pwc-archive/papers-with-abstracts huggingface dataset:
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging
Authors: Naif Alkhunaizi, Dmitry Kamzolov, Martin Takáč, Karthik Nandakumar
Date: 2022-07-15 00:00:00
Tasks: Federated Learning, Outlier Detection

Collaboration among multiple data-owning entities (e.g., hospitals) can accelerate the training process and yield better machine learning models due to the availability and diversity of data. However, privacy concerns make it challenging to exchange data while preserving confidentiality. Federated Learning (FL) is a promising solution that enables collaborative training through exchange of model parameters instead of raw data. However, most existing FL solutions work under the assumption that participating clients are \emph{honest} and thus can fail against poisoning attacks from malicious parties, whose goal is to deteriorate the global model performance. In this work, we propose a robust aggregation rule called Distance-based Outlier Suppression (DOS) that is resilient to byzantine failures. The proposed method computes the distance between local parameter updates of different clients and obtains an outlier score for each client using Copula-based Outlier Detection (COPOD). The resulting outlier scores are converted into normalized weights using a softmax function, and a weighted average of the local parameters is used for updating the global model. DOS aggregation can effectively suppress parameter updates from malicious clients without the need for any hyperparameter selection, even when the data distributions are heterogeneous. Evaluation on two medical imaging datasets (CheXpert and HAM10000) demonstrates the higher robustness of DOS method against a variety of poisoning attacks in comparison to other state-of-the-art methods. The code can be found here https://github.com/Naiftt/SPAFD.
PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool
Authors: Rizka Purwanto, Arindam Pal, Alan Blair, Sanjay Jha
Date: 2022-07-13 00:00:00
Tasks: Incremental Learning, Phishing Website Detection

In this paper, we propose a feature-free method for detecting phishing websites using the Normalized Compression Distance (NCD), a parameter-free similarity measure which computes the similarity of two websites by compressing them, thus eliminating the need to perform any feature extraction. It also removes any dependence on a specific set of website features. This method examines the HTML of webpages and computes their similarity with known phishing websites, in order to classify them. We use the Furthest Point First algorithm to perform phishing prototype extractions, in order to select instances that are representative of a cluster of phishing webpages. We also introduce the use of an incremental learning algorithm as a framework for continuous and adaptive detection without extracting new features when concept drift occurs. On a large dataset, our proposed method significantly outperforms previous methods in detecting phishing websites, with an AUC score of 98.68%, a high true positive rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of 0.58%. Our approach uses prototypes, eliminating the need to retain long term data in the future, and is feasible to deploy in real systems with a processing time of roughly 0.3 seconds.
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework
Authors: Jianing Ye, Chenghao Li, Jianhao Wang, Chongjie Zhang
Date: 2022-07-12 00:00:00
Tasks: Multi-agent Reinforcement Learning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning (RL)

Decentralized execution is one core demand in cooperative multi-agent reinforcement learning (MARL). Recently, most popular MARL algorithms have adopted decentralized policies to enable decentralized execution and use gradient descent as their optimizer. However, there is hardly any theoretical analysis of these algorithms taking the optimization method into consideration, and we find that various popular MARL algorithms with decentralized policies are suboptimal in toy tasks when gradient descent is chosen as their optimization method. In this paper, we theoretically analyze two common classes of algorithms with decentralized policies -- multi-agent policy gradient methods and value-decomposition methods to prove their suboptimality when gradient descent is used. In addition, we propose the Transformation And Distillation (TAD) framework, which reformulates a multi-agent MDP as a special single-agent MDP with a sequential structure and enables decentralized execution by distilling the learned policy on the derived ``single-agent" MDP. This approach uses a two-stage learning paradigm to address the optimization problem in cooperative MARL, maintaining its performance guarantee. Empirically, we implement TAD-PPO based on PPO, which can theoretically perform optimal policy learning in the finite multi-agent MDPs and shows significant outperformance on a large set of cooperative multi-agent tasks.
Humans plan for the near future to walk economically on uneven terrain
Authors: Osman Darici, Arthur D. Kuo
Date: 2022-07-16 00:00:00

Humans experience small fluctuations in their gait when walking on uneven terrain. The fluctuations deviate from the steady, energy-minimizing pattern for level walking, and have no obvious organization. But humans often look ahead when they walk, and could potentially plan anticipatory fluctuations for the terrain. Such planning is only sensible if it serves some an objective purpose, such as maintaining constant speed or reducing energy expenditure, that is also attainable within finite planning capacity. Here we show that humans do plan and perform optimal control strategies on uneven terrain. Rather than maintain constant speed, they make purposeful, anticipatory speed adjustments that are consistent with minimizing energy expenditure. A simple optimal control model predicts economical speed fluctuations that agree well with experiments with humans (N = 12) walking on seven different terrain profiles (correlated with model ro=0.55+-0.11, P<0.05 all terrains). Participants made repeatable speed fluctuations starting about six to eight steps ahead of each terrain feature (up to +-7.5 cm height difference each step, up to 16 consecutive features). Nearer features matter more, because energy is dissipated with each succeeding step collision with ground, preventing momentum from persisting indefinitely. A finite horizon of continuous look ahead and motor working space thus suffice to practically optimize for any length of terrain. Humans reason about walking in the near future to plan complex optimal control sequences.
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF
Authors: Aditya Arie Nugraha, Kouhei Sekiguchi, Mathieu Fontaine, Yoshiaki Bando, Kazuyoshi Yoshii
Date: 2022-07-22 00:00:00
Tasks: blind source separation, Speech Enhancement

This paper describes a practical dual-process speech enhancement system that adapts environment-sensitive frame-online beamforming (front-end) with help from environment-free block-online source separation (back-end). To use minimum variance distortionless response (MVDR) beamforming, one may train a deep neural network (DNN) that estimates time-frequency masks used for computing the covariance matrices of sources (speech and noise). Backpropagation-based run-time adaptation of the DNN was proposed for dealing with the mismatched training-test conditions. Instead, one may try to directly estimate the source covariance matrices with a state-of-the-art blind source separation method called fast multichannel non-negative matrix factorization (FastMNMF). In practice, however, neither the DNN nor the FastMNMF can be updated in a frame-online manner due to its computationally-expensive iterative nature. Our DNN-free system leverages the posteriors of the latest source spectrograms given by block-online FastMNMF to derive the current source covariance matrices for frame-online beamforming. The evaluation shows that our frame-online system can quickly respond to scene changes caused by interfering speaker movements and outperformed an existing block-online system with DNN-based beamforming by 5.0 points in terms of the word error rate.
Two-Port Feedback Analysis On Miller-Compensated Amplifiers
Authors: Myungjun Kim
Date: 2022-07-22 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, various Miller-compensated amplifiers are analyzed by using the two-port feedback analysis together with the root-locus diagram. The proposed analysis solves problems of Miller theorem/approximation that fail to predict a pole-splitting and that require an impractical assumption that an initial lower frequency pole before connecting a Miller capacitor in a two-stage amplifier should be associated with the input of the amplifier. Since the proposed analysis sheds light on how the closed-loop poles originate from the open-loop poles in the s-plane, it allows the association of the closed-loop poles with the circuit components and thus provides a design insight for frequency compensation. The circuits analyzed are two-stage Miller-compensated amplifiers with and without a current buffer and a three-stage nested Miller-compensated amplifier.
On the Spatial Pattern of Input-Output Metrics for a Network Synchronization Process
Authors: Subir Sarker, Sandip Roy
Date: 2022-07-22 00:00:00

A graph-theoretic analysis is undertaken for a compendium of input-output (transfer) metrics of a standard discrete-time linear synchronization model, including lp gains, frequency responses, frequency-band energy, and Markov parameters. We show that these transfer metrics exhibit a spatial degradation, such that they are monotonically nonincreasing along vertex cutsets away from an exogenous input. We use this spatial analysis to characterize signal-to-noise ratios (SNRs) in diffusive networks driven by process noise, and to develop a notion of propagation stability for dynamical networks. Finally, the formal results are illustrated through an example.
Learn and Control while Switching: with Guaranteed Stability and Sublinear Regret
Authors: Jafar Abbaszadeh Chekan, Cédric Langbort
Date: 2022-07-22 00:00:00

Over-actuated systems often make it possible to achieve specific performances by switching between different subsets of actuators. However, when the system parameters are unknown, transferring authority to different subsets of actuators is challenging due to stability and performance efficiency concerns. This paper presents an efficient algorithm to tackle the so-called "learn and control while switching between different actuating modes" problem in the Linear Quadratic (LQ) setting. Our proposed strategy is constructed upon Optimism in the Face of Uncertainty (OFU) based algorithm equipped with a projection toolbox to keep the algorithm efficient, regret-wise. Along the way, we derive an optimum duration for the warm-up phase, thanks to the existence of a stabilizing neighborhood. The stability of the switched system is also guaranteed by designing a minimum average dwell time. The proposed strategy is proved to have a regret bound of $\mathcal{\bar{O}}\big(\sqrt{T}\big)+\mathcal{O}\big(ns\sqrt{T}\big)$ in horizon $T$ with $(ns)$ number of switches, provably outperforming naively applying the basic OFU algorithm.
Integral surface based Second Order Sliding Mode Controller design for Inverted Pendulum with PD SMC Compensation
Authors: Kirtiman Singh, Prabin Kumar Padhy
Date: 2022-07-20 00:00:00
Tasks: Position

Stabilization of a nonlinear single stage inverted pendulum is a complicated control problem, as nonlinearity is present inherently and external factors affect the equilibrium position. In this paper, a PD sliding mode controller is connected with Second order PI (Proportional+Integral) sliding mode controller, which is designed to improve the performance for nonlinear state differential equations with unknown parameters. This paper throws light on the sliding surface design and highlights the important features of multiplexing sliding mode control inputs resulting in robustness and higher convergence of output, through extensive mathematical modeling. Simulations and experimental application is done on the system to evaluate the controller for performance, complexity of implementation and also on the impact of the nonlinear IP system on its stability.
Design of Sliding Mode PID Controller with Improved reaching laws for Nonlinear Systems
Authors: Kirtiman Singh
Date: 2022-07-20 00:00:00

In this thesis, advanced design technique in sliding mode control (SMC) is presented with focus on PID (Proportional-Integral-Derivative) type Sliding surfaces based Sliding mode control with improved power rate exponential reaching law for Non-linear systems using Modified Particle Swarm Optimization (MPSO). To handle large non-linearities directly, sliding mode controller based on PID-type sliding surface has been designed in this work, where Integral term ensures fast finite convergence time. The controller parameter for various modified structures can be estimated using Modified PSO, which is used as an offline optimization technique. Various reaching law were implemented leading to the proposed improved exponential power rate reaching law, which also improves the finite convergence time. To implement the proposed algorithm, nonlinear mathematical model has to be decrypted without linearizing, and used for the simulation purposes. Their performance is studied using simulations to prove the proposed behavior. The problem of chattering has been overcome by using boundary method and also second order sliding mode method. PI-type sliding surface based second order sliding mode controller with PD surface based SMC compensation is also proposed and implemented. The proposed algorithms have been analyzed using Lyapunov stability criteria. The robustness of the method is provided using simulation results including disturbance and 10% variation in system parameters. Finally process control based hardware is implemented (conical tank system).
Network analysis of the human structural connectome including the brainstem
Authors: Salma Salhi, Youssef Kora, Gisu Ham, Hadi Zadeh Haghighi, Christoph Simon
Date: 2022-07-22 00:00:00

The underlying anatomical structure is fundamental to the study of brain networks, but the role of brainstem from a structural perspective is not very well understood. We conduct a computational and graph-theoretical study of the human structural connectome incorporating a variety of subcortical structures including the brainstem. Our computational scheme involves the use of Python DIPY and Nibabel libraries to develop structural connectomes using 100 healthy adult subjects. We then compute degree, eigenvector, and betweenness centralities to identify several highly connected structures and find that the brainstem ranks highest across all examined metrics, a result that holds even when the connectivity matrix is normalized by volume. We also investigated some global topological features in the connectomes, such as the balance of integration and segregation, and found that the domination of the brainstem generally causes networks to become less integrated and segregated. Our results highlight the importance of including the brainstem in structural network analyses.
Mechanics of Morphogenesis in Neural Development: in vivo, in vitro, and in silico
Authors: Joseph Sutlive, Hamed Seyyedhosseinzadeh, Zheng Ao, Haning Xiu, Kun Gou, Feng Guo, Zi Chen
Date: 2022-07-22 00:00:00

Morphogenesis in the central nervous system has received intensive attention as elucidating fundamental mechanisms of morphogenesis will shed light on the physiology and pathophysiology of the developing central nervous system. Morphogenesis of the central nervous system is of a vast topic that includes important morphogenetic events such as neurulation and cortical folding. Here we review three types of methods used to improve our understanding of morphogenesis of the central nervous system: in vivo experiments, organoids (in vitro), and computational models (in silico). The in vivo experiments are used to explore cellular- and tissue-level mechanics and interpret them on the roles of neurulation morphogenesis. Recent advances in human brain organoids have provided new opportunities to study morphogenesis and neurogenesis to compensate for the limitations of in vivo experiments, as organoid models are able to recapitulate some critical neural morphogenetic processes during early human brain development. Due to the complexity and costs of in vivo and in vitro studies, a variety of computational models have been developed and used to explain the formation and morphogenesis of brain structures. We review and discuss the Pros and Cons of these methods and their usage in the studies on morphogenesis of the central nervous system. Notably, none of these methods alone is sufficient to unveil the biophysical mechanisms of morphogenesis, thus calling for the interdisciplinary approaches using a combination of these methods in order to test hypotheses and generate new insights on both normal and abnormal development of the central nervous system.
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
Authors: Hao Li, Zeyu Tang, Yang Nan, Guang Yang
Date: 2022-07-12 00:00:00
Tasks: Computed Tomography (CT), Prognosis

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.
Retinex-qDPC: automatic background rectified quantitative differential phase contrast imaging
Authors: Shuhe Zhang, Tao Peng, Zeyu Ke, Han Yang, Tos T. J. M. Berendschot, Jinhua Zhou
Date: 2022-07-21 00:00:00

The quality of quantitative differential phase contrast reconstruction (qDPC) can be severely degenerated by the mismatch of the background of two oblique illuminated images, yielding problematic phase recovery results. These background mismatches may result from illumination patterns, inhomogeneous media distribution, or other defocusing layers. In previous reports, the background is manually calibrated which is time-consuming, and unstable, since new calibrations are needed if any modification to the optical system was made. It is also impossible to calibrate the background from the defocusing layers, or for high dynamic observation as the background changes over time. To tackle the mismatch of background and increases the experimental robustness, we propose the Retinex-qDPC in which we use the images edge features as data fidelity term yielding L2-Retinex-qDPC and L1-Retinex-qDPC for high background-robustness qDPC reconstruction. The split Bregman method is used to solve the L1-Retinex DPC. We compare both Retinex-qDPC models against state-of-the-art DPC reconstruction algorithms including total-variation regularized qDPC, and isotropic-qDPC using both simulated and experimental data. Results show that the Retinex qDPC can significantly improve the phase recovery quality by suppressing the impact of mismatch background. Within, the L1-Retinex-qDPC is better than L2-Retinex and other state-of-the-art DPC algorithms. In general, the Retinex-qDPC increases the experimental robustness against background illumination without any modification of the optical system, which will benefit all qDPC applications.
A Conditional Linear Combination Test with Many Weak Instruments
Authors: Dennis Lim, Wenjie Wang, Yichong Zhang
Date: 2022-07-22 00:00:00

We consider a linear combination of jackknife Anderson-Rubin (AR), jackknife Lagrangian multiplier (LM), and orthogonalized jackknife LM tests for inference in IV regressions with many weak instruments and heteroskedasticity. Following I.Andrews (2016), we choose the weights in the linear combination based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed test controls asymptotic size and is admissible among certain class of tests. Under strong identification, our linear combination test has optimal power against local alternatives among the class of invariant or unbiased tests which are constructed based on jackknife AR and LM tests. Simulations and an empirical application to Angrist and Krueger's (1991) dataset confirm the good power properties of our test.
Time-Varying Poisson Autoregression
Authors: Giovanni Angelini, Giuseppe Cavaliere, Enzo D'Innocenzo, Luca De Angelis
Date: 2022-07-22 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper we propose a new time-varying econometric model, called Time-Varying Poisson AutoRegressive with eXogenous covariates (TV-PARX), suited to model and forecast time series of counts. {We show that the score-driven framework is particularly suitable to recover the evolution of time-varying parameters and provides the required flexibility to model and forecast time series of counts characterized by convoluted nonlinear dynamics and structural breaks.} We study the asymptotic properties of the TV-PARX model and prove that, under mild conditions, maximum likelihood estimation (MLE) yields strongly consistent and asymptotically normal parameter estimates. Finite-sample performance and forecasting accuracy are evaluated through Monte Carlo simulations. The empirical usefulness of the time-varying specification of the proposed TV-PARX model is shown by analyzing the number of new daily COVID-19 infections in Italy and the number of corporate defaults in the US.
Improved $α$-GAN architecture for generating 3D connected volumes with an application to radiosurgery treatment planning
Authors: Sanaz Mohammadjafari, Mucahit Cevik, Ayse Basar
Date: 2022-07-13 00:00:00
Tasks: Diagnostic, Synthetic Data Generation

Generative Adversarial Networks (GANs) have gained significant attention in several computer vision tasks for generating high-quality synthetic data. Various medical applications including diagnostic imaging and radiation therapy can benefit greatly from synthetic data generation due to data scarcity in the domain. However, medical image data is typically kept in 3D space, and generative models suffer from the curse of dimensionality issues in generating such synthetic data. In this paper, we investigate the potential of GANs for generating connected 3D volumes. We propose an improved version of 3D $\alpha$-GAN by incorporating various architectural enhancements. On a synthetic dataset of connected 3D spheres and ellipsoids, our model can generate fully connected 3D shapes with similar geometrical characteristics to that of training data. We also show that our 3D GAN model can successfully generate high-quality 3D tumor volumes and associated treatment specifications (e.g., isocenter locations). Similar moment invariants to the training data as well as fully connected 3D shapes confirm that improved 3D $\alpha$-GAN implicitly learns the training data distribution, and generates realistic-looking samples. The capability of improved 3D $\alpha$-GAN makes it a valuable source for generating synthetic medical image data that can help future research in this domain.