Semi-supervised Active Salient Object Detection

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Emotion is an increasingly important concept in ethics, education, and engineering. It is also important for the intersection of these three domains: engineering ethics education. Despite extensive research being conducted independently in each field, ther ...

Neural Networks (NNs), renowned for their universal approximation capabilities, have proven well-suited for parametrizing nonlinear control policies and modeling complex processes from available data. However, several NN architectures are physics-agnostic, ...

We present a general variational framework for the training of freeform nonlinearities in layered computational architectures subject to some slope constraints. The regularization that we add to the traditional training loss penalizes the second-order tota ...

Self-organization and emergent behavior lie at the core of many natural phenomena, from pattern formation in biological tissues to the collective motion of flocking birds. Harnessing these principles in computational models can unlock powerful approaches f ...

First-generation students (FGS) are traditionally underrepresented in higher education, including engineering education. To ensure equity in access and success in engineering, and thereby ensure that we educate responsible engineers who come from a diversi ...

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG ...

This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied ran ...

Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a spectral bias ...

We consider the problem of learning to play a repeated contextual game with unknown reward and unknown constraints functions. Such games arise in applications where each agent’s action needs to belong to a feasible set, but the feasible set is a priori unk ...

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality leve ...

Image restoration reconstructs, as faithfully as possible, an original image from a potentially degraded version of it. Image degradations can be of various types, for instance haze, unwanted reflections, optical or spectral aberrations, or other physicall ...

Predictive scene parsing is a task of assigning pixel-level semantic labels to a future frame of a video. It has many applications in vision-based artificial intelligent systems, e.g., autonomous driving and robot navigation. Although previous work has sho ...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

Integrated optics has shown itself very convenient for exploiting nonlinear processes as it results in high confinement factor, freedom of dispersion engineering and compactness. However, the choice of materials is crucial for the development of nonlinear ...

The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting i ...

In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic concepts ...

The need to produce more sustainable concrete is proving imminent given the rising environmental concerns facing the industry. Blended cement concrete, based on any of the prominent supplementary cementitious materials (SCMs) such as fly ash, ground granul ...

The sharing economy (SE) has gained attention in recent years in both academia and practice. This derives from the economic and social benefits that are often attributed to it, but also from its limited anticipated environmental impacts. However, its role ...

Semi-supervised Active Salient Object Detection

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Emotions in Engineering Ethics Education: Systematic Review and Ways Forward

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Concept Discovery for The Interpretation of Landscape Scenicness

Data-driven optimization tool for the functional, economic, and environmental properties of blended cement concrete using supplementary cementitious materials

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