Word-level language detection is necessary for analyzing code-switched text, where multiple languages could be mixed within a sentence. Existing models are restricted to code-switching between two specific languages and fail in real-world scenarios as text input rarely has a priori information on the languages used. We present a novel unsupervised word-level language detection technique for cod...

Beaudry and Portier (2006) propose an identification scheme to study the effects of news shocks about future productivity in Vector Error Correction Models (VECM). This comment shows that their methodology does not have a unique solution, when applied to their VECMs with more than two variables. The problem arises from the interplay of cointegration assumptions and long-run restrictions imposed...

Forecast DNI values in hourly resolution for one day ahead are evaluated by a comparison with pyrheliometer ground measurements. Three months of such day ahead forecasts from four different providers for a site close to Questa, NM, USA are analyzed firstly by calculating the RMSE and the mean bias error. Secondly, cumulative distributions of the DNI forecast errors are calculated as they better...

In order to increase the reliability of data transmission, using multiple antennas in radio frequency identification (RFID) systems has been investigated by researchers, mainly through measurements and simulations. The multiple-input multiple-output (MIMO) RFID backscattering channel exhibits a special type of cascaded structure rather than that of other better-studied cascaded channels such as...

In this paper, we present an approach to the automatic identification and correction of preposition and determiner errors in nonnative (L2) English writing. We show that models of use for these parts of speech can be learned with an accuracy of 70.06% and 92.15% respectively on L1 text, and present first results in an error detection task for L2 writing.

In this paper we explore the use of lexical information for language identification (LID). Our reference LID system uses language-dependent acoustic phone models and phone-based bigram language models. For each language, lexical information is introduced by augmenting the phone vocabulary with the N most frequent words in the training data. Combined phone and word bigram models are used to prov...

The high cost of model predictive control (MPC) technology has hampered its wide application in process industries beyond the refining/petrochemical industry. This work aims to increase the efficiency of MPC deployment. First a semi-automatic MPC system is introduced. It consists of three modules: an MPC control module, an online identification module and a control monitor module. The goal of t...

background:  aeromonas spp. can cause diarrhea and various infections in humans. access to rapid techniques with a high sensitivity and specificity is strongly needed for the identification of aeromonas species. the aim of this study was to evaluate two different methods including api 20e bacterial identification tests and the molecular detection using pcr primers specific for 16s-rrna and 23s-...

This thesis aims at investigating automatic identification of Chinese named entities (NEs) and their relations (NERs) in a specific domain. We have proposed a three-stage pipeline computational model for the error correction of word segmentation and POS tagging, NE recognition and NER identification. In this model, an error repair module utilizing machine learning techniques is developed in the...

Parametric system identification of dynamic systems is the process of building mathematical, time domain models of plants, based on excitation and response signals. In contrast to its nonparametric counterpart, this model based procedure leads to fixed descriptions, by means of finitely parameterized transfer function representations. This fact provides increased flexibility and makes model-bas...