pwc-archive/papers-with-abstracts huggingface dataset:
Assessment of Vehicular Vision Obstruction Due to Driver-Side B-Pillar and Remediation with Blind Spot Eliminator
Authors: Dilara Baysal
Date: 2023-02-08 00:00:00

Blind spots created by the driver-side B-pillar impair the ability of the driver to assess their surroundings accurately, significantly contributing to the frequency and severity of vehicular accidents. Vehicle manufacturers are unable to readily eliminate the B-pillar due to regulatory guidelines intended to protect vehicular occupants in the event of side collisions and rollover incidents. Furthermore, assistance implements utilized to counteract the adverse effects of blind spots remain ineffective due to technological limitations and optical impediments. This paper introduces mechanisms to quantify the obstruction caused by the B-pillar when the head of the driver is facing forward and turning 90 degrees, typical of an over-the-shoulder blind spot check. It uses the metrics developed to demonstrate the relationship between B-pillar width and the obstruction angle. The paper then creates a methodology to determine the movement required of the driver to eliminate blind spots. Ultimately, this paper proposes a solution, the Blind Spot Eliminator, and demonstrates that it successfully decreases both the obstruction angle and, consequently, the required driver movement. A prototype of the Blind Spot Eliminator is also constructed and experimented with using a mannequin to model human vision in a typical passenger vehicle. The results of this experiment illustrated a substantial improvement in viewing ability, as predicted by earlier calculations. Therefore, this paper concludes that the proposed Blind Spot Eliminator has excellent potential to improve driver safety and reduce vehicular accidents. Keywords: B-pillar, driver vision, active safety, blind spots, transportation, crash avoidance, side-view assist.
Multi-Source Contrastive Learning from Musical Audio
Authors: Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
Date: 2023-02-14 00:00:00
Tasks: CoLA, Contrastive Learning, Genre classification, Music Auto-Tagging, Music Genre Classification, Representation Learning, Self-Supervised Learning, TAG

Contrastive learning constitutes an emerging branch of self-supervised learning that leverages large amounts of unlabeled data, by learning a latent space, where pairs of different views of the same sample are associated. In this paper, we propose musical source association as a pair generation strategy in the context of contrastive music representation learning. To this end, we modify COLA, a widely used contrastive learning audio framework, to learn to associate a song excerpt with a stochastically selected and automatically extracted vocal or instrumental source. We further introduce a novel modification to the contrastive loss to incorporate information about the existence or absence of specific sources. Our experimental evaluation in three different downstream tasks (music auto-tagging, instrument classification and music genre classification) using the publicly available Magna-Tag-A-Tune (MTAT) as a source dataset yields competitive results to existing literature methods, as well as faster network convergence. The results also show that this pre-training method can be steered towards specific features, according to the selected musical source, while also being dependent on the quality of the separated sources.
Speaker-Independent Acoustic-to-Articulatory Speech Inversion
Authors: Peter Wu, Li-Wei Chen, Cheol Jun Cho, Shinji Watanabe, Louis Goldstein, Alan W Black, Gopala K. Anumanchipalli
Date: 2023-02-14 00:00:00
Tasks: Resynthesis

To build speech processing methods that can handle speech as naturally as humans, researchers have explored multiple ways of building an invertible mapping from speech to an interpretable space. The articulatory space is a promising inversion target, since this space captures the mechanics of speech production. To this end, we build an acoustic-to-articulatory inversion (AAI) model that leverages self-supervision to generalize to unseen speakers. Our approach obtains 0.784 correlation on an electromagnetic articulography (EMA) dataset, improving the state-of-the-art by 12.5\%. Additionally, we show the interpretability of these representations through directly comparing the behavior of estimated representations with speech production behavior. Finally, we propose a resynthesis-based AAI evaluation metric that does not rely on articulatory labels, demonstrating its efficacy with an 18-speaker dataset.
Analysis of Wind Energy Curtailment in the Ireland and Northern Ireland Power Systems
Authors: Manuel Hurtado, Taulant Kerci, Simon Tweed, Eoin Kennedy, Nezar Kamaluddin, Federico Milano
Date: 2023-02-14 00:00:00

The All-Island power system (AIPS) of Ireland and Northern Ireland currently accommodates up to 75% of system non-synchronous penetration (SNSP) (e.g., wind). These unprecedented levels of renewable penetration challenge the operation of the power system. The AIPS is not always able to accommodate all of the available renewable generation due to binding operational and technical constraints. In this context, this paper analyses wind energy curtailment in the AIPS using actual data. It is found that there is a positive correlation between the installed wind capacity and curtailment levels, and that the trend is that these levels increase. The paper also shows that the main driver for curtailment in AIPS during 2020-2021 was the operational constraint that imposes a minimum number of conventional units online (MUON) (80% of the time), with the SNSP limit accounting for less than 20%. Other system-wide limits, such as rate of change of frequency (RoCoF) and inertia are found to have a negligible impact on wind curtailment.
Equilibria, Stability, and Sensitivity for the Aerial Suspended Beam Robotic System subject to Parameter Uncertainty
Authors: Chiara Gabellieri, Marco Tognon, Dario Sanalitro, Antonio Franchi
Date: 2023-02-14 00:00:00
Tasks: Sensitivity

This work studies how parametric uncertainties affect the cooperative manipulation of a cable-suspended beam-shaped load by means of two aerial robots not explicitly communicating with each other. In particular, the work sheds light on the impact of the uncertain knowledge of the model parameters available to an established communication-less force-based controller. First, we find the closed-loop equilibrium configurations in the presence of the aforementioned uncertainties, and then we study their stability. Hence, we show the fundamental role played in the robustness of the load attitude control by the internal force induced in the manipulated object by non-vertical cables. Furthermore, we formally study the sensitivity of the attitude error to such parametric variations, and we provide a method to act on the load position error in the presence of the uncertainties. Eventually, we validate the results through an extensive set of numerical tests in a realistic simulation environment including underactuated aerial vehicles and sagging-prone cables, and through hardware experiments.
Deep Learning-Based Modeling of 5G Core Control Plane for 5G Network Digital Twin
Authors: Zhenyu Tao, Yongliang Guo, Guanghui He, Yongming Huang, Xiaohu You
Date: 2023-02-14 00:00:00
Tasks: Decoder

Digital twin serves as a crucial facilitator in the advancement and implementation of emerging technologies within 5G and beyond networks. However, the intricate structure and diverse functionalities of the existing 5G core network, especially the control plane, present challenges in constructing core network digital twins. In this paper, we propose two novel data-driven architectures for modeling the 5G control plane and implement corresponding deep learning models, namely 5GC-Seq2Seq and 5GC-former, based on the Vanilla Seq2Seq model and Transformer decoder respectively. We also present a solution enabling the interconversion of signaling messages and length-limited vectors to construct a dataset. The experiments are based on 5G core network signaling messages collected by the Spirent C50 network tester, encompassing various procedures such as registration, handover, and PDU sessions. The results show that 5GC-Seq2Seq achieves a 99.997\% F1-score (a metric measuring the accuracy of positive samples) in single UE scenarios with a simple structure, but exhibits significantly reduced performance in handling concurrency. In contrast, 5GC-former surpasses 99.999\% F1-score while maintaining robust performance under concurrent UE scenarios by constructing a more complex and highly parallel model. These findings validate that our method accurately replicates the principal functionalities of the 5G core network control plane.
Self-triggered Resilient Stabilization of Linear Systems with Quantized Output
Authors: Wenjie Liu, Masashi Wakaiki, Jian Sun, Gang Wang, Jie Chen
Date: 2023-02-14 00:00:00

This paper studies the problem of stabilizing a self-triggered control system with quantized output. Employing a standard observer-based state feedback control law, a self-triggering mechanism that dictates the next sampling time based on quantized output is co-developed with an output encoding scheme. If, in addition, the transmission protocols at the controller-to-actuator (C-A) and sensor-to-controller (S-C) channels can be adapted, the self-triggered control architecture can be considerably simplified, leveraging a delicate observer-based deadbeat controller to eliminate the need for running the controller in parallel at the encoder side. To account for denial-of-service (DoS) in the S-C channel, the proposed output encoding and self-triggered control schemes are further made resilient. It is shown that a linear time-invariant system can be exponentially stabilized if some conditions on the average DoS duration time are met. There is a trade-off between the maximum inter-sampling time and the resilience against DoS attacks. Finally, a numerical example is presented to demonstrate the practical merits of the proposed self-triggered control schemes and associated theory.
Cyber-Physical Power System Layers: Classification, Characterization, and Interactions
Authors: Michael Abdelmalak, Narayan Bhusal, Mukesh Gautam, Mohammed Ben-Idris
Date: 2023-02-14 00:00:00
Tasks: Classification

This paper provides a strategy to identify layers and sub-layers of cyber-physical power systems (CPPS) and characterize their inter- and intra-actions. The physical layer usually consists of the power grid and protection devices whereas the cyber layer consists of communication, and computation and control components. Combining components of the cyber layer in one layer complicates the process of modeling intra-actions because each component has different failure modes. On the other hand, dividing the cyber layers into a large number of sub-layers may unnecessarily increase the number of system states and increase the computational burden. In this paper, we classify system layers based on their common, coupled, and shared functions. Also, interactions between the classified layers are identified, characterized, and clustered based on their impact on the system. Furthermore, based on the overall function of each layer and types of its components, intra-actions within layers are characterized. The strategies developed in this paper for comprehensive classification of system layers and characterization of their inter- and intra-actions contribute toward the goal of accurate and detailed modeling of state transition and failure and attack propagation in CPPS, which can be used for various reliability assessment studies.
Coalitional Game Theory in Power Systems: Applications, Challenges, and Future Directions
Authors: Mukesh Gautam, Mohammed Ben-Idris
Date: 2023-02-14 00:00:00

Game theory-based approaches have recently gained traction in a wide range of applications, importantly in power and energy systems. With the onset of cooperation as a new perspective for solving power system problems, as well as the nature of power system problems, it is now necessary to seek appropriate game theory-based tools that permit the investigation and analysis of the behavior and relationships of various players in power system problems. In this context, this paper performs a literature review on coalitional game theory's most recent advancements and applications in power and energy systems. First, we provide a brief overview of the coalitional game theory's fundamental ideas, current theoretical advancements, and various solution concepts. Second, we examine the recent applications in power and energy systems. Finally, we explore the challenges, limitations, and future research possibilities with applications in power and energy systems in the hopes of furthering the literature by strengthening the applications of coalitional game theory in power and energy systems.
Tetris-inspired detector with neural network for radiation mapping
Authors: Ryotaro Okabe, Shangjie Xue, Jiankai Yu, Tongtong Liu, Benoit Forget, Stefanie Jegelka, Gordon Kohse, Lin-wen Hu, Mingda Li
Date: 2023-02-07 00:00:00

In recent years, radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. In terms of both materials and their configurations, radiation detectors have been developed to locate the directions and positions of the radiation sources. In this process, algorithm is essential in converting detector signals to radiation source information. However, due to the complex mechanisms of radiation-matter interaction and the current limitation of data collection, high-performance, low-cost radiation mapping is still challenging. Here we present a computational framework using Tetris-inspired detector pixels and machine learning for radiation mapping. Using inter-pixel padding to increase the contrast between pixels and neural network to analyze the detector readings, a detector with as few as four pixels can achieve high-resolution directional mapping. By further imposing Maximum a Posteriori (MAP) with a moving detector, further radiation position localization is achieved. Non-square, Tetris-shaped detector can further improve performance beyond the conventional grid-shaped detector. Our framework offers a new avenue for high quality radiation mapping with least number of detector pixels possible, and is anticipated to be capable to deploy for real-world radiation detection with moderate validation.
Sequential Estimation of Multivariate Factor Stochastic Volatility Models
Authors: Giorgio Calzolari, Roxana Halbleib, Christian Mücher
Date: 2023-02-14 00:00:00

We provide a simple method to estimate the parameters of multivariate stochastic volatility models with latent factor structures. These models are very useful as they alleviate the standard curse of dimensionality, allowing the number of parameters to increase only linearly with the number of the return series. Although theoretically very appealing, these models have only found limited practical application due to huge computational burdens. Our estimation method is simple in implementation as it consists of two steps: first, we estimate the loadings and the unconditional variances by maximum likelihood, and then we use the efficient method of moments to estimate the parameters of the stochastic volatility structure with GARCH as an auxiliary model. In a comprehensive Monte Carlo study we show the good performance of our method to estimate the parameters of interest accurately. The simulation study and an application to real vectors of daily returns of dimensions up to 148 show the method's computation advantage over the existing estimation procedures.
Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform
Authors: Sagarjit Aujla, Adel Mohamed, Ryan Tan, Randy Tan, Lei Gao, Naimul Khan, Karthikeyan Umapathy
Date: 2023-02-14 00:00:00
Tasks: Diagnostic, image-classification, Image Classification, Respiratory Failure

Annually 8500 neonatal deaths are reported in the US due to respiratory failure. Recently, Lung Ultrasound (LUS), due to its radiation free nature, portability, and being cheaper is gaining wide acceptability as a diagnostic tool for lung conditions. However, lack of highly trained medical professionals has limited its use especially in remote areas. To address this, an automated screening system that captures characteristics of the LUS patterns can be of significant assistance to clinicians who are not experts in lung ultrasound (LUS) images. In this paper, we propose a feature extraction method designed to quantify the spatially-localized line patterns and texture patterns found in LUS images. Using the dual-tree complex wavelet transform (DTCWT) and four types of common image features we propose a method to classify the LUS images into 6 common neonatal lung conditions. These conditions are normal lung, pneumothorax (PTX), transient tachypnea of the newborn (TTN), respiratory distress syndrome (RDS), chronic lung disease (CLD) and consolidation (CON) that could be pneumonia or atelectasis. The proposed method using DTCWT decomposition extracted global statistical, grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and linear binary pattern (LBP) features to be fed to a linear discriminative analysis (LDA) based classifier. Using 15 best DTCWT features along with 3 clinical features the proposed approach achieved a per-image classification accuracy of 92.78% with a balanced dataset containing 720 images from 24 patients and 74.39% with the larger unbalanced dataset containing 1550 images from 42 patients. Likewise, the proposed method achieved a maximum per-subject classification accuracy of 81.53% with 43 DTCWT features and 3 clinical features using the balanced dataset and 64.97% with 13 DTCWT features and 3 clinical features using the unbalanced dataset.
Explainable artificial intelligence toward usable and trustworthy computer-aided early diagnosis of multiple sclerosis from Optical Coherence Tomography
Authors: Monica Hernandez, Ubaldo Ramon-Julvez, Elisa Vilades, Beatriz Cordon, Elvira Mayordomo, Elena Garcia-Martin
Date: 2023-02-13 00:00:00
Tasks: Clinical Knowledge, Explainable artificial intelligence

Background: Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily understandable explanations. Materials and methods: A total of 216 eyes from 111 healthy controls and 100 eyes from 59 patients with relapsing-remitting MS were enrolled. The feature set was obtained from the thickness of the ganglion cell layer (GCL) and the retinal nerve fiber layer (RNFL). Measurements were acquired by the novel Posterior Pole protocol from Spectralis Optical Coherence Tomography (OCT) device. We compared two black-box methods (gradient boosting and random forests) with a glass-box method (explainable boosting machine). Explainability was studied using SHAP for the black-box methods and the scores of the glass-box method. Results: The best-performing models were obtained for the GCL layer. Explainability pointed out to the temporal location of the GCL layer that is usually broken or thinning in MS and the relationship between low thickness values and high probability of MS, which is coherent with clinical knowledge. Conclusions: The insights on how to use explainability shown in this work represent a first important step toward a trustworthy computer-aided solution for the diagnosis of MS with OCT.
Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey
Authors: Song Wu, Yazhou Ren, Aodi Yang, Xinyue Chen, Xiaorong Pu, Jing He, Liqiang Nie, Philip S. Yu
Date: 2023-02-13 00:00:00
Tasks: COVID-19 Diagnosis, Deep Learning, Diagnostic, image-classification, Image Classification

COVID-19 (Coronavirus disease 2019) has been quickly spreading since its outbreak, impacting financial markets and healthcare systems globally. Countries all around the world have adopted a number of extraordinary steps to restrict the spreading virus, where early COVID-19 diagnosis is essential. Medical images such as X-ray images and Computed Tomography scans are becoming one of the main diagnostic tools to combat COVID-19 with the aid of deep learning-based systems. In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis. We also provide a summary of the X-ray and CT image datasets used in various studies for COVID-19 detection. The key difficulties and potential applications of deep learning in fighting against COVID-19 are finally discussed. This work summarizes the latest methods of deep learning using medical images to diagnose COVID-19, highlighting the challenges and inspiring more studies to keep utilizing the advantages of deep learning to combat COVID-19.
Non-uniform array design for robust LoS MIMO via convex optimization
Authors: Michail Palaiologos, Mario H. Castãneda García, Anastasios Kakkavas, Richard A. Stirling-Gallacher, Giuseppe Caire
Date: 2023-02-14 00:00:00

The array design problem of multiple-input multiple-output (MIMO) systems in a line-of-sight (LoS) transmit environment is examined. As uniform array configurations at the transmitter (Tx) and receiver (Rx) are optimal at specific transmit distances only, they lead to reduced spectral efficiency over a range of transmit distances. To that end, the joint design of nonuniform Tx and Rx arrays towards maximizing the minimum capacity of a LoS MIMO system across a range of transmit distances is investigated in this paper. By introducing convex relaxation, the joint Tx and Rx array design is cast as a convex optimization problem, which is solved in a iterative manner. In addition, we also implement a local search to obtain a refined solution that achieves an improved performance. It is shown that the non-uniform configurations designed with our proposed approach outperform uniform and non-uniform array designs of the literature in terms of capacity and/or complexity.
Self-Supervised and Invariant Representations for Wireless Localization
Authors: Artan Salihu, Stefan Schwarz, Markus Rupp
Date: 2023-02-14 00:00:00
Tasks: Linear evaluation

In this work, we present a wireless localization method that operates on self-supervised and unlabeled channel estimates. Our self-supervising method learns general-purpose channel features robust to fading and system impairments. Learned representations are easily transferable to new environments and ready to use for other wireless downstream tasks. To the best of our knowledge, the proposed method is the first joint-embedding self-supervised approach to forsake the dependency on contrastive channel estimates. Our approach outperforms fully-supervised techniques in small data regimes under fine-tuning and, in some cases, linear evaluation. We assess the performance in centralized and distributed massive MIMO systems for multiple datasets. Moreover, our method works indoors and outdoors without additional assumptions or design changes.