Can Curriculum Changes Improve the Deliverables the Business Studies Departments of Maine’s Community Colleges Provide its Stakeholders?

Recent seminars and meetings between state officials and business leaders have established that a perceived disconnect exists between the skill\u27s, knowledge, and abilities needed by their enterprises and the skill\u27s, knowledge, and abilities being taught by the state’s educational systems. The Maine Community College System\u27s vision states that the system answers to a number of stakeholders by providing a two year comprehensive, affordable, and accessible college education... dedicated to building a quality workforce for Maine” (Maine Community College, n.d.). While the MCCS is a relatively new institution, this suggests that the community college arena is the most logical starting place for investigating and identifying these disconnects. Through interviews with students and businesses, surveys for both groups of stakeholders were developed. The resulting survey data was used to ascertain any disconnects and suggest possible venues for determining if curriculum changes would address them. Suggestions for future directions are offered

The Full Two-Loop R-parity Violating Renormalization Group Equations for All Minimal Supersymmetric Standard Model Couplings

We present the full two-loop $\beta$-functions for the minimal supersymmetric standard model couplings, extended to include R-parity violating couplings through explicit R-parity violation

Fast Predictive Simple Geodesic Regression

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets.Comment: 19 pages, 10 figures, 13 table

An Improved Encoder-Decoder Framework for Food Energy Estimation

Dietary assessment is essential to maintaining a healthy lifestyle. Automatic image-based dietary assessment is a growing field of research due to the increasing prevalence of image capturing devices (e.g. mobile phones). In this work, we estimate food energy from a single monocular image, a difficult task due to the limited hard-to-extract amount of energy information present in an image. To do so, we employ an improved encoder-decoder framework for energy estimation; the encoder transforms the image into a representation embedded with food energy information in an easier-to-extract format, which the decoder then extracts the energy information from. To implement our method, we compile a high-quality food image dataset verified by registered dietitians containing eating scene images, food-item segmentation masks, and ground truth calorie values. Our method improves upon previous caloric estimation methods by over 10\% and 30 kCal in terms of MAPE and MAE respectively.Comment: Accepted for Madima'23 in ACM Multimedi

Constraints on Finite Soft Supersymmetry-Breaking Terms

Requiring the soft supersymmetry-breaking (SSB) parameters in finite gauge-Yukawa unified models to be finite up to and including two-loop order, we derive a two-loop sum rule for the soft scalar-masses. It is shown that this sum rule coincides with that of a certain class of string models in which the massive string states are organized into N=4 supermultiplets. We investigate the SSB sector of two finite SU(5) models. Using the sum rule which allows the non-universality of the SSB terms and requiring that the lightest superparticle particleis neutral, we constrain the parameter space of the SSB sector in each model.Comment: 34 page

QARV: Quantization-Aware ResNet VAE for Lossy Image Compression

This paper addresses the problem of lossy image compression, a fundamental problem in image processing and information theory that is involved in many real-world applications. We start by reviewing the framework of variational autoencoders (VAEs), a powerful class of generative probabilistic models that has a deep connection to lossy compression. Based on VAEs, we develop a novel scheme for lossy image compression, which we name quantization-aware ResNet VAE (QARV). Our method incorporates a hierarchical VAE architecture integrated with test-time quantization and quantization-aware training, without which efficient entropy coding would not be possible. In addition, we design the neural network architecture of QARV specifically for fast decoding and propose an adaptive normalization operation for variable-rate compression. Extensive experiments are conducted, and results show that QARV achieves variable-rate compression, high-speed decoding, and a better rate-distortion performance than existing baseline methods. The code of our method is publicly accessible at https://github.com/duanzhiihao/lossy-vaeComment: Technical repor