2,187 research outputs found
A pitfall of piecewise-polytropic equation of state inference
The only messenger radiation in the Universe which one can use to
statistically probe the Equation of State (EOS) of cold dense matter is that
originating from the near-field vicinities of compact stars. Constraining
gravitational masses and equatorial radii of rotating compact stars is a major
goal for current and future telescope missions, with a primary purpose of
constraining the EOS. From a Bayesian perspective it is necessary to carefully
discuss prior definition; in this context a complicating issue is that in
practice there exist pathologies in the general relativistic mapping between
spaces of local (interior source matter) and global (exterior spacetime)
parameters. In a companion paper, these issues were raised on a theoretical
basis. In this study we reproduce a probability transformation procedure from
the literature in order to map a joint posterior distribution of Schwarzschild
gravitational masses and radii into a joint posterior distribution of EOS
parameters. We demonstrate computationally that EOS parameter inferences are
sensitive to the choice to define a prior on a joint space of these masses and
radii, instead of on a joint space interior source matter parameters. We focus
on the piecewise-polytropic EOS model, which is currently standard in the field
of astrophysical dense matter study. We discuss the implications of this issue
for the field.Comment: 16 pages, 9 figures. Accepted for publication in MNRA
Robustness Verification for Classifier Ensembles
We give a formal verification procedure that decides whether a classifier
ensemble is robust against arbitrary randomized attacks. Such attacks consist
of a set of deterministic attacks and a distribution over this set. The
robustness-checking problem consists of assessing, given a set of classifiers
and a labelled data set, whether there exists a randomized attack that induces
a certain expected loss against all classifiers. We show the NP-hardness of the
problem and provide an upper bound on the number of attacks that is sufficient
to form an optimal randomized attack. These results provide an effective way to
reason about the robustness of a classifier ensemble. We provide SMT and MILP
encodings to compute optimal randomized attacks or prove that there is no
attack inducing a certain expected loss. In the latter case, the classifier
ensemble is provably robust. Our prototype implementation verifies multiple
neural-network ensembles trained for image-classification tasks. The
experimental results using the MILP encoding are promising both in terms of
scalability and the general applicability of our verification procedure
Thermal Imidization Kinetics of Ultrathin Films of Hybrid Poly(POSS-imide)s
In the thermal imidization of an alternating inorganicâorganic hybrid network, there is an inverse relationship between the length and flexibility of the organic bridges and the extent of the layer shrinkage. The hybrid material studied here consists of polyhedral oligomeric silsesquioxanes that are covalently bridged by amic acid groups. During heat treatment, shrinkage of the materials occurs due to the removal of physically bound water, imidization of the amic acid groups, and silanol condensation. For five different bridging groups with different lengths and flexibilities, comparable mass reductions are observed. For the shorter bridging groups, the dimensional changes are hindered by the limited network mobility. Longer, more flexible bridging groups allow for much greater shrinkage. The imidization step can be described by a decelerating reaction mechanism with an onset at 150 °C and shows a higher activation energy than in the case of entirely organic polyimides. The differences in the imidization kinetics between hybrid and purely organic materials demonstrates the need for close study of the thermal processing of hybrid, hyper-cross-linked material
Equation of state sensitivities when inferring neutron star and dense matter properties
Understanding the dense matter equation of state at extreme conditions is an important open problem. Astrophysical observations of neutron stars promise to solve this, with NICER poised to make precision measurements of mass and radius for several stars using the waveform modelling technique. What has been less clear, however, is how these mass-radius measurements might translate into equation of state constraints and what are the associated equation of state sensitivities. We use Bayesian inference to explore and contrast the constraints that would result from different choices for the equation of state parametrization; comparing the well-established piecewise polytropic parametrization to one based on physically motivated assumptions for the speed of sound in dense matter. We also compare the constraints resulting from Bayesian inference to those from simple compatibility cuts. We find that the choice of equation of state parametrization and particularly its prior assumptions can have a significant effect on the inferred global mass-radius relation and the equation of state constraints. Our results point to important sensitivities when inferring neutron star and dense matter properties. This applies also to inferences from gravitational wave observations
Integration of risk and asset management for sustainable management of European coastal zones
Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment
VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided.
The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the âBeeldenstormâ collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Other approaches included using topic changes, elevated speaking pitch, increased speaking intensity and radical visual changes. The Linking Task, also called âFinding Related Resources Across Languages,â involved linking video to material on the same subject in a different language.
Participants were provided with a list of multimedia anchors (short video segments) in the Dutch-language âBeeldenstormâ collection and were expected to return target pages drawn from English-language Wikipedia. The best performing methods used the transcript of the
speech spoken during the multimedia anchor to build a query to search an index of the Dutch language Wikipedia. The Dutch Wikipedia pages returned were used to identify related English pages. Participants also experimented with pseudo-relevance feedback, query translation and methods that targeted proper names
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