arXiv subjects

Qi Wu

Publications and source records attributed to Qi Wu.

At least 217 records · Page 12Linked to original sources

arXiv2024

Single electron charge spectra of 8-inch high-collection-efficiency MCP-PMTs

The atomic layer deposition(ALD) coating lengthens the lifetime of microchannel plates(MCP), which are used as the electron amplifier of the photomultiplier tubes(PMT). In the Jinping Neutrino Experiment, the newly developed 8-inch MCP-PMT achieves high collection efficiency by coating with high secondary emission materials. The resulting single electron response(SER) charge distribution deviates from the Gaussian distribution in large charge regions.To understand the nature of the jumbo-charged SER, we designed a voltage-division experiment to quantify the dependence of the MCP gain on the energy of incident electrons. Combining the relationship with the Furman probabilistic model, we reproduced the SER charge spectra by an additional amplification stage on the input electrode of the first MCP. Our results favor a Gamma-Tweedie mixture to describe the SER charge spectra of the MCP-PMTs.

physics.ins-det↗

arXiv2024

SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection

Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks. The social graph, constructed from social network interactions, contains benign and bot accounts that influence each other. However, previous graph-based detection methods that follow the transductive message-passing paradigm may not fully utilize hidden graph information and are vulnerable to adversarial bot behavior. The indiscriminate message passing between nodes from different categories and communities results in excessively homogeneous node representations, ultimately reducing the effectiveness of social bot detectors. In this paper, we propose SEBot, a novel multi-view graph-based contrastive learning-enabled social bot detector. In particular, we use structural entropy as an uncertainty metric to optimize the entire graph's structure and subgraph-level granularity, revealing the implicitly existing hierarchical community structure. And we design an encoder to enable message passing beyond the homophily assumption, enhancing robustness to adversarial behaviors of social bots. Finally, we employ multi-view contrastive learning to maximize mutual information between different views and enhance the detection performance through multi-task learning. Experimental results demonstrate that our approach significantly improves the performance of social bot detection compared with SOTA methods.

cs.SI↗

arXiv2024

$X_0(2900)$ and its spin partners production in the $B^+$ decay process

In the present work, we investigate $X_0(2900)$ and its spin partners productions in the $B^+$ decay processes, where $X_0(2900)$ is regarded as a $\bar{D}^\ast K^\ast$ molecular state. We infer that the initial $B^+$ meson and the final $D^+ X_0(2900)$ are connected through $D_{s1}^+(2460) \bar{D}^{\ast 0} $ mesons by exchanging a $K^{\ast 0}$ meson. The contributions from such a meson loop to the process $B^+ \to D^+ X_0(2900)$ are evaluated and our estimations indicate that the measured branching fraction of $B^+\to D^+ X_0(2900)$ could be well reproduced in a reasonable model parameter range, which indicates that $X_0(2900)$ could be interpreted as a $\bar{D}^\ast K^\ast $ molecular state. In addition, the production processes $B^+ \to D^+ \tilde{X}_{1,2}$, $B^+ \to D^{\ast +} X_0(2900)$, and $B^+ \to D^{\ast +}\tilde{X}_{1,2}$ are investigated, and we find that the fit fraction of $\tilde{X}_1$ in the process $B^+ \to D^{\ast +} D^{\ast -} K^+$ is aobut $6\%$, which should be accessible experimentally.

hep-ph↗

arXiv2024

BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers

Detecting social bots has evolved into a pivotal yet intricate task, aimed at combating the dissemination of misinformation and preserving the authenticity of online interactions. While earlier graph-based approaches, which leverage topological structure of social networks, yielded notable outcomes, they overlooked the inherent dynamicity of social networks -- In reality, they largely depicted the social network as a static graph and solely relied on its most recent state. Due to the absence of dynamicity modeling, such approaches are vulnerable to evasion, particularly when advanced social bots interact with other users to camouflage identities and escape detection. To tackle these challenges, we propose BotDGT, a novel framework that not only considers the topological structure, but also effectively incorporates dynamic nature of social network. Specifically, we characterize a social network as a dynamic graph. A structural module is employed to acquire topological information from each historical snapshot. Additionally, a temporal module is proposed to integrate historical context and model the evolving behavior patterns exhibited by social bots and legitimate users. Experimental results demonstrate the superiority of BotDGT against the leading methods that neglected the dynamic nature of social networks in terms of accuracy, recall, and F1-score.

cs.SI↗

arXiv2024

Production of $P^Λ_{ψs}(4338)$ from $Ξ_b$ decay

In the present work, we investigate the production of the newly observed $P^Λ_{ψs}(4338)$ state in $Ξ_b^-$ decay, where the $P^Λ_{ψs}(4338)$ is assigned as a $Ξ_c \bar{D}$ molecular state. By using an effective Lagrangian approach, we evaluate the branching fractions of $Ξ_b^-\rightarrow P^Λ_{ψs}(4338)K^-$ via the triangle loop mechanism. The branching fractions of $Ξ_b^-\rightarrow P^Λ_{ψs}(4338)K^-$ are in the order of $10^{-4}$; the result is compared with our previous work of $Ξ_b^-\rightarrow P^Λ_{ψs}(4459)K^-$. We also predict the ratio of $P^Λ_{ψs}(4459)$ and $P^Λ_{ψs}(4338)$ productions in the decay $Ξ_b^- \to P^Λ_{ψs} K^- \to J/ψΛK^-$. The predicted branching fractions and their ratios could be tested experimentally, which may be helpful for understanding the molecular picture of $P^Λ_{ψs}(4338)$ as well as other hidden-charm pentaquark states with strangeness. Moreover, the experimental potential of observing $P^Λ_{ψs}(4338)$ in the $Ξ^-_b\to K^- J/ψΛ$ is discussed.

hep-ph↗

arXiv2024

G-NeRF: Geometry-enhanced Novel View Synthesis from Single-View Images

Novel view synthesis aims to generate new view images of a given view image collection. Recent attempts address this problem relying on 3D geometry priors (e.g., shapes, sizes, and positions) learned from multi-view images. However, such methods encounter the following limitations: 1) they require a set of multi-view images as training data for a specific scene (e.g., face, car or chair), which is often unavailable in many real-world scenarios; 2) they fail to extract the geometry priors from single-view images due to the lack of multi-view supervision. In this paper, we propose a Geometry-enhanced NeRF (G-NeRF), which seeks to enhance the geometry priors by a geometry-guided multi-view synthesis approach, followed by a depth-aware training. In the synthesis process, inspired that existing 3D GAN models can unconditionally synthesize high-fidelity multi-view images, we seek to adopt off-the-shelf 3D GAN models, such as EG3D, as a free source to provide geometry priors through synthesizing multi-view data. Simultaneously, to further improve the geometry quality of the synthetic data, we introduce a truncation method to effectively sample latent codes within 3D GAN models. To tackle the absence of multi-view supervision for single-view images, we design the depth-aware training approach, incorporating a depth-aware discriminator to guide geometry priors through depth maps. Experiments demonstrate the effectiveness of our method in terms of both qualitative and quantitative results.

cs.CV↗

arXiv2024

PairAug: What Can Augmented Image-Text Pairs Do for Radiology?

Current vision-language pre-training (VLP) methodologies predominantly depend on paired image-text datasets, a resource that is challenging to acquire in radiology due to privacy considerations and labelling complexities. Data augmentation provides a practical solution to overcome the issue of data scarcity, however, most augmentation methods exhibit a limited focus, prioritising either image or text augmentation exclusively. Acknowledging this limitation, our objective is to devise a framework capable of concurrently augmenting medical image and text data. We design a Pairwise Augmentation (PairAug) approach that contains an Inter-patient Augmentation (InterAug) branch and an Intra-patient Augmentation (IntraAug) branch. Specifically, the InterAug branch of our approach generates radiology images using synthesised yet plausible reports derived from a Large Language Model (LLM). The generated pairs can be considered a collection of new patient cases since they are artificially created and may not exist in the original dataset. In contrast, the IntraAug branch uses newly generated reports to manipulate images. This process allows us to create new paired data for each individual with diverse medical conditions. Our extensive experiments on various downstream tasks covering medical image classification zero-shot and fine-tuning analysis demonstrate that our PairAug, concurrently expanding both image and text data, substantially outperforms image-/text-only expansion baselines and advanced medical VLP baselines. Our code is released at \url{https://github.com/YtongXie/PairAug}.

cs.CV↗

arXiv2024

ModaVerse: Efficiently Transforming Modalities with LLMs

Humans possess the capability to comprehend diverse modalities and seamlessly transfer information between them. In this work, we introduce ModaVerse, a Multi-modal Large Language Model (MLLM) capable of comprehending and transforming content across various modalities including images, videos, and audio. Predominant MLLM frameworks have largely relied on the alignment of latent spaces of textual and non-textual features. This alignment process, which synchronizes a language model trained on textual data with encoders and decoders trained on multi-modal data, often necessitates extensive training of several projection layers in multiple stages. Inspired by LLM-as-agent methodologies, we propose a novel Input/Output (I/O) alignment mechanism that operates directly at the level of natural language. It aligns the LLM's output with the input of generative models, avoiding the complexities associated with latent feature alignments, and simplifying the multiple training stages of existing MLLMs into a single, efficient process. This conceptual advancement leads to significant reductions in both data and computational costs. By conducting experiments on several benchmarks, we demonstrate that our approach attains comparable performance with the state of the art while achieving considerable efficiencies in data usage and training duration.

cs.CV↗

arXiv2024

Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework

Medical vision language pre-training (VLP) has emerged as a frontier of research, enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease. Due to the complex semantics of biomedical texts, current methods struggle to align medical images with key pathological findings in unstructured reports. This leads to the misalignment with the target disease's textual representation. In this paper, we introduce a novel VLP framework designed to dissect disease descriptions into their fundamental aspects, leveraging prior knowledge about the visual manifestations of pathologies. This is achieved by consulting a large language model and medical experts. Integrating a Transformer module, our approach aligns an input image with the diverse elements of a disease, generating aspect-centric image representations. By consolidating the matches from each aspect, we improve the compatibility between an image and its associated disease. Additionally, capitalizing on the aspect-oriented representations, we present a dual-head Transformer tailored to process known and unknown diseases, optimizing the comprehensive detection efficacy. Conducting experiments on seven downstream datasets, ours improves the accuracy of recent methods by up to 8.56% and 17.26% for seen and unseen categories, respectively. Our code is released at https://github.com/HieuPhan33/MAVL.

cs.CV↗

arXiv2024

VL-Mamba: Exploring State Space Models for Multimodal Learning

Multimodal large language models (MLLMs) have attracted widespread interest and have rich applications. However, the inherent attention mechanism in its Transformer structure requires quadratic complexity and results in expensive computational overhead. Therefore, in this work, we propose VL-Mamba, a multimodal large language model based on state space models, which have been shown to have great potential for long-sequence modeling with fast inference and linear scaling in sequence length. Specifically, we first replace the transformer-based backbone language model such as LLama or Vicuna with the pre-trained Mamba language model. Then, we empirically explore how to effectively apply the 2D vision selective scan mechanism for multimodal learning and the combinations of different vision encoders and variants of pretrained Mamba language models. The extensive experiments on diverse multimodal benchmarks with competitive performance show the effectiveness of our proposed VL-Mamba and demonstrate the great potential of applying state space models for multimodal learning tasks.

cs.CV↗

arXiv2024

TON-VIO: Online Time Offset Modeling Networks for Robust Temporal Alignment in High Dynamic Motion VIO

Temporal misalignment (time offset) between sensors is common in low cost visual-inertial odometry (VIO) systems. Such temporal misalignment introduces inconsistent constraints for state estimation, leading to a significant positioning drift especially in high dynamic motion scenarios. In this article, we focus on online temporal calibration to reduce the positioning drift caused by the time offset for high dynamic motion VIO. For the time offset observation model, most existing methods rely on accurate state estimation or stable visual tracking. For the prediction model, current methods oversimplify the time offset as a constant value with white Gaussian noise. However, these ideal conditions are seldom satisfied in real high dynamic scenarios, resulting in the poor performance. In this paper, we introduce online time offset modeling networks (TON) to enhance real-time temporal calibration. TON improves the accuracy of time offset observation and prediction modeling. Specifically, for observation modeling, we propose feature velocity observation networks to enhance velocity computation for features in unstable visual tracking conditions. For prediction modeling, we present time offset prediction networks to learn its evolution pattern. To highlight the effectiveness of our method, we integrate the proposed TON into both optimization-based and filter-based VIO systems. Simulation and real-world experiments are conducted to demonstrate the enhanced performance of our approach. Additionally, to contribute to the VIO community, we will open-source the code of our method on: https://github.com/Franky-X/FVON-TPN.

cs.RO↗

arXiv2024

Thermal-NeRF: Neural Radiance Fields from an Infrared Camera

In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction. However, the predominant reliance on RGB imaging presupposes ideal lighting conditions: a premise frequently unmet in robotic applications plagued by poor lighting or visual obstructions. This limitation overlooks the capabilities of infrared (IR) cameras, which excel in low-light detection and present a robust alternative under such adverse scenarios. To tackle these issues, we introduce Thermal-NeRF, the first method that estimates a volumetric scene representation in the form of a NeRF solely from IR imaging. By leveraging a thermal mapping and structural thermal constraint derived from the thermal characteristics of IR imaging, our method showcasing unparalleled proficiency in recovering NeRFs in visually degraded scenes where RGB-based methods fall short. We conduct extensive experiments to demonstrate that Thermal-NeRF can achieve superior quality compared to existing methods. Furthermore, we contribute a dataset for IR-based NeRF applications, paving the way for future research in IR NeRF reconstruction.

cs.CV↗

arXiv2024

One-way Valley-locked waveguide with large channel achieved by all-dielectric Photonic Crystals

Nonreciprocity, which denotes the asymmetric or even unidirectional transmission of light, constitutes the cornerstone of modern photonic circuits. In the realm of photonic devices, it has been widely utilized in isolators, circulators and so on. Recent topology in artificial materials, an unprecedented degree of freedom, has been proposed to solve the effect of impurities on nonreciprocal transmission. However, in view of the bulk-edge correspondence, the spatial width of the transmission channel with uniform field distribution is quite narrow and needs further exploration. In this paper, we proposed a one-way valley-locked waveguide with a large channel in an all-dielectric photonic crystal. Quite different from the topological edge modes, the unidirectional property of our waveguide comes from the bulk modes with valley-lock, which can fully utilize the whole dimension of the structure with an efficiency of 100%. Additionally, the electrical field is uniformly distributed across the entire channel, which opens a new avenue for low-loss nonreciprocity devices.

physics.app-ph↗

arXiv2024

Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors

This paper introduces a novel human pose estimation approach using sparse inertial sensors, addressing the shortcomings of previous methods reliant on synthetic data. It leverages a diverse array of real inertial motion capture data from different skeleton formats to improve motion diversity and model generalization. This method features two innovative components: a pseudo-velocity regression model for dynamic motion capture with inertial sensors, and a part-based model dividing the body and sensor data into three regions, each focusing on their unique characteristics. The approach demonstrates superior performance over state-of-the-art models across five public datasets, notably reducing pose error by 19\% on the DIP-IMU dataset, thus representing a significant improvement in inertial sensor-based human pose estimation. Our codes are available at {\url{https://github.com/dx118/dynaip}}.

cs.CV↗

arXiv2024

Explicit Interaction for Fusion-Based Place Recognition

Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and autonomous vehicles. Recent fusion-based place recognition methods combine multi-modal features in implicit manners. While achieving remarkable results, they do not explicitly consider what the individual modality affords in the fusion system. Therefore, the benefit of multi-modal feature fusion may not be fully explored. In this paper, we propose a novel fusion-based network, dubbed EINet, to achieve explicit interaction of the two modalities. EINet uses LiDAR ranges to supervise more robust vision features for long time spans, and simultaneously uses camera RGB data to improve the discrimination of LiDAR point clouds. In addition, we develop a new benchmark for the place recognition task based on the nuScenes dataset. To establish this benchmark for future research with comprehensive comparisons, we introduce both supervised and self-supervised training schemes alongside evaluation protocols. We conduct extensive experiments on the proposed benchmark, and the experimental results show that our EINet exhibits better recognition performance as well as solid generalization ability compared to the state-of-the-art fusion-based place recognition approaches. Our open-source code and benchmark are released at: https://github.com/BIT-XJY/EINet.

cs.CV↗

arXiv2024

Capacitive coupling study of the HERD SCD prototype: preliminary results

The Silicon Charge Detector (SCD) is a subdetector of the High Energy Cosmic Radiation Detection payload. The dynamic range of the silicon microstrip detector can be extended by the capacitive coupling effect, which is related to the interstrip capacitance and the coupling capacitance. A detector prototype with several sets of parameters was designed and tested in the ion beams at the CERN Super Proton Synchrotron. The capacitive coupling fractions with readout strip and floating strip incidences were studied using the beam test data and SPICE simulation.

physics.ins-det↗

arXiv2024

A novel method for determining the resistivity of compressed superconducting materials

The resistivity of a superconductor in its normal state plays a critical role in determining its superconducting ground state. However, measuring the resistivity of a material under high pressure has long presented a significant technical challenge due to pressure-induced changes in the crystallographic directions, especially for samples with anisotropic layered structures like high-Tc superconductors and other intriguing quantum materials. Here, we are the first to propose a novel and effective method for determining high-pressure resistivity, which relies on the ambient-pressure resistivity, initial sample sizes, lattice parameters, high-pressure resistance, and lattice parameters measured from the same sample. Its validity has been confirmed through our investigations of pressurized copper-oxide superconductors, which demonstrates that this method provides new possibilities for researchers conducting high-pressure studies related to resistivity of these materials.

cond-mat.str-el↗

arXiv2024

Productions of $X(3872)$, $Z_c(3900)$, $X_2(4013)$, and $Z_c(4020)$ in $B_{(s)}$ decays offer strong clues on their molecular nature

The exotic states $X(3872)$ and $Z_c(3900)$ have long been conjectured as isoscalar and isovector $\bar{D}^*D$ molecules. In this work, we first propose the triangle diagram mechanism to investigate their productions in $B$ decays as well as their heavy quark spin symmetry partners, $X_2(4013)$ and $Z_c(4020)$. We show that the large isospin breaking of the ratio $\mathcal{B}[B^+ \to X(3872) K^+]/\mathcal{B}[B^0 \to X(3872) K^0] $ can be attributed to the isospin breaking of the neutral and charged $\bar{D}^*D$ components in their wave functions. For the same reason, the branching fractions of $Z_c(3900)$ in $B$ decays are smaller than the corresponding ones of $X(3872)$ by at least one order of magnitude, which naturally explains its non-observation. A hierarchy for the production fractions of $X(3872)$, $Z_c(3900)$, $X_2(4013)$, and $Z_c(4020)$ in $B$ decays, consistent with all existing data, is predicted. Furthermore, with the factorization ansatz we extract the decay constants of $X(3872)$, $Z_c(3900)$, and $Z_c(4020)$ as $\bar{D}^*D^{(*)}$ molecules via the $B$ decays, and then calculate their branching fractions in the relevant $B_{(s)}$ decays, which turn out to agree with all existing experimental data. The mechanism we proposed is useful to elucidate the internal structure of the many exotic hadrons discovered so far and to extract the decay constants of hadronic molecules,which can be used to predict their production in related processes.

hep-ph↗