Optimizing Two Sided Promotion for IS Enabled Transportation Network: A Conditional Bayesian Learning Model

This paper investigates whether taxi apps provide attribute value for taxi driver, and how two-sided sales promotion interacted with consumer learning about attribute value to influence taxi drivers’ decision of adoption of taxi app. We propose a conditional Bayesian learning model to allow learning about multiple attributes. We find the evidence of taxi driver’s learning about attribute of app, transaction successful rate and the probability of earning cash back from app provider. We also find measurable evidence that sales promotion during product introduction has indirect effect through learning

Exploring the Nudging and Counter-Nudging Effects of Campaign Updates in Crowdfunding

Crowdfunding has emerged as a vital financing avenue for entrepreneurs to realize their ventures. With limited information availability, crowd-funders may choose to first follow the progress of interested crowdfunding campaigns, such as monitoring project updates to acquire more information for justifying investment decision, before making pledges. Although campaign updates have been touted to be a salient driver of fundraising success, the underlying mechanism for this relationship remains unclear. Subscribing to nudge theory, we strive to shed light on how update strategies, such as frequency and message length, can serve as nudges to convert project followers to actual funders. Specifically, we posit a dual-role of campaign updates whereby an over-zealous update strategy may induce a counter-nudging effect that deters prospective funders, what we labelled as ‘over-nudging’. This study advances a model to account for both the nudging and counter-nudging effects of campaign updates in crowdfunding, which could yield insights for fundraisers to optimize their update strategy and in turn, get their business off the ground

One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER

Cross-domain NER is a challenging task to address the low-resource problem in practical scenarios. Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain. Owing to the mismatch issue among entity types in different domains, previous approaches normally tune all parameters of PLMs, ending up with an entirely new NER model for each domain. Moreover, current models only focus on leveraging knowledge in one general source domain while failing to successfully transfer knowledge from multiple sources to the target. To address these issues, we introduce Collaborative Domain-Prefix Tuning for cross-domain NER (CP-NER) based on text-to-text generative PLMs. Specifically, we present text-to-text generation grounding domain-related instructors to transfer knowledge to new domain NER tasks without structural modifications. We utilize frozen PLMs and conduct collaborative domain-prefix tuning to stimulate the potential of PLMs to handle NER tasks across various domains. Experimental results on the Cross-NER benchmark show that the proposed approach has flexible transfer ability and performs better on both one-source and multiple-source cross-domain NER tasks. Codes will be available in https://github.com/zjunlp/DeepKE/tree/main/example/ner/cross.Comment: Work in progres

Deployment of Accounting Analytics Models for Workforce and Project Management

We provide several innovative industry solutions and analytical tools for our industry partner, HANDS Enterprise Solutions, to better assess their cost structure and improve their HR policies using various big data analysis and data visualization tools. Based on our analysis, we identify several areas of weakness in their HR policies, highlight salient points to buttress their internal control policies, and provide policy recommendations for future improvement and analysis. Overall, we have improved the quality of the company’s claims submission. The productivity improvement is applicable for both their consultants and staff, and the improvement in cost control can be observed at both the project and employee levels

Early Triassic microbialites from the Changxing Region of Zhejiang Province, South China

© 2019, The Author(s). Microbialites, often considered as a signal of extreme marine environment, are common in the Lower Triassic strata of South China where they flourished in the aftermath of the end-Permian mass extinction. Early Triassic microbialite facies are known to vary palaeogeographically, perhaps due to differing climates, ocean chemistry, and water depths. This paper provides the first record of a brief, but spectacular development of microbialites in the aftermath of the end-Permian mass extinction at Panjiazhuang section, Changxing Region of Zhejiang Province (eastern South China). Here, the Upper Permian Changxing Formation comprises typical shallow platform facies rich in calcareous algae and foraminifera, the development of which was terminated by the major end-Permian regression. A 3.4-m-thick microbialite began to form at the onset of the transgression in the earliest Triassic. The microbialite at Panjiazhuang section is composed of thrombolite that contains abundant calcified cyanobacteria, small gastropods, microconchid tubes and ostracods, representing a low-diversity shallow marine community in the aftermath of the end-Permian crisis. The microbialites are succeeded by thin-bedded micrites bearing thin-shelled bivalves, which record a rapid sea-level rise in the Early Triassic. Abundant populations of small pyrite framboids are observed in the upper part of the microbialites and the overlying thin-bedded micrites, suggesting that dysoxic water conditions developed at that time. The appearance of microbialites near the Permian–Triassic boundary (PTB) at Panjiazhuang section was the result of peculiar marine conditions following the end-Permian regression, whilst their disappearance was due to the increasing water depth and the development of dysoxia

Soft modes in hot QCD matter

The chiral crossover of QCD at finite temperature and vanishing baryon density turns into a second order phase transition if lighter than physical quark masses are considered. If this transition occurs sufficiently close to the physical point, its universal critical behaviour would largely control the physics of the QCD phase transition. We quantify the size of this region in QCD using functional approaches, both Dyson-Schwinger equations and the functional renormalisation group. The latter allows us to study both critical and non-critical effects on an equal footing, facilitating a precise determination of the scaling regime. We find that the physical point is far away from the critical region. Importantly, we show that the physics of the chiral crossover is dominated by soft modes even far beyond the critical region. While scaling functions determine all thermodynamic properties of the system in the critical region, the order parameter potential is the relevant quantity away from it. We compute this potential in QCD using the functional renormalisation group and Dyson-Schwinger equations and provide a simple parametrisation for phenomenological applications.Comment: 7+8 pages, 5+4 figure

Fine-grained parallel RNAalifold algorithm for RNA secondary structure prediction on FPGA

<p>Abstract</p> <p>Background</p> <p>In the field of RNA secondary structure prediction, the RNAalifold algorithm is one of the most popular methods using free energy minimization. However, general-purpose computers including parallel computers or multi-core computers exhibit parallel efficiency of no more than 50%. Field Programmable Gate-Array (FPGA) chips provide a new approach to accelerate RNAalifold by exploiting fine-grained custom design.</p> <p>Results</p> <p>RNAalifold shows complicated data dependences, in which the dependence distance is variable, and the dependence direction is also across two dimensions. We propose a systolic array structure including one master Processing Element (PE) and multiple slave PEs for fine grain hardware implementation on FPGA. We exploit data reuse schemes to reduce the need to load energy matrices from external memory. We also propose several methods to reduce energy table parameter size by 80%.</p> <p>Conclusion</p> <p>To our knowledge, our implementation with 16 PEs is the only FPGA accelerator implementing the complete RNAalifold algorithm. The experimental results show a factor of 12.2 speedup over the RNAalifold (<it>ViennaPackage </it>– 1.6.5) software for a group of aligned RNA sequences with 2981-residue running on a Personal Computer (PC) platform with Pentium 4 2.6 GHz CPU.</p

High Correlation Between Structure Development and Chemical Variation During Biofilm Formation by Vibrio parahaemolyticus

The complex three-dimensional structure of biofilms is supported by extracellular polymeric substances (EPSs) and additional insight on chemical variations in EPS and biofilm structure development will inform strategies for control of biofilms. Vibrio parahaemolyticus VPS36 biofilm development was studied using confocal laser scanning microscopy (CLSM) and Raman spectroscopy (RM). The structural parameters of the biofilm (biovolume, mean thickness, and porosity) were characterized by CLSM and the results showed that VPS36 biofilm formed dense structures after 48 h incubation. There were concurrent variations in carbohydrates and nucleic acids contents in the EPS as evidenced by RM. The Raman intensities of the chemical component in EPS, measured using Pearson’s correlation coefficient, were positively correlated with biovolume and mean thickness, and negatively correlated with porosity. The Raman intensity for carbohydrates correlated closely with mean thickness (p-value &lt; 0.01) and the Raman intensity for nucleic acid correlated closely with porosity (p-value &lt; 0.01). Additional evidence for these correlations were confirmed using scanning electron microscopic (SEM) and crystal violet staining

Impact of stress hyperglycemia ratio on mortality in patients with critical acute myocardial infarction: insight from American MIMIC-IV and the Chinese CIN-II study

Background: Among patients with acute coronary syndrome and percutaneous coronary intervention, stress hyperglycemia ratio (SHR) is primarily associated with short-term unfavorable outcomes. However, the relationship between SHR and long-term worsen prognosis in acute myocardial infarction (AMI) patients admitted in intensive care unit (ICU) are not fully investigated, especially in those with different ethnicity. This study aimed to clarify the association of SHR with all-cause mortality in critical AMI patients from American and Chinese cohorts. Methods: Overall 4,337 AMI patients with their first ICU admission from the American Medical Information Mart for Intensive Care (MIMIC)-IV database (n = 2,166) and Chinese multicenter registry cohort Cardiorenal ImprovemeNt II (CIN-II, n = 2,171) were included in this study. The patients were divided into 4 groups based on quantiles of SHR in both two cohorts. Results: The total mortality was 23.8% (maximum follow-up time: 12.1 years) in American MIMIC-IV and 29.1% (maximum follow-up time: 14.1 years) in Chinese CIN-II. In MIMIC-IV cohort, patients with SHR of quartile 4 had higher risk of 1-year (adjusted hazard radio [aHR] = 1.87; 95% CI: 1.40–2.50) and long-term (aHR = 1.63; 95% CI: 1.27–2.09) all-cause mortality than quartile 2 (as reference). Similar results were observed in CIN-II cohort (1-year mortality: aHR = 1.44; 95%CI: 1.03–2.02; long-term mortality: aHR = 1.32; 95%CI: 1.05–1.66). In both two group, restricted cubic splines indicated a J-shaped correlation between SHR and all-cause mortality. In subgroup analysis, SHR was significantly associated with higher 1-year and long-term all-cause mortality among patients without diabetes in both MIMIC-IV and CIN-II cohort. Conclusion: Among critical AMI patients, elevated SHR is significantly associated with and 1-year and long-term all-cause mortality, especially in those without diabetes, and the results are consistently in both American and Chinese cohorts

Privacy-preserving continual learning methods for medical image classification: a comparative analysis

BackgroundThe implementation of deep learning models for medical image classification poses significant challenges, including gradual performance degradation and limited adaptability to new diseases. However, frequent retraining of models is unfeasible and raises concerns about healthcare privacy due to the retention of prior patient data. To address these issues, this study investigated privacy-preserving continual learning methods as an alternative solution.MethodsWe evaluated twelve privacy-preserving non-storage continual learning algorithms based deep learning models for classifying retinal diseases from public optical coherence tomography (OCT) images, in a class-incremental learning scenario. The OCT dataset comprises 108,309 OCT images. Its classes include normal (47.21%), drusen (7.96%), choroidal neovascularization (CNV) (34.35%), and diabetic macular edema (DME) (10.48%). Each class consisted of 250 testing images. For continuous training, the first task involved CNV and normal classes, the second task focused on DME class, and the third task included drusen class. All selected algorithms were further experimented with different training sequence combinations. The final model's average class accuracy was measured. The performance of the joint model obtained through retraining and the original finetune model without continual learning algorithms were compared. Additionally, a publicly available medical dataset for colon cancer detection based on histology slides was selected as a proof of concept, while the CIFAR10 dataset was included as the continual learning benchmark.ResultsAmong the continual learning algorithms, Brain-inspired-replay (BIR) outperformed the others in the continual learning-based classification of retinal diseases from OCT images, achieving an accuracy of 62.00% (95% confidence interval: 59.36-64.64%), with consistent top performance observed in different training sequences. For colon cancer histology classification, Efficient Feature Transformations (EFT) attained the highest accuracy of 66.82% (95% confidence interval: 64.23-69.42%). In comparison, the joint model achieved accuracies of 90.76% and 89.28%, respectively. The finetune model demonstrated catastrophic forgetting in both datasets.ConclusionAlthough the joint retraining model exhibited superior performance, continual learning holds promise in mitigating catastrophic forgetting and facilitating continual model updates while preserving privacy in healthcare deep learning models. Thus, it presents a highly promising solution for the long-term clinical deployment of such models