Verification and control of partially observable probabilistic systems

We present automated techniques for the verification and control of partially observable, probabilistic systems for both discrete and dense models of time. For the discrete-time case, we formally model these systems using partially observable Markov decision processes; for dense time, we propose an extension of probabilistic timed automata in which local states are partially visible to an observer or controller. We give probabilistic temporal logics that can express a range of quantitative properties of these models, relating to the probability of an event’s occurrence or the expected value of a reward measure. We then propose techniques to either verify that such a property holds or synthesise a controller for the model which makes it true. Our approach is based on a grid-based abstraction of the uncountable belief space induced by partial observability and, for dense-time models, an integer discretisation of real-time behaviour. The former is necessarily approximate since the underlying problem is undecidable, however we show how both lower and upper bounds on numerical results can be generated. We illustrate the effectiveness of the approach by implementing it in the PRISM model checker and applying it to several case studies from the domains of task and network scheduling, computer security and planning

Supporting Validation of UAV Sense-and-Avoid Algorithms with Agent-Based Simulation and Evolutionary Search

A Sense-and-Avoid (SAA) capability is required for the safe integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace. Given their safety-critical nature, SAA algorithms must undergo rigorous verification and validation before deployment. The validation of UAV SAA algorithms requires identifying challenging situations that the algorithms have difficulties in handling. By building on ideas from Search-Based Software Testing, this thesis proposes an evolutionary-search-based approach that automatically identifies such situations to support the validation of SAA algorithms. Specifically, in the proposed approach, the behaviours of UAVs under the control of selected SAA algorithms are examined with agent-based simulations. Evolutionary search is used to guide the simulations to focus on increasingly challenging situations in a large search space defined by (the variations of) parameters that configure the simulations. An open-source tool has been developed to support the proposed approach so that the process can be partially automated. Positive results were achieved in a preliminary evaluation of the proposed approach using a simple two-dimensional SAA algorithm. The proposed approach was then further demonstrated and evaluated using two case studies, applying it to a prototype of an industry-level UAV collision avoidance algorithm (specifically, ACAS XU) and a multi-UAV conflict resolution algorithm (specifically, ORCA-3D). In the case studies, the proposed evolutionary-search-based approach was empirically compared with some plausible rivals (specifically, random-search-based approaches and a deterministic-global-search-based approach). The results show that the proposed approach can identify the required challenging situations more effectively and efficiently than the random-search-based approaches. The results also show that even though the proposed approach is a little less competitive than the deterministic-global-search-based approach in terms of effectiveness in relatively easy cases, it is more effective and efficient in more difficult cases, especially when the objective function becomes highly discontinuous. Thus, the proposed evolutionary-search-based approach has the potential to be used for supporting the validation of UAV SAA algorithms although it is not possible to show that it is the best approach

Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search

A new approach to testing multi-UAV conflict resolution algorithms is presented. The problem is formulated as a multi-objective search problem with two objectives: finding air traffic encounters that 1) are able to reveal faults in conflict resolution algorithms and 2) are likely to happen in the real world. The method uses agent-based simulation and multi-objective search to automatically find encounters satisfying these objectives. It describes pairwise encounters in three-dimensional space using a parameterized geometry representation, which allows encounters involving multiple UAVs to be generated by combining several pairwise encounters. The consequences of the encounters, given the conflict resolution algorithm, are explored using a fast-time agent-based simulator. To find encounters meeting the two objectives, a genetic algorithm approach is used. The method is applied to test ORCA-3D, a widely cited open-source multi-UAV conflict resolution algorithm, and the method’s performance is compared with a plausible random testing approach. The results show that the method can find the required encounters more efficiently than the random search. The identified safety incidents are then the starting points for understanding limitations of the conflict resolution algorithm

On the Validation of a UAV Collision Avoidance System Developed by Model-Based Optimization: : Challenges and a Tentative Partial Solution

The development of the new generation of airborne collision avoidance system ACAS X adopts a model-based optimization approach, where the collision avoidance logic is automatically generated based on a probabilistic model and a set of preferences. It has the potential for safety benefits and shortening the development cycle, but it poses new challenges for safety assurance. In this paper, we introduce the new development process and explain its key ideas using a simple collision avoidance example. Based on this explanation, we analyze the challenges it poses to safety assurance, with a particular focus on system validation. We then propose a Genetic-Algorithm-based approach that can efficiently search for undesired situations to help the development and validation of the system. We introduce an open-source tool we have developed to support this approach and demonstrate it on searching for challenging situations for ACAS XU

Comparison of beta diversity measures in clustering the high-dimensional microbial data.

The heterogeneity of disease is a major concern in medical research and is commonly characterized as subtypes with different pathogeneses exhibiting distinct prognoses and treatment effects. The classification of a population into homogeneous subgroups is challenging, especially for complex diseases. Recent studies show that gut microbiome compositions play a vital role in disease development, and it is of great interest to cluster patients according to their microbial profiles. There are a variety of beta diversity measures to quantify the dissimilarity between the compositions of different samples for clustering. However, using different beta diversity measures results in different clusters, and it is difficult to make a choice among them. Considering microbial compositions from 16S rRNA sequencing, which are presented as a high-dimensional vector with a large proportion of extremely small or even zero-valued elements, we set up three simulation experiments to mimic the microbial compositional data and evaluate the performance of different beta diversity measures in clustering. It is shown that the Kullback-Leibler divergence-based beta diversity, including the Jensen-Shannon divergence and its square root, and the hypersphere-based beta diversity, including the Bhattacharyya and Hellinger, can capture compositional changes in low-abundance elements more efficiently and can work stably. Their performance on two real datasets demonstrates the validity of the simulation experiments

Identification and Analysis of <i>Phosphatidylethanolamine-Binding Protein</i> Family Genes in the Hangzhou White Chrysanthemum (<i>Chrysanthemum morifolium</i> Ramat)

The Hangzhou White Chrysanthemum (Chrysanthemum morifolium Ramat) is one of the “Zhejiang eight flavors” in traditional Chinese medicine. The phosphatidylethanolamine-binding protein (PEBP) plays an important role in flowering and floral organ development. Even so, the biological role of PEBPs in the Hangzhou White Chrysanthemum has not been studied, which attracted us. Here, nine CmPEBP genes that contain the PF01161 domain were identified in the Hangzhou White Chrysanthemum for the first time, and their biological role in flowering was preliminarily studied. A phylogenetic analysis classified the CmPEBP genes into three subfamilies: MFT-like, TFL-like, and FT-like genes. The differential expression analysis was performed under different tissues and different stressors using qRT-PCR. It showed that each CmPEBP displayed tissue-specific expression patterns. Expression patterns in response to different temperatures and hormone stressors were investigated. They were finally demonstrated to be differentially expressed. TFL-like gene expression, which delayed reproductive growth, was upregulated under heat stress. Conversely, FT-like gene expression was upregulated under low temperatures. CmFT1 expression could be inhibited by GA (gibberellin), 6-BA (benzylaminopurine), ET (ethylene), and MeSA (methyl salicylate) but could be activated by IAA (indole-3-aceticacid), ABA (abscisic acid), and SA (salicylic acid) in the dark, whereas CmFT2 and CmFT3 expression levels were upregulated by ET, MeJA (methyl jasmonate), and ABA but were downregulated by 6-BA, SA, and MeSA. GA, IAA, SA, and MeSA inhibited CmTFL gene expression under light and dark treatments. Further research on CmPEBP genes in the Hangzhou White Chrysanthemum could better determine their roles in flowering and floral organ development, especially in response to the prolonged spraying of exogenous hormones

Identification and Analysis of Phosphatidylethanolamine-Binding Protein Family Genes in the Hangzhou White Chrysanthemum (Chrysanthemum morifolium Ramat)

The Hangzhou White Chrysanthemum (Chrysanthemum morifolium Ramat) is one of the &ldquo;Zhejiang eight flavors&rdquo; in traditional Chinese medicine. The phosphatidylethanolamine-binding protein (PEBP) plays an important role in flowering and floral organ development. Even so, the biological role of PEBPs in the Hangzhou White Chrysanthemum has not been studied, which attracted us. Here, nine CmPEBP genes that contain the PF01161 domain were identified in the Hangzhou White Chrysanthemum for the first time, and their biological role in flowering was preliminarily studied. A phylogenetic analysis classified the CmPEBP genes into three subfamilies: MFT-like, TFL-like, and FT-like genes. The differential expression analysis was performed under different tissues and different stressors using qRT-PCR. It showed that each CmPEBP displayed tissue-specific expression patterns. Expression patterns in response to different temperatures and hormone stressors were investigated. They were finally demonstrated to be differentially expressed. TFL-like gene expression, which delayed reproductive growth, was upregulated under heat stress. Conversely, FT-like gene expression was upregulated under low temperatures. CmFT1 expression could be inhibited by GA (gibberellin), 6-BA (benzylaminopurine), ET (ethylene), and MeSA (methyl salicylate) but could be activated by IAA (indole-3-aceticacid), ABA (abscisic acid), and SA (salicylic acid) in the dark, whereas CmFT2 and CmFT3 expression levels were upregulated by ET, MeJA (methyl jasmonate), and ABA but were downregulated by 6-BA, SA, and MeSA. GA, IAA, SA, and MeSA inhibited CmTFL gene expression under light and dark treatments. Further research on CmPEBP genes in the Hangzhou White Chrysanthemum could better determine their roles in flowering and floral organ development, especially in response to the prolonged spraying of exogenous hormones