Fair Is Fair—Reshaping Alaska’s Unfair Trade Practices and Consumer Protection Act

Few fields of law impact as wide a swath of population as consumer protection law. Alaska adopted its consumer protection statute, the Unfair Trade Practices and Consumer Protection Act (UTPCPA), amid a national movement to strengthen consumer protection laws. The UTPCPA uses broad language to encompass a wide range of conduct. However, creative pleading and recent applications of the UTPCPA have expanded the law in ways that threaten Alaska businesses even in the absence of culpable conduct. This Note reviews the history of consumer protection, Alaska’s UTPCPA, and the incentives leading to an expanding application of the UTPCPA. The Note concludes by proposing potential legislative solutions to rein in abuse of the Act

Origin and Detection of Microstructural Clustering in Fluids with Spatial-Range Competitive Interactions

Fluids with competing short-range attractions and long-range repulsions mimic dispersions of charge-stabilized colloids that can display equilibrium structures with intermediate range order (IRO), including particle clusters. Using simulations and analytical theory, we demonstrate how to detect cluster formation in such systems from the static structure factor and elucidate links to macrophase separation in purely attractive reference fluids. We find that clusters emerge when the thermal correlation length encoded in the IRO peak of the structure factor exceeds the characteristic lengthscale of interparticle repulsions. We also identify qualitative differences between the dynamics of systems that form amorphous versus micro-crystalline clusters.Comment: 6 pages, 5 figure

The Use of Online Panel Data in Management Research: A Review and Recommendations

Management scholars have long depended on convenience samples to conduct research involving human participants. However, the past decade has seen an emergence of a new convenience sample: online panels and online panel participants. The data these participants provide—online panel data (OPD)—has been embraced by many management scholars owing to the numerous benefits it provides over “traditional” convenience samples. Despite those advantages, OPD has not been warmly received by all. Currently, there is a divide in the field over the appropriateness of OPD in management scholarship. Our review takes aim at the divide with the goal of providing a common understanding of OPD and its utility and providing recommendations regarding when and how to use OPD and how and where to publish it. To accomplish these goals, we inventoried and reviewed OPD use across 13 management journals spanning 2006 to 2017. Our search resulted in 804 OPD-based studies across 439 articles. Notably, our search also identified 26 online panel platforms (“brokers”) used to connect researchers with online panel participants. Importantly, we offer specific guidance to authors, reviewers, and editors, having implications for both micro and macro management scholars

Development: Sketch for a Theory of Oct4

SummaryHow is it that Oct4, a transcription factor that controls pluripotency in stem cells, also controls lineage specification? A recent study investigating common Oct4 targets in vertebrate species indicates an evolutionarily conserved role in mediating cell adhesion. This finding may help decipher Oct4’s versatility in governing stem cell behaviors

Funnel control of nonlinear systems

Tracking of reference signals is addressed in the context of a class of nonlinear controlled systems modelled by $r$-th order functional differential equations, encompassing inter alia systems with unknown "control direction" and dead-zone input effects. A control structure is developed which ensures that, for every member of the underlying system class and every admissible reference signal, the tracking error evolves in a prescribed funnel chosen to reflect transient and asymptotic accuracy objectives. Two fundamental properties underpin the system class: bounded-input bounded-output stable internal dynamics, and a high-gain property (an antecedent of which is the concept of sign-definite high-frequency gain in the context of linear systems)

Funnel control of nonlinear systems

Tracking of reference signals is addressed in the context of a class of nonlinear controlled systems modelled by r-th-order functional differential equations, encompassing inter alia systems with unknown "control direction" and dead-zone input effects. A control structure is developed which ensures that, for every member of the underlying system class and every admissible reference signal, the tracking error evolves in a prescribed funnel chosen to reflect transient and asymptotic accuracy objectives. Two fundamental properties underpin the system class: bounded-input bounded-output stable internal dynamics, and a high-gain property (an antecedent of which is the concept of sign-definite high-frequency gain in the context of linear systems)

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions

Demonstration of Robust Quantum Gate Tomography via Randomized Benchmarking

Typical quantum gate tomography protocols struggle with a self-consistency problem: the gate operation cannot be reconstructed without knowledge of the initial state and final measurement, but such knowledge cannot be obtained without well-characterized gates. A recently proposed technique, known as randomized benchmarking tomography (RBT), sidesteps this self-consistency problem by designing experiments to be insensitive to preparation and measurement imperfections. We implement this proposal in a superconducting qubit system, using a number of experimental improvements including implementing each of the elements of the Clifford group in single `atomic' pulses and custom control hardware to enable large overhead protocols. We show a robust reconstruction of several single-qubit quantum gates, including a unitary outside the Clifford group. We demonstrate that RBT yields physical gate reconstructions that are consistent with fidelities obtained by randomized benchmarking