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Qi Xia

Publications and source records attributed to Qi Xia.

16 recordsLinked to original sources

The compactness of Moser-Trudinger functionals with conical metric in the unit ball

Let $\mathbb{B}$ be the unit ball in $\mathbb{R}^2$, $W_0^{1,2} \left( \mathbb{B} \right)$ is a standard Sobolev space. Suppose a function $h_{\epsilon}(x)$ is radially symmetric, nonnegative, continuous on $\overline{\mathbb{B}}$ and satifies $\underset{x \rightarrow 0}{\lim} h_{\epsilon}(x) |x|^{- 2 \epsilon} =1 $, with $h_{\epsilon} (x) >0$ on $\overline{\mathbb{B}} \setminus \{0\}$. In \citep{26}, Zhang proved that the supremum in the following inequality can be attained by some function $u_{\epsilon}$, i.e. , \begin{align} \int_{ \mathbb{B} } h_{\epsilon} (x) e^{ 4 \pi \left(1 + \epsilon \right) {u_{\epsilon}}^2 } dx = \underset{u \in W_0^{1,2} \left( \mathbb{B} \right) \cap \mathcal{S} \setminus \{0\} , ~ \int_{ \mathbb{B} } |\nabla u|^2 dx \leq 1}{\sup} \int_{\mathbb{B}} h_{\epsilon} (x) e^{4 \pi (1 + \epsilon) u^2 } dx, \label{eq: 0.1} \end{align} where $4 \pi$ is the best constant in the classical Moser-Trudinger inequality, and $\mathcal{S}$ is the set of radially symmetric functions. In this paper, we consider the compactness of the sequence $\{ u_{\epsilon} \}_{\epsilon} $ and prove that the limit of this sequence is a function $u_0 \in C^1 (\overline{ \mathbb{B}} )$. Moreover, the $u_0$ is an extremal function of the supremum \begin{align*} \underset{u \in W_0^{1,2} \left( \mathbb{B} \right) \cap \mathcal{S} \setminus \{0\} , ~ \int_{ \mathbb{B} } |\nabla u|^2 dx \leq 1}{\sup} \int_{\mathbb{B}} e^{4 \pi u^2 } dx. \end{align*}

math.AP

The compactness of Moser-Trudinger functionals with conical metric in the unit ball of $\mathbb{R}^N$

Let $\mathbb{B}$ be the unit ball in $\mathbb{R}^N$, $W_0^{1,N} \left( \mathbb{B} \right)$ is a standard Sobolev space. Zhang proved the extremal function of the Moser-Trudinger inequality as follows, \begin{align*} \int_{ \mathbb{B}} h_\epsilon(x) e^{ \alpha_N \left( 1 + \epsilon \right) |u_{\epsilon}|^{ \frac{N}{N-1} } } dx, \quad u_{\epsilon} \in W_0^{1,N} ( \mathbb{B} ) \cap \mathcal{S}, \end{align*} where $\alpha_N = \omega_N^{ \frac{1}{N-1} }$, $\omega_N $ is the area of the unit sphere in $\mathbb{R}^N$(see \citep{26}) . In this paper, we consider the compactness of the sequence $\{ u_{\epsilon} \}_{\epsilon} $ and prove that it has a subsequence converging to a function in $C^1 \left(\overline{ \mathbb{B}} \right)$.

math.AP

Controllable Video Object Insertion via Multi-View Priors

Video object insertion places a user-specified object in an existing dynamic scene. Existing methods typically condition generation on text or a single reference image. Consequently, object appearance is underconstrained under viewpoint changes, often leading to identity drift, incorrect foreground-background layering, boundary artifacts, and temporal flickering. In this paper, we propose a video object insertion framework that incorporates multi-view object priors to address these limitations. The framework lifts a 2D reference image into a multi-view representation and uses view-consistent conditioning to provide stable identity guidance and view-adaptive appearance cues. A quality-aware weighting mechanism reduces the influence of noisy or imperfect reconstructed views. We further introduce an Integration-Aware Consistency Module that promotes plausible occlusion, clean boundaries, and temporal continuity. Experiments demonstrate that the proposed framework improves visual quality, controllability, identity consistency, and foreground-background integration for video object insertion compared to the baseline methods. Project page: https://polarisxq.github.io/MOVI/.

cs.CV

SocialMirror: Reconstructing 3D Human Interaction Behaviors from Monocular Videos with Semantic and Geometric Guidance

Accurately reconstructing human behavior in close-interaction scenarios is crucial for enabling realistic virtual interactions in augmented reality, precise motion analysis in sports, and natural collaborative behavior in human-robot tasks. Reliable reconstruction in these contexts significantly enhances the realism and effectiveness of AI-driven interactive applications. However, human reconstruction from monocular videos in close-interaction scenarios remains challenging due to severe mutual occlusions, leading local motion ambiguity, disrupted temporal continuity and spatial relationship error. In this paper, we propose SocialMirror, a diffusion-based framework that integrates semantic and geometric cues to effectively address these issues. Specifically, we first leverage high-level interaction descriptions generated by a vision-language model to guide a semantic-guided motion infiller, hallucinating occluded bodies and resolving local pose ambiguities. Next, we propose a sequence-level temporal refiner that enforces smooth, jitter-free motions, while incorporating geometric constraints during sampling to ensure plausible contact and spatial relationships. Evaluations on multiple interaction benchmarks show that SocialMirror achieves state-of-the-art performance in reconstructing interactive human meshes, demonstrating strong generalization across unseen datasets and in-the-wild scenarios. The code will be released upon publication.

cs.CV

TINC: Trusted Intelligent NetChain

Blockchain technology facilitates the development of decentralized systems that ensure trust and transparency without the need for expensive centralized intermediaries. However, existing blockchain architectures particularly consortium blockchains face critical challenges related to scalability and efficiency. State sharding has emerged as a promising approach to enhance blockchain scalability and performance. However, current shard-based solutions often struggle to guarantee fair participation and a balanced workload distribution among consortium members. To address these limitations, we propose Trusted Intelligent NetChain (TINC), a multi-plane sharding architecture specifically designed for consortium blockchains. TINC incorporates intelligent mechanisms for adaptive node assignment and dynamic workload balancing, enabling the system to respond effectively to changing network conditions while maintaining equitable shard utilization. By decoupling the control and data planes, TINC allows control nodes to focus on consensus operations, while data nodes handle large-scale storage, thus improving overall resource efficiency. Extensive experimental evaluation and formal analysis demonstrate that TINC significantly outperforms existing shard-based blockchain frameworks. It achieves higher throughput, lower latency, balanced node and transaction distributions, and reduced transaction failure rates. Furthermore, TINC maintains essential blockchain security guarantees, exhibiting resilience against Byzantine faults and dynamic network environments. The integration of Dynamic Decentralized Identifiers (DDIDs) further strengthens trust and security management within the consortium network.

cs.NI

CoNi-MOF laccase-like nanozymes prepared by dielectric barrier discharge plasma for treatment of antibiotic pollution

Laccase is a natural green catalyst and utilized in pollution treatment. Nevertheless, its practical application is constrained by limitations including high cost, poor stability, and difficulties in recovery. Herein, with inspiration from catalytic mechanism of natural laccase, we designed and prepared a bimetallic metal-organic framework, namely, CoNi-MOF, using low-temperature plasma (LTP) technology. We employed dielectric barrier discharge (DBD) plasma to prepare CoNi-MOF, and by precisely modulating the N2/O2 gas ratio, we could modulate the distribution concentration of oxygen vacancies in CoNi-MOF. Experimental investigations and density functional theory (DFT) calculations elucidated that the critical role of the oxygen vacancies in enhancing the laccase-like activity, which promoted the activation of molecular oxygen (O2) for generation of reactive oxygen species (ROS). Compared to natural laccase, CoNi-MOF exhibited superior catalytic performance in the degradation of antibiotic tetracycline (TC), along with enhanced resistance to harsh environmental conditions, improved stability, and low biotoxicity. Notably, aeration increased the dissolved oxygen (DO) content, further improving the TC degradation efficiency. As such, this study not only proposes a facile and efficient low-temperature plasma technology for synthesizing high-performance laccase-like nanozymes but also provides a promising and environmentally friendly strategy for the remediation of antibiotic contamination in the environment.

cond-mat.mtrl-sci

Kinetic Simulations of Particle Acceleration in Relativistic Perpendicular Electron-positron Shocks with Proton Admixture

Particle acceleration in relativistic shocks of electron-positron plasmas with proton admixture is investigated through two-dimensional (2D) particle-in-cell (PIC) simulations. The upstream plasma, with a bulk Lorentz factor of $10$ and a magnetization parameter of 0.02, includes a small fraction of protons ($\sim 5\%$ by number). A relativistic perpendicular shock is formed by reflecting the flow off a conducting wall. The shock structure, electromagnetic fields, and particle energy spectra are analyzed. The particle density and the magnetic field have fluctuations. In the far-downstream region of the shock, positrons are accelerated to energies comparable to protons and develop a hard nonthermal component with a spectral index of $\sim 2$ in their energy spectrum, while electrons remain confined to lower energies. This asymmetry is attributed to the polarization properties of proton-driven electromagnetic waves, which favor positron acceleration. The results highlight the importance of plasma composition in shaping particle acceleration and nonthermal emission in relativistic shocks. These findings provide new insights into the microphysics of particle acceleration in astrophysical sources containing relativistic shocks.

astro-ph.HE

Effect of Numerically Controlled Oscillator Bit Width in Phase Meters

Projects aiming to detect gravitational waves (GWs) in space in the millihertz range will utilize interferometers to measure the separations between free-falling test masses. The phasemeter measures the phase changes of the interference signals caused by the test masses' relative movements. The measurement sensitivity of the phasemeter is one of the key factors in the detection. In this work, we reviewed the core metrology of the phasemeter and evaluated the ultra-low noise performance of the phasemeter with analog signals. Frequency readout noise related to the bit width of the numerically controlled oscillator (NCO) inside the phasemeter is identified as one of the main noise sources of phase measurement theoretically and experimentally. After increasing the NCO bit widths, the single-channel phase noise of the phasemeter reached 2.0 {\mu}rad/Hz^{1/2} at 6 mHz, and the differential phase noise reached 0.4 {\mu}rad/Hz^{1/2} at 6 mHz. The phase noise performances remained consistent within the carrier frequency range of 4.9 MHz to 25.1 MHz.

astro-ph.IM

Advanced pure tilt actuator for testing tilt-to-length coupling in space-based gravitational wave detection

Tilt-to-length (TTL) coupling, caused by the jitter of test masses or satellites, is a significant noise source in space-based gravitational wave detection. Calibrating and suppressing TTL coupling noise at the sub-nanometer level is essential. One main challenge in current ground-based TTL coupling testing is the residual translational movement of the tilt actuator. This paper introduces the development of an advanced pure tilt actuator (APTA) specifically designed for testing TTL coupling. The APTA provides precise tilt motion and is monitored by a four-beam interferometer, which measures the displacement of attached array pyramids. We present a detailed theoretical model and experimental setup. Experimental results demonstrate that this optical test bed, equipped with the APTA, can achieve subnanometer-level TTL coupling calibration. In addition, a typical heterodyne interferometer was tested using the APTA test bed. Comparative testing demonstrated that the imaging system is capable of effectively suppressing TTL coupling errors. The TTL coupling coefficients were reduced from over plus-minus 30 micrometers per radian to within plus-minus 5 micrometers per radian across a range of plus-minus 200 microradians, meeting the preliminary requirements for the TianQin mission. This APTA test platform has the potential to be widely utilized for ground-based TTL coupling inspection.

astro-ph.IM

Nonthermal Acceleration of Electrons, Positrons and Protons at a Nonrelativistic Quasiparallel Collisionless Shock

Energetic positrons have been observed in the interstellar medium, and high-energy positrons with relativistic energies up to approximately 1 TeV have been detected in Galactic cosmic rays. We conducted a study on the acceleration of particles, specifically positrons, in a nonrelativistic quasiparallel collisionless shock induced by a plasma consisting of protons, electrons, and positrons. The positron-to-proton number density ratio in the plasma is 0.1. We focused on a representative shock with a sonic Mach number of 17.1 and an Alfvénic Mach number of 16.8 in the rest frame of the shock. To investigate the acceleration mechanisms of particles including positrons in the shock, we utilized one-dimensional particle-in-cell (PIC) simulations. It was found that all three species of particles in the shock can be accelerated and exhibit power law spectra. At the shock front, a significant portion of incoming upstream particles are reflected and undergo significant energy increase, and these reflected particles can be efficiently injected into the process of diffusive shock acceleration (DSA). Moveover, the reflected positrons can be further accelerated by an electric field parallel to the magnetic field when they move along the magnetic field upstream of the shock. As a result, positrons can be preferentially accelerated to be injected in the DSA process compared to electrons.

astro-ph.HE

Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections

Sparse data are common. The traditional ``handcrafted'' features are often sparse. Embedding vectors from trained models can also be very sparse, for example, embeddings trained via the ``ReLu'' activation function. In this paper, we report our exploration of efficient search in sparse data with graph-based ANN algorithms (e.g., HNSW, or SONG which is the GPU version of HNSW), which are popular in industrial practice, e.g., search and ads (advertising). We experiment with the proprietary ads targeting application, as well as benchmark public datasets. For ads targeting, we train embeddings with the standard ``cosine two-tower'' model and we also develop the ``chi-square two-tower'' model. Both models produce (highly) sparse embeddings when they are integrated with the ``ReLu'' activation function. In EBR (embedding-based retrieval) applications, after we the embeddings are trained, the next crucial task is the approximate near neighbor (ANN) search for serving. While there are many ANN algorithms we can choose from, in this study, we focus on the graph-based ANN algorithm (e.g., HNSW-type). Sparse embeddings should help improve the efficiency of EBR. One benefit is the reduced memory cost for the embeddings. The other obvious benefit is the reduced computational time for evaluating similarities, because, for graph-based ANN algorithms such as HNSW, computing similarities is often the dominating cost. In addition to the effort on leveraging data sparsity for storage and computation, we also integrate ``sign cauchy random projections'' (SignCRP) to hash vectors to bits, to further reduce the memory cost and speed up the ANN search. In NIPS'13, SignCRP was proposed to hash the chi-square similarity, which is a well-adopted nonlinear kernel in NLP and computer vision. Therefore, the chi-square two-tower model, SignCRP, and HNSW are now tightly integrated.

cs.IR

QuantumFed: A Federated Learning Framework for Collaborative Quantum Training

With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machines wish to train a global model using the local data on each machine, it may be very difficult to copy the data into one machine and train the model. Therefore, a collaborative quantum neural network framework is necessary. In this article, we borrow the core idea of federated learning to propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together. Our experiments show the feasibility and robustness of our framework.

cs.LG

LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks

Variational quantum algorithms (VQAs) have recently received significant attention from the research community due to their promising performance in Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run on parameterized quantum circuits (PQC) with randomly initialized parameters are characterized by barren plateaus (BP) where the gradient vanishes exponentially in the number of qubits. In this paper, we first review quantum natural gradient (QNG), which is one of the most popular algorithms used in VQA, from the classical first-order optimization point of view. Then, we proposed a \underline{L}ook \underline{A}round \underline{W}arm-\underline{S}tart QNG (LAWS) algorithm to mitigate the widespread existing BP issues. LAWS is a combinatorial optimization strategy taking advantage of model parameter initialization and fast convergence of QNG. LAWS repeatedly reinitializes parameter search space for the next iteration parameter update. The reinitialized parameter search space is carefully chosen by sampling the gradient close to the current optimal. Moreover, we present a unified framework (WS-SGD) for integrating parameter initialization techniques into the optimizer. We provide the convergence proof of the proposed framework for both convex and non-convex objective functions based on Polyak-Lojasiewicz (PL) condition. Our experiment results show that the proposed algorithm could mitigate the BP and have better generalization ability in quantum classification problems.

quant-ph

Kinetic Simulation on Electron, Proton and Helium Acceleration in a Nonrelativistic Quasiparallel shock

In addition to electrons and protons, nonrelativistic quasiparallel shocks are expected to possess the ability to accelerate heavy ions. The shocks in supernova remnants are generally supposed to be accelerators of the Galactic cosmic rays, which consist of many species of particles. We investigate diffusive shock acceleration (DSA) of electrons, protons and helium ions in a nonrelativistic quasiparallel shock through 1D particle-in-cell (PIC) simulation with a helium-to-proton number density ratio of $0.1$, which is relevant for the Galactic cosmic rays. The simulation indicates that waves can be excited by the flow of the energetic protons and helium ions upstream of the nonrelativistic quasiparallel shock with a sonic Mach number of 14 and an alfvén Mach number of 19.5 in the shock rest frame, and the charged particles are scattered by the self-generated waves and accelerated gradually. Moreover, the spectra of the charged particles downstream of the shock are thermal plus a nonthermal tail, and the acceleration is efficient with about $7\%$ and $5.4\%$ of the bulk kinetic energy transferred into the nonthermal protons and helium ions in the near downstream region at the end of the simulation, respectively.

astro-ph.HE

Object-Based Image Coding: A Learning-Driven Revisit

The Object-Based Image Coding (OBIC) that was extensively studied about two decades ago, promised a vast application perspective for both ultra-low bitrate communication and high-level semantical content understanding, but it had rarely been used due to the inefficient compact representation of object with arbitrary shape. A fundamental issue behind is how to efficiently process the arbitrary-shaped objects at a fine granularity (e.g., feature element or pixel wise). To attack this, we have proposed to apply the element-wise masking and compression by devising an object segmentation network for image layer decomposition, and parallel convolution-based neural image compression networks to process masked foreground objects and background scene separately. All components are optimized in an end-to-end learning framework to intelligently weigh their (e.g., object and background) contributions for visually pleasant reconstruction. We have conducted comprehensive experiments to evaluate the performance on PASCAL VOC dataset at a very low bitrate scenario (e.g., $\lesssim$0.1 bits per pixel - bpp) which have demonstrated noticeable subjective quality improvement compared with JPEG2K, HEVC-based BPG and another learned image compression method. All relevant materials are made publicly accessible at https://njuvision.github.io/Neural-Object-Coding/.

eess.IV

Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing

Mobile crowdsensing engages a crowd of individuals to use their mobile devices to cooperatively collect data about social events and phenomena for special interest customers. It can reduce the cost on sensor deployment and improve data quality with human intelligence. To enhance data trustworthiness, it is critical for service provider to recruit mobile users based on their personal features, e.g., mobility pattern and reputation, but it leads to the privacy leakage of mobile users. Therefore, how to resolve the contradiction between user privacy and task allocation is challenging in mobile crowdsensing. In this paper, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation from geographic information and credit points of mobile users. In SPOON, the service provider enables to recruit mobile users based on their locations, and select proper sensing reports according to their trust levels without invading user privacy. By utilizing proxy re-encryption and BBS+ signature, sensing tasks are protected and reports are anonymized to prevent privacy leakage. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users. Finally, we show the security properties of SPOON and demonstrate its efficiency on computation and communication.

cs.CR