pwc-archive/papers-with-abstracts huggingface dataset:
Scoring and Assessment in Medical VR Training Simulators with Dynamic Time Series Classification
Authors: Neil Vaughan, Bogdan Gabrys
Date: 2020-06-11 00:00:00
Tasks: Dynamic Time Warping, General Classification, Time Series, Time Series Analysis, Time Series Classification

This research proposes and evaluates scoring and assessment methods for Virtual Reality (VR) training simulators. VR simulators capture detailed n-dimensional human motion data which is useful for performance analysis. Custom made medical haptic VR training simulators were developed and used to record data from 271 trainees of multiple clinical experience levels. DTW Multivariate Prototyping (DTW-MP) is proposed. VR data was classified as Novice, Intermediate or Expert. Accuracy of algorithms applied for time-series classification were: dynamic time warping 1-nearest neighbor (DTW-1NN) 60%, nearest centroid SoftDTW classification 77.5%, Deep Learning: ResNet 85%, FCN 75%, CNN 72.5% and MCDCNN 28.5%. Expert VR data recordings can be used for guidance of novices. Assessment feedback can help trainees to improve skills and consistency. Motion analysis can identify different techniques used by individuals. Mistakes can be detected dynamically in real-time, raising alarms to prevent injuries.
Finite-Sample Analysis of Proximal Gradient TD Algorithms
Authors: Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik
Date: 2020-06-06 00:00:00

In this paper, we analyze the convergence rate of the gradient temporal difference learning (GTD) family of algorithms. Previous analyses of this class of algorithms use ODE techniques to prove asymptotic convergence, and to the best of our knowledge, no finite-sample analysis has been done. Moreover, there has been not much work on finite-sample analysis for convergent off-policy reinforcement learning algorithms. In this paper, we formulate GTD methods as stochastic gradient algorithms w.r.t.~a primal-dual saddle-point objective function, and then conduct a saddle-point error analysis to obtain finite-sample bounds on their performance. Two revised algorithms are also proposed, namely projected GTD2 and GTD2-MP, which offer improved convergence guarantees and acceleration, respectively. The results of our theoretical analysis show that the GTD family of algorithms are indeed comparable to the existing LSTD methods in off-policy learning scenarios.
Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction
Authors: Yunsheng Bai, Ken Gu, Yizhou Sun, Wei Wang
Date: 2020-06-11 00:00:00
Tasks: Link Prediction

We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI). Taking drug-drug interaction as an example, existing methods using machine learning either only utilize the link structure between drugs without using the graph representation of each drug molecule, or only leverage the individual drug compound structures without using graph structure for the higher-level DDI graph. The key idea of our method is to fundamentally view the data as a bi-level graph, where the highest level graph represents the interaction between biological entities (interaction graph), and each biological entity itself is further expanded to its intrinsic graph representation (representation graphs), where the graph is either flat like a drug compound or hierarchical like a protein with amino acid level graph, secondary structure, tertiary structure, etc. Our model not only allows the usage of information from both the high-level interaction graph and the low-level representation graphs, but also offers a baseline for future research opportunities to address the bi-level nature of the data.
Validating psychometric survey responses
Authors: Alberto Mastrotto, Anderson Nelson, Dev Sharma, Ergeta Muca, Kristina Liapchin, Luis Losada, Mayur Bansal, Roman S. Samarev
Date: 2020-06-08 00:00:00
Tasks: BIG-bench Machine Learning, Survey

We present an approach to classify user validity in survey responses by using a machine learning techniques. The approach is based on collecting user mouse activity on web-surveys and fast predicting validity of the survey in general without analysis of specific answers. Rule based approach, LSTM and HMM models are considered. The approach might be used in web-survey applications to detect suspicious users behaviour and request from them proper answering instead of false data recording.
Hyperparameter Optimization for Forecasting Stock Returns
Authors: Sang Il Lee
Date: 2020-01-28 00:00:00
Tasks: Feature Importance, Hyperparameter Optimization

In recent years, hyperparameter optimization (HPO) has become an increasingly important issue in the field of machine learning for the development of more accurate forecasting models. In this study, we explore the potential of HPO in modeling stock returns using a deep neural network (DNN). The potential of this approach was evaluated using technical indicators and fundamentals examined based on the effect the regularization of dropouts and batch normalization for all input data. We found that the model using technical indicators and dropout regularization significantly outperforms three other models, showing a positive predictability of 0.53% in-sample and 1.11% out-of-sample, thereby indicating the possibility of beating the historical average. We also demonstrate the stability of the model in terms of the changes in its feature importance over time.
A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories
Authors: Daria B. Kokha, Bernd Doser, Stefan Richter, Fabian Ormersbach, Xingyi Cheng, Rebecca C. Wade
Date: 2020-06-19 00:00:00
Tasks: Drug Design

The dissociation of ligands from proteins and other biomacromolecules occurs over a wide range of timescales. For most pharmaceutically relevant inhibitors, these timescales are far beyond those that are accessible by conventional molecular dynamics (MD) simulation. Consequently, to explore ligand egress mechanisms and compute dissociation rates, it is necessary to enhance the sampling of ligand unbinding. Random Acceleration MD (RAMD) is a simple method to enhance ligand egress from a macromolecular binding site, which enables the exploration of ligand egress routes without prior knowledge of the reaction coordinates. Furthermore, the tauRAMD procedure can be used to compute the relative residence times of ligands. When combined with a machine-learning analysis of protein-ligand interaction fingerprints (IFPs), molecular features that affect ligand unbinding kinetics can be identified. Here, we describe the implementation of RAMD in GROMACS 2020, which provides significantly improved computational performance, with scaling to large molecular systems. For the automated analysis of RAMD results, we developed MD-IFP, a set of tools for the generation of IFPs along unbinding trajectories and for their use in the exploration of ligand dynamics. We demonstrate that the analysis of ligand dissociation trajectories by mapping them onto the IFP space enables the characterization of ligand dissociation routes and metastable states. The combined implementation of RAMD and MD-IFP provides a computationally efficient and freely available workflow that can be applied to hundreds of compounds in a reasonable computational time and will facilitate the use of tauRAMD in drug design.
Leverage efficiency
Authors: Ole Peters, Alexander Adamou
Date: 2011-01-24 00:00:00

Peters (2011a) defined an optimal leverage which maximizes the time-average growth rate of an investment held at constant leverage. It was hypothesized that this optimal leverage is attracted to 1, such that, e.g., leveraging an investment in the market portfolio cannot yield long-term outperformance. This places a strong constraint on the stochastic properties of prices of traded assets, which we call "leverage efficiency." Market conditions that deviate from leverage efficiency are unstable and may create leverage-driven bubbles. Here we expand on the hypothesis and its implications. These include a theory of noise that explains how systemic stability rules out smooth price changes at any pricing frequency; a resolution of the so-called equity premium puzzle; a protocol for central bank interest rate setting to avoid leverage-driven price instabilities; and a method for detecting fraudulent investment schemes by exploiting differences between the stochastic properties of their prices and those of legitimately-traded assets. To submit the hypothesis to a rigorous test we choose price data from different assets: the S&P500 index, Bitcoin, Berkshire Hathaway Inc., and Bernard L. Madoff Investment Securities LLC. Analysis of these data supports the hypothesis.
RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments
Authors: Kévin Cortacero, Tobias Fischer, Yiannis Demiris
Date: 2019-10-27 00:00:00
Proceeding: IEEE International Conference on Computer Vision Workshops 2019 10
Tasks: Blink estimation, Gaze Estimation

In recent years gaze estimation methods have made substantial progress, driven by the numerous application areas including human-robot interaction, visual attention estimation and foveated rendering for virtual reality headsets. However, many gaze estimation methods typically assume that the subject's eyes are open; for closed eyes, these methods provide irregular gaze estimates. Here, we address this assumption by first introducing a new open-sourced dataset with annotations of the eye-openness of more than 200,000 eye images, including more than 10,000 images where the eyes are closed. We further present baseline methods that allow for blink detection using convolutional neural networks. In extensive experiments, we show that the proposed baselines perform favourably in terms of precision and recall. We further incorporate our proposed RT-BENE baselines in the recently presented RT-GENE gaze estimation framework where it provides a real-time inference of the openness of the eyes. We argue that our work will benefit both gaze estimation and blink estimation methods, and we take steps towards unifying these methods.
Twin-Net Descriptor: Twin Negative Mining With Quad Loss for Patch-Based Matching
Authors: Aman Irshad, Rehan Hafiz, Mohsen Ali, Muhammad Faisal, Yongju Cho, Jeongil Seo
Date: 2019-09-19 00:00:00
Proceeding: IEEE Access 2019 9
Tasks: Patch Matching, Triplet

Local keypoint matching is an important step for computer vision based tasks. In recent years, Deep Convolutional Neural Network (CNN) based strategies have been employed to learn descriptor generation to enhance keypoint matching accuracy. Recent state-of-art works in this direction primarily rely upon a triplet based loss function (and its variations) utilizing three samples: an anchor, a positive and a negative. In this work we propose a novel “Twin Negative Mining” based sampling strategy coupled with a Quad loss function to train a deep neural network based pipeline (Twin-Net) for generating a robust descriptor that provides an increased discriminatory power to differentiate between patches that do not correspond to each other. Our sampling strategy and choice of loss function is aimed at placing an upper bound that descriptors of two patches representing same location could be at worst no more dissimilar than the descriptors of two similar looking patches that do-not belong to same 3D location. This results in an increase in the generalization capability of the network and outperforms its existing counterparts when trained over the same datasets. Twin-Net outputs a 128-dimensional descriptor and uses L 2 Distance as the similarity metric, and hence conforms to the classical descriptor matching pipelines such as that of SIFT. Our results on Brown and HPatches datasets demonstrate Twin-Net's consistently better performance as well as better discriminatory and generalization capability as compared to the state-of-art.
Pose Encoding for Robust Skeleton-Based Action Recognition
Authors: Demisse, G. G., Papadopoulos, K., Aouada, D., & Ottersten, B.
Date: 2018-06-18 00:00:00
Proceeding: In Proceedings of the IEEE conference on computer vision and pattern recognition workshops 2018 6
Tasks: Action Recognition, Skeleton Based Action Recognition

Some of the main challenges in skeleton-based action recognition systems are redundant and noisy pose transformations. Earlier works in skeleton-based action recognition explored different approaches for filtering linear noise transformations, but neglect to address potential nonlinear transformations. In this paper, we present an unsupervised learning approach for estimating nonlinear noise transformations in pose estimates. Our approach starts by decoupling linear and nonlinear noise transformations. While the
linear transformations are modelled explicitly the nonlinear transformations are learned from data. Subsequently, we use an autoencoder with L 2 -norm reconstruction error and show that it indeed does capture nonlinear noise transformations, and recover a denoised pose estimate which in turn improves performance significantly. We validate our approach on a publicly available dataset, NW-UCLA.
A Study of Position Bias in Digital Library Recommender Systems
Authors: Andrew Collins, Dominika Tkaczyk, Akiko Aizawa, Joeran Beel
Date: 2018-02-19 00:00:00
Tasks: Position, Recommendation Systems

"Position bias" describes the tendency of users to interact with items on top of a list with higher probability than with items at a lower position in the list, regardless of the items' actual relevance. In the domain of recommender systems, particularly recommender systems in digital libraries, position bias has received little attention. We conduct a study in a real-world recommender system that delivered ten million related-article recommendations to the users of the digital library Sowiport, and the reference manager JabRef. Recommendations were randomly chosen to be shuffled or non-shuffled, and we compared click-through rate (CTR) for each rank of the recommendations. According to our analysis, the CTR for the highest rank in the case of Sowiport is 53% higher than expected in a hypothetical non-biased situation (0.189% vs. 0.123%). Similarly, in the case of Jabref the highest rank received a CTR of 1.276%, which is 87% higher than expected (0.683%). A chi-squared test confirms the strong relationship between the rank of the recommendation shown to the user and whether the user decided to click it (p < 0.01 for both Jabref and Sowiport). Our study confirms the findings from other domains, that recommendations in the top positions are more often clicked, regardless of their actual relevance.
A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection
Authors: Archontis Politis, Sharath Adavanne, Tuomas Virtanen
Date: 2020-06-02 00:00:00
Tasks: Sound Event Localization and Detection

This report presents the dataset and the evaluation setup of the Sound Event Localization & Detection (SELD) task for the DCASE 2020 Challenge. The SELD task refers to the problem of trying to simultaneously classify a known set of sound event classes, detect their temporal activations, and estimate their spatial directions or locations while they are active. To train and test SELD systems, datasets of diverse sound events occurring under realistic acoustic conditions are needed. Compared to the previous challenge, a significantly more complex dataset was created for DCASE 2020. The two key differences are a more diverse range of acoustical conditions, and dynamic conditions, i.e. moving sources. The spatial sound scenes are created using real room impulse responses captured in a continuous manner with a slowly moving excitation source. Both static and moving sound events are synthesized from them. Ambient noise recorded on location is added to complete the generation of scene recordings. A baseline SELD method accompanies the dataset, based on a convolutional recurrent neural network, to provide benchmark scores for the task. The baseline is an updated version of the one used in the previous challenge, with input features and training modifications to improve its performance.
Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning
Authors: Di Cao, Junbo Zhao, Weihao Hu, Fei Ding, Qi Huang, Zhe Chen, Frede Blaabjerg
Date: 2020-06-24 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, model

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice. This paper develops a model-free approach based on the surrogate model and deep reinforcement learning (DRL). We have also extended it to deal with unbalanced three-phase scenarios. The key idea is to learn a surrogate model to capture the relationship between the power injections and voltage fluctuation of each node from historical data instead of using the original inaccurate model affected by errors and uncertainties. This allows us to integrate the DRL with the learned surrogate model. In particular, DRL is applied to learn the optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The integrated framework contains training three networks, i.e., surrogate model, actor, and critic networks, which fully leverage the strong nonlinear fitting ability of deep learning and DRL for online decision making. Several single-phase approaches have also been extended to deal with three-phase unbalance scenarios and the simulation results on the IEEE 123-bus system show that our proposed method can achieve similar performance as those that use accurate physical models.
CLC: Complex Linear Coding for the DNS 2020 Challenge
Authors: Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B., Andreas Maier
Date: 2020-06-23 00:00:00
Tasks: Speech Enhancement

Complex-valued processing brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the noise reduction process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram. Complex masks (CM) usually outperform real-valued masks due to their ability to modify the phase. Recent work proposed to use a complex linear combination of coefficients called complex linear coding (CLC) instead of a point-wise multiplication with a mask. This allows to incorporate information from previous and optionally future time steps which results in superior performance over mask-based enhancement for certain noise conditions. In fact, the linear combination enables to model quasi-steady properties like the spectrum within a frequency band. In this work, we apply CLC to the Deep Noise Suppression (DNS) challenge and propose CLC as an alternative to traditional mask-based processing, e.g. used by the baseline. We evaluated our models using the provided test set and an additional validation set with real-world stationary and non-stationary noises. Based on the published test set, we outperform the baseline w.r.t. the scale independent signal distortion ratio (SI-SDR) by about 3dB.
Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN
Authors: Yudi Dong, Huaxia Wang, Yu-Dong Yao
Date: 2020-06-19 00:00:00

Channel estimation is a challenging task, especially in a massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADC). Traditional deep learning (DL) methods, that learn the mapping from inputs to real channels, have significant difficulties in estimating accurate channels because their loss functions are not well designed and investigated. In this paper, a conditional generative adversarial networks (cGAN) is developed to predict more realistic channels by adversarially training two DL networks. cGANs not only learn the mapping from quantized observations to real channels but also learn an adaptive loss function to correctly train the networks. Numerical results show that the proposed cGAN based approach outperforms existing DL methods and achieves high robustness in massive MIMO systems.
CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization
Authors: Tanvir Mahmud, Md Awsafur Rahman, Shaikh Anowarul Fattah
Date: 2020-07-01 00:00:00
Proceeding: Computers in Biology and Medicine 2020 7
Tasks: COVID-19 Diagnosis, Diagnostic, Pneumonia Detection

With the recent outbreak of COVID-19, fast diagnostic testing has become one of the major challenges due to the critical shortage of test kit. Pneumonia, a major effect of COVID-19, needs to be urgently diagnosed along with its underlying reasons. In this paper, deep learning aided automated COVID-19 and other pneumonia detection schemes are proposed utilizing a small amount of COVID-19 chest X-rays. A deep convolutional neural network (CNN) based architecture, named as CovXNet, is proposed that utilizes depthwise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. Since the chest X-ray images corresponding to COVID-19 caused pneumonia and other traditional pneumonias have significant similarities, at first, a large number of chest X-rays corresponding to normal and (viral/bacterial) pneumonia patients are used to train the proposed CovXNet. Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients. In the proposed method, different forms of CovXNets are designed and trained with X-ray images of various resolutions and for further optimization of their predictions, a stacking algorithm is employed. Finally, a gradient-based discriminative localization is integrated to distinguish the abnormal regions of X-ray images referring to different types of pneumonia. Extensive experimentations using two different datasets provide very satisfactory detection performance with accuracy of 97.4% for COVID/Normal, 96.9% for COVID/Viral pneumonia, 94.7% for COVID/Bacterial pneumonia, and 90.2% for multiclass COVID/normal/Viral/Bacterial pneumonias. Hence, the proposed schemes can serve as an efficient tool in the current state of COVID-19 pandemic.