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External pressure and associated changes in lattice structures are key to realizing exotic quantum phases such as high-$T_{\rm c}$ superconductivity. While applying external pressure is a standard method to induce novel lattice structures, its impact on organic molecular crystals has been less explored. Here we report a unique structural phase transition in (DMET-TTF)$_2$AuBr$_2$ under pressure. By combining advanced high-pressure techniques and $ab$ $initio$ calculations, we elucidate that (DMET-TTF)$_2$AuBr$_2$ undergoes a transition from a quasi-one-dimensional lattice to a nearly perfect rectangular lattice at 0.9 GPa. This transition leads to the realization of an antiferromagnetic Mott insulator with $T_{\rm N}=66$ K, the highest $T_{\rm N}$ in low-dimensional molecular crystal solids to date. Upon increasing the pressure, the antiferromagnetic ordering is suppressed, and a superconducting phase with $T_{\rm c}=4.8$ K emerges around 6 GPa. Our study reveals the significant impact of external pressure on lattice structures of organic molecular crystals and highlights the intricate relationship between geometrical frustration and superconductivity. Our findings also pave the way for realizing functional organic molecular crystals through changes in lattice structures by pressure., Comment: 11 pages, 7 figures, 3 Tables

Large language model (LLM)-based recommender models that bridge users and items through textual prompts for effective semantic reasoning have gained considerable attention. However, few methods consider the underlying rationales behind interactions, such as user preferences and item attributes, limiting the reasoning capability of LLMs for recommendations. This paper proposes a rationale distillation recommender (RDRec), a compact model designed to learn rationales generated by a larger language model (LM). By leveraging rationales from reviews related to users and items, RDRec remarkably specifies their profiles for recommendations. Experiments show that RDRec achieves state-of-the-art (SOTA) performance in both top-N and sequential recommendations. Our source code is released at https://github.com/WangXFng/RDRec., Comment: 10 pages. Accepted to ACL 2024 Main as a short paper

The radioisotope Th-229 is renowned for its extraordinarily low-energy, long-lived nuclear first-excited state. This isomeric state can be excited by VUV lasers and the transition from the ground state has been proposed as a reference transition for ultra-precise nuclear clocks. Such nuclear clocks will find multiple applications, ranging from fundamental physics studies to practical implementations. Recent investigations extracted valuable constraints on the nuclear transition energy and lifetime, populating the isomer in stochastic nuclear decay of U-233 or Ac-229. However, to assess the feasibility and performance of the (solid-state) nuclear clock concept, time-controlled excitation and depopulation of the $^{229}$Th isomer together with time-resolved monitoring of the radiative decay are imperative. Here we report the population of the $^{229}$Th isomeric state through resonant X-ray pumping and detection of the radiative decay in a VUV transparent $^{229}$Th-doped CaF$_2$ crystal. The decay half-life is measured to $447\pm 25$ s, with a transition wavelength of $148.18 \pm 0.42$ nm and a radiative decay fraction consistent with unity. Furthermore, we report a new ``X-ray quenching'' effect which allows to de-populate the isomer on demand and effectively reduce the half-life by at least a factor 50. Such controlled quenching can be used to significantly speed up the interrogation cycle in future nuclear clock schemes. Our results show that full control over the $^{229}$Th nuclear isomer population can be achieved in a crystal environment. In particular, non-radiative decay processes that might lead to a broadening of the isomer transition linewidth are negligible, paving the way for the development of a compact and robust solid-state nuclear clock. Further studies are needed to reveal the underlying physical mechanism of the X-ray quenching effect., Comment: 14 pages with 8 figures and 2 tables

For a family $\mathcal{H}$ of graphs, a graph $G$ is said to be {\it $\mathcal{H}$-free} if $G$ contains no member of $\mathcal{H}$ as an induced subgraph. We let $\tilde{\mathcal{G}}_{3}(\mathcal{H})$ denote the family of connected $\mathcal{H}$-free graphs having minimum degree at least $3$. In this paper, we characterize the non-caterpillar trees $T$ having diameter at least $7$ such that $\tilde{\mathcal{G}}_{3}(\{C_{3},C_{4},T\})$ is a finite family, where $C_{n}$ is a cycle of order $n$., Comment: 31 pages, 19 figures

Recommender models aimed at mining users' behavioral patterns have raised great attention as one of the essential applications in daily life. Recent work on graph neural networks (GNNs) or debiasing methods has attained remarkable gains. However, they still suffer from (1) over-smoothing node embeddings caused by recursive convolutions with GNNs, and (2) the skewed distribution of interactions due to popularity and user-individual biases. This paper proposes a contextualized and debiased recommender model (CaDRec). To overcome the over-smoothing issue, we explore a novel hypergraph convolution operator that can select effective neighbors during convolution by introducing both structural context and sequential context. To tackle the skewed distribution, we propose two strategies for disentangling interactions: (1) modeling individual biases to learn unbiased item embeddings, and (2) incorporating item popularity with positional encoding. Moreover, we mathematically show that the imbalance of the gradients to update item embeddings exacerbates the popularity bias, thus adopting regularization and weighting schemes as solutions. Extensive experiments on four datasets demonstrate the superiority of the CaDRec against state-of-the-art (SOTA) methods. Our source code and data are released at https://github.com/WangXFng/CaDRec., Comment: Accepted to SIGIR 2024

Graph neural network (GNN)-based models have been extensively studied for recommendations, as they can extract high-order collaborative signals accurately which is required for high-quality recommender systems. However, they neglect the valuable information gained through negative feedback in two aspects: (1) different users might hold opposite feedback on the same item, which hampers optimal information propagation in GNNs, and (2) even when an item vastly deviates from users' preferences, they might still choose it and provide a negative rating. In this paper, we propose a negative feedback-aware recommender model (NFARec) that maximizes the leverage of negative feedback. To transfer information to multi-hop neighbors along an optimal path effectively, NFARec adopts a feedback-aware correlation that guides hypergraph convolutions (HGCs) to learn users' structural representations. Moreover, NFARec incorporates an auxiliary task - predicting the feedback sentiment polarity (i.e., positive or negative) of the next interaction - based on the Transformer Hawkes Process. The task is beneficial for understanding users by learning the sentiment expressed in their previous sequential feedback patterns and predicting future interactions. Extensive experiments demonstrate that NFARec outperforms competitive baselines. Our source code and data are released at https://github.com/WangXFng/NFARec., Comment: Accepted to SIGIR 2024

Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional noise-reduction processing. This study uses deep learning to validate noise reduction techniques, with autoencoders serving as the learning model. Focusing on the diffraction patterns of nanoparticles, we simulated a large dataset treating the nanoparticles as composed of many independent atoms. Three neural network architectures are investigated: neural network, convolutional neural network and U-net, with U-net showing superior performance in noise reduction and subphoton reproduction. We also extended our models to apply to diffraction patterns of particle shapes different from those in the simulated data. We then applied the U-net model to a coherent diffractive imaging study, wherein a nanoparticle in a microfluidic device is exposed to a single X-ray free-electron laser pulse. After noise reduction, the reconstructed nanoparticle image improved significantly even though the nanoparticle shape was different from the training data, highlighting the importance of transfer learning., Comment: 17 pages, 10 figures

Aspect-category-based sentiment analysis (ACSA), which aims to identify aspect categories and predict their sentiments has been intensively studied due to its wide range of NLP applications. Most approaches mainly utilize intrasentential features. However, a review often includes multiple different aspect categories, and some of them do not explicitly appear in the review. Even in a sentence, there is more than one aspect category with its sentiments, and they are entangled intra-sentence, which makes the model fail to discriminately preserve all sentiment characteristics. In this paper, we propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) for ACSA tasks. Specifically, we explore coherence modeling to capture the contexts across the whole review and to help the implicit aspect and sentiment identification. To address the issue of multiple aspect categories and sentiment entanglement, we propose a hierarchical disentanglement module to extract distinct categories and sentiment features. Extensive experimental and visualization results show that our ECAN effectively decouples multiple categories and sentiments entangled in the coherence representations and achieves state-of-the-art (SOTA) performance. Our codes and data are available online: \url{https://github.com/cuijin-23/ECAN}., Comment: Accepted by LREC-COLING 2024

We present a combined experimental and theoretical study on the quasi-one-dimensional organic conductor (TMTTF)$_2$PF$_6$, and elucidate the variation of its physical properties under pressure. We fully resolve the crystal structure by single crystal x-ray diffraction measurements using a diamond anvil cell up to 8 GPa, and based on the structural data, we perform first-principles density-functional theory calculations and derive the $ab$ $initio$ extended Hubbard-type Hamiltonians. Furthermore, we compare the behavior of the resistivity measured up to 3 GPa using a BeCu clamp-type cell and the ground state properties of the obtained model numerically calculated by the many-variable variational Monte Carlo method. Our main findings are as follows: i) The crystal was rapidly compressed up to about 3 GPa where the volume drops to 80% and gradually varies down to 70% at 8 GPa. The transfer integrals increase following such behavior whereas the screened Coulomb interactions decrease, resulting in a drastic reduction of correlation effect. ii) The degree of dimerization in the intrachain transfer integrals, as the result of the decrease in structural dimerization together with the change in the intermolecular configuration, almost disappears above 4 GPa; the interchain transfer integrals also show characteristic variations under pressure. iii) The results of identifying the characteristic temperatures in the resistivity and the charge and spin orderings in the calculations show an overall agreement: The charge ordering sensitively becomes unstable above 1 GPa, while the spin ordering survives up to higher pressures. These results shed light on the similarities and differences between applying external pressure and substituting the chemical species (chemical pressure)., Comment: 13 pages, 10 figures

This study investigates the aural and visual factors that influence appropriateness perception in soundscape evaluations in residential spaces, where people may spend most of their time in. Appropriateness in soundscape is derived from the expectation of sound sources in a specific environment, place, or function heard by a listener. The appropriateness of soundscapes in 30 locations in an urban residential environment is investigated with varying landscape, visual, and aural elements through a questionnaire. Participants experienced the soundscape in situ and were asked to evaluate the appropriateness of soundscape as well as the dominance of specific sound sources such as traffic, human activities, and birdsongs in the residential space. The effect of the type of traffic on appropriateness is also investigated. A strong relationship is found between appropriateness and affective soundscape qualities such as pleasantness, highlighting the importance of considering appropriateness in soundscape research., Comment: Internoise 2021

The study of the magnetic order has recently been invigorated by the discovery of exotic collinear antiferromagnets with time-reversal symmetry breaking. Examples include altermagnetism and compensated ferrimagnets, which show spin splittings of the electronic band structures even at zero net magnetization, leading to several unique transport phenomena, notably spin-current generation. Altermagnets demonstrate anisotropic spin splitting, such as $d$-wave, in momentum space, whereas compensated ferrimagnets exhibit isotropic spin splitting. However, methods to realize compensated ferrimagnets are limited. Here, we demonstrate a method to realize a fully compensated ferrimagnet with isotropic spin splitting utilizing the dimer structures inherent in organic compounds. Moreover, based on $ab$ $initio$ calculations, we find that this ferrimagnet can be realized in the recently discovered organic compound (EDO-TTF-I)$_2$ClO$_4$. Our findings provide an unprecedented strategy for using the dimer degrees of freedom in organic compounds to realize fully compensated ferrimagnets with colossal spin splitting., Comment: 15 pages, 10 figures

We numerically and experimentally demonstrate efficient light couplers between topological slow light waveguides in valley photonic crystals (VPhCs) and wire waveguides. By numerical simulations, we obtained a high coupling efficiency of -0.84 dB/coupler on average in the slow light regime of a group index ng = 10 - 30. Experimentally, we fabricated the couplers in a Si slab and measured the transmitted power of the devices. We realized a high coupling efficiency of approximately -1.2 dB/coupler in the slow light region of ng = 10 - 30, which is close to the result from the numerical simulations. These demonstrations will lay the groundwork for low-loss photonic integrated circuits using topological slow light waveguides.

We investigate a physical characterization of the gradient flow structure of variational fracture models for brittle materials: a Griffith-type fracture model and an irreversible fracture phase field model. We derive the Griffith-type fracture model by assuming that the fracture energy in Griffith's theory is an increasing function of the crack tip velocity. Such a velocity dependence of the fracture energy is typically observed in polymers. We also prove an energy dissipation identity of the Griffith-type fracture model, in other words, its gradient flow structure. On the other hand, the irreversible fracture phase field model is derived as a unidirectional gradient flow of a regularized total energy with a small time relaxation parameter based on the variational fracture theory by Francfort and Marigo (1998) and a mathematical space regularization proposed by Ambrosio and Tortorelli (1992). We have considered the time relaxation parameter a mathematical approximation parameter, which we should choose as small as possible. In this research, however, we reveal the physical origin of the gradient flow structure of the fracture phase field model and show that the small time relaxation parameter is characterized as the rate of velocity dependence of the fracture energy. It is verified by comparing the energy dissipation properties of those two models and by analyzing a traveling wave solution of the irreversible fracture phase field model., Comment: 16 pages, 6 figures

Engineering heterostructures with various types of quantum materials can provide an intriguing playground for studying exotic physics induced by the proximity effect. Here, we report the successful synthesis of iron-based superconductor FeSe$_{x}$Te$_{1-x}$ (FST) thin films across the entire composition range of $0 \leq x \leq 1$ and its heterostructure with a magnetic topological insulator by using molecular beam epitaxy. Superconductivity is observed in the FST films with an optimal superconducting transition temperature $T_c$ $\sim$ 12 K at around x = 0.1. We found that superconductivity survives in the very Te-rich films ($x \leq 0.05$), showing stark contrast to bulk crystals with suppression of superconductivity due to an appearance of bicollinear antiferromagnetism accompanied by a monoclinic structural transition. By examining thickness $t$ dependence of magnetic susceptibility and electrical transport properties, we observed a trend where anomalies associated with the first order structural transition broaden in films with below $t \sim$ 100 nm. We infer this observation suggests a suppression of the structural instability near substrates. Furthermore, we fabricated an all chalcogenide-based heterointerface between FST and a magnetic topological insulator (Cr,Bi,Sb)$_{2}$Te$_{3}$ for the first time, observing both superconductivity and a large anomalous Hall conductivity. The anomalous Hall conductivity increases with decreasing temperature, approaching the quantized value of $e^2/h$ down to the measurable minimum temperature at $T_c$. The result suggests coexistence of magnetic and superconducting gaps at low temperatures opening at the top and bottom surfaces, respectively. Our novel magnetic topological insulator/superconductor heterostructure could be an ideal platform to explore chiral Majorana edge mode.

Surface states of a topological insulator demonstrate interesting quantum phenomena, such as the quantum anomalous Hall (QAH) effect and the quantum magnetoelectric effect. Fermi energy tuning plays a role in inducing phase transitions and developing future device functions. Here, we report on controlling the topological phases in a dual-gate field-effect transistor of a semi-magnetic topological insulator heterostructure. The heterostructure consists of magnetized one-surface and non-magnetic other-surface. By tuning the Fermi energy to the energy gap of the magnetized surface, the Hall conductivity $\sigma_{xy}$ becomes close to the half-integer quantized Hall conductivity $e^2/2h$, exemplifying parity anomaly. The dual-gate control enables the band structure alignment to the two quantum Hall states with $\sigma_{xy} = e^2/h$ and 0 under a strong magnetic field. These states are topologically equivalent to the QAH and axion insulator states, respectively. Precise and independent control of the band alignment of the top and bottom surfaces successively induces various topological phase transitions among the QAH, axion insulator, and parity anomaly states in magnetic topological insulators., Comment: 20 pages, 4 figures

Simulation of the intermediate levels of disorder found in multi-component multi-sublattice systems in various functional materials is a challenging issue, even for state-of-the-art methodologies based on first-principles calculation. Here, we introduce our open-source package ab Initio Configuration Sampling Toolkit (abICS), which combines high-throughput first-principles calculations, machine learning, and parallel extended ensemble sampling in an active learning setting to enable such simulations. The theoretical background is reviewed in some detail followed by brief notes on usage of the software. In addition, our recent applications of abICS to multi-component ionic systems and their interfaces for energy applications are reviewed as demonstration of the power of this approach., Comment: 25 pages, 6 figures

MnP is a metal that shows successive magnetic transitions from paramagnetic to ferromagnetic and helical magnetic phases at ambient pressure with decreasing temperature. With applied pressure, the magnetic transition temperatures decrease and superconductivity appears around 8 GPa where the magnetic order is fully suppressed and the quantum critical behavior is observed. These results suggest that MnP is an unconventional superconductor in which magnetic fluctuations may be relevant to the superconducting pairing mechanism. In order to elucidate the magnetic ground state adjacent to the superconducting phase first discovered in Mn-based materials, high-pressure neutron diffraction measurements have been performed in hydrostatic pressure up to 7.5 GPa. The helical magnetic structure with the propagation vector along the $b$ axis, reported previously at 3.8 GPa, was found to be robust up to 7.5 GPa. First principles and classical Monte Carlo calculations have also been performed to understand how the pressure-driven magnetic phase transitions are coupled with change of the exchange interactions. The calculations, which qualitatively reproduce the magnetic structures as a function of pressure, suggest that the exchange interactions change drastically with applied pressure and the further-neighbor interactions become more influential at high pressures. Combining the experimental and theoretical results, we describe the detail of exchange interactions in the vicinity of the superconducting phase which is critical to understand the pairing mechanism of the unconventional superconductivity in MnP., Comment: 15 pages, 10 figures

H-wave is an open-source software package for performing the Hartree--Fock approximation (HFA) and random phase approximation (RPA) for a wide range of Hamiltonians of interacting fermionic systems. In HFA calculations, H-wave examines the stability of several symmetry-broken phases, such as anti-ferromagnetic and charge-ordered phases, in the given Hamiltonians at zero and finite temperatures. Furthermore, H-wave calculates the dynamical susceptibilities using RPA to examine the instability toward the symmetry-broken phases. By preparing a simple input file for specifying the Hamiltonians, users can perform HFA and RPA for standard Hamiltonians in condensed matter physics, such as the Hubbard model and its extensions. Additionally, users can use a Wannier90-like format to specify fermionic Hamiltonians. A Wannier90 format is implemented in RESPACK to derive ab initio Hamiltonians for solids. HFA and RPA for the ab initio Hamiltonians can be easily performed using H-wave. In this paper, we first explain the basis of HFA and RPA, and the basic usage of H-wave, including download and installation. Thereafter, the input file formats implemented in H-wave, including the Wannier90-like format for specifying the interacting fermionic Hamiltonians, are discussed. Finally, we present several examples of H-wave such as zero-temperature HFA calculations for the extended Hubbard model on a square lattice, finite-temperature HFA calculations for the Hubbard model on a cubic lattice, and RPA in the extended Hubbard model on a square lattice., Comment: 15 pages, 7 figures