pwc-archive/papers-with-abstracts huggingface dataset:
Time-Frequency Distributions of Heart Sound Signals: A Comparative Study using Convolutional Neural Networks
Authors: Xinqi Bao, Yujia Xu, Hak-Keung Lam, Mohamed Trabelsi, Ines Chihi, Lilia Sidhom, Ernest N. Kamavuako
Date: 2022-08-05 00:00:00
Tasks: Classification, Sound Classification

Time-Frequency Distributions (TFDs) support the heart sound characterisation and classification in early cardiac screening. However, despite the frequent use of TFDs in signal analysis, no study comprehensively compared their performances on deep learning for automatic diagnosis. Furthermore, the combination of signal processing methods as inputs for Convolutional Neural Networks (CNNs) has been proved as a practical approach to increasing signal classification performance. Therefore, this study aimed to investigate the optimal use of TFD/ combined TFDs as input for CNNs. The presented results revealed that: 1) The transformation of the heart sound signal into the TF domain achieves higher classification performance than using of raw signals. Among the TFDs, the difference in the performance was slight for all the CNN models (within $1.3\%$ in average accuracy). However, Continuous wavelet transform (CWT) and Chirplet transform (CT) outperformed the rest. 2) The appropriate increase of the CNN capacity and architecture optimisation can improve the performance, while the network architecture should not be overly complicated. Based on the ResNet or SEResNet family results, the increase in the number of parameters and the depth of the structure do not improve the performance apparently. 3) Combining TFDs as CNN inputs did not significantly improve the classification results. The findings of this study provided the knowledge for selecting TFDs as CNN input and designing CNN architecture for heart sound classification.
AID: Open-source Anechoic Interferer Dataset
Authors: Philipp Götz, Cagdas Tuna, Andreas Walther, Emanuël A. P. Habets
Date: 2022-08-05 00:00:00

A dataset of anechoic recordings of various sound sources encountered in domestic environments is presented. The dataset is intended to be a resource of non-stationary, environmental noise signals that, when convolved with acoustic impulse responses, can be used to simulate complex acoustic scenes. Additionally, a Python library is provided to generate random mixtures of the recordings in the dataset, which can be used as non-stationary interference signals.
Integrated Adaptive Control and Reference Governors for Constrained Systems with State-Dependent Uncertainties
Authors: Pan Zhao, Ilya Kolmanovsky, Naira Hovakimyan
Date: 2022-08-05 00:00:00

This paper presents an adaptive reference governor (RG) framework for a linear system with matched nonlinear uncertainties that can depend on both time and states, subject to both state and input constraints. The proposed framework leverages an L1 adaptive controller (L1AC) that estimates and compensates for the uncertainties, and provides guaranteed transient performance, in terms of uniform bounds on the error between actual states and inputs and those of a nominal (i.e., uncertainty-free) system. The uniform performance bounds provided by the L1AC are used to tighten the pre-specified state and control constraints. A reference governor is then designed for the nominal system using the tightened constraints, and guarantees robust constraint satisfaction. Moreover, the conservatism introduced by the constraint tightening can be systematically reduced by tuning some parameters within the L1AC. Compared with existing solutions, the proposed adaptive RG framework can potentially yield less conservative results for constraint enforcement due to the removal of uncertainty propagation along a prediction horizon, and improved tracking performance due to the inherent uncertainty compensation mechanism. Simulation results for a flight control example illustrate the efficacy of the proposed framework.
Effect of State of Charge Uncertainty on Battery Energy Storage Systems
Authors: Sonia Martin, Simona Onori, Ram Rajagopal
Date: 2022-08-04 00:00:00

Battery energy storage systems (BESSs) provide many benefits to the electricity grid, including stability, backup power, and flexibility in introducing more clean energy sources. As BESS penetration grows, knowledge of the uncertainty in the battery's state of charge (SOC) estimate is crucial for planning optimal BESS power injection trajectories. This paper proposes a framework for quantifying SOC estimation uncertainty based on battery rest periods. An uncertainty analysis is presented for a BESS participating in the frequency regulation market.
Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach
Authors: Mushuang Liu, H. Eric Tseng, Dimitar Filev, Anouck Girard, Ilya Kolmanovsky
Date: 2022-08-04 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making

This paper proposes a novel decision-making framework for autonomous vehicles (AVs), called predictor-corrector potential game (PCPG), composed of a Predictor and a Corrector. To enable human-like reasoning and characterize agent interactions, a receding-horizon multi-player game is formulated. To address the challenges caused by the complexity in solving a multi-player game and by the requirement of real-time operation, a potential game (PG) based decision-making framework is developed. In the PG Predictor, the agent cost functions are heuristically predefined. We acknowledge that the behaviors of other traffic agents, e.g., human-driven vehicles and pedestrians, may not necessarily be consistent with the predefined cost functions. To address this issue, a best response-based PG Corrector is designed. In the Corrector, the action deviation between the ego vehicle prediction and the surrounding agent actual behaviors are measured and are fed back to the ego vehicle decision-making, to correct the prediction errors caused by the inaccurate predefined cost functions and to improve the ego vehicle strategies. Distinguished from most existing game-theoretic approaches, this PCPG 1) deals with multi-player games and guarantees the existence of a pure-strategy Nash equilibrium (PSNE), convergence of the PSNE seeking algorithm, and global optimality of the derived PSNE when multiple PSNE exist; 2) is computationally scalable in a multi-agent scenario; 3) guarantees the ego vehicle safety under certain conditions; and 4) approximates the actual PSNE of the system despite the unknown cost functions of others. Comparative studies between the PG, the PCPG, and the control barrier function (CBF) based approaches are conducted in diverse traffic scenarios, including oncoming traffic scenario and multi-vehicle intersection-crossing scenario.
Effective Non-Iterative Phase Retrieval of 2-D Bandlimited Signals with Applications to Antenna Characterization and Diagnostics
Authors: Giada Maria Battaglia, Andrea Francesco Morabito, Roberta Palmeri, Tommaso Isernia
Date: 2022-08-05 00:00:00
Tasks: Retrieval

The Phase Retrieval problem is dealt with for the challenging case where just a single set of (phaseless) radiated field data is available. In particular, even still emulating the solution of crosswords puzzles, we provide decisive improvements over our recent approaches. In fact, by exploiting bandlimitedness and a suitable set of intersecting curves, we definitively lower the computational complexity (thus eliminating drawbacks) of our previous techniques. Numerical examples, concerning applications of actual interest, support the given theory and confirm the effectiveness of the developed procedure.
Factor Network Autoregressions
Authors: Matteo Barigozzi, Giuseppe Cavaliere, Graziano Moramarco
Date: 2022-08-04 00:00:00
Tasks: Dimensionality Reduction, Time Series, Time Series Analysis

We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents ("multilayer network"), which are summarized into a smaller number of network matrices ("network factors") through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the factors, their loadings, and the coefficients of the FNAR, as the number of layers, nodes and time points diverges to infinity. Our approach combines two different dimension-reduction techniques and can be applied to high-dimensional datasets. Simulation results show the goodness of our estimators in finite samples. In an empirical application, we use the FNAR to investigate the cross-country interdependence of GDP growth rates based on a variety of international trade and financial linkages. The model provides a rich characterization of macroeconomic network effects as well as good forecasts of GDP growth rates.
Subgame perfect Nash equilibrium for dynamic pricing competition with finite planning horizon
Authors: Niloofar Fadavi
Date: 2022-08-04 00:00:00
Tasks: Management

Having fixed capacities, homogeneous products and price sensitive customer purchase decision are primary distinguishing characteristics of numerous revenue management systems. Even with two or three rivals, competition is still highly fierce. This paper studies sub-game perfect Nash equilibrium of a price competition in an oligopoly market with perishable assets. Sellers each has one unit of a good that cannot be replenished, and they compete in setting prices to sell their good over a finite sales horizon. Each period, buyers desire one unit of the good and the number of buyers coming to the market in each period is random. All sellers' prices are accessible for buyers, and search is costless. Using stochastic dynamic programming methods, the best response of sellers can be obtained from a one-shot price competition game regarding remained periods and the current-time demand structure. Assuming a binary demand model, we demonstrate that the duopoly model has a unique Nash equilibrium and the oligopoly model does not reveal price dispersion with respect to a particular metric. We illustrate that, when considering a generalized demand model, the duopoly model has a unique mixed strategy Nash equilibrium while the oligopoly model has a unique symmetric mixed strategy Nash equilibrium.
Expanded Adaptive Scaling Normalization for End to End Image Compression
Authors: Chajin Shin, Hyeongmin Lee, Hanbin Son, Sangjin Lee, Dogyoon Lee, Sangyoun Lee
Date: 2022-08-05 00:00:00
Tasks: Image Compression

Recently, learning-based image compression methods that utilize convolutional neural layers have been developed rapidly. Rescaling modules such as batch normalization which are often used in convolutional neural networks do not operate adaptively for the various inputs. Therefore, Generalized Divisible Normalization(GDN) has been widely used in image compression to rescale the input features adaptively across both spatial and channel axes. However, the representation power or degree of freedom of GDN is severely limited. Additionally, GDN cannot consider the spatial correlation of an image. To handle the limitations of GDN, we construct an expanded form of the adaptive scaling module, named Expanded Adaptive Scaling Normalization(EASN). First, we exploit the swish function to increase the representation ability. Then, we increase the receptive field to make the adaptive rescaling module consider the spatial correlation. Furthermore, we introduce an input mapping function to give the module a higher degree of freedom. We demonstrate how our EASN works in an image compression network using the visualization results of the feature map, and we conduct extensive experiments to show that our EASN increases the rate-distortion performance remarkably, and even outperforms the VVC intra at a high bit rate.
Phase Difference based Doppler Disambiguation Method for TDM-MIMOFMCW Radars
Authors: Qingshan Shen, Qingbo Wang
Date: 2022-08-05 00:00:00

State-of-the-art automotive radar sensors use a Mutliple-Input Mutiple-Output (MIMO) approach to obtain a better angular resolution. Time-Division Multiplexing (TDM) scheme is commonly applied to realize the orthogonality in time at the transmitter. Apart from its simplicity in implementation, TDM scheme has the drawback of a reduced maximum unambiguous Doppler proportional to the number of transmitters. In this paper, a phase difference based Doppler disambiguation method is proposed to regain the maximum unambiguous Doppler which is equivalent to only one transmitter. This method works well when the number of transmitters is large. The proposed method is demonstrated with simulation and measurement data.
Microwave QR Code: An IRS-Based Solution
Authors: Sai Xu, Yanan Du, Jiliang Zhang, Jie Zhang
Date: 2022-08-05 00:00:00

This letter proposes to employ intelligent reflecting surface (IRS) as an information media to display a microwave quick response (QR) code for Internet-of-Things applications. To be specific, an IRS is used to form a dynamic bitmap image thanks to its tunable elements. With a QR code shown on the IRS, the transmitting and receiving antenna arrays are jointly designed to scan it by radiating electromagnetic wave as well as receiving and detecting the reflected signal. Based on such an idea, an IRS enabled information and communication system is modelled. Accordingly, some fundamental systematic operating mechanisms are investigated, involving derivation of average bit error probability for signal modulation, QR code implementation on an IRS, transmission design, detection, etc. The simulations are performed to show the achievable communication performance of system and confirm the feasibility of IRS-based microwave QR code.
Continuous Beam Alignment for Mobile MIMO
Authors: Mohamed Naguib, Yahia Shabara, Can Emre Koksal
Date: 2022-08-05 00:00:00

Millimeter-wave transceivers use large antenna arrays to form narrow high-directional beams and overcome severe attenuation. Narrow beams require large signaling overhead to be aligned if no prior information about beam directions is available. Moreover, beams drift with time due to user mobility and may need to be realigned. Beam tracking is commonly used to keep the beams tightly coupled and eliminate the overhead associated with realignment. Hence, with periodic measurements, beams are adjusted before they lose alignment. We propose a model where the receiver adjusts beam direction "continuously" over each physical-layer sample according to a carefully calculated estimate of the continuous variation of the beams. In our approach, the change of direction is updated using the rate variation prediction of beam angles via three different solutions. Our approach incurs no additional overhead in pilots, yet, the performance of beam tracking is improved significantly. Numerical results reveal an SNR enhancement associated with reducing the MSE of the beam directions. In addition, our approach reduces the pilot overhead by 60% and up to 87% while achieving a similar total tracking duration as the state-of-the-art.
Rate Splitting Multiple Access for Cognitive Radio GEO-LEO Co-Existing Satellite Networks
Authors: Wali Ullah Khan, Zain Ali, Eva Lagunas, Symeon Chatzinotas, Björn Ottersten
Date: 2022-08-04 00:00:00

LEO satellite communication has drawn particular attention recently due to its high data rate services and low round-trip latency. It is low-cost to launch and can provide global coverage. However, the spectrum scarcity might be one of the critical challenges in the growth of LEO satellites, impacting severe restrictions on the development of ground-space integrated networks. To address this issue, we propose RSMA for CR enabled GEO-LEO coexisting satellite network. In particular, this work aims to maximize the system's sum rate by simultaneously optimizing the power allocation and subcarrier beam assignment of LEO satellite communication while restricting the interference temperature to GEO satellite users. The problem of sum rate maximization is formulated as non-convex and a Global optimal solution is challenging to obtain. Therefore, we first employ the successive convex approximation technique to reduce the complexity and make the problem more tractable. Then for the power allocation, we exploit KKT condition and adopt an efficient algorithm based on the greedy approach for subcarrier beam assignment. We also propose two suboptimal schemes with fixed power allocation and random subcarrier beam assignment as the benchmark. Results demonstrate the benefits of the proposed scheme compared to the benchmark schemes.
Protein Folding: From Classical Issues to a New Perspective
Authors: Jorge A. Vila
Date: 2022-08-05 00:00:00
Tasks: Protein Folding

The Levinthal paradox exposes many critical questions on the protein folding problem, among which we could point out why proteins can reach their native state in a biologically reasonable time. A proper answer to this question is of foremost importance for evolutive biology since it enables us to understand life as we know it. Preliminary results, based on the upper bound protein marginal-stability limit, together with transition state theory arguments, lead us to show that two-state proteins must reach their native state in, at most, seconds rather than ($\sim 10^{27}$) years -- as indicated by a naive solution of the Levinthal paradox. This outcome -- added to the amide hydrogen-exchange protection factors analysis -- makes it possible for us to suggest how a protein point mutations and/or post-translational modifications impact its folding time scales but not its upper bound limit that obeys the physics ruling the process. Noteworthy for almost 50 years, the protein folding problem --mas the Levinthal paradox -- has been a topic of passionate debate because Anfinsen's challenge -- how a sequence encodes its folding -- remains unsolved despite the smashing success of accurately predicting the protein tridimensional structures by state-of-the-art numerical-methods. Aimed to unlock this long-standing challenge, we propose a new perspective of protein folding, specifically, as a problem that should be devised as an 'analytic whole' -- a Leibniz & Kant's notion. This viewpoint might help us decode Anfinsen's challenge and, thus, open new avenues for future research in the protein folding field.
Common Synaptic Input, Synergies, and Size Principle: Control of Spinal Motor Neurons for Movement Generation
Authors: François Hug, Simon Avrillon, Jaime Ibáñez, Dario Farina
Date: 2022-07-27 00:00:00

Understanding how movement is controlled by the central nervous system remains a major challenge, with ongoing debate about basic features underlying this control. In this review, we introduce a new conceptual framework for the distribution of common input to spinal motor neurons. Specifically, this framework is based on the following assumptions: 1) motor neurons are grouped into functional groups (clusters) based on the common inputs they receive; 2) clusters may significantly differ from the classical definition of motor neuron pools, such that they may span across muscles and/or involve only a portion of a muscle; 3) clusters represent functional modules used by the central nervous system to reduce the dimensionality of the control; and 4) selective volitional control of single motor neurons within a cluster receiving common inputs cannot be achieved. We discuss this framework and its underlying theoretical and experimental evidence.
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis
Authors: Amin Gasmi
Date: 2022-08-05 00:00:00
Tasks: Data Augmentation, Deep Learning, Diagnostic, Management, Transfer Learning, Tumor Segmentation

The connection between the design and delivery of health care services using information technology is known as health informatics. It involves data usage, validation, and transfer of an integrated medical analysis using neural networks of multi-layer deep learning techniques to analyze complex data. For instance, Google incorporated ''DeepMind'' health mobile tool that integrates \& leverage medical data needed to enhance professional healthcare delivery to patients. Moorfield Eye Hospital London introduced DeepMind Research Algorithms with dozens of retinal scans attributes while DeepMind UCL handled the identification of cancerous tissues using CT \& MRI Scan tools. Atomise analyzed drugs and chemicals with Deep Learning Neural Networks to identify accurate pre-clinical prescriptions. Health informatics makes medical care intelligent, interactive, cost-effective, and accessible; especially with DL application tools for detecting the actual cause of diseases. The extensive use of neural network tools leads to the expansion of different medical disciplines which mitigates data complexity and enhances 3-4D overlap images using target point label data detectors that support data augmentation, un-semi-supervised learning, multi-modality and transfer learning architecture. Health science over the years focused on artificial intelligence tools for care delivery, chronic care management, prevention/wellness, clinical supports, and diagnosis. The outcome of their research leads to cardiac arrest diagnosis through Heart Signal Computer-Aided Diagnostic tool (CADX) and other multifunctional deep learning techniques that offer care, diagnosis \& treatment. Health informatics provides monitored outcomes of human body organs through medical images that classify interstitial lung disease, detects image nodules for reconstruction \& tumor segmentation. The emergent medical research applications gave rise to clinical-pathological human-level performing tools for handling Radiological, Ophthalmological, and Dental diagnosis. This research will evaluate methodologies, Deep learning architectures, approaches, bio-informatics, specified function requirements, monitoring tools, ANN (artificial neural network), data labeling \& annotation algorithms that control data validation, modeling, and diagnosis of different diseases using smart monitoring health informatics applications.
Estimation of Historical volatility and Allocation strategies using Variance Swaps
Authors: Lucio Fiorin
Date: 2022-08-05 00:00:00

In this memorie de fin d'etudes, we review some techniques to estimate historical volatility and to price Variance Swaps
TLPG-Tracker: Joint Learning of Target Localization and Proposal Generation for Visual Tracking.
Authors: Siyuan Li, Zhi Zhang, Ziyu Liu, Anna Wang, Linglong Qiu, Feng Du
Date: 2020-06-01 00:00:00
Proceeding: International Joint Conference on Artificial Intelligence 2020 6
Tasks: Visual Tracking

Target localization and proposal generation are two essential subtasks in generic visual tracking, and it is a challenge to address both the two efficiently. In this paper, we propose an efficient two-stage architecture which makes full use of the complementarity of two subtasks to achieve robust localization and high-quality proposals generation of the target jointly. Specifically, our model performs a novel deformable central correlation operation by an online learning model in both two stages to locate new target centers while generating target proposals in the vicinity of these centers. The proposals are refined in the refinement stage to further improve accuracy and robustness. Moreover, the model benefits from multi-level features aggregation in a neck module and a feature enhancement module. We conduct extensive ablation studies to demonstrate the effectiveness of our proposed methods. Our tracker runs at over 30 FPS and sets a new state-ofthe-art on five tracking benchmarks, including La-SOT, VOT2018, TrackingNet, GOT10k, OTB2015.