A hierarchical control framework for autonomous decision-making systems: Integrating HMDP and MPC

This paper proposes a comprehensive hierarchical control framework for autonomous decision-making arising in robotics and autonomous systems. In a typical hierarchical control architecture, high-level decision making is often characterised by discrete state and decision/control sets. However, a rational decision is usually affected by not only the discrete states of the autonomous system, but also the underlying continuous dynamics even the evolution of its operational environment. This paper proposes a holistic and comprehensive design process and framework for this type of challenging problems, from new modelling and design problem formulation to control design and stability analysis. It addresses the intricate interplay between traditional continuous systems dynamics utilized at the low levels for control design and discrete Markov decision processes (MDP) for facilitating high-level decision making. We model the decision making system in complex environments as a hybrid system consisting of a controlled MDP and autonomous (i.e. uncontrolled) continuous dynamics. Consequently, the new formulation is called as hybrid Markov decision process (HMDP). The design problem is formulated with a focus on ensuring both safety and optimality while taking into account the influence of both the discrete and continuous state variables of different levels. With the help of the model predictive control (MPC) concept, a decision maker design scheme is proposed for the proposed hybrid decision making model. By carefully designing key ingredients involved in this scheme, it is shown that the recursive feasibility and stability of the proposed autonomous decision making scheme are guaranteed. The proposed framework is applied to develop an autonomous lane changing system for intelligent vehicles.Comment: 11 pages, 14 figures, submitted to Automatic

Genetic Studies of Complex Human Diseases: Characterizing SNP-Disease Associations Using Bayesian Networks

Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis, and treatment of complex human diseases. Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype. RESULTS: To address the problems of computational methods in epistatic interaction detection, we propose a score-based Bayesian network structure learning method, EpiBN, to detect epistatic interactions. We apply the proposed method to both simulated datasets and three real disease datasets. Experimental results on simulation data show that our method outperforms some other commonly-used methods in terms of power and sample-efficiency, and is especially suitable for detecting epistatic interactions with weak or no marginal effects. Furthermore, our method is scalable to real disease data. CONCLUSIONS: We propose a Bayesian network-based method, EpiBN, to detect epistatic interactions. In EpiBN, we develop a new scoring function, which can reflect higher-order epistatic interactions by estimating the model complexity from data, and apply a fast Branch-and-Bound algorithm to learn the structure of a two-layer Bayesian network containing only one target node. To make our method scalable to real data, we propose the use of a Markov chain Monte Carlo (MCMC) method to perform the screening process. Applications of the proposed method to some real GWAS (genome-wide association studies) datasets may provide helpful insights into understanding the genetic basis of Age-related Macular Degeneration, late-onset Alzheimer's disease, and autism

New environmental dependent modelling with Gaussian particle filtering based implementation for ground vehicle tracking

This paper proposes a new domain knowledge aided Gaussian particle filtering based approach for the ground vehicle tracking application. Firstly, a new form of modelling is proposed to reflect the influences of different types of environmental domain knowledge on the vehicle dynamic: i) a non-Markov jump model is applied with multiple models while transition probabilities between models are environmental dependent ii) for a particular model, both the constraints and potential forces obtained from the surrounding environment have been applied to refine the vehicle state distribution. Based on the proposed modelling approach, a Gaussian particle filtering based method is developed to implement the related Bayesian inference for the target state estimation. Simulation studies from multiple Monte Carlo simulations confirm the advantages of the proposed method over traditional ones, from both the modelling and implementation aspects

Depletion of OLFM4 gene inhibits cell growth and increases sensitization to hydrogen peroxide and tumor necrosis factor-alpha induced-apoptosis in gastric cancer cells

<p>Abstract</p> <p>Background</p> <p>Human olfactomedin 4 (OLFM4) gene is a secreted glycoprotein more commonly known as the anti-apoptotic molecule GW112. OLFM4 is found to be frequently up-regulated in many types of human tumors including gastric cancer and it was believed to play significant role in the progression of gastric cancer. Although the function of OLFM4 has been indicated in many studies, recent evidence strongly suggests a cell or tissue type-dependent role of OLFM4 in cell growth and apoptosis. The aim of this study is to examine the role of gastric cancer-specific expression of OLFM4 in cell growth and apoptosis resistance.</p> <p>Methods</p> <p>OLFM4 expression was eliminated by RNA interference in SGC-7901 and MKN45 cells. Cell proliferation, anchorage-independent growth, cell cycle and apoptosis were characterized in vitro. Tumorigenicity was analyzed in vivo. The apoptosis and caspase-3 activation in response to hydrogen peroxide (H₂O₂) or tumor necrosis factor-alpha (TNF α) were assessed in the presence or absence of caspase inhibitor Z-VAD-fmk.</p> <p>Results</p> <p>The elimination of OLFM4 protein by RNA interference in SGC-7901 and MKN45 cells significantly inhibits tumorigenicity both in vitro and in vivo by induction of cell G1 arrest (all P < 0.01). OLFM4 knockdown did not trigger obvious cell apoptosis but increased H₂O₂or TNF α-induced apoptosis and caspase-3 activity (all P < 0.01). Treatment of Z-VAD-fmk attenuated caspase-3 activity and significantly reversed the H₂O₂or TNF α-induced apoptosis in OLFM4 knockdown cells (all P < 0.01).</p> <p>Conclusion</p> <p>Our study suggests that depletion of OLFM4 significantly inhibits tumorigenicity of the gastric cancer SGC-7901 and MKN45 cells. Blocking OLFM4 expression can sensitize gastric cancer cells to H₂O₂or TNF α treatment by increasing caspase-3 dependent apoptosis. A combination strategy based on OLFM4 inhibition and anticancer drugs treatment may provide therapeutic potential in gastric cancer intervention.</p

Surviving disturbances: A predictive control framework with guaranteed safety

Rejecting all disturbances is an extravagant hope in safety-critical control systems, hence surviving them where possible is a sensible objective a controller can deliver. In order to build a theoretical framework starting from surviving all disturbances but taking the appropriate opportunity to reject them, a sufficient condition on surviving disturbances is first established by exploring the relation among steady sets of state, input, and disturbance, followed by an output reachability condition on rejecting disturbances. A new robust safety-critical model prediction control (MPC) framework is then developed by embedding the quartet of pseudo steady input, output, state, and disturbance (IOSD) into the optimisation. Unlike most existing tracking MPC setups, a new and unique formulation is adopted by taking the pseudo steady disturbance as an optimisation decision variable, rather than directly driven by the disturbance estimate. This new setup is able to decouple estimation error dynamics, significantly contributing to the guarantee of recursive feasibility, even if the disturbance or its estimate changes rapidly. Moreover, towards optimal coexistence with disturbances, offset-free tracking of a compromised reference can be achieved, if rejecting the disturbance conflicts with safety-critical specifications. Finally, the benefits of the proposed method have been demonstrated by both numerical simulations and experiments on aerial physical interaction

Novel superconducting structures of BH2 under high pressure

The crystal structures of boron hydrides in a pressure range of 50–400 GPa were studied using the genetic algorithm (GA) method combined with first-principles density functional theory calculations. BH4 and BH5 are predicted to be thermodynamically unstable. Two new BH2 structures with Cmcm and C2/c space group symmetries, respectively, were predicted, in which the B atoms tend to form two-dimensional sheets. The calculated band structures showed that in the pressure range of 50–150 GPa, the Cmcm-BH2 phase has very small gaps, while the C2/c-BH2 phase at 200–400 GPa is metallic. The superconductivity of the C2/c-BH2 structure was also investigated, and electron–phonon coupling calculations revealed that the estimated Tc values of C2/c-BH2 are about 28.18–37.31 K at 250 GPa

Characterization of the early fiber development gene, Ligon-lintless 1 (Li1), using microarray

AbstractCotton fiber length is a key factor in determining fiber quality in the textile industry throughout the world. Understanding the molecular basis of fiber elongation would allow for improvement of fiber length. Ligon-lintless 1 (Li1) is a monogenic dominant mutation that results in short fibers. This mutant provides an excellent model system to study the molecular mechanisms of cotton fiber elongation. Microarray technology and quantitative real time PCR (qRT-PCR) were used to evaluate differentially expressed genes (DEGs) in the Ligon-lintless 1 (Li1) mutant compared to the wild-type. Although the results showed only a few differentially expressed genes at −1, 3 and 7days post anthesis (DPA); at 5 DPA, there were 1915 DEGs, including 984 up-regulated genes and 931 down-regulated genes. The critical stage for early termination of Li1 fiber elongation was 5 DPA, as there were the most differentially expressed genes in this sample. The transcription factors and other proteins identified might contribute to understanding the molecular basis of early fiber elongation. Gene ontology analysis identified some key GO terms that impact the regulation of fiber development during early elongation. These results provide some fundamental information about the TFs that might provide new insight into understanding the molecular mechanisms governing cotton fiber development

Brominated Selinane Sesquiterpenes from the Marine Brown Alga Dictyopteris divaricata

Two new brominated selinane sesquiterpenes, 1-bromoselin-4(14),11-diene (1) and 9-bromoselin-4(14),11-diene (2), one known cadinane sesquiterpene, cadalene (3), and four known selinane sesquiterpenes, α-selinene (4), β-selinene (5), β-dictyopterol (6), and cyperusol C (7), were isolated from a sample of marine brown alga Dictyopteris divaricata collected off the coast of Yantai (China). Their structures were established by detailed MS and NMR spectroscopic analysis, as well as comparison with literature data