Experimental entanglement of temporal order

The study of causal relations has recently been applied to the quantum realm, leading to the discovery that not all physical processes have a definite causal structure. While indefinite causal processes have previously been experimentally shown, these proofs relied on the quantum description of the experiments. Yet, the same experimental data could also be compatible with definite causal structures within different descriptions. Here, we present the first demonstration of indefinite temporal order outside of quantum formalism. We show that our experimental outcomes are incompatible with a class of generalised probabilistic theories satisfying the assumptions of locality and definite temporal order. To this end, we derive physical constraints (in the form of a Bell-like inequality) on experimental outcomes within such a class of theories. We then experimentally invalidate these theories by violating the inequality using entangled temporal order. This provides experimental evidence that there exist correlations in nature which are incompatible with the assumptions of locality and definite temporal order.

Inverting the structure–property map of truss metamaterials by deep learning

Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties.

Einstein-Roscoe Regression for the Slag Viscosity Prediction Problem in Steelmaking

In classical machine learning, regressors are trained without attempting to gain insight into the mechanism connecting inputs and outputs. Natural sciences, however, are interested in finding a robust interpretable function for the target phenomenon, that can return predictions even outside of the training domains. This paper focuses on viscosity prediction problem in steelmaking, and proposes Einstein-Roscoe regression (ERR), which learns the coefficients of the Einstein-Roscoe equation, and is able to extrapolate to unseen domains. Besides, it is often the case in the natural sciences that some measurements are much more expensive than the others due to physical constraints. To this end, we employ a transfer learning framework based on Gaussian process, which allows us to estimate the regression parameters using the auxiliary measurements available in a reasonable cost. In experiments using the viscosity measurements in high temperature slag system, ERR is compared favorably with various machine learning approaches in interpolation settings, while outperformed all of them in extrapolation settings. Furthermore, after estimating parameters using the auxiliary dataset obtained at room temperature, increase in accuracy is observed in the high temperature dataset, which corroborates the effectiveness of the proposed approach.

On “Savage and Deformed Slave”: Bodily Difference in Selected Contemporary Productions of The Tempest in Poland

Dis/ability is a dynamic category produced in a complex constellation of factors that includes not only stigmatised mental and physical constraints or physiological differences, but also a manifestation of incapacity that is recognised or produced by law, social norms and the very way of thinking about the nature of bodily vulnerability. The meanings of dis/ability are thus culturally and historically dependent. Therefore, the manner in which dis/ability is presented on a theatrical stage can be considered not only as an important factor influencing the interpretation of a given production but also as a test for the dominant thinking of disability at a given point of time, in a given culture. The departure point for this paper is a brief discussion of the visibility of medieval models of dis/ability in Shakespeare’s plays and a reflection on how the reception of these dramatic texts has changed over time depending on the paradigmatic shifts in thinking about dis/ability, especially with the emergence of disability studies and the growing theoretical reflection on the position of dis/ability in theatre. An especially interesting case in point is the reception of Caliban as a character whose stigmatisation can be expressed through bodily difference. Thus, the paper focuses on what seems to be a systematic aberrant decoding of The Tempest in three twenty-first century Polish productions of the play.

Optimal Geno-Fuzzy Lateral Control of Powered Parachute Flying Vehicles

In this work, we propose a systematic procedure to design a fuzzy logic controller (FLC) to control the lateral motion of powered parachute (PPC) flying vehicles. The design process does not require knowing the details of vehicle dynamics. Moreover, the physical constraints of the system, such as the maximum error of the yaw angle and the maximum allowed steering angle, are naturally included in the designed controller. The effectiveness of the proposed controller was assessed using the nonlinear six degrees of freedom (6DOF) mathematical model of the PPC. The genetic algorithm (GA) optimization technique was used to optimize the distribution of the fuzzy membership functions in order to improve the performance of the suggested controller. The robustness of the proposed controller was evaluated by changing the values of the parafoil aerodynamic coefficients and the initial flight conditions.

‘The troubles of collecting’: William Henry Harvey and the practicalities of natural-history collecting in Britain's nineteenth-century world

In recent decades, historians have become increasingly interested in the logistical challenges and difficulties encountered by those responsible for the collection, preservation and safe transport of specimens from the field to the museum or laboratory. This article builds on this trend by looking beyond apparent successes to consider the practices and practicalities of shipboard travel and maritime and coastal collecting activities. The discussion focuses on the example of William Henry Harvey, who travelled to Australia in pursuit of cryptogams – non-flowering plants like mosses, lichens and algae – in 1853. In his private correspondence to family and friends, Harvey offered insights into the challenges and obstacles faced by all collectors in the period. His experiences were fundamentally shaped by the material culture, embodied knowledge and physical constraints he encountered on the way. On one level, shipboard and onshore collecting activities were facilitated by the connections forged by new technologies and Britain's global empire. But they also depended on specific contexts and relied on local agents and actors, as well as on the physical and technical facilities (and limitations) of those doing the collecting. The examples of Harvey and others shed light on the real, ‘lived’ experiences of individual collectors, the difficulties and challenges they encountered in amassing their collections, and the networks of people on which they relied.

If time had no beginning: growth dynamics for past-infinite causal sets

We explore whether the growth dynamics paradigm of Causal Set Theory is compatible with past-infinite causal sets. We modify the Classical Sequential Growth dynamics of Rideout and Sorkin to accommodate growth "into the past" and discuss what form physical constraints such as causality could take in this new framework. We propose convex-suborders as the "observables" or "physical properties" in a theory in which causal sets can be past-infinite and use this proposal to construct a manifestly covariant framework for dynamical models of growth for past-infinite causal sets.

Triple oxygen isotope constraints on atmospheric O2 and biological productivity during the mid-Proterozoic

Reconstructing the history of biological productivity and atmospheric oxygen partial pressure (pO2) is a fundamental goal of geobiology. Recently, the mass-independent fractionation of oxygen isotopes (O-MIF) has been used as a tool for estimating pO2 and productivity during the Proterozoic. O-MIF, reported as Δ′17O, is produced during the formation of ozone and destroyed by isotopic exchange with water by biological and chemical processes. Atmospheric O-MIF can be preserved in the geologic record when pyrite (FeS2) is oxidized during weathering, and the sulfur is redeposited as sulfate. Here, sedimentary sulfates from the ∼1.4-Ga Sibley Formation are reanalyzed using a detailed one-dimensional photochemical model that includes physical constraints on air–sea gas exchange. Previous analyses of these data concluded that pO2 at that time was <1% PAL (times the present atmospheric level). Our model shows that the upper limit on pO2 is essentially unconstrained by these data. Indeed, pO2 levels below 0.8% PAL are possible only if atmospheric methane was more abundant than today (so that pCO2 could have been lower) or if the Sibley O-MIF data were diluted by reprocessing before the sulfates were deposited. Our model also shows that, contrary to previous assertions, marine productivity cannot be reliably constrained by the O-MIF data because the exchange of molecular oxygen (O2) between the atmosphere and surface ocean is controlled more by air–sea gas transfer rates than by biological productivity. Improved estimates of pCO2 and/or improved proxies for Δ′17O of atmospheric O2 would allow tighter constraints to be placed on mid-Proterozoic pO2.

Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Battery energy storage systems (BESSs) play a critical role in eliminating uncertainties associated with renewable energy generation, to maintain stability and improve flexibility of power networks. In this paper, a BESS is used to provide energy arbitrage (EA) and frequency regulation (FR) services simultaneously to maximize its total revenue within the physical constraints. The EA and FR actions are taken at different timescales. The multitimescale problem is formulated as two nested Markov decision process (MDP) submodels. The problem is a complex decision-making problem with enormous high-dimensional data and uncertainty (e.g., the price of the electricity). Therefore, a novel co-optimization scheme is proposed to handle the multitimescale problem, and also coordinate EA and FR services. A triplet deep deterministic policy gradient with exploration noise decay (TDD–ND) approach is used to obtain the optimal policy at each timescale. Simulations are conducted with real-time electricity prices and regulation signals data from the American PJM regulation market. The simulation results show that the proposed approach performs better than other studied policies in literature.