ELASTIC: Improving CNNs with Dynamic Scaling Policies

Scale variation has been a challenge from traditional to modern approaches in computer vision. Most solutions to scale issues have a similar theme: a set of intuitive and manually designed policies that are generic and fixed (e.g. SIFT or feature pyramid). We argue that the scaling policy should be learned from data. In this paper, we introduce ELASTIC, a simple, efficient and yet very effective approach to learn a dynamic scale policy from data. We formulate the scaling policy as a non-linear function inside the network's structure that (a) is learned from data, (b) is instance specific, (c) does not add extra computation, and (d) can be applied on any network architecture. We applied ELASTIC to several state-of-the-art network architectures and showed consistent improvement without extra (sometimes even lower) computation on ImageNet classification, MSCOCO multi-label classification, and PASCAL VOC semantic segmentation. Our results show major improvement for images with scale challenges. Our code is available here: https://github.com/allenai/elasticComment: CVPR 2019 oral, code available https://github.com/allenai/elasti

Analytical and computational modeling of multiphase flow in ferrofluid charged oscillating heat pipes

Electromagnetic-based energy harvesting materials and devices have emerged as a prominent research area in the last ten years, especially systems using ferrofluidic induction—a process that generates voltage via the pulsation of a ferrofluid (iron-based nanofluid) through a solenoid. This work includes the development of an analytical model and computational modeling methods to investigate ferrofluid pulsating flow within an energy harvesting device and the mass and heat transfer performance of a two-phase closed thermosyphon (TPCT) and oscillating heat pipe (OHP). First, an analytical model is proposed to predict the induced electromotive force (EMF) based on the flow behavior and magnetic properties of a pulsating ferrofluid energy harvesting device. The model identifies key parameters for describing and optimizing induction for ferrofluid pulsing through a solenoid. Data from a previously documented experimental study was used to validate the analytical model, and both the experimental data and analytical model show the same trends with the induced EMF increasing as a function of pulsating frequency and magnetic field strength as a higher percentage of the ferrofluid nanoparticle moments are aligned. Second, computational fluid dynamics (CFD) simulations were performed to predict the heat transfer performance of a TPCT. Simulations were performed using a three-dimensional finite-volume flow solver (ANSYS Fluent) with a pressure-based scheme for the solution of the continuity and momentum equations, volume-of-fluid method for resolution of the liquid-vapor phase interface, and a temperature-dependent model for interphase mass transfer by evaporation and condensation. Different model and numerical scheme combinations were investigated to identify an efficient and consistently accurate method using currently available software tools. To address issues with previously published simulation methods violating the conservation of mass, a new variable saturation temperature model was tested along with mass transfer coefficients based on the vapor-liquid density ratio and more physically realistic boundary conditions. The variable saturation temperature model significantly mitigated mass and energy imbalance in the simulations, for both constant heat flux and convection condenser boundary conditions. In addition, for the VOF discretization the Geo-Reconstruct scheme was found to be more accurate than the Compressive scheme available in Fluent without additional computational cost. Third, simulations of a vertical OHP were performed using the CFD methodology developed for the TPCT system. Results show simulations using appropriate values for the evaporation and condensation mass transfer time relaxation parameters and the new variable saturation temperature model are in good agreement with the available experimental data. For the OHP system, using the Compressive discretization scheme for the VOF model allowed for computationally efficient simulation. It is believed that the advances in analytical and computational modeling developed in this research project will contribute important steps toward development of an accurate, efficient, and comprehensive simulation methodology to predict multiphase flow, heat transfer, and energy harvesting in ferrofluid charged oscillating heat pipes

On Modeling Long-Range Dependencies for Visual Perception

One of the ultimate goals of computer vision is to extract useful information from visual inputs. An example is to recognize and segment objects from natural images. Recently, deep networks enable us to do a wide range of these tasks better than ever. These are mostly achieved with convolutional neural networks that model pixel relations within a small convolution kernel. Despite such success of convolution, the local window approximation makes it challenging to capture long-range relations. This limitation results in problems, such as unsatisfactory generalization and robustness to out-of-distribution examples. In this dissertation, I aim to model long-range dependencies in the context of natural image perception. The first part of the dissertation is focused on designing neural architectures that are flexible enough to capture long-range relations. We start by improving convolutional networks with dynamic scaling policies. Then, we explore an alternative solution that completely replaces convolution with global self-attention to capture more context. The attention mechanism is further extended to modeling relations between the pixels and the objects with a transformer, enabling panoptic segmentation in an end-to-end manner. These flexible long-range models usually require a large amount of labeled data to train. In order to address this issue, we discuss self-supervised techniques that learn representation effectively without human annotation in the second part of the dissertation. We regularize the contrastive learning framework with a consistency term that refines self-supervision signals. We also study a more general pretext task, masked image modeling, and train transformers to learn better representations with an online semantic tokenizer

Financing Sources, R&D Investment and Enterprise Risk

AbstractResearch and development (R&D) investment of high-tech enterprises has an impact on enterprise risk, but the effect is different when funding sources are different. This paper aims to study the relationships among financing sources, R&D investment and enterprise risk. The empirical results suggest that the relationship between endogenous financing rate and R&D investment is significantly positive, and asset-liability ratio has a significantly negative impact on R&D investment. Furthermore, the study shows that relationship between enterprise risk and R&D investment can be described with a quadratic parabola