Poesis and Sympathy: Community Through Craftsmanship

A community is no more than a mass of individuals that is held together by the objects that connect them. The independent spaces occupied by a craftsman or consumer become dependent on their counterparts. These connected environments create the ecosystem of the community. Objects originating from the craftsmen of the community allow for sympathy to flourish. When these objects are created by well-educated craftsmen of the community, they originate with natural understanding, further extending their reach. While nonnatural things such as plastic items have an intrinsic connection to modern life, they do not encourage folk to connect with others in the living environment. These natural connections among the community are what allow for a well rooted society to flourish and for all people to feel included and heard, forming a better union. Developing ideas primarily from Victorian thinkers John Ruskin and William Morris, a deeper understanding what how a community connects and relates is possibl

Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos

This study investigates the feasibility of applying state of the art deep learning techniques to detect precancerous stages of squamous cell carcinoma (SCC) cancer in real time to address the challenges while diagnosing SCC with subtle appearance changes as well as video processing speed. Two deep learning models are implemented, which are to determine artefact of video frames and to detect, segment and classify those no-artefact frames respectively. For detection of SCC, both mask-RCNN and YOLOv3 architectures are implemented. In addition, in order to ascertain one bounding box being detected for one region of interest instead of multiple duplicated boxes, a faster non-maxima suppression technique (NMS) is applied on top of predictions. As a result, this developed system can process videos at 16-20 frames per second. Three classes are classified, which are ‘suspicious’, ‘high grade’ and ‘cancer’ of SCC. With the resolution of 1920x1080 pixels of videos, the average processing time while apply YOLOv3 is in the range of 0.064-0.101 seconds per frame, i.e. 10-15 frames per second, while running under Windows 10 operating system with 1 GPU (GeForce GTX 1060). The averaged accuracies for classification and detection are 85% and 74% respectively. Since YOLOv3 only provides bounding boxes, to delineate lesioned regions, mask-RCNN is also evaluated. While better detection result is achieved with 77% accuracy, the classification accuracy is similar to that by YOLOYv3 with 84%. However, the processing speed is more than 10 times slower with an average of 1.2 second per frame due to creation of masks. The accuracy of segmentation by mask-RCNN is 63%. These results are based on the date sets of 350 images. Further improvement is hence in need in the future by collecting, annotating or augmenting more datasets

SB39-15/16: Restructure the Student Political Action Committee

SB39-15/16: Restructure the Student Political Action Committee. This resolution was tabled at the March 9, 2016 meeting of the Associated Students of the University of Montana (ASUM) and tabled indefinitely at the March 16, 2016 meeting of ASUM

SB46-15/16: Restructure the Student Political Action Committee

SB46-15/16-Restructure the Student Political Action Committee. This resolution was passed by roll call vote at the March 23, 2016 meeting of the Associated Students of the University of Montana (ASUM

Reciprocal Asymptotically Decoupled Hamiltonian for Cavity Quantum Electrodynamics

We develop a new theoretical framework for describing light-matter interactions in cavity quantum electrodynamics (QED), optimized for efficient convergence at arbitrarily strong coupling strengths and is naturally applicable to low-dimensional materials. This new Hamiltonian is obtained by applying a unitary gauge transformation on the p$\cdot$A Hamiltonian, with a shift on both the matter coordinate and the photonic coordinate, then performing a phase rotation and transforming in the reciprocal space of the matter. By formulating the light-matter interaction in terms of an upper-bounded effective coupling parameter, this method allows one to easily converge eigenspectra calculations for any coupling strength, even far into the ultra-strong and deep-strong coupling regimes. We refer to this new approach as the Reciprocal Asymptotically Decoupled (RAD) Hamiltonian. The RAD Hamiltonian allows for a fast convergence of the polariton eigenspectrum with a much smaller matter and photon basis, compared to the commonly used p$\cdot$A or dipole gauge Hamiltonians. The RAD Hamiltonian also allows one to go beyond the commonly used long-wavelength approximation and accurately describes the spatial variations of the field inside the cavity, which ensures the conservation of momentum between light and matter

Analysis of potential for supplemental irrigation in southern Illinois

The aim of this study is to determine the potential for supplemental crop irrigation of the tight subsoil area of Southern Illinois with surface water impounded in small catchment reservoirs. The geographic area of the tight soils (mainly the southern 1/3 of Illinois includes almost 25 percent of the state. Random statistical sampling was used to select topographic quadrangles in this area for investigation of reservoir sites. Costs and water volume were then computed for sites with potential for reservoir siting. The results of the survey of potential reservoirs and cost analysis indicate about 1.2 million acres of land in the claypan area of Southern Illinois can be irrigated under current cost conditions depending on the price of corn and soybeans. From inspection of the best potential reservoir sites, watersheds, and irrigation areas, a specific site was selected for detailed analysis. Site analysis showed the most profitable management practice to be a corn-soybean rotation with reduced tillage, up-and-down slopes plowing, and irrigation. Further analysis was performed concerning the effect of sedimentation on reservoir capacity and, optimal land use. The results indicate that, over a thirty-year period, sedimentation will not have any appreciable effect on reservoir capacity and on land use practice. Finally, the supplemental irrigation system was analysed to determine its overall economic feasibility. A supply curve for irrigation from reservoirs was developed.U.S. Department of the InteriorU.S. Geological SurveyOpe

Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time

Standard white light (WL) endoscopy often misses precancerous oesophageal changes due to their only subtle differences to the surrounding normal mucosa. While deep learning (DL) based decision support systems benefit to a large extent, they face two challenges, which are limited annotated data sets and insufficient generalisation. This paper aims to fuse a DL system with human perception by exploiting computational enhancement of colour contrast. Instead of employing conventional data augmentation techniques by alternating RGB values of an image, this study employs a human colour appearance model, CIECAM, to enhance the colours of an image. When testing on a frame of endoscopic videos, the developed system firstly generates its contrast-enhanced image, then processes both original and enhanced images one after another to create initial segmentation masks. Finally, fusion takes place on the assembled list of masks obtained from both images to determine the finishing bounding boxes, segments and class labels that are rendered on the original video frame, through the application of non-maxima suppression technique (NMS). This deep learning system is built upon real-time instance segmentation network Yolact. In comparison with the same system without fusion, the sensitivity and specificity for detecting early stage of oesophagus cancer, i.e. low-grade dysplasia (LGD) increased from 75% and 88% to 83% and 97%, respectively. The video processing/play back speed is 33.46 frames per second. The main contribution includes alleviation of data source dependency of existing deep learning systems and the fusion of human perception for data augmentation

Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos

This study investigates the feasibility of applying state of the art deep learning techniques to detect precancerous stages of squamous cell carcinoma (SCC) cancer in real time to address the challenges while diagnosing SCC with subtle appearance changes as well as video processing speed. Two deep learning models are implemented, which are to determine artefact of video frames and to detect, segment and classify those no-artefact frames respectively. For detection of SCC, both mask-RCNN and YOLOv3 architectures are implemented. In addition, in order to ascertain one bounding box being detected for one region of interest instead of multiple duplicated boxes, a faster non-maxima suppression technique (NMS) is applied on top of predictions. As a result, this developed system can process videos at 16-20 frames per second. Three classes are classified, which are ‘suspicious’, ‘high grade’ and ‘cancer’ of SCC. With the resolution of 1920x1080 pixels of videos, the average processing time while apply YOLOv3 is in the range of 0.064-0.101 seconds per frame, i.e. 10-15 frames per second, while running under Windows 10 operating system with 1 GPU (GeForce GTX 1060). The averaged accuracies for classification and detection are 85% and 74% respectively. Since YOLOv3 only provides bounding boxes, to delineate lesioned regions, mask-RCNN is also evaluated. While better detection result is achieved with 77% accuracy, the classification accuracy is similar to that by YOLOYv3 with 84%. However, the processing speed is more than 10 times slower with an average of 1.2 second per frame due to creation of masks. The accuracy of segmentation by mask-RCNN is 63%. These results are based on the date sets of 350 images. Further improvement is hence in need in the future by collecting, annotating or augmenting more datasets