ISAC-NET: Model-driven Deep Learning for Integrated Passive Sensing and Communication

Recent advances in wireless communication with the enormous demands of sensing ability have given rise to the integrated sensing and communication (ISAC) technology, among which passive sensing plays an important role. The main challenge of passive sensing is how to achieve high sensing performance in the condition of communication demodulation errors. In this paper, we propose an ISAC network (ISAC-NET) that combines passive sensing with communication signal detection by using model-driven deep learning (DL). Dissimilar to existing passive sensing algorithms that first demodulate the transmitted symbols and then obtain passive sensing results from the demodulated symbols, ISAC-NET obtains passive sensing results and communication demodulated symbols simultaneously. Different from the data-driven DL method, we adopt the block-by-block signal processing method that divides the ISAC-NET into the passive sensing module, signal detection module and channel reconstruction module. From the simulation results, ISAC-NET obtains better communication performance than the traditional signal demodulation algorithm, which is close to OAMP-Net2. Compared to the 2D-DFT algorithm, ISAC-NET demonstrates significantly enhanced sensing performance. In summary, ISAC-NET is a promising tool for passive sensing and communication in wireless communications.Comment: 29 pages, 11 figure

The impact of electronic health records on risk management of information systems in Australian residential aged care homes

To obtain indications of the influence of electronic health records (EHR) in managing risks and meeting information system accreditation standard in Australian residential aged care (RAC) homes. The hypothesis to be tested is that the RAC homes using EHR have better performance in meeting information system standards in aged care accreditation than their counterparts only using paper records for information management. Content analysis of aged care accreditation reports from the Aged Care Standards and Accreditation Agency produced between April 2011 and December 2013. Items identified included types of information systems, compliance with accreditation standards, and indicators of failure to meet an expected outcome for information systems. The Chi-square test was used to identify difference between the RAC homes that used EHR systems and those that used paper records in not meeting aged care accreditation standards. 1,031 (37.4%) of 2,754 RAC homes had adopted EHR systems. Although the proportion of homes that met all accreditation standards was significantly higher for those with EHR than for homes with paper records, only 13 RAC homes did not meet one or more expected outcomes. 12 used paper records and nine of these failed the expected outcome for information systems. The overall contribution of EHR to meeting aged care accreditation standard in Australia was very small. Risk indicators for not meeting information system standard were no access to accurate and appropriate information, failure in monitoring mechanisms, not reporting clinical incidents, insufficient recording of residents\u27 clinical changes, not providing accurate care plans, and communication processes failure. The study has provided indications that use of EHR provides small, yet significant advantages for RAC homes in Australia in managing risks for information management and in meeting accreditation requirements. The implication of the study for introducing technology innovation in RAC in Australia is discussed

EGC: Image Generation and Classification via a Diffusion Energy-Based Model

Learning image classification and image generation using the same set of network parameters is a challenging problem. Recent advanced approaches perform well in one task often exhibit poor performance in the other. This work introduces an energy-based classifier and generator, namely EGC, which can achieve superior performance in both tasks using a single neural network. Unlike a conventional classifier that outputs a label given an image (i.e., a conditional distribution $p(y|\mathbf{x})$), the forward pass in EGC is a classifier that outputs a joint distribution $p(\mathbf{x},y)$, enabling an image generator in its backward pass by marginalizing out the label $y$. This is done by estimating the energy and classification probability given a noisy image in the forward pass, while denoising it using the score function estimated in the backward pass. EGC achieves competitive generation results compared with state-of-the-art approaches on ImageNet-1k, CelebA-HQ and LSUN Church, while achieving superior classification accuracy and robustness against adversarial attacks on CIFAR-10. This work represents the first successful attempt to simultaneously excel in both tasks using a single set of network parameters. We believe that EGC bridges the gap between discriminative and generative learning

2-(4-Fluoro­phen­yl)quinoxaline

In the title compound, C14H9FN2, the dihedral angle between the benzene ring and the quinoxaline ring system is 22.2 (3)°. Any aromatic π–π stacking in the crystal must be very weak, with a minimum centroid–centroid separation of 3.995 (2) Å

41540R01 KINETICS OF SLURRY PHASE FISCHER-TROPSCH SYNTHESIS First Annual Technical Progress Report Reporting Period Start Disclaimer

Abstract This report covers the first year of this three-year research grant under the University Coal Research program. The overall objective of this project is to develop a comprehensive kinetic model for slurry phase Fischer-Tropsch synthesis on iron catalysts. This model will be validated with experimental data obtained in a stirred tank slurry reactor (STSR) over a wide range of process conditions. The model will be able to predict concentrations of all reactants and major product species (H 2 O, CO 2 , linear 1-and 2-olefins, and linear paraffins) as a function of reaction conditions in the STSR. During the reporting period we have completed one STSR test with precipitated iron catalyst obtained from Ruhrchemie AG (Oberhausen-Holten, Germany). This catalyst was initially in commercial fixed bed reactors at Sasol in South Africa. The catalyst was tested at 13 different sets of process conditions, and had experienced a moderate deactivation during the first 500 h of testing (decrease in conversion from 56% to 50% at baseline process conditions). The second STSR test has been initiated and after 270 h on stream, the catalyst was tested at 6 different sets of process conditions.

Suppression of Superconductivity by Twin Boundaries in FeSe

Low-temperature scanning tunneling microscopy and spectroscopy are employed to investigate twin boundaries in stoichiometric FeSe films grown by molecular beam epitaxy. Twin boundaries can be unambiguously identified by imaging the 90{\deg} change in the orientation of local electronic dimers from Fe site impurities on either side. Twin boundaries run at approximately 45{\deg} to the Fe-Fe bond directions, and noticeably suppress the superconducting gap, in contrast with the recent experimental and theoretical findings in other iron pnictides. Furthermore, vortices appear to accumulate on twin boundaries, consistent with the degraded superconductivity there. The variation in superconductivity is likely caused by the increased Se height in the vicinity of twin boundaries, providing the first local evidence for the importance of this height to the mechanism of superconductivity.Comment: 6 pages, 7 figure