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A major objective of evolutionary biology is to understand the processes by which organisms have adapted to various environments, and to predict the response of organisms to new or future conditions. The availability of large genomic and environmental data sets provides an opportunity to address those questions, and the R package LEA has been introduced to facilitate population and ecological genomic analyses in this context. By using latent factor models, the program computes ancestry coefficients from population genetic data, and performs genotype-environment association analyses with correction for unobserved confounding variables. In this study, we present new functionalities of LEA, which include imputation of missing genotypes, fast algorithms for latent factor mixed models using multivariate predictors for genotype-environment association studies, population differentiation tests for admixed or continuous populations, and estimation of genetic offset based on climate models. The new functionalities are implemented in version 3.0 and higher releases of the package. Using simulated and real data sets, our study provides evaluations and examples of applications, outlining important practical considerations when analyzing ecological genomic data in R.

We present an efficient approach to retrieve behavioural models from reactive software systems in the form of Finite State Machines by testing them. The system is accessed in black box mode; thus, no source or binary code is needed. The novelty of the approach is that it does not require to reset the system between tests (queries) and does not require any knowledge of the system apart from its input domain. Experiments have shown that it can scale up to systems that may have thousands of states.

International audience; This work tackles the reachability problem of uncertain discrete-time linear systems controlled by estimated state feedback regulators. First an augmented system is considered in order to include the effect of the estimation error on the behavior of the closed loop system. Then, based on interval analysis, an interval predictor is proposed to compute tight trajectory tubes that contain in a guaranteed way the actual state trajectory of the augmented system. Moreover, under the standard controllability and observability assumptions of linear systems, the convergence of the width of these trajectory tubes is shown.

Efficient detection and instrumentation of function calls is fundamental for a variety of dynamic analysis techniques, including dynamic callgraph construction, control-flow integrity, and automatic vulnerability discovery. A common way of detecting calls at the machine code level is to look for CALL instructions. However, optimizing compilers frequently implement function tail calls with JMP instructions instead, and distinguishing an intra-procedural jump from a JMP-based function call is not straightforward. Despite the importance of making this distinction, prior research has not produced a reliable solution. In this paper, we address the problem of dynamic function call detection in real-time. We propose a heuristic-based approach named iCi to efficiently and automatically instrument calls, including conventional CALLs and JMP-based calls, at runtime. iCi does not rely on source code, debug information, symbol tables or static analysis. We show that iCi achieves an f-score of 0.95 in the worst case, regardless of optimization level. We open-source our implementation as well as the oracle we used for our evaluation.

Active inference algorithms that are used to extract behavioural models of software systems usually assume that the System Under Inference (SUI) can be reset. Two approaches have been proposed to infer systems that cannot be reset. Rivest and Schapire proposed an adaptation of the \(L^*\) algorithm that relies on having a homing sequence for the SUI. We detail here another approach that is based on characterization sequences. More precisely, we assume classical testing hypotheses, namely that we are given a bound n on the number of states and a set W of characterizing sequences to distinguish states. Contrary to \(L^*\), it does not require an external oracle to decide on equivalence. The length of the test sequence is polynomial in n and the exponent depends on the cardinality |W| of the characterization set. For systems where resetting is impossible or expensive, this approach can be a viable alternative to classical learning methods.

Machine learning in the form of inference of state machine models has gained popularity in model-based testing as a means of retrieving models from software systems. By combining an old idea from machine inference with methods from automata testing in a heuristic approach, we propose a new promising direction for inferring black box systems that cannot be reset. Preliminary experiments show that this heuristic approach scales up well and outperforms more systematic approaches.

The paper focuses on the problems of passive and active FSM inference as well as checking sequence generation. We consider the setting where an FSM cannot be reset so that its inference is constrained to a single trace either given a priori in a passive inference scenario or to be constructed in an active inference scenario or aiming at obtaining checking sequence for a given FSM. In each of the last two cases, the expected result is a trace representing a checking sequence for an inferred machine, if it was not given. We demonstrate that this can be achieved by a repetitive use of a procedure that infers an FSM from a given trace (identifying a minimal machine consistent with a trace) avoiding equivalent conjectures. We thus show that FSM inference and checking sequence construction are two sides of the same coin. Following an existing approach of constructing conjectures by SAT solving, we elaborate first such a procedure and then based on it the methods for obtaining checking sequence for a given FSM and inferring a machine from a black box. The novelty of our approach is that it does not use any state identification facilities. We demonstrate that the proposed approach can also be constrained to find a solution in a subset of FSMs represented by a nondeterministic mutation machine. Experiments with a prototype implementation of the developed approach using an existing SAT solver indicate that it scales for FSMs with up to a dozen of states and requires relatively short sequences to identify a black box machine.

International audience; —When designing new solutions for Low Power Wide Area (LPWA) networks, coexistence and integration into existing 4G frameworks should be considered to ease the deployment procedure and reduce costs. In a previous work, Turbo-Frequency Shift Keying (FSK) was proposed as a potential physical layer for LPWA networks. We propose to study the system in the Orthogonal Frequency Division Multiplexing (OFDM) framework , currently used by existing cellular networks and also considered for the recently standardized Narrow-Band IoT. The new architectures of the Turbo-FSK transmitter and receiver are presented. Simulations are run under various conditions including Rayleigh fading channel with mobility, demonstrating the Turbo-FSK scheme to achieve performance close to Turbo-Coded OFDM. The scheme also exhibits a constant envelope, which may multiply the Power Amplifier (PA) efficiency by up to a factor of 2 and makes the solution a promising candidate for 5G LPWA transmission.