812 research outputs found
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
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
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