Irradiation experiments (IE) are an essential step in the development of High-Energy Physics (HEP) particle accelerators and detectors. They assess the radiation hardness of materials used in HEP experimental devices by simulating, in a short time, the common long-term degradation effects due to their bombardment by high-energy particles. IEs are also used in other scientific and industrial fields such as medicine (e.g., for cancer treatment, medical imaging, etc.), space/avionics (e.g., for radiation testing of payload equipment) as well as in industry (e.g., for food sterilization). Usually carried out with ionizing radiation, these complex processes require highly specialized infrastructures: the irradiation facilities. Currently, hundreds of such facilities exist worldwide. To help develop best practices and promote computer-assisted handling and management of IEs, we introduce IEDM, a new OWL-based Irradiation Experiment Data Management ontology. This paper provides an overview of the classes and properties of IEDM. Since one of the key design choices for IEDM was to maximize the reuse of existing foundational ontologies such as the Ontology of Scientific Experiments (EXPO), the Ontology of Units of Measure (OM) and the Friend-of-a-Friend Ontology (FOAF), we discuss the methodological issues of the integration of IEDM with these imported ontologies. We illustrate the use of IEDM via an actual IE recently performed at IRRAD, the CERN proton irradiation facility. Finally, we discuss other motivations for this work, including the use of IEDM for the generation of user interfaces for IE management, and their impact on our methodology.

The challenges of making the technology industry a more welcoming place for women are numerous, especially in the booming field of artificial intelligence. To get a sense of just how monumental a task the tech community faces, look no further than the marquee gathering for AI's top scientists. Preparations for this year's event drew controversy not only because there weren't enough female speakers or study authors. The biggest debate was over the conference's name. The annual Conference and Workshop on Neural Information Processing Systems, formerly known as NIPS, had become a punchline symbol about just how bad the gender imbalance is for artificial intelligence.

Satisfiability Modulo Theories (SMT) is essential for many practical applications, e.g., in hard- and software verification, and increasingly also in other scientific areas like computational biology. A large number of applications in these areas benefit from bit-precise reasoning over finite-domain variables. Current approaches in this area translate a formula over bit-vectors to an equisatisfiable propositional formula, which is then given to a SAT solver. In this paper, we present a novel stochastic local search (SLS) algorithm to solve SMT problems, especially those in the theory of bit-vectors, directly on the theory level. We explain how several successful techniques used in modern SLS solvers for SAT can be lifted to the SMT level. Experimental results show that our approach can compete with state-of-the-art bit-vector solvers on many practical instances and, sometimes, outperform existing solvers. This offers interesting possibilities in combining our approach with existing techniques, and, moreover, new insights into the importance of exploiting problem structure in SLS solvers for SAT. Our approach is modular and, therefore, extensible to support other theories, potentially allowing SLS to become part of the more general SMT framework.

As a robot formation increases in size or explores places where it is difficult for a human operator to interact, autonomous control becomes critical. We propose a distributed autonomous method for evaluating properties of multi-robot systems, and then discuss how this information can be applied to improve performance with respect to a given operation. We present this as an extension of our previous work on robot formations; however, the techniques described could be adapted to other multi-robot systems.

Deneb Robotics (Auburn Hills, Mich.) has been awarded a $2.3 million contract from the National Institute of Standards and Technology (NIST) to develop the agent network for task scheduling and execution. This intelligent agent-based project is designed to improve existing factory-scheduling systems with a new task scheduling and execution system in which Shell U.K. Exploration and Production availability and prevent cars from agents represent factory resources, systems, (Aberdeen, U.K.) has implemented being damaged while they are parked. The Arvin Industries (Columbus, Ind.) is Cisco Systems (San Jose, Calif.), a supplier expert system helped Shell achieve working with the U.S. Air Force to of network technology, is using over $1.6 million in cost savings for develop a neural network system that intelligent-agent technology to integrate its Brent Field site within 2 months of can determine the quality of noise in CD-ROM and online web information implementation. The neural network will help The addition of intelligent The National Research Council has determine what exactly an annoying search-and-retrieval capabilities has awarded Nestor (Providence, R.I.) a sound is and how it can be fixed. Mercedes-Benz plans This system has helped cut specialty Neural Computer Sciences (NCS) to establish three vrf test sites in clinic costs by 40 percent.

But even if it isn't way, and addresses significant tasks.