Extending the Finite Domain Solver of GNU Prolog

International audienceThis paper describes three significant extensions for the Finite Domain solver of GNU Prolog. First, the solver now supports negative integers. Second, the solver detects and prevents integer overflows from occurring. Third, the internal representation of sparse domains has been redesigned to overcome its current limitations. The preliminary performance evaluation shows a limited slowdown factor with respect to the initial solver. This factor is widely counterbalanced by the new possibilities and the robustness of the solver. Furthermore these results are preliminary and we propose some directions to limit this overhead

Development and implementation of in silico molecule fragmentation algorithms for the cheminformatics analysis of natural product spaces

Computational methodologies extracting specific substructures like functional groups or molecular scaffolds from input molecules can be grouped under the term “in silico molecule fragmentation”. They can be used to investigate what specifically characterises a heterogeneous compound class, like pharmaceuticals or Natural Products (NP) and in which aspects they are similar or dissimilar. The aim is to determine what specifically characterises NP structures to transfer patterns favourable for bioactivity to drug development. As part of this thesis, the first algorithmic approach to in silico deglycosylation, the removal of glycosidic moieties for the study of aglycones, was developed with the Sugar Removal Utility (SRU) (Publication A). The SRU has also proven useful for investigating NP glycoside space. It was applied to one of the largest open NP databases, COCONUT (COlleCtion of Open Natural prodUcTs), for this purpose (Publication B). A contribution was made to the Chemistry Development Kit (CDK) by developing the open Scaffold Generator Java library (Publication C). Scaffold Generator can extract different scaffold types and dissect them into smaller parent scaffolds following the scaffold tree or scaffold network approach. Publication D describes the OngLai algorithm, the first automated method to identify homologous series in input datasets, group the member structures of each group, and extract their common core. To support the development of new fragmentation algorithms, the open Java rich client graphical user interface application MORTAR (MOlecule fRagmenTAtion fRamework) was developed as part of this thesis (Publication E). MORTAR allows users to quickly execute the steps of importing a structural dataset, applying a fragmentation algorithm, and visually inspecting the results in different ways. All software developed as part of this thesis is freely and openly available (see https://github.com/JonasSchaub)

Learning in clustered spiking networks

Neurons spike on a millisecond time scale while behaviour typically spans hundreds of milliseconds to seconds and longer. Neurons have to bridge this time gap when computing and learning behaviours of interest. Recent computational work has shown that neural circuits can bridge this time gap when connected in specific ways. Moreover, the connectivity patterns can develop using plasticity rules typically considered to be biologically plausible. In this thesis, we focus on one type of connectivity where excitatory neurons are grouped in clusters. Strong recurrent connectivity within the clusters reverberates the activity and prolongs the time scales in the network. This way, the clusters of neurons become the basic functional units of the circuit, in line with an increasing number of experimental studies. We study a general architecture where plastic synapses connect the clustered network to a read-out network. We demonstrate the usefulness of this architecture for two different problems: 1) learning and replaying sequences; 2) learning statistical structure. The time scales in both problems range from hundreds of milliseconds to seconds and we address the problems through simulation and analysis of spiking networks. We show that the clustered organization circumvents the need for non-bio-plausible mathematical optimizations and instead allows the use of unsupervised spike-timing-dependent plasticity rules. Additionally, we make qualitative links to experimental findings and predictions for both problems studied. Finally, we speculate about future directions that could extend upon our findings.Open Acces

Kiel Declarative Programming Days 2013

This report contains the papers presented at the Kiel Declarative Programming Days 2013, held in Kiel (Germany) during September 11-13, 2013. The Kiel Declarative Programming Days 2013 unified the following events: * 20th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2013) * 22nd International Workshop on Functional and (Constraint) Logic Programming (WFLP 2013) * 27th Workshop on Logic Programming (WLP 2013) All these events are centered around declarative programming, an advanced paradigm for the modeling and solving of complex problems. These specification and implementation methods attracted increasing attention over the last decades, e.g., in the domains of databases and natural language processing, for modeling and processing combinatorial problems, and for high-level programming of complex, in particular, knowledge-based systems

Approximation methods in geometry and topology: learning, coarsening, and sampling

Data materialize in many different forms and formats. These can be continuous or discrete, from algebraic expressions to unstructured pointclouds and highly structured graphs and simplicial complexes. Their sheer volume and plethora of different modalities used to manipulate and understand them highlight the need for expressive abstractions and approximations, enabling novel insights and efficiency. Geometry and topology provide powerful and intuitive frameworks for modelling structure, form, and connectivity. Acting as a multi-focal lens, they enable inspection and manipulation at different levels of detail, from global discriminant features to local intricate characteristics. However, these fundamentally algebraic theories do not scale well in the digital world. Adjusting topology and geometry to the computational setting is a non-trivial task, adhering to the “no free lunch” adage. The necessary discretizations can be inaccurate, the underlying combinatorial structures can grow unmanageably in size, and computing salient topological and geometric features can become computationally taxing. Approximations are a necessity when theory cannot accommodate for efficient algorithms. This thesis explores different approaches to simplifying computations pertaining to geometry and topology via approximations. Our methods contribute to the approximation of topological features on discrete domains, and employ geometry and topology to efficiently guide discretizations and approximations. This line of work fits un der the umbrella of Topological Data Analysis (TDA) and Discrete Geometry, which aim to bridge the continuous algebraic mindset with the discrete. We construct topological and geometric approximation methods operating on three different levels. We approximate topological features on discrete combinatorial spaces; we approximate the combinatorial spaces themselves; and we guide processes that allow us to discretize domains via sampling. With our Dist2Cycle model we learn geometric manifestations of topological features, the “optimal” homology generating cycles. This is achieved by a novel simplicial complex neural network that exploits the kernel of Hodge Laplacian operators to localize concise homology generators. Compression of meshes and arbitrary simplicial complexes is made possible by our general spectral coarsening strategy. Functional and structural properties are preserved by optimizing for important eigenspaces of general differential operators, the Hodge Laplacians, at multiple dimensions. Finally, we offer a geometry-driven sampling strategy for data accumulation and stochastic integration. By employing the kd-tree geometric partitioning algorithm we construct a sample set with provable equidistribution guarantees. Our findings are contextualized within prior and recent work, and our methods are thoroughly discussed and evaluated on diverse settings. Ultimately, we are making a claim towards the usefulness of examining the ever-present topological and geometric properties of data, not only in terms of feature discovery, but also as informed generation, manipulation, and simplification tools

Decryption Failure Attacks on Post-Quantum Cryptography

This dissertation discusses mainly new cryptanalytical results related to issues of securely implementing the next generation of asymmetric cryptography, or Public-Key Cryptography (PKC).PKC, as it has been deployed until today, depends heavily on the integer factorization and the discrete logarithm problems.Unfortunately, it has been well-known since the mid-90s, that these mathematical problems can be solved due to Peter Shor's algorithm for quantum computers, which achieves the answers in polynomial time.The recently accelerated pace of R&D towards quantum computers, eventually of sufficient size and power to threaten cryptography, has led the crypto research community towards a major shift of focus.A project towards standardization of Post-quantum Cryptography (PQC) was launched by the US-based standardization organization, NIST. PQC is the name given to algorithms designed for running on classical hardware/software whilst being resistant to attacks from quantum computers.PQC is well suited for replacing the current asymmetric schemes.A primary motivation for the project is to guide publicly available research toward the singular goal of finding weaknesses in the proposed next generation of PKC.For public key encryption (PKE) or digital signature (DS) schemes to be considered secure they must be shown to rely heavily on well-known mathematical problems with theoretical proofs of security under established models, such as indistinguishability under chosen ciphertext attack (IND-CCA).Also, they must withstand serious attack attempts by well-renowned cryptographers both concerning theoretical security and the actual software/hardware instantiations.It is well-known that security models, such as IND-CCA, are not designed to capture the intricacies of inner-state leakages.Such leakages are named side-channels, which is currently a major topic of interest in the NIST PQC project.This dissertation focuses on two things, in general:1) how does the low but non-zero probability of decryption failures affect the cryptanalysis of these new PQC candidates?And 2) how might side-channel vulnerabilities inadvertently be introduced when going from theory to the practice of software/hardware implementations?Of main concern are PQC algorithms based on lattice theory and coding theory.The primary contributions are the discovery of novel decryption failure side-channel attacks, improvements on existing attacks, an alternative implementation to a part of a PQC scheme, and some more theoretical cryptanalytical results

Automated Reasoning

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book

Interactions between structure and stochasticity in demogaphic models

Demography is the study of population dynamics. Populations can be considered as groups of individuals living within a given region. These simple statements encompass highly disparate systems, which respond to demographic and environmental stochasticity in predictable and unpredictable ways. The responses depend on the structure of the population, since individuals can have vastly different survival and recruitment, which, with dispersal, determine population abundance. Whilst some variation is inter-st(age) – increases in reproductive performance with age, for example – substantial intra-st(age) variation is not uncommon. Using longterm individual-based data on two disparate vertebrate populations, the focus of this thesis is the interaction between structure and stochasticity in demographic models, and consequences on resultant aspects of population growth. Structured models that incorporated variation in demographic rates detected marked differences within, between and across diverse habitats for different age-classes in both populations. These results were consistent for a wide range of scaling and definition to account for mathematical dependence. Spatial structure was more influential than age-structure in responses to stochastic predation. Despite significant changes in performance and phenotype with age, individual heterogeneity within ageclasses was vast. These results are of importance for conservation and management action, as well as predictors of evolutionary change. The population is a fundamental force in ecology and evolution. This work adds weight to the argument that characteristics of individual performance in response to variability in their environments are pivotal to increased understanding of changes in population abundance. These individual responses are dependent upon the opportunity generated by population structure. A failure to incorporate either structure or stochasticity neglects crucial aspects in population regulation, and therefore ecological and evolutionary change