Coalgebraic Monads

This paper introduces coalgebraic monads as a unified model of term algebras covering fundamental examples such as initial algebras, final coalgebras, rational terms and term graphs. We develop a general method for obtaining finitary coalgebraic monads which allows us to generalise the notion of rational term and term graph to categories other than Set. As an application we sketch part of the correctness of the the term graph implementation of functional programming languages.     

Parallel integer sorting using small operations

We consider the problem of sortingn integers in the range [0,n ^c -1], wherec is a constant. It has been shown by Rajasekaran and Sen [14] that this problem can be solved optimally inO(logn) steps on an EREW PRAM withO(n) n -bit operations, for any constant >O. Though the number of operations is optimal, each operation is very large. In this paper, we show thatn integers in the range [0,n ^c -1] can be sorted inO(logn) time withO(nlogn)O(1)-bit operations andO(n) O(logn)-bit operations. The model used is a non-standard variant of an EREW PRAMtthat permits processors to have word-sizes ofO(1)-bits and (logn)-bits. Clearly, the speed of the proposed algorithm is optimal. Considering that the input to the problem consists ofO (n logn) bits, the proposed algorithm performs an optimal amount of work, measured at the bit level.This work was partially supported by The Northeast Parallel Architectures Center (NPAC) at Syracuse University, Syracuse, NY 13244 and The Rome Air Development Center, under contract F30602-88-D-0027.     

Classification of strongly correlated f-electron systems

Ce-based heavy-fermion (HF) metals behave as Kondo-lattice systems and can be classified with the aid of a single coupling parameter, ¦J ¦/W,where J < 0is the local exchange integral and W the conduction-band width. Depending on its actual composition, the exemplary material CeCu₂Si₂ chooses one out of two ground states: HF superconductivity and a new magnetic HF phase A. In a narrow composition range, these two phases are nearly degenerate and expell each other upon varying either the temperature or the external magnetic field. The one-parameter-scaling approach appears inapplicable to the U-based HF metals. For the exemplary material UPd₂Al₃, antiferromagnetic ordering between seemingly local 5f moments coexists, on a microscopic scale, with HF superconductivity. Whether this coexistence can be explained by assuming 5 f states localized on a tetravalent U-ion with non-magnetic crystal-field ground state remains to be shown. We discuss arguments which invokeitinerant 5 f states in the U-based HF metals to be distinguished from thelocalized 4f states in the Ce-based counterparts.     

Digital recording and numerical reconstruction of holograms: a new method for displaying light in flight

We present a new method for displaying light in flight. Fresnel holograms are recorded directly on a CCD sensor, electronically stored, and numerically reconstructed. Experimental results are shown. From different parts of a single holographic recording, different views of a wave front can be reconstructed. This means that the temporal evolution of a wave front can be observed by numerical methods.     

Fabrication Method,Ferromagnetic Resonance Spectroscopy and Spintronics Devices Based on the Organic-Based Ferrimagnet Vanadium Tetracyanoethylene

Organic-based magnetic materials have been used for spintronic device applications as electrodes of spin aligned carriers and spin-pumping substrates. Their advantages over more traditional inorganic magnets include reduced magnetic damping and lower fabrication costs. Vanadium tetracyanoethylene, V[TCNE]_x (x ≈ 2), is an organic-based ferrimagnet with an above room-temperature magnetic order temperature (T_c ≈ 400 K). V[TCNE]_x has deposition flexibility and can be grown on a variety of substrates via low-temperature chemical vapor deposition (CVD). A systematic study of V[TCNE]_x thin-film CVD parameters to achieve optimal film quality, reproducibility, and excellent magnetic properties is reported. This is assessed by broadband ferromagnetic resonance (FMR) that shows most narrow linewidth of ≈1.5 Gauss and an extremely low Gilbert damping coefficient. The neat V[TCNE]_x films are shown to be efficient spin injectors via spin pumping into an adjacent platinum layer. Also, under an optimized FMR linewidth, the V[TCNE]_x films exhibit Fano-type resonance with a continuum broadband absorption in the microwave range, which can be readily tuned by the microwave frequency.     

Going with the Flow: Tunable Flow‐Induced Polymer Mechanochemistry

Mechanical forces can drive chemical transformations in polymers, directing reactions along otherwise inaccessible pathways, providing exciting possibilities for developing smart, responsive materials. The state‐of‐the‐art test for solution‐based polymer mechanochemistry development is ultrasonication. However, this does not accurately model the forces that will be applied during device fabrication using processes such as 3D printing or spray coating. Here, a step is taken toward predictably translating mechanochemistry from molecular design to manufacturing by demonstrating a highly controlled nozzle flow setup in which the shear forces being delivered are precisely tuned. The results show that solvent viscosity, fluid strain rate, and the nature of the breaking bond can be individually studied. Importantly, it is shown that the influence of each is different to that suggested by ultrasonication (altered quantity of chain breakage and critical polymer chain length). Significant development is presented in the understanding of polymer bond breakage during manufacturing flows to help guide design of active components that trigger on demand. Using an anthracene‐based mechanophore, the triggering of a fluorescence turn‐on is demonstrated through careful selection of the flow parameters. This work opens the avenue for programmed chemical transformations during inline manufacturing processes leading to tunable, heterogeneous final products from a single source material.     

Designing a shared freight service intelligence platform for transport stakeholders using mobile telematics

Internet of Things (IoT) technology transforms freight transport operations by adopting novel data-driven services and enables information sharing among actors involved in global transport chains. Mobile telematics represents emerging IoT technologies for global forwarding increasingly applied to full loads conveyed by freight transport assets (FTAs) (e.g., ISO containers) facilitating intelligent services. In this light, telematics-enabled FTAs support freight transport operations utilized by individual stakeholders in three overarching service dimensions: transport management, fleet management, and risk management. This topic is, however, understudied by information systems (IS) research and service science. For this reason, we establish a design science research project, conceptualize a shared Freight Service Intelligence Platform (FSIP), and introduce freight service intelligence as an interdisciplinary research field. To this aim, we first review related literature, interview 14 transport stakeholders, and theorize six meta-requirements. Second, we propose five design principles that indicate how the meta-requirements may be associated. Third, we develop a web-based prototype application to instantiate the proposed design principles comprising performance analytics, anomaly detection, risk assessment including prediction, data exchange, communication, and IS integration. Subsequently, we evaluate the application with six transport stakeholders and logistics software vendors. Finally, we conclude with a discussion on the implications of an emerging topic addressed by this paper.

     

Guiding the evolution of product-line configurations

Software and Systems Modeling - A product line is an approach for systematically managing configuration options of customizable systems, usually by means of features. Products are generated for...     

Learning to recommend similar items from human judgments

Similar item recommendations—a common feature of many Web sites—point users to other interesting objects given a currently inspected item. A common way of computing such recommendations is to use a similarity function, which expresses how much alike two given objects are. Such similarity functions are usually designed based on the specifics of the given application domain. In this work, we explore how such functions can be learned from human judgments of similarities between objects, using two domains of “quality and taste”—cooking recipe and movie recommendation—as guiding scenarios. In our approach, we first collect a few thousand pairwise similarity assessments with the help of crowdworkers. Using these data, we then train different machine learning models that can be used as similarity functions to compare objects. Offline analyses reveal for both application domains that models that combine different types of item characteristics are the best predictors for human-perceived similarity. To further validate the usefulness of the learned models, we conducted additional user studies. In these studies, we exposed participants to similar item recommendations using a set of models that were trained with different feature subsets. The results showed that the combined models that exhibited the best offline prediction performance led to the highest user-perceived similarity, but also to recommendations that were considered useful by the participants, thus confirming the feasibility of our approach.