Work in progress: Data explorer - Assessment data integration, analytics, and visualization for STEM education research

Citation: Weese, J. L., & Hsu, W. H. (2016). Work in progress: Data explorer - Assessment data integration, analytics, and visualization for STEM education research.We describe a comprehensive system for comparative evaluation of uploaded and preprocessed data in physics education research with applicability to standardized assessments for discipline-based education research, especially in science, technology, mathematics, and engineering. Views are provided for inspection of aggregate statistics about student scores, comparison over time within one course, or comparison across multiple years. The design of this system includes a search facility for retrieving anonymized data from classes similar to the uploader's own. These visualizations include tracking of student performance on a range of standardized assessments. These assessments can be viewed as pre- and post-tests with comparative statistics (e.g., normalized gain), decomposed by answer in the case of multiple-choice questions, and manipulated using pre-specified data transformations such as aggregation and refinement (drill down and roll up). Furthermore, the system is designed to incorporate a scalable framework for machine learning-based analytics, including clustering and similarity-based retrieval, time series prediction, and probabilistic reasoning. © American Society for Engineering Education, 2016

High speed quantum gates with cavity quantum electrodynamics

Cavity quantum electrodynamic schemes for quantum gates are amongst the earliest quantum computing proposals. Despite continued progress, and the dramatic recent demonstration of photon blockade, there are still issues with optimal coupling and gate operation involving high-quality cavities. Here we show dynamic control techniques that allow scalable cavity-QED based quantum gates, that use the full bandwidth of the cavities. When applied to quantum gates, these techniques allow an order of magnitude increase in operating speed, and two orders of magnitude reduction in cavity Q, over passive cavity-QED architectures. Our methods exploit Stark shift based Q-switching, and are ideally suited to solid-state integrated optical approaches to quantum computing.Comment: 4 pages, 3 figures, minor revision

Data-Enhanced Modeling of Sea and Swell on the Continental Shelf

LONG-TERM GOAL: Our long-term goal is to contribute to the accurate prediction of surface gravity wave generation, propagation, and dissipation in coastal regions through the combined use of measurements and models.Award #s: N00014-98-1-0019; N0001499WX30036; N0001499WR3000

Optical properties of pyrochlore oxide Pb2Ru2O7−δPb_{2}Ru_{2}O_{7-{\delta}}

We present optical conductivity spectra for $Pb_{2}Ru_{2}O_{7-{\delta}}$ single crystal at different temperatures. Among reported pyrochlore ruthenates, this compound exhibits metallic behavior in a wide temperature range and has the least resistivity. At low frequencies, the optical spectra show typical Drude responses, but with a knee feature around 1000 \cm. Above 20000 \cm, a broad absorption feature is observed. Our analysis suggests that the low frequency responses can be understood from two Drude components arising from the partially filled Ru $t_{2g}$ bands with different plasma frequencies and scattering rates. The high frequency broad absorption may be contributed by two interband transitions: from occupied Ru $t_{2g}$ states to empty $e_{g}$ bands and from the fully filled O 2p bands to unoccupied Ru $t_{2g}$ states.Comment: 4 pages, 6 figure

Mapping Lesion-Related Epilepsy to a Human Brain Network

Importance: It remains unclear why lesions in some locations cause epilepsy while others do not. Identifying the brain regions or networks associated with epilepsy by mapping these lesions could inform prognosis and guide interventions. Objective: To assess whether lesion locations associated with epilepsy map to specific brain regions and networks. Design, setting, and participants: This case-control study used lesion location and lesion network mapping to identify the brain regions and networks associated with epilepsy in a discovery data set of patients with poststroke epilepsy and control patients with stroke. Patients with stroke lesions and epilepsy (n = 76) or no epilepsy (n = 625) were included. Generalizability to other lesion types was assessed using 4 independent cohorts as validation data sets. The total numbers of patients across all datasets (both discovery and validation datasets) were 347 with epilepsy and 1126 without. Therapeutic relevance was assessed using deep brain stimulation sites that improve seizure control. Data were analyzed from September 2018 through December 2022. All shared patient data were analyzed and included; no patients were excluded. Main outcomes and measures: Epilepsy or no epilepsy. Results: Lesion locations from 76 patients with poststroke epilepsy (39 [51%] male; mean [SD] age, 61.0 [14.6] years; mean [SD] follow-up, 6.7 [2.0] years) and 625 control patients with stroke (366 [59%] male; mean [SD] age, 62.0 [14.1] years; follow-up range, 3-12 months) were included in the discovery data set. Lesions associated with epilepsy occurred in multiple heterogenous locations spanning different lobes and vascular territories. However, these same lesion locations were part of a specific brain network defined by functional connectivity to the basal ganglia and cerebellum. Findings were validated in 4 independent cohorts including 772 patients with brain lesions (271 [35%] with epilepsy; 515 [67%] male; median [IQR] age, 60 [50-70] years; follow-up range, 3-35 years). Lesion connectivity to this brain network was associated with increased risk of epilepsy after stroke (odds ratio [OR], 2.82; 95% CI, 2.02-4.10; P \u3c .001) and across different lesion types (OR, 2.85; 95% CI, 2.23-3.69; P \u3c .001). Deep brain stimulation site connectivity to this same network was associated with improved seizure control (r, 0.63; P \u3c .001) in 30 patients with drug-resistant epilepsy (21 [70%] male; median [IQR] age, 39 [32-46] years; median [IQR] follow-up, 24 [16-30] months). Conclusions and relevance: The findings in this study indicate that lesion-related epilepsy mapped to a human brain network, which could help identify patients at risk of epilepsy after a brain lesion and guide brain stimulation therapies

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ