A simple approach to neutral atom microscopy

Scanning surfaces using a beam of noncharged atoms or molecules allows for especially nondestructive and low-energy surface imaging, with the potential to obtain new information about surfaces that cannot be easily obtained otherwise. We have developed a new approach, operating with the sample at a close working distance from an aperture, the need for optics to focus the beam is obviated. Compared to more complex approaches, the theoretical performance has no other disadvantage than the short working distance. Resolution of 1.5 μm has been achieved, and submicron resolution appears to be practical. Construction of the microscope and results are presented, including first images done in reflection mode, theory for optimization of the design and avenues for future improvement.     

Note: The intermodulation lockin analyzer

Nonlinear systems can be probed by driving them with two or more pure tones while measuring the intermodulation products of the drive tones in the response. We describe a digital lockin analyzer which is designed explicitly for this purpose. The analyzer is implemented on a field-programmable gate array, providing speed in analysis, real-time feedback, and stability in operation. The use of the analyzer is demonstrated for intermodulation atomic force microscopy. A generalization of the intermodulation spectral technique to arbitrary drive waveforms is discussed.     

An optimization algorithm for multimodal functions inspired by collective animal behavior

Interest in multimodal function optimization is expanding rapidly as real-world optimization problems often demand locating multiple optima within a search space. This article presents a new multimodal optimization algorithm named as the collective animal behavior. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents are a group of animals which interact with each other based on the biologic laws of collective motion. Experimental results demonstrate that the proposed algorithm is capable of finding global and local optima of benchmark multimodal optimization problems with a higher efficiency in comparison with other methods reported in the literature.     

Topology and thickness optimization of laminated composites including manufacturing constraints

This paper presents a gradient based optimization method for large-scale topology and thickness optimization of fiber reinforced monolithic laminated composite structures including certain manufacturing constraints to attain industrial relevance. This facilitates application of predefined fiber mats and reduces the risk of failure such as delamination and matrix cracking problems. The method concerns simultaneous determination of the optimum thickness and fiber orientation throughout a laminated structure with fixed outer geometry. The laminate thickness may vary as an integer number of plies, and possible fiber orientations are limited to a finite set. The conceptual combinatorial problem is relaxed to a continuous problem and solved on basis of interpolation schemes with penalization through the so-called Discrete Material Optimization method, explicitly including manufacturing constraints as a large number of sparse linear constraints. The methodology is demonstrated on several numerical examples.     

Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions

We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.     

Optimally Adaptive Integration of Univariate Lipschitz Functions

We consider the problem of approximately integrating a Lipschitz function f (with a known Lipschitz constant) over an interval. The goal is to achieve an additive error of at most ε using as few samples of f as possible. We use the adaptive framework: on all problem instances an adaptive algorithm should perform almost as well as the best possible algorithm tuned for the particular problem instance. We distinguish between and , the performances of the best possible deterministic and randomized algorithms, respectively. We give a deterministic algorithm that uses samples and show that an asymptotically better algorithm is impossible. However, any deterministic algorithm requires samples on some problem instance. By combining a deterministic adaptive algorithm and Monte Carlo sampling with variance reduction, we give an algorithm that uses at most samples. We also show that any algorithm requires samples in expectation on some problem instance (f,ε), which proves that our algorithm is optimal.     

Subquadratic Algorithms for 3SUM

We obtain subquadratic algorithms for 3SUM on integers and rationals in several models. On a standard word RAM with w-bit words, we obtain a running time of . In the circuit RAM with one nonstandard AC ⁰ operation, we obtain . In external memory, we achieve O(n ²/(MB)), even under the standard assumption of data indivisibility. Cache-obliviously, we obtain a running time of . In all cases, our speedup is almost quadratic in the “parallelism” the model can afford, which may be the best possible. Our algorithms are Las Vegas randomized; time bounds hold in expectation, and in most cases, with high probability.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.     

Neck postures in air traffic controllers with and without neck/shoulder disorders

Prolonged computer work with an extended neck is commonly believed to be associated with an increased risk of neck-shoulder disorders. The aim of this study was to compare neck postures during computer work between female cases with neck-shoulder disorders, and healthy referents. Based on physical examinations, 13 cases and 11 referents were selected among 70 female air traffic controllers with the same computer-based work tasks and identical workstations. Postures and movements were measured by inclinometers, placed on the forehead and upper back (C7/Th1) during authentic air traffic control. A recently developed method was applied to assess flexion/extension in the neck, calculated as the difference between head and upper back flexion/extension. Results: cases and referents did not differ significantly in neck posture (median neck flexion/extension: -10 degrees vs. -9 degrees ; p=0.9). Hence, the belief that neck extension posture is associated with neck-shoulder disorders in computer work is not supported by the present data.     

Light diffraction from rough gratings

Two holographic diffraction gratings with very similar parameters, designed for maximum performance inTM polarization, are investigated, their diffraction efficiency measured, an electron microscopic picture of their surface observed, and numerical simulation of light diffraction done with the surface roughness taken into account. It is demonstrated numerically that a small-scale roughness imposed on the grating surface could increase significantly both scattering and absorption from the surface, this influence being greater in the TM case. A very good coincidence between the numerical and experimental data is obtained in TE polarization, whereas in TM polarization only a qualitative agreement exists.