In this paper, we present a Conditional Random Fields (CRFs) framework for the Clause Splitting problem. We adapt the CRFs model to this problem in order to use a very large sets of arbitrary, overlapping and non-independent features. In addition, we propose the use of rich linguistic information along with a new bottomup dynamic algorithm for decoding to split a sentence into clauses. The expe...

This study proposes a two-pass spoken term detection (STD) method. The first pass uses a phoneme-based dynamic time warping (DTW)-based STD, and the second pass recomputes detection scores produced by the first pass using conditional random fields (CRF)-based triphone detectors. In the second-pass, we treat STD as a sequence labeling problem. We use CRF-based triphone detection models based on ...

A key problem in financial econometrics is the modeling, estimation and forecasting of conditional return volatility and correlation. Having accurate forecasting models for conditional volatility and correlation is important for accurate derivatives pricing, risk management and asset allocation decisions. It is well known that conditional volatility and correlation are highly predictable. An in...

We consider the problem of detecting past activities as well as anticipating which activity will happen in the future and how. We start by modeling the rich spatio-temporal relations between human poses and objects (called affordances) using a conditional random field (CRF). However, because of the ambiguity in the temporal segmentation of the sub-activities that constitute an activity, in the ...

Green bonds play a pivotal role in the financing of sustainable infrastructure systems. Likewise, CO 2 emissions and oil prices can cause an impact on green market. In order to better understand this issue, study analyzes relationship among bonds, futures’ prices, using daily data set that includes 2,206 observations corresponding information from 1 January 2014 15 June 2022. The Granger Causal...

The development of single-shot, high-dynamic-range temporal measurement devices has become necessary for characterizing high-energy petawatt laser pulses. Because of the low repetition rate of such systems, no device offers high-dynamic-range measurements in a single-shot mode. In addition, investigating the unexpected temporal behavior of pulses amplified by new techniques such as analyzed in ...

Economic and financial time series typically exhibit time varying conditional (given the past) standard deviations and correlations. The conditional standard deviation is also called the volatility. Higher volatilities increase the risk of assets, and higher conditional correlations cause an increased risk in portfolios. Therefore, models of time varying volatilities and correlations are essent...

The estimation of a large covariance matrix is challenging when the dimension p relative to sample size n. Common approaches deal with challenge have been based on thresholding or shrinkage methods in estimating matrices. However, many applications (e.g., regression, forecast combination, portfolio selection), what we need not but its inverse (the precision matrix). In this paper introduce meth...

Longitudinal and survival sub-models are two building blocks for joint modelling of longitudinal time-to-event data. Extensive research indicates separate analysis these processes could result in biased outputs due to their associations. Conditional independence between measurements biomarkers event time process given latent classes or random effects is a conventional approach characterising th...

Abstract: In this paper, we consider compositions of conditional risk measures in order to obtain time-consistent dynamic risk measures and determine the solvency capital of a life insurer selling pension liabilities or a pension fund with a single cash-flow at maturity. We first recall the notion of conditional, dynamic and time-consistent risk measures. We link the latter with its iterated pr...