pwc-archive/papers-with-abstracts huggingface dataset:
Attaining entropy production and dissipation maps from Brownian movies via neural networks
Authors: Youngkyoung Bae, Dong-Kyum Kim, Hawoong Jeong
Date: 2021-06-29 00:00:00
Tasks: Time Series, Time Series Analysis

Quantifying entropy production (EP) is essential to understand stochastic systems at mesoscopic scales, such as living organisms or biological assemblies. However, without tracking the relevant variables, it is challenging to figure out where and to what extent EP occurs from recorded time-series image data from experiments. Here, applying a convolutional neural network (CNN), a powerful tool for image processing, we develop an estimation method for EP through an unsupervised learning algorithm that calculates only from movies. Together with an attention map of the CNN's last layer, our method can not only quantify stochastic EP but also produce the spatiotemporal pattern of the EP (dissipation map). We show that our method accurately measures the EP and creates a dissipation map in two nonequilibrium systems, the bead-spring model and a network of elastic filaments. We further confirm high performance even with noisy, low spatial resolution data, and partially observed situations. Our method will provide a practical way to obtain dissipation maps and ultimately contribute to uncovering the nonequilibrium nature of complex systems.
FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis
Authors: Taejun Bak, Jae-Sung Bae, Hanbin Bae, Young-Ik Kim, Hoon-Young Cho
Date: 2021-06-29 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

Methods for modeling and controlling prosody with acoustic features have been proposed for neural text-to-speech (TTS) models. Prosodic speech can be generated by conditioning acoustic features. However, synthesized speech with a large pitch-shift scale suffers from audio quality degradation, and speaker characteristics deformation. To address this problem, we propose a feed-forward Transformer based TTS model that is designed based on the source-filter theory. This model, called FastPitchFormant, has a unique structure that handles text and acoustic features in parallel. With modeling each feature separately, the tendency that the model learns the relationship between two features can be mitigated.
GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis
Authors: Jinhyeok Yang, Jae-Sung Bae, Taejun Bak, Youngik Kim, Hoon-Young Cho
Date: 2021-06-29 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Vocal Bursts Intensity Prediction

Recent advances in neural multi-speaker text-to-speech (TTS) models have enabled the generation of reasonably good speech quality with a single model and made it possible to synthesize the speech of a speaker with limited training data. Fine-tuning to the target speaker data with the multi-speaker model can achieve better quality, however, there still exists a gap compared to the real speech sample and the model depends on the speaker. In this work, we propose GANSpeech, which is a high-fidelity multi-speaker TTS model that adopts the adversarial training method to a non-autoregressive multi-speaker TTS model. In addition, we propose simple but efficient automatic scaling methods for feature matching loss used in adversarial training. In the subjective listening tests, GANSpeech significantly outperformed the baseline multi-speaker FastSpeech and FastSpeech2 models, and showed a better MOS score than the speaker-specific fine-tuned FastSpeech2.
End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping
Authors: Fayçal Ait Aoudia, Jakob Hoydis
Date: 2021-06-29 00:00:00

As communication systems are foreseen to enable new services such as joint communication and sensing and utilize parts of the sub-THz spectrum, the design of novel waveforms that can support these emerging applications becomes increasingly challenging. We present in this work an end-to-end learning approach to design waveforms through joint learning of pulse shaping and constellation geometry, together with a neural network (NN)-based receiver. Optimization is performed to maximize an achievable information rate, while satisfying constraints on out-of-band emission and power envelope. Our results show that the proposed approach enables up to orders of magnitude smaller adjacent channel leakage ratios (ACLRs) with peak-to-average power ratios (PAPRs) competitive with traditional filters, without significant loss of information rate on an additive white Gaussian noise (AWGN) channel, and no additional complexity at the transmitter.
Machine learning for plant microRNA prediction: A systematic review
Authors: Shyaman Jayasundara, Sandali Lokuge, Puwasuru Ihalagedara, Damayanthi Herath
Date: 2021-06-29 00:00:00
Tasks: BIG-bench Machine Learning

MicroRNAs (miRNAs) are endogenous small non-coding RNAs that play an important role in post-transcriptional gene regulation. However, the experimental determination of miRNA sequence and structure is both expensive and time-consuming. Therefore, computational and machine learning-based approaches have been adopted to predict novel microRNAs. With the involvement of data science and machine learning in biology, multiple research studies have been conducted to find microRNAs with different computational methods and different miRNA features. Multiple approaches are discussed in detail considering the learning algorithm/s used, features considered, dataset/s used and the criteria used in evaluations. This systematic review focuses on the machine learning methods developed for miRNA identification in plants. This will help researchers to gain a detailed idea about past studies and identify novel paths that solve drawbacks occurred in past studies. Our findings highlight the need for plant-specific computational methods for miRNA identification.
TUCaN: Progressively Teaching Colourisation to Capsules
Authors: Rita Pucci, Niki Martinel
Date: 2021-06-29 00:00:00

Automatic image colourisation is the computer vision research path that studies how to colourise greyscale images (for restoration). Deep learning techniques improved image colourisation yielding astonishing results. These differ by various factors, such as structural differences, input types, user assistance, etc. Most of them, base the architectural structure on convolutional layers with no emphasis on layers specialised in object features extraction. We introduce a novel downsampling upsampling architecture named TUCaN (Tiny UCapsNet) that exploits the collaboration of convolutional layers and capsule layers to obtain a neat colourisation of entities present in every single image. This is obtained by enforcing collaboration among such layers by skip and residual connections. We pose the problem as a per pixel colour classification task that identifies colours as a bin in a quantized space. To train the network, in contrast with the standard end to end learning method, we propose the progressive learning scheme to extract the context of objects by only manipulating the learning process without changing the model. In this scheme, the upsampling starts from the reconstruction of low resolution images and progressively grows to high resolution images throughout the training phase. Experimental results on three benchmark datasets show that our approach with ImageNet10k dataset outperforms existing methods on standard quality metrics and achieves state of the art performances on image colourisation. We performed a user study to quantify the perceptual realism of the colourisation results demonstrating: that progressive learning let the TUCaN achieve better colours than the end to end scheme; and pointing out the limitations of the existing evaluation metrics.
Predicting Depth from Semantic Segmentation using Game Engine Dataset
Authors: Mohammad Amin Kashi
Date: 2021-06-12 00:00:00
Tasks: Depth Estimation, Semantic Segmentation

Depth perception is fundamental for robots to understand the surrounding environment. As the view of cognitive neuroscience, visual depth perception methods are divided into three categories, namely binocular, active, and pictorial. The first two categories have been studied for decades in detail. However, research for the exploration of the third category is still in its infancy and has got momentum by the advent of deep learning methods in recent years. In cognitive neuroscience, it is known that pictorial depth perception mechanisms are dependent on the perception of seen objects. Inspired by this fact, in this thesis, we investigated the relation of perception of objects and depth estimation convolutional neural networks. For this purpose, we developed new network structures based on a simple depth estimation network that only used a single image at its input. Our proposed structures use both an image and a semantic label of the image as their input. We used semantic labels as the output of object perception. The obtained results of performance comparison between the developed network and original network showed that our novel structures can improve the performance of depth estimation by 52\% of relative error of distance in the examined cases. Most of the experimental studies were carried out on synthetic datasets that were generated by game engines to isolate the performance comparison from the effect of inaccurate depth and semantic labels of non-synthetic datasets. It is shown that particular synthetic datasets may be used for training of depth networks in cases that an appropriate dataset is not available. Furthermore, we showed that in these cases, usage of semantic labels improves the robustness of the network against domain shift from synthetic training data to non-synthetic test data.
MU-MIMO Grouping For Real-time Applications
Authors: Hannaneh Barahouei Pasandi, Tamer Nadeem, Hadi Amirpour
Date: 2021-06-29 00:00:00

Over the last decade, the bandwidth expansion and MU-MIMO spectral efficiency have promised to increase data throughput by allowing concurrent communication between one Access Point and multiple users. However, we are still a long way from enjoying such MU-MIMO MAC protocol improvements for bandwidth hungry applications such as video streaming in practical WiFi network settings due to heterogeneous channel conditions and devices, unreliable transmissions, and lack of useful feedback exchange among the lower and upper layers' requirements. This paper introduces MuViS, a novel dual-phase optimization framework that proposes a Quality of Experience (QoE) aware MU-MIMO optimization for multi-user video streaming over IEEE 802.11ac. MuViS first employs reinforcement learning to optimize the MU-MIMO user group and mode selection for users based on their PHY/MAC layer characteristics. The video bitrate is then optimized based on the user's mode (Multi-User (MU) or Single-User (SU)). We present our design and its evaluation on smartphones and laptops using 802.11ac WiFi. Our experimental results in various indoor environments and configurations show a scalable framework that can support a large number of users with streaming at high video rates and satisfying QoE requirements.
Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data
Authors: Daniel de Barros Soares, François Andrieux, Bastien Hell, Julien Lenhardt, Jordi Badosa, Sylvain Gavoille, Stéphane Gaiffas, Emmanuel Bacry
Date: 2021-05-28 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Estimating the amount of electricity that can be produced by rooftop photovoltaic systems is a time-consuming process that requires on-site measurements, a difficult task to achieve on a large scale. In this paper, we present an approach to estimate the solar potential of rooftops based on their location and architectural characteristics, as well as the amount of solar radiation they receive annually. Our technique uses computer vision to achieve semantic segmentation of roof sections and roof objects on the one hand, and a machine learning model based on structured building features to predict roof pitch on the other hand. We then compute the azimuth and maximum number of solar panels that can be installed on a rooftop with geometric approaches. Finally, we compute precise shading masks and combine them with solar irradiation data that enables us to estimate the yearly solar potential of a rooftop.
Autonomous Driving Implementation in an Experimental Environment
Authors: Namig Aliyev, Oguzhan Sezer, Mehmet Turan Guzel
Date: 2021-05-23 00:00:00
Tasks: Autonomous Driving, Optical Flow Estimation

Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the experimental environment designed for this purpose. In this system, a model vehicle having a camera is used to trace the lanes and avoid obstacles to experimentally study autonomous driving behavior. Convolutional Neural Network models were trained for Lane tracking. For the vehicle to avoid obstacles, corner detection, optical flow, focus of expansion, time to collision, balance calculation, and decision mechanism were created, respectively.
On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery
Authors: Maria Pia Del Rosso, Alessandro Sebastianelli, Dario Spiller, Pierre Philippe Mathieu, Silvia Liberata Ullo
Date: 2021-06-29 00:00:00

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study and a first prototype for an Artificial Intelligence (AI) model to be deployed on board satellites are presented in this work. As a case study, the detection of volcanic eruptions has been investigated as a method to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been proposed and designed, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements.
Similarity Embedding Networks for Robust Human Activity Recognition
Authors: Chenglin Li, Carrie Lu Tong, Di Niu, Bei Jiang, Xiao Zuo, Lei Cheng, Jian Xiong, Jianming Yang
Date: 2021-05-31 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex real-world HAR data is limited by the availability of high-quality labeled activity data, which are hard to obtain. In this paper, we design a similarity embedding neural network that maps input sensor signals onto real vectors through carefully designed convolutional and LSTM layers. The embedding network is trained with a pairwise similarity loss, encouraging the clustering of samples from the same class in the embedded real space, and can be effectively trained on a small dataset and even on a noisy dataset with mislabeled samples. Based on the learned embeddings, we further propose both nonparametric and parametric approaches for activity recognition. Extensive evaluation based on two public datasets has shown that the proposed similarity embedding network significantly outperforms state-of-the-art deep models on HAR classification tasks, is robust to mislabeled samples in the training set, and can also be used to effectively denoise a noisy dataset.
Anomaly Detection and Automated Labeling for Voter Registration File Changes
Authors: Sam Royston, Ben Greenberg, Omeed Tavasoli, Courtenay Cotton
Date: 2021-06-16 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Voter eligibility in United States elections is determined by a patchwork of state databases containing information about which citizens are eligible to vote. Administrators at the state and local level are faced with the exceedingly difficult task of ensuring that each of their jurisdictions is properly managed, while also monitoring for improper modifications to the database. Monitoring changes to Voter Registration Files (VRFs) is crucial, given that a malicious actor wishing to disrupt the democratic process in the US would be well-advised to manipulate the contents of these files in order to achieve their goals. In 2020, we saw election officials perform admirably when faced with administering one of the most contentious elections in US history, but much work remains to secure and monitor the election systems Americans rely on. Using data created by comparing snapshots taken of VRFs over time, we present a set of methods that make use of machine learning to ease the burden on analysts and administrators in protecting voter rolls. We first evaluate the effectiveness of multiple unsupervised anomaly detection methods in detecting VRF modifications by modeling anomalous changes as sparse additive noise. In this setting we determine that statistical models comparing administrative districts within a short time span and non-negative matrix factorization are most effective for surfacing anomalous events for review. These methods were deployed during 2019-2020 in our organization's monitoring system and were used in collaboration with the office of the Iowa Secretary of State. Additionally, we propose a newly deployed model which uses historical and demographic metadata to label the likely root cause of database modifications. We hope to use this model to predict which modifications have known causes and therefore better identify potentially anomalous modifications.
Evaluating Deep Neural Networks for Image Document Enhancement
Authors: Lucas N. Kirsten, Ricardo Piccoli, Ricardo Ribani
Date: 2021-06-11 00:00:00
Tasks: Deep Learning, Document Enhancement, Image Enhancement, Image Quality Assessment

This work evaluates six state-of-the-art deep neural network (DNN) architectures applied to the problem of enhancing camera-captured document images. The results from each network were evaluated both qualitatively and quantitatively using Image Quality Assessment (IQA) metrics, and also compared with an existing approach based on traditional computer vision techniques. The best performing architectures generally produced good enhancement compared to the existing algorithm, showing that it is possible to use DNNs for document image enhancement. Furthermore, the best performing architectures could work as a baseline for future investigations on document enhancement using deep learning techniques. The main contributions of this paper are: a baseline of deep learning techniques that can be further improved to provide better results, and a evaluation methodology using IQA metrics for quantitatively comparing the produced images from the neural networks to a ground truth.
Reliable and Fast Recurrent Neural Network Architecture Optimization
Authors: Andrés Camero, Jamal Toutouh, Enrique Alba
Date: 2021-06-29 00:00:00

This article introduces Random Error Sampling-based Neuroevolution (RESN), a novel automatic method to optimize recurrent neural network architectures. RESN combines an evolutionary algorithm with a training-free evaluation approach. The results show that RESN achieves state-of-the-art error performance while reducing by half the computational time.
US Fatal Police Shooting Analysis and Prediction
Authors: YuAn Wang, Yangxin Fan
Date: 2021-03-24 00:00:00
Tasks: Prediction, regression

We believe that "all men are created equal". With the rise of the police shootings reported by media, more people in the U.S. think that police use excessive force during law enforcement, especially to a specific group of people. We want to apply multidimensional statistical analysis to reveal more facts than the monotone mainstream media. Our paper has three parts. First, we proposed a new method to quantify fatal police shooting news reporting deviation of mainstream media, which includes CNN, FOX, ABC, and NBC. Second, we analyzed the most comprehensive US fatal police shooting dataset from Washington Post. We used FP-growth to reveal the frequent patterns and DBSCAN clustering to find fatal shooting hotspots. We brought multi-attributes (social economics, demographics, political tendency, education, gun ownership rate, police training hours, etc.) to reveal connections under the iceberg. We found that the police shooting rate of a state depends on many variables. The top four most relevant attributes were state joined year, state land area, gun ownership rate, and violent crime rate. Third, we proposed four regression models to predict police shooting rates at the state level. The best model Kstar could predict the fatal police shooting rate with about 88.53% correlation coefficient. We also proposed classification models, including Gradient Boosting Machine, Multi-class Classifier, Logistic Regression, and Naive Bayes Classifier, to predict the race of fatal police shooting victims. Our classification models show no significant evidence to conclude that racial discrimination happened during fatal police shootings recorded by the WP dataset.
VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds
Authors: Monami Banerjee, Rudrasis Chakraborty, Jose Bouza, Baba C. Vemuri
Date: 2021-06-05 00:00:00
Tasks: Diffusion  MRI, Translation

Convolutional neural networks have been highly successful in image-based learning tasks due to their translation equivariance property. Recent work has generalized the traditional convolutional layer of a convolutional neural network to non-Euclidean spaces and shown group equivariance of the generalized convolution operation. In this paper, we present a novel higher order Volterra convolutional neural network (VolterraNet) for data defined as samples of functions on Riemannian homogeneous spaces. Analagous to the result for traditional convolutions, we prove that the Volterra functional convolutions are equivariant to the action of the isometry group admitted by the Riemannian homogeneous spaces, and under some restrictions, any non-linear equivariant function can be expressed as our homogeneous space Volterra convolution, generalizing the non-linear shift equivariant characterization of Volterra expansions in Euclidean space. We also prove that second order functional convolution operations can be represented as cascaded convolutions which leads to an efficient implementation. Beyond this, we also propose a dilated VolterraNet model. These advances lead to large parameter reductions relative to baseline non-Euclidean CNNs. To demonstrate the efficacy of the VolterraNet performance, we present several real data experiments involving classification tasks on spherical-MNIST, atomic energy, Shrec17 data sets, and group testing on diffusion MRI data. Performance comparisons to the state-of-the-art are also presented.