Analysis of the Retention Mechanism of Knowledge Sharing Platforms - Taking Zhihu Platform as an Example

With the rapid development of the Internet era, it is difficult to distinguish the truth from the falsehood of the massive amount of online information. Due to the improvement of people's material level, spiritual needs and other aspects, the users' desire for knowledge has become stronger and stronger, and knowledge sharing platforms have emerged. This paper aims to deeply analyse the retention mechanism of knowledge sharing platforms to reveal the success factors of knowledge sharing platforms. Based on existing research, this paper discusses the Zhihu platform from multiple dimensions, such as high-quality content, incentive mechanism, speech control, and pushing mechanism. This paper concludes that Zhihu continuously improves the quality of knowledge through cooperation, diversified forms, and technological innovation. User experience is guaranteed through user reach and incentive mechanisms. Regarding speech control, Zhihu balances freedom of speech and legal regulations to ensure the smooth operation of the platform. In the future, this kind of knowledge sharing platform will introduce more science and technology to improve user stickiness. Through a deep understanding of Zhihu's operation mechanism, this paper helps similar platforms understand the leaders' success factors. It provides opinions and improvement experiences for the development of similar platforms

Effects of introducing low-cost high-speed rail on air-rail competition:Modelling and numerical analysis for Paris-Marseille

Given the trend of railway liberalization in Europe and Asia, we explore the effects of introducing low-cost high-speed rail as an answer to the railway reform on air-rail competition. In particular, by proposing a vertically differentiated model, we first derive the optimal pricing policies as well as the corresponding profits and market shares for low-cost high-speed rail (LCR), full-service high-speed rail (FSR) and air transport (Air). We do so for two types of LCR entrants, namely the incumbent owned entrant (to the FSR company) and the independently owned entrants. For both situations, we prove analytically that introducing LCR leads to reduced FSR and Air fares as well as to reduced Air traffic. The fare and traffic reductions increase with the passenger's time value and with the LCR travel time, while they decrease with the Air unit seat cost. Moreover, all LCR effects are stronger for an independently operated LCR. We apply our model to the Paris-Marseille route, based on data collected from publicly available sources. It is found that introducing an independently owned (incumbent owned) LCR on this route leads to 39% (33%) less air traffic, 20% (14%) less FSR traffic and a 37% (29%) increase in total rail traffic. Furthermore, this comes with increases of 2% (8%) in combined railway profit and 6% (5%) in total social welfare. These results support the decision of French policy makers to have LCR and FSR operated by the same company, as it comes with much higher combined railway profits and almost the same welfare increase as independently owned LCR. Further sensitivity analyses suggest that most LCR passengers would otherwise have traveled by FSR or Air, although LCR also attracts new passengers. In addition, offering a low-cost alternative is more effective if passengers value time more highly. Implications in terms of methodology and industry are provided.</p

LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset

As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192 candidates extracted from 4.3 million criminal case documents. To the best of our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval datasets, providing extensive coverage of criminal charges. Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. Furthermore, we propose a two-level candidate set pooling strategy that effectively identify potential candidates for each query case. It's important to note that all cases in the dataset have been annotated by multiple legal experts specializing in criminal law. Their expertise ensures the accuracy and reliability of the annotations. We evaluate several state-of-the-art retrieval models at LeCaRDv2, demonstrating that there is still significant room for improvement in legal case retrieval. The details of LeCaRDv2 can be found at the anonymous website https://github.com/anonymous1113243/LeCaRDv2

TSST: A Benchmark and Evaluation Models for Text Speech-Style Transfer

Text style is highly abstract, as it encompasses various aspects of a speaker's characteristics, habits, logical thinking, and the content they express. However, previous text-style transfer tasks have primarily focused on data-driven approaches, lacking in-depth analysis and research from the perspectives of linguistics and cognitive science. In this paper, we introduce a novel task called Text Speech-Style Transfer (TSST). The main objective is to further explore topics related to human cognition, such as personality and emotion, based on the capabilities of existing LLMs. Considering the objective of our task and the distinctive characteristics of oral speech in real-life scenarios, we trained multi-dimension (i.e. filler words, vividness, interactivity, emotionality) evaluation models for the TSST and validated their correlation with human assessments. We thoroughly analyze the performance of several large language models (LLMs) and identify areas where further improvement is needed. Moreover, driven by our evaluation models, we have released a new corpus that improves the capabilities of LLMs in generating text with speech-style characteristics. In summary, we present the TSST task, a new benchmark for style transfer and emphasizing human-oriented evaluation, exploring and advancing the performance of current LLMs.Comment: Working in progres

Caseformer: Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions

Legal case retrieval aims to help legal workers find relevant cases related to their cases at hand, which is important for the guarantee of fairness and justice in legal judgments. While recent advances in neural retrieval methods have significantly improved the performance of open-domain retrieval tasks (e.g., Web search), their advantages have not been observed in legal case retrieval due to their thirst for annotated data. As annotating large-scale training data in legal domains is prohibitive due to the need for domain expertise, traditional search techniques based on lexical matching such as TF-IDF, BM25, and Query Likelihood are still prevalent in legal case retrieval systems. While previous studies have designed several pre-training methods for IR models in open-domain tasks, these methods are usually suboptimal in legal case retrieval because they cannot understand and capture the key knowledge and data structures in the legal corpus. To this end, we propose a novel pre-training framework named Caseformer that enables the pre-trained models to learn legal knowledge and domain-specific relevance information in legal case retrieval without any human-labeled data. Through three unsupervised learning tasks, Caseformer is able to capture the special language, document structure, and relevance patterns of legal case documents, making it a strong backbone for downstream legal case retrieval tasks. Experimental results show that our model has achieved state-of-the-art performance in both zero-shot and full-data fine-tuning settings. Also, experiments on both Chinese and English legal datasets demonstrate that the effectiveness of Caseformer is language-independent in legal case retrieval

Highly efficient influenza virus production: A MDCK-based high-cell-density process

Seasonal vaccination campaigns for influenza in developed and developing countries create a massive demand for 500 million (2015) vaccine doses every year [1]. Besides egg-based vaccine manufacturing, production platforms based on animal cell culture increasingly contribute to this overall growing market. In order to intensify cell culture-based influenza virus production, high-cell-density (HCD) cultivation of suspension cells can be applied to improve virus titer, process productivity and production costs [2]. For process optimization and evaluation of HCD conditions, cells cultivated using semi-perfusion approaches in small shakers can be used as a scale-down model for bioreactors operating in full perfusion mode [3]. In this study, a previously developed MDCK suspension cell line [4] was adapted to a new serum free medium [5] to facilitate higher growth rate, cell density and virus titer both in batch and in HCD. Therefore, MDCK cells cultivated in Smif-8 medium were slowly adapted to a new cultivation medium (Xeno™) by stepwise increasing the Xeno content. Fully adapted cells were cultivated in shaker flasks to evaluate the performance of influenza A virus production in batch and HCD. Cell densities exceeding 2∙107 cells/mL were achieved in shakers using semi-perfusion, where cell free medium was manually replaced with fresh medium. Volume and time interval of media replacement were chosen to achieve a constant cell-specific perfusion rate of 2.5 pL/(cell h). Cell cultures were infected with influenza virus (A/PR/8/34 H1N1 RKI) with trypsin addition. Cell count, viability, main metabolites and virus titer (HA-assay & TCID50) were monitored pre and post infection. Medium adaptation resulted in a MDCK suspension cell line with morphological, growth, and metabolic characteristics different from parental cells. Cells fully adapted to Xeno medium were growing to higher cell densities (1.4∙107 vs 6∙106 cells/mL) with higher specific growth rate (µmax: 0.036 vs 0.026 1/h), cells were bigger (15-16 vs 13-14 µm) and grew without aggregate formation. Due to higher cell densities at time of infection, virus titers up to 3.6 log10(HAU/100µL) were reached. In semi-perfusion, adapted MDCK cells were grown up to 6∙107 cells/mL, however, maximum virus titer and productivity were observed with 4∙107 cells/mL. In multiple harvests, very high virus titer exceeding 4 log10(HAU/100µL) and up to 9∙109 virions/mL (TCID50) were measured, which corresponded to an accumulated titer of 4.5 log10(HAU/100µL). Cell-specific virus titer was similar or higher compared to the reference batch infections, depending on perfusion and infection strategy. Overall, results in this semi-perfusion scale-down model for influenza A virus production suggest a highly efficient and productive upstream process for influenza virus production, with an up to six-fold improved space time yield compared to batch mode. [1] Palache A. et al., Vaccine 35 (2017): 4681–4686. doi: 10.1016/j.vaccine.2017.07.053 [2] Genzel Y. et al., Vaccine 32 (2014): 2770–2781. doi: 10.1016/j.vaccine.2014.02.016 [3] Vázquez-Ramírez D. et al., Vaccine (2018): article in press. doi: 10.1016/j.vaccine.2017.10.112 [4] Lohr V. et al., Vaccine 28 (2010): 6256–6264. doi: 10.1016/j.vaccine.2010.07.004 [5] Xeno™-S001S MDCK Cell Serum Free Medium (#FG0100402), Bioengine, Shanghai, Chin

Efficient influenza vaccine manufacturing: Single MDCK suspension cells in chemically defined medium

Facing the constant global high demand for influenza vaccines, improving production capacity is most important. For influenza vaccine production, cell culture-based processes have advantages regarding flexibility, efficiency, and safety in comparison with the traditional egg-based processes. To avoid problems related to microcarrier-based approaches and serum containing media, growth of suspension cells in chemically-defined media is favoured. In addition, such a process has advantages regarding the improvement of virus titers, the scale-up of the production process, and overall productivity in up- and downstream processing. In this study, a previously developed MDCK suspension cell line [1] was cultivated in an in-house chemically defined medium to evaluate cell growth and virus production. For the purpose of process intensification, virus adaptation and infection strategies were investigated to achieve high cell densities and to maximize virus titers. Therefore, an adapted influenza virus strain (A/PR/8/34 H1N1 RK1) was generated by a series of virus passages with low multiplicity of infection (MOI). Virus infections were carried out by supplementing 100% of fresh medium, infecting cells with a MOI of 10-3, and with trypsin addition at 72 h of cell cultivations in shake flasks and bioreactors. For scale-up, MDCK cells were cultivated in a DASGIP bioreactor system, optimizing stirring speed, time of infection, pH and DO levels both for cell growth and virus infection. Cell count, viability, main extracellular metabolites, and virus titers were measured to compare productivity between shake flasks and bioreactors. In batch culture (shake flasks and bioreactors), single MDCK cells were grown to maximum cell densities of 1.2 x107 cells/ml with cell viabilities exceeding 98% at high cell specific growth rates of 0.036 h-1. Virus adaptation to the MDCK suspension cell line led to a fast infection and stable virus titers over time. Regarding process optimization, optimal pH (cell growth: 7.00, infection: 7.20), DO (40%) and agitation speed (80 rpm) were chosen for influenza A virus production in three parallel bioreactors. Cell densities of 1.0 x107 cells/ml were achieved at time of infection (72 h) before performing a dilution step. Post infection, similar virus infection dynamics were observed in shake flasks and bioreactors. For both cultivation systems maximal HA titers of 3.6 log10(HAU/100µl) were achieved without reduction of cell-specific virus titer (12,000 virions/cell). Overall, a highly efficient and scalable upstream process was realized by cultivation of MDCK suspension cells as single cells in chemically defined medium. This is a strong basis for a promising application in large-scale influenza vaccine manufacturing and potential process intensification towards high cell density virus production. [1] Huang D. et al., PloS One 10 (2015): e0141686. doi: 10.1371/journal.pone.014168

π\pi-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation

Foundation models have achieved great advances in multi-task learning with a unified interface of unimodal and multimodal tasks. However, the potential of such multi-task learners has not been exploited during transfer learning. In this work, we present a universal parameter-efficient transfer learning method, termed Predict-Interpolate Tuning ($\pi$-Tuning), for vision, language, and vision-language tasks. It aggregates the parameters of lightweight task-specific experts learned from similar tasks to aid the target downstream task. The task similarities are predicted in a unified modality-independent space, yielding a scalable graph to demonstrate task relationships. $\pi$-Tuning has several appealing benefits. First, it flexibly explores both intra- and inter-modal transferability between similar tasks to improve the accuracy and robustness of transfer learning, especially in data-scarce scenarios. Second, it offers a systematical solution for transfer learning with multi-task prediction-and-then-interpolation, compatible with diverse types of parameter-efficient experts, such as prompt and adapter. Third, an extensive study of task-level mutual benefits on 14 unimodal and 6 multimodal datasets shows that $\pi$-Tuning surpasses fine-tuning and other parameter-efficient transfer learning methods both in full-shot and low-shot regimes. The task graph also enables an in-depth interpretable analysis of task transferability across modalities.Comment: To appear in ICML 202

The Prospects for Immigration Amendments

Obg proteins are a family of P-loop GTPases, conserved from bacteria to human. The Obg protein in Escherichia coli (ObgE) has been implicated in many diverse cellular functions, with proposed molecular roles in two global processes, ribosome assembly and stringent response. Here, using pre-steady state fast kinetics we demonstrate that ObgE is an anti-association factor, which prevents ribosomal subunit association and downstream steps in translation by binding to the 50S subunit. ObgE is a ribosome dependent GTPase; however, upon binding to guanosine tetraphosphate (ppGpp), the global regulator of stringent response, ObgE exhibits an enhanced interaction with the 50S subunit, resulting in increased equilibrium dissociation of the 70S ribosome into subunits. Furthermore, our cryo-electron microscopy (cryo-EM) structure of the 50S? ObgE? GMPPNP complex indicates that the evolutionarily conserved N-terminal domain (NTD) of ObgE is a tRNA structural mimic, with specific interactions with peptidyl-transferase center, displaying a marked resemblance to Class I release factors. These structural data might define ObgE as a specialized translation factor related to stress responses, and provide a framework towards future elucidation of functional interplay between ObgE and ribosome-associated (p) ppGpp regulators. Together with published data, our results suggest that ObgE might act as a checkpoint in final stages of the 50S subunit assembly under normal growth conditions. And more importantly, ObgE, as a (p) ppGpp effector, might also have a regulatory role in the production of the 50S subunit and its participation in translation under certain stressed conditions. Thus, our findings might have uncovered an under-recognized mechanism of translation control by environmental cues

An Intent Taxonomy of Legal Case Retrieval

Legal case retrieval is a special Information Retrieval~(IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users' information needs in legal case retrieval could be significantly different from those in Web search and traditional ad-hoc retrieval tasks. While there are several studies that retrieve legal cases based on text similarity, the underlying search intents of legal retrieval users, as shown in this paper, are more complicated than that yet mostly unexplored. To this end, we present a novel hierarchical intent taxonomy of legal case retrieval. It consists of five intent types categorized by three criteria, i.e., search for Particular Case(s), Characterization, Penalty, Procedure, and Interest. The taxonomy was constructed transparently and evaluated extensively through interviews, editorial user studies, and query log analysis. Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval. Furthermore, we apply the proposed taxonomy to various downstream legal retrieval tasks, e.g., result ranking and satisfaction prediction, and demonstrate its effectiveness. Our work provides important insights into the understanding of user intents in legal case retrieval and potentially leads to better retrieval techniques in the legal domain, such as intent-aware ranking strategies and evaluation methodologies.Comment: 28 pages, work in proces