Grazing Incidence Small Angle X-Ray Scattering (GISAXS) on Small Targets Using Large Beams

GISAXS is often used as a versatile tool for the contactless and destruction-free investigation of nanostructured surfaces. However, due to the shallow incidence angles, the footprint of the X-ray beam is significantly elongated, limiting GISAXS to samples with typical target lengths of several millimetres. For many potential applications, the production of large target areas is impractical, and the targets are surrounded by structured areas. Because the beam footprint is larger than the targets, the surrounding structures contribute parasitic scattering, burying the target signal. In this paper, GISAXS measurements of isolated as well as surrounded grating targets in Si substrates with line lengths from $50\,{\rm\mu m}$ down to $4\,{\rm\mu m}$ are presented. For the isolated grating targets, the changes in the scattering patterns due to the reduced target length are explained. For the surrounded grating targets, the scattering signal of a $15\,{\rm\mu m}\,\times\,15\,{\rm\mu m}$ target grating structure is separated from the scattering signal of $100\,{\rm\mu m}\,\times\,100\,{\rm\mu m}$ nanostructured surroundings by producing the target with a different orientation with respect to the predominant direction of the surrounding structures. The described technique allows to apply GISAXS, e.g. for characterization of metrology fields in the semiconductor industry, where up to now it has been considered impossible to use this method due to the large beam footprint

Reduction of slaughterhouse stress in beef cattle by facilitating animal tameness

The relationship between animals and humans is important for animal husbandry and welfare. Loosehousing and grazing systems with low management input often result in frail relationships between humans and animals. This study investigated whether a positive handling, applied during the first days of the animals’ life, had a calming and stress reducing effect on suckler beef calves at slaughter

Using Evolutionary Strategies for the Real-Time Learning of Controllers for Autonomous Agents in Xpilot-AI

Real-time learning is the process of an artificial intelligence agent learning behavior(s) at the same pace as it operates in the real world. Video games tend to be an excellent locale for testing real-time learning agents, as the action happens at real speeds with a good visual feedback mechanism, coupled with the possibility of comparing human performance to that of the agent\u27s. In addition, players want to be competing against a consistently challenging opponent. This paper is a discussion of a controller for an agent in the space combat game Xpilot and the evolution of said controller using two different methods. The controller is a multilayer neural network, which controls all facets of the agent\u27s behavior that are not created in the initial set-up. The neural network is evolved using 1-to-1 evolutionary strategies in one method and genetic algorithms in the other method. Using three independent trials per methodology, it was shown that evolutionary strategies learned faster, while genetic algorithms learned more consistently, leading to the idea that genetic algorithms may be superior when there is ample time before use, but evolutionary strategies are better when pressed for learning time as in real-time learning

FOXP3 and GARP (LRRC32): the master and its minion

The transcription factor FOXP3 is essential for the development and function of CD4+CD25hiFOXP3+ regulatory T (Treg) cells, but also expressed in activated human helper T cells without acquisition of a regulatory phenotype. This comment focuses on glycoprotein-A repetitions predominant (GARP or LRRC32) recently identified as specific marker of activated human Treg cells, which may provide the missing link toward a better molecular definition of the regulatory phenotype

Integrated Java Bytecode Verification

AbstractExisting Java verifiers perform an iterative data-flow analysis to discover the unambiguous type of values stored on the stack or in registers. Our novel verification algorithm uses abstract interpretation to obtain definition/use information for each register and stack location in the program, which in turn is used to transform the program into Static Single Assignment form. In SSA, verification is reduced to simple type compatibility checking between the definition type of each SSA variable and the type of each of its uses. Inter-adjacent transitions of a value through stack and registers are no longer verified explicitly. This integrated approach is more efficient than traditional bytecode verification but still as safe as strict verification, as overall program correctness can be induced once the data flow from each definition to all associated uses is known to be type-safe

A Multilevel Introspective Dynamic Optimization System For Holistic Power-Aware Computing

Power consumption is rapidly becoming the dominant limiting factor for further improvements in computer design. Curiously, this applies both at the "high end" of workstations and servers and the "low end" of handheld devices and embedded computers. At the high-end, the challenge lies in dealing with exponentially growing power densities. At the low-end, there is a demand to make mobile devices more powerful and longer lasting, but battery technology is not improving at the same rate that power consumption is rising. Traditional power-management research is fragmented; techniques are being developed at specific levels, without fully exploring their synergy with other levels. Most software techniques target either operating systems or compilers but do not explore the interaction between the two layers. These techniques also have not fully explored the potential of virtual machines for power management. In contrast, we are developing a system that integrates information from multiple levels of software and hardware, connecting these levels through a communication channel. At the heart of this system are a virtual machine that compiles and dynamically profiles code, and an optimizer that reoptimizes all code, including that of applications and the virtual machine itself. We believe this introspective, holistic approach enables more informed power-management decisions

Opening the black box: Unpacking board involvement in innovation

Corporate governance research suggests that boards of directors play key roles in governing company strategy. Although qualitative research has examined board-management relationships to describe board involvement in strategy, we lack detailed insights into how directors engage with organizational members for governing a complex and long-term issue such as product innovation. Our multiple-case study of four listed pharmaceutical firms reveals a sequential process of board involvement: Directors with deep expertise govern scientific innovation, followed by the full board's involvement in its strategic aspects. The nature of director involvement varies across board levels in terms of the direction (proactive or reactive), timing (regular or spontaneous), and the extent of formality of exchanges between directors and organizational members. Our study contributes to corporate governance research by introducing the concept of board behavioral diversity and by theorizing about the multilevel, structural, and temporal dimensions of board behavior and its relational characteristics