POLYTHESIS. Filologia, Interpretazione e Teoria della Letteratura

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Aquilegia, Vol. 26 No. 5, September-October 2002: Newsletter of the Colorado Native Plant Society

https://epublications.regis.edu/aquilegia/1095/thumbnail.jp

Building a hybrid : chatterbot - dialog system

Generic conversational agents often use hard-coded stimulus- response data to generate responses, for which little to no effort is attributed to effectively understand and comprehend the input. The limitation of these types of systems is obvious: the general and linguistic knowledge of the system is limited to what the developer of the system explicitly defined. Therefore, a system which analyses user input at a deeper level of abstraction which backs its knowledge with common sense information will essentially result in a system that is capable of providing more adequate responses which in turn result in a better over- all user experience. From this premise, a framework was proposed, and a working prototype was implemented upon this framework. The prototype makes use of various natural language processing tools, online and offline knowledge bases, and other information sources, to enable it to comprehend and construct relevant responses.peer-reviewe

Successful Yukawa structures in Warped Extra Dimensions

For a RS model, with SM fields in the bulk and the Higgs boson on the TeV-brane, we suggest two specific structures for the Yukawa couplings, one based on a permutation symmetry and the other on the Universal Strength of Yukawa couplings hypothesis (USY). In USY, all Yukawa couplings have equal strength and the difference in the Yukawa structure lies in some complex phase. In both scenarios, all Yukawa couplings are of the same order of magnitude. Thus, the main features of the fermion hierarchies are explained through the RS geometrical mechanism, and not because some Yukawa coupling is extremely small. We find that the RS model is particularly appropriate to incorporate the suggested Yukawa configurations. Indeed, the RS geometrical mechanism of fermion locations along the extra dimension, combined with the two Yukawa scenarios, reproduces all the present experimental data on fermion masses and mixing angles. It is quite remarkable that in the USY case, only two complex phases of definite value +-Pi/2 are sufficient to generate the known neutrino mass differences, while at same time, permitting large leptonic mixing in agreement with experiment.Comment: 11 page

Intraoperative ventilator settings and their association with postoperative pulmonary complications in neurosurgical patients : post-hoc analysis of LAS VEGAS study

Background Limited information is available regarding intraoperative ventilator settings and the incidence of postoperative pulmonary complications (PPCs) in patients undergoing neurosurgical procedures. The aim of this post-hoc analysis of the 'Multicentre Local ASsessment of VEntilatory management during General Anaesthesia for Surgery' (LAS VEGAS) study was to examine the ventilator settings of patients undergoing neurosurgical procedures, and to explore the association between perioperative variables and the development of PPCs in neurosurgical patients. Methods Post-hoc analysis of LAS VEGAS study, restricted to patients undergoing neurosurgery. Patients were stratified into groups based on the type of surgery (brain and spine), the occurrence of PPCs and the assess respiratory risk in surgical patients in Catalonia (ARISCAT) score risk for PPCs. Results Seven hundred eighty-four patients were included in the analysis; 408 patients (52%) underwent spine surgery and 376 patients (48%) brain surgery. Median tidal volume (V-T) was 8 ml [Interquartile Range, IQR = 7.3-9] per predicted body weight; median positive end-expiratory pressure (PEEP) was 5 [3 to 5] cmH(2)0. Planned recruitment manoeuvres were used in the 6.9% of patients. No differences in ventilator settings were found among the sub-groups. PPCs occurred in 81 patients (10.3%). Duration of anaesthesia (odds ratio, 1.295 [95% confidence interval 1.067 to 1.572]; p = 0.009) and higher age for the brain group (odds ratio, 0.000 [0.000 to 0.189]; p = 0.031), but not intraoperative ventilator settings were independently associated with development of PPCs. Conclusions Neurosurgical patients are ventilated with low V-T and low PEEP, while recruitment manoeuvres are seldom applied. Intraoperative ventilator settings are not associated with PPCs

Feature-based Transferable Disruption Prediction for future tokamaks using domain adaptation

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak only using a few discharges based on a domain adaptation algorithm called CORAL. It is the first attempt at applying domain adaptation in the disruption prediction task. In this paper, this disruption prediction approach aligns a few data from the future tokamak (target domain) and a large amount of data from the existing tokamak (source domain) to train a machine learning model in the existing tokamak. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak. To simulate the lack of disruptive data in future tokamak, we only selected 100 non-disruptive discharges and 10 disruptive discharges from EAST as the target domain training data. We have improved CORAL to make it more suitable for the disruption prediction task, called supervised CORAL. Compared to the model trained by mixing data from the two tokamaks, the supervised CORAL model can enhance the disruption prediction performance for future tokamaks (AUC value from 0.764 to 0.890). Through interpretable analysis, we discovered that using the supervised CORAL enables the transformation of data distribution to be more similar to future tokamak. An assessment method for evaluating whether a model has learned a trend of similar features is designed based on SHAP analysis. It demonstrates that the supervised CORAL model exhibits more similarities to the model trained on large data sizes of EAST. FTDP provides a light, interpretable, and few-data-required way by aligning features to predict disruption using small data sizes from the future tokamak.Comment: 15 pages, 9 figure

Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection

The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption precursors is a tedious and challenging task, as some precursors are difficult to accurately identify, limiting the potential of machine learning models. To address this issue, commonly used labeling methods assume that the precursor onset occurs at a fixed time before the disruption, which may not be consistent for different types of disruptions or even the same type of disruption, due to the different speeds at which plasma instabilities escalate. This leads to mislabeled samples and suboptimal performance of the supervised learning predictor. In this paper, we present a disruption prediction method based on anomaly detection that overcomes the drawbacks of unbalanced positive and negative data samples and inaccurately labeled disruption precursor samples. We demonstrate the effectiveness and reliability of anomaly detection predictors based on different algorithms on J-TEXT and EAST to evaluate the reliability of the precursor onset time inferred by the anomaly detection predictor. The precursor onset times inferred by these predictors reveal that the labeling methods have room for improvement as the onset times of different shots are not necessarily the same. Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.Comment: 21 pages, 11 figure