Artificial Intelligence is still a nascent technology; much of the groundbreaking work moving the industry forward is done inside AI research labs. It's often from those labs that open source projects are started. Institutes like Open AI, NASA's JPL, Google Deepmind, MIT CSAIL, BAIR, The Turing Institute, and Max Planck -- to name just a handful -- are presenting at ODSC in 2019, helping us bring our community to the leading edge of AI. To learn more about the labs' role at ODSC, visit ODSC West. Since our next conference is in San Francisco, we're looking west at a few exciting research labs in the area that are participating in ODSC this year.

Nielsen (NLSN) and the University of Oxford today announced a two-year collaboration to advance the use of artificial intelligence (AI) to identify and classify consumer packaged goods (CPG) products on shelves in retail stores. Facilitated between Nielsen's Image Recognition group and the Visual Geometry Group (VGG) at the University of Oxford, this partnership brings together the world's largest pool of product reference data with industry-leading brainpower around AI technology to yield greater accuracy in product identification and discovery. Through this partnership, Nielsen is working directly with University of Oxford Professors Andrew Zisserman and Andrea Vedaldi (Department of Engineering Science), world-renowned computer scientists and pioneers in image recognition and AI research. Zisserman, Vedaldi and their team of research scientists will work together with Nielsen to more precisely and quickly identify and classify in-store products based on product images captured through Nielsen's eCollection solution. The Oxford researchers will focus on building and enhancing the eCollection algorithms with increasingly advanced deep learning capabilities, enabling a more automatic detection of store products, promotions and prices without the need for manual intervention.

To predict the impacts of human emissions, researchers need a complete picture of the atmosphere's methane cycle. They need to know the size of the inputs -- both natural and human -- as well as the outputs. They also need to know how long methane resides in the atmosphere. To help develop this understanding, Tom Weber, an assistant professor of earth and environmental sciences at the University of Rochester; undergraduate researcher Nicola Wiseman '18, now a graduate student at the University of California, Irvine; and their colleague Annette Kock at the GEOMAR Helmholtz Centre for Ocean Research in Germany, used data science to determine how much methane is emitted from the ocean into the atmosphere each year. Their results, published in the journal Nature Communications, fill a longstanding gap in methane cycle research and will help climate scientists better assess the extent of human perturbations.

Dr. Ganapathi Pulipaka was a recipient of the Top 50 Technology Leader awards for recognition of his contribution to artificial intelligence, machine learning, and data science; for the past five years on Twitter as a machine learning and data science influencer; as a contributor to thought leadership and of project implementation articles on Medium, Data Driven Investor, LinkedIn, GitHub; as a best-selling author of two books on Amazon - "The Future of Data Science and Parallel Computing: A Road to Technological Singularity," published on June 29, 2018, and "Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data," published Dec. 8, 2015 - and other eBooks that have reached all-time high rankings from the world's largest book ratings authority (featured on Forbes), BookAuthority; and also for writing another 400 research papers as part of academic research programs for PostDoc and PhD. He is an American data scientist and AI luminary who has been featured in top-tier magazines and news and industry publications and was a speaker for multiple media distribution networks and some of the top media station affiliates, including ABC, FoxNews, NBC, Yahoo Finance, MarketWatch, The CW, VentureBeat, MirrorReview, CIOReview, SAP, Erie News Now, USA Today, Double T 97.3 Lubbock's Radio station, 100.7 KFM BFM San Diego, KITV, Telemundo Lubbock 46, AZCentral, Insights Success, NewsOk, Pittsburgh Post-Gazette, MarketWatch, and Ask.

This paper presents BiPaR, a bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support multilingual and cross-lingual reading comprehension. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written parallelly in two languages. We collect 3,667 bilingual parallel paragraphs from Chinese and English novels, from which we construct 14,668 parallel question-answer pairs via crowdsourced workers following a strict quality control procedure. We analyze BiPaR in depth and find that BiPaR offers good diversification in prefixes of questions, answer types and relationships between questions and passages. We also observe that answering questions of novels requires reading comprehension skills of coreference resolution, multi-sentence reasoning, and understanding of implicit causality, etc. With BiPaR, we build monolingual, multilingual, and cross-lingual MRC baseline models. Even for the relatively simple monolingual MRC on this dataset, experiments show that a strong BERT baseline is over 30 points behind human in terms of both EM and F1 score, indicating that BiPaR provides a challenging testbed for monolingual, multilingual and cross-lingual MRC on novels. The dataset is available at https://multinlp.github.io/BiPaR/.

The design of recommendations strategies in the adaptive learning system focuses on utilizing currently available information to provide individual-specific learning instructions for learners. As a critical motivate for human behaviors, curiosity is essentially the drive to explore knowledge and seek information. In a psychologically inspired view, we aim to incorporate the element of curiosity for guiding learners to study spontaneously. In this paper, a curiosity-driven recommendation policy is proposed under the reinforcement learning framework, allowing for a both efficient and enjoyable personalized learning mode. Given intrinsic rewards from a well-designed predictive model, we apply the actor-critic method to approximate the policy directly through neural networks. Numeric analyses with a large continuous knowledge state space and concrete learning scenarios are used to further demonstrate the power of the proposed method.

Emotions recognition is the task of recognizing people's emotions. Usually it is achieved by analyzing expression of peoples faces. There are two ways for representing emotions: The categorical approach and the dimensional approach by using valence and arousal values. Valence shows how negative or positive an emotion is and arousal shows how much it is activated. Recent deep learning models, that have to do with emotions recognition, are using the second approach, valence and arousal. Moreover, a more interesting concept, which is useful in real life is the "in the wild" emotions recognition. "In the wild" means that the images analyzed for the recognition task, come from from real life sources(online videos, online photos, etc.) and not from staged experiments. So, they introduce unpredictable situations in the images, that have to be modeled. The purpose of this project is to study the previous work that was done for the "in the wild" emotions recognition concept, design a new dataset which has as a standard the "Aff-wild" database, implement new deep learning models and evaluate the results. First, already existing databases and deep learning models are presented. Then, inspired by them a new database is created which includes 507.208 frames in total from 106 videos, which were gathered from online sources. Then, the data are tested in a CNN model based on CNN-M architecture, in order to be sure about their usability. Next, the main model of this project is implemented. That is a Regression GAN which can execute unsupervised and supervised learning at the same time. More specifically, it keeps the main functionality of GANs, which is to produce fake images that look as good as the real ones, while it can also predict valence and arousal values for both real and fake images. Finally, the database created earlier is applied to this model and the results are presented and evaluated.

In the context of HCI, building an automatic system to recognize affect of human facial expression in real-world condition is very crucial to make machine interact naturallisticaly with a man. However, existing facial emotion databases usually contain expression in the limited scenario under well-controlled condition. Aff-Wild is currently the largest database consisting of spontaneous facial expression in the wild annotated with valence and arousal. The first contribution of this project is the completion of extending Aff-Wild database which is fulfilled by collecting videos from YouTube on which the videos have spontaneous facial expressions in the wild, annotating videos with valence and arousal ranging in [-1,1], detecting faces in frames using FFLD2 detector and partitioning the whole data set into train, validate and test set, with 527056, 94223 and 135145 frames. The diversity is guaranteed regarding age, ethnicity and values of valence and arousal. The ratio of male to female is close to 1. Regarding the techniques used to build the automatic system, deep learning is outstanding since almost all winning methods in emotion challenges adopt DNN techniques. The second contribution of this project is that an end-to-end DNN is constructed to have joint CNN and RNN block and gives the estimation on valence and arousal for each frame in sequential data. VGGFace, ResNet, DenseNet with the corresponding pre-trained model for CNN block and LSTM, GRU, IndRNN, Attention mechanism for RNN block are experimented aiming to find the best combination. Fine tuning and transfer learning techniques are also tried out. By comparing the CCC evaluation value on test data, the best model is found to be pre-trained VGGFace connected with 2 layers GRU with attention mechanism. The models test performance is 0.555 CCC for valence with sequence length 80 and 0.499 CCC for arousal with sequence length 70.

Here, we use the nomenclature derived from the meta-learning literature which is the most prevalent at time of writing. Let S {( x l,y l)} L l 1 be a support-set containing input-output pairs, with L equal to one (1-shot) or five (5-shot), and Q { (x m,y m)} M m 1be a query-set (sometimes referred to in the literature as a target-set), with M typically one order of magnitude greater than L. For ease of notation, the support and query sets are grouped in a task T {S, Q}, with the dataset D {T n} N n 1 defined as a collection of such tasks. Models are trained on random tasks sampled from D . Then, given a new task T {S, Q } sampled from a test set, the objective is to condition the model on the samples of the support S to estimate the membership of the samples in the query set Q . In the most common scenario, the inputs x D belong to the same distribution p(x) and are distributed across training, validation, and test sets such that their class membership is non-overlapping. Note that y can be a continuous value (regression) or a discrete one (classification), even though most of the previous work has focused on classification. We also consider the cross-domain scenario, where the inputs are sampled from different distributions at training and test time; this is more representative of real-world scenarios.

The typical multi-task learning methods for spatio-temporal data prediction involve low-rank tensor computation. However, such a method have relatively weak performance when the task number is small, and we cannot integrate it into non-linear models. In this paper, we propose a two-step suboptimal unitary method (SUM) to combine a meta-learning strategy into multi-task models. In the first step, it searches for a global pattern by optimising the general parameters with gradient descents under constraints, which is a geological regularizer to enable model learning with less training data. In the second step, we derive an optimised model on each specific task from the global pattern with only a few local training data. Compared with traditional multi-task learning methods, SUM shows advantages of generalisation ability on distant tasks. It can be applied on any multi-task models with the gradient descent as its optimiser regardless if the prediction function is linear or not. Moreover, we can harness the model to enable traditional prediction model to make coKriging. The experiments on public datasets have suggested that our framework, when combined with current multi-task models, has a conspicuously better prediction result when the task number is small compared to low-rank tensor learning, and our model has a quite satisfying outcome when adjusting the current prediction models for coKriging.