The propagation of acoustic waves through bone remains a longstanding challenge in transcranial ultrasound imaging. As a highly scattering medium, the skull causes significant distortions in the ultrasonic wavefield, introducing complex aberrations that hinder precise image reconstruction. Conventional delay-and-sum (DAS) algorithms, which process pixels independently, fail to account for inter-pixel relationships, limiting their ability to correct such distortions. To address this issue, we propose a Pixel-Responsive Optimization (PRO) Beamforming Method that leverages backscattered signals from compound plane waves. By constructing a pixel-response matrix and simulating a virtual acoustic lens, PRO isolates and aligns distorted fields with reference phases to restore near-ideal propagation. Experiments on bovine femur plates and a human skull demonstrate improved image resolution, recovery of submerged signals, and artifact suppression. PRO achieves up to a 90% improvement in full-width at half-maximum (FWHM) compared to DAS, requiring no prior assumptions and showing strong generalizability in complex scenarios through bone. This advancement holds promise for future in vivo transcranial brain imaging applications.

Haolin Deng, Chang Wang, Li Xin, Dezhang Yuan, Junlang Zhan, Tian Zhou, Jin Ma, Jun Gao, Ruifeng Xu. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

The embedding-based retrieval (EBR) approach is widely used in mainstream search engine retrieval systems and is crucial in recent retrieval-augmented methods for eliminating LLM illusions. However, existing EBR models often face the “semantic drift” problem and insufficient focus on key information, leading to a low adoption rate of retrieval results in subsequent steps. This issue is especially noticeable in real-time search scenarios, where the various expressions of popular events on the Internet make real-time retrieval heavily reliant on crucial event information. To tackle this problem, this paper proposes a novel approach called EER, which enhances real-time retrieval performance by improving the dual-encoder model of traditional EBR. We incorporate contrastive learning to accompany pairwise learning for encoder optimization. Furthermore, to strengthen the focus on critical event information in events, we include a decoder module after the document encoder, introduce a generative event triplet extraction scheme based on prompt-tuning, and correlate the events with query encoder optimization through comparative learning. This decoder module can be removed during inference. Extensive experiments demonstrate that EER can significantly improve the real-time search retrieval performance. We believe that this approach will provide new perspectives in the field of information retrieval. The codes and dataset are available at https://github.com/open-event-hub/Event-enhanced_Retrieval.

This paper addresses the challenge of the semantic gap between user queries and web content, commonly referred to as asymmetric text matching, within the domain of web search.By leveraging BERT for reading comprehension, current algorithms enable significant advancements in query understanding, but still encounter limitations in effectively resolving the asymmetrical ranking problem due to model comprehension and summarization constraints.To tackle this issue, we propose the QAGR (Question-Answer Generation and Ranking) method, comprising an offline module called QAGeneration and an online module called QARanking.The QAGeneration module utilizes large language models (LLMs) to generate high-quality question-answering pairs for each web page.This process involves two steps: generating question-answer pairs and performing verification to eliminate irrelevant questions, resulting in high-quality questions associated with their respective documents.The QARanking module combines and ranks the generated questions and web page content.To ensure efficient online inference, we design the QARanking model as a homogeneous dual-tower model, incorporating query intent to drive score fusion while balancing keyword matching and asymmetric matching.Additionally, we conduct a preliminary screening of questions for each document, selecting only the top-N relevant questions for further relevance calculation.Empirical results demonstrate the substantial performance improvement of our proposed method in web search.We achieve over

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19 586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn.

Query correction is a task that automatically detects and corrects errors in what users type into a search engine. Misspelled queries can lead to user dissatisfaction and churn. However, correcting a user query accurately is not an easy task. One major challenge is that a correction model must be capable of high-level language comprehension. Recently, pre-trained language models (PLMs) have been successfully applied to text correction tasks, but few works have been done on query correction. However, it is nontrivial to directly apply these PLMs to query correction in large-scale search systems due to the following challenging issues: 1) Expensive deployment. Deploying such a model requires expensive computations. 2) Lacking domain knowledge. A neural correction model needs massive training data to activate its power.

Haolin Deng, Yanan Zhang, Yangfan Zhang, Wangyang Ying, Changlong Yu, Jun Gao, Wei Wang, Xiaoling Bai, Nan Yang, Jin Ma, Xiang Chen, Tianhua Zhou. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.