Hidden Markov Model Identifiability via Tensors

The prevalence of hidden Markov models (HMMs) in various applications of statistical signal processing and communications is a testament to the power and flexibility of the model. In this paper, we link the identifiability problem with tensor decomposition, in particular, the Canonical Polyadic decomposition. Using recent results in deriving uniqueness conditions for tensor decomposition, we are able to provide a necessary and sufficient condition for the identification of the parameters of discrete time finite alphabet HMMs. This result resolves a long standing open problem regarding the derivation of a necessary and sufficient condition for uniquely identifying an HMM. We then further extend recent preliminary work on the identification of HMMs with multiple observers by deriving necessary and sufficient conditions for identifiability in this setting.Comment: Accepted to ISIT 2013. 5 pages, no figure

Tracking Control for Affine Time-Varying Nonlinear Systems with Bounds

In practice, there exist systems with high nonlinearity and time-varying functions. Time-varying nonlinear systems (TVNS) present inherent challenges due to their high nonlinearity and time-varying nature, especially when unknown input disturbance and model uncertainties occur. In this work, a class of single input single output (SISO) uncertain affine TVNS is considered for tracking controller design in the presence of unknown disturbance, in which both the disturbance and model uncertainties are assumed to be bounded. Based on these bounds, a tracking controller will be proposed for first-order uncertain TVNS with unknown input disturbance, and then it is extended for second-order uncertain affine TVNS with unknown input disturbance. Unlike other existing works, the proposed controller does not use fuzzy systems, neural networks or any adaptive mechanism to cope with uncertainties and disturbances. It only uses the bounds of disturbance and model uncertainties, the information of tracking error to compute the control signal, and Lyapunov stability theory is applied to analyze stability of the closed-loop system. In addition, the convergence rate of tracking error can be adjusted by tuning parameters. Some numerical simulations with a first-order system and a model of inverted pendulum are given to verify the developed controller. These systems are uncertain and disturbed by unknown external signals and the proposed controller does not know this information but the tracking error still converges to a small circle containing the origin. The proposed controller can be extended for higher-order systems or MIMO systems such as robotic manipulators

Impact of climate policy uncertainty on return spillover among green assets and portfolio implications

Peer reviewe

Does the U.S. export inflation? Evidence from the dynamic inflation spillover between the U.S. and EAGLEs

Peer reviewe

VLSP SHARED TASK: SENTIMENT ANALYSIS

Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns

Deux applications de processus ponctuels aux réseaux de communication

- Cet article résume des résultats récents obtenus en utilisant des processus ponctuels (plus précisémment, des bruits impulsionnels) pour deux applications dans le contexte des réseaux de communication: d'une part, la modélisation du trafic TCP/IP dans les réseaux d'épine dorsale, et d'autre part les propriétés de connectivité des réseaux ad hoc sans fil sous des contraintes de débit