Several compounds were tested on the differential-reinforcement-of-low-rate 72-sec (DRL 72-sec) schedule, a behavioral screen to determine putative antidepressants; these compounds were evaluated in two outbred stocks of rats, Harlan and Holtzman Sprague-Dawley rats. A dose-response determination for the tricyclic antidepressants, imipramine and desipramine, the selective serotonin (5-hydroxytr...

Considering the advancement of medical sciences, diagnostic tests have been developed to distinguish patients from healthy population. Therefore, Determining and evaluation of the diagnostic accuracy tests is of great importance. The accuracy of a test under evaluation is determined through the amount of agreement between its results with the results of the gold standard, and this test accuracy...

Network virtualization is a main paradigm of Future Internet research. It allows for automatic creation of virtual networks with application specific resource management, routing, topology and naming. Since those virtual networks need to be implemented by means of the underlying physical network, the Virtual Network Mapping Problem (VNMP) arises. In this work, we introduce the Virtual Network M...

The DRL (Delayed Reinforcement Learning) problem is classical in Reinforcement Learning theory. There were several agent architectures solving that problem including some connectionist architectures. This work describes an early connectionist agent architecture, the CAA architecture, that solved the problem using the concept of emotion it its learning rule. The architecture is compared to anoth...

The vertebrate heart muscle (myocardium) develops from the first heart field (FHF) and expands by adding second heart field (SHF) cells. While both lineages exist already in teleosts, the primordial contributions of FHF and SHF to heart structure and function remain incompletely understood. Here we delineate the functional contribution of the FHF and SHF to the zebrafish heart using the cis-reg...

Deep reinforcement learning (DRL) demonstrates its potential in learning a model-free navigation policy for robot visual navigation. However, the data-demanding algorithm relies on a large number of navigation trajectories in training. Existing datasets supporting training such robot navigation algorithms consist of either 3D synthetic scenes or reconstructed scenes. Synthetic data suffers from...

Using Deep Reinforcement Learning (DRL) can be a promising approach to handle various tasks in the field of (simulated) autonomous driving. However, recent publications mainly consider learning in unusual driving environments. This paper presents Driving School for Autonomous Agents (DSA2), a software for validating DRL algorithms in more usual driving environments based on artificial and reali...

Cognitive dysfunction may be a core feature of major depressive disorder, including affective processing bias, abnormal response to negative feedback, changes in decision making, and increased impulsivity. Accordingly, a translational medicine paradigm predicts clinical action of novel antidepressants by examining drug-induced changes in affective processing bias. With some exceptions, these co...

An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful tha...

Mobile network that millions of people use every day is one of the most complex systems in real world. Optimization of mobile network to meet exploding customer demand and reduce CAPEX/OPEX poses greater challenges than in prior works. Actually, learning to solve complex problems in real world to benefit everyone and make the world better has long been ultimate goal of AI. However, it still rem...