Quantum Propensity in Economics

This paper describes an approach to economics that is inspired by quantum computing, and is motivated by the need to develop a consistent quantum mathematical framework for economics. The traditional neoclassical approach assumes that rational utility-optimisers drive market prices to a stable equilibrium, subject to external perturbations or market failures. While this approach has been highly influential, it has come under increasing criticism following the financial crisis of 2007/8. The quantum approach, in contrast, is inherently probabilistic and dynamic. Decision-makers are described, not by a utility function, but by a propensity function which specifies the probability of transacting. We show how a number of cognitive phenomena such as preference reversal and the disjunction effect can be modelled by using a simple quantum circuit to generate an appropriate propensity function. Conversely, a general propensity function can be quantized, via an entropic force, to incorporate effects such as interference and entanglement that characterise human decision-making. Applications to some common problems and topics in economics and finance, including the use of quantum artificial intelligence, are discussed.

MICROSCOPE: systematic errors

The MICROSCOPE mission aims to test the Weak Equivalence Principle (WEP) in orbit with an unprecendented precision of 10-15 on the Eövös parameter thanks to electrostatic accelerometers on board a drag-free microsatellite. The precision of the test is determined by statistical errors, due to the environment and instrument noises, and by systematic errors to which this paper is devoted. Sytematic error sources can be divided into three categories: external perturbations, such as the residual atmospheric drag or the gravity gradient at the satellite altitude, perturbations linked to the satellite design, such as thermal or magnetic perturbations, and perturbations from the instrument internal sources. Each systematic error is evaluated or bounded in order to set a reliable upper bound on the WEP parameter estimation uncertainty.

Development and Evaluation of BenchBalance: A System for Benchmarking Balance Capabilities of Wearable Robots and Their Users

Recent advances in the control of overground exoskeletons are being centered on improving balance support and decreasing the reliance on crutches. However, appropriate methods to quantify the stability of these exoskeletons (and their users) are still under development. A reliable and reproducible balance assessment is critical to enrich exoskeletons’ performance and their interaction with humans. In this work, we present the BenchBalance system, which is a benchmarking solution to conduct reproducible balance assessments of exoskeletons and their users. Integrating two key elements, i.e., a hand-held perturbator and a smart garment, BenchBalance is a portable and low-cost system that provides a quantitative assessment related to the reaction and capacity of wearable exoskeletons and their users to respond to controlled external perturbations. A software interface is used to guide the experimenter throughout a predefined protocol of measurable perturbations, taking into account antero-posterior and mediolateral responses. In total, the protocol is composed of sixteen perturbation conditions, which vary in magnitude and location while still controlling their orientation. The data acquired by the interface are classified and saved for a subsequent analysis based on synthetic metrics. In this paper, we present a proof of principle of the BenchBalance system with a healthy user in two scenarios: subject not wearing and subject wearing the H2 lower-limb exoskeleton. After a brief training period, the experimenter was able to provide the manual perturbations of the protocol in a consistent and reproducible way. The balance metrics defined within the BenchBalance framework were able to detect differences in performance depending on the perturbation magnitude, location, and the presence or not of the exoskeleton. The BenchBalance system will be integrated at EUROBENCH facilities to benchmark the balance capabilities of wearable exoskeletons and their users.

Control of Thruster-Assisted, Bipedal Legged Locomotion of the Harpy Robot

Fast constraint satisfaction, frontal dynamics stabilization, and avoiding fallovers in dynamic, bipedal walkers can be pretty challenging. The challenges include underactuation, vulnerability to external perturbations, and high computational complexity that arise when accounting for the system full-dynamics and environmental interactions. In this work, we study the potential roles of thrusters in addressing some of these locomotion challenges in bipedal robotics. We will introduce a thruster-assisted bipedal robot called Harpy. We will capitalize on Harpy’s unique design to propose an optimization-free approach to satisfy gait feasibility conditions. In this thruster-assisted legged locomotion, the reference trajectories can be manipulated to fulfill constraints brought on by ground contact and those prescribed for states and inputs. Unintended changes to the trajectories, especially those optimized to produce periodic orbits, can adversely affect gait stability and hybrid invariance. We will show our approach can still guarantee stability and hybrid invariance of the gaits by employing the thrusters in Harpy. We will also show that the thrusters can be leveraged to robustify the gaits by dodging fallovers or jumping over large obstacles.

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

Understanding and predicting molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CellOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CellOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

Emergent $${{{{{{{\mathcal{PT}}}}}}}}$$-symmetry breaking of collective modes with topological critical phenomena

The spontaneous breaking of parity-time ($${{{{{{{\mathcal{PT}}}}}}}}$$ PT ) symmetry yields rich critical behavior in non-Hermitian systems, and has stimulated much interest, albeit most previous studies were performed within the single-particle or mean-field framework. Here, by studying the collective excitations of a Fermi superfluid with $${{{{{{{\mathcal{PT}}}}}}}}$$ PT -symmetric spin-orbit coupling, we uncover an emergent $${{{{{{{\mathcal{PT}}}}}}}}$$ PT -symmetry breaking in the Anderson-Bogoliubov (AB) collective modes, even as the superfluid ground state retains an unbroken $${{{{{{{\mathcal{PT}}}}}}}}$$ PT symmetry. The critical point of the transition is marked by a non-analytic kink in the speed of sound, which derives from the coalescence and annihilation of the AB mode and its hole partner, reminiscent of the particle-antiparticle annihilation. The system consequently becomes immune to low-frequency external perturbations at the critical point, a phenomenon associated with the spectral topology of the complex quasiparticle dispersion. This critical phenomenon offers a fascinating route toward perturbation-free quantum states.

Asynchronous ecological upheavals on the Western Mediterranean islands: New insights on the extinction of their autochthonous small mammals

Comparative studies on extinction scenarios are an invaluable contribution to enhance our understanding of their patterns and mechanisms underpinning them. This paper presents new radiocarbon dates based on specimens of five extinct mammal species from Mallorca and Sardinia. The new evidence permits to reanalyse the extinction dynamics on both islands. Radiocarbon ages directly obtained on bone collagen from these species show evidence of different extinction patterns on Mallorca and Sardinia. For Mallorca the most reliable scenario is a mass extinction of all non-volant mammal species as an immediate consequence of the first human irruption on the island. However, for Sardinia, the extinction of autochthonous mammals lasted over several millennia. The new radiocarbon dates of the last occurrence of endemic mammals suggest a sequence of punctuated extinction events throughout the late Sardinian Holocene. These events are here tentatively related to successive human colonisation waves. The current lack of chronological dates for some Sardinian fossil mammals impedes to outline a more accurate pattern of extinction events. The present paper points that Mallorca have been more vulnerable than Sardinia to the external disturbances introduced by humans. We suggest that the capacity of each island to absorb external perturbations could be related to the island area, the duration of the isolated evolution and the degree of faunal complexity.

Hollow Core Bragg Fiber-Based Sensor for Simultaneous Measurement of Curvature and Temperature

In this paper, the hollow core Bragg fiber (HCBF)-based sensor based on anti-resonant reflecting optical waveguide (ARROW) model is proposed and experimentally demonstrated for simultaneous measurement of curvature and temperature by simply sandwiching a segment of HCBF within two single-mode fibers (SMFs). The special construction of a four-bilayer Bragg structure provides a well-defined periodic interference envelope in the transmission spectrum for sensing external perturbations. Owing to different sensitivities of interference dips, the proposed HCBF-based sensor is capable of dual-parameter detection by monitoring the wavelength shift. The highest curvature sensitivity of the proposed sensor is measured to be 74.4 pm/m−1 in the range of 1.1859–2.9047 m−1 with the adjusted R square value of 0.9804. In the meanwhile, the best sensitivity of temperature sensing was detected to be 16.8 pm/°C with the linearity of 0.997 with temperature range varying from 25 to 55 °C. Furthermore, with the aid of the 2 × 2 matrix, the dual demodulation of curvature and temperature can be carried out to realize the simultaneous measurement of these two parameters. Besides dual-parameter sensing based on wavelength shift, the proposed sensor can also measure temperature-insensitive curvature by demodulating the intensity of resonant dips.

Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering

Optimal operation of hydropower plants (HP) is a crucial task for the control of several variables involved in the power generation process, including hydraulic level and power generation rate. In general, there are three main problems that an optimal operation approach must address: (i) maintaining a hydraulic head level which satisfies the energy demand at a given time, (ii) regulating operation to match with certain established conditions, even in the presence of system’s parametric variations, and (iii) managing external disturbances at the system’s input. To address these problems, in this paper we propose an approach for optimal hydraulic level tracking based on an Inverse Optimal Controller (IOC), devised with the purpose of regulating power generation rates on a specific HP infrastructure. The Closed–Loop System (CLS) has been simulated using data collected from the HP through a whole year of operation as a tracking reference. Furthermore, to combat parametric variations, an accumulative action is incorporated into the control scheme. In addition, a Recurrent Neural Network (RNN) based on Feature Engineering (FE) techniques has been implemented to aid the system in the prediction and management of external perturbations. Besides, a landslide is simulated, causing the system’s response to show a deviation in reference tracking, which is corrected through the control action. Afterward, the RNN is including of the aforementioned system, where the trajectories tracking deviation is not perceptible, at the hand of, a better response with respect to use a single scheme. The results show the robustness of the proposed control scheme despite climatic variations and landslides in the reservoir operation process. This proposed combined scheme shows good performance in presence of parametric variations and external perturbations.