State-specific Parameters Research Articles

Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market

The role of cryptocurrencies within the financial systems has been expanding rapidly in recent years among investors and institutions. It is therefore crucial to investigate this phenomenon and develop statistical methods able to capture their interrelationships, the links with other global systems and, at the same time, the serial heterogeneity. Here we introduce hidden Markov regression models for jointly estimating quantiles and expectiles of cryptocurrency returns using regime-switching copulas. The proposed approach allows us to focus on extreme returns and describe their temporal evolution by introducing time-dependent coefficients, evolving according to a latent Markov chain. Moreover to model their time-varying dependence structure, we consider elliptical copula functions defined by state-specific parameters. Maximum likelihood estimates are obtained via an expectation-maximization algorithm. The empirical analysis investigates the relationship between daily returns of five cryptocurrencies and major world market indices.

Augmenting progress on the elimination of vertical transmissions of HIV in India: Insights from Spectrum-based HIV burden estimations.

The government of India has adopted the elimination of vertical transmission of HIV as one of the five high-level goals under phase V of the National AIDS and STD Control Programme (NACP). In this paper, we present the data from HIV estimations 2021 for India and select States detailing the progress as well as the attributable causes for vertical transmissions. The NACP spearheads work on mathematical modelling to estimate HIV burden based on the periodically conducted sentinel surveillance for guiding program implementation and policymaking. Using the results of the latest round of HIV Estimations in 2021, we analysed the mother-to-child transmission (MTCT) during the perinatal and postnatal (breastfeeding) period. In 2021, overall, around 5,000 [3,000-7,800] vertical transmissions were estimated nationally with 58% being perinatal infections and remaining during breastfeeding. MTCT at 6 weeks was around 12.95% [9.45-16.02] with the final transmission rate at 24.25% [18.50-29.50]. Overall, 57% of vertical transmissions were among HIV-positive mothers who did not receive ART during pregnancy or breastfeeding, 19% among mothers who dropped off ART during pregnancy or delivery, and 18% among mothers who were infected during pregnancy or breastfeeding. There were significant variations between States. Depending upon the States, the programme needs to focus on the intervention domains of timely engagement in antenatal care-HIV testing-ART initiation as well as programme retention and adherence support. Equally important would be strengthening the strategic information to generate related evidence for inputting India and State-specific parameters improving the MTCT-related modelled estimates.

Dissecting unsupervised learning through hidden Markov modeling in electrophysiological data.

Unsupervised, data-driven methods are commonly used in neuroscience to automatically decompose data into interpretable patterns. These patterns differ from one another depending on the assumptions of the models. How these assumptions affect specific data decompositions in practice, however, is often unclear, which hinders model applicability and interpretability. For instance, the hidden Markov model (HMM) automatically detects characteristic, recurring activity patterns (so-called states) from time series data. States are defined by a certain probability distribution, whose state-specific parameters are estimated from the data. But what specific features, from all of those that the data contain, do the states capture? That depends on the choice of probability distribution and on other model hyperparameters. Using both synthetic and real data, we aim to better characterize the behavior of two HMM types that can be applied to electrophysiological data. Specifically, we study which differences in data features (such as frequency, amplitude, or signal-to-noise ratio) are more salient to the models and therefore more likely to drive the state decomposition. Overall, we aim at providing guidance for the appropriate use of this type of analysis on one- or two-channel neural electrophysiological data and an informed interpretation of its results given the characteristics of the data and the purpose of the analysis.NEW & NOTEWORTHY Compared with classical supervised methods, unsupervised methods of analysis have the advantage to be freer of subjective biases. However, it is not always clear what aspects of the data these methods are most sensitive to, which complicates interpretation. Focusing on the hidden Markov model, commonly used to describe electrophysiological data, we explore in detail the nature of its estimates through simulations and real data examples, providing important insights about what to expect from these models.

Energy- and charge-state-resolved spectrometry of tin laser-produced plasma using a retarding field energy analyzer

We present a method to obtain the individual charge-state-dependent kinetic-energy distributions of tin ions emanating from a laser-produced plasma from their joint overlapping energy distributions measured by means of a retarding field energy analyzer (RFA). The method of extracting charge state specific parameters from the ion signals is described mathematically, and reinforced with experimental results. The absolute charge-state-resolved ion energy distributions is obtained from ns-pulse Nd:YAG-laser-produced microdroplet tin plasmas in a setting relevant for state-of-the-art extreme ultraviolet nanolithography.

Leveraging Device-Arterial Coupling to Determine Cardiac and Vascular State.

Limitations in available diagnostic metrics restrict the efficacy of managing therapies for cardiogenic shock. In current clinical practice, cardiovascular state is inferred through measurement of pulmonary capillary wedge pressure and reliance on linear approximations between pressure and flow to estimate peripheral vascular resistance. Mechanical circulatory support devices residing within the left ventricle and aorta provide an opportunity for both determining cardiac and vascular state and offering therapeutic benefit. We leverage the controllable mode of operation and transvalvular position of an indwelling percutaneous ventricular assist device to assess vascular and, in turn, cardiac state through the effects of device-arterial coupling across different levels of device support. Vascular state is determined by measuring changes in the pressure waveforms induced through intentional variation in the device generated blood flow. We evaluate this impact by applying a lumped parameter model to quantify state-specific vascular resistance and compliance and calculate beat-to-beat stroke volume and cardiac output in both animal models and retrospective patient data without external calibration. Vascular state was accurately predicted in patients and animals in both baseline and experimental conditions. In the animal, stroke volume was predicted within a total root mean square error of 3.71 mL (n = 482). We demonstrate that device-arterial coupling is a powerful tool for evaluating patient and state specific parameters of cardiovascular function. These insights may yield improved clinical care and support the development of next generation mechanical circulatory support devices that determine and operate in tandem with the supported organ.

Accommodating environmental thresholds and extreme events in hydrological models: A Bayesian approach

Extreme events appear to play an important role in pollutant export and the overall functioning of watershed systems. Because they are expected to increase in frequency as urbanization and recent climate change trends continue, the development of techniques that can effectively accommodate the behavior of watersheds during extreme events is one of the challenges of the contemporary modeling practice. In this regard, we present a Bayesian framework which postulates that the watershed response to precipitation occurs in distinct states. Precipitation depth above a certain threshold triggers an extreme state, which is characterized by a qualitatively different response of the watershed to precipitation. Our calibration framework allows us to identify these extreme states and to characterize the different watershed behavior by allowing parameter values to vary between states. We applied this framework to SWAT model implementations in two creeks in the Hamilton Harbour watershed of Redhill Creek, an urban catchment, and Grindstone Creek, an agricultural one. We found that our framework is able to coherently identify watershed states and state-specific parameters, with extreme states being characterized by a higher propensity for runoff generation. Our framework resulted in better model fit above the precipitation threshold, although there were not consistent improvements of model fit overall. We demonstrate that accommodating threshold-type of behavior may improve the use of models in locating critical source areas of non-point source pollution.

Cross-Lingual Subspace Gaussian Mixture Models for Low-Resource Speech Recognition

This paper studies cross-lingual acoustic modeling in the context of subspace Gaussian mixture models (SGMMs). SGMMs factorize the acoustic model parameters into a set that is globally shared between all the states of a hidden Markov model (HMM) and another that is specific to the HMM states. We demonstrate that the SGMM global parameters are transferable between languages, particularly when the parameters are trained multilingually. As a result, acoustic models may be trained using limited amounts of transcribed audio by borrowing the SGMM global parameters from one or more source languages, and only training the state-specific parameters on the target language audio. Model regularization using l1-norm penalty is shown to be particularly effective at avoiding overtraining and leading to lower word error rates. We investigate maximum a posteriori (MAP) adaptation of subspace parameters in order to reduce the mismatch between the SGMM global parameters of the source and target languages. In addition, monolingual and cross-lingual speaker adaptive training is used to reduce the model variance introduced by speakers. We have systematically evaluated these techniques by experiments on the GlobalPhone corpus.

Fourier transform spectroscopy of the CO-stretching band of O-18 methanol

The high-resolution Fourier transform spectrum of the ν 8 CO-stretching band of CH 3 18OH between 900 and 1100 cm −1 has been recorded at the Canadian Light Source (CLS) synchrotron facility in Saskatoon, and the majority of the torsion–rotation structure has been analyzed. For the ν t = 0 torsional ground state, subbands have been identified for K values from 0 to 11 for A and E torsional symmetries up to J values typically well over 30. For ν t = 1, A and E subbands have been assigned up to K = 7, and several ν t = 2 subbands have also been identified. Upper-state term values determined from the assigned transitions using the Ritz program have been fitted to J( J + 1) power-series expansions to obtain substate origins and sets of state-specific parameters giving a compact representation of the substate J-dependence. The ν t = 0 subband origins have been fitted to effective molecular constants for the excited CO-stretching state and a torsional barrier of 377.49(32) cm −1 is found, representing a 0.89% increase over the ground-state value. The vibrational energy for the CO-stretch state was found to be 1007.49(7) cm −1. A number of subband-wide and J-localized perturbations have been seen in the spectrum, arising both from anharmonic and Coriolis interactions, and several of the interacting states have been identified.

Evaluation of Interstate Highway Capacity for Short-Term Work Zone Lane Closures

The need to maintain adequate traffic flow through short-term Interstate work zones is paramount on today's heavily traveled freeways. Numerous states have policies related to traffic flow thresholds, vehicle delay, and vehicle queue lengths that provide guidance on conditions when shortterm lane closures can be instituted. Generally, traffic flow threshold limits are a function of traffic-stream characteristics, highway geometry, work zone location, type of construction activities, and work zone configuration. A research study was initiated in South Carolina to examine quantitatively the development and implementation of a methodology for use in determining an updated lane-closure policy based on statespecific parameters. The study focused on determining the number of vehicles per lane per hour that can pass through short-term Interstate work zone lane closures with minimal or acceptable levels of delay. Research tasks included a survey of policies of other states, field-data collection, and methodology development. A methodology was developed with field data collected from 22 work zone sites along South Carolina's Interstate system. Data were obtained during a 12-month period from September 2001 to August 2002. Data collection procedures and data analysis methods employed in model development are summarized. Results of this study may be helpful to state highway agencies interested in developing similar research-based policy guidelines for short-term Interstate lane closures in their jurisdictions.

Fourier transform spectroscopy of the CO-stretching band of C-13 methanol in the torsional ground state

The Fourier transform spectrum of the CO-stretching fundamental band of 13CH3OH has been investigated at 0.002 cm−1 resolution. For the n=0 torsional ground state, 35 subbands containing some 2300 lines have been identified for K values from 0 to 10 for the three torsional symmetries A, E1, and E2 up to J values typically well over 30. The assigned transitions have been fitted to J(J+1) power-series expansions to obtain subband origins and sets of phenomenological state-specific parameters describing the J dependence of the subbands. These parameters reproduce the observed wave numbers to within the experimental uncertainty of ±0.0005 cm−1 for unblended lines, except when particularly large asymmetry shifts or perturbations due to Fermi or Coriolis resonances are present. The subband origins have been fitted to effective molecular constants for the excited CO-stretching state and a torsional barrier of 378.7(8) cm−1 is found, representing a 1.3% increase over the ground state value. A wide variety of global or J-localized perturbations has been seen, and most of the interacting states have been identified. New assignments have been obtained for far-infrared laser lines optically pumped by the 10R(2) and 9P(12) CO2 laser lines.