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Quantum spin liquids (QSLs) represent a novel state where spins are highly entangled but do not order even at zero temperature due to strong quantum fluctuations. Such a state is mostly studied in Heisenberg models defined on geometrically frustrated lattices. Here, we turn to a new triangular-lattice antiferromagnet PrMgAl$_{11}$O$_{19}$, in which the interactions are believed to be of Ising type. Magnetic susceptibility measured with an external field along the $c$ axis is two orders of magnitude larger than that with a field in the $ab$ plane, displaying an ideal easy-axis behavior. Meanwhile, there is no magnetic phase transition or spin freezing observed down to 1.8 K. Ultralow-temperature specific heat measured down to 50 mK does not capture any phase transition either, but a hump at 4.5 K, below which the magnetic specific heat exhibits a quasi-quadratic temperature dependence that is consistent with a Dirac QSL state. Inelastic neutron scattering technique is also employed to elucidate the nature of its ground state. In the magnetic excitation spectra, there is a gapless broad continuum at the base temperature 55~mK, in favor of the realization of a gapless QSL. Our results provide a scarce example for the QSL behaviors observed in an Ising-type magnet, which can serve as a promising platform for future research on QSL physics based on an Ising model., Comment: 11 pages, 5 figures

In this paper, we construct suitable weak martingale solutions for $3$-dimensional incompressible stochastic Navier-Stokes equations with linear multiplicative noise. In deterministic setting, as widely known, ``suitable weak solutions'' are Leray-Hopf weak solutions enjoying two different types of local energy inequalities. In stochastic setting, we are able to show corresponding stochastic versions of the two local energy inequalities, where a semi-martingale term occurs as the effect of the noise. Note that the exponential formulas, which are widely used to transform the stochastic PDEs system into a random one, DO NOT show up in our formulations of local energy inequality. This is different to \cite{FR02,Rom10} where the additive noise case is dealt. Also, a path-wise result in terms of ``a.e. super-martingale'' is derived from the stochastic local energy inequalities. Further more, if the initial vorticity is a finite Radon measure, we are able to bound the $L^1\big(\Omega;L^\infty([0,T];L^1)\big)$ norm of the vorticity and $L^{\frac{4}{3+\delta}}\big(\Omega\times[0,T]\times\mathbb T^3\big)$ norm of the gradient of the vorticity.

A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a semantically meaningful way, such that the resulting new NeRF retains the target geometry but has an appearance that is an analogy to the source NeRF. To this end, we generalize classic image analogies from 2D images to NeRFs. We leverage correspondence transfer along semantic affinity that is driven by semantic features from large, pre-trained 2D image models to achieve multi-view consistent appearance transfer. Our method allows exploring the mix-and-match product space of 3D geometry and appearance. We show that our method outperforms traditional stylization-based methods and that a large majority of users prefer our method over several typical baselines., Comment: Project page: https://mfischer-ucl.github.io/nerf_analogies/

While numerous 3D reconstruction and novel-view synthesis methods allow for photorealistic rendering of a scene from multi-view images easily captured with consumer cameras, they bake illumination in their representations and fall short of supporting advanced applications like material editing, relighting, and virtual object insertion. The reconstruction of physically based material properties and lighting via inverse rendering promises to enable such applications. However, most inverse rendering techniques require high dynamic range (HDR) images as input, a setting that is inaccessible to most users. We present a method that recovers the physically based material properties and spatially-varying HDR lighting of a scene from multi-view, low-dynamic-range (LDR) images. We model the LDR image formation process in our inverse rendering pipeline and propose a novel optimization strategy for material, lighting, and a camera response model. We evaluate our approach with synthetic and real scenes compared to the state-of-the-art inverse rendering methods that take either LDR or HDR input. Our method outperforms existing methods taking LDR images as input, and allows for highly realistic relighting and object insertion., Comment: Project Website: https://irisldr.github.io/

In heavy-fermion systems with $f$ electrons, there is an intricate interplay between Kondo screening and magnetic correlations, which can give rise to various exotic phases. Recently, similar interplay appears to also occur in $d$-electron systems, but the underlying mechanism remains elusive. Here, using inelastic neutron scattering, we investigate the temperature evolution of the low-energy spin waves in a metallic van der Waals ferromagnet Fe$_{3-x}$GeTe$_{2}$ (Curie temperature $T_{\rm C}\sim160$ K), where the Kondo-lattice behavior emerges in the ferromagnetic phase below a characteristic temperature $T^*\sim90$ K. We observe that the magnon damping constant diverges at both low and high temperatures, exhibiting a minimum coincidentally around $T^*$. Such an observation is analogous to the resistivity minimum as due to the single-impurity Kondo effect. This unusual behavior is described by a formula that combines logarithmic and power terms, representing the dominant contributions from Kondo screening and thermal fluctuations, respectively. Furthermore, we find that the magnon damping increases with momentum below $T_{\rm C}$. These findings can be explained by considering spin-flip electron-magnon scattering, which serves as a magnonic analog of the Kondo-impurity scattering, and thus provides a measure of the Kondo coupling through magnons. Our results provide critical insights into how Kondo coupling manifests itself in a system with magnetic ordering and shed light on the coexistence of and interplay between magnetic order and Kondo effect in itinerant 3$d$-electron systems., Comment: 8 pages, 4 figures

Magnon polarons are novel elementary excitations possessing hybrid magnonic and phononic signatures, and are responsible for many exotic spintronic and magnonic phenomena. Despite long-term sustained experimental efforts in chasing for magnon polarons, direct spectroscopic evidence of their existence is hardly observed. Here, we report the direct observation of magnon polarons using neutron spectroscopy on a multiferroic Fe$_{2}$Mo$_{3}$O$_{8}$ possessing strong magnon-phonon coupling. Specifically, below the magnetic ordering temperature, a gap opens at the nominal intersection of the original magnon and phonon bands, leading to two separated magnon-polaron bands. Each of the bands undergoes mixing, interconverting and reversing between its magnonic and phononic components. We attribute the formation of magnon polarons to the strong magnon-phonon coupling induced by Dzyaloshinskii-Moriya interaction. Intriguingly, we find that the band-inverted magnon polarons are topologically nontrivial. These results uncover exotic elementary excitations arising from the magnon-phonon coupling, and offer a new route to topological states by considering hybridizations between different types of fundamental excitations., Comment: 11 pages, 5 figures, published in Nature Communications

Fractional magnetisation plateaus, in which the magnetisation is pinned at a fraction of its saturated value within a range of external magnetic field, are spectacular macroscopic manifestations of the collective quantum behaviours. One prominent example of the plateau phase is found in spin-1/2 triangular-lattice antiferromagnets featuring strong geometrical frustration, and is often interpreted as quantum-fluctuation-stabilised state in magnetic field via the "order-by-disorder" mechanism. Here, we observe an unprecedented 1/3 magnetisation plateau between 5.2 and 7.4 T at 2 K in a spin-1 antiferromagnet Na$_3$Ni$_2$BiO$_6$ with a honeycomb lattice, where conventionally no geometrical frustration is anticipated. By carrying out elastic neutron scattering measurements, we propose the spin structure of the plateau phase to be an unusual partial spin-flop ferrimagnetic order, transitioning from the zigzag antiferromagnetic order in zero field. Our theoretical calculations show that the plateau phase is stabilised by the bond-anisotropic Kitaev interaction. These results provide a new paradigm for the exploration of rich quantum phases in frustrated magnets and exotic Kitaev physics in high-spin systems., Comment: Submitted version, 10 pages, 5 figures. Final version has been published in Nature Physics

Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency. However, this action may also incur potential security threats, which have not been fully recognized so far. To this end, this article analyzes potential threats incurred by applying LLMs to power systems, emphasizing the need for urgent research and development of countermeasures.

Most existing data-driven power system short-term voltage stability assessment (STVSA) approaches presume class-balanced input data. However, in practical applications, the occurrence of short-term voltage instability following a disturbance is minimal, leading to a significant class imbalance problem and a consequent decline in classifier performance. This work proposes a Transformer-based STVSA method to address this challenge. By utilizing the basic Transformer architecture, a stability assessment Transformer (StaaT) is developed {as a classification model to reflect the correlation between the operational states of the system and the resulting stability outcomes}. To combat the negative impact of imbalanced datasets, this work employs a conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for synthetic data generation, aiding in the creation of a balanced, representative training set for the classifier. Semi-supervised clustering learning is implemented to enhance clustering quality, addressing the lack of a unified quantitative criterion for short-term voltage stability. {Numerical tests on the IEEE 39-bus test system extensively demonstrate that the proposed method exhibits robust performance under class imbalances up to 100:1 and noisy environments, and maintains consistent effectiveness even with an increased penetration of renewable energy}. Comparative results reveal that the CWGAN-GP generates more balanced datasets than traditional oversampling methods and that the StaaT outperforms other deep learning algorithms. This study presents a compelling solution for real-world STVSA applications that often face class imbalance and data noise challenges., Comment: Accepted by Renewable and Sustainable Energy Reviews

We introduce differentiable indirection -- a novel learned primitive that employs differentiable multi-scale lookup tables as an effective substitute for traditional compute and data operations across the graphics pipeline. We demonstrate its flexibility on a number of graphics tasks, i.e., geometric and image representation, texture mapping, shading, and radiance field representation. In all cases, differentiable indirection seamlessly integrates into existing architectures, trains rapidly, and yields both versatile and efficient results., Comment: Project website: https://sayan1an.github.io/din.html

We study the motion of a hole with internal degrees of freedom, introduced to the zigzag magnetic ground state of Na$_2$IrO$_3$, by using the self-consistent Born approximation. We find that the low, intermediate, and high-energy spectra are primarily attributed to the singlet, triplet, and quintet hole contributions, respectively. The spectral functions exhibit distinct features such as the electron-like dispersion of low-energy states near the $\Gamma$ point, the maximum M-point intensity of mid-energy states, and the hole-like dispersion of high-energy states. These features are robust and almost insensitive to the exchange model and Hund's coupling, and are in qualitative agreement with the angular-resolved photoemission spectra observed in Na$_2$IrO$_3$. Our results reveal that the interference between internal degrees of freedom in different sublattices plays an important role in inducing the complex dispersions.

Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL., Comment: Survey of quantum federated learning (QFL)

We propose a physically-motivated deep learning framework to solve a general version of the challenging indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Particularly, when the input is an LDR video sequence, our framework not only progressively refines the lighting prediction as it sees more regions, but also preserves temporal consistency by keeping the refinement smooth. Our framework reconstructs a spherical Gaussian lighting volume (SGLV) through a tailored 3D encoder-decoder, which enables spatially consistent lighting prediction through volume ray tracing, a hybrid blending network for detailed environment maps, an in-network Monte-Carlo rendering layer to enhance photorealism for virtual object insertion, and recurrent neural networks (RNN) to achieve temporally consistent lighting prediction with a video sequence as the input. For training, we significantly enhance the OpenRooms public dataset of photorealistic synthetic indoor scenes with around 360K HDR environment maps of much higher resolution and 38K video sequences, rendered with GPU-based path tracing. Experiments show that our framework achieves lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods, leading to photorealistic AR applications such as object insertion.

Reconstructing the shape and spatially varying surface appearances of a physical-world object as well as its surrounding illumination based on 2D images (e.g., photographs) of the object has been a long-standing problem in computer vision and graphics. In this paper, we introduce an accurate and highly efficient object reconstruction pipeline combining neural based object reconstruction and physics-based inverse rendering (PBIR). Our pipeline firstly leverages a neural SDF based shape reconstruction to produce high-quality but potentially imperfect object shape. Then, we introduce a neural material and lighting distillation stage to achieve high-quality predictions for material and illumination. In the last stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction of object shape, material, and illumination. Experimental results demonstrate our pipeline significantly outperforms existing methods quality-wise and performance-wise., Comment: ICCV 2023. Project page at https://neural-pbir.github.io/ Update Stanford-ORB results

We present a neural extension of basic shadow mapping for fast, high quality hard and soft shadows. We compare favorably to fast pre-filtering shadow mapping, all while producing visual results on par with ray traced hard and soft shadows. We show that combining memory bandwidth-aware architecture specialization and careful temporal-window training leads to a fast, compact and easy-to-train neural shadowing method. Our technique is memory bandwidth conscious, eliminates the need for post-process temporal anti-aliasing or denoising, and supports scenes with dynamic view, emitters and geometry while remaining robust to unseen objects., Comment: Project Page: https://sayan1an.github.io/neuralShadowMapping.html

In this paper we establish a sharp non-uniqueness result for stochastic $d$-dimensional ($d\geq2$) incompressible Navier-Stokes equations. First, for every divergence free initial condition in $L^2$ we show existence of infinite many global in time probabilistically strong and analytically weak solutions in the class $L^\alpha\big(\Omega,L^p_tL^\infty\big)$ for any $1\leq p<2,\alpha\geq1$. Second, we prove the above result is sharp in the sense that pathwise uniqueness holds in the class of $L^p_tL^q$ for some $p\in[2,\infty],q\in(2,\infty]$ such that $\frac2{p}+\frac{d}{q}\leq1$, which is a stochastic version of Ladyzhenskaya-Prodi-Serrin criteria. Moreover, for stochastic $d$-dimensional incompressible Euler equation, existence of infinitely many global in time probabilistically strong and analytically weak solutions is obtained. Compared to the stopping time argument used in \cite{HZZ19, HZZ21a}, we developed a new stochastic version of the convex integration. More precisely, we introduce expectation during convex integration scheme and construct directly solutions on the whole time interval $[0,\infty)$., Comment: 36 pages

In neuroimaging analysis, fMRI can well assess the function changes for brain diseases with no obvious structural lesions. To date, most deep-learning-based fMRI studies have employed functional connectivity (FC) as the basic feature for disease classification. However, FC is calculated on time series of predefined regions of interest and neglects detailed information contained in each voxel. Another drawback of using FC is the limited sample size for the training of deep models. The low representation ability of FC leads to poor performance in clinical practice, especially when dealing with multimodal medical data involving multiple types of visual signals and textual records for brain diseases. To overcome this bottleneck problem in the fMRI feature modality, we propose BrainFormer, an end-to-end functional interaction learning method for brain disease classification with single fMRI volume. Unlike traditional deep learning methods that construct convolution and transformers on FC, BrainFormer learns the functional interaction from fMRI signals, by modeling the local cues within each voxel with 3D convolutions and capturing the global correlations among distant regions with specially designed global attention mechanisms from shallow layers to deep layers. Meanwhile, BrainFormer can deal with multimodal medical data including fMRI volume, structural MRI, FC features and phenotypic data to achieve more comprehensive brain disease diagnosis. We evaluate BrainFormer on five independent multi-site datasets on autism, Alzheimer's disease, depression, attention deficit hyperactivity disorder and headache disorders. The results demonstrate its effectiveness and generalizability for multiple brain diseases diagnosis with multimodal features. BrainFormer may promote precision of neuroimaging-based diagnosis in clinical practice and motivate future studies on fMRI analysis.

Recently, a SU(4) $\pi$-flux Hubbard model on the honeycomb lattice has been proposed to study the spin-orbit excitations of $\alpha$-ZrCl$_3$ [Phys.~Rev.~Lett. 121.097201~(2017)]. Based on this model with a zigzag edge, we show the edge defects can induce edge flat bands that result in a SU(4) edge ferromagnetism. We develop an effective one-dimensional interaction Hamiltonian to study the corresponding SU(4) spin excitations. Remarkably, SU(4) spin excitations of the edge ferromagnet appear as a continuum covering the entire energy region rather than usual magnons. Through further entanglement entropy analysis, we suggest that the continuum consists of fractionalized spin excitations from the disappeared magnons, except for that from the particle-hole Stoner excitations. Moreover, in ribbon systems with finite widths, the disappeared magnons can be restored in the gap formed by the finite-size effect and the optical branch of the restored magnons are found to be topological nontrivial.

We introduce the spin entanglement entropy (EE) to characterize spin excitations. The scalings of EEs are elaborated as $\ln N$, $\ln N+D_k$ and $2\ln N+D$ for the single-magnon states, two-magnon bound states and two-magnon continuum, respectively, based on the Bethe ansatz solutions of the ferromagnetic spin chain with $N$ sites. The $\ln N$ divergence for the bound states reveals the two magnons emerge as a new quasiparticle. More importantly, the nonzero intercepts ($D_k,D$) embody the many-body effects of two-magnon states. In particular, an exact relation between the intercept of EEs and an observable quantity, the two-magnon distance, is established for the bound states. In such a case, we quantify the two-magnon confinement in a spin system by the increasing entanglement with the distance as a particle physics analogue. Moreover, the EEs can also be used to study the evolution of the excitations. When the bound states are immersed in the continuum in the alternating chain, they undergo an oscillating confinement-deconfinement crossover, shown by the oscillations of the EEs and two-magnon distance with the chain length., Comment: 5 pages, 3 figures

The stochastic $p$-Laplace equation with multiplicative transport noise is studied on the torus $\mathbb T^d\, (d\geq 2)$. It is shown that the dissipation is enhanced by transport noise in both the averaged sense and the pathwise sense., Comment: 20 pages. We have made some small corrections

Mathematically representing the shape of an object is a key ingredient for solving inverse rendering problems. Explicit representations like meshes are efficient to render in a differentiable fashion but have difficulties handling topology changes. Implicit representations like signed-distance functions, on the other hand, offer better support of topology changes but are much more difficult to use for physics-based differentiable rendering. We introduce a new physics-based inverse rendering pipeline that uses both implicit and explicit representations. Our technique enjoys the benefit of both representations by supporting both topology changes and differentiable rendering of complex effects such as environmental illumination, soft shadows, and interreflection. We demonstrate the effectiveness of our technique using several synthetic and real examples.

As one of the most promising Kitaev quantum-spin-liquid (QSL) candidates, $\alpha$-RuCl$_3$ has received a great amount of attention. However, its ground state exhibits a long-range zigzag magnetic order, which defies the QSL phase. Nevertheless, the magnetic order is fragile and can be completely suppressed by applying an external magnetic field. Here, we explore the evolution of magnetic excitations of $\alpha$-RuCl$_3$ under an in-plane magnetic field, by carrying out inelastic neutron scattering measurements on high-quality single crystals. Under zero field, there exist spin-wave excitations near the $M$ point and a continuum near the $\mit\Gamma$ point, which are believed to be associated with the zigzag magnetic order and fractional excitations of the Kitaev QSL state, respectively. By increasing the magnetic field, the spin-wave excitations gradually give way to the continuous excitations. On the verge of the critical field $\mu_0H_{\rm c}=7.5$ T, the former vanish and only the latter is left, indicating the emergence of a pure QSL state. By further increasing the field strength, the excitations near the $\mit\Gamma$ point become more intense. By following the gap evolution of the excitations near the $\mit\Gamma$ point, we are able to establish a phase diagram composed of three interesting phases, including a gapped zigzag order phase at low fields, possibly-gapless QSL phase near $\mu_0H_{\rm c}$, and gapped partially polarized phase at high fields. These results demonstrate that an in-plane magnetic field can drive $\alpha$-RuCl$_3$ into a long-sought QSL state near the critical field., Comment: 9 pages, 4 figures

We investigate the dynamical behavior of pull-back trajectories for nonautonomous stochastic feedback systems with multiplicative noise. We proved that there exists a random periodic solution of this system and all pull-back trajectories converge to this random periodic solution as time goes to infinitely almost surely. Our results can be applied to nonautonomous stochastic Goodwin negative feedback system, nonautonomous stochastic Othmer-Tyson positive feedback system and nonautonomous stochastic competitive systems etc.

Dimerized magnets forming alternating Heisenberg chains exhibit quantum coherence and entanglement and thus can find potential applications in quantum information and computation. However, magnetic systems typically undergo thermal decoherence at finite temperatures. Here, we show inelastic neutron scattering results on an alternating antiferromagnetic-ferromagnetic chain compound Na$_2$Cu$_2$TeO$_6$ that the excited quasiparticles can counter thermal decoherence and maintain strong correlations at elevated temperatures. At low temperatures, we observe clear dispersive singlet-triplet excitations arising from the dimers formed along the crystalline $b$-axis. The excitation gap is of $\sim$18 meV and the bandwidth is about half of the gap. The band top energy has a weak modulation along the [100] direction, indicative of a small interchain coupling. The gap increases while the bandwidth decreases with increasing temperature, leading to a strong reduction in the available phase space for the triplons. As a result, the Lorentzian-type energy broadening becomes highly asymmetric as the temperature is raised. These results are associated with a strongly correlated state resulting from hard-core constraint and quasiparticle interactions. We consider these results to be not only evidence for strong correlations at finite temperatures in Na$_2$Cu$_2$TeO$_6$, but also for the universality of the strongly correlated state in a broad range of quantum magnetic systems., Comment: Published in PRB, 8 pages, 6 figures

Disorder is important in the study of quantum spin liquids, but its role on the spin dynamics remains elusive. Here, we explore the disorder effect by investigating the magnetic-field dependence of the low-energy magnetic excitations in a triangular-lattice frustrated magnet YbZnGaO$_4$ with inelastic neutron scattering. With an intermediate field of 2.5 T applied along the $c$-axis, the broad continuum at zero field becomes more smeared both in energy and momentum. With a field up to 10 T, which fully polarizes the magnetic moments, we observe clear spin-wave excitations with a gap of $\sim$1.4 meV comparable to the bandwidth. However, the spectra are significantly broadened. The excitation spectra both at zero and high fields can be reproduced by performing classical Monte Carlo simulations which take into account the disorder effect arising from the random site mixing of nonmagnetic Zn$^{2+}$ and Ga$^{3+}$ ions. These results elucidate the critical role of disorder in broadening the magnetic excitation spectra and mimicking the spin-liquid features in frustrated quantum magnets., Comment: Published in PRB, 10 pages, 3 figures

$\alpha$-RuCl$_3$ has been studied extensively because of its proximity to the Kitaev quantum-spin-liquid (QSL) phase and the possibility of approaching it by tuning the competing interactions. Here we present the first polarized inelastic neutron scattering study on $\alpha$-RuCl$_3$ single crystals to explore the scattering continuum around the $\Gamma$ point at the Brillouin zone center, which was hypothesized to be resulting from the Kitaev QSL state but without concrete evidence. With polarization analyses, we find that while the spin-wave excitations around the M point vanish above the transition temperature $T_{\rm N}$, the pure magnetic continuous excitations around the $\Gamma$ point are robust against temperature. Furthermore, by calculating the dynamical spin-spin correlation function using the cluster perturbation theory, we derive magnetic dispersion spectra based on the $K$-$\Gamma$ model, which involves with a ferromagnetic Kitaev interaction of -7.2 meV and an off-diagonal interaction of 5.6 meV. We find this model can reproduce not only the spin-wave excitation spectra around the M point, but also the non-spin-wave continuous magnetic excitations around the $\Gamma$ point. These results provide evidence for the existence of fractional excitations around the $\Gamma$ point originating from the Kitaev QSL state, and further support the validity of the $K$-$\Gamma$ model as the effective minimal spin model to describe $\alpha$-RuCl$_3$., Comment: Published in Chinese Physics Letters as Express Letter, 8 pages, 4 figures

In the local or itinerant extreme, magnetic excitations can be described by the Heisenberg model which treats electron spins as localized moments, or by the itinerant-electron model where the exchange interaction between electrons leads to unequal numbers of electrons with up and down spins. However, it has been elusive when both local moments and itinerant electrons are present in the intermediate range. Using inelastic neutron scattering, we provide direct spectroscopic evidence on the coexistence of and interplay between local moments and itinerant electrons in a van der Waals metallic ferromagnet Fe$_{2.72}$GeTe$_{2}$, which can sustain tunable room-temperature ferromagnetism down to the monolayer limit. We find that there exist ferromagnetic spin-wave excitations dispersing from the zone center at low energies resulting from local moments, and a column-like broad continuum at the zone boundary at high energies up to over 100 meV resulting from itinerant electrons. Unlike the two-dimensional crystal structure, the low-energy mode exhibits a three-dimensional nature, and the high-energy mode also has an out-of-plane dependence. Both modes persist well above the Curie temperature of 160 K. Our neutron spectroscopic data reveal that the low-energy spin waves at 100 K are more coherent than those at 4 K, which is evidence of the weakening of the Kondo screening at high temperatures. These results unambiguously demonstrate the coexistence of local moments and itinerant electrons, and the Kondo effect between these two components in Fe$_{2.72}$GeTe$_{2}$. Such behaviors are generally expected in heavy-fermion systems with heavy $f$ electrons but rarely clearly observed in materials with light $d$ electrons. These findings shed light on the understanding of magnetism in transition-metal compounds., Comment: Main text 15 pages, 5 figures. Supplementary Materials available on PRX