Auditory stimuli suppress contextual fear responses in safety learning independent of a possible safety meaning.

Safety learning allows the identification of non-threatening situations, a learning process instrumental for survival and psychic health. In contrast to fear learning, in which a sensory cue (conditioned stimulus, CS) is temporally linked to a mildly aversive stimulus (US), safety learning is studied by presenting the CS and US in an explicitly unpaired fashion. This leads to conditioned inhibition of fear responses, in which sensory cues can acquire a safety meaning (CS-).

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data.

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increase available data (e.g., in low-density regions-i.

Computational models of episodic-like memory in food-caching birds.

Birds of the crow family adapt food-caching strategies to anticipated needs at the time of cache recovery and rely on memory of the what, where and when of previous caching events to recover their hidden food. It is unclear if this behavior can be explained by simple associative learning or if it relies on higher cognitive processes like mental time-travel. We present a computational model and propose a neural implementation of food-caching behavior.

Testing two competing hypotheses for Eurasian jays' caching for the future.

Previous research reported that corvids preferentially cache food in a location where no food will be available or cache more of a specific food in a location where this food will not be available. Here, we consider possible explanations for these prospective caching behaviours and directly compare two competing hypotheses. The Compensatory Caching Hypothesis suggests that birds learn to cache more of a particular food in places where that food was less frequently available in the past.

Biologically plausible deep learning - But how far can we go with shallow networks?

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining success as reaching around 98% test accuracy on the MNIST data set. Here, we investigate how far we can go on digit (MNIST) and object (CIFAR10) classification with biologically plausible, local learning rules in a network with one hidden layer and a single readout layer.

New Multi-Keyword Ciphertext Search Method for Sensor Network Cloud Platforms.

This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt when performing multi-keyword ciphertext searches on privacy-protected sensor network cloud platforms.

Polarity control in WSe2 double-gate transistors.

As scaling of conventional silicon-based electronics is reaching its ultimate limit, considerable effort has been devoted to find new materials and new device concepts that could ultimately outperform standard silicon transistors. In this perspective two-dimensional transition metal dichalcogenides, such as MoS2 and WSe2, have recently attracted considerable interest thanks to their electrical properties. Here, we report the first experimental demonstration of a doping-free, polarity-controllable device fabricated on few-layer WSe2.

Grassmannian regularized structured multi-view embedding for image classification.

Images are usually represented by features from multiple views, e.g., color and texture.

Topological measure locating the effective crossover between segregation and integration in a modular network.

We introduce an easily computable topological measure which locates the effective crossover between segregation and integration in a modular network. Segregation corresponds to the degree of network modularity, while integration is expressed in terms of the algebraic connectivity of an associated hypergraph. The rigorous treatment of the simplified case of cliques of equal size that are gradually rewired until they become completely merged, allows us to show that this topological crossover can be made to coincide with a dynamical crossover from cluster to global synchronization of a system of coupled phase oscillators.

Subspaces indexing model on Grassmann manifold for image search.

Conventional linear subspace learning methods like principal component analysis (PCA), linear discriminant analysis (LDA) derive subspaces from the whole data set. These approaches have limitations in the sense that they are linear while the data distribution we are trying to model is typically nonlinear. Moreover, these algorithms fail to incorporate local variations of the intrinsic sample distribution manifold.

Efficient rewirings for enhancing synchronizability of dynamical networks.

In this paper, we present an algorithm for optimizing synchronizability of complex dynamical networks. Starting with an undirected and unweighted network, we end up with an undirected and unweighted network with the same number of nodes and edges having enhanced synchronizability. To this end, based on some network properties, rewirings, i.