A Novel Loss Function Utilizing Wasserstein Distance to Reduce Subject-Dependent Noise for Generalizable Models in Affective Computing

Emotions are an essential part of human behavior that can impact thinking, decision-making, and communication skills. Thus, the ability to accurately monitor and identify emotions can be useful in many human-centered applications such as behavioral training, tracking emotional well-being, and development of human-computer interfaces. The correlation between patterns in physiological data and affective states has allowed for the utilization of deep learning techniques which can accurately detect the affective states of a person. However, the generalisability of existing models is often limited by the subject-dependent noise in the physiological data due to variations in a subject's reactions to stimuli. Hence, we propose a novel cost function that employs Optimal Transport Theory, specifically Wasserstein Distance, to scale the importance of subject-dependent data such that higher importance is assigned to patterns in data that are common across all participants while decreasing the importance of patterns that result from subject-dependent noise. The performance of the proposed cost function is demonstrated through an autoencoder with a multi-class classifier attached to the latent space and trained simultaneously to detect different affective states. An autoencoder with a state-of-the-art loss function i.e., Mean Squared Error, is used as a baseline for comparison with our model across four different commonly used datasets. Centroid and minimum distance between different classes are used as a metrics to indicate the separation between different classes in the latent space. An average increase of 14.75% and 17.75% (from benchmark to proposed loss function) was found for minimum and centroid euclidean distance respectively over all datasets.Comment: 9 page

Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations

In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational efficiency but often lack precision. Applying conventional super-resolution to these simulations poses a significant challenge due to the fundamental contrast between downsampling high-resolution images and authentically emulating low-resolution physics. The former method conserves more of the underlying physics, surpassing the usual constraints of real-world scenarios. We propose a novel definition of super-resolution tailored for PDE-based problems. Instead of simply downsampling from a high-resolution dataset, we use coarse-grid simulated data as our input and predict fine-grid simulated outcomes. Employing a physics-infused UNet upscaling method, we demonstrate its efficacy across various 2D-CFD problems such as discontinuity detection in Burger's equation, Methane combustion, and fouling in Industrial heat exchangers. Our method enables the generation of fine-mesh solutions bypassing traditional simulation, ensuring considerable computational saving and fidelity to the original ground truth outcomes. Through diverse boundary conditions during training, we further establish the robustness of our method, paving the way for its broad applications in engineering and scientific CFD solvers.Comment: Accepted at Machine Learning and the Physical Sciences Workshop, NeurIPS 202

Ultrafast Green Single Photon Emission from an InGaN Quantum Dot-in-a-GaN Nanowire at Room Temperature

In recent years, there has been a growing demand for room-temperature visible single-photon emission from InGaN nanowire-quantum-dots (NWQDs) due to its potential in developing quantum computing, sensing, and communication technologies. Despite various approaches explored for growing InGaN quantum dots on top of nanowires (NWs), achieving the emission of a single photon at room temperature with sensible efficiency remains a challenge. This challenge is primarily attributed to difficulties in accomplishing the radial confinement limit and the inherent giant built-in potential of the NWQD. In this report, we have employed a novel Plasma Assisted Molecular Beam Epitaxy (PAMBE) growth approach to reduce the diameter of the QD to the excitonic Bohr radius of InGaN, thereby achieving strong lateral confinement. Additionally, we have successfully suppressed the strong built-in potential by reducing the QD diameter. Toward the end of the report, we have demonstrated single-photon emission (${\lambda}$ = 561 nm) at room-temperature from the NWQD and measured the second-order correlation function $g^{2}(0)$ as 0.11, which is notably low compared to other reported findings. Furthermore, the lifetime of carriers in the QD is determined to be 775 ps, inferring a high operational speed of the devices

Investigation of Magnesium Silicate as an Effective Gate Dielectric for AlGaN/GaN Metal Oxide High Electron Mobility Transistors (MOSHEMT)

In this study, a 6 nm layer of Magnesium Silicate (Mg-Silicate) was deposited on AlGaN/GaN heterostructure by sputtering of multiple stacks of MgO and SiO$_{2}$, followed by rapid thermal annealing in a nitrogen (N$_{2}$) environment. The X-ray photoelectron spectroscopy (XPS) analysis confirmed the stoichiometric Mg-Silicate (MgSiO$_{3}$) after being annealed at a temperature of 850 $^\circ$C for 70 seconds. Atomic force microscopy (AFM) was employed to measure the root mean square (RMS) roughness (2.20 nm) of the Mg-Silicate. A significant reduction in reverse leakage current, by a factor of three orders of magnitude, was noted for the Mg-Silicate/AlGaN/GaN metal-oxide-semiconductor (MOS) diode in comparison to the Schottky diode. The dielectric constant of Mg-Silicate($\mathcal{E}_{Mg-Silicate}$) and the interface density of states (D$_{it}$) with AlGaN were approximated at $\sim$ 6.6 and 2.0 $\times$ 10$^{13}$ cm$^{-2}$eV$^{-1}$ respectively, utilizing capacitance-voltage (CV) characteristics