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Researcher profile

Jie Feng

Jie Feng contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionMachine LearningArtificial Intelligencephysics.flu-dynphysics.plasm-phSocial and Information Networksastro-ph.HEastro-ph.IMeess.IVhep-exhep-phnucl-exphysics.optics

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Published work

12 published item(s)

preprint2026arXiv

LMM-Track4D: Eliciting 4D Dynamic Reasoning in LMMs via Trajectory-Grounded Dialogue

Recent large multimodal models (LMMs) have become increasingly capable on image and video understanding, yet still struggle to sustain 4D continuous spatiotemporal dynamic reasoning. To study this capability gap, we formulate trajectory-grounded multi-turn spatiotemporal dialogue, a new task in which a model must answer spatiotemporal queries while returning structured 3D target trajectories over an entire short clip or a specified segment of a longer clip, and introduce Track4D-Bench, a benchmark with 526 clip-level dialogue samples spanning 23.5k frames and 7.5k object annotations, for training and evaluation. Building on this task, we propose LMM-Track4D, which combines RTGE (Ray--Time Geometry Encoding), a dedicated streaming state token TRK for long-horizon dynamic propagation, and an Object-Slot Kinematic, Residual-Anchor (OSK-RA) decoder for stable 4-step 3D state estimation under occlusion and viewpoint variation. Experiments on Track4D-Bench show consistent improvements over strong baselines, suggesting that explicit dynamic state modeling is a useful design principle for eliciting 4D dynamic reasoning in LMMs. Our code and dataset will be publicly available at https://github.com/mikubaka88/LMM-Track4D.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Tabletop X-ray ghost video of moving objects

X-ray imaging is widely employed in clinical medicine, industrial inspection, and various scientific research fields. Unfortunately, most currently used X-ray two-dimensional (2D) detectors suffer from a fundamental trade-off between the number of pixels and readout time, making them unsuitable for fast moving objects imaging, as well as the readout dead time causes frame losses. X-ray ghost imaging (XGI) offers an alternative approach to image an object using only a highly sensitive single-pixel detector. However, a critical limitation of existing XGI methods is the excessive total acquisition time required, rendering it impractical for real applications. In this paper, we propose a rapid spatial modulation scheme based on random binary patterns encoded onto a fast-spinning mask. Clear X-ray visualization of moving objects is demonstrated with imaging rates up to 200 frames per second with a resolution of 225 um. For the first time, our method has greatly improved the XGI imaging speed and paves the way for X-ray imaging application of motion objects, such as the inspection of rotating aero-engines and in vivo medical imaging.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Oscillatory flows in three-dimensional deformable microchannels

Deformable microchannels emulate a key characteristic of soft biological systems and flexible engineering devices: the flow-induced deformation of the conduit due to slow viscous flow within. Elucidating the two-way coupling between oscillatory flow and deformation of a three-dimensional (3D) rectangular channel is crucial for designing lab- and organ-on-a-chip microsystems and eventually understanding flow-structure instabilities that can enhance mixing and transport. To this end, we determine the axial variations of the primary flow, pressure, and deformation for Newtonian fluids in the canonical geometry of a slender (long) and shallow (wide) 3D rectangular channel with a deformable top wall under the assumption of weak compliance and without restriction on the oscillation frequency (\textit{i.e.}, on the Womersley number). Unlike rigid conduits, the pressure distribution is not linear with the axial coordinate. To validate this prediction, we design a PDMS-based experimental platform with a speaker-based flow-generation apparatus and a pressure acquisition system with multiple ports along the axial length of the channel. The experimental measurements show good agreement with the predicted pressure profiles across a wide range of the key dimensionless quantities: the Womersley number, the compliance number, and the elastoviscous number. Finally, we explore how the nonlinear flow-deformation coupling leads to self-induced streaming (rectification of the oscillatory flow). Following Zhang and Rallabandi (\textit{J.\ Fluid Mech.}, vol.~996, 2024, A16), we develop a theory for the cycle-averaged pressure based on the primary problem's solution, and we validate the predictions for the axial distribution of the streaming pressure against the experimental measurements.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CoSimGNN: Towards Large-scale Graph Similarity Computation

The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is important in many real-world applications. Computing exact GED values is typically an NP-hard problem and traditional algorithms usually achieve an unsatisfactory trade-off between accuracy and efficiency. Recently, Graph Neural Networks (GNNs) provide a data-driven solution for this task, which is more efficient while maintaining prediction accuracy in small graph (around 10 nodes per graph) similarity computation. Existing GNN-based methods, which either respectively embeds two graphs (lack of low-level cross-graph interactions) or deploy cross-graph interactions for whole graph pairs (redundant and time-consuming), are still not able to achieve competitive results when the number of nodes in graphs increases. In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores. Furthermore, we create several synthetic datasets which provide new benchmarks for graph similarity computation. Detailed experiments on both synthetic and real-world datasets have been conducted and CoSimGNN achieves the best performance while the inference time is at most 1/3 of that of previous state-of-the-art.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Femtosecond pumping of nuclear isomeric states by the Coulomb collision of ions with quivering electrons

Efficient production of nuclear isomers is critical for pioneering applications, like nuclear clocks, nuclear batteries, clean nuclear energy, and nuclear γ-ray lasers. However, due to small production cross sections and quick decays, it is extremely difficult to acquire a significant amount of isomers with short lifetimes via traditional accelerators or reactors because of low beam intensity. Here, for the first time, we experimentally present femtosecond pumping of nuclear isomeric states by the Coulomb excitation of ions with the quivering electrons induced by laser fields. Nuclei populated on the third excited state of 83Kr are generated with a peak efficiency of 2.34*10^15 particles=s from a tabletop hundred-TW laser system. It can be explained by the Coulomb excitation of ions with the quivering electrons during the interaction between laser pulses and clusters at nearly solid densities. This efficient and universal production method can be widely used for pumping isotopes with excited state lifetimes down to picoseconds, and could be a benefit for fields like nuclear transition mechanisms and nuclear γ-ray lasers.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Laser plasma accelerated ultra-intense electron beam for efficiently exciting nuclear isomers

Utilizing laser plasma wakefield to accelerate ultra-high charge electron beam is critical for many pioneering applications, for example to efficiently produce nuclear isomers with short lifetimes which may be widely used. However, because of the beam loading effect, electron charge in a single plasma bubble is limited in level of hundreds picocoulomb. Here, we experimentally present that a hundred kilo-ampere, twenty nanocoulomb, tens of MeV collimated electron beam is produced from a chain of wakefield acceleration, via a tightly focused intense laser pulse transversely matched in dense plasma. This ultra-intense electron beam ascribes to a novel efficient injection that the nitrogen atom inner shell electrons are ionized and continuously injected into multiple plasma bubbles. This intense electron beam has been utilized to exciting nuclear isomers with an ultra-high peak efficiency of $1.76\times10^{15}$ particles/s via photonuclear reactions. This efficient production method of isomers can be widely used for pumping isotopes with excited state lifetimes down to picosecond, which is benefit for deep understanding nuclear transition mechanisms and stimulating gamma-ray lasers.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SparseDet: Towards End-to-End 3D Object Detection

In this paper, we propose SparseDet for end-to-end 3D object detection from point cloud. Existing works on 3D object detection rely on dense object candidates over all locations in a 3D or 2D grid following the mainstream methods for object detection in 2D images. However, this dense paradigm requires expertise in data to fulfill the gap between label and detection. As a new detection paradigm, SparseDet maintains a fixed set of learnable proposals to represent latent candidates and directly perform classification and localization for 3D objects through stacked transformers. It demonstrates that effective 3D object detection can be achieved with none of post-processing such as redundant removal and non-maximum suppression. With a properly designed network, SparseDet achieves highly competitive detection accuracy while running with a more efficient speed of 34.5 FPS. We believe this end-to-end paradigm of SparseDet will inspire new thinking on the sparsity of 3D object detection.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Water-to-air transfer of nano/micro-sized particulates: enrichment effect in bubble bursting jet drops

Bubbles dispersed in liquids are widely present in many natural and industrial processes, and play a key role in mediating mass transfer during their lifetime from formation to rising to bursting. In particular, nano/micro-sized particulates and organisms, present in the bulk water can be highly enriched in the jet drops ejected during bubble bursting, impacting global climate and public health. However, the detailed mechanism of this enrichment remains obscure, with the enrichment factor being difficult to predict. Here, we experimentally investigate the enrichment of nano/micro-sized particles in bubble bursting jet drops and highlight the underlying hydrodynamic mechanism, combining the effects of bubble scavenge and bursting on the transport of particles. Scaling laws for the enrichment factor are subsequently proposed that describe both our and prior experimental results reasonably well. Our study may provide new insights for water-to-air transfer of microbes related to bubble bursting.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

AttnMove: History Enhanced Trajectory Recovery via Attentional Network

A considerable amount of mobility data has been accumulated due to the proliferation of location-based service. Nevertheless, compared with mobility data from transportation systems like the GPS module in taxis, this kind of data is commonly sparse in terms of individual trajectories in the sense that users do not access mobile services and contribute their data all the time. Consequently, the sparsity inevitably weakens the practical value of the data even it has a high user penetration rate. To solve this problem, we propose a novel attentional neural network-based model, named AttnMove, to densify individual trajectories by recovering unobserved locations at a fine-grained spatial-temporal resolution. To tackle the challenges posed by sparsity, we design various intra- and inter- trajectory attention mechanisms to better model the mobility regularity of users and fully exploit the periodical pattern from long-term history. We evaluate our model on two real-world datasets, and extensive results demonstrate the performance gain compared with the state-of-the-art methods. This also shows that, by providing high-quality mobility data, our model can benefit a variety of mobility-oriented down-stream applications.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation

Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, such as finding the most similar chemical compounds similar to a query compound or Fewshot 3D Action Recognition. Recently, some graph similarity computation models based on neural networks have been proposed, which are either based on graph-level interaction or node-level comparison. However, when the number of nodes in the graph increases, it will inevitably bring about reduced representation ability or high computation cost. Motivated by this observation, we propose a graph partitioning and graph neural network-based model, called PSimGNN, to effectively resolve this issue. Specifically, each of the input graphs is partitioned into a set of subgraphs to extract the local structural features directly. Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector. Some of these subgraph pairs are automatically selected for node-level comparison to supplement the subgraph-level embedding with fine-grained information. Finally, coarse-grained interaction information among subgraphs and fine-grained comparison information among nodes in different subgraphs are integrated to predict the final similarity score. Experimental results on graph datasets with different graph sizes demonstrate that PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks using approximate Graph Edit Distance (GED) as the graph similarity metric.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

End-to-end Optimized Video Compression with MV-Residual Prediction

We present an end-to-end trainable framework for P-frame compression in this paper. A joint motion vector (MV) and residual prediction network MV-Residual is designed to extract the ensembled features of motion representations and residual information by treating the two successive frames as inputs. The prior probability of the latent representations is modeled by a hyperprior autoencoder and trained jointly with the MV-Residual network. Specially, the spatially-displaced convolution is applied for video frame prediction, in which a motion kernel for each pixel is learned to generate predicted pixel by applying the kernel at a displaced location in the source image. Finally, novel rate allocation and post-processing strategies are used to produce the final compressed bits, considering the bits constraint of the challenge. The experimental results on validation set show that the proposed optimized framework can generate the highest MS-SSIM for P-frame compression competition.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

A Genetic Algorithm for Astroparticle Physics Studies

Precision measurements of charged cosmic rays have recently been carried out by space-born (e.g. AMS-02), or ground experiments (e.g. HESS). These measured data are important for the studies of astro-physical phenomena, including supernova remnants, cosmic ray propagation, solar physics and dark matter. Those scenarios usually contain a number of free parameters that need to be adjusted by observed data. Some techniques, such as Markov Chain Monte Carlo and MultiNest, are developed in order to solve the above problem. However, it is usually required a computing farm to apply those tools. In this paper, a genetic algorithm for finding the optimum parameters for cosmic ray injection and propagation is presented. We find that this algorithm gives us the same best fit results as the Markov Chain Monte Carlo but consuming less computing power by nearly 2 orders of magnitudes.

0 citations0 reviewsNo code linkedOpen access