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.
The paper uses CNNs to classify road conditions in real-time.
Engine predicts real-time air quality with high resolution.
Study improves accuracy of weather data for real-time building simulations.
PRISM provides real-time SLAM with uncertainty estimates for agent and map states.
BusTr predicts bus travel times from real-time traffic forecasts.
New simulation method tackles sign problem in quantum fields.
FedCM measures contributions in real-time for federated learning.
Though distribution system operators have been adding more sensors to their networks, they still often lack an accurate real-time picture of the behavior of distributed energy resources such as demand responsive electric loads and residential solar generation. Such information could improve system reliability, economic…
Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of …
This thesis builds a real-time VaR calculation workflow for crypto derivatives.
Near real-time damage diagnosis of building structures after extreme events (e.g., earthquakes) is of great importance in structural health monitoring. Unlike conventional methods that are usually time-consuming and require human expertise, pattern recognition algorithms have the potential to interpret sensor recording…
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by the problem of real-time detection of flexible demand available. In this paper we pr…
LAD-BNet improves real-time energy forecasting on edge devices.
This paper proposes a real-time embedded fall detection system using a DVS(Dynamic Vision Sensor) that has never been used for traditional fall detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal Network). The first contribution is building a DVS Falls Dataset, which made our network to recogn…
Adaptive ML learns complex time-varying systems without new data.
Efficiently learns and transports posterior densities for real-time inference.
Linear and non-linear measures of heart rate variability (HRV) are widely investigated as non-invasive indicators of health. Stress has a profound impact on heart rate, and different meditation techniques have been found to modulate heartbeat rhythm. This paper aims to explore the process of identifying appropriate met…
RePAD detects anomalies in streaming time series data in real-time.
Predicts the age of astronomical transients from real-time data.
Neural process model improves real-time condition monitoring signal prediction.
Research proves limits on harmonic map orders into Euclidean buildings.
Proposes a non-autoregressive Transformer for time series forecasting.
TensorWatch enables real-time interactive analysis of deep learning training.
Proves unique maps from certain spaces to others.
This paper constructs and proves the uniqueness of pluriharmonic maps to Euclidean buildings.
Harmonic maps to Euclidean buildings have rectifiable singular strata.
In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty. We first formulate this problem as a Markov decision process, and then develop a deep reinforcement learning based algorithm to learn a stochastic control policy th…
In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions while using the energy resources efficiently, we assume that power c…
Develops an efficient method for real-time data analysis and visualization.
We propose a technique to develop (and localize in) topological maps from light detection and ranging (Lidar) data. Localizing an autonomous vehicle with respect to a reference map in real-time is crucial for its safe operation. Owing to the rich information provided by Lidar sensors, these are emerging as a promising …
Proves regularity of harmonic maps into Euclidean buildings and applies to superrigidity of algebraic groups.
With the rapid emergence of a spectrum of high-end mobile devices, many applications that required desktop-level computation capability formerly can now run on these devices without any problem. However, without a careful optimization, executing Deep Neural Networks (a key building block of the real-time video stream p…
Automated rock fragmentation assessment using deep learning and spatial statistics.
Statistical arbitrage strategies, such as pairs trading and its generalizations, rely on the construction of mean-reverting spreads enjoying a certain degree of predictability. Gaussian linear state-space processes have recently been proposed as a model for such spreads under the assumption that the observed process is…
IVMs help identify dangerous traffic conditions in real-time.
In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable s…
This paper proposes a vision-based fire and smoke segmentation system which use spatial, temporal and motion information to extract the desired regions from the video frames. The fusion of information is done using multiple features such as optical flow, divergence and intensity values. These features extracted from th…
Satellite-based positioning system such as GPS often suffers from large amount of noise that degrades the positioning accuracy dramatically especially in real-time applications. In this work, we consider a data-mining approach to enhance the GPS signal. We build a large-scale high precision GPS receiver grid system to …
DefogGAN predicts hidden RTS game information to aid strategic decision-making.
Efficient superpixel method for real-time segmentation.
We describe a novel spiking neural network (SNN) for automated, real-time handwritten digit classification and its implementation on a GP-GPU platform. Information processing within the network, from feature extraction to classification is implemented by mimicking the basic aspects of neuronal spike initiation and prop…
Model predicts global financial market risks and asset allocation.
It takes skill to build a meaningful predictive model even with the abundance of implementations of modern machine learning algorithms and readily available computing resources. Building a model becomes challenging if hundreds of terabytes of data need to be processed to produce the training data set. In a digital adve…
Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot system to make decisions that trade off between reaction time and accuracy in the predictions. Despite the recent advances in deep learning, it i…
Paper introduces TS-GPT for engineering time series forecasting.
Unified model improves multi-task learning by accounting for temporal misalignment.
Fast Bayesian inference with adaptable priors for real-time applications.
Affine maps reveal higher rank structures in certain spaces.