Deep Learning in the Automotive Industry: Applications and Tools

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.Comment: 10 page

Tuning the transport properties of layer-by-layer thin films for fuel cell applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 138-148).The increasing global focus on alternative energy sources has led to a renewed interest in fuel cells. For low power, portable applications, direct methanol fuel cells (DMFCs) are the most promising type of fuel cell. DMFCs can operate at ambient conditions and only require dilute methanol solutions and air to be input to the devices. At the core of these devices is a proton exchange membrane (PEM) that allows rapid proton transport through the polymer matrix while preventing fuel from permeating across. Additionally, PEMs must have long-term stability in the fuel cell environment, the ability to operate over a wide range of conditions (temperature and humidity), and be cost effective. A promising, robust method for fabricating polymer films with tunable properties is layer-by-layer (LbL) assembly. This technique consists of building a polymer film by sequential dipping into polymer solutions with complementary interactions, such as opposite electrostatic charges. The LbL method allows the formation of thin films that have perm-selective properties and high ionic conductivity values. This work describes the optimization of multilayer systems for use as the PEM in DMFCs. First, LbL assembled films of poly[bis(methoxyethoxyethoxy)-phosphazene] (MEEP) and poly (acrylic acid) (PAA) are demonstrated by utilizing the hydrogen bonding between these two polymers. These films show controlled thickness growth, high ionic conductivity, and excellent hydrolytic stability. The ionic conductivity of these films is optimized by tuning the assembly pH of initial polymer solutions and thereby controlling the hydrogen bonding characteristics.(cont.) Despite similar film composition, MEEP/PAA LbL films assembled at higher pH values have enhanced water uptake and transport properties, which play a key role in increasing ion transport within the films. At fully humidified conditions, the ionic conductivity of MEEP/PAA is over one order of magnitude higher than previously studied hydrogen bonded LbL systems. The next LbL systems studied consist of a highly sulfonated aromatic polyether (sPPO) paired with amine containing polycations. The best performing sPPO system has ionic conductivity values which are the same order of magnitude as commercially relevant PEMs and has the highest ionic conductivity ever obtained from a LbL assembled film. Additionally, these LbL systems have methanol permeability values over two orders of magnitude lower than traditional PEMs. Incorporating the sPPO systems into DMFCs results in a 53% improvement in power output as compared with DMFCs using traditional PEMs. In-depth structure property studies are performed to understand the nature of the high ionic conductivity of the sPPO LbL systems with respect to film growth, composition, water uptake, and ionic crosslink density. Lastly, the mechanical properties of highly conducting LbL films are improved by forming the LbL matrix on highly tunable electrospun fiber mat (EFM) supports. Free-standing LbL films have moderate mechanical properties when dry, but are mechanically deficient when hydrated. Coating an EFM with the LbL dipping process produces composite membranes with interesting "bridged" morphologies, while still maintaining high ionic conductivity values.(cont.) The spray LbL assembly is studied as a means for the rapid formation of LbL films on EFMs. At optimized conditions, the LbL materials conformally coat the individual fibers throughout the bulk of the EFM and have uniform surface coatings. The mechanical properties of the spray coated EMFs are shown to be superior to the pristine LbL systems.by James Nathan Ashcraft.Ph.D

Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity

The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e.g., a vision model for classifying street signs should work in rural, city, and highway settings under varying lighting/weather conditions). However, these one-size-fits-all models are typically optimized for average case performance, encouraging them to achieve high performance in nominal conditions but exposing them to unexpected behavior in challenging or rare contexts. To address this concern, we develop a new method for training context-dependent models. We extend Bridge-Mode Connectivity (BMC) (Garipov et al., 2018) to train an infinite ensemble of models over a continuous measure of context such that we can sample model parameters specifically tuned to the corresponding evaluation context. We explore the definition of context in image classification tasks through multiple lenses including changes in the risk profile, long-tail image statistics/appearance, and context-dependent distribution shift. We develop novel extensions of the BMC optimization for each of these cases and our experiments demonstrate that model performance can be successfully tuned to context in each scenario.Comment: Accepted to the NeurIPS 2022 ML Safety Worksho

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Affect, motivation, working memory, and mathematics

This article reviews the topics of affect, motivation, working memory, and their relationships to mathematics learning and performance. The underlying factors of interest, motivation, self-efficacy, and maths anxiety, as well as an approach concerning people’s beliefs about fixed versus malleable intelligence, can be grouped into an approach and an avoidance constellation of attitudes and beliefs, with opposite relationships to outcome measures of learning and mastery in maths. This article then considers the research on working memory, showing it to be central to arithmetic and maths processing, and also the principle mental component being disrupted by affective and emotional reactions during problem solving. After discussing the disruptive effects of maths anxiety, choking under pressure, and stereotype threat, the article closes with a brief consideration of how these affective disruptions might be minimized or eliminated