Physical Sciences and Mathematics Commons^™

Chapter Five: The San Bernardino Iphone Case, Tracy Mitrano

Tracy Mitrano

Chapter Four: Information Security, Tracy Mitrano

Tracy Mitrano

No abstract provided.

Chapter One: Free Speech, Tracy Mitrano

Tracy Mitrano

No abstract provided.

Chapter Two: Privacy, Tracy Mitrano

Tracy Mitrano

Chapter Three: Intellectual Property, Tracy Mitrano

Tracy Mitrano

No abstract provided.

Targeted Estimation And Inference For The Sample Average Treatment Effect In Trials With And Without Pair-Matching, Laura Balzer, M. Petersen, M. Van Der Laan, The Search Collaboration

Laura B. Balzer

The Changing Values Of Digital Legacies: E-Books And The Challenges Of Data Mobility And The Perceived Value Of Books, Derani Nathasha Dissanayake, David M. Cook

Dr. David M Cook

Strategies For The Production Of Cell Wall-Deconstructing Enzymes In Lignocellulosic Biomass And Their Utilization For Biofuel Production, Sang-Hyuck Park, Rebecca Garlock Ong, Mariam Sticklen

Rebecca Ong

Microbial cell wall-deconstructing enzymes are widely used in the food, wine, pulp and paper, textile, and detergent industries and will be heavily utilized by cellulosic biorefineries in the production of fuels and chemicals. Due to their ability to use freely available solar energy, genetically engineered bioenergy crops provide an attractive alternative to microbial bioreactors for the production of cell wall-deconstructing enzymes. This review article summarizes the efforts made within the last decade on the production of cell wall-deconstructing enzymes in planta for use in the deconstruction of lignocellulosic biomass. A number of strategies have been employed to increase enzyme yields …

Modeling Count Data; Errata And Additions

Joseph M Hilbe

Odm Tools Python: Open Source Software For Managing Continuous Sensor Data, Jeffery S. Horsburgh, Stephanie Reeder, Amber Spackman Jones

Stephanie Reeder

Hydrologic and water quality data is being collected at high frequencies, for extended durations, and with spatial distributions that require infrastructure for data storage and management. The Observations Data Model (ODM), which is part of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), was developed as a framework in which to organize, store, and describe point observations data. In this paper we describe ODM Tools Python, which is an open source software application that allows ODM users to query and export, visualize, and edit data stored in an ODM database. Previous versions …

Merelaniite, Mo4pb4vsbs15, A New Molybdenum-Essential Member Of The Cylindrite Group, From The Merelani Tanzanite Deposit, Lelatema Mountains, Manyara Region, Tanzania, John A. Jaszczak, Michael S. Rumsey, Luca Bindi, Stephen A. Hackney, Michael A. Wise, Chris J. Stanley, John Spratt

John Jaszczak

No abstract provided.

Validation Of Orion Cockpit Displays Using Eggplant Functional And Python Programming, M. A. Rafe Biswas

M. A. Rafe Biswas

No abstract provided.

Femtosecond Pulse Trains Of Polychromatic Inverse Compton Γ-Rays From Designer Electron Beams Produced By Laser-Plasma Acceleration In Plasma Channels, Serge Y. Kalmykov, Isaac Ghebregziabher, X. Davoine, Remi Lehe, Agustin F. Lifschitz, Victor Malka, Bradley A. Shadwick

Serge Youri Kalmykov

Accordion Effect Revisited: Generation Of Comb-Like Electron Beams In Plasma Channels, Serge Y. Kalmykov, X. Davoine, Remi Lehe, Agustin F. Lifschitz, Bradley A. Shadwick

Serge Youri Kalmykov

Performance-Constrained Binary Classification Using Ensemble Learning: An Application To Cost-Efficient Targeted Prep Strategies, Wenjing Zheng, Laura Balzer, Maya L. Petersen, Mark J. Van Der Laan

Laura B. Balzer

Binary classifications problems are ubiquitous in health and social science applications. In many cases, one wishes to balance two conflicting criteria for an optimal binary classifier. For instance, in resource-limited settings, an HIV prevention program based on offering Pre-Exposure Prophylaxis (PrEP) to select high-risk individuals must balance the sensitivity of the binary classifier in detecting future seroconverters (and hence offering them PrEP regimens) with the total number of PrEP regimens that is financially and logistically feasible for the program to deliver. In this article, we consider a general class of performance-constrained binary classification problems wherein the objective function and the …

A Study Of The Christian Public's Engagement With The New Geology Of The 19th Century And Its Implications For The Succeeding Centuries, Cornelis Bootsman, Kevin C. De Berg, Lynden Rogers

Lynden Rogers

While Christian communities had no problem engaging positively with the sciences of astronomy, physics and chemistry, they had difficulty engaging with the emerging geology and biology of the 19^th century. The ancient earth and evolutionary models of geology and biology respectively were seen as a direct attack on the biblical Genesis model of a young earth and a creation that took place over the period of a week. Some Christian apologists used Baconianism and the Scottish Common Sense philosophy to suggest that geology was not a real science. Geology was characterised as consisting of wild speculation, hypotheses and theories …

Examination Of The Nonlinear Dynamics And Possible Chaos Encryption In A Zeroth-Order Acousto-Optic Bragg Modulator With Feedback, Fares S. Almehmadi, Monish Ranjan Chatterjee

Monish R. Chatterjee

Zeroth-order chaos modulation in a Bragg cell is examined such that tracking problems due to spatial deflections of the first-order AO beam at the receiver may be avoided by switching to the undeviated zeroth-order beam.

Anisoplanatic Electromagnetic Image Propagation Through Narrow Or Extended Phase Turbulence Using Altitude-Dependent Structure Parameter, Monish Ranjan Chatterjee, Ali Mohamed

Monish R. Chatterjee

The effects of turbulence on anisoplanatic imaging are often modeled through the use of a sequence of phase screens distributed along the optical path. We implement the split-step wave algorithm to examine turbulence-corrupted images.

Person Identification From Streaming Surveillance Video Using Mid-Level Features From Joint Action-Pose Distribution, Binu M. Nair, Vijayan K. Asari

Vijayan K. Asari

We propose a real time person identification algorithm for surveillance based scenarios from low-resolution streaming video, based on mid-level features extracted from the joint distribution of various types of human actions and human poses. The proposed algorithm uses the combination of an auto-encoder based action association framework which produces per-frame probability estimates of the action being performed, and a pose recognition framework which gives per-frame body part locations. The main focus in this manuscript is to effectively combine these per-frame action probability estimates and pose trajectories from a short temporal window to obtain mid-level features. We demonstrate that these mid-level …

Volume Component Analysis For Classification Of Lidar Data, Nina M. Varney, Vijayan K. Asari

Vijayan K. Asari

One of the most difficult challenges of working with LiDAR data is the large amount of data points that are produced. Analysing these large data sets is an extremely time consuming process. For this reason, automatic perception of LiDAR scenes is a growing area of research. Currently, most LiDAR feature extraction relies on geometrical features specific to the point cloud of interest. These geometrical features are scene-specific, and often rely on the scale and orientation of the object for classification. This paper proposes a robust method for reduced dimensionality feature extraction of 3D objects using a volume component analysis (VCA) …

State Preserving Extreme Learning Machine For Face Recognition, Md. Zahangir Alom, Paheding Sidike, Vijayan K. Asari, Tarek M. Taha

Vijayan K. Asari

Extreme Learning Machine (ELM) has been introduced as a new algorithm for training single hidden layer feed-forward neural networks (SLFNs) instead of the classical gradient-based algorithms. Based on the consistency property of data, which enforce similar samples to share similar properties, ELM is a biologically inspired learning algorithm with SLFNs that learns much faster with good generalization and performs well in classification applications. However, the random generation of the weight matrix in current ELM based techniques leads to the possibility of unstable outputs in the learning and testing phases. Therefore, we present a novel approach for computing the weight matrix …

Video-To-Video Pose And Expression Invariant Face Recognition Using Volumetric Directional Pattern, Vijayan K. Asari, Almabrok Essa

Vijayan K. Asari

Face recognition in video has attracted attention as a cryptic method of human identification in surveillance systems. In this paper, we propose an end-to-end video face recognition system, addressing a difficult problem of identifying human faces in video due to the presence of large variations in facial pose and expression, and poor video resolution. The proposed descriptor, named Volumetric Directional Pattern (VDP), is an oriented and multi-scale volumetric descriptor that is able to extract and fuse the information of multi frames, temporal (dynamic) information, and multiple poses and expressions of faces in input video to produce feature vectors, which are …

Multiple Object Detection In Hyperspectral Imagery Using Spectral Fringe-Adjusted Joint Transform Correlator, Paheding Sidike, Vijayan K. Asari, Mohammad S. Alam

Vijayan K. Asari

Hyperspectral imaging (HSI) sensors provide plenty of spectral information to uniquely identify materials by their reflectance spectra, and this information has been effectively used for object detection and identification applications. Joint transform correlation (JTC) based object detection techniques in HSI have been proposed in the literatures, such as spectral fringe-adjusted joint transform correlation (SFJTC) and with its several improvements. However, to our knowledge, the SFJTC based techniques were designed to detect only similar patterns in hyperspectral data cube and not for dissimilar patterns. Thus, in this paper, a new deterministic object detection approach using SFJTC is proposed to perform multiple …

Efficient Thermal Image Segmentation Through Integration Of Nonlinear Enhancement With Unsupervised Active Contour Model, Fatema Albalooshi, Evan Krieger, Paheding Sidike, Vijayan K. Asari

Vijayan K. Asari

Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions. Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity …

Gaussian Weighted Neighborhood Connectivity Of Nonlinear Line Attractor For Learning Complex Manifolds, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla

Vijayan K. Asari

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 …

Histogram Of Oriented Phase And Gradient (Hopg) Descriptor For Improved Pedestrian Detection, Hussin Ragb, Vijayan K. Asari

Vijayan K. Asari

This paper presents a new pedestrian detection descriptor named Histogram of Oriented Phase and Gradient (HOPG) based on a combination of the Histogram of Oriented Phase (HOP) features and the Histogram of Oriented Gradient features (HOG). The proposed descriptor extracts the image information using both the gradient and phase congruency concepts. Although the HOG based method has been widely used in the human detection systems, it lacks to deal effectively with the images impacted by the illumination variations and cluttered background. By fusing HOP and HOG features, more structural information can be identified and localized in order to obtain more …

Directional Ringlet Intensity Feature Transform For Tracking, Evan Krieger, Paheding Sidike, Theus H. Aspiras, Vijayan K. Asari

Vijayan K. Asari

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 …

Histogram Of Oriented Phase (Hop): A New Descriptor Based On Phase Congruency, Hussin Ragb, Vijayan K. Asari

Vijayan K. Asari

In this paper we present a low level image descriptor called Histogram of Oriented Phase based on phase congruency concept and the Principal Component Analysis (PCA). Since the phase of the signal conveys more information regarding signal structure than the magnitude, the proposed descriptor can precisely identify and localize image features over the gradient based techniques, especially in the regions affected by illumination changes. The proposed features can be formed by extracting the phase congruency information for each pixel in the image with respect to its neighborhood. Histograms of the phase congruency values of the local regions in the image …

Intensity And Resolution Enhancement Of Local Regions For Object Detection And Tracking In Wide Area Surveillance, Evan Krieger, Vijayan K. Asari, Saibabu Arigela, Theus H. Aspiras

Vijayan K. Asari

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 …

Gaussian Nonlinear Line Attractor For Learning Multidimensional Data, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla

Vijayan K. Asari

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 …