The quantum theory of measurement within dynamical reduction models

We analyze in mathematical detail, within the framework of the QMUPL model of spontaneous wave function collapse, the von Neumann measurement scheme for the measurement of a 1/2 spin particle. We prove that, according to the equation of the model: i) throughout the whole measurement process, the pointer of the measuring device is always perfectly well localized in space; ii) the probabilities for the possible outcomes are distributed in agreement with the Born probability rule; iii) at the end of the measurement the state of the microscopic system has collapsed to the eigenstate corresponding to the measured eigenvalue. This analysis shows rigorously how dynamical reduction models provide a consistent solution to the measurement problem of quantum mechanics.Comment: 24 pages, RevTeX. Minor changes mad

Reply to Comments of Bassi, Ghirardi, and Tumulka on the Free Will Theorem

We show that the authors in the title have erred in claiming that our axiom FIN is false by conflating it with Bell locality. We also argue that the predictions of quantum mechanics, and in particular EPR, are fully Lorentz invariant, whereas the Free Will Theorem shows that theories with a mechanism of reduction, such as GRW, cannot be made fully invariant.Comment: We sharpen our theorem by replacing axiom FIN by a weaker axiom MIN to answer the above authors' objection

Numerical investigation of the conditioning for plane wave discontinuous Galerkin methods

We present a numerical study to investigate the conditioning of the plane wave discontinuous Galerkin discretization of the Helmholtz problem. We provide empirical evidence that the spectral condition number of the plane wave basis on a single element depends algebraically on the mesh size and the wave number, and exponentially on the number of plane wave directions; we also test its dependence on the element shape. We show that the conditioning of the global system can be improved by orthogonalization of the local basis functions with the modified Gram-Schmidt algorithm, which results in significantly fewer GMRES iterations for solving the discrete problem iteratively.Comment: Submitted as a conference proceeding; minor revisio

Consciousness and the Wigner's friend problem

It is generally agreed that decoherence theory is, if not a complete answer, at least a great step forward towards a solution of the quantum measurement problem. It is shown here however that in the cases in which a sentient being is explicitly assumed to take cognizance of the outcome the reasons we have for judging this way are not totally consistent, so that the question has to be considered anew. It is pointed out that the way the Broglie-Bohm model solves the riddle suggests a possible clue, consisting in assuming that even very simple systems may have some sort of a proto-consciousness, but that their ``internal states of consciousness'' are not predictive. It is, next, easily shown that if we imagine the systems get larger, in virtue of decoherence their internal states of consciousness progressively gain in predictive value. So that, for macro-systems, they may be identified (in practice) with the predictive states of consciousness on which we ground our observational predictions. The possibilities of carrying over this idea to standard quantum mechanics are then investigated. Conditions of conceptual consistency are considered and found rather strict, and, finally, two solutions emerge, differing conceptually very much from one another but in both of which the, possibly non-predictive, generalized internal states of consciousness play a crucial role

Entangling macroscopic diamonds at room temperature: Bounds on the continuous-spontaneous-localization parameters

A recent experiment [K. C. Lee et al., Science 334, 1253 (2011)] succeeded in detecting entanglement between two macroscopic specks of diamonds, separated by a macroscopic distance, at room temperature. This impressive results is a further confirmation of the validity of quantum theory in (at least parts of) the mesoscopic and macroscopic domain, and poses a challenge to collapse models, which predict a violation of the quantum superposition principle, which is the bigger the larger the system. We analyze the experiment in the light of such models. We will show that the bounds placed by experimental data are weaker than those coming from matter-wave interferometry and non-interferometric tests of collapse models.Comment: 7 pages, 3 figures, v2: close to the published version, LaTe

Towards Quantum Superpositions of a Mirror: an Exact Open Systems Analysis

We analyze the recently proposed mirror superposition experiment of Marshall, Simon, Penrose, and Bouwmeester, assuming that the mirror's dynamics contains a non-unitary term of the Lindblad type proportional to -[q,[q,\rho]], with q the position operator for the center of mass of the mirror, and \rho the statistical operator. We derive an exact formula for the fringe visibility for this system. We discuss the consequences of our result for tests of environmental decoherence and of collapse models. In particular, we find that with the conventional parameters for the CSL model of state vector collapse, maintenance of coherence is expected to within an accuracy of at least 1 part in 10^{8}. Increasing the apparatus coupling to environmental decoherence may lead to observable modifications of the fringe visibility, with time dependence given by our exact result.Comment: 4 pages, RevTeX. Substantial changes mad

Effect of lhcsr gene dosage on oxidative stress and light use efficiency by Chlamydomonas reinhardtii cultures

Unicellular green algae, a promising source for renewable biofuels, produce lipid-rich biomass from light and CO2. Productivity in photo-bioreactors is affected by inhomogeneous light distribution from high cell pigment causing heat dissipation of light energy absorbed in excess and shading of the deep layers. Contrasting reports have been published on the relation between photoprotective energy dissipation and productivity. Here, we have re-investigated the relation between energy quenching (qE) activity, photodamage and light use efficiency by comparing WT and two Chlamydomonas reinhardtii strains differing for their complement in LHCSR proteins, which catalyse dissipation of excitation energy in excess (qE). Strains were analysed for ROS production, protein composition, rate of photodamage and productivity assessed under wide light and CO2 conditions.The strain lacking LHCSR1 and knocked down in LHCSR3, thus depleted in qE, produced O-2 at significantly higher rate under high light, accompanied by enhanced singlet oxygen release and PSII photodamage. However, biomass productivity of WT was delayed in respect for mutant strains under intermittent light conditions only, implying that PSII activity was not the limiting factor under excess light. Contrary to previous proposals, domestication of Chlamydomonas for carbon assimilation rate in photo-bioreactors by down-regulation of photoprotective energy dissipation was ineffective in increasing algal biomass productivity

Effect of lhcsr gene dosage on oxidative stress and light use efficiency by Chlamydomonas reinhardtii cultures

Unicellular green algae, a promising source for renewable biofuels, produce lipid-rich biomass from light and CO2. Productivity in photo-bioreactors is affected by inhomogeneous light distribution from high cell pigment causing heat dissipation of light energy absorbed in excess and shading of the deep layers. Contrasting reports have been published on the relation between photoprotective energy dissipation and productivity. Here, we have re-investigated the relation between energy quenching (qE) activity, photodamage and light use efficiency by comparing WT and two Chlamydomonas reinhardtii strains differing for their complement in LHCSR proteins, which catalyse dissipation of excitation energy in excess (qE). Strains were analysed for ROS production, protein composition, rate of photodamage and productivity assessed under wide light and CO2 conditions. The strain lacking LHCSR1 and knocked down in LHCSR3, thus depleted in qE, produced O2 at significantly higher rate under high light, accompanied by enhanced singlet oxygen release and PSII photodamage. However, biomass productivity of WT was delayed in respect for mutant strains under intermittent light conditions only, implying that PSII activity was not the limiting factor under excess light. Contrary to previous proposals, domestication of Chlamydomonas for carbon assimilation rate in photo-bioreactors by down-regulation of photoprotective energy dissipation was ineffective in increasing algal biomass productivity

COVID-19 detection using chest X-rays: is lung segmentation important for generalization?

We evaluated the generalization capability of deep neural networks (DNNs), trained to classify chest X-rays as COVID-19, normal or pneumonia, using a relatively small and mixed dataset. We proposed a DNN to perform lung segmentation and classification, stacking a segmentation module (U-Net), an original intermediate module and a classification module (DenseNet201). To evaluate generalization, we tested the DNN with an external dataset (from distinct localities) and used Bayesian inference to estimate probability distributions of performance metrics. Our DNN achieved 0.917 AUC on the external test dataset, and a DenseNet without segmentation, 0.906. Bayesian inference indicated mean accuracy of 76.1% and [0.695, 0.826] 95% HDI (high density interval, which concentrates 95% of the metric's probability mass) with segmentation and, without segmentation, 71.7% and [0.646, 0.786]. We proposed a novel DNN evaluation technique, using Layer-wise Relevance Propagation (LRP) and Brixia scores. LRP heatmaps indicated that areas where radiologists found strong COVID-19 symptoms and attributed high Brixia scores are the most important for the stacked DNN classification. External validation showed smaller accuracies than internal, indicating difficulty in generalization, which segmentation improves. Performance in the external dataset and LRP analysis suggest that DNNs can be trained in small and mixed datasets and detect COVID-19.Comment: This revision mainly changed the text to make explanations clearer and it added better comparisons to related works. Reported results and models did not chang