arXiv subjects

Zhen Bai

Publications and source records attributed to Zhen Bai.

At least 19 recordsLinked to original sources

arXiv2026

FocusAdapt: Context-aware Adaptive Focus Assistance in Diminished Reality

Diminished Reality (DR) can reduce visual clutter by removing irrelevant objects. However, removing all task-irrelevant objects may eliminate useful contextual information and reduce situational awareness. We present FocusAdapt, a context-aware DR system that predicts object-level distraction by integrating visual saliency, semantic relevance, and gaze behavior. Based on findings from a formative study, FocusAdapt selectively diminishes highly distracting objects while preserving useful context, enabling adaptive focus assistance during procedural tasks.

cs.HC↗

arXiv2026

IPDiff: Diffusion-driven ORSI Salient Object Detection with Information Reconstruction and Multi-Prior Guidance

Existing Salient Object Detection in Optical Remote Sensing Image (ORSI-SOD) methods mainly adopt the static inference strategy, which uses fixed trained model parameters for saliency inference in the testing phase. This means that even if the generated saliency map has errors, it cannot be further optimized. In this paper, we propose the novel IPDiff, a Diffusion-driven ORSI-SOD method with Information Reconstruction and Multi-Prior Guidance. We build IPDiff based on a unique dynamic optimization strategy, which endows IPDiff with the ability to iteratively optimize saliency maps with a dynamic parameter. Specifically, we formulate ORSI-SOD as a conditional diffusion problem in IPDiff. IPDiff first extracts informative conditional priors from ORSIs, including the saliency prior and the hierarchical priors, in the prior network with the assistance of the information reconstruction-driven attention module. The saliency prior can provide positional information of salient objects, while the hierarchical priors can provide specific detail and semantic information of salient objects. Under the guidance of these priors, IPDiff then iteratively denoises random noise as the timestep dynamically changes in the denoising network, generating saliency maps that are close to ground truths. Notably, we simultaneously supervise IPDiff in both spatial and spectral domains through a hybrid loss function to achieve efficient network training. Comprehensive experiments on public ORSSD, EORSSD, and ORSI-4199 datasets demonstrate that our proposed IPDiff achieves the best performance compared to 46 state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/IPDiff.

cs.CV↗

arXiv2026

A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood?

Protein-ligand modeling underpins computational drug discovery and molecular design. Existing protein-ligand benchmarks typically evaluate whether a protein and ligand interact and how strongly they bind, through tasks such as binary binding prediction and affinity regression. However, these evaluations provide limited evidence of whether models can localize binding sites or identify the non-covalent interactions underlying molecular recognition. To address this gap, we introduce InteractBind, a large-scale protein-ligand dataset comprising approximately 100k protein-ligand pairs, together with a benchmark for fine-grained evaluation. The core fine-grained task is that of binding-site localization, which uses protein-residue and ligand-atom interaction maps spanning six major types of non-covalent interactions to assess whether model-derived interaction maps localize binding sites. InteractBind further includes binding affinity and protein similarity-controlled splits to support realistic generalization assessment. Using InteractBind, we evaluate eight existing sequence-based and interaction-aware models, assessing binary binding prediction and binding-site localization. Results reveal limited binding-site localization despite strong binary binding prediction, with marked variation across non-covalent interaction types. Overall, InteractBind establishes a benchmark paradigm that encourages the development of more interpretable and physically grounded protein-ligand models.

cs.LG↗

arXiv2025

Fragmentation of neutron-rich carbon isotopes on light targets at 27.5 MeV/nucleon

Experimental and theoretical investigation of the fragmentation reaction in Fermi-energy domain is currently of particular importance for not only the nuclear physics but also some interdisciplinary fields. In the present work, neutron-rich $^{14}$C and $^{16}$C ion beams at 27.5 MeV/nucleon were used to bombard carbon and polyethylene (CD$_{2}$)$_{n}$ targets. Energy and angular distributions of the produced fragments were measured. Background events originating from the carbon content in (CD$_{2}$)$_{n}$ target were efficiently excluded using an extended $E-P$ plot method. Experimental results are systematically analyzed by using HIPSE-SIMON dynamic model. The comparison reveals that, for the carbon target, the HIPSE-SIMON calculation overestimates the yields of the beam-velocity component for fragments near the projectile and also the energy phase space for fragments far away from the projectile, suggesting fine tuning of the overall interaction profile adopted in the model. In contrast, for reactions with the deuteron target, the model calculation can reasonably reproduce the experimental data. The implication of the fragmentation mechanism to the validity of the invariant mass method, as frequently used to reconstruct the clustering resonant structures in light nuclei, is also discussed.

nucl-ex↗

arXiv2025

Experimental Study of Bremsstrahlung Gamma Ray Emission and Short-Range Correlations in $^{124}$Sn+$^{124}$Sn Collisions at 25 MeV/u

Short-range correlation (SRC) in nuclei refers to nucleons forming temporally correlated pairs in close proximity, giving rise to the high momentum of the nucleons beyond the Fermi surface. It has been reported that bremsstrahlung $\gamma$ production from neutron-proton process in heavy-ion reactions provides a potential probe to the SRC abundance in nuclei. In this paper, we present in detail the precision measurement of bremsstrahlung $\gamma$-rays in $\rm ^{124}Sn$+$\rm ^{124}Sn$ reactions at 25 MeV/u using the Compact Spectrometer for Heavy IoN Experiment (CSHINE). A comprehensive experimental and analysis framework is established to ensure the reliability and robustness of the extracted results. Background contributions are evaluated and subtracted using independent methods, and the consistency of the analysis is systematically validated. By comparing the experimental $\gamma$ spectrum with the Isospin-dependent Boltzmann-Uehling-Uhlenbeck simulations, the high momentum tail (HMT) fraction of $R_{\rm HMT}=(20 \pm 3)\%$ is derived in $^{124}$Sn nuclei. This work provides a detailed and validated experimental framework for extracting SRC information from bremsstrahlung $\gamma$-ray emission and demonstrates the feasibility of studying nucleon SRCs with high precision in low-energy heavy-ion collisions.

nucl-ex↗

arXiv2025

Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning

Large reasoning models (LRMs) have demonstrated impressive capabilities in complex problem-solving, yet their internal reasoning mechanisms remain poorly understood. In this paper, we investigate the reasoning trajectories of LRMs from an information-theoretic perspective. By tracking how mutual information (MI) between intermediate representations and the correct answer evolves during LRM reasoning, we observe an interesting MI peaks phenomenon: the MI at specific generative steps exhibits a sudden and significant increase during LRM's reasoning process. We theoretically analyze such phenomenon and show that as MI increases, the probability of model's prediction error decreases. Furthermore, these MI peaks often correspond to tokens expressing reflection or transition, such as ``Hmm'', ``Wait'' and ``Therefore,'' which we term as the thinking tokens. We then demonstrate that these thinking tokens are crucial for LRM's reasoning performance, while other tokens has minimal impacts. Building on these analyses, we propose two simple yet effective methods to improve LRM's reasoning performance, by delicately leveraging these thinking tokens. Overall, our work provides novel insights into the reasoning mechanisms of LRMs and offers practical ways to improve their reasoning capabilities. The code is available at https://github.com/ChnQ/MI-Peaks.

cs.AI↗

arXiv2025

Precise Measurement of Short-Range Correlations in Nuclei from Bremsstrahlung Gamma Ray Emission in Low-Energy Heavy-Ion Collisions

Atomic nuclei and dense nucleonic matter in neutron stars exhibit short-range correlations (SRCs), where nucleons form temporally correlated pairs in proximity beyond mean-field approximation. It is essential to make precision measurement of the fraction of SRC since it carries the signature of underlying quark dynamics in nuclear medium. In this letter, we present the first high-precision measurement of neutron-proton bremsstrahlung $\gamma$-ray emission from the symmetric $\rm ^{124}Sn$+$\rm ^{124}Sn$ reactions at 25 MeV/u. From the observed spectral hardening, the precise SRC fraction in the $\rm ^{124}Sn$ nucleus is extracted to be $(20 \pm 3)\%$. This result provides a novel, direct and unambiguous evidence of SRCs, and demonstrates that low-energy heavy-ion collisions offers a new approach to studying nuclear structure in connection with quark-level dynamics.

nucl-ex↗

arXiv2025

Briteller: Shining a Light on AI Recommendations for Children

Understanding how AI recommendations work can help the younger generation become more informed and critical consumers of the vast amount of information they encounter daily. However, young learners with limited math and computing knowledge often find AI concepts too abstract. To address this, we developed Briteller, a light-based recommendation system that makes learning tangible. By exploring and manipulating light beams, Briteller enables children to understand an AI recommender system's core algorithmic building block, the dot product, through hands-on interactions. Initial evaluations with ten middle school students demonstrated the effectiveness of this approach, using embodied metaphors, such as "merging light" to represent addition. To overcome the limitations of the physical optical setup, we further explored how AR could embody multiplication, expand data vectors with more attributes, and enhance contextual understanding. Our findings provide valuable insights for designing embodied and tangible learning experiences that make AI concepts more accessible to young learners.

cs.HC↗

arXiv2025

Extract neutron-neutron interaction strength and spatial-temporal dynamics of neutron emission from two-particle correlation function

The neutron-neutron ($nn$) correlation function has been measured in 25 MeV/u $^{124}$Sn+$^{124}$Sn reactions. Using the Lednick\'y-Lyuboshitz approach, the $nn$ scattering length and effective range ($f_{0}^{nn}$, $d_{0}^{nn}$), as well as the reduced space-time size $R^{(0)}$ of the neutron emission source are simultaneously extracted as ($18.9^{+1.3}_{-1.2}$ fm, $1.9^{+1.3}_{-1.0}$ fm) and $4.12 \pm 0.12$ fm, respectively. The measured $nn$ scattering length is consistent with the results obtained in the low-energy scattering $^{2}{\rm H}(\pi^{-},\gamma)2n$, indicating heavy-ion collisions can serve as an effective approach for measuring $nn$ interactions and further investigating the charge symmetry breaking of nuclear force. The space-time size extracted from momentum-gated correlation functions exhibits clear dependence on the pair momentum, with $R^{(0)}=2.8 \pm 0.1 $ fm and $4.9 \pm 0.2$ fm being determined for the high and low momentum neutrons, respectively.

nucl-ex↗

arXiv2024

The neutron array of the compact spectrometer for heavy ion experiments in Fermi energy region

The emission of neutrons from heavy ion reactions is an important observable for studying the asymmetric nuclear equation of state and the reaction dynamics. A 20-unit neutron array has been developed and mounted on the compact spectrometer for heavy ion experiments (CSHINE) to measure the neutron spectra, neutron-neutron and neutron-proton correlation functions. Each unit consists of a $\rm 15\times 15\times 15~cm^3$ plastic scintillator coupled to a $ \phi=52 ~\rm mm$ photomultiplier. The Geant4 simulation with optical process is performed to investigate the time resolution and the neutron detection efficiency. The inherent time resolution of 212 ps is obtained by cosmic ray coincidence test. The n-$\gamma$ discrimination and time-of-flight performance are given by $\rm ^{252}Cf$ radioactive source test and beam test. The neutron energy spectra have been obtained in the angle range $30^\circ \le \theta_{\rm lab} \le 51^\circ$ in the beam experiment of $^{124}$Sn+$^{124}$Sn at 25 MeV/u with CSHINE.

physics.ins-det↗

arXiv2023

Texture-Semantic Collaboration Network for ORSI Salient Object Detection

Salient object detection (SOD) in optical remote sensing images (ORSIs) has become increasingly popular recently. Due to the characteristics of ORSIs, ORSI-SOD is full of challenges, such as multiple objects, small objects, low illuminations, and irregular shapes. To address these challenges, we propose a concise yet effective Texture-Semantic Collaboration Network (TSCNet) to explore the collaboration of texture cues and semantic cues for ORSI-SOD. Specifically, TSCNet is based on the generic encoder-decoder structure. In addition to the encoder and decoder, TSCNet includes a vital Texture-Semantic Collaboration Module (TSCM), which performs valuable feature modulation and interaction on basic features extracted from the encoder. The main idea of our TSCM is to make full use of the texture features at the lowest level and the semantic features at the highest level to achieve the expression enhancement of salient regions on features. In the TSCM, we first enhance the position of potential salient regions using semantic features. Then, we render and restore the object details using the texture features. Meanwhile, we also perceive regions of various scales, and construct interactions between different regions. Thanks to the perfect combination of TSCM and generic structure, our TSCNet can take care of both the position and details of salient objects, effectively handling various scenes. Extensive experiments on three datasets demonstrate that our TSCNet achieves competitive performance compared to 14 state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/TSCNet.

cs.CV↗

arXiv2023

Revisit to the yield ratio of triton and $^3$He as an indicator of neutron-rich neck emission

The neutron rich neck zone created in heavy ion reaction is experimentally probed by the production of the $A=3$ isobars. The energy spectra and angular distributions of triton and $^3$He are measured with the CSHINE detector in $^{86}$Kr +$^{208}$Pb reactions at 25 MeV/u. While the energy spectrum of $^{3}$He is harder than that of triton, known as "$^{3}$He-puzzle", the yield ratio $R({\rm t/^3He})$ presents a robust rising trend with the polar angle in laboratory. Using the fission fragments to reconstruct the fission plane, the enhancement of out-plane $R({\rm t/^3He})$ is confirmed in comparison to the in-plane ratios. Transport model simulations reproduce qualitatively the experimental trends, but the quantitative agreement is not achieved. The results demonstrate that a neutron rich neck zone is formed in the reactions. Further studies are called for to understand the clustering and the isospin dynamics related to neck formation.

nucl-ex↗

arXiv2023

Variation of Tensor Force due to Nuclear Medium Effect

The enhancement of $J^π(T)$=3$^{+}$(0) state with isospin $T=0$ excited by the tensor force in the free $^{6}$Li nucleus has been observed, for the first time, relative to a shrinkable excitation in the $^{6}$Li cluster component inside its host nucleus. Comparatively, the excitation of $J^π(T)$=0$^{+}$(1) state with isospin $T=1$ for these two $^{6}$Li formations take on an approximately equal excitation strength. The mechanism of such tensor force effect was proposed due to the intensive nuclear medium role on isospin $T$=0 state.

nucl-ex↗

arXiv2023

Aspect of Clusters Correlation at Light Nuclei Excited State

The correlation of $αα$ was probed via measuring the transverse momentum $p_{T}$ and width $δp_{T}$ of one $α$, for the first time, which represents the spatial and dynamical essentialities of the initial coupling state in $^{8}$Be nucleus. The weighted interaction vertex of 3$α$ reflected by the magnitudes of their relative momentums and relative emission angles proves the isosceles triangle configuration for 3$α$ at the high excited energy analogous Hoyle states.

nucl-ex↗

arXiv2023

Multi-alpha Boson Gas state in Fusion Evaporation Reaction and Three-body Force

The experimental evidence for the $α$ Boson gas state in the $^{11}$C+$^{12}$C$\rightarrow$$^{23}$Mg$^{\ast}$ fusion evaporation reaction is presented. By measuring the $α$ emission spectrum with multiplicity 2 and 3, we provide insight into the existence of a three-body force among $α$ particles. The observed spectrum exhibited distinct tails corresponding to $α$ particles emitted in pairs and triplets consistent well with the model-calculations of AV18-UX and chiral effective field theory of NV2-3-la*, indicating the formation of $α$ clusters with three-body force in the Boson gas state.

nucl-ex↗

arXiv2023

Salient Object Detection in Optical Remote Sensing Images Driven by Transformer

Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) mainly adopt Convolutional Neural Networks (CNNs) as the backbone, such as VGG and ResNet. Since CNNs can only extract features within certain receptive fields, most ORSI-SOD methods generally follow the local-to-contextual paradigm. In this paper, we propose a novel Global Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the global-to-local paradigm. Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local interactions, and a Knowledge Transfer Module (KTM) to further enhance cross-level contextual interactions. D-SWSAM comprehensively perceives the orientation information in the lowest-level features through directional convolutions to adapt to various orientations of salient objects in ORSIs, and effectively enhances the details of salient objects with an improved attention mechanism. SWSAM discards the direction-aware part of D-SWSAM to focus on localizing salient objects in the highest-level features. KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features. Finally, a saliency predictor is used to generate the saliency map based on the outputs of the above three modules. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/GeleNet.

cs.CV↗

arXiv2023

Probing high-momentum component in nucleon momentum distribution by neutron-proton bremsstrahlung γ-rays in heavy ion reactions

The high momentum tail (HMT) of nucleons, as a signature of the short-range correlations in nuclei, has been investigated by the high-energy bremsstrahlung $γ$ rays produced in $^{86}$Kr + $^{124}$Sn at 25 MeV/u. The energetic photons are measured by a CsI(Tl) hodoscope mounted on the spectrometer CSHINE. The energy spectrum above 30 MeV can be reproduced by the IBUU model calculations incorporating the photon production channel from $np$ process in which the HMTs of nucleons is considered. A non-zero HMT ratio of about $15\%$ is favored by the data. The effect of the capture channel $np \to dγ$ is demonstrated.

nucl-ex↗

arXiv2023

Participatory Design of AI with Children: Reflections on IDC Design Challenge

Children growing up in the era of Artificial Intelligence (AI) will be most impacted by the technology across their life span. Participatory Design (PD) is widely adopted by the Interaction Design and Children (IDC) community, which empowers children to bring their interests, needs, and creativity to the design process of future technologies. While PD has drawn increasing attention to human-centered AI design, it remains largely untapped in facilitating the design process of AI technologies relevant to children and their community. In this paper, we report intriguing children's design ideas on AI technologies resulting from the "Research and Design Challenge" of the 22nd ACM Interaction Design and Children (IDC 2023) conference. The diversity of design problems, AI applications and capabilities revealed by the children's design ideas shed light on the potential of engaging children in PD activities for future AI technologies. We discuss opportunities and challenges for accessible and inclusive PD experiences with children in shaping the future of AI-powered society.

cs.HC↗