MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. For example, symptom \emph{runny nose} highly indicates the existence of disease \emph{whooping cough} when the patient is a baby rather than the people at other ages. Such conditions for medical knowledge are crucial for decision-making in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the amount of available EMRs is limited due to reasons such as regularization. Meanwhile, a large amount of medical question answering (QA) data is available, which can greatly help the studied task. However, the quality of medical QA data is quite diverse, which may degrade the quality of the discovered medical knowledge conditions. In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation. We conduct series of experiments on real-world medical datasets to demonstrate that the proposed method can discover meaningful and accurate conditions for medical knowledge by leveraging both EMR and QA data. Further, the proposed method is tested on synthetic datasets to validate its effectiveness under various scenarios.Comment: Accepted as CIKM2019 long pape

Poly[di-μ2-azido-μ3-pyrazine-2-carboxyl­ato-cadmium(II)]

The title compound, [Cd(C5H3N2O2)(N3)]n, has been pre­pared by the reaction of pyrazine-2-carboxylic acid, cadmium(II) nitrate and sodium azide. In the structure, the CdII atom is six-coordinated by two azide anions and three pyrazine-2-carboxyl­ate ligands. Each pyrazine-2-carboxyl­ate ligand bridges three CdII atoms, whereas the azide ligand bridges two CdII atoms, resulting in the formation of a two-dimensional metal–organic polymer developing parallel to the (100) plane

Millimeter Spectral Line Mapping Observations Toward Four Massive Star Forming HII Regions

We present spectral line mapping observations toward four massive star-forming regions (Cepheus A, DR21S, S76E and G34.26+0.15), with the IRAM 30 meter telescope at 2 mm and 3 mm bands. Totally 396 spectral lines from 51 molecules, one helium recombination line, ten hydrogen recombination lines, and 16 unidentified lines were detected in these four sources. An emission line of nitrosyl cyanide (ONCN, 14$_{0,14}$-13$_{0,13}$) was detected in G34.26+0.15, as first detection in massive star-forming regions. We found that the $c$-C$_{3}$H$_{2}$ and NH$_{2}$D show enhancement in shocked regions as suggested by evidences of SiO and/or SO emission. Column density and rotational temperature of CH$_{3}$CN were estimated with the rotational diagram method for all four sources. Isotope abundance ratios of $^{12}$C/$^{13}$C were derived using HC$_{3}$N and its $^{13}$C isotopologue, which were around 40 in all four massive star-forming regions and slightly lower than the local interstellar value ($\sim$65). $^{14}$N/$^{15}$N and $^{16}$O/$^{18}$O abundance ratios in these sources were also derived using double isotopic method, which were slightly lower than that in local interstellar medium. Except for Cep A, $^{33}$S/$^{34}$S ratio in the other three targets were derived, which were similar to that in the local interstellar medium. The column density ratios of N(DCN)/N(HCN) and N(DCO$^{+}$)/N(HCO$^{+}$) in these sources were more than two orders of magnitude higher than the elemental [D]/[H] ratio, which is 1.5$\times$10$^{-5}$. Our results show the later stage sources, G34.26+0.15 in particular, present more molecular species than earlier stage ones. Evidence of shock activity is seen in all stages studied.Comment: 32 pages, 11 figures, 8 tables, accepted for publication in MNRA

Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms