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.
A new method improves few-shot event classification accuracy.
New deep learning method handles rare and imbalanced events in time series.
The study uses financial events to predict stock market movements.
Deep learning for particle classification on large event images.
Paper tackles event stream outliers using a novel weight function.
Reduces false positives in classifying rare online platforms.
Generative model creates EEG data for RSVP experiments.
Improved audio event recognition using audiovisual transformers.
A real-world dataset is provided from a pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x's) and the event label (y). The pri…
Combining multiple collider events improves machine learning performance.
Paper analyzes cyber risk classifications for forecasting performance.
We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an input signal, each …
AI classifies tourist events for better service.
Adversarial domain adaptation reduces sample bias in high energy physics classifier.
Smart city surveillance benefits from sound event recognition.
In this paper, we describe our method for DCASE2019 task3: Sound Event Localization and Detection (SELD). We use four CRNN SELDnet-like single output models which run in a consecutive manner to recover all possible information of occurring events. We decompose the SELD task into estimating number of active sources, est…
In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven health monitoring systems is gaining in popularity due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as Deep Learning. The main purpose of this pa…
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…
Simple methods boost sound event classifier accuracy by 2.5%.
Crowd opinions in microblogs can predict event outcomes, matching with expert opinions.
Due to globalization, geographic boundaries no longer serve as effective shields for the spread of infectious diseases. In order to aid bio-surveillance analysts in disease tracking, recent research has been devoted to developing information retrieval and analysis methods utilizing the vast corpora of publicly availabl…
Quantum hybrid vision transformers improve event classification in high energy physics.
Proposes a method to improve rare event prediction in healthcare.
New method clusters hydrological and sediment data for storm event analysis.
Shapelet transform improves time series classification for earthquake, wind, and wave events.
Classifies and analyzes the stability of black hole event horizon birth points using contact geometry.
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages the power of convolutional recurrent neural network architectures; convolutional layers learn effective features over which higher recurren…
Meta-learning improves few-shot acoustic event detection.
Study improves radio show segmentation using audio embeddings.
Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds. We consider several class-agnostic semantic constraints that apply to unlabeled nonspeech audio: (i) noise and translations in time do not change t…
Crowdsourcing and active learning reduce manual annotation in social media event classification.
As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the `Event Detection - Office Synthetic' task of DCASE 2013,…
In this paper, we consider the problem of event classification with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex temporal dependencies between the variables combined with sparsity of the data makes the event classification problem particularly challengin…
This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time …
Develops FSC for maxima nominated samples, improving classification in rare-event data.
Paper proposes a hierarchical approach for early anomaly detection in time series data for critical health events.
Various and ubiquitous information systems are being used in monitoring, exchanging, and collecting information. These systems are generating massive amount of event sequence logs that may help us understand underlying phenomenon. By analyzing these logs, we can learn process models that describe system procedures, pre…
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little r…
Quantum-enhanced classifier improves EEG data classification accuracy.
We describe a novel weakly labeled Audio Event Classification approach based on a self-supervised attention model. The weakly labeled framework is used to eliminate the need for expensive data labeling procedure and self-supervised attention is deployed to help a model distinguish between relevant and irrelevant parts …
Study shows SEC crypto classification led to significant market reactions.
The study examines how class imbalance impacts logistic regression models in low-default credit portfolios.
This monograph introduces deep learning models for predicting time-to-event outcomes.
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA detector to perform classification and regression tasks. We show that the resul…
Machine learning tools are commonly used in modern high energy physics (HEP) experiments. Different models, such as boosted decision trees (BDT) and artificial neural networks (ANN), are widely used in analyses and even in the software triggers. In most cases, these are classification models used to select the "signal"…
Event-based control improves neural network training speed and accuracy.
Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackl…
A novel transformer model improves classification of partially ordered sequences.