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.
Paper discusses ASD challenge for machine condition monitoring.
Paper improves ETF tail-risk monitoring reliability.
New monitoring method detects ML risk models' performance changes in medical interventions.
KQT-EWMA monitors multivariate data streams online with flexible and practical change detection.
The paper develops methods for monitoring TPL machine health.
Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type …
Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is …
For large-scale industrial processes under closed-loop control, process dynamics directly resulting from control action are typical characteristics and may show different behaviors between real faults and normal changes of operating conditions. However, conventional distributed monitoring approaches do not consider the…
Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial intelligence in an effort to avoid time-consuming manual analysis and handcrafted feature extraction. Many of these only analyze a single ma…
A new method monitors unstructured 3D shapes without registration.
Study improves data quality assessment for structural monitoring data.
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator c…
In approachability with full monitoring there are two types of conditions that are known to be equivalent for convex sets: a primal and a dual condition. The primal one is of the form: a set C is approachable if and only all containing half-spaces are approachable in the one-shot game; while the dual one is of the form…
Continuous monitoring of cardiac health under free living condition is crucial to provide effective care for patients undergoing post operative recovery and individuals with high cardiac risk like the elderly. Capacitive Electrocardiogram (cECG) is one such technology which allows comfortable and long term monitoring t…
The method and characteristics of several approaches to the pricing of discretely monitored arithmetic Asian options on stocks with discrete, absolute dividends are described. The contrast between method behaviors for options with an Asian tail and those with monitoring throughout their lifespan is emphasized. Rates of…
Study compares semi-supervised learning methods for anomaly detection in hydraulic systems.
DCASE 2022 Task 2 tackles domain shifts in ASD for machine condition monitoring.
Deep learning reconstructs pressure fields and classifies leakage rates in CCS storage sites.
Novel method detects faults in helicopter transmissions using healthy data only.
Adaptive monitoring for AI systems detects and diagnoses shifts in data distribution.
Monitoring means to observe a system for any changes which may occur over time, using a monitor or measuring device of some sort. In this paper we formulate a problem of monitoring dates of maximal risk of a financial position. Thus, the systems we are going to observe arise from situations in finance. The measuring de…
Study reviews machine learning techniques for stress monitoring.
Deep learning speeds up real-time emission monitoring.
Partial monitoring is a rich framework for sequential decision making under uncertainty that generalizes many well known bandit models, including linear, combinatorial and dueling bandits. We introduce information directed sampling (IDS) for stochastic partial monitoring with a linear reward and observation structure. …
In recent years, intelligent condition-based monitor-ing of rotary machinery systems has become a major researchfocus of machine fault diagnosis. In condition-based monitoring,it is challenging to form a large-scale well-annotated datasetdue to the expense of data acquisition and costly annotation.The generated data ha…
Optimizes state monitoring in Markovian systems with cost constraints.
Neural process model improves real-time condition monitoring signal prediction.
Percolation on complex networks has been used to study computer viruses, epidemics, and other casual processes. Here, we present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of node states resulting from actively monitoring the network. Since tradi…
New method detects bearing faults using multivariate statistical process control.
The monitoring of large dynamic networks is a major chal- lenge for a wide range of application. The complexity stems from properties of the underlying graphs, in which slight local changes can lead to sizable variations of global prop- erties, e.g., under certain conditions, a single link cut that may be overlooked du…
Expert-guided model improves seismic compliance monitoring.
Condition monitoring is one of the routine tasks in all major process industries. The mechanical parts such as a motor, gear, bearings are the major components of a process industry and any fault in them may cause a total shutdown of the whole process, which may result in serious losses. Therefore, it is very crucial t…
Online monitor detects classifier drift and adapts predictions.
AI systems that explain their decisions can be monitored for harmful intentions.
Fast feature selection for SHM using canonical correlation.
The failure of a complex and safety critical industrial asset can have extremely high consequences. Close monitoring for early detection of abnormal system conditions is therefore required. Data-driven solutions to this problem have been limited for two reasons: First, safety critical assets are designed and maintained…
Daytime hypoglycemia should be accurately predicted to achieve normoglycemia and to avoid disastrous situations. Hypoglycemia, an abnormally low blood glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia. Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In this paper, …
Quantum computer method for pricing lookback options with jumps.
Training data-driven approaches for complex industrial system health monitoring is challenging. When data on faulty conditions are rare or not available, the training has to be performed in a unsupervised manner. In addition, when the observation period, used for training, is kept short, to be able to monitor the syste…
Simple online monitor detects unsafe LLM outputs.
This research tackles monitoring machine learning algorithms post-deployment, addressing performativity issues.
This paper reviews traditional and modern methods for detecting structural damage using vibrations.
PITMonitor monitors model calibration over time with formal error guarantees.
The paper adds explanation to predictive process monitoring.
IDS algorithm optimizes sequential decisions in various monitoring settings.
A deep learning framework learns wavelet packet transforms for efficient feature extraction.
Focuses on monitoring and explaining models in real-world applications.
ShapShift explains shifts in model predictions due to data distribution changes.