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Xi Sun

Publications and source records attributed to Xi Sun.

9 recordsLinked to original sources

Geometric Spin-Orbit Coupling Resolves the Contradictory CISS Effect in Chiral Single Molecules

Some studies have reported clear chirality-induced spin selectivity (CISS) effect in four classes of chiral single molecules with remarkable spin polarization. In contrast, a recent high-precision measurement involving nearly a thousand individual tests failed to detect significant CISS signals in the same molecular systems (J. Am. Chem. Soc. 2025, \textbf{147}, 25043). These conflicting results cast doubt on whether CISS truly occurs in these chiral systems at the single-molecular level. To resolve this discrepancy, we develop a theoretical framework incorporating geometric spin-orbit coupling and environmental decoherence, enabling systematic study of the CISS in four chiral single molecules with distinct geometries and sizes. Our calculations show that the CISS effect is completely suppressed in both strong-coherence and strong-decoherence regimes, but becomes pronounced in the intermediate-decoherence regime, where observable spin polarization emerges. In the strong-coherence regime, both electron-electron interaction and electron-vibration coupling enhance the CISS effect: the former is more effective in large molecules, whereas the latter plays a more significant role in smaller ones. Increasing temperature further enhances spin polarization. The proposed mechanism unifies contradictory experimental observations and reveals how the CISS effect evolves from regular helical (helical symmetric) to irregular helical (point-symmetric or axially symmetric) chirality. This framework thus provides a basis for unifying CISS phenomena across single-molecule systems, regardless of their specific molecular configurations or symmetry classes.

cond-mat.mes-hall

What Is the Real-Time Atomistic Mechanism Behind Chirality-Induced Spin Selectivity in Donor-Chiral Bridge-Acceptor Molecules?

Chiral-induced spin selectivity (CISS) has been experimentally observed in photo-excited donor-chiral bridge-acceptor (D-B{\chi}-A) molecules [Science 382, 197-201 (2023)]. However, the microscopic mechanism underlying CISS in such chiral systems remains elusive. Here we develop a quantum dynamical model that precisely maps the atomic structure of binaphthyl-type bridge dimers in isolated D-B{\chi}-A molecules and introduce a geometric spin-orbit coupling (SOC) mechanism to unveil the intrinsic origin of CISS in axially chiral systems. During photo-excited electron transport along the twisted pathways, the geometric SOC coupling strength exceeds the intrinsic coupling of light atoms by one to two orders of magnitude, readily producing observable high spin polarizations. The resulting spin polarization comprises two components: the CISS-associated polarizations along and perpendicular to the chiral axis are intrinsic to axial chirality, requiring neither external fields nor spin-superexchange transfer, while a non-Abelian curvature correction provides a rigorous mathematical definition of the chiral axis direction. Our calculated polarization components, chirality dependence, and relative magnitudes (30-40\%) quantitatively match time-resolved electron paramagnetic resonance measurements. This geometric SOC framework offers a self-consistent and general physical picture of CISS in axially chiral molecules and provides explicit theoretical guidance for the design of chiral spintronic devices.

cond-mat.mes-hall

Synergy and Competition of Dual Chirality in the Chirality-Induced Spin Selectivity of Supramolecular Helices

Recent progress in constructing supramolecular assemblies with hierarchical chirality offers new opportunities to investigate the chirality-induced spin selectivity (CISS) effect and its potential applications. In this work, we systematically examine the CISS effect in such multichiral systems by designing a class of multilayer helical architectures constructed of stacked and interfaced individual helical rings, each possessing well-defined local chirality. Through controlled interlayer twisting, a global helical handedness is further imposed, forming a multichiral tubular helix. Theoretical calculations reveal that these two distinct chiral hierarchies lead to several unprecedented CISS phenomena, such as enhanced spin polarization arising from cooperative dual chirality, along with the simultaneous emergence of transverse and longitudinal CISS signals. Moreover, interlayer torsional competition modulates the system's response to external fields. The dual-chiral geometry breaks the conventional symmetry of single helices, inducing an anomalous angular phase shift in magnetoresistance. Furthermore, Floquet analysis reveals that the interplay between local and global chirality enables controlled spin polarization switching under circularly polarized light. These findings provide a basic theoretical framework for studying the CISS in multichiral superstructures and establish design principles for coupled optical, magnetic, and spin manipulations, thereby facilitating the development of multichiral spintronic devices.

cond-mat.mes-hall

Knot-Driven Spin Selectivity: Topological Chirality-Induced Robust Spin Polarization in Molecular Knots

Compared to traditional structural chiral materials (e.g., DNA, helicene), topological chirality in trefoil knot molecules has demonstrated multiple remarkable advantages in chirality-induced spin selectivity (CISS), including ultra-high spin polarization of nearly 90%, conductivity increased by two orders of magnitude, and high-temperature stability (up to 350$^{\circ}$C). However, the underlying physical mechanism remains elusive. This work establishes, for the first time, a fundamental theoretical framework for topological chirality-induced spin selectivity (TCISS) in trefoil knot molecules and identifies the necessary conditions for knot-driven spin selectivity. Our calculation results reveal that a trefoil knot molecule can exhibit spin polarization exceeding 60% along with significant conductivity. Notably, neither reducing the lattice number nor applying strain regulation significantly diminishes this ultra-high spin polarization, highlighting its robustness. Importantly, when the topological knot degenerates into a trivial structure, accompanied by the transition from topological chirality to structural chirality, the spin polarization sharply declines, demonstrating a strong correlation between the ultrahigh spin polarization and the knot topology. Our theory not only successfully elucidates the physical mechanism of TCISS, but also uncovers a new spin-polarized transport phenomenon termed knot-driven spin selectivity, offering new guiding principles for designing nonmagnetic materials for spintronics device applications.

cond-mat.mtrl-sci

SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging

Model merging has gained increasing attention due to its intriguing property: interpolating the parameters of different task-specific fine-tuned models leads to multi-task abilities. However, despite its empirical success, the underlying mechanisms of model merging remain poorly understood. In this work, we delve into the mechanism behind model merging from a representation perspective. Our analysis reveals that model merging achieves multi-task abilities through two key capabilities: i) distinguishing samples from different tasks, and ii) adapting to the corresponding expert model for each sample. These two capabilities allow the merged model to retain task-specific expertise, enabling efficient multi-task adaptation. Building on these insights, we propose \texttt{SE-Merging}, a self-enhanced model merging framework that leverages these two characteristics to dynamically identify the corresponding task for each sample and then adaptively rescales the merging coefficients to further enhance task-specific expertise in the merged model. Notably, \texttt{SE-Merging} achieves dynamic model merging without additional training. Extensive experiments demonstrate that \texttt{SE-Merging} achieves significant performance improvements while remaining compatible with existing model merging techniques.

cs.AI

MOSS: Motion-based 3D Clothed Human Synthesis from Monocular Video

Single-view clothed human reconstruction holds a central position in virtual reality applications, especially in contexts involving intricate human motions. It presents notable challenges in achieving realistic clothing deformation. Current methodologies often overlook the influence of motion on surface deformation, resulting in surfaces lacking the constraints imposed by global motion. To overcome these limitations, we introduce an innovative framework, Motion-Based 3D Clo}thed Humans Synthesis (MOSS), which employs kinematic information to achieve motion-aware Gaussian split on the human surface. Our framework consists of two modules: Kinematic Gaussian Locating Splatting (KGAS) and Surface Deformation Detector (UID). KGAS incorporates matrix-Fisher distribution to propagate global motion across the body surface. The density and rotation factors of this distribution explicitly control the Gaussians, thereby enhancing the realism of the reconstructed surface. Additionally, to address local occlusions in single-view, based on KGAS, UID identifies significant surfaces, and geometric reconstruction is performed to compensate for these deformations. Experimental results demonstrate that MOSS achieves state-of-the-art visual quality in 3D clothed human synthesis from monocular videos. Notably, we improve the Human NeRF and the Gaussian Splatting by 33.94% and 16.75% in LPIPS* respectively. Codes are available at https://wanghongsheng01.github.io/MOSS/.

cs.CV

3DFusion, A real-time 3D object reconstruction pipeline based on streamed instance segmented data

This paper presents a real-time segmentation and reconstruction system that utilizes RGB-D images to generate accurate and detailed individual 3D models of objects within a captured scene. Leveraging state-of-the-art instance segmentation techniques, the system performs pixel-level segmentation on RGB-D data, effectively separating foreground objects from the background. The segmented objects are then reconstructed into distinct 3D models in a high-performance computation platform. The real-time 3D modelling can be applied across various domains, including augmented/virtual reality, interior design, urban planning, road assistance, security systems, and more. To achieve real-time performance, the paper proposes a method that effectively samples consecutive frames to reduce network load while ensuring reconstruction quality. Additionally, a multi-process SLAM pipeline is adopted for parallel 3D reconstruction, enabling efficient cutting of the clustering objects into individuals. This system employs the industry-leading framework YOLO for instance segmentation. To improve YOLO's performance and accuracy, modifications were made to resolve duplicated or false detection of similar objects, ensuring the reconstructed models align with the targets. Overall, this work establishes a robust real-time system with a significant enhancement for object segmentation and reconstruction in the indoor environment. It can potentially be extended to the outdoor scenario, opening up numerous opportunities for real-world applications.

cs.CV

When We First Met: Visual-Inertial Person Localization for Co-Robot Rendezvous

We aim to enable robots to visually localize a target person through the aid of an additional sensing modality -- the target person's 3D inertial measurements. The need for such technology may arise when a robot is to meet person in a crowd for the first time or when an autonomous vehicle must rendezvous with a rider amongst a crowd without knowing the appearance of the person in advance. A person's inertial information can be measured with a wearable device such as a smart-phone and can be shared selectively with an autonomous system during the rendezvous. We propose a method to learn a visual-inertial feature space in which the motion of a person in video can be easily matched to the motion measured by a wearable inertial measurement unit (IMU). The transformation of the two modalities into the joint feature space is learned through the use of a contrastive loss which forces inertial motion features and video motion features generated by the same person to lie close in the joint feature space. To validate our approach, we compose a dataset of over 60,000 video segments of moving people along with wearable IMU data. Our experiments show that our proposed method is able to accurately localize a target person with 80.7% accuracy using only 5 seconds of IMU data and video.

cs.CV

Improving the Load Balance of MapReduce Operations based on the Key Distribution of Pairs

Load balance is important for MapReduce to reduce job duration, increase parallel efficiency, etc. Previous work focuses on coarse-grained scheduling. This study concerns fine-grained scheduling on MapReduce operations. Each operation represents one invocation of the Map or Reduce function. Scheduling MapReduce operations is difficult due to highly screwed operation loads, no support to collect workload statistics, and high complexity of the scheduling problem. So current implementations adopt simple strategies, leading to poor load balance. To address these difficulties, we design an algorithm to schedule operations based on the key distribution of intermediate pairs. The algorithm involves a sub-program for selecting operations for task slots, and we name it the Balanced Subset Sum (BSS) problem. We discuss properties of BSS and design exact and approximation algorithms for it. To transparently incorporate these algorithms into MapReduce, we design a communication mechanism to collect statistics, and a pipeline within Reduce tasks to increase resource utilization. To the best of our knowledge, this is the first work on scheduling MapReduce workload at this fine-grained level. Experiments on PUMA [T+12] benchmarks show consistent performance improvement. The job duration can be reduced by up to 37%, compared with standard MapReduce.

cs.DC