Are tiled display walls needed for astronomy?

Clustering commodity displays into a Tiled Display Wall (TDW) provides a cost-effective way to create an extremely high resolution display, capable of approaching the image sizes now gen- erated by modern astronomical instruments. Astronomers face the challenge of inspecting single large images, many similar images simultaneously, and heterogeneous but related content. Many research institutions have constructed TDWs on the basis that they will improve the scientific outcomes of astronomical imagery. We test this concept by presenting sample images to astronomers and non- astronomers using a standard desktop display (SDD) and a TDW. These samples include standard English words, wide field galaxy surveys and nebulae mosaics from the Hubble telescope. These experiments show that TDWs provide a better environment for searching for small targets in large images than SDDs. It also shows that astronomers tend to be better at searching images for targets than non-astronomers, both groups are generally better when employing physical navigation as opposed to virtual navigation, and that the combination of two non-astronomers using a TDW rivals the experience of a single astronomer. However, there is also a large distribution in aptitude amongst the participants and the nature of the content also plays a significant role is success.Comment: 19 pages, 15 figures, accepted for publication in PASA (Publications of the Astronomical Society of Australia

Conformal Magnetic Composite RFID for Wearable RF and Bio-Monitoring Applications

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.10.1109/TMTT.2008.2006810This paper introduces for the first time a novel flexible magnetic composite material for RF identification (RFID) and wearable RF antennas. First, one conformal RFID tag working at 480 MHz is designed and fabricated as a benchmarking prototype and the miniaturization concept is verified. Then, the impact of the material is thoroughly investigated using a hybrid method involving electromagnetic and statistical tools. Two separate statistical experiments are performed, one for the analysis of the impact of the relative permittivity and permeability of the proposed material and the other for the evaluation of the impact of the dielectric and magnetic loss on the antenna performance. Finally, the effect of the bending of the antenna is investigated, both on the S-parameters and on the radiation pattern. The successful implementation of the flexible magnetic composite material enables the significant miniaturization of RF passives and antennas in UHF frequency bands, especially when conformal modules that can be easily fine-tuned are required in critical biomedical and pharmaceutical applications

Knowledge-aware Complementary Product Representation Learning

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness. We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases. The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers

Numerical integration of variational equations

We present and compare different numerical schemes for the integration of the variational equations of autonomous Hamiltonian systems whose kinetic energy is quadratic in the generalized momenta and whose potential is a function of the generalized positions. We apply these techniques to Hamiltonian systems of various degrees of freedom, and investigate their efficiency in accurately reproducing well-known properties of chaos indicators like the Lyapunov Characteristic Exponents (LCEs) and the Generalized Alignment Indices (GALIs). We find that the best numerical performance is exhibited by the \textit{`tangent map (TM) method'}, a scheme based on symplectic integration techniques which proves to be optimal in speed and accuracy. According to this method, a symplectic integrator is used to approximate the solution of the Hamilton's equations of motion by the repeated action of a symplectic map $S$, while the corresponding tangent map $TS$, is used for the integration of the variational equations. A simple and systematic technique to construct $TS$ is also presented.Comment: 27 pages, 11 figures, to appear in Phys. Rev.

Mapping moral language on US presidential primary campaigns reveals rhetorical networks of political division and unity

During political campaigns, candidates use rhetoric to advance competing visions and assessments of their country. Research reveals that the moral language used in this rhetoric can significantly influence citizens’ political attitudes and behaviors; however, the moral language actually used in the rhetoric of elites during political campaigns remains understudied. Using a data set of every tweet (N=139,412) published by 39 US presidential candidates during the 2016 and 2020 primary elections, we extracted moral language and constructed network models illustrating how candidates’ rhetoric is semantically connected. These network models yielded two key discoveries. First, we find that party affiliation clusters can be reconstructed solely based on the moral words used in candidates’ rhetoric. Within each party, popular moral values are expressed in highly similar ways, with Democrats emphasizing careful and just treatment of individuals and Republicans emphasizing in-group loyalty and respect for social hierarchies. Second, we illustrate the ways in which outsider candidates like Donald Trump can separate themselves during primaries by using moral rhetoric that differs from their parties’ common language. Our findings demonstrate the functional use of strategic moral rhetoric in a campaign context and show that unique methods of text network analysis are broadly applicable to the study of campaigns and social movements

Synthesis and Structural Characterization of a Metal Cluster and a Coordination Polymer Based on the [Mn6(μ4-O)2]10+ Unit

A new 1-D coordination polymer {[Mn6O2(O2CMe)10(H2O)4]·2.5H2O}∞ (1·2.5H2O)∞ and the cluster [Mn6O2(O2(O2CPh)10 (py)2(MeCN)(H2O)]·2MeCN (2·2MeCN) are reported. Both compounds were synthesized by room temperature reactions of [Mn3(μ3-O)(O2CR)6(L)2(L′)] (R = Me, L = L′ = py, (1·2.5H2O)∞; R = Ph, L = py, L′ = H2O, 2·2MeCN) in the presence of 3-hydroxymethylpyridine (3hmpH) in acetonitrile. The structures of these complexes are based on hexanuclear mixed-valent manganese carboxylate clusters containing the [Mn4IIMn2III(μ4-O)2]10+ structural core. (1·2.5H2O)∞ consists of zigzag chain polymers constructed from [Mn6O2(O2CMe)10(H2O)4] repeating units linked through acetate ligands, whereas 2·2MeCN comprises a discrete Mn6-benzoate cluster

Multimode bolometer development for the PIXIE instrument

The Primordial Inflation Explorer (PIXIE) is an Explorer-class mission concept designed to measure the polarization and absolute intensity of the cosmic microwave background. In the following, we report on the design, fabrication, and performance of the multimode polarization-sensitive bolometers for PIXIE, which are based on silicon thermistors. In particular we focus on several recent advances in the detector design, including the implementation of a scheme to greatly raise the frequencies of the internal vibrational modes of the large-area, low-mass optical absorber structure consisting of a grid of micromachined, ion-implanted silicon wires. With $\sim30$ times the absorbing area of the spider-web bolometers used by Planck, the tensioning scheme enables the PIXIE bolometers to be robust in the vibrational and acoustic environment at launch of the space mission. More generally, it could be used to reduce microphonic sensitivity in other types of low temperature detectors. We also report on the performance of the PIXIE bolometers in a dark cryogenic environment.Comment: 10 pages, 7 figure

Tagged Arithmetic

A special tagged arithmetic system has been developed for use with FORTRAN pro grams written to process experimental data. The tagged arithmetic system carries a condition code with every numerical value and uses a special output to call attention to answers computed by using questionable input data. The questionable input data may result from instrumentation o r data recording system malfunctions, which can cause ill-conditioned calculations that result in process-time faults or error conditions.https://thekeep.eiu.edu/archives_armstead_publications/1000/thumbnail.jp