Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision

Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences.Comment: Published in IEEE-Transactions on Intelligent Transportation Systems, 2021 14 pages, 10 figure

Early warning models in real time

Each quarter, banks file a call report, or Report of Condition and Income, containing hundreds of accounting items pertaining to their financial condition. Because call reports are filed quarterly, whereas banks are typically examined about once every twelve to eighteen months, statistical early warning models using call report data potentially provide a more up-to-date picture of a bank's condition than on-site exams alone. Often neglected, however, is the fact that call report data are subject to revision. We find evidence of a strong relationship between on-site exams and call report revisions. In addition, we evaluate a major class of early warning models using both originally published and revised data to assess whether model accuracy in real time is appreciably lower than accuracy measured using revised data. The findings indicate revised data overstate the accuracy of early warning models. The substantial effect of revisions on the accuracy of early warning models, coupled with the finding of a relationship between revisions and exams, points to a substantial auditing role for on-site exams. More generally, our findings point to the need for care in the use of call report data for research in which the real-time flow of financial information is of some concern.Econometrics ; Banks and banking - Accounting ; Bank supervision

Functional overview of financial crises development and propagation

The U.S. sub-prime crise developed in the last few months as a dangerous syncope for the entire international financial system, recall for the rethinking of market functionality, revealing the international institutional weakness in financial system supervision on global scale. The mortgage volatility induced by the international dereglementation and derivates contemporary burst, correlated with a relaxed supervision framework, transformed progressively the credit market into a system “bubble”, making possible the distortion of real estates values toward those levels forced by creditors. Throughout a weakness chain, many financial institutions, determined by a savage competition on this sector, left away the prudence and borrowed money from different investors, guarantying the long terms transactions, with short time derivates from speculative short-term market, supplying the bubble. In this context, the paperwork is meant to recall for reinventing the risks models, so that the crises to be anticipated earlier than its development moment.globalization, financial crise, global economy, monetary system, international management

Single Particle Models for the Numerical Simulation of Lithium-Ion Cells

In the design of Battery Management Systems (BMS) for a lithium-ion cell, it is crucial to accurately simulate the device in real time using mathematical models. Often, Equivalent Circuit Models (ECM) are used to this end, due to their simplicity and efficiency. However, they are purely phenomenological (their parameters are fitted to emulate empirical data) and their internal variables lack physical meaning. On the other hand, the most popular physics-based electrochemical model in the literature, the pseudo-two-dimensional (P2D) model, presents a high computational cost. In this paper, we review the single particle model (SPM), a physics-based model of reduced complexity that is suitable for real-time applications.Under the academic supervision of Dr. Jerónimo Rodríguez García, this work was carried out as a Master’s thesis for the Máster en matemática industrial program, offered by Universidade de Santiago de Compostela (www.m2i.es). The project was proposed by Itmati, with Repsol collaboration, who temporally ceded software to obtain some of the numerical results presented