Adaptability Checking in Multi-Level Complex Systems

A hierarchical model for multi-level adaptive systems is built on two basic levels: a lower behavioural level B accounting for the actual behaviour of the system and an upper structural level S describing the adaptation dynamics of the system. The behavioural level is modelled as a state machine and the structural level as a higher-order system whose states have associated logical formulas (constraints) over observables of the behavioural level. S is used to capture the global and stable features of B, by a defining set of allowed behaviours. The adaptation semantics is such that the upper S level imposes constraints on the lower B level, which has to adapt whenever it no longer can satisfy them. In this context, we introduce weak and strong adaptabil- ity, i.e. the ability of a system to adapt for some evolution paths or for all possible evolutions, respectively. We provide a relational characterisation for these two notions and we show that adaptability checking, i.e. deciding if a system is weak or strong adaptable, can be reduced to a CTL model checking problem. We apply the model and the theoretical results to the case study of motion control of autonomous transport vehicles.Comment: 57 page, 10 figures, research papaer, submitte

On the ISW-cluster cross-correlation in future surveys

We investigate the cosmological information contained in the cross-correlation between the Integrated Sachs-Wolfe (ISW) of the Cosmic Microwave Background (CMB) anisotropy pattern and galaxy clusters from future wide surveys. Future surveys will provide cluster catalogues with a number of objects comparable with galaxy catalogues currently used for the detection of the ISW signal by cross-correlation with the CMB anisotropy pattern. By computing the angular power spectra of clusters and the corresponding cross-correlation with CMB, we perform a signal-to-noise ratio (SNR) analysis for the ISW detection as expected from the eROSITA and the Euclid space missions. We discuss the dependence of the SNR of the ISW-cluster cross-correlation on the specifications of the catalogues and on the reference cosmology. We forecast that the SNRs for ISW-cluster cross-correlation are alightly smaller compared to those which can be obtained from future galaxy surveys but the signal is expected to be detected at high significance, i.e. more than $> 3\,\sigma$. We also forecast the joint constraints on parameters of model extensions of the concordance $\Lambda$CDM cosmology by combining CMB and the ISW-cluster cross-correlation.Comment: 12 pages, 10 figures. Matches version accepted in MNRA

Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes

We consider the problem of predictive monitoring (PM), i.e., predicting at runtime the satisfaction of a desired property from the current system's state. Due to its relevance for runtime safety assurance and online control, PM methods need to be efficient to enable timely interventions against predicted violations, while providing correctness guarantees. We introduce \textit{quantitative predictive monitoring (QPM)}, the first PM method to support stochastic processes and rich specifications given in Signal Temporal Logic (STL). Unlike most of the existing PM techniques that predict whether or not some property $\phi$ is satisfied, QPM provides a quantitative measure of satisfaction by predicting the quantitative (aka robust) STL semantics of $\phi$. QPM derives prediction intervals that are highly efficient to compute and with probabilistic guarantees, in that the intervals cover with arbitrary probability the STL robustness values relative to the stochastic evolution of the system. To do so, we take a machine-learning approach and leverage recent advances in conformal inference for quantile regression, thereby avoiding expensive Monte-Carlo simulations at runtime to estimate the intervals. We also show how our monitors can be combined in a compositional manner to handle composite formulas, without retraining the predictors nor sacrificing the guarantees. We demonstrate the effectiveness and scalability of QPM over a benchmark of four discrete-time stochastic processes with varying degrees of complexity

Benchmarking Evolutionary Community Detection Algorithms in Dynamic Networks

In dynamic complex networks, entities interact and form network communities that evolve over time. Among the many static Community Detection (CD) solutions, the modularity-based Louvain, or Greedy Modularity Algorithm (GMA), is widely employed in real-world applications due to its intuitiveness and scalability. Nevertheless, addressing CD in dynamic graphs remains an open problem, since the evolution of the network connections may poison the identification of communities, which may be evolving at a slower pace. Hence, naively applying GMA to successive network snapshots may lead to temporal inconsistencies in the communities. Two evolutionary adaptations of GMA, sGMA and $\alpha$GMA, have been proposed to tackle this problem. Yet, evaluating the performance of these methods and understanding to which scenarios each one is better suited is challenging because of the lack of a comprehensive set of metrics and a consistent ground truth. To address these challenges, we propose (i) a benchmarking framework for evolutionary CD algorithms in dynamic networks and (ii) a generalised modularity-based approach (NeGMA). Our framework allows us to generate synthetic community-structured graphs and design evolving scenarios with nine basic graph transformations occurring at different rates. We evaluate performance through three metrics we define, i.e. Correctness, Delay, and Stability. Our findings reveal that $\alpha$GMA is well-suited for detecting intermittent transformations, but struggles with abrupt changes; sGMA achieves superior stability, but fails to detect emerging communities; and NeGMA appears a well-balanced solution, excelling in responsiveness and instantaneous transformations detection.Comment: Accepted at the 4th Workshop on Graphs and more Complex structures for Learning and Reasoning (GCLR) at AAAI 202

On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers

Pietropolli, G., Manzoni, L., Paoletti, A., & Castelli, M. (2023). On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers. Genetic Programming And Evolvable Machines, 24(2 Special Issue on Highlights of Genetic Programming 2022 Events), 1-20. [16]. https://doi.org/10.21203/rs.3.rs-2229748/v1, https://doi.org/10.1007/s10710-023-09463-1---Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the Project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMSGeometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.publishersversionepub_ahead_of_prin

Statistical Guarantees for the Robustness of Bayesian Neural Networks

We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set such that the BNN prediction differs between the two. Such a measure can be used, for instance, to quantify the probability of the existence of adversarial examples. Building on statistical verification techniques for probabilistic models, we develop a framework that allows us to estimate probabilistic robustness for a BNN with statistical guarantees, i.e., with a priori error and confidence bounds. We provide experimental comparison for several approximate BNN inference techniques on image classification tasks associated to MNIST and a two-class subset of the GTSRB dataset. Our results enable quantification of uncertainty of BNN predictions in adversarial settings.Comment: 9 pages, 6 figure

The Mediterranean European hake, Merluccius merluccius: Detecting drivers influencing the Anisakis spp. larvae distribution

The European hake Merluccius merluccius is one of the most commercially important and widely distributed fish species, occurring both in European and Mediterranean Sea fisheries. We analyzed the distribution and infection rates of different species of Anisakis in M. merluccius (N = 1130 hakes), by site of infection in the fish host (viscera, dorsal and ventral fillets) from 13 different fishing grounds of the Mediterranean Sea (FAO area 37). The fillets were examined using the UV-Press method. A large number of Anisakis specimens (N = 877) were identified by diagnostic allozymes, sequence analysis of the partial EF1 α-1 region of nDNA and mtDNA cox2 gene. Among these, 813 larvae corresponded to A. pegreffii, 62 to A. physeteris, 1 to A. simplex (s. s.), whereas one resulted as a F1 hybrid between A. pegreffii and A. simplex (s. s.). Remarkably high levels of infection with A. pegreffii were recorded in hakes from the Adriatic/Ionian Sea compared to the fish of similar length obtained from the western Mediterranean fishing grounds. A positive correlation between fish length and abundance of A. pegreffii was observed. Concerning the localization of A. pegreffii larvae in the fish, 28.3% were detected in the liver, 62.9% in the rest of the viscera, 6.6% in the ventral part of the flesh, whereas 2.1% in the dorsal flesh

HDAC-inhibitor (S)-8 disrupts HDAC6-PP1 complex prompting A375 melanoma cell growth arrest and apoptosis.

Histone deacetylase inhibitors (HDACi) are agents capable of inducing growth arrest and apoptosis in different tumour cell types. Previously, we reported a series of novel HDACi obtained by hybridizing SAHA or oxamflatin with 1,4-benzodiazepines. Some of these hybrids proved effective against haematological and solid cancer cells and, above all, compound (S)-8 has emerged for its activities in various biological systems. Here, we describe the effectiveness of (S)-8 against highly metastatic human A375 melanoma cells by using normal PIG1 melanocytes as control. (S)-8 prompted: acetylation of histones H3/H4 and α-tubulin; G(0)/G(1) and G(2)/M cell cycle arrest by rising p21 and hypophos-phorylated RB levels; apoptosis involving the cleavage of PARP and caspase 9, BAD protein augmentation and cytochrome c release; decrease in cell motility, invasiveness and pro-angiogenic potential as shown by results of wound-healing assay, down-regulation of MMP-2 and VEGF-A/VEGF-R2, besides TIMP-1/TIMP-2 up-regulation; and also intracellular accumulation of melanin and neutral lipids. The pan-caspase inhibitor Z-VAD-fmk, but not the antioxidant N-acetyl-cysteine, contrasted these events. Mechanistically, (S)-8 allows the disruption of cytoplasmic HDAC6-protein phosphatase 1 (PP1) complex in A375 cells thus releasing the active PP1 that dephosphorylates AKT and blocks its downstream pro-survival signalling. This view is consistent with results obtained by: inhibiting PP1 with Calyculin A; using PPP1R2-transfected cells with impaired PP1 activity; monitoring drug-induced HDAC6-PP1 complex re-shuffling; and, abrogating HDAC6 expression with specific siRNA. Altogether, (S)-8 proved very effective against melanoma A375 cells, but not normal melanocytes, and safe to normal mice thus offering attractive clinical prospects for treating this aggressive malignancy