Ferroelectricity in the Magnetic E-Phase of Orthorhombic Perovskites

We show that the symmetry of the spin zigzag chain E phase of the orthorhombic perovskite manganites and nickelates allows for the existence of a finite ferroelectric polarization. The proposed microscopic mechanism is independent of spin-orbit coupling. We predict that the polarization induced by the E-type magnetic order can potentially be enhanced by up to two orders of magnitude with respect to that in the spiral magnetic phases of TbMnO3 and similar multiferroic compounds.Comment: 4 pages, 2 figures, somewhat changed emphases, accepted to PR

Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry

The backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks; however, its biological plausibility has been strongly criticized, and it remains an open question whether the brain employs supervised learning mechanisms akin to it. Here, we propose correlative information maximization between layer activations as an alternative normative approach to describe the signal propagation in biological neural networks in both forward and backward directions. This new framework addresses many concerns about the biological-plausibility of conventional artificial neural networks and the backpropagation algorithm. The coordinate descent-based optimization of the corresponding objective, combined with the mean square error loss function for fitting labeled supervision data, gives rise to a neural network structure that emulates a more biologically realistic network of multi-compartment pyramidal neurons with dendritic processing and lateral inhibitory neurons. Furthermore, our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths, a significant critique against the plausibility of the conventional backpropagation algorithm. This is achieved by leveraging two alternative, yet equivalent forms of the correlative mutual information objective. These alternatives intrinsically lead to forward and backward prediction networks without weight symmetry issues, providing a compelling solution to this long-standing challenge

Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources

Extraction of latent sources of complex stimuli is critical for making sense of the world. While the brain solves this blind source separation (BSS) problem continuously, its algorithms remain unknown. Previous work on biologically-plausible BSS algorithms assumed that observed signals are linear mixtures of statistically independent or uncorrelated sources, limiting the domain of applicability of these algorithms. To overcome this limitation, we propose novel biologically-plausible neural networks for the blind separation of potentially dependent/correlated sources. Differing from previous work, we assume some general geometric, not statistical, conditions on the source vectors allowing separation of potentially dependent/correlated sources. Concretely, we assume that the source vectors are sufficiently scattered in their domains which can be described by certain polytopes. Then, we consider recovery of these sources by the Det-Max criterion, which maximizes the determinant of the output correlation matrix to enforce a similar spread for the source estimates. Starting from this normative principle, and using a weighted similarity matching approach that enables arbitrary linear transformations adaptable by local learning rules, we derive two-layer biologically-plausible neural network algorithms that can separate mixtures into sources coming from a variety of source domains. We demonstrate that our algorithms outperform other biologically-plausible BSS algorithms on correlated source separation problems.Comment: NeurIPS 2022, 37 page

Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation

The brain effortlessly extracts latent causes of stimuli, but how it does this at the network level remains unknown. Most prior attempts at this problem proposed neural networks that implement independent component analysis which works under the limitation that latent causes are mutually independent. Here, we relax this limitation and propose a biologically plausible neural network that extracts correlated latent sources by exploiting information about their domains. To derive this network, we choose maximum correlative information transfer from inputs to outputs as the separation objective under the constraint that the outputs are restricted to their presumed sets. The online formulation of this optimization problem naturally leads to neural networks with local learning rules. Our framework incorporates infinitely many source domain choices and flexibly models complex latent structures. Choices of simplex or polytopic source domains result in networks with piecewise-linear activation functions. We provide numerical examples to demonstrate the superior correlated source separation capability for both synthetic and natural sources.Comment: Preprint, 32 page

The effect of initial pH and retention time on boron removal by continuous electrocoagulation process

In this study, factors influencing boron removal via the continuous electrocoagulation process were investigated at lab-scale. Different influent pH values (4, 5, 6, 7.45 and 9) and contact times (10, 25, 50 and 100 min) were examined as variable parameters. Plate-type aluminium electrodes with 5 mm distance between them were used. All the experiments were conducted in continuous mode and the current density was kept constant at 5 A throughout the whole experimental period. The initial boron concentration was selected to be 1000 mg L-1. The first set of experiments concerning the influence of the influent pH showed that the highest boron removal (67%) was obtained at pH=6 since it was the optimal pH for boron precipitation through aluminium borate formation. Under the constant current density of the study and with the initial pH adjusted to 6, increasing the duration of the electrocoagulation process from 10 to 100 min resulted in raising the boron removal from 45 to 79% during the second set of experiments. The greater duration of the electrocagulation process enabled higher aluminium dissolution, thus allowing the existence of a higher number of coagulants within the reactor. Moreover, it enhanced boron precipitation because of the longer contact time between the boron ions and the coagulants. After optimizing significant parameters such as the influent pH and the electrocagulation duration, the continuous electrocoagulation process was found to constitute an effective alternative for boron removal

Computerized Nurse Charting

journal articleBiomedical Informatic

Dynamics of the chiral phase transition from AdS/CFT duality

We use Lorentzian signature AdS/CFT duality to study a first order phase transition in strongly coupled gauge theories which is akin to the chiral phase transition in QCD. We discuss the relation between the latent heat and the energy (suitably defined) of the component of a D-brane which lies behind the horizon at the critical temperature. A numerical simulation of a dynamical phase transition in an expanding, cooling Quark-Gluon plasma produced in a relativistic collision is carried out.Comment: 30 pages, 5 figure

3D segmentation of intervertebral discs: from concept to the fabrication of patient-specific scaffolds

Aim: To develop a methodology for producing patient-specific scaffolds that mimic the annulus fibrosus (AF) of the human intervertebral disc (IVD) by means of combining magnetic resonance imaging (MRI) and 3D bioprinting. Methods: In order to obtain the AF 3D model from patientâ s volumetric MRI dataset, the RheumaSCORE segmentation software was used. Polycaprolactone scaffolds with three different internal architectures were fabricated by 3D bioprinting, and characterized by micro-computed tomography. Results: The demonstrated methodology of a geometry reconstruction pipeline enabled to successfully obtain an accurate AF model and 3D print patient-specific scaffolds with different internal architectures. Conclusion: The results guide us towards patient-specific IVD tissue engineering as demonstrated a way of manufacturing personalized scaffolds using patient's MRI data.The authors would like to acknowledge the financial support provided by the Portuguese Foundation for Science and Technology (FCT) through the project EPIDisc (UTAPEXPL/BBB-ECT/0050/2014), funded in the Framework of the ‘International Collaboratory for Emerging Technologies, CoLab’, UT justin|Portugal Program. FCT is also acknowledged for the PhD scholarship attributed to IF Cengiz (SFRH/ BD/99555/2014) and the financial support provided to J Silva-Correia (SFRH/BPD/100590/2014 and IF/00115/2015). JM Oliveira also thanks the FCT for the funds provided under the program Investigador FCT (IF/00423/2012 and IF/01285/2015). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.info:eu-repo/semantics/publishedVersio