Time Differential Pricing Model of Urban Rail Transit Considering Passenger Exchange Coefficient

Passenger exchange coefficient is a significant factor which has great impact on the pricing model of urban rail transit. This paper introduces passenger exchange coefficient into a bi-level programming model with time differential pricing for urban rail transit by analysing variation regularity of passenger flow characteristics. Meanwhile, exchange cost coefficient is also considered as a restrictive factor in the pricing model. The improved particle swarm optimisation algorithm (IPSO) was applied to solve the model, and simulation results show that the proposed improved pricing model can effectively realise stratification of fares for different time periods with different routes. Taking Line 2 and Line 8 of the Beijing rail transit network as an example, the simulation result shows that passenger flows of Line 2 and Line 8 in peak hours decreased by 9.94% and 19.48% and therefore increased by 32.23% and 44.96% in off-peak hours, respectively. The case study reveals that dispersing passenger flows by means of fare adjustment can effectively drop peak load and increase off-peak load. The time differential pricing model of urban rail transit proposed in this paper has great influences on dispersing passenger flow and ensures safety operation of urban rail transit. It is also a valuable reference for other metropolitan rail transit operating companies

Multi-scale Attention Flow for Probabilistic Time Series Forecasting

The probability prediction of multivariate time series is a notoriously challenging but practical task. On the one hand, the challenge is how to effectively capture the cross-series correlations between interacting time series, to achieve accurate distribution modeling. On the other hand, we should consider how to capture the contextual information within time series more accurately to model multivariate temporal dynamics of time series. In this work, we proposed a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF), where we integrate multi-scale attention and relative position information and the multivariate data distribution is represented by the conditioned normalizing flow. Additionally, compared with autoregressive modeling methods, our model avoids the influence of cumulative error and does not increase the time complexity. Extensive experiments demonstrate that our model achieves state-of-the-art performance on many popular multivariate datasets

Efficient Test-Time Model Adaptation without Forgetting

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important for deep models when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two practical challenges: 1) existing methods have to perform backward computation for each test sample, resulting in unbearable prediction cost to many applications; 2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). In this paper, we point out that not all the test samples contribute equally to model adaptation, and high-entropy ones may lead to noisy gradients that could disrupt the model. Motivated by this, we propose an active sample selection criterion to identify reliable and non-redundant samples, on which the model is updated to minimize the entropy loss for test-time adaptation. Furthermore, to alleviate the forgetting issue, we introduce a Fisher regularizer to constrain important model parameters from drastic changes, where the Fisher importance is estimated from test samples with generated pseudo labels. Extensive experiments on CIFAR-10-C, ImageNet-C, and ImageNet-R verify the effectiveness of our proposed method.Comment: 15 pages, conferenc

Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as predictive markers in hepatoblastoma

BackgroundThe neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been presented to be a prognostic indicator in several cancers. We were supposed to evaluate the prognostic role of such inflammatory markers in hepatoblastoma (HB).MethodsTotal of 101 children, diagnosed with hepatoblastoma between January 2010 and January 2018, were enrolled before treatment in the study. The clinicopathological parameters, and outcomes were collected through laboratory analyses and patient follow-up. The association between NLR, PLR, and clinicopathological characters were analyzed with Wilcoxon test, Chi-Squared test, Kaplan-Meier, Log-rank and Cox regression analyses.ResultsNLR and PLR were significantly elevated in HB patients (P < 0.001), and related to age (P < 0.001), risk stratification system (P < 0.001), and pretreatment extent of disease (P < 0.0001). NLR was significantly related to alpha-fetoprotein (P = 0.034) and lactate dehydrogenase (P = 0.026). The 3-year overall survival (OS) and event-free survival (EFS) were poor in the high-NLR group (OS: 44.3% vs. 90.3%, P < 0.0001, EFS: 38.6% vs. 80.6%, P = 0.0001). The 3-year OS and EFS were poor in the high-PLR group (OS: 49.1% vs. 68.8%, P = 0.016, EFS: 39.6% vs. 64.6%, P = 0.0117). The multivariate analysis suggested that NLR (HR: 11.359, 95% CI: 1.218–105.947; P = 0.033) and risk stratification (HR: 44.905, 95% CI: 2.458–820.36; P = 0.01), were independent predictors of OS.ConclusionOur research showed that elevated NLR and PLR were the poor prognostic factors in HB patients before treatment. The NLR was an independent prognostic factor for OS

Privacy-Preserving Face Recognition Using Random Frequency Components

The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.Comment: ICCV 202

Dissolvable Magnesium Alloys in Oil and Gas Industry

Invented and continuously optimized during two decades of the shale revolution that completely transformed the global energy market, dissolvable magnesium (DM) alloy technology has advanced significantly in both performance improvement and cost reduction, and thousands of tons of Mg alloy are consumed annually by oil and gas industry to fabricate downhole tools, including dissolvable hydraulic frac plugs. In this book chapter, every aspect of this technology will be reviewed, including history, development routes, manufacturing process, dissolving mechanism and control, failure analysis and prevention, influence of environments, delayed coating, field experience, etc., and outlook will be provided at the end for future development

Study protocol of a phase II clinical trial evaluating the efficacy of neoadjuvant intraperitoneal and systemic albumin-bound paclitaxel combined with camrelizumab and S-1 in the treatment of patients with exfoliative cell-positive gastric cancer

BackgroundCurrently, gastric cancer with positive lavage cytology without gross peritoneal dissemination (GC-CY1) is a special type of metastatic form with poor prognosis. Consensus guidelines on treatment strategies for patients with GC-CY1 have not been established. This study involves a single-arm, prospective, phase II clinical trial to examine the efficacy and safety of neoadjuvant intraperitoneal and systemic (NIPS) albumin-bound paclitaxel combined with Camrelizumab and S-1 in the treatment of GC-CY1 patients.Methods/designThis is a prospective single-center exploratory study, and the primary endpoints of the trial are R0 resection rate and conversion rate of abdominal free cancer cells (FCCs), with secondary endpoints of 3-year progression-free survival (PFS); 3-year overall survival (OS); objective remission rate (ORR); disease control rate (DCR); safety and TRG classification.DiscussionThis study is the first to apply NIPS albumin-bound paclitaxel combined with Camrelizumab and S-1 to the conversion therapy of GC-CY1 patients. It is speculated that this combination of regimens will increase the negative conversion rate of FCCs by 20%, which will provide innovative insights into conversion treatment ideas for GC-CY1 patients to be managed in a more comprehensive and optimized manner.Clinical trial registrationhttp://clinicaltrials.gov/, identifier NCT05410847