Magnetically induced ferroelectricity in Cu2MnSnS4 and Cu2MnSnSe4

We investigate magnetically-induced ferroelectricity in Cu2MnSnS4 by means of Landau theory of phase transitions and of ab initio density functional theory. As expected from the Landau approach, ab initio calculations show that a non-zero ferroelectric polarization P along the y direction is induced by the peculiar antiferromagnetic configuration of Mn spins occurring in Cu2MnSnS4. The comparison between P, calculated either via density-functional-theory or according to Landau approach, clearly shows that ferroelectricity is mainly driven by Heisenberg-exchange terms and only to a minor extent by relativistic terms. At variance with previous examples of collinear antiferromagnets with magnetically-induced ferroelectricity (such as AFM-E HoMnO3), the ionic displacements occurring upon magnetic ordering are very small, so that the exchange-striction mechanism (i.e. displacement of ions so as to minimize the magnetic coupling energy) is not effective here. Rather, the microscopic mechanism at the basis of polarization has mostly an electronic origin. In this framework, we propose the small magnetic moment at Cu sites induced by neighboring Mn magnetic moments to play a relevant role in inducing P. Finally, we investigate the effect of the anion by comparing Cu2MnSnSe4 and Cu2MnSnS4: Se-4p states, more delocalized compared to S-3p states, are able to better mediate the Mn-Mn interaction, in turn leading to a higher ferroelectric polarization in the Se-based compound

Ferroelectricity in multiferroic magnetite Fe3O4 driven by noncentrosymmetric Fe2+/Fe3+ charge-ordering: First-principles study

By means of first-principles simulations, we unambiguously show that improper ferroelectricity in magnetite in the low-temperature insulating phase is driven by charge-ordering. An accurate comparison between monoclinic ferroelectric Cc and paraelectric P2/c structures shows that the polarization arises because of "shifts" of electronic charge between octahedral Fe sites, leading to a non-centrosymmetric Fe2+/Fe3+ charge-ordered pattern. Our predicted values for polarization, in good agreement with available experimental values, are discussed in terms of point-charge dipoles located on selected Fe tetrahedra, pointing to a manifest example of electronic ferroelectricity driven by charge rearrangement.Comment: 5 pages, 4 figures, accepted for publication in Phys. Rev.

Impact of Hfq on the Intrinsic Drug Resistance of Salmonella Enterica Serovar Typhimurium

Salmonella enterica is an important enteric pathogen, and its various serovars cause both systemic and intestinal diseases in humans and domestic animals. The emergence of multidrug-resistant strains of Salmonella, leading to increased morbidity and mortality, has further complicated its management. Hfq is an RNA chaperon that mediates the binding of small RNAs to mRNA and assists in post-transcriptional gene regulation in bacteria. Although Hfq is related to important phenotypes including virulence in Salmonella, its role in the drug resistance of this organism is unknown. The aim of this study was to investigate the role of Hfq in intrinsic drug resistance of S. enterica serovar Typhimurium. hfq Mutant was susceptible to acriflavine. Although there is a relationship between the production of the AcrB multidrug efflux pump and Hfq in Escherichia coli, the deletion of the drug efflux acrB did not impair the effect of hfq deletion on Salmonella susceptibility. In contrast, the deletion of another drug efflux gene, smvA, impaired the effect of hfq deletion on acriflavine susceptibility. These results indicate that Hfq regulates the intrinsic drug resistance, and it may influence drug susceptibility by regulating SmvA in Salmonella

Feature Extraction using Spiking Convolutional Neural Networks

Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing dependant plasticity. Training deep convolutional neural networks is a memory and power intensive job. Spiking networks could potentially help in reducing the power usage. There is a large pool of tools for one to chose to train artificial neural networks of any size, on the other hand all the available tools to simulate spiking neural networks are geared towards computational neuroscience applications and they are not suitable for real life applications. In this work we focus on implementing a spiking CNN using Tensorflow to examine behaviour of the network and study catastrophic forgetting in the spiking CNN and weight initialization problem in R-STDP using MNIST data set. We also report classification accuracies that are achieved using N-MNIST and MNIST data sets

Are You Tampering With My Data?

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.Comment: 18 page

Bi-stability of mixed states in neural network storing hierarchical patterns

We discuss the properties of equilibrium states in an autoassociative memory model storing hierarchically correlated patterns (hereafter, hierarchical patterns). We will show that symmetric mixed states (hereafter, mixed states) are bi-stable on the associative memory model storing the hierarchical patterns in a region of the ferromagnetic phase. This means that the first-order transition occurs in this ferromagnetic phase. We treat these contents with a statistical mechanical method (SCSNA) and by computer simulation. Finally, we discuss a physiological implication of this model. Sugase et al. analyzed the time-course of the information carried by the firing of face-responsive neurons in the inferior temporal cortex. We also discuss the relation between the theoretical results and the physiological experiments of Sugase et al.Comment: 18 pages, 6 figure

Brain Dp140 alters glutamatergic transmission and social behaviour in the mdx52 mouse model of Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is a muscle disorder caused by DMD mutations and is characterized by neurobehavioural comorbidities due to dystrophin deficiency in the brain. The lack of Dp140, a dystrophin short isoform, is clinically associated with intellectual disability and autism spectrum disorders (ASDs), but its postnatal functional role is not well understood. To investigate synaptic function in the presence or absence of brain Dp140, we utilized two DMD mouse models, mdx23 and mdx52 mice, in which Dp140 is preserved or lacking, respectively. ASD-like behaviours were observed in pups and 8-week-old mdx52 mice lacking Dp140. Paired-pulse ratio of excitatory postsynaptic currents, glutamatergic vesicle number in basolateral amygdala neurons, and glutamatergic transmission in medial prefrontal cortex-basolateral amygdala projections were significantly reduced in mdx52 mice compared to those in wild-type and mdx23 mice. ASD-like behaviour and electrophysiological findings in mdx52 mice were ameliorated by restoration of Dp140 following intra-cerebroventricular injection of antisense oligonucleotide drug-induced exon 53 skipping or intra-basolateral amygdala administration of Dp140 mRNA-based drug. Our results implicate Dp140 in ASD-like behaviour via altered glutamatergic transmission in the basolateral amygdala of mdx52 mice