163 research outputs found
Pair correlation functions and limiting distributions of iterated cluster point processes
We consider a Markov chain of point processes such that each state is a super
position of an independent cluster process with the previous state as its
centre process together with some independent noise process. The model extends
earlier work by Felsenstein and Shimatani describing a reproducing population.
We discuss when closed term expressions of the first and second order moments
are available for a given state. In a special case it is known that the pair
correlation function for these type of point processes converges as the Markov
chain progresses, but it has not been shown whether the Markov chain has an
equilibrium distribution with this, particular, pair correlation function and
how it may be constructed. Assuming the same reproducing system, we construct
an equilibrium distribution by a coupling argument
Functional summary statistics for point processes on the sphere with an application to determinantal point processes
We study point processes on , the -dimensional unit sphere
, considering both the isotropic and the anisotropic case, and
focusing mostly on the spherical case . The first part studies reduced
Palm distributions and functional summary statistics, including nearest
neighbour functions, empty space functions, and Ripley's and inhomogeneous
-functions. The second part partly discusses the appealing properties of
determinantal point process (DPP) models on the sphere and partly considers the
application of functional summary statistics to DPPs. In fact DPPs exhibit
repulsiveness, but we also use them together with certain dependent thinnings
when constructing point process models on the sphere with aggregation on the
large scale and regularity on the small scale. We conclude with a discussion on
future work on statistics for spatial point processes on the sphere
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
We present the Mind the Gap Model (MGM), an approach for interpretable feature extraction and selection. By placing interpretability criteria directly into the model, we allow for the model to both optimize parameters related to interpretability and to directly report a global set of distinguishable dimensions to assist with further data exploration and hypothesis generation. MGM extracts distinguishing features on real-world datasets of animal features, recipes ingredients, and disease co-occurrence. It also maintains or improves performance when compared to related approaches. We perform a user study with domain experts to show the MGM's ability to help with dataset explorationNational Science Foundation (U.S.) (ACI 1544628
Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach
Recent years have witnessed a resurgence of interest in video summarization.
However, one of the main obstacles to the research on video summarization is
the user subjectivity - users have various preferences over the summaries. The
subjectiveness causes at least two problems. First, no single video summarizer
fits all users unless it interacts with and adapts to the individual users.
Second, it is very challenging to evaluate the performance of a video
summarizer.
To tackle the first problem, we explore the recently proposed query-focused
video summarization which introduces user preferences in the form of text
queries about the video into the summarization process. We propose a memory
network parameterized sequential determinantal point process in order to attend
the user query onto different video frames and shots. To address the second
challenge, we contend that a good evaluation metric for video summarization
should focus on the semantic information that humans can perceive rather than
the visual features or temporal overlaps. To this end, we collect dense
per-video-shot concept annotations, compile a new dataset, and suggest an
efficient evaluation method defined upon the concept annotations. We conduct
extensive experiments contrasting our video summarizer to existing ones and
present detailed analyses about the dataset and the new evaluation method
Universal hidden order in amorphous cellular geometries
Partitioning space into cells with certain extreme geometrical properties is a central problem in many fields of science and technology. Here we investigate the Quantizer problem, defined as the optimisation of the moment of inertia of Voronoi cells, i.e., similarly-sized ‘sphere-like’ polyhedra that tile space are preferred. We employ Lloyd’s centroidal Voronoi diagram algorithm to solve this problem and find that it converges to disordered states associated with deep local minima. These states are universal in the sense that their structure factors are characterised by a complete independence of a wide class of initial conditions they evolved from. They moreover exhibit an anomalous suppression of long-wavelength density fluctuations and quickly become effectively hyperuniform. Our findings warrant the search for novel amorphous hyperuniform phases and cellular materials with unique physical properties
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