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Metasurfaces offer complete control of optical wavefront at the subwavelength scale, advancing a new class of artificial planar optics, including lenses, waveplates, and holograms, with unprecedented merits over conventional optical components. In particular, the ultrathin, flat, and compact characteristics of metasurfaces facilitate their integration with semiconductor devices for the development of miniaturized and multifunctional optoelectronic systems. In this work, generation of structured light is implemented at an ultracompact wafer‐level through the monolithic integration of metasurface with standard vertical cavity surface‐emitting lasers (VCSELs). This work opens new perspectives for the design of structured light systems with compactness, lightweight, and scalability. Ultracompact beam structured laser chips with versatile functionalities are experimentally demonstrated, including multichannel beams array generation, on‐chip large‐angle beam steering up to 60°, and wafer‐level holographic beam shaping with a wide field of view (about 124°). The results will promote the development of compact light structuring systems with great potential in 3D imaging, displays, robotic vision, human–computer interaction, and augmented/virtual reality.

Sarcasm is a pervasive phenomenon in today's social media platforms such as Twitter and Reddit. These platforms allow users to create multi-modal messages, including texts, images, and videos. Existing multi-modal sarcasm detection methods either simply concatenate the features from multi modalities or fuse the multi modalities information in a designed manner. However, they ignore the incongruity character in sarcastic utterance, which is often manifested between modalities or within modalities. Inspired by this, we propose a BERT architecture-based model, which concentrates on both intra and inter-modality incongruity for multi-modal sarcasm detection. To be specific, we are inspired by the idea of self-attention mechanism and design intermodality attention to capturing inter-modality incongruity. In addition, the co-attention mechanism is applied to model the contradiction within the text. The incongruity information is then used for prediction. The experimental results demonstrate that our model achieves state-of-the-art performance on a public multi-modal sarcasm detection dataset.

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Conversational Emotion Recognition (CER) is a task to predict the emotion of an utterance in the context of a conversation. Although modeling the conversational context and interactions between speakers has been studied broadly, it is important to consider the speaker's psychological state, which controls the action and intention of the speaker. The state-of-the-art method introduces CommonSense Knowledge (CSK) to model psychological states in a sequential way (forwards and backwards). However, it ignores the structural psychological interactions between utterances. In this paper, we propose a pSychological-Knowledge-Aware Interaction Graph (SKAIG). In the locally connected graph, the targeted utterance will be enhanced with the information of action inferred from the past context and intention implied by the future context. The utterance is self-connected to consider the present effect from itself. Furthermore, we utilize CSK to enrich edges with knowledge representations and process the SKAIG with a graph transformer. Our method achieves state-of-theart and competitive performance on four popular CER datasets.

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Graphene is an ideal material for wide spectrum detector owing to its special band structure, but its low light absorption and fast composite of photogenerated carriers lead to a weak response performance. In this paper, we designed a unique photoconductive graphene-InGaAs photodetector. The built-in electric field was formed between graphene and InGaAs, which can prolong the lifetime of photogenerated carriers and improve the response of devices by confining the holes. Compared with graphene-Si structure, a higher built-in electric field and reach to 0.54 eV is formed. It enables the device to achieve a responsivity of 60 AW−1 and a photoconductive gain of 79.4 at 792 nm. In the 1550 nm communication band, the responsivity of the device is also greater than 10 AW−1 and response speed is less than 2 ms. Meanwhile, the saturation phenomenon of light response was also found in this photoconductive graphene heterojunction detector during the experiment, we have explained the phenomenon by the capacitance theory of the built-in electric field, and the maximum optical responsivity of the detector is calculated theoretically, which is in good agreement with the measurement result.

Zero-shot intent detection (ZSID) aims to deal with the continuously emerging intents without annotated training data. However, existing ZSID systems suffer from two limitations: 1) They are not good at modeling the relationship between seen and unseen intents. 2) They cannot effectively recognize unseen intents under the generalized intent detection (GZSID) setting. A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage. To address this problem, we propose a novel framework that utilizes unseen class labels to learn Class-Transductive Intent Representations (CTIR). Specifically, we allow the model to predict unseen intents during training, with the corresponding label names serving as input utterances. On this basis, we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately. CTIR is easy to implement and can be integrated with existing ZSID and GZSID methods. Experiments on two real-world datasets show that CTIR brings considerable improvement to the baseline systems.