Letters, speeches, articles and memos written by a pioneer Muslim educationalist.

In order to reduce the dimensionality of parameter space and enhance out-of-sample forecasting performance, this research compares regularization techniques with Autometrics in time-series modeling. We mainly focus on comparing weighted lag adaptive LASSO with Autometrics, but as a benchmark, we estimate other popular regularization methods LASSO, AdaLASSO, SCAD, and MCP. For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out-of-sample Root Mean Square Error, Gauge, and Potency. The comparison is assessed with (...) varying autocorrelation coefficients and sample sizes. The simulation experiment indicates that, compared to Autometrics and other regularization approaches, the WLAdaLASSO outperforms the others in covariate selection and forecasting, especially when there is a greater linear dependency between predictors. In contrast, the computational efficiency of Autometrics decreases with a strong linear dependency between predictors. However, under the large sample and weak linear dependency between predictors, the Autometrics potency ⟶ 1 and gauge ⟶ α. In contrast, LASSO, AdaLASSO, SCAD, and MCP select more covariates and possess higher RMSE than Autometrics and WLAdaLASSO. To compare the considered techniques, we made the Generalized Unidentified Model for covariate selection and out-of-sample forecasting for the trade balance of Pakistan. We train the model on 1985–2015 observations and 2016–2020 observations as test data for the out-of-sample forecast. (shrink)

On Western philosophy from the point of view of Burmese philosophers.

Aesthetics of art and literature; articles.

The theory of rough sets starts with the notion of an approximation space , which is a pair ( U , R ), U being the domain of discourse, and R an equivalence relation on U . R is taken to represent the knowledge base of an agent, and the induced partition reflects a granularity of U that is the result of a lack of complete information about the objects in U . The focus then is on approximations of concepts (...) on the domain, in the context of the granularity. The present article studies the theory in the situation where information is obtained from different sources. The notion of approximation space is extended to define a multiple-source approximation system with distributed knowledge base , which is a tuple , where N is a set of sources and P ranges over all finite subsets of N . Each R P is an equivalence relation on U satisfying some additional conditions, representing the knowledge base of the group P of sources. Thus each finite group of sources and hence individual source perceives the same domain differently (depending on what information the group/individual source has about the domain), and the same concept may then have approximations that differ with the groups. In order to express the notions and properties related with rough set theory in this multiple-source situation, a quantified modal logic LMSAS D is proposed. In LMSAS D , quantification ranges over modalities, making it different from modal predicate logic and modal logic with propositional quantifiers. Some fragments of LMSAS D are discussed and it is shown that the modal system KTB is embedded in LMSAS D . The epistemic logic is also embedded in LMSAS D , and cannot replace the latter to serve our purpose. The relationship of LMSAS D with first and second-order logics is presented. Issues of expressibility, axiomatization and decidability are addressed. (shrink)

Khan Abdul Ghaffar Khan, also called Badshah Khan, is a nearly unknown champion of nonviolence in South Asia and a forgotten Muslim ally of Mohandas Gandhi. The story of Khan's Khudai Khidmatgars movement in what was to become Pakistan is not only inspirational but also instructive, exploding as it does several widespread myths about nonviolence. Today, the United States is embroiled in that region in the longest war in American history and among the Pashtun people from (...) whom Khan arose. Thus his story, according to this article, has a revolutionary potential. When young Malala Yousafzai cited Badshah Khan in a speech at the United Nations in 2013, therefore, she may have done more good than she realized. (shrink)

Pratyakṣakhaṇḍa of Tattvacintāmaṇi of Gaṅgeśa, 13th cent., basic work on Hindu logic and epistimelogy of the Neo-Nyaya school in Hindu philosophy; a study.

Classical commentary, with a supercommentary, on Jaimini's Mīmāṃsāsūtra, presenting the tradition of Bhāṭṭa school; includes Mīmāṃsāśāstrasāraḥ, by N.S. Anantakrishna Sastri.

On Sikh ethics; includes brief biographies of Sikh religious leaders.

Works on the refutation of some fundamentals of Advaita philosophy.

Jamal al-Din al-Afghani (1838-97) was a pan-Islamic thinker, political activist, and journalist, who sought to revive Islamic thought and liberate the Muslim world from Western influence. Many aspects of his life and his background remain unknown or controversial, including his birthplace, his religious affiliation, and the cause of his death. He was likely born in Asadabad, near present-day Hamadan, Iran. His better known history begins when he was 18, with a one-year stay in India that coincided with the Sepoy Mutiny (...) of 1857-59. In what would become a life of constant travel, he soon went to Mecca to perform Hajj, before returning to Afghanistan to join the service of the country's ruler, Dost Mohammad Khan (1793-1863). He later sided with Dost's son Mohammad Aʻzam, who ultimately lost in a power struggle with his British-supported brother Sher Ali. Al-Afghani's political activism eventually took him to Paris, London, Tehran, Saint Petersburg, and Constantinople. It was during his second stay in Egypt (1871-79) that he cemented his role as a reformer. He found in Cairo a class of young intellectuals who gathered around him, established newspapers, and used these papers to disseminate his ideas. Chief among al-Afghani's Egyptian disciples were scholar Muhammad ʻAbduh, journalist ʻAbd Allah al-Nadim, and nationalist politicians Mustafa Kamil and Saʻd Zaghlul. Al-Afghani's influence on both modernist and traditionalist Islamic thought continues to the present. An activist who sought to effect change through political journalism and public speaking, he did not write many books. This treatise, entitled al-Radd ʻalā al-dahrīyīn (Refutation of the materialists), was a rebuttal of the views of the pro-British Sir Sayyid Ahmad Khan, who had argued that science is more important than religion in the rise of civilizations. First written in Persian following al-Afghani's exile from Egypt to India, it was translated into Arabic by his student Muhammad ʻAbduh, with the help of al-Afghani's assistant Arif Efendi. (shrink)

Half a million species of plants could be existing in the world. Classification of plants based on leaf features is a critical job as feature extraction from binary images of leaves may result in duplicate identification. However, leaves are an effective means of differentiating plant species because of their unique characteristics like area, diameter, perimeter, circularity, aspect ratio, solidity, eccentricity, and narrow factor. This paper presents the extraction of plant leaf gas alongside other features from the camera images or a (...) dataset of images by applying a convolutional neural network. The extraction of leaf gas enables identification of the actual level of chlorophyll and nitrogen which may help to interpret future predictions. Our contribution includes the study of texture and geometric features, analyzing ratio of Ch and N in both healthy and dead leaves, and the study of color-based methods via CNN. Several steps are included to obtain the results: image preprocessing, testing, training, enhancement, segmentation, feature extraction, and aggregation of results. A vital contrast of the results can be seen by considering the kind of image, whether a healthy or dead leaf. (shrink)

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates. BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ’learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the (...) relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. (shrink)

1. Yŏn'gyŏngjae Sŏng Hae-ŭng. "Yŏn'gyŏngjae chŏnjip", innŭn sasil ŭl kŭdaero kirok hada -- 2. P'ungsŏk Sŏ Yu-gu. "Imwŏn kyŏngjeji", hŭlkuk kwa chongittŏk in hangmun ŭn anŭrira -- 3. Oju Yi Kyu-gyŏng. "Ojuyŏn munjang chŏnsan'go", pakhak kwa kojŭnghak ŭro modŭn kŏt ŭl pyŏnjŭng hara -- 4. Tasan Chŏng Yag-yong. "Mongmin simsŏ", sidae rŭl ap'ahago paeksŏngdŭl ŭi pich'am han sam e punno haeya handa -- 5. Ch'ujae Cho Su-sam. "Ch'ujae chip", nara ka mangharyŏmyŏn pandŭsi yomul i naonda -- 6. Nakhasaeng Yi Hak-kyu. (...) "Yŏngnam akpu", mal hanŭn cha nŭn choe ka ŏpta -- 7. Kuhwajae Hong Sŏng-mo. "Tongguk sesigi", sot e kadŭkhan kuk ŭl han sutkal ro mat poda -- 8. Hosan Cho Hŭi-ryong. "Sŏgu mangnyŏllok", hanŭl arae kajang t'ongk'wae han il ida -- 9. Sŏp'a Yu Hŭi. "Munt'ong", ilsaeng ŭl nŭl sibi sok esŏ saranne -- 10. Hyegang Ch'oe Han-gi. "Kihak", taedong ilt'ong ŭi isang segye rŭl kuhyŏn hada -- 11. Kosanja Kim Chŏng-ho. "Taedong yŏjido", chido ro ch'ŏnha ŭi hyŏngse rŭl salp'il su itta -- 12. Paegun Sim Tae-yun. "Pongni chŏnsŏ", mani ilgŭlsurok pongni ka tŏuk manajinda -- 13. Suun Ch'oe Che-u. "Tonggyŏng taejŏn", hangmun ŭro marhajamyŏn 'tonghak' irago haeya handa -- 14. Tongmu Yi Che-ma. "Kyŏkch'igo", i ch'aek i ch'ŏllima ka toeji ank'ennŭn'ga? (shrink)