Predicts arterial road incident duration with extreme gradient boosting.
problem Predicting incident duration on arterial roads, especially with limited data.
method Bi-level framework combining classification and regression models.
result Extreme gradient boosting outperformed other models by 53%.
Model predicts traffic incident duration and identifies key features.
problem Predict traffic incident duration and identify critical features.
method Multi-task learning framework with sparsity optimization and ADMM algorithm.
result Model predicts incident duration and identifies key features effectively.
The paper formalizes incidence tensors and their decomposition for geometric deep learning.
problem Representing structured data like graphs and simplicial complexes.
method Formalizes incidence tensors, analyzes their structure, and presents equivariant networks.
result Incidence tensors decompose into invariant subsets, leading to efficient linear map implementations.
Model predicts traffic speed using urban incidents.
problem Accurately predicting traffic speed in urban areas.
method Deep Incident-Aware Graph Convolutional Network (DIGC-Net).
result Model outperforms competing benchmarks in traffic speed prediction.
This paper aims to optimize incident-specific cyber insurance design.
problem Complexity in determining optimal risk retention and transfer.
method Economic foundation for incident-specific cyber insurance with Pareto optimality.
result Illustrates feasibility of designing incident-specific indemnities for both parties.
Crowdsourced data helps detect incidents faster, balancing accuracy and practicality.
problem Detecting incidents from crowdsourced data is challenging due to noise and uncertainty.
method CROME (Crowdsourced Multi-objective Event Detection) uses CNN and Pareto optimization.
result The approach outperforms existing methods in incident detection and practicality.
Study uses vehicle trajectory data to predict traffic incidents on highways.
problem Early detection of traffic incidents to reduce secondary crashes.
method Machine learning algorithms (Logistic Regression, Random Forest, Extreme Gradient Boost, Artificial Neural Network) applied to vehicle trajectory data.
result Random Forest model performs best for incident prediction.
HYVINT generates hypergraphs with intensity-driven incidence formation and variational learning.
problem Challenges in generating hypergraphs with mechanistic interpretation and limited latent space.
method HYVINT uses intensity-driven incidence formation and a lower-bound variational estimator for latent representations.
result HYVINT achieves strong fidelity and novelty on synthetic and real-world hypergraphs.
Study shows data breaches cause significant financial losses for firms, especially in health sector.
problem Understanding the economic impact of cyber incidents on listed firms.
method Event study using abnormal returns over 2012-2022, adjusting for event-induced variance and residual cross-correlation.
result Data breaches cause significant financial losses for firms, especially in health sector.
Automated suggestions help train technicians diagnose incidents faster.
problem Manual and time-consuming incident diagnosis by train maintenance technicians.
method Developed and deployed a learning machine to suggest diagnostics to technicians.
result The model refines its accuracy through feedback from experts and uses feature engineering.
Market valuation duration is 175 years, but drops to 46 years during crises.
problem Understanding the duration of market valuation and its impact on returns.
method Comparing market valuation ratios and dividends to estimate duration, analyzing the discount rate effect.
result Valuation duration is negatively correlated with market returns, with a robust out-of-sample R2 of 15%.
New model predicts financial transaction durations using quantiles.
problem Modeling financial transaction durations using traditional mean duration.
method Proposes a new autoregressive conditional duration model based on log-symmetric distributions reparametrized by quantiles.
result Proposed model allows for modeling different percentiles of financial transaction durations.
In this letter we borrow from the inference techniques developed for unbounded state-cardinality (nonparametric) variants of the HMM and use them to develop a tuning-parameter free, black-box inference procedure for Explicit-state-duration hidden Markov models (EDHMM). EDHMMs are HMMs that have latent states consisting…
Investigates proving geometric theorems over complex and real numbers using tilings.
problem Proving incidence theorems over C and R using the master theorem.
method Formalizes tiling proofs and introduces a hierarchy of theorems based on topological spaces.
result Identifies which theorems can or cannot be proved over C and R.
New model improves inference on asset market durations.
problem Statistical artifacts in trade aggregation.
method Flexible stochastic duration model with uncertainty in related trades.
result Conditional hazard function varies less than previous studies.
This paper proposes a real-time signal plan recommendation system for traffic incidents.
problem Limited effectiveness of traffic incident management due to late response and workload.
method Decomposes recommendation task into real-time traffic prediction and plan association, learning from historical data and metric learning.
result Precision score of 96.75% and recall of 87.5% on testing plan, with 22.5 minutes lead time ahead of Waze alerts.
We characterize the boundary at infinity of a complex hyperbolic space as a compact Ptolemy space that satisfies four incidence axioms.
New econometric results for financial duration models under varying tail behaviors.
problem Estimation and inference challenges in financial durations models with random event counts.
method Analysis of likelihood estimators for ACD models, focusing on tail behavior and stationarity.
result Asymptotic normality breaks down for tail indices smaller than one, leading to mixed Gaussian estimators with non-standard rates of convergence.
The paper studies how points and lines can move while preserving incidences.
problem Understanding how point-line configurations can move while maintaining their geometric relationships.
method Developed a projective rigidity matrix to analyze the infinitesimal motions and dependencies of point-line configurations.
result The symmetry-adapted projective rigidity matrix provides a more detailed analysis of symmetric configurations and their motions.
Unified asymptotic theory and tests for ACD models reveal infinite-mean durations in cryptocurrency trading.
problem Challenges in asymptotic theory for ACD models, especially for integrated ACD.
method Unified asymptotic theory for quasi-maximum likelihood estimator, hypothesis testing framework.
result Infinite-mean durations in cryptocurrency trading, rejected integrated ACD hypothesis.
We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronol…
Crimes emerge out of complex interactions of human behaviors and situations. Linkages between crime incidents are highly complex. Detecting crime linkage given a set of incidents is a highly challenging task since we only have limited information, including text descriptions, incident times, and locations. In practice,…
Develops regression trees for estimating cumulative incidence curves in competing risks.
problem Estimating cumulative incidence functions in competing risks settings.
method Uses augmented estimators of the Brier score risk to build and prune regression trees.
result Demonstrates the utility of the proposed methods through simulation studies and real data.
The paper explains the topological origin of the distinction between incidence theorems over division rings and fields.
problem Understanding the distinction between incidence theorems over division rings and fields.
method Extending the surface-graph approach to noncommutative settings, the paper analyzes the topological properties of graphs embedded on surfaces of different genera.
result Theorems associated with graphs on the sphere hold over any division ring, while those on surfaces of positive genus typically hold only if the ground ring is a field.
New conditions for ACD model consistency and normality.
problem Random number of durations in ACD model.
method Additional sufficient conditions for consistency and normality of QMLE.
result Finite mean of durations is required for consistency and normality.
In a recent work of Ayaka Shimizu[5], she defined an operation named region crossing change on link diagrams, and showed that region crossing change is an unknotting operation for knot diagrams. In this paper, we prove that region crossing change on a 2-component link diagram is an unknotting operation if and only…
Enhances cyber risk assessment with entity-specific features.
problem Lack of high-quality public cyber incident data.
method Develops an InsurTech framework to enrich cyber incident data with entity-specific attributes and implements machine learning models.
result InsurTech features improve prediction robustness and provide customized risk profiles.
Study finds companies react negatively to material cybersecurity incident disclosures.
problem Understanding market reactions to cybersecurity incidents.
method Examined daily stock price movements of companies disclosing material cybersecurity incidents.
result Companies tend to experience negative price reactions after disclosing material cybersecurity incidents.
In this paper we continue the study of generic properties of the Novikov complex, began in the work "The incidence coefficients in the Novikov complex are generically rational functions" ( dg-ga/9603006). For a Morse map f:M→S1 there is a refined version of Novikov complex, defined over the Novikov completion of …
An unsupervised method clusters patient incident reports for content analysis.
problem Lack of methods to extract interpretable content from electronic healthcare records.
method Combines text-embedding with paragraph vectors and graph-theoretical multiscale community detection.
result Extracts high-intrinsic-consistency groups of patient incident reports.
The distribution of intertrade durations, defined as the waiting times between two consecutive transactions, is investigated based upon the limit order book data of 23 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. A scaling pattern is observed in the distributions of intertrade dur…
Study shows training duration impacts model merging quality, suggesting joint selection of duration and method.
problem Impact of expert training duration on model merging quality for large language models (LLMs).
method Systematically fine-tuned experts on five domains across three model sizes, evaluating five merging methods at each duration.
result Training duration affects merging quality, with simple averaging degrading sharply and sparsification-based methods performing well past the validation optimum.
A new method uses burst and inter-burst duration to test long-range memory in financial markets.
problem Varying results from long-range memory estimators in financial markets.
method Burst and inter-burst duration statistical analysis of limit order book data.
result The new method provides a more reliable evaluation of the Hurst exponent.
Study shows training duration affects model merging quality, suggesting joint selection of duration and method.
problem Impact of expert training duration on model merging quality for large language models (LLMs).
method Systematically fine-tuned experts on five domains across three model sizes, evaluated five merging methods at each duration.
result Training duration and merging method should be chosen jointly, not independently.
Study predicts stock transaction durations using LSTM and attention mechanism.
problem Estimating the probability density function of transaction durations in financial markets.
method Proposes a hybrid model combining LSTM networks and attention mechanism to extend ACD model.
result Demonstrates superior performance of the hybrid model on large-scale financial data.
QTIP improves traffic prediction in sudden disruptions.
problem Traffic models fail during sudden disruptions.
method Simulation-based framework for real-time adaptation.
result QTIP improves traffic prediction in critical minutes of incidents.
Proposes a new model for better speech segmentation.
problem Improving speech segmentation accuracy.
method Integrates recurrent explicit duration variables into rSLDS and uses Pólya-gamma augmentation for inference.
result Demonstrates improved segmentation on various datasets.
This paper introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility model of Calvet and Fisher (2004) to the duration setting. Although the MSMD process is exponential β-mixing as we show in the paper, it is capable of generating highly persistent autocorrelation. W…
Characterizes groups with specific boundary properties.
problem Groups with Schottky set boundaries.
method Study relatively hyperbolic group pairs with Schottky boundaries.
result Groups with boundaries where Schottky sets have 1 or 2 component incidence graphs.
Finite rigid sets found in surface curve complexes.
problem Finding rigid sets in surface curve complexes.
method Incidence-preserving maps to find rigid subcomplexes.
result Finite rigid subcomplexes identified in surface curve complexes.
Statistical models outperform mechanistic models in short-term COVID-19 incidence forecasts.
problem Comparing accuracy of mechanistic vs statistical models for short-term COVID-19 incidence forecasts.
method Empirical comparison of forecasts from mechanistic and statistical models using daily incidence data from six US states.
result Statistical models are at least as accurate as mechanistic models and better capture volatility.
We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis. We extrapolate to…
A key barrier to making phonetic studies scalable and replicable is the need to rely on subjective, manual annotation. To help meet this challenge, a machine learning algorithm was developed for automatic measurement of a widely used phonetic measure: vowel duration. Manually-annotated data were used to train a model t…
Intertrade duration of equities is an important financial measure characterizing the trading activities, which is defined as the waiting time between successive trades of an equity. Using the ultrahigh-frequency data of a liquid Chinese stock and its associated warrant, we perform a comparative investigation of the sta…
The intraday pattern, long memory, and multifractal nature of the intertrade durations, which are defined as the waiting times between two consecutive transactions, are investigated based upon the limit order book data and order flows of 23 liquid Chinese stocks listed on the Shenzhen Stock Exchange in 2003. An inverse…
This paper tackles JSSP with uncertain task durations using DRL.
problem Job Shop Scheduling Problem with uncertain task durations.
method Integrates Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL) to generate robust schedules.
result Advances DRL applications to JSSPs, enhancing generalization and scalability.
This paper detects anomalies in cellular network traffic using hybrid methods.
problem Detecting anomalies in network traffic for security and analysis.
method Hybrid method combining GARCH, K-means, and Neural Network.
result Anomaly detection in cellular network traffic successfully achieved.
The paper extends group constructions to coset geometries, creating new ways to combine geometries.
problem Combining and gluing incidence geometries in a general framework.
method Extending classical group-theoretic constructions to coset geometries.
result Provides a general framework for combining or gluing incidence geometries.