Zero-Mode Contribution in Nucleon-Delta Transition

We investigate the transition form factors between nucleon and $\Delta$(1232) particles by using a covariant quark-spectator-diquark field theory model in (3+1) dimensions. Performing a light-front calculation in parallel with the manifestly covariant calculation in light-front helicity basis, we examine the light-front zero-mode contribution to the helicity components of light-front good ("+") current matrix elements. Choosing the light-front gauge ($\epsilon^+_{h=\pm}=0$) with circular polarization in Drell-Yan-West frame, we find that only the helicity components $({1\over 2}, {1\over 2})$ and $({1\over 2},-{1\over 2})$ of the good current receive the zero-mode contribution. Taking into account the zero-mode, we find the prescription independence in obtaining the light-front solution of form factors from any three helicity matrix elements with smeared light-front wavefunctions. The angular condition, which guarantees the full covariance of different schemes, is recovered.Comment: 16 latex pages, 7 figures, to appear in PR

Modeling of whole-space transient electromagnetic responses based on FDTD and its application in the mining industry

Hidden, water-abundant areas in coal mines pose a serious threat to mine safety and production. Underground transient electromagnetic method (TEM) is one of the most effective means of detecting water-abundant areas in front of the roadway head. Traditional TEM theories and applications are interpreted mainly on the vertical component. In this study, multicomponent responses of TEM in underground roadways were modeled using the finite-difference time-domain method. Physical simulation was also used for advanced detection of TEM in the roadway. Both the numerical and physical simulation results show that the horizontal component is more sensitive to the location of water-abundant areas. The results of the joint interpretation with both horizontal and vertical components were verified in a practical coal mine application, indicating that it is feasible to use the horizontal component in interpreting TEM data. Thus, the horizontal component could serve as a new approach for coal mine TEM data processing and interpretation.The State Key Research Development Program of China (NO. 2017YFC0804401), in part by the China Postdoctoral Science Foundation (NO.110101/3445), and in part by the National Research Foundation, South Africa (RDYR160404161474).http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424hj2018Electrical, Electronic and Computer Engineerin

Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning-Aided Particle Filtering Approach

Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.Comment: Under Revie

How Can Recommender Systems Benefit from Large Language Models: A Survey

Recommender systems (RS) play important roles to match users' information needs for Internet applications. In natural language processing (NLP) domains, large language model (LLM) has shown astonishing emergent abilities (e.g., instruction following, reasoning), thus giving rise to the promising research direction of adapting LLM to RS for performance enhancements and user experience improvements. In this paper, we conduct a comprehensive survey on this research direction from an application-oriented view. We first summarize existing research works from two orthogonal perspectives: where and how to adapt LLM to RS. For the "WHERE" question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller. For the "HOW" question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLMs or not, and whether to involve conventional recommendation model (CRM) for inference. Detailed analysis and general development trajectories are provided for both questions, respectively. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We also actively maintain a GitHub repository for papers and other related resources in this rising direction: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.Comment: 15 pages; 3 figures; summarization table in appendi

Ultra-compact lithium niobate photonic chip for high-capacity and energy-efficient wavelength-division-multiplexing transmitters

Recently, high-performance thin-film lithium niobate optical modulators have emerged that, together with advanced multiplexing technologies, are highly expected to satisfy the ever-growing demand for high-capacity optical interconnects utilizing multiple channels. Accordingly, in this study, a compact lithium-niobate-on-insulator (LNOI) photonic chip was adopted to establish four-channel wavelength-division-multiplexing (WDM) transmitters, comprising four optical modulators based on ultracompact 2 × 2 Fabry-Perot cavities and a four-channel WDM filter based on multimode waveguide gratings. The fabricated chip with four wavelength channels has a total footprint as compact as 0.3 × 2.8 mm2, and exhibits an excess loss of ~0.8 dB as well as low inter-channel crosstalk of < –22 dB. Using this LNOI photonic chip, high-capacity data transmissions of 320 Gbps (4 × 80 Gbps) on-off-keying signals and 400 Gbps (4 × 100 Gbps) four-level pulse amplitude signals were successfully realized with the ultra-low power consumption of 11.9 fJ/bit

CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models

With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers. We propose CodeApex, a bilingual benchmark dataset focusing on the programming comprehension and code generation abilities of LLMs. CodeApex comprises three types of multiple-choice questions: conceptual understanding, commonsense reasoning, and multi-hop reasoning, designed to evaluate LLMs on programming comprehension tasks. Additionally, CodeApex utilizes algorithmic questions and corresponding test cases to assess the code quality generated by LLMs. We evaluate 14 state-of-the-art LLMs, including both general-purpose and specialized models. GPT exhibits the best programming capabilities, achieving approximate accuracies of 50% and 56% on the two tasks, respectively. There is still significant room for improvement in programming tasks. We hope that CodeApex can serve as a reference for evaluating the coding capabilities of LLMs, further promoting their development and growth. Datasets are released at https://github.com/APEXLAB/CodeApex.git. CodeApex submission website is https://apex.sjtu.edu.cn/codeapex/.Comment: 21 page

Full-length single-cell RNA-seq applied to a viral human cancer:applications to HPV expression and splicing analysis in HeLa S3 cells

Background: Viral infection causes multiple forms of human cancer, and HPV infection is the primary factor in cervical carcinomas Recent single-cell RNA-seq studies highlight the tumor heterogeneity present in most cancers, but virally induced tumors have not been studied HeLa is a well characterized HPV+ cervical cancer cell line Result: We developed a new high throughput platform to prepare single-cell RNA on a nanoliter scale based on a customized microwell chip Using this method, we successfully amplified full-length transcripts of 669 single HeLa S3 cells and 40 of them were randomly selected to perform single-cell RNA sequencing Based on these data, we obtained a comprehensive understanding of the heterogeneity of HeLa S3 cells in gene expression, alternative splicing and fusions Furthermore, we identified a high diversity of HPV-18 expression and splicing at the single-cell level By co-expression analysis we identified 283 E6, E7 co-regulated genes, including CDC25, PCNA, PLK4, BUB1B and IRF1 known to interact with HPV viral proteins Conclusion: Our results reveal the heterogeneity of a virus-infected cell line It not only provides a transcriptome characterization of HeLa S3 cells at the single cell level, but is a demonstration of the power of single cell RNA-seq analysis of virally infected cells and cancers