ChatEL: Entity Linking with Chatbots

Entity Linking (EL) is an essential and challenging task in natural language processing that seeks to link some text representing an entity within a document or sentence with its corresponding entry in a dictionary or knowledge base. Most existing approaches focus on creating elaborate contextual models that look for clues the words surrounding the entity-text to help solve the linking problem. Although these fine-tuned language models tend to work, they can be unwieldy, difficult to train, and do not transfer well to other domains. Fortunately, Large Language Models (LLMs) like GPT provide a highly-advanced solution to the problems inherent in EL models, but simply naive prompts to LLMs do not work well. In the present work, we define ChatEL, which is a three-step framework to prompt LLMs to return accurate results. Overall the ChatEL framework improves the average F1 performance across 10 datasets by more than 2%. Finally, a thorough error analysis shows many instances with the ground truth labels were actually incorrect, and the labels predicted by ChatEL were actually correct. This indicates that the quantitative results presented in this paper may be a conservative estimate of the actual performance. All data and code are available as an open-source package on GitHub at https://github.com/yifding/In_Context_EL

LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation

Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems. Most recent reports, however, focus heavily on new model architectures. This may bring some gaps when applying GNN in the industrial setup, where, besides the model, constructing the graph and handling data sparsity also play critical roles in the overall success of the project. In this work, we report how GNN is applied for large-scale e-commerce item retrieval at Shopee. We introduce our simple yet novel and impactful techniques in graph construction, modeling, and handling data skewness. Specifically, we construct high-quality item graphs by combining strong-signal user behaviors with high-precision collaborative filtering (CF) algorithm. We then develop a new GNN architecture named LightSAGE to produce high-quality items' embeddings for vector search. Finally, we design multiple strategies to handle cold-start and long-tail items, which are critical in an advertisement (ads) system. Our models bring improvement in offline evaluations, online A/B tests, and are deployed to the main traffic of Shopee's Recommendation Advertisement system

Cryo-EM structure of a fungal mitochondrial calcium uniporter.

The mitochondrial calcium uniporter (MCU) is a highly selective calcium channel localized to the inner mitochondrial membrane. Here, we describe the structure of an MCU orthologue from the fungus Neosartorya fischeri (NfMCU) determined to 3.8 Å resolution by phase-plate cryo-electron microscopy. The channel is a homotetramer with two-fold symmetry in its amino-terminal domain (NTD) that adopts a similar structure to that of human MCU. The NTD assembles as a dimer of dimers to form a tetrameric ring that connects to the transmembrane domain through an elongated coiled-coil domain. The ion-conducting pore domain maintains four-fold symmetry, with the selectivity filter positioned at the start of the pore-forming TM2 helix. The aspartate and glutamate sidechains of the conserved DIME motif are oriented towards the central axis and separated by one helical turn. The structure of NfMCU offers insights into channel assembly, selective calcium permeation, and inhibitor binding

A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model

In online advertising scenario, sellers often create multiple creatives to provide comprehensive demonstrations, making it essential to present the most appealing design to maximize the Click-Through Rate (CTR). However, sellers generally struggle to consider users preferences for creative design, leading to the relatively lower aesthetics and quantities compared to Artificial Intelligence (AI)-based approaches. Traditional AI-based approaches still face the same problem of not considering user information while having limited aesthetic knowledge from designers. In fact that fusing the user information, the generated creatives can be more attractive because different users may have different preferences. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. The ranking model can predict the CTR score for each creative considering user features. However, the two above stages are regarded as two different tasks and are optimized separately. In this paper, we proposed a new automated Creative Generation pipeline for Click-Through Rate (CG4CTR) with the goal of improving CTR during the creative generation stage. Our contributions have 4 parts: 1) The inpainting mode in stable diffusion is firstly applied to creative generation task in online advertising scene. A self-cyclic generation pipeline is proposed to ensure the convergence of training. 2) Prompt model is designed to generate individualized creatives for different user groups, which can further improve the diversity and quality. 3) Reward model comprehensively considers the multimodal features of image and text to improve the effectiveness of creative ranking task, and it is also critical in self-cyclic pipeline. 4) The significant benefits obtained in online and offline experiments verify the significance of our proposed method

Effect of Songyu Anshen Fang on expression of hypothalamic GABA and GABA(B) receptor proteins in insomniac rats induced by para-chlorophenylalanine

Purpose: To investigate the effects of the Chinese compound, Songyu Anshen Fang (SYF) on levels of GABA and GABA(B) receptor proteins in insomniac rats induced by para-chlorophenylalanine (PCPA).Methods: All rats were randomly separated into either a control group, insomnia group, or a SYF group (at a dose of 8.5 g/kg or 17 g/kg body weight per day). The rat model of insomnia was induced by intraperitoneal injection of PCPA, and SYF was administered intragastrically in suspension. All experimental groups were treated with a corresponding agent for one week. The levels of glutamic acid (Glu) and γ-aminobutyric acid (GABA) were determined by high performance liquid chromatography (HPLC); mRNA and protein expressions, and GABA(B) receptor levels were detected by real-time polymerase chain reaction (RT-PCR) and western blot.Results: SYF treatment with 8.5 or 17 g/kg/day decreased the levels of Glu and Glu/GABA ratios in the hypothalamus following abnormal increase by PCPA. Moreover, GABA(B) receptor, mRNA and protein expression decreased by PCPA in hypothalamus were significantly normalized by SYF.Conclusion: The study indicates that the effects of PCPA-induced insomnia can be alleviated by SYF modulation of neurotransmitter levels and the expression of GABA(B) receptor in the hypothalamus. This suggests that clinical application of SYF to treat insomnia may be feasible.Keywords: Songyu Anshen Fang, Para-chlorophenylalanine (PCPA), γ-Aminobutyric acid (GABA), GABA(B) receptor, Insomni

Simultaneous Resonant and Broadband Detection for Dark Sectors

Electromagnetic resonant systems, such as cavities or LC circuits, have emerged as powerful detectors for probing ultralight boson dark matter and high-frequency gravitational waves. However, the limited resonant bandwidth of conventional single-mode resonators, imposed by quantum fluctuations, necessitates numerous scan steps to cover broad unexplored frequency regions. The incorporation of multiple auxiliary modes can realize a broadband detector while maintaining a substantial signal response. The broadened sensitive width can be on the same order as the resonant frequency, encompassing several orders of the source frequency for heterodyne detection, where a background cavity mode transitions into another. Consequently, our approach enables significantly deeper exploration of the parameter space within the same integration time compared to single-mode detection.Comment: 18 pages, 6 figure