pwc-archive/papers-with-abstracts huggingface dataset:
AI-based Identification of Most Critical Cyberattacks in Industrial Systems
Authors: Bruno Paes Leao, Jagannadh Vempati, Siddharth Bhela, Tobias Ahlgrim, Daniel Arnold
Date: 2023-06-07 00:00:00
Tasks: Decision Making, Sequential Decision Making

Modern industrial systems face a growing threat from sophisticated cyberattacks that can cause significant operational disruptions. This work presents a novel methodology for identification of the most critical cyberattacks that may disrupt the operation of such a system. Application of the proposed framework can enable the design and development of advanced cybersecurity solutions for a wide range of industrial applications. Attacks are assessed taking into direct consideration how they impact the system operation as measured by a defined Key Performance Indicator (KPI). A simulation model (SM), of the industrial process is employed for calculation of the KPI based on operating conditions. Such SM is augmented with a layer of information describing the communication network topology, connected devices, and potential actions an adversary can take based on each device or network link. Each possible action is associated with an abstract measure of effort, which is interpreted as a cost. It is assumed that the adversary has a corresponding budget that constrains the selection of the sequence of actions defining the progression of the attack. A dynamical system comprising a set of states associated with the cyberattack (cyber-states) and transition logic for updating their values is also proposed. The resulting augmented simulation model (ASM) is then employed in an artificial intelligence-based sequential decision-making optimization to yield the most critical cyberattack scenarios as measured by their impact on the defined KPI. The methodology is successfully tested based on an electrical power distribution system use case.
Traction and Stability Control using Fuzzy-based Controller Integration for Electric Vehicles
Authors: Nimantha Dasanayake, Shehara Perera
Date: 2023-06-07 00:00:00

Adverse road conditions can cause vehicle yaw instability and loss of traction. To compensate for the instability under such conditions, corrective actions must be taken. In comparison to a mechanical differential, an electronic differential can independently control the two drive wheels and provide means of generating more effective corrective actions. As a solution for traction and stability issues in automobiles, this study has developed a controller for a vehicle electronic differential consisting of two program-controlled rear motors. The control algorithm adjusts to changing road conditions. Traction was controlled using a motor reaction torque observer-based slip ratio estimation, and yaw stability was achieved by tracking a reference yaw rate calculated using estimated tyre cornering stiffnesses. A recursive least squares algorithm was used to estimate cornering stiffness. The yaw rate of the vehicle, as well as its longitudinal and lateral accelerations, were measured, and the body slip angle was estimated using an observer. A fuzzy inference system was used to integrate the independently developed traction control and yaw control schemes. The fuzzy inference system modifies the commanded voltage generated by the driver's input to account for the traction and yaw stability controller outputs. A vehicle simulator was used to numerically simulate the integrated controller. For the racetrack simulation, the root-mean-square slip ratio error reduced by 96.14% and the RMS yaw rate error reduced by 88.17%. For the double lane change test at 40 km/h and 100 km/h, the RMS yaw rate error reduced by 86.96% and 92.34%, respectively.
Heterogeneous Autoregressions in Short T Panel Data Models
Authors: M. Hashem Pesaran, Liying Yang
Date: 2023-06-08 00:00:00

This paper considers a first-order autoregressive panel data model with individual-specific effects and heterogeneous autoregressive coefficients defined on the interval (-1,1], thus allowing for some of the individual processes to have unit roots. It proposes estimators for the moments of the cross-sectional distribution of the autoregressive (AR) coefficients, assuming a random coefficient model for the autoregressive coefficients without imposing any restrictions on the fixed effects. It is shown the standard generalized method of moments estimators obtained under homogeneous slopes are biased. Small sample properties of the proposed estimators are investigated by Monte Carlo experiments and compared with a number of alternatives, both under homogeneous and heterogeneous slopes. It is found that a simple moment estimator of the mean of heterogeneous AR coefficients performs very well even for moderate sample sizes, but to reliably estimate the variance of AR coefficients much larger samples are required. It is also required that the true value of this variance is not too close to zero. The utility of the heterogeneous approach is illustrated in the case of earnings dynamics.
Losing a Gold Mine?
Authors: Syed Abul Basher, Salim Rashid, Mohammad Riad Uddin
Date: 2023-06-08 00:00:00

Four rounds of surveys of slum dwellers in Dhaka city during the 2020-21 COVID-19 pandemic raise questions about whether the slum dwellers possess some form of immunity to the effects of COVID-19? If the working poor of Bangladesh are practically immune to COVID-19, why has this question not been more actively investigated? We shed light on some explanations for these pandemic questions and draw attention to the role of intellectual elites and public policy, suggesting modifications needed for pandemic research.
Perspectives in closed-loop supply chains network design considering risk and uncertainty factors
Authors: Yang Hu
Date: 2023-06-07 00:00:00

Risk and uncertainty in each stage of CLSC have greatly increased the complexity and reduced process efficiency of the closed-loop networks, impeding the sustainable and resilient development of industries and the circular economy. Recently, increasing interest in academia have been raised on the risk and uncertainty analysis of closed-loop supply chain, yet there is no comprehensive review paper focusing on closed-loop network design considering risk and uncertainty. This paper examines previous research on the domain of closed-loop network design under risk and uncertainties to provide constructive prospects for future study. We selected 106 papers published in the Scopus database from the year 2004 to 2022. We analyse the source of risk and uncertainties of the CLSC network and identified appropriate methods for handling uncertainties in addition to algorithms for solving uncertain CLSCND problems. We also illustrate the evolution of objectives for designing a closed-loop supply chain that is expos to risk or uncertainty, and investigate the application of uncertain network design models in practical industry sectors. Finally, we draw proper research gaps for each category and clarify some novel insights for future study. By considering the impacts of risk or uncertainties of different sources on closed-loop supply chain network design, we can approach the economical, sustainable, social, and resilient objectives effectively and efficiently.
Chevron's Sliding Scale in Wyeth v. Levine, 129 S. Ct. 1187 (2009)
Authors: Gregory M. Dickinson
Date: 2023-06-05 00:00:00

In Wyeth v. Levine the Supreme Court once again failed to reconcile the interpretive presumption against preemption with the sometimes competing Chevron doctrine of deference to agencies' reasonable statutory interpretations. Rather than resolve the issue of which principle should govern where the two principles point toward opposite results, the Court continued its recent practice of applying both principles halfheartedly, carving exceptions, and giving neither its proper weight. This analysis situates Wyeth within the larger framework of the Court's recent preemption decisions in an effort to explain the Court's hesitancy to resolve the conflict. The analysis concludes that the Court, motivated by its strong respect for congressional intent and concern to protect federalism, applies both the presumption against preemption and the Chevron doctrine on a sliding scale. Where congressional intent to preempt is clear and vague only as to scope, the Court is usually quite deferential to agency determinations, but where congressional preemptive intent is unclear, agency views are accorded less weight. The Court's variable approach to deference is defensible as necessary to prevent unauthorized incursion into areas of traditional state sovereignty, but its inherent unpredictability sows confusion among regulated parties, and the need for flexibility prevents the Court from adopting any of the more predictable across-the-board approaches to deference proposed by the Court's critics. A superior approach would combine the Court's concern for federalism with the certainty of a bright-line rule by granting deference to agency views where Congress has spoken via a preemption clause of ambiguous scope and no deference where Congress has remained silent.
TransTIC: Transferring Transformer-based Image Compression from Human Perception to Machine Perception
Authors: Yi-Hsin Chen, Ying-Chieh Weng, Chia-Hao Kao, Cheng Chien, Wei-Chen Chiu, Wen-Hsiao Peng
Date: 2023-06-08 00:00:00
Proceeding: ICCV 2023 1
Tasks: Decoder, Image Compression, Visual Prompt Tuning

This work aims for transferring a Transformer-based image compression codec from human perception to machine perception without fine-tuning the codec. We propose a transferable Transformer-based image compression framework, termed TransTIC. Inspired by visual prompt tuning, TransTIC adopts an instance-specific prompt generator to inject instance-specific prompts to the encoder and task-specific prompts to the decoder. Extensive experiments show that our proposed method is capable of transferring the base codec to various machine tasks and outperforms the competing methods significantly. To our best knowledge, this work is the first attempt to utilize prompting on the low-level image compression task.
JDLL: A library to run Deep Learning models on Java bioimage informatics platforms
Authors: Carlos Garcia Lopez de Haro, Stephane Dallongeville, Thomas Musset, Estibaliz Gomez de Mariscal, Daniel Sage, Wei Ouyang, Arrate Munoz-Barrutia, Jean-Yves Tinevez, Jean-Christophe Olivo-Marin
Date: 2023-06-07 00:00:00

We present JDLL, an agile Java library that offers a comprehensive toolset/API to unify the development of high-end applications of DL for bioimage analysis and to streamline their installation and maintenance. JDLL provides all the functions required to consume DL models seamlessly, without being burdened by the configuration of the Python-based DL frameworks, within Java bioimage informatics platforms. Moreover, it allows the deployment of pre-trained models in the Bioimage Model Zoo (BMZ) by shipping the logic to connect to the BMZ website, download and run a selected model inference.
Rate Forecaster based Energy Aware Band Assignment in Multiband Networks
Authors: Brijesh Soni, Siddhartan Govindasamy, Dhaval K. Patel
Date: 2023-06-08 00:00:00

The high frequency communication bands (mmWave and sub-THz) promise tremendous data rates, however, they also have very high power consumption which is particularly significant for battery-power-limited user-equipment (UE). In this context, we design an energy aware band assignment system which reduces the power consumption while also achieving a target sum rate of M in T time-slots. We do this by using 1) Rate forecaster(s); 2) Channel forecaster(s) which forecasts T direct multistep ahead using a stacked (long short term memory) LSTM architecture. We propose an iterative rate updating algorithm which updates the target rate based on current rate and future predicted rates in a frame. The proposed approach is validated on the publicly available `DeepMIMO' dataset. Research findings shows that the rate forecaster based approach performs better than the channel forecaster. Furthermore, LSTM based predictions outperforms well celebrated Transformer predictions in terms of NRMSE and NMAE. Research findings reveals that the power consumption with this approach is ~ 300 mW lower compared to a greedy band assignment at a 1.5Gb/s target rate.
A Review of the Recent Developments in the Fabrication Processes of CMOS Image Sensors for Smartphones
Authors: Kirthika Nahalingam, Linda P. B. Katehi
Date: 2023-06-08 00:00:00

CMOS Image Sensors are experiencing significant growth due to their capabilities to be integrated in smartphones with refined image quality. One of the major contributions to the growth of image sensors is the innovation brought about in their fabrication processes. This paper presents a detailed review of the different fabrication processes of the CMOS Image Sensors and its impact on the image quality of smartphone pictures. Fabrication of CMOS image sensors using wafer bonding technologies such as Through Silicon Vias and CuCu hybrid bonding along with their experimental results are discussed. A 2 layer architecture of photodiode and pixel transistors has adopted the 3D sequential integration, by which the wafers are bonded together one after the other in the fabrication process. Electrical characteristics and reliability test results are presented for the former two fabrication processes and the improvements in the pixels performance such as conversion gain, quantum efficiency, full well capacity and dynamic range for the 2 layer architecture are discussed.
Iterative Signal Processing for Integrated Sensing and Communication Systems
Authors: Zhiqing Wei, Hanyang Qu, Wangjun Jiang, Kaifeng Han, Huici Wu, Zhiyong Feng
Date: 2023-06-08 00:00:00
Tasks: Integrated sensing and communication, ISAC, Quantization

Integrated sensing and communication (ISAC), with sensing and communication sharing the same wireless resources and hardware, has the advantages of high spectrum efficiency and low hardware cost, which is regarded as one of the key technologies of the fifth generation advanced (5G-A) and sixth generation (6G) mobile communication systems. ISAC has the potential to be applied in the intelligent applications requiring both communication and high accurate sensing capabilities. The fundamental challenges of ISAC system are the ISAC signal design and ISAC signal processing. However, the existing ISAC signal has low anti-noise capability. And the existing ISAC signal processing algorithms have the disadvantages of quantization errors and high complexity, resulting in large energy consumption. In this paper, phase coding is applied in ISAC signal design to improve the anti-noise performance of ISAC signal. Then, the effect of phase coding method on improving the sensing accuracy is analyzed. In order to improve the sensing accuracy with low-complexity algorithm, the iterative ISAC signal processing methods are proposed. The proposed methods improve the sensing accuracy with low computational complexity, realizing energy efficient ISAC signal processing. Taking the scenarios of short distance and long distance sensing into account, the iterative two-dimensional (2D) fast Fourier transform (FFT) and iterative cyclic cross-correlation (CC) methods are proposed, respectively, realizing high sensing accuracy and low computational complexity. Finally, the feasibility of the proposed ISAC signal processing methods are verified by simulation results.
Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss
Authors: Nuwanthika Rajapaksha, Jafar Mohammadi, Stefan Wesemann, Thorsten Wild, Nandana Rajatheva
Date: 2023-06-08 00:00:00
Tasks: Classification, Combinatorial Optimization

Transmit antenna muting (TAM) in multiple-user multiple-input multiple-output (MU-MIMO) networks allows reducing the power consumption of the base station (BS) by properly utilizing only a subset of antennas in the BS. In this paper, we consider the downlink transmission of an MU-MIMO network where TAM is formulated to minimize the number of active antennas in the BS while guaranteeing the per-user throughput requirements. To address the computational complexity of the combinatorial optimization problem, we propose an algorithm called neural antenna muting (NAM) with an asymmetric custom loss function. NAM is a classification neural network trained in a supervised manner. The classification error in this scheme leads to either sub-optimal energy consumption or lower quality of service (QoS) for the communication link. We control the classification error probability distribution by designing an asymmetric loss function such that the erroneous classification outputs are more likely to result in fulfilling the QoS requirements. Furthermore, we present three heuristic algorithms and compare them with the NAM. Using a 3GPP compliant system-level simulator, we show that NAM achieves $\sim73\%$ energy saving compared to the full antenna configuration in the BS with $\sim95\%$ reliability in achieving the user throughput requirements while being around $1000\times$ and $24\times$ less computationally intensive than the greedy heuristic algorithm and the fixed column antenna muting algorithm, respectively.
A Novel Blind Adaptive Beamformer with Robustness against Mutual Coupling and Miscalibration Effects
Authors: M. Yaser Yağan, Ahmet F. Coşkun, Ali E. Pusane
Date: 2023-06-08 00:00:00

Beamforming techniques utilized either at the transmitter or the receiver terminals have achieved superior quality-of-service performances from both the multi-antenna wireless communications systems, communications intelligence and radar target detection perspectives. Despite the overwhelming advantages in ideal operating conditions, beamforming approaches have been shown to face substantial performance degradations due to unknown mutual coupling effects and miscalibrated array elements. As a promising solution, blind beamformers have been proposed as a class of receiver beamformers that do not require a reference signal to operate. In this paper, a novel gradient-based blind beamformer is introduced with the aim of mitigating the deteriorating effects of unknown mutual coupling or miscalibration effects. The proposed approach is shown to find the optimal weights in different antenna array configurations in the presence of several unknown imperfections (e.g., mutual coupling effects, miscalibration effects due to gain and phase variations, inaccurate antenna positions). By providing numerical results related to the proposed algorithm for different array configurations, and bench-marking with the other existing approaches, the proposed scheme has been shown to achieve superior performance in many aspects. Additionally, a measurement-based analysis has been included with validation purposes.
Orthogonal Sampling based Broad-Band Signal Generation with Low-Bandwidth Electronics
Authors: Mohamed I. Hosni, Janosch Meier, Younus Mandalawi, Karanveer Singh, Paulomi Mandal, Ahmed H. Elghandour, Thomas Schneider
Date: 2023-06-08 00:00:00

High-bandwidth signals are needed in many applications like radar, sensing, measurement and communications. Especially in optical networks, the sampling rate and analog bandwidth of digital-to-analog converters (DACs) is a bottleneck for further increasing data rates. To circumvent the sampling rate and bandwidth problem of electronic DACs, we demonstrate the generation of wide-band signals with low-bandwidth electronics. This generation is based on orthogonal sampling with sinc-pulse sequences in N parallel branches. The method not only reduces the sampling rate and bandwidth, at the same time the effective number of bits (ENOB) is improved, dramatically reducing the requirements on the electronic signal processing. In proof of concept experiments the generation of analog signals, as well as Nyquist shaped and normal data will be shown. In simulations we investigate the performance of 60 GHz data generation by 20 and 12 GHz electronics. The method can easily be integrated together with already existing electronic DAC designs and would be of great interest for all high-bandwidth applications.
Neuromorphic Sampling of Signals in Shift-Invariant Spaces
Authors: Abijith Jagannath Kamath, Chandra Sekhar Seelamantula
Date: 2023-06-08 00:00:00

Neuromorphic sampling is a paradigm shift in analog-to-digital conversion where the acquisition strategy is opportunistic and measurements are recorded only when there is a significant change in the signal. Neuromorphic sampling has given rise to a new class of event-based sensors called dynamic vision sensors or neuromorphic cameras. The neuromorphic sampling mechanism utilizes low power and provides high-dynamic range sensing with low latency and high temporal resolution. The measurements are sparse and have low redundancy making it convenient for downstream tasks. In this paper, we present a sampling-theoretic perspective to neuromorphic sensing of continuous-time signals. We establish a close connection between neuromorphic sampling and time-based sampling - where signals are encoded temporally. We analyse neuromorphic sampling of signals in shift-invariant spaces, in particular, bandlimited signals and polynomial splines. We present an iterative technique for perfect reconstruction subject to the events satisfying a density criterion. We also provide necessary and sufficient conditions for perfect reconstruction. Owing to practical limitations in meeting the sufficient conditions for perfect reconstruction, we extend the analysis to approximate reconstruction from sparse events. In the latter setting, we pose signal reconstruction as a continuous-domain linear inverse problem whose solution can be obtained by solving an equivalent finite-dimensional convex optimization program using a variable-splitting approach. We demonstrate the performance of the proposed algorithm and validate our claims via experiments on synthetic signals.
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers
Authors: Su Wang, Rajeev Sahay, Adam Piaseczny, Christopher G. Brinton
Date: 2023-06-08 00:00:00
Tasks: Adversarial Attack, Federated Learning, Model Poisoning

Recent interest in leveraging federated learning (FL) for radio signal classification (SC) tasks has shown promise but FL-based SC remains susceptible to model poisoning adversarial attacks. These adversarial attacks mislead the ML model training process, damaging ML models across the network and leading to lower SC performance. In this work, we seek to mitigate model poisoning adversarial attacks on FL-based SC by proposing the Underlying Server Defense of Federated Learning (USD-FL). Unlike existing server-driven defenses, USD-FL does not rely on perfect network information, i.e., knowing the quantity of adversaries, the adversarial attack architecture, or the start time of the adversarial attacks. Our proposed USD-FL methodology consists of deriving logits for devices' ML models on a reserve dataset, comparing pair-wise logits via 1-Wasserstein distance and then determining a time-varying threshold for adversarial detection. As a result, USD-FL effectively mitigates model poisoning attacks introduced in the FL network. Specifically, when baseline server-driven defenses do have perfect network information, USD-FL outperforms them by (i) improving final ML classification accuracies by at least 6%, (ii) reducing false positive adversary detection rates by at least 10%, and (iii) decreasing the total number of misclassified signals by over 8%. Moreover, when baseline defenses do not have perfect network information, we show that USD-FL achieves accuracies of approximately 74.1% and 62.5% in i.i.d. and non-i.i.d. settings, outperforming existing server-driven baselines, which achieve 52.1% and 39.2% in i.i.d. and non-i.i.d. settings, respectively.