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.
Study on how SEU affects neural networks and proposes remedies.
Paper models entropy-based impact of soft errors on neural network inference.
The study analyzes games and social hierarchies, incorporating luck and depth of competition.
GNNRank uses neural networks to learn global rankings from competition match data.
Combining multiple collider events improves machine learning performance.
Optimal Kelly strategy for multi-outcome parlay bets proven using implicit cash approach.
Harmoniums model multiple time-to-event variables in survival analysis.
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
Graph neural networks detect anomalies in object-centric business processes.
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…
Defines SETR to measure carbon transition risk for investors.
A new method for optimizing stakes in a single event with multiple outcomes.
A deep neural network detects sleep events in polysomnograms with high accuracy.
Support selection and eventwise decoupling for simultaneous bets proven.
Study investigates micro-event detection on FLOSS version releases from Stack Overflow.
Rugby-Bot predicts multiple metrics from a single source using fine-grain data.
New convergence guarantees for learning with unknown nuisance parameters.
The paper categorizes and analyzes various event-linked perpetual futures contracts.
Events in the world may be caused by other, unobserved events. We consider sequences of events in continuous time. Given a probability model of complete sequences, we propose particle smoothing---a form of sequential importance sampling---to impute the missing events in an incomplete sequence. We develop a trainable fa…
We show that a stationary asymptotically flat electro-vacuum solution of Einstein's equations that is everywhere locally "almost isometric" to a Kerr-Newman solution cannot admit more than one event horizon. Axial symmetry is not assumed. In particular this implies that the assumption of a single event horizon in Alexa…
The logic of uncertainty is not the logic of experience and as well as it is not the logic of chance. It is the logic of experience and chance. Experience and chance are two inseparable poles. These are two dual reflections of one essence, which is called co~event. The theory of experience and chance is the theory of c…
Corporate defaults may be triggered by some major market news or events such as financial crises or collapses of major banks or financial institutions. With a view to develop a more realistic model for credit risk analysis, we introduce a new type of reduced-form intensity-based model that can incorporate the impacts o…
Develops a framework for modeling set-valued data in continuous-time.
EventFlow forecasts event sequences without autoregression, improving accuracy.
Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the even…
Paper improves sound event detection using semi-supervised learning.
Polysomnography (PSG) is the gold standard for diagnosing sleep obstructive apnea (OSA). It allows monitoring of breathing events throughout the night. The detection of these events is usually done by trained sleep experts. However, this task is tedious, highly time-consuming and subject to important inter-scorer varia…
Event detection has been one of the most important research topics in social media analysis. Most of the traditional approaches detect events based on fixed temporal and spatial resolutions, while in reality events of different scales usually occur simultaneously, namely, they span different intervals in time and space…
SurvSet offers a repository of 76 T2E datasets for ML benchmarking.
Method estimates exogenous and endogenous factors from event times.
A new methodology for incorporating LGD correlation effects into the Basel II risk weight functions is introduced. This methodology is based on modelling of LGD and default event with a single loss variable. The resulting formulas for capital charges are numerically compared to the current proposals by the Basel Commit…
Study on price formation in financial markets with a single default event.
The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks standardization. Common quantitative scalar evaluation metrics such as sensitivity and specificity can often be misleading depending on the requirements of the application. Evaluation metrics must ultimat…
Generates synthetic health data from patient visits.
Fine-grained event tagging system for SEC 8-K filings improves precision to 96%.
The P300 event-related potential (ERP), evoked in scalp-recorded electroencephalography (EEG) by external stimuli, has proven to be a reliable response for controlling a BCI. The P300 component of an event related potential is thus widely used in brain-computer interfaces to translate the subjects' intent by mere thoug…
Research shows Twitter is permeable to financial events, influencing its content and sentiment.
Early detection and precise characterization of emerging topics in text streams can be highly useful in applications such as timely and targeted public health interventions and discovering evolving regional business trends. Many methods have been proposed for detecting emerging events in text streams using topic modeli…
Study compares and accelerates deep learning for extreme events modeling.
Finding the optimal -means clustering is NP-hard in general and many heuristics have been designed for minimizing monotonically the -means objective. We first show how to extend Lloyd's batched relocation heuristic and Hartigan's single-point relocation heuristic to take into account empty-cluster and single-poin…
We present a new volatility model, simple to implement, that includes a leverage effect whose return-volatility correlation function fits to empirical observations. This model is able to capture both the "retarded effect" induced by the specific risk, and the "panic effect", which occurs whenever systematic risk become…
This note presents a kind of the strong law of large numbers for an insurance risk caused by a single catastrophic event rather than by an accumulation of independent and identically distributed risks. We derive this result by a large diversification effect resulting from optimal allocation of the risk to many reinsure…
The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data dis…
Predict human lives using sequences of life events.
Deep learning improves survival analysis for complex data types.
Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common methods for centrality estimation in A-A and p-A collisions usually rely on a single…
New method falsifies causal graphs using outlier events.
New algorithm identifies best arm in rare event scenarios.