Discretized Bayesian pursuit – A new scheme for reinforcement learning

The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction ( L RI )-like schemes, can be explained by their ability to pursue the actions with the highest reward probability estimates. Without access to reward probability estimates, it makes sense for schemes like the L RI to first make large exploring steps, and then to gradually turn exploration into exploitation by making progressively smaller learning steps. However, this behavior becomes counter-intuitive when pursuing actions based on their estimated reward probabilities. Learning should then ideally proceed in progressively larger steps, as the reward probability estimates turn more accurate. This paper introduces a new estimator algorithm, the Discretized Bayesian Pursuit Algorithm (DBPA), that achieves this. The DBPA is implemented by linearly discretizing the action probability space of the Bayesian Pursuit Algorithm (BPA) [1]. The key innovation is that the linear discrete updating rules mitigate the counter-intuitive behavior of the corresponding linear continuous updating rules, by augmenting them with the reward probability estimates. Extensive experimental results show the superiority of DBPA over previous estimator algorithms. Indeed, the DBPA is probably the fastest reported LA to date

Utilizing vegetation indices as a proxy to characterize the stability of a railway embankment in a permafrost region

Degrading permafrost conditions around the world are posing stability issues for infrastructure constructed on them. Railway lines have exceptionally low tolerances for differential settlements associated with permafrost degradation due to the potential for train derailments. Railway owners with tracks in permafrost regions therefore make it a priority to identify potential settlement locations so that proper maintenance or embankment stabilization measures can be applied to ensure smooth and safe operations. The extensive discontinuous permafrost zone along the Hudson Bay Railway (HBR) in Northern Manitoba, Canada, has been experiencing accelerated deterioration, resulting in differential settlements that necessitate continuous annual maintenance to avoid slow orders and operational interruptions. This paper seeks to characterize the different permafrost degradation susceptibilities present at the study site. Track geometry exceptions were compared against remotely sensed vegetation indices to establish a relationship between track quality and vegetation density. This relationship was used as a proxy for subsurface condition verified by electrical resistivity tomography. The established relationship was then used to develop a three-level degradation susceptibility chart to indicate low, moderate and high susceptibility regions. The defined susceptibility regions can be used to better allocate the limited maintenance resources and also help inform potentially long-term stabilization measures for the severely affected sections

Characterizing soil stiffness using thermal remote sensing and machine learning

Soil strength characterization is essential for any problem that deals with geomechanics, including terramechanics/terrain mobility. Presently, the primary method of collecting soil strength parameters through in situ measurements but sending a team of people out to a site to collect data this has significant cost implications and accessing the location with the necessary equipment can be difficult. Remote sensing provides an alternate approach to in situ measurements. In this lab study, we compare the use of Apparent Thermal Inertia (ATI) against a GeoGauge for the direct testing of soil stiffness. ATI correlates with stiffness, so it allows one to predict the soil strength remotely using machine-learning algorithms. The best performing regression algorithm among the ones tested with different predictor variable combinations was found to be KNN with an R2 of 0.824 and a RMSE of 0.141. This study demonstrates the potential for using remote sensing to acquire thermal images that characterize terrain strength for mobility utilizing different machine-learning algorithms

Utilizing hyperspectral remote sensing for soil gradation

Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil

An unusual presentation of catastrophic antiphospholipid antibody syndrome in the background of sepsis

A 59-year-old female presented with complaints of gangrenous changes over right ring finger and reduced renal output. She was in hypotension and had to be started on ionotropes. She also had acute kidney injury and was initiated on hemodialyis. Inspite of culture directed antibiotics and amputation of the necrotic region, her condition worsened. Considering the acute multisystem worsening, i.e., less than a week, concomitant autoimmune etiology was considered. Antiphospholipid antibodies were positive. Her tissue biopsy was suggestive of vasculitis. Hence the diagnosis of catastrophic antiphospholipid antibody syndrome (CAPS) was made. Quick recognition and appropriate treatment play a cornerstone in treatment of CAPS. She was pulsed with methylprednisolone and also treated with intravenous immunoglobulins and anticoagulants. She showed remarkable improvement and responded to the treatment. CAPS should always be kept as a differential in case of multisystem acute deterioration even in the background of sepsis. The treatment is a big challenge to physicians given the associated mortality rate if not briskly treated

Challenges of modeling rainfall triggered landslides in a data-sparse region: A case study from the Western Ghats, India

Accurate rainfall estimates are required to forecast the spatio-temporal distribution of rain-triggered landslides. In this study, a comparison between rain gauge and satellite rainfall data for assessing landslide distribution in a data-sparse region, the mountainous district of Idukki, along the Western Ghats of southwestern India, is carried out. Global Precipitation Mission Integrated Multi-satellitE Retrievals for GPM-Late (GPM IMERG-L) rainfall products were compared with rain gauge measurements, and it was found that the satellite rainfall observations were underpredicting the actual rainfall. A conditional merging algorithm was applied to develop a product that combines the accuracy of rain gauges and the spatial variability of satellite precipitation data. Correlation Coefficient (CC) and Root Mean Squared Error (RMSE) were used to check the performance of the conditional merging process. An example from a station with the least favorable statistics shows the CC increasing from 0.589 to 0.974 and the RMSE decreasing from 65.22 to 20.01. A case scenario was considered that evaluated the performance of a landslide prediction model by relying solely on a sparse rain gauge network. Rainfall thresholds computed from both the conditionally merged GPM IMERG-L and the rain gauge data were compared and the differences indicated that relying solely on a discrete, sparse rain gauge network would create false predictions. A total of 18.7% of landslide predictions only were identified as true positives, while 60.7% was the overall false-negative rate, and the remaining were false-positives. This pointed towards the need of having a continuous data that is both accurate in measurement and efficient in capturing spatial variability of rainfall

On merging the fields of neural networks and adaptive data structures to yield new pattern recognition methodologies

The aim of this talk is to explain a pioneering exploratory research endeavour that attempts to merge two completely different fields in Computer Science so as to yield very fascinating results. These are the well-established fields of Neural Networks (NNs) and Adaptive Data Structures (ADS) respectively. The field of NNs deals with the training and learning capabilities of a large number of neurons, each possessing minimal computational properties. On the other hand, the field of ADS concerns designing, implementing and analyzing data structures which adaptively change with time so as to optimize some access criteria. In this talk, we shall demonstrate how these fields can be merged, so that the neural elements are themselves linked together using a data structure. This structure can be a singly-linked or doubly-linked list, or even a Binary Search Tree (BST). While the results themselves are quite generic, in particular, we shall, as a prima facie case, present the results in which a Self-Organizing Map (SOM) with an underlying BST structure can be adaptively re-structured using conditional rotations. These rotations on the nodes of the tree are local and are performed in constant time, guaranteeing a decrease in the Weighted Path Length of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution. Besides, the neighborhood properties of the neurons suit the best BST that represents the data

Observing CMB polarisation through ice

Ice crystal clouds in the upper troposphere can generate polarisation signals at the uK level. This signal can seriously affect very sensitive ground based searches for E- and B-mode of Cosmic Microwave Background polarisation. In this paper we estimate this effect within the ClOVER experiment observing bands (97, 150 and 220 GHz) for the selected observing site (Llano de Chajnantor, Atacama desert, Chile). The results show that the polarisation signal from the clouds can be of the order of or even bigger than the CMB expected polarisation. Climatological data suggest that this signal is fairly constant over the whole year in Antarctica. On the other hand the stronger seasonal variability in Atacama allows for a 50% of clean observations during the dry season.Comment: 7 Pages, 4 figure

Landslides near Enguri dam (Caucasus, Georgia) and possible seismotectonic effects

The Enguri dam and water reservoir, nested in the southwestern Caucasus (Republic of Georgia), are surrounded by steep mountain slopes. At a distance of 2.5&thinsp;km from the dam, a mountain ridge along the reservoir is affected by active deformations with a double vergence. The western slope, directly facing the reservoir, has deformations that affect a subaerial area of 1.2&thinsp;km2. The head scarp affects the Jvari–Khaishi–Mestia main road with offsets of man-made features that indicate slip rates of 2–9&thinsp;cm&thinsp;yr−1. Static, pseudostatic and Newmark analyses, based on field and seismological data, suggest different unstable rock volumes based on the environmental conditions. An important effect of variation of the water table is shown, as well as the possible destabilization of the slope following seismic shaking, compatible with the expected local peak ground acceleration. This worst-case scenario corresponds to an unstable volume on the order of up to 48±12×106&thinsp;m3. The opposite, eastern slope of the same mountain ridge is also affected by wide deformation affecting an area of 0.37&thinsp;km2. Here, field data indicate 2–5&thinsp;cm&thinsp;yr−1 of slip rates. All this evidence is interpreted as resulting from two similar landslides, whose possible causes are discussed, comprising seismic triggering, mountain rapid uplift, river erosion and lake variations.</p

Lrp4 Modulates Extracellular Integration of Cell Signaling Pathways in Development

The extent to which cell signaling is integrated outside the cell is not currently appreciated. We show that a member of the low-density receptor-related protein family, Lrp4 modulates and integrates Bmp and canonical Wnt signalling during tooth morphogenesis by binding the secreted Bmp antagonist protein Wise. Mouse mutants of Lrp4 and Wise exhibit identical tooth phenotypes that include supernumerary incisors and molars, and fused molars. We propose that the Lrp4/Wise interaction acts as an extracellular integrator of epithelial-mesenchymal cell signaling. Wise, secreted from mesenchyme cells binds to BMP's and also to Lrp4 that is expressed on epithelial cells. This binding then results in the modulation of Wnt activity in the epithelial cells. Thus in this context Wise acts as an extracellular signaling molecule linking two signaling pathways. We further show that a downstream mediator of this integration is the Shh signaling pathway