Bis(2-hydroxy­benzaldehyde oximato-κO)triphenyl­anti­mony(V)

The mol­ecule of the title compound, [Sb(C6H5)3(C7H6NO2)2], is located on a twofold axis defined by the metal center and two C atoms of a coordinated phenyl group. The Sb center has a slightly distorted trigonal-bipyramidal geometry, with the axial positions occupied by the O atoms of symmetry-related 2-hydroxy­benzaldehyde oximate ligands. An intra­molecular O—H⋯N inter­action is present. The crystal structure is stabilized by C—H⋯O inter­actions

Tetra­kis(μ3-2-{[1,1-bis­(hydroxy­meth­yl)-2-oxidoeth­yl]imino­meth­yl}-6-methoxy­phenol­ato)tetra­nickel(II) tetra­hydrate

The title complex, [Ni4(C12H15NO4)4]·4H2O, has crystal­lographic fourfold inversion symmetry, with each NiII ion coordinated in a slightly distorted square-pyramidal coordination environment and forming an Ni4O4 cubane-like core. In the crystal structure, inter­molecular O—H⋯O hydrogen bonds connect complex and water mol­ecules to form a three-dimensional network. The O atom of one of the unique hydroxy­methyl groups is disordered over two sites, with the ratio of occupancies being approximately 0.79:0.21

1,5-Bis(2-chloro­benzyl­idene)carbonohydrazide

In the title mol­ecule, C15H12Cl2N4O, the two benzene rings are inclined at a dihedral angle of 14.5 (2)°. In the crystal, inter­molecular N—H⋯O hydrogen bonds link mol­ecules into chains propagated in [001]

Effect of Grain Coalescence on Dislocation and Stress Evolution of GaN Films Grown on Nanoscale Patterned Sapphire Substrates

Two types of nucleation layers (NLs), including in-situ low-temperature grown GaN (LT-GaN) and ex-situ sputtered physical vapor deposition AlN (PVD-AlN), are applied on cone-shaped nanoscale patterned sapphire substrate (NPSS). The initial growth process of GaN on these two NLs is comparably investigated by a series of growth interruptions. The coalescence process of GaN grains is modulated by adjusting the three-dimensional (3D) temperatures. The results indicate that higher 3D temperatures reduce the edge dislocation density while increasing the residual compressive stress in GaN films. Compared to the LT-GaN NLs, the PVD-AlN NLs effectively resist Ostwald ripening and facilitate the uniform growth of GaN grains on NPSS. Furthermore, GaN films grown on NPSS with PVD-AlN NLs exhibit a reduction of over 50% in both screw and edge dislocation densities compared to those grown on LT-GaN NLs. Additionally, PVD-AlN NLs result in an increase of about 0.5 GPa in the residual compressive stress observed in GaN films

The Observable Mind: Enabling an Autonomous Agent Sharing Its Conscious Contents Using a Cognitive Architecture

We enable an autonomous agent sharing its artificial mind to its audiences like humans. This supports the autonomous human robot interactions relying on a cognitive architecture, LIDA, which explains and predicts how minds work and is used as the controllers of intelligent autonomous agents. We argue that LIDA’s cognitive representations and processes may serve as the source of the mind content its agent shares out, autonomously. We proposed a new description (sub) model into LIDA, letting its agent describing its conscious contents. Through this description, the agent’s mind is more observable so we can understand the agent’s entity and intelligence more directly. Also, this helps the agent explains its behaviors to its audiences so engage into its living society better. We built an initial LIDA agent embedding with this description model. The agent shares its conscious content autonomously, reasonably explaining its behaviors

Sensory Motor System: Modeling the process of action execution

Abstract This paper presents a cognitive model—the Sensory Motor System (SMS)—for an action execution process, as a new module of the LIDA systems-level cognitive model. Action execution refers to a situation in which a software agent or robot executes a selected goal-directed action in the real world so as to output pertinent movement. Action execution requires transforming a selected goal-directed action into lower-level executable actions, and executing them. A sensorimotor system derived from the subsumption architecture has been implemented into the SMS; and several cognitive neuroscience hypotheses have been incorporated as well, including the two visual systems and others. A computational SMS has been created inside a LIDA-based software agent in Webots to model the execution of a grip action. The grip’s design is inspired by the arm controller of the robot Herbert and the current study of the human action execution. Simulated results are compared to human data

Action Execution, Its Estimation and Learning for a Systems Level Cognitive Architecture

An agent or robot achieves its goals by interacting with its environment, cyclically choosing and executing suitable actions. Cognitive architectures are considered the control structures of the agent, helping it decide what to do next, while the designs resemble how minds work, be they human, animal, or artificial. An action execution process is a critical part of an entire cognitive architecture, because the process of generating executable motor commands is not only driven by low-level environmental information, but is also initiated and affected by the agent’s high-level mental processes. I give a review of the cognitive models of the action execution process as implemented in a set of popular cognitive architectures, and conclude with some general observations regarding the nature of action execution. Next, I present a cognitive model—the Sensory Motor System (SMS)—for an action execution process, as a new module of the LIDA (for “Learning Intelligent Distribution Agent”) systems-level cognitive model. A sensorimotor system derived from the subsumption architecture has been implemented into the SMS; and several cognitive neuroscience hypotheses have been incorporated as well. Inspired by the hypothesis that humans estimate their movements based on their knowledge of the dynamics of the environment, and on actual sensory data (Wolpert, Ghahramani, & Jordan, 1995), I create a model of the estimation process of action execution using SMS in LIDA. Also, based on a recent study in neuroscience (Herzfeld, Vaswani, Marko, & Shadmehr, 2014), I introduce a new factor—memory of errors—into this model of estimation. The historical errors help humans determine the stability of the environment, so as to decide the degree to which knowledge of the environment may affect the estimation. Learning is significant for for allowing an agent to act more intelligently. I present a new model of sensorimotor learning in LIDA, one that helps an agent properly interact with its environment using past experiences. Following Global Workspace Theory, the primary basis of LIDA, this learning is cued by the agent’s conscious content, the most salient portion of the agent’s understanding of the current situation. Furthermore, I add a dynamic learning rate to control the extent to which newly arriving conscious content may affect the learning. Finally, I introduce an extension of the SMS. This extension allows, and explains, the use of the sensory data, the prime, perceived before a participant starts his or her movement, by the SMS during action execution. Furthermore, this extension allows the replication by a LIDA-based agent, of some human experiments (T. Schmidt, 2002) studying the priming process in motor control

Sensory Motor System: Modeling the Process of Action Execution

This paper presents a cognitive model—the Sensory Motor System (SMS)—for an action execution process, as a new module of the LIDA systems-level cognitive model. Action execution refers to a situation in which a software agent or robot executes a selected goal-directed action in the real world so as to output pertinent movement. Action execution requires transforming a selected goal-directed action into lower-level executable actions, and executing them. A sensorimotor system derived from the subsumption architecture has been implemented into the SMS; and several cognitive neuroscience hypotheses have been incorporated as well, including the two visual systems and others. A computational SMS has been created inside a LIDA-based software agent in Webots to model the execution of a grip action. The grip’s design is inspired by the arm controller of the robot Herbert and the current study of human’s action. Simulated results are compared to human performance