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Here we employed optical spectroscopy and ultrafast reflectivity measurements to investigate the density wave instability of trilayer nickelate La4Ni3O10 at ambient pressure. Our optical spectroscopy measurements indicate that La4Ni3O10 is metallic with a large plasma frequency at room temperature. As the temperature decreases, we observe the formation of an energy gap in reflectivity below TDW, signaling the charge/spin density wave transition. The Drude component was largely removed due to the gap opening in the Fermi surface. Our Drude-Lorentz analysis reveals that the energy gap in La4Ni3O10 is approximately 61 meV, which is three times larger than that obtained from ARPES measurements. The density wave gap feature is more prominent than that observed in bilayer nickelate La3Ni2O7, suggesting more carriers are gapped at the Fermi surface across the density wave transition. By comparing the measured plasma frequency with the first-principles calculation, we categorize La4Ni3O10 as a moderately electronic correlation material, similar to the parent compound of iron-based superconductors, however, being weaker than the bilayer nickelate La3Ni2O7. Our ultrafast pump-probe experiments also show that the relaxation time diverges near the transition temperature. By analyzing the amplitude and relaxation time with the Rothwarf-Taylor model, we estimate the energy gap to be 58 meV, which agrees with the result of optical spectroscopy. The more prominent gap feature and weaker electronic correlation might be the cause of a lower superconductivity transition temperature in La4Ni3O10 under high pressure. These findings significantly contribute to understanding the origin of density wave and superconductivity in trilayer nickelate La4Ni3O10.

ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT. A key challenge for the evaluation is that ChatGPT is still closed-source and traditional benchmark datasets may have been used by ChatGPT as the training data. In this paper, (i) we survey recent studies which uncover the real performance levels of ChatGPT in seven categories of NLP tasks, (ii) review the social implications and safety issues of ChatGPT, and (iii) emphasize key challenges and opportunities for its evaluation. We hope our survey can shed some light on its blackbox manner, so that researchers are not misleaded by its surface generation., Comment: 18 pages, 2 tables

Metaphors play a significant role in our everyday communication, yet detecting them presents a challenge. Traditional methods often struggle with improper application of language rules and a tendency to overlook data sparsity. To address these issues, we integrate knowledge distillation and prompt learning into metaphor detection. Our approach revolves around a tailored prompt learning framework specifically designed for metaphor detection. By strategically masking target words and providing relevant prompt data, we guide the model to accurately predict the contextual meanings of these words. This approach not only mitigates confusion stemming from the literal meanings of the words but also ensures effective application of language rules for metaphor detection. Furthermore, we've introduced a teacher model to generate valuable soft labels. These soft labels provide a similar effect to label smoothing and help prevent the model from becoming over confident and effectively addresses the challenge of data sparsity. Experimental results demonstrate that our model has achieved state-of-the-art performance, as evidenced by its remarkable results across various datasets.

Learning 3D representation plays a critical role in masked autoencoder (MAE) based pre-training methods for point cloud, including single-modal and cross-modal based MAE. Specifically, although cross-modal MAE methods learn strong 3D representations via the auxiliary of other modal knowledge, they often suffer from heavy computational burdens and heavily rely on massive cross-modal data pairs that are often unavailable, which hinders their applications in practice. Instead, single-modal methods with solely point clouds as input are preferred in real applications due to their simplicity and efficiency. However, such methods easily suffer from limited 3D representations with global random mask input. To learn compact 3D representations, we propose a simple yet effective Point Feature Enhancement Masked Autoencoders (Point-FEMAE), which mainly consists of a global branch and a local branch to capture latent semantic features. Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct. Meanwhile, to further enhance features in the local branch, we propose a Local Enhancement Module with local patch convolution to perceive fine-grained local context at larger scales. Our method significantly improves the pre-training efficiency compared to cross-modal alternatives, and extensive downstream experiments underscore the state-of-the-art effectiveness, particularly outperforming our baseline (Point-MAE) by 5.16%, 5.00%, and 5.04% in three variants of ScanObjectNN, respectively. The code is available at https://github.com/zyh16143998882/AAAI24-PointFEMAE., Comment: Accepted to AAAI 2024

Utilizing ultrafast light-matter interaction to manipulate electronic states of quantum materials is an emerging area of research in condensed matter physics. It has significant implications for the development of future ultrafast electronic devices. However, the ability to induce long-lasting metastable electronic states in a fully reversible manner is a long-standing challenge.Here, by using ultrafast laser excitations, we demonstrate the capability to manipulate the electronic polar states in the charge-density-wavematerial EuTe4 in a non-volatile manner. The process is completely reversible and is achieved at room temperature with an all-optical approach. Each induced non-volatile state brings about modifications to the electrical resistance and second harmonic generation intensity. The results point to layer-specific phase inversion dynamics by which photoexcitation mediates the stacking polar order of the system. Our findings extend the scope of non-volatile all-optical control of electronic states to ambient conditions, and highlight a distinct role of layerdependent phase manipulation in quasi-two-dimensional systems with inherent sublayer stacking orders., Comment: 19 pages, 4 figures

Meta learning have achieved promising performance in low-resource text classification which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. However, due to the limited training data in the meta-learning scenario and the inherent properties of parameterized neural networks, poor generalization performance has become a pressing problem that needs to be addressed. To deal with this issue, we propose a meta-learning based method called Retrieval-Augmented Meta Learning(RAML). It not only uses parameterization for inference but also retrieves non-parametric knowledge from an external corpus to make inferences, which greatly alleviates the problem of poor generalization performance caused by the lack of diverse training data in meta-learning. This method differs from previous models that solely rely on parameters, as it explicitly emphasizes the importance of non-parametric knowledge, aiming to strike a balance between parameterized neural networks and non-parametric knowledge. The model is required to determine which knowledge to access and utilize during inference. Additionally, our multi-view passages fusion network module can effectively and efficiently integrate the retrieved information into low-resource classification task. The extensive experiments demonstrate that RAML significantly outperforms current SOTA low-resource text classification models., Comment: Under Review

Generalized Few-Shot Intent Detection (GFSID) is challenging and realistic because it needs to categorize both seen and novel intents simultaneously. Previous GFSID methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup as they do not explicitly learn the classification of seen categories and the knowledge of seen intents. To address the dilemma, we propose to convert the GFSID task into the class incremental learning paradigm. Specifically, we propose a two-stage learning framework, which sequentially learns the knowledge of different intents in various periods via prompt learning. And then we exploit prototypes for categorizing both seen and novel intents. Furthermore, to achieve the transfer knowledge of intents in different stages, for different scenarios we design two knowledge preservation methods which close to realistic applications. Extensive experiments and detailed analyses on two widely used datasets show that our framework based on the class incremental learning paradigm achieves promising performance., Comment: Under Review

In condensed matter physics, materials with kagome lattice display a range of exotic quantum states, including charge density wave (CDW), superconductivity and magnetism. Recently, the intermetallic kagome metal ScV6Sn6 was discovered to undergo a first-order structural phase transition with the formation of a root3xroot3x3 CDW at around 92 K. The bulk electronic band properties are crucial to understanding the origin of the structural phase transition. Here, we conducted an optical spectroscopy study in combination with band structure calculations across the structural transition. Our findings showed abrupt changes in the optical reflectivity/conductivity spectra as a result of the structural transition, without any observable gap formation behavior. The optical measurements and band calculations actually reveal a sudden change of the band structure after transition. It is important to note that this phase transition is of the first-order type, which distinguishes it from conventional density-wave type condensations. Our results provide an insight into the origin of the structural phase transition in this new and unique kagome lattice., Comment: 9 pages, 6 figures

The topological Weyl semimetals with peculiar band structure exhibit novel nonlinear optical enhancement phenomena even for light at optical wavelengths. While many intriguing nonlinear optical effects were constantly uncovered in type-I semimetals, few experimental works focused on basic nonlinear optical properties in type-II Weyl semimetals. Here we perform a fundamental static and time-resolved second harmonic generation (SHG) on the three dimensional Type-II Weyl semimetal candidate $\beta$-WP$_2$. Although $\beta$-WP$_2$ exhibits extremely high conductivity and an extraordinarily large mean free path, the second harmonic generation is unscreened by conduction electrons, we observed rather strong SHG response compared to non-topological polar metals and archetypal ferroelectric insulators. Additionally, our time-resolved SHG experiment traces ultrafast symmetry switching and reveals that polar metal $\beta$-WP$_2$ tends to form inversion symmetric metastable state after photo-excitation. Intense femtosecond laser pulse could optically drive symmetry switching and tune nonlinear optical response on ultrafast timescales although the interlayer coupling of $\beta$-WP$_2$ is very strong. Our work is illuminating for the polar metal nonlinear optics and potential ultrafast topological optoelectronic applications., Comment: 8 pages, 5 figures

Nonlinear responses of superconductors to intense terahertz radiation has been an active research frontier. Using terahertz pump-terahertz probe spectroscopy, we investigate the c-axis nonlinear optical response of a high-temperature superconducting cuprate. After excitation by a single-cycle terahertz pump pulse, the reflectivity of the probe pulse oscillates as the pump-probe delay is varied. Interestingly, the oscillatory central frequency scales linearly with the probe frequency, a fact widely overlooked in pump-probe experiments. By theoretically solving the nonlinear optical reflection problem on the interface, we show that our observation is well explained by the Josephson-type third-order nonlinear electrodynamics, together with the emission coefficient from inside the material into free space. The latter results in a strong enhancement of the emitted signal whose physical frequency is around the Josephson plasma edge. Our result offers a benchmark for and new insights into strong-field terahertz spectroscopy of related quantum materials., Comment: 8 pages, 4 figures