Nonlinear Saturation of g-modes in Proto-Neutron Stars: Quieting the Acoustic Engine

According to Burrows et al.'s acoustic mechanism for core-collapse supernova explosions, the primary, l=1, g-mode in the core of the proto-neutron star is excited to an energy of ~ 10^{50} ergs and damps by the emission of sound waves. Here we calculate the damping of the primary mode by the parametric instability, i.e., by nonlinear, 3-mode coupling between the low-order primary mode and pairs of high-order g-modes. We show that the primary mode is strongly coupled to highly resonant, neutrino damped pairs with n>10; such short wavelength interactions cannot be resolved in the simulations. We find that the parametric instability saturates the primary mode energy at ~10^{48} ergs, well below the energy needed to drive an explosion. We therefore conclude that acoustic power is unlikely to be energetically significant in core-collapse supernova explosions.Comment: 6 pages, 3 figures, fixed minor typos, matches version published in MNRAS Letter

Techniques for applying reinforcement learning to routing and wavelength assignment problems in optical fiber communication networks

We propose a novel application of reinforcement learning (RL) with invalid action masking and a novel training methodology for routing and wavelength assignment (RWA) in fixed-grid optical networks and demonstrate the generalizability of the learned policy to a realistic traffic matrix unseen during training. Through the introduction of invalid action masking and a new training method, the applicability of RL to RWA in fixed-grid networks is extended from considering connection requests between nodes to servicing demands of a given bit rate, such that lightpaths can be used to service multiple demands subject to capacity constraints. We outline the additional challenges involved for this RWA problem, for which we found that standard RL had low performance compared to that of baseline heuristics, in comparison with the connection requests RWA problem considered in the literature. Thus, we propose invalid action masking and a novel training method to improve the efficacy of the RL agent. With invalid action masking, domain knowledge is embedded in the RL model to constrain the action space of the RL agent to lightpaths that can support the current request, reducing the size of the action space and thus increasing the efficacy of the agent. In the proposed training method, the RL model is trained on a simplified version of the problem and evaluated on the target RWA problem, increasing the efficacy of the agent compared with training directly on the target problem. RL with invalid action masking and this training method outperforms standard RL and three state-of-the-art heuristics, namely, k shortest path first fit, first-fit k shortest path, and k shortest path most utilized, consistently across uniform and nonuniform traffic in terms of the number of accepted transmission requests for two real-world core topologies, NSFNET and COST - 239. The RWA runtime of the proposed RL model is comparable to that of these heuristic approaches, demonstrating the potential for real-world applicability. Moreover, we show that the RL agent trained on uniform traffic is able to generalize well to a realistic nonuniform traffic distribution not seen during training, thus outperforming the heuristics for this traffic. Visualization of the learned RWA policy reveals an RWA strategy that differs significantly from those of the heuristic baselines in terms of the distribution of services across channels and the distribution across links

Machine learning for optical fiber communication systems: An introduction and overview

Optical networks generate a vast amount of diagnostic, control and performance monitoring data. When information is extracted from this data, reconfigurable network elements and reconfigurable transceivers allow the network to adapt both to changes in the physical infrastructure but also changing traffic conditions. Machine learning is emerging as a disruptive technology for extracting useful information from this raw data to enable enhanced planning, monitoring and dynamic control. We provide a survey of the recent literature and highlight numerous promising avenues for machine learning applied to optical networks, including explainable machine learning, digital twins and approaches in which we embed our knowledge into the machine learning such as physics-informed machine learning for the physical layer and graph-based machine learning for the networking layer

Radiotherapy Optimal Design: An Academic Radiotherapy Treatment Design System

Optimally designing radiotherapy and radiosurgery treatments to increase the likelihood of a successful recovery from cancer is an important application of operations research. Researchers have been hindered by the lack of academic software that supports head-to-head comparisons of different techniques, and this article addresses the inherent difficulties of designing and implementing an academic treatment planning system. In particular, this article details the algorithms and the software design of Radiotherapy optimAl Design (RAD)

The Full Kepler Phase Curve of the Eclipsing Hot White Dwarf Binary System KOI-964

We analyze the full Kepler phase curve of KOI-964, a binary system consisting of a hot white dwarf on an eclipsing orbit around an A-type host star. Using all 18 quarters of long-cadence photometry, we carry out a joint light-curve fit and obtain improved phase-curve amplitudes, occultation depths, orbital parameters, and transit ephemeris over the previous results of Carter et al. A periodogram of the residuals from the phase-curve fit reveals an additional stellar variability signal from the host star with a characteristic period of 0.620276 ± 0.000011 days and a full amplitude of 24 ± 2 ppm. We also present new Keck/HIRES radial velocity observations, which we use to measure the orbit and obtain a mass ratio of q = 0.106 ± 0.012. Combining this measurement with the results of a stellar isochrone analysis, we find that the masses of the host star and white dwarf companion are 2.23 ± 0.12 M⊙ and 0.236^(+0.028)_(−0.027) M⊙, respectively. The effective temperatures of the two components are 9940^(+260)_(−230) K and 15,080 ± 400 K, respectively, and we determine the age of the system to be 0.21^(+0.11)_(−0.08) Gyr. We use the measured system properties to compute predicted phase-curve amplitudes and find that while the measured Doppler-boosting and mutual illumination components agree well with theory, the ellipsoidal distortion amplitude is significantly underestimated. We detail possible explanations for this discrepancy, including interactions between the dynamical tide of the host star and the tidal bulge and possible nonsynchronous rotation of the host star

Quantized Vortex Rings and Loop Solitons

From Springer Nature via Jisc Publications RouterHistory: received 2019-07-15, registration 2020-08-08, accepted 2020-08-08, pub-electronic 2020-08-29, online 2020-08-29, pub-print 2020-10Publication status: PublishedFunder: University of ManchesterFunder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/PO25625/1Abstract: The vortex filament model is used to investigate the interaction of a quantized vortex ring with a straight vortex line and also the interaction of two solitons traveling in opposite directions along a vortex. When a ring reconnects with a line, we find that a likely outcome is the formation of a loop soliton. When they collide, loop solitons reconnect as they overlap each other producing either one or two vortex rings. These simulations are relevant for experiments on quantum turbulence in the zero temperature limit where small vortex rings are expected to be numerous. It seems that loop solitons might also commonly occur on vortex lines as they act as transient states between the absorption of a vortex ring before another ring is emitted when the soliton is involved in a reconnection

Understanding the town centre customer experience (TCCE)

This research enhances the understanding of consumer behaviour and customer experience in the context of town centres. First, it defines town centre customer experience (TCCE) as a multifaceted journey that combines interactions with a diverse range of public and private organisations, including retailers and social and community elements; this results in a unique experience co-created with the consumer across a series of functional and experiential touchpoints. Second, combining qualitative and quantitative insights, this research reveals a series of specific functional and experiential TCCE touchpoints, which underpin the consumer internal response (motivation to visit) and outward behaviour (desire to stay and revisit intentions) in the town centre. In addition to enhancing town centre and customer experience knowledge, these findings offer important new insights to those managing town centres and seeking to retain customer loyalty in the high street. Above all, these findings can help identify the touchpoints that need to be reinforced and/or improved to differentiate a town from its competing centres and to create tailored marketing strategies. Taken together, such initiatives have the potential to positively impact the revitalisation of the high street and the town centre economy