pwc-archive/papers-with-abstracts huggingface dataset:
Implementation of Noise-Shaped Signaling System through Software-Defined Radio
Authors: Junsung Choi, Dongryul Park, Suil Kim, Seungyoung Ahn
Date: 2021-11-17 00:00:00

As developments of electromagnetic weapons, Electronic Warfare (EW) has been rising as the future form of war. Especially in wireless communications, the high security defense systems, such as Low Probability of Detection (LPD), Low Probability of Interception (LPI), or Low Prob-ability of Exploitation (LPE) communication algorithms, are studied to prevent the military force loss. One of the LPD, LPI, and LPE communication algorithm, physical-layer security, has been discussed and studied. We propose a noise signaling system, a type of physical-layer secu-rity, which modifies conventionally modulated I/Q data into a noise-like shape. For presenting the possibility of realistic implementation, we use Software-Defined Radio (SDR). Since there are certain limitations of hardware, we present the limitations, requirements, and preferences of practical implementation of noise signaling system, and the proposed system is ring-shaped signaling. We present the ring-shaped signaling system algorithm, SDR implementation meth-odology, and performance evaluations of the system by the metrics of Bit Error Rate (BER) and Probability of Modulation Identification (PMI), which we obtain by Convolutional Neural Net-work (CNN) algorithm. We conclude that the ring-shaped signaling system can perform a high LPI/LPE communication function due to the eavesdropper cannot obtain the correct used modu-lation scheme information, and the performance can vary by the configurations of the I/Q data modifying factors.
Edge Computing and Communication for Energy-Efficient Earth Surveillance with LEO Satellites
Authors: Marc Martinez-Gost, Israel Leyva-Mayorga, Ana Pérez-Neira, Miguel Ángel Vázquez, Beatriz Soret, Marco Moretti
Date: 2021-11-17 00:00:00
Tasks: Edge-computing, Management

Modern satellites deployed in low Earth orbit (LEO) accommodate processing payloads that can be exploited for edge computing. Furthermore, by implementing inter-satellite links, the LEO satellites in a constellation can route the data end-to-end (E2E). These capabilities can be exploited to greatly improve the current store-and-forward approaches in Earth surveillance systems. However, they give rise to an NP-hard problem of joint communication and edge computing resource management (RM). In this paper, we propose an algorithm that allows the satellites to select between computing the tasks at the edge or at a cloud server and to allocate an adequate power for communication. The overall objective is to minimize the energy consumption at the satellites while fulfilling specific service E2E latency constraints for the computing tasks. Experimental results show that our algorithm achieves energy savings of up to 18% when compared to the selected benchmarks with either 1) fixed edge computing decisions or 2) maximum power allocation.
Distributed Dual Gradient Tracking for Economic Dispatch in Power Systems with Noisy Information
Authors: Wenwen Wu, Shuai Liu, Shanying Zhu
Date: 2021-11-17 00:00:00

Distributed algorithms can be efficiently used for solving economic dispatch problem (EDP) in power systems. To implement a distributed algorithm, a communication network is required, making the algorithm vulnerable to noise which may cause detrimental decisions or even instability. In this paper, we propose an agent-based method which enables a fully distributed solution of the EDP in power systems with noisy information exchange. Through the novel design of the gradient tracking update and introducing suppression parameters, the proposed algorithm can effectively alleviate the impact of noise and it is shown to be more robust than the existing distributed algorithms. The convergence of the algorithm is also established under standard assumptions. Moreover, a strategy are presented to accelerate our proposed algorithm. Finally, the algorithm is tested on several IEEE bus systems to demonstrate its effectiveness and scalability.
Adversarial Tradeoffs in Robust State Estimation
Authors: Thomas T. C. K. Zhang, Bruce D. Lee, Hamed Hassani, Nikolai Matni
Date: 2021-11-17 00:00:00
Tasks: State Estimation

Adversarially robust training has been shown to reduce the susceptibility of learned models to targeted input data perturbations. However, it has also been observed that such adversarially robust models suffer a degradation in accuracy when applied to unperturbed data sets, leading to a robustness-accuracy tradeoff. Inspired by recent progress in the adversarial machine learning literature which characterize such tradeoffs in simple settings, we develop tools to quantitatively study the performance-robustness tradeoff between nominal and robust state estimation. In particular, we define and analyze a novel $\textit{adversarially robust Kalman Filtering problem}$. We show that in contrast to most problem instances in adversarial machine learning, we can precisely derive the adversarial perturbation in the Kalman Filtering setting. We provide an algorithm to find this perturbation given data realizations, and develop upper and lower bounds on the adversarial state estimation error in terms of the standard (non-adversarial) estimation error and the spectral properties of the resulting observer. Through these results, we show a natural connection between a filter's robustness to adversarial perturbation and underlying control theoretic properties of the system being observed, namely the spectral properties of its observability gramian.
NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems
Authors: Xiaowu Sun, Yasser Shoukry
Date: 2021-11-17 00:00:00
Tasks: Imitation Learning

In this paper, we introduce NNSynth, a new framework that uses machine learning techniques to guide the design of abstraction-based controllers with correctness guarantees. NNSynth utilizes neural networks (NNs) to guide the search over the space of controllers. The trained neural networks are "projected" and used for constructing a "local" abstraction of the system. An abstraction-based controller is then synthesized from such "local" abstractions. If a controller that satisfies the specifications is not found, then the best found controller is "lifted" to a neural network for additional training. Our experiments show that this neural network-guided synthesis leads to more than $50\times$ or even $100\times$ speedup in high dimensional systems compared to the state-of-the-art.
One hundred percent renewable energy generation in 2030 with the lowest cost commercially available power plants
Authors: Manfred G. Kratzenberg, Hans Helmut Zürn, Ricardo Rüther
Date: 2021-11-16 00:00:00

We hypothesize that the present expansion of energy generation by variable renewable energy (VRE) power plants, such as wind and photovoltaic power plants, leads to a 100% renewable energy supply in 2030 because of its inherent exponential growth function. This behavior is related to the exponential cost reduction of its generated energy and the nearly unconstrained available potential of its natural resources. The cost reduction results from the continuous improvements in development, research, manufacturing, and installation, also showing a growth of its installation power per power plant or aero generator. We prove that if the historic exponential growth is followed in the future, it is possible to decarbonize the world's electric energy systems' power supply in 2026. Furthermore, the global demand on primary energy can be supplied in 2030, which leads to the total suppression of CO2 emissions related to the energy need of humanity. Because of the related cost reduction, energy costs are not anymore relevant. Our extrapolation is based on the continuation of the historic growth functions of the globally installed PV and the wind power plants, and we also discuss the conditions necessary to enable a transition to such a 100% renewable energy production. Considering a non-constrained growth of VRE power plants' installation power, decarbonization related to energy generation and use can be accomplished in a much shorter time frame as previously scheduled. As a result, climate change mitigation, energy cost reduction, and high employment are attained much earlier than previously planned.
Formal Synthesis of Controllers for Uncertain Linear Systems against $ω$-Regular Properties: A Set-based Approach
Authors: Bingzhuo Zhong, Majid Zamani, Marco Caccamo
Date: 2021-11-16 00:00:00

In this paper, we present how to synthesize controllers to enforce $\omega$-regular properties over linear control systems affected by bounded disturbances. In particular, these controllers are synthesized based on so-called hybrid controlled invariant (HCI) sets. To compute these sets, we first construct a product system between the linear control system and the deterministic Streett automata (DSA) modeling the desired property. Then, we compute the maximal HCI set over the state set of the product system by leveraging a set-based approach. To ensure termination of the computation of the HCI sets within a finite number of iterations, we also propose two iterative schemes to compute approximations of the maximal HCI set. Finally, we show the effectiveness of our results via two case studies.
Scaling of population resilience with dispersal length and habitat size
Authors: Rodrigo Crespo-Miguel, Javier Jarillo, Francisco J. Cao-García
Date: 2021-11-16 00:00:00

Environmental fluctuations can create population-depleted areas and even extinct areas for the population. This effect is more severe in the presence of the Allee effect (decreasing growth rate at low population densities). Dispersal inside the habitat provides a rescue effect on population-depleted areas, enhancing the population resilience to environmental fluctuations. Habitat reduction decreases the effectiveness of the dispersal rescue mechanism. We report here how the population resilience to environmental fluctuations decreases when the dispersal length or the habitat size are reduced. The resilience reduction is characterized by a decrease of the extinction threshold for environmental fluctuations. The extinction threshold is shown to scale with the ratio between the dispersal length and the scale of environmental synchrony, i.e., it is the dispersal connection between non-environmentally-correlated regions that provides resilience to environmental fluctuations. Habitat reduction also decreases the resilience to environmental fluctuations, when the habitat size is similar to or smaller than the characteristic dispersal distances. The power laws of these scaling behaviors are characterized here. Alternative scaling functions with spatial scales of population synchrony are found to fit the simulations worse. These results support the dispersal length as the critical scale for extinction induced by habitat reduction.
A simple model of epidemic dynamics with memory effects
Authors: Michael Bestehorn, Thomas M. Michelitsch, Bernard A. Collet, Alejandro P. Riascos, Andrzej F. Nowakowski
Date: 2021-11-17 00:00:00

We introduce a modified SIR model with memory for the dynamics of epidemic spreading in a constant population of individuals. Each individual is in one of the states susceptible (${\bf S}$), infected (${\bf I}$) or recovered (${\bf R}$). In the state ${\bf R}$ an individual is assumed to stay immune within a finite time interval. In the first part, we introduce a random life time or duration of immunity which is drawn from a certain probability density function. Once the time of immunity is elapsed an individual makes an instantaneous transition to the susceptible state. By introducing a random duration of immunity a memory effect is introduced into the process which crucially determines the epidemic dynamics. In the second part, we investigate the influence of the memory effect on the space-time dynamics of the epidemic spreading by implementing this approach into computer simulations and employ a multiple random walker's model. If a susceptible walker meets an infectious one on the same site, then the susceptible one gets infected with a certain probability. The computer experiments allow us to identify relevant parameters for spread or extinction of an epidemic. In both parts, the finite duration of immunity causes persistent oscillations in the number of infected individuals with ongoing epidemic activity preventing the system from relaxation to a steady state solution. Such oscillatory behavior is supported by real-life observations and cannot be captured by standard SIR models.
Effect of decreasing population growth-rate on deforestation and population sustainability
Authors: Gerardo Aquino, Mauro Bologna
Date: 2021-11-16 00:00:00

We consider the effect of non-constant parameters on the human-forest interaction logistic model coupled with human technological growth introduced in "Deforestation and world population sustainability: a quantitative analysis"[1]. In recent years in fact, a decrease in human population growth rate has emerged which can be measured to about 1.7% drop per year since 1960 value which coincides with latest UN projections for next decades up to year 2100 [2]. We therefore consider here the effect of decreasing human population growth-rate on the aforementioned model and we evaluate its effect on the probability of survival of human civilisation without going through a catastrophic collapse in population. We find that for realistic values of the human population carrying capacity of the earth (measured by parameter beta) this decrease would not affect previous results leading to a low probability of avoiding a catastrophic collapse. For larger more optimistic values of beta instead, a decrease in growth-rate would tilt the probability in favour of a positive outcome, i.e. from 10-20% up to even 95% likelihood of avoiding collapse.
Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects
Authors: Jiti Gao, Fei Liu, Bin Peng, Yayi Yan
Date: 2020-12-06 00:00:00

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we establish a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and achieve the asymptotic distributions accordingly. In addition, we conduct intensive simulations to examine the theoretical findings. In the empirical study, we focus on the sign prediction of stock returns, and then use the results of sign forecast to conduct portfolio analysis.
A Hierarchical Multi-Task Approach to Gastrointestinal Image Analysis
Authors: Adrian Galdran, Gustavo Carneiro, Miguel A. González Ballester
Date: 2021-11-16 00:00:00
Tasks: Segmentation

A large number of different lesions and pathologies can affect the human digestive system, resulting in life-threatening situations. Early detection plays a relevant role in the successful treatment and the increase of current survival rates to, e.g., colorectal cancer. The standard procedure enabling detection, endoscopic video analysis, generates large quantities of visual data that need to be carefully analyzed by an specialist. Due to the wide range of color, shape, and general visual appearance of pathologies, as well as highly varying image quality, such process is greatly dependent on the human operator experience and skill. In this work, we detail our solution to the task of multi-category classification of images from the gastrointestinal (GI) human tract within the 2020 Endotect Challenge. Our approach is based on a Convolutional Neural Network minimizing a hierarchical error function that takes into account not only the finding category, but also its location within the GI tract (lower/upper tract), and the type of finding (pathological finding/therapeutic intervention/anatomical landmark/mucosal views' quality). We also describe in this paper our solution for the challenge task of polyp segmentation in colonoscopies, which was addressed with a pretrained double encoder-decoder network. Our internal cross-validation results show an average performance of 91.25 Mathews Correlation Coefficient (MCC) and 91.82 Micro-F1 score for the classification task, and a 92.30 F1 score for the polyp segmentation task. The organization provided feedback on the performance in a hidden test set for both tasks, which resulted in 85.61 MCC and 86.96 F1 score for classification, and 91.97 F1 score for polyp segmentation. At the time of writing no public ranking for this challenge had been released.
Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification
Authors: Merel Kuijs, Catherine R. Jutzeler, Bastian Rieck, Sarah C. Brüningk
Date: 2021-11-15 00:00:00

Owing to its pristine soft-tissue contrast and high resolution, structural magnetic resonance imaging (MRI) is widely applied in neurology, making it a valuable data source for image-based machine learning (ML) and deep learning applications. The physical nature of MRI acquisition and reconstruction, however, causes variations in image intensity, resolution, and signal-to-noise ratio. Since ML models are sensitive to such variations, performance on out-of-distribution data, which is inherent to the setting of a deployed healthcare ML application, typically drops below acceptable levels. We propose an interpretability aware adversarial training regime to improve robustness against out-of-distribution samples originating from different MRI hardware. The approach is applied to 1.5T and 3T MRIs obtained from the Alzheimer's Disease Neuroimaging Initiative database. We present preliminary results showing promising performance on out-of-distribution samples.
Survey on Symbiotic Radio: A Paradigm Shift in Spectrum Sharing and Coexistence
Authors: Muhammad Bilal Janjua, Huseyin Arslan
Date: 2021-11-17 00:00:00

Sixth-generation (6G) of mobile communication aims to connect this world digitally through green communication networks that provide secure, ubiquitous, and unlimited connectivity in an attempt to improve the overall quality of life.The driving force behind the development of these networks is the rapid evolution of Internet-of-Things (IoT), which has stimulated the proliferation of wireless applications in health,education, agriculture, utilities, etc. However, these applications are accompanied by the deployment of a massive number of IoT devices that require a significant radio spectrum for wireless connectivity. IoT devices usually have low data rate requirements and limited power provision but desirably a long life.Recently, the development of passive radio systems has opened new paradigms of spectrum sharing and coexistence. These systems utilize the radio resources and infrastructure of the active radio systems to perform their functionalities. By enabling the dependent coexistence, a new technology named symbiotic radio (SRad) enables the symbiotic relationships between the different radio systems ranging from mutual benefits or competition in terms of sharing the resources, in particular for IoT devices. This survey first provides the motivation for dependent coexistence and background of spectrum sharing through coexistence along with existing literature. Then, it describes the active and passive radio systems definition and a brief overview. Afterward, the history of symbiosis and the role of SRad technology in spectrum sharing and coexistence are defined while focusing on symbiotic communication. Lastly, we discuss research challenges, future directions, and applications scenarios.
Reconstruction-Computation-Quantization (RCQ): A Paradigm for Low Bit Width LDPC Decoding
Authors: Linfang Wang, Caleb Terrill, Maximilian Stark, Zongwang Li, Sean Chen, Chester Hulse, Calvin Kuo, Richard Wesel, Gerhard Bauch, Rekha Pitchumani
Date: 2021-11-17 00:00:00
Tasks: Decoder, Quantization

This paper uses the reconstruction-computation-quantization (RCQ) paradigm to decode low-density parity-check (LDPC) codes. RCQ facilitates dynamic non-uniform quantization to achieve good frame error rate (FER) performance with very low message precision. For message-passing according to a flooding schedule, the RCQ parameters are designed by discrete density evolution (DDE). Simulation results on an IEEE 802.11 LDPC code show that for 4-bit messages, a flooding MinSum RCQ decoder outperforms table-lookup approaches such as information bottleneck (IB) or Min-IB decoding, with significantly fewer parameters to be stored. Additionally, this paper introduces layer-specific RCQ (LS-RCQ), an extension of RCQ decoding for layered architectures. LS-RCQ uses layer-specific message representations to achieve the best possible FER performance. For LS-RCQ, this paper proposes using layered DDE featuring hierarchical dynamic quantization (HDQ) to design LS-RCQ parameters efficiently. Finally, this paper studies field-programmable gate array (FPGA) implementations of RCQ decoders. Simulation results for a (9472, 8192) quasi-cyclic (QC) LDPC code show that a layered MinSum RCQ decoder with 3-bit messages achieves more than a $10\%$ reduction in LUTs and routed nets and more than a $6\%$ decrease in register usage while maintaining comparable decoding performance, compared to a 5-bit offset MinSum decoder.
A TCN-based Hybrid Forecasting Framework for Hours-ahead Utility-scale PV Forecasting
Authors: Yiyan Li, Lidong Song, Si Zhang, Laura Kraus, Taylor Adcox, Roger Willardson, Abhishek Komandur, Ning Lu
Date: 2021-11-16 00:00:00

This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven cloud-event forecasting (CF) model. Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the CF model to achieve more accurate forecast of intra-hour PV power drops, and iii) reconciling TF and CF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved. To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the CF model on capturing large power fluctuations caused by cloud movements. The framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina. Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by approximately 20% compared with state-of-the-art methods.