Correlations and Clustering in Wholesale Electricity Markets

We study the structure of locational marginal prices in day-ahead and real-time wholesale electricity markets. In particular, we consider the case of two North American markets and show that the price correlations contain information on the locational structure of the grid. We study various clustering methods and introduce a type of correlation function based on event synchronization for spiky time series, and another based on string correlations of location names provided by the markets. This allows us to reconstruct aspects of the locational structure of the grid.Comment: 30 pages, several picture

Hybrid Comfort: 3D Printing Interwoven

Under the concept of Maker Movement, apparel researchers and designers are exploring the potentials of three-dimensional printing (3DP) and seeking ways to take the advantages of 3DP and apply it to wearable products. This design case study aimed to integrate 3DP textiles in a beach vest to allow new properties and functions to emerge with aesthetics, and explore the properties of 3D textiles by manipulating the structure of TPU materials using the FDM 3DP method. The hybrid 3DP textile was developed by mimicking and integrating the structures of traditional woven and knitted fabrics, tried to take advantages of both fabrics. The final 3DP textile structure evaluations suggested some expected properties (e.g., flexible, strong), while revealed new properties (e.g., porous, cushioning). Further, the specialty 3D printed TPU material was unique in its resilient and flexible properties, and fit the functions of the man’s beach vest design

Smooth Dynamic

With technology advancement and product customization demands, more people with mobility issues are seeking specialty assistive tools to help facilitate and manage their daily lives. This case study adopts research through design methodology and explores the explores the workflow approach in developing wearable assistive glove for female wheelchair users using 3D CAD modeling program, 3D scanning, and 3DP technology. The assistive glove was developed using both and 3D printed nylon filament. It consists of a custom fit glove and a 3D printed nylon wrist protection portion. Friction pads are custom designed on fit model to support the potential hand overuse in the pushrim gripping motion. Heat dissipation is also considered through incorporating a 3D printed textile inset on the glove dorsal side. Key findings reflect challenges in manipulating 3DP materials for assistive tool development and virtual fit evaluation in the 3D CAD modeling process

How Much Can Brands Deviate from their Brand Aesthetic? The Moderating Role of Brand’s Luxury Statu

Brand aesthetics is fundamental to maintaining a competitive advantage, especially within the luxury sector. Brand design consistency (BDC) plays a pivotal role in building a successful luxury brand through the formation of strong brand associations. However, creating new and distinct product designs that deviate from a brand\u27s aesthetic may have the potential to increase the brand\u27s interest. The purpose of this study is to examine the impact of BDC on aesthetic judgment and purchase intention and to investigate the moderating effect of brand\u27s luxury status in the above relationship. The concepts of halo effect and biased assimilation relating to luxury brands form the conceptual basis of our study. Findings of this study exhibit that high BDC (vs. low BDC) evoked the most positive consumer response for luxury and non-luxury brands. The overall BDC effect however, was more salient for non-luxury brands which indicates that they have less latitude to deviate from their brand aesthetic than luxury fashion brands

Informative Bayesian Neural Network Priors for Weak Signals

Funding Information: ∗This work was supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence, FCAI, grants 319264, 292334, 286607, 294015, 336033, 315896, 341763), and EU Horizon 2020 (INTERVENE, grant no. 101016775). We also acknowledge the computational resources provided by the Aalto Science-IT Project from Computer Science IT. †Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Finland, [email protected] ‡Finnish Institute for Health and Welfare (THL), Finland §Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland ¶Department of Computer Science, University of Manchester, UK ‖Equal contribution. Funding Information: This work was supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence, FCAI, grants 319264, 292334, 286607, 294015, 336033, 315896, 341763), and EU Horizon 2020 (INTERVENE, grant no. 101016775). We also acknowledge the computational resources provided by the Aalto Science-IT Project from Computer Science IT. Publisher Copyright: © 2022 International Society for Bayesian AnalysisEncoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals. Two types of domain knowledge are commonly available in scientific applications: 1. feature sparsity (fraction of features deemed relevant); 2. signal-to-noise ratio, quantified, for instance, as the proportion of variance explained. We show how to encode both types of domain knowledge into the widely used Gaussian scale mixture priors with Automatic Relevance Determination. Specifically, we propose a new joint prior over the local (i.e., feature-specific) scale parameters that encodes knowledge about feature sparsity, and a Stein gradient optimization to tune the hyperparameters in such a way that the distribution induced on the model’s proportion of variance explained matches the prior distribution. We show empirically that the new prior improves prediction accuracy compared to existing neural network priors on publicly available datasets and in a genetics application where signals are weak and sparse, often outperforming even computationally intensive cross-validation for hyperparameter tuning.Peer reviewe

On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets

Different distribution shifts require different algorithmic and operational interventions. Methodological research must be grounded by the specific shifts they address. Although nascent benchmarks provide a promising empirical foundation, they implicitly focus on covariate shifts, and the validity of empirical findings depends on the type of shift, e.g., previous observations on algorithmic performance can fail to be valid when the $Y|X$ distribution changes. We conduct a thorough investigation of natural shifts in 5 tabular datasets over 86,000 model configurations, and find that $Y|X$-shifts are most prevalent. To encourage researchers to develop a refined language for distribution shifts, we build WhyShift, an empirical testbed of curated real-world shifts where we characterize the type of shift we benchmark performance over. Since $Y|X$-shifts are prevalent in tabular settings, we identify covariate regions that suffer the biggest $Y|X$-shifts and discuss implications for algorithmic and data-based interventions. Our testbed highlights the importance of future research that builds an understanding of how distributions differ.Comment: 41 page

Efficient Implementation of Ab Initio Quantum Embedding in Periodic Systems: Density Matrix Embedding Theory

We describe an efficient quantum embedding framework for realistic ab initio density matrix embedding theory (DMET) calculations in solids. We discuss in detail the choice of orbitals and mapping to a lattice, treatment of the virtual space and bath truncation, and the lattice-to-embedded integral transformation. We apply DMET in this ab initio framework to a hexagonal boron nitride monolayer, crystalline silicon, and nickel monoxide in the antiferromagnetic phase, using large embedded clusters with up to 300 embedding orbitals. We demonstrate our formulation of ab initio DMET in the computation of ground-state properties such as the total energy, equation of state, magnetic moment, and correlation functions