Automatically learning topics and difficulty levels of problems in online judge systems

Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online learning systems, such as Massive Open Online Course, most OJ systems are designed for self-directed learning without the intervention of teachers. Also, in most OJ systems, problems are simply listed in volumes and there is no clear organization of them by topics or difficulty levels. As such, problems in the same volume are mixed in terms of topics or difficulty levels. By analyzing large-scale users’ learning traces, we observe that there are two major learning modes (or patterns). Users either practice problems in a sequential manner from the same volume regardless of their topics or they attempt problems about the same topic, which may spread across multiple volumes. Our observation is consistent with the findings in classic educational psychology. Based on our observation, we propose a novel two-mode Markov topic model to automatically detect the topics of online problems by jointly characterizing the two learning modes. For further predicting the difficulty level of online problems, we propose a competition-based expertise model using the learned topic information. Extensive experiments on three large OJ datasets have demonstrated the effectiveness of our approach in three different tasks, including skill topic extraction, expertise competition prediction and problem recommendation

City-level comparison of urban land-cover configurations from 2000-2015 across 65 countries within the global Belt and Road

The configuration of urban land-covers is essential for improving dwellers' environments and ecosystem services. A city-level comparison of land-cover changes along the Belt and Road is still unavailable due to the lack of intra-urban land products. A synergistic classification methodology of sub-pixel un-mixing, multiple indices, decision tree classifier, unsupervised (SMDU) classification was established in the study to examine urban land covers across 65 capital cities along the Belt and Road during 2000-2015. The overall accuracies of the 15 m resolution urban products (i.e., the impervious surface area, vegetation, bare soil, and water bodies) derived from Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) images were 92.88% and 93.19%, with kappa coefficients of 0.84 and 0.85 in 2000 and 2015, respectively. The built-up areas of 65 capital cities increased from 23,696.25 km(2) to 29,257.51 km(2), with an average growth rate of 370.75 km(2)/y during 2000-2015. Moreover, urban impervious surface area (ISA) expanded with an average rate of 401.92 km(2)/y, while the total area of urban green space (UGS) decreased with an average rate of 17.59 km(2)/y. In different regions, UGS changes declined by 7.37% in humid cities but increased by 14.61% in arid cities. According to the landscape ecology indicators, urban land-cover configurations became more integrated (oShannon's Diversity Index (SHDI) = -0.063; oPatch Density (PD) = 0.054) and presented better connectivity (oConnectance Index (CON) = +0.594). The proposed method in this study improved the separation between ISA and bare soil in mixed pixels, and the 15 m intra-urban land-cover product provided essential details of complex urban landscapes and urban ecological needs compared with contemporary global products. These findings provide valuable information for urban planners dealing with human comfort and ecosystem service needs in urban areas

Controllability, Reachability, and Stabilizability of Finite Automata: A Controllability Matrix Method

This paper investigates the controllability, reachability, and stabilizability of finite automata by using the semitensor product of matrices. Firstly, by expressing the states, inputs, and outputs as vector forms, an algebraic form is obtained for finite automata. Secondly, based on the algebraic form, a controllability matrix is constructed for finite automata. Thirdly, some necessary and sufficient conditions are presented for the controllability, reachability, and stabilizability of finite automata by using the controllability matrix. Finally, an illustrative example is given to support the obtained new results

2D Covalent Organic Frameworks as Intrinsic Photocatalysts for Visible Light-Driven COMsub\u3e2\u3c/sub\u3e Reduction

Covalent organic framework (COF) represents an emerging class of porous materials that have exhibited great potential in various applications, particularly in catalysis. In this work, we report a newly designed 2D COF with incorporated Re complex, which exhibits intrinsic light absorption and charge separation (CS) properties. We show that this hybrid catalyst can efficiently reduce CO2 to form CO under visible light illumination with high electivity (98%) and better activity than its homogeneous Re counterpart. More importantly, using advanced transient optical and X-ray absorption spectroscopy and in situ diffuse reflectance spectroscopy, we unraveled three key intermediates that are responsible for CS, the induction period, and rate limiting step in catalysis. This work not only demonstrates the potential of COFs as next generation photocatalysts for solar fuel conversion but also provide unprecedented insight into the mechanistic origins for light-driven CO2 reduction

Ammonia Gas Detection by Tannic Acid Functionalized and Reduced Graphene Oxide at Room Temperature

Reduced graphene oxide (rGO) based chemiresistor gas sensor has received much attention in gas sensing for high sensitivity, room temperature operation, and reversible. Here, for the first time, we present a promising chemiresistor for ammonia gas detection based on tannic acid (TA) functionalized and reduced graphene oxide (rGOTA functionalized). Green reductant of TA plays a major role in both reducing process and enhancing the gas sensing properties of rGOTA functionalized. Our results show rGOTA functionalized only selective to ammonia with excellent respond, recovery, respond time, and recovery times. rGOTA functionalized electrical resistance decreases upon exposure to NH3 where we postulated that it is due to n-doping by TA and charge transfer between rGOTA functionalized and NH3 through hydrogen bonding. Furthermore, rGOTA functionalized hinders the needs for stimulus for both recovery and respond. The combination of greener sensing material and simplicity in overall sensor design provides a new sight for green reductant approach of rGO based chemiresistor gas sensor

Jump-seq: Genome-Wide Capture and Amplification of 5-Hydroxymethylcytosine Sites

5-Hydroxymethylcytosine (5hmC) arises from the oxidation of 5-methylcytosine (5mC) by Fe2+ and 2-oxoglutarate-dependent 10–11 translocation (TET) family proteins. Substantial levels of 5hmC accumulate in many mammalian tissues, especially in neurons and embryonic stem cells, suggesting a potential active role for 5hmC in epigenetic regulation beyond being simply an intermediate of active DNA demethylation. 5mC and 5hmC undergo dynamic changes during embryogenesis, neurogenesis, hematopoietic development, and oncogenesis. While methods have been developed to map 5hmC, more efficient approaches to detect 5hmC at base resolution are still highly desirable. Herein, we present a new method, Jump-seq, to capture and amplify 5hmC in genomic DNA. The principle of this method is to label 5hmC by the 6-N3-glucose moiety and connect a hairpin DNA oligonucleotide carrying an alkyne group to the azide-modified 5hmC via Huisgen cycloaddition (click) chemistry. Primer extension starts from the hairpin motif to the modified 5hmC site and then continues to “land” on genomic DNA. 5hmC sites are inferred from genomic DNA sequences immediately spanning the 5-prime junction. This technology was validated, and its utility in 5hmC identification was confirmed