A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
MIP framework improves urban flow prediction by adapting to distribution shifts.
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and…
ATFM predicts traffic flow using attention mechanism and neural networks.
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality rel…
Proposes a method to forecast spatial-temporal data with limited training data.
ST-SAN predicts flow with spatial-temporal dependencies using self-attention.
Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The…
New BNN method reduces training time and model size.
Diffusion Transformer captures spatial-temporal dependencies in sequential data.
Survey of urban flows prediction methods using various datasets.
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities ma…
Proposes a GNN for multivariate time-series prediction with filtering.
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance…
New model predicts travel demand uncertainty with high accuracy.
Study compares deep learning models for traffic forecasting, highlighting graph elements' impact.
Deep neural network predicts event ticket prices considering spatial-temporal data sparsity.
New training algorithm enhances SNNs for temporal signal processing.
STCA discovers dynamic functional brain networks using spatial-temporal convolution and attention.
Proposes a non-autoregressive Transformer for time series forecasting.
STOIC improves energy demand forecasting with reliable uncertainty estimates.
GACAN combines multi-granularity time series for traffic forecasting.
A new method detects financial fraud using graph transformers.
Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demons…
Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of …
Proposes KStar Diffuser for kinematics-aware bimanual robotic manipulation.
We propose a mixed deep neural network strategy, incorporating parallel combination of Convolutional (CNN) and Recurrent Neural Networks (RNN), cascaded with deep autoencoders and fully connected layers towards automatic identification of imagined speech from EEG. Instead of utilizing raw EEG channel data, we compute t…
The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network to sign language recognition based on the human skeletal movements. The method uses graphs to capture the si…
Proposes a new model to predict travel demand with zero-inflated and long-tail characteristics.
Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.
PSTN improves traffic condition forecasting with deep neural networks.
Traffic speed prediction is a critically important component of intelligent transportation systems (ITS). Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed, which achieved high accuracy and large-scale predicti…
Dynamic model captures spatial, temporal, and spatiotemporal volatility effects.
Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples. TDA, thus, yields key shape descriptors in the form of persistent topological features that can be used for any supervised or unsupervised learning…
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial dependency on a fixed graph structure, assuming that the underlying relation between entities is pre-determined. However, the explicit graph…
LightGBM outperforms other models in predicting pH values in Georgia, USA.
Paper improves generalization bounds for multi-kernel learning with mixed datasets.
DeepONets enhance spatial-temporal surrogates for structural dynamics.
Novel Bayesian model improves EEG-based BCI character selection.
Develops a tensor decomposition method with side information.
Finite element method applied to Leland's model for option pricing with transaction costs.
Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction wit…
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger deman…
We propose two methods for exact Gaussian process (GP) inference and learning on massive image, video, spatial-temporal, or multi-output datasets with missing values (or "gaps") in the observed responses. The first method ignores the gaps using sparse selection matrices and a highly effective low-rank preconditioner is…
Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing…
Kernel Dynamic Mode Decomposition reconstructs dynamical systems using Laplacian kernel.
Action recognition has attracted increasing attention from RGB input in computer vision partially due to potential applications on somatic simulation and statistics of sport such as virtual tennis game and tennis techniques and tactics analysis by video. Recently, deep learning based methods have achieved promising per…
Hybrid model predicts flow and pressure in water systems.
Hybrid model improves COVID-19 case forecasting accuracy.