TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References

In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.Comment: MICCAI2017 Ora

Symbiotic effectiveness of pea-rhizobia associations and the implications for farming systems in the western Loess Plateau, China

Interactions between pea (Pisum sativa L.) cultivars and Rhizobium strain affect the symbiotic relationship and ultimately both the nitrogen fixing capacity and the yield. Since Pisum sativum L. is poorly nodulated in the Loess Plateau of China where this crop is grown, the response of pea cultivars Yannong No.2 and Luwan to inoculation with reference Rhizobium ACCC16101, ACCC16103 and selected strains, namely SY3.1, SY12, GWW16, GWC7.3, GDB27, GQW28, GQZ5, GLC1, QGW19 and XC3.1, was studied in two soil types in a greenhouse. Selected strains were isolated from the root nodules of pea (Pisum sativum L.), broad bean (Vicia faba L.) and lentil (Lens culinaris L.) plants in the Loess Plateau of China. Analyses focused on the nodule number, nodule dry weight, plant dry weight, nitrogenase activity, total N accumulation of per plant and seed yield. A significant interaction between pea and rhizobia was observed. The selected strain GDB27 was considered to be the best symbiotically efficient for all pea cultivars in the various soils. Strain XC3.1 evidenced relatively superior symbiotic effectiveness with pea cultivar Yannong No.2. Strains QGW19 and GWW16 performed well with Luwan. Correlation among the parameters for the plants showed that N2-fixation was positively and strongly correlated with nodule dry weight, whole plant dry weight and Acetylene Reduction Assay (ARA) in both soils, but not always significantly correlated with nodule number. Yield increases will follow widespread adoption of inoculation with cultivar-specific strains of Rhizobium.Key words: Nitrogen fixation, Rhizobium, Loess Plateau, soil type, yield increas

SU(3) and Nonet Breaking Effects in KL→γγK_L \to \gamma \gamma Induced by s→d+2gluons \to d + 2{gluon} due to Anomaly

In this paper we study the effects of $s\to d + 2{gluon}$ on $K_L \to \gamma\gamma$ in the Standard Model. We find that this interaction can induce new sizeable SU(3) and U(3) nonet breaking effects in $K_L - \eta, \eta'$ transitions and therefore in $K_L\to \gamma\gamma$ due to large matrix elements of $$ from QCD anomaly. These new effects play an important role in explaining the observed value. We also study the effects of this interaction on the contribution to $\Delta m_{K_L-K_S}$.Comment: RevTex, 12 Pages, no figures. Version to be published in PR

A Novel Large Moment Antiferromagnetic Order in K0.8Fe1.6Se2 Superconductor

The discovery of cuprate high Tc superconductors has inspired searching for unconventional su- perconductors in magnetic materials. A successful recipe has been to suppress long-range order in a magnetic parent compound by doping or high pressure to drive the material towards a quantum critical point, which is replicated in recent discovery of iron-based high TC superconductors. The long-range magnetic order coexisting with superconductivity has either a small magnetic moment or low ordering temperature in all previously established examples. Here we report an exception to this rule in the recently discovered potassium iron selenide. The superconducting composition is identified as the iron vacancy ordered K0.8Fe1.6Se2 with Tc above 30 K. A novel large moment 3.31 {\mu}B/Fe antiferromagnetic order which conforms to the tetragonal crystal symmetry has the unprecedentedly high an ordering temperature TN = 559 K for a bulk superconductor. Staggeredly polarized electronic density of states thus is suspected, which would stimulate further investigation into superconductivity in a strong spin-exchange field under new circumstance.Comment: 5 figures, 5 pages, and 2 tables in pdf which arXiv.com cannot tak

Wireless sensor networks for heritage object deformation detection and tracking algorithm

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection