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Diffusion models excel in generating high-quality images. However, current diffusion models struggle to produce reliable images without guidance methods, such as classifier-free guidance (CFG). Are guidance methods truly necessary? Observing that noise obtained via diffusion inversion can reconstruct high-quality images without guidance, we focus on the initial noise of the denoising pipeline. By mapping Gaussian noise to `guidance-free noise', we uncover that small low-magnitude low-frequency components significantly enhance the denoising process, removing the need for guidance and thus improving both inference throughput and memory. Expanding on this, we propose \ours, a novel method that replaces guidance methods with a single refinement of the initial noise. This refined noise enables high-quality image generation without guidance, within the same diffusion pipeline. Our noise-refining model leverages efficient noise-space learning, achieving rapid convergence and strong performance with just 50K text-image pairs. We validate its effectiveness across diverse metrics and analyze how refined noise can eliminate the need for guidance. See our project page: https://cvlab-kaist.github.io/NoiseRefine/., Comment: Project page: https://cvlab-kaist.github.io/NoiseRefine/

In this paper, we develop monolithic limiting techniques for enforcing nonlinear stability constraints in enriched Galerkin (EG) discretizations of nonlinear scalar hyperbolic equations. To achieve local mass conservation and gain control over the cell averages, the space of continuous (multi-)linear finite element approximations is enriched with piecewise-constant functions. The resulting spatial semi-discretization has the structure of a variational multiscale method. For linear advection equations, it is inherently stable but generally not bound preserving. To satisfy discrete maximum principles and ensure entropy stability in the nonlinear case, we use limiters adapted to the structure of our locally conservative EG method. The cell averages are constrained using a flux limiter, while the nodal values of the continuous component are constrained using a clip-and-scale limiting strategy for antidiffusive element contributions. The design and analysis of our new algorithms build on recent advances in the fields of convex limiting and algebraic entropy fixes for finite element methods. In addition to proving the claimed properties of the proposed approach, we conduct numerical studies for two-dimensional nonlinear hyperbolic problems. The numerical results demonstrate the ability of our limiters to prevent violations of the imposed constraints, while preserving the optimal order of accuracy in experiments with smooth solutions.

Geothermal energy, a promising renewable source, relies on efficiently utilizing geothermal reservoirs, especially in Enhanced Geothermal Systems (EGS), where fractures in hot rock formations enhance permeability. Understanding fracture behavior, influenced by temperature changes, is crucial for optimizing energy extraction. To address this, we propose a novel high-accuracy phase-field interface model integrating temperature dynamics into a comprehensive hydraulic-mechanical approach, aiming for a thermo-fluid-structure interaction representation. Therein, the key technical development is a four-step algorithm. This consists of computing the fracture width, reconstructing the sharp interface geometry, solving the thermo-fluid-structure interaction (TFSI) problem, and employing a phase-field approach coupled to the temperature and pressure from the TFSI problem. By coupling temperature-hydraulic-mechanical processes with our newly proposed high-accuracy phase-field interface approach, we investigate how temperature impacts fracture width values, which are crucial for permeability in EGS reservoirs. Through this model and three different numerical simulations, we aim to provide an approach to deepen understanding of the complex interplay between temperature, mechanical deformation, and permeability evolution. Therein, we substantiate our formulations and algorithms through mesh convergence results of crack width and total crack volumes for static fractures, and crack lengths in the case of propagating fractures.

The real-world traffic networks undergo expansion through the installation of new sensors, implying that the traffic patterns continually evolve over time. Incrementally training a model on the newly added sensors would make the model forget the past knowledge, i.e., catastrophic forgetting, while retraining the model on the entire network to capture these changes is highly inefficient. To address these challenges, we propose a novel Traffic Forecasting Mixture of Experts (TFMoE) for traffic forecasting under evolving networks. The main idea is to segment the traffic flow into multiple homogeneous groups, and assign an expert model responsible for a specific group. This allows each expert model to concentrate on learning and adapting to a specific set of patterns, while minimizing interference between the experts during training, thereby preventing the dilution or replacement of prior knowledge, which is a major cause of catastrophic forgetting. Through extensive experiments on a real-world long-term streaming network dataset, PEMSD3-Stream, we demonstrate the effectiveness and efficiency of TFMoE. Our results showcase superior performance and resilience in the face of catastrophic forgetting, underscoring the effectiveness of our approach in dealing with continual learning for traffic flow forecasting in long-term streaming networks.

Accurate traffic flow forecasting is a crucial research topic in transportation management. However, it is a challenging problem due to rapidly changing traffic conditions, high nonlinearity of traffic flow, and complex spatial and temporal correlations of road networks. Most existing studies either try to capture the spatial dependencies between roads using the same semantic graph over different time steps, or assume all sensors on the roads are equally likely to be connected regardless of the distance between them. However, we observe that the spatial dependencies between roads indeed change over time, and two distant roads are not likely to be helpful to each other when predicting the traffic flow, both of which limit the performance of existing studies. In this paper, we propose Temporal Graph Learning Recurrent Neural Network (TGLRN) to address these problems. More precisely, to effectively model the nature of time series, we leverage Recurrent Neural Networks (RNNs) to dynamically construct a graph at each time step, thereby capturing the time-evolving spatial dependencies between roads (i.e., microscopic view). Simultaneously, we provide the Adaptive Structure Information to the model, ensuring that close and consecutive sensors are considered to be more important for predicting the traffic flow (i.e., macroscopic view). Furthermore, to endow TGLRN with robustness, we introduce an edge sampling strategy when constructing the graph at each time step, which eventually leads to further improvements on the model performance. Experimental results on four commonly used real-world benchmark datasets show the effectiveness of TGLRN.

We present a novel training method for deep operator networks (DeepONets), one of the most popular neural network models for operators. DeepONets are constructed by two sub-networks, namely the branch and trunk networks. Typically, the two sub-networks are trained simultaneously, which amounts to solving a complex optimization problem in a high dimensional space. In addition, the nonconvex and nonlinear nature makes training very challenging. To tackle such a challenge, we propose a two-step training method that trains the trunk network first and then sequentially trains the branch network. The core mechanism is motivated by the divide-and-conquer paradigm and is the decomposition of the entire complex training task into two subtasks with reduced complexity. Therein the Gram-Schmidt orthonormalization process is introduced which significantly improves stability and generalization ability. On the theoretical side, we establish a generalization error estimate in terms of the number of training data, the width of DeepONets, and the number of input and output sensors. Numerical examples are presented to demonstrate the effectiveness of the two-step training method, including Darcy flow in heterogeneous porous media.

This paper explores an iterative coupling approach to solve linear thermo-poroelasticity problems, with its application as a high-fidelity discretization utilizing finite elements during the training of projection-based reduced order models. One of the main challenges in addressing coupled multi-physics problems is the complexity and computational expenses involved. In this study, we introduce a decoupled iterative solution approach, integrated with reduced order modeling, aimed at augmenting the efficiency of the computational algorithm. The iterative coupling technique we employ builds upon the established fixed-stress splitting scheme that has been extensively investigated for Biot's poroelasticity. By leveraging solutions derived from this coupled iterative scheme, the reduced order model employs an additional Galerkin projection onto a reduced basis space formed by a small number of modes obtained through proper orthogonal decomposition. The effectiveness of the proposed algorithm is demonstrated through numerical experiments, showcasing its computational prowess.

Ferromagnetism in two-dimensional materials presents a promising platform for the development of ultrathin spintronic devices with advanced functionalities. Recently discovered ferromagnetic van der Waals crystals such as CrI$_{3}$, readily isolated two-dimensional crystals, are highly tunable through external fields or structural modifications. However, there remains a challenge because of material instability under air exposure. Here, we report the observation of an air stable and layer dependent ferromagnetic (FM) van der Waals crystal, CrPS$_{4}$, using magneto-optic Kerr effect microscopy. In contrast to the antiferromagnetic (AFM) bulk, the FM out-of-plane spin orientation is found in the monolayer crystal. Furthermore, alternating AFM and FM properties observed in even and odd layers suggest robust antiferromagnetic exchange interactions between layers. The observed ferromagnetism in these crystals remains resilient even after the air exposure of about a day, providing possibilities for the practical applications of van der Waals spintronics., Comment: 22 pages, 5 figures

We present a new enriched Galerkin (EG) scheme for the Stokes equations based on piecewise linear elements for the velocity unknowns and piecewise constant elements for the pressure. The proposed EG method augments the conforming piecewise linear space for velocity by adding an additional degree of freedom which corresponds to one discontinuous linear basis function per element. Thus, the total number of degrees of freedom is significantly reduced in comparison with standard conforming, non-conforming, and discontinuous Galerkin schemes for the Stokes equation. We show the well-posedness of the new EG approach and prove that the scheme converges optimally. For the solution of the resulting large-scale indefinite linear systems we propose robust block preconditioners, yielding scalable results independent of the discretization and physical parameters. Numerical results confirm the convergence rates of the discretization and also the robustness of the linear solvers for a variety of test problems.

Behavior of quantum liquids is a fascinating topic in physics. Even in a strongly correlated case, the linear response of a given system to an external field is described by the fluctuation-dissipation relations based on the two-body correlations in the equilibrium. However, to explore nonlinear non-equilibrium behaviors of the system beyond this well-established regime, the role of higher order correlations starting from the three-body correlations must be revealed. In this work, we experimentally investigate a controllable quantum liquid realized in a Kondo-correlated quantum dot and prove the relevance of the three-body correlations in the nonlinear conductance at finite magnetic field, which validates the recent Fermi liquid theory extended to the non-equilibrium regime.

We show that a cryogenic amplifier composed of a homemade GaAs high-electron-mobility transistor (HEMT) is suitable for current-noise measurements in a mesoscopic device at dilution-refrigerator temperatures. The lower noise characteristics of our homemade HEMT leads to a lower noise floor in the experimental setup and enables more efficient current-noise measurement than is available with a commercial HEMT. We present the dc transport properties of the HEMT and the gain and noise characteristics of the amplifier. With the amplifier employed for current-noise measurements in a quantum point contact, we demonstrate the high resolution of the measurement setup by comparing it with that of the conventional one using a commercial HEMT., Comment: 10 pages, 13 figures

We present a novel constitutive model using the framework of strain-limiting theories of elasticity for an evolution of quasi-static anti-plane fracture. The classical linear elastic fracture mechanics (LEFM), with conventional linear relationship between stress and strain, has a well documented inconsistency through which it predicts a singular cracktip strain. This clearly violates the basic tenant of the theory which is a first order approximation to finite elasticity. To overcome the issue, we investigate a new class of material models which predicts uniform and bounded strain throughout the body. The nonlinear model allows the strain value to remain small even if the stress value tends to infinity, which is achieved by an implicit relationship between stress and strain. A major objective of this paper is to couple a nonlinear bulk energy with diffusive crack employing the phase-field approach. Towards that end, an iterative L-scheme is employed and the numerical model is augmented with a penalization technique to accommodate irreversibility of crack. Several numerical experiments are presented to illustrate the capability and the performance of the proposed framework We observe the naturally bounded strain in the neighborhood of the crack-tip, leading to different bulk and crack energies for fracture propagation.

The conventional model governing the spread of fractures in elastic material is formulated by coupling linear elasticity with deformation systems. The classical linear elastic fracture mechanics (LEFM) model is derived based on the assumption of small strain values. However, since the strain values in the model are linearly proportional to the stress values, the strain value can be large if the stress value increases. Thus this results in the contradiction of the assumption to LEFM and it is one of the major disadvantages of the model. In particular, this singular behavior of the strain values is often observed especially near the crack-tip, and it may not accurately predict realistic phenomena. Thus, we investigate the framework of a new class of theoretical model, which is known as the nonlinear strain-limiting model. The advantage of the nonlinear strain-limiting models over LEFM is that the strain value remains bounded even if the stress value tends to the infinity. This is achieved by assuming the nonlinear relation between the strain and stress in the derivation of the model. Moreover, we consider the quasi-static fracture propagation by coupling with the phase-field approach to present the effectiveness of the proposed strain-limiting model. Several numerical examples to evaluate and validate the performance of the new model and algorithms are presented. Detailed comparisons of the strain values, fracture energy, and fracture propagation speed between nonlinear strain-limiting model and LEFM for the quasi-static fracture propagation are discussed.

In this paper, the optimal choice of the interior penalty parameter of the discontinuous Galerkin finite element methods for both the elliptic problems and the Biot's systems are studied by utilizing the neural network and machine learning. It is crucial to choose the optimal interior penalty parameter, which is not too small or not too large for the stability, robustness, and efficiency of the numerical discretized solutions. Both linear regression and nonlinear artificial neural network methods are employed and compared using several numerical experiments to illustrate the capability of our proposed computational framework. This framework is an integral part of a developing automated numerical simulation platform because it can automatically identify the optimal interior penalty parameter. Real-time feedback could also be implemented to update and improve model accuracy on the fly.

We have performed magnetotransport measurements in CeTe$_{3}$ thin films down to $0.2~{\rm K}$. It is known that CeTe$_{3}$ has two magnetic transitions at $T_{\rm N1} \approx 3~{\rm K}$ and $T_{\rm N2} \approx 1~{\rm K}$. A clear Shubnikov-de-Haas (SdH) oscillation was observed at $4~{\rm K}$, demonstrating the strong two-dimensional nature in this material. Below $T_{\rm N2}$, the SdH oscillation has two frequencies, indicating that the Fermi surface could be slightly modulated due to the second magnetic transition. We also observed a magnetic hysteresis in the SdH oscillation below $T_{\rm N1}$. Especially, there is a unique spike in the magnetoresistance at $B \approx 0.6~{\rm T}$ only when the magnetic field is swept from a high enough field (more than $2~{\rm T}$) to zero field., Comment: 5 pages, 4 figures, accepted for publication in Applied Physics Letters

Chemical doping is one of the most important strategies for tuning electrical properties of semiconductors, particularly thermoelectric materials. Generally, the main role of chemical doping lies in optimizing the carrier concentration, but there can potentially be other important effects. Here, we show that chemical doping plays multiple roles for both electron and phonon transport properties in half-Heusler thermoelectric materials. With ZrNiSn-based half-Heusler materials as an example, we use high-quality single and polycrystalline crystals, various probes, including electrical transport measurements, inelastic neutron scattering measurement, and first-principles calculations, to investigate the underlying electron-phonon interaction. We find that chemical doping brings strong screening effects to ionized impurities, grain boundary, and polar optical phonon scattering, but has negligible influence on lattice thermal conductivity. Furthermore, it is possible to establish a carrier scattering phase diagram, which can be used to select reasonable strategies for optimization of the thermoelectric performance., Comment: 21 pages, 5 figures

The layered perovskite PrBaCo2O5.5+x demonstrates a strong negative thermal expansion (NTE) which holds potential for being fabricated into composites with zero thermal expansion. The NTE was found to be intimately associated with the spontaneous magnetic ordering, known as magnetovolume effect (MVE). Here we report with compelling evidences that the continuous-like MVE in PrBaCo2O5.5+x is intrinsically of discontinuous character, originating from an magnetoelectric transition from an antiferromagnetic insulating large-volume (AFILV) phase to a ferromagnetic metallic small-volume (FMSV) phase. Furthermore, the magnetoelectric effect (ME) shows high sensitivity to multiple external stimuli such as temperature, carrier doping, hydrostatic pressure, magnetic field etc. In contrast to the well-known ME such as colossal magnetoresistance and multiferroic effect which involve symmetry breaking of crystal structure, the ME in the cobaltite is purely isostructural. Our discovery provides a new pathway to realizing the ME as well as the NTE, which may find applications in new techniques.