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Data mining can hardly solve but always faces a problem that there is little meaningful information within the dataset serving a given requirement. Faced with multiple unknown datasets, to allocate data mining resources to acquire more desired data, it is necessary to establish a data quality assessment framework based on the relevance between the dataset and requirements. This framework can help the user to judge the potential benefits in advance, so as to optimize the resource allocation to those candidates. However, the unstructured data (e.g., image data) often presents dark data states, which makes it tricky for the user to understand the relevance based on content of the dataset in real time. Even if all data have label descriptions, how to measure the relevance between data efficiently under semantic propagation remains an urgent problem. Based on this, we propose a Deep Hash-based Relevance-aware Data Quality Assessment framework, which contains off-line learning and relevance mining parts as well as an on-line assessing part. In the off-line part, we first design a Graph Convolution Network (GCN)-AutoEncoder hash (GAH) algorithm to recognize the data (i.e., lighten the dark data), then construct a graph with restricted Hamming distance, and finally design a Cluster PageRank (CPR) algorithm to calculate the importance score for each node (image) so as to obtain the relevance representation based on semantic propagation. In the on-line part, we first retrieve the importance score by hash codes and then quickly get the assessment conclusion in the importance list. On the one hand, the introduction of GCN and co-occurrence probability in the GAH promotes the perception ability for dark data. On the other hand, the design of CPR utilizes hash collision to reduce the scale of graph and iteration matrix, which greatly decreases the consumption of space and computing resources. We conduct extensive experiments on both single-label and multi-label datasets to assess the relevance between data and requirements as well as test the resources allocation. Experimental results show our framework can gain the most desired data with the same mining resources. Besides, the test results on Tencent1M dataset demonstrate the framework can complete the assessment with a stability for given different requirements.

The ability to control the potential landscape in a medium of interacting particles could lead to intriguing collective behavior and innovative functionalities. Here, we utilize spatially reconfigurable magnetic potentials of a pinwheel artificial spin ice structure to tailor the motion of superconducting vortices. The reconstituted chain structures of the magnetic charges in the artificial pinwheel spin ice and the strong interaction between magnetic charges and superconducting vortices allow significant modification of the transport properties of the underlying superconducting thin film, resulting in a reprogrammable resistance state that enables a reversible and switchable vortex Hall effect. Our results highlight an effective and simple method of using artificial spin ice as an in-situ reconfigurable nanoscale energy landscape to design reprogrammable superconducting electronics, which could also be applied to the in-situ control of properties and functionalities in other magnetic particle systems, such as magnetic skyrmions., Comment: To appear in Nano Letters

A superconducting diode is an electronic device that conducts supercurrent and exhibits zero resistance primarily for one direction of applied current. Such a dissipationless diode is a desirable unit for constructing electronic circuits with ultralow power consumption. However, realizing a superconducting diode is fundamentally and technologically challenging, as it usually requires a material structure without a centre of inversion, which is scarce among superconducting materials. Here, we demonstrate a superconducting diode achieved in a conventional superconducting film patterned with a conformal array of nanoscale holes, which breaks the spatial inversion symmetry. We showcase the superconducting diode effect through switchable and reversible rectification signals, which can be three orders of magnitude larger than that from a flux-quantum diode. The introduction of conformal potential landscapes for creating a superconducting diode is thereby proven as a convenient, tunable, yet vastly advantageous tool for superconducting electronics. This could be readily applicable to any superconducting materials, including cuprates and iron-based superconductors that have higher transition temperatures and are desirable in device applications., 25 pages, 4 figures

Negative longitudinal magnetoresistance (NLMR) has been reported in a variety of materials and has attracted extensive attention as an electrotransport hallmark of topological Weyl semimetals. However, its origin is still under debate. Here, we demonstrate that the NLMR in a two-dimensional electron gas can be influenced by the measurement current. While the NLMR persists up to 130 K, its magnitude and magnetic field response become dependent on the applied current below 60 K. The tunable NLMR at low and high currents can be best attributed to quantum interference and disorder scattering effects, respectively. This work uncovers non-Ohmic NLMR in a non-Weyl material and highlights potential effects of the measurement current in elucidating electrotransport phenomena. We also demonstrate that NLMRs can be a valuable phenomenon in revealing the origins of other properties, such as negative MRs in perpendicular magnetic fields.

We study the ratchet effect in a narrow pinning-free superconductive ring based on time-dependent Ginzburg-Landau (TDGL) equations. Voltage responses to external dc an ac currents at various magnetic fields are studied. Due to asymmetric barriers for flux penetration and flux exit in the ring-shaped superconductor, the critical current above which the flux-flow state is reached, as well as the critical current for the transition to the normal state, are different for the two directions of applied current. These effects cooperatively cause ratchet signal reversal at high magnetic fields, which has not been reported to date in a pinning-free system. The ratchet signal found here is larger than those induced by asymmetric pinning potentials. Our results also demonstrate the feasibility of using mesoscopic superconductors to employ superconducting diode effect in versatile superconducting devices., 8 pages,6 figures

It has always been a tricky task to generate image hashing function via deep learning without labels and allocate the relative distance between data through their features. Existing methods can complete this task and prevent the overfitting problem using shallow graph embedding technique. However, they only capture the first-order proximity. To address this problem, we design DSTGeH, a deep self-taught graph embedding hashing framework which learns hash function without labels for image retrieval. DSTGeH introduces deep graph embedding means to capture more complex topological relationships (the second-order proximity) on the graph and maps these relationships into pseudo labels, which enables an end-to-end hash model and helps recognize the samples outside the graph. We present the ablation studies and compare DSTGeH with the state-of-the-art label-free hashing algorithms. Extensive experiments show DSTGeH can achieve the best performances and produce an overwhelming advantage on multi-object datasets.

Magnetic induction tomography (MIT) is a promising technique for cerebral hemorrhage detection due to its non-radioactive, non-invasive, low cost features and capacity to penetrate the low-conductivity skull. In the cerebral hemorrhage detection by MIT, multi-frequency decomposition method can reduce the artifacts caused by the background conductivity changes in the existing frequency-difference method. In order to improve the accuracy of multi-frequency decomposition, a cambered sensor array with focusing coil was proposed. The performance of focusing coil was tested by 3D anatomical head models with different hemorrhage depths. Target to noise ratio was investigated to evaluate the antiinterference performance. Decomposition error and correlation coefficient were calculated to evaluate the accuracy of multifrequency decomposition. After introducing the focusing coil, the decomposition error is reduced by 39.0%, 25.6% and 15.3% respectively at the hemorrhagic depth 12 mm, 16 mm and 20 mm. The results show that the focusing coil is effective in enhancing the anti-interference performance against the noise signal generated by the conductivity changes of other tissues and improving the accuracy of multi-frequency decomposition. This method provides the possibility of cerebral hemorrhage detection with smaller volume in frequency-difference MIT.

We present extensive investigations of the crystallographic phase diagram and electronic properties of the Fe-based superconductor FeSe under extreme conditions (high pressure (HP) and low temperature (LT)) by synchrotron X-ray diffraction (XRD) and X-ray absorption spectroscopy (XAS). An isostructural phase transition (Tetragonal (T) →high-pressure Tetragonal (Tʹ)) is discovered in FeSe at ∼2.8 GPa based on the axial ratio c/a with finer pressure step as observed in Fe-As-based superconductor such as EuFe2As2. We also find a pressure-induced Tʹ → MnP-type phase transition at 7.6 GPa in FeSe, which is consistent with the documented pressure-induced high-spin → low-spin transition (∼6–7 GPa). These results reveal the pressure-induced structural phase transition sequence in FeSe at room temperature to be T → Tʹ → Tʹ+MnP-type at pressures of 0–10.6 GPa, enriching the crystallographic phase diagram. The HPLT XRD data also indicate that a sluggish structural phase transition (Cmma → Pnma) begins at 7.5 GPa, and these two phases coexist up to 26.5 GPa. The HP X-ray absorption near-edge spectroscopy (XANES) measurement shows that Eo of Se experiences a pressure-induced shift to high energy, evidencing strongly charge transfer between Fe and Se under high pressure. Our results shed lights on the correlation between crystallographic/electronic structure and superconductivity in this material.

Fascination with glassy states has persisted since Fisher introduced the vortex-glass as a new thermodynamic phase that is a true superconductor that lacks conventional long-range order. Though Fisher’s original model considered point disorder, it was later predicted that columnar defects (CDs) could also induce glassiness — specifically, a Bose-glass phase. In YBa2Cu3O7−x (YBCO), glassy states can cause distinct behavior in the temperature (T ) dependent rate of thermally activated vortex motion (S). The vortex-glass state produces a plateau in S(T ) whereas a Bose-glass can transition into a state hosting vortex excitations called double-kinks that can expand, creating a large peak in S(T ). Although glass phases have been well-studied in YBCO, few studies exist of other materials containing CDs that could contribute to distinguishing universal behavior. Here, we report on the effectiveness of CDs tilted ~30° from the c-axis in reducing S in a NbSe2 crystal. The magnetization is 5 times higher and S is minimized when the field is parallel to the defects versus aligned with the c-axis. We see signatures of glassiness in both field orientations, but do not observe a peak in S(T ) nor a plateau at values observed in YBCO. Finally, we discuss the possibility that competing disorder induces a field-orientation-driven transition from a Bose-glass to an anisotropic glass involving both point and columnar disorder.

Pinning and creep determine the current--voltage characteristic of a type II superconductor and thereby its potential for technological applications. The recent development of strong pinning theory provides us with a tool to assess a superconductor's electric properties in a quantitative way. Motivated by the observation of typical excess-current characteristics and field-scaling of critical currents, here, we analyze current--voltage characteristics measured on 2H-NbSe$_2$ and $a$-MoGe type II superconductors within the setting provided by strong pinning theory. The experimentally observed shift and rounding of the voltage-onset is consistent with the predictions of strong pinning in the presence of thermal fluctuations. We find the underlying parameters determining pinning and creep and discuss their consistency.

Magnetic induction tomography (MIT) is a promising technology for intracranial hematomas imaging. The existing problems in MIT are mainly low sensitivity and low resolution of the sensor array, which is necessary to be optimized. A number of optimizations exist to enhance the resolution of MIT, but so far, studies only considered the eigenvalue or singular value of the sensitivity, or the magnetic flux with noise-free data or low level of noise. In this paper, the optimization considers the intensity and uniformity of the sensitivity matrix with added noise. In order to enhance the resolution of dual frequency-difference MIT, the coil parameters optimization is studied by simulations from three aspects: the inner radius, the shape, and the number of turns. After the sensitivity analysis, the absolute dimensions of the optimal coil structure are determined. Based on the optimal structure, the brain hematoma is simulated with a human brain model and finite element method. Images are reconstructed from the modeled data with a 26-dB signal-to-noise ratio noise contaminated. The results indicated that the optimal sensor array can achieve the conductivity resolution of 0.25 S/m and the spatial resolution is 12.38% of the brain radius.

Electrochemical doping was used to dope lithium into perovskite CsPbBr3 crystal. Due to the doping, diamagnetic behavior was observed with a transition temperature (Tc) of 7.17 K. Burstein–Moss effect related photoluminescence (PL) blue-shift was observed as long as 15 nm. UV–vis-NIR spectra indicate that the absorption has been enhanced. The doped CsPbBr3 crystal generated 8 nA photocurrent, which is higher than the undoped CsPbBr3 crystal.

The storage system in large scale data centers is typically built upon thousands or even millions of disks, where disk failures constantly happen. A disk failure could lead to serious data loss and thus system unavailability or even catastrophic consequences if the lost data cannot be recovered. While replication and erasure coding techniques have been widely deployed to guarantee storage availability and reliability, disk failure prediction is gaining popularity as it has the potential to prevent disk failures from occurring in the first place. Recent trends have turned toward applying machine learning approaches based on disk SMART attributes for disk failure predictions. However, traditional machine learning (ML) approaches require a large set of training data in order to deliver good predictive performance. In large-scale storage systems, new disks enter gradually to augment the storage capacity or to replace failed disks, leading storage systems to consist of small amounts of new disks from different vendors and/or different models from the same vendor as time goes on. We refer to this relatively small amount of disks as minority disks. Due to the lack of sufficient training data, traditional ML approaches fail to deliver satisfactory predictive performance in evolving storage systems which consist of heterogeneous minority disks. To address this challenge and improve the predictive performance for minority disks in large data centers, we propose a minority disk failure prediction model named TLDFP based on a transfer learning approach. Our evaluation results on two realistic datasets have demonstrated that TLDFP can deliver much more precise results, compared to four popular prediction models based on traditional ML algorithms and two state-of-the-art transfer learning methods.

Understanding and forecasting user visits is of great importance for a variety of tasks, e.g., online advertising, which is one of the most profitable business models for Internet services. Publishers sell advertising spaces in advance with user visit volume and attributes guarantees. There are usually tens of thousands of attribute combinations in an online advertising system. The key problem is how to accurately forecast the number of user visits for each attribute combination. Many traditional work characterizing temporal trends of every single time series are quite inefficient for large-scale time series. Recently, a number of models based on deep learning or matrix factorization have been proposed for high-dimensional time series forecasting. However, most of them neglect correlations among attribute combinations, or are tailored for specific applications, resulting in poor adaptability for different business scenarios.Besides, sophisticated deep learning models usually cause high time and space complexity. There is still a lack of an efficient highly scalable and adaptable solution for accurate high-dimensional time series forecasting. To address this issue, in this work, we conduct a thorough analysis on large-scale user visits data and propose a novel deep spatial-temporal tensor factorization framework, which provides a general design for high-dimensional time series forecasting. We deployed the proposed framework in Tencent online guaranteed delivery advertising system, and extensively evaluated the effectiveness and efficiency of the framework in two different large-scale application scenarios. The results show that our framework outperforms existing methods in prediction accuracy. Meanwhile, it significantly reduces the parameter number and is resistant to incomplete data with up to 20% missing values.

Configuration tuning is vital to optimize the performance of database management system (DBMS). It becomes more tedious and urgent for cloud databases (CDB) due to the diverse database instances and query workloads, which make the database administrator (DBA) incompetent. Although there are some studies on automatic DBMS configuration tuning, they have several limitations. Firstly, they adopt a pipelined learning model but cannot optimize the overall performance in an end-to-end manner. Secondly, they rely on large-scale high-quality training samples which are hard to obtain. Thirdly, there are a large number of knobs that are in continuous space and have unseen dependencies, and they cannot recommend reasonable configurations in such high-dimensional continuous space. Lastly, in cloud environment, they can hardly cope with the changes of hardware configurations and workloads, and have poor adaptability. To address these challenges, we design an end-to-end automatic CDB tuning system, CDBTune, using deep reinforcement learning (RL). CDBTune utilizes the deep deterministic policy gradient method to find the optimal configurations in high-dimensional continuous space. CDBTune adopts a try-and-error strategy to learn knob settings with a limited number of samples to accomplish the initial training, which alleviates the difficulty of collecting massive high-quality samples. CDBTune adopts the reward-feedback mechanism in RL instead of traditional regression, which enables end-to-end learning and accelerates the convergence speed of our model and improves efficiency of online tuning. We conducted extensive experiments under 6 different workloads on real cloud databases to demonstrate the superiority of CDBTune. Experimental results showed that CDBTune had a good adaptability and significantly outperformed the state-of-the-art tuning tools and DBA experts.

The charge and spin of the electrons in solids have been extensively exploited in electronic devices and in the development of spintronics. Another attribute of electrons—their orbital nature—is attracting growing interest for understanding exotic phenomena and in creating the next-generation of quantum devices such as orbital qubits. Here, we report on orbital-flop induced magnetoresistance anisotropy in CeSb. In the low temperature high magnetic-field driven ferromagnetic state, a series of additional minima appear in the angle-dependent magnetoresistance. These minima arise from the anisotropic magnetization originating from orbital-flops and from the enhanced electron scattering from magnetic multidomains formed around the first-order orbital-flop transition. The measured magnetization anisotropy can be accounted for with a phenomenological model involving orbital-flops and a spin-valve-like structure is used to demonstrate the viable utilization of orbital-flop phenomenon. Our results showcase a contribution of orbital behavior in the emergence of intriguing phenomena., The orbital degree of freedom can be as important as the charge and spin of the electron to the electronic phenomena. Here the authors show additional minimum in the angle-dependent magnetoresistance (MR) for the low temperature high magnetic field driven ferromagnetic state in CeSb which indicates the orbital flop induced MR anisotropy.

Blindly applying data mining techniques on image dark data whose content and value are not clear, is highly likely to bring undesired result. Therefore, we propose an assessment framework which includes offline and online stages for image dark data. In offline stage, we first transform images into hash codes by Deep Self-taught Hashing (DSTH) algorithm, then construct a semantic graph, and finally use our designed Semantic Hash Ranking (SHR) algorithm to calculate the importance score. During online stage, we first translate the user’s query into hash codes, then match the suitable data contained in the dark data, and finally return the weighted average value of these matched data to help the user cognize the dark data. The results on real-world dataset show our framework can apply to large-scale datasets, help the user conduct subsequent data mining work.

Review sentiment analysis has drawn a lot of active research interest because of the explosive growth in the amount of available reviews in our day-to-day activities. The current review sentiment classification work often models each sentence as a sequence of words, thus simply training sequence-structured recurrent neural networks (RNNs) end-to-end and optimizing via stochastic gradient descent (SGD). However, such sequence-structured architectures overlook the syntactic hierarchy among the words in a sentence. As a result, they fail to capture the syntactic properties that would naturally combine words to phrases. In this paper, we propose to model each sentence of a review with an attention-based dependency tree-LSTM, where a sentence embedding is obtained relying on the dependency tree of the sentence as well as the attention mechanism in the tree structure. Then, we feed all the sentence representations into a sequence-structured long short-term memory network (LSTM) and exploit attention mechanism to generate the review embedding for final sentiment classification. We evaluate our attention-based tree-LSTM model on three public datasets, and experimental results turn out that it outperforms the state-of-the-art baselines.

Two-dimensional topological materials bearing time reversal-breaking magnetic fields support protected one-way edge modes. Normally, these edge modes adhere to physical edges where material properties change abruptly. However, even in homogeneous materials, topology still permits a unique form of edge modes – kink modes – residing at the domain boundaries of magnetic fields within the materials. This scenario, despite being predicted in theory, has rarely been demonstrated experimentally. Here, we report our observation of topologically-protected high-frequency kink modes – kink magnetoplasmons (KMPs) – in a GaAs/AlGaAs two-dimensional electron gas (2DEG) system. These KMPs arise at a domain boundary projected from an externally-patterned magnetic field onto a uniform 2DEG. They propagate unidirectionally along the boundary, protected by a difference of gap Chern numbers (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm1$$\end{document}±1) in the two domains. They exhibit large tunability under an applied magnetic field or gate voltage, and clear signatures of nonreciprocity even under weak-coupling to evanescent photons., Topological kink modes are peculiar edge excitations that take place at domain boundaries of magnetic fields inside homogeneous materials. Here, the authors experimentally observe kink magnetoplasmons in a 2D electron gas using custom-shaped strong permanent magnets on top of a GaAs/AlGaAs heterojunction.

Nanoscale single-domain bar magnets are building blocks for a variety of fundamental and applied mesoscopic magnetic systems, such as artificial spin ices, magnetic shape-morphing microbots as well as magnetic majority logic gates. The magnetization reversal switching field of the bar nanomagnets is a crucial parameter that determines the physical properties and functionalities of their constituted artificial systems. Previous methods on tuning the magnetization reversal switching field of a bar nanomagnet usually rely on modifying its aspect ratio, such as its length, width and/or thickness. Here, we show that the switching field of a bar nanomagnet saturates when extending its length beyond a certain value, preventing further tailoring of the magnetization reversal via aspect ratios. We showcase highly tunable switching field of a bar nanomagent by tailoring its end geometry without altering its size. This provides an easy method to control the magnetization reversal of a single-domain bar nanomagnet. It would enable new research and/or applications, such as designing artificial spin ices with additional tuning parameters, engineering magnetic microbots with more flexibility as well as developing magnetic quantum-dot cellular automata systems for low power computing., Comment: 24 pages, 4 figures

Cs4PbBr6 crystals have been synthesized by an ultrasonication-assisted trace amount polar solvent method. The prepared green Cs4PbBr6 material was found to have high photoluminescence (PL) brightness and 10 ns PL lifetime. The prepared green light emitting device can achieve the maximum brightness of 3.8 × 109 cd/m2, which is nearly 100 times higher than a wick. The white light emitting device based on Cs4PbBr6 crystals has better performance than commercial products, which can cover 32.3% of the color gamut and achieves 107.3% coverage for International Commission on Illumination (CIE)1931 and 84.8% coverage for CIE1976.

Geometric frustration emerges when local interaction energies in an ordered lattice structure cannot be simultaneously minimized, resulting in a large number of degenerate states. The numerous degenerate configurations may lead to practical applications in microelectronics, such as data storage, memory and logic. However, it is difficult to achieve extensive degeneracy, especially in a two-dimensional system. Here, we showcase in-situ controllable geometric frustration with massive degeneracy in a two-dimensional flux quantum system. We create this in a superconducting thin film placed underneath a reconfigurable artificial-spin-ice structure. The tunable magnetic charges in the artificial-spin-ice strongly interact with the flux quanta in the superconductor, enabling the switching between frustrated and crystallized flux quanta states. The different states have measurable effects on the superconducting critical current profile, which can be reconfigured by precise selection of the spin ice magnetic state through application of an external magnetic field. We demonstrate the applicability of these effects by realizing a reprogrammable flux quanta diode. The tailoring of the energy landscape of interacting 'particles' using artificial-spin-ices provides a new paradigm for the design of geometric frustration, which allows us to control new functionalities in other material systems, such as magnetic skyrmions, electrons/holes in two-dimensional materials and topological insulators, as well as colloids in soft materials., 32 pages, 14 figures

In the current reconstruction of magnetic induction tomography (MIT) in brain imaging, cylindrical and hemispherical coil arrays are developed to obtain the whole head or a cross-section of the whole head. Since the center sensitivity of these kinds of MIT coil array is poor, an open cambered MIT coil array is proposed to focus on the local hemorrhage reconstruction. Different curvatures of cambered coil arrays were investigated to validate improvement of local sensitivity. Absolute average sensitivity values and quartiles were calculated for evaluating the performance of different curvatures of cambered coil arrays within different regions of interest (ROI). The results show that the larger curvature of cambered MIT coil array can obtain the best local sensitivity for the region of interest with a comparable size. The cambered MIT coil arrays improve the local sensitivity within a range of 40mm from the edge of ROI relative to the planar coil array. Cambered MIT coil array provides an open and more portable way for local hemorrhage detection.

Objective Frequency-difference magnetic induction tomography (fdMIT) is a promising technique for the continuous monitoring of intracranial hemorrhage. The dual-frequency difference MIT (dfdMIT) will sometimes produce artefacts in the reconstructed images. To overcome this problem, a multi-frequency difference MIT (mfdMIT) method is proposed. Approach To test the performance of this algorithm, 2D head models comprising six tissue types with different hemorrhage sizes were simulated, and images of the hemorrhage were reconstructed by mfdMIT and dfdMIT. Imaging errors and correlation coefficients were calculated with different levels of added phase noise and conductivity noise. Main results A hemorrhage with a diameter of 20 mm can be reconstructed with 20 dB noise by the mfdMIT method, and the imaging error is reduced by about 60%. The results show that the conductivity of brain tissues changing with frequency will cause artefacts on the reconstructed images of the hemorrhage by dfdMIT, but the artefacts can be effectively suppressed with mfdMIT by separating the hemorrhage from other brain tissues. Significance The mfdMIT method increases the reconstructed image precision, which promotes the development of medical imaging by MIT.

The motion of Abrikosov vortices in type-II superconductors results in a finite resistance in the presence of an applied electric current. Elimination or reduction of the resistance via immobilization of vortices is the "holy grail" of superconductivity research. Common wisdom dictates that an increase in the magnetic field escalates the loss of energy since the number of vortices increases. Here we show that this is no longer true if the magnetic field and the current are applied parallel to each other.Our experimental studies on the resistive behavior of a superconducting Mo$_{0.79}$Ge$_{0.21}$ nanostrip reveal the emergence of a dissipative state with increasing magnetic field, followed by a pronounced resistance drop, signifying a reentrance to the superconducting state. Large-scale simulations of the 3D time-dependent Ginzburg-Landau model indicate that the intermediate resistive state is due to an unwinding of twisted vortices. When the magnetic field increases, this instability is suppressed due to a better accommodation of the vortex lattice to the pinning configuration. Our findings show that magnetic field and geometrical confinement can suppress the dissipation induced by vortex motion and thus radically improve the performance of superconducting materials., Comment: 10 pages, 4 figures

Electron-hole (e-h) compensation is a hallmark of multi-band semimetals with extremely large magnetoresistance (XMR) and has been considered to be the basis for XMR. Recent spectroscopic experiments, however, reveal that YSb with non-saturating magnetoresistance is uncompensated, questioning the e-h compensation scenario for XMR. Here we demonstrate with magnetoresistivity and angle dependent Shubnikov - de Haas (SdH) quantum oscillation measurements that YSb does have nearly perfect e-h compensation, with a density ratio of $0.95$ for electrons and holes. The density and mobility anisotropy of the charge carriers revealed in the SdH experiments allow us to quantitatively describe the magnetoresistance with an anisotropic multi-band model that includes contributions from all Fermi pockets. We elucidate the role of compensated multi-bands in the occurrence of XMR by demonstrating the evolution of calculated magnetoresistances for a single band and for various combinations of electron and hole Fermi pockets., Comment: 22 pages, 7 figures

We report investigations on the magnetotransport in LaSb, which exhibits extremely large magnetoresistance (XMR). Foremost, we demonstrate that the resistivity plateau can be explained without invoking topological protection. We then determine the Fermi surface from Shubnikov - de Haas (SdH) quantum oscillation measurements and find good agreement with the bulk Fermi pockets derived from first principle calculations. Using a semiclassical theory and the experimentally determined Fermi pocket anisotropies, we quantitatively describe the orbital magnetoresistance, including its angle dependence. We show that the origin of XMR in LaSb lies in its high mobility with diminishing Hall effect, where the high mobility leads to a strong magnetic field dependence of the longitudinal magnetoconductance. Unlike a one-band material, when a system has two or more bands (Fermi pockets) with electron and hole carriers, the added conductance arising from the Hall effect is reduced, hence revealing the latent XMR enabled by the longitudinal magnetoconductance. With diminishing Hall effect, the magnetoresistivity is simply the inverse of the longitudinal magnetoconductivity, enabling the differentiation of the electron and hole contributions to the XMR, which varies with the strength and orientation of the magnetic field. This work demonstrates a convenient way to separate the dynamics of the charge carriers and to uncover the origin of XMR in multi-band materials with anisotropic Fermi surfaces. Our approach can be readily applied to other XMR materials., 34 pages, 8 figures

Cerebral hemorrhage can be detected and reconstructed by frequency-difference imaging method in magnetic induction tomography(MIT). The traditional sensitivity matrix used in solving the inverse problem of MIT was usually based on reciprocity theorem which cannot reconstruct the other brain tissues. Based on the frequency-dependent change of biological tissues conductivity, exact normal distribution of brain tissue was implemented as prior information to produce a new sensitivity matrix. A 2D normal realistic head model with 6 kinds of tissues was simulated by finite element analysis software to calculate the brain tissue based sensitivity matrix. Brain tissue based sensitivity maps were investigated and compared with the traditional sensitivity maps. Frequency-difference images of hemorrhage were reconstructed by Tikhonov regularization method using the brain tissue based sensitivity matrix. The results show that the proposed sensitivity matrix can reconstruct the hemorrhage more accurate relative to the other brain tissues than traditional sensitivity matrix by reconstructing the other brain tissues.

Understanding the effect of pinning on the vortex dynamics in superconductors is a key factor towards controlling critical current values. Large-scale simulations of vortex dynamics can provide a rational approach to achieve this goal. Here, we use the time-dependent Ginzburg-Landau equations to study thin superconducting films with artificially created pinning centers arranged periodically in hexagonal lattices. We calculate the critical current density for various geometries of the pinning centers --- varying their size, strength, and density. Furthermore, we shed light upon the influence of pattern distortion on the magnetic-field-dependent critical current. We compare our result directly with available experimental measurements on patterned molybdenum-germanium films, obtaining good agreement. Our results give important systematic insights into the mechanisms of pinning in these artificial pinning landscapes and open a path for tailoring superconducting films with desired critical current behavior., Comment: 10 pages, 8 figures

We report the occurrence of reentrant metallic behavior in the Weyl semimetal NbP. When the applied magnetic field $H$ is above a critical value $H_c$, a reentrance appears as a peak in the temperature dependent resistivity $\rho_{xx}(T)$ at $T$ = $T_p$, similar to that observed in graphite where it was attributed to local superconductivity. The $T_p(H)$ relationship follows a power-law dependence $T_p\sim(H-H_c)^{1/v}$ where $v$ can be derived from the temperature dependence of the zero-field resistivity $\rho_0(T) \sim T^v$. From concurrent measurements of the transverse $\rho_{xx}(T)$ and Hall $\rho_{xy}(T)$ magnetoresistivities, we reveal a clear correlation between the rapidly increasing $\rho_{xy}(T)$ and the occurrence of a peak in the $\rho_{xx}(T)$ curve. Quantitative analysis indicates that the reentrant metallic behavior arises from the competition of the magneto conductivity $\sigma_{xx}(T)$ with an additional component $\Delta\sigma_{xx}(T)=\kappa_H\sigma_{xx}(T)$ where $\kappa_H=[\rho_{xy}(T)/\rho_{xx}(T)]^2$ is the Hall factor. We find that the Hall factor ($\kappa_H \approx 0.4$) at peak temperature $T_p$ is nearly field-independent, leading to the observed $T_p(H)$ relationship. Furthermore, the reentrant metallic behavior in $\rho_{xx}(T)$ also is reflected in the behavior of $\rho_{xx}(H)$ that ranges from non-saturating at $T>70$ K to saturation at liquid helium temperatures. The latter can be explained with the magnetic field dependence of the Hall factor $\kappa_H(H)$. Our studies demonstrate that a semiclassical theory can account for the 'anomalies' in the magnetotransport phenomena of NbP without invoking an exotic mechanism., Comment: To appear in Phys. Rev. B

A conformal pinning array can be created by conformally transforming a uniform triangular pinning lattice to produces a new structure in which the six-fold ordering of the original lattice is conserved but where there is a spatial gradient in the density of pinning sites. Here we examine several aspects of vortices interacting with conformal pinning arrays and how they can be used to create a flux flow diode effect for driving vortices in different directions across the arrays. Under the application of an ac drive, a pronounced vortex ratchet effect occurs where the vortices flow in the easy direction of the array asymmetry. When the ac drive is applied perpendicular to the asymmetry direction of the array, it is possible to realize a transverse vortex ratchet effect where there is a generation of a dc flow of vortices perpendicular to the ac drive due to the creation of a noise correlation ratchet by the plastic motion of the vortices. We also examine vortex transport simulations in experiments and compare the pinning effectiveness of conformal arrays to uniform triangular pinning arrays. We find that a triangular array generally pins the vortices more effectively at the first matching field and below, while the conformal array is more effective at higher fields where interstitial vortex flow occurs., 9 pages, 7 figures

The key ingredient of high critical currents in a type-II superconductor is defect sites that 'pin' vortices. Contrary to earlier understanding on nano-patterned artificial pinning, here we show unequivocally the advantages of a random pinscape over an ordered array in a wide magnetic field range. We reveal that the better performance of a random pinscape is due to the variation of its local-density-of-pinning-sites (LDOPS), which mitigates the motion of vortices. This is confirmed by achieving even higher enhancement of the critical current through a conformally mapped random pinscape, where the distribution of the LDOPS is further enlarged. The demonstrated key role of LDOPS in enhancing superconducting critical currents gets at the heart of random versus commensurate pinning. Our findings highlight the importance of random pinscapes in enhancing the superconducting critical currents of applied superconductors., Comment: To appear in Physical Review B

Artificial ices enable the study of geometrical frustration by design and through direct observation. However, it has proven difficult to achieve tailored long-range ordering of their diverse configurations, limiting both fundamental and applied research directions. We designed an artificial spin structure that produces a magnetic charge ice with tunable long-range ordering of eight different configurations. We also developed a technique to precisely manipulate the local magnetic charge states and demonstrate write-read-erase multifunctionality at room temperature. This globally reconfigurable and locally writable magnetic charge ice could provide a setting for designing magnetic monopole defects, tailoring magnonics, and controlling the properties of other two-dimensional materials.

Magnetic induction tomography (MIT) is a promising technique for continuous monitoring of intracranial hemorrhage due to its contactless nature, low cost and capacity to penetrate the high-resistivity skull. The inter-tissue inductive coupling increases with frequency, which may lead to errors in multi-frequency imaging at high frequency. The effect of inter-tissue inductive coupling was investigated to improve the multi-frequency imaging of hemorrhage. An analytical model of inter-tissue inductive coupling based on the equivalent circuit was established. A set of new multi-frequency decomposition equations separating the phase shift of hemorrhage from other brain tissues was derived by employing the coupling information to improve the multi-frequency imaging of intracranial hemorrhage. The decomposition error and imaging error are both decreased after considering the inter-tissue inductive coupling information. The study reveals that the introduction of inter-tissue inductive coupling can reduce the errors of multi-frequency imaging, promoting the development of intracranial hemorrhage monitoring by multi-frequency MIT.

We report on a resistance anomaly in disordered superconducting films containing arrays of irregularly distributed nanoscale holes. At high driving currents, peaks appear in the resistance as a function of temperature, with peak values up to 2% above the classic normal-state resistance. We attribute the observed resistance anomaly to dissipation-induced granularity which enhances the contributions from fluctuation-induced reduction of the density of states of the quasiparticles. The granular feature of a disordered superconducting film originates from the inhomogeneous temperature distribution caused by the variation of the local dissipation and/or heat transfer., 14 pages, 3 figures, accepted for publication in Applied Physics Letters

Nb films containing extended arrays of holes with 45-nm diameter and 100-nm spacing have been fabricated using anodized aluminum oxide (AAO) as substrate. Pronounced matching effects in the magnetization and Little-Parks oscillations of the superconducting critical temperature have been observed in fields up to 9 kOe. Flux pinning in the patterned samples is enhanced by two orders of magnitude as compared to unpatterned reference samples in applied fields exceeding 5 kOe. Matching effects are a dominant contribution to vortex pinning at temperatures as low as 4.2 K due to the extremely small spacing of the holes.