FinSphere improves stock analysis quality with AI and expert-curated data.
problem Lack of objective evaluation metrics and depth in stock analysis by FinLLMs.
method Developed AnalyScore, curated Stocksis dataset, and FinSphere AI agent.
result FinSphere outperforms general and domain-specific LLMs in generating high-quality stock analysis reports.
BusTr predicts bus travel times from real-time traffic forecasts.
problem Improving accuracy of bus travel time predictions.
method Neural sequence model trained on real-time traffic forecasts.
result BusTr outperforms DeepTTE by 30% in Mean Absolute Percentage Error (MAPE).
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.
problem Evaluating LLMs' stock prediction accuracy and robustness.
method Real-time benchmark with 110 U.S. stocks, internal and external information expansion.
result LLMs perform suboptimally in long-term predictions due to fake news and specific industries.
Improved real-time UAV terrain following with RVM-RLS filter.
problem Accurate real-time waypoints estimation under measurement noise in nonlinear, time-varying systems.
method Residual Variance Matching Recursive Least Squares (RVM-RLS) filter guided by RVME criterion.
result Improved waypoints estimation accuracy by approximately 88% compared to benchmarks.
Paper proposes a risk-averse approach to energy storage price arbitrage using conformal uncertainty quantification.
problem Inherent volatility and uncertainty of real-time electricity prices create financial risks for storage arbitrage.
method Two-layer prediction model with conformal uncertainty quantification for high coverage of real-time price uncertainty.
result The framework achieves good profit margins with minimal losses, demonstrating effectiveness in real-time market.
Study uses DNNs for real-time EM inversion, highlighting model errors and proposing solutions.
problem Model errors in DNNs affect real-time geosteering decisions in EM measurements.
method Bayesian ensemble smoothing with DNNs for thousands of model evaluations, identifying multimodality.
result Model errors can lead to biased estimates, necessitating error reduction techniques.
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.
problem Real-time speech enhancement in noisy environments.
method Causal speech enhancement model using encoder-decoder architecture with skip-connections, optimized in time and frequency domains.
result The model matches state-of-the-art performance while working directly on raw waveform.
Optimizes RTB bidding without exploration, improving performance under various budgets.
problem Lack of clear evaluation and generalization issues in RTB systems.
method Maximum entropy principle and conditional independence structures to train a model that generalizes to unseen budget conditions.
result Significantly improved performance under various budget settings compared to baselines.
Real-time policy distillation speeds up and improves reinforcement learning.
problem Slow and inefficient policy distillation in reinforcement learning.
method Simultaneous training and distillation of a teacher and student model.
result Significantly reduced distillation time and improved small model performance.
Dividing local Gaussian processes improve real-time prediction efficiency.
problem Efficient online prediction for large data sets.
method Iterative data-driven division of input space for sublinear computational complexity.
result Sublinear computational complexity in real-time prediction.
DeepONet accelerates nuclear DT inference with high accuracy and efficiency.
problem Real-time prediction and model evaluation in nuclear systems.
method Deep Neural Operator (DeepONet) for surrogate modeling.
result DeepONet outperforms traditional ML methods in accuracy and speed.
Paper optimizes urban navigation with deep learning models.
problem Real-time urban pathfinding challenges.
method Enhanced A* algorithm and neural network model.
result Neural network model outperforms traditional methods, reducing travel times by up to 40%.
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…
KryptoOracle predicts cryptocurrency prices using Twitter sentiments.
problem Real-time price prediction for high-volatility cryptocurrencies.
method Spark-based architecture, sentiment analysis, online learning.
result Real-time adaptation of learning algorithms to new data.
Geometric method improves uncertainty estimation in real-time.
problem Improving uncertainty estimation in machine learning models.
method Geometric distance from training inputs for uncertainty estimation, post-hoc calibration.
result Method yields better uncertainty estimations than existing approaches.
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.
problem Challenges in real-time financial decision-making by autonomous agents.
method Fully automated, live evaluation benchmark with minimal human intervention.
result General intelligence does not translate to effective trading, highlighting limitations.
AI framework predicts invoice dilution in supply chain finance.
problem Invoice dilution risk in supply chain finance.
method AI, machine learning, dynamic credit limits, real-time projections.
result Supplemental AI model improves prediction accuracy.
Action guidance helps agents learn true objectives in games with sparse rewards.
problem Training agents in games with sparse rewards requires significant exploration.
method Action guidance, a novel technique that combines exploration with reward shaping.
result Action guidance enables agents to optimize true objectives efficiently.
New method allows real-time audio synthesis using non-causal convolutions.
problem Real-time audio synthesis limitations due to offline model constraints.
method Post-training reconfiguration of non-causal models for real-time buffer-based processing.
result Non-causal streaming models can be transformed from offline-trained models without quality loss.
Real-time semantic segmentation for autonomous vehicles on FPGA reduces latency and power consumption.
problem Efficient real-time semantic segmentation for autonomous vehicles.
method Compressed ENet architecture, FPGA deployment, batch processing, filter reduction, quantization-aware training.
result Reduced latency to 3 ms per image with batch size of ten and 40% resource utilization.
CyPhERS provides real-time event info for CPSs, avoiding downtime.
problem Real-time event identification in CPSs is challenging due to complex interdependencies and rare events.
method CyPhERS integrates cyber and physical components, generating event signatures for known and unknown events.
result Event signatures provide relevant and inferable information on both known and unknown event types.
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…
The paper proposes a new method to forecast winning prices in real-time bidding.
problem Accurately forecasting winning prices in real-time bidding with limited data.
method The paper introduces a heteroscedastic fully parametric censored regression approach and a mixture density censored network.
result The proposed method significantly improves winning price forecasting compared to existing methods.
GC 2022 challenges real-time trend detection in financial tick data.
problem Efficiently detect trading trends in high-volume financial tick data.
method Real-time complex event processing of tick data, focusing on trend indicators and patterns.
result Participants must build reusable and practical solutions for real-life trading decisions.
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.
Amazon SageMaker Model Monitor detects drift in deployed ML models.
problem Ensuring high performance of ML models in production environments.
method Automatically detects data, concept, bias, and feature attribution drift in real-time.
result Maintains high quality models by providing alerts and corrective actions.
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.
problem Current reinforcement learning requires thousands of samples to converge, and is prone to catastrophic failures.
method Integrates human interaction modalities (demonstrations, interventions, evaluations) into the reinforcement learning loop.
result Human interaction accelerates learning and improves sample efficiency.
ARTEO algorithm optimizes safety-critical systems with uncertainty.
problem Decision-making under uncertainty with safety constraints in real-time optimization.
method ARTEO algorithm uses multi-armed bandits as a mathematical programming problem subject to safety constraints, learning unknown characteristics through exploration and incorporating uncertainty quantification.
result ARTEO achieves less cumulative regret with accurate and safe decisions.
Framework for real-time win probability and player ability in sports.
problem Real-time evaluation and player assessment in team sports.
method Continuous dominance indicator (T-score) and T-process formulation.
result Decomposes competitive advantage into interpretable statistical components.
Noise2Filter improves 3D tomography reconstruction efficiency and accuracy.
problem Efficiently reconstructing 3D tomographic images in real-time with limited data.
method Self-supervised learning and a learned filter method.
result Noise2Filter achieves real-time reconstruction with limited loss of accuracy.
Efficiently learns and transports posterior densities for real-time inference.
problem High computational cost of Bayesian inference for complex posterior densities.
method Tensor-train (TT) format for offline learning, conditional transport for online inference.
result Significant improvement in inference performance for high-dimensional problems.
New NIDS uses hypergraphs for real-time detection of evolving port scans.
problem Challenges in offline NIDS for auto-generated port scans.
method Hypergraph-based metrics for real-time ML ensemble NIDS.
result High accuracy, precision, and recall in detecting port scans and attacks.
Paper proposes a deep learning model for real-time ECG signal segmentation.
problem Real-time analysis of large ECG datasets for tele-health monitoring.
method Combines CNN and LSTM for detecting heartbeats' waveforms.
result Achieved high sensitivity and precision in QRS detection.
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.
problem Recovering spatial detail information and fusing it with semantic information in real-time.
method Proposes a new architecture (MCFNet) with feature refinement and fusion modules, and a gating unit.
result Achieves competitive performance with high speed (75.5% mIOU, 151.3 FPS on Cityscapes).
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.
problem Detecting abrupt changes in high-volume scientific data streams.
method Kernel-based Cumulative Sum (KCUSUM) algorithm using Maximum Mean Discrepancy (MMD).
result KCUSUM outperforms traditional CUSUM in online change point detection.
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…
Automated video conferencing system improves user experience with ASD and VC.
problem Improve remote video conferencing experience through automated speaker detection and virtual cinematography.
method Uses 4K wide-FOV camera, depth camera, and microphone array to extract features and train machine learning models for ASD and VC.
result System performs within 0.3 MOS of an expert cinematographer, as rated by users.
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.
problem Limited real-world training for LLMs in financial markets.
method Virtual zero-sum stock market with competitive multi-agent trading.
result LLMs perform better with chart-based visualizations and a reflection module.
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' …