Normal zone in YBa2Cu3O6+xYBa_2Cu_3O_{6+x}-coated conductors

We consider the distribution of an electric field in YBCO-coated conductors for a situation in which the DC transport current is forced into the copper stabilizer due to a weak link -- a section of the superconducting film with a critical current less than the transport current. The electric field in the metal substrate is also discussed. The results are compared with recent experiments on normal zone propagation in coated conductors for which the substrate and stabilizer are insulated from each other. The potential difference between the substrate and stabilizer, and the electric field in the substrate outside the normal zone can be accounted for by a large screening length in the substrate, comparable to the length of the sample. During a quench, the electric field inside the interface between YBCO and stabilizer, as well as in the buffer layer, can be several orders of magnitude greater than the longitudinal macroscopic electric field inside the normal zone. We speculate on the possibility of using possible microscopic electric discharges caused by this large ($\sim$kV/cm) electric field as a means to detect a quench.Comment: 8 pages, 4 figure

Polymorphic Students

Objective: In an effort to break away from the stale classifications of community college students that stem from the hegemonic perspective of previous literature, this work utilizes the perceptions of community college practitioners to demonstrate new ways of understanding the identities of community college students. Method: By utilizing Gee’s identity theory and Grillo’s theory of intersectionality, we analyze interviews with community college practitioners from three different community colleges on the West coast of the United States to answer these questions: What identities (i.e., natural, institutional, and discursive) do faculty and administrators recognize in community college students? In what ways do community college faculty and administrators describe and conceptualize community college students? Findings: First, community college student identities are intricate and have changed with time; there are two different institutional views held by organizational members—the educational view and the managerial view—which both shape the construction of student identities and play a prominent role in determining which students are disadvantaged. Second, organizational members constructed meanings of student achievement and value (i.e., attributes or outcomes of the ideal student, or what policy makers and institutions refer to as success) according to organizational priorities and perspectives. Conclusion: This investigation encapsulates and elucidates the portrayals and understandings of community college students held by community college administrators and faculty as a means to acknowledge the diverse identities among these students. Scholars and practitioners are encouraged to acknowledge the polymorphic identities of this diverse population to improve scholarship and practice

Winning Play in Spectrum Auctions

We describe factors that make bidding in large spectrum auctions complex -- including exposure and budget problems, the role of timing within an ascending auction, and the possibilities for price forecasting -- and how economic and game-theoretic analysis can assist bidders in overcoming these problems. We illustrate with the case of the FCC's Advanced Wireless Service auction, in which a new entrant, SpectrumCo, faced all these problems yet managed to purchase nationwide coverage at a discount of roughly a third relative to the prices paid by its incumbent competitors in the same auction, saving more than a billion dollars.

4D Frequency Analysis of Computational Cameras for Depth of Field Extension

Depth of field (DOF), the range of scene depths that appear sharp in a photograph, poses a fundamental tradeoff in photography---wide apertures are important to reduce imaging noise, but they also increase defocus blur. Recent advances in computational imaging modify the acquisition process to extend the DOF through deconvolution. Because deconvolution quality is a tight function of the frequency power spectrum of the defocus kernel, designs with high spectra are desirable. In this paper we study how to design effective extended-DOF systems, and show an upper bound on the maximal power spectrum that can be achieved. We analyze defocus kernels in the 4D light field space and show that in the frequency domain, only a low-dimensional 3D manifold contributes to focus. Thus, to maximize the defocus spectrum, imaging systems should concentrate their limited energy on this manifold. We review several computational imaging systems and show either that they spend energy outside the focal manifold or do not achieve a high spectrum over the DOF. Guided by this analysis we introduce the lattice-focal lens, which concentrates energy at the low-dimensional focal manifold and achieves a higher power spectrum than previous designs. We have built a prototype lattice-focal lens and present extended depth of field results

Understanding Silent Failures in Medical Image Classification

To ensure the reliable use of classification systems in medical applications, it is crucial to prevent silent failures. This can be achieved by either designing classifiers that are robust enough to avoid failures in the first place, or by detecting remaining failures using confidence scoring functions (CSFs). A predominant source of failures in image classification is distribution shifts between training data and deployment data. To understand the current state of silent failure prevention in medical imaging, we conduct the first comprehensive analysis comparing various CSFs in four biomedical tasks and a diverse range of distribution shifts. Based on the result that none of the benchmarked CSFs can reliably prevent silent failures, we conclude that a deeper understanding of the root causes of failures in the data is required. To facilitate this, we introduce SF-Visuals, an interactive analysis tool that uses latent space clustering to visualize shifts and failures. On the basis of various examples, we demonstrate how this tool can help researchers gain insight into the requirements for safe application of classification systems in the medical domain. The open-source benchmark and tool are at: https://github.com/IML-DKFZ/sf-visuals.Comment: Accepted at MICCAI 2

Sulfur hexafluoride - A powerful new atmospheric tracer

Long-term observations of the atmospheric trace gas sulfur hexafluoride (SF6) at four background monitoring stations, Neumayer, Antarctica (1986-1994), Cape Grim, Tasmania (1978-1994), Izana, Canary Islands (1991-1994) and Alert, Canada (1993-1994) are presented. These data sets are supplemented by two meridional profiles collected over the Atlantic Ocean (1990 and 1993) and occasional observations at the regional site Fraserdale, Canada (1994). The analytical system and the method of SF6 calibration are described. Compared with data from Neumayer and Izana reported earlier, measurements are updated for all sites until the end of 1994 and the precision has improved by more than a factor of 2. With the Cape Grim archived air samples, the atmospheric SF6 chronology is extended by 8 more years back to 1978. For the period from January 1978 to December 1994 the data confirm a stable and unbroken quadratic rise in tropospheric SF6 from 0.50 to 3.11 ppt in the southern hemisphere and for July 1991 to December 1994 from 2.69 to 3.44 ppt in the northern hemisphere. The global mean tropospheric increase rate in late 1994 was 0.225 ppt/yr (6.9%/yr). The long term trend and interhemispheric gradients are due to industrial production and emission, rising approximately linearly with time and located predominantly (94%) in the northern hemisphere. The interhemispheric exchange time (1.7+/-0.2 yr) derived from SF6 ground level observations when using a two-box model of the atmosphere is considerably larger if compared to the exchange time derived from two- and three-dimensional models (1.1 yr). The chemical and biological inertness of SF6 up to stratospheric conditions results in an atmospheric lifetime of more than 800 years and makes SF6 a powerful tool for modelling transport processes in the atmosphere. Moreover, the tropospheric SF6 chronology is a very valuable input function for mixing studies in linked compartments like the stratosphere, the hydrosphere and the cryosphere

Fidelity and quantum phase transitions

It is shown that the fidelity, a basic notion of quantum information science, may be used to characterize quantum phase transitions, regardless of what type of internal order is present in quantum many-body states. If the fidelity of two given states vanishes, then there are two cases: (1) they are in the same phase if the distinguishability results from irrelevant local information; or (2) they are in different phases if the distinguishability results from relevant long-distance information. The different effects of irrelevant and relevant information are quantified, which allows us to identify unstable and stable fixed points (in the sense of renormalization group theory). A physical implication of our results is the occurrence of the orthogonality catastrophe near the transition points.Comment: 5 pages, 2 figure

Data-driven Optimization for Drone Delivery Service Planning with Online Demand

In this study, we develop an innovative data-driven optimization approach to solve the drone delivery service planning problem with online demand. Drone-based logistics are expected to improve operations by enhancing flexibility and reducing congestion effects induced by last-mile deliveries. With rising digitalization and urbanization, however, logistics service providers are constantly grappling with the challenge of uncertain real-time demand. This study investigates the problem of planning drone delivery service through an urban air traffic network to fulfil online and stochastic demand. Customer requests, if accepted, generate profit and are serviced by individual drone flights as per request origins, destinations and time windows. We cast this stochastic optimization problem as a Markov decision process. We present a novel data-driven optimization approach which generates predictive prescriptions of parameters of a surrogate optimization formulation. Our solution method consists of synthesizing training data via lookahead simulations to train a supervised machine learning model for predicting relative link priority based on the state of the network. This knowledge is then leveraged to selectively create weighted reserve capacity in the network and via a surrogate objective function that controls the trade-off between reserve capacity and profit maximization to maximize the cumulative profit earned. Using numerical experiments based on benchmarking transportation networks, the resulting data-driven optimization policy is shown to outperform a myopic policy. Sensitivity analyses on learning parameters reveal insights into the design of efficient policies for drone delivery service planning with online demand