arXiv subjects

Zhan Wang

Publications and source records attributed to Zhan Wang.

At least 19 recordsLinked to original sources

arXiv2026

Streamwise dispersion with interphase mass transfer through a tube

Reactive solute transport in shear flows is of broad importance in chemical engineering, liquid--solid chromatography, and environmental mechanics, particularly in flow reactors and fluidic devices involving interphase mass transfer. Based on the recently developed streamwise dispersion theory for inert solutions (Guan & Chen, 2024, J. Fluid Mech., vol. 980, A33), we present an analytical model for reactive dispersion of chemically active solutes characterized by first-order, irreversible surface reactions. The extension is not a simple perturbation because the reactive boundary destroys mass conservation with streamwise and radial derivatives coupled. The model establishes how the microscopic kinetic reactivity and radial eigenmodes combine to determine a newly defined (effective) local exchange rate. We derive rigorous explicit solutions for the local moments, concentration, and phenomenological transport coefficients at arbitrary reactivity, in quantitative agreement with numerical simulations across all regimes. For tube Poiseuille flows, we reveal pre-asymptotic behaviours including sign-changing radial exchange, reactivity-enhanced drift, reactivity-reduced dispersivity, and non-Gaussian concentration profiles. Small- and large-Damköhler-number asymptotics for the local and global transport coefficients identify the reaction-limited and diffusion-limited regimes, connecting the conservative case to the Dirichlet limit at infinite reactivity. This streamwise perspective could advance our understanding of macroscale transport in chemically active systems.

physics.flu-dyn↗

arXiv2026

$d$-wave superconductivity in quintuple-layer bulk superconductor La$_6$Ni$_5$O$_{12}$

The recent realization of bulk superconductivity in the quintuple-layer nickelate La$_6$Ni$_5$O$_{12}$ opens new opportunities to probe the pairing symmetry of the reduced Ruddlesden--Popper (RP) nickelate family. Using an effective five-layer Hubbard model and variational Monte Carlo calculations, we find that a spin-singlet $d_{x^2-y^2}$-wave state is energetically favored, while extended-$s$-wave pairing is strongly suppressed. Based on the optimized superconducting state, we further calculate several experimentally accessible observables. The tunneling density of states exhibits a characteristic V-shaped low-energy spectrum, the superfluid density shows a linear low-temperature depletion, the Knight shift decreases below $T_c$, and the spin-lattice relaxation rate follows $1/T_1T\propto T^2$, together providing consistent signatures of a clean nodal superconductor. Extending the same framework to related reduced RP nickelates, we find a robust $d$-wave tendency in infinite-layer LaNiO$_{2}$, while trilayer La$_4$Ni$_3$O$_{8}$ at its nominal filling lies close to the boundary between $d$-wave superconductivity and a non-superconducting state. These results establish bulk La$_6$Ni$_5$O$_{12}$ as a promising platform for determining the pairing symmetry of nickelate superconductors and suggest a common $d_{x^2-y^2}$-wave pairing tendency across the reduced RP nickelate family.

cond-mat.supr-con↗

arXiv2026

Infrared Subtraction with Artificial Intelligence

We present AI-developed local infrared subtraction, building on projection to Born and EFT matching. The framework separates an integrable radiation term from a finite contribution at Born kinematics, referred to as the Born contact. The contact is determined using the EFT singular distribution in a resolution observable such as N-jettiness $τ_N$. Under human physics guidance, an LLM develops two implementations. One uses a neural network for phase space projection and fits the contact by matching to EFT cumulants. The other uses an analytic construction that keeps the Born momenta fixed while integrating over radiation. It combines the EFT $δ(τ_N)$ coefficient with finite 4-dimensional radiation integrals to calculate the contact term directly. This gives a local subtraction formula without a slicing parameter, while reusing existing lower-order radiation calculations and EFT singular predictions. As a demonstration, we reconstruct the full NLO correction for massless 3- and 4-jet production in electron-positron annihilation. The attempt to the NNLO dijet production is also made by recursively using the NLO P2B construction with the LLM designing machine-learning controls to reduce the variance of the contact integral. The tested predictions are in good agreement with EERAD3. The numerical calculation and projection-network training use a 2020 Apple M1 MacBook, without GPU acceleration, illustrating the feasibility of the construction with modest computing resources. The appendices develop an extension of the local subtraction to 3-jet NNLO, giving explicit radiation maps and a proposed contact formula. We also show how to integrate over NNLO radiation while keeping the Born momenta fixed, for any number of massless final-state jets. Our results demonstrate how AI can help higher-order calculations by constructing infrared subtraction and improving its numerical integration.

hep-ph↗

arXiv2026

Counting and Min-Cost Encoding for Tokenization in Large Language Models

Mainstream large language models rely on a tokenizer to encode text into a token sequence. Different tokenizers may yield token sequences of substantially different lengths for the same text. With a fixed model architecture, shorter token sequences correspond to lower inference time. We propose a tokenizer training approach named Counting and Filtering (CNF) and a text encoding algorithm called Min-Cost Encoding (MCE). MCE defines a cost function over a text segment, and determines the best segmentation by globally minimizing the overall segmentation cost. CNF builds a raw vocabulary by directly counting valid substrings, and then constructs the final vocabulary through a filtering step based on actual token usage when segmenting the training corpus with MCE. The CNF-MCE conbination offers several advantages over BPE, including higher token efficiency, greater scalability, and lower dependency. Across six text categories and two vocabulary-size groups, CNF-MCE consistently achieves better compression than the evaluated BPE tokenizers. With a 250K vocabulary, CNF-MCE increases compression rate by 26% and 30% on English web text over the o200k_base and qwen250k tokenizers. Experiments scaling the vocabulary to 1M entries on English web text demonstrate sustained improvements over BPE, with a token efficiency improvement of over 60% and vocabulary utilization rising from 52.9% to 96.9%. The MCE algorithm does not depend on a merge list (as in BPE) or token probability (as in UnigramLM), making it applicable to a wide range of vocabularies, including those built from BPE, UnigramLM, CNF, and others. Language models trained from scratch at the 1.8B and 8B scales achieve comparable average performance to models using the BPE tokenizers across 11 benchmarks. These results demonstrate that CNF-MCE can improve token efficiency significantly while maintaining competitive downstream performance.

cs.CL↗

arXiv2026

A Hybrid POD-Autoencoder Framework for Reduced Order Modeling of Turbulent Flow via Strategic Field Decomposition

This study proposes a hybrid reduced-order modeling (ROM) framework for the simulation of turbulent flow. The central idea is to decompose flow dynamics according to their temporal characteristics and predict the resulting components individually. The full field is first divided into a sub-field represented by a limited number of proper orthogonal decomposition (POD) modes (named as POD-retained field) and the corresponding residual sub-field (named as POD-truncated field). A frequency-informed POD strategy identifies the retained modes by considering both modal energy and dominant frequency. The evolution of retained POD coefficients, which feature similar temporal scales, is described using a vector autoregressive (VAR) model. In parallel, the POD-truncated field is compressed into a low-dimensional latent space using a Fourier-neural-operator-based Koopman $β$-variational autoencoder (FK-$β$-VAE), with the latent variables subsequently predicted by a switching-VAR model. Turbulent statistics of the full field are recovered by combining the contributions from the two components. The framework is assessed using turbulent channel flow at a friction Reynolds number of $110$. The predicted Reynolds-stress components, turbulent kinetic energy (TKE), and dominant wavenumber spectra show good agreement with the reference. Moreover, in comparison with an alternative framework of full-field modeling (i.e., without field decomposition), the proposed framework yields more accurate and robust long-term statistical predictions.

physics.flu-dyn↗

arXiv2026

Predict Before Replay: Joint FEC and Flight Control for Reliable Scale-Up Links

Scale-up accelerator fabrics send latency-sensitive flits over serial links at hundreds of gigabits per second. Their reliability pipeline first relies on FEC, then detects residual failures and replays unacknowledged data. At these line rates, delayed feedback lets later flits enter the replay window before a residual failure is reported, so standard replay can amplify one corrupted flit into a suffix retransmission. This paper presents PREFACE, a pre-FEC controller for temporally correlated burst errors. A two-state Bayesian filter converts corrected-symbol observations into a next-flit burst posterior and jointly selects FEC strength with an outstanding-flit cap. We implement PREFACE in ns-3 with publicly verifiable UALink 200G 1.0 replay semantics. PREFACE improves goodput by 10.52%, lowers P99 latency by 50.75%, cuts replay by 47.52%, and improves modeled ring AllReduce by 13.1--27.0%.

cs.NI↗

arXiv2026

Completion-Path Credits: Multi-Resource Control for Scale-Up Fabrics

Scale-up fabrics connecting GPUs and AI accelerators carry tensor transfers together with remote reads, writes, atomics, and notifications over shared target-side receiver resources. Byte-denominated credits protect link buffers and streaming HBM traffic, but poorly represent small operations dominated by Atomic execution or response injection. This paper presents SemaCredit, a receiver controller that admits each remote-memory operation against a vector of target-resource demands and returns each component when its corresponding HBM, Atomic, or response stage completes. In a deterministic event simulator with multipath queues, eight HBM partitions, a serialized Atomic engine, and a response engine, SemaCredit matches a strong per-resource byte baseline on HBM-hotspot traffic while reducing small-operation P99 latency by 52.4% under Atomic contention and 10.2% under response incast. Application-shaped mixes show 57.7% and 14.5% P99 latency improvements for AllReduce-shaped and remote-read-shaped traffic while matching byte credits on HBM-dominated MoE traffic.

cs.NI↗

arXiv2026

Rogue Wave Statistics from a Sparse Coherent Structure Decomposition

While conventional rogue wave statistical models rely on linear or weakly nonlinear descriptions of random seas, we demonstrate experimentally that moderately or strongly nonlinear wave fields can be represented by sparse ensembles of coherent soliton-like packets. These packets exhibit log-normal amplitude distributions together with uniformly distributed phases and peak emergence times. This sparse coherent structure framework naturally leads to an extreme value description in which the probability of exceedance is governed by the tail of the coherent-structure amplitude distribution. The prediction is validated against laboratory hydrodynamic experiments across a variety of unidirectional sea states, showing good agreement with the experimental observations and comparing favourably with conventional statistical prediction models while retaining analytical simplicity. Our results provide a direct physics-based link between sparse coherent structures and rogue wave probabilities, with broader implications for nonlinear wave physics in optics, cold gases, and plasmas.

nlin.PS↗

arXiv2026

BladeYOLO: Wind Turbine Blade Defect Detection with Limited Annotations and Weak-Saliency Awareness

Wind turbine blade defect detection remains highly challenging in real-world inspection scenarios due to limited on-site data and the subtle visual characteristics of defects. In practice, blade defects are often small-scale, low-contrast, and difficult to distinguish from complex backgrounds, which significantly limits the robustness of existing detectors. To address these challenges, we propose BladeYOLO, a defect detection framework for wind turbine blades. Specifically, we integrate a Vision Transformer (ViT) backbone initialized with DINOv3 self-supervised pre-trained weights into YOLOv12-L, enabling the transfer of large-scale generic visual priors to blade defect detection and improving feature representation under limited training annotations. To enhance the perception of subtle defects, we further develop a Mamba-guided Weak-Defect Enhancement module, which consists of a Detail-Enhanced Multi-scale Branch for preserving high-frequency structural cues and a Cross-Mamba module for progressively propagating high-level semantic guidance to shallow features. In addition, we introduce a lightweight Style-Injector module that captures environment-related style information via Fourier decomposition and injects it into selected ViT self-attention layers, thereby improving robustness against environment-induced appearance variations. Extensive experiments demonstrate that BladeYOLO achieves superior performance on the WTBlade-Defect dataset, with additional annotation-budget experiments showing its favorable performance under reduced training annotations. Evaluation on the public Wind Surface Defect dataset further provides supportive evidence for the cross-dataset robustness of BladeYOLO. In particular, on this public dataset, BladeYOLO outperforms the best competing method by 3.5\% in mAP$_{50}$ and 2.5\% in mAP$_{50-95}$.

cs.CV↗

arXiv2026

Electronically Inactive Intercalated La$_2$NiO$_4$ Layer in Superconducting La$_5$Ni$_3$O$_{11}$

The recent discovery of superconductivity in La$_5$Ni$_3$O$_{11}$ extends the family of superconducting Ruddlesden--Popper nickelates beyond La$_3$Ni$_2$O$_7$. Unlike conventional members of a single Ruddlesden--Popper series, La$_5$Ni$_3$O$_{11}$ contains an intercalated La$_2$NiO$_4$ layer between La$_3$Ni$_2$O$_7$ blocks, raising the question of whether this additional layer participates in the low-energy electronic structure. Here, we combine density functional theory, Wannier-based tight-binding modeling, and rotationally invariant slave-boson calculations to investigate the electronic role of the intercalated layer. We find that realistic electronic parameters place the La$_2$NiO$_4$ layer in gapped insulating regimes rather than a paramagnetic metallic state. Furthermore, realistic interlayer hybridization fails to generate any appreciable La$_2$NiO$_4$-derived spectral weight at the Fermi level. Our results demonstrate that the low-energy electronic structure of La$_5$Ni$_3$O$_{11}$ is governed primarily by the La$_3$Ni$_2$O$_7$ block, with the intercalated La$_2$NiO$_4$ layer remaining electronically inactive. This establishes a minimal low-energy description of La$_5$Ni$_3$O$_{11}$ and provides a unified framework for understanding superconductivity in intercalated Ruddlesden--Popper nickelates.

cond-mat.supr-con↗

arXiv2026

Magnetic hopfions at room temperature

Hopfions are three-dimensional (3D) topological solitons predicted to exist in diverse magnetic systems, yet their practical utility has been largely restricted to cryogenic environments. Here, we overcome this temperature constraint by demonstrating stable magnetic hopfions in the chiral magnet Co8Zn8Mn4 at and above room temperature. Using a transmission electron microscope equipped for in situ optical excitation, we generate magnetic hopfions with femtosecond laser pulses. Long-term observations further reveal Brownian-like motion at room temperature and thermally activated collapse upon approaching the high-temperature regime. Together with micromagnetic simulations and homotopy group analysis, our experimental observations uncover the hopfion formation mechanism through the fusion of bimeron pairs. These findings establish room-temperature magnetic hopfions and provide a framework for their further studies under technologically relevant conditions.

cond-mat.mtrl-sci↗

arXiv2026

EPIC: Abstraction and Polymorphism of In-Network Collectives on Ethernet

In-Network Collective (INC) acceleration holds immense potential for optimizing AI training and inference; however, its cross-layer nature has historically hindered investment and adoption within the open Ethernet ecosystem. To bridge this gap, we propose EPIC (Ethernet Polymorphic In-network Collective), an INC protocol specification and reference system built on the principle of "Unified Abstraction, Polymorphic Realization." EPIC introduces an abstraction compatible with standard Ethernet that aligns functional boundaries with participant roles, while offering polymorphic realizations tailored to varying hardware capabilities. We address three fundamental challenges: first, we employ a modular design that enables an evolutionary path from simple to complex implementations, allowing vendors to iterate their hardware incrementally; second, we apply formal verification methodologies to prove the correctness of all proposed polymorphic modes; and third, we develop a unified resource management model versatile enough for diverse INC scenarios. Extensive validation -- spanning model checking, packet/flow simulations, VM emulation, Tofino Testbed, and FPGA/RTL verification -- confirms EPIC's correctness, performance gain, and feasibility.

cs.DC↗

arXiv2026

Active phase-space topology unifies depletion and alignment in bacterial flows

Transport at small scales is classically understood within an equilibrium framework, where dispersion theory successfully describes shear-enhanced diffusion for passive particles in the continuum limit. However, as most bacteria can move on their own, their motility in flows, inherently out of thermal equilibrium, fundamentally challenges this framework. A minimal, predictive unified theory of bacterial transport in low-Reynolds-number flows remains lacking. Here, from first principles, we develop an analytical hydrodynamic model that enforces consistent no-flux boundary conditions and uses the method of images to characterize the flow-wall coupling. The model quantitatively reproduces measured bacterial distributions and reveals a hydrodynamic locking mechanism accompanied by mean-drift invariance -- an active counterpart to Taylor dispersion. We clarify that shear-induced depletion and alignment are dual manifestations of a single active phase-space topology, ruling out explanations based solely on the local shear magnitude. The theory is validated against microfluidic experiments spanning multiple bacterial species and shear geometries, from one-dimensional to fully three-dimensional flows. Our findings establish a unified phase-space framework for bacterial hydrodynamics, advancing the fundamental understanding of active matter.

physics.flu-dyn↗

arXiv2026

Gossamer Superconductivity in Moiré WSe$_2$ Bilayer

Moiré transition metal dichalcogenides have served as a versatile platform for simulating Hubbard physics. Recent experiments have identified robust superconductivity in moiré bilayer WSe$_2$ for certain twist angles. Here, we propose the gossamer nature of the superconductivity recently discovered at half-filling and zero displacement field in twisted WSe$_2$. By mapping the moiré continuum system to an effective extended single-orbital Hubbard model on the triangular lattice, we employ renormalized mean-field theory to investigate the strong-coupling phase diagram. We find that a moderate Coulomb repulsion partially suppresses charge fluctuations while preserving a finite density of mobile doublons and holes. In this regime, the interplay between extended kinetic hoppings and antiferromagnetic superexchange stabilizes a chiral $d+id$ superconducting phase. Our results naturally account for the twist-angle-dependent evolution from a Mott insulator to a superconductor and eventually to a correlated metal. Furthermore, the model demonstrates that this half-filled pairing state vanishes rapidly upon density doping, consistent with experimental observations.

cond-mat.str-el↗

arXiv2026

Jahn-Teller distortion on strained La$_3$Ni$_2$O$_7$ thin films

We present a systematic study of the electronic structure of strained La$_3$Ni$_2$O$_7$ thin films. We show that biaxial compressive strain mainly elongates the outer apical Ni-O bond while leaving the inner apical Ni-O bond nearly unchanged. As a result, the Jahn-Teller splitting $Δ_{JT}$ is strongly enhanced, whereas the interlayer $d_{z^2}$ hopping $t_\perp^z$ changes only weakly. Since superconductivity is widely believed to emerge only below a critical in-plane lattice constant, our results identify the strain-enhanced $Δ_{JT}$ as the relevant microscopic tuning parameter. Consistently, the calculated Fermi surfaces and Hall response for LaAlO$_3$ and SrLaAlO$_4$ substrates agree with ARPES and Hall measurements. Our results identify Jahn-Teller distortion as a key tuning parameter in strained La$_3$Ni$_2$O$_7$ and support its central role in optimizing superconductivity in bilayer nickelates.

cond-mat.supr-con↗

arXiv2026

CCL-D: A High-Precision Diagnostic System for Slow and Hang Anomalies in Large-Scale Model Training

As training scales grow, collective communication libraries (CCL) increasingly face anomalies arising from complex interactions among hardware, software, and environmental factors. These anomalies typically manifest as slow/hang communication, the most frequent and time-consuming category to diagnose. However, traditional diagnostic methods remain inaccurate and inefficient, frequently requiring hours or even days for root cause analysis. To address this, we propose CCL-D, a high-precision diagnostic system designed to detect and locate slow/hang anomalies in large-scale distributed training. CCL-D integrates a rank-level real-time probe with an intelligent decision analyzer. The probe measures cross-layer anomaly metrics using a lightweight distributed tracing framework to monitor communication traffic. The analyzer performs automated anomaly detection and root-cause location, precisely identifying the faulty GPU rank. Deployed on a 4,000-GPU cluster over one year, CCL-D achieved near-complete coverage of known slow/hang anomalies and pinpointed affected ranks within 6 minutes-substantially outperforming existing solutions.

cs.DC↗

arXiv2026

Discovery of parity-violating chiral polar-nematic charge density wave and superconductivity in kagome metals

Nonmagnetic kagome metals and superconductors AV3Sb5 (A = K, Rb, Cs) host unconventional charge density wave (CDW) and superconducting (SC) phases accompanied by multiple electronic symmetry breaking. Due to the centrosymmetric crystal structure, inversion symmetry has generally been assumed to hold. Here, using scanning tunneling microscopy complemented by atomic force microscopy and optical second-harmonic generation, we directly reveal that inversion symmetry in the kagome plane is spontaneously broken in the CDW state. The mixed-parity CDW state exhibits ferroelectric dipolar and nematic quadrupolar ordered moments. The coexistence and coupling between the dipole and quadrupole favor noncollinear ferro-polar and nematic alignment that breaks all mirror symmetries and gives rise to robust electronic chirality in the 3Q CDW. The multipolar coupling to in-plane electric field enables electric field control and manipulation of the chiral polar-nematic CDW state, including its chirality. Below the SC transition, we observe parity-violating pair density modulations at both the original and the CDW lattice wavevectors. Our findings of parity-violating electronic chiral multipolar order provide microscopic insights into the magnetoelectric and nonreciprocal transport, loop current order, pairing density waves, and unconventional superconductivity in kagome metals and related quantum materials.

cond-mat.supr-con↗

arXiv2026

Orbital-Selective $d$-wave Superconductivity in the Two-Band $t$-$J$ Model: Possible Applications to La$_3$Ni$_2$O$_7$

We investigate superconductivity in a two-band $t$-$J$ model consisting of an itinerant orbital (orbital-0) and a quasi-localized orbital (orbital-1) using variational Monte Carlo. A robust orbital-selective $d$-wave superconducting state is found to emerge exclusively from the itinerant orbital. An analysis of the superexchange energy hierarchy shows that the quasi-localized orbital-1 competes with superconductivity by favoring local inter-orbital bound states, which act as energy defects and disrupt phase coherence. Consistently, the superconducting order parameter is monotonically suppressed as the occupancy of orbital-1 increases. Motivated by superconductivity in nickelate La$_3$Ni$_2$O$_7$, these results highlight the essential role of multi-orbital physics beyond the single-band $t$-$J$ framework and point to a concrete route to enhance $T_c$: suppressing the involvement of localized $d_{z^2}$-derived orbitals.

cond-mat.str-el↗