پروفیسر علی محمد خسرو
سخت افسوس ہے کہ ۲۴؍ اگست کی شب میں ساڑھے گیارہ بجے مشہور مسلم دانشور، ملک کے ممتاز ماہر اقتصادیات اور علی گڑھ مسلم یونیورسٹی کے وائس چانسلر اور چانسلر پروفیسر سید علی محمد خسرو نے داعی اجل کو لبیک کہا، اناﷲ وانا الیہ راجعون۔
۷؍ اگست کو دل کا شدید دورہ پڑا تو اسپتال میں داخل کیے گئے لیکن مرض بڑھتا گیا اور آخر دنوں میں حالت اتنی خراب ہوگئی تھی کہ خود سے سانس نہیں لے سکتے تھے اور آلہ تنفس کا سہارا لینا پڑا بلڈپریشر بہت لو ہوگیا تھا بالآخر ۷۹ برس کی عمر میں وقت موعود آگیا، پس ماندگان میں ایک صاحبزادے اور ایک صاحبزادی ہیں۔
۲۵؍ اگست کو غالب اکیڈمی بستی حضرت نظام الدین کے قریب عرس محل میں عصر بعد نماز جنازہ ادا کی گئی اور درگاہ عمادالدین فردوسی کے پاس خسر و باغ میں تدفین ہوئی۔
موت تو ہر ایک کو آنی لابد ہے لیکن خسرو صاحب کی موت ایک بڑا قومی و ملی سانحہ ہے، وہ ملک کے مایہ ناز فرد، قومی اہمیت کے حامل اور زرعی و مالی اقتصادیات میں عالم گیر شہرت کے مالک تھے اور جس ملت سے ان کا تعلق تھا اس میں بڑا قحط الرجال ہے، اس کے یہاں جو جگہ خالی ہوتی ہے وہ پر نہیں ہوتی، خسرو صاحب جیسے بلند پایہ، عالی دماغ، کامل الفن اور یگانہ شخص کی خالی جگہ بھی پر ہوتی نظر نہیں آتی۔
سید علی محمد خسرو کا تعلق حیدر آباد کے ایک ممتاز خاندان سے تھا، وہ یہیں ۱۹۲۵ء میں پیدا ہوئے تھے، مدرسہ عالیہ اور نظام کالج سے فارغ التحصیل ہونے کے بعد لندن چلے گئے اور لیڈز یونیورسٹی سے معاشیات میں ایم۔اے اور پی۔ایچ۔ڈی کیا، وطن واپس آنے کے بعد عثمانیہ یونیورسٹی میں درس و تدریس کی خدمت انجام دی،...
This study discusses the management of climacteric obstetrics and menopause. Menopause is the final feminine cycle or when the final monthly cycle happens, one of the mental viewpoints of changing self-concept amid menopause is unquestionably menopausal ladies ended up on edge around their bodies and frame self-concept approximately how their bodies are. The side effects experienced by ladies some time recently menopause cause the mother to be ill-equipped approximately physical and mental changes. To decrease this, ladies must get ready themselves both physically and mentally for menopause. Ladies who are going through menopause go through the primary stages counting premenopause, perimenopause, menopause, and postmenopause, and menopause for the most part happens in ladies matured 45-50 a long time.
With the advancement in information and communication technologies, sensing devices have now become pervasive. The pervasiveness of camera devices has enabled recording of video data at anytime and anywhere. It gives rise to a massive amount of untrimmed video data being produced, which consist of several human-related activities and actions including some background activities as well. It is important to detect the actions of interest in such long and untrimmed videos so that it can be further used in numerous applications i.e., video analysis, video summarization, surveillance, retrieval and captioning etc. This thesis targets temporal human action detection in long and untrimmed videos. Given a long and untrimmed video, the task of the temporal action detection is to detect starting and ending time of all occurrences of actions of interest and to predict action label of the detected intervals. Detecting human actions in long untrimmed videos is important but a challenging problem because of the unconstrained nature of long untrimmed videos in both space and time. In this work we solve the temporal action detection problem using two di erent paradigms: \proposal + classi cation" and \end-to-end temporal action detection". In proposal + classi cation approach, the regions which likely to contain human actions, known as proposals, arerst generated from untrimmed videos which are then classi ed into the targeted actions. To this end, we propose two di erent methods to generate action proposals: (1) un-supervised and (2) supervised temporal action proposal methods. In therst method, we propose unsupervised proposal generation method named as Proposals from Motion History Images (PMHI). PMHI discriminates actions from non-action regions by clustering the MHIs into actions and nonaction segments by detecting minima from the energy of MHIs. The strength of PMHI is that it is unsupervised, which alleviates the requirement for any training data. PMHI outperforms the existing proposal methods on the Multi-view Human Action video (MuHAVi)- uncut and Computer Vision and Pattern recognition (CVPR) 2012 Change Detection datasets.PMHI depends upon precise silhouettes extraction which is challenging for realistic videos and for moving cameras. To solve aforementioned problem, we propose a supervised temporal action proposal method named as Temporally Aggregated Bag-of-Discriminant-Words (TAB) which work directly on RGB videos. TAB is based on the observation that there are many overlapping frames in action and background temporal regions of untrimmed videos, which cause di culties in segmenting actions from non-action regions. TAB solve this issue by extracting class-speci c codewords from the action and background videos and extracting the discriminative weights of these codewords based on their ability to discriminate between these two classes. We integrate these discriminative weights with Bag of Word encoding, which we then call Bag-of-Discriminant-Words (BoDW). We sample the untrimmed videos into non-overlapping snippets and temporally aggregate the BoDW representation of multiple snippets into action proposals. We present the e ectiveness of TAB proposal method on two challenging temporal action detection datasets: MSR-II and Thumos14, where it improves upon state-ofthe- art methods. \Proposal + classi cation", requires multiple passes through testing data for these two stages, therefore, it is di cult to use these methods in an end-to-end manner. To solve this problem, we propose an end-to-end temporal action detection method known as Bag of Discriminant Snippets (BoDS). BoDS is based on the observation that multiple actions and the background classes have similar snippets, which cause incorrect classi cation of action regions and imprecise boundaries. We solve this issue bynding the key-snippets from the training data of each class and compute their discriminative power which is used in BoDS encoding. During testing of an untrimmed video, wend the BoDS representation for multiple candidate regions andnd their class label based on a majority voting scheme. We test BoDS on the Thumos14 and ActivityNet datasets and obtain state-of-the-art results.