arXiv subjects

Junbin Zhang

Publications and source records attributed to Junbin Zhang.

9 recordsLinked to original sources

arXiv2026

Graph-PiT: Enhancing Structural Coherence in Part-Based Image Synthesis via Graph Priors

Achieving fine-grained and structurally sound controllability is a cornerstone of advanced visual generation. Existing part-based frameworks treat user-provided parts as an unordered set and therefore ignore their intrinsic spatial and semantic relationships, which often results in compositions that lack structural integrity. To bridge this gap, we propose Graph-PiT, a framework that explicitly models the structural dependencies of visual components using a graph prior. Specifically, we represent visual parts as nodes and their spatial-semantic relationships as edges. At the heart of our method is a Hierarchical Graph Neural Network (HGNN) module that performs bidirectional message passing between coarse-grained part-level super-nodes and fine-grained IP+ token sub-nodes, refining part embeddings before they enter the generative pipeline. We also introduce a graph Laplacian smoothness loss and an edge-reconstruction loss so that adjacent parts acquire compatible, relation-aware embeddings. Quantitative experiments on controlled synthetic domains (character, product, indoor layout, and jigsaw), together with qualitative transfer to real web images, show that Graph-PiT improves structural coherence over vanilla PiT while remaining compatible with the original IP-Prior pipeline. Ablation experiments confirm that explicit relational reasoning is crucial for enforcing user-specified adjacency constraints. Our approach not only enhances the plausibility of generated concepts but also offers a scalable and interpretable mechanism for complex, multi-part image synthesis. The code is available at https://github.com/wolf-bailang/Graph-PiT.

cs.CV↗

arXiv2026

Shallow- and Deep-fake Image Manipulation Localization Using Vision Mamba and Guided Graph Neural Network

Image manipulation localization is a critical research task, given that forged images may have a significant societal impact of various aspects. Such image manipulations can be produced using traditional image editing tools (known as "shallowfakes") or advanced artificial intelligence techniques ("deepfakes"). While numerous studies have focused on image manipulation localization on either shallowfake images or deepfake videos, few approaches address both cases. In this paper, we explore the feasibility of using a deep learning network to localize manipulations in both shallow- and deep-fake images, and proposed a solution for such purpose. To precisely differentiate between authentic and manipulated pixels, we leverage the Vision Mamba network to extract feature maps that clearly describe the boundaries between tampered and untouched regions. To further enhance this separation, we propose a novel Guided Graph Neural Network (G-GNN) module that amplifies the distinction between manipulated and authentic pixels. Our evaluation results show that our proposed method achieved higher inference accuracy compared to other state-of-the-art methods.

cs.CV↗

arXiv2025

Downregulation of aquaporin 3 promotes hyperosmolarity-induced apoptosis of nucleus pulposus cells through PI3K/Akt/mTOR pathway suppression

Hyperosmolarity is a key contributor to nucleus pulposus cell (NPC) apoptosis during intervertebral disc degeneration (IVDD). Aquaporin 3 (AQP3), a membrane channel protein, regulates cellular osmotic balance by transporting water and osmolytes. Although AQP3 downregulation is associated with disc degeneration, its role in apoptosis under hyperosmotic conditions remains unclear. Here, we demonstrate that hyperosmolarity induces AQP3 depletion, suppresses the PI3K/AKT/mTOR signaling pathway, and promotes mitochondrial dysfunction and ROS accumulation in NPCs. Lentiviral overexpression of AQP3 restores this pathway, attenuates oxidative damage, and reduces apoptosis, preserving disc structure in IVDD rat models. In contrast, pharmacological inhibition of AQP3 exacerbates ECM catabolism and NP tissue loss. Our findings reveal that AQP3 deficiency under hyperosmolarity contributes to NPC apoptosis via suppression of PI3K/AKT/mTOR signaling, potentially creating a pathological cycle of disc degeneration. These results highlight AQP3 as a promising therapeutic target for IVDD.

q-bio.BM↗

arXiv2024

Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines

Synchrotron radiation sources play a crucial role in fields such as materials science, biology, and chemistry. The beamline, a key subsystem of the synchrotron, modulates and directs the radiation to the sample for analysis. However, the alignment of beamlines is a complex and time-consuming process, primarily carried out manually by experienced engineers. Even minor misalignments in optical components can significantly affect the beam's properties, leading to suboptimal experimental outcomes. Current automated methods, such as bayesian optimization (BO) and reinforcement learning (RL), although these methods enhance performance, limitations remain. The relationship between the current and target beam properties, crucial for determining the adjustment, is not fully considered. Additionally, the physical characteristics of optical elements are overlooked, such as the need to adjust specific devices to control the output beam's spot size or position. This paper addresses the alignment of beamlines by modeling it as a Markov Decision Process (MDP) and training an intelligent agent using RL. The agent calculates adjustment values based on the current and target beam states, executes actions, and iterates until optimal parameters are achieved. A policy network with action attention is designed to improve decision-making by considering both state differences and the impact of optical components. Experiments on two simulated beamlines demonstrate that our algorithm outperforms existing methods, with ablation studies highlighting the effectiveness of the action attention-based policy network.

eess.SY↗

arXiv2022

Semantic2Graph: Graph-based Multi-modal Feature Fusion for Action Segmentation in Videos

Video action segmentation have been widely applied in many fields. Most previous studies employed video-based vision models for this purpose. However, they often rely on a large receptive field, LSTM or Transformer methods to capture long-term dependencies within videos, leading to significant computational resource requirements. To address this challenge, graph-based model was proposed. However, previous graph-based models are less accurate. Hence, this study introduces a graph-structured approach named Semantic2Graph, to model long-term dependencies in videos, thereby reducing computational costs and raise the accuracy. We construct a graph structure of video at the frame-level. Temporal edges are utilized to model the temporal relations and action order within videos. Additionally, we have designed positive and negative semantic edges, accompanied by corresponding edge weights, to capture both long-term and short-term semantic relationships in video actions. Node attributes encompass a rich set of multi-modal features extracted from video content, graph structures, and label text, encompassing visual, structural, and semantic cues. To synthesize this multi-modal information effectively, we employ a graph neural network (GNN) model to fuse multi-modal features for node action label classification. Experimental results demonstrate that Semantic2Graph outperforms state-of-the-art methods in terms of performance, particularly on benchmark datasets such as GTEA and 50Salads. Multiple ablation experiments further validate the effectiveness of semantic features in enhancing model performance. Notably, the inclusion of semantic edges in Semantic2Graph allows for the cost-effective capture of long-term dependencies, affirming its utility in addressing the challenges posed by computational resource constraints in video-based vision models.

cs.CV↗

arXiv2021

An Efficient Probe-based Routing for Content-Centric Networking

With the development of new technologies and applications, such as the Internet of Things, smart cities, 5G, and edge computing, traditional Internet Protocol-based (IP-based) networks have been exposed as having many problems. Information-Centric Networking (ICN), Named Data Networking (NDN), and Content-Centric Networking (CCN) are therefore proposed as an alternative for future networks. However, unlike IP-based networks, CCN routing is nondeterministic and difficult to optimize due to frequent in-network caching replacement. This paper presents a novel probe-based routing algorithm that explores real-time in-network caching to ensure the routing table storing the optimal paths to the nearest content provider is up-to-date. Effective probe-selections, Pending Interest Table (PIT) probe, and Forwarding Information Base (FIB) probe are discussed and analyzed by simulation with different performance measurements. Compared with the basic CCN, in terms of qualitative analysis, the additional computational overhead of our approach is O(*) and O(*) on processing interest packets and data packets, respectively. However, in terms of quantitative analysis, our approach reduces the number of timeout interests by 6% and the average response time by 0.6 s. Furthermore, although basic CCN and our approach belong to the same Quality of Service category, our approach outperforms basic CCN in terms of real values. Additionally, our probe-based approach performs better than * and *. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when accounting for router failures. In summary, the results demonstrate that compared to basic CCN, our probe-based routing approach raises FIB accuracy and reduces network congestion and response time, resulting in efficient routing.

cs.NI↗

arXiv2018

Readout Electronics for CEPC Semi-Digital Hadron Calorimeter Pre-prototype

CEPC (Circular Electron and Positron Collider) is a large experiment facility proposed by Chinese particle physics community. One of its running option is being the Higgs factory. Calorimeter is the main part of this experiment to measure the jet energy. Semi-digital hadron calorimeter (SDHCAL) is one of the options for the hadron measurement. GEM detector with its high position resolution and flexible configuration is one of the candidates for the active layer of the SDHCAL. The main purpose of this paper is to provide a feasible readout method for the GEM-based semi-digital hadron calorimeter. A small-scale prototype is designed and implemented, including front-end board (FEB) and data interface board (DIF). The prototype electronics has been tested. The equivalent RMS noise of all channels is below 0.35fC. The dynamic range is up to 500fC and the gain variation is less than 1%. The readout electronics is applied on a double-layer GEM detector with 1cm*1cm readout pad. Result indicates that the electronics works well with the detector. The detection efficiency of MIP is over 95% with 5fC threshold.

physics.ins-det↗

arXiv2016

The calibration and electron energy reconstruction of the BGO ECAL of the DAMPE detector

The DArk Matter Particle Explorer (DAMPE) is a space experiment designed to search for dark matter indirectly by measuring the spectra of photons, electrons, and positrons up to 10 TeV. The BGO electromagnetic calorimeter (ECAL) is its main sub-detector for energy measurement. In this paper, the instrumentation and development of the BGO ECAL is briefly described. The calibration on the ground, including the pedestal, minimum ionizing particle (MIP) peak, dynode ratio, and attenuation length with the cosmic rays and beam particles is discussed in detail. Also, the energy reconstruction results of the electrons from the beam test are presented.

physics.ins-det↗

arXiv2014

Design of the Readout Electronics for the Qualification Model of DAMPE BGO Calorimeter

The DAMPE (DArk Matter Particle Explorer) is a scientific satellite being developed in China, aimed at cosmic ray study, gamma ray astronomy, and searching for the clue of dark matter particles, with a planned mission period of more than 3 years and an orbit altitude of about 500 km. The BGO Calorimeter, which consists of 308 BGO (Bismuth Germanate Oxid) crystal bars, 616 PMTs (photomultiplier tubes) and 1848 dynode signals, has approximately 32 radiation lengths. It is a crucial sub-detector of the DAMPE payload, with the functions of precisely measuring the energy of cosmic particles from 5 GeV to 10TeV, distinguishing positrons/electrons and gamma rays from hadron background, and providing trigger information for the whole DAMPE payload. The dynamic range for a single BGO crystal is about 2?105 and there are 1848 detector signals in total. To build such an instrument in space, the major design challenges for the readout electronics come from the large dynamic range, the high integrity inside the very compact structure, the strict power supply budget and the long term reliability to survive the hush environment during launch and in orbit. Currently the DAMPE mission is in the end of QM (Qualification Model) stage. This paper presents a detailed description of the readout electronics for the BGO calorimeter.

astro-ph.IM↗