Low-Complexity Non-uniform Constellation Demapping Algorithm for Broadcasting System

This paper presents a novel low-complexity soft demapping algorithm for  two-dimensional non-uniform spaced constellations (2D-NUCs) and massive order one-dimensional NUCs (1D-NUCs). NUCs have been implemented in a wide range of new broadcasting systems to approach the Shannon limit further, such as DVB-NGH, ATSC 3.0 and NGB-W. However, the soft demapping complexity is extreme due to the substantial distance calculations. In the proposed scheme, the demapping process is classified into four cases based on different quadrants. To deal with the complexity problem, four groups of reduced subsets in terms of the quadrant for each bit are separately calculated and stored in advance. Analysis and simulation prove that the proposed demapper only introduces a small penalty under 0.02dB with respect to Max-Log-MAP demapper, whereas a significant complexity reduction ranging from 68.75\% to 88.54\% is obtained

Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework

Factuality is important to dialogue summarization. Factual error correction (FEC) of model-generated summaries is one way to improve factuality. Current FEC evaluation that relies on factuality metrics is not reliable and detailed enough. To address this problem, we are the first to manually annotate a FEC dataset for dialogue summarization containing 4000 items and propose FERRANTI, a fine-grained evaluation framework based on reference correction that automatically evaluates the performance of FEC models on different error categories. Using this evaluation framework, we conduct sufficient experiments with FEC approaches under a variety of settings and find the best training modes and significant differences in the performance of the existing approaches on different factual error categories.Comment: Accepted to ACL 2023 Main Conferenc

Track Anything: Segment Anything Meets Videos

Recently, the Segment Anything Model (SAM) gains lots of attention rapidly due to its impressive segmentation performance on images. Regarding its strong ability on image segmentation and high interactivity with different prompts, we found that it performs poorly on consistent segmentation in videos. Therefore, in this report, we propose Track Anything Model (TAM), which achieves high-performance interactive tracking and segmentation in videos. To be detailed, given a video sequence, only with very little human participation, i.e., several clicks, people can track anything they are interested in, and get satisfactory results in one-pass inference. Without additional training, such an interactive design performs impressively on video object tracking and segmentation. All resources are available on {https://github.com/gaomingqi/Track-Anything}. We hope this work can facilitate related research.Comment: Tech-repor

Comparing glycemic traits in defining diabetes among rural Chinese older adults

The protocol of MIND-China was registered in the Chinese Clinical Trial Registry (ChiCTR, www.chictr.org.cn; registration no.: ChiCTR1800017758).Background: We sought to identify the optimal cut-off of glycated hemoglobin (HbA1c) for defining diabetes and to assess the agreements of fasting plasma glucose (FPG), fasting serum glucose (FSG), and HbA1c in defining diabetes among rural older adults in China. Methods: This population-based cross-sectional study included 3547 participants (age ≥61 years, 57.8% women) from the Multidomain Interventions to Delay Dementia and Disability in Rural China from 2018-2019; of these, 3122 had no previously diagnosed diabetes. We identified the optimal cut-off of HbA1c against FPG ≥7.0 mmol/L for defining diabetes by using receiver operating characteristic curve and Youden index. The agreements of FPG, FSG, and HbA1c in defining diabetes were assessed using kappa statistics. Results: Among participants without previously diagnosed diabetes (n = 3122), the optimal HbA1c cut-off for defining diabetes was 6.5% (48 mmol/mol), with the sensitivity of 88.9%, specificity of 93.7%, and Youden index of 0.825. The correlation coefficients were 0.845 between FPG and FSG, 0.574 between FPG and HbA1c, and 0.529 between FSG and HbA1c in the total sample (n = 3547). The kappa statistic for defining diabetes was 0.962 between FSG and FPG, and 0.812 between HbA1c and FPG. Conclusions: The optimal cut-off of HbA1c for diagnosing diabetes against FPG >7.0 mmol/L is ≥6.5% in Chinese rural-dwelling older adults. The agreement in defining diabetes using FPG, FSG, and HbA1c is nearly perfect. These results have relevant implications for diabetes research and clinical practice among older adults in China. Clinical trial registration: The protocol of MIND-China was registered in the Chinese Clinical Trial Registry (ChiCTR, www.chictr.org.cn; registration no.: ChiCTR1800017758).Y Du was supported by the major grant from the National Key R&D Program of the Ministry of Sciences and Technology of China (Grant No.: 2017YFC1310100) and by additional grants from the National Nature Science Foundation of China (Grants No.: 81861138008 and 82011530139), the Academic Promotion Program of Shandong First Medical University (2019QL020), and the Taishan Scholar Program of Shandong Province, China (Tsqn201909182). C Qiu received grants from the Swedish Research Council (Grants No.: 2017-05819 and 2020-01574), the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) (Grant No.: CH2019-8320) for the Joint China-Sweden Mobility program, and the Karolinska Institutet, Stockholm, Sweden. The funding agency had no role in the study design, data collection and analysis, the writing of this manuscript, and in the decision to submit the work for publication.S

ConvFormer: Revisiting Transformer for Sequential User Modeling

Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behavior sequences. Despite the remarkable success of Transformer-based models across various domains, their full potential in comprehending user behavior remains untapped. In this paper, we re-examine Transformer-like architectures aiming to advance state-of-the-art performance. We start by revisiting the core building blocks of Transformer-based methods, analyzing the effectiveness of the item-to-item mechanism within the context of sequential user modeling. After conducting a thorough experimental analysis, we identify three essential criteria for devising efficient sequential user models, which we hope will serve as practical guidelines to inspire and shape future designs. Following this, we introduce ConvFormer, a simple but powerful modification to the Transformer architecture that meets these criteria, yielding state-of-the-art results. Additionally, we present an acceleration technique to minimize the complexity associated with processing extremely long sequences. Experiments on four public datasets showcase ConvFormer's superiority and confirm the validity of our proposed criteria

Segregated Nanocompartments Containing Therapeutic Enzymes and Imaging Compounds within DNA-Zipped Polymersome Clusters for Advanced Nanotheranostic Platform

Abstract Nanotheranostics is an emerging field that brings together nanoscale-engineered materials with biological systems providing a combination of therapeutic and diagnostic strategies. However, current theranostic nanoplatforms have serious limitations, mainly due to a mismatch between the physical properties of the selected nanomaterials and their functionalization ease, loading ability, or overall compatibility with bioactive molecules. Herein, a nanotheranostic system is proposed based on nanocompartment clusters composed of two different polymersomes linked together by DNA. Careful design and procedure optimization result in clusters segregating the therapeutic enzyme human Dopa decarboxylase (DDC) and fluorescent probes for the detection unit in distinct but colocalized nanocompartments. The diagnostic compartment provides a twofold function: trackability via dye loading as the imaging component and the ability to attach the cluster construct to the surface of cells. The therapeutic compartment, loaded with active DDC, triggers the cellular expression of a secreted reporter enzyme via production of dopamine and activation of dopaminergic receptors implicated in atherosclerosis. This two-compartment nanotheranostic platform is expected to provide the basis of a new treatment strategy for atherosclerosis, to expand versatility and diversify the types of utilizable active molecules, and thus by extension expand the breadth of attainable applications