Perhaps he won the presidency thanks to his specialized knowledge of voting behavior and public opinion, or maybe it was his casual tweets in conversations with students, his use of mocking buzzwords or his adoption of slang used by his pupils. Whatever the case, 67-year-old Aiji Tanaka assumed the presidency of Tokyo's Waseda University last month, becoming the institution's first leader selected from the political science and economics department over the past 50 years. Tanaka envisions raising Waseda into the ranks of the world's top schools, with clear measures he says must be "effective first, and then efficient." Tanaka said he intends to boost the university into the top 30 to 40 institutions worldwide. "To be a top university in the world, serious determination and commitment are necessary. That was the first thing I thought of when becoming president," said Tanaka.

Showbox, a mysterious app that allows people to watch new films and TV shows for free, is back after a strange outage. But the circumstances around that return is largely unknown, and anyone trying to use the popular app might be endangering themselves and their computer. Showbox is a hugely popular app that allows for a Netflix-like experience but includes apparently torrented versions of new movies and TV shows. More specifically, there appears to be a number of versions of the app, all of which present themselves as the legitimate app. The service stopped working recently, prompting concerns that the service might have been taken down entirely – but it seems to have emerged once again.

Chinese artificial intelligence provider Megvii, commonly known as Face, is targeting to raise $500 million in a new funding round that pegs the current valuation of the firm at $3.5 billion, people with knowledge of the matter said. Bank of China Group Investment Ltd, the state bank's private equity (PE) arm, is looking to lead the fundraising with $200 million, two of the people told Reuters, declining to be named as the information is confidential. Terms of the fundraising have not been finalised, the people added. Beijing-based Megvii declined to comment. Bank of China's PE arm did not respond to a request for comment.

The Softmax function is used in the final layer of nearly all existing sequence-to-sequence models for language generation. However, it is usually the slowest layer to compute which limits the vocabulary size to a subset of most frequent types; and it has a large memory footprint. We propose a general technique for replacing the softmax layer with a continuous embedding layer. Our primary innovations are a novel probabilistic loss, and a training and inference procedure in which we generate a probability distribution over pre-trained word embeddings, instead of a multinomial distribution over the vocabulary obtained via softmax. We evaluate this new class of sequence-to-sequence models with continuous outputs on the task of neural machine translation. We show that our models obtain upto 2.5x speed-up in training time while performing on par with the state-of-the-art models in terms of translation quality. These models are capable of handling very large vocabularies without compromising on translation quality. They also produce more meaningful errors than in the softmax-based models, as these errors typically lie in a subspace of the vector space of the reference translations.

In a densely populated city like Dhaka (Bangladesh), a growing number of high-rise buildings is an inevitable reality. However, they pose mental health risks for citizens in terms of detachment from natural light, sky view, greenery, and environmental landscapes. The housing economy and rent structure in different areas may or may not take account of such environmental factors. In this paper, we build a computer vision based pipeline to study factors like sky visibility, greenery in the sidewalks, and dominant colors present in streets from a pedestrian's perspective. We show that people in lower economy classes may suffer from lower sky visibility, whereas people in higher economy classes may suffer from lack of greenery in their environment, both of which could be possibly addressed by implementing rent restructuring schemes.

Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes \textit{random drop} of nodes from the hidden layers of a Neural Network. It is our hypothesis that a guided selection of nodes for intelligent dropout can lead to better generalization as compared to the traditional dropout. In this research, we propose "guided dropout" for training deep neural network which drop nodes by measuring the strength of each node. We also demonstrate that conventional dropout is a specific case of the proposed guided dropout. Experimental evaluation on multiple datasets including MNIST, CIFAR10, CIFAR100, SVHN, and Tiny ImageNet demonstrate the efficacy of the proposed guided dropout.

We report a data mining pipeline and subsequent analysis to understand the core periphery power structure created in three national newspapers in Bangladesh, as depicted by statements made by people appearing in news. Statements made by one actor about another actor can be considered a form of public conversation. Named entity recognition techniques can be used to create a temporal actor network from such conversations, which shows some unique structure, and reveals much room for improvement in news reporting and also the top actors' conversation preferences. Our results indicate there is a presence of cliquishness between powerful political leaders when it comes to their appearance in news. We also show how these cohesive cores form through the news articles, and how, over a decade, news cycles change the actors belonging in these groups.

In this paper, we investigate the significance of choosing an appropriate tessellation strategy for a spatio-temporal taxi demand-supply modeling framework. Our study compares (i) the variable-sized polygon based Voronoi tessellation, and (ii) the fixed-sized grid based Geohash tessellation, using taxi demand-supply GPS data for the cities of Bengaluru, India and New York, USA. Long Short-Term Memory (LSTM) networks are used for modeling and incorporating information from spatial neighbors into the model. We find that the LSTM model based on input features extracted from a variable-sized polygon tessellation yields superior performance over the LSTM model based on fixed-sized grid tessellation. Our study highlights the need to explore multiple spatial partitioning techniques for improving the prediction performance in neural network models.

Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost. This letter introduces a ramp cost function into the plane-based clustering to propose a new clustering method, called ramp-based twin support vector clustering (RampTWSVC). RampTWSVC is more robust because of its boundness, and thus it is more easier to find the intrinsic clusters than other plane-based clustering methods. The non-convex programming problem in RampTWSVC is solved efficiently through an alternating iteration algorithm, and its local solution can be obtained in a finite number of iterations theoretically. In addition, the nonlinear manifold-based formation of RampTWSVC is also proposed by kernel trick. Experimental results on several benchmark datasets show the better performance of our RampTWSVC compared with other plane-based clustering methods.

SoftBank Group has made several big name investments across insurtech and real estate, including deals to WeWork, OYO Rooms, PolicyBazaar, and Lemonade. SoftBank wants its portfolio companies to get along. Since 2014, SoftBank has been investing aggressively in companies modernizing insurance and real estate. Armed with its massive $98B Vision Fund, it hasn't been shy to write huge checks to startups disrupting these areas. The average size of Vision Fund-backed equity deals to insurtech and real estate startups exceeds $400M, and the Vision Fund has accounted for 8% of all deals by SoftBank Group in these areas since 2014.