The iLabs platform - a low cost, highly scalable way to provide online learning for remote and virtual laboratory experiments is introduced into a classroom setting for the first time. As the COVID-19 pandemic has disrupted conventional laboratory experiment-based teaching, the iLabs platform can be a potential solution to include remote and virtual experiments in the curricula. As a case study, the process and challenges of integrating an iLabs experiment into a course are discussed. The described process includes converting a physical experiment into an iLabs experiment and using it as a teaching tool in one course at Stanford University during the Autumn 2020-2021 quarter. Feedback collected from the students is discussed. Although performed on a small scale, the case study suggests that the platform has the potential to be a helpful teaching tool for science and technology classes. In addition to being a substitute for a physical laboratory during the pandemic, the platform can be included as a supplementary teaching tool complementing physical laboratory experiments after the pandemic as well and will aid remote education.

This article presents a general way to enable automatic generation of digitized laboratories (a sort of digital twin for laboratory experimental setups) from remote laboratories and their easy deployment and publication. To demonstrate its effectiveness, we use two existing tools to generate and publish two digitized laboratories online from two implementations of a Snell's law remote laboratory, although they could be applied to many other remote laboratories. The first of these tools is a communication protocol that was designed to manipulate laboratory equipment through the Internet. This protocol can be used to automatically loop through different possible laboratory states and store them. The second one is a web platform that allows uploading files, that contain data sets of the laboratory states, to publish the digitized laboratory as a web application that is generated automatically.

Reasoning about unexpected outcomes is crucial to scientific exploration and STEM education. To stimulate conversations about these outcomes, we integrated noise characteristics and uncertainty into the framework of Massively Scalable Online Laboratories (MSOLs), which provide a collaborative and interactive online environment for education. The MSOL approach reduces a physical experiment to a set of states and displays it in an interactive environment. With the approach described here, those stages exhibit noise characteristics and nondeterministic outcomes. As a demonstration, we successfully recorded and displayed an example experiment, which contains quantum noise and limited repeatability. We thus display experiments more realistically and lead students towards a deeper understanding of the physical reality by evoking their curiosity about the variation of the displayed outcomes.

Educating peers and broader audiences is of great significance within the scientific community. The education process aids the communication of ideas and allows for engagement with science. We present a ready-to-use tool to exchange data collected in laboratories in a scalable, graphical and easily accessible manner using Massively Scalable Online Laboratories (MSOL) embedded within Virtual Interactive Scalable Online Notebooks for Science (VISONS). The tool we present replicates the experiment in a visual and interactive way, dynamically displaying the experiment in all of its states. Using VISONS, researchers can more easily engage in discussions about their experimental results, which allows for a deeper understanding of their data and scientific challenges. The form of data exchange and graphic interface we present here helps to stimulate purposeful conversations about cutting-edge science by using VISONS during discussions with peers or in publications. Moreover, our approach, which uses efficient data storage and retrieval capabilities, provides scalable data management that laboratory environments currently lack. Simultaneously, we provide a more effective way for researchers manage their own work. VISONS offer a clear and concise version control system, assisting scientists at all stages of the development process. To provide information security during preliminary experimental work, before official publications, we incorporated a feature to choose the target audience for each experiment. To demonstrate the merits of VISONS, we recorded different versions of a cutting-edge science experiment, a quantum-noise limited differential phase contrast X-Ray imaging system, during its development process.

An ideal visualization tool that has not been used before in studying the optical behavior of near-field apertures is three-dimensional vector field topology. The global view of the vector field structure is deduced by locating singularities (critical points) within the field and augmenting these points with nearby streamlines. We have used for the first time, to the best of our knowledge, three-dimensional topology to analyze the topological differences between a resonant C-shaped nano-aperture and various nonresonant conventional apertures. The topological differences between these apertures are related to the superiority in power throughput of the C-aperture versus conventional round and square sub-wavelength apertures. We demonstrate how topological visualization techniques provide significant insight into the energy enhancement mechanism of the C aperture, and also shed light on critical issues related to the interaction between multiple apertures located in close proximity to each other, which gives rise to cross-talk, for example as a function of distance. Topological techniques allow us to develop design rules for the geometry of these apertures and their desired spot sizes and brightness. The performance of various sub-wavelength apertures can also be compared quantitatively based on their topology. Since topological methods are generically applicable to tensor and vector fields, our approach can be readily extended to provide insight into the broader category of finite-difference-time-domain nano-photonics and nano-science problems.

In this paper, we discuss fundamental issues underlying holographic data storage: grating formation, recording and readout of thick and thin holograms, multiplexing techniques, signal-to-noise ratio considerations, and readout techniques suitable for conventional, phase conjugate, and associative search data retrieval. Next, we consider holographic materials characteristics for digital data storage, followed by a discussion on photorefractive media, fixing techniques, and noise in photovoltaic and other media with a local response. Subsequently, we discuss photopolymer materials, followed by a discussion on system tradeoffs and a section on signal processing and en/decoding techniques, succeeded by a discussion on electronic implementations for control, signal encoding, and recovery. We proceed further by presenting significant demonstrations of digital holographic systems. We close by discussing the outlook for future holographic data storage systems and potential applications for which holographic data storage systems would be particularly suited.

A method for comparing three-dimensional vector fields constructed from simple critical points is described. This method is a natural extension of previous work (Y. Lavin et al., 1998), which defined a distance metric for comparing two-dimensional fields. The extension to three-dimensions follows the path of our previous work, rethinking the representation of a critical point signature and the distance measure between the points. Since the method relies on topologically based information, problems such as grid matching and vector alignment which often complicate other comparison techniques are avoided. In addition, since only feature information is used to represent, and is therefore stored for each field, a significant amount of compression occurs.

A novel approach is introduced to define a quantitative measure of closeness between vector fields. The usefulness of this measurement can be seen when comparing computational and experimental flow fields under the same conditions. Furthermore, its applicability can be extended to more cumbersome tasks, such as navigating through a large database, searching for similar topologies. This new measure relies on the use of critical points, which are a key feature in vector field topology. In order to characterize critical points, /spl alpha/ and /spl beta/ parameters are introduced. They are used to form a closed set of eight unique patterns for simple critical points. These patterns are also basic building blocks for higher-order nonlinear vector fields. In order to study and compare a given set of vector fields, a measure of distance between different patterns of critical points is introduced. The basic patterns of critical points are mapped onto a unit circle in /spl alpha/-/spl beta/ space. The concept of the "Earth mover's distance" is used to compute the closeness between various pairs of vector fields, and a nearest-neighbor query is thus produced to illustrate the relationship between the given set of vector fields. This approach quantitatively measures the similarity and dissimilarity between vector fields. It is ideal for data compression of a large flow field, since only the number and types of critical points along with their corresponding /spl alpha/ and /spl beta/ parameters are necessary to reconstruct the whole field. It can also be used to better quantify the changes in time-varying data sets.

The authors study the topology of symmetric, second-order tensor fields. The results of the study can be readily extended to include general tensor fields through linear combination of symmetric tensor fields and vector fields. The goal is to represent their complex structure by a simple set of carefully chosen points, lines, and surfaces analogous to approaches in vector field topology. They extract topological skeletons of the eigenvector fields and use them for a compact, comprehensive description of the tensor field. Their approach is based on the premise: "analyze, then visualize". The basic constituents of tensor topology are the degenerate points, or points where eigenvalues are equal to each other. Degenerate points play a similar role as critical points in vector fields. In tensor fields they identify two kinds of elementary degenerate points, which they call wedge points and trisector points. They can combine to form more familiar singularities-such as saddles, nodes, centers, or foci. However, these are generally unstable structures in tensor fields. Based on the notions developed for 2D tensor fields, they extend the theory to include 3D degenerate points. Examples are given on the use of tensor field topology for the interpretation of physical systems.

We study the topology of symmetric, second-order tensor fields. The goal is to represent their complex structure by a simple set of carefully chosen points and lines analogous to vector field topology. We extract topological skeletons of the eigenvector fields, and we track their evolution over time. We study tensor topological transitions and correlate tensor and vector data. The basic constituents of tensor topology are the degenerate points, or points where eigenvalues are equal to each other. Degenerate points play a similar role as critical points in vector fields. We identify two kinds of elementary degenerate points, which we call wedges and trisectors. They can combine to form more familiar singularities-such as saddles, nodes, centers, or foci. However, these are generally unstable structures in tensor fields. Finally, we show a topological rule that puts a constraint on the topology of tensor fields defined across surfaces, extending to tensor fields the Poincare-Hopf theorem for vector fields.>

Volume rendering has been proposed as a useful tool for extracting information from large datasets, where non-visual analysis alone may not be feasible. The scale of these applications implies that data management is an important issue that needs to be addressed. Most volume rendering algorithms, however, process data in raw, uncompressed form. In previous work, we introduced a compressed volume format that may be volume rendered directly with minimal impact on rendering time. In this paper, we extend these ideas to a new volume format that not only reduces storage space and transmission time, but is designed for fast volume rendering as well. The volume dataset is represented as indices into a small codebook of representative blocks. With the data structure, volume shading calculations need only be performed on the codebook and image generation is accelerated by reusing precomputed block projections.>

New display technologies have begun to provide more innovative and potentially powerful methods to present information to a viewer. However, many of these techniques struggle to deliver accurate full color. In this paper, we address this difficulty by employing the dichromatic theory of color reflection, which implies that many objects can be rendered accurately using only two primaries. Complex display systems with two primaries can be produced with significantly less work than is required for the traditional three primaries. We discuss methods for selecting objects that can be rendered accurately on two-color displays, and we present our experiments with a two-color display using monochromatic primaries.>

The visualization of 3-D second-order tensor fields and matrix data is studied. The general problem of visualizing unsymmetric real or complex Hermitian second-order tensor fields can be reduced to the simultaneous visualization of a real and symmetric second-order tensor field and a real vector field. The emphasis is on exploiting the mathematical properties of tensor fields in order to facilitate their visualization and to produce a continuous representation of the data. The focus is on interactively sensing and exploring real and symmetric second-order tensor data by generalizing the vector notion of streamline to the tensor concept of hyperstreamline. The importance of a structural analysis of the data field analogous to the techniques of vector field topology extraction in order to obtain a unique and objective representation of second-order tensor fields is stressed.>

The use of critical point analysis to generate representations of the vector field topology of numerical flow data sets is discussed. Critical points are located and characterized in a two-dimensional domain, which may be either a two-dimensional flow field or the tangential velocity field near a three-dimensional body. Tangent curves are then integrated out along the principal directions of certain classes of critical points. The points and curves are linked to form a skeleton representing the two-dimensional vector field topology. When generated from the tangential velocity field near a body in a three-dimensional flow, the skeleton includes the critical points and curves which provide a basis for analyzing the three-dimensional structure of the flow separation.>