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.
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.
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…
This paper addresses the problem of predicting duration of unplanned power outages, using historical outage records to train a series of neural network predictors. The initial duration prediction is made based on environmental factors, and it is updated based on incoming field reports using natural language processing …
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.
Todays interactive devices such as smart-phone assistants and smart speakers often deal with short-duration speech segments. As a result, speaker recognition systems integrated into such devices will be much better suited with models capable of performing the recognition task with short-duration utterances. In this pap…
LSTM-based speaker verification usually uses a fixed-length local segment randomly truncated from an utterance to learn the utterance-level speaker embedding, while using the average embedding of all segments of a test utterance to verify the speaker, which results in a critical mismatch between testing and training. T…
We search for digital biomarkers from Parkinson's Disease by observing approximate repetitive patterns matching hypothesized step and stride periodic cycles. These observations were modeled as a cycle of hidden states with randomness allowing deviation from a canonical pattern of transitions and emissions, under the hy…
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…
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.
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.
Generative model predicts daily activity sequences with duration-aware dynamics.
problem Accurately forecasting granular daily activity sequences for energy demand.
method Hierarchical semi-Markov models with duration-aware dynamics.
result Explicitly modeling activity durations improves predictive performance.
Graph Attention Networks predict power outage durations from natural disasters.
problem Accurately predicting power outage durations from geospatial and weather data.
method Graph Attention Networks (GAT) for semi-supervised learning.
result GAT model outperforms existing methods by 2% - 15% in accuracy.
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.
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.
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…
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.
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…
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 article presents valuation of Treasury Bonds (T-Bonds) on Macedonian Stock Exchange (MSE) and empirical test of duration, modified duration and convexity of the T-bonds at MSE in order to determine sensitivity of bonds prices on interest rate changes. The main goal of this study is to determine how standard valuat…
In finance, durations between successive transactions are usually modeled by the autoregressive conditional duration model based on a continuous distribution omitting zero values. Zero or close-to-zero durations can be caused by either split transactions or independent transactions. We propose a discrete model allowing…
Predicting traffic incident duration is a major challenge for many traffic centres around the world. Most research studies focus on predicting the incident duration on motorways rather than arterial roads, due to a high network complexity and lack of data. In this paper we propose a bi-level framework for predicting th…
Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training s…
In this paper we study the probabilistic properties of the posteriors in a speech recognition system that uses a deep neural network (DNN) for acoustic modeling. We do this by reducing Kaldi's DNN shared pdf-id posteriors to phone likelihoods, and using test set forced alignments to evaluate these using a calibration s…
This paper considers magnitude, asymptotics and duration of drawdowns for some Lévy processes. First, we revisit some existing results on the magnitude of drawdowns for spectrally negative Lévy processes using an approximation approach. For any spectrally negative Lévy process whose scale functions are well-behaved at …
Extends hyperparameter transfer across model sizes and modules, improving training speed.
problem Training stability and performance of large-scale models with optimal hyperparameters.
method Complete(d) Parameterisation, per-module hyperparameter optimisation and transfer. result Hyperparameter transfer holds even in the per-module hyperparameter regime, improving training speed.
Scheduling surgeries is a challenging task due to the fundamental uncertainty of the clinical environment, as well as the risks and costs associated with under- and over-booking. We investigate neural regression algorithms to estimate the parameters of surgery case durations, focusing on the issue of heteroscedasticity…
Improves speaker verification for variable-duration utterances using a feature pyramid module.
problem Improving robustness for variable-duration utterances in speaker verification.
method Integrates a feature pyramid module into multi-scale aggregation to enhance speaker-discriminative information from multiple layers.
result Improves performance for both short and long utterances compared to state-of-the-art approaches.
Paper forecasts financial trading durations using a new point process model.
problem Forecasting limit order book durations in high-frequency financial data.
method Self-exciting flexible residual point process incorporating empirical distributional features.
result The model achieves strong predictive performance compared to alternative approaches.
Text-independent speaker recognition using short utterances is a highly challenging task due to the large variation and content mismatch between short utterances. I-vector based systems have become the standard in speaker verification applications, but they are less effective with short utterances. In this paper, we fi…
New model for time series classification from single example.
problem Classifying time series patterns from limited data.
method Developed a Hidden semi-Markov Model with variable state duration.
result Different representations of state duration have distinct strengths and weaknesses.
Ormerod and Mounfield analysed GDP data of 17 leading capitalist economies from 1870 to 1994 and concluded that the frequency of the duration of recessions is consistent with a power-law. But in fact the data is consistent with an exponential (Boltzmann-Gibbs) law.
The order submission and cancelation processes are two crucial aspects in the price formation of stocks traded in order-driven markets. We investigate the dynamics of order cancelation by studying the statistical properties of inter-cancelation durations defined as the waiting times between consecutive order cancelatio…
Study on scheduling jobs with unknown types, achieving sublinear excess cost.
problem Optimizing job scheduling with unknown job types and varying durations.
method Design of algorithms for non-preemptive and preemptive scenarios, proving lower bounds.
result Preemptive algorithms can significantly outperform non-preemptive ones when job types have distinct durations.
A new method prioritizes project risks using Monte Carlo Simulation.
problem Determining the relative importance of project risks.
method Monte Carlo Simulation (MCS) for quantitative prioritization.
result Differentiates critical risks based on their impact on project duration and cost.
Critical incident stages identification and reasonable prediction of traffic incident duration are essential in traffic incident management. In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of tempo…
Study optimizes portfolio to minimize relative drawdown duration, penalizing unfavorable performance states.
problem Minimizing relative drawdown duration in portfolio optimization relative to a benchmark.
method Introduces a benchmark-relative drawdown-duration criterion penalizing unfavorable performance states. Uses a one-dimensional Markovian representation and Hamilton-Jacobi-Bellman equation.
result Derives explicit projection-based characterization of the optimal feedback control and identifies geometric settings for unique strong solutions.
A new method tests Expected Shortfall by analyzing both duration and severity of VaR violations.
problem Lack of separate testing for frequency and severity in ES backtesting.
method Uses bivariate orthogonal polynomials to derive moment conditions for durations and severities.
result Proposes a Wald test for identifying mis-specified components in ES models.
The extension of image generation to video generation turns out to be a very difficult task, since the temporal dimension of videos introduces an extra challenge during the generation process. Besides, due to the limitation of memory and training stability, the generation becomes increasingly challenging with the incre…
Study models parking duration using machine learning and interpretable methods.
problem Parking issues in developing countries like India.
method Artificial neural networks (ANNs) for capturing relationships; Garson algorithm and LIME for model interpretation.
result LIME shows higher prediction accuracy and can be universally adopted.
Two methods estimate effect size for online experiments, improving accuracy and efficiency.
problem Determining the correct effect size for online experiment duration.
method Two approaches: hierarchical models and utility theory.
result Proposed methods outperform baseline approaches in accuracy and efficiency.
We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning pap…
Study reveals 1/f noise in signals made from nonoverlapping rectangular pulses.
problem Analyzing 1/f noise in signals composed of nonoverlapping pulses. method Derived a general formula for power spectral density, analyzed rectangular pulse case.
result Observed pure 1/f noise until very low frequencies with long pulse durations.