Mach Cones and Hydrodynamic Flow: Probing Big Bang Matter in the Laboratory

A critical discussion of the present signals for the phase transition to quark-gluon plasma (QGP) is given. Since hadronic rescattering models predict much larger flow than observed from 1 to 50 A GeV laboratory bombarding energies, this observation is interpreted as potential evidence for a first-order phase transition at high baryon density. A detailed discussion of the collective flow as a barometer for the equation of state (EoS) of hot dense matter at RHIC follows. Here, hadronic rescattering models can explain < 30 % of the observed elliptic flow v_2 for $p_T > 2$ GeV/c. This is interpreted as an evidence for the production of superdense matter at RHIC. The connection of v_2 to jet suppression is examined. A study of Mach shocks generated by fast partonic jets propagating through the QGP is given. The main goal is to take into account different types of collective motion during the formation and evolution of this matter. A significant deformation of Mach shocks in central Au+Au collisions at RHIC and LHC energies as compared to the case of jet propagation in a static medium is predicted. A new hydrodynamical study of jet energy loss is presented.Comment: 18 pages, 12 figures, presented at the IWCF 2006, Nov. 21-24, Hangzhou, Chin

Maximal information component analysis: a novel non-linear network analysis method.

BackgroundNetwork construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.ResultsWe have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.ConclusionsIn making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions

How Different are Pre-trained Transformers for Text Ranking?

In recent years, large pre-trained transformers have led to substantial gains in performance over traditional retrieval models and feedback approaches. However, these results are primarily based on the MS Marco/TREC Deep Learning Track setup, with its very particular setup, and our understanding of why and how these models work better is fragmented at best. We analyze effective BERT-based cross-encoders versus traditional BM25 ranking for the passage retrieval task where the largest gains have been observed, and investigate two main questions. On the one hand, what is similar? To what extent does the neural ranker already encompass the capacity of traditional rankers? Is the gain in performance due to a better ranking of the same documents (prioritizing precision)? On the other hand, what is different? Can it retrieve effectively documents missed by traditional systems (prioritizing recall)? We discover substantial differences in the notion of relevance identifying strengths and weaknesses of BERT that may inspire research for future improvement. Our results contribute to our understanding of (black-box) neural rankers relative to (well-understood) traditional rankers, help understand the particular experimental setting of MS-Marco-based test collections.Comment: ECIR 202