46,414 research outputs found
Data-driven detection of multi-messenger transients
The primary challenge in the study of explosive astrophysical transients is
their detection and characterisation using multiple messengers. For this
purpose, we have developed a new data-driven discovery framework, based on deep
learning. We demonstrate its use for searches involving neutrinos, optical
supernovae, and gamma rays. We show that we can match or substantially improve
upon the performance of state-of-the-art techniques, while significantly
minimising the dependence on modelling and on instrument characterisation.
Particularly, our approach is intended for near- and real-time analyses, which
are essential for effective follow-up of detections. Our algorithm is designed
to combine a range of instruments and types of input data, representing
different messengers, physical regimes, and temporal scales. The methodology is
optimised for agnostic searches of unexpected phenomena, and has the potential
to substantially enhance their discovery prospects.Comment: 16 page
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
Chasing a consistent picture for dark matter direct searches
In this paper we assess the present status of dark matter direct searches by
means of Bayesian statistics. We consider three particle physics models for
spin-independent dark matter interaction with nuclei: elastic, inelastic and
isospin violating scattering. We shortly present the state of the art for the
three models, marginalising over experimental systematics and astrophysical
uncertainties. Whatever the scenario is, XENON100 appears to challenge the
detection region of DAMA, CoGeNT and CRESST. The first aim of this study is to
rigorously quantify the significance of the inconsistency between XENON100 data
and the combined set of detection (DAMA, CoGeNT and CRESST together),
performing two statistical tests based on the Bayesian evidence. We show that
XENON100 and the combined set are inconsistent at least at 2 sigma level in all
scenarios but inelastic scattering, for which the disagreement drops to 1 sigma
level. Secondly we consider only the combined set and hunt the best particle
physics model that accounts for the events, using Bayesian model comparison.
The outcome between elastic and isospin violating scattering is inconclusive,
with the odds 2:1, while inelastic scattering is disfavoured with the odds of
1:32 because of CoGeNT data. Our results are robust under reasonable prior
assumptions. We conclude that the simple elastic scattering remains the best
model to explain the detection regions, since the data do not support extra
free parameters. Present direct searches therefore are not able to constrain
the particle physics interaction of the dark matter. The outcome of consistency
tests implies that either a better understanding of astrophysical and
experimental uncertainties is needed, either the dark matter theoretical model
is at odds with the data.Comment: 18 pages, 8 figures and 7 tables; minor revisions following referee
report. Accepted for publication in Phys.Rev.
Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data
Background. A large number of algorithms is being developed to reconstruct
evolutionary models of individual tumours from genome sequencing data. Most
methods can analyze multiple samples collected either through bulk multi-region
sequencing experiments or the sequencing of individual cancer cells. However,
rarely the same method can support both data types.
Results. We introduce TRaIT, a computational framework to infer mutational
graphs that model the accumulation of multiple types of somatic alterations
driving tumour evolution. Compared to other tools, TRaIT supports multi-region
and single-cell sequencing data within the same statistical framework, and
delivers expressive models that capture many complex evolutionary phenomena.
TRaIT improves accuracy, robustness to data-specific errors and computational
complexity compared to competing methods.
Conclusions. We show that the application of TRaIT to single-cell and
multi-region cancer datasets can produce accurate and reliable models of
single-tumour evolution, quantify the extent of intra-tumour heterogeneity and
generate new testable experimental hypotheses
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
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