SRNI-CAR: A comprehensive dataset for analyzing the Chinese automotive market

The automotive industry plays a critical role in the global economy, and particularly important is the expanding Chinese automobile market due to its immense scale and influence. However, existing automotive sector datasets are limited in their coverage, failing to adequately consider the growing demand for more and diverse variables. This paper aims to bridge this data gap by introducing a comprehensive dataset spanning the years from 2016 to 2022, encompassing sales data, online reviews, and a wealth of information related to the Chinese automotive industry. This dataset serves as a valuable resource, significantly expanding the available data. Its impact extends to various dimensions, including improving forecasting accuracy, expanding the scope of business applications, informing policy development and regulation, and advancing academic research within the automotive sector. To illustrate the dataset's potential applications in both business and academic contexts, we present two application examples. Our developed dataset enhances our understanding of the Chinese automotive market and offers a valuable tool for researchers, policymakers, and industry stakeholders worldwide

RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition

Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric. However, the lack of large-scale public palmprint datasets limits further research and development of palmprint recognition. In this paper, we propose a novel realistic pseudo-palmprint generation (RPG) model to synthesize palmprints with massive identities. We first introduce a conditional modulation generator to improve the intra-class diversity. Then an identity-aware loss is proposed to ensure identity consistency against unpaired training. We further improve the B\'ezier palm creases generation strategy to guarantee identity independence. Extensive experimental results demonstrate that synthetic pretraining significantly boosts the recognition model performance. For example, our model improves the state-of-the-art B\'ezierPalm by more than $5\%$ and $14\%$ in terms of TAR@FAR=1e-6 under the $1:1$ and $1:3$ Open-set protocol. When accessing only $10\%$ of the real training data, our method still outperforms ArcFace with $100\%$ real training data, indicating that we are closer to real-data-free palmprint recognition.Comment: 12 pages,8 figure

HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting

Multivariate time series (MTS) prediction has been widely adopted in various scenarios. Recently, some methods have employed patching to enhance local semantics and improve model performance. However, length-fixed patch are prone to losing temporal boundary information, such as complete peaks and periods. Moreover, existing methods mainly focus on modeling long-term dependencies across patches, while paying little attention to other dimensions (e.g., short-term dependencies within patches and complex interactions among cross-variavle patches). To address these challenges, we propose a pure MLP-based HDMixer, aiming to acquire patches with richer semantic information and efficiently modeling hierarchical interactions. Specifically, we design a Length-Extendable Patcher (LEP) tailored to MTS, which enriches the boundary information of patches and alleviates semantic incoherence in series. Subsequently, we devise a Hierarchical Dependency Explorer (HDE) based on pure MLPs. This explorer effectively models short-term dependencies within patches, long-term dependencies across patches, and complex interactions among variables. Extensive experiments on 9 real-world datasets demonstrate the superiority of our approach. The code is available at https://github.com/hqh0728/HDMixer

A Compressed-Domain Robust Video Watermarking Against Recompression Attack

International audienc

Association between platelet counts and morbidity and mortality after endovascular repair for type B aortic dissection

This study aimed to assess the association of postoperative platelet counts with early and late outcomes after thoracic endovascular aortic repair (TEVAR) for type B aortic dissection (TBAD). We retrospectively evaluated 892 patients with TBAD who underwent TEVAR from a prospectively maintained database. Postoperative nadir platelet counts were evaluated as a continuous variable, and a categorical variable (thrombocytopenia), which was defined as platelet count≤ the lowest 10% percentile (108 × 109/l). Multivariable logistic regression analyses were conducted to assess the impact of postoperative thrombocytopenia on early outcomes, and multivariable cox regression analyses on long-term mortality. Patients with postoperative thrombocytopenia experienced significantly higher rates of postoperative mortality, prolonged intensive care unit stay, death, stroke, limb ischemia, mesenteric ischemia, acute kidney injury (AKI), and puncture-related hematoma (P< .05 for each), but similar rates of immediate type I endoleak and spinal cord ischemia. Multivariable logistic analyses showed that postoperative thrombocytopenia was independently associated with postoperative stroke, limb ischemia, and AKI. Similar results were observed when postoperative nadir platelet count was modeled as a continuous predictor (P< .05 for each). By multivariable Cox analyses, postoperative thrombocytopenia was an independent predictor for long-term all-cause mortality (hazard ratio 2.72, 95% CI, 1.72–4.29, P< .001). For every 30 × 109/L decrease in postoperative platelet count, the risk of long-term all-cause mortality increased by 15% (HR 1.15; 95% CI 1.07–1.25; P< .001). Therefore, postoperative thrombocytopenia might be a useful tool for risk stratification after TEVAR