Context based API recommendation is an important way to help developers find the needed APIs effectively and efficiently. For effective API recommendation, we need not only a joint view of both structural and textual code information, but also a holistic view of correlated API usage in control and data flow graph as a whole. Unfortunately, existing API recommendation methods exploit structural or textual code information separately. In this work, we propose a novel API recommendation approach called APIRec-CST (API Recommendation by Combining Structural and Textual code information). APIRec-CST is a deep learning model that combines the API usage with the text information in the source code based on an API Context Graph Network and a Code Token Network that simultaneously learn structural and textual features for API recommendation. We apply APIRec-CST to train a model for JDK library based on 1,914 open-source Java projects and evaluate the accuracy and MRR (Mean Reciprocal Rank) of API recommendation with another 6 open-source projects. The results show that our approach achieves respectively a top-1, top-5, top-10 accuracy and MRR of 60.3%, 81.5%, 87.7% and 69.4%, and significantly outperforms an existing graph-based statistical approach and a tree-based deep learning approach for API recommendation. A further analysis shows that textual code information makes sense and improves the accuracy and MRR. We also conduct a user study in which two groups of students are asked to finish 6 programming tasks with or without our APIRec-CST plugin. The results show that APIRec-CST can help the students to finish the tasks faster and more accurately and the feedback on the usability is overwhelmingly positive.

We propose a new framework for reasoning about generalization in deep learning. The core idea is to couple the Real World, where optimizers take stochastic gradient steps on the empirical loss, to an Ideal World, where optimizers take steps on the population loss. This leads to an alternate decomposition of test error into: (1) the Ideal World test error plus (2) the gap between the two worlds. If the gap (2) is universally small, this reduces the problem of generalization in offline learning to the problem of optimization in online learning. We then give empirical evidence that this gap between worlds can be small in realistic deep learning settings, in particular supervised image classification. For example, CNNs generalize better than MLPs on image distributions in the Real World, but this is "because" they optimize faster on the population loss in the Ideal World. This suggests our framework is a useful tool for understanding generalization in deep learning, and lays a foundation for future research in the area.

Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it is usually hard for existing NTMs to achieve good document representation and coherent/diverse topics at the same time. Moreover, they often degrade their performance severely on short documents. The requirement of reparameterisation could also comprise their training quality and model flexibility. To address these shortcomings, we present a new neural topic model via the theory of optimal transport (OT). Specifically, we propose to learn the topic distribution of a document by directly minimising its OT distance to the document's word distributions. Importantly, the cost matrix of the OT distance models the weights between topics and words, which is constructed by the distances between topics and words in an embedding space. Our proposed model can be trained efficiently with a differentiable loss. Extensive experiments show that our framework significantly outperforms the state-of-the-art NTMs on discovering more coherent and diverse topics and deriving better document representations for both regular and short texts.

We formalize human language understanding as a structured prediction task where the output is a partially ordered set (poset). Current encoder-decoder architectures do not take the poset structure of semantics into account properly, thus suffering from poor compositional generalization ability. In this paper, we propose a novel hierarchical poset decoding paradigm for compositional generalization in language. Intuitively: (1) the proposed paradigm enforces partial permutation invariance in semantics, thus avoiding overfitting to bias ordering information; (2) the hierarchical mechanism allows to capture high-level structures of posets. We evaluate our proposed decoder on Compositional Freebase Questions (CFQ), a large and realistic natural language question answering dataset that is specifically designed to measure compositional generalization. Results show that it outperforms current decoders.

Though it was held this year in October instead of September, Apple's "Hi, Speed" media event was a largely typical iPhone launch party, opening with the expansion of its Siri-powered line of HomePod speakers ("Hi"), and concluding with the long-awaited addition of 5G cellular connectivity to the iPhone lineup ("Speed"). Some companies might have tread cautiously on these topics -- Siri and 5G have both been dogged by complaints -- but Apple didn't hold anything back, using a seemingly endless parade of spokespeople to hype the new devices ahead of preorders. The 5G iPhone 12 family, it promised, will "blast past fast," while the $99 HomePod mini will become a hub to "control your smart home," bringing "intelligent assistant" access to the lowest price yet for any Siri device. Having covered Apple for a long time, I'm not surprised that its latest pitches were all sunshine and roses, but I couldn't help but feel that it was making big promises that could come back to bite the company and its partners. As of October 2020, the only thing less likely to thrill someone than a Siri speaker is typical U.S. 5G network performance, which despite boasts of 1-4Gbps downloads has seen average speeds that are barely better than 4G/LTE. Siri and 5G are both theoretically moving targets -- they're services that could improve at any time and in any region without advance notice -- but prior to this event, neither has delivered on its transformative potential.

SAN FRANCISCO, CA, USA, October 14, 2020 – Gorilla Corporation, a leader in global partner marketing and strategy, today announced the expansion of their corporate social responsibility (CSR) program, by including Tortora Brayda Partnership Excellence. Gorilla Corporation, with a long-standing commitment to their corporate social responsibility program, will allocate further sponsorship to the non-profit organization Tortora Brayda Partnership Excellence. Tortora Brayda focuses on engaging business, cultural, government, and thought leaders worldwide to reshape and develop collaboration and partnering practices with a view to improving and securing business and economies. "This official partnership with Gorilla Corporation is powerful for our Think Tank, Gorilla's reputation as a leader in partner marketing internationally, is relevant and strategic. Their contribution and support are pivotal for us," said Carlo Tortora Brayda, founder and chairperson of Tortora Brayda Partnership Excellence.

In Milan, Italy, STIIMA, the National Research Council's Institute for Smart Industrial Technology Systems for Advanced Manufacturing, and the Polytechnic University of Milan have set up a joint experimental "re-manufacturing" and "de-manufacturing" facility. While still at a pilot experimental level, this is an excellent example of the enormous potential of artificial intelligence in the circular economy. This is because there are no similar plants in the world capable of managing electronic waste, understanding what the items are, dismantling them and recovering their useful or valuable components. For this reason, millions of tonnes of old TVs, monitors, broken PCs, telephones, and electrical appliances of every type, are piling up at waste sites, from where they are often taken to fuel an illegal and extremely polluting market. Its real size is difficult to estimate, but according to UNEP, the United Nations Environmental Protection agency, the global market for electronic waste is worth more than 62 billion dollars and only 20% of it is officially recycled.

Artificial Intelligence is the new buzzword that no one can go without. The reasons are numerous; AI has given as self-driven cars, fancy robots that have close to human intelligence, and many many more. Experts predict that AI will significantly improve the lives of humans in years to come. Even now, we are enjoying some of the benefits of this awesome technology. Artificial Intelligence is the act of giving machines the ability to perform human-level tasks without explicit programming.

Just like any other day, we start our morning with a coffee and a snack to go from our favorite bakery. Later on the same day, we check out our mail where we find letters, newspapers, magazines, and possibly a package that just arrived. Finally at night, after a rough week, we decide to go out to have drinks with friends. Sounds like a pretty uneventful day, right? Except that we produced lots of trash in the form of plastic, glass, paper, ad more.

Want to grow food sustainably on a global scale? You're going to need more than a few tractors and plows. Enter Alphabet's X lab moonshot Mineral--a "computational agriculture" project. "Alongside experts in the field--literally and figuratively--we've been developing and testing a range of software and hardware prototypes based on breakthroughs in artificial intelligence, simulation, sensors, robotics, and more," Elliott Grant, project lead at X, wrote in a blog post. "From strawberry fields in California to soybean fields in Illinois, we've been learning about crops from sprout to harvest," he continued.