Surrogate models have been widely used for solving computationally expensive multi-objective optimization problems (MOPs). The efficient global optimization (EGO) algorithm, a Bayesian approach to ...surrogate-assisted optimization, has become very popular in surrogate-assisted evolutionary optimization. In this paper, we propose an adaptive Bayesian approach to surrogate-assisted evolutionary algorithm to solve expensive MOPs. The main idea is to tune the hyperparameter in the acquisition function according to the search dynamics to determine which candidate solutions are to be evaluated using the expensive real objective functions. In addition, the sampling selection criterion switches between an angle based distance and an angle-penalized distance over the course of optimization to achieve a better balance between exploration and exploitation. The performance of the proposed algorithm is examined on a set of benchmark problems and an airfoil design optimization problem using a maximum of 300 real fitness evaluations. Our experimental results show that the proposed algorithm is competitive compared to four popular multi-objective evolutionary algorithms.
Are starburst galaxies proton calorimeters? Wang (王夕露), Xilu; Fields, Brian D
Monthly Notices of the Royal Astronomical Society,
03/2018, Volume:
474, Issue:
3
Journal Article
Peer reviewed
Open access
AbstractSeveral starburst galaxies have been observed in the GeV and TeV bands. In these dense environments, gamma-ray emission should be dominated by cosmic ray (CR) interactions with the ...interstellar medium (pcr pism rarr π0 rarr γγ). Indeed, starbursts may act as proton 'calorimeters' where a substantial fraction of CR energy input is emitted in gamma-rays. Here, we build a one-zone, 'thick-target' model implementing calorimetry and placing a firm upper bound on gamma-ray emission from CR interactions. The model assumes that CRs are accelerated by supernovae (SNe), and all suffer nuclear interactions rather than escape. Our model has only two free parameters: the CR proton acceleration energy per SN εcr , and the proton injection spectral index s. We calculate the pionic gamma-ray emission from 10 MeV to 10 TeV, and derive thick-target parameters for six galaxies with Fermi, H.E.S.S., and/or VERITAS data. Our model provides good fits for the M82 and NGC 253, and yields εcr and s values suggesting that SN CR acceleration is similar in starbursts and in our Galaxy. We find that these starbursts are indeed nearly if not fully proton calorimeters. For NGC 4945 and NGC 1068, the models are consistent with calorimetry but are less well-constrained due to the lack of TeV data. However, the Circinus galaxy and the ultra-luminous infrared galaxy Arp 220 exceed our pionic upper-limit; possible explanations are discussed.
Administration of cisplatin, a common chemotherapeutic drug, has an inevitable side effect of sensorineural hearing loss. The main etiologies are stria vascularis injury, spiral ganglion ...degeneration, and hair cell death. Over several decades, the research scope of cisplatin-induced ototoxicity has expanded with the discovery of the molecular mechanism mediating inner ear cell death, highlighting the roles of reactive oxygen species and transport channels for cisplatin uptake into inner ear cells. Upon entering hair cells, cisplatin disrupts organelle metabolism, induces oxidative stress, and targets DNA to cause intracellular damage. Recent studies have also reported the role of inflammation in cisplatin-induced ototoxicity. In this article, we preform a narrative review of the latest reported molecular mechanisms of cisplatin-induced ototoxicity, from extracellular to intracellular. We build up a signaling network starting with cisplatin entering into the inner ear through the blood labyrinth barrier, disrupting cochlear endolymph homeostasis, and activating inflammatory responses of the outer hair cells. After entering the hair cells, cisplatin causes hair cell death via DNA damage, redox system imbalance, and mitochondrial and endoplasmic reticulum dysfunction, culminating in programmed cell death including apoptosis, necroptosis, autophagic death, pyroptosis, and ferroptosis. Based on the mentioned mechanisms, prominent therapeutic targets, such as channel-blocking drugs of cisplatin transporter, construction of cisplatin structural analogues, anti-inflammatory drugs, antioxidants, cell death inhibitors, and others, were collated. Considering the recent research efforts, we have analyzed the feasibility of the aforementioned therapeutic strategies and proposed our otoprotective approaches to overcome cisplatin-induced ototoxicity.
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•We provide an overview of the signaling network of cisplatin-induced ototoxicity,from extracellular to intracellular.•Cisplatin disrupts cochlear endolymph homeostasis, and activates inflammatory responses after entering into the inner ear.•Intracellularly, cisplatin causes DNA damage, redox system imbalance, and organelle dysfunction, culminating in PCD.•Potentrial therapeutic targets for treating CIO and their future feasibilities were discussed.•Contains figures summarizing various mechanisms of CIO and a table summarizing potential therapeutic for treating CIO.
Abstract
Long-lived massive magnetars are expected to be remnants of some binary neutron star (BNS) mergers. In this paper, we argue that the magnetic powered flaring activities of these merged ...magnetars would occur dominantly in their early millisecond-period-spin phase, which is in the timescale of days. Such flares endure significant absorption by the ejecta from the BNS collision, and their detectable energy range is from 0.1 to 10 MeV, in a time lag of approximately days after the merger events indicated by the gravitational wave chirps. We estimate the rate of such flares in different energy ranges, and find that there could have been 0.1–10 cases detected by Fermi/GBM. A careful search for ∼10 ms spin-period modulation in weak short gamma-ray bursts (GRBs) may identify them from the archival data. The next-generation megaelectronvolt detectors could detect them at a mildly higher rate. The recent report on the Quasi-Period-Oscillation found in two BASTE GRBs should not be considered as cases of such flares, for they were detected in a lower energy range and with a much shorter period spin modulation.
Neutron star mergers (NSMs) are the first verified sites of rapid neutron capture (r-process) nucleosynthesis, and could emit gamma rays from the radioactive isotopes synthesized in the neutron-rich ...ejecta. These MeV gamma rays may provide a unique and direct probe of the NSM environment as well as insight into the nature of the r process, just as observed gammas from the 56Ni radioactive decay chain provide a window into supernova nucleosynthesis. In this work, we include the photons from fission processes for the first time in estimates of the MeV gamma-ray signal expected from an NSM event. We consider NSM ejecta compositions with a range of neutron richness and find a dramatic difference in the predicted signal depending on whether or not fissioning nuclei are produced. The difference is most striking at photon energies above ∼3.5 MeV and at a relatively late time, several days after the merger event, when the ejecta is optically thin. We estimate that a Galactic NSM could be detectable by a next generation gamma-ray detector such as AMEGO in the MeV range, up to ∼104 days after the merger, if fissioning nuclei are robustly produced in the event.
Abstract
The astrophysical sites where
r
-process elements are synthesized remain mysterious: it is clear that neutron star mergers (kilonovae (KNe)) contribute, and some classes of core-collapse ...supernovae (SNe) are also possible sources of at least the lighter
r
-process species. The discovery of
60
Fe on the Earth and Moon implies that one or more astrophysical explosions have occurred near the Earth within the last few million years, probably SNe. Intriguingly,
244
Pu has now been detected, mostly overlapping with
60
Fe pulses. However, the
244
Pu flux may extend to before 12 Myr ago, pointing to a different origin. Motivated by these observations and difficulties for
r
-process nucleosynthesis in SN models, we propose that ejecta from a KN enriched the giant molecular cloud that gave rise to the Local Bubble, where the Sun resides. Accelerator mass spectrometry (AMS) measurements of
244
Pu and searches for other live isotopes could probe the origins of the
r
-process and the history of the solar neighborhood, including triggers for mass extinctions, e.g., that at the end of the Devonian epoch, motivating the calculations of the abundances of live
r
-process radioisotopes produced in SNe and KNe that we present here. Given the presence of
244
Pu, other
r
-process species such as
93
Zr,
107
Pd,
129
I,
135
Cs,
182
Hf,
236
U,
237
Np, and
247
Cm should be present. Their abundances and well-resolved time histories could distinguish between the SN and KN scenarios, and we discuss prospects for their detection in deep-ocean deposits and the lunar regolith. We show that AMS
129
I measurements in Fe–Mn crusts already constrain a possible nearby KN scenario.
Gaussian processes (GPs) are widely used in surrogate-assisted evolutionary optimization of expensive problems mainly due to the ability to provide a confidence level of their outputs, making it ...possible to adopt principled surrogate management methods, such as the acquisition function used in the Bayesian optimization. Unfortunately, GPs become less practical for high-dimensional multiobjective and many-objective optimization as their computational complexity is cubic in the number of training samples. In this article, we propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multiobjective and many-objective expensive optimization problems. While the conventional dropout neural network needs to save a large number of network models during the training for calculating the confidence level, only one single network model is needed in the EDN to estimate the fitness and its confidence level by randomly ignoring neurons in both training and testing the neural network. Extensive experimental studies on benchmark problems with up to 100 decision variables and 20 objectives demonstrate that, compared to state of the art, the proposed algorithm is not only highly competitive in performance but also computationally more scalable to high-dimensional many-objective optimization problems. Finally, the proposed algorithm is validated on an operational optimization problem of crude oil distillation units, further confirming its capability of handling expensive problems given a limited computational budget.
Particle filters, also known as sequential Monte Carlo (SMC) methods, constitute a class of importance sampling and resampling techniques designed to use simulations to perform on-line filtering. ...Recently, particle filters have been extended for optimization by utilizing the ability to track a sequence of distributions. In this work, we incorporate transfer learning capabilities into the optimizer by using particle filters. To achieve this, we propose a novel particle-filter-based multi-objective optimization algorithm (PF-MOA) by transferring knowledge acquired from the search experience. The key insight adopted here is that, if we can construct a sequence of target distributions that can balance the multiple objectives and make the degree of the balance controllable, we can approximate the Pareto optimal solutions by simulating each target distribution via particle filters. As the importance weight updating step takes the previous target distribution as the proposal distribution and takes the current target distribution as the target distribution, the knowledge acquired from the previous run can be utilized in the current run by carefully designing the set of target distributions. The experimental results on the DTLZ and WFG test suites show that the proposed PF-MOA achieves competitive performance compared with state-of-the-art multi-objective evolutionary algorithms on most test instances.
Abstract
A Milky Way Type Ia supernova (SNIa) could be unidentified or even initially unnoticed, being dim in radio, X-rays, and neutrinos, and suffering large optical/IR extinction in the Galactic ...plane. But SNIa emit nuclear gamma-ray lines from 56Ni → 56Co → 56Fe radioactive decays. These lines fall within the Fermi/GBM energy range, and the 56Ni 158 keV line is detectable by Swift/BAT. Both instruments frequently monitor the Galactic plane, which is transparent to gamma rays. Thus GBM and BAT are ideal Galactic SNIa early warning systems. We simulate SNIa MeV light curves and spectra to show that GBM and BAT could confirm a Galactic SNIa explosion, followed by Swift localization and observation in X-rays and UVOIR band. The time of detection depends sensitively on the 56Ni distribution, and can be as early as a few days if ${\gtrsim } 10{{\ \rm per\ cent}}$ of the 56Ni is present in the surface as suggested by SN2014J gamma data.
In many real-world applications of interest, several related optimization tasks can be encountered, where each task is associated with a specific context or personalized information. Moreover, the ...amount of available data for each task may be highly limited due to the expensive cost involved. Although Bayesian optimization (BO) has emerged as a promising paradigm for handling black-box optimization problems, addressing such a sequence of optimization tasks can be intractable due to the cold start issues in BO. The key challenge is to speed up the optimization by leveraging the transferable information, while taking the personalization into consideration. In this paper, optimization problems with personalized variables are formally defined at first. Subsequently, a personalized evolutionary Bayesian algorithm is proposed to consider the personalized information and the measurement noise. Specifically, a contextual Gaussian process is used to jointly learn a surrogate model in different contexts with regard to the varying personalized parameter, and an evolutionary algorithm is tailored for optimizing an acquisition function for handling the presence of personalized information. Finally, we demonstrate the effectiveness of the proposed algorithm by testing it on widely used single- and multi-objective benchmark problems with personalized variables.
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