1,008 research outputs found

    Only Aggressive Elephants are Fast Elephants

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    Yellow elephants are slow. A major reason is that they consume their inputs entirely before responding to an elephant rider's orders. Some clever riders have trained their yellow elephants to only consume parts of the inputs before responding. However, the teaching time to make an elephant do that is high. So high that the teaching lessons often do not pay off. We take a different approach. We make elephants aggressive; only this will make them very fast. We propose HAIL (Hadoop Aggressive Indexing Library), an enhancement of HDFS and Hadoop MapReduce that dramatically improves runtimes of several classes of MapReduce jobs. HAIL changes the upload pipeline of HDFS in order to create different clustered indexes on each data block replica. An interesting feature of HAIL is that we typically create a win-win situation: we improve both data upload to HDFS and the runtime of the actual Hadoop MapReduce job. In terms of data upload, HAIL improves over HDFS by up to 60% with the default replication factor of three. In terms of query execution, we demonstrate that HAIL runs up to 68x faster than Hadoop. In our experiments, we use six clusters including physical and EC2 clusters of up to 100 nodes. A series of scalability experiments also demonstrates the superiority of HAIL.Comment: VLDB201

    Parity effect in ground state localization of antiferromagnetic chains coupled to a ferromagnet

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    We investigate the ground states of antiferromagnetic Mn nanochains on Ni(110) by spin-polarized scanning tunneling microscopy in combination with theory. While the ferrimagnetic linear trimer experimentally shows the predicted collinear classical ground state, no magnetic contrast was observed for dimers and tetramers where non-collinear structures were expected based on ab-initio theory. This striking observation can be explained by zero-point energy motion for even numbered chains derived within a classical equation of motion leading to non classical ground states. Thus, depending on the parity of the chain length, the system shows a classical or a quantum behavior.Comment: 5 pages, 4 figures and supplementary informatio

    Multistability and spin diffusion enhanced lifetimes in dynamic nuclear polarization in a double quantum dot

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    The control of nuclear spins in quantum dots is essential to explore their many-body dynamics and exploit their prospects for quantum information processing. We present a unique combination of dynamic nuclear spin polarization and electric-dipole-induced spin resonance in an electrostatically defined double quantum dot (DQD) exposed to the strongly inhomogeneous field of two on-chip nanomagnets. Our experiments provide direct and unrivaled access to the nuclear spin polarization distribution and allow us to establish and characterize multiple fixed points. Further, we demonstrate polarization of the DQD environment by nuclear spin diffusion which significantly stabilizes the nuclear spins inside the DQD

    Large nuclear spin polarization in gate-defined quantum dots using a single-domain nanomagnet

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    The electron-nuclei (hyperfine) interaction is central to spin qubits in solid state systems. It can be a severe decoherence source but also allows dynamic access to the nuclear spin states. We study a double quantum dot exposed to an on-chip single-domain nanomagnet and show that its inhomogeneous magnetic field crucially modifies the complex nuclear spin dynamics such that the Overhauser field tends to compensate external magnetic fields. This turns out to be beneficial for polarizing the nuclear spin ensemble. We reach a nuclear spin polarization of ~50%, unrivaled in lateral dots, and explain our manipulation technique using a comprehensive rate equation model

    Function Analysis for Selecting Automated Machine Learning Solutions

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    Methods of machine learning (ML) are notoriously difficult for enterprises to employ productively. Data science is not a core skill of most companies, and acquiring external talent is expensive. Automated machine learning (Auto-ML) aims to alleviate this, democratising machine learning by introducing elements such as low-code / no-code functionalities into its model creation process. Multiple applications are possible for Auto-ML, such as Natural Language Processing (NLP), predictive modelling and optimization. However, employing Auto-ML still proves difficult for companies due to the dynamic vendor market: The solutions vary in scope and functionality while providers do little to delineate their offerings from related solutions like industrial IoT-Platforms. Additionally, the current research on Auto-ML focuses on mathematical optimization of the underlying algorithms, with diminishing returns for end users. The aim of this paper is to provide an overview over available, user-friendly ML technology through a descriptive model of the functions of current Auto-ML solutions. The model was created based on case studies of available solutions and an analysis of relevant literature. This method yielded a comprehensive function tree for Auto-ML solutions along with a methodology to update the descriptive model in case the dynamic provider market changes. Thus, the paper catalyses the use of ML in companies by providing companies and stakeholders with a framework to assess the functional scope of Auto-ML solutions

    Transport through (Ga,Mn)As nanoislands: Coulomb-blockade and temperature dependence of the conductance

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    We report on magnetotransport measurements of nanoconstricted (Ga,Mn)As devices showing very large resistance changes that can be controlled by both an electric and a magnetic field. Based on the bias voltage and temperature dependent measurements down to the millikelvin range we compare the models currently used to describe transport through (Ga,Mn)As nanoconstrictions. We provide an explanation for the observed spin-valve like behavior during a magnetic field sweep by means of the magnetization configurations in the device. Furthermore, we prove that Coulomb-blockade plays a decisive role for the transport mechanism and show that modeling the constriction as a granular metal describes the temperature and bias dependence of the conductance correctly and allows to estimate the number of participating islands located in the constriction.Comment: 5 pages, 3 figures, completed affiliations and corrected typo

    Dynamic optical coherence tomography of chronic venous ulcers

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    Background Chronic ulcers, especially venous leg ulcers, are a major burden on the healthcare system. To date there are only few non-invasive established procedures for evaluation of blood perfusion in wounds. Dynamic optical coherence tomography (D-OCT) provides images of the skin's superficial vascularisation. Objectives This study aims to investigate if and how the D-OCT measurement of chronic wounds can provide new information about the vascularisation during the healing process. Methods We examined 16 venous ulcers over 16 weeks and evaluated the vessel morphology and density using D-OCT at the wound bed, borders, two centimetres adjacent to the wound und at non-ulcerated skin on the contralateral leg. Results In D-OCT scans clumps were unique and the most common vessel type in the wound area of venous ulcers, whereas lines and serpiginous vessels were the most common in non-ulcerated skin. At the wound border mottle and cluster patterns occurred more frequently. Healthy skin showed a significant increase of mesh pattern. Vessel density significantly increased at the wound area compared to non-ulcerated skin. During the healing process the wound border showed the most vascular changes while only an increase in curves was observed in the wound centre. Non-healing wounds had fewer dots and blobs at the borders, fewer dots, coils, clumps, lines and serpiginous vessels at the centre and fewer dots in adjacent skin. Temperature analysis showed higher temperatures in non-ulcerated skin, followed by the wound margin and centre. Non-healing wounds showed the lowest temperatures in the wound centre. Conclusions These results highlight the non-invasive use of D-OCT for the examination and monitoring of wound healing in chronic venous ulcers. D-OCT imaging of blood vessels may offer the potential to detect disorders of wound healing at an early stage, differentiate ulcers of different genesis and to tailor more individualized, patient-oriented therapy

    Simultaneous Detection of Longitudinal and Transverse Bunch Signals at a Storage Ring

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    To understand and control the dynamics in the longitudinal phase space, time-resolved measurements of different bunch parameters are required. For a reconstruction of this phase space, the detector systems have to be synchronized. This reconstruction can be used e.g. for studies of the micro-bunching instability. It occurs if the interaction of the bunch with its own radiation leads to the formation of sub-structures on the longitudinal bunch profile. These sub-structures can grow rapidly -- leading to a sawtooth-like behaviour of the bunch. At KARA, we use a fast-gated intensified camera for energy spread studies, Schottky diodes for coherent synchrotron radiation studies as well as electro-optical spectral decoding for longitudinal bunch profile measurements. For a synchronization, a hardware synchronization scheme is used which compensates for eventual hardware delays. In this paper, the different experimental setups and their synchronization are discussed and first results of synchronous measurements are presented
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