arXiv subjects

Bokai Zhang

Publications and source records attributed to Bokai Zhang.

17 recordsLinked to original sources

arXiv2026

Frequency-domain extended-effective-source gravitational self-force for eccentric Schwarzschild orbits

Effective-source formulations provide a practical route to gravitational self-force (GSF) calculations when singular retarded fields cannot be handled directly mode by mode. Up to now, only frequency-domain first-order GSF for circular orbits in Schwarzschild spacetime was obtained with the effective-source method. We construct and implement a frequency-domain extended effective-source (EES) formulation for first-order Lorenz-gauge GSF on eccentric orbits in Schwarzschild spacetime. The central obstacle is that radial libration forces the physical puncture and effective source to switch between interior and exterior branches, limiting their differentiability and producing slow spectral convergence. We overcome this obstruction by analytically extending both branches across the libration region and solving the coupled Lorenz-gauge perturbation equations for the resulting smooth extended sources. This provides an end-to-end gravitational EES implementation for eccentric motion and validates a computational ingredient needed for extending frequency-domain effective-source calculations toward second order.

gr-qc↗

arXiv2026

Self-Interaction Controls Vortex Scale in Soliton Mergers

This study investigates the impact of self-interaction strength on the formation and scale of turbulent vortex structures during the merger of Bose stars, using numerical simulations of the Gross-Pitaevskii-Poisson (GPP) equations. We find that vortex formation is a universal outcome of soliton mergers, with the vortex size strongly dependent on the self-interaction coupling parameter $g$. Through analysis of velocity correlations, kinetic energy spectra, and vorticity distributions, we conclude that for repulsive self-interaction, the vortex region expands as self-interaction strength increases; conversely, for attractive self-interaction, the vortex region shrinks as self-interaction strength increases.

hep-ph↗

arXiv2026

Self-Forces as Nonlocal Probes of Gravastar Interiors

Compact objects with the same exterior metric are locally indistinguishable to test particles, yet observables built from retarded fields can retain information about the spacetime outside the particle's immediate neighbourhood. The self-force acting on a particle in curved spacetime provides a unique probe of both the local geometry and the global structure of the background spacetime. We calculate the static, conservative self-force on minimally coupled scalar and electric charges in the simplest thin-shell gravastar: a de Sitter core matched to a Schwarzschild exterior. Weak-field expansions in the compactness $M/R$ are obtained analytically and summed in closed form. For a scalar charge outside the gravastar the self-force is nonzero---in contrast to the exactly vanishing result for a Schwarzschild black hole of the same mass---and behaves as $\tfrac{2}{5}q^2 M R^2/r_0^5$ at large distances, while for an electric charge the universal Smith--Will force $e^2M/r_0^3$ is corrected by a structure-dependent term $\tfrac{4}{5}e^2 M R^2/r_0^5$. Inside the gravastar the scalar self-force is directed toward the center at leading order in $M/R$, vanishes linearly at the center, and produces harmonic oscillations of the charge about the center; the electromagnetic self-force inside the gravastar behaves similarly. The results demonstrate explicitly that the self-force depends not only on the local curvature surrounding the particle but also on the global structure of spacetime: although the exterior geometry of a gravastar is identical to that of a Schwarzschild black hole, the interior boundary conditions modify the regular field and therefore produce distinct self-forces.

gr-qc↗

arXiv2026

iResolveX: Multi-Layered Indirect Call Resolution via Static Reasoning and Learning-Augmented Refinement

Indirect call resolution remains a key challenge in reverse engineering and control-flow graph recovery, especially for stripped or optimized binaries. Static analysis is sound but often over-approximates, producing many false positives, whereas machine-learning approaches can improve precision but may sacrifice completeness and generalization. We present iResolveX, a hybrid multi-layered framework that combines conservative static analysis with learning-based refinement. The first layer applies a conservative value-set analysis (BPA) to ensure high recall. The second layer adds a learning-based soft-signature scorer (iScoreGen) and selective inter-procedural backward analysis with memory inspection (iScoreRefine) to reduce false positives. The final output, p-IndirectCFG, annotates indirect edges with confidence scores, enabling downstream analyses to choose appropriate precision--recall trade-offs. Across SPEC CPU2006 and real-world binaries, iScoreGen reduces predicted targets by 19.2% on average while maintaining BPA-level recall (98.2%). Combined with iScoreRefine, the total reduction reaches 44.3% over BPA with 97.8% recall (a 0.4% drop). iResolveX supports both conservative, recall-preserving and F1-optimized configurations and outperforms state-of-the-art systems.

cs.SE↗

arXiv2025

Bacterial turbulence at compressible fluid interfaces

Dense bacterial suspensions at fluid interfaces provide a natural platform to explore active turbulence in a dimensional mismatch: active units are restricted to a two-dimensional surface, while the induced flows extend into the surrounding three-dimensional liquid. Using hydrophobic Serratia marcescens at the air-water interface, we realize interfacial bacterial turbulence as a distinct class of active turbulence. The system exhibits compressible in-plane flows, with vortex size initially increasing with the thickness of the underlying fluid and saturating near 100 $μ$ m, independent of bacterial length. This behavior contrasts sharply with bulk active turbulence, where correlation length typically scales with system size. Hydrodynamic theory, together with direct measurements of the three-dimensional flow field, shows that the coupling between interfacial and bulk flows sets the emergent length scale. Our results uncover the fundamental physics of interfacial bacterial turbulence and open new strategies for geometric control of collective active flows.

cond-mat.soft↗

arXiv2024

Activity-Induced Stiffness, Entanglement Network and Dynamic Slowdown in Unentangled Semidilute Polymer Solutions

Active polymers possess numerous unique properties that are quite different from those observed in the system of small active molecule due to the intricate interplay between their activity and topological constraints. This study focuses on the conformational changes induced by activity, impacting effective stiffness and crucially influencing entanglement and dynamics. When the two terminals of a linear chain undergo active modification through coupling to a high-temperature thermal bath, there is a substantial increase in chain size, indicating a notable enhancement in effective stiffness. Unlike in passive semiflexible chains where stiffness predominantly affects local bond angles, activity-induced stiffness manifests at the scale of tens of monomers. While activity raises the ambient temperature, it significantly decreases diffusion by over an order of magnitude. The slowdown of dynamics observed can be attributed to increased entanglement due to chain elongation.

cond-mat.soft↗

arXiv2024

Glass formation in mechanically interlocked ring polymers: the role of induced chain stiffness

Polymer-related materials exhibit rich glassy behaviors at different length scales due to their various molecular structures and topological constraints. Recent studies have identified transient interpenetration of the long-chain rings contributing to dynamic arrest on the center-of-mass level. Interpenetration of rings is proposed as an approach to facilitate glass formation in polymer melts. In this work, inspired by recent advances in the synthesis of mechanically interlocked polymers, we investigate glass transition on the nanometer-scale segments influenced by permanent interpenetration of rings using molecular dynamics simulations. We find that decreasing chain length in the mechanically interlocked system is equivalent to inducing an effective chain stiffness on the sub-rings. The induced stiffness provides a unified explanation for these unique structural features and transient dynamic arrest in the system of interlocked rings with rather short chains. Further, a crossover is observed in the scaling relation between localization and glassy depth upon cooling. Our work reveals a dynamic transition from weak to strong caging at the crossover temperature. According to the localization model, we demonstrate that the chain stiffness increases the critical temperature and oscillation distance, therefore leads to more fragile dynamics and deeper glassy state. These findings are consistent with the predictions of molecular simulations and theories for polymers with real local stiffness. Our work deepens the understanding of the role of induced stiffness on glass transition, and opens up a new direction to design rich glass materials by manipulating stiffness through mechanical bonds.

cond-mat.soft↗

arXiv2024

Conformational and static properties of tagged chains in solvents: effect of chain connectivity in solvent molecules

Polymer chains immersed in different solvent molecules exhibit diverse properties due to multiple spatiotemporal scales and complex interactions. Using molecular dynamics simulations, we study the conformational and static properties of tagged chains in different solvent molecules. Two types of solvent molecules were examined: one type consisted of chain molecules connected by bonds, while the other type consisted of individual bead molecules without any bonds. The only difference between the two solvent molecules lay in the chain connectivity. Our results show a compression of the tagged chains with the addition of bead or chain molecules. Chain molecule confinement induces a stronger compression compared to bead molecule confinement. In chain solvent molecules, the tagged chain's radius of gyration reached a minimum at a monomer volume fraction of $\sim0.3$. Notably, the probability distributions of chain size remain unchanged at different solvent densities, irrespective of whether the solvent consists of beads or polymers. Furthermore, as solvent density increases, a crossover from a unimodal to a bimodal distribution of bond angles is observed, indicating the presence of both compressed and expanded regions within the chain. The effective monomer-solvent interaction is obtained by calculating the partial radial distribution function and the potential of the mean force. In chain solvent, the correlation hole effect results in a reduced number of nearest neighbors around tagged monomers compared to bead solvents. The calculation of pore size distribution reveals that the solvent nonhomogeneity induced by chain connectivity leads to a broader distribution of pore sizes and larger pore dimensions at low volume fractions. These findings provide a deeper understanding of the conformational behavior of polymer chains in different solvent environments.

cond-mat.soft↗

arXiv2024

Omnidirectional 3D printing of PEDOT:PSS aerogels with tunable electromechanical performance for unconventional stretchable interconnects and thermoelectrics

The next generation of soft electronics will expand to the third dimension. This will require the integration of mechanically-compliant three-dimensional functional structures with stretchable materials. This study demonstrates omnidirectional direct ink writing (DIW) of Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) aerogels with tunable electrical and mechanical performance, which can be integrated with soft substrates. Several PEDOT:PSS hydrogels were formulated for DIW and freeze-dried directly on stretchable substrates to form integrated aerogels displaying high shape fidelity and minimal shrinkage. The effect of additives and processing in the PEDOT:PSS hydro and aerogels morphology, and the link with their electromechanical properties was elucidated. This technology demonstrated 3D-structured stretchable interconnects and planar thermoelectric generators (TEGs) for skin electronics, as well as vertically-printed high aspect ratio thermoelectric pillars with a high ZT value of 3.2 10^-3 and ultra-low thermal conductivity of 0.065 W/(m K). Despite their comparatively low ZT, the aerogel pillars outpowered their dense counterparts in realistic energy harvesting scenarios where contact resistances cannot be ignored, and produced up to 26 nW/cm2 (corresponding to a gravimetric power density of 0.76 mW/kg) for a difference of temperature of 15 K. This work suggests promising advancements in soft and energy-efficiency electronic systems relevant to soft robotics and wearable.

physics.app-ph↗

arXiv2024

Friends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition

Automatic surgical phase recognition is a core technology for modern operating rooms and online surgical video assessment platforms. Current state-of-the-art methods use both spatial and temporal information to tackle the surgical phase recognition task. Building on this idea, we propose the Multi-Scale Action Segmentation Transformer (MS-AST) for offline surgical phase recognition and the Multi-Scale Action Segmentation Causal Transformer (MS-ASCT) for online surgical phase recognition. We use ResNet50 or EfficientNetV2-M for spatial feature extraction. Our MS-AST and MS-ASCT can model temporal information at different scales with multi-scale temporal self-attention and multi-scale temporal cross-attention, which enhances the capture of temporal relationships between frames and segments. We demonstrate that our method can achieve 95.26% and 96.15% accuracy on the Cholec80 dataset for online and offline surgical phase recognition, respectively, which achieves new state-of-the-art results. Our method can also achieve state-of-the-art results on non-medical datasets in the video action segmentation domain.

cs.CV↗

arXiv2023

Scalable Electrodeposition of Eutectic Indium Gallium from an Acetonitrile-Based Electrolyte for Integrated Stretchable Electronics

For the advancement of highly-integrated stretchable electronics, the development of scalable sub-micrometer conductor patterning is required. Eutectic gallium indium EGaIn is an attractive conductor for stretchable electronics, as its liquid metallic character grants it high electrical conductivity upon deformation. However, its high surface energy precludes patterning it with (sub)-micron resolution. Herein, we overcome this limitation by reporting for the first time the electrodeposition of EGaIn. We use a non-aqueous acetonitrile-based electrolyte that exhibits high electrochemical stability and chemical orthogonality. The electrodeposited material led to low-resistance lines that remained stable upon (repeated) stretching to a 100 percent strain. Because electrodeposition benefits from the resolution of mature nanofabrication methods used to pattern the base metal, the proposed bottom-up approach achieved a record-high density integration of EGaIn regular lines of 300 nm half-pitch on an elastomer substrate by plating on a gold seed layer pre-patterned by nanoimprinting. Moreover, vertical integration was enabled by filling high aspect ratio vias. This capability was conceptualized by the fabrication of an omnidirectionally stretchable 3D electronic circuit, and demonstrates a soft-electronic analogue of the stablished damascene process used to fabricate microchip interconnects. Overall, this work proposes a simple route to address the challenge of metallization in highly integrated (3D) stretchable electronics.

physics.app-ph↗

arXiv2023

SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition

Automatic surgical phase recognition is one of the key technologies to support Video-Based Assessment (VBA) systems for surgical education. Utilizing temporal information is crucial for surgical phase recognition, hence various recent approaches extract frame-level features to conduct full video temporal modeling. For better temporal modeling, we propose SlowFast Temporal Modeling Network (SF-TMN) for surgical phase recognition that can not only achieve frame-level full video temporal modeling but also achieve segment-level full video temporal modeling. We employ a feature extraction network, pre-trained on the target dataset, to extract features from video frames as the training data for SF-TMN. The Slow Path in SF-TMN utilizes all frame features for frame temporal modeling. The Fast Path in SF-TMN utilizes segment-level features summarized from frame features for segment temporal modeling. The proposed paradigm is flexible regarding the choice of temporal modeling networks. We explore MS-TCN and ASFormer models as temporal modeling networks and experiment with multiple combination strategies for Slow and Fast Paths. We evaluate SF-TMN on Cholec80 surgical phase recognition task and demonstrate that SF-TMN can achieve state-of-the-art results on all considered metrics. SF-TMN with ASFormer backbone outperforms the state-of-the-art Not End-to-End(TCN) method by 2.6% in accuracy and 7.4% in the Jaccard score. We also evaluate SF-TMN on action segmentation datasets including 50salads, GTEA, and Breakfast, and achieve state-of-the-art results. The improvement in the results shows that combining temporal information from both frame level and segment level by refining outputs with temporal refinement stages is beneficial for the temporal modeling of surgical phases.

cs.CV↗

arXiv2023

Molecular chaos in dense active systems

The hypothesis of molecular chaos plays the central role in kinetic theory, which provides a closure leading to the Boltzmann equation for quantitative description of classic fluids. Yet how to properly extend it to active systems is still an open question in nonequilibrium physics. Combining experiment, simulation, and theory, we investigate the emergent collective behaviors of self-propelled particles that exhibit collision avoidance, a moving strategy commonly adopted in natural and engineering active systems. This dense active system shows unusual phase dynamics strongly regulated by many-body interactions, which cannot be explained by theories assuming molecular chaos. To rationalize the interplay between different emergent phases, a simple kinetic model is proposed with a revised molecular chaos hypothesis, which treats the many-body effect implicitly via categorizing different types of particle pair collisions. Our model predicts an optimal growth rate of flocking and illustrates a generic approach for understanding dense active systems.

cond-mat.soft↗

arXiv2022

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

cs.CV↗

arXiv2022

Dynamic path dependence of phase behaviors in dense active system

There are rich emergent phase behaviors in non-equilibrium active systems. Flocking and clustering are two representative dynamic phases. The relationship between these two phases is still unclear. In the paper, we numerically investigate the evolution of flocking and clustering in a system consisting of self-propelled particles with active reorientation. We consider the interplay between flocking and clustering phases under different initial states, and observe an unstable domain in order parameters phase diagrams due to initial states even in the absence of an explicit attraction. This point is different from the previous finding that active angular fluctuations lead to an earlier breakdown of collective motion and the emergence of a new bi-stable regime in the aligned active particles [R.Grossmann et al, New J. Phys.073033,14 (2012)]. In particular, we find that the existence of bi-stable states is due to the diversity of dynamic paths arising from different initial states. By increasing (decreasing) the initial degree of ordering, the bi-stable state can be shifted to a more ordered flocking (disordered clustering) state. These results enlighten us pave the way to manipulate emergent behaviors and collective motions of active system.

cond-mat.soft↗

arXiv2021

Theory of polymer diffusion in polymer-nanoparticle mixtures: effect of nanoparticle concentration and polymer length

The dynamics of polymer-nanoparticle (NP) mixtures, which involves multiple scales and system-specific variables, has posed a long-standing challenge on its theoretical description. In this paper, we construct a microscopic theory for polymer diffusion in the mixtures based on a combination of generalized Langevin equation, mode-coupling approach, and polymer physics ideas. The parameter-free theory has an explicit expression and remains tractable on pair correlation level with system-specific equilibrium structures as input. Taking a minimal polymer-NP mixture as an example, our theory correctly captures the dependence of polymer diffusion on NP concentration and average interparticle distance. Importantly, the polymer diffusion exhibits a power law decay as the polymer length increases at dense NPs and/or long chain, which marks the emergence of entanglement-like motion. The work provides a first-principle theoretical foundation to investigate dynamic problems in diverse polymer nanocomposites.

cond-mat.soft↗

arXiv2020

Active suspensions of bacteria and passive objects: a model for the near field pair dynamics

Near field hydrodynamic interactions are essential to determine many important emergent behaviors observed in active suspensions, but have not been successfully modeled so far. In this work we propose an effective model capable of efficiently capturing the essence of the near field hydrodynamic interactions, validated numerically by a pedagogic model system consisting of an E. coli and a spherical tracer. The proposed model effectively captures all the details of near field hydrodynamics through only a tensorial coefficient of resistance, which is fundamentally different from, and thus cannot be replaced by, an effective interaction of conservative nature. In a critical test case that studies the scattering angle of the bacterium-tracer pair dynamics, calculations based on the proposed model reveals a region in parameter space where the bacterium is trapped by the spherical tracer, a phenomenon that is regularly observed in experiments but cannot be explained by any existing model.

cond-mat.soft↗