Ziqian Lin
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View article: CMDMamba: dual-layer Mamba architecture with dual convolutional feed-forward networks for efficient financial time series forecasting
CMDMamba: dual-layer Mamba architecture with dual convolutional feed-forward networks for efficient financial time series forecasting Open
Introduction Transformer models have demonstrated remarkable performance in financial time series forecasting. However, they suffer from inefficiencies in computational efficiency, high operational costs, and limitations in capturing tempo…
View article: The impact of academic burnout on academic achievement: a moderated chain mediation effect from the Stimulus-Organism-Response perspective
The impact of academic burnout on academic achievement: a moderated chain mediation effect from the Stimulus-Organism-Response perspective Open
Objective With the increasing academic pressure faced by university students, academic burnout has gradually become a critical factor affecting students' learning outcomes, drawing widespread attention in the field of education. Methods A …
View article: Statistical Inference for Regression with Imputed Binary Covariates with Application to Emotion Recognition
Statistical Inference for Regression with Imputed Binary Covariates with Application to Emotion Recognition Open
In the flourishing live streaming industry, accurate recognition of streamers' emotions has become a critical research focus, with profound implications for audience engagement and content optimization. However, precise emotion coding typi…
View article: A Novel Approach to Dual Feature Selection of Atrial Fibrillation Based on HC-MFS
A Novel Approach to Dual Feature Selection of Atrial Fibrillation Based on HC-MFS Open
This investigation sought to discern the risk factors for atrial fibrillation within Shanghai’s Chongming District, analyzing data from 678 patients treated at a tertiary hospital in Chongming District, Shanghai, from 2020 to 2023, collect…
View article: Pre-trained Recommender Systems: A Causal Debiasing Perspective
Pre-trained Recommender Systems: A Causal Debiasing Perspective Open
Recent studies on pre-trained vision/language models have demonstrated the practical benefit of a new, promising solution-building paradigm in AI where models can be pre-trained on broad data describing a generic task space and then adapte…
View article: Dual Operating Modes of In-Context Learning
Dual Operating Modes of In-Context Learning Open
In-context learning (ICL) exhibits dual operating modes: task learning, i.e., acquiring a new skill from in-context samples, and task retrieval, i.e., locating and activating a relevant pretrained skill. Recent theoretical work investigate…
View article: Pre-trained Recommender Systems: A Causal Debiasing Perspective
Pre-trained Recommender Systems: A Causal Debiasing Perspective Open
Recent studies on pre-trained vision/language models have demonstrated the practical benefit of a new, promising solution-building paradigm in AI where models can be pre-trained on broad data describing a generic task space and then adapte…
View article: Structure Detection in Three-Dimensional Cellular Cryoelectron Tomograms by Reconstructing Two-Dimensional Annotated Tilt Series
Structure Detection in Three-Dimensional Cellular Cryoelectron Tomograms by Reconstructing Two-Dimensional Annotated Tilt Series Open
The revolutionary technique cryoelectron tomography (cryo-ET) enables imaging of cellular structure and organization in a near-native environment at submolecular resolution, which is vital to subsequent data analysis and modeling. The conv…
View article: Context-aware Spatial-Temporal Neural Network for Citywide Crowd Flow Prediction via Modeling Long-range Spatial Dependency
Context-aware Spatial-Temporal Neural Network for Citywide Crowd Flow Prediction via Modeling Long-range Spatial Dependency Open
Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims at predicting the inflow (the traffic of crowds entering a region in a given time interval) and out…
View article: MOOD: Multi-level Out-of-distribution Detection
MOOD: Multi-level Out-of-distribution Detection Open
Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from causing a model to fail during deployment. While improved OOD detection methods have emerged, they often rely on the final layer outputs and require a full f…
View article: A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling
A Sequential Convolution Network for Population Flow Prediction with Explicitly Correlation Modelling Open
Population flow prediction is one of the most fundamental components in many applications from urban management to transportation schedule. It is challenging due to the complicated spatial-temporal correlation.While many studies have been …
View article: DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis
DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis Open
Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflo…