pwc-archive/papers-with-abstracts huggingface dataset:
NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing
Authors: Tanish Tyagi, Colin G. Magdamo, Ayush Noori, Zhaozhi Li, Xiao Liu, Mayuresh Deodhar, Zhuoqiao Hong, Wendong Ge, Elissa M. Ye, Yi-han Sheu, Haitham Alabsi, Laura Brenner, Gregory K. Robbins, Sahar Zafar, Nicole Benson, Lidia Moura, John Hsu, Alberto Serrano-Pozo, Dimitry Prokopenko, Rudolph E. Tanzi, Bradley T. Hyman, Deborah Blacker, Shibani S. Mukerji, M. Brandon Westover, Sudeshna Das
Date: 2022-01-12 00:00:00

Dementia related cognitive impairment (CI) is a neurodegenerative disorder, affecting over 55 million people worldwide and growing rapidly at the rate of one new case every 3 seconds. 75% cases go undiagnosed globally with up to 90% in low-and-middle-income countries, leading to an estimated annual worldwide cost of USD 1.3 trillion, forecasted to reach 2.8 trillion by 2030. With no cure, a recurring failure of clinical trials, and a lack of early diagnosis, the mortality rate is 100%. Information in electronic health records (EHR) can provide vital clues for early detection of CI, but a manual review by experts is tedious and error prone. Several computational methods have been proposed, however, they lack an enhanced understanding of the linguistic context in complex language structures of EHR. Therefore, I propose a novel and more accurate framework, NeuraHealth, to identify patients who had no earlier diagnosis. In NeuraHealth, using patient EHR from Mass General Brigham BioBank, I fine-tuned a bi-directional attention-based deep learning natural language processing model to classify sequences. The sequence predictions were used to generate structured features as input for a patient level regularized logistic regression model. This two-step framework creates high dimensionality, outperforming all existing state-of-the-art computational methods as well as clinical methods. Further, I integrate the models into a real-world product, a web app, to create an automated EHR screening pipeline for scalable and high-speed discovery of undetected CI in EHR, making early diagnosis viable in medical facilities and in regions with scarce health services.
Black-box Bayesian inference for economic agent-based models
Authors: Joel Dyer, Patrick Cannon, J. Doyne Farmer, Sebastian Schmon
Date: 2022-02-01 00:00:00
Tasks: Bayesian Inference, Benchmarking, Decision Making, Density Ratio Estimation, parameter estimation, Time Series Analysis

Simulation models, in particular agent-based models, are gaining popularity in economics. The considerable flexibility they offer, as well as their capacity to reproduce a variety of empirically observed behaviours of complex systems, give them broad appeal, and the increasing availability of cheap computing power has made their use feasible. Yet a widespread adoption in real-world modelling and decision-making scenarios has been hindered by the difficulty of performing parameter estimation for such models. In general, simulation models lack a tractable likelihood function, which precludes a straightforward application of standard statistical inference techniques. Several recent works have sought to address this problem through the application of likelihood-free inference techniques, in which parameter estimates are determined by performing some form of comparison between the observed data and simulation output. However, these approaches are (a) founded on restrictive assumptions, and/or (b) typically require many hundreds of thousands of simulations. These qualities make them unsuitable for large-scale simulations in economics and can cast doubt on the validity of these inference methods in such scenarios. In this paper, we investigate the efficacy of two classes of black-box approximate Bayesian inference methods that have recently drawn significant attention within the probabilistic machine learning community: neural posterior estimation and neural density ratio estimation. We present benchmarking experiments in which we demonstrate that neural network based black-box methods provide state of the art parameter inference for economic simulation models, and crucially are compatible with generic multivariate time-series data. In addition, we suggest appropriate assessment criteria for future benchmarking of approximate Bayesian inference procedures for economic simulation models.
Data-driven emergence of convolutional structure in neural networks
Authors: Alessandro Ingrosso, Sebastian Goldt
Date: 2022-02-01 00:00:00
Tasks: Tensor Decomposition, Translation

Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their inputs is thus crucial in machine learning and neuroscience. Convolutional neural networks, for example, were designed to exploit translation symmetry and their capabilities triggered the first wave of deep learning successes. However, learning convolutions directly from translation-invariant data with a fully-connected network has so far proven elusive. Here, we show how initially fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs, resulting in localised, space-tiling receptive fields. These receptive fields match the filters of a convolutional network trained on the same task. By carefully designing data models for the visual scene, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs, which has long been recognised as the hallmark of natural images. We provide an analytical and numerical characterisation of the pattern-formation mechanism responsible for this phenomenon in a simple model and find an unexpected link between receptive field formation and tensor decomposition of higher-order input correlations. These results provide a new perspective on the development of low-level feature detectors in various sensory modalities, and pave the way for studying the impact of higher-order statistics on learning in neural networks.
Iterative regularization for low complexity regularizers
Authors: Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa
Date: 2022-02-01 00:00:00

Iterative regularization exploits the implicit bias of an optimization algorithm to regularize ill-posed problems. Constructing algorithms with such built-in regularization mechanisms is a classic challenge in inverse problems but also in modern machine learning, where it provides both a new perspective on algorithms analysis, and significant speed-ups compared to explicit regularization. In this work, we propose and study the first iterative regularization procedure able to handle biases described by non smooth and non strongly convex functionals, prominent in low-complexity regularization. Our approach is based on a primal-dual algorithm of which we analyze convergence and stability properties, even in the case where the original problem is unfeasible. The general results are illustrated considering the special case of sparse recovery with the $\ell_1$ penalty. Our theoretical results are complemented by experiments showing the computational benefits of our approach.
Improving BERT-based Query-by-Document Retrieval with Multi-Task Optimization
Authors: Amin Abolghasemi, Suzan Verberne, Leif Azzopardi
Date: 2022-02-01 00:00:00
Tasks: Information Retrieval, Representation Learning, Retrieval

Query-by-document (QBD) retrieval is an Information Retrieval task in which a seed document acts as the query and the goal is to retrieve related documents -- it is particular common in professional search tasks. In this work we improve the retrieval effectiveness of the BERT re-ranker, proposing an extension to its fine-tuning step to better exploit the context of queries. To this end, we use an additional document-level representation learning objective besides the ranking objective when fine-tuning the BERT re-ranker. Our experiments on two QBD retrieval benchmarks show that the proposed multi-task optimization significantly improves the ranking effectiveness without changing the BERT re-ranker or using additional training samples. In future work, the generalizability of our approach to other retrieval tasks should be further investigated.
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR
Authors: Anshul Paigwar, David Sierra-Gonzalez, Özgür Erkent, Christian Laugier
Date: 2021-10-11 00:00:00
Proceeding: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2021 10
Tasks: 2D Object Detection, 3D Object Detection, Autonomous Vehicles, Birds Eye View Object Detection, Decision Making, Object, object-detection, Object Detection, Object Localization

Accurate 3D object detection is a key part of the perception module for autonomous vehicles. A better understanding of the objects in 3D facilitates better decision-making and path planning. RGB Cameras and LiDAR are the most commonly used sensors in autonomous vehicles for environment perception. Many approaches have shown promising results for 2D detection with RGB Images, but efficiently localizing small objects like pedestrians in the 3D point cloud of large scenes has remained a challenging area of research. We propose a novel method, Frustum-PointPillars, for 3D object detection using LiDAR data. Instead of solely relying on point cloud features, we leverage the mature field of 2D object detection to reduce the search space in the 3D space. Then, we use the Pillar Feature Encoding network for object localization in the reduced point cloud. We also propose a novel approach for masking point clouds to further improve the localization of objects. We train our network on the KITTI dataset and perform experiments to show the effectiveness of our network. On the KITTI test set our method outperforms other multi-sensor SOTA approaches for 3D pedestrian localization (Bird’s Eye View) while achieving a significantly faster runtime of 14 Hz.
MatchMaker: A Deep Learning Framework for Drug Synergy Prediction
Authors: Halil Ibrahim Kuru, Oznur Tastan, Ercument Cicek
Date: 2022-02-02 00:00:00
Proceeding: IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022 2
Tasks: Deep Learning, Prediction

Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with high-throughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to 15% correlation and 33% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at https://github.com/tastanlab/matchmaker.
XLSR53 Wav2Vec2 Portuguese by Orlem Santos
Authors: Orlem Santos, Rodrigo Frassetto Nogueira, Roberto Lotufo
Date: 2022-02-01 00:00:00
Proceeding: - 2022 2
Tasks: Speech Recognition

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Differentially Private Top-k Selection via Canonical Lipschitz Mechanism
Authors: Michael Shekelyan, Grigorios Loukides
Date: 2022-01-31 00:00:00

Selecting the top-$k$ highest scoring items under differential privacy (DP) is a fundamental task with many applications. This work presents three new results. First, the exponential mechanism, permute-and-flip and report-noisy-max, as well as their oneshot variants, are unified into the Lipschitz mechanism, an additive noise mechanism with a single DP-proof via a mandated Lipschitz property for the noise distribution. Second, this new generalized mechanism is paired with a canonical loss function to obtain the canonical Lipschitz mechanism, which can directly select k-subsets out of $d$ items in $O(dk+d \log d)$ time. The canonical loss function assesses subsets by how many users must change for the subset to become top-$k$. Third, this composition-free approach to subset selection improves utility guarantees by an $\Omega(\log k)$ factor compared to one-by-one selection via sequential composition, and our experiments on synthetic and real-world data indicate substantial utility improvements.
Handling Bias in Toxic Speech Detection: A Survey
Authors: Tanmay Garg, Sarah Masud, Tharun Suresh, Tanmoy Chakraborty
Date: 2022-01-26 00:00:00
Tasks: Survey

Detecting online toxicity has always been a challenge due to its inherent subjectivity. Factors such as the context, geography, socio-political climate, and background of the producers and consumers of the posts play a crucial role in determining if the content can be flagged as toxic. Adoption of automated toxicity detection models in production can thus lead to a sidelining of the various groups they aim to help in the first place. It has piqued researchers' interest in examining unintended biases and their mitigation. Due to the nascent and multi-faceted nature of the work, complete literature is chaotic in its terminologies, techniques, and findings. In this paper, we put together a systematic study of the limitations and challenges of existing methods for mitigating bias in toxicity detection. We look closely at proposed methods for evaluating and mitigating bias in toxic speech detection. To examine the limitations of existing methods, we also conduct a case study to introduce the concept of bias shift due to knowledge-based bias mitigation. The survey concludes with an overview of the critical challenges, research gaps, and future directions. While reducing toxicity on online platforms continues to be an active area of research, a systematic study of various biases and their mitigation strategies will help the research community produce robust and fair models.
Runtime Monitoring and Statistical Approaches for Correlation Analysis of ECG and PPG
Authors: Abhinandan Panda, Srinivas Pinisetty, Partha Roop
Date: 2022-01-20 00:00:00
Tasks: Sensor Fusion

Biophysical signals such as Electrocardiogram (ECG) and Photoplethysmogram (PPG) are key to the sensing of vital parameters for wellbeing. Coincidentally, ECG and PPG are signals, which provide a "different window" into the same phenomena, namely the cardiac cycle. While they are used separately, there are no studies regarding the exact correction of the different ECG and PPG events. Such correlation would be helpful in many fronts such as sensor fusion for improved accuracy using cheaper sensors and attack detection and mitigation methods using multiple signals to enhance the robustness, for example. Considering this, we present the first approach in formally establishing the key relationships between ECG and PPG signals. We combine formal run-time monitoring with statistical analysis and regression analysis for our results.
A Privacy-Preserving Image Retrieval Scheme with a Mixture of Plain and EtC Images
Authors: Kenta Iida, Hitoshi Kiya
Date: 2022-02-01 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Privacy Preserving, Retrieval

In this paper, we propose a novel content-based image-retrieval scheme that allows us to use a mixture of plain images and compressible encrypted ones called "encryption-then-compression (EtC) images." In the proposed scheme, extended SIMPLE descriptors are extracted from EtC images as well as from plain ones, so the mixed use of plain and encrypted images is available for image retrieval. In an experiment, the proposed scheme was demonstrated to have almost the same retrieval performance as that for plain images, even with a mixture of plain and encrypted images.
EPHS: A Port-Hamiltonian Modelling Language
Authors: Markus Lohmayer, Sigrid Leyendecker
Date: 2022-02-01 00:00:00

A prevalent theme throughout science and engineering is the ongoing paradigm shift away from isolated systems towards open and interconnected systems. Port-Hamiltonian theory developed as a synthesis of geometric mechanics and network theory. The possibility to model complex multiphysical systems via interconnection of simpler components is often advertised as one of its most attractive features. The development of a port-Hamiltonian modelling language however remains a topic which has not been sufficiently addressed. We report on recent progress towards the formalization and implementation of a modelling language for exergetic port-Hamiltonian systems. Its diagrammatic syntax inspired by bond graphs and its functorial semantics together enable a modular and hierarchical approach to model specification.
Underwater Differential Game: Finite-Time Target Hunting Task with Communication Delay
Authors: Wei Wei, Jingjing Wang, Jun Du, Zhengru Fang, Chunxiao Jiang, Yong Ren
Date: 2022-02-01 00:00:00
Tasks: Deep Reinforcement Learning

This work considers designing an unmanned target hunting system for a swarm of unmanned underwater vehicles (UUVs) to hunt a target with high maneuverability. Differential game theory is used to analyze combat policies of UUVs and the target within finite time. The challenge lies in UUVs must conduct their control policies in consideration of not only the consistency of the hunting team but also escaping behaviors of the target. To obtain stable feedback control policies satisfying Nash equilibrium, we construct the Hamiltonian function with Leibniz's formula. For further taken underwater disturbances and communication delay into consideration, modified deep reinforcement learning (DRL) is provided to investigate the underwater target hunting task in an unknown dynamic environment. Simulations show that underwater disturbances have a large impact on the system considering communication delay. Moreover, consistency tests show that UUVs perform better consistency with a relatively small range of disturbances.
Position-Dependent Snap Feedforward: A Gaussian Process Framework
Authors: Max van Haren, Maurice Poot, Jim Portegies, Tom Oomen
Date: 2022-02-01 00:00:00
Tasks: Position

Mechatronic systems have increasingly high performance requirements for motion control. The low-frequency contribution of the flexible dynamics, i.e. the compliance, should be compensated for by means of snap feedforward to achieve high accuracy. Position-dependent compliance, which often occurs in motion systems, requires the snap feedforward parameter to be modeled as a function of position. Position-dependent compliance is compensated for by using a Gaussian process to model the snap feedforward parameter as a continuous function of position. A simulation of a flexible beam shows that a significant performance increase is achieved when using the Gaussian process snap feedforward parameter to compensate for position-dependent compliance.
A self-organizing multi-agent system for distributed voltage regulation
Authors: Badr Al Faiya, Dimitrios Athanasiadis, Minjiang Chen, Stephen McArthur, Ivana Kockar, Haowei Lu, Francisco de Leon
Date: 2022-02-01 00:00:00

This paper presents a distributed voltage regulation method based on multi-agent system control and network self-organization for a large distribution network. The network autonomously organizes itself into small subnetworks through the epsilon decomposition of the sensitivity matrix, and agents group themselves into these subnetworks with the communication links being autonomously determined. Each subnetwork controls its voltage by locating the closest local distributed generation and optimizing their outputs. This simplifies and reduces the size of the optimization problem and the interaction requirements. This approach also facilitates adaptive grouping of the network by self-reorganizing to maintain a stable state in response to time-varying network requirements and changes. The effectiveness of the proposed approach is validated through simulations on a model of a real heavily-meshed secondary distribution network. Simulation results and comparisons with other methods demonstrate the ability of the subnetworks to autonomously and independently regulate the voltage and to adapt to unpredictable network conditions over time, thereby enabling autonomous and flexible distribution networks.
Design Constraints of Disturbance Observer-based Motion Control Systems are Stricter in the Discrete-Time Domain
Authors: Emre Sariyildiz
Date: 2022-02-01 00:00:00

This paper shows that the design constraints of the Disturbance Observer (DOb) based robust motion control systems become stricter when they are implemented using computers or microcontrollers. The stricter design constraints put new upper bounds on the plant-model mismatch and the bandwidth of the DOb, thus limiting the achievable robustness against disturbances and the phase-lead effect in the inner-loop. Violating the design constraints may yield severe stability and performance issues in practice; therefore, they should be considered in tuning the control parameters of the robust motion controller. This paper also shows that continuous-time analysis methods fall-short in deriving the fundamental design constraints on the nominal plant model and the bandwidth of the digital DOb. Therefore, we may observe unexpected stability and performance issues when tuning the control parameters of the digital robust motion controllers in the continuous-time domain. To improve the robust stability and performance of the motion controllers, this paper explains the fundamental design constraints of the DOb by employing the generalised continuous and discrete Bode Integral Theorems in a unified framework. Simulation and experimental results are given to verify the proposed analysis method.
Reply to comment on "Failure of the simultaneous block diagonalization technique applied to complete and cluster synchronization of random networks"
Authors: Shirin Panahi, Nelson Amaya, Isaac Klickstein, Galen Novello, Francesco Sorrentino
Date: 2022-01-31 00:00:00

We respond briefly to a comment [1, arXiv:2110.15493] recently posted online on our paper [2, arXiv:2108.07893]. Complete and cluster synchronization of random networks is undoubtedly a topic of interest in the Physics, Engineering, and Nonlinear Dynamics literature. In [3] we study both complete and cluster synchronization of networks and introduce indices that measure success (or failure) of application of the SBD technique in decoupling the stability problem into problems of lower dimensionality. Our usage of the word `failure' indicates that the technique does not produce a decomposition which results in a system which is easier to analyze, not that the technique fails in correctly decoupling these problems.