The study of the thermal behavior of a new semicrystalline polyimide

Thermal properties of a new semicrystalline polyimide synthesized from 3,3',4,4' benzophenone tetracarboxylic dianhydride (BTDA) and 2,2 dimethyl 1,2-(4 aminophenoxy) propane (DMDA) were studied. Heat capacities in the solid and liquid states of BTDA-DMDA were measured. The heat capacity increase at the glass transition temperature (T sub g = 230 C) is 145 J/(C mol) for amorphous BTDA-DMDA. The equilibrium heat of fusion of the BTDA-DMDA crystals was obtained using wide angle X ray diffraction and differential scanning calorimetry measurements, and it is 75.8 kJ/mol. Based on the information of crystallinity and the heat capacity increase at T sub g, a rigid amorphous fraction is identified in semicrystalline BTDA-DMDA samples. The rigid amorphous fraction represents an interfacial region between the crystalline and amorphous states. In particular, this fraction increases with the crystallinity of the sample which should be associated with crystal sizes, and therefore, with crystal morphology. It was also found that this polymer has a high temperature crystal phase upon annealing above its original melting temperature. The thermal degradation activation energies are determined to be 154 and 150 kJ/mol in nitrogen and air, respectively

A simple nearest-neighbor two-body Hamiltonian system for which the ground state is a universal resource for quantum computation

We present a simple quantum many-body system - a two-dimensional lattice of qubits with a Hamiltonian composed of nearest-neighbor two-body interactions - such that the ground state is a universal resource for quantum computation using single-qubit measurements. This ground state approximates a cluster state that is encoded into a larger number of physical qubits. The Hamiltonian we use is motivated by the projected entangled pair states, which provide a transparent mechanism to produce such approximate encoded cluster states on square or other lattice structures (as well as a variety of other quantum states) as the ground state. We show that the error in this approximation takes the form of independent errors on bonds occurring with a fixed probability. The energy gap of such a system, which in part determines its usefulness for quantum computation, is shown to be independent of the size of the lattice. In addition, we show that the scaling of this energy gap in terms of the coupling constants of the Hamiltonian is directly determined by the lattice geometry. As a result, the approximate encoded cluster state obtained on a hexagonal lattice (a resource that is also universal for quantum computation) can be shown to have a larger energy gap than one on a square lattice with an equivalent Hamiltonian.Comment: 5 pages, 1 figure; v2 has a simplified lattice, an extended analysis of errors, and some additional references; v3 published versio

Screening of Pneumonia and Urinary Tract Infection at Triage using TriNet

Due to the steady rise in population demographics and longevity, emergency department visits are increasing across North America. As more patients visit the emergency department, traditional clinical workflows become overloaded and inefficient, leading to prolonged wait-times and reduced healthcare quality. One of such workflows is the triage medical directive, impeded by limited human workload, inaccurate diagnoses and invasive over-testing. To address this issue, we propose TriNet: a machine learning model for medical directives that automates first-line screening at triage for conditions requiring downstream testing for diagnosis confirmation. To verify screening potential, TriNet was trained on hospital triage data and achieved high positive predictive values in detecting pneumonia (0.86) and urinary tract infection (0.93). These models outperform current clinical benchmarks, indicating that machine-learning medical directives can offer cost-free, non-invasive screening with high specificity for common conditions, reducing the risk of over-testing while increasing emergency department efficiency.Comment: Index Terms: Downstream testing, Machine Learning, Medical directives, Modelling, Modular network, Pneumonia, Positive predictive value, Screening, Triage, Urinary tract infectio

Bayesian Recurrent Neural Networks for Real Time Object Detection

Neural networks have become increasingly popular in real time object detection algorithms. A major concern with these algorithms is their ability to quantify their own uncertainty, leading to many high profile failures. This research proposes three novel real time detection algorithms. The first of leveraging Bayesian convolutional neural layers producing a predictive distribution, the second leveraging predictions from previous frames, and the third model combining these two techniques together. These augmentations seek to mitigate the calibration problem of modern detection algorithms. These three models are compared to the state of the art YOLO architecture; with the strongest contending model achieving a 0.6% increase in precision for a 3.7% decrease in recall. This research also investigates and provides insights into what neural networks do under uncertainty. This research showed that on average for every 0.92% increase in the total number of annotations, above the mean, for a given class, the object detection model becomes 0.7% more likely to have a false positive for that class. Consequently this research presents insights that neural networks defer to the highest frequency classes from their training when they are unsure what the actual classification is

Financial Statement Insurance: A New Approach to Ivestor Protection

The accounting profession rapidly is moving toward a crisis in liability. Members of the investing public are suing accountants with mounting frequency and success. This article will analyze briefly the origin and present dimensions of the crisis, and then propose a plan for replacing court-imposed liability with insured liability through the offering of financial statement insurance. The essentials of the plan can be simply stated. Insurance would be offered by accountants to investors on a voluntary basis in conjunction with purchases and sales of corporate stock and securities. Individual investors would be able to purchase from the auditors of a corporation assurance that the most recent, audited financial statements of that corporation in fact fairly represent its financial condition as of the date of the statements. All investors would be more confident of the independence of accountants, since a portion of the accountants\u27 compensation no longer would come directly from their corporate clients. Amendments to the Securities Acts would insulate accountants from liability to uninsured investors except in instances of fraud or gross negligence constituting fraud

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Recent and upcoming BCI progress: overview, analysis, and recommendations

Brain–computer interfaces (BCIs) are finally moving out of the laboratory and beginning to gain acceptance in real-world situations. As BCIs gain attention with broader groups of users, including persons with different disabilities and healthy users, numerous practical questions gain importance. What are the most practical ways to detect and analyze brain activity in field settings? Which devices and applications are most useful for different people? How can we make BCIs more natural and sensitive, and how can BCI technologies improve usability? What are some general trends and issues, such as combining different BCIs or assessing and comparing performance? This book chapter provides an overview of the different sections of this book, providing a summary of how authors address these and other questions. We also present some predictions and recommendations that ensue from our experience from discussing these and other issues with our authors and other researchers and developers within the BCI community. We conclude that, although some directions are hard to predict, the field is definitely growing and changing rapidly, and will continue doing so in the next several years

First-order melting of a weak spin-orbit Mott insulator into a correlated metal

The electronic phase diagram of the weak spin-orbit Mott insulator (Sr(1-x)Lax)3Ir2O7 is determined via an exhaustive experimental study. Upon doping electrons via La substitution, an immediate collapse in resistivity occurs along with a narrow regime of nanoscale phase separation comprised of antiferromagnetic, insulating regions and paramagnetic, metallic puddles persisting until x~0.04. Continued electron doping results in an abrupt, first-order phase boundary where the Neel state is suppressed and a homogenous, correlated, metallic state appears with an enhanced spin susceptibility and local moments. As the metallic state is stabilized, a weak structural distortion develops and suggests a competing instability with the parent spin-orbit Mott state.Comment: 5 pages, 4 figure