Lattice QCD and Hydro/Cascade Model of Heavy Ion Collisions

We report here on a recent lattice study of the QCD transition region at finite temperature and zero chemical potential using domain wall fermions (DWF). We also present a parameterization of the QCD equation of state obtained from lattice QCD that is suitable for use in hydrodynamics studies of heavy ion collisions. Finally, we show preliminary results from a multi-stage hydrodynamics/hadron cascade model of a heavy ion collision, in an attempt to understand how well the experimental data (e.g. particle spectra, elliptic flow, and HBT radii) can constrain the inputs (e.g. initial temperature, freezeout temperature, shear viscosity, equation of state) of the theoretical model.Comment: 10 pages, 12 figures. Proceedings for the 26th Winter Workshop on Nuclear Dynamics, Ocho Rios, Jamaica, Jan 2-9, 201

Infrared Surface Brightness Fluctuations of the Coma Elliptical NGC 4874 and the Value of the Hubble Constant

We have used the Keck I Telescope to measure K-band surface brightness fluctuations (SBFs) of NGC 4874, the dominant elliptical galaxy in the Coma cluster. We use deep HST WFPC2 optical imaging to account for the contamination due to faint globular clusters and improved analysis techniques to derive measurements of the SBF apparent magnitude. Using a new SBF calibration which accounts for the dependence of K-band SBFs on the integrated color of the stellar population, we measure a distance modulus of 34.99+/-0.21 mag (100+/-10 Mpc) for the Coma cluster. The resulting value of the Hubble constant is 71+/-8 km/s/Mpc, not including any systematic error in the HST Cepheid distance scale.Comment: ApJ Letters, in press. Uses emulateapj5.st

Hardwiring of fine synaptic layers in the zebrafish visual pathway

<p>Abstract</p> <p>Background</p> <p>Neuronal connections are often arranged in layers, which are divided into sublaminae harboring synapses with similar response properties. It is still debated how fine-grained synaptic layering is established during development. Here we investigated two stratified areas of the zebrafish visual pathway, the inner plexiform layer (IPL) of the retina and the neuropil of the optic tectum, and determined if activity is required for their organization.</p> <p>Results</p> <p>The IPL of 5-day-old zebrafish larvae is composed of at least nine sublaminae, comprising the connections between different types of amacrine, bipolar, and ganglion cells (ACs, BCs, GCs). These sublaminae were distinguished by their expression of cell type-specific transgenic fluorescent reporters and immunohistochemical markers, including protein kinase Cβ (PKC), parvalbumin (Parv), zrf3, and choline acetyltransferase (ChAT). In the tectum, four retinal input layers abut a laminated array of neurites of tectal cells, which differentially express PKC and Parv. We investigated whether these patterns were affected by experimental disruptions of retinal activity in developing fish. Neither elimination of light inputs by dark rearing, nor a D, L-amino-phosphono-butyrate-induced reduction in the retinal response to light onset (but not offset) altered IPL or tectal lamination. Moreover, thorough elimination of chemical synaptic transmission with <it>Botulinum </it>toxin B left laminar synaptic arrays intact.</p> <p>Conclusion</p> <p>Our results call into question a role for activity-dependent mechanisms – instructive light signals, balanced <it>on </it>and <it>off </it>BC activity, Hebbian plasticity, or a permissive role for synaptic transmission – in the synaptic stratification we examined. We propose that genetically encoded cues are sufficient to target groups of neurites to synaptic layers in this vertebrate visual system.</p

Explainable search

Search-based AI agents are state of the art in many challenging sequential decision-making domains. However, contemporary approaches lack the ability to explain, summarize, or visualize their plans and decisions, and how they are derived from traversing complex spaces of possible futures, contingencies, and eventualities, spanned by the available actions of the agent. This limits human trust in high-stakes scenarios, as well as effective human-AI collaboration. In this paper, we pr

Novelty and MCTS

Novelty search has become a popular technique in different fields such as evolutionary computing, classical AI planning, and deep reinforcement learning. Searching for novelty instead of, or in addition to, directly maximizing the search objective, aims at avoiding dead ends and local minima, and overall improving exploration. We propose and test the integration of novelty into Monte Carlo Tree Search (MCTS), a state-of-the-art framework for online RL planning, by linearly combining value estim

ME-MCTS: Online generalization by combining multiple value estimators

This paper addresses the challenge of online gen- eralization in tree search. We propose Multiple Estimator Monte Carlo Tree Search (ME-MCTS), with a two-fold contribution: first, we introduce a formalization of online generalization that can rep- resent existing techniques such as “history heuris- tics”, “RAVE”, or “OMA” – contextual action value estimators or abstractors that generalize across spe- cific contexts. Second, we incorporate recent ad- vances in estimator averaging that enable guiding search by combining the online action value esti- mates of any number of such abstractors or sim- ilar types of action value estimators. Unlike pre- vious work, which usually proposed a single ab- stractor for either the selection or the rollout phase of MCTS simulations, our approach focuses on the combination of multiple estimators and applies them to all move choices in MCTS simulations. As the MCTS tree itself is just another value estima- tor – unbiased, but without abstraction – this blurs the traditional distinction between action choices inside and outside of the MCTS tree. Experi- ments with three abstractors in four board games show significant improvements of ME-MCTS over MCTS using only a single abstractor, both for MCTS with random rollouts as well as for MCTS with static evaluation functions. While we used deterministic, fully observable games, ME-MCTS naturally extends to more challenging settings

Guiding multiplayer MCTS by focusing on yourself

In n-player sequential move games, the second root-player move appears at tree depth n + 1. Depending on n and time, tree search techniques can struggle to expand the game tree deeply enough to find multiple-move plans of the root player, which is often more important for strategic play than considering every possible opponent move in between. The minimax-based Paranoid search and BRS+ algorithms currently achieve state-of-the-art performance, especially at short time settings, by using a generally incorrect opponent model.

Towards explainable MCTS

Monte-Carlo Tree Search (MCTS) is a family of sampling-based search algorithms widely used for online planning in sequential decision-making domains, and at the heart of many recent breakthroughs in AI. Understanding the behavior of MCTS agents is non-trivial for developers and users, as it results from often large and complex search trees, consisting of many simulated possible futures, their evaluations, and relationships to each other. This p

Color-Octet Contributions to J/ψJ/\psi Photoproduction

We have calculated the leading color-octet contributions to the production of $J/\psi$ particles in photon-proton collisions. Using the values for the color-octet matrix elements extracted from fits to prompt $J/\psi$ data at the Tevatron, we demonstrate that distinctive color-octet signatures should be visible in $J/\psi$ photoproduction. However, these predictions appear at variance with recent experimental data obtained at HERA, indicating that the phenomenological importance of the color-octet contributions is smaller than expected from theoretical considerations and suggested by the Tevatron fits.Comment: 10 pages, LaTeX, epsfig, 4 figure

Value targets in off-policy AlphaZero: A new greedy backup

This article presents and evaluates a family of AlphaZero value targets, subsuming previous variants and introducing AlphaZero with greedy backups (A0GB). Current state-of-the-art algorithms for playing board games use sample-based planning, such as Monte Carlo Tree Search (MCTS), combined with deep neural networks (NN) to approximate the value function. These algorithms, of which AlphaZero is a prominent example, are computationally extremely expensive to train, due to their reliance on many neural network evaluations. This limits their practical performance. We improve the training process of AlphaZero by using more effective training targets for the neural network. We introduce a three-dimensional space to describe a family of training targets, covering the original AlphaZero training target as well as the soft-Z and A0C variants from the literature. We demonstrate that A0GB, using a specific new value target from this family, is able to find the optimal policy in a small tabular domain, whereas the original AlphaZero target fails to do so. In addition, we show that soft-Z, A0C and A0GB achieve better performance and faster training than the original AlphaZero target on two benchmark board games (Connect-Four and Breakthrough). Finally, we juxtapose tabular learning with neural network-based value function approximation in Tic-Tac-Toe, and compare the effects of learning targets therein