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.
Framework detects fake news using weak social signals from multiple sources.
Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementati…
Detecting depression early from social media texts.
TDA detects financial bubbles through early warning signals.
Paper uses ensemblers to predict sepsis early from patient records.
This paper improves neural network predictions with early stopping using conformal calibration.
Novel approach detects early warning indicators in complex systems.
Paper proposes a hierarchical approach for early anomaly detection in time series data for critical health events.
Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the exi…
Study uses machine learning to detect early COVID-19 from CT images.
EagerNet detects network attacks quickly with less resources.
SRR detects early signs of financial crises using multi-layer graphs.
Machine learning monitors detect motor overheating, adapting to concept drift.
We describe two recently proposed machine learning approaches for discovering emerging trends in fatal accidental drug overdoses. The Gaussian Process Subset Scan enables early detection of emerging patterns in spatio-temporal data, accounting for both the non-iid nature of the data and the fact that detecting subtle p…
The IM effect helps detect anomalies by memorizing inliers early.
The paper develops a method to predict the latent deterioration phase in limit order books before stress is observed.
Sepsis is a life-threatening host response to infection associated with high mortality, morbidity, and health costs. Its management is highly time-sensitive since each hour of delayed treatment increases mortality due to irreversible organ damage. Meanwhile, despite decades of clinical research, robust biomarkers for s…
Study uses DNM theory to detect early warning signals of market instability.
Deep neural networks achieve superior performance in challenging tasks such as image classification. However, deep classifiers tend to incorrectly classify out-of-distribution (OOD) inputs, which are inputs that do not belong to the classifier training distribution. Several approaches have been proposed to detect OOD i…
Crowdsourced data helps detect incidents faster, balancing accuracy and practicality.
Machine learning detects subtle glucose changes for early diabetes diagnosis.
Lung cancer is one of the death threatening diseases among human beings. Early and accurate detection of lung cancer can increase the survival rate from lung cancer. Computed Tomography (CT) images are commonly used for detecting the lung cancer.Using a data set of thousands of high-resolution lung scans collected from…
New method detects bearing faults using multivariate statistical process control.
Early detection is a crucial goal in the study of Alzheimer's Disease (AD). In this work, we describe several techniques to boost the performance of 3D deep convolutional neural networks (CNNs) trained to detect AD using structural brain MRI scans. Specifically, we provide evidence that (1) instance normalization outpe…
HyPV-LEAD detects cryptocurrency anomalies proactively, improving financial security.
Real-time fuel leakage detection framework MOCPD improves accuracy.
Survival rates for colorectal cancer are higher when polyps are detected at an early stage and can be removed before they develop into malignant tumors. Automated polyp detection, which is dominated by deep learning based methods, seeks to improve early detection of polyps. However, current efforts rely heavily on the …
Random matrix theory explains transient signal detectability in early-stopped gradient flow.
A new control chart detects shifts in binary data streams quickly and reliably.
QC-SPHARM detects Alzheimer's Disease early using hippocampal surface geometry.
Deep neural networks predict CVCM track circuit failures early.
Novel framework detects CKD in diabetic patients using sparse EHR representations.
XAI identifies key time steps for early crop classification.
Paper presents a spatio-temporal Bayesian model for early detection of COVID-19 hotspots.
Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness if left untreated. One of the essential challenges is early detection, which is very important for treatment success. Unfortunately, the exact identification of the diabetic retinopathy stage is notoriously t…
Study uses satellite data to predict tailings dam collapse risk.
Paper characterizes early-stage dementia signatures from sensor data.
Alzheimer's disease is the most common dementia leading to an irreversible neurodegenerative process. To date, subject revealed advanced brain structural alterations when the diagnosis is established. Therefore, an earlier diagnosis of this dementia is crucial although it is a challenging task. Recently, many studies h…
Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection…
Telescope detects LLM generated text by measuring token repetition probability.
With pressure to increase graduation rates and reduce time to degree in higher education, it is important to identify at-risk students early. Automated early warning systems are therefore highly desirable. In this paper, we use unsupervised clustering techniques to predict the graduation status of declared majors in fi…
Machine learning detects regime shifts in online game-experiments with high accuracy.
In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these are difficult to identify in most data sources. However, by using citizen sens…
Early fault detection using instrumented sensor data is one of the promising application areas of machine learning in industrial facilities. However, it is difficult to improve the generalization performance of the trained fault-detection model because of the complex system configuration in the target diagnostic system…
We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute cri…
3D object detection is a common function within the perception system of an autonomous vehicle and outputs a list of 3D bounding boxes around objects of interest. Various 3D object detection methods have relied on fusion of different sensor modalities to overcome limitations of individual sensors. However, occlusion, l…
Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomal…
Big data analytics improves healthcare through early detection and quality life.