OpenRaiser/Intern-Atlas huggingface dataset:
Explicitly Conditioned Melody Generation: A Case Study with Interdependent RNNs
Venue: arXiv cs.SD 2019
Year: 2019

Deep generative models for symbolic music are typically designed to model temporal dependencies in music so as to predict the next musical event given previous events. In many cases, such models are expected to learn abstract concepts such as harmony, meter, and rhythm from raw musical data without any additional information. In this study, we investigate the effects of explicitly conditioning deep generative models with musically relevant information. Specifically, we study the effects of four different conditioning inputs on the performance of a recurrent monophonic melody generation model. Several combinations of these conditioning inputs are used to train different model variants which are then evaluated using three objective evaluation paradigms across two genres of music. The results indicate musically relevant conditioning significantly improves learning and performance, and reveal how this information affects learning of musical features related to pitch and rhythm. An informal subjective evaluation suggests a corresponding improvement in the aesthetic quality of generations.
The Illusion of Animal Body Ownership and Its Potential for Virtual Reality Games
Venue: arXiv cs.HC 2019
Year: 2019

Virtual reality offers the unique possibility to experience a virtual representation as our own body. In contrast to previous research that predominantly studied this phenomenon for humanoid avatars, our work focuses on virtual animals. In this paper, we discuss different body tracking approaches to control creatures such as spiders or bats and the respective virtual body ownership effects. Our empirical results demonstrate that virtual body ownership is also applicable for nonhumanoids and can even outperform human-like avatars in certain cases. An additional survey confirms the general interest of people in creating such experiences and allows us to initiate a broad discussion regarding the applicability of animal embodiment for educational and entertainment purposes.
Influence of Pointing on Learning to Count: A Neuro-Robotics Model
Venue: arXiv cs.CV 2019
Year: 2019

In this paper a neuro-robotics model capable of counting using gestures is introduced. The contribution of gestures to learning to count is tested with various model and training conditions. Two studies were presented in this article. In the first, we combine different modalities of the robot's neural network, in the second, a novel training procedure for it is proposed. The model is trained with pointing data from an iCub robot simulator. The behaviour of the model is in line with that of human children in terms of performance change depending on gesture production.
Introduction to Camera Pose Estimation with Deep Learning
Venue: arXiv cs.CV 2019
Year: 2019

Over the last two decades, deep learning has transformed the field of computer vision. Deep convolutional networks were successfully applied to learn different vision tasks such as image classification, image segmentation, object detection and many more. By transferring the knowledge learned by deep models on large generic datasets, researchers were further able to create fine-tuned models for other more specific tasks. Recently this idea was applied for regressing the absolute camera pose from an RGB image. Although the resulting accuracy was sub-optimal, compared to classic feature-based solutions, this effort led to a surge of learning-based pose estimation methods. Here, we review deep learning approaches for camera pose estimation. We describe key methods in the field and identify trends aiming at improving the original deep pose regression solution. We further provide an extensive cross-comparison of existing learning-based pose estimators, together with practical notes on their execution for reproducibility purposes. Finally, we discuss emerging solutions and potential future research directions.
Affine Disentangled GAN for Interpretable and Robust AV Perception
Venue: arXiv cs.CV 2019
Year: 2019

Autonomous vehicles (AV) have progressed rapidly with the advancements in computer vision algorithms. The deep convolutional neural network as the main contributor to this advancement has boosted the classification accuracy dramatically. However, the discovery of adversarial examples reveals the generalization gap between dataset and the real world. Furthermore, affine transformations may also confuse computer vision based object detectors. The degradation of the perception system is undesirable for safety critical systems such as autonomous vehicles. In this paper, a deep learning system is proposed: Affine Disentangled GAN (ADIS-GAN), which is robust against affine transformations and adversarial attacks. It is demonstrated that conventional data augmentation for affine transformation and adversarial attacks are orthogonal, while ADIS-GAN can handle both attacks at the same time. Useful information such as image rotation angle and scaling factor are also generated in ADIS-GAN. On MNIST dataset, ADIS-GAN can achieve over 98 percent classification accuracy within 30 degrees rotation, and over 90 percent classification accuracy against FGSM and PGD adversarial attack.
Vision-based Pedestrian Alert Safety System (PASS) for Signalized Intersections
Venue: arXiv cs.CV 2019
Year: 2019

Although Vehicle-to-Pedestrian (V2P) communication can significantly improve pedestrian safety at a signalized intersection, this safety is hindered as pedestrians often do not carry hand-held devices (e.g., Dedicated short-range communication (DSRC) and 5G enabled cell phone) to communicate with connected vehicles nearby. To overcome this limitation, in this study, traffic cameras at a signalized intersection were used to accurately detect and locate pedestrians via a vision-based deep learning technique to generate safety alerts in real-time about possible conflicts between vehicles and pedestrians. The contribution of this paper lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds). We develop a pedestrian alert safety system (PASS) to generate a safety alert of an imminent pedestrian-vehicle crash using generated PSMs to improve pedestrian safety at a signalized intersection. Our approach estimates the location and velocity of a pedestrian more accurately than existing DSRC-enabled pedestrian hand-held devices. A connected vehicle application, the Pedestrian in Signalized Crosswalk Warning (PSCW), was developed to evaluate the vision-based PASS. Numerical analyses show that our vision-based PASS is able to satisfy the accuracy and latency requirements of pedestrian safety applications in a connected vehicle environment.
A Study of Network Congestion in Two Supercomputing High-Speed Interconnects
Venue: arXiv cs.DC 2019
Year: 2019

Network congestion in high-speed interconnects is a major source of application run time performance variation. Recent years have witnessed a surge of interest from both academia and industry in the development of novel approaches for congestion control at the network level and in application placement, mapping, and scheduling at the system-level. However, these studies are based on proxy applications and benchmarks that are not representative of field-congestion characteristics of high-speed interconnects. To address this gap, we present (a) an end-to-end framework for monitoring and analysis to support long-term field-congestion characterization studies, and (b) an empirical study of network congestion in petascale systems across two different interconnect technologies: (i) Cray Gemini, which uses a 3-D torus topology, and (ii) Cray Aries, which uses the DragonFly topology.
Minimax Theorems for Finite Blocklength Lossy Joint Source-Channel Coding over an AVC
Venue: arXiv cs.IT 2019
Year: 2019

Motivated by applications in the security of cyber-physical systems, we pose the finite blocklength communication problem in the presence of a jammer as a zero-sum game between the encoder-decoder team and the jammer, by allowing the communicating team as well as the jammer only locally randomized strategies. The communicating team's problem is non-convex under locally randomized codes, and hence, in general, a minimax theorem need not hold for this game. However, we show that approximate minimax theorems hold in the sense that the minimax and maximin values of the game approach each other asymptotically. In particular, for rates strictly below a critical threshold, both the minimax and maximin values approach zero, and for rates strictly above it, they both approach unity. We then show a second order minimax theorem, i.e., for rates exactly approaching the threshold with along a specific scaling, the minimax and maximin values approach the same constant value, that is neither zero nor one. Critical to these results is our derivation of finite blocklength bounds on the minimax and maximin values of the game and our derivation of second order dispersion-based bounds.
A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts
Venue: arXiv cs.CL 2019
Year: 2019

End-to-end Task-oriented Dialogue Systems (TDSs) have attracted a lot of attention for their superiority (e.g., in terms of global optimization) over pipeline modularized TDSs. Previous studies on end-to-end TDSs use a single-module model to generate responses for complex dialogue contexts. However, no model consistently outperforms the others in all cases. We propose a neural Modular Task-oriented Dialogue System(MTDS) framework, in which a few expert bots are combined to generate the response for a given dialogue context. MTDS consists of a chair bot and several expert bots. Each expert bot is specialized for a particular situation, e.g., one domain, one type of action of a system, etc. The chair bot coordinates multiple expert bots and adaptively selects an expert bot to generate the appropriate response. We further propose a Token-level Mixture-of-Expert (TokenMoE) model to implement MTDS, where the expert bots predict multiple tokens at each timestamp and the chair bot determines the final generated token by fully taking into consideration the outputs of all expert bots. Both the chair bot and the expert bots are jointly trained in an end-to-end fashion. To verify the effectiveness of TokenMoE, we carry out extensive experiments on a benchmark dataset. Compared with the baseline using a single-module model, our TokenMoE improves the performance by 8.1% of inform rate and 0.8% of success rate.
Abelian-square factors and binary words
Venue: arXiv cs.FL 2019
Year: 2019

In this work, we affirm the conjecture proposed by Gabriele Fici and Filippo Mignosi at the 10th Conference on Combinatorics on Words.
Grounding Value Alignment with Ethical Principles
Venue: arXiv cs.AI 2019
Year: 2019

An important step in the development of value alignment (VA) systems in AI is understanding how values can interrelate with facts. Designers of future VA systems will need to utilize a hybrid approach in which ethical reasoning and empirical observation interrelate successfully in machine behavior. In this article we identify two problems about this interrelation that have been overlooked by AI discussants and designers. The first problem is that many AI designers commit inadvertently a version of what has been called by moral philosophers the "naturalistic fallacy," that is, they attempt to derive an "ought" from an "is." We illustrate when and why this occurs. The second problem is that AI designers adopt training routines that fail fully to simulate human ethical reasoning in the integration of ethical principles and facts. Using concepts of quantified modal logic, we proceed to offer an approach that promises to simulate ethical reasoning in humans by connecting ethical principles on the one hand and propositions about states of affairs on the other.
Online Learning to Estimate Warfarin Dose with Contextual Linear Bandits
Venue: arXiv cs.LG 2019
Year: 2019

Warfarin is one of the most commonly used oral blood anticoagulant agent in the world, the proper dose of Warfarin is difficult to establish not only because it is substantially variant among patients, but also adverse even severe consequences of taking an incorrect dose. Typical practice is to prescribe an initial dose, then doctor closely monitor patient response and adjust accordingly to the correct dosage. The three commonly used strategies for an initial dosage are the fixed-dose approach, the Warfarin Clinical algorithm, and the Pharmacogenetic algorithm developed by the IWPC (International Warfarin Pharmacogenetics Consortium). It is always best to prescribe correct initial dosage, motivated by this challenge, this work explores the performance of multi-armed bandit algorithms to best predict the correct dosage of Warfarin instead of trial-and-error procedure. Real data from the Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) is used, with it a series of linear bandit algorithms and variants are developed and evaluated on Warfarin dataset. All proposed algorithms outperformed the fixed-dose baseline algorithm, and some even matched up the Warfarin Clinical Dosing Algorithm. In addition, a few promising future directions are given for further exploration and development.
The Gain of Energy Accumulation in Multi-hop Wireless Network Broadcast
Venue: arXiv cs.NI 2019
Year: 2019

Broadcast is a fundamental network operation, widely used in wireless networks to disseminate messages. The energy-efficiency of broadcast is important particularly when devices in the network are energy constrained. To improve the efficiency of broadcast, different approaches have been taken in the literature. One of these approaches is broadcast with energy accumulation. Through simulations, it has been shown in the literature that broadcast with energy accumulation can result in energy saving. The amount of this saving, however, has only been analyzed for linear multi-hop wireless networks. In this work, we extend this analysis to two-dimensional (2D) multi-hop networks. The analysis of saving in 2D networks is much more challenging than that in linear networks. It is because, unlike in linear networks, in 2D networks, finding minimum-energy broadcasts with or without energy accumulation are both NP-hard problems. Nevertheless, using a novel approach, we prove that this saving is constant when the path loss exponent alpha is strictly greater than two. Also, we prove that the saving is theta(log n) when alpha=2, where n denotes the number of nodes in the network.
Geographical Distribution of Biomedical Research in the USA and China
Venue: arXiv cs.DL 2019
Year: 2019

We analyze nearly 20 million geocoded PubMed articles with author affiliations. Using K-means clustering for the lower 48 US states and mainland China, we find that the average published paper is within a relatively short distance of a few centroids. These centroids have shifted very little over the past 30 years, and the distribution of distances to these centroids has not changed much either. The overall country centroids have gradually shifted south (about 0.2° for the USA and 1.7° for China), while the longitude has not moved significantly. These findings indicate that there are few large scientific hubs in the USA and China and the typical investigator is within geographical reach of one such hub. This sets the stage to study centralization of biomedical research at national and regional levels across the globe, and over time.
R-Transformer: Recurrent Neural Network Enhanced Transformer
Venue: arXiv cs.LG 2019
Year: 2019

Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently. Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks. Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts. In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks. The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings. We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks. We have made the code publicly available at \url{https://github.com/DSE-MSU/R-transformer}.
Narvis: Authoring Narrative Slideshows for Introducing Data Visualization Designs
Venue: arXiv cs.HC 2019
Year: 2019

Visual designs can be complex in modern data visualization systems, which poses special challenges for explaining them to the non-experts. However, few if any presentation tools are tailored for this purpose. In this study, we present Narvis, a slideshow authoring tool designed for introducing data visualizations to non-experts. Narvis targets two types of end-users: teachers, experts in data visualization who produce tutorials for explaining a data visualization, and students, non-experts who try to understand visualization designs through tutorials. We present an analysis of requirements through close discussions with the two types of end-users. The resulting considerations guide the design and implementation of Narvis. Additionally, to help teachers better organize their introduction slideshows, we specify a data visualization as a hierarchical combination of components, which are automatically detected and extracted by Narvis. The teachers craft an introduction slideshow through first organizing these components, and then explaining them sequentially. A series of templates are provided for adding annotations and animations to improve efficiency during the authoring process. We evaluate Narvis through a qualitative analysis of the authoring experience, and a preliminary evaluation of the generated slideshows.
Detecting coherent explorations in SQL workloads
Venue: arXiv cs.DB 2019
Year: 2019

This paper presents a proposal aiming at better understanding a workload of SQL queries and detecting coherent explorations hidden within the workload. In particular, our work investigates SQLShare [11], a database-as-a-service platform targeting scientists and data scientists with minimal database experience, whose workload was made available to the research community. According to the authors of [11], this workload is the only one containing primarily ad-hoc hand-written queries over user-uploaded datasets. We analyzed this workload by extracting features that characterize SQL queries and we show how to use these features to separate sequences of SQL queries into meaningful explorations. We ran several tests over various query workloads to validate empirically our approach.
AVD: Adversarial Video Distillation
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we present a simple yet efficient approach for video representation, called Adversarial Video Distillation (AVD). The key idea is to represent videos by compressing them in the form of realistic images, which can be used in a variety of video-based scene analysis applications. Representing a video as a single image enables us to address the problem of video analysis by image analysis techniques. To this end, we exploit a 3D convolutional encoder-decoder network to encode the input video as an image by minimizing the reconstruction error. Furthermore, weak supervision by an adversarial training procedure is imposed on the output of the encoder to generate semantically realistic images. The encoder learns to extract semantically meaningful representations from a given input video by mapping the 3D input into a 2D latent representation. The obtained representation can be simply used as the input of deep models pre-trained on images for video classification. We evaluated the effectiveness of our proposed method for video-based activity recognition on three standard and challenging benchmark datasets, i.e. UCF101, HMDB51, and Kinetics. The experimental results demonstrate that AVD achieves interesting performance, outperforming the state-of-the-art methods for video classification.
Space Efficient Approximation to Maximum Matching Size from Uniform Edge Samples
Venue: arXiv cs.DS 2019
Year: 2019

Given a source of iid samples of edges of an input graph $G$ with $n$ vertices and $m$ edges, how many samples does one need to compute a constant factor approximation to the maximum matching size in $G$? Moreover, is it possible to obtain such an estimate in a small amount of space? We show that, on the one hand, this problem cannot be solved using a nontrivially sublinear (in $m$) number of samples: $m^{1-o(1)}$ samples are needed. On the other hand, a surprisingly space efficient algorithm for processing the samples exists: $O(\log^2 n)$ bits of space suffice to compute an estimate. Our main technical tool is a new peeling type algorithm for matching that we simulate using a recursive sampling process that crucially ensures that local neighborhood information from `dense' regions of the graph is provided at appropriately higher sampling rates. We show that a delicate balance between exploration depth and sampling rate allows our simulation to not lose precision over a logarithmic number of levels of recursion and achieve a constant factor approximation. The previous best result on matching size estimation from random samples was a $\log^{O(1)} n$ approximation [Kapralov et al'14]. Our algorithm also yields a constant factor approximate local computation algorithm (LCA) for matching with $O(d\log n)$ exploration starting from any vertex. Previous approaches were based on local simulations of randomized greedy, which take $O(d)$ time {\em in expectation over the starting vertex or edge} (Yoshida et al'09, Onak et al'12), and could not achieve a better than $d^2$ runtime. Interestingly, we also show that unlike our algorithm, the local simulation of randomized greedy that is the basis of the most efficient prior results does take $\wtΩ(d^2)\gg O(d\log n)$ time for a worst case edge even for $d=\exp(Θ(\sqrt{\log n}))$.
Equal bi-Vectorized (EBV) method to high performance on GPU
Venue: arXiv cs.DC 2019
Year: 2019

Due to importance of reducing of time solution in numerical codes, we propose an algorithm for parallel LU decomposition solver for dense and sparse matrices on GPU. This algorithm is based on first bi-vectorizing a triangular matrices of decomposed coefficient matrix and then equalizing vectors. So we improve performance of LU decomposition on equal contributed scheme on threads. This algorithm also is convenient for other parallelism method and multi devices. Several test cases show advantage of this method over other familiar method.
Exploration by Optimisation in Partial Monitoring
Venue: arXiv cs.LG 2019
Year: 2019

We provide a simple and efficient algorithm for adversarial $k$-action $d$-outcome non-degenerate locally observable partial monitoring game for which the $n$-round minimax regret is bounded by $6(d+1) k^{3/2} \sqrt{n \log(k)}$, matching the best known information-theoretic upper bound. The same algorithm also achieves near-optimal regret for full information, bandit and globally observable games.