Gaussian Nonlinear Line Attractor for Learning Multidimensional Data

The human brain’s ability to extract information from multidimensional data modeled by the Nonlinear Line Attractor (NLA), where nodes are connected by polynomial weight sets. Neuron connections in this architecture assumes complete connectivity with all other neurons, thus creating a huge web of connections. We envision that each neuron should be connected to a group of surrounding neurons with weighted connection strengths that reduces with proximity to the neuron. To develop the weighted NLA architecture, we use a Gaussian weighting strategy to model the proximity, which will also reduce the computation times significantly. Once all data has been trained in the NLA network, the weight set can be reduced using a locality preserving nonlinear dimensionality reduction technique. By reducing the weight sets using this technique, we can reduce the amount of outputs for recognition tasks. An appropriate distance measure can then be used for comparing testing data and the trained data when processed through the NLA architecture. It is observed that the proposed GNLA algorithm reduces training time significantly and is able to provide even better recognition using fewer dimensions than the original NLA algorithm. We have tested this algorithm and showed that it works well in different datasets, including the EO Synthetic Vehicle database and the Sheffield face database

Gaussian Weighted Neighborhood Connectivity of Nonlinear Line Attractor for Learning Complex Manifolds

The human brain has the capability to process high quantities of data quickly for detection and recognition tasks. These tasks are made simpler by the understanding of data, which intentionally removes redundancies found in higher dimensional data and maps the data onto a lower dimensional space. The brain then encodes manifolds created in these spaces, which reveal a specific state of the system. We propose to use a recurrent neural network, the nonlinear line attractor (NLA) network, for the encoding of these manifolds as specific states, which will draw untrained data towards one of the specific states that the NLA network has encoded. We propose a Gaussian-weighted modular architecture for reducing the computational complexity of the conventional NLA network. The proposed architecture uses a neighborhood approach for establishing the interconnectivity of neurons to obtain the manifolds. The modified NLA network has been implemented and tested on the Electro-Optic Synthetic Vehicle Model Database created by the Air Force Research Laboratory (AFRL), which contains a vast array of high resolution imagery with several different lighting conditions and camera views. It is observed that the NLA network has the capability for representing high dimensional data for the recognition of the objects of interest through its new learning strategy. A nonlinear dimensionality reduction scheme based on singular value decomposition has found to be very effective in providing a low dimensional representation of the dataset. Application of the reduced dimensional space on the modified NLA algorithm would provide fast and more accurate recognition performance for real time applications

Intensity and Resolution Enhancement of Local Regions for Object Detection and Tracking in Wide Area Surveillance

Object tracking in wide area motion imagery is a complex problem that consists of object detection and target tracking over time. This challenge can be solved by human analysts who naturally have the ability to keep track of an object in a scene. A computer vision solution for object tracking has the potential to be a much faster and efficient solution. However, a computer vision solution faces certain challenges that do not affect a human analyst. To overcome these challenges, a tracking process is proposed that is inspired by the known advantages of a human analyst. First, the focus of a human analyst is emulated by doing processing only the local object search area. Second, it is proposed that an intensity enhancement process should be done on the local area to allow features to be detected in poor lighting conditions. This simulates the ability of the human eye to discern objects in complex lighting conditions. Third, it is proposed that the spatial resolution of the local search area is increased to extract better features and provide more accurate feature matching. A quantitative evaluation is performed to show tracking improvement using the proposed method. The three databases, each grayscale sequences that were obtained from aircrafts, used for these evaluations include the Columbus Large Image Format database, the Large Area Image Recorder database, and the Sussex database

The effect of inoculum source and fluid shear force on the development of in vitro oral multispecies biofilms

AimsSaliva has been previously used as an inoculum for in vitro oral biofilm studies. However, the microbial community profile of saliva is markedly different from hardâ and softâ tissueâ associated oral biofilms. Here, we investigated the changes in the biofilm architecture and microbial diversity of in vitro oral biofilms developed from saliva, tongue or plaqueâ derived inocula under different salivary shear forces.Methods and ResultsFour inoculum types (saliva, bacteria harvested from the tongue, toothbrush and curetteâ harvested plaque) were collected and pooled. Biofilms (n ⠥ 15) were grown for 20 h in cellâ free human saliva flowing at three different shear forces. Stained biofilms were imaged using a confocal laser scanning microscope. Biomass, thickness and roughness were determined by image analysis and bacterial community composition analysed using Ion Torrent. All developed biofilms showed a significant reduction in observed diversity compared with their respective original inoculum. Shear force altered biofilm architecture of saliva and curetteâ collected plaque and community composition of saliva, tongue and curetteâ harvested plaque.ConclusionsDifferent intraoral inocula served as precursors of in vitro oral polymicrobial biofilms which can be influenced by shear.Significance and Impact of the StudyInoculum selection and shear force are key factors to consider when developing multispecies biofilms within in vitro models.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136333/1/jam13376-sup-0001-FigS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136333/2/jam13376-sup-0002-TableS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136333/3/jam13376_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136333/4/jam13376.pd

Directional Ringlet Intensity Feature Transform for Tracking

The challenges existing for current intensity-based histogram feature tracking methods in wide area motion imagery include object structural information distortions and background variations, such as different pavement or ground types. All of these challenges need to be met in order to have a robust object tracker, while attaining to be computed at an appropriate speed for real-time processing. To achieve this we propose a novel method, Directional Ringlet Intensity Feature Transform (DRIFT), that employs Kirsch kernel filtering and Gaussian ringlet feature mapping. We evaluated the DRIFT on two challenging datasets, namely Columbus Large Image Format (CLIF) and Large Area Image Recorder (LAIR), to evaluate its robustness and efficiency. Experimental results show that the proposed approach yields the highest accuracy compared to state-of-the-art object tracking methods

Teaching in Tumultuous Times: Unraveling Teachers’ Experiences amidst the COVID-19 Pandemic

Teachers are the most significant assets in any educational institution. They serve as an avenue for conveying knowledge, skills, and values to students. They play a vital role in reforming and strengthening the education system of any country. However, education in the new normal requires numerous adaptations, as teachers were unprepared when the pandemic struck. This qualitative study sought to discover the strengths, weaknesses, opportunities, and threats (SWOT) from teachers' lived experiences in teaching during the pandemic. A total of 28 participants were involved, who had first-hand experiences of teaching tertiary level in the new normal in a university. The qualitative phenomenological research design was used in this study. Thus, teaching in the wake of the COVID-19 pandemic provided teachers’ deficiencies in some course delivery; however, they worked diligently to transform and demonstrate resilience in teaching in the new normal amidst pandemics, converting them into strengths and opportunities. On the other hand, instructors' and professors’ strengths should be recognized, and professional development opportunities should be provided to help them become more competent educators

College students\u27 engagement and academic outcomes in online learning during the COVID-19 pandemic

This study examined the relationship among motivation, engagement, and academic outcomes in online learning during the COVID-19 pandemic. Participants were 41 students enrolled in at least two online classes. They completed a survey measuring motivation, engagement, and academic performance in their online courses. It was hypothesized that greater motivation and engagement would predict greater academic outcomes, and that engagement mediates the relationship between motivation and academic outcomes. Regression analyses showed that both motivation and engagement significantly predicted academic outcomes. Engagement was not a significant mediator between motivation and academic outcomes; rather, mediation analyses found that motivation mediated the relationship between engagement and academic outcomes. Findings suggest that both motivation and engagement are important for understanding academic outcomes in online courses

GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping

In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed observations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an earlier proposed deep-learning-based approach, GlacierNet, which was developed to exploit a convolutional neural-network segmentation model to accurately outline regional DCG ablation zones. Specifically, we developed an enhanced GlacierNet2 architecture thatincorporates multiple models, automatic post-processing, and basin-level hydrological flow techniques to improve the mapping of DCGs such that it includes both the ablation and accumulation zones. Experimental evaluations demonstrate that GlacierNet2 improves the estimation of the ablation zone and allows a high level of intersection over union (IOU: 0.8839) score. The proposed architecture provides complete glacier (both accumulation and ablation zone) outlines at regional scales, with an overall IOU score of 0.8619. This is a crucial first step in automating complete glacier mapping that can be used for accurate glacier modeling or mass-balance analysis

High-velocity microsprays enhance antimicrobial activity in S. mutans biofilms

Streptococcus mutans in dental plaque biofilms play a role in caries development. The biofilm’s complex structure enhances the resistance to antimicrobial agents by limiting the transport of active agents inside the biofilm. We assessed the ability of high-velocity water microsprays to enhance delivery of antimicrobials into 3-days old S. mutans biofilms. Biofilms were exposed to a 90° or 30° impact, firstly using a 1-?m tracer beads solution (109 beads/mL) and secondly, a 0.2% Chlorhexidine (CHX) or 0.085% Cetylpyridinium chloride (CPC) solution. For comparison, a 30-sec diffusive transport and simulated mouthwash were also performed. Confocal microscopy was used to determine number and relative bead penetration depth (RD) into the biofilm. Assessment of antimicrobial penetration was determined by calculating the killing depth (KD) detected by live/dead viability staining. We firstly demonstrated that the microspray was able to deliver significantly more microbeads deeper in the biofilm compared to diffusion and mouthwashing exposures. Next our experiments revealed that the microspray yielded better antimicrobial penetration evidenced by deeper killing inside the biofilm and a wider killing zone around the zone of clearance than a diffusion transport with the same antimicrobials. Interestingly the 30° impact in the distal position delivered approximately 16 times more microbeads and yielded approximately 20% more bacteria killing (for both CHX and CPC) than the 90o impact. These data suggest that high-velocity water microsprays can be used as an effective mechanism to deliver micro-particles and antimicrobials inside S. mutans biofilms. High shear stresses generated at the biofilm/burst interface might have enhanced beads and antimicrobials delivery inside the remaining biofilm by combining forced advection into the biofilm matrix and physical restructuring of the biofilm itself. Further, the impact angle has potential to be optimized both for biofilm removal and active agents’ delivery inside biofilm in those protected areas where some biofilm might remai