For the first time, it seems, the entire world knows what it’s like to live inside my head.

The United States faces a major meat shortage due to virus infections at processing plants. It means millions of pigs could be put down without ever making it to table. This is what the predicament looks like on a Minnesota farm. ... According to the Minnesota Pork Producers Association, an estimated 10,000 pigs are being euthanised every day in the state. ... [Farmer Mike Boerboom:] "On the same day that we're euthanising pigs - and it's a horrible day - is the same day that a grocery store 10 miles away may not get a shipment of pork. It's just that the supply chain is broken at this point."

We review some recent results on the phenomenon of surface superconductivity in the framework of Ginzburg-Landau theory for extreme type-II materials. In particular, we focus on the response of the superconductor to a strong longitudinal magnetic field in the regime where superconductivity survives only along the boundary of the wire. We derive the energy and density asymptotics for samples with smooth cross section, up to curvature-dependent terms. Furthermore, we discuss the corrections in presence of corners at the boundary of the sample.

Starting from the Hamiltonian formulation of ${cal N}{=},2$ supersymmetric Calogero models associated with the classical $A_n, B_n, C_n$ and $D_n$ series and their hyperbolic/trigonometric cousins, we provide their superspace description. The key ingredients include $n$ bosonic and $2n(n{-}1)$ fermionic ${cal N}{=},2$ superfields, the latter being subject to a nonlinear chirality constraint. This constraint has a universal form valid for all Calogero models. With its help we find more general supercharges (and a superspace Lagrangian), which provide the ${cal N}{=},2$ supersymmetrization for bosonic potentials with arbitrary repulsive two-body interactions.

The aim of this paper is to obtain a generalized CK-extension theorem in superspace for the bi-axial Dirac operator. In the classical commuting case, this result can be written as a power series of Bessel type of certain differential operators acting on a single initial function. In the superspace setting, novel structures appear in the cases of negative even superdimensions. In these cases, the CK-extension depends on two initial functions on which two power series of differential operators act. These series are not only of Bessel type but they give rise to an additional structure in terms of Appell polynomials. This pattern also is present in the structure of the Pizzetti formula, which describes integration over the supersphere in terms of differential operators. We make this relation explicit by studying the decomposition of the generalized CK-extension into plane waves integrated over the supersphere. Moreover, these results are applied to obtain a decomposition of the Cauchy kernel in superspace into monogenic plane waves, which shall be useful for inverting the super Radon transform.

Deep neural networks have exhibited promising performance in image super-resolution (SR). Most SR models follow a hierarchical architecture that contains both the cell-level design of computational blocks and the network-level design of the positions of upsampling blocks. However, designing SR models heavily relies on human expertise and is very labor-intensive. More critically, these SR models often contain a huge number of parameters and may not meet the requirements of computation resources in real-world applications. To address the above issues, we propose a Hierarchical Neural Architecture Search (HNAS) method to automatically design promising architectures with different requirements of computation cost. To this end, we design a hierarchical SR search space and propose a hierarchical controller for architecture search. Such a hierarchical controller is able to simultaneously find promising cell-level blocks and network-level positions of upsampling layers. Moreover, to design compact architectures with promising performance, we build a joint reward by considering both the performance and computation cost to guide the search process. Extensive experiments on five benchmark datasets demonstrate the superiority of our method over existing methods.

Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.

Noisy Intermediate-Scale Quantum (NISQ) computers are entering an era in which they can perform computational tasks beyond the capabilities of the most powerful classical computers, thereby achieving "Quantum Supremacy", a major milestone in quantum computing. NISQ Supremacy requires comparison with a state-of-the-art classical simulator. We report HPC simulations of hard random quantum circuits (RQC), which have been recently used as a benchmark for the first experimental demonstration of Quantum Supremacy, sustaining an average performance of 281 Pflop/s (true single precision) on Summit, currently the fastest supercomputer in the World. These simulations were carried out using qFlex, a tensor-network-based classical high-performance simulator of RQCs. Our results show an advantage of many orders of magnitude in energy consumption of NISQ devices over classical supercomputers. In addition, we propose a standard benchmark for NISQ computers based on qFlex.

In ASPIC-style structured argumentation an argument can rebut another argument by attacking its conclusion. Two ways of formalizing rebuttal have been proposed: In restricted rebuttal, the attacked conclusion must have been arrived at with a defeasible rule, whereas in unrestricted rebuttal, it may have been arrived at with a strict rule, as long as at least one of the antecedents of this strict rule was already defeasible. One systematic way of choosing between various possible definitions of a framework for structured argumentation is to study what rationality postulates are satisfied by which definition, for example whether the closure postulate holds, i.e. whether the accepted conclusions are closed under strict rules. While having some benefits, the proposal to use unrestricted rebuttal faces the problem that the closure postulate only holds for the grounded semantics but fails when other argumentation semantics are applied, whereas with restricted rebuttal the closure postulate always holds. In this paper we propose that ASPIC-style argumentation can benefit from keeping track not only of the attack relation between arguments, but also the relation of deductive joint support that holds between a set of arguments and an argument that was constructed from that set using a strict rule. By taking this deductive joint support relation into account while determining the extensions, the closure postulate holds with unrestricted rebuttal under all admissibility-based semantics. We define the semantics of deductive joint support through the flattening method.

Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The saliency and separability of the hidden representations when trained on MNIST dataset is studied using an external classifier, and compared with other unsupervised learning methods that include restricted Boltzmann machines and autoencoders.

In this paper, we propose a new nonlinear detector with improved interference suppression in Multi-User Multiple Input, Multiple Output (MU-MIMO) system. The proposed detector is a combination of the following parts: QR decomposition (QRD), low complexity users sorting before QRD, sorting-reduced (SR) K-best method and minimum mean square error (MMSE) pre-processing. Our method outperforms a linear interference rejection combining (IRC, i.e. MMSE naturally) method significantly in both strong interference and additive white noise scenarios with both ideal and real channel estimations. This result has wide application importance for scenarios with strong interference, i.e. when co-located users utilize the internet in stadium, highway, shopping center, etc. Simulation results are presented for the non-line of sight 3D-UMa model of 5G QuaDRiGa 2.0 channel for 16 highly correlated single-antenna users with QAM16 modulation in 64 antennas of Massive MIMO system. The performance was compared with MMSE and other detection approaches.

In this paper, we analyze and implement the Dirichlet spectral-Galerkin method for approximating simply supported vibrating plate eigenvalues with variable coefficients. This is a Galerkin approximation that uses the approximation space that is the span of finitely many Dirichlet eigenfunctions for the Laplacian. Convergence and error analysis for this method is presented for two and three dimensions. Here we will assume that the domain has either a smooth or Lipschitz boundary with no reentrant corners. An important component of the error analysis is Weyl's law for the Dirichlet eigenvalues. Numerical examples for computing the simply supported vibrating plate eigenvalues for the unit disk and square are presented. In order to test the accuracy of the approximation, we compare the spectral-Galerkin method to the separation of variables for the unit disk. Whereas for the unit square we will numerically test the convergence rate for a variable coefficient problem.

Rescue vessels are the main actors in maritime safety and rescue operations. At the same time, aerial drones bring a significant advantage into this scenario. This paper presents the research directions of the AutoSOS project, where we work in the development of an autonomous multi-robot search and rescue assistance platform capable of sensor fusion and object detection in embedded devices using novel lightweight AI models. The platform is meant to perform reconnaissance missions for initial assessment of the environment using novel adaptive deep learning algorithms that efficiently use the available sensors and computational resources on drones and rescue vessel. When drones find potential objects, they will send their sensor data to the vessel to verity the findings with increased accuracy. The actual rescue and treatment operation are left as the responsibility of the rescue personnel. The drones will autonomously reconfigure their spatial distribution to enable multi-hop communication, when a direct connection between a drone transmitting information and the vessel is unavailable.

Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data. This paper presents a self-supervised method that can be trained on YouTube videos without known depth, which makes training data collection simple and improves the generalization of the learned network. The self-supervised learning is achieved by minimizing a photo-consistency loss, which is evaluated between a video frame and its neighboring frames warped according to the estimated depth and the 3D non-rigid motion of the human body. To solve this non-rigid motion, we first estimate a rough SMPL model at each video frame and compute the non-rigid body motion accordingly, which enables self-supervised learning on estimating the shape details. Experiments demonstrate that our method enjoys better generalization and performs much better on data in the wild.

Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels. In this paper, we implement the CORAL on quantum devices by two different methods. One method utilizes quantum basic linear algebra subroutines (QBLAS) to implement the CORAL with exponential speedup in the number and dimension of the given data samples. The other method is achieved through a variational hybrid quantum-classical procedure. In addition, the numerical experiments of the CORAL with three different types of data sets, namely the synthetic data, the synthetic-Iris data, the handwritten digit data, are presented to evaluate the performance of our work. The simulation results prove that the variational quantum correlation alignment algorithm (VQCORAL) can achieve competitive performance compared with the classical CORAL.

Low-resolution text images are often seen in natural scenes such as documents captured by mobile phones. Recognizing low-resolution text images is challenging because they lose detailed content information, leading to poor recognition accuracy. An intuitive solution is to introduce super-resolution (SR) techniques as pre-processing. However, previous single image super-resolution (SISR) methods are trained on synthetic low-resolution images (e.g.Bicubic down-sampling), which is simple and not suitable for real low-resolution text recognition. To this end, we pro-pose a real scene text SR dataset, termed TextZoom. It contains paired real low-resolution and high-resolution images which are captured by cameras with different focal length in the wild. It is more authentic and challenging than synthetic data, as shown in Fig. 1. We argue improv-ing the recognition accuracy is the ultimate goal for Scene Text SR. In this purpose, a new Text Super-Resolution Network termed TSRN, with three novel modules is developed. (1) A sequential residual block is proposed to extract the sequential information of the text images. (2) A boundary-aware loss is designed to sharpen the character boundaries. (3) A central alignment module is proposed to relieve the misalignment problem in TextZoom. Extensive experiments on TextZoom demonstrate that our TSRN largely improves the recognition accuracy by over 13%of CRNN, and by nearly 9.0% of ASTER and MORAN compared to synthetic SR data. Furthermore, our TSRN clearly outperforms 7 state-of-the-art SR methods in boosting the recognition accuracy of LR images in TextZoom. For example, it outperforms LapSRN by over 5% and 8%on the recognition accuracy of ASTER and CRNN. Our results suggest that low-resolution text recognition in the wild is far from being solved, thus more research effort is needed.

Bitvector filtering is an important query processing technique that can significantly reduce the cost of execution, especially for complex decision support queries with multiple joins. Despite its wide application, however, its implication to query optimization is not well understood.

In this work, we study how bitvector filters impact query optimization. We show that incorporating bitvector filters into query optimization straightforwardly can increase the plan space complexity by an exponential factor in the number of relations in the query. We analyze the plans with bitvector filters for star and snowflake queries in the plan space of right deep trees without cross products. Surprisingly, with some simplifying assumptions, we prove that, the plan of the minimal cost with bitvector filters can be found from a linear number of plans in the number of relations in the query. This greatly reduces the plan space complexity for such queries from exponential to linear.

Motivated by our analysis, we propose an algorithm that accounts for the impact of bitvector filters in query optimization. Our algorithm optimizes the join order for an arbitrary decision support query by choosing from a linear number of candidate plans in the number of relations in the query. We implement our algorithm in Microsoft SQL Server as a transformation rule. Our evaluation on both industry standard benchmarks and customer workload shows that, compared with the original Microsoft SQL Server, our technique reduces the total CPU execution time by 22%-64% for the workloads, with up to two orders of magnitude reduction in CPU execution time for individual queries.

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.

Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. However, it is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising yet under-explored in unsupervised multimodal MT (MMT). In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT. Our model employs multimodal back-translation and features pseudo visual pivoting in which we learn a shared multilingual visual-semantic embedding space and incorporate visually-pivoted captioning as additional weak supervision. The experimental results on the widely used Multi30K dataset show that the proposed model significantly improves over the state-of-the-art methods and generalizes well when the images are not available at the testing time.

Dialogue engines that incorporate different types of agents to converse with humans are popular.

However, conversations are dynamic in the sense that a selected response will change the conversation on-the-fly, influencing the subsequent utterances in the conversation, which makes the response selection a challenging problem.

We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a emph{Neural Response Selection} method.

The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism.

Our experimental results show that the proposed method can accurately select the most appropriate responses, thereby significantly improving the user experience in dialogue systems.

Despite multiple concussions, a high school freshman continues to play football. Will family tradition outweigh the risks?

By The Office of Energy Efficiency & Renewable Energy Within 25 years, the United States could produce enough biomass to support a bioeconomy, including renewable aquatic and terrestrial biomass resources that could be used for energy and to develop products … Continue reading →

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If you've ever taken a stroll around downtown Spokane's west side, you may have come across Barrister Winery, tucked into a historic brick and timber building alongside the railroad tracks at 1213 Railroad Ave.…

For parents of budding artists and creative types, it can often seem like the arts get short shrift in the K-12 curricula, especially at a time when STEM — short for science, technology, engineering and math — is the buzzword in education and the most visible casualties of school budget cuts are librarians and music teachers.…

As soon as state Rep. Marcus Riccelli returned home from Olympia, he jumpstarted a community-wide effort to feed Spokane constituents deeply affected by the COVID-19 crisis.…

A system and method that facilitates urban traffic state detection based on support vector machine (SVM) and multilayer perceptron (MLP) classifiers is provided. Moreover, the SVM and MLP classifiers are fused into a cascaded two-tier classifier that improves the accuracy of the traffic state classification. To further improve the accuracy, the cascaded two-tier classifier (e.g., MLP-SVM), a single SVM classifier and a single MLP classifier are fused to determine a final decision for a traffic state. In addition, fusion strategies are employed during training and implementation phases to compensate for data acquisition and classification errors caused by noise and/or outliers.

A particle-polymer hybrid material can include: a substance having the structure of Formula 1 Z(Y-FP)m, wherein Z is a particle smaller than 1 mm; m is an integer; Y is a linker including a silicon atom linked to the particle; FP is a functionalized polymer having: a first structure derived from a first norbornene compound linked to the Y; and one or more repeating units linked to the first structure, each unit being derived from a second norbornene compound and having a functional group.

The present invention discloses a method and apparatus for processing and error correction of a GFP-T superblock, where the 64 bytes of payload data of a first superblock are buffered in the first page of a two-page buffer. The flag byte is buffered in a separate buffer, and a CRC operation is performed in a separate logic element. The result of the CRC operation is checked against a single syndrome table which may indicate single- or multi-bit errors. As the payload data of the first superblock is processed and read out of the first page of the two-page buffer, the payload data of a second superblock is written into the second page of the two-page buffer to be processed and corrected.

The present invention provides a polymerization reactor for producing a super absorbent polymer comprising: a reaction unit; a monomer composition supply unit being connected to the reaction unit and supplying a monomer composition solution containing a monomer, a photoinitiator, and a solvent; an agitating shaft extended in the reaction unit from one end of the reaction unit connected to the monomer composition supply unit to the other end of the reaction unit; a plurality of agitating blades installed around the agitating shaft; and a light irradiation unit providing light to the monomer composition solution furnished from the monomer composition supply unit, and a method of producing super absorbent polymers by using the same.

A crosslinkable curing super-branched polyester and the cured product and the preparation method thereof are disclosed. The super-branched polyester has high refractive index and comprises a compound represented by the following structural formula (I). In the formula (I), HBP is the backbone of the super-branched polyester; both a and b are positive integers; the sum of a and b is less than or equal to n; n is more than or equal to 10 and less than 80. In the super-branched polyester, A is represented by formula (II) and N is represented by formula (III), wherein R is methyl or hydrogen atom; the mole ratio of N relative to the total mole of A and N is more than 30 mol %, and the ratio of the total mole of A and N relative to the product of the total mole of HBP backbone and n is more than 0.5 and less than or equal to 1.

Methods of increasing the solubility of a base in supercritical carbon dioxide include forming a complex of a Lewis acid and the base, and dissolving the complex in supercritical carbon dioxide. The Lewis acid is soluble in supercritical carbon dioxide, and the base is substantially insoluble in supercritical carbon dioxide. Methods for increasing the solubility of water in supercritical carbon dioxide include dissolving an acid or a base in supercritical carbon dioxide to form a solution and dissolving water in the solution. The acid or the base is formulated to interact with water to solubilize the water in the supercritical carbon dioxide. Some compositions include supercritical carbon dioxide, a hydrolysable metallic compound, and at least one of an acid and a base. Some compositions include an alkoxide and at least one of an acid and a base.

A multi-threaded in-order superscalar processor 2 includes an issue stage 12 including issue circuitry 22, 24 for selecting instructions to be issued to execution units 14, 16 in dependence upon a currently selected issue policy. A plurality of different issue policies are provided by associated different policy circuitry 28, 30, 32 and a selection between which of these instances of the policy circuitry 28, 30, 32 is active is made by policy selecting circuitry 34 in dependence upon detected dynamic behavior of the processor 2.

A hypervisor and one or more guest operating systems resident in a data processing system and hosted by the hypervisor are configured to selectively enable or disable branch prediction logic through separate hypervisor-mode and guest-mode instructions. By doing so, different branch prediction strategies may be employed for different operating systems and user applications hosted thereby to provide finer grained optimization of the branch prediction logic for different operating scenarios.

A via disposition information acquiring unit acquires via disposition information indicating a disposition of the plurality of first vias (212). A second conductor information acquiring unit acquires second conductor information indicating disposition positions of a plurality of second conductors (232) repeatedly disposed in the second conductor layer (230). A via extracting unit extracts an extraction via with respect to each of the plurality of second conductors (232). The extraction via is each of the first vias (212) overlapping the second conductor (232). A via selecting unit selects a selection via with respect to each of the plurality of second conductors (232). The selection via is each of first vias (212) selected in a predetermined number from the extraction vias. An opening introducing unit introduces a first opening (234) to each of the plurality of second conductors (232). The first opening (234) overlaps the extraction via not selected by the via selecting unit in plan view.

A circuit-design support method includes obtaining information for a circuit having a logic circuit in which signal lines are connected to input terminals, signals of the signal lines being output via the logic circuit; obtaining information concerning a control circuit that has a first flip-flop for scanning and that can control a value of a given signal line by a value set by the first flip-flop; selecting, based on the circuit information, a second flip-flop at an output destination of a signal from the logic circuit, among second flip-flops of the circuit; and generating, based on the control circuit information, information indicating a serial connection of the control circuit between an output source of the signal of the given signal line and the given signal line and a connection of a data input terminal of the first flip-flop and an output terminal of the selected second flip-flop.

Fault Current Limiters (FCL) provide protection for upstream and/or downstream devices in electric power grids. Conventional FCL require the use of expensive conductors and liquid or gas cryogen handling. Disclosed embodiments describe FCL systems and devices that use lower cost superconductors, require no liquid cryogen, and are fast cycling. These improved FCL can sustain many sequential faults and require less time to clear faults while avoiding the use of liquid cryogen. Disclosed embodiments describe a FCL with a superconductor and cladding cooled to cryogenic temperatures; these are connected in parallel with a second resistor across two nodes in a circuit. According to disclosed embodiments, the resistance of the superconducting components and its sheath in the fault mode are sufficiently high to minimize energy deposition within the cryogenic system, minimizing recovery time. A scheme for intermediate heat storage also is described which allows a useful compromise between conductor length enabled energy minimization and allowable number of sequential faults to enable an overall system design which is affordable, and yet allows conduction cooled (cryogen free) systems which have fast recovery and allows for multiple sequential faults.

A transmission system is provided with a superconductive cable having three phase conductors and a cryostat, surrounding the phase conductors, and encasing a hollow space, for conducting a cooling agent. For the three phase conductors, a common neutral conductor is provided, being made of electrically normally conducting material, carried out as insulating round conductor and placed outside the cryostat and next to it. The cryostat is made of a circumferentially enclosed, thermally insulated sheath.

Provided is a method of manufacturing a superconducting accelerator cavity in which a plurality of half cells having opening portions (equator portions and iris portions) at both ends thereof in an axial direction are placed one after another in the axial direction, contact portions where the corresponding opening portions come into contact with each other are joined by welding, and thus, a superconducting accelerator cavity is manufactured, the half cells to be joined are arranged so that the axial direction thereof extends in a vertical direction; and concave portions that are concave towards an outer side are also formed at inner circumferential surfaces located below the contact portions of the half cells positioned at a bottom; and the contact portions are joined from outside by penetration welding.

A superconductive magnet includes a superconductive coil that is an air-core coil; a pair of bobbin bodies that support the superconductive coil while interposing the superconductive coil therebetween on both sides of a center axial line direction of the superconductive coil; an outer circumference-side binding portion that extends in the center axial line direction on an outer circumferential side of the superconductive coil to bind the pair of bobbin bodies; and a belt-shaped or a wire-shaped inner circumference-side tension imparted portion which extends in the center axial line direction on an inner circumferential side of the superconductive coil to connect the pair of bobbin bodies, and on which tension is imparted in the center axial line direction.

Low-loss superconducting devices and methods for fabricating low loss superconducting devices. For example, superconducting devices, such as superconducting resonator devices, are formed with a (200)-oriented texture titanium nitride (TiN) layer to provide high Q, low loss resonator structures particularly suitable for application to radio-frequency (RF) and/or microwave superconducting resonators, such as coplanar waveguide superconducting resonators. In one aspect, a method of forming a superconducting device includes forming a silicon nitride (SiN) seed layer on a substrate, and forming a (200)-oriented texture titanium nitride (TiN) layer on the SiN seed layer.

A superconductive electrical direct current cable with at least two conductors insulated relative to each other is indicated, where the cable is placed with at least two conductors insulated relative to each other, where the conductors are arranged in a cryostat suitable for guidance of the cooling agent, wherein the cryostat is composed of at least one metal pipe which is surrounded by a circumferentially closed layer with thermally insulating properties. In the cryostat is arranged a strand-shaped carrier composed of insulating material, where the carrier has at least two diametrically oppositely located outwardly open grooves in each of which is arranged one of the conductors. Each conductor is composed of a plurality of superconductive elements.

The present disclosure relates to a superconducting rotating electrical machine and a manufacturing method for a high temperature superconducting film thereof. The superconducting rotating electrical machine includes a stator, and a rotor rotatable with respect to the stator, the rotor having a rotary shaft and a rotor winding. Here, the rotor winding includes tubes disposed on a circumference of the rotary shaft and each forming a passage for a cooling fluid therein, superconducting wires accommodated within the tubes, and a cooling fluid flowing through the inside of the tubes. This configuration may allow for direct heat exchange between the superconducting wires and a refrigerant, resulting in improvement of heat exchange efficiencies of the superconducting wires.

A persistent-mode High Temperature Superconductor (HTS) shim coil is provided having at least one rectangular shaped thin sheet of HTS, wherein the thin sheet of HTS contains a first long portion, a second long portion parallel to first long portion, a first end, and a second end parallel to the first end. The rectangular shaped thin sheet of high-temperature superconductor has a hollow center and forms a continuous loop. In addition, the first end and the second end are folded toward each other forming two rings, and the thin sheet of high-temperature superconductor has a radial build that is less than 5 millimeters (mm) and able to withstand very strong magnetic field ranges of greater than approximately 12 Tesla (T) within a center-portion of a superconducting magnet of a superconducting magnet assembly.

Disclosed are an oxide superconductor tape and a method of manufacturing the oxide superconductor tape capable of improving the length and characteristics of superconductor tape and obtaining stabilized characteristics across the entire length thereof. A Y-class superconductor tape (10), as an oxide superconductor tape, comprises a tape (13) further comprising a tape-shaped non-oriented metallic substrate (11), and a first buffer layer (sheet layer) (12) that is formed by IBAD upon the tape-shaped non-oriented metallic substrate (11); and a second buffer layer (gap layer) (14), further comprising a lateral face portion (14a) that is extended to the lateral faces of the first buffer layer (sheet layer) (12) upon the tape (13) by RTR RF-magnetron sputtering.