pwc-archive/papers-with-abstracts huggingface dataset:
Controller-Aware Dynamic Network Management for Industry 4.0
Authors: Efe C. Balta, Mohammad H. Mamduhi, John Lygeros, Alisa Rupenyan
Date: 2022-05-28 00:00:00
Tasks: Management

In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected, constrained environment to perform industrial tasks. The CPMS has a centralized control plane with digital twins (DTs) of the physical resources, computational resources, and a network manager that allocates network resources. Existing approaches for allocation of network resources are typically fixed with respect to controller-dependent run-time specifications, which may impact the performance of physical processes. We propose a dynamic network management framework, where the network resource allocation schemes are controller-aware. The information about the controllers of the physical resources is implemented at the DT level, and metrics, such as regret bounds, take the process performance measures into account. The proposed network management schemes optimize physical system performance by balancing the shared resources between the physical assets on the plant floor, and by considering their control requirements, providing a new perspective for dynamic resource allocation. A simulation study is provided to illustrate the performance of the proposed network management approaches and compare their efficiencies.
Incentive Mechanism Design for Emergency Frequency Control in Multi-Infeed Hybrid AC-DC System
Authors: Ye Liu, Chen Shen, Zhaojian Wang, Feng Liu
Date: 2022-05-28 00:00:00

In multi-infeed hybrid AC-DC (MIDC) systems, the emergency frequency control (EFC) with LCC-HVDC systems participating is of vital importance for system frequency stability. Nevertheless, when regional power systems are operated by different decision-makers, the LCC-HVDC systems and their connected AC systems might be unwilling to participate in the EFC due to the costs and losses. In this paper, to incentivize the LCC-HVDC systems and their connected adjacent AC systems to participate in the droop-based EFC, a novel control-parameter-based incentive mechanism is proposed, which can deal with various possible emergency frequency faults. Then, a non-cooperative-based incentive game model is formulated to implement the incentive mechanism in the MIDC system. An algorithm for seeking the Nash equilibrium is designed, and the uniqueness of Nash equilibrium is proven. Moreover, the individual rationality, incentive compatibility and social optimality of the proposed mechanism are analyzed and proven. The effectiveness of the proposed incentive mechanism is verified through a case study.
Two-Leg Deep Space Relay Architectures: Performance, Challenges, and Perspectives
Authors: Dario Modenini, Alfredo Locarini, Lorenzo Valentini, Alberto Faedi, Paolo Tortora, Davide Rovelli, Nicolò Mazzali, Marco Chiani, Enrico Paolini
Date: 2022-05-27 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, architectures for interplanetary communications that feature the use of a data relay are investigated. In the considered "two-leg" architecture, a spacecraft orbiting the Earth, or in orbit at a Lagrange point, receives data from a deep space probe (leg-1) and relays them towards ground (leg-2). Different wireless technologies for the interplanetary link, namely, radio frequencies above the Ka band and optical frequencies, are considered. Moreover, the cases of transparent and regenerative relaying as well as different different orbital configurations are addressed, offering a thorough analysis of such systems from different viewpoints. Results show that, under certain constraints in terms of pointing accuracy and onboard antenna size, the adoption of a two-leg architecture can achieve the data rates supported by direct space-to-Earth link configurations with remarkably smaller ground station antennas.
A Novel Control-Oriented Cell Transmission Model Including Service Stations on Highways
Authors: Carlo Cenedese, Michele Cucuzzella, Antonella Ferrara, John Lygeros
Date: 2022-05-23 00:00:00

In this paper, we propose a novel model that describes how the traffic evolution on a highway stretch is affected by the presence of a service station. The presented model enhances the classical CTM dynamics by adding the dynamics associated with the service stations, where the vehicles may stop before merging back into the mainstream. We name it CTMs. We discuss its flexibility in describing different complex scenarios where multiple stations are characterized by different drivers' average stopping times corresponding to different services. The model has been developed to help design control strategies aimed at decreasing traffic congestion. Thus, we discuss how classical control schemes can interact with the proposed \gls{CTMs}. Finally, we validate the proposed model through numerical simulations and assess the effects of service stations on traffic evolution, which appear to be beneficial, especially for relatively short congested periods.
Metabolic scaling is governed by Murray's network in animals and by hydraulic conductance and photosynthesis in plants
Authors: Jinkui Zhao
Date: 2022-05-30 00:00:00

The prevailing theory for metabolic scaling is based on area-preserved, space-filling fractal vascular networks. However, it's known both theoretically and experimentally that animals' vascular systems obey Murray's cubic branching law. Area-preserved branching conflicts with energy minimization and hence the least-work principle. Additionally, while Kleiber's law is the dominant rule for both animals and plants, small animals are observed to follow the 2/3-power law, large animals have larger than 3/4 scaling exponents, and small plants have near-linear scaling behaviors. No known theory explains all the observations. Here, I show that animals' metabolism is determined by their Murray's vascular systems. For plants, the scaling is determined by the trunks' hydraulic conductance and the leaves' photosynthesis. Both analyses agree with data of various body sizes. Animals' scaling has a concave curvature while plants have a convex one. The empirical power laws are approximations within selected mass ranges. Generally, the 3/4-power law applies to animals of ~15 g to 10,000 kg and the 2/3-power law to those of ~1 g to 10 kg. For plants, the scaling exponent is 1 for small plants and decreases to 3/4 for those greater than ~10 kg.
Multi-Dimensional self-exciting NBD process and Default portfolios
Authors: Masato Hisakado, Kodai Hattori, Shintaro Mori
Date: 2022-05-20 00:00:00

In this study, we apply a multidimensional self-exciting negative binomial distribution (SE-NBD) process to default portfolios with 13 sectors. The SE-NBD process is a Poisson process with a gamma-distributed intensity function. We extend the SE-NBD process to a multidimensional process. Using the multidimensional SE-NBD process (MD-SE-NBD), we can estimate interactions between these 13 sectors as a network. By applying impact analysis, we can classify upstream and downstream sectors. The upstream sectors are real-estate and financial institution (FI) sectors. From these upstream sectors, shock spreads to the downstream sectors. This is an amplifier of the shock. This is consistent with the analysis of bubble bursts. We compare these results to the multidimensional Hawkes process (MD-Hawkes) that has a zero-variance intensity function.
Noise analysis, error estimates, and Gamma Radiation Measurement for limited detector computerized tomography application
Authors: Kajal Kumari, Mayank Goswami
Date: 2022-05-16 00:00:00
Tasks: CT Reconstruction

Computed Tomography is one of the efficient and vital modalities of non-destructive techniques (NDT). Various factors influence the CT reconstruction result, including limited projection data, detector electronics optimization, background noise, detection noise, discretized nature of projection data, and many more. Radiation hardening and other aging factors that affect the operational settings may require recalibration of electronics parameters. Two well-known exercises are utilized with the motivation to improve reliability and accuracy in inverse recovery. The first exercise brute-forces an optimal candidate from the set of calibration methods for minimum error in inverse recovery. The second exercise, Kanpur Theorem-1 (KT-1) examines if optimal calibration sets electronics to impart minimum noise. The mutual conformity between statistics-derived CLT and Riemann integral transform-based KT-1 is shown first time using gamma radiation measurement. The analysis shows that measurement data with normal distribution inflicts the least noise in inverse recovery.
The Effect of Increased Access to IVF on Women's Careers
Authors: Lingxi Chen
Date: 2022-05-21 00:00:00

Motherhood is the main contributor to gender gaps in the labor market. IVF is a method of assisted reproduction that can delay fertility, which results in decreased motherhood income penalty. In this research, I estimate the effects of expanded access to in vitro fertilization (IVF) arising from state insurance mandates. I use a difference-in-differences model to estimate the effect of increased IVF accessibility for delaying childbirth and decreasing the motherhood income penalty. Using the fertility supplement dataset from the Current Population Survey (CPS), I estimate how outcomes change in states when they implement their mandates compared to how outcomes change in states that are not changing their policies. The results indicate that IVF mandates increase the probability of motherhood by 38 by 3.1 percentage points (p<0.01). However, the results provide no evidence that IVF insurance mandates impact women's earnings.
The Relationship between Digital RMB and Digital Economy in China
Authors: Chang Su, Wenbo Lyu, Yueting Liu
Date: 2022-05-28 00:00:00

By comparing the historical patterns of currency development, this paper pointed out the inevitability of the development of digital currency and the relationship between digital currency and the digital economy. With the example of China, this paper predicts the future development trend of digital currency. In the context of the rapid development of private cryptocurrency, China launched the digital currency based on serving the digital economy and committed to the globalization of the digital renminbi (RMB) and the globalization of the digital economy. The global economy in 2022 ushered in stagnation, and China treats digital fiat currency and the digital economy development as a breakthrough to pursue economic transformation and new growth. It has become one of the forefront countries with numerous experiences that can be learned by countries around the world.
Assortativity in cognition
Authors: Ennio Bilancini, Leonardo Boncinelli, Eugenio Vicario
Date: 2022-05-30 00:00:00

In pairwise interactions assortativity in cognition means that pairs where both decision-makers use the same cognitive process are more likely to occur than what happens under random matching. In this paper we study both the mechanisms determining assortativity in cognition and its effects. In particular, we analyze an applied model where assortativity in cognition helps explain the emergence of cooperation and the degree of prosociality of intuition and deliberation, which are the typical cognitive processes postulated by the dual process theory in psychology. Our findings rely on agent-based simulations, but analytical results are also obtained in a special case. We conclude with examples showing that assortativity in cognition can have different implications in terms of its societal desirability.
Is Lip Region-of-Interest Sufficient for Lipreading?
Authors: Jing-Xuan Zhang, Gen-Shun Wan, Jia Pan
Date: 2022-05-28 00:00:00
Tasks: Lipreading, Self-Supervised Learning, speech-recognition, Speech Recognition, Visual Speech Recognition

Lip region-of-interest (ROI) is conventionally used for visual input in the lipreading task. Few works have adopted the entire face as visual input because lip-excluded parts of the face are usually considered to be redundant and irrelevant to visual speech recognition. However, faces contain much more detailed information than lips, such as speakers' head pose, emotion, identity etc. We argue that such information might benefit visual speech recognition if a powerful feature extractor employing the entire face is trained. In this work, we propose to adopt the entire face for lipreading with self-supervised learning. AV-HuBERT, an audio-visual multi-modal self-supervised learning framework, was adopted in our experiments. Our experimental results showed that adopting the entire face achieved 16% relative word error rate (WER) reduction on the lipreading task, compared with the baseline method using lip as visual input. Without self-supervised pretraining, the model with face input achieved a higher WER than that using lip input in the case of limited training data (30 hours), while a slightly lower WER when using large amount of training data (433 hours).
Deep Representation Decomposition for Rate-Invariant Speaker Verification
Authors: Fuchuan Tong, Siqi Zheng, Haodong Zhou, Xingjia Xie, Qingyang Hong, Lin Li
Date: 2022-05-28 00:00:00
Tasks: Speaker Verification

While promising performance for speaker verification has been achieved by deep speaker embeddings, the advantage would reduce in the case of speaking-style variability. Speaking rate mismatch is often observed in practical speaker verification systems, which may actually degrade the system performance. To reduce intra-class discrepancy caused by speaking rate, we propose a deep representation decomposition approach with adversarial learning to learn speaking rate-invariant speaker embeddings. Specifically, adopting an attention block, we decompose the original embedding into an identity-related component and a rate-related component through multi-task training. Additionally, to reduce the latent relationship between the two decomposed components, we further propose a cosine mapping block to train the parameters adversarially to minimize the cosine similarity between the two decomposed components. As a result, identity-related features become robust to speaking rate and then are used for verification. Experiments are conducted on VoxCeleb1 data and HI-MIA data to demonstrate the effectiveness of our proposed approach.
Image Super-resolution with An Enhanced Group Convolutional Neural Network
Authors: Chunwei Tian, Yixuan Yuan, Shichao Zhang, Chia-Wen Lin, WangMeng Zuo, David Zhang
Date: 2022-05-29 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN.
Q-LIC: Quantizing Learned Image Compression with Channel Splitting
Authors: Heming Sun, Lu Yu, Jiro Katto
Date: 2022-05-28 00:00:00
Tasks: Image Compression, MS-SSIM, Quantization, SSIM

Learned image compression (LIC) has reached a comparable coding gain with traditional hand-crafted methods such as VVC intra. However, the large network complexity prohibits the usage of LIC on resource-limited embedded systems. Network quantization is an efficient way to reduce the network burden. This paper presents a quantized LIC (QLIC) by channel splitting. First, we explore that the influence of quantization error to the reconstruction error is different for various channels. Second, we split the channels whose quantization has larger influence to the reconstruction error. After the splitting, the dynamic range of channels is reduced so that the quantization error can be reduced. Finally, we prune several channels to keep the number of overall channels as origin. By using the proposal, in the case of 8-bit quantization for weight and activation of both main and hyper path, we can reduce the BD-rate by 0.61%-4.74% compared with the previous QLIC. Besides, we can reach better coding gain compared with the state-of-the-art network quantization method when quantizing MS-SSIM models. Moreover, our proposal can be combined with other network quantization methods to further improve the coding gain. The moderate coding loss caused by the quantization validates the feasibility of the hardware implementation for QLIC in the future.
PO-ELIC: Perception-Oriented Efficient Learned Image Coding
Authors: Dailan He, Ziming Yang, Hongjiu Yu, Tongda Xu, Jixiang Luo, Yuan Chen, Chenjian Gao, Xinjie Shi, Hongwei Qin, Yan Wang
Date: 2022-05-28 00:00:00
Tasks: Image Compression, MS-SSIM, SSIM

In the past years, learned image compression (LIC) has achieved remarkable performance. The recent LIC methods outperform VVC in both PSNR and MS-SSIM. However, the low bit-rate reconstructions of LIC suffer from artifacts such as blurring, color drifting and texture missing. Moreover, those varied artifacts make image quality metrics correlate badly with human perceptual quality. In this paper, we propose PO-ELIC, i.e., Perception-Oriented Efficient Learned Image Coding. To be specific, we adapt ELIC, one of the state-of-the-art LIC models, with adversarial training techniques. We apply a mixture of losses including hinge-form adversarial loss, Charbonnier loss, and style loss, to finetune the model towards better perceptual quality. Experimental results demonstrate that our method achieves comparable perceptual quality with HiFiC with much lower bitrate.
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
Authors: Gourav Datta, Souvik Kundu, Zihan Yin, Joe Mathai, Zeyu Liu, Zixu Wang, Mulin Tian, Shunlin Lu, Ravi T. Lakkireddy, Andrew Schmidt, Wael Abd-Almageed, Ajey P. Jacob, Akhilesh R. Jaiswal, Peter A. Beerel
Date: 2022-05-28 00:00:00
Tasks: Autonomous Vehicles, Object, object-detection, Object Detection

Today's high resolution, high frame rate cameras in autonomous vehicles generate a large volume of data that needs to be transferred and processed by a downstream processor or machine learning (ML) accelerator to enable intelligent computing tasks, such as multi-object detection and tracking. The massive amount of data transfer incurs significant energy, latency, and bandwidth bottlenecks, which hinders real-time processing. To mitigate this problem, we propose an algorithm-hardware co-design framework called Processing-in-Pixel-in-Memory-based object Detection and Tracking (P2M-DeTrack). P2M-DeTrack is based on a custom faster R-CNN-based model that is distributed partly inside the pixel array (front-end) and partly in a separate FPGA/ASIC (back-end). The proposed front-end in-pixel processing down-samples the input feature maps significantly with judiciously optimized strided convolution and pooling. Compared to a conventional baseline design that transfers frames of RGB pixels to the back-end, the resulting P2M-DeTrack designs reduce the data bandwidth between sensor and back-end by up to 24x. The designs also reduce the sensor and total energy (obtained from in-house circuit simulations at Globalfoundries 22nm technology node) per frame by 5.7x and 1.14x, respectively. Lastly, they reduce the sensing and total frame latency by an estimated 1.7x and 3x, respectively. We evaluate our approach on the multi-object object detection (tracking) task of the large-scale BDD100K dataset and observe only a 0.5% reduction in the mean average precision (0.8% reduction in the identification F1 score) compared to the state-of-the-art.
Predictive Rate Selection for Ultra-Reliable Communication using Statistical Radio Maps
Authors: Tobias Kallehauge, Pablo Ramìrez-Espinosa, Anders E. Kalør, Christophe Biscio, Petar Popovski
Date: 2022-05-30 00:00:00
Tasks: Gaussian Processes, Position

This paper proposes exploiting the spatial correlation of wireless channel statistics beyond the conventional received signal strength maps by constructing statistical radio maps to predict any relevant channel statistics to assist communications. Specifically, from stored channel samples acquired by previous users in the network, we use Gaussian processes (GPs) to estimate quantiles of the channel distribution at a new position using a non-parametric model. This prior information is then used to select the transmission rate for some target level of reliability. The approach is tested with synthetic data, simulated from urban micro-cell environments, highlighting how the proposed solution helps to reduce the training estimation phase, which is especially attractive for the tight latency constraints inherent to ultra-reliable low-latency (URLLC) deployments.
Abnormal Signal Recognition with Time-Frequency Spectrogram: A Deep Learning Approach
Authors: Tingyan Kuang, Huichao Chen, Lu Han, Rong He, Wei Wang, Guoru Ding
Date: 2022-05-30 00:00:00
Tasks: image-classification, Image Classification

With the increasingly complex and changeable electromagnetic environment, wireless communication systems are facing jamming and abnormal signal injection, which significantly affects the normal operation of a communication system. In particular, the abnormal signals may emulate the normal signals, which makes it very challenging for abnormal signal recognition. In this paper, we propose a new abnormal signal recognition scheme, which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals. Firstly, we emulate synthetic abnormal communication signals including seven jamming patterns. Then, we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver. To improve the performance, we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm. Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations, even under low signal-to-noise ratio (SNR) and low jamming-to-signal ratio (JSR) conditions.