pinecone/core-2020-05-10-deduplication huggingface dataset:
Prospects for asteroseismology of rapidly rotating B-type stars

In rapidly rotating stars Coriolis forces and centrifugal deformations modify
the properties of oscillations; the Coriolis force is important for
low-frequency modes, while the centrifugal deformation affects mainly p-modes.
Here, we discuss properties of g- and r-mode oscillations in rotating stars.
Predicted frequency spectra of high-order g-modes (and r-modes) excited in
rapidly rotating stars show frequency groupings associated with azimuthal order
$m$. We compare such properties with observations in rapidly rotating Be stars,
and discuss what is learnt from such comparisons.Comment: 18 pages, 7 figurs, in "Studying stellar Rotation and Convection",
  Lecture Notes in Physics (Springer Verlag, Berlin), M.J. Goupil, K. Belkacem,
  C. Neiner, F. Ligni\`eres, and J. J. Green (eds
The Unary Fragments of Metric Interval Temporal Logic: Bounded versus
  Lower bound Constraints (Full Version)

We study two unary fragments of the well-known metric interval temporal logic
MITL[U_I,S_I] that was originally proposed by Alur and Henzinger, and we pin
down their expressiveness as well as satisfaction complexities. We show that
MITL[F_\inf,P_\inf] which has unary modalities with only lower-bound
constraints is (surprisingly) expressively complete for Partially Ordered 2-Way
Deterministic Timed Automata (po2DTA) and the reduction from logic to automaton
gives us its NP-complete satisfiability. We also show that the fragment
MITL[F_b,P_b] having unary modalities with only bounded intervals has
\nexptime-complete satisfiability. But strangely, MITL[F_b,P_b] is strictly
less expressive than MITL[F_\inf,P_\inf]. We provide a comprehensive picture of
the decidability and expressiveness of various unary fragments of MITL.Comment: Presented at ATVA, 201
Counterexample Guided Synthesis of Monitors for Realizability Enforcement

International audienceMany of today's software systems are built using distributed services, which evolve in different organizations. In order to facilitate their integration, it is necessary to provide a contract that the services participating in a composition should adhere to. A contract specifies interactions among a set of services from a global point of view. One important problem in a top-down development process is figuring out whether such a contract can be implemented by a set of services, obtained by projection and communicating via message passing. It was only recently shown, that this problem, known as realizability, is decidable if asynchronous communication (communication via FIFO buffers) is considered. It can be verified using the synchronizability property. If the system is not synchronizable, the system is not realizable either. In this paper, we propose a new, automatic approach, which enforces both synchronizability and realizability by generating local monitors through successive equivalence checks and refinement
An Experiment on Parallel Model Checking of a CTL Fragment

We propose a parallel algorithm for local, on the fly, model checking of a
fragment of CTL that is well-suited for modern, multi-core architectures. This
model-checking algorithm takes bene t from a parallel state space construction
algorithm, which we described in a previous work, and shares the same basic set
of principles: there are no assumptions on the models that can be analyzed; no
restrictions on the way states are distributed; and no restrictions on the way
work is shared among processors. We evaluate the performance of diff erent
versions of our algorithm and compare our results with those obtained using
other parallel model checking tools. One of the most novel contributions of
this work is to study a space-e fficient variant for CTL model-checking that
does not require to store the whole transition graph but that operates on a
reverse spanning tree.Comment: 10th International Symposium, ATVA 2012, Automated Technology for
  Verification and Analysis, Thiruvananthapuram : India (2012
Local implicit modeling of blood vessels for interactive simulation

International audienceIn the context of computer-based simulation, contact management requires an accurate, smooth, but still efficient surface model for the blood vessels. A new implicit model is proposed, consisting of a tree of local implicit surfaces generated by skeletons ({\em blobby models}). The surface is reconstructed from data points by minimizing an energy, alternating with an original blob selection and subdivision scheme. The reconstructed models are very efficient for simulation and were shown to provide a sub-voxel approximation of the vessel surface on 5 patients
Curvilinear structure enhancement with the polygonal path image - Application to guide-wire segmentation in X-ray fluoroscopy.

International audienceCurvilinear structures are common in medical imaging, which typically require dedicated processing techniques. We present a new structure to process these, that we call the polygonal path image, denoted $\mathfrak{P}$. We derive from $\mathfrak{P}$ some curvilinear structure enhancement and analysis algorithms. We show that $\mathfrak{P}$ has some interesting properties: it generalizes several concepts found in other methods; it makes it possible to control the smoothness and length of the structures under study; and it can be computed efficiently. We estimate quantitatively its performance in the context of interventional cardiology for the detection of guide-wires in X-ray images. We show that $\mathfrak{P}$ is particularly well suited for this task where it appears to outperform previous state of the art techniques
Automated diffeomorphic registration of anatomical structures with rigid parts: application to dynamic cervical MRI.

International audienceWe propose an iterative two-step method to compute a diffeomorphic non-rigid transformation between images of anatomical structures with rigid parts, without any user intervention or prior knowledge on the image intensities. First we compute spatially sparse, locally optimal rigid transformations between the two images using a new block matching strategy and an efficient numerical optimiser (BOBYQA). Then we derive a dense, regularised velocity field based on these local transformations using matrix logarithms and M-smoothing. These two steps are iterated until convergence and the final diffeomorphic transformation is defined as the exponential of the accumulated velocity field. We show our algorithm to outperform the state-of-the-art log-domain diffeomorphic demons method on dynamic cervical MRI data
Joint Tumor Segmentation and Dense Deformable Registration of Brain MR Images

International audienceIn this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopt- ing a similar graphical model, using image-based classi cation techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of tumors as well as a segmentation through a registration term aiming the separation be- tween healthy and diseased tissues. E cient linear programming is used to solve both problems simultaneously. State of the art results demon- strate the potential of our method on a large and challenging low-grade glioma data set
Exploring the role of commercial stakeholders in open source software evolution

Copyright @ 2012 SpringerIt has been lately established that a major success or failure factor of an OSS project is whether it involves a commercial company,\ud
or more extremely, when the project management is in the hands of a commercial software corporation. As documented recently, the success of the Eclipse IDE can be largely attributed to the project management of IBM, since the upper part of the developer hierarchy is dominated\ud
by its staff. This paper reports on the study of the evolution of three different Free, Libre, Open Source (FLOSS) projects – the Eclipse and jEdit IDE’s, and the Moodle e-learning system – looking at whether they have benefited from the contribution of commercial companies. With the involvement of commercial companies, it is found that FLOSS projects achieve sustained productivity, increasing amounts of output\ud
produced and intake of new developers. It is also found that individual and commercial contributions show similar stages: developer intake, learning effect, sustained contributions and, finally, abandonment of the project. This preliminary evidence suggests that a major success factor for FLOSS is the involvement of a commercial company, or more radically, when project management is in hands of a commercial entity
Accelerated Diffusion Spectrum Imaging with Compressed Sensing Using Adaptive Dictionaries

Diffusion Spectrum Imaging (DSI) offers detailed information on complex distributions of intravoxel fiber orientations at the expense of extremely long imaging times (~1 hour). It is possible to accelerate DSI by sub-Nyquist sampling of the q-space followed by nonlinear reconstruction to estimate the diffusion probability density functions (pdfs). Recent work by Menzel et al. imposed sparsity constraints on the pdfs under wavelet and Total Variation (TV) transforms. As the performance of Compressed Sensing (CS) reconstruction depends strongly on the level of sparsity in the selected transform space, a dictionary specifically tailored for sparse representation of diffusion pdfs can yield higher fidelity results. To our knowledge, this work is the first application of adaptive dictionaries in DSI, whereby we reduce the scan time of whole brain DSI acquisition from 50 to 17 min while retaining high image quality. In vivo experiments were conducted with the novel 3T Connectome MRI, whose strong gradients are particularly suited for DSI. The RMSE from the proposed reconstruction is up to 2 times lower than that of Menzel et al.’s method, and is actually comparable to that of the fully-sampled 50 minute scan. Further, we demonstrate that a dictionary trained using pdfs from a single slice of a particular subject generalizes well to other slices from the same subject, as well as to slices from another subject.National Institutes of Health (U.S.) (NIH R01 EB007942)National Institute for Biomedical Imaging and Bioengineering (U.S.) (NIBIB K99EB012107)National Institute for Biomedical Imaging and Bioengineering (U.S.) (NIBIB R01EB006847)National Institute for Biomedical Imaging and Bioengineering (U.S.) (K99/R00 EB008129)National Center for Research Resources (U.S.) (NCRR P41RR14075)National Institutes of Health (U.S.) (NIH Blueprint for Neuroscience Research U01MH093765)National Institutes of Health (U.S.) (The Human Connectome project)Siemens Aktiengesellschaft (Siemens-MIT Alliance)Center for Integration of Medicine and Innovative Technology (MIT-CIMIT Medical Engineering Fellowship
Spectral Label Fusion

We present a new segmentation approach that combines the strengths of label fusion and spectral clustering. The result is an atlas-based segmentation method guided by contour and texture cues in the test image. This offers advantages for datasets with high variability, making the segmentation less prone to registration errors. We achieve the integration by letting the weights of the graph Laplacian depend on image data, as well as atlas-based label priors. The extracted contours are converted to regions, arranged in a hierarchy depending on the strength of the separating boundary. Finally, we construct the segmentation by a region-wise, instead of voxel-wise, voting, increasing the robustness. Our experiments on cardiac MRI show a clear improvement over majority voting and intensity-weighted label fusion.National Institutes of Health (U.S.) (NIH NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NIH NCRR NAC P41-RR13218)National Science Foundation (U.S.) (NSF CAREER 0642971
Non-local robust detection of DTI white matter differences with small databases.

International audienceDiffusion imaging, through the study of water diffusion, allows for the characterization of brain white matter, both at the population and individual level. In recent years, it has been employed to detect brain abnormalities in patients suffering from a disease, e.g., from multiple sclerosis (MS). State-of-the-art methods usually utilize a database of matched (age, sex, ...) controls, registered onto a template, to test for differences in the patient white matter. Such approaches however suffer from two main drawbacks. First, registration algorithms are prone to local errors, thereby degrading the comparison results. Second, the database needs to be large enough to obtain reliable results. However, in medical imaging, such large databases are hardly available. In this paper, we propose a new method that addresses these two issues. It relies on the search for samples in a local neighborhood of each pixel to increase the size of the database. Then, we propose a new test based on these samples to perform a voxelwise comparison of a patient image with respect to a population of controls. We demonstrate on simulated and real MS patient data how such a framework allows for an improve detection power and a better robustness and reproducibility, even with a small database
An efficiently computable support measure for frequent subgraph pattern mining

Graph support measures are functions measuring how frequently a given subgraph pattern occurs in a given database graph. An important class of support measures relies on overlap graphs. A major advantage of the overlap graph based approaches is that they combine anti-monotonicity with counting occurrences of a pattern which are independent according to certain criteria. However, existing overlap graph based support measures are expensive to compute. 
In this paper, we propose a new support measure which is based on a new notion of independence. We show that our measure is the solution to a linear program which is usually sparse, and using interior point methods can be computed efficiently. We show experimentally that for large networks, in contrast to earlier overlap graph based proposals, pattern mining based on our support measure is feasible.status: publishe
An Experimental Comparison of Hybrid Algorithms for Bayesian Network
  Structure Learning

We present a novel hybrid algorithm for Bayesian network structure learning,
called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian
network and then performs a Bayesian-scoring greedy hill-climbing search to
orient the edges. It is based on a subroutine called HPC, that combines ideas
from incremental and divide-and-conquer constraint-based methods to learn the
parents and children of a target variable. We conduct an experimental
comparison of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the
most powerful state-of-the-art algorithm for Bayesian network structure
learning, on several benchmarks with various data sizes. Our extensive
experiments show that H2PC outperforms MMHC both in terms of goodness of fit to
new data and in terms of the quality of the network structure itself, which is
closer to the true dependence structure of the data. The source code (in R) of
H2PC as well as all data sets used for the empirical tests are publicly
available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other authors.
  Lecture notes in computer science, springer, 2012, Machine Learning and
  Knowledge Discovery in Databases, 7523, pp.58-7
Volumetric real-time particle-based representation of large unstructured tetrahedral polygon meshes

In this paper we propose a particle-based volume rendering approach for unstructured, three-dimensional, tetrahedral polygon meshes. We stochastically generate millions of particles per second and project them on the screen in real-time. In contrast to previous rendering techniques of tetrahedral volume meshes, our method does not need a prior depth sorting of geometry. Instead, the rendered image is generated by choosing particles closest to the camera. Furthermore, we use spatial superimposing. Each pixel is constructed from multiple subpixels. This approach not only increases projection accuracy, but allows also a combination of subpixels into one superpixel that creates the well-known translucency effect of volume rendering. We show that our method is fast enough for the visualization of unstructured three-dimensional grids with hard real-time constraints and that it scales well for a high number of particles
A Model Theoretic Proof of Completeness of an Axiomatization of Monadic Second-Order Logic on Streams

International audienceWe discuss the completeness of an axiomatization of Monadic Second- Order Logic (MSO) on infinite words (or streams). By using model-theoretic tools, we give an alternative proof of D. Siefkes' result that a fragment with full comprehension and induction of second-order Peano's arithmetic is com- plete w.r.t. the validity of MSO-formulas on streams. We rely on Feferman- Vaught Theorems and the Ehrenfeucht-Fra ̈ıss ́e method for Henkin models of second-order arithmetic. Our main technical contribution is an infinitary Feferman-Vaught Fusion of such models. We show it using Ramseyan fac- torizations similar to those for standard infinite words. We also discuss a Ramsey's theorem for MSO-definable colorings, and show that in linearly ordered Henkin models, Ramsey's theorem for additive MSO-definable col- orings implies Ramsey's theorem for all MSO-definable colorings
Indifferentiable Hashing to Barreto–Naehrig Curves

International audienceA number of recent works have considered the problem of constructing constant-time hash functions to various families of elliptic curves over finite fields. In the relevant literature, it has been occasionally asserted that constant-time hashing to certain special elliptic curves, in particular so-called BN elliptic curves, was an open problem. It turns out, however, that a suitably general encoding function was constructed by Shallue and van de Woestijne back in 2006. In this paper, we show that, by specializing the construction of Shallue and van de Woestijne to BN curves, one obtains an encoding function that can be implemented rather efficiently and securely, that reaches about 9/16ths of all points on the curve, and that is well-distributed in the sense of Farashahi et al., so that one can easily build from it a hash function that is indifferentiable from a random oracle
Bayesian Nonparametric Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given the transition function and a set of observed demonstrations in the form of state-action pairs. Current IRL algorithms attempt to find a single reward function which explains the entire observation set. In practice, this leads to a computationally-costly search over a large (typically infinite) space of complex reward functions. This paper proposes the notion that if the observations can be partitioned into smaller groups, a class of much simpler reward functions can be used to explain each group. The proposed method uses a Bayesian nonparametric mixture model to automatically partition the data and find a set of simple reward functions corresponding to each partition. The simple rewards are interpreted intuitively as subgoals, which can be used to predict actions or analyze which states are important to the demonstrator. Experimental results are given for simple examples showing comparable performance to other IRL algorithms in nominal situations. Moreover, the proposed method handles cyclic tasks (where the agent begins and ends in the same state) that would break existing algorithms without modification. Finally, the new algorithm has a fundamentally different structure than previous methods, making it more computationally efficient in a real-world learning scenario where the state space is large but the demonstration set is small
Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization

International audienceThe use of Reinforcement Learning in real-world scenarios is strongly limited by issues of scale. Most RL learning algorithms are unable to deal with problems composed of hundreds or sometimes even dozens of possible actions, and therefore cannot be applied to many real-world problems. We consider the RL problem in the supervised classification framework where the optimal policy is obtained through a multiclass classifier, the set of classes being the set of actions of the problem. We introduce error-correcting output codes (ECOCs) in this setting and propose two new methods for reducing complexity when using rollouts-based approaches. The first method consists in using an ECOC-based classifier as the multiclass classifier, reducing the learning complexity from O(A2) to O(Alog(A)) . We then propose a novel method that profits from the ECOC's coding dictionary to split the initial MDP into O(log(A)) separate two-action MDPs. This second method reduces learning complexity even further, from O(A2) to O(log(A)) , thus rendering problems with large action sets tractable. We finish by experimentally demonstrating the advantages of our approach on a set of benchmark problems, both in speed and performance
MDL-based analysis of time series at multiple time-scales

The behavior of many complex physical systems is affected by a variety of phenomena occurring at different temporal scales. Time series data produced by measuring properties of such systems often mirrors this fact by appearing as a composition of signals across different time scales. When the final goal of the analysis is to model the individual phenomena affecting a system, it is crucial to be able to recognize the right temporal scales and to separate the individual components of the data. In this paper, we approach this challenge through a combination of the Minimum Description Length (MDL) principle, feature selection strategies, and convolution techniques from the signal processing field. As a result, our algorithm produces a good decomposition of a given time series and, as a side effect, builds a compact representation of its identified components. Experiments demonstrate that our method manages to identify correctly both the number and the temporal scale of the components for real-world as well as artificial data and show the usefulness of our method as an exploratory tool for analyzing time series data.status: publishe