Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.

Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models

Background Many models are published which predict outcomes in hospitalized COVID-19 patients. The generalizability of many is unknown. We evaluated the performance of selected models from the literature and our own models to predict outcomes in patients at our institution. Methods We searched the literature for models predicting outcomes in inpatients with COVID-19. We produced models of mortality or criticality (mortality or ICU admission) in a development cohort. We tested external models which provided sufficient information and our models using a test cohort of our most recent patients. The performance of models was compared using the area under the receiver operator curve (AUC). Results Our literature review yielded 41 papers. Of those, 8 were found to have sufficient documentation and concordance with features available in our cohort to implement in our test cohort. All models were from Chinese patients. One model predicted criticality and seven mortality. Tested against the test cohort, internal models had an AUC of 0.84 (0.74–0.94) for mortality and 0.83 (0.76–0.90) for criticality. The best external model had an AUC of 0.89 (0.82–0.96) using three variables, another an AUC of 0.84 (0.78–0.91) using ten variables. AUC’s ranged from 0.68 to 0.89. On average, models tested were unable to produce predictions in 27% of patients due to missing lab data. Conclusion Despite differences in pandemic timeline, race, and socio-cultural healthcare context some models derived in China performed well. For healthcare organizations considering implementation of an external model, concordance between the features used in the model and features available in their own patients may be important. Analysis of both local and external models should be done to help decide on what prediction method is used to provide clinical decision support to clinicians treating COVID-19 patients as well as what lab tests should be included in order sets.

External Evaluation of Two Pediatric Population Pharmacokinetics Models of Oral Trimethoprim and Sulfamethoxazole

The antibiotic combination trimethoprim (TMP)-sulfamethoxazole (SMX) has a broad spectrum of activity and it is used for the treatment of numerous infections, but pediatric pharmacokinetic (PK) data are limited. We previously published population PK (popPK) models of oral TMP-SMX in pediatric patients based on sparse opportunistically collected data (POPS study, Antimicrob Agents Chemother 62:e01813-17, 2017, https://doi.org/10.1128/AAC.01813-17). We performed a separate PK study of oral TMP-SMX in infants and children with more traditional PK sample collection, and independently developed new popPK models of TMP-SMX using this external dataset. The POPS dataset and the External dataset were each used to evaluate both popPK models. The External TMP model had identical model and error structure as the POPS TMP model with typical values for PK parameters within 20%. The External SMX model did not identify the covariates in the POPS SMX model as significant. The External popPK models predicted higher exposures of TMP (median overprediction of 0.13 mg/L for the POPS dataset and 0.061 mg/L for the External dataset) and SMX (median overprediction of 1.7 mg/L and 0.90 mg/L) than the POPS TMP (median underprediction of 0.016 mg/L and 0.39 mg/L) and SMX (median underprediction of 1.2 mg/L and 14 mg/L) models. Nonetheless, both models supported TMP-SMX dose increases in infants and young children for more resistant pathogens with a minimum inhibitory concentration of 1 mg/L, although the required dose increase based on the External model is lower.

Grammatical replication: First-person non-singular verbal indexes in Eastern Toba, Western Toba (Guaicuruan) and Tapiete (Tupi-Guaraní)

In this paper we suggest that linguistic features can show traces of the frequency and intensity of social interactions between indigenous peoples. We focus on peoples of the alluvial fan of the Pilcomayo River (South American Chaco), and analyze first-person non-singular verbal encoding in their languages. The corpus is composed of (a) data obtained during fieldwork, (b) descriptive grammars, and (c) published reports by missionaries, army officers, and European travelers. Combining environmental and ethnohistorical information, we propose that the first-person non-singular subject verbal indexes split visible in Eastern Toba of the Lower Pilcomayo River, Western Toba from the Upper-Middle Pilcomayo River (Guaicuruan), and Tapiete of the Upper Pilcomayo River area (Tupi-Guaraní) could be an outcome of language-internal resources used by the speakers of these languages to replicate the Matacoan (Maká, Nivaclé, and Wichí) external model.

DESIGN AND DEVELOPMENT OF AN LADM-BASED EXTERNAL DATA MODEL FOR LAND REGISTRY AND CADASTRE TRANSACTIONS IN TURKEY: A CASE STUDY OF TREASURY REAL PROPERTIES

The processes starting with the identification and registration of treasury properties have an essential place in the cadastral systems. Spatial data modelling studies were conducted in 2002 to establish a common standard structure on the fundamental similarities of land management systems. These studies were stated as a beginning named Core Cadastral Domain Model (CCDM), since 2006, it has been started to be made under the name of LADM. This model was accepted in 2012 as a standard model in the field of land administration by the International Organization for Standardization (ISO). In this study, an external model class is proposed for LADM’s transactions related to Treasury’s real estates properties which are related National Property Automation Project (MEOP). In order to determine the deficiency of this current external model, databases containing records related to spatial data and property rights were examined, and the deficiencies related to transactions on treasury properties were determined. The created external class is associated with the LADM’s LA_Party, LA_RRR, LA_SpatialUnit and LA_BAUnit master classes. Herewith the standardization of the external data model is ensured. If the external model is implemented by the responsible standardization of the archiving processes will be more comfortable and faster to register.

Hermeneutic, Comparative, and Syncretic Philosophy: Or, On Ricoeurian, Confucian and Aztec Philosophy

Hermeneutic philosophy, and Paul Ricoeur’s formulation of hermeneutics in particular, faces a serious challenge, not from external sources, but from internal proponents of the program. In what might be called the Collapse Challenge, Ricoeur’s understanding of the hermeneutic circle is criticized for making use of structuralist methods that are no longer considered viable. Rather than look to replace Ricoeur’s work with an external model, the present essay draws on his late model of translation to suggest two viable paths forward beyond the Collapse Challenge. To develop these paths, the essay gives two concrete cases, one using Confucian philosophy, which is comparative, another using Aztec philosophy, which is syncretic.

Efficiency of bond graph and external model integration for alarm processing of a central air conditioning system

<p><span>The design of a supervision system based on the external model by structuring the industrial process according to several modes of operation (degraded and normal). The disadvantage of this model is that it describes the industrial process components as functions regardless of their dynamics without going into detail. Hence the interest of the bond graph model to fill the external model limits. The performance of the proposed supervisory system using both models lies in the detection and location of faults for each mode of operation. The bond graph model enriched by the concept of causality and thanks to these structural properties can clearly display the elements of the physical system taking into account their dynamics in normal and abnormal operation. The results of our research have been applied to central air conditioning system; the development of the proposed project has proceeded from the modeling stage to the reconfiguration stage of the system.</span></p>

Improving PSI Processing of Mining Induced Large Deformations with External Models

Ground subsidences, either caused by natural phenomena or human activities, can threaten the safety of nearby infrastructures and residents. Among the different causes, mining operations can trigger strong subsidence phenomena with a fast nonlinear temporal behaviour. Therefore, a reliable and precise deformation monitoring is of great significance for safe mining and protection of facilities located above or near the mined-out area. Persistent Scatterer Interferometry (PSI) is a technique that uses stacks Synthetic Aperture Radar (SAR) images to remotely monitor the ground deformation of large areas with a high degree of precision at a reasonable cost. Unfortunately, PSI presents limitations when monitoring large gradient deformations when there is phase ambiguity among adjacent Persistent Scatterer (PS) points. In this paper, an improvement of PSI processing, named as External Model-based Deformation Decomposition PSI (EMDD-PSI), is proposed to address this limitation by taking advantage of an external model. The proposed method first uses interferograms generated from SAR Single Look Complex (SLC) images to optimize the parameter adjustments of the external model. Then, the modelled spatial distribution of subsidence is utilized to reduce the fringes of the interferograms generated from the SAR images and to ease the PSI processing. Finally, the ground deformation is retrieved by jointly adding the external model and PSI results. In this paper, fourteen Radarsat-2 SAR images over Fengfeng mining area (China) are used to demonstrate the capabilities of the proposed method. The results are evaluated by comparing them with leveling data of the area covering the same temporal period. Results have shown that, after the optimization, the model is able to mimic the real deformation and the fringes of the interferograms can be effectively reduced. As a consequence, the large gradient deformation then can be better retrieved with the preservation of the nonlinear subsidence term. The ground truth shows that, comparing with the classical PSI and PSI with unadjusted parameters, the proposed scheme reduces the error by 35.2% and 20.4%, respectively.