On the development of a Bayesian optimisation framework for complex unknown systems

Bayesian optimisation provides an effective method to optimise expensive black box functions. It has recently been applied to problems in fluid dynamics. This paper studies and compares common Bayesian optimisation algorithms empirically on a range of synthetic test functions. It investigates the choice of acquisition function and number of training samples, exact calculation of acquisition functions and Monte Carlo based approaches and both single-point and multi-point optimisation. The test functions considered cover a wide selection of challenges and therefore serve as an ideal test bed to understand the performance of Bayesian optimisation and to identify general situations where Bayesian optimisation performs well and poorly. This knowledge can be utilised in applications, including those in fluid dynamics, where objective functions are unknown. The results of this investigation show that the choices to be made are less relevant for relatively simple functions, while optimistic acquisition functions such as Upper Confidence Bound should be preferred for more complex objective functions. Furthermore, results from the Monte Carlo approach are comparable to results from analytical acquisition functions. In instances where the objective function allows parallel evaluations, the multi-point approach offers a quicker alternative, yet it may potentially require more objective function evaluations.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Optimisation and analysis of streamwise-varying wall-normal blowing in a turbulent boundary layer

Skin-friction drag is a major engineering concern, with wide-ranging consequences across many industries. Active flow-control techniques targeted at minimising skin friction have the potential to significantly enhance aerodynamic efficiency, reduce operating costs, and assist in meeting emission targets. However, they are difficult to design and optimise. Furthermore, any performance benefits must be balanced against the input power required to drive the control. Bayesian optimisation is a technique that is ideally suited to problems with a moderate number of input dimensions and where the objective function is expensive to evaluate, such as with high-fidelity computational fluid dynamics simulations. In light of this, this work investigates the potential of low-intensity wall-normal blowing as a skin-friction drag reduction strategy for turbulent boundary layers by combining a high-order flow solver (Incompact3d) with a Bayesian optimisation framework. The optimisation campaign focuses on streamwise-varying wall-normal blowing, parameterised by a cubic spline. The inputs to be optimised are the amplitudes of the spline control points, whereas the objective function is the net-energy saving (NES), which accounts for both the skin-friction drag reduction and the input power required to drive the control (with the input power estimated from real-world data). The results of the optimisation campaign are mixed, with significant drag reduction reported but no improvement over the canonical case in terms of NES. Selected cases are chosen for further analysis and the drag reduction mechanisms and flow physics are highlighted. The results demonstrate that low-intensity wall-normal blowing is an effective strategy for skin-friction drag reduction and that Bayesian optimisation is an effective tool for optimising such strategies. Furthermore, the results show that even a minor improvement in the blowing efficiency of the device used in the present work will lead to meaningful NES

A collaborative approach to socio-economic assessment to increase coastal marsh and community resilience on the Chesapeake Bay

Sea level rise and other stressors in the mid-Atlantic U.S. are impacting the resilience of coastal communities, and increase their overall physical and socio-economic vulnerabilities. The Deal Island Peninsula on the Eastern Shore of the Chesapeake Bay, MD is used as a case study of a coastal heritage community that is undergoing these stressors and is involved in stakeholder-driven resilience and adaptation planning. In this interdisciplinary socio-ecological project funded by the NERRS Science Collaborative, a socio-economic analysis of a culturally rich coastal community is performed as a sub-study. The goals of the socio-economic analysis are to 1) better understand stakeholder relationships with marsh ecosystems and services they provide, 2) bring stakeholder perceptions and values of socio-ecological services into a coastal decision-making framework, and 3) bridge the gap between science and decision-making through improved communication and collaboration. The methodologies employed take the nature of a collaborative learning approach, coupled with the Q-sort technique. In this presentation, discussion topics include the collaborative approach taken toward a socio-economic assessment, preliminary results of the Q-sort, and indicators of community adaptation efforts

Language effects on bargaining

Language is critical to coordination in groups. Though, how language affects coordination in groups is not well understood. We prime distributive and integrative language in a bargaining experiment to better understand the links between group outcomes and communication. We accomplish this by priming interests or positions language in randomized groups. We find that priming positions as opposed to interests language leads to agreements where controllers, subjects with unilateral authority over the group outcome, receive a larger share of the benefits but where the total benefits to the group are unaffected. In contrast to common justifications for the use of integrative language in bargaining, our experimental approach revealed no significant differences between priming interests and positions language in regards to increasing joint outcomes for the groups. Across treatments, we find subjects that use gain frames and make reference to visuals aids during bargaining experience larger gains for the group, while loss frames and pro-self language experience larger gains for the individual through side payments. This finding suggests a bargainer’s dilemma: whether to employ language that claims a larger share of group’s assets or employ language to increase joint gains

橘の花散る里のほととぎす--「万葉集」巻8,1472,1473番歌をめぐって

R-symmetry leads to a distinct low energy realisation of SUSY with a signicantly modified colour-charged sector featuring a Dirac gluino and scalar colour octets (sgluons). In the present work we recast results from LHC BSM searches to discuss the impact of R-symmetry on the squark and gluino mass limits. We work in the framework of the Minimal R-symmetric Supersymmetric Standard Model and take into account the NLO corrections to the squark production cross sections in the MRSSM that have become available recently. We find substantially weaker limits on squark masses compared to the MSSM: for simple scenarios with heavy gluinos and degenerate squarks, the MRSSM mass limit is ${m}_{\tilde{g}}$ > 1:7TeV, approximately 600 GeV lower than in the MSSM

Doubling up on supersymmetry in the Higgs sector

We explore the possibility that physics at the TeV scale possesses approximate N =2 supersymmetry, which is reduced to the N =1 minimal supersymmetric extension of the Standard Model (MSSM) at the electroweak scale. This doubling of supersymmetry modifies the Higgs sector of the theory, with consequences for the masses, mixings and couplings of the MSSM Higgs bosons, whose phenomenological consequences we explore in this paper. The mass of the lightest neutral Higgs boson h is independent of tan β at the tree level, and the decoupling limit is realized whatever the values of the heavy Higgs boson masses. Radiative corrections to the top quark and stop squarks dominate over those due to particles in N = 2 gauge multiplets. We assume that these radiative corrections fix mh ≃ 125 GeV, whatever the masses of the other neutral Higgs bosons H, A, a scenario that we term the h2MSSM. Since the H, A bosons decouple from the W and Z bosons in the h2MSSM at tree level, only the LHC constraints on H, A and H± couplings to fermions are applicable. These and the indirect constraints from LHC measurements of h couplings are consistent with mA ≳ 200 GeV for tan β ∈ (2, 8) in the h2MSSM

Tumor-derived GDF-15 blocks LFA-1 dependent T cell recruitment and suppresses responses to anti-PD-1 treatment

Immune checkpoint blockade therapy is beneficial and even curative for some cancer patients. However, the majority don’t respond to immune therapy. Across different tumor types, pre-existing T cell infiltrates predict response to checkpoint-based immunotherapy. Based on in vitro pharmacological studies, mouse models and analyses of human melanoma patients, we show that the cytokine GDF-15 impairs LFA-1/β2-integrin-mediated adhesion of T cells to activated endothelial cells, which is a pre-requisite of T cell extravasation. In melanoma patients, GDF-15 serum levels strongly correlate with failure of PD-1-based immune checkpoint blockade therapy. Neutralization of GDF-15 improves both T cell trafficking and therapy efficiency in murine tumor models. Thus GDF-15, beside its known role in cancer-related anorexia and cachexia, emerges as a regulator of T cell extravasation into the tumor microenvironment, which provides an even stronger rationale for therapeutic anti-GDF-15 antibody development. Experimental cancer immunology and therap

Interleukin-6 trans-signaling is a candidate mechanism to drive progression of human DCCs during clinical latency

Although thousands of breast cancer cells disseminate and home to bone marrow until primary surgery, usually less than a handful will succeed in establishing manifest metastases months to years later. To identify signals that support survival or outgrowth in patients, we profile rare bone marrow-derived disseminated cancer cells (DCCs) long before manifestation of metastasis and identify IL6/PI3K-signaling as candidate pathway for DCC activation. Surprisingly, and similar to mammary epithelial cells, DCCs lack membranous IL6 receptor expression and mechanistic dissection reveals IL6 trans-signaling to regulate a stem-like state of mammary epithelial cells via gp130. Responsiveness to IL6 trans-signals is found to be niche-dependent as bone marrow stromal and endosteal cells down-regulate gp130 in premalignant mammary epithelial cells as opposed to vascular niche cells. PIK3CA activation renders cells independent from IL6 trans-signaling. Consistent with a bottleneck function of microenvironmental DCC control, we find PIK3CA mutations highly associated with late-stage metastatic cells while being extremely rare in early DCCs. Our data suggest that the initial steps of metastasis formation are often not cancer cell-autonomous, but also depend on microenvironmental signals. Metastatic dissemination in breast cancer patients occurs early in malignant transformation, raising questions about how disseminated cancer cells (DCC) progress at distant sites. Here, the authors show that DCCs in bone marrow are activated via IL6-trans-signaling and thereby acquire stemness traits relevant for metastasis formation