For certain industrial control applications an explicit function capturing the non-trivial trade-off between competing objectives in closed loop performance is not available. In such scenarios it is common practice to use the human innate ability to implicitly learn such a relationship and manually tune the corresponding controller to achieve the desirable closed loop performance. This approach has its deficiencies because of individual variations due to experience levels and preferences in the absence of an explicit calibration metric. Moreover, as the complexity of the underlying system and/or the controller increase, in the effort to achieve better performance, so does the tuning time and the associated tuning cost. To reduce the overall tuning cost, a tuning framework is proposed herein, whereby a supervised machine learning is used to extract the human-learned cost function and an optimisation algorithm that can efficiently deal with a large number of variables, is used for optimising the extracted cost function. Given the interest in the implementation across many industrial domains and the associated high degree of freedom present in the corresponding tuning process, a Model Predictive Controller applied to air path control in a diesel engine is tuned for the purpose of demonstrating the potential of the framework.

Abstract Let R be an S-graded ring inducing S, that is, a ring which is the direct sum of a family of its additive subgroups indexed by a nonempty set S, under the assumption that the product of homogeneous elements is again homogeneous. We introduce a graded version of the subring and discuss its homogeneity, where U(R) denotes the group of units of R. Communicated by Pavel Kolesnikov

Abstract We study the graded isoradical of a ring graded by a group. In particular, we compare the graded isoradical and the classical isoradical of a graded ring, examine the question of how the (graded) isoradical of a graded ring depends on the classical isoradical of a ring which corresponds to the identity element of the grading group, and we also give some sufficient conditions under which the classical isoradical of a graded ring is homogeneous.

Principled computational approaches for tumor phylogeny reconstruction via single-cell sequencing typically aim to build the most likely perfect phylogeny tree from the noisy genotype matrix - which represents genotype calls of single-cells. This problem is NP-hard, and as a result, existing approaches aim to solve relatively small instances of it through combinatorial optimization techniques or Bayesian inference. As expected, even when the goal is to infer basic topological features of the tumor phylogeny - rather than reconstructing the topology entirely, these approaches could be prohibitively slow. In this paper, we introduce fast deep-learning solutions to the problems of inferring whether the most likely tree has a linear (chain) or branching topology and whether a perfect phylogeny is feasible from a given genotype matrix. We also present a reinforcement learning approach for reconstructing the most likely tumor phylogeny. This preliminary work demonstrates that data-driven approaches can reconstruct key features of tumor evolution.

Motivation Recent advances in single cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program (ILP), or a constraint satisfaction program (CSP), which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain (MCMC) or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology. Results We introduce PhISCS-BnB, a Branch and Bound algorithm to compute the most likely perfect phylogeny (PP) on an input genotype matrix extracted from a SCS data set. PhISCS-BnB not only offers an optimality guarantee, but is also 10 to 100 times faster than the best available methods on simulated tumor SCS data. We also applied PhISCS-BnB on a large melanoma data set derived from the sub-lineages of a cell line involving 24 clones with 3574 mutations, which returned the optimal tumor phylogeny in less than 2 hours. The resulting phylogeny also agrees with bulk exome sequencing data obtained from in vivo tumors growing out from the same cell line. Availability https://github.com/algo-cancer/PhISCS-BnB

Abstract We present a small collection of examples and counterexamples for selected problems, mostly in spectral graph theory, that have occupied our minds over a number of years without being completely resolved.

In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can be computationally expensive or even intractable if the observations are high-dimensional (e.g. images). For this reason, previous works have considered partial models, which model only part of the observation. In this paper, we show that partial models can be causally incorrect: they are confounded by the observations they don't model, and can therefore lead to incorrect planning. To address this, we introduce a general family of partial models that are provably causally correct, yet remain fast because they do not need to fully model future observations.

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity. The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3–5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes. Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

Contemporary living is marked by powerful presence and all present use of new technologies. We might boldly state that people might not function well without new media. We heedlessly witness large part of contemporary adolescent’s social and emotional development occurring while on the Internet and on cell phones. Many parents and caregivers today use technology incredibly well and feel comfortable and capable with the programs and online venues that their children and adolescents are using. Nevertheless, some parents and adults are concerned about adolescent’s overuse of new media due to their potential risks and negative impact on adolescent’s psycho-social development. Some parents and caregivers may find it difficult to relate to their digitally savvy youngsters online for valid reasons. Such people may lack some basic understanding of adolescents and the new forms of socialization which is happening online, which are integral to their children's lives. Adolescent’s limited capacity for self-regulation and susceptibility to peer pressure make youth particularly vulnerable and at risk for various risks as they navigate and experiment with social media. Primary aim of this paper is to shed some light on adolescent’s online behavior and choices given their physical, cognitive, emotional, social, and behavioral characteristics and discuss potential negative and positive impact of new media on youth, family and social participation.

The conditions that led to the formation of the first organisms and the ways that life originates from a lifeless chemical soup are poorly understood. The recent hypothesis of “RNA-peptide coevolution” suggests that the current close relationship between amino acids and nucleobases may well have extended to the origin of life. We now show how the interplay between these compound classes can give rise to new self-replicating molecules using a dynamic combinatorial approach. We report two strategies for the fabrication of chimeric amino acid/nucleobase self-replicating macrocycles capable of exponential growth. The first one relies on mixing nucleobase- and peptide-based building blocks, where the ligation of these two gives rise to highly specific chimeric ring structures. The second one starts from peptide nucleic acid (PNA) building blocks in which nucleobases are already linked to amino acids from the start. While previously reported nucleic acid-based self-replicating systems rely on presynthesis of (short) oligonucleotide sequences, self-replication in the present systems start from units containing only a single nucleobase. Self-replication is accompanied by self-assembly, spontaneously giving rise to an ordered one-dimensional arrangement of nucleobase nanostructures.