Applying machine learning techniques to graph drawing has become an emergent area of research in visualization. In this paper, we interpret graph drawing as a multi-agent reinforcement learning (MARL) problem. We first demonstrate that a large number of classic graph drawing algorithms, including force-directed layouts and stress majorization, can be interpreted within the framework of MARL. Using this interpretation, a node in the graph is assigned to an agent with a reward function. Via multi-agent reward maximization, we obtain an aesthetically pleasing graph layout that is comparable to the outputs of classic algorithms. The main strength of a MARL framework for graph drawing is that it not only unifies a number of classic drawing algorithms in a general formulation but also supports the creation of novel graph drawing algorithms by introducing a diverse set of reward functions.

The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets and metrics are used as AI markers for AI literature. These entities can be used to trace the research process described in the bodies of papers, which opens up new perspectives for seeking and mining more valuable academic information. Firstly, the entity extraction model is used in this study to extract AI markers from large-scale AI literature. Secondly, original papers are traced for AI markers. Statistical and propagation analysis are performed based on tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters and the influencing relationships amongst different research scene clusters are explored. The above-mentioned mining based on AI markers yields many meaningful discoveries. For example, the propagation of effective methods on the datasets is rapidly increasing with the development of time; effective methods proposed by China in recent years have increasing influence on other countries, whilst France is the opposite. Saliency detection, a classic computer vision research scene, is the least likely to be affected by other research scenes.

We present a method for adversarial input generation against black box models for reading comprehension based question answering. Our approach is composed of two steps. First, we approximate a victim black box model via model extraction (Krishna et al., 2020). Second, we use our own white box method to generate input perturbations that cause the approximate model to fail. These perturbed inputs are used against the victim. In experiments we find that our method improves on the efficacy of the AddAny---a white box attack---performed on the approximate model by 25% F1, and the AddSent attack---a black box attack---by 11% F1 (Jia and Liang, 2017).

Despite continuous investments in data technologies, the latency of querying data still poses a significant challenge. Modern analytic solutions require near real-time responsiveness both to make them interactive and to support automated processing. Current technologies (Hadoop, Spark, Dataflow) scan the dataset to execute queries. They focus on providing a scalable data storage to maximize task execution speed. We argue that these solutions fail to offer an adequate level of interactivity since they depend on continual access to data. In this paper we present a method for query approximation, also known as approximate query processing (AQP), that reduce the need to scan data during inference (query calculation), thus enabling a rapid query processing tool. We use LSTM network to learn the relationship between queries and their results, and to provide a rapid inference layer for predicting query results. Our method (referred as ``Hunch``) produces a lightweight LSTM network which provides a high query throughput. We evaluated our method using 12 datasets. The results show that our method predicted queries' results with a normalized root mean squared error (NRMSE) ranging from approximately 1\% to 4\%. Moreover, our method was able to predict up to 120,000 queries in a second (streamed together), and with a single query latency of no more than 2ms.

The most remarkable thing about shopping in a smart store is how unremarkable it feels -- once you get over the whole no-checkout thing, of course. At the same time, there's a whole mess of complex computer vision modeling going on all around you to facilitate the experience. Those models have to be sophisticated enough to handle all manner of image challenges: object recognition, activity recognition, pose estimation. Asking a system to differentiate between two similar bags of chips, grabbed by people in similar coats and gloves, with no margin for error, is a big ask. Getting it right requires data.

University of California San Diego School of Medicine and Moores Cancer Center say they have created a new artificial intelligence (AI) system called DrugCell that reportedly matches tumors to the best drug combinations, but does so in way that clearly makes sense. "That's because right now we can't match the right combination of drugs to the right patients in a smart way," said Trey Ideker, PhD, professor at University of California San Diego School of Medicine and Moores Cancer Center. "And especially for cancer, where we can't always predict which drugs will work best given the unique, complex inner workings of a person's tumor cells." Currently, Only four percent of all cancer therapeutic drugs under development earn final approval by the FDA. In a paper "Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells" published in Cancer Cell, Ideker, Brent Kuenzi, PhD, and Jisoo Park, PhD, postdoctoral researchers in his lab, published a paper on their work.

Generative adversarial networks (GANs), formed in 2014 [1], is a state of the art deep neural network with many applications. Unlike the traditional machine learning in unsupervised learning (it does not require a targeted label), GANs is a generative model which generates new content by given data. The analogy of GANs is known as a fake-currency detection game between a counterfeiter and police [1]. According to the tutorial of GANs by Goodfellow [2], GANs consists of two characters, namely, the generator (counterfeiter) and the discriminator (police). The counterfeiter tries to produce fake money and deceive the police (discriminator) by looking at the real banknote.

In the winter of 2011, Daniel Yamins, a postdoctoral researcher in computational neuroscience at the Massachusetts Institute of Technology, would at times toil past midnight on his machine vision project. He was painstakingly designing a system that could recognize objects in pictures, regardless of variations in size, position, and other properties--something that humans do with ease. The system was a deep neural network, a type of computational device inspired by the neurological wiring of living brains. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research develop ments and trends in mathe matics and the physical and life sciences. "I remember very distinctly the time when we found a neural network that actually solved the task," he said.

Efficient nonconvex reformulations of neural network convex relaxations [Hinder et al, NeurIPS 2020]: Verification algorithms for neural networks are often derived from a convex relaxation, that replaces the nonlinear relations between network activations by a weaker set of convex (often linear) constraints between inputs and outputs of neurons (Ehlers 2017). This enables incomplete verification via convex optimisation, with tightness governed by the gap between the weaker convex and the original nonlinear constraints. However, off-the-shelf convex optimisation solvers still don't scale efficiently to modern neural networks, and most attempts at developing scalable methods has required using weaker relaxations (Fast-Lin(Wong and Kolter 2017, Weng et al 2018), CROWN (Zhang et al 2018)). In this work, we develop a novel non-convex reformulation of convex relaxations of neural network verification. Despite the nonconvexity, we are able to derive algorithms that are guaranteed to converge quickly to the global optimum.

A cloud image can provide significant information, such as precipitation and solar irradiation. Predicting short-time cloud motion from images is the primary means of making intra-hour irradiation forecasts for solar-energy production and is also important for precipitation forecasts. However, it is very challenging to predict cloud motion (especially nonlinear motion) accurately. Traditional methods of cloud-motion prediction are based on block matching and the linear extrapolation of cloud features; they largely ignore nonstationary processes, such as inversion and deformation, and the boundary conditions of the prediction region. In this paper, the prediction of cloud motion is regarded as a spatiotemporal sequence-forecasting problem, for which an end-to-end deep-learning model is established; both the input and output are spatiotemporal sequences. The model is based on gated recurrent unit (GRU)- recurrent convolutional network (RCN), a variant of the gated recurrent unit (GRU), which has convolutional structures to deal with spatiotemporal features. We further introduce surrounding context into the prediction task. We apply our proposed Multi-GRU-RCN model to FengYun-2G satellite infrared data and compare the results to those of the state-of-the-art method of cloud-motion prediction, the variational optical flow (VOF) method, and two well-known deep-learning models, namely, the convolutional long short-term memory (ConvLSTM) and GRU. The Multi-GRU-RCN model predicts intra-hour cloud motion better than the other methods, with the largest peak signal-to-noise ratio and structural similarity index. The results prove the applicability of the GRU-RCN method for solving the spatiotemporal data prediction problem and indicate the advantages of our model for further applications.