Ensuring message embedding in wet paper steganography

International audienceSyndrome coding has been proposed by Crandall in 1998 as a method to stealthily embed a message in a cover-medium through the use of bounded decoding. In 2005, Fridrich et al. introduced wet paper codes to improve the undetectability of the embedding by nabling the sender to lock some components of the cover-data, according to the nature of the cover-medium and the message. Unfortunately, almost all existing methods solving the bounded decoding syndrome problem with or without locked components have a non-zero probability to fail. In this paper, we introduce a randomized syndrome coding, which guarantees the embedding success with probability one. We analyze the parameters of this new scheme in the case of perfect codes

Modules for Experiments in Stellar Astrophysics (MESA): Convective Boundaries, Element Diffusion, and Massive Star Explosions

We update the capabilities of the software instrument Modules for Experiments in Stellar Astrophysics (MESA) and enhance its ease of use and availability. Our new approach to locating convective boundaries is consistent with the physics of convection, and yields reliable values of the convective core mass during both hydrogen and helium burning phases. Stars with $M<8\,{\rm M_\odot}$ become white dwarfs and cool to the point where the electrons are degenerate and the ions are strongly coupled, a realm now available to study with MESA due to improved treatments of element diffusion, latent heat release, and blending of equations of state. Studies of the final fates of massive stars are extended in MESA by our addition of an approximate Riemann solver that captures shocks and conserves energy to high accuracy during dynamic epochs. We also introduce a 1D capability for modeling the effects of Rayleigh-Taylor instabilities that, in combination with the coupling to a public version of the STELLA radiation transfer instrument, creates new avenues for exploring Type II supernovae properties. These capabilities are exhibited with exploratory models of pair-instability supernova, pulsational pair-instability supernova, and the formation of stellar mass black holes. The applicability of MESA is now widened by the capability of importing multi-dimensional hydrodynamic models into MESA. We close by introducing software modules for handling floating point exceptions and stellar model optimization, and four new software tools -- MESAWeb, MESA-Docker, pyMESA, and mesastar.org -- to enhance MESA's education and research impact.Comment: 64 pages, 61 figures; Accepted to AAS Journal

Sparse and skew hashing of K-mers

Motivation: A dictionary of k-mers is a data structure that stores a set of n distinct k-mers and supports membership queries. This data structure is at the hearth of many important tasks in computational biology. High-Throughput sequencing of DNA can produce very large k-mer sets, in the size of billions of strings-in such cases, the memory consumption and query efficiency of the data structure is a concrete challenge. Results: To tackle this problem, we describe a compressed and associative dictionary for k-mers, that is: A data structure where strings are represented in compact form and each of them is associated to a unique integer identifier in the range [0,n). We show that some statistical properties of k-mer minimizers can be exploited by minimal perfect hashing to substantially improve the space/time trade-off of the dictionary compared to the best-known solutions

MIL primitives for querying a fragmented world

In query-intensive database application areas, like decision support and data mining, systems that use vertical fragmentation have a significant performance advantage. In order to support relational or object oriented applications on top of such a fragmented data model, a flexible yet powerful intermediate language is needed. This problem has been successfully tackled in Monet, a modern extensible database kernel developed by our group. We focus on the design choices made in the Monet Interpreter Language (MIL), its algebraic query language, and outline how its concept of tactical optimization enhances and simplifies the optimization of complex queries. Finally, we summarize the experience gained in Monet by creating a highly efficient implementation of MIL

Navigating Diverse Datasets in the Face of Uncertainty

When exploring big volumes of data, one of the challenging aspects is their diversity of origin. Multiple files that have not yet been ingested into a database system may contain information of interest to a researcher, who must curate, understand and sieve their content before being able to extract knowledge. Performance is one of the greatest difficulties in exploring these datasets. On the one hand, examining non-indexed, unprocessed files can be inefficient. On the other hand, any processing before its understanding introduces latency and potentially un- necessary work if the chosen schema matches poorly the data. We have surveyed the state-of-the-art and, fortunately, there exist multiple proposal of solutions to handle data in-situ performantly. Another major difficulty is matching files from multiple origins since their schema and layout may not be compatible or properly documented. Most surveyed solutions overlook this problem, especially for numeric, uncertain data, as is typical in fields like astronomy. The main objective of our research is to assist data scientists during the exploration of unprocessed, numerical, raw data distributed across multiple files based solely on its intrinsic distribution. In this thesis, we first introduce the concept of Equally-Distributed Dependencies, which provides the foundations to match this kind of dataset. We propose PresQ, a novel algorithm that finds quasi-cliques on hypergraphs based on their expected statistical properties. The probabilistic approach of PresQ can be successfully exploited to mine EDD between diverse datasets when the underlying populations can be assumed to be the same. Finally, we propose a two-sample statistical test based on Self-Organizing Maps (SOM). This method can outperform, in terms of power, other classifier-based two- sample tests, being in some cases comparable to kernel-based methods, with the advantage of being interpretable. Both PresQ and the SOM-based statistical test can provide insights that drive serendipitous discoveries

Learning spatio-temporal patterns with Neural Cellular Automata

Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning rules that capture the dynamics of Turing pattern formation in nonlinear Partial Differential Equations (PDEs). We demonstrate that NCA can generalise very well beyond their PDE training data, we show how to constrain NCA to respect given symmetries, and we explore the effects of associated hyperparameters on model performance and stability. Being able to learn arbitrary dynamics gives NCA great potential as a data driven modelling framework, especially for modelling biological pattern formation.Comment: For videos referenced in appendix, see: https://github.com/AlexDR1998/NCA/tree/main/Video