Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme.
Judgment documents contain a wealth of valuable information. The original judgment documents are written in pure text format, so we cannot obtain information directly, which hinders the study of the judgment documents. We propose an approach to parse Chinese judgment documents into structured documents to solve this problem. Divide a judgment document into logical segments, and then extract and label information items from these logical segments. Use information items to build analytic document information model and the model is output into a structured XML document.
Pretreatments of wheat straw are necessary before preparation of superabsorbent resins(SAR).Both the effects on wheat straw and the superabsorbent resins were analyzed and compared after acid hydrolysis,dipping in sodium hydroxide solution or ammonia,alkali cooking and their united pretreatments.Besides,the morphology and chemical structure of wheat straw and the chemical structure of SAR were confirmed by metallurgical microscope and IR spectra before and after the pretreatments.The results showed that the appropriate pretreatments were alkali cooking(water solution of w(NaOH)=14%,150 ℃,0.6 MPa,30 min)followed by hydrolysis with 1 mol/L nitric acid at 100 ℃ for 30 min,or dipping in w(NH_3·H_2O)=10% ammonia for 48 h at room temperature followed by hydrolysis with 1 mol/L nitric acid at 100 ℃ for 45 min.SAR prepared with the wheat straw after the two pretreatments can absorb distilled water of 405 g and 294 g per 1 g respectively,and can absorb the water solution(w(compound fertilizer) = 0.1% in which w(N)=w(P)=w(K)=10%,N existing in urea,P existing in single superphosphate and K existing in KCl) of 124 g and 84 g per 1 g,respectively.
Based on proposed joint human connectome project multi-modal parcellation (JHCPMMP), the study on the binary classification of Alzheimer's disease was conducted. We tried to build a novel classification model, which can be interpretative and have the ability to deal with the complexity and individual differences of brain networks. The subclass weighted logistic regression (SWLR) based on logistic regression was proposed in this paper. We conducted five groups of experiments, in which the accuracy of HC vs. AD was 95.8%, HC vs. EMCI was 91.6%, HC vs. LMCI was 93.7%, EMCI vs. LMCI was 89.5%, and LMCI vs. AD was 91.6%. In addition, we conducted a follow-up analysis of the coefficient matrix and found that the distribution of core deterioration brain regions in different stages is different in the development of Alzheimer's disease. We located these brain regions in two-dimensional images and found that they generally show a trend of continuous counterclockwise migration.
Caged antisense oligodeoxynucleotides (asODNs) are synthesized by linking two ends of linear oligodeoxynucleotides using a photocleavable linker. Two of them (H30 and H40) have hairpin-like structures which show a large difference in thermal stability (Delta T(m) = 17.5 degrees C and 11.6 degrees C) comparing to uncaged ones. The other three (C20, C30 and C40) without stable secondary structures have the middle 20 deoxynucleotides complementary to 40-mer RNA. All caged asODNs have restricted opening which provides control over RNA/asODN interaction. RNase H assay results showed that 40-mer RNA digestion could be photo-modulated 2- to 3-fold upon light-activation with H30, H40, C30 and C40, while with C20, RNA digestion was almost not detectable; however, photo-activation triggered >20-fold increase of RNA digestion. And gel shift assays showed that it needed >0.04 microM H40 and 0.5 microM H30 to completely bind 0.02 microM 40-mer RNA, and for C40 and C30, it needed >0.2 microM and 0.5 microM for 0.02 microM 40-mer RNA binding. However, even 4 microM C20 was not able to fully bind the same concentration of 40-mer RNA. By simple adjustment of ring size of caged asODNs, we could successfully photoregulate their hybridization with mRNA and target RNA hydrolysis by RNase H with light activation.
With the continuous expansion of the software market and the updating of the maturity of the software development process, the performance requirements of software users are becoming increasingly prominent. Performance issues are essentially related to the source code. For solving the same problem, different programmers may write completely different "correct" code with the same functionality but have different performance. Most online judge system on programming make use of automated grading systems, usually rely on test results to quantify the correctness and performance for the submitted source code. However, traditional dynamic testing takes a lot of time, and the discovery of performance problems is usually after the fact even for those small scale programs. Therefore, we proposed DeepTLE which is used to effectively predict the performance of submitted source code before it runs. DeepTLE can automatically learn the semantic and structural features of the source code. In order to verify the effect of our approach, we applied it to the source code collected from the program competition website to predict if the source code would be time limit exceed or not without running its test cases. Experiment results show that our method can save 96% of the time cost compared to the dynamic testing, and the accuracy of the prediction reaches 82%.
Effect of polysaccharides and water extract from Fructus tribuli on growth of Lactobacillus brevis was studied by measuring the optical density at 600 nm and pH using commercial MRS broth as the control. The addition (mg/mL) of steroidal saponins was 0.10, 0.20, 0.30, 0.40 and 0.50 and the addition (%, v/v) of water extract was 1, 2, 3, 4 and 5. Results were as follows: The additions of polysaccharides and water extract had a significant promotion on the growth of Lactobacillus brevis. The optimum concentrations of steroidal saponins and water extract were 0.40 mg/mL and 5%, respectively.
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