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In scientific machine learning, the task of identifying partial differential equations accurately from sparse and noisy data poses a significant challenge. Current sparse regression methods may identify inaccurate equations on sparse and noisy datasets and are not suitable for varying coefficients. To address this issue, we propose a hybrid framework that combines two alternating direction optimization phases: discovery and embedding. The discovery phase employs current well-developed sparse regression techniques to preliminarily identify governing equations from observations. The embedding phase implements a recurrent convolutional neural network (RCNN), enabling efficient processes for time-space iterations involved in discretized forms of wave equation. The RCNN model further optimizes the imperfect sparse regression results to obtain more accurate functional terms and coefficients. Through alternating update of discovery-embedding phases, essential physical equations can be robustly identified from noisy and low-resolution measurements. To assess the performance of proposed framework, numerical experiments are conducted on various scenarios involving wave equation in elastic/viscoelastic and homogeneous/inhomogeneous media. The results demonstrate that the proposed method exhibits excellent robustness and accuracy, even when faced with high levels of noise and limited data availability in both spatial and temporal domains.

Two-dimensional semiconductors and their moir\'e superlattices have emerged as important platforms for investigating correlated electrons. However, many key properties of these systems, such as the frequency-dependent conductivity, remain experimentally inaccessible because of the mesoscopic sample size. Here we report a technique to directly measure the complex conductivity of electrostatically gated two-dimensional semiconductors in the terahertz frequency range. Applying this technique to a WSe2 monolayer encapsulated in hBN, we observe clear Drude-like response between 0.1 and 1 THz, in a density range challenging to access even in DC transport. Our work opens a new avenue for studying tunable van der Waals heterostructures using terahertz spectroscopy.

Physics-informed neural networks (PINNs) have great potential for flexibility and effectiveness in forward modeling and inversion of seismic waves. However, coordinate-based neural networks (NNs) commonly suffer from the "spectral bias" pathology, which greatly limits their ability to model high-frequency wave propagation in sharp and complex media. We propose a unified framework of Fourier feature physics-informed neural networks (FF-PINNs) for solving the time-domain wave equations. The proposed framework combines the stochastic gradient descent (SGD) strategy with a pre-trained wave velocity surrogate model to mitigate the singularity at the point source. The performance of the activation functions and gradient descent strategies are discussed through ablation experiments. In addition, we evaluate the accuracy comparison of Fourier feature mappings sampled from different families of distributions (Gaussian, Laplace, and uniform). The second-order paraxial approximation-based boundary conditions are incorporated into the loss function as a soft regularizer to eliminate spurious boundary reflections. Through the non-smooth Marmousi and Overthrust model cases, we emphasized the necessity of the absorbing boundary conditions (ABCs) constraints. The results of a series of numerical experiments demonstrate the accuracy and effectiveness of the proposed method for modeling high-frequency wave propagation in sharp and complex media.

Spin- and charge-lattice interactions are potential key factors in the microscopic mechanism of high-temperature superconductivity in cuprates. Although both interactions can dramatically shape the low-energy electronic structure, their phenomenological roles in superconductivity are usually investigated independently. Employing angle-resolved photoemission spectroscopy, we reveal the spectroscopic fingerprint of short-range antiferromagnetic order in conjunction with enhanced electron-phonon interaction in the electron-doped cuprate superconductor $\mathrm{Nd_{1.85}Ce_{0.15}CuO_4}$. The observed mode coupling exhibits a strong momentum dependence that is in striking contrast to the node-antinode dichotomy previously observed in the hole-doped cuprates. Our results reveal an intimate relationship between electron-phonon coupling and antiferromagnetic fluctuations, which collectively sets the stage for unconventional superconductivity in the electron-doped cuprates.

Recently entanglement suppression was proposed to be one possible origin of emergent symmetries. Here we test this conjecture in the context of heavy meson scatterings. The low-energy interactions of $D^{(*)}\bar D^{(*)}$ and $D^{(*)} D^{(*)}$ are closely related to the hadronic molecular candidates $X(3872)$ and $T_{cc}(3875)^+$, respectively, and can be described by a nonrelativistic effective Lagrangian manifesting heavy-quark spin symmetry, which includes only constant contact potentials at leading order. We explore entanglement suppression in a tensor-product framework to treat both the isospin and spin degrees of freedom. Using the $X(3872)$ and $T_{cc}(3875)^+$ as inputs, we find that entanglement suppression indeed leads to an emergent symmetry, namely, a light-quark spin symmetry, and as such the $D^{(*)}\bar D^{(*)}$ or $D^{(*)} D^{(*)}$ interaction strengths for a given total isospin do not depend on the total angular momentum of light (anti)quarks. The $X(3872)$ and $T_{cc}(3875)^+$ are predicted to have five and one isoscalar partner, respectively, while the corresponding partner numbers derived solely from heavy-quark spin symmetry are three and one, respectively. The predictions need to be confronted with experimental data and lattice quantum chromodynamics results to further test the entanglement suppression conjecture., Comment: 28 pages, 3 figures; Version to appear in Phys. Rev. D

Microwave impedance microscopy (MIM) is an emerging scanning probe technique for nanoscale complex permittivity mapping and has made significant impacts in diverse fields from semiconductors to quantum materials. To date, the most significant hurdles that limit its widespread use are the requirements of specialized microwave probes and high-precision cancellation circuits. Here we show that forgoing both elements not only is feasible but actually enhances MIM performance. Using monolithic silicon cantilever probes and a cancellation-free architecture, we demonstrate thermal Johnson-noise-limited, drift-free MIM operation with 15 nm spatial resolution, minimal topography crosstalk, and an unprecedented sensitivity of 0.26 zF/$\sqrt{\text{Hz}}$. We accomplish this by taking advantage of the high mechanical resonant frequency and spatial resolution of silicon probes, the inherent common-mode phase noise rejection of self-referenced homodyne detection, and the exceptional stability of the streamlined architecture. Our approach makes MIM drastically more accessible and paves the way for more advanced operation modes and integration with complementary techniques., Comment: 10 pages, 7 figures

In the underdoped n-type cuprate Nd2-xCexCuO4, long-ranged antiferromagnetic order reconstructs the Fermi surface, resulting in a putative antiferromagnetic metal with small pockets. Using angle-resolved photoemission spectroscopy, we observe an anomalous energy gap, an order of magnitude smaller than the antiferromagnetic gap, in a wide range of the underdoped regime and smoothly connecting to the superconducting gap at optimal doping. After carefully considering all the known ordering tendencies in tandem with the phase diagram, we hypothesize that the normal state gap in the underdoped n-type cuprates originates from Cooper pairing. The high temperature scale of the normal state gap raises the prospect of engineering higher transition temperatures in the n-type cuprates comparable to that of the p-type cuprates.

Deep learning is commonly used to reconstruct HDR images from LDR images. LDR stack-based methods are used for single-image HDR reconstruction, generating an HDR image from a deep learning-generated LDR stack. However, current methods generate the stack with predetermined exposure values (EVs), which may limit the quality of HDR reconstruction. To address this, we propose the continuous exposure value representation (CEVR), which uses an implicit function to generate LDR images with arbitrary EVs, including those unseen during training. Our approach generates a continuous stack with more images containing diverse EVs, significantly improving HDR reconstruction. We use a cycle training strategy to supervise the model in generating continuous EV LDR images without corresponding ground truths. Our CEVR model outperforms existing methods, as demonstrated by experimental results., Comment: ICCV 2023. Project page: https://skchen1993.github.io/CEVR_web/

In contrast to hole-doped cuprates, electron-doped cuprates consistently exhibit strong antiferromagnetic correlations with a commensurate ({\pi}, {\pi}) ordering wave vector, leading to the prevalent belief that antiferromagnetic spin fluctuations mediate Cooper pairing in these unconventional superconductors. However, early investigations produced two paradoxical findings: while antiferromagnetic spin fluctuations create the largest pseudogap at "hot spots" in momentum space, Raman scattering and angle-resolved photoemission spectroscopy measurements using the leading-edge method seem to suggest the superconducting gap is also maximized at these locations. This presented a dilemma for spin-fluctuation-mediated pairing: Cooper pairing is strongest at momenta where normal state low energy spectral weight is most suppressed. Here we investigate this dilemma in Nd2-xCexCuO4 using angle-resolved photoemission spectroscopy under significantly improved experimental conditions. The unprecedented signal-to-noise ratio and resolution allow us to directly observe the Bogoliubov quasiparticles, demonstrating the existence and importance of two sectors of states: 1. The reconstructed main band and the states gapped by the antiferromagnetic pseudogap around the hot spots. 2. The gossamer Fermi surface states with distinct dispersion inside the pseudogap, from which Bogoliubov quasiparticle coherence peaks emerge below Tc. Supported by numerical results, we propose that the non-zero modulus of the antiferromagnetic order parameter causes the former, while fluctuations in the antiferromagnetic order parameter orientation are responsible for the latter. Our revelations of the gossamer Fermi surface reconcile the paradoxical observations, deepening our understanding of superconductivity in electron-doped cuprates in particular, and unconventional superconductivity in general., Comment: Submitted version 30 pages, 4 main figures, 8 extended data figures. Accepted version in press at Nature Physics

High pressure plays a crucial role in the field of superconductivity. Compressed hydride superconductors are leaders in the race for a material that can conduct electricity without resistance at high or even room temperature. In the present work, we have discovered new lanthanum superhydride, cubic A15-type La$_4$H$_{23}$, with lower stabilization pressure compared to the reported $\textit{fcc}$ LaH$_{10}$. Superconducting La$_4$H$_{23}$ was obtained by laser heating of LaH$_3$ with ammonia borane at about 120 GPa. Transport measurements reveal the maximum critical temperature $\textit{T}$$_{C}$(onset) = 105 K and the critical field $\textit{H}$$_{C2}$(0) = 32 T at 118 GPa, as evidenced by the sharp drop of electrical resistance and the displacement of superconducting transitions in applied magnetic fields. Moreover, we provide evidence for unconventional transport associated with a pseudogap phase in La$_4$H$_{23}$ using pulsed magnetic fields up to 68 T. A large negative magnetoresistance in the non-superconducting state below 40 K, quasi $\textit{T}$-linear electrical resistance, and a sign-change of its temperature dependence mark the emergence of pseudogap in this hydride. Discovered lanthanum hydride is a new member of the A15 family of superconductors with $\textit{T}$$_C$ exceeding the boiling point of liquid nitrogen., Comment: A mistake in Figure 3(a) was fixed

Ground-motion model (GMM) is the basis of many earthquake engineering studies. In this study, a novel physics-informed symbolic learner (PISL) method based on the Nest Generation Attenuation-West2 database is proposed to automatically discover mathematical equation operators as symbols. The sequential threshold ridge regression algorithm is utilized to distill a concise and interpretable explicit characterization of complex systems of ground motions. In addition to the basic variables retrieved from previous GMMs, the current PISL incorporates two a priori physical conditions, namely, distance and amplitude saturation. GMMs developed using the PISL, an empirical regression method (ERM), and an artificial neural network (ANN) are compared in terms of residuals and extrapolation based on obtained data of peak ground acceleration and velocity. The results show that the inter- and intra-event standard deviations of the three methods are similar. The functional form of the PISL is more concise than that of the ERM and ANN. The extrapolation capability of the PISL is more accurate than that of the ANN. The PISL-GMM used in this study provide a new paradigm of regression that considers both physical and data-driven machine learning and can be used to identify the implied physical relationships and prediction equations of ground motion variables in different regions.

The effect of Lifshitz transition on thermodynamics and superconductivity in hole-doped cuprates has been heavily debated but remains an open question. In particular, an observed peak of electronic specific heat is proposed to originate from fluctuations of a putative quantum critical point p* (e.g. the termination of pseudogap at zero temperature), which is close to, but distinguishable from the Lifshitz transition in La-based cuprates. Here, we report an in situ angle-resolved photoemission spectroscopy study of three-dimensional Fermi surfaces in La2-xSrxCuO4 thin films(x = 0.06 - 0.35). With accurate kz dispersion quantification, the Lifshitz transition is determined to happen within a finite range around x = 0.21. Normal state electronic specific heat, calculated from spectroscopy-derived band parameters, agrees with previous thermodynamic microcalorimetry measurements. The account of the specific heat maximum by underlying band structures excludes the need for additionally dominant contribution from the quantum fluctuations at p*. A d-wave superconducting gap smoothly across the Lifshitz transition demonstrates the insensitivity of superconductivity to the dramatic density of states enhancement.

We extend the $SU(3)_c \times SU(2)_L \times U(1)_Y$ standard model by a $U(1)_{Y'}$ gauge symmetry. Three right-handed neutrinos are introduced to cancel the gauge anomaly. One Higgs singlet is responsible for spontaneously breaking the $U(1)_{Y'}$ symmetry while the standard model Higgs doublet does not carry any $U(1)_{Y'}$ charges. The down-type quarks, up-type quarks, charged leptons and neutral neutrinos obtain their Dirac masses through four types of dimension-5 operators constructed by the fermion doublets and singlets with the Higgs doublet and singlet. This effective theory is realized in three renormalizable contexts with heavy fermion singlets, scalar doublets and fermion doublets. The heavy fermion singlets and doublets for generating the neutrino masses also accommodate a successful Dirac leptogenesis to explain the baryon asymmetry in the universe., Comment: 9 pages, 6 figures

A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has information that characterizes the learning behaviors of its users and can adjust its pedagogy accordingly. This study investigated an intelligent tutoring system with computer agents ("AutoTutor") designed to improve comprehension skills in adults with low reading literacy. One goal of this study was to classify adults into different clusters based on their behavioral patterns (accuracy and response time to answer questions) while they interacted with AutoTutor to help them improve their reading comprehension skills. A second goal was to investigate whether adults' behaviors were associated with different reading components. A third goal was to assess improvements in reading comprehension skills, based on psychometric tests, of different clusters of readers. Performance on AutoTutor was collected in a targeted 100-hour hybrid intervention for adult readers (n = 252) that included both human teachers and the AutoTutor system. The adults' average accuracy and response time in AutoTutor were used to cluster the adults into four categories: higher performers (comparatively fast and accurate), conscientious readers (slow but accurate), under-engaged readers (fast at the expense of somewhat lower accuracy) and struggling readers (slow and inaccurate). Two psychometric tests of comprehension were used to assess comprehension. Gains in comprehension scores were highest for conscientious readers, lowest for struggling readers, with higher performing readers and under-engaged readers in between. The results provide guidance to enhance the adaptivity of AutoTutor. [This is the online version of an article published in "International Journal of Artificial Intelligence in Education."]

There has been an increasing interest in integrating physics knowledge and machine learning for modeling dynamical systems. However, very limited studies have been conducted on seismic wave modeling tasks. A critical challenge is that these geophysical problems are typically defined in large domains (i.e., semi-infinite), which leads to high computational cost. In this paper, we present a novel physics-informed neural network (PINN) model for seismic wave modeling in semi-infinite domain without the nedd of labeled data. In specific, the absorbing boundary condition is introduced into the network as a soft regularizer for handling truncated boundaries. In terms of computational efficiency, we consider a sequential training strategy via temporal domain decomposition to improve the scalability of the network and solution accuracy. Moreover, we design a novel surrogate modeling strategy for parametric loading, which estimates the wave propagation in semin-infinite domain given the seismic loading at different locations. Various numerical experiments have been implemented to evaluate the performance of the proposed PINN model in the context of forward modeling of seismic wave propagation. In particular, we define diverse material distributions to test the versatility of this approach. The results demonstrate excellent solution accuracy under distinctive scenarios., Comment: 22 pages

Identifying quantum critical points (QCPs) and their associated fluctuations may hold the key to unraveling the unusual electronic phenomena observed in cuprate superconductors. Recently, signatures of quantum fluctuations associated with charge order (CO) have been inferred from the anomalous enhancement of CO excitations that accompany the reduction of the CO order parameter in the superconducting state. To gain more insight about the interplay between CO and superconductivity, here we investigate the doping dependence of this phenomenon throughout the Bi-2212 cuprate phase diagram using resonant inelastic x-ray scattering (RIXS) at the Cu L3- edge. As doping increases, the CO wavevector decreases, saturating at a commensurate value of 0.25 r.l.u. beyond a characteristic doping pc, where the correlation length becomes shorter than the apparent periodicity (4a0). Such behavior is indicative of the fluctuating nature of the CO; and the proliferation of CO excitations in the superconducting state also appears strongest at pc, consistent with expected behavior at a CO QCP. Intriguingly, pc appears to be near optimal doping, where the superconducting transition temperature Tc is maximal., Comment: This is a submitted version of the manuscript. The revised manuscript is now published on Physical Review B

The standard model left-handed neutrinos and several right-handed neutrinos can obtain a tiny Dirac mass matrix through their mixings with relatively heavy Dirac fermions. In this Dirac seesaw scenario, the mixings involving the left-handed neutrinos can be allowed much larger than those involving the right-handed neutrinos. This undemocratic parameter choice is attractive to phenomenology. We show that the small mixings between the heavy Dirac fermions and the right-handed neutrinos can have a common origin with the observed baryon asymmetry in the universe. We also connect the introduction of right-handed neutrinos to the existence and stability of dark matter by a new $U(1)$ gauge symmetry for dark photon or baryon-minus-lepton number. We then specify how to embed our scenario into a left-right symmetric theory or a grand unification theory., Comment: 6 pages, 2 figures