pwc-archive/papers-with-abstracts huggingface dataset:
RIS Enhanced Massive Non-orthogonal Multiple Access Networks: Deployment and Passive Beamforming Design
Authors: Xiao Liu, Yuanwei Liu, Yue Chen, H. Vincent Poor
Date: 2020-01-28 00:00:00

A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology. The problem of joint deployment, phase shift design, as well as power allocation is formulated for maximizing the energy efficiency with considering users' particular data requirements. To tackle this pertinent problem, machine learning approaches are adopted in two steps. Firstly, a novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. Secondly, a decaying double deep Q-network (D3QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS. In the proposed algorithm, the base station, which controls the RIS by a controller, acts as an agent. The agent periodically observes the state of the RIS-enhanced system for attaining the optimal deployment and design policies of the RIS by learning from its mistakes and the feedback of users. Additionally, it is proved that the proposed D3QN based deployment and design algorithm is capable of converging within mild conditions. Simulation results are provided for illustrating that the proposed LSTM-based ESN algorithm is capable of striking a tradeoff between the prediction accuracy and computational complexity. Finally, it is demonstrated that the proposed D3QN based algorithm outperforms the benchmarks, while the NOMA-enhanced RIS system is capable of achieving higher energy efficiency than orthogonal multiple access (OMA) enabled RIS system.
Statistical Exploration of Relationships Between Routine and Agnostic Features Towards Interpretable Risk Characterization
Authors: Eric Wolsztynski
Date: 2020-01-28 00:00:00

As is typical in other fields of application of high throughput systems, radiology is faced with the challenge of interpreting increasingly sophisticated predictive models such as those derived from radiomics analyses. Interpretation may be guided by the learning output from machine learning models, which may however vary greatly with each technique. Whatever this output model, it will raise some essential questions. How do we interpret the prognostic model for clinical implementation? How can we identify potential information structures within sets of radiomic features, in order to create clinically interpretable models? And how can we recombine or exploit potential relationships between features towards improved interpretability? A number of statistical techniques are explored to assess (possibly nonlinear) relationships between radiological features from different angles.
WISDoM: characterizing neurological timeseries with the Wishart distribution
Authors: Carlo Mengucci, Daniel Remondini, Gastone Castellani, Enrico Giampieri
Date: 2020-01-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Time Series, Time Series Analysis

WISDoM (Wishart Distributed Matrices) is a new framework for the quantification of deviation of symmetric positive-definite matrices associated to experimental samples, like covariance or correlation matrices, from expected ones governed by the Wishart distribution WISDoM can be applied to tasks of supervised learning, like classification, in particular when such matrices are generated by data of different dimensionality (e.g. time series with same number of variables but different time sampling). We show the application of the method in two different scenarios. The first is the ranking of features associated to electro encephalogram (EEG) data with a time series design, providing a theoretically sound approach for this type of studies. The second is the classification of autistic subjects of the ABIDE study, using brain connectivity measurements.
Multi-Source Deep Domain Adaptation for Quality Control in Retail Food Packaging
Authors: Mamatha Thota, Stefanos Kollias, Mark Swainson, Georgios Leontidis
Date: 2020-01-28 00:00:00
Tasks: Domain Adaptation

Retail food packaging contains information which informs choice and can be vital to consumer health, including product name, ingredients list, nutritional information, allergens, preparation guidelines, pack weight, storage and shelf life information (use-by / best before dates). The presence and accuracy of such information is critical to ensure a detailed understanding of the product and to reduce the potential for health risks. Consequently, erroneous or illegible labeling has the potential to be highly detrimental to consumers and many other stakeholders in the supply chain. In this paper, a multi-source deep learning-based domain adaptation system is proposed and tested to identify and verify the presence and legibility of use-by date information from food packaging photos taken as part of the validation process as the products pass along the food production line. This was achieved by improving the generalization of the techniques via making use of multi-source datasets in order to extract domain-invariant representations for all domains and aligning distribution of all pairs of source and target domains in a common feature space, along with the class boundaries. The proposed system performed very well in the conducted experiments, for automating the verification process and reducing labeling errors that could otherwise threaten public health and contravene legal requirements for food packaging information and accuracy. Comprehensive experiments on our food packaging datasets demonstrate that the proposed multi-source deep domain adaptation method significantly improves the classification accuracy and therefore has great potential for application and beneficial impact in food manufacturing control systems.
f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation
Authors: Konstantin Sofiiuk, Ilia Petrov, Olga Barinova, Anton Konushin
Date: 2020-01-28 00:00:00
Proceeding: CVPR 2020 6
Tasks: Interactive Segmentation, Segmentation

Deep neural networks have become a mainstream approach to interactive segmentation. As we show in our experiments, while for some images a trained network provides accurate segmentation result with just a few clicks, for some unknown objects it cannot achieve satisfactory result even with a large amount of user input. Recently proposed backpropagating refinement (BRS) scheme introduces an optimization problem for interactive segmentation that results in significantly better performance for the hard cases. At the same time, BRS requires running forward and backward pass through a deep network several times that leads to significantly increased computational budget per click compared to other methods. We propose f-BRS (feature backpropagating refinement scheme) that solves an optimization problem with respect to auxiliary variables instead of the network inputs, and requires running forward and backward pass just for a small part of a network. Experiments on GrabCut, Berkeley, DAVIS and SBD datasets set new state-of-the-art at an order of magnitude lower time per click compared to original BRS. The code and trained models are available at https://github.com/saic-vul/fbrs_interactive_segmentation .
Margin Maximization as Lossless Maximal Compression
Authors: Nikolaos Nikolaou, Henry Reeve, Gavin Brown
Date: 2020-01-28 00:00:00
Tasks: General Classification

The ultimate goal of a supervised learning algorithm is to produce models constructed on the training data that can generalize well to new examples. In classification, functional margin maximization -- correctly classifying as many training examples as possible with maximal confidence --has been known to construct models with good generalization guarantees. This work gives an information-theoretic interpretation of a margin maximizing model on a noiseless training dataset as one that achieves lossless maximal compression of said dataset -- i.e. extracts from the features all the useful information for predicting the label and no more. The connection offers new insights on generalization in supervised machine learning, showing margin maximization as a special case (that of classification) of a more general principle and explains the success and potential limitations of popular learning algorithms like gradient boosting. We support our observations with theoretical arguments and empirical evidence and identify interesting directions for future work.
Spatial-Adaptive Network for Single Image Denoising
Authors: Meng Chang, Qi Li, Huajun Feng, Zhihai Xu
Date: 2020-01-28 00:00:00
Proceeding: ECCV 2020 8
Tasks: Decoder, Denoising, Image Denoising

Previous works have shown that convolutional neural networks can achieve good performance in image denoising tasks. However, limited by the local rigid convolutional operation, these methods lead to oversmoothing artifacts. A deeper network structure could alleviate these problems, but more computational overhead is needed. In this paper, we propose a novel spatial-adaptive denoising network (SADNet) for efficient single image blind noise removal. To adapt to changes in spatial textures and edges, we design a residual spatial-adaptive block. Deformable convolution is introduced to sample the spatially correlated features for weighting. An encoder-decoder structure with a context block is introduced to capture multiscale information. With noise removal from the coarse to fine, a high-quality noisefree image can be obtained. We apply our method to both synthetic and real noisy image datasets. The experimental results demonstrate that our method can surpass the state-of-the-art denoising methods both quantitatively and visually.
Discrete Signal Processing with Set Functions
Authors: Markus Püschel, Chris Wendler
Date: 2020-01-28 00:00:00
Tasks: Image Segmentation, Informativeness, Recommendation Systems, Semantic Segmentation

Set functions are functions (or signals) indexed by the powerset (set of all subsets) of a finite set N. They are fundamental and ubiquitous in many application domains and have been used, for example, to formally describe or quantify loss functions for semantic image segmentation, the informativeness of sensors in sensor networks the utility of sets of items in recommender systems, cooperative games in game theory, or bidders in combinatorial auctions. In particular, the subclass of submodular functions occurs in many optimization and machine learning problems. In this paper, we derive discrete-set signal processing (SP), a novel shift-invariant linear signal processing framework for set functions. Discrete-set SP considers different notions of shift obtained from set union and difference operations. For each shift it provides associated notions of shift-invariant filters, convolution, Fourier transform, and frequency response. We provide intuition for our framework using the concept of generalized coverage function that we define, identify multivariate mutual information as a special case of a discrete-set spectrum, and motivate frequency ordering. Our work brings a new set of tools for analyzing and processing set functions, and, in particular, for dealing with their exponential nature. We show two prototypical applications and experiments: compression in submodular function optimization and sampling for preference elicitation in combinatorial auctions.
Distal Explanations for Model-free Explainable Reinforcement Learning
Authors: Prashan Madumal, Tim Miller, Liz Sonenberg, Frank Vetere
Date: 2020-01-28 00:00:00
Tasks: model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we introduce and evaluate a distal explanation model for model-free reinforcement learning agents that can generate explanations for `why' and `why not' questions. Our starting point is the observation that causal models can generate opportunity chains that take the form of `A enables B and B causes C'. Using insights from an analysis of 240 explanations generated in a human-agent experiment, we define a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models. A recurrent neural network is employed to learn opportunity chains, and decision trees are used to improve the accuracy of task prediction and the generated counterfactuals. We computationally evaluate the model in 6 reinforcement learning benchmarks using different reinforcement learning algorithms. From a study with 90 human participants, we show that our distal explanation model results in improved outcomes over three scenarios compared with two baseline explanation models.
Real-time calibration of coherent-state receivers: learning by trial and error
Authors: M. Bilkis, M. Rosati, R. Morral Yepes, J. Calsamiglia
Date: 2020-01-28 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

The optimal discrimination of coherent states of light with current technology is a key problem in classical and quantum communication, whose solution would enable the realization of efficient receivers for long-distance communications in free-space and optical fiber channels. In this article, we show that reinforcement learning (RL) protocols allow an agent to learn near-optimal coherent-state receivers made of passive linear optics, photodetectors and classical adaptive control. Each agent is trained and tested in real time over several runs of independent discrimination experiments and has no knowledge about the energy of the states nor the receiver setup nor the quantum-mechanical laws governing the experiments. Based exclusively on the observed photodetector outcomes, the agent adaptively chooses among a set of ~3 10^3 possible receiver setups, and obtains a reward at the end of each experiment if its guess is correct. At variance with previous applications of RL in quantum physics, the information gathered in each run is intrinsically stochastic and thus insufficient to evaluate exactly the performance of the chosen receiver. Nevertheless, we present families of agents that: (i) discover a receiver beating the best Gaussian receiver after ~3 10^2 experiments; (ii) surpass the cumulative reward of the best Gaussian receiver after ~10^3 experiments; (iii) simultaneously discover a near-optimal receiver and attain its cumulative reward after ~10^5 experiments. Our results show that RL techniques are suitable for on-line control of quantum receivers and can be employed for long-distance communications over potentially unknown channels.
Reservoir computing model of two-dimensional turbulent convection
Authors: Sandeep Pandey, Jörg Schumacher
Date: 2020-01-28 00:00:00
Tasks: Vocal Bursts Valence Prediction

Reservoir computing is applied to model the large-scale evolution and the resulting low-order turbulence statistics of a two-dimensional turbulent Rayleigh-B\'{e}nard convection flow at a Rayleigh number ${\rm Ra}=10^7$ and a Prandtl number ${\rm Pr}=7$ in an extended domain with an aspect ratio of 6. Our data-driven approach which is based on a long-term direct numerical simulation of the convection flow comprises a two-step procedure. (1) Reduction of the original simulation data by a Proper Orthogonal Decomposition (POD) snapshot analysis and subsequent truncation to the first 150 POD modes which are associated with the largest total energy amplitudes. (2) Setup and optimization of a reservoir computing model to describe the dynamical evolution of these 150 degrees of freedom and thus the large-scale evolution of the convection flow. The quality of the prediction of the reservoir computing model is comprehensively tested. At the core of the model is the reservoir, a very large sparse random network charcterized by the spectral radius of the corresponding adjacency matrix and a few further hyperparameters which are varied to investigate the quality of the prediction. Our work demonstrates that the reservoir computing model is capable to model the large-scale structure and low-order statistics of turbulent convection which can open new avenues for modeling mesoscale convection processes in larger circulation models.
Causal query in observational data with hidden variables
Authors: Debo Cheng, Jiuyong Li, Lin Liu, Jixue Liu, Kui Yu, Thuc Duy Le
Date: 2020-01-28 00:00:00

This paper discusses the problem of causal query in observational data with hidden variables, with the aim of seeking the change of an outcome when "manipulating" a variable while given a set of plausible confounding variables which affect the manipulated variable and the outcome. Such an "experiment on data" to estimate the causal effect of the manipulated variable is useful for validating an experiment design using historical data or for exploring confounders when studying a new relationship. However, existing data-driven methods for causal effect estimation face some major challenges, including poor scalability with high dimensional data, low estimation accuracy due to heuristics used by the global causal structure learning algorithms, and the assumption of causal sufficiency when hidden variables are inevitable in data. In this paper, we develop a theorem for using local search to find a superset of the adjustment (or confounding) variables for causal effect estimation from observational data under a realistic pretreatment assumption. The theorem ensures that the unbiased estimate of causal effect is included in the set of causal effects estimated by the superset of adjustment variables. Based on the developed theorem, we propose a data-driven algorithm for causal query. Experiments show that the proposed algorithm is faster and produces better causal effect estimation than an existing data-driven causal effect estimation method with hidden variables. The causal effects estimated by the proposed algorithm are as accurate as those by the state-of-the-art methods using domain knowledge.
Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure
Authors: Milad Ramezani, Georgi Tinchev, Egor Iuganov, Maurice Fallon
Date: 2020-01-28 00:00:00
Tasks: CPU

In this paper, we present a factor-graph LiDAR-SLAM system which incorporates a state-of-the-art deeply learned feature-based loop closure detector to enable a legged robot to localize and map in industrial environments. These facilities can be badly lit and comprised of indistinct metallic structures, thus our system uses only LiDAR sensing and was developed to run on the quadruped robot's navigation PC. Point clouds are accumulated using an inertial-kinematic state estimator before being aligned using ICP registration. To close loops we use a loop proposal mechanism which matches individual segments between clouds. We trained a descriptor offline to match these segments. The efficiency of our method comes from carefully designing the network architecture to minimize the number of parameters such that this deep learning method can be deployed in real-time using only the CPU of a legged robot, a major contribution of this work. The set of odometry and loop closure factors are updated using pose graph optimization. Finally we present an efficient risk alignment prediction method which verifies the reliability of the registrations. Experimental results at an industrial facility demonstrated the robustness and flexibility of our system, including autonomous following paths derived from the SLAM map.
Controlling generative models with continuous factors of variations
Authors: Antoine Plumerault, Hervé Le Borgne, Céline Hudelot
Date: 2020-01-28 00:00:00
Proceeding: ICLR 2020 1
Tasks: Translation

Recent deep generative models are able to provide photo-realistic images as well as visual or textual content embeddings useful to address various tasks of computer vision and natural language processing. Their usefulness is nevertheless often limited by the lack of control over the generative process or the poor understanding of the learned representation. To overcome these major issues, very recent work has shown the interest of studying the semantics of the latent space of generative models. In this paper, we propose to advance on the interpretability of the latent space of generative models by introducing a new method to find meaningful directions in the latent space of any generative model along which we can move to control precisely specific properties of the generated image like the position or scale of the object in the image. Our method does not require human annotations and is particularly well suited for the search of directions encoding simple transformations of the generated image, such as translation, zoom or color variations. We demonstrate the effectiveness of our method qualitatively and quantitatively, both for GANs and variational auto-encoders.
Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach
Authors: Jialin Dong, Jun Zhang, Yuanming Shi, Jessie Hui Wang
Date: 2020-01-28 00:00:00
Tasks: Action Detection, Activity Detection

This paper investigates the grant-free random access with massive IoT devices. By embedding the data symbols in the signature sequences, joint device activity detection and data decoding can be achieved, which, however, significantly increases the computational complexity. Coordinate descent algorithms that enjoy a low per-iteration complexity have been employed to solve the detection problem, but previous works typically employ a random coordinate selection policy which leads to slow convergence. In this paper, we develop multi-armed bandit approaches for more efficient detection via coordinate descent, which make a delicate trade-off between exploration and exploitation in coordinate selection. Specifically, we first propose a bandit based strategy, i.e., Bernoulli sampling, to speed up the convergence rate of coordinate descent, by learning which coordinates will result in more aggressive descent of the objective function. To further improve the convergence rate, an inner multi-armed bandit problem is established to learn the exploration policy of Bernoulli sampling. Both convergence rate analysis and simulation results are provided to show that the proposed bandit based algorithms enjoy faster convergence rates with a lower time complexity compared with the state-of-the-art algorithm. Furthermore, our proposed algorithms are applicable to different scenarios, e.g., massive random access with low-precision analog-to-digital converters (ADCs).