The granitic rocks around the Utla area（Gadoon）, north western, Pakistan are studied in terms of their petrographic features and geochemical characteristics. Although predominantly mega-porphyritic, some of the Utla...The granitic rocks around the Utla area（Gadoon）, north western, Pakistan are studied in terms of their petrographic features and geochemical characteristics. Although predominantly mega-porphyritic, some of the Utla granites are massive and display fine-grained equi-granular texture. Some of the mega-porphyritic varieties exhibit foliation and seem to be restricted to shear zones. In addition to being distributed largely as phenocrysts, all the essential minerals（plagioclase, perthitic alkali feldspar and quartz） also constitute the groundmass. The studied samples also contain minor to accessory amounts of tourmaline, muscovite and biotite and accessory to trace amounts of apatite, andalusite, garnet, zircon, monazite, epidote and sphene. A detailed geochemical investigation reveals a calc-alkaline and peraluminous character of the Utla granites. The peraluminous character and total lack of hornblende designate their S-type character while a volcanic arc or syn-collisional tectonic setting for their emplacement is indicated by discrimination diagrams. Further examination shows that the melt parental to the Utla granite was derived from a plagioclase-poor, clay-rich rock, i.e., pelite. The petrogenetically significant petrographic and geochemical features of the Utla granite show greater similarity with the Mansehra than the Ambela granites. These include（i） the predominantly megaporphyritic texture,（ii） the presence of andalusite and tourmaline,（iii） the calc-alkaline geochemical signature and（iv） an indication of similar melt source rock character.展开更多
Petrographic and geo-mechanical properties of samples representing quartzose rocks of Tanawal Formation (Baja Bamkhel area, Swabi) and Misri Banda quartzite (Nowshera) from Peshawar Basin, NW Pakistan, have been inves...Petrographic and geo-mechanical properties of samples representing quartzose rocks of Tanawal Formation (Baja Bamkhel area, Swabi) and Misri Banda quartzite (Nowshera) from Peshawar Basin, NW Pakistan, have been investigated. Although formerly referred to as quartzite, mineralogical composition and textural details support characterization of the studied quartzose samples of Tanawal Formation as blasto-psammite and those of Misri Banda as sub-arkose. The two rock types also show significant differences in terms of matrix and heavy mineral concentrations as well as the degree and frequency of intra-granular deformation. On the basis of unconfined compressive strength (UCS), both fall in the category of very strong rocks. Correspondingly, their specific gravity and water absorption values are high and low respectively and lie well within the range permissible for use as construction material. However, both contain high amounts of deleterious components, i.e. reactive forms of silica (chert and/or strained quartz) and clay minerals. Therefore, they cannot be used as coarse aggregate with Ordinary Portland Cement (OPC) and asphalt. The modal abundance of quartz is higher in the Misri Banda than the Tanawal samples, but the quartz to feldspar ratios are higher for the latter. Yet, the sub-arkosic Misri Banda rocks are stronger than the Tanawal blasto-psammites, most probably because they are i) almost totally devoid of matrix;ii) finer grained;iii) having a higher percentage of grains with irregular and sutured contacts and iv) lesser abundance of grains displaying intra-granular deformation.展开更多
Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence i...Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures that the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss which has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than other models;we attribute this to the dataset’s low dimensionality as the most probable explanation.展开更多
文摘The granitic rocks around the Utla area（Gadoon）, north western, Pakistan are studied in terms of their petrographic features and geochemical characteristics. Although predominantly mega-porphyritic, some of the Utla granites are massive and display fine-grained equi-granular texture. Some of the mega-porphyritic varieties exhibit foliation and seem to be restricted to shear zones. In addition to being distributed largely as phenocrysts, all the essential minerals（plagioclase, perthitic alkali feldspar and quartz） also constitute the groundmass. The studied samples also contain minor to accessory amounts of tourmaline, muscovite and biotite and accessory to trace amounts of apatite, andalusite, garnet, zircon, monazite, epidote and sphene. A detailed geochemical investigation reveals a calc-alkaline and peraluminous character of the Utla granites. The peraluminous character and total lack of hornblende designate their S-type character while a volcanic arc or syn-collisional tectonic setting for their emplacement is indicated by discrimination diagrams. Further examination shows that the melt parental to the Utla granite was derived from a plagioclase-poor, clay-rich rock, i.e., pelite. The petrogenetically significant petrographic and geochemical features of the Utla granite show greater similarity with the Mansehra than the Ambela granites. These include（i） the predominantly megaporphyritic texture,（ii） the presence of andalusite and tourmaline,（iii） the calc-alkaline geochemical signature and（iv） an indication of similar melt source rock character.
文摘Petrographic and geo-mechanical properties of samples representing quartzose rocks of Tanawal Formation (Baja Bamkhel area, Swabi) and Misri Banda quartzite (Nowshera) from Peshawar Basin, NW Pakistan, have been investigated. Although formerly referred to as quartzite, mineralogical composition and textural details support characterization of the studied quartzose samples of Tanawal Formation as blasto-psammite and those of Misri Banda as sub-arkose. The two rock types also show significant differences in terms of matrix and heavy mineral concentrations as well as the degree and frequency of intra-granular deformation. On the basis of unconfined compressive strength (UCS), both fall in the category of very strong rocks. Correspondingly, their specific gravity and water absorption values are high and low respectively and lie well within the range permissible for use as construction material. However, both contain high amounts of deleterious components, i.e. reactive forms of silica (chert and/or strained quartz) and clay minerals. Therefore, they cannot be used as coarse aggregate with Ordinary Portland Cement (OPC) and asphalt. The modal abundance of quartz is higher in the Misri Banda than the Tanawal samples, but the quartz to feldspar ratios are higher for the latter. Yet, the sub-arkosic Misri Banda rocks are stronger than the Tanawal blasto-psammites, most probably because they are i) almost totally devoid of matrix;ii) finer grained;iii) having a higher percentage of grains with irregular and sutured contacts and iv) lesser abundance of grains displaying intra-granular deformation.
文摘Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures that the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss which has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than other models;we attribute this to the dataset’s low dimensionality as the most probable explanation.
