A Model for the Evaluation of Society’s Progress Towards Cashlessness: A Comprehensive Analysis of the World and Norway

Master's thesis in Business administration (BE501)The purpose of this thesis is to determine Norway as well as other countries in terms of the progress in cashless societies. When it comes to payment services, there are constantly new and better innovations that suppliers are attempting to find solutions for and that are easily available to consumers. The main aim of this thesis is to determine which phases a society is in by using a model for cashless society phases. As there are no fitting models for analyzing such cashless societies, we introduce a new model of cashnessless, and use it as the base of the analysis. Some countries with advanced technology and rising markets are discussed to see what kind of payment methods the country provides to its citizens. The benefits and drawbacks of establishing a cashless society are also presented to determine which groups will be most affected, as well as determining the importance of physical money to the public

Training Curricula for Open Domain Answer Re-Ranking

In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking strategy would be to learn how to identify the easiest correct answers first (i.e., assign a high ranking score to answers that have characteristics that usually indicate relevance, and a low ranking score to those with characteristics that do not), before incorporating more complex logic to handle difficult cases (e.g., semantic matching or reasoning). In this work, we apply this idea to the training of neural answer rankers using curriculum learning. We propose several heuristics to estimate the difficulty of a given training sample. We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process. As the training process progresses, our approach gradually shifts to weighting all samples equally, regardless of difficulty. We present a comprehensive evaluation of our proposed idea on three answer ranking datasets. Results show that our approach leads to superior performance of two leading neural ranking architectures, namely BERT and ConvKNRM, using both pointwise and pairwise losses. When applied to a BERT-based ranker, our method yields up to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model trained without a curriculum). This results in models that can achieve comparable performance to more expensive state-of-the-art techniques.Comment: Accepted at SIGIR 2020 (long

Improving Mechanical Ventilator Clinical Decision Support Systems with A Machine Learning Classifier for Determining Ventilator Mode

Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have com-plete information on state of supporting physiologic monitor-ing devices, which can limit the input data available to CDSS. This is especially true in the use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS to make accurate recommendations related to ventilator mode, we developed a highly performant ma-chine learning model that is able to perform per-breath clas-sification of 5 of the most widely used ventilation modes in the USA with an average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and that it is highly robust to missing data caused by software/sensor error

Robust Feedback Linearization Approach for Fuel-Optimal Oriented Control of Turbocharged Spark-Ignition Engines

This chapter proposes a new control approach for the turbocharged air system of a gasoline engine. To simplify the control implementation task, static lookup tables (LUTs) of engine data are used to estimate the engine variables in place of complex dynamical observer and/or estimators. The nonlinear control design is based on the concept of robust feedback linearization which can account for the modeling uncertainty and the estimation errors induced by the use of engine lookup tables. The control feedback gain can be effectively computed from a convex optimization problem. Two control strategies have been investigated for this complex system: drivability optimization and fuel reduction. The effectiveness of the proposed control approach is clearly demonstrated with an advanced engine simulator

Exploring the Impact of Activity-Dependent Stimulation on Neuroanatomy

Background: Activity-dependent stimulation (ADS) uses recorded neural activity to trigger stimulation of the brain. Open-loop stimulation (OLS) is independent of neural feedback, thus relying on a machine-generated pattern of stimulation. A previous study found that ADS and OLS both promote fine motor control recovery in a rat model of traumatic brain injury in the primary motor cortex. ADS enhanced recovery of motor function compared to OLS. Investigation of the underlying mechanisms driving motor recovery in response to ADS and OLS is needed. Methods: Six healthy, adult male rats were implanted with recording electrodes in the premotor cortex (PM) and stimulating electrodes in the somatosensory cortex (S1). Three of the rats were treated with ADS; action potentials (spikes) recorded in the premotor cortex triggered stimulation in S1. Three rats were treated with random OLS mimicking the same rate of stimulation as ADS. After 21 days of stimulation, brain tissue was processed for evidence of morphological differences between the two types of stimulation. Immunohistochemistry was used to label sections for synaptophysin, BDNF, GluR1, and GluR2. Densitometry was used for semiquantitative analysis. Results: As this was a pilot study with a small sample size, analyses were exploratory. Observed synaptophysin and GluR1 immunoreactivity (IR) was greater in ADS rats compared to OLS rats (p=0.0253 and p=0.0253 respectively), whereas BDNF and GluR2 lacked such trends (p=0.456 and p=0.456 respectively). In all rats the stimulated hemispheres expressed significantly more synaptophysin (p= 0.0132) and GluR1 (p=0.0062) than the non-stimulated hemispheres. BDNF and GluR2 expression were significantly lower in the stimulated hemispheres (p=0.0030 and p=0.0054 respectively). Conclusions: The data suggests that ADS and OLS both enhance synaptogenesis and GluR1 expression. Results are consistent with the hypothesis that ADS induces greater synaptogenesis and GluR1 expression than OLS. Data does not support the hypothesis that BDNF expression is higher after ADS treatment than OLS treatment. This pilot study elucidates the impact of intracortical stimulation on synaptic plasticity in the cerebral cortex

Automated Scenario Generation for Human-in-the-Loop Simulations

Automated Multi-Aircraft Control System scenario generation for Human-in-the-Loop (HITL) evaluations of air traffic management concepts is described. The objective is to replace the difficult manual process with the automated process for creating an initial (seed) scenario that serves as a starting point for manual adjustments for creating the Human-in-the-Loop scenario. Methods for analyzing and comparing the seed-scenario generated using the automated process and the Human-in-the-Loop-scenario derived from it to meet the experiment objectives are discussed. Results of comparison of input Human-in-the-Loop-scenario with the Multi-Aircraft Control System output are also presented. The main findings are: (1) many of the characteristics of the seed-scenario used for constructing the Human-in-the-Loop-scenario are preserved in the Human-in-the-Loop-scenario, (2) landing rate profile of the traffic generated by the Multi-Aircraft Control System (MACS) using the input scenario compares reasonably well with that intended in the input scenario, and (3) many of the desired characteristics of the Human-in-the-Loop-scenario can be achieved by further automation

Evaluation of the Terminal Area Precision Scheduling and Spacing System for Performance-Based Navigation Arrivals

The growth of global demand for air transportation has put increasing strain on the nation's air traffic management system. To relieve this strain, the International Civil Aviation Organization has urged all nations to adopt Performance-Based Navigation (PBN), which can help to reduce air traffic congestion, decrease aviation fuel consumption, and protect the environment. NASA has developed a Terminal Area Precision Scheduling and Spacing (TAPSS) system that can support increased use of PBN during periods of high traffic, while supporting fuel-efficient, continuous descent approaches. In the original development of this system, arrival aircraft are assigned fuel-efficient Area Navigation (RNAV) Standard Terminal Arrival Routes before their initial descent from cruise, with routing defined to a specific runway. The system also determines precise schedules for these aircraft that facilitate continuous descent through the assigned routes. To meet these schedules, controllers are given a set of advisory tools to precisely control aircraft. The TAPSS system has been evaluated in a series of human-in-the-loop (HITL) air traffic simulations during 2010 and 2011. Results indicated increased airport arrival throughput up to 10 over current operations, and maintained fuel-efficient aircraft decent profiles from the initial descent to landing with reduced controller workload. This paper focuses on results from a joint NASA and FAA HITL simulation conducted in 2012. Due to the FAA rollout of the advance terminal area PBN procedures at mid-sized airports first, the TAPSS system was modified to manage arrival aircraft as they entered Terminal Radar Approach Control (TRACON). Dallas-Love Field airport (DAL) was selected by the FAA as a representative mid-sized airport within a constrained TRACON airspace due to the close proximity of a major airport, in this case Dallas-Ft Worth International Airport, one of the busiest in the world. To address this constraint, RNAV routes and Required Navigation Performance with the particular capability known as Radius-to-Fix (RNP-RF) approaches to a short final were used. The purpose of this simulation was to get feedback on how current operations could benefit with the TAPSS system and also to evaluate the efficacy of the advisory tools to support the broader use of PBN in the US National Airspace System. For this NASA-FAA joint experiment, an Air Traffic Control laboratory at NASA Ames was arranged to simulate arrivals into DAL in Instrument Meteorological Conditions utilizing parallel dependent approaches, with two feeder positions that handed off traffic to one final position. Four FAA controllers participated, alternately covering these three positions. All participants were Full-Performance Level terminal controllers and members of the National Air Traffic Controllers Association. During the simulation, PBN arrival operations were compared and contrasted in three conditions. They were the Baseline, where none of the TAPSS systems TRACON controller decision support advisories were provided, the Limited Advisories, reflecting the existing but dormant capabilities of the current terminal automation equipment with providing a subset of the TAPSS systems advisories; numerical delay, landing sequence, and runway assignment information, and the Full Advisories, with providing the following in addition to the ones in the Limited condition; trajectory slot markers, timelines of estimated times of arrivals and sche

Evaluation of the Terminal Sequencing and Spacing System for Performance Based Navigation Arrivals

NASA has developed the Terminal Sequencing and Spacing (TSS) system, a suite of advanced arrival management technologies combining timebased scheduling and controller precision spacing tools. TSS is a ground-based controller automation tool that facilitates sequencing and merging arrivals that have both current standard ATC routes and terminal Performance-Based Navigation (PBN) routes, especially during highly congested demand periods. In collaboration with the FAA and MITRE's Center for Advanced Aviation System Development (CAASD), TSS system performance was evaluated in human-in-the-loop (HITL) simulations with currently active controllers as participants. Traffic scenarios had mixed Area Navigation (RNAV) and Required Navigation Performance (RNP) equipage, where the more advanced RNP-equipped aircraft had preferential treatment with a shorter approach option. Simulation results indicate the TSS system achieved benefits by enabling PBN, while maintaining high throughput rates-10% above baseline demand levels. Flight path predictability improved, where path deviation was reduced by 2 NM on average and variance in the downwind leg length was 75% less. Arrivals flew more fuel-efficient descents for longer, spending an average of 39 seconds less in step-down level altitude segments. Self-reported controller workload was reduced, with statistically significant differences at the p less than 0.01 level. The RNP-equipped arrivals were also able to more frequently capitalize on the benefits of being "Best-Equipped, Best- Served" (BEBS), where less vectoring was needed and nearly all RNP approaches were conducted without interruption

UC-246 Spudify

Spotify’s yearly wrapped report is extremely popular amongst its users. Unfortunately users must wait a year from every report to view statistics about their listening habits. Our app will allow users to generate reports displaying their top songs and artists whenever they want. Additionally, our app will allow users to generate recommendations for new music based on their favorite songs/artists. Users will also be able to generate advanced recommendations by inputting custom artists/genres/songs and customizing a variety of parameters such as the recommended song’s tempo, loudness, and danceability. Our app will give Spotify users the freedom to view statistics regarding their listening habits whenever they want. Additionally, users will never run out of new music to listen to due to the custom song recommendation feature of our app

Healthy Colon, Healthy Life (Colon Sano, Vida Sana): Colorectal Cancer Screening Among Latinos in Santa Clara, California

Colorectal cancer (CRC) screening rates are low among Latinos. To identify factors associated with CRC screening, we conducted a telephone survey of Latino primary care patients aged 50–79 years. Among 1,013 participants, 38% were up-to-date (UTD) with fecal occult blood test (FOBT); 66% were UTD with any CRC screening (FOBT, sigmoidoscopy, or colonoscopy). Individuals less than 65, females, those less acculturated, and patients of female physicians were more likely to be UTD with FOBT. CRC screening among Latinos is low. Younger patients, women, and patients of female physicians receive more screening