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.
BusTr predicts bus travel times from real-time traffic forecasts.
FinSphere improves stock analysis quality with AI and expert-curated data.
We examined the use of three conventional anomaly detection methods and assess their potential for on-line tool wear monitoring. Through efficient data processing and transformation of the algorithm proposed here, in a real-time environment, these methods were tested for fast evaluation of cutting tools on CNC machines…
PriceSeer benchmarks LLMs in real-time stock prediction.
Improved real-time UAV terrain following with RVM-RLS filter.
Paper proposes a risk-averse approach to energy storage price arbitrage using conformal uncertainty quantification.
Study uses DNNs for real-time EM inversion, highlighting model errors and proposing solutions.
Gesture recognition and hand motion tracking are important tasks in advanced gesture based interaction systems. In this paper, we propose to apply a sliding windows filtering approach to sample the incoming streams of data from data gloves and a decision tree model to recognize the gestures in real time for a manual gr…
Real-time speech enhancement model removes various noises and reverb.
Optimizes RTB bidding without exploration, improving performance under various budgets.
Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification. However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handl…
Dividing local Gaussian processes improve real-time prediction efficiency.
DeepONet accelerates nuclear DT inference with high accuracy and efficiency.
Paper optimizes urban navigation with deep learning models.
Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new "Learning-to-Infer" method is developed for efficient inference of every line statu…
Geometric method improves uncertainty estimation in real-time.
This research investigates the Statistical Machine Translation approaches to translate speech in real time automatically. Such systems can be used in a pipeline with speech recognition and synthesis software in order to produce a real-time voice communication system between foreigners. We obtained three main data sets …
Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user…
AI-Trader benchmarks LLMs in live financial markets, revealing poor trading performance.
AI framework predicts invoice dilution in supply chain finance.
Action guidance helps agents learn true objectives in games with sparse rewards.
New method allows real-time audio synthesis using non-causal convolutions.
Real-time semantic segmentation for autonomous vehicles on FPGA reduces latency and power consumption.
CyPhERS provides real-time event info for CPSs, avoiding downtime.
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…
GC 2022 challenges real-time trend detection in financial tick data.
Flexible algorithm of multicurrency trade on Forex market has been built on the grounds of non-linear stochastic wavelets (NSW) model. Probability of the loss-free trade has been evaluated. Results of the algorithm's real-time testing and issues of the algorithm's development are discussed.
Policy distillation in deep reinforcement learning provides an effective way to transfer control policies from a larger network to a smaller untrained network without a significant degradation in performance. However, policy distillation is underexplored in deep reinforcement learning, and existing approaches are compu…
Amazon SageMaker Model Monitor detects drift in deployed ML models.
Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics. The problem is challenging due to the data variability among individual users, and sensor artefacts. In this work, we propose an end-to-end framework for real-time cognitive wor…
This research integrates human interaction into reinforcement learning to improve sample efficiency and real-time learning.
ARTEO algorithm optimizes safety-critical systems with uncertainty.
Framework for real-time win probability and player ability in sports.
Noise2Filter improves 3D tomography reconstruction efficiency and accuracy.
Efficiently learns and transports posterior densities for real-time inference.
New NIDS uses hypergraphs for real-time detection of evolving port scans.
Paper proposes a deep learning model for real-time ECG signal segmentation.
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…
MCFNet recovers spatial detail and fuses it with semantic information for real-time segmentation.
Surface inspection systems are an important application domain for computer vision, as they are used for defect detection and classification in the manufacturing industry. Existing systems use hand-crafted features which require extensive domain knowledge to create. Even though Convolutional neural networks (CNNs) have…
KCUSUM detects abrupt changes in real-time data streams efficiently.
Graphs are widely adopted for modeling complex systems, including financial, biological, and social networks. Nodes in networks usually entail attributes, such as the age or gender of users in a social network. However, real-world networks can have very large size, and nodal attributes can be unavailable to a number of…
Timely detection of abrupt anomalies is crucial for real-time monitoring and security of modern systems producing high-dimensional data. With this goal, we propose effective and scalable algorithms. Proposed algorithms are nonparametric as both the nominal and anomalous multivariate data distributions are assumed unkno…
Markov Decision Processes (MDPs), the mathematical framework underlying most algorithms in Reinforcement Learning (RL), are often used in a way that wrongfully assumes that the state of an agent's environment does not change during action selection. As RL systems based on MDPs begin to find application in real-world sa…
Agent Trading Arena trains LLMs in real-time financial markets to improve numerical reasoning.
Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei. Different from traditional recommendation where individual preference is mostly static, O2O recommendation should be dynamic to capture variation of users' …
Convolutional Neural Networks (CNN) are successfully used for various visual perception tasks including bounding box object detection, semantic segmentation, optical flow, depth estimation and visual SLAM. Generally these tasks are independently explored and modeled. In this paper, we present a joint multi-task network…
We present a queuing model of parking dynamics and a model-based prediction method to provide real-time probabilistic forecasts of future parking occupancy. The queuing model has a non-homogeneous arrival rate and time-varying service time distribution. All statistical assumptions of the model are verified using data f…