pwc-archive/papers-with-abstracts huggingface dataset:
Cannot Predict Comment Volume of a News Article before (a few) Users Read It
Authors: Lihong He, Chen Shen, Arjun Mukherjee, Slobodan Vucetic, Eduard Dragut
Date: 2020-08-14 00:00:00
Tasks: Articles

Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., comments. An article may attract an apathetic user engagement (several tens of comments) or a spontaneous fervent user engagement (thousands of comments). In this paper, we study the problem of predicting the total number of user comments a news article will receive. Our main insight is that the early dynamics of user comments contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence. This appears to be an interesting and understudied phenomenon: collective social behavior at a news outlet shapes user response and may even downplay the content of an article. We compile and analyze a large number of features, both old and novel from literature. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. We show that the early arrival rate of comments is the best indicator of the eventual number of comments. We conduct an in-depth analysis of this feature across several dimensions, such as news outlets and news article categories. We show that the relationship between the early rate and the final number of comments as well as the prediction accuracy vary considerably across news outlets and news article categories (e.g., politics, sports, or health).
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Authors: Stefanos Laskaridis, Stylianos I. Venieris, Mario Almeida, Ilias Leontiadis, Nicholas D. Lane
Date: 2020-08-14 00:00:00
Tasks: Collaborative Inference

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices. A popular alternative comprises offloading CNN processing to powerful cloud-based servers. Nevertheless, by relying on the cloud to produce outputs, emerging mission-critical and high-mobility applications, such as drone obstacle avoidance or interactive applications, can suffer from the dynamic connectivity conditions and the uncertain availability of the cloud. In this paper, we propose SPINN, a distributed inference system that employs synergistic device-cloud computation together with a progressive inference method to deliver fast and robust CNN inference across diverse settings. The proposed system introduces a novel scheduler that co-optimises the early-exit policy and the CNN splitting at run time, in order to adapt to dynamic conditions and meet user-defined service-level requirements. Quantitative evaluation illustrates that SPINN outperforms its state-of-the-art collaborative inference counterparts by up to 2x in achieved throughput under varying network conditions, reduces the server cost by up to 6.8x and improves accuracy by 20.7% under latency constraints, while providing robust operation under uncertain connectivity conditions and significant energy savings compared to cloud-centric execution.
Renormalization for Initialization of Rolling Shutter Visual-Inertial Odometry
Authors: Branislav Micusik, Georgios Evangelidis
Date: 2020-08-14 00:00:00

In this paper we deal with the initialization problem of a visual-inertial odometry system with rolling shutter cameras. Initialization is a prerequisite for using inertial signals and fusing them with visual data. We propose a novel statistical solution to the initialization problem on visual and inertial data simultaneously, by casting it into the renormalization scheme of Kanatani. The renormalization is an optimization scheme which intends to reduce the inherent statistical bias of common linear systems. We derive and present the necessary steps and methodology specific to the initialization problem. Extensive evaluations on ground truth exhibit superior performance and a gain in accuracy of up to $20\%$ over the originally proposed Least Squares solution. The renormalization performs similarly to the optimal Maximum Likelihood estimate, despite arriving at the solution by different means. With this paper we are adding to the set of Computer Vision problems which can be cast into the renormalization scheme.
Supervised Topological Maps
Authors: Francesco Mannella
Date: 2020-08-14 00:00:00

Controlling the internal representation space of a neural network is a desirable feature because it allows to generate new data in a supervised manner. In this paper we will show how this can be achieved while building a low-dimensional mapping of the input stream, by deriving a generalized algorithm starting from Self Organizing Maps (SOMs). SOMs are a kind of neural network which can be trained with unsupervised learning to produce a low-dimensional discretized mapping of the input space. They can be used for the generation of new data through backward propagation of interpolations made from the mapping grid. Unfortunately the final topology of the mapping space of a SOM is not known before learning, so interpolating new data in a supervised way is not an easy task. Here we will show a variation from the SOM algorithm consisting in constraining the update of prototypes so that it is also a function of the distance of its prototypes from extrinsically given targets in the mapping space. We will demonstrate how such variants, that we will call Supervised Topological Maps (STMs), allow for a supervised mapping where the position of internal representations in the mapping space is determined by the experimenter. Controlling the internal representation space in STMs reveals to be an easier task than what is currently done using other algorithms such as variational or adversarial autoencoders.
Sample-efficient Cross-Entropy Method for Real-time Planning
Authors: Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Jan Achterhold, Joerg Stueckler, Michal Rolinek, Georg Martius
Date: 2020-08-14 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse-reward environments. However, their sampling inefficiency prevents them from being used for real-time planning and control. We propose an improved version of the CEM algorithm for fast planning, with novel additions including temporally-correlated actions and memory, requiring 2.7-22x less samples and yielding a performance increase of 1.2-10x in high-dimensional control problems.
PointMixup: Augmentation for Point Clouds
Authors: Yunlu Chen, Vincent Tao Hu, Efstratios Gavves, Thomas Mensink, Pascal Mettes, Pengwan Yang, Cees G. M. Snoek
Date: 2020-08-14 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Point Cloud Classification, 3D Point Cloud Data Augmentation, Data Augmentation, Point Cloud Classification

This paper introduces data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in the image domain. Such a mixup is however not directly transferable to point clouds, as we do not have a one-to-one correspondence between the points of two different objects. In this paper, we define data augmentation between point clouds as a shortest path linear interpolation. To that end, we introduce PointMixup, an interpolation method that generates new examples through an optimal assignment of the path function between two point clouds. We prove that our PointMixup finds the shortest path between two point clouds and that the interpolation is assignment invariant and linear. With the definition of interpolation, PointMixup allows to introduce strong interpolation-based regularizers such as mixup and manifold mixup to the point cloud domain. Experimentally, we show the potential of PointMixup for point cloud classification, especially when examples are scarce, as well as increased robustness to noise and geometric transformations to points. The code for PointMixup and the experimental details are publicly available.
Survey of XAI in digital pathology
Authors: Milda Pocevičiūtė, Gabriel Eilertsen, Claes Lundström
Date: 2020-08-14 00:00:00
Tasks: Diagnostic, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Survey

Artificial intelligence (AI) has shown great promise for diagnostic imaging assessments. However, the application of AI to support medical diagnostics in clinical routine comes with many challenges. The algorithms should have high prediction accuracy but also be transparent, understandable and reliable. Thus, explainable artificial intelligence (XAI) is highly relevant for this domain. We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs. The review includes several contributions. Firstly, we give a thorough overview of current XAI techniques of potential relevance for deep learning methods in pathology imaging, and categorise them from three different aspects. In doing so, we incorporate uncertainty estimation methods as an integral part of the XAI landscape. We also connect the technical methods to the specific prerequisites in digital pathology and present findings to guide future research efforts. The survey is intended for both technical researchers and medical professionals, one of the objectives being to establish a common ground for cross-disciplinary discussions.
Partial Orders, Residuation, and First-Order Linear Logic
Authors: Richard Moot
Date: 2020-08-14 00:00:00

We will investigate proof-theoretic and linguistic aspects of first-order linear logic. We will show that adding partial order constraints in such a way that each sequent defines a unique linear order on the antecedent formulas of a sequent allows us to define many useful logical operators. In addition, the partial order constraints improve the efficiency of proof search.
Feature Extraction Functions for Neural Logic Rule Learning
Authors: Shashank Gupta, Antonio Robles-Kelly, Mohamed Reda Bouadjenek
Date: 2020-08-14 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification

Combining symbolic human knowledge with neural networks provides a rule-based ante-hoc explanation of the output. In this paper, we propose feature extracting functions for integrating human knowledge abstracted as logic rules into the predictive behavior of a neural network. These functions are embodied as programming functions, which represent the applicable domain knowledge as a set of logical instructions and provide a modified distribution of independent features on input data. Unlike other existing neural logic approaches, the programmatic nature of these functions implies that they do not require any kind of special mathematical encoding, which makes our method very general and flexible in nature. We illustrate the performance of our approach for sentiment classification and compare our results to those obtained using two baselines.
OR-Gym: A Reinforcement Learning Library for Operations Research Problems
Authors: Christian D. Hubbs, Hector D. Perez, Owais Sarwar, Nikolaos V. Sahinidis, Ignacio E. Grossmann, John M. Wassick
Date: 2020-08-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings. We introduce OR-Gym, an open-source library for developing reinforcement learning algorithms to address operations research problems. In this paper, we apply reinforcement learning to the knapsack, multi-dimensional bin packing, multi-echelon supply chain, and multi-period asset allocation model problems, as well as benchmark the RL solutions against MILP and heuristic models. These problems are used in logistics, finance, engineering, and are common in many business operation settings. We develop environments based on prototypical models in the literature and implement various optimization and heuristic models in order to benchmark the RL results. By re-framing a series of classic optimization problems as RL tasks, we seek to provide a new tool for the operations research community, while also opening those in the RL community to many of the problems and challenges in the OR field.
Interpretable Real-Time Win Prediction for Honor of Kings, a Popular Mobile MOBA Esport
Authors: Zelong Yang, Zhufeng Pan, Yan Wang, Deng Cai, Xiaojiang Liu, Shuming Shi, Shao-Lun Huang
Date: 2020-08-14 00:00:00
Tasks: Attribute

With the rapid prevalence and explosive development of MOBA esports (Multiplayer Online Battle Arena electronic sports), much research effort has been devoted to automatically predicting game results (win predictions). While this task has great potential in various applications, such as esports live streaming and game commentator AI systems, previous studies fail to investigate the methods to interpret these win predictions. To mitigate this issue, we collected a large-scale dataset that contains real-time game records with rich input features of the popular MOBA game Honor of Kings. For interpretable predictions, we proposed a Two-Stage Spatial-Temporal Network (TSSTN) that can not only provide accurate real-time win predictions but also attribute the ultimate prediction results to the contributions of different features for interpretability. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective both in prediction accuracy and interpretability.
Computation Offloading in Heterogeneous Vehicular Edge Networks: On-line and Off-policy Bandit Solutions
Authors: Arash Bozorgchenani, Setareh Maghsudi, Daniele Tarchi, Ekram Hossain
Date: 2020-08-14 00:00:00
Tasks: Edge-computing

With the rapid advancement of Intelligent Transportation Systems (ITS) and vehicular communications, Vehicular Edge Computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.
Provable More Data Hurt in High Dimensional Least Squares Estimator
Authors: Zeng Li, Chuanlong Xie, Qinwen Wang
Date: 2020-08-14 00:00:00
Tasks: Prediction, Vocal Bursts Intensity Prediction

This paper investigates the finite-sample prediction risk of the high-dimensional least squares estimator. We derive the central limit theorem for the prediction risk when both the sample size and the number of features tend to infinity. Furthermore, the finite-sample distribution and the confidence interval of the prediction risk are provided. Our theoretical results demonstrate the sample-wise nonmonotonicity of the prediction risk and confirm "more data hurt" phenomenon.
A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis
Authors: Maria Hügle, Gabriel Kalweit, Thomas Huegle, Joschka Boedecker
Date: 2020-08-14 00:00:00
Tasks: BIG-bench Machine Learning, Disease Prediction, Management

Clinical data from electronic medical records, registries or trials provide a large source of information to apply machine learning methods in order to foster precision medicine, e.g. by finding new disease phenotypes or performing individual disease prediction. However, to take full advantage of deep learning methods on clinical data, architectures are necessary that 1) are robust with respect to missing and wrong values, and 2) can deal with highly variable-sized lists and long-term dependencies of individual diagnosis, procedures, measurements and medication prescriptions. In this work, we elaborate limitations of fully-connected neural networks and classical machine learning methods in this context and propose AdaptiveNet, a novel recurrent neural network architecture, which can deal with multiple lists of different events, alleviating the aforementioned limitations. We employ the architecture to the problem of disease progression prediction in rheumatoid arthritis using the Swiss Clinical Quality Management registry, which contains over 10.000 patients and more than 65.000 patient visits. Our proposed approach leads to more compact representations and outperforms the classical baselines.
Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?
Authors: Luis H. John, Jan A. Kors, Jenna M. Reps, Patrick B. Ryan, Peter R. Rijnbeek
Date: 2020-08-14 00:00:00
Tasks: All, Future prediction, Prediction

Objective: Provide guidance on sample size considerations for developing predictive models by empirically establishing the adequate sample size, which balances the competing objectives of improving model performance and reducing model complexity as well as computational requirements. Materials and Methods: We empirically assess the effect of sample size on prediction performance and model complexity by generating learning curves for 81 prediction problems (23 outcomes predicted in a depression cohort, 58 outcomes predicted in a hypertension cohort) in three large observational health databases, requiring training of 17,248 prediction models. The adequate sample size was defined as the sample size for which the performance of a model equalled the maximum model performance minus a small threshold value. Results: The adequate sample size achieves a median reduction of the number of observations of 9.5%, 37.3%, 58.5%, and 78.5% for the thresholds of 0.001, 0.005, 0.01, and 0.02, respectively. The median reduction of the number of predictors in the models was 8.6%, 32.2%, 48.2%, and 68.3% for the thresholds of 0.001, 0.005, 0.01, and 0.02, respectively. Discussion: Based on our results a conservative, yet significant, reduction in sample size and model complexity can be estimated for future prediction work. Though, if a researcher is willing to generate a learning curve a much larger reduction of the model complexity may be possible as suggested by a large outcome-dependent variability. Conclusion: Our results suggest that in most cases only a fraction of the available data was sufficient to produce a model close to the performance of one developed on the full data set, but with a substantially reduced model complexity.
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes
Authors: Weidong Zhang, Wei zhang, yinda zhang
Date: 2020-08-14 00:00:00
Proceeding: ECCV 2020 8
Tasks: Keypoint Detection, Room Layout Estimation, Semantic Segmentation

The task of room layout estimation is to locate the wall-floor, wall-ceiling, and wall-wall boundaries. Most recent methods solve this problem based on edge/keypoint detection or semantic segmentation. However, these approaches have shown limited attention on the geometry of the dominant planes and the intersection between them, which has significant impact on room layout. In this work, we propose to incorporate geometric reasoning to deep learning for layout estimation. Our approach learns to infer the depth maps of the dominant planes in the scene by predicting the pixel-level surface parameters, and the layout can be generated by the intersection of the depth maps. Moreover, we present a new dataset with pixel-level depth annotation of dominant planes. It is larger than the existing datasets and contains both cuboid and non-cuboid rooms. Experimental results show that our approach produces considerable performance gains on both 2D and 3D datasets.
Rb-PaStaNet: A Few-Shot Human-Object Interaction Detection Based on Rules and Part States
Authors: Shenyu Zhang, Zichen Zhu, Qingquan Bao
Date: 2020-08-14 00:00:00
Tasks: Human-Object Interaction Detection

Existing Human-Object Interaction (HOI) Detection approaches have achieved great progress on nonrare classes while rare HOI classes are still not well-detected. In this paper, we intend to apply human prior knowledge into the existing work. So we add human-labeled rules to PaStaNet and propose Rb-PaStaNet aimed at improving rare HOI classes detection. Our results show a certain improvement of the rare classes, while the non-rare classes and the overall improvement is more considerable.
Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
Authors: Cong Quan, Jinjie Zhou, Yuanzheng Zhu, Yang Chen, Shan-Shan Wang, Dong Liang, Qiegen Liu
Date: 2020-08-14 00:00:00
Tasks: Denoising, Image Reconstruction, MRI Reconstruction

Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently. Rather than the existing generative models that often optimize the density priors, in this work, by taking advantage of the denoising score matching, homotopic gradients of generative density priors (HGGDP) are proposed for magnetic resonance imaging (MRI) reconstruction. More precisely, to tackle the low-dimensional manifold and low data density region issues in generative density prior, we estimate the target gradients in higher-dimensional space. We train a more powerful noise conditional score network by forming high-dimensional tensor as the network input at the training phase. More artificial noise is also injected in the embedding space. At the reconstruction stage, a homotopy method is employed to pursue the density prior, such as to boost the reconstruction performance. Experiment results imply the remarkable performance of HGGDP in terms of high reconstruction accuracy; only 10% of the k-space data can still generate images of high quality as effectively as standard MRI reconstruction with the fully sampled data.