pwc-archive/papers-with-abstracts huggingface dataset:
Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data
Authors: Jan Kieseler
Date: 2020-02-10 00:00:00
Tasks: Clustering, Object, object-detection, Object Detection, Object Reconstruction

High-energy physics detectors, images, and point clouds share many similarities in terms of object detection. However, while detecting an unknown number of objects in an image is well established in computer vision, even machine learning assisted object reconstruction algorithms in particle physics almost exclusively predict properties on an object-by-object basis. Traditional approaches from computer vision either impose implicit constraints on the object size or density and are not well suited for sparse detector data or rely on objects being dense and solid. The object condensation method proposed here is independent of assumptions on object size, sorting or object density, and further generalises to non-image-like data structures, such as graphs and point clouds, which are more suitable to represent detector signals. The pixels or vertices themselves serve as representations of the entire object, and a combination of learnable local clustering in a latent space and confidence assignment allows one to collect condensates of the predicted object properties with a simple algorithm. As proof of concept, the object condensation method is applied to a simple object classification problem in images and used to reconstruct multiple particles from detector signals. The latter results are also compared to a classic particle flow approach.
How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?
Authors: Sven Koehler, Animesh Tandon, Tarique Hussain, Heiner Latus, Thomas Pickardt, Samir Sarikouch, Philipp Beerbaum, Gerald Greil, Sandy Engelhardt, Ivo Wolf
Date: 2020-02-10 00:00:00

Planning the optimal time of intervention for pulmonary valve replacement surgery in patients with the congenital heart disease Tetralogy of Fallot (TOF) is mainly based on ventricular volume and function according to current guidelines. Both of these two biomarkers are most reliably assessed by segmentation of 3D cardiac magnetic resonance (CMR) images. In several grand challenges in the last years, U-Net architectures have shown impressive results on the provided data. However, in clinical practice, data sets are more diverse considering individual pathologies and image properties derived from different scanner properties. Additionally, specific training data for complex rare diseases like TOF is scarce. For this work, 1) we assessed the accuracy gap when using a publicly available labelled data set (the Automatic Cardiac Diagnosis Challenge (ACDC) data set) for training and subsequent applying it to CMR data of TOF patients and vice versa and 2) whether we can achieve similar results when applying the model to a more heterogeneous data base. Multiple deep learning models were trained with four-fold cross validation. Afterwards they were evaluated on the respective unseen CMR images from the other collection. Our results confirm that current deep learning models can achieve excellent results (left ventricle dice of $0.951\pm{0.003}$/$0.941\pm{0.007}$ train/validation) within a single data collection. But once they are applied to other pathologies, it becomes apparent how much they overfit to the training pathologies (dice score drops between $0.072\pm{0.001}$ for the left and $0.165\pm{0.001}$ for the right ventricle).
Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Authors: Theofanis Karaletsos, Thang D. Bui
Date: 2020-02-10 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Active Learning

Probabilistic neural networks are typically modeled with independent weight priors, which do not capture weight correlations in the prior and do not provide a parsimonious interface to express properties in function space. A desirable class of priors would represent weights compactly, capture correlations between weights, facilitate calibrated reasoning about uncertainty, and allow inclusion of prior knowledge about the function space such as periodicity or dependence on contexts such as inputs. To this end, this paper introduces two innovations: (i) a Gaussian process-based hierarchical model for network weights based on unit embeddings that can flexibly encode correlated weight structures, and (ii) input-dependent versions of these weight priors that can provide convenient ways to regularize the function space through the use of kernels defined on contextual inputs. We show these models provide desirable test-time uncertainty estimates on out-of-distribution data, demonstrate cases of modeling inductive biases for neural networks with kernels which help both interpolation and extrapolation from training data, and demonstrate competitive predictive performance on an active learning benchmark.
Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition
Authors: Bing-Yi Jing, Ting Li, Zhongyuan Lyu, Dong Xia
Date: 2020-02-10 00:00:00
Tasks: Community Detection, Stochastic Block Model, Tensor Decomposition

We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multi-layer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.
Automating App Review Response Generation
Authors: Cuiyun Gao, Jichuan Zeng, Xin Xia, David Lo, Michael R. Lyu, Irwin King
Date: 2020-02-10 00:00:00
Tasks: Response Generation

Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a template-based strategy where the templates can express appreciation for using the app or mention the company email address for users to follow up. However, reading a large number of user reviews every day is not an easy task for developers. Thus, there is a need for more automation to help developers respond to user reviews. Addressing the aforementioned need, in this work we propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4 (an accuracy measure that is widely used to evaluate dialogue response generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating relevant and accurate responses.
Pairwise Neural Networks (PairNets) with Low Memory for Fast On-Device Applications
Authors: Luna M. Zhang
Date: 2020-02-10 00:00:00
Tasks: Hyperparameter Optimization, Incremental Learning

A traditional artificial neural network (ANN) is normally trained slowly by a gradient descent algorithm, such as the backpropagation algorithm, since a large number of hyperparameters of the ANN need to be fine-tuned with many training epochs. Since a large number of hyperparameters of a deep neural network, such as a convolutional neural network, occupy much memory, a memory-inefficient deep learning model is not ideal for real-time Internet of Things (IoT) applications on various devices, such as mobile phones. Thus, it is necessary to develop fast and memory-efficient Artificial Intelligence of Things (AIoT) systems for real-time on-device applications. We created a novel wide and shallow 4-layer ANN called "Pairwise Neural Network" ("PairNet") with high-speed non-gradient-descent hyperparameter optimization. The PairNet is trained quickly with only one epoch since its hyperparameters are directly optimized one-time via simply solving a system of linear equations by using the multivariate least squares fitting method. In addition, an n-input space is partitioned into many n-input data subspaces, and a local PairNet is built in a local n-input subspace. This divide-and-conquer approach can train the local PairNet using specific local features to improve model performance. Simulation results indicate that the three PairNets with incremental learning have smaller average prediction mean squared errors, and achieve much higher speeds than traditional ANNs. An important future work is to develop better and faster non-gradient-descent hyperparameter optimization algorithms to generate effective, fast, and memory-efficient PairNets with incremental learning on optimal subspaces for real-time AIoT on-device applications.
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention
Authors: Yuxiang Ren, Jiawei Zhang
Date: 2020-02-05 00:00:00
Tasks: Articles, Fake News Detection, Graph Attention, Graph Representation Learning, Node Classification, Representation Learning

The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language models and invalidating simple text-based models. In fact, news articles and other related components (such as news creators and news topics) can be modeled as a heterogeneous information network (HIN for short). In this paper, we propose a novel fake news detection framework, namely Hierarchical Graph Attention Network(HGAT), which uses a novel hierarchical attention mechanism to perform node representation learning in HIN, and then detects fake news by classifying news article nodes. Experiments on two real-world fake news datasets show that HGAT can outperform text-based models and other network-based models. In addition, the experiment proved the expandability and generalizability of our for graph representation learning and other node classification related applications in heterogeneous graphs.
Understanding Crowd Behaviors in a Social Event by Passive WiFi Sensing and Data Mining
Authors: Yuren Zhou, Billy Pik Lik Lau, Zann Koh, Chau Yuen, Benny Kai Kiat Ng
Date: 2020-02-05 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Management

Understanding crowd behaviors in a large social event is crucial for event management. Passive WiFi sensing, by collecting WiFi probe requests sent from mobile devices, provides a better way to monitor crowds compared with people counters and cameras in terms of free interference, larger coverage, lower cost, and more information on people's movement. In existing studies, however, not enough attention has been paid to the thorough analysis and mining of collected data. Especially, the power of machine learning has not been fully exploited. In this paper, therefore, we propose a comprehensive data analysis framework to fully analyze the collected probe requests to extract three types of patterns related to crowd behaviors in a large social event, with the help of statistics, visualization, and unsupervised machine learning. First, trajectories of the mobile devices are extracted from probe requests and analyzed to reveal the spatial patterns of the crowds' movement. Hierarchical agglomerative clustering is adopted to find the interconnections between different locations. Next, k-means and k-shape clustering algorithms are applied to extract temporal visiting patterns of the crowds by days and locations, respectively. Finally, by combining with time, trajectories are transformed into spatiotemporal patterns, which reveal how trajectory duration changes over the length and how the overall trends of crowd movement change over time. The proposed data analysis framework is fully demonstrated using real-world data collected in a large social event. Results show that one can extract comprehensive patterns from data collected by a network of passive WiFi sensors.
Traffic Data Imputation using Deep Convolutional Neural Networks
Authors: Ouafa Benkraouda, Bilal Thonnam Thodi, Hwasoo Yeo, Monica Menendez, Saif Eddin Jabari
Date: 2020-01-21 00:00:00
Tasks: Decoder, Imputation, Traffic Data Imputation

We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from NGSIM. Our results show that with probe vehicle penetration levels as low as 5\%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model's reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation.
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
Authors: Lu Bai, Lixin Cui, Edwin R. Hancock
Date: 2020-02-08 00:00:00

In this paper, we develop a new graph kernel, namely the Hierarchical Transitive-Aligned kernel, by transitively aligning the vertices between graphs through a family of hierarchical prototype graphs. Comparing to most existing state-of-the-art graph kernels, the proposed kernel has three theoretical advantages. First, it incorporates the locational correspondence information between graphs into the kernel computation, and thus overcomes the shortcoming of ignoring structural correspondences arising in most R-convolution kernels. Second, it guarantees the transitivity between the correspondence information that is not available for most existing matching kernels. Third, it incorporates the information of all graphs under comparisons into the kernel computation process, and thus encapsulates richer characteristics. By transductively training the C-SVM classifier, experimental evaluations demonstrate the effectiveness of the new transitive-aligned kernel. The proposed kernel can outperform state-of-the-art graph kernels on standard graph-based datasets in terms of the classification accuracy.
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Authors: Alexander Tong, Jessie Huang, Guy Wolf, David van Dijk, Smita Krishnaswamy
Date: 2020-02-09 00:00:00
Proceeding: ICML 2020 1

It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take in these systems. To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time. Continuous normalizing flows are generally under constrained, as they are allowed to take an arbitrary path from the source to the target distribution. We present TrajectoryNet, which controls the continuous paths taken between distributions to produce dynamic optimal transport. We show how this is particularly applicable for studying cellular dynamics in data from single-cell RNA sequencing (scRNA-seq) technologies, and that TrajectoryNet improves upon recently proposed static optimal transport-based models that can be used for interpolating cellular distributions.
pymoo: Multi-objective Optimization in Python
Authors: Julian Blank, Kalyanmoy Deb
Date: 2020-01-22 00:00:00
Tasks: Decision Making

Python has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning. Since optimization is an inherent part of these research fields, more optimization related frameworks have arisen in the past few years. Only a few of them support optimization of multiple conflicting objectives at a time, but do not provide comprehensive tools for a complete multi-objective optimization task. To address this issue, we have developed pymoo, a multi-objective optimization framework in Python. We provide a guide to getting started with our framework by demonstrating the implementation of an exemplary constrained multi-objective optimization scenario. Moreover, we give a high-level overview of the architecture of pymoo to show its capabilities followed by an explanation of each module and its corresponding sub-modules. The implementations in our framework are customizable and algorithms can be modified/extended by supplying custom operators. Moreover, a variety of single, multi and many-objective test problems are provided and gradients can be retrieved by automatic differentiation out of the box. Also, pymoo addresses practical needs, such as the parallelization of function evaluations, methods to visualize low and high-dimensional spaces, and tools for multi-criteria decision making. For more information about pymoo, readers are encouraged to visit: https://pymoo.org
Optimization by Hybridization of a Genetic Algorithm with the PROMOTHEE Method: Management of Multicriteria Localization
Authors: Myriem Alijo, Otman Abdoun, Mostafa Bachran, Amal Bergam
Date: 2020-01-10 00:00:00
Tasks: Management

The decision to locate an economic activity of one or several countries is made taking into account numerous parameters and criteria. Several studies have been carried out in this field, but they generally use information in a reduced context. The majority are based solely on parameters, using traditional methods which often lead to unsatisfactory solutions.This work consists in hybridizing through genetic algorithms, economic intelligence (EI) and multicriteria analysis methods (MCA) to improve the decisions of territorial localization. The purpose is to lead the company to locate its activity in the place that would allow it a competitive advantage. This work also consists of identifying all the parameters that can influence the decision of the economic actors and equipping them with tools using all the national and international data available to lead to a mapping of countries, regions or departments favorable to the location. Throughout our research, we have as a goal the realization of a hybrid conceptual model of economic intelligence based on multicriteria on with genetic algorithms in order to optimize the decisions of localization, in this perspective we opted for the method of PROMETHEE (Preference Ranking Organization for Method of Enrichment Evaluation), which has made it possible to obtain the best compromise between the various visions and various points of view.
An experiment exploring the theoretical and methodological challenges in developing a semi-automated approach to analysis of small-N qualitative data
Authors: Sandro Tsang
Date: 2020-02-03 00:00:00

This paper experiments with designing a semi-automated qualitative data analysis (QDA) algorithm to analyse 20 transcripts by using freeware. Text-mining (TM) and QDA were guided by frequency and association measures, because these statistics remain robust when the sample size is small. The refined TM algorithm split the text into various sizes based on a manually revised dictionary. This lemmatisation approach may reflect the context of the text better than uniformly tokenising the text into one single size. TM results were used for initial coding. Code repacking was guided by association measures and external data to implement a general inductive QDA approach. The information retrieved by TM and QDA was depicted in subgraphs for comparisons. The analyses were completed in 6-7 days. Both algorithms retrieved contextually consistent and relevant information. However, the QDA algorithm retrieved more specific information than TM alone. The QDA algorithm does not strictly comply with the convention of TM or of QDA, but becomes a more efficient, systematic and transparent text analysis approach than a conventional QDA approach. Scaling up QDA to reliably discover knowledge from text was exactly the research purpose. This paper also sheds light on understanding the relations between information technologies, theory and methodologies.
A One-to-One Correspondence between Natural Numbers and Binary Trees
Authors: Osvaldo Skliar, Sherry Gapper, Ricardo E. Monge
Date: 2020-02-07 00:00:00

A characterization is provided for each natural number except one (1) by means of an ordered pair of elements. The first element is a natural number called the type of the natural number characterized, and the second is a natural number called the order of the number characterized within those of its type. A one-to-one correspondence is specified between the set of binary trees such that a) a given node has no child nodes (that is, it is a terminal node), or b) it has exactly two child nodes. Thus, binary trees such that one of their parent nodes has only one child node are excluded from the set considered here.
SafeNet: An Assistive Solution to Assess Incoming Threats for Premises
Authors: Shahinur Alam, Md Sultan Mahmud, Mohammed Yeasin
Date: 2020-01-27 00:00:00
Tasks: Change Detection, Computational Efficiency

An assistive solution to assess incoming threats (e.g., robbery, burglary, gun violence) for homes will enhance the safety of the people with or without disabilities. This paper presents "SafeNet"- an integrated assistive system to generate context-oriented image descriptions to assess incoming threats. The key functionality of the system includes the detection and identification of human and generating image descriptions from the real-time video streams obtained from the cameras placed in strategic locations around the house. In this paper, we focus on developing a robust model called "SafeNet" to generate image descriptions. To interact with the system, we implemented a dialog enabled interface for creating a personalized profile from face images or videos of friends/families. To improve computational efficiency, we apply change detection to filter out frames that do not have any activity and use Faster-RCNN to detect the human presence and extract faces using Multitask Cascaded Convolutional Networks (MTCNN). Subsequently, we apply LBP/FaceNet to identify a person. SafeNet sends image descriptions to the users with an MMS containing a person's name if any match found or as "Unknown", scene image, facial description, and contextual information. SafeNet identifies friends/families/caregiver versus intruders/unknown with an average F-score 0.97 and generates image descriptions from 10 classes with an average F-measure 0.97.