As the number of online job postings and users grows dramatically, the accuracy and explainability of personjob fit systems are of increasing concern. An explainable personjob fit system can show reasons when making recommendations to both Human Resources and Job Seekers, building trust between uses and recommendation system while providing accurate recommendation results. However, the existing research on content-based person-job fit mainly focuses on 1) dealing with unstructured statements without effectively using structured information in resumes and jobs, and 2) the explanations of the model stay at the level of giving a few sentences, which leads to a lack of explanations. In this paper, we propose an explainable person-job fit model based on the attention mechanism. We model the resume text through a hierarchical attention mechanism and capture the semantic connections between the resume, structured job text, and unstructured job text through a collaborative attention mechanism to better model the job content and provide both structured and unstructured levels of recommendation explanation. Experiments on a large real dataset show that our model outperforms existing baseline models and provides job recommendation reasons at both levels.
Background: Atelectasis and attic retraction pocket are two common tympanic membranes changes. However, general practitioners, pediatricians and otolaryngologists showed low diagnostic accuracy for these ear diseases. Therefore, there is a need to develop a deep learning model to detect atelectasis and attic retraction pocket automatically. Method: 6393 OME otoscopic images from 3 centers were used to develop and validate a deep learning model to detect atelectasis and attic retraction pocket. 3-fold random cross validation was adopted to divided dataset into training set and validation set. A team of otologists were assigned to diagnose and label. Receiver operating characteristic (ROC) curve, 3-fold average classification accuracy, sensitivity and specificity were used to assess the performance of deep learning model. Class Activation Mapping (CAM) was applied to show the discriminative region in the otoscopic images. Result: Among all the otoscopic images, 3564 (55.74%) images were identified with attic retraction pocket, and 2460 (38.48%) images were identified with atelectasis. The automatically diagnostic model of attic retraction pocket and atelectasis achieved 3-fold cross validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, sensitivity of 0.93 and 0.71, and specificity of 0.62 and 0.84 respectively. Bigger and deeper atelectasis and attic retraction pocket showed more weight with red color in the heat map of CAM. Conclusion: Deep learning algorithm could be used to identify atelectasis and attic retraction pocket, which could be used as a tool to assist general practitioners, pediatricians and otolaryngologists. Key words: deep learning, otoscopic images, atelectasis, attic retraction pocket
Guanfu base A (GFA) is a novel heterocyclic antiarrhythmic drug isolated from Aconitum coreanum (Lèvl.) rapaics and is currently in a phase IV clinical trial in China. However, no study has investigated the influence of GFA on cytochrome P450 (P450) drug metabolism. We characterized the potency and specificity of GFA CYP2D inhibition based on dextromethorphan O-demethylation, a CYP2D6 probe substrate of activity in human, mouse, rat, dog, and monkey liver microsomes. In addition, (+)-bufuralol 1′-hydroxylation was used as a CYP2D6 probe for the recombinant form (rCYP2D6), 2D1 (rCYP2D1), and 2D2 (rCYP2D2) activities. Results show that GFA is a potent noncompetitive inhibitor of CYP2D6, with inhibition constant Ki = 1.20 ± 0.33 μM in human liver microsomes (HLMs) and Ki = 0.37 ± 0.16 μM for the human recombinant form (rCYP2D6). GFA is also a potent competitive inhibitor of CYP2D in monkey (Ki = 0.38 ± 0.12 μM) and dog (Ki = 2.4 ± 1.3 μM) microsomes. However, GFA has no inhibitory activity on mouse or rat CYP2Ds. GFA did not exhibit any inhibition activity on human recombinant CYP1A2, 2A6, 2C8, 2C19, 3A4, or 3A5, but showed slight inhibition of 2B6 and 2E1. Preincubation of HLMs and rCYP2D6 resulted in the inactivation of the enzyme, which was attenuated by GFA or quinidine. Beagle dogs treated intravenously with dextromethorphan (2 mg/ml) after pretreatment with GFA injection showed reduced CYP2D metabolic activity, with the Cmax of dextrorphan being one-third that of the saline-treated group and area under the plasma concentration–time curve half that of the saline-treated group. This study suggests that GFA is a specific CYP2D6 inhibitor that might play a role in CYP2D6 medicated drug-drug interaction.
An efficient audio indexing and retrieval algorithm is proposed to locate similar audio segments in the database.A new boundary detection technique based on audio shot is proposed for audio segmentation.Subsequently, a new method is employed to convert the audio shot sequence to audio word sequence, which utilizes a self-learning audio shot dictionary.We also borrow the idea of inverted file from text retrieval to locate candidates efficiently.Furthermore, a similarity measure combining content and temporal order matching is proposed.Experiment results show a retrieval precision of 94.70% within an average response time of 6.344 seconds.
Traditional algorithms can only access to a single network, which leads to low utilization ratio of network resource. With the development of mobile communication technology, heterogeneous all-IP network architecture will be used in wireless mobile communication systems. In heterogeneous network, the key is to find a network access algorithm which can achieve high utilization ratio of network resource under the premise of user satisfaction. In this paper, we propose an IP flow Parallel Access Algorithm based on Multi-factor (IPAM). We build the objective cost function by using bandwidth and price as parameters, and then get the optimal flow distributing proportion by making the objective cost function minimum. The algorithm can also be used when multiple networks transmit at the same time. The simulation results show that the algorithm can reduce the price in low network load and balance load in high network load.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
