Cooperation-A ware Lane Change Control in Dense Traffic Sangjae Bae 1, Dhruv Saxena 2, Alireza Nakhaei 3, Chiho Choi 3, Kikuo Fujimura 3, and Scott Moura 1 Abstract -- This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of human drivers. This paper especially focuses on heavy traffic where vehicles cannot change lane without cooperating with other drivers. In this case, classical robust controls may not apply since there is no "safe" area to merge to. That said, modeling complex and interactive human behaviors is nontrivial from the perspective of control engineers. We propose a mathematical control framework based on Model Predictive Control (MPC) encompassing a state-of-the-art Recurrent Neural network (RNN) architecture. In particular, RNN predicts interactive motions of human drivers in response to potential actions of the autonomous vehicle, which are then be systematically evaluated in safety constraints. We also propose a real-time heuristic algorithm to find locally optimal control inputs. Finally, quantitative and qualitative analysis on simulation studies are presented, showing a strong potential of the proposed framework. I NTRODUCTION An autonomous-driving vehicle is no longer a futuristic concept and extensive researches have been conducted in various aspects, spanning from localization, perceptions, and controls to implementations and validations. Particularly from the perspective of control engineers, designing a controller that secures safety, in various traffic conditions, such as driving on arterial-road/highway in free-flow/dense traffic with/without traffic lights, has been a principal research focus.

To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.

BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pre-trained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction. In this paper we introduce a new pre-training task inspired by reading comprehension and an effort to avoid encoding general knowledge in the transformer network itself. We find significant and consistent improvements over both BERT-BASE and BERT-LARGE on multiple reading comprehension (MRC) and paraphrasing datasets. Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction. We also establish a new SOTA in HotpotQA, improving answer prediction F1 by 4 F1 points and supporting fact prediction by 1 F1 point. Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount.

--In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the product recommendation in the online advertising. Through extensive testing with RecoGym, an OpenAI Gym-based reinforcement learning environment for the product recommendation in online advertising, the proposed method outperforms the widespread reinforcement learning schemes such as null -Greedy, Upper Confidence (UCB1) and Exponential Weights for Exploration and Exploitation (EXP3). I NTRODUCTION Online advertising is becoming increasingly popular and is the main motivation for the development of almost free internet platforms such as search engines, social networks, recruitment sites, multimedia contents (e.g., videos, images, musics, ...) sharing, etc. From the point of view of the internet users, the product recommendation on online advertising can be genuinely useful if it meets the real immediate needs of users. Instead of spending a lot of time and effort searching for a huge number of thousands or even millions of choices, most internet users will be quite satisfied if recommendation systems propose exactly what they need. Finding a good recommendation system, therefore, continues to be the goal of many studies [1], [2]. Online and offline approaches for learning optimal recommendation policies can be found in the literature.

We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In addition to the volumes of brain structures, the FNN uses cortical WM/GM contrast and cortical thickness at 78 cortical regions. These last two measurements were derived from the T1-weighted MR images using cortical surfaces produced by the CIVET pipeline. The age and gender of the subjects and the scanner manufacturer are also used as features for the learning algorithm. This yielded 283 features provided to the FNN with two hidden layers of 20 and 15 nodes. The method was applied to the data from the ABCD study. Trained with a training set of 3736 subjects, the proposed method achieved a MSE of 71.596 and a correlation of 0.151 in the validation set of 415 subjects. For the final submission, the model was trained with 3568 subjects and it achieved a MSE of 94.0270 in the test set comprised of 4383 subjects.

The study of dexterous manipulation has provided important insights in humans sensorimotor control as well as inspiration for manipulation strategies in robotic hands. Previous work focused on experimental environment with restrictions. Here we describe a method using the deformation and color distribution of the fingernail and its surrounding skin, to estimate the fingertip forces, torques and contact surface curvatures for various objects, including the shape and material of the contact surfaces and the weight of the objects. The proposed method circumvents limitations associated with sensorized objects, gloves or fixed contact surface type. In addition, compared with previous single finger estimation in an experimental environment, we extend the approach to multiple finger force estimation, which can be used for applications such as human grasping analysis. Four algorithms are used, c.q., Gaussian process (GP), Convolutional Neural Networks (CNN), Neural Networks with Fast Dropout (NN-FD) and Recurrent Neural Networks with Fast Dropout (RNN-FD), to model a mapping from images to the corresponding labels. The results further show that the proposed method has high accuracy to predict force, torque and contact surface.

The Large Synoptic Survey Telescope will complete its survey in 2022 and produce terabytes of imaging data each night. To work with this massive onset of data, automated algorithms to classify astronomical light curves are crucial. Here, we present a method for automated classification of photometric light curves for a range of astronomical objects. Our approach is based on the gradient boosting of decision trees, feature extraction and selection, and augmentation. The solution was developed in the context of The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) and achieved one of the top results in the challenge.

--The past few decades has seen increased interest in the application of social robots to interventions for Autism Spectrum Disorder as behavioural coaches [4]. We consider that robots embedded in therapies could also provide quantitative diagnostic information by observing patient behaviours. The social nature of ASD symptoms means that, to achieve this, robots need to be able to recognize the internal states their human interaction partners are experiencing, e.g. In this paper we discuss these two questions in depth and propose a novel, conceptor-based classifier . We report the initial results of this system in a proof-of-concept study and outline plans for future work. The development of socially interactive robots has inspired research into various applications for these tools.

Although numerous instantiations [ He et al., 2017; Liang et al., 2018 ] of CF have been proposed in recent years, matrix factorization (MF) [ Mnih and Salakhut-dinov, 2007; Koren et al., 2009 ] remains the most popular one due to its simplicity and effectiveness, and has been used for large scale recommendations of news [ Das et al., 2007], movies [ Koren et al., 2009 ] and products [ Linden et al., 2003 ] . Recent studies extend the MF framework for item cold-start recommendation by incorporating content information of items. The majority of methods for item cold-start recommendation employ a latent space sharing model. For example, Saveski te al. [ 2014] and Barjasteh et al. [ 2016 ] propose to use MF as the prjection function for both interactions and item contents. LDA [ Wang and Blei, 2011 ], CNN [ Kim et al., 2016 ], DNN [ Ebesu and Fang, 2017 ], SDAE [ Wang et al., 2015; Ying et al., 2016 ] and mDA [ Li et al., 2015 ] are proposed to learn the latent vectors of items from their textual contents. V an den Oord et al. [ 2013] and Wang et al. [ 2014] propose to use CNN to learn the latent vectors of music from their audio signals. The Wasserstein distance, which originates from optimal transport theory [ Rubner et al., 1998; Levina and Bickel, 2001], is a distance metric on probabilistic space and able to leverage the information on feature space. It has been successfully applied to many applications, such as computer vision [ Arjovsky et al., 2017 ] and natural language processing Figure 2: An illustration of problem definition.

Network embedding aims to embed nodes into a low-dimensional space, while capturing the network structures and properties. Although quite a few promising network embedding methods have been proposed, most of them focus on static networks. In fact, temporal networks, which usually evolve over time in terms of microscopic and macroscopic dynamics, are ubiquitous. The micro-dynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale. Both micro- and macro-dynamics are the key factors to network evolution; however, how to elegantly capture both of them for temporal network embedding, especially macro-dynamics, has not yet been well studied. In this paper, we propose a novel temporal network embedding method with micro- and macro-dynamics, named $\rm{M^2DNE}$. Specifically, for micro-dynamics, we regard the establishments of edges as the occurrences of chronological events and propose a temporal attention point process to capture the formation process of network structures in a fine-grained manner. For macro-dynamics, we define a general dynamics equation parameterized with network embeddings to capture the inherent evolution pattern and impose constraints in a higher structural level on network embeddings. Mutual evolutions of micro- and macro-dynamics in a temporal network alternately affect the process of learning node embeddings. Extensive experiments on three real-world temporal networks demonstrate that $\rm{M^2DNE}$ significantly outperforms the state-of-the-arts not only in traditional tasks, e.g., network reconstruction, but also in temporal tendency-related tasks, e.g., scale prediction.