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Qi Lu

Publications and source records attributed to Qi Lu.

At least 37 records · Page 2Linked to original sources
arXiv2026

Paint by Odor: An Exploration of Odor Visualization through Large Language Model and Generative AI

Odor visualization translates odor information and perception into visual outcomes and arouses the corresponding olfactory synesthesia, surpassing the spatial limitation that odors can only be perceived where they are present. Traditional odor visualization has typically relied on unidimensional mappings, such as odor-to-color associations, and has required extensive manual design efforts. However, the advent of generative AI (Gen AI) and large language models (LLMs) presents a new opportunity for automatic odor visualization. Nonetheless, gaps remain in bridging olfactory perception with generative tools to produce odor images. To address these gaps, this paper introduces Paint by Odor, a pipeline that leverages Gen AI and LLMs to transform olfactory perceptions into rich, aesthetically engaging visual representations. Two experiments were conducted, where 30 participants smelled real-world odors and provided descriptive data and 28 participants evaluated 560 generated odor images through seven systematically designed prompts. Our findings explored the capability of LLMs in producing olfactory perception by comparing it with human responses and revealed the underlying mechanisms and effects of language-based descriptions and several abstraction styles on odor visualization. Our work further discussed the possibility of automatic odor visualization without human participation. These explorations and results have bridged the research gap in odor visualization using LLMs and Gen AI, offering valuable design insights and various possibilities for future applications.

cs.HC↗
arXiv2026

Quaternion optical computing chip for parallel high-dimensional data processing

Optical computing chips have emerged as a transformative computing technology due to their high computational density, low energy consumption, and compact footprint. While real- and complex-valued computing chips have been well developed, their fundamental limitations in representing high-dimensional data significantly constrain their applicability in modern signal processing. Quaternions enable direct operations on three- and four-dimensional data, powering high-dimensional processing in data analytics and artificial intelligence. Here we demonstrate a quaternion optical computing chip (QOCC) for the first time and benchmark its performance in several typical application scenarios: three-dimensional point cloud processing, RGB chromatic transformation, and quaternion convolutional neural network for color image recognition. The QOCC harnesses high parallelism of light by wavelength-division multiplexing, processing high-dimensional data simultaneously through multiple optical wavelength channels. Compared to the electronic computing counterpart, our QOCC achieves higher computational fidelity (root mean square error < 0.035) and substantially reduced computational load (2/3 lower). It paves the way towards next-generation optical computing, overcoming the limitations of traditional computing systems in high-dimensional data processing.

physics.optics↗
arXiv2025

Nanosecond-Scale Proton Emission from Triaxially Deformed Lu-148 Predicted with High Accuracy Qp Value via Novel Bayesian Evaluation

The half-life of the odd-odd deformed proton emitter $^{148}$Lu is predicted to be $196_{-129}^{+420}$ ns via the Wentzel-Kramers-Brillouin (WKB) approximation, in which the potential is extracted from the triaxial relativistic Hartree-Bogoliubov theory in continuum (TRHBc) and the proton decay energy $Q_{\rm p}$ is computed as 2.015(89) MeV by the Bayesian Neural Network - Beihang (BNN-BH) model for the first time. As a decisive factor, the uncertainty of $S_{\rm p}$ has been improved from 411 keV (Bayesian Machine Learning, BML) to 89 keV (BNN-BH) by taking the ensemble uncertainty into account and confining the error estimation to the neighboring nuclei. In consequence, the magnitude of the half-life's uncertainty can be reduced from 4 orders to 1 order, compared to that ($5.5_{-5.3}^{+636}$ ns) with $S_{\rm p}$ from the BML model. We also found that the range of half-life predicted by the TRHBc + WKB approach is consistent with those from the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) + WKB approach, and with those from an empirical formalism with the $S_{\rm p}$ obtained with the BNN-BH model. Furthermore, the means from the above 3 ways agree well with the experimental data for $^{149}$Lu, which gives us confidence to recommend a measurement of the half-life of proton emitter $^{148}$Lu.

nucl-th↗
arXiv2025

QCD axions and domain walls in dense matter under compact stellar conditions

In compact stellar environments, the stability of dense QCD matter requires the simultaneous fulfillment of charge neutrality and beta equilibrium. In this work, we study how temperature and finite chemical potential affect QCD topology and axion properties within this medium, analyzing both cases with and without the charge neutrality condition. Our results show that the topological susceptibility and axion properties are highly sensitive to the critical behavior of the chiral phase transition in both cases. In particular, the axion mass is strongly suppressed near the transition, while the axion self-coupling constant develops a pronounced peak whose magnitude depends on the temperature and density of the medium. Remarkably, around the critical point at $T\simeq70$ MeV and $μ\simeq346$ MeV, the self-coupling constant is enhanced by more than a factor of seven compared to its vacuum value, a feature that to the best of our knowledge has not been reported in previous studies. Such a strong amplification at the phase boundary indicates that axion-mediated interactions could play an important role in shaping the structure and stability of compact stars, with potential implications for their evolution and observable astrophysical signatures.

hep-ph↗
arXiv2025

HeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search

Feature selection is a combinatorial optimization problem that is NP-hard. Conventional approaches often employ heuristic or greedy strategies, which are prone to premature convergence and may fail to capture subtle yet informative features. This limitation becomes especially critical in high-dimensional datasets, where complex and interdependent feature relationships prevail. We introduce the HeFS (Helper-Enhanced Feature Selection) framework to refine feature subsets produced by existing algorithms. HeFS systematically searches the residual feature space to identify a Helper Set - features that complement the original subset and improve classification performance. The approach employs a biased initialization scheme and a ratio-guided mutation mechanism within a genetic algorithm, coupled with Pareto-based multi-objective optimization to jointly maximize predictive accuracy and feature complementarity. Experiments on 18 benchmark datasets demonstrate that HeFS consistently identifies overlooked yet informative features and achieves superior performance over state-of-the-art methods, including in challenging domains such as gastric cancer classification, drug toxicity prediction, and computer science applications. The code and datasets are available at https://healthinformaticslab.org/supp/.

cs.LG↗
arXiv2025

SegTrans: Transferable Adversarial Examples for Segmentation Models

Segmentation models exhibit significant vulnerability to adversarial examples in white-box settings, but existing adversarial attack methods often show poor transferability across different segmentation models. While some researchers have explored transfer-based adversarial attack (i.e., transfer attack) methods for segmentation models, the complex contextual dependencies within these models and the feature distribution gaps between surrogate and target models result in unsatisfactory transfer success rates. To address these issues, we propose SegTrans, a novel transfer attack framework that divides the input sample into multiple local regions and remaps their semantic information to generate diverse enhanced samples. These enhanced samples replace the original ones for perturbation optimization, thereby improving the transferability of adversarial examples across different segmentation models. Unlike existing methods, SegTrans only retains local semantic information from the original input, rather than using global semantic information to optimize perturbations. Extensive experiments on two benchmark datasets, PASCAL VOC and Cityscapes, four different segmentation models, and three backbone networks show that SegTrans significantly improves adversarial transfer success rates without introducing additional computational overhead. Compared to the current state-of-the-art methods, SegTrans achieves an average increase of 8.55% in transfer attack success rate and improves computational efficiency by more than 100%.

cs.CV↗
arXiv2025

Electrically Reconfigurable Arbitrary Splitting-Ratio Optical Splitter Based on Low-Loss Sb2Se3

Reconfigurable beam splitters capable of being arbitrarily programmed for the power splitting ratios are vital for the adaptive optical networks and photonic computing. Conventional mechanisms such as thermo-optic, free-carrier, or mechanical tuning are usually volatile and require continuous power, limiting their suitability for low-frequency and low power-consumption programmable operations. Here, we experimentally demonstrate an electrically reconfigurable beam splitter based on the low-loss phase-change material Sb2Se3, enabling multi-level and arbitrary splitting-ratio (SR) control. By locally triggering phase transitions in the coupling region with integrated micro-electrodes, we exploit the high refractive-index contrast between different phases and negligible absorption in the near-infrared wavelength of Sb2Se3 to precisely tune the coupling strength with non-volatile retention. 8-level of power splitting states is achieved within a compact footprint of ~14.5-μm in the experiments, with insertion loss is ~1 dB across 1515-1550 nm and near-zero static power. Combining the advantages of compactness, broad bandwidth, low loss, non-volatility, and multi-level control experimentally, this device provides a universal building block for scalable, energy-efficient reconfigurable photonic circuits, with great prospects in optical computing and intelligent communication systems.

physics.optics↗
arXiv2025

New contribution to the anomalous $π^0\rightarrowγγ$ decay in SU(2) chiral perturbation theory

The introduction of axions gives rise to additional one-loop diagrams for the two-photon decays of neutral pions via axion-pion mixing. We compute this correction that has been overlooked in existing calculations, within the framework of SU(2) chiral perturbation theory. Our analysis shows that the correction is proportional to the axion-photon coupling and the square of the axion mass. In the classical axion parameter space, this correction is strongly suppressed by the axion decay constant. However, for QCD axions in the MeV or higher mass range, the correction may become significant. Furthermore, when combined with experimental measurements of the decay width of the $π^0 \rightarrow γγ$ process, our results rule out the standard QCD axion as a viable explanation for the observed discrepancy between chiral perturbation theory predictions and experimental data.

hep-ph↗
arXiv2025

MolProphecy: Bridging Medicinal Chemists' Knowledge and Molecular Pre-Trained Models via a Multi-Modal Framework

MolProphecy is a human-in-the-loop (HITL) multi-modal framework designed to integrate chemists' domain knowledge into molecular property prediction models. While molecular pre-trained models have enabled significant gains in predictive accuracy, they often fail to capture the tacit, interpretive reasoning central to expert-driven molecular design. To address this, MolProphecy employs ChatGPT as a virtual chemist to simulate expert-level reasoning and decision-making. The generated chemist knowledge is embedded by the large language model (LLM) as a dedicated knowledge representation and then fused with graph-based molecular features through a gated cross-attention mechanism, enabling joint reasoning over human-derived and structural features. Evaluated on four benchmark datasets (FreeSolv, BACE, SIDER, and ClinTox), MolProphecy outperforms state-of-the-art (SOTA) models, achieving a 15.0 percent reduction in RMSE on FreeSolv and a 5.39 percent improvement in AUROC on BACE. Analysis reveals that chemist knowledge and structural features provide complementary contributions, improving both accuracy and interpretability. MolProphecy offers a practical and generalizable approach for collaborative drug discovery, with the flexibility to incorporate real chemist input in place of the current simulated proxy--without the need for model retraining. The implementation is publicly available at https://github.com/zhangruochi/MolProphecy.

cs.LG↗
arXiv2025

New axion contribution to the two-photon decays of neutral pions

The presence of axions introduces new diagrams at one-loop order to the two-photon decays of the neutral pion through axion-pion mixing. In this work, we calculate this correction, missing in all current calculations, in the framework of SU(2) chiral perturbation theory. We show that the correction is proportional to the axion-photon coupling and the square of axion mass, which in turn is strongly suppressed by the axion decay constant for the classical space window but may not be negligible for the QCD axion in the MeV or even larger mass range. On the other hand, in combination with the experimental measurement of the decay width of $π^0\rightarrowγγ$ process, this result rules out the standard QCD axion as an explanation for the possible discrepancy between the chiral perturbation theory prediction and the experimental data.

hep-ph↗
arXiv2024

Exploration on $1n$ halo nucleus $^{19}$C from D-RHFB structure to reaction observables

We utilize the axially deformed relativistic Hartree-Fock-Bogoliubov (D-RHFB) model to describe the structure of neutron-rich carbon isotopes, taking into account the continuum, pairing correlations, tensor force and their interplay. In this scheme, one- and two-neutron separation energies of neutron-rich carbon isotopes agree well with measured data, as well as the spin and parity $J^π=1/2^+$ for the ground state of $^{19}$C, which is a long-standing problem for theoretical structure models. With the structure input extracted from the microscopic D-RHFB model, the reaction observables are well described the Glauber model. In particular, this unified approach accurately reproduces the inclusive longitudinal momentum distributions of the breakup reaction $^{19}$C + $^{12}$C at 240 MeV/nucleon, which rule out the possibility of the ground state of $^{19}$C being $J^π=3/2^+$. Moreover, the continuum plays a crucial role in the formation of the halo, which is further confirmed by the reaction cross sections and longitudinal momentum distributions. However, the tensor force components carried by the $π$-coupling are not as significant as anticipated. Consequently, the D-RHFB + Glauber approach turns out to be a promising tool to search for halo candidates from the structure to the reaction.

nucl-th↗
arXiv2024

QCD topology and axion properties in an isotropic hot and dense medium

We study the QCD topology and axion properties at finite temperature and chemical potential in the framework of the two-flavor Nambu$-$Jona-Lasinio model. We find that the behaviors of the two lowest cumulants of the QCD topological charge distribution and axion properties are highly sensitive to the critical behavior of the chiral phase transition. In particular, the topological susceptibility and the axion mass follow the response of the chiral condensate to temperature and chemical potential, showing that both quantities decrease monotonically with the increment of temperature and/or chemical potential. However, it is important to note that the normalized fourth cumulant behaves differently depending on the temperature. At low temperatures, it is a non-monotonic function of the chemical potential, while at high temperatures, it monotonically decreases. Additionally, its value invariably approaches the asymptotic value of $b_2^{\text {inst }}=-1/12$, predicted by the dilute instanton gas model. We also observe that with the increase in chemical potential at relatively low temperatures, the axion self-coupling constant exhibits a sharp peak around the critical point, which can even be more than twice its vacuum value. After that, the self-coupling drops sharply to a much lower value than its vacuum value, eventually approaching zero in the high chemical potential limit. The finding that the axion self-coupling constant is significantly enhanced in high-density environments near the chiral phase transition could lead to the creation or enhancement of an axion Bose-Einstein condensate in compact astrophysical objects.

hep-ph↗
arXiv2024

Triaxial shape of the one-proton emitter $^{149}$Lu

We revisit the proton emitter $^{149}$Lu utilizing the recently developed triaxial relativistic Hartree-Bogoliubov theory in continuum (TRHBc). By incorporating the microscopic nuclear structure properties from the TRHBc theory into the WKB approximation, we successfully reproduce the measured proton-emission half-life of $^{149}$Lu within experimental uncertainties. A triaxial ground state characterized by ($β=0.17,γ=31^\circ$) has been clarified for $^{149}$Lu. The inclusion of triaxiality significantly changes nuclear density distributions and potentials, which results in enhanced binding of both the nuclear system and the proton-emitting orbital. As a result, a slightly extended half-life for the proton emission of $^{149}$Lu is achieved after considering triaxial deformation degrees of freedom.

nucl-th↗
arXiv2023

Transient dynamics of the phase transition in VO2 revealed by mega electron-volt ultrafast electron diffraction

Vanadium dioxide (VO2) exhibits an insulator-to-metal transition accompanied by a structural transition near room temperature. This transition can be triggered by an ultrafast laser pulse. Exotic transient states, such as a metallic state without structural transition, were also proposed. These unique characteristics let VO2 have great potential in thermal switchable devices and photonic applications. Although great efforts have been made, the atomic pathway during the photoinduced phase transition is still not clear. Here, we synthesized freestanding quasi-single-crystal VO2 films and examined their photoinduced structural phase transition with mega-electron-volt ultrafast electron diffraction. Leveraging the high signal-to-noise ratio and high temporal resolution, we observe that the disappearance of vanadium dimers and zigzag chains does not coincide with the transformation of crystal symmetry. After photoexcitation, the initial structure is strongly modified within 200 femtoseconds, resulting in a transient monoclinic structure without vanadium dimers and zigzag chains. Then, it continues to evolve to the final tetragonal structure in approximately 5 picoseconds. In addition, only one laser fluence threshold instead of two thresholds suggested in polycrystalline samples was observed in our quasi-single-crystal samples. Our findings provide new essential information for a comprehensive understanding of the photoinduced ultrafast phase transition in VO2.

cond-mat.mtrl-sci↗
arXiv2023

CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving

Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors. Widely-applied methods either heavily rely on hand-crafted rules or learn from limited human experience, which makes them hard to generalize to rare but critical scenarios. In this paper, we present a novel CAscade Deep REinforcement learning framework, CADRE, to achieve model-free vision-based autonomous urban driving. In CADRE, to derive representative latent features from raw observations, we first offline train a Co-attention Perception Module (CoPM) that leverages the co-attention mechanism to learn the inter-relationships between the visual and control information from a pre-collected driving dataset. Cascaded by the frozen CoPM, we then present an efficient distributed proximal policy optimization framework to online learn the driving policy under the guidance of particularly designed reward functions. We perform a comprehensive empirical study with the CARLA NoCrash benchmark as well as specific obstacle avoidance scenarios in autonomous urban driving tasks. The experimental results well justify the effectiveness of CADRE and its superiority over the state-of-the-art by a wide margin.

cs.CV↗
arXiv2023

One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation

Deep learning based methods have achieved state-of-the-art performance for automated white matter (WM) tract segmentation. In these methods, the segmentation model needs to be trained with a large number of manually annotated scans, which can be accumulated throughout time. When novel WM tracts, i.e., tracts not included in the existing annotated WM tracts, are to be segmented, additional annotations of these novel WM tracts need to be collected. Since tract annotation is time-consuming and costly, it is desirable to make only a few annotations of novel WM tracts for training the segmentation model, and previous work has addressed this problem by transferring the knowledge learned for segmenting existing WM tracts to the segmentation of novel WM tracts. However, accurate segmentation of novel WM tracts can still be challenging in the one-shot setting, where only one scan is annotated for the novel WM tracts. In this work, we explore the problem of one-shot segmentation of novel WM tracts. Since in the one-shot setting the annotated training data is extremely scarce, based on the existing knowledge transfer framework, we propose to further perform extensive data augmentation for the single annotated scan, where synthetic annotated training data is produced. We have designed several different strategies that mask out regions in the single annotated scan for data augmentation. Our method was evaluated on public and in-house datasets. The experimental results show that our method improves the accuracy of one-shot segmentation of novel WM tracts.

cs.CV↗
arXiv2023

Structured air lasing of N2+

Structured light has attracted great interest in scientific and technical fields. Here, we demonstrate the first generation of structured air lasing in N2+ driven by 800 nm femtosecond laser pulses. By focusing a vortex pump beam at 800 nm in N2 gas, we generate a vortex superfluorescent radiation of N2+ at 391 nm, which carries the same photon orbital angular momentum as the pump beam. With the injection of a Gaussian seed beam at 391 nm, the coherent radiation is amplified, but the vorticity is unchanged. A new physical mechanism is revealed in the vortex N2+ superfluorescent radiation: the vortex pump beam transfers the spatial spiral phase into the N2+ gain medium, and the Gaussian seed beam picks up the spatial spiral phase and is then amplified into a vortex beam. Moreover, when we employ a pump beam with a cylindrical vector mode, the Gaussian seed beam is correspondingly amplified into a cylindrical vector beam. Surprisingly, the spatial polarization state of the amplified radiation is identical to that of the vector pump beam regardless of whether the Gaussian seed beam is linearly, elliptically, or circularly polarized. Solving three-dimensional coupled wave equations, we show how a Gaussian beam becomes a cylindrical vector beam in a cylindrically symmetric gain medium. This study provides a novel approach to generating structured light via N2+ air lasing.

physics.optics↗