arXiv subjects

Qianqian Li

Publications and source records attributed to Qianqian Li.

At least 19 recordsLinked to original sources

arXiv2026

Global small data radial symmetric solutions of 3D semilinear Euler-Poisson-Darboux equations

For the 3D semilinear Euler-Poisson-Darboux equation $\square u+\fracμ{t}\partial_tu=|u|^p$, where $t\geq1$, $μ>0$ and $p>1$, it is conjectured that there is a critical exponent $p_{crit}(3,μ)=\max\{p_s(3+μ), p_f(3)\}$ with the Strauss exponent $p_s(3+μ)=\frac{μ+4+\sqrt{μ^2+16μ+32}}{2(μ+2)}$ and the Fujita exponent $p_f(3)=\frac{5}{3}$ such that when $p>p_{crit}(3,μ)$, the small data solution $u$ exists globally, otherwise, when $1 p_{crit}(3,μ)$. Note that $p_{crit}(3,μ)=p_s(3+μ)$ for $0<μ<\frac{14}{5}$ and $p_{crit}(3,μ)=p_f(3)$ for $μ\geq\frac{14}{5}$. In the recent paper [16], the authors have obtained the global small solution $u$ for $p>\max\{\frac{5}{3},1+\frac{2}μ\}$ and $μ\geq\frac{14}{5}$. In this paper, by utilizing the hypergeometric Riemann representation and establishing some delicate pointwise spacetime weighted estimates, we prove the global existence of small data radial solution $u$ in the remaining range of $\frac{5}{3} p_{crit}(3,μ)=\frac{5}{3}$.

math.AP↗

arXiv2026

Few-Shot Class-Incremental Audio Classification Using Pseudo-Incrementally Trained Embedding Learner and Continually Updated Stochastic Classifier

Few-shot Class-incremental Audio Classification (FCAC) aims to progressively recognize incremental classes with few tagged samples and meanwhile memorize base classes. To achieve satisfactory FCAC performance, the model needs to have high stability (memorizing base classes) and strong plasticity (adapting to incremental classes). In this work, we design a model which can be decoupled into two independent modules, namely an embedding learner and a stochastic classifier. The former is the backbone of a residual convolutional network, while the latter is composed of distributions and each distribution consists of a mean vector and a variance vector for representing one class. After being trained in the base session, the embedding learner is not updated in each incremental session and thus can memorize the knowledge of base classes. To make the embedding learner possess strong representation ability for incremental classes, we propose a strategy to pseudo-incrementally train the embedding learner using data augmentation in the base session. On the other hand, the stochastic classifier is continually updated in each incremental session and thus can adapt to incremental classes. Our model which consists of a pseudo-incrementally trained embedding learner and a continually updated stochastic classifier can increasingly identify incremental classes without forgetting base classes. Three datasets (FSC-89, NSynth-100 and LS-100) are used to verify the effectiveness of our method. Experiments show that our method exceeds the comparison methods in accuracy, and has lower complexity than most of the comparison methods. The code is at https://github.com/vinceasvp/PITEL-CUSC.

eess.AS↗

arXiv2026

Global existence of small data solutions to 3-D semilinear Euler-Poisson-Darboux equations

There is an interesting open question: for $n$-D ($n\ge 1$) semilinear Euler-Poisson-Darboux equation $\partial_t^2u-Δu+\fracμ{t}\partial_tu=|u|^p$, where $t\ge 1$, $p>1$ and $μ>0$, the global small data weak solution $u$ will exist when $p>p_{crit}(n,μ)=\max\{p_s(n+μ), p_f(n)\}$ with the Strauss exponent $p_{s}(n+μ)=\frac{n+μ+1+\sqrt{(n+μ)^2+10(n+μ)-7}}{2(n+μ-1)}$ and the Fujita exponent $p_f(n)=1+\frac{2}{n}$. The blowup of weak solution $u$ has been shown when $1 \max\{\frac53, 1+\frac{2}μ\}$.

math.AP↗

arXiv2026

Few-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes

Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of unseen classes). In this study, we propose a method for Few-shot Open-set Audio Classification (FOAC), which can recognize query samples of seen classes after updating the model using a few support samples, and meanwhile reject query samples from unseen classes. We design a model consisting of an encoder and a classifier. The encoder is the backbone of a ResNet used for extracting embeddings. The classifier consists of prototype generators of few-shot classes and open-set classes. Prototypes of few-shot classes are obtained by fusing the class-discriminative information of support and query embeddings and by assigning larger weighting coefficient to representative part of the support embeddings. One prototype is generated for open-set classes using the proposed prototype generator. The encoder is trained with abundant samples of base classes in supervised manner, and then the prototypes of base classes are generated under the supervision of a joint loss. The classifier is trained using a few samples of few-shot classes in a meta-training way. Three public datasets (LS-100, NSynth-100, and FSC-89) are used to assess the performance of our method. Experiments show that our method has advantage over prior methods in AUROC and accuracy. This advantage has statistical significance for most prior methods. Our method has lower computational complexity than most prior methods. The code is at https://github.com/Jessytan/FOAC-AIFP.

eess.AS↗

arXiv2026

Few-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training

In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibility of decrease. However, the number of classes generally increases or decreases in practice. In this paper, we investigate a problem of Few-shot Class-variable Incremental Audio Classification (FCIAC), in which the number of classes increases or decreases. We propose a FCIAC method using prototype adaptation and pseudo class-variable training. The model in our method consists of an encoder and a classifier. The classifier is initialized by a class-variable prototype adaptation network, whose structure dynamically changes with the change of classes. In addition, we design a pseudo class-variable training strategy to enhance the model's adaptability to changing classes. Experiments on three public datasets show that our method exceeds previous methods in average accuracy. The code is at: https://github.com/cgq2971-afk/FCIAC.

eess.AS↗

arXiv2025

Pattern Division Random Access (PDRA) for M2M Communications with Massive MIMO Systems

In this work, we introduce the pattern-domain pilot design paradigm based on a "superposition of orthogonal-building-blocks" with significantly larger contention space to enhance the massive machine-type communications (mMTC) random access (RA) performance in massive multiple-input multiple-output (MIMO) systems.Specifically, the pattern-domain pilot is constructed based on the superposition of $L$ cyclically-shifted Zadoff-Chu (ZC) sequences. The pattern-domain pilots exhibit zero correlation values between non-colliding patterns from the same root and low correlation values between patterns from different roots. The increased contention space, i.e., from N to $\binom{N}{L}$, where $\binom{N}{L}$ denotes the number of all L-combinations of a set N, and low correlation valueslead to a significantly lower pilot collision probability without compromising excessively on channel estimation performance for mMTC RA in massive MIMO systems.We present the framework and analysis of the RA success probability of the pattern-domain based scheme with massive MIMO systems.Numerical results demonstrate that the proposed pattern division random access (PDRA) scheme achieves an appreciable performance gain over the conventional one,while preserving the existing physical layer virtually unchanged. The extension of the "superposition of orthogonal-building-blocks" scheme to "superposition of quasi-orthogonal-building-blocks" is straightforward.

cs.IT↗

arXiv2025

Impact of RHIs and ipSIC on Active RIS-NOMA Systems with Low-Precision ADCs

This study evaluates the performance of an active reconfigurable intelligent surface (ARIS)-assisted non-orthogonal multiple access (NOMA) system employing low-precision analog-to-digital converters (ADCs). Analytical approximations for the outage probability (OP) are derived, considering residual hardware impairments (RHIs) and imperfect successive interference cancellation (ipSIC). Additionally, we analyze the asymptotic OP, system throughput, and diversity order at high signal-to-noise ratios (SNRs). Simulation results demonstrate that the proposed quantized ARIS-NOMA system outperforms its passive counterpart (PRIS-NOMA), achieving lower OP and higher throughput with reduced transmit power requirements and fewer reflecting elements. Moreover, the outage performance of both quantized ARIS-NOMA and PRIS-NOMA systems demonstrates significant improvement as the number of reflecting elements increases. The negative impacts of low-precision ADCs can be effectively mitigated by optimizing transmit power and scaling the number of reflecting elements.

eess.SP↗

arXiv2025

On the Design of Capacity-Achieving Distributions for Discrete-Time Poisson Channel with Low-Precision ADCs

This paper investigates the design of the capacity-achieving input distribution for the discrete-time Poisson channel (DTPC) under dark current effects with low-precision analog-to-digital converters (ADCs). This study introduces an efficient optimization algorithm that integrates the Newton-Raphson and Blahut-Arimoto (BA) methods to determine the capacity-achieving input distribution and the corresponding amplitudes of input mass points for the DTPC, subject to both peak and average power constraints. Additionally, the Karush-Kuhn-Tucker (KKT) conditions are established to provide necessary and sufficient conditions for the optimality of the obtained capacity-achieving distribution. Simulation results illustrate that the proposed algorithm attains $72\%$ and $83\%$ of the theoretical capacity at 5 dB for 1-bit and 2-bit quantized DTPC, respectively. Furthermore, for a finite-precision quantized DTPC (i.e., ${\log _2}K$ bits), the capacity can be achieved by a non-uniform discrete input distribution with support for $K$ mass points, under the given power constraints.

eess.SP↗

arXiv2025

Global small data weak solutions of 2-D semilinear wave equations with scale-invariant damping, III

For the $2$-D semilinear wave equation with scale-invariant damping $\square u+\fracμ{t}\partial_tu=|u|^p$, where $t\geq 1$, $μ>0$ and $p>1$, it is conjectured that the global small data weak solution $u$ exists when $p>p_{s}(2+μ) =\frac{μ+3+\sqrt{μ^2+14μ+17}}{2(μ+1)}$ for $0<μ\leq 2$ and $p>p_f(2)=2$ for $μ\geq 2$. In our previous papers, the global small solution $u$ has been obtained for $p>p_{s}(2+μ)$ and $0<μ<2$ but $μ\not=1$. In the present paper, by the vector field method together with the delicate analysis on the Bessel functions, we will show the global existence of small solution $u$ for $p>2$ and $μ>2$. In forthcoming paper, for $μ=1$ and $p>p_{s}(2+μ)=p_{s}(3)=1+\sqrt 2$, the global solution $u$ is also obtained. Therefore, collecting our series of conclusions together with partial results from others, this open question has been solved completely.

math.AP↗

arXiv2025

Global small data weak solutions of 2-D semilinear wave equations with scale-invariant damping, II

For the $2$-D semilinear wave equation with scale-invariant damping $\partial_t^2u-Δu+\fracμ{t}\partial_tu=|u|^p$, where $t\ge 1$ and $p>1$, in the paper [T. Imai, M. Kato, H. Takamura, K. Wakasa, The lifespan of solutions of semilinear wave equations with the scale-invariant damping in two space dimensions, J. Differential Equations 269 (2020), no. 10, 8387-8424], it is conjectured that the global small data weak solution $u$ exists when $p>p_{s}(2+μ) =\frac{μ+3+\sqrt{μ^2+14μ+17}}{2(μ+1)}$ for $μ\in (0, 2)$ and $p>p_f(2)=2$ for $μ\geq 2$. In our previous paper, the global small solution $u$ has been obtained for $p_{s}(2+μ) 2, p>2$ or $μ=1, p>p_s(μ+2)=1+\sqrt 2$.

math.AP↗

arXiv2025

Global existence of small data weak solutions to the semilinear wave equations with time-dependent scale-invariant damping

In this paper, we are concerned with the global existence of small data weak solutions to the $n-$dimensional semilinear wave equation $\partial_t^2u-Δu+\fracμ{t}\partial_tu=|u|^p$ with time-dependent scale-invariant damping, where $n\geq 2$, $t\geq 1$, $μ\in(0,1)\cup(1,2]$ and $p>1$. This equation can be changed into the semilinear generalized Tricomi equation $\partial_t^2u-t^mΔu=t^{α(m)}|u|^p$, where $m=m(μ)>0$ and $α(m)\in\Bbb R$ are two suitable constants. At first, for the more general semilinear Tricomi equation $\partial_t^2v-t^mΔv=t^α|v|^p$ with any fixed constant $m>0$ and arbitrary parameter $α\in\Bbb R$, we shall show that in the case of $α\leq -2$, $n\geq 3$ and $p>1$, the small data weak solution $v$ exists globally; in the case of $α>-2$, through determining the conformal exponent $p_{conf}(n,m,α)>1$, the global small data weak solution $v$ exists when some extra restrictions of $p\geq p_{conf}(n,m,α)$ are given. Returning to the original equation $\partial_t^2u-Δu+\fracμ{t}\partial_tu=|u|^p$, the corresponding global existence results on the small data solution $u$ can be obtained.

math.AP↗

arXiv2024

SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation

The modeling of users' behaviors is crucial in modern recommendation systems. A lot of research focuses on modeling users' lifelong sequences, which can be extremely long and sometimes exceed thousands of items. These models use the target item to search for the most relevant items from the historical sequence. However, training lifelong sequences in click through rate (CTR) prediction or personalized search ranking (PSR) is extremely difficult due to the insufficient learning problem of ID embedding, especially when the IDs in the lifelong sequence features do not exist in the samples of training dataset. Additionally, existing target attention mechanisms struggle to learn the multi-modal representations of items in the sequence well. The distribution of multi-modal embedding (text, image and attributes) output of user's interacted items are not properly aligned and there exist divergence across modalities. We also observe that users' search query sequences and item browsing sequences can fully depict users' intents and benefit from each other. To address these challenges, we propose a unified lifelong multi-modal sequence model called SEMINAR-Search Enhanced Multi-Modal Interest Network and Approximate Retrieval. Specifically, a network called Pretraining Search Unit (PSU) learns the lifelong sequences of multi-modal query-item pairs in a pretraining-finetuning manner with multiple objectives: multi-modal alignment, next query-item pair prediction, query-item relevance prediction, etc. After pretraining, the downstream model restores the pretrained embedding as initialization and finetunes the network. To accelerate the online retrieval speed of multi-modal embedding, we propose a multi-modal codebook-based product quantization strategy to approximate the exact attention calculati

cs.IR↗

arXiv2023

Detect Depression from Social Networks with Sentiment Knowledge Sharing

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK.

cs.CL↗

arXiv2022

Resonantly Enhanced Electric-Field Sensing of Etchless Thin Film Lithium Niobate via Quasibound Sates in the Continuum

Electric field detection has been widely utilized in many fields such as scientific research and integrated circuits. To enhance the tuning sensitivity of E-field sensor, in this paper, we theoretically proposed a highly sensitive E-field sensor composed of etchless lithium niobate (LN) material and hybrid coupling-grating systems in the visible near-infrared regime. Such configuration supports high-quality factor quasi-BIC resonance, which generates strong localized field confinement. Due to the large electro-optic coefficient of LN material, one can shift the wavelength and reflection ratio of resonance by tuning the refractive index of LN material. An analytical theory is carried out to explain the relationship between the refractive index variations and the applied voltages, so we successfully obtained a tuning sensitivity of 40.8 nm/V and a minimum detectable electric field amplitude of 24.5 mV with wavelength resolution of 1 nm. Due to the low parasitic capacitance of LN material and high conductivity of gold film and ITO layer which are utilized as the electrodes, the 3dB bandwidth of the devices should exceed 154 GHz. And we believe that such a surface-normal E-field sensor has extensive potential for the extremely weak electric field detection.

physics.optics↗

arXiv2022

A Novel Approach to Climate Resilience of Infrastructure Networks

With a changing climate, the frequency and intensity of extreme weather events are likely to increase, posing a threat to infrastructure systems' resilience. The response of infrastructure systems to localised failures depends on whether assets are affected randomly, in a targeted strategic way, or any way in between. More than that, infrastructure decisions today, including new routes or improvements to existing assets, will underpin the behaviour of the systems over the next century. It is important to separate and analyse the case of climate-based disruptions and how they affect systems' resilience. This paper presents a probabilistic resilience assessment framework where failure scenarios and network disruptions are generated using weather profile data from climate prediction models with component-level fragility functions. A case study is then carried out to quantify the resilience of Great Britain's railway passenger transport system to high-temperature-related track buckling under the Representative Concentration Pathway 8.5 (RCP8.5) climate change scenario. A 95-year horizon on the resilience of the railway system is drawn. The results also reveal the non-linear responses of the railway system to the increasing temperature and show that models considering random asset failures overestimate the system's resilience.

eess.SY↗

arXiv2022

An improved approach to manufacture CNT reinforced magnesium AZ91 composites with increased strength and ductility

Multiwalled carbon nanotubes (MWCNTs) are decorated with Pt nanoparticles by a "layer-by-layer" approach using poly (sodium 4-styrene sulfonate) (PSS) and poly (diallyl dimethylammonium chloride) (PDDA). Transmission electron microscopy (TEM) images and Energy Dispersive X-Ray (EDX) analysis of the samples confirm Pt deposition on surfaces of CNTs. Dispersibility and dispersion stability of MWCNTs in the solvents are enhanced when MWCNTs are coated with Pt nanoparticles. Mg AZ91 composites reinforced with MWCNTs are then produced by a melt stirring process. Compression tests of the composites show that adding 0.05\% wt Pt-coated MWCNTs in AZ91 improves the composite's mechanical properties compared to the pure AZ91 and pristine MWCNT/AZ91. Fracture surface analysis of the composite using a scanning electron microscope (SEM) shows individuals pulled out MWCNTs in the case of the Pt-coated MWCNT/AZ91 composites. We attribute this finding to the uniform dispersion of Pt-coated MWCNTs in Mg due to the improved wettability of Pt-coated MWCNTs in Mg melts. Molecular dynamics (MD) simulations of the interaction between Pt-coated MWCNTs and Mg support this interpretation.

cond-mat.mtrl-sci↗

arXiv2021

Atomistic aspects of load transfer and fracture in CNT-reinforced aluminium

This paper describes atomistic simulations of deformation and fracture of Al reinforced with carbon nanotubes (CNTs). We use density functional theory (DFT) to understand the energetics of Al-graphene interfaces and gain reference data for the parameterization of Al-C empirical potentials. We then investigate the load transfer between CNTs and Al and its effect on composite strengthening. To this end, we perform uniaxial tensile simulations of an Al crystal reinforced with CNTs of various volume fractions. We also study the interaction of the embedded CNTs with a crack. We show that the interaction between CNTs and Al is weak such that, under tensile loading, CNTs can easily slide inside the Al matrix and get pulled out from the cracked surface. This effect is almost independent of CNT length and volume fraction. Little load transfer and consequently no crack bridging are observed during the simulation of pristine CNTs threading the crack surfaces. CNTs that are geometrically fixated inside Al, on the other hand, can increase the fracture stress and enhance plastic dissipation in the matrix. CNTs located in front of a growing crack blunt the crack and induce plastic deformation of the Al matrix. Depending on the CNT orientation, these processes can either increase or decrease the failure stress of the composite.

cond-mat.mtrl-sci↗

arXiv2020

Multilayer Structures of Graphene and Pt Nanoparticles -- a Multiscale Computational Study

We report results of a multiscale simulation study of multilayer structures consisting of graphene sheets with embedded Pt nanoparticles. Density functional theory is used to understand the energetics of Pt-graphene interfaces and provide reference data for the parameterization of a Pt-graphene interaction potential. Molecular dynamics simulations then provide the conformation and energetics of graphene sheets with embedded Pt nanoparticles of varying density, form and size. These results are interpreted using a continuum mechanical model of sheet deformation, and serve to parameterize a meso-scale Monte Carlo model to investigate the question under which conditions the free volume around the Pt nanoparticles forms a percolating cluster, such that the structures can be used in catalytic applications. We conclude with a discussion of potential applications of such multilayer structures.

physics.app-ph↗