We report on the realization of a matter-wave interferometer based on single-photon interaction on the ultranarrow optical clock transition of strontium atoms. We experimentally demonstrate its operation as a gravimeter and as a gravity gradiometer. No reduction of interferometric contrast was observed for a total interferometer time up to $\sim 10\mathrm{ms}$, limited by geometric constraints of the apparatus. Single-photon interferometers represent a new class of high-precision sensors that could be used for the detection of gravitational waves in so far unexplored frequency ranges and to enlighten the boundary between quantum mechanics and general relativity.

Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media. Existing multimodal rumor detection has focused on mixing tokens among spatial and sequential locations for unimodal representation or fusing clues of rumor veracity across modalities. However, they suffer from less discriminative unimodal representation and are vulnerable to intricate location dependencies in the time-consuming fusion of spatial and sequential tokens. This work makes the first attempt at multimodal rumor detection in the frequency domain, which efficiently transforms spatial features into the frequency spectrum and obtains highly discriminative spectrum features for multimodal representation and fusion. A novel Frequency Spectrum Representation and fUsion network (FSRU) with dual contrastive learning reveals the frequency spectrum is more effective for multimodal representation and fusion, extracting the informative components for rumor detection. FSRU involves three novel mechanisms: utilizing the Fourier transform to convert features in the spatial domain to the frequency domain, the unimodal spectrum compression, and the cross-modal spectrum co-selection module in the frequency domain. Substantial experiments show that FSRU achieves satisfactory multimodal rumor detection performance.

Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.

A transaction-based recommender system (TBRS) attempts to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependency considered are intra-transaction dependency and inter-transaction dependency. Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item. However, not all recent transactions are relevant to the current one and next items, such that the relevant ones should be prioritized. In this paper, we propose a novel hierarchical attentive transaction embedding (HATE) model to tackle these issues. Specifically, a two-level attention mechanism integrates both item embeddings and transaction embeddings to build an attentive context representation incorporating both intra- and inter-transaction dependencies and to recommend the next item. Experimental evaluations using two real-world datasets of shopping transactions show that HATE significantly outperforms the state-of-the-art methods in terms of recommendation accuracy.

In this paper, we experimentally demonstrate an ultra-long haul and multi-access fiber-optic time synchronization system over the equivalent 13 134 km fiber link based on the bidirectional time division multiplexing transmission over a single fiber with the same wavelength (BTDM-SFSW) scheme. The 13 134 km fiber link is simulated by re-circulating the time signals 33 laps in the 398 km fiber loop. Intermediate stations containing bidirectional optical-electrical-optical repeaters are simultaneously used to implement a high-symmetry low-noise bidirectional signal regeneration for extending transmission distance, and provide the time signals for multi-access time synchronization applications. The results illustrate that time signals aligned with the master station can be available at the slave station and arbitrary intermediate stations without requiring link calibrations. Time synchronization stability in terms of time deviation (TDEV) of less than 32 ps/10 s and the combined uncertainty of less than 89.6 ps can be obtained for the 13 134 km fiber recirculating link. The intermediate stations also have the similar performance with the main fiber link. The simple, robust and accurate ultra-long haul fiber-optic time synchronization technique demonstrated here represents a new class of high-accuracy time synchronization that could be used for national/intercontinental atomic clock comparison campaigns and opens up a possibility for providing synchronized time signals to multiple users over the large-scale fiber-optic networks.