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Polymorphism, commonly denoting the variety of molecular or crystal structures, is a vital element in many natural science disciplines. In van der Waals layered antiferromagnets, a new type of magnetic polymorphism is allowed by having multiple layer-selective magnetic structures with the same total magnetization. However, resolving and manipulating such magnetic polymorphs remain a great challenge. Here we use the phase-resolved magnetic second-harmonic generation microscopy to elucidate such magnetic polymorphism in the 2D semiconducting layered antiferromagnet chromium sulfur bromide (CrSBr), and demonstrate how the magnetic polymorphs can be deterministically switched in an unprecedented layer-selective manner. With the nonlinear magneto-optical technique unveiling the magnetic symmetry information through the amplitude and phase of light, we could unambiguously resolve the polymorphic spin-flip transitions in CrSBr bilayers and tetralayers. Remarkably, the deterministic routing of polymorphic transitions originates from the breaking of energy degeneracy via a magnetic layer-sharing effect: the spin-flip transitions in a tetralayer are governed by the laterally extended bilayer, which acts as a control bit. We envision such controllable magnetic polymorphism to be ubiquitous for van der Waals layered antiferromagnets, and could lead to conceptually new design and construction of spintronic and opto-spintronic devices for probabilistic computation and neuromorphic engineering., Comment: 22 pages, 6 figures

Instructional documents are rich sources of knowledge for completing various tasks, yet their unique challenges in conversational question answering (CQA) have not been thoroughly explored. Existing benchmarks have primarily focused on basic factual question-answering from single narrative documents, making them inadequate for assessing a model`s ability to comprehend complex real-world instructional documents and provide accurate step-by-step guidance in daily life. To bridge this gap, we present InsCoQA, a novel benchmark tailored for evaluating large language models (LLMs) in the context of CQA with instructional documents. Sourced from extensive, encyclopedia-style instructional content, InsCoQA assesses models on their ability to retrieve, interpret, and accurately summarize procedural guidance from multiple documents, reflecting the intricate and multi-faceted nature of real-world instructional tasks. Additionally, to comprehensively assess state-of-the-art LLMs on the InsCoQA benchmark, we propose InsEval, an LLM-assisted evaluator that measures the integrity and accuracy of generated responses and procedural instructions.

Van der Waals (vdW) antiferromagnets are exceptional platforms for exploring the spin dynamics of antiferromagnetic materials owing to their weak interlayer exchange coupling. In this study, we examined the antiferromagnetic resonance spectra of anisotropic Van der Waals antiferromagnet CrSBr. In addition to the ordinary resonance modes, we observed a dipolar spin wave mode when the microwave field was oriented perpendicular to the in-plane easy axis of CrSBr. Furthermore, our results uncovered a pronounced dependency of various resonant modes on the orientation of the microwave field, which is pivotal for the accurate determination of exchange coupling constants. Numerical simulations have elucidated this orientation dependence of spin dynamics arises from the magnetostatic effect. This discovery underscores the previously underappreciated significance of dipolar interactions in shaping the dynamical properties of two-dimensional AFM materials, thereby enhancing our understanding of the intrinsic dynamic properties of vdW magnets.

A well-known dilemma in large vision-language models (e.g., GPT-4, LLaVA) is that while increasing the number of vision tokens generally enhances visual understanding, it also significantly raises memory and computational costs, especially in long-term, dense video frame streaming scenarios. Although learnable approaches like Q-Former and Perceiver Resampler have been developed to reduce the vision token burden, they overlook the context causally modeled by LLMs (i.e., key-value cache), potentially leading to missed visual cues when addressing user queries. In this paper, we introduce a novel approach to reduce vision compute by leveraging redundant vision tokens "skipping layers" rather than decreasing the number of vision tokens. Our method, VideoLLM-MoD, is inspired by mixture-of-depths LLMs and addresses the challenge of numerous vision tokens in long-term or streaming video. Specifically, for each transformer layer, we learn to skip the computation for a high proportion (e.g., 80\%) of vision tokens, passing them directly to the next layer. This approach significantly enhances model efficiency, achieving approximately \textasciitilde42\% time and \textasciitilde30\% memory savings for the entire training. Moreover, our method reduces the computation in the context and avoid decreasing the vision tokens, thus preserving or even improving performance compared to the vanilla model. We conduct extensive experiments to demonstrate the effectiveness of VideoLLM-MoD, showing its state-of-the-art results on multiple benchmarks, including narration, forecasting, and summarization tasks in COIN, Ego4D, and Ego-Exo4D datasets.

The rapid increase in multimedia data has spurred advancements in Multimodal Summarization with Multimodal Output (MSMO), which aims to produce a multimodal summary that integrates both text and relevant images. The inherent heterogeneity of content within multimodal inputs and outputs presents a significant challenge to the execution of MSMO. Traditional approaches typically adopt a holistic perspective on coarse image-text data or individual visual objects, overlooking the essential connections between objects and the entities they represent. To integrate the fine-grained entity knowledge, we propose an Entity-Guided Multimodal Summarization model (EGMS). Our model, building on BART, utilizes dual multimodal encoders with shared weights to process text-image and entity-image information concurrently. A gating mechanism then combines visual data for enhanced textual summary generation, while image selection is refined through knowledge distillation from a pre-trained vision-language model. Extensive experiments on public MSMO dataset validate the superiority of the EGMS method, which also prove the necessity to incorporate entity information into MSMO problem., Comment: In ACL-Findings 2024

Recent Large Language Models have been enhanced with vision capabilities, enabling them to comprehend images, videos, and interleaved vision-language content. However, the learning methods of these large multimodal models typically treat videos as predetermined clips, making them less effective and efficient at handling streaming video inputs. In this paper, we propose a novel Learning-In-Video-Stream (LIVE) framework, which enables temporally aligned, long-context, and real-time conversation within a continuous video stream. Our LIVE framework comprises comprehensive approaches to achieve video streaming dialogue, encompassing: (1) a training objective designed to perform language modeling for continuous streaming inputs, (2) a data generation scheme that converts offline temporal annotations into a streaming dialogue format, and (3) an optimized inference pipeline to speed up the model responses in real-world video streams. With our LIVE framework, we built VideoLLM-online model upon Llama-2/Llama-3 and demonstrate its significant advantages in processing streaming videos. For instance, on average, our model can support streaming dialogue in a 5-minute video clip at over 10 FPS on an A100 GPU. Moreover, it also showcases state-of-the-art performance on public offline video benchmarks, such as recognition, captioning, and forecasting. The code, model, data, and demo have been made available at https://showlab.github.io/videollm-online., Comment: CVPR 2024. This arxiv version is upgraded with Llama-3

People's social relationships are often manifested through their surroundings, with certain objects or interactions acting as symbols for specific relationships, e.g., wedding rings, roses, hugs, or holding hands. This brings unique challenges to recognizing social relationships, requiring understanding and capturing the essence of these contexts from visual appearances. However, current methods of social relationship understanding rely on the basic classification paradigm of detected persons and objects, which fails to understand the comprehensive context and often overlooks decisive social factors, especially subtle visual cues. To highlight the social-aware context and intricate details, we propose a novel approach that recognizes \textbf{Con}textual \textbf{So}cial \textbf{R}elationships (\textbf{ConSoR}) from a social cognitive perspective. Specifically, to incorporate social-aware semantics, we build a lightweight adapter upon the frozen CLIP to learn social concepts via our novel multi-modal side adapter tuning mechanism. Further, we construct social-aware descriptive language prompts (e.g., scene, activity, objects, emotions) with social relationships for each image, and then compel ConSoR to concentrate more intensively on the decisive visual social factors via visual-linguistic contrasting. Impressively, ConSoR outperforms previous methods with a 12.2\% gain on the People-in-Social-Context (PISC) dataset and a 9.8\% increase on the People-in-Photo-Album (PIPA) benchmark. Furthermore, we observe that ConSoR excels at finding critical visual evidence to reveal social relationships.

Large Language Models (LLMs) have demonstrated exceptional text understanding. Existing works explore their application in text embedding tasks. However, there are few works utilizing LLMs to assist multimodal representation tasks. In this work, we investigate the potential of LLMs to enhance multimodal representation in multimodal item-to-item (I2I) recommendations. One feasible method is the transfer of Multimodal Large Language Models (MLLMs) for representation tasks. However, pre-training MLLMs usually requires collecting high-quality, web-scale multimodal data, resulting in complex training procedures and high costs. This leads the community to rely heavily on open-source MLLMs, hindering customized training for representation scenarios. Therefore, we aim to design an end-to-end training method that customizes the integration of any existing LLMs and vision encoders to construct efficient multimodal representation models. Preliminary experiments show that fine-tuned LLMs in this end-to-end method tend to overlook image content. To overcome this challenge, we propose a novel training framework, NoteLLM-2, specifically designed for multimodal representation. We propose two ways to enhance the focus on visual information. The first method is based on the prompt viewpoint, which separates multimodal content into visual content and textual content. NoteLLM-2 adopts the multimodal In-Content Learning method to teach LLMs to focus on both modalities and aggregate key information. The second method is from the model architecture, utilizing a late fusion mechanism to directly fuse visual information into textual information. Extensive experiments have been conducted to validate the effectiveness of our method., Comment: 19 pages, 5 figures

People enjoy sharing "notes" including their experiences within online communities. Therefore, recommending notes aligned with user interests has become a crucial task. Existing online methods only input notes into BERT-based models to generate note embeddings for assessing similarity. However, they may underutilize some important cues, e.g., hashtags or categories, which represent the key concepts of notes. Indeed, learning to generate hashtags/categories can potentially enhance note embeddings, both of which compress key note information into limited content. Besides, Large Language Models (LLMs) have significantly outperformed BERT in understanding natural languages. It is promising to introduce LLMs into note recommendation. In this paper, we propose a novel unified framework called NoteLLM, which leverages LLMs to address the item-to-item (I2I) note recommendation. Specifically, we utilize Note Compression Prompt to compress a note into a single special token, and further learn the potentially related notes' embeddings via a contrastive learning approach. Moreover, we use NoteLLM to summarize the note and generate the hashtag/category automatically through instruction tuning. Extensive validations on real scenarios demonstrate the effectiveness of our proposed method compared with the online baseline and show major improvements in the recommendation system of Xiaohongshu., Comment: Published as a WWW'24 full paper

Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compression in one iteration with higher communication cost, or bidirectional compression with slower convergence rate. In this work, we propose the Local Immediate Error Compensated SGD (LIEC-SGD) optimization algorithm to break the above bottlenecks based on bidirectional compression and carefully designed compensation approaches. Specifically, the bidirectional compression technique is to reduce the communication cost, and the compensation technique compensates the local compression error to the model update immediately while only maintaining the global error variable on the server throughout the iterations to boost its efficacy. Theoretically, we prove that LIEC-SGD is superior to previous works in either the convergence rate or the communication cost, which indicates that LIEC-SGD could inherit the dual advantages from unidirectional compression and bidirectional compression. Finally, experiments of training deep neural networks validate the effectiveness of the proposed LIEC-SGD algorithm.

Topological properties in quantum materials are often governed by symmetry and tuned by crystal structure and external fields, and hence symmetry-sensitive nonlinear optical measurements in a magnetic field are a valuable probe. Here we report nonlinear magneto-optical second harmonic generation (SHG) studies of non-magnetic topological materials including bilayer WTe2, monolayer WSe2 and bulk TaAs. The polarization-resolved patterns of optical SHG under magnetic field show nonlinear Kerr rotation in these time-reversal symmetric materials. For materials with three-fold rotational symmetric lattice structure, the SHG polarization pattern rotates just slightly in a magnetic field, whereas in those with mirror or two-fold rotational symmetry the SHG polarization pattern rotates greatly and distorts. These different magneto-SHG characters can be understood by considering the superposition of the magnetic field-induced time-noninvariant nonlinear optical tensor and the crystal-structure-based time-invariant counterpart. The situation is further clarified by scrutinizing the Faraday rotation, whose subtle interplay with crystal symmetry accounts for the diverse behavior of the extrinsic nonlinear Kerr rotation in different materials. Our work illustrates the application of magneto-SHG techniques to directly probe nontrivial topological properties, and underlines the importance of minimizing extrinsic nonlinear Kerr rotation in polarization-resolved magneto-optical studies., Comment: 25 pages, 6 figures

The discovery of atomically thin van der Waals ferroelectric and magnetic materials encourages the exploration of 2D multiferroics, which holds the promise to understand fascinating magnetoelectric interactions and fabricate advanced spintronic devices. In addition to building a heterostructure consisting of ferroelectric and magnetic ingredients, thinning down layered multiferroics of spin origin such as NiI2 becomes a natural route to realize 2D multiferroicity. However, the layer-dependent behavior, widely known in the community of 2D materials, necessitates a rigorous scrutiny of the multiferroic order in the few-layer limit. Here, we interrogate the layer thickness crossover of helimagnetism in NiI2 that drives the ferroelectricity and thereby type-II multiferroicity. By using wavelength-dependent polarization-resolved optical second harmonic generation (SHG) to probe the ferroic symmetry, we find that the SHG arises from the inversion-symmetry-breaking magnetic order, not previously assumed ferroelectricity. This magnetism-induced SHG is only observed in bilayer or thicker layers, and vanishes in monolayer, suggesting the critical role of interlayer exchange interaction in breaking the degeneracy of geometrically frustrated spin structures in triangular lattice and stabilizing the type-II multiferroic magnetism in few-layers. While the helimagnetic transition temperature is layer dependent, the few-layer NiI2 exhibits another thickness evolution and reaches the bulk-like behavior in trilayer, indicated by the intermediate centrosymmetric antiferromagnetic state as revealed in Raman spectroscopy. Our work therefore highlights the magnetic contribution to SHG and Raman spectroscopy in reduced dimension and guides the optical study of 2D multiferroics., Comment: 23 pages, 4 figures, 6 supplementary figures

Multimodal entity linking (MEL) task, which aims at resolving ambiguous mentions to a multimodal knowledge graph, has attracted wide attention in recent years. Though large efforts have been made to explore the complementary effect among multiple modalities, however, they may fail to fully absorb the comprehensive expression of abbreviated textual context and implicit visual indication. Even worse, the inevitable noisy data may cause inconsistency of different modalities during the learning process, which severely degenerates the performance. To address the above issues, in this paper, we propose a novel Multi-GraIned Multimodal InteraCtion Network $\textbf{(MIMIC)}$ framework for solving the MEL task. Specifically, the unified inputs of mentions and entities are first encoded by textual/visual encoders separately, to extract global descriptive features and local detailed features. Then, to derive the similarity matching score for each mention-entity pair, we device three interaction units to comprehensively explore the intra-modal interaction and inter-modal fusion among features of entities and mentions. In particular, three modules, namely the Text-based Global-Local interaction Unit (TGLU), Vision-based DuaL interaction Unit (VDLU) and Cross-Modal Fusion-based interaction Unit (CMFU) are designed to capture and integrate the fine-grained representation lying in abbreviated text and implicit visual cues. Afterwards, we introduce a unit-consistency objective function via contrastive learning to avoid inconsistency and model degradation. Experimental results on three public benchmark datasets demonstrate that our solution outperforms various state-of-the-art baselines, and ablation studies verify the effectiveness of designed modules., Comment: Accepted by KDD 2023

Affordance-centric Question-driven Task Completion (AQTC) for Egocentric Assistant introduces a groundbreaking scenario. In this scenario, through learning instructional videos, AI assistants provide users with step-by-step guidance on operating devices. In this paper, we present a solution for enhancing video alignment to improve multi-step inference. Specifically, we first utilize VideoCLIP to generate video-script alignment features. Afterwards, we ground the question-relevant content in instructional videos. Then, we reweight the multimodal context to emphasize prominent features. Finally, we adopt GRU to conduct multi-step inference. Through comprehensive experiments, we demonstrate the effectiveness and superiority of our method, which secured the 2nd place in CVPR'2023 AQTC challenge. Our code is available at https://github.com/zcfinal/LOVEU-CVPR23-AQTC., Comment: 5 pages, 1 figure, technical report for track3 of CVPR 2023 LOVEU challenge

Optical phase-matching involves establishing a proper phase relationship between the fundamental and generated waves to enable efficient optical parametric processes. It is typically achieved through either birefringence or periodically assigned polarization. Here, we report that twist angle in two-dimensional (2D) materials can generate a nonlinear Berry optical phase to compensate the phase mismatch in the process of nonlinear optical frequency conversion, and the vertical assembly of 2D layers with a proper twist sequence will generate a nontrivial "twist-phase-matching" (twist-PM) regime. The twist-PM model offers superior flexibility in the design of optical crystals, which works for twisted layers with either periodic or random thickness distribution. The designed crystals from twisted rhombohedra boron nitride films give rise to a second-harmonic generation conversion efficiency of ~8% within a thickness of only 3.2 um, and a facile polarization controllability that is absent in conventional crystals (from linear to left-/right-handed circular/elliptical polarizations). Our methodology establishes a platform for the rational designing and atomic manufacturing of nonlinear optical crystals based on abundant 2D materials for various functionalities.

Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs. When solving this problem, previous works generally lack in considering the structures of human faces and the correspondence between expressions and relevant facial muscles. To address this issue for better performance of ME spotting, this paper seeks to extract finer spatial features by modeling the relationships between facial Regions of Interest (ROIs). Specifically, we propose a graph convolutional-based network, called Action-Unit-aWare Graph Convolutional Network (AUW-GCN). Furthermore, to inject prior information and to cope with the problem of small datasets, AU-related statistics are encoded into the network. Comprehensive experiments show that our results outperform baseline methods consistently and achieve new SOTA performance in two benchmark datasets,CAS(ME)^2 and SAMM-LV. Our code is available at https://github.com/xjtupanda/AUW-GCN., Comment: Accepted by ICME-2023

Affordance-centric Question-driven Task Completion for Egocentric Assistant(AQTC) is a novel task which helps AI assistant learn from instructional videos and scripts and guide the user step-by-step. In this paper, we deal with the AQTC via a two-stage Function-centric approach, which consists of Question2Function Module to ground the question with the related function and Function2Answer Module to predict the action based on the historical steps. We evaluated several possible solutions in each module and obtained significant gains compared to the given baselines. Our code is available at \url{https://github.com/starsholic/LOVEU-CVPR22-AQTC}.

The charge frustration with the mixed-valence state inherent to LiTi$_2$O$_4$, which is found to be a unique spinel oxide superconductor, is the impetus for paying special attention to reveal the existence of intriguing superconducting properties. Here, we report a pronounced fourfold rotational symmetry of the superconductivity in high-quality single-crystalline LiTi$_2$O$_4$ (001) thin films. Both the magnetoresistivity and upper critical field under an applied magnetic field manifest striking fourfold oscillations deep inside the superconducting state, whereas the anisotropy vanishes in the normal state, demonstrating that it is an intrinsic property of the superconducting phase. We attribute this behavior to the unconventional $d$-wave superconducting Cooper pairs with the irreducible representation of $E_g$ protected by $O_h$ point group in LiTi$_2$O$_4$. Our findings demonstrate the unconventional character of the pairing interaction in a three-dimensional spinel oxide superconductor and shed new light on the pairing mechanism of unconventional superconductivity., Comment: 8 pages, 4 figures

The study of exotic superconductivity in two dimensions has been a central theme in solid state and materials research communities. Experimentally exploring and identifying new fascinating interface superconductors with a high transition temperature ($T_c$) is challenging. Here, we report an experimental observation of intriguing two-dimensional superconductivity with a $T_c$ up to 3.8 K at the interface between Mott insulator Ti$_2$O$_3$ and polar semiconductor GaN. At the verge of superconductivity, we also observe a striking quantum metallic-like state, demonstrating that it is a precursor to the two-dimensional superconductivity as the temperature is decreased. Our work is the first time finding this intriguing superconducting state at the heterointerface, which not only brings a new broad of perspective on the emergent quantum phenomena at the heterointerfaces but also sheds new light on exploiting the novel heterointerface superconductivity with high-$T_c$ via heterostructure engineering., Comment: 8 pages, 4 figures

The valleys of two-dimensional transition metal dichalcogenides (TMDCs) offer a new degree of freedom for information processing. To take advantage of this valley degree of freedom, on one hand, it is feasible to control valleys by utilizing different external stimuli like optical and electric fields. On the other hand, nanostructures are also used to separate the valleys by near field coupling. However, for both above methods, either required low-temperature environment or low degree of coherence properties limit their further applications. Here, we demonstrate all-dielectric photonic crystal (PhC) slabs without in-plane inversion symmetry (C2 symmetry) could separate and route valley photons in a WS2 monolayer at room temperature. Coupling with circularly polarized photonic Bloch modes of such PhC slabs, valley photons emitted by a WS2 monolayer are routed directionally and efficiently separated in the far field. In addition, the far-field emission is directionally enhanced and with long-distance spatial coherence property., Comment: 14 pages, 5 figures

Two-dimensional (2D) materials have become a new territory for exploring novel properties and functionalities because of their superior tunability. The unprecedented tunability of 2D materials is also accompanied by many and equally great challenges, as they are susceptible to defects and disorders. The presence of defects and disorders induces the inhomogeneity of electronic states, often making it difficult to directly probe the intrinsic properties of materials. Therefore, many efforts have been devoted to improve the electronic homogeneity, for example, by reducing the density of defects and disorders in the materials and at the interface. However, little attention is paid to the disorders embedded in underlying substrates. Here we investigate how individual dopants in substrate interact with 2D materials and give rise to the electron-hole puddles by low temperature scanning tunneling microscopy (STM) and spectroscopy (STS). Specifically, we take the epitaxial bilayer graphene grown on nitrogen doped silicon carbide (SiC) substrates as a model system, which has played the essential roles in many applications and fundamental studies. The nitrogen dopants inside SiC substrate were imaged over the epitaxial graphene by STM for the first time. The charged nitrogen dopants from the substrate induce the electron-lack puddles in graphene with a diameter of ~2 nm, via Coulomb interaction. The Fermi level with respect to the Dirac point is modulated by the puddles with an amplitude of ~40 meV, causing the electronic inhomogeneity of epitaxial graphene. Our findings on this prototype material are expected to facilitate the quality improvement of graphene and other 2D materials in general., Comment: 18 pages, 5 figures

To train accurate deep object detectors under the extreme foreground-background imbalance, heuristic sampling methods are always necessary, which either re-sample a subset of all training samples (hard sampling methods, \eg biased sampling, OHEM), or use all training samples but re-weight them discriminatively (soft sampling methods, \eg Focal Loss, GHM). In this paper, we challenge the necessity of such hard/soft sampling methods for training accurate deep object detectors. While previous studies have shown that training detectors without heuristic sampling methods would significantly degrade accuracy, we reveal that this degradation comes from an unreasonable classification gradient magnitude caused by the imbalance, rather than a lack of re-sampling/re-weighting. Motivated by our discovery, we propose a simple yet effective \emph{Sampling-Free} mechanism to achieve a reasonable classification gradient magnitude by initialization and loss scaling. Unlike heuristic sampling methods with multiple hyperparameters, our Sampling-Free mechanism is fully data diagnostic, without laborious hyperparameters searching. We verify the effectiveness of our method in training anchor-based and anchor-free object detectors, where our method always achieves higher detection accuracy than heuristic sampling methods on COCO and PASCAL VOC datasets. Our Sampling-Free mechanism provides a new perspective to address the foreground-background imbalance. Our code is released at \url{https://github.com/ChenJoya/sampling-free}., Comment: Accepted by IEEE Transactions on Image Processing (14 pages)

Controlling the crystal structure is a powerful approach for manipulating the fundamental properties of solids. Unique to two-dimensional (2D) van der Waals materials, the control can be achieved by modifying the stacking order through rotation and translation between the layers. Here, we report the first observation of stacking dependent interlayer magnetism in the 2D magnetic semiconductor, chromium tribromide (CrBr3), enabled by the successful growth of its monolayer and bilayer through molecular beam epitaxy. Using in situ spin-polarized scanning tunneling microscopy and spectroscopy, we directly correlated the atomic lattice structure with observed magnetic order. We demonstrated that while individual CrBr3 monolayer is ferromagnetic, the interlayer coupling in bilayer depends strongly on the stacking order and can be either ferromagnetic or antiferromagnetic. Our observations provide direct experimental evidence for exploring the stacking dependent layered magnetism, and pave the way for manipulating 2D magnetism with unique layer twist angle control., Comment: originally submitted version, 12 pages, 4 figures

Layered antiferromagnetism is the spatial arrangement of ferromagnetic layers with antiferromagnetic interlayer coupling. Recently, the van der Waals magnet, chromium triiodide (CrI3), emerged as the first layered antiferromagnetic insulator in its few-layer form, opening up ample opportunities for novel device functionalities. Here, we discovered an emergent nonreciprocal second order nonlinear optical effect in bilayer CrI3. The observed second harmonic generation (SHG) is giant: several orders of magnitude larger than known magnetization induced SHG and comparable to SHG in the best 2D nonlinear optical materials studied so far (e.g. MoS2). We showed that while the parent lattice of bilayer CrI3 is centrosymmetric and thus does not contribute to the SHG signal, the observed nonreciprocal SHG originates purely from the layered antiferromagnetic order, which breaks both spatial inversion and time reversal symmetries. Furthermore, polarization-resolved measurements revealed the underlying C2h symmetry, and thus monoclinic stacking order in CrI3 bilayers, providing crucial structural information for the microscopic origin of layered antiferromagnetism. Our results highlight SHG as a highly sensitive probe that can reveal subtle magnetic order and open novel nonlinear and nonreciprocal optical device possibilities based on 2D magnets.

Structural symmetry of crystals plays important roles in physical properties of two-dimensional (2D) materials, particularly in the nonlinear optics regime. It has been a long-term exploration on the physical properties in 2D materials with various stacking structures, which correspond to different structural symmetries. Usually, the manipulation of rotational alignment between layers in 2D heterostructures has been realized at the synthetic stage through artificial stacking like assembling Lego bricks. However, the reconfigurable control of translational symmetry of crystalline structure is still challenging. High pressure, as a powerful external control knob, provides a very promising route to circumvent this constraint. Here, we experimentally demonstrate a pressure-controlled symmetry transition in layered InSe. The continuous and reversible evolution of structural symmetries can be in-situ monitored by using the polarization-resolved second harmonic generation (SHG) spectroscopy. As pressure changes, the reconfigurable symmetry transition of the SHG pattern from three-fold rotational symmetry to mirror symmetry was experimentally observed in a layered InSe samples and was successfully explained by the proposed interlayer-translation model. This opens new routes towards potential applications of manipulating crystal symmetry of 2D materials.

We examined the chirality dependent optical selection rules in two-dimensional monolayer materials with the honeycomb lattice, and based on symmetry argument, we generalized these rules to multi-photon transitions of arbitrary orders. We also presented the phase relations between incident and outgoing photons in such processes. The results agreed nicely with our experimental observations of second and third harmonic generations. In particular, we demonstrated that the phase relation of chiral second harmonic generation can serve as a handy tool for imaging domains and domain boundaries of these monolayers. Our results can benefit future studies on chirality related optical phenomena and opto-electronic applications of such materials., Comment: 15 pages, 4 figures