Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach

Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all (OSFA) approach to train single deep neural network models that do not account for spatial variability. Quantification of spatial variability can be challenging due to the influence of many geophysical factors. In preliminary work, we proposed a spatial variability aware neural network (SVANN-I, formerly called SVANN ) approach where weights are a function of location but the neural network architecture is location independent. In this work, we explore a more flexible SVANN-E approach where neural network architecture varies across geographic locations. In addition, we provide a taxonomy of SVANN types and a physics inspired interpretation model. Experiments with aerial imagery based wetland mapping show that SVANN-I outperforms OSFA and SVANN-E performs the best of all.

METODE KOMBINASI DALAM MENAFSIRKAN SURAT AL-AHZAB (33) : 33

This paper describes the method of interpreting the Koran, the author considers that the interpretation of the Koran is carried out with various methods and ways that have been determined in the rules of interpretation, both through approaches, history, linguistics, social, science, and looking at the interpretations of the commentators. interpretation into four kinds. First, this Tahli>li method seeks to explain the content of the verses of the Qur'an from various aspects, according to the views, tendencies, and wishes of the commentators which are presented in a coherent manner in accordance with the order of the verses in the manuscripts. Second, this ijma>li or global method describes the general meanings contained by the verse. Third, muqa>ran or comparison. The interpretation model in this method is to present verses of the Koran with different editorials from one another, to present verses that have different information content from the hadith and to present various interpretations of the scholars. Fourth, maudhu>'i or thematic, namely methods that direct view on a particular theme. Then look for a particular theme by collecting all the verses that talk about it, analyzing and understanding verse by verse to get a complete interpretation of the theme being discussed.

BELENGGU RASIONALITAS DALAM TAFSĪR BI AL-MA’THŪR

The Interpretation with transmitted text (riwāyah) which uses to get meaning far away from the influence of ratio (al-ra'y) still cannot occur. Various different narrations for interpreting the same verse in the exegesis via narration (al-tafsīr bi al-ma'thūr) can be evidence of the use of ijtihad in that interpretation model. In this context, this study aims to find evidence and reconceptualize the terms to prove the existence ratio on exegesis via narration. This study uses a qualitative method with the type of study that uses literature research and uses content analysis as an analytical tool. This study finds that the interpreters in the category of exegesis via narration used ratio in the form of transmitted text selection to strengthen the meaning desired. The unawareness of the existence of rationality in the exegesis via narration is caused by the confusion of the terms used which are borrowed from the term of hadith study (muṣṭalah al-ḥadīth). Based on this, this study proposes to redefine that term to illustrate that dichotomy does not refer to the use of transmitted text or ratios in interpretation, but that dichotomy occurs from the proximity of the resulting message to the literal meaning content in interpretation.

News Feed Advertising and Positive Attitude: An Interpretation Model Based on Information Processing

This study mainly examines the different effects and mechanisms of news feed advertising (vs. traditional display advertising) on advertising attitude and brand attitude by using an observational experiment that categorized participants based on their past experiences. By analyzing the video advertising of the “Li Ning” brand, three conclusion were drawn. First, compared with traditional display advertising, news feed advertising led to more positive advertising attitude and brand attitude. Second, generation and transmission mechanism of attitudes were applicable to the interpretation rules of the classical information processing process model, which showed that the information processing process of the news feed advertising and the traditional display advertising were consistent. Third, consumer’s brand experience did not affect the direct effects of “attention” on “memory,” but affected the mediate role of “interpretation,” which showed that news feed advertising may have a stronger “drainage” effect when promoting new products and developing new markets. However, if the consumer’s experience is not enhanced, the brand attitude triggered by news feed advertising will still not be stable. These findings are conducive to further understanding the effect of news feed advertising on advertising attitude and brand attitude.

Abnormal Findings on “T1WI or DWI or MRCP:” An Effective Boolean Interpretation Model in Discriminating Small Pancreatic Ductal Adenocarcinoma from Control Group

Objectives: The objectives of the study was to evaluate the diagnostic performance of findings on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and magnetic resonance cholangiopancreatography (MRCP) separately and to identify an optimal Boolean interpretation model for discriminating patients with small pancreatic ductal adenocarcinoma (PDAC) from control groups in clinical practice. Material and Methods: We retrospectively enrolled 30 patients with surgery confirmed small PDAC (≤20 mm) and 302 patients without pancreatic abnormality between April 2008 and February 2020. The presence of masses was evaluated by T1WI, T2WI, and DWI. Abnormality of the main pancreatic duct (MPD) was evaluated by T2WI and MRCP. Multivariate logistic regression analysis was performed to select significant sequences for discriminating the small PDAC and control groups. Boolean operators “OR” or “AND” were used to construct sequence combinations. Diagnostic performances of these sequences and combinations were evaluated by X2 tests. Results: The sensitivity of T2WI was lowest (20%) for detecting masses. For evaluating MPD abnormality, sensitivity was higher for MRCP than for T2WI (86.7% vs. 53.3%). Multivariate logistic regression analysis showed that T1WI and DWI for detecting the presence of masses and MRCP for evaluating MPD abnormality were significantly associated with differentiation between the two groups (P = 0.0002, P = 0.0484, and P < 0.0001, respectively). Seven combinations were constructed with T1WI, DWI, and MRCP. The combination of findings on “T1WI or DWI or MRCP” achieved the highest sensitivity of 96.7% and negative predictive value of 99.6%. Conclusion: The combination of findings on “T1WI or DWI or MRCP” might be an optimal interpretation model for discriminating small PDAC from control groups in clinical practice.

Sign Language Interpreter using Deep Learning

Sign Language is invaluable to hearing and speaking impaired people and is their only way of communicating among themselves. However, it has limitations with its reach as the rest of the people have no information regarding sign language interpretation. Sign language is communicated via hand gestures and visual modes and is therefore used by hearing and speaking impaired people to intercommunicate. These languages have alphabets and grammar of their own, which cannot be understood by people who have no knowledge about the specific symbols and rules. Thus, it has become essential for everyone to interpret, understand and communicate via sign language to overcome and alleviate the barriers of speech and communication. This can be tackled with the help of machine learning. This model is a Sign Language Interpreter that uses a dataset of images and interprets the sign language alphabets and sentences with 90.9% accuracy. For this paper, we have used an ASL (American Sign Language) Alphabet. We have used the CNN algorithm for this project. This paper ends with a summary of the model’s viability and its usefulness for interpretation of Sign Language. Keywords: Sign Language, Machine Learning, Interpretation model, Convoluted Neural Networks, American Sign Language

Parameter Evaluations for Vertical Wells with Hydraulic Fracture Using Well-Testing and Deep Learning Method

The reliability of well-testing interpretation largely depends on the experience of reservoir engineers, which make the issue of non-unique solution serious and increase its application threshold. Virtually, deep learning assistive techniques are good strategies in well-testing interpretation. Although some work has been done based on automatic interpretation techniques, there is still a lack of an automatic interpretation model with wide applicability and fast interpretation on parameter evaluation of vertically fractured well. To improve this situation and make the well-testing interpretation easier to apply, this paper uses deep learning methods to build an automatic interpretation model of well-testing data for vertically fractured well. The model can automatically identify the corresponding parameters. The results in the validation set show that the median relative error of the curve parameter inversion is less than 10%. In addition, the accuracy of parameter prediction can be improved by increasing the weight of some important parameters in deep learning model training, such as permeability and fracture half-length. Finally, the automatic interpretation model is tested on a field case. The test results prove that the model has high accuracy and interpretation speed.