4,095 research outputs found

    Sowing the seeds of massive black holes in small galaxies: Young clusters as the building blocks of Ultra-Compact-Dwarf Galaxies

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    Interacting galaxies often have complexes of hundreds of young stellar clusters of individual masses 1046 M\sim 10^{4-6}~M_\odot in regions that are a few hundred parsecs across. These cluster complexes interact dynamically, and their coalescence is a candidate for the origin of some ultracompact dwarf galaxies (UCDs). Individual clusters with short relaxation times are candidates for the production of intermediate-mass black holes of a few hundred solar masses, via runaway stellar collisions prior to the first supernovae in a cluster. It is therefore possible that a cluster complex hosts multiple intermediate-mass black holes that may be ejected from their individual clusters due to mergers or binary processes, but bound to the complex as a whole. Here we explore the dynamical interaction between initially free-flying massive black holes and clusters in an evolving cluster complex. We find that, after hitting some clusters, it is plausible that the massive black hole will be captured in an ultracompact dwarf forming near the center of the complex. In the process, the hole typically triggers electromagnetic flares via stellar disruptions, and is also likely to be a prominent source of gravitational radiation for the advanced ground-based detectors LIGO and VIRGO. We also discuss other implications of this scenario, notably that the central black hole could be considerably larger than expected in other formation scenarios for ultracompact dwarfs.Comment: 15 pages, published in ApJ; for movies, please visit http://members.aei.mpg.de/amaro-seoane/ultra-compact-dwarf-galaxie

    Putting bandits into context: How function learning supports decision making

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    The authors introduce the contextual multi-armed bandit task as a framework to investigate learning and decision making in uncertain environments. In this novel paradigm, participants repeatedly choose between multiple options in order to maximize their rewards. The options are described by a number of contextual features which are predictive of the rewards through initially unknown functions. From their experience with choosing options and observing the consequences of their decisions, participants can learn about the functional relation between contexts and rewards and improve their decision strategy over time. In three experiments, the authors explore participants’ behavior in such learning environments. They predict participants’ behavior by context-blind (mean-tracking, Kalman filter) and contextual (Gaussian process and linear regression) learning approaches combined with different choice strategies. Participants are mostly able to learn about the context-reward functions and their behavior is best described by a Gaussian process learning strategy which generalizes previous experience to similar instances. In a relatively simple task with binary features, they seem to combine this learning with a probability of improvement decision strategy which focuses on alternatives that are expected to lead to an improvement upon a current favorite option. In a task with continuous features that are linearly related to the rewards, participants seem to more explicitly balance exploration and exploitation. Finally, in a difficult learning environment where the relation between features and rewards is nonlinear, some participants are again well-described by a Gaussian process learning strategy, whereas others revert to context-blind strategies

    Pseudomembranous Trigonitis: A Common but Underrecognized Urological Entity

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    Pseudomembranous trigonitis is the term used to describe squamous metaplastic changes of the bladder trigone, which affect nearly 40% of adult females. We present the characteristics of this underrecognized clinical entity and encourage further relevant research

    Feasibility studies of a Level-1 Tracking Trigger for ATLAS

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    The existing ATLAS Level-1 trigger system is seriously challenged at the SLHC's higher luminosity. A hardware tracking trigger might be needed, but requires a detailed understanding of the detector. Simulation of high pile-up events, with various data-reduction techniques applied will be described. Two scenarios are envisaged: (a) regional readout - calorimeter and muon triggers are used to identify portions of the tracker; and (b) track-stub finding using special trigger layers. A proposed hardware system, including data reduction on the front-end ASICs, readout within a super-module and integrating regional triggering into all levels of the readout system, will be discussed

    Preoperative tumor marking with indocyanine green (ICG) prior to minimally invasive colorectal cancer: a systematic review of current literature

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    AIMS: To describe the currently available evidence regarding the efficacy and safety of preoperative tumor marking using indocyanine green (ICG) prior to laparoscopic or robotic colorectal resections. METHODS: A systematic search for relevant studies was conducted using the following databases: Embase (OVID), MEDLINE® (OVID), APA PsycInfo (OVID), Global Health (OVID) and HMIC Health Management Information Consortium (OVID) through June 2022 reported according to PRISMA 2020 guidelines. Primary outcome was the detection rate of the tumor sites preoperatively marked with ICG. Secondary outcomes were timing of ICG injection in days prior to the operation and technique-related complications. RESULTS: Eight single center studies, published between 2008 and 2022, were identified yielding a total of 1,061 patients, of whom 696 were preoperatively tattooed with ICG. Injection dosage of diluted ICG ranged from 0.1–1.5 ml. Four studies used the saline test injection method prior to ICG injection. When the marking was placed within one week, the visualization rate was 650/668 (97%), whereas when it was longer than one week, the detection rate was 8/56 (14%). No severe complications were reported. CONCLUSION: Preoperative tumor marking using ICG prior to minimally invasive colorectal resections is safe and effective, allowing intraoperative tumor site location when performed up to a week prior to surgery without disturbing the surgical view in potential mild complications

    Long-time electron spin storage via dynamical suppression of hyperfine-induced decoherence in a quantum dot

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    The coherence time of an electron spin decohered by the nuclear spin environment in a quantum dot can be substantially increased by subjecting the electron to suitable dynamical decoupling sequences. We analyze the performance of high-level decoupling protocols by using a combination of analytical and exact numerical methods, and by paying special attention to the regimes of large inter-pulse delays and long-time dynamics, which are outside the reach of standard average Hamiltonian theory descriptions. We demonstrate that dynamical decoupling can remain efficient far beyond its formal domain of applicability, and find that a protocol exploiting concatenated design provides best performance for this system in the relevant parameter range. In situations where the initial electron state is known, protocols able to completely freeze decoherence at long times are constructed and characterized. The impact of system and control non-idealities is also assessed, including the effect of intra-bath dipolar interaction, magnetic field bias and bath polarization, as well as systematic pulse imperfections. While small bias field and small bath polarization degrade the decoupling fidelity, enhanced performance and temporal modulation result from strong applied fields and high polarizations. Overall, we find that if the relative errors of the control parameters do not exceed 5%, decoupling protocols can still prolong the coherence time by up to two orders of magnitude.Comment: 16 pages, 10 figures, submitted to Phys. Rev.

    Uncertainty and exploration in a restless bandit task

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    Decision-making in noisy and changing environments requires a fine balance between exploiting knowledge about good courses of action and exploring the environment in order to improve upon this knowledge. We present an experiment in which participants made repeated choices between options for which the average rewards changed over time. Comparing a number of computational models of participants' behaviour in this task, we find evidence that a substantial number of them balanced exploration and exploitation by considering the probability that an option offers the maximum reward out of all the available options
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