pwc-archive/papers-with-abstracts huggingface dataset:
CxSE: Chest X-ray Slow Encoding CNN forCOVID-19 Diagnosis
Authors: Thangarajah Akilan
Date: 2021-06-23 00:00:00
Tasks: Sensitivity

The coronavirus continues to disrupt our everyday lives as it spreads at an exponential rate. It needs to be detected quickly in order to quarantine positive patients so as to avoid further spread. This work proposes a new convolutional neural network (CNN) architecture called 'slow Encoding CNN. The proposed model's best performance wrt Sensitivity, Positive Predictive Value (PPV) found to be SP=0.67, PP=0.98, SN=0.96, and PN=0.52 on AI AGAINST COVID19 - Screening X-ray images for COVID-19 Infections competition's test data samples. SP and PP stand for the Sensitivity and PPV of the COVID-19 positive class, while PN and SN stand for the Sensitivity and PPV of the COVID-19 negative class.
Diabetic Retinopathy Detection using Ensemble Machine Learning
Authors: Israa Odeh, Mouhammd Alkasassbeh, Mohammad Alauthman
Date: 2021-06-22 00:00:00
Tasks: BIG-bench Machine Learning, Diabetic Retinopathy Detection, Diagnostic, Nutrition

Diabetic Retinopathy (DR) is among the worlds leading vision loss causes in diabetic patients. DR is a microvascular disease that affects the eye retina, which causes vessel blockage and therefore cuts the main source of nutrition for the retina tissues. Treatment for this visual disorder is most effective when it is detected in its earliest stages, as severe DR can result in irreversible blindness. Nonetheless, DR identification requires the expertise of Ophthalmologists which is often expensive and time-consuming. Therefore, automatic detection systems were introduced aiming to facilitate the identification process, making it available globally in a time and cost-efficient manner. However, due to the limited reliable datasets and medical records for this particular eye disease, the obtained predictions accuracies were relatively unsatisfying for eye specialists to rely on them as diagnostic systems. Thus, we explored an ensemble-based learning strategy, merging a substantial selection of well-known classification algorithms in one sophisticated diagnostic model. The proposed framework achieved the highest accuracy rates among all other common classification algorithms in the area. 4 subdatasets were generated to contain the top 5 and top 10 features of the Messidor dataset, selected by InfoGainEval. and WrapperSubsetEval., accuracies of 70.7% and 75.1% were achieved on the InfoGainEval. top 5 and original dataset respectively. The results imply the impressive performance of the subdataset, which significantly conduces to a less complex classification process
Fine-Tuning StyleGAN2 For Cartoon Face Generation
Authors: Jihye Back
Date: 2021-06-22 00:00:00
Tasks: Face Generation, Image-to-Image Translation, Translation

Recent studies have shown remarkable success in the unsupervised image to image (I2I) translation. However, due to the imbalance in the data, learning joint distribution for various domains is still very challenging. Although existing models can generate realistic target images, it's difficult to maintain the structure of the source image. In addition, training a generative model on large data in multiple domains requires a lot of time and computer resources. To address these limitations, we propose a novel image-to-image translation method that generates images of the target domain by finetuning a stylegan2 pretrained model. The stylegan2 model is suitable for unsupervised I2I translation on unbalanced datasets; it is highly stable, produces realistic images, and even learns properly from limited data when applied with simple fine-tuning techniques. Thus, in this paper, we propose new methods to preserve the structure of the source images and generate realistic images in the target domain. The code and results are available at https://github.com/happy-jihye/Cartoon-StyleGan2
Decentralized Linear MMSE Equalizer Under Colored Noise for Massive MIMO Systems
Authors: Xiaotong Zhao, Xin Guan, Mian Li, Qingjiang Shi
Date: 2021-06-23 00:00:00

Conventional uplink equalization in massive MIMO systems relies on a centralized baseband processing architecture. However, as the number of base station antennas increases, centralized baseband processing architectures encounter two bottlenecks, i.e., the tremendous data interconnection and the high-dimensional computation. To tackle these obstacles, decentralized baseband processing was proposed for uplink equalization, but only applicable to the scenarios with unpractical white Gaussian noise assumption. This paper presents an uplink linear minimum mean-square error (L-MMSE) equalization method in the daisy chain decentralized baseband processing architecture under colored noise assumption. The optimized L-MMSE equalizer is derived by exploiting the block coordinate descent method, which shows near-optimal performance both in theoretical and simulation while significantly mitigating the bottlenecks.
Arbitrary Angle of Arrival in Radar Target Simulation
Authors: Axel Diewald, Benjamin Nuß, Mario Pauli, Thomas Zwick
Date: 2021-06-23 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

Automotive radar sensors play a key role in the current development of autonomous driving. Their ability to detect objects even under adverse conditions makes them indispensable for environment-sensing tasks in autonomous vehicles. The thorough and in-place validation of radar sensors demands for an integrative test system. Radar Target Simulators (RTS) are capable of performing over-the-air validation tests by creating artificial radar echos that are perceived as targets by the radar under test (RuT). Since the authenticity and credibility of these targets is based on the accuracy with which they are generated, their simulated position must be arbitrarily adjustable. In this paper, a new approach to synthesize virtual radar targets at an arbitrary angle of arrival is presented. The concept is based on the superposition of the returning signals of two adjacent RTS channels. A theoretical model describing the basic principle and its constraints is developed. A measurement campaign is conducted that verifies the practical functionality of the proposed scheme.
Terahertz Wireless Communications with Flexible Index Modulation Aided Pilot Design
Authors: Tianqi Mao, Zhaocheng Wang
Date: 2021-06-23 00:00:00

Terahertz (THz) wireless communication is envisioned as a promising technology, which is capable of providing ultra-high-rate transmission up to Terabit per second. However, some hardware imperfections, which are generally neglected in the existing literature concerning lower data rates and traditional operating frequencies, cannot be overlooked in the THz systems. Hardware imperfections usually consist of phase noise, in-phase/quadrature imbalance, and nonlinearity of power amplifier. Due to the time-variant characteristic of phase noise, frequent pilot insertion is required, leading to decreased spectral efficiency. In this paper, to address this issue, a novel pilot design strategy is proposed based on index modulation (IM), where the positions of pilots are flexibly changed in the data frame, and additional information bits can be conveyed by indices of pilots. Furthermore, a turbo receiving algorithm is developed, which jointly performs the detection of pilot indices and channel estimation in an iterative manner. It is shown that the proposed turbo receiver works well even under the situation where the prior knowledge of channel state information is outdated. Analytical and simulation results validate that the proposed schemes achieve significant enhancement of bit-error rate performance and channel estimation accuracy, whilst attaining higher spectral efficiency in comparison with its classical counterpart.
Regret-optimal Estimation and Control
Authors: Gautam Goel, Babak Hassibi
Date: 2021-06-22 00:00:00
Tasks: Model Predictive Control

We consider estimation and control in linear time-varying dynamical systems from the perspective of regret minimization. Unlike most prior work in this area, we focus on the problem of designing causal estimators and controllers which compete against a clairvoyant noncausal policy, instead of the best policy selected in hindsight from some fixed parametric class. We show that the regret-optimal estimator and regret-optimal controller can be derived in state-space form using operator-theoretic techniques from robust control and present tight,data-dependent bounds on the regret incurred by our algorithms in terms of the energy of the disturbances. Our results can be viewed as extending traditional robust estimation and control, which focuses on minimizing worst-case cost, to minimizing worst-case regret. We propose regret-optimal analogs of Model-Predictive Control (MPC) and the Extended KalmanFilter (EKF) for systems with nonlinear dynamics and present numerical experiments which show that our regret-optimal algorithms can significantly outperform standard approaches to estimation and control.
Coupling Bayesian theory and static acoustic detector data to model bat motion and locate roosts
Authors: Lucy Henley, Owen Jones, Fiona Mathews, Thomas E. Woolley
Date: 2021-06-22 00:00:00

We propose a novel approach for modelling bat motion dynamics and use it to predict roost locations using data from static acoustic detectors. Specifically, radio tracking studies of Greater Horseshoe bats demonstrate that bat movement can be split into two phases: dispersion and return. Dispersion is easily understood and can be modelled as simple random motion. The return phase is much more complex, as it requires intelligent directed motion and results in all agents returning home in a stereotypical manner. Critically, combining reaction-diffusion theory and domain shrinking we deterministically and stochastically model a ``leap-frogging'' motion, which fits favourably with the observed tracking data.
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning
Authors: Hua Huang, Fanhua Shang, Yuanyuan Liu, Hongying Liu
Date: 2021-06-23 00:00:00
Tasks: Federated Learning

Federated Learning (FL) has become an active and promising distributed machine learning paradigm. As a result of statistical heterogeneity, recent studies clearly show that the performance of popular FL methods (e.g., FedAvg) deteriorates dramatically due to the client drift caused by local updates. This paper proposes a novel Federated Learning algorithm (called IGFL), which leverages both Individual and Group behaviors to mimic distribution, thereby improving the ability to deal with heterogeneity. Unlike existing FL methods, our IGFL can be applied to both client and server optimization. As a by-product, we propose a new attention-based federated learning in the server optimization of IGFL. To the best of our knowledge, this is the first time to incorporate attention mechanisms into federated optimization. We conduct extensive experiments and show that IGFL can significantly improve the performance of existing federated learning methods. Especially when the distributions of data among individuals are diverse, IGFL can improve the classification accuracy by about 13% compared with prior baselines.
Learned Interpretable Residual Extragradient ISTA for Sparse Coding
Authors: Lin Kong, Wei Sun, Fanhua Shang, Yuanyuan Liu, Hongying Liu
Date: 2021-06-22 00:00:00

Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number of experiments as well as some theories have proved the high efficiency of LISTA for solving sparse coding problems. However, existing LISTA methods are all serial connection. To address this issue, we propose a novel extragradient based LISTA (ELISTA), which has a residual structure and theoretical guarantees. In particular, our algorithm can also provide the interpretability for Res-Net to a certain extent. From a theoretical perspective, we prove that our method attains linear convergence. In practice, extensive empirical results verify the advantages of our method.
A Unified Approach to Fair Online Learning via Blackwell Approachability
Authors: Evgenii Chzhen, Christophe Giraud, Gilles Stoltz
Date: 2021-06-23 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Fairness

We provide a setting and a general approach to fair online learning with stochastic sensitive and non-sensitive contexts. The setting is a repeated game between the Player and Nature, where at each stage both pick actions based on the contexts. Inspired by the notion of unawareness, we assume that the Player can only access the non-sensitive context before making a decision, while we discuss both cases of Nature accessing the sensitive contexts and Nature unaware of the sensitive contexts. Adapting Blackwell's approachability theory to handle the case of an unknown contexts' distribution, we provide a general necessary and sufficient condition for learning objectives to be compatible with some fairness constraints. This condition is instantiated on (group-wise) no-regret and (group-wise) calibration objectives, and on demographic parity as an additional constraint. When the objective is not compatible with the constraint, the provided framework permits to characterise the optimal trade-off between the two.
Bayesian Joint Chance Constrained Optimization: Approximations and Statistical Consistency
Authors: Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
Date: 2021-06-23 00:00:00
Tasks: Stochastic Optimization

This paper considers data-driven chance-constrained stochastic optimization problems in a Bayesian framework. Bayesian posteriors afford a principled mechanism to incorporate data and prior knowledge into stochastic optimization problems. However, the computation of Bayesian posteriors is typically an intractable problem, and has spawned a large literature on approximate Bayesian computation. Here, in the context of chance-constrained optimization, we focus on the question of statistical consistency (in an appropriate sense) of the optimal value, computed using an approximate posterior distribution. To this end, we rigorously prove a frequentist consistency result demonstrating the convergence of the optimal value to the optimal value of a fixed, parameterized constrained optimization problem. We augment this by also establishing a probabilistic rate of convergence of the optimal value. We also prove the convex feasibility of the approximate Bayesian stochastic optimization problem. Finally, we demonstrate the utility of our approach on an optimal staffing problem for an M/M/c queueing model.
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study
Authors: Sai Sukruth Bezugam
Date: 2021-06-23 00:00:00
Tasks: Dimensionality Reduction, Medical Diagnosis, Multi-class Classification, Segmentation

The diagnosis of blood-based diseases often involves identifying and characterizing patient blood samples. Automated methods to detect and classify blood cell subtypes have important medical applications. Automated medical image processing and analysis offers a powerful tool for medical diagnosis. In this work we tackle the problem of white blood cell classification based on the morphological characteristics of their outer contour, color. The work we would explore a set of preprocessing and segmentation (Color-based segmentation, Morphological processing, contouring) algorithms along with a set of features extraction methods (Corner detection algorithms and Histogram of Gradients(HOG)), dimensionality reduction algorithms (Principal Component Analysis(PCA)) that are able to recognize and classify through various Unsupervised(k-nearest neighbors) and Supervised (Support Vector Machine, Decision Trees, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Naive Bayes) algorithms different categories of white blood cells to Eosinophil, Lymphocyte, Monocyte, and Neutrophil. We even take a step forwards to explore various Deep Convolutional Neural network architecture (Sqeezent, MobilenetV1,MobilenetV2, InceptionNet etc.) without preprocessing/segmentation and with preprocessing. We would like to explore many algorithms to identify the robust algorithm with least time complexity and low resource requirement. The outcome of this work can be a cue to selection of algorithms as per requirement for automated blood cell classification.
Optimal sensing and control of run-and-tumble chemotaxis
Authors: Kento Nakamura, Tetsuya J. Kobayashi
Date: 2021-06-23 00:00:00

Run-and-tumble chemotaxis is one of the representative search strategies of an odor source via sensing its spatial gradient. The optimal ways of sensing and control in the run-and-tumble chemotaxis have been analyzed theoretically to elucidate the efficiency of strategies implemented in organisms. However, because of theoretical difficulties, most of attempts have been limited only to either linear or deterministic analysis even though real biological chemotactic systems involve considerable stochasticity and nonlinearity in their sensory processes and controlled responses. In this paper, by combining the theories of optimal filtering and Kullback-Leibler control of partially observed Markov decision process (POMDP), we derive the optimal and fully nonlinear strategy for controlling run-and-tumble motion depending on noisy sensing of ligand gradient. The derived optimal strategy consists of the optimal filtering dynamics to estimate the run-direction from noisy sensory input and the control function to regulate the motor output. We further show that this optimal strategy can be associated naturally with a standard biochemical model and experimental data of the Escherichia coli's chemotaxis. These results demonstrate that our theoretical framework can work as a basis for analyzing the efficiency and optimality of run-and-tumble chemotaxis.
Network analytics for drug repurposing in COVID-19
Authors: Nicoleta Siminea, Victor Popescu, Jose Angel Sanchez Martin, Daniela Florea, Georgiana Gavril, Ana-Maria Gheorghe, Corina Itcus, Krishna Kanhaiya, Octavian Pacioglu, Laura Ioana Popa, Romica Trandafir, Maria Iris Tusa, Manuela Sidoroff, Mihaela Paun, Eugen Czeizler, Andrei Paun, Ion Petre
Date: 2021-06-23 00:00:00

To better understand the potential of drug repurposing in COVID-19, we analyzed control strategies over essential host factors for SARS-CoV-2 infection. We constructed comprehensive directed protein-protein interaction networks integrating the top ranked host factors, drug target proteins, and directed protein-protein interaction data. We analyzed the networks to identify drug targets and combinations thereof that offer efficient control over the host factors. We validated our findings against clinical studies data and bioinformatics studies. Our method offers a new insight into the molecular details of the disease and into potentially new therapy targets for it. Our approach for drug repurposing is significant beyond COVID-19 and may be applied also to other diseases.
Promoter methylation in a mixed feedback loop circadian clock model
Authors: Turner Silverthorne, Edward Saehong Oh, Adam R Stinchcombe
Date: 2021-06-22 00:00:00

We introduce and analyze an extension of the mixed feedback loop model of Fran\c{c}ois and Hakim. Our extension includes an additional promoter state and allows for reversible protein sequestration, which was absent from the original studies of the mixed feedback loop model. Motivated by experimental observations that link DNA methylation with circadian gene expression, we use our extended model to investigate the role of DNA methylation(s) in the mammalian circadian clock. We extend the perturbation analysis of Fran\c{c}ois and Hakim to determine how methylation affects the presence and the periodicity of oscillations. We derive a modified Goodwin oscillator model as an approximation to show that although methylation contributes to period control, excessive methylation can abolish rhythmicity.
Motion Illusion-like Patterns Extracted from Photo and Art Images Using Predictive Deep Neural Networks
Authors: Taisuke Kobayashi, Akiyoshi Kitaoka, Manabu Kosaka, Kenta Tanaka, Eiji Watanabe
Date: 2021-06-23 00:00:00

In our previous study, we successfully reproduced the illusory motion of the rotating snakes illusion using deep neural networks incorporating predictive coding theory. In the present study, we further examined the properties of the networks using a set of 1500 images, including ordinary static images of paintings and photographs and images of various types of motion illusions. Results showed that the networks clearly classified illusory images and others and reproduced illusory motions against various types of illusions similar to human perception. Notably, the networks occasionally detected anomalous motion vectors, even in ordinally static images where humans were unable to perceive any illusory motion. Additionally, illusion-like designs with repeating patterns were generated using areas where anomalous vectors were detected, and psychophysical experiments were conducted, in which illusory motion perception in the generated designs was detected. The observed inaccuracy of the networks will provide useful information for further understanding information processing associated with human vision.
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models
Authors: Louis Rouillard, Demian Wassermann
Date: 2021-06-23 00:00:00
Proceeding: ICLR 2022 4
Tasks: Density Estimation, Variational Inference

Frequently, population studies feature pyramidally-organized data represented using Hierarchical Bayesian Models (HBM) enriched with plates. These models can become prohibitively large in settings such as neuroimaging, where a sample is composed of a functional MRI signal measured on 300 brain locations, across 4 measurement sessions, and 30 subjects, resulting in around 1 million latent parameters.Such high dimensionality hampers the usage of modern, expressive flow-based techniques.To infer parameter posterior distributions in this challenging class of problems, we designed a novel methodology that automatically produces a variational family dual to a target HBM. This variational family, represented as a neural network, consists in the combination of an attention-based hierarchical encoder feeding summary statistics to a set of normalizing flows. Our automatically-derived neural network exploits exchangeability in the plate-enriched HBM and factorizes its parameter space. The resulting architecture reduces by orders of magnitude its parameterization with respect to that of a typical flow-based representation, while maintaining expressivity.Our method performs inference on the specified HBM in an amortized setup: once trained, it can readily be applied to a new data sample to compute the parameters' full posterior.We demonstrate the capability and scalability of our method on simulated data, as well as a challenging high-dimensional brain parcellation experiment. We also open up several questions that lie at the intersection between normalizing flows, SBI, structured Variational Inference, and inference amortization.