| 1. |
- Adcox, K, et al.
(författare)
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PHENIX detector overview
- 2003
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Ingår i: Nuclear Instruments & Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment. - 0167-5087. ; 499:2-3, s. 469-479
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Tidskriftsartikel (refereegranskat)abstract
- The PHENIX detector is designed to perform a broad study of A-A, p-A, and p-p collisions to investigate nuclear matter under extreme conditions. A wide variety of probes, sensitive to all timescales, are used to study systematic variations with species and energy as well as to measure the spin structure of the nucleon. Designing for the needs of the heavy-ion and polarized-proton programs has produced a detector with unparalleled capabilities. PHENIX measures electron and muon pairs, photons, and hadrons with excellent energy and momentum resolution. The detector consists of a large number of subsystems that are discussed in other papers in this volume. The overall design parameters of the detector are presented. (C) 2002 Elsevier Science B.V. All rights reserved.
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| 2. |
- Albrecht, Stefano V., et al.
(författare)
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Reports on the 2015 AAAI Workshop Series
- 2015
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Ingår i: The AI Magazine. - : Wiley. - 0738-4602 .- 2371-9621. ; 36:2, s. 90-101
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Tidskriftsartikel (refereegranskat)abstract
- AAAI's 2015 Workshop Program was held Sunday and Monday, January 25-26, 2015, at the Hyatt Regency Austin Hotel in Austin, Texas, USA. The AAAI-15 workshop program included 16 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included Algorithm Configuration; Artificial Intelligence and Ethics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Artificial Intelligence for Cities; Artificial Intelligence for Transportation: Advice, Inter-activity, and Actor Modeling; Beyond the Turing Test; Computational Sustainability; Computer Poker and Imperfect Information; Incentive and Trust in E-Communities; Knowledge, Skill, and Behavior Transfer in Autonomous Robots; Learning for General Competency in Video Games; Multiagent Interaction without Prior Coordination; Planning, Search, and Optimization; Scholarly Big Data: AI Perspectives, Challenges, and Ideas; Trajectory-Based Behaviour Analytics; and World Wide Web and Public Health Intelligence.
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| 3. |
- Hagleitner, Daniel R., et al.
(författare)
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Bulk and surface characterization of In2O3(001) single crystals
- 2012
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Ingår i: Physical Review B (Condensed Matter and Materials Physics). - 1098-0121. ; 85:11
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Tidskriftsartikel (refereegranskat)abstract
- A comprehensive bulk and surface investigation of high-quality In2O3(001) single crystals is reported. The transparent-yellow, cube-shaped single crystals were grown using the flux method. Inductively coupled plasma mass spectrometry (ICP-MS) reveals small residues of Pb, Mg, and Pt in the crystals. Four-point-probe measurements show a resistivity of 2.0 +/- 0.5 x 10(5) Omega cm, which translates into a carrier concentration of approximate to 10(12) cm(-3). The results from x-ray diffraction (XRD) measurements revise the lattice constant to 10.1150(5) angstrom from the previously accepted value of 10.117 angstrom. Scanning tunneling microscopy (STM) images of a reduced (sputtered/annealed) and oxidized (exposure to atomic oxygen at 300 degrees C) surface show a step height of 5 angstrom, which indicates a preference for one type of surface termination. The surfaces stay flat without any evidence for macroscopic faceting under any of these preparation conditions. A combination of low-energy ion scattering (LEIS) and atomically resolved STM indicates an indium-terminated surface with small islands of 2.5 angstrom height under reducing conditions, with a surface structure corresponding to a strongly distorted indium lattice. Scanning tunneling spectroscopy (STS) reveals a pronounced surface state at the Fermi level (E-F). Photoelectron spectroscopy (PES) shows additional, deep-lying band gap states, which can be removed by exposure of the surface to atomic oxygen. Oxidation also results in a shoulder at the O 1s core level at a higher binding energy, possibly indicative of a surface peroxide species. A downward band bending of 0.4 eV is observed for the reduced surface, while the band bending of the oxidized surface is of the order of 0.1 eV or less.
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| 4. |
- Hvarfner, Carl, et al.
(författare)
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Joint Entropy Search for Maximally-Informed Bayesian Optimization
- 2022
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Ingår i: Advances in Neural Information Processing Systems 35 (NeurIPS 2022).
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Konferensbidrag (refereegranskat)abstract
- Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Search and Predictive Entropy Search both consider the entropy over the optimum in the input space, while the recent Max-value Entropy Search considers the entropy over the optimal value in the output space. We propose Joint Entropy Search (JES), a novel information-theoretic acquisition function that considers an entirely new quantity, namely the entropy over the joint optimal probability density over both input and output space. To incorporate this information, we consider the reduction in entropy from conditioning on fantasized optimal input/output pairs. The resulting approach primarily relies on standard GP machinery and removes complex approximations typically associated with information-theoretic methods. With minimal computational overhead, JES shows superior decision-making, and yields state-of-the-art performance for information-theoretic approaches across a wide suite of tasks. As a light-weight approach with superior results, JES provides a new go-to acquisition function for Bayesian optimization.
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| 5. |
- Hvarfner, Carl, et al.
(författare)
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πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
- 2022
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Konferensbidrag (refereegranskat)abstract
- Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not utilize readily available prior beliefs the practitioner has on the potential location of the optimum. Thus, BO disregards a valuable source of information, reducing its appeal to ML practitioners. To address this issue, we propose PiBO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user. In contrast to previous approaches, PiBO is conceptually simple and can easily be integrated with existing libraries and many acquisition functions. We provide regret bounds when PiBO is applied to the common Expected Improvement acquisition function and prove convergence at regular rates independently of the prior. Further, our experiments show that BO outperforms competing approaches across a wide suite of benchmarks and prior characteristics. We also demonstrate that PiBO improves on the state-of-the-art performance for a popular deep learning task, with a 12.5 time-to-accuracy speedup over prominent BO approaches.
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| 6. |
- Joshi, Chaitanya, et al.
(författare)
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Quantum entanglement of nanocantilevers
- 2010
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Ingår i: Physical Review A. - 1050-2947. ; 82
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Tidskriftsartikel (refereegranskat)abstract
- We propose a scheme to entangle two mechanical nanocantilevers through indirect interactions mediated by a gas of ultracold atoms. We envisage a system of nanocantilevers magnetically coupled to a Bose-Einstein condensate of atoms and focus on studying the dark states of the system. These dark states are entangled states of the two nanocantilevers, with no coupling to the atomic condensate. In the absence of dissipation, the degree of entanglement is found to oscillate with time, while if dissipation is included, the system is found to relax to a statistical mixture of dark states which remains time independent until the inevitable thermal dephasing destroys the nanocantilever coherence. This opens up the possibility of achieving long-lived entangled nanocantilever states.
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| 7. |
- Lehman, Joel, et al.
(författare)
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The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
- 2020
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Ingår i: Artificial Life. - : MIT Press - Journals. - 1530-9185 .- 1064-5462. ; 26:2, s. 274-306
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Tidskriftsartikel (refereegranskat)abstract
- Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.
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| 8. |
- Schädlich, Philip, et al.
(författare)
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Domain Boundary Formation Within an Intercalated Pb Monolayer Featuring Charge-Neutral Epitaxial Graphene
- 2023
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Ingår i: Advanced Materials Interfaces. - 2196-7350. ; 10:27
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Tidskriftsartikel (refereegranskat)abstract
- The synthesis of new graphene-based quantum materials by intercalation is an auspicious approach. However, an accompanying proximity coupling depends crucially on the structural details of the new heterostructure. It is studied in detail the Pb monolayer structure after intercalation into the graphene buffer layer on the SiC(0001) interface by means of photoelectron spectroscopy, x-ray standing waves, and scanning tunneling microscopy. A coherent fraction close to unity proves the formation of a flat Pb monolayer on the SiC surface. An interlayer distance of 3.67 Å to the suspended graphene underlines the formation of a truly van der Waals heterostructure. The 2D Pb layer reveals a quasi ten-fold periodicity due to the formation of a grain boundary network, ensuring the saturation of the Si surface bonds. Moreover, the densely-packed Pb layer also efficiently minimizes the doping influence by the SiC substrate, both from the surface dangling bonds and the SiC surface polarization, giving rise to charge-neutral monolayer graphene. The observation of a long-ranged ((Formula presented.)) reconstruction on the graphene lattice at tunneling conditions close to Fermi energy is most likely a result of a nesting condition to be perfectly fulfilled.
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| 9. |
- Souza, Artur, et al.
(författare)
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Bayesian Optimization with a Prior for the Optimum
- 2021
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Ingår i: Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2021, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030865221 ; 12977 LNAI, s. 265-296
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Konferensbidrag (refereegranskat)abstract
- While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts. This causes BO to waste function evaluations on bad design choices (e.g., machine learning hyperparameters) that the expert already knows to work poorly. To address this issue, we introduce Bayesian Optimization with a Prior for the Optimum (BOPrO). BOPrO allows users to inject their knowledge into the optimization process in the form of priors about which parts of the input space will yield the best performance, rather than BO’s standard priors over functions, which are much less intuitive for users. BOPrO then combines these priors with BO’s standard probabilistic model to form a pseudo-posterior used to select which points to evaluate next. We show that BOPrO is around 6.67 × faster than state-of-the-art methods on a common suite of benchmarks, and achieves a new state-of-the-art performance on a real-world hardware design application. We also show that BOPrO converges faster even if the priors for the optimum are not entirely accurate and that it robustly recovers from misleading priors.
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