pwc-archive/papers-with-abstracts huggingface dataset:
A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
Authors: Richard Shaw, Carole H. Sudre, Sebastien Ourselin, M. Jorge Cardoso
Date: 2020-01-31 00:00:00
Proceeding: MIDL 2019 7

Quality control (QC) of medical images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visually at significant time and operator cost. We aim to automate the process by formulating a probabilistic network that estimates uncertainty through a heteroscedastic noise model, hence providing a proxy measure of task-specific image quality that is learnt directly from the data. By augmenting the training data with different types of simulated k-space artefacts, we propose a novel cascading CNN architecture based on a student-teacher framework to decouple sources of uncertainty related to different k-space augmentations in an entirely self-supervised manner. This enables us to predict separate uncertainty quantities for the different types of data degradation. While the uncertainty measures reflect the presence and severity of image artefacts, the network also provides the segmentation predictions given the quality of the data. We show models trained with simulated artefacts provide informative measures of uncertainty on real-world images and we validate our uncertainty predictions on problematic images identified by human-raters.
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning
Authors: Gregory J. Zelinsky, Yupei Chen, Seoyoung Ahn, Hossein Adeli, Zhibo Yang, Lihan Huang, Dimitrios Samaras, Minh Hoai
Date: 2020-01-31 00:00:00
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Understanding how goal states control behavior is a question ripe for interrogation by new methods from machine learning. These methods require large and labeled datasets to train models. To annotate a large-scale image dataset with observed search fixations, we collected 16,184 fixations from people searching for either microwaves or clocks in a dataset of 4,366 images (MS-COCO). We then used this behaviorally-annotated dataset and the machine learning method of Inverse-Reinforcement Learning (IRL) to learn target-specific reward functions and policies for these two target goals. Finally, we used these learned policies to predict the fixations of 60 new behavioral searchers (clock = 30, microwave = 30) in a disjoint test dataset of kitchen scenes depicting both a microwave and a clock (thus controlling for differences in low-level image contrast). We found that the IRL model predicted behavioral search efficiency and fixation-density maps using multiple metrics. Moreover, reward maps from the IRL model revealed target-specific patterns that suggest, not just attention guidance by target features, but also guidance by scene context (e.g., fixations along walls in the search of clocks). Using machine learning and the psychologically-meaningful principle of reward, it is possible to learn the visual features used in goal-directed attention control.
A Tool for Conducting User Studies on Mobile Devices
Authors: Luca Costa, Mohammad Aliannejadi, Fabio Crestani
Date: 2020-01-31 00:00:00
Tasks: Information Retrieval, Retrieval

With the ever-growing interest in the area of mobile information retrieval and the ongoing fast development of mobile devices and, as a consequence, mobile apps, an active research area lies in studying users' behavior and search queries users submit on mobile devices. However, many researchers require to develop an app that collects useful information from users while they search on their phones or participate in a user study. In this paper, we aim to address this need by providing a comprehensive Android app, called Omicron, which can be used to collect mobile query logs and perform user studies on mobile devices. Omicron, at its current version, can collect users' mobile queries, relevant documents, sensor data as well as user activity and interaction data in various study settings. Furthermore, we designed Omicron in such a way that it is conveniently extendable to conduct more specific studies and collect other types of sensor data. Finally, we provide a tool to monitor the participants and their data both during and after the collection process.
Verifying Tree Ensembles by Reasoning about Potential Instances
Authors: Laurens Devos, Wannes Meert, Jesse Davis
Date: 2020-01-31 00:00:00
Tasks: Attribute, Fairness

Imagine being able to ask questions to a black box model such as "Which adversarial examples exist?", "Does a specific attribute have a disproportionate effect on the model's prediction?" or "What kind of predictions could possibly be made for a partially described example?" This last question is particularly important if your partial description does not correspond to any observed example in your data, as it provides insight into how the model will extrapolate to unseen data. These capabilities would be extremely helpful as they would allow a user to better understand the model's behavior, particularly as it relates to issues such as robustness, fairness, and bias. In this paper, we propose such an approach for an ensemble of trees. Since, in general, this task is intractable we present a strategy that (1) can prune part of the input space given the question asked to simplify the problem; and (2) follows a divide and conquer approach that is incremental and can always return some answers and indicates which parts of the input domains are still uncertain. The usefulness of our approach is shown on a diverse set of use cases.
An efficient automated data analytics approach to large scale computational comparative linguistics
Authors: Gabija Mikulyte, David Gilbert
Date: 2020-01-31 00:00:00
Tasks: Clustering

This research project aimed to overcome the challenge of analysing human language relationships, facilitate the grouping of languages and formation of genealogical relationship between them by developing automated comparison techniques. Techniques were based on the phonetic representation of certain key words and concept. Example word sets included numbers 1-10 (curated), large database of numbers 1-10 and sheep counting numbers 1-10 (other sources), colours (curated), basic words (curated). To enable comparison within the sets the measure of Edit distance was calculated based on Levenshtein distance metric. This metric between two strings is the minimum number of single-character edits, operations including: insertions, deletions or substitutions. To explore which words exhibit more or less variation, which words are more preserved and examine how languages could be grouped based on linguistic distances within sets, several data analytics techniques were involved. Those included density evaluation, hierarchical clustering, silhouette, mean, standard deviation and Bhattacharya coefficient calculations. These techniques lead to the development of a workflow which was later implemented by combining Unix shell scripts, a developed R package and SWI Prolog. This proved to be computationally efficient and permitted the fast exploration of large language sets and their analysis.
Learning Unitaries by Gradient Descent
Authors: Bobak Toussi Kiani, Seth Lloyd, Reevu Maity
Date: 2020-01-31 00:00:00

We study the hardness of learning unitary transformations in $U(d)$ via gradient descent on time parameters of alternating operator sequences. We provide numerical evidence that, despite the non-convex nature of the loss landscape, gradient descent always converges to the target unitary when the sequence contains $d^2$ or more parameters. Rates of convergence indicate a "computational phase transition." With less than $d^2$ parameters, gradient descent converges to a sub-optimal solution, whereas with more than $d^2$ parameters, gradient descent converges exponentially to an optimal solution.
Lossless Attention in Convolutional Networks for Facial Expression Recognition in the Wild
Authors: Chuang Wang, Ruimin Hu, Min Hu, Jiang Liu, Ting Ren, Shan He, Ming Jiang, Jing Miao
Date: 2020-01-31 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Unlike the constraint frontal face condition, faces in the wild have various unconstrained interference factors, such as complex illumination, changing perspective and various occlusions. Facial expressions recognition (FER) in the wild is a challenging task and existing methods can't perform well. However, for occluded faces (containing occlusion caused by other objects and self-occlusion caused by head posture changes), the attention mechanism has the ability to focus on the non-occluded regions automatically. In this paper, we propose a Lossless Attention Model (LLAM) for convolutional neural networks (CNN) to extract attention-aware features from faces. Our module avoids decay information in the process of generating attention maps by using the information of the previous layer and not reducing the dimensionality. Sequentially, we adaptively refine the feature responses by fusing the attention map with the feature map. We participate in the seven basic expression classification sub-challenges of FG-2020 Affective Behavior Analysis in-the-wild Challenge. And we validate our method on the Aff-Wild2 datasets released by the Challenge. The total accuracy (Accuracy) and the unweighted mean (F1) of our method on the validation set are 0.49 and 0.38 respectively, and the final result is 0.42 (0.67 F1-Score + 0.33 Accuracy).
Hybrid Tiled Convolutional Neural Networks for Text Sentiment Classification
Authors: Maria Mihaela Trusca, Gerasimos Spanakis
Date: 2020-01-31 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

The tiled convolutional neural network (tiled CNN) has been applied only to computer vision for learning invariances. We adjust its architecture to NLP to improve the extraction of the most salient features for sentiment analysis. Knowing that the major drawback of the tiled CNN in the NLP field is its inflexible filter structure, we propose a novel architecture called hybrid tiled CNN that applies a filter only on the words that appear in the similar contexts and on their neighbor words (a necessary step for preventing the loss of some n-grams). The experiments on the datasets of IMDB movie reviews and SemEval 2017 demonstrate the efficiency of the hybrid tiled CNN that performs better than both CNN and tiled CNN.
CNN-based fast source device identification
Authors: Sara Mandelli, Davide Cozzolino, Paolo Bestagini, Luisa Verdoliva, Stefano Tubaro
Date: 2020-01-31 00:00:00
Tasks: Image Forensics

Source identification is an important topic in image forensics, since it allows to trace back the origin of an image. This represents a precious information to claim intellectual property but also to reveal the authors of illicit materials. In this paper we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks (CNNs). Specifically, we propose a 2-channel-based CNN that learns a way of comparing camera fingerprint and image noise at patch level. The proposed solution turns out to be much faster than the conventional approach and to ensure an increased accuracy. This makes the approach particularly suitable in scenarios where large databases of images are analyzed, like over social networks. In this vein, since images uploaded on social media usually undergo at least two compression stages, we include investigations on double JPEG compressed images, always reporting higher accuracy than standard approaches.
Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks
Authors: Stavros Deligiannidis, Adonis Bogris, Charis Mesaritakis, Yannis Kopsinis
Date: 2020-01-31 00:00:00

We introduce for the first time the utilization of Long short-term memory (LSTM) neural network architectures for the compensation of fiber nonlinearities in digital coherent systems. We conduct numerical simulations considering either C-band or O-band transmission systems for single channel and multi-channel 16-QAM modulation format with polarization multiplexing. A detailed analysis regarding the effect of the number of hidden units and the length of the word of symbols that trains the LSTM algorithm and corresponds to the considered channel memory is conducted in order to reveal the limits of LSTM based receiver with respect to performance and complexity. The numerical results show that LSTM Neural Networks can be very efficient as post processors of optical receivers which classify data that have undergone non-linear impairments in fiber and provide superior performance compared to digital back propagation, especially in the multi-channel transmission scenario. The complexity analysis shows that LSTM becomes more complex as the number of hidden units and the channel memory increase can be less complex than DBP in long distances (> 1000 km).
Noise2Inverse: Self-supervised deep convolutional denoising for tomography
Authors: Allard A. Hendriksen, Daniel M. Pelt, K. Joost Batenburg
Date: 2020-01-31 00:00:00
Tasks: Denoising, Image Denoising, Image Reconstruction

Recovering a high-quality image from noisy indirect measurements is an important problem with many applications. For such inverse problems, supervised deep convolutional neural network (CNN)-based denoising methods have shown strong results, but the success of these supervised methods critically depends on the availability of a high-quality training dataset of similar measurements. For image denoising, methods are available that enable training without a separate training dataset by assuming that the noise in two different pixels is uncorrelated. However, this assumption does not hold for inverse problems, resulting in artifacts in the denoised images produced by existing methods. Here, we propose Noise2Inverse, a deep CNN-based denoising method for linear image reconstruction algorithms that does not require any additional clean or noisy data. Training a CNN-based denoiser is enabled by exploiting the noise model to compute multiple statistically independent reconstructions. We develop a theoretical framework which shows that such training indeed obtains a denoising CNN, assuming the measured noise is element-wise independent and zero-mean. On simulated CT datasets, Noise2Inverse demonstrates an improvement in peak signal-to-noise ratio and structural similarity index compared to state-of-the-art image denoising methods and conventional reconstruction methods, such as Total-Variation Minimization. We also demonstrate that the method is able to significantly reduce noise in challenging real-world experimental datasets.
A comparison of Vector Symbolic Architectures
Authors: Kenny Schlegel, Peer Neubert, Peter Protzel
Date: 2020-01-31 00:00:00

Vector Symbolic Architectures combine a high-dimensional vector space with a set of carefully designed operators in order to perform symbolic computations with large numerical vectors. Major goals are the exploitation of their representational power and ability to deal with fuzziness and ambiguity. Over the past years, several VSA implementations have been proposed. The available implementations differ in the underlying vector space and the particular implementations of the VSA operators. This paper provides an overview of eleven available VSA implementations and discusses their commonalities and differences in the underlying vector space and operators. We create a taxonomy of available binding operations and show an important ramification for non self-inverse binding operations using an example from analogical reasoning. A main contribution is the experimental comparison of the available implementations in order to evaluate (1) the capacity of bundles, (2) the approximation quality of non-exact unbinding operations, (3) the influence of combining binding and bundling operations on the query answering performance, and (4) the performance on two example applications: visual place- and language-recognition. We expect this comparison and systematization to be relevant for development of VSAs, and to support the selection of an appropriate VSA for a particular task. The implementations are available.
On Calibration Neural Networks for extracting implied information from American options
Authors: Shuaiqiang Liu, Álvaro Leitao, Anastasia Borovykh, Cornelis W. Oosterlee
Date: 2020-01-31 00:00:00
Tasks: BIG-bench Machine Learning

Extracting implied information, like volatility and/or dividend, from observed option prices is a challenging task when dealing with American options, because of the computational costs needed to solve the corresponding mathematical problem many thousands of times. We will employ a data-driven machine learning approach to estimate the Black-Scholes implied volatility and the dividend yield for American options in a fast and robust way. To determine the implied volatility, the inverse function is approximated by an artificial neural network on the computational domain of interest, which decouples the offline (training) and online (prediction) phases and thus eliminates the need for an iterative process. For the implied dividend yield, we formulate the inverse problem as a calibration problem and determine simultaneously the implied volatility and dividend yield. For this, a generic and robust calibration framework, the Calibration Neural Network (CaNN), is introduced to estimate multiple parameters. It is shown that machine learning can be used as an efficient numerical technique to extract implied information from American options.
A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
Authors: Pallavi Bagga, Nicola Paoletti, Bedour Alrayes, Kostas Stathis
Date: 2020-01-31 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning-based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.
iCap: Interactive Image Captioning with Predictive Text
Authors: Zhengxiong Jia, Xirong Li
Date: 2020-01-31 00:00:00
Tasks: Image Captioning, Sentence, Sentence Completion

In this paper we study a brand new topic of interactive image captioning with human in the loop. Different from automated image captioning where a given test image is the sole input in the inference stage, we have access to both the test image and a sequence of (incomplete) user-input sentences in the interactive scenario. We formulate the problem as Visually Conditioned Sentence Completion (VCSC). For VCSC, we propose asynchronous bidirectional decoding for image caption completion (ABD-Cap). With ABD-Cap as the core module, we build iCap, a web-based interactive image captioning system capable of predicting new text with respect to live input from a user. A number of experiments covering both automated evaluations and real user studies show the viability of our proposals.
A Review of Personality in Human Robot Interactions
Authors: Lionel P. Robert, Rasha Alahmad, Connor Esterwood, Sangmi Kim, Sangseok You, Qiaoning Zhang
Date: 2020-01-31 00:00:00
Tasks: Articles

Personality has been identified as a vital factor in understanding the quality of human robot interactions. Despite this the research in this area remains fragmented and lacks a coherent framework. This makes it difficult to understand what we know and identify what we do not. As a result our knowledge of personality in human robot interactions has not kept pace with the deployment of robots in organizations or in our broader society. To address this shortcoming, this paper reviews 83 articles and 84 separate studies to assess the current state of human robot personality research. This review: (1) highlights major thematic research areas, (2) identifies gaps in the literature, (3) derives and presents major conclusions from the literature and (4) offers guidance for future research.
Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm
Authors: Daesung Kim, Hye Won Chung
Date: 2020-01-31 00:00:00
Tasks: Active Learning, Attribute, Binary Classification, General Classification, Recommendation Systems

We consider a query-based data acquisition problem for binary classification of unknown labels, which has diverse applications in communications, crowdsourcing, recommender systems and active learning. To ensure reliable recovery of unknown labels with as few number of queries as possible, we consider an effective query type that asks "group attribute" of a chosen subset of objects. In particular, we consider the problem of classifying $m$ binary labels with XOR queries that ask whether the number of objects having a given attribute in the chosen subset of size $d$ is even or odd. The subset size $d$, which we call query degree, can be varying over queries. We consider a general noise model where the accuracy of answers on queries changes depending both on the worker (the data provider) and query degree $d$. For this general model, we characterize the information-theoretic limit on the optimal number of queries to reliably recover $m$ labels in terms of a given combination of degree-$d$ queries and noise parameters. Further, we propose an efficient inference algorithm that achieves this limit even when the noise parameters are unknown.
Encoding-based Memory Modules for Recurrent Neural Networks
Authors: Antonio Carta, Alessandro Sperduti, Davide Bacciu
Date: 2020-01-31 00:00:00
Tasks: Memorization

Learning to solve sequential tasks with recurrent models requires the ability to memorize long sequences and to extract task-relevant features from them. In this paper, we study the memorization subtask from the point of view of the design and training of recurrent neural networks. We propose a new model, the Linear Memory Network, which features an encoding-based memorization component built with a linear autoencoder for sequences. We extend the memorization component with a modular memory that encodes the hidden state sequence at different sampling frequencies. Additionally, we provide a specialized training algorithm that initializes the memory to efficiently encode the hidden activations of the network. The experimental results on synthetic and real-world datasets show that specializing the training algorithm to train the memorization component always improves the final performance whenever the memorization of long sequences is necessary to solve the problem.