of aspects and infer their respective sentimental polarities. State-of-the-art approaches are built on fine-tuning pre-trained language models, focusing on learning aspect-specific representations from the corpus. However, aspects are often expressed implicitly, making implicit mapping challenging without sufficient labeled examples, which may be scarce in real-world scenarios. This paper proposes a unified framework to address aspect categorization and aspect-based sentiment subtasks. We introduce a mechanism to construct an auxiliary-sentence for the implicit aspect using the corpus's semantic information. We then encourage BERT to learn aspect-specific representation in response to this auxiliary-sentence, not the aspect itself. We evaluate our approach on real benchmark datasets for both ABSA and Targeted-ABSA tasks. Our experiments show that it consistently achieves state-of-the-art performance in aspect categorization and aspect-based sentiment across all datasets, with considerable improvement margins. The BERT-ASC code is available at https://github.com/amurtadha/BERT-ASC.

cs.CL↗

arXiv2024

AlcLaM: Arabic Dialectal Language Model

Pre-trained Language Models (PLMs) are integral to many modern natural language processing (NLP) systems. Although multilingual models cover a wide range of languages, they often grapple with challenges like high inference costs and a lack of diverse non-English training data. Arabic-specific PLMs are trained predominantly on modern standard Arabic, which compromises their performance on regional dialects. To tackle this, we construct an Arabic dialectal corpus comprising 3.4M sentences gathered from social media platforms. We utilize this corpus to expand the vocabulary and retrain a BERT-based model from scratch. Named AlcLaM, our model was trained using only 13 GB of text, which represents a fraction of the data used by existing models such as CAMeL, MARBERT, and ArBERT, compared to 7.8%, 10.2%, and 21.3%, respectively. Remarkably, AlcLaM demonstrates superior performance on a variety of Arabic NLP tasks despite the limited training data. AlcLaM is available at GitHub https://github.com/amurtadha/Alclam and HuggingFace https://huggingface.co/rahbi.

cs.CL↗

arXiv2024

Naive Bayes-based Context Extension for Large Language Models

Large Language Models (LLMs) have shown promising in-context learning abilities. However, conventional In-Context Learning (ICL) approaches are often impeded by length limitations of transformer architecture, which pose challenges when attempting to effectively integrate supervision from a substantial number of demonstration examples. In this paper, we introduce a novel framework, called Naive Bayes-based Context Extension (NBCE), to enable existing LLMs to perform ICL with an increased number of demonstrations by significantly expanding their context size. Importantly, this expansion does not require fine-tuning or dependence on particular model architectures, all the while preserving linear efficiency. NBCE initially splits the context into equal-sized windows fitting the target LLM's maximum length. Then, it introduces a voting mechanism to select the most relevant window, regarded as the posterior context. Finally, it employs Bayes' theorem to generate the test task. Our experimental results demonstrate that NBCE substantially enhances performance, particularly as the number of demonstration examples increases, consistently outperforming alternative methods. The NBCE code will be made publicly accessible. The code NBCE is available at: https://github.com/amurtadha/NBCE-master

cs.CL↗

arXiv2024

Miniature narrow-linewidth 1 μm Laser

Self-injection locking scheme has the potential to narrow the linewidth of lasers in a compact setup. Here, we report a narrow linewidth laser source near 1 μm by self-injection locking scheme using a Fabry-Perot (FP) hollow resonator with a high-quality factor (Q>10^8). The measured fundamental linewidth of the laser is 41 Hz, and a coarse tuning range over 5.5 nm is achieved by changing the driving current of the laser source. Meanwhile, a fine-tuning range of 373 MHz is achieved without mode hops by changing the voltage applied to the PZT on the resonator. More importantly, benefiting from the low thermal refractive noise and low thermal expansion of the FP hollow resonator, the beat-note linewidth and the frequency Allan deviation are measured to be 510.3 Hz in and 10^-11 (1s averaging time), respectively, by using a fully stabilized frequency comb as reference. Such a high-performance laser is fully integrated with a palm-sized package (52.3 mL) for field-deployable applications.

physics.optics↗

arXiv2023

RoFormer: Enhanced Transformer with Rotary Position Embedding

Position encoding recently has shown effective in the transformer architecture. It enables valuable supervision for dependency modeling between elements at different positions of the sequence. In this paper, we first investigate various methods to integrate positional information into the learning process of transformer-based language models. Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information. Specifically, the proposed RoPE encodes the absolute position with a rotation matrix and meanwhile incorporates the explicit relative position dependency in self-attention formulation. Notably, RoPE enables valuable properties, including the flexibility of sequence length, decaying inter-token dependency with increasing relative distances, and the capability of equipping the linear self-attention with relative position encoding. Finally, we evaluate the enhanced transformer with rotary position embedding, also called RoFormer, on various long text classification benchmark datasets. Our experiments show that it consistently overcomes its alternatives. Furthermore, we provide a theoretical analysis to explain some experimental results. RoFormer is already integrated into Huggingface: \url{https://huggingface.co/docs/transformers/model_doc/roformer}.

cs.CL↗

arXiv2023

Numerical study on mechanism of C-J deflagration

The mechanism of detonation instability and deflagration-to-detonation transition is studied by one-dimensional numerical simulation with overall one-step chemical reaction kinetics in this paper. The detonation is ignited at the left closed end of the one-dimensional detonation tube and propagates downstream. The activation energy is increased to trigger the instability of detonation. The numerical results show that the C-J detonation is stable at lower activation energy. The stable detonation does not have the von Neumann spike and the gas Mach number at detonation front is subsonic. The von Neumann spike appears and the gas Mach number becomes supersonic as the activation energy is increased. The detonation instability appears with the von Neumann spike synchronously. At very higher activation energy, the detonation quenches abruptly and degenerates into a C-J deflagration. The detonation is extinguished abruptly by the rarefaction wave induced by the higher von Neumann spike. Then the rarefaction wave moves in front of the heat release region and weakens the leading shock wave gradually. The C-J deflagration is composed of a precursor shock wave and a flame front, and the flame front is completely decoupled from the shock wave. The gas static temperature behind the leading shock wave is too low to ignite the mixture. The rarefaction wave from the wall ceases the mixture behind the leading shock, increases its static temperature and decrease its pressure. As a result, the combustion takes place at the interface. The pressure rise caused by the combustion at the interface offsets the influence of rarefaction wave, and this mechanism makes the C-J deflagration propagate downstream with a relatively constant velocity for a long time.

physics.flu-dyn↗

arXiv2023

Rank-Aware Negative Training for Semi-Supervised Text Classification

Semi-supervised text classification-based paradigms (SSTC) typically employ the spirit of self-training. The key idea is to train a deep classifier on limited labeled texts and then iteratively predict the unlabeled texts as their pseudo-labels for further training. However, the performance is largely affected by the accuracy of pseudo-labels, which may not be significant in real-world scenarios. This paper presents a Rank-aware Negative Training (RNT) framework to address SSTC in learning with noisy label manner. To alleviate the noisy information, we adapt a reasoning with uncertainty-based approach to rank the unlabeled texts based on the evidential support received from the labeled texts. Moreover, we propose the use of negative training to train RNT based on the concept that ``the input instance does not belong to the complementary label''. A complementary label is randomly selected from all labels except the label on-target. Intuitively, the probability of a true label serving as a complementary label is low and thus provides less noisy information during the training, resulting in better performance on the test data. Finally, we evaluate the proposed solution on various text classification benchmark datasets. Our extensive experiments show that it consistently overcomes the state-of-the-art alternatives in most scenarios and achieves competitive performance in the others. The code of RNT is publicly available at:https://github.com/amurtadha/RNT.

cs.CL↗

arXiv2022

The criterion on the Propulsive Balance of Oblique Detonation Engine

The oblique detonation engine (ODE) has established a clear superiority for hypersonic flight because of its high thermal efficiency and compact structure. It has become the research hot spot all over the world in recent years. The aim of this study is to derive a criterion on the propulsive balance of ODE, from which we can find the key parameters governing the propulsive performance explicitly. A physical model of ODE is put forth, which consists of the inlet, the constant cross-section combustor and the divergent nozzle. The mathematical equations to calculate the thrust generated by the divergent nozzle and the pressure drag produced by the inlet are deduced. The net thrust of ODE is then obtained. The criterion shows clearly that the static temperature at the engine inlet exit is a very important parameter. The lower the inlet exit temperature is, the higher the specific impulse will be. The specific impulse of ODE with stoichiometric H2/air mixture and hydrocarbon/air mixture are calculated by using these equations. The results show that ODE can obtain positive net thrust from Ma8 to Ma15.

physics.flu-dyn↗

arXiv2022

Automated turnkey microcomb for low-noise microwave synthesis

Microresonator-based optical frequency comb (microcomb) has the potential to revolutionize the accuracy of frequency synthesizer in radar and communication applications. However, fundamental limit exists for low noise microcomb generation, especially in low size, weight, power and cost (SWaP-C) package. Here we resolve this limit, by the demonstration of an automated turnkey microcomb, operating close to its low quantum-limited phase noise, within a compact setup size of 85 mm * 90 mm * 25 mm. High quality factor fiber Fabry-Perot resonator (FFPR), with Q up to 4.0 * 10^9, is the key for both low quantum noise and pump noise limit, in the diode-pump case in a self-injection locking scheme. Low phase noise of -80 and -105 dBc/Hz at 100 Hz, -106 and -125 dBc/Hz at 1 kHz, -133 and -148 dBc/Hz at 10 kHz is achieved at 10.1 GHz and 1.7 GHz repetition frequencies, respectively. With the simultaneous automated turnkey, low-noise and direct-diode-pump capability, our microcomb is ready to be used as a low-noise frequency synthesizer with low SWaP-C and thus field deployability.

physics.optics↗

arXiv2022

ZLPR: A Novel Loss for Multi-label Classification

In the era of deep learning, loss functions determine the range of tasks available to models and algorithms. To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zero-bounded log-sum-exp \& pairwise rank-based) loss in this paper. Compared to other rank-based losses for MLC, ZLPR can handel problems that the number of target labels is uncertain, which, in this point of view, makes it equally capable with the other two strategies often used in MLC, namely the binary relevance (BR) and the label powerset (LP). Additionally, ZLPR takes the corelation between labels into consideration, which makes it more comprehensive than the BR methods. In terms of computational complexity, ZLPR can compete with the BR methods because its prediction is also label-independent, which makes it take less time and memory than the LP methods. Our experiments demonstrate the effectiveness of ZLPR on multiple benchmark datasets and multiple evaluation metrics. Moreover, we propose the soft version and the corresponding KL-divergency calculation method of ZLPR, which makes it possible to apply some regularization tricks such as label smoothing to enhance the generalization of models.

cs.LG↗

arXiv2022

Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition

Named entity recognition (NER) task aims at identifying entities from a piece of text that belong to predefined semantic types such as person, location, organization, etc. The state-of-the-art solutions for flat entities NER commonly suffer from capturing the fine-grained semantic information in underlying texts. The existing span-based approaches overcome this limitation, but the computation time is still a concern. In this work, we propose a novel span-based NER framework, namely Global Pointer (GP), that leverages the relative positions through a multiplicative attention mechanism. The ultimate goal is to enable a global view that considers the beginning and the end positions to predict the entity. To this end, we design two modules to identify the head and the tail of a given entity to enable the inconsistency between the training and inference processes. Moreover, we introduce a novel classification loss function to address the imbalance label problem. In terms of parameters, we introduce a simple but effective approximate method to reduce the training parameters. We extensively evaluate GP on various benchmark datasets. Our extensive experiments demonstrate that GP can outperform the existing solution. Moreover, the experimental results show the efficacy of the introduced loss function compared to softmax and entropy alternatives.

cs.CL↗

arXiv2021

Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention

Emotion recognition from speech is a challenging task. Re-cent advances in deep learning have led bi-directional recur-rent neural network (Bi-RNN) and attention mechanism as astandard method for speech emotion recognition, extractingand attending multi-modal features - audio and text, and thenfusing them for downstream emotion classification tasks. Inthis paper, we propose a simple yet efficient neural networkarchitecture to exploit both acoustic and lexical informationfrom speech. The proposed framework using multi-scale con-volutional layers (MSCNN) to obtain both audio and text hid-den representations. Then, a statistical pooling unit (SPU)is used to further extract the features in each modality. Be-sides, an attention module can be built on top of the MSCNN-SPU (audio) and MSCNN (text) to further improve the perfor-mance. Extensive experiments show that the proposed modeloutperforms previous state-of-the-art methods on IEMOCAPdataset with four emotion categories (i.e., angry, happy, sadand neutral) in both weighted accuracy (WA) and unweightedaccuracy (UA), with an improvement of 5.0% and 5.2% respectively under the ASR setting.

cs.SD↗