pwc-archive/papers-with-abstracts huggingface dataset:
FixCaps: An Improved Capsules Network for Diagnosis of Skin Cancer
Authors: Zhangli Lan, Songbai Cai, Xu He, Xinpeng Wen
Date: 2022-06-08 00:00:00
Proceeding: IEEE Access 2022 6
Tasks: image-classification, Image Classification, Lesion Classification

The early detection of skin cancer substantially improves the five-year survival rate of patients. It is often difficult to distinguish early malignant tumors from skin images, even by expert dermatologists. Therefore, several classification methods of dermatoscopic images have been proposed, but they have been found to be inadequate or defective for skin cancer detection, and often require a large amount of calculations. This study proposes an improved capsule network called FixCaps for dermoscopic image classification. FixCaps has a larger receptive field than CapsNets by applying a high-performance large-kernel at the bottom convolution layer whose kernel size is as large as 31×31 , in contrast to commonly used 9×9 . The convolutional block attention module was used to reduce the losses of spatial information caused by convolution and pooling. The group convolution was used to avoid model underfitting in the capsule layer. The network can improve the detection accuracy and reduce a great amount of calculations, compared with several existing methods. The experimental results showed that FixCaps is better than IRv2-SA for skin cancer diagnosis, which achieved an accuracy of 96.49% on the HAM10000 dataset.
Visual Speech Recognition in a Driver Assistance System
Authors: Denis Ivanko, Dmitry Ryumin, Alexey Kashevnik, Alexandr Axyonov, Alexey Karpov
Date: 2022-08-29 00:00:00
Proceeding: 30th European Signal Processing Conference (EUSIPCO) 2022 8
Tasks: Data Augmentation, Lipreading, Lip Reading, speech-recognition, Speech Recognition, Visual Speech Recognition

Visual speech recognition or automated lipreading is a field of growing attention. Video data proved its usefulness in multimodal speech recognition, especially when acoustic data is heavily noised or even inaccessible. In this paper, we present a novel method for visual speech recognition. We benchmark it on the famous LRW lip-reading dataset by outperforming the existing approaches. After a comprehensive evaluation, we adapt the developed method and test it on the collected RUSAVIC corpus we recorded in-the-wild for vehicle driver. The results obtained demonstrate not only the high performance of the proposed method, but also the fundamental possibility of recognizing speech only by using video modality, even in such difficult natural conditions as driving.
Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark
Authors: Elham Azhir, Mehdi Hosseinzadeh, Faheem Khan, Amir Mosavi
Date: 2022-09-17 00:00:00
Tasks: Clustering, Distributed Computing

Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method. The performance evaluation is performed based on execution time. The results of the experiments demonstrated the effectiveness of parallel query clustering in achieving high scalability. Furthermore, Apache Spark achieved better performance than Apache Hadoop, reaching an average speedup of 2x.
Who Wrote this? How Smart Replies Impact Language and Agency in the Workplace
Authors: Kilian Wenker
Date: 2022-10-07 00:00:00

AI-mediated communication is designed to help us do our work more quickly and efficiently. But does it come at a cost? This study uses smart replies (SRs) to show how AI influences humans without any intent on the part of the developer - the very use of AI is sufficient. I propose a loss of agency theory as a viable approach for studying the impact of AI on human agency. This theory focusses on the transfer of agency that is forced by circumstances (such as time pressure), human weaknesses (such as complacency), and conceptual priming. Mixed methods involving a crowdsourced experiment test that theory. The quantitative results reveal that machine agency affects the content we author and the behavior we generate. But it is a non-zero-sum game. The transfers between human and machine agency are fluid; they complement, replace, and reinforce each other at the same time.
Simulating Ride-Pooling Services with Pre-Booking and On-Demand Customers
Authors: Roman Engelhardt, Florian Dandl, Klaus Bogenberger
Date: 2022-10-13 00:00:00

If private vehicle trips can be replaced, ride-pooling services can decrease parking space needed by higher vehicle utilization and increase traffic efficiency by increasing vehicle occupancy. Nevertheless, substantial benefits can only be achieved if a certain market penetration is passed to find enough shareable rides for pooling to take place. Additionally, because of their highly dynamic and stochastic nature on-demand ride-pooling services cannot always guarantee that a request is served. Allowing customers to pre-book their trip in advance could provide benefits for both aspects. Additional knowledge helps an operator to better plan vehicle schedules to improve service efficiency while an accepted trip or a rejection can be communicated early on to the customer. This study presents a simulation framework where a ride-pooling provider offers a service in mixed operation: Customers can either use the service on-demand or pre-book trips. A graph-based batch optimization formulation is proposed to create offline schedules for pre-booking customers. Using two rolling horizons, this offline solution is forwarded to an online optimization for on-demand and pre-booking customers simultaneously. The framework is tested in a case study for Manhattan, NYC. That the graph-based batch optimization is superior to a basic insertion method in terms of solution quality and run-time. Due to additional knowledge, the ride-pooling operator can improve the solution quality significantly by serving more customers while pooling efficiency can be increased. Additionally, customers have shorter waiting and detour times the more customers book a trip in advance.
Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion
Authors: Yuxiang Zhang, Jingze Lu, Xingming Wang, Zhuo Li, Runqiu Xiao, Wenchao Wang, Ming Li, Pengyuan Zhang
Date: 2022-10-13 00:00:00
Tasks: Data Augmentation, DeepFake Detection, Face Swapping

This paper describes the deepfake audio detection system submitted to the Audio Deep Synthesis Detection (ADD) Challenge Track 3.2 and gives an analysis of score fusion. The proposed system is a score-level fusion of several light convolutional neural network (LCNN) based models. Various front-ends are used as input features, including low-frequency short-time Fourier transform and Constant Q transform. Due to the complex noise and rich synthesis algorithms, it is difficult to obtain the desired performance using the training set directly. Online data augmentation methods effectively improve the robustness of fake audio detection systems. In particular, the reasons for the poor improvement of score fusion are explored through visualization of the score distributions and comparison with score distribution on another dataset. The overfitting of the model to the training set leads to extreme values of the scores and low correlation of the score distributions, which makes score fusion difficult. Fusion with partially fake audio detection system improves system performance further. The submission on track 3.2 obtained the weighted equal error rate (WEER) of 11.04\%, which is one of the best performing systems in the challenge.
An Analysis Method for Metric-Level Switching in Beat Tracking
Authors: Ching-Yu Chiu, Meinard Müller, Matthew E. P. Davies, Alvin Wen-Yu Su, Yi-Hsuan Yang
Date: 2022-10-13 00:00:00
Tasks: Beat Tracking

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not reflect such behaviors, as they typically assume a fixed relationship between the reference beats and estimated beats. In this paper, we propose a new performance analysis method, called annotation coverage ratio (ACR), that accounts for a variety of possible metric-level switching behaviors of beat trackers. The idea is to derive sequences of modified reference beats of all metrical levels for every two consecutive reference beats, and compare every sequence of modified reference beats to the subsequences of estimated beats. We show via experiments on three datasets of different genres the usefulness of ACR when utilized alongside existing metrics, and discuss the new insights to be gained.
Stability analysis of optimal control problems with time-dependent costs
Authors: Sifeddine Benahmed, Romain Postoyan, Mathieu Granzotto, Lucian Buşoniu, Jamal Daafouz, Dragan Nešić
Date: 2022-10-13 00:00:00

We present stability conditions for deterministic time-varying nonlinear discrete-time systems whose inputs aim to minimize an infinite-horizon time-dependent cost. Global asymptotic and exponential stability properties for general attractors are established. This work covers and generalizes the related results on discounted optimal control problems to more general systems and cost functions.
Decentralized State Estimation In A Dimension-Reduced Linear Regression
Authors: Robin Forsling, Fredrik Gustafsson, Zoran Sjanic, Gustaf Hendeby
Date: 2022-10-13 00:00:00
Tasks: Dimensionality Reduction, regression, State Estimation

Decentralized state estimation in a communication-constrained sensor network is considered. The exchanged estimates are dimension-reduced to reduce the communication load using a linear mapping to a lower-dimensional space. The mean squared error optimal linear mapping depends on the particular estimation method used. Several dimension-reducing algorithms are proposed, where each algorithm corresponds to a commonly applied decentralized estimation method. All except one of the algorithms are shown to be optimal. For the remaining algorithm, we provide a convergence analysis where it is theoretically shown that this algorithm converges to a stationary point and numerically shown that the convergence rate is fast. A message-encoding solution is proposed that allows for efficient communication when using the proposed dimension reduction techniques. We also derive different properties from the proposed framework and show its superiority in relation to baseline methods. The applicability of the different algorithms is demonstrated using a simple fusion example and a more realistic target tracking scenario.
Conflict-free Charging and Real-time Control for an Electric Bus Network
Authors: Rémi Lacombe, Nikolce Murgovski, Sébastien Gros, Balázs Kulcsár
Date: 2022-10-13 00:00:00
Tasks: Scheduling

The rapid adoption of electric buses by transit agencies around the world is leading to new challenges in the planning and operation of bus networks. In particular, the limited driving range of electric vehicles imposes operational constraints such as the need to charge buses during service. Research on this topic has mostly focused on the strategic and tactical planning aspects until now, and very little work has been done on the real-time operational aspect. To remedy this, we propose integrating the charging scheduling problem with a real-time speed control strategy in this paper. The control problem is formulated as a mixed-integer linear program and solved to optimality with the branch-and-bound method. Simulations are carried out by repeatedly solving the control problem in a receding horizon fashion over a full day of operation. The results show that the proposed controller manages to anticipate and avoid potential conflicts where the charging demand exceeds the charger capacity. This feature makes the controller achieve lower operational costs, both in terms of service regularity and energy consumption, compared to a standard first-come, first-served charging scheme.
Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models
Authors: Jing Xie, Fabio Bonassi, Marcello Farina, Riccardo Scattolini
Date: 2022-10-13 00:00:00
Tasks: Model Predictive Control

This paper presents a robust Model Predictive Control (MPC) scheme that provides offset-free setpoint tracking for systems described by Neural Nonlinear AutoRegressive eXogenous (NNARX) models. The NNARX model learns the dynamics of the plant from input-output data, and during the training the Incremental Input-to-State Stability (${\delta}$ISS) property is forced to guarantee stability. The trained NNARX model is then augmented with an explicit integral action on the output tracking error, which allows the control scheme to enjoy offset-free tracking ability. A tube-based MPC is finally designed, leveraging the unique structure of the model, to ensure robust stability and robust asymptotic zero error regulation for constant reference signals in the presence of model-plant mismatch or unknown disturbances. Numerical simulations on a water heating system show the effectiveness of the proposed control algorithm.
Optimal Control of Material Micro-Structures
Authors: Aayushman Sharma, Zirui Mao, Haiying Yang, Suman Chakravorty, Michael J Demkowicz, Dileep Kalathil
Date: 2022-10-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we consider the optimal control of material micro-structures. Such material micro-structures are modeled by the so-called phase field model. We study the underlying physical structure of the model and propose a data based approach for its optimal control, along with a comparison to the control using a state of the art Reinforcement Learning (RL) algorithm. Simulation results show the feasibility of optimally controlling such micro-structures to attain desired material properties and complex target micro-structures.
Output Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model
Authors: Tochukwu Elijah Ogri, S. M. Nahid Mahmud, Zachary I. Bell, Rushikesh Kamalapurkar
Date: 2022-10-13 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Real-world control applications in complex and uncertain environments require adaptability to handle model uncertainties and robustness against disturbances. This paper presents an online, output-feedback, critic-only, model-based reinforcement learning architecture that simultaneously learns and implements an optimal controller while maintaining stability during the learning phase. Using multiplier matrices, a convenient way to search for observer gains is designed along with a controller that learns from simulated experience to ensure stability and convergence of trajectories of the closed-loop system to a neighborhood of the origin. Local uniform ultimate boundedness of the trajectories is established using a Lyapunov-based analysis and demonstrated through simulation results, under mild excitation conditions.
Real-Time Dense Field Phase-to-Space Simulation of Imaging through Atmospheric Turbulence
Authors: Nicholas Chimitt, Xingguang Zhang, Zhiyuan Mao, Stanley H. Chan
Date: 2022-10-13 00:00:00

Numerical simulation of atmospheric turbulence is one of the biggest bottlenecks in developing computational techniques for solving the inverse problem in long-range imaging. The classical split-step method is based upon numerical wave propagation which splits the propagation path into many segments and propagates every pixel in each segment individually via the Fresnel integral. This repeated evaluation becomes increasingly time-consuming for larger images. As a result, the split-step simulation is often done only on a sparse grid of points followed by an interpolation to the other pixels. Even so, the computation is expensive for real-time applications. In this paper, we present a new simulation method that enables \emph{real-time} processing over a \emph{dense} grid of points. Building upon the recently developed multi-aperture model and the phase-to-space transform, we overcome the memory bottleneck in drawing random samples from the Zernike correlation tensor. We show that the cross-correlation of the Zernike modes has an insignificant contribution to the statistics of the random samples. By approximating these cross-correlation blocks in the Zernike tensor, we restore the homogeneity of the tensor which then enables Fourier-based random sampling. On a $512\times512$ image, the new simulator achieves 0.025 seconds per frame over a dense field. On a $3840 \times 2160$ image which would have taken 13 hours to simulate using the split-step method, the new simulator can run at approximately 60 seconds per frame.
Getting higher on rugged landscapes: Inversion mutations open access to fitter adaptive peaks in NK fitness landscapes
Authors: Leonardo Trujillo, Paul Banse, Guillaume Beslon
Date: 2022-10-13 00:00:00

Molecular evolution is often conceptualised as adaptive walks on rugged fitness landscapes, driven by mutations and constrained by incremental fitness selection. It is well known that epistasis shapes the ruggedness of the landscape's surface, outlining their topography (with high-fitness peaks separated by valleys of lower fitness genotypes). However, within the strong selection weak mutation (SSWM) limit, once an adaptive walk reaches a local peak, natural selection restricts passage through downstream paths and hampers any possibility of reaching higher fitness values. Here, in addition to the widely used point mutations, we introduce a minimal model of sequence inversions to simulate adaptive walks. We use the well known NK model to instantiate rugged landscapes. We show that adaptive walks can reach higher fitness values through inversion mutations, which, compared to point mutations, allows the evolutionary process to escape local fitness peaks. To elucidate the effects of this chromosomal rearrangement, we use a graph-theoretical representation of accessible mutants and show how new evolutionary paths are uncovered. The present model suggests a simple mechanistic rationale to analyse escapes from local fitness peaks in molecular evolution driven by (intragenic) structural inversions and reveals some consequences of the limits of point mutations for simulations of molecular evolution.
Biologically plausible solutions for spiking networks with efficient coding
Authors: Veronika Koren, Stefano Panzeri
Date: 2022-10-13 00:00:00

Understanding how the dynamics of neural networks is shaped by the computations they perform is a fundamental question in neuroscience. Recently, the framework of efficient coding proposed a theory of how spiking neural networks can compute low-dimensional stimulus signals with high efficiency. Efficient spiking networks are based on time-dependent minimization of a loss function related to information coding with spikes. To inform the understanding of the function and dynamics of biological networks in the brain, however, the mathematical models have to be informed by biology and obey the same constraints as biological networks. Currently, spiking network models of efficient coding have been extended to include some features of biological plausibility, such as architectures with excitatory and inhibitory neurons. However, biological realism of efficient coding theories is still limited to simple cases and does not include single neuron and network properties that are known to be key in biological circuits. Here, we revisit the theory of efficient coding with spikes to develop spiking neural networks that are closer to biological circuits. Namely, we find a biologically plausible spiking model realizing efficient coding in the case of a generalized leaky integrate-and-fire network with excitatory and inhibitory units, equipped with fast and slow synaptic currents, local homeostatic currents such as spike-triggered adaptation, hyperpolarization-activated rebound current, heterogeneous firing thresholds and resets, heterogeneous postsynaptic potentials, and structured, low-rank connectivity. We show how the complexity of E-E connectivity matrix shapes network responses.
Robust Electromagnetic Interference (EMI) Elimination via Simultaneous Sensing and Deep Learning Prediction for RF Shielding-free MRI
Authors: Yujiao Zhao, Linfang Xiao, Vick Lau, Yilong Liu, Alex T. Leong, Ed X. Wu
Date: 2022-10-13 00:00:00

At present, MRI scans are performed inside a fully-enclosed RF shielding room, posing stringent installation requirement and unnecessary patient discomfort. We aim to develop an electromagnetic interference (EMI) cancellation strategy for MRI with no or incomplete RF shielding. In this study, a simultaneous sensing and deep learning driven EMI cancellation strategy is presented to model, predict and remove EMI signals from acquired MRI signals. Specifically, during each MRI scan, separate EMI sensing coils placed in various spatial locations are utilized to simultaneously sample environmental and internal EMI signals within two windows (for both conventional MRI signal acquisition and EMI characterization acquisition). Then a CNN model is trained using the EMI characterization data to relate EMI signals detected by EMI sensing coils to EMI signals in MRI receive coil. This model is utilized to retrospectively predict and remove EMI signals components detected by MRI receive coil during the MRI signal acquisition window. We implemented and demonstrated this strategy for various EMI sources on a mobile ultra-low-field 0.055 T permanent magnet MRI scanner and a 1.5 T superconducting magnet MRI scanner with no or incomplete RF shielding. Our experimental results demonstrate that the method is highly effective and robust in predicting and removing various EMI sources from both external environments and internal scanner electronics at both 0.055 T (2.3 MHz) and 1.5 T (64 MHz), producing final image signal-to-noise ratios that are comparable to those obtained using a fully enclosed RF shielding. Our proposed strategy enables MRI operation with no or incomplete RF shielding, alleviating MRI installation and operational requirements. It is also potentially applicable to other scenarios of accurate RF signal detection or discrimination in presence of external and internal EMI or RF sources.
Robust Estimation and Inference in Panels with Interactive Fixed Effects
Authors: Timothy B. Armstrong, Martin Weidner, Andrei Zeleneev
Date: 2022-10-13 00:00:00
Tasks: valid

We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure). We demonstrate that existing estimators and confidence intervals (CIs) can be heavily biased and size-distorted when some of the factors are weak. We propose estimators with improved rates of convergence and bias-aware CIs that remain valid uniformly, regardless of factor strength. Our approach applies the theory of minimax linear estimation to form a debiased estimate, using a nuclear norm bound on the error of an initial estimate of the interactive fixed effects. Our resulting bias-aware CIs take into account the remaining bias caused by weak factors. Monte Carlo experiments show substantial improvements over conventional methods when factors are weak, with minimal costs to estimation accuracy when factors are strong.