pwc-archive/papers-with-abstracts huggingface dataset:
Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants
Authors: Zahra Tayebi, Sarwan Ali, Murray Patterson
Date: 2021-10-18 00:00:00
Tasks: Clustering, feature selection

The widespread availability of large amounts of genomic data on the SARS-CoV-2 virus, as a result of the COVID-19 pandemic, has created an opportunity for researchers to analyze the disease at a level of detail unlike any virus before it. One one had, this will help biologists, policy makers and other authorities to make timely and appropriate decisions to control the spread of the coronavirus. On the other hand, such studies will help to more effectively deal with any possible future pandemic. Since the SARS-CoV-2 virus contains different variants, each of them having different mutations, performing any analysis on such data becomes a difficult task. It is well known that much of the variation in the SARS-CoV-2 genome happens disproportionately in the spike region of the genome sequence -- the relatively short region which codes for the spike protein(s). Hence, in this paper, we propose an approach to cluster spike protein sequences in order to study the behavior of different known variants that are increasing at very high rate throughout the world. We use a k-mers based approach to first generate a fixed-length feature vector representation for the spike sequences. We then show that with the appropriate feature selection, we can efficiently and effectively cluster the spike sequences based on the different variants. Using a publicly available set of SARS-CoV-2 spike sequences, we perform clustering of these sequences using both hard and soft clustering methods and show that with our feature selection methods, we can achieve higher F1 scores for the clusters.
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey
Authors: Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley
Date: 2021-10-18 00:00:00
Tasks: Dimensionality Reduction, regression

This is a tutorial and survey paper on various methods for Sufficient Dimension Reduction (SDR). We cover these methods with both statistical high-dimensional regression perspective and machine learning approach for dimensionality reduction. We start with introducing inverse regression methods including Sliced Inverse Regression (SIR), Sliced Average Variance Estimation (SAVE), contour regression, directional regression, Principal Fitted Components (PFC), Likelihood Acquired Direction (LAD), and graphical regression. Then, we introduce forward regression methods including Principal Hessian Directions (pHd), Minimum Average Variance Estimation (MAVE), Conditional Variance Estimation (CVE), and deep SDR methods. Finally, we explain Kernel Dimension Reduction (KDR) both for supervised and unsupervised learning. We also show that supervised KDR and supervised PCA are equivalent.
Further Generalizations of the Jaccard Index
Authors: Luciano da F. Costa
Date: 2021-10-18 00:00:00

Quantifying the similarity between two mathematical structures or datasets constitutes a particularly interesting and useful operation in several theoretical and applied problems. Aimed at this specific objective, the Jaccard index has been extensively used in the most diverse types of problems, also motivating some respective generalizations. The present work addresses further generalizations of this index, including its modification into a coincidence index capable of accounting also for the level of relative interiority between the two compared entities, as well as respective extensions for sets in continuous vector spaces, the generalization to multiset addition, densities and generic scalar fields, as well as a means to quantify the joint interdependence between two random variables. The also interesting possibility to take into account more than two sets has also been addressed, including the description of an index capable of quantifying the level of chaining between three structures. Several of the described and suggested eneralizations have been illustrated with respect to numeric case examples. It is also posited that these indices can play an important role while analyzing and integrating datasets in modeling approaches and pattern recognition activities, including as a measurement of clusters similarity or separation and as a resource for representing and analyzing complex networks.
Interpolating between sampling and variational inference with infinite stochastic mixtures
Authors: Richard D. Lange, Ari Benjamin, Ralf M. Haefner, Xaq Pitkow
Date: 2021-10-18 00:00:00
Tasks: Variational Inference

Sampling and Variational Inference (VI) are two large families of methods for approximate inference that have complementary strengths. Sampling methods excel at approximating arbitrary probability distributions, but can be inefficient. VI methods are efficient, but may misrepresent the true distribution. Here, we develop a general framework where approximations are stochastic mixtures of simple component distributions. Both sampling and VI can be seen as special cases: in sampling, each mixture component is a delta-function and is chosen stochastically, while in standard VI a single component is chosen to minimize divergence. We derive a practical method that interpolates between sampling and VI by solving an optimization problem over a mixing distribution. Intermediate inference methods then arise by varying a single parameter. Our method provably improves on sampling (reducing variance) and on VI (reducing bias+variance despite increasing variance). We demonstrate our method's bias/variance trade-off in practice on reference problems, and we compare outcomes to commonly used sampling and VI methods. This work takes a step towards a highly flexible yet simple family of inference methods that combines the complementary strengths of sampling and VI.
A Survey on Machine Learning Techniques for Source Code Analysis
Authors: Tushar Sharma, Maria Kechagia, Stefanos Georgiou, Rohit Tiwari, Indira Vats, Hadi Moazen, Federica Sarro
Date: 2021-10-18 00:00:00
Tasks: BIG-bench Machine Learning, Vulnerability Detection

The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such a large number of studies hinders the community from understanding the current research landscape. This paper aims to summarize the current knowledge in applied machine learning for source code analysis. We review studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we conducted an extensive literature search and identified 479 primary studies published between 2011 and 2021. We summarize our observations and findings with the help of the identified studies. Our findings suggest that the use of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task and summarize machine learning techniques employed. We identify a comprehensive list of available datasets and tools useable in this context. Finally, the paper discusses perceived challenges in this area, including the availability of standard datasets, reproducibility and replicability, and hardware resources.
Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models
Authors: Sarwan Ali, Yijing Zhou, Murray Patterson
Date: 2021-10-18 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before, by leveraging such big data. This data is of many different varieties, and can be of different levels of veracity e.g., precise, imprecise, uncertain, and missing, making it challenging to extract important information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform -- often in real-time -- the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning based algorithms to this big data is a natural approach to take to this aim, since they can quickly scale to such data, and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19, and for potential future pandemics in general. In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation, and then propose a model that first performs efficient feature selection from such representation. We apply this approach on two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90\% in most cases. We also computed the importance of different attributes in the dataset using information gain. This can help the policy makers to focus on only certain attributes for the purposes of studying this disease rather than focusing on multiple random factors that may not be very informative to patient outcomes.
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
Authors: Michael Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken
Date: 2021-10-18 00:00:00
Tasks: Descriptive, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering, Visual Question Answering, Visual Question Answering (VQA)

State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors, but also gives a way to act and improve the classifier. We propose to discover those feature-value combinations (i.e., patterns) that strongly correlate with correct resp. erroneous predictions to obtain a global and interpretable description for arbitrary classifiers. We show this is an instance of the more general label description problem, which we formulate in terms of the Minimum Description Length principle. To discover a good pattern set, we develop the efficient Premise algorithm. Through an extensive set of experiments we show it performs very well in practice on both synthetic and real-world data. Unlike existing solutions, it ably recovers ground truth patterns, even on highly imbalanced data over many features. Through two case studies on Visual Question Answering and Named Entity Recognition, we confirm that Premise gives clear and actionable insight into the systematic errors made by modern NLP classifiers.
Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices
Authors: Stanisław Kacprzak, Konrad Kowalczyk
Date: 2021-10-18 00:00:00
Tasks: Acoustic Scene Classification, Classification, Domain Adaptation, Scene Classification

In classification tasks, the classification accuracy diminishes when the data is gathered in different domains. To address this problem, in this paper, we investigate several adversarial models for domain adaptation (DA) and their effect on the acoustic scene classification task. The studied models include several types of generative adversarial networks (GAN), with different loss functions, and the so-called cycle GAN which consists of two interconnected GAN models. The experiments are performed on the DCASE20 challenge task 1A dataset, in which we can leverage the paired examples of data recorded using different devices, i.e., the source and target domain recordings. The results of performed experiments indicate that the best performing domain adaptation can be obtained using the cycle GAN, which achieves as much as 66% relative improvement in accuracy for the target domain device, while only 6\% relative decrease in accuracy on the source domain. In addition, by utilizing the paired data examples, we are able to improve the overall accuracy over the model trained using larger unpaired data set, while decreasing the computational cost of the model training.
A-Optimal Active Learning
Authors: Tue Boesen, Eldad Haber
Date: 2021-10-18 00:00:00
Tasks: Active Learning, Experimental Design

In this work we discuss the problem of active learning. We present an approach that is based on A-optimal experimental design of ill-posed problems and show how one can optimally label a data set by partially probing it, and use it to train a deep network. We present two approaches that make different assumptions on the data set. The first is based on a Bayesian interpretation of the semi-supervised learning problem with the graph Laplacian that is used for the prior distribution and the second is based on a frequentist approach, that updates the estimation of the bias term based on the recovery of the labels. We demonstrate that this approach can be highly efficient for estimating labels and training a deep network.
Permutation Invariance of Deep Neural Networks with ReLUs
Authors: Diganta Mukhopadhyay, Kumar Madhukar, Mandayam Srivas
Date: 2021-10-18 00:00:00

Consider a deep neural network (DNN) that is being used to suggest the direction in which an aircraft must turn to avoid a possible collision with an intruder aircraft. Informally, such a network is well-behaved if it asks the own ship to turn right (left) when an intruder approaches from the left (right). Consider another network that takes four inputs -- the cards dealt to the players in a game of contract bridge -- and decides which team can bid game. Loosely speaking, if you exchange the hands of partners (north and south, or east and west), the decision would not change. However, it will change if, say, you exchange north's hand with east. This permutation invariance property, for certain permutations at input and output layers, is central to the correctness and robustness of these networks. This paper proposes a sound, abstraction-based technique to establish permutation invariance in DNNs with ReLU as the activation function. The technique computes an over-approximation of the reachable states, and an under-approximation of the safe states, and propagates this information across the layers, both forward and backward. The novelty of our approach lies in a useful tie-class analysis, that we introduce for forward propagation, and a scalable 2-polytope under-approximation method that escapes the exponential blow-up in the number of regions during backward propagation. An experimental comparison shows the efficiency of our algorithm over that of verifying permutation invariance as a two-safety property (using FFNN verification over two copies of the network).
Wideband and Entropy-Aware Deep Soft Bit Quantization
Authors: Marius Arvinte, Jonathan I. Tamir
Date: 2021-10-18 00:00:00
Tasks: Quantization

Deep learning has been recently applied to physical layer processing in digital communication systems in order to improve end-to-end performance. In this work, we introduce a novel deep learning solution for soft bit quantization across wideband channels. Our method is trained end-to-end with quantization- and entropy-aware augmentations to the loss function and is used at inference in conjunction with source coding to achieve near-optimal compression gains over wideband channels. To efficiently train our method, we prove and verify that a fixed feature space quantization scheme is sufficient for efficient learning. When tested on channel distributions never seen during training, the proposed method achieves a compression gain of up to $10 \%$ in the high SNR regime versus previous state-of-the-art methods. To encourage reproducible research, our implementation is publicly available at https://github.com/utcsilab/wideband-llr-deep.
Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems
Authors: Mohamed Elsayed, Ahmad A. Aziz El-Banna, Octavia A. Dobre, Wanyi Shiu, Peiwei Wang
Date: 2021-10-18 00:00:00

Full-duplex (FD) systems have been introduced to provide high data rates for beyond fifth-generation wireless networks through simultaneous transmission of information over the same frequency resources. However, the operation of FD systems is practically limited by the self-interference (SI), and efficient SI cancelers are sought to make the FD systems realizable. Typically, polynomial-based cancelers are employed to mitigate the SI; nevertheless, they suffer from high complexity. This article proposes two novel hybrid-layers neural network (NN) architectures to cancel the SI with low complexity. The first architecture is referred to as hybrid-convolutional recurrent NN (HCRNN), whereas the second is termed as hybrid-convolutional recurrent dense NN (HCRDNN). In contrast to the state-of-the-art NNs that employ dense or recurrent layers for SI modeling, the proposed NNs exploit, in a novel manner, a combination of different hidden layers (e.g., convolutional, recurrent, and/or dense) in order to model the SI with lower computational complexity than the polynomial and the state-of-the-art NN-based cancelers. The key idea behind using hybrid layers is to build an NN model, which makes use of the characteristics of the different layers employed in its architecture. More specifically, in the HCRNN, a convolutional layer is employed to extract the input data features using a reduced network scale. Moreover, a recurrent layer is then applied to assist in learning the temporal behavior of the input signal from the localized feature map of the convolutional layer. In the HCRDNN, an additional dense layer is exploited to add another degree of freedom for adapting the NN settings in order to achieve the best compromise between the cancellation performance and computational complexity. Complexity analysis and numerical simulations are provided to prove the superiority of the proposed architectures.
CycleFlow: Purify Information Factors by Cycle Loss
Authors: Haoran Sun, Chen Chen, Lantian Li, Dong Wang
Date: 2021-10-18 00:00:00
Tasks: Voice Conversion

SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies. A potential problem of SpeechFlow, however, is that if the IB channels are not well designed, the resultant factors cannot be well disentangled. In this study, we propose a CycleFlow model that combines random factor substitution and cycle loss to solve this problem. Experiments on voice conversion tasks demonstrate that this simple technique can effectively reduce mutual information among individual factors, and produce clearly better conversion than the IB-based SpeechFlow. CycleFlow can also be used as a powerful tool for speech editing. We demonstrate this usage by an emotion perception experiment.
Eigenbehaviour as an Indicator of Cognitive Abilities
Authors: Angela Botros, Narayan Schütz, Christina Röcke, Robert Weibel, Mike Martin, René Müri, Tobias Nef
Date: 2021-10-18 00:00:00
Tasks: Binary Classification

With growing usage of machine learning algorithms and big data in health applications, digital biomarkers have become an important key feature to ensure the success of those applications. In this paper, we focus on one important use-case, the long-term continuous monitoring of the cognitive ability of older adults. The cognitive ability is a factor both for long-term monitoring of people living alone as well as an outcome in clinical studies. In this work, we propose a new digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indoor location information obtained from passive infrared sensors is used to build a location matrix covering several weeks of measurement. Based on the eigenvectors of this matrix, the reconstruction error is calculated for various numbers of used eigenvectors. The reconstruction error is used to predict cognitive ability scores collected at baseline, using linear regression. Additionally, classification of normal versus pathological cognition level is performed using a support-vector-machine. Prediction performance is strong for high levels of cognitive ability, but grows weaker for low levels of cognitive ability. Classification into normal versus pathological cognitive ability level reaches high accuracy with a AUC = 0.94. Due to the unobtrusive method of measurement based on contactless ambient sensors, this digital biomarker of cognitive ability is easily obtainable. The usage of the reconstruction error is a strong digital biomarker for the binary classification and, to a lesser extent, for more detailed prediction of interindividual differences in cognition.
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
Authors: Hengrui Zhang, Zhongming Yu, Guohao Dai, Guyue Huang, Yufei Ding, Yuan Xie, Yu Wang
Date: 2021-10-18 00:00:00

Graph Neural Networks (GNNs) have been widely used in various domains, and GNNs with sophisticated computational graph lead to higher latency and larger memory consumption. Optimizing the GNN computational graph suffers from: (1) Redundant neural operator computation. The same data are propagated through the graph structure to perform the same neural operation multiple times in GNNs, leading to redundant computation which accounts for 92.4% of total operators. (2) Inconsistent thread mapping. Efficient thread mapping schemes for vertex-centric and edge-centric operators are different. This inconsistency prohibits operator fusion to reduce memory IO. (3) Excessive intermediate data. For GNN training which is usually performed concurrently with inference, intermediate data must be stored for the backward pass, consuming 91.9% of the total memory requirement. To tackle these challenges, we propose following designs to optimize the GNN computational graph from a novel coordinated computation, IO, and memory perspective: (1) Propagation-postponed operator reorganization. We reorganize operators to perform neural operations before the propagation, thus the redundant computation is eliminated. (2) Unified thread mapping for fusion. We propose a unified thread mapping scheme for both vertex- and edge-centric operators to enable fusion and reduce IO. (3) Intermediate data recomputation. Intermediate data are recomputed during the backward pass to reduce the total memory consumption. Extensive experimental results on three typical GNN models show that, we achieve up to 2.75x end-to-end speedup, 6.89x less memory IO, and 7.73x less memory consumption over state-of-the-art frameworks.