pwc-archive/papers-with-abstracts huggingface dataset:
Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods
Authors: Md Rakibul Hasan, Md Zakir Hossain, Shreya Ghosh, Aneesh Krishna, Tom Gedeon
Date: 2023-10-30 00:00:00
Tasks: Privacy Preserving, Systematic Literature Review

Empathy indicates an individual's ability to understand others. Over the past few years, empathy has drawn attention from various disciplines, including but not limited to Affective Computing, Cognitive Science, and Psychology. Detecting empathy has potential applications in society, healthcare and education. Despite being a broad and overlapping topic, the avenue of empathy detection leveraging Machine Learning remains underexplored from a systematic literature review perspective. We collected 849 papers from 10 well-known academic databases, systematically screened them and analysed the final 82 papers. Our analyses reveal several prominent task formulations - including empathy on localised utterances or overall expressions, unidirectional or parallel empathy, and emotional contagion - in monadic, dyadic and group interactions. Empathy detection methods are summarised based on four input modalities - text, audiovisual, audio and physiological signals - thereby presenting modality-specific network architecture design protocols. We discuss challenges, research gaps and potential applications in the Affective Computing-based empathy domain, which can facilitate new avenues of exploration. We further enlist the public availability of datasets and codes. This paper, therefore, provides a structured overview of recent advancements and remaining challenges towards developing a robust empathy detection system that could meaningfully contribute to enhancing human well-being.
Interactive AI Alignment: Specification, Process, and Evaluation Alignment
Authors: Michael Terry, Chinmay Kulkarni, Martin Wattenberg, Lucas Dixon, Meredith Ringel Morris
Date: 2023-10-23 00:00:00

Modern AI enables a high-level, declarative form of interaction: Users describe the intended outcome they wish an AI to produce, but do not actually create the outcome themselves. In contrast, in traditional user interfaces, users invoke specific operations to create the desired outcome. This paper revisits the basic input-output interaction cycle in light of this declarative style of interaction, and connects concepts in AI alignment to define three objectives for interactive alignment of AI: specification alignment (aligning on what to do), process alignment (aligning on how to do it), and evaluation alignment (assisting users in verifying and understanding what was produced). Using existing systems as examples, we show how these user-centered views of AI alignment can be used descriptively, prescriptively, and as an evaluative aid.
Mathematical Properties of the Zadoff-Chu Sequences
Authors: David Gregoratti, Xavier Arteaga, Joaquim Broquetas
Date: 2023-11-02 00:00:00

This paper is a compilation of well-known results about Zadoff-Chu sequences, including all proofs with a consistent mathematical notation, for easy reference. Moreover, for a Zadoff-Chu sequence $x_u[n]$ of prime length $N_{\text{ZC}}$ and root index $u$, a formula is derived that allows computing the first term (frequency zero) of its discrete Fourier transform, $X_u[0]$, with constant complexity independent of the sequence length, as opposed to accumulating all its $N_{\text{ZC}}$ terms. The formula stems from a famous result in analytic number theory and is an interesting complement to the fact that the discrete Fourier transform of a Zadoff-Chu sequence is itself a Zadoff-Chu sequence whose terms are scaled by $X_u[0]$. Finally, the paper concludes with a brief analysis of time-continuous signals derived from Zadoff-Chu sequences, especially those obtained by OFDM-modulating a Zadoff-Chu sequence.
Intelligent Surface Empowered Integrated Sensing and Communication: From Coexistence to Reciprocity
Authors: Kaitao Meng, Qingqing Wu, Christos Masouros, Wen Chen, Deshi Li
Date: 2023-11-01 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has attracted growing interests for sixth-generation (6G) and beyond wireless networks. The primary challenges faced by highly efficient ISAC include limited sensing and communication (S&C) coverage, constrained integration gain between S&C under weak channel correlations, and unknown performance boundary. Intelligent reflecting/refracting surfaces (IRSs) can effectively expand S&C coverage and control the degree of freedom of channels between the transmitters and receivers, thereby realizing increasing integration gains. In this work, we first delve into the fundamental characteristics of IRS-empowered ISAC and innovative IRS-assisted sensing architectures. Then, we discuss various objectives for IRS channel control and deployment optimization in ISAC systems. Furthermore, the interplay between S&C in different deployment strategies is investigated and some promising directions for IRS enhanced ISAC are outlined.
Multi-functional OFDM Signal Design for Integrated Sensing, Communications, and Power Transfer
Authors: Yumeng Zhang, Sundar Aditya, Bruno Clerckx
Date: 2023-10-31 00:00:00

The wireless domain is witnessing a flourishing of integrated systems, e.g. (a) integrated sensing and communications, and (b) simultaneous wireless information and power transfer, due to their potential to use resources (spectrum, power) judiciously. Inspired by this trend, we investigate integrated sensing, communications and powering (ISCAP), through the design of a wideband OFDM signal to power a sensor while simultaneously performing target-sensing and communication. To characterize the ISCAP performance region, we assume symbols with non-zero mean asymmetric Gaussian distribution (i.e., the input distribution), and optimize its mean and variance at each subcarrier to maximize the harvested power, subject to constraints on the achievable rate (communications) and the average side-to-peak-lobe difference (sensing). The resulting input distribution, through simulations, achieves a larger performance region than that of (i) a symmetric complex Gaussian input distribution with identical mean and variance for the real and imaginary parts, (ii) a zero-mean symmetric complex Gaussian input distribution, and (iii) the superposed power-splitting communication and sensing signal (the coexisting solution). In particular, the optimized input distribution balances the three functions by exhibiting the following features: (a) symbols in subcarriers with strong communication channels have high variance to satisfy the rate constraint, while the other symbols are dominated by the mean, forming a relatively uniform sum of mean and variance across subcarriers for sensing; (b) with looser communication and sensing constraints, large absolute means appear on subcarriers with stronger powering channels for higher harvested power. As a final note, the results highlight the great potential of the co-designed ISCAP system for further efficiency enhancement.
Minimum Snap Trajectory Generation and Control for an Under-actuated Flapping Wing Aerial Vehicle
Authors: Chen Qian, Rui Chen, Peiyao Shen, Yongchun Fang, Jifu Yan, Tiefeng Li
Date: 2023-11-02 00:00:00

Minimum Snap Trajectory Generation and Control for an Under-actuated Flapping Wing Aerial VehicleThis paper presents both the trajectory generation and tracking control strategies for an underactuated flapping wing aerial vehicle (FWAV). First, the FWAV dynamics is analyzed in a practical perspective. Then, based on these analyses, we demonstrate the differential flatness of the FWAV system, and develop a general-purpose trajectory generation strategy. Subsequently, the trajectory tracking controller is developed with the help of robust control and switch control techniques. After that, the overall system asymptotic stability is guaranteed by Lyapunov stability analysis. To make the controller applicable in real flight, we also provide several instructions. Finally, a series of experiment results manifest the successful implementation of the proposed trajectory generation strategy and tracking control strategy. This work firstly achieves the closed-loop integration of trajectory generation and control for real 3-dimensional flight of an underactuated FWAV to a practical level.
Synthesis and verification of robust-adaptive safe controllers
Authors: Simin Liu, Kai S. Yun, John M. Dolan, Changliu Liu
Date: 2023-11-01 00:00:00

Safe control with guarantees generally requires the system model to be known. It is far more challenging to handle systems with uncertain parameters. In this paper, we propose a generic algorithm that can synthesize and verify safe controllers for systems with constant, unknown parameters. In particular, we use robust-adaptive control barrier functions (raCBFs) to achieve safety. We develop new theories and techniques using sum-of-squares that enable us to pose synthesis and verification as a series of convex optimization problems. In our experiments, we show that our algorithms are general and scalable, applying them to three different polynomial systems of up to moderate size (7D). Our raCBFs are currently the most effective way to guarantee safety for uncertain systems, achieving 100% safety and up to 55% performance improvement over a robust baseline.
Adaptive Control of Euler-Lagrange Systems under Time-varying State Constraints without a Priori Bounded Uncertainty
Authors: Viswa Narayanan Sankaranarayanan, Sumeet Gajanan Satpute, Spandan Roy, George Nikolakopoulos
Date: 2023-10-31 00:00:00

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the overall efficacy of the proposed scheme is verified using a simulated robotic arm compared to the state of the art.
C2C: Cough to COVID-19 Detection in BHI 2023 Data Challenge
Authors: Woo-Jin Chung, Miseul Kim, Hong-Goo Kang
Date: 2023-11-01 00:00:00
Tasks: COVID-19 Diagnosis, Data Augmentation, Diagnostic

This report describes our submission to BHI 2023 Data Competition: Sensor challenge. Our Audio Alchemists team designed an acoustic-based COVID-19 diagnosis system, Cough to COVID-19 (C2C), and won the 1st place in the challenge. C2C involves three key contributions: pre-processing of input signals, cough-related representation extraction leveraging Wav2vec2.0, and data augmentation. Through experimental findings, we demonstrate C2C's promising potential to enhance the diagnostic accuracy of COVID-19 via cough signals. Our proposed model achieves a ROC-AUC value of 0.7810 in the context of COVID-19 diagnosis. The implementation details and the python code can be found in the following link: https://github.com/Woo-jin-Chung/BHI_2023_challenge_Audio_Alchemists
Genetic Algorithm-based Beamforming in Subarray Architectures for GEO Satellites
Authors: Juan Andrés Vásquez-Peralvo, Jorge Querol, Eva Lagunas, Flor Ortiz, Luis Manuel Garcés-Socarrás, Jorge Luis González-Rios, Victor Monzon Baeza, Symeon Chatzinotas
Date: 2023-11-02 00:00:00

The incorporation of subarrays in Direct Radiating Arrays for satellite missions is fundamental in reducing the number of Radio Frequency chains, which correspondingly diminishes cost, power consumption, space, and mass. Despite the advantages, previous beamforming schemes incur significant losses during beam scanning, particularly when hybrid beamforming is not employed. Consequently, this paper introduces an algorithm capable of compensating for these losses by increasing the power, for this, the algorithm will activate radiating elements required to address a specific Effective Isotropic Radiated Power for a beam pattern over Earth, projected from a GeoStationary satellite. In addition to the aforementioned compensation, other beam parameters have been addressed in the algorithm, such as beamwidth and Side Lobe Levels. To achieve these objectives, we propose employing the array thinning concept through the use of genetic algorithms, which enable beam shaping with the desired characteristics and power. The full array design considers an open-ended waveguide, configured to operate in circular polarization within the Ka-band frequency range of 17.7-20.2 GHz.
Stabilizing Large-Scale Electric Power Grids with Adaptive Inertia
Authors: Julian Fritzsch, Philippe Jacquod
Date: 2023-11-02 00:00:00

The stability of AC power grids relies on ancillary services that mitigate frequency fluctuations. The electromechanical inertia of large synchronous generators is currently the only resource to absorb frequency disturbances on sub-second time scales. Replacing standard thermal power plants with inertialess new renewable sources of energy (NRE) therefore jeopardizes grid stability against e.g. sudden power generation losses. To guarantee system stability and compensate the lack of electromechanical inertia in grids with large penetrations of NREs, virtual synchronous generators, that emulate conventional generators, have been proposed. Here, we propose a novel control scheme for virtual synchronous generators, where the provided inertia is large at short times -- thereby absorbing faults as efficiently as conventional generators -- but decreases over a tunable time scale to prevent coherent frequency oscillations from setting in. We evaluate the performance of this adaptive inertia scheme under sudden power losses in large-scale transmission grids. We find that it systematically outperforms conventional, electromechanical inertia and that it is more stable than previously suggested schemes. Numerical simulations show how a quasi-optimal geographical distribution of adaptive inertia devices not only absorbs local faults efficiently, but also significantly increases the damping of inter-area oscillations. Our results show that the proposed adaptive inertia control scheme is an excellent solution to strengthen grid stability in future low-inertia power grids with large penetrations of NREs.
Adaptive Identification of SIS Models
Authors: Chi Ho Leung, William E. Retnaraj, Ashish R. Hota, Philip E. Paré
Date: 2023-11-02 00:00:00

Effective containment of spreading processes such as epidemics requires accurate knowledge of several key parameters that govern their dynamics. In this work, we first show that the problem of identifying the underlying parameters of epidemiological spreading processes is often ill-conditioned and lacks the persistence of excitation required for the convergence of adaptive learning schemes. To tackle this challenge, we leverage a relaxed property called initial excitation combined with a recursive least squares algorithm to design an online adaptive identifier to learn the parameters of the susceptible-infected-susceptible (SIS) epidemic model from the knowledge of its states. We prove that the iterates generated by the proposed algorithm minimize an auxiliary weighted least squares cost function. We illustrate the convergence of the error of the estimated epidemic parameters via several numerical case studies and compare it with results obtained using conventional approaches.
Experiencing Urban Air Mobility: How Passengers evaluate a simulated flight with an Air Taxi
Authors: Anne Papenfuss, Maria Stolz, Nele Riedesel, Franziska Dunkel, Johannes Maria Ernst, Tim Laudien, Helge Lenz, Aytek Korkmaz, Albert End, Bianca Isabella Schuchardt
Date: 2023-11-02 00:00:00
Tasks: Mixed Reality, Novel Concepts

For the successful development and implementation of novel concepts and technology, the acceptance of potential users is crucial. Therefore, within the project HorizonUAM of the German Aerospace Center (DLR), we investigated passengers' acceptance of air taxis. One challenge is that not many people have real experiences with urban air mobility (UAM) at the moment and thus requirements formulated by potential users refer to rather abstract concepts. To allow participants to gain realistic impressions of UAM concepts, a Mixed Reality (MR) Air Taxi Simulator was set up. In a study, 30 participants experienced an inner-city business shuttle flight. We assessed the influence of another person on board on wellbeing and information needs in nominal (experiment 1) and non-nominal situations (experiment 2). For the latter, participants experienced a re-routing of the flight due to landing side unavailability. During and after the flights, participants answered questionnaires and extensive interviews were conducted. The study produced empirical data on relevant factors regarding interaction, information needs and comfort within an air taxi. The findings show that passengers want to be informed about intentions of the vehicle. The presence of a flight attendant on board is not necessary but can increase wellbeing especially during non-nominal situations.
Dynamic Uploading Scheduling in mmWave-Based Sensor Networks via Mobile Blocker Detection
Authors: Yifei Sun, Bojie Lv, Rui Wang, Haisheng Tan, Francis C. M. Lau
Date: 2023-11-02 00:00:00
Tasks: Scheduling

The freshness of information, measured as Age of Information (AoI), is critical for many applications in next-generation wireless sensor networks (WSNs). Due to its high bandwidth, millimeter wave (mmWave) communication is seen to be frequently exploited in WSNs to facilitate the deployment of bandwidth-demanding applications. However, the vulnerability of mmWave to user mobility typically results in link blockage and thus postponed real-time communications. In this paper, joint sampling and uploading scheduling in an AoI-oriented WSN working in mmWave band is considered, where a single human blocker is moving randomly and signal propagation paths may be blocked. The locations of signal reflectors and the real-time position of the blocker can be detected via wireless sensing technologies. With the knowledge of blocker motion pattern, the statistics of future wireless channels can be predicted. As a result, the AoI degradation arising from link blockage can be forecast and mitigated. Specifically, we formulate the long-term sampling, uplink transmission time and power allocation as an infinite-horizon Markov decision process (MDP) with discounted cost. Due to the curse of dimensionality, the optimal solution is infeasible. A novel low-complexity solution framework with guaranteed performance in the worst case is proposed where the forecast of link blockage is exploited in a value function approximation. Simulations show that compared with several heuristic benchmarks, our proposed policy, benefiting from the awareness of link blockage, can reduce average cost up to 49.6%.
Adaptive Threshold Selection for Set Membership State Estimation with Quantized Measurements
Authors: Marco Casini, Andrea Garulli, Antonio Vicino
Date: 2023-11-01 00:00:00
Tasks: State Estimation

State estimation for discrete-time linear systems with quantized measurements is addressed. By exploiting the set-theoretic nature of the information provided by the quantizer, the problem is cast in the set membership estimation setting. Motivated by the possibility of suitably tuning the quantizer thresholds in sensor networks, the optimal design of adaptive quantizers is formulated in terms of the minimization of the radius of information associated to the state estimation problem. The optimal solution is derived for first-order systems and the result is exploited to design adaptive quantizers for generic systems, minimizing the size of the feasible output signal set. Then, the minimum number of sensor thresholds for which the adaptive quantizers guarantee asymptotic boundedness of the state estimation uncertainty is established. Threshold adaptation mechanisms based on several types of outer approximations of the feasible state set are also proposed. The effectiveness of the designed adaptive quantizers is demonstrated on numerical tests involving a specific case study and randomly generated systems, highlighting the trade off between the resulting estimation uncertainty and the computational burden required by recursive set approximations.
Experimental Validation of a Grid-Aware Optimal Control of Hybrid AC/DC Microgrids
Authors: Willem Lambrichts, Jules Mace, Mario Paolone
Date: 2023-11-01 00:00:00

This paper presents the experimental validation of a grid-aware real-time control method for hybrid AC/DC microgrids. The optimal control is leveraged by the voltage sensitivity coefficients (SC) that are computed analytically using the close-form expression proposed in the authors' previous work. The SCs are based on the unified power flow model for hybrid AC/DC grids that accounts for the AC grid, DC grid, and the Interfacing Converters (IC), which can operate in different control modes, e.g. voltage or power control. The SCs are used to express the grid constraints in the optimal control problem in a fully linear way and, therefore, allow for second- to subsecond control actions. The validation of the model is performed on the hybrid AC/DC grid, available at the EPFL. The network consists of 18 AC nodes, 8 DC nodes, and 4 converters to interface the AC and DC network. The network hosts multiple controllable and uncontrollable resources. The SC-based optimal control is validated in a generic experiment. It is shown that the real-time control is able to control the ICs optimally to redirect power through the DC grid, to avoid grid constraint violations while providing reactive power support to the upper layer AC grid. Furthermore, the computational time of the optimal control is analysed to validate its application in critical real-time applications.
Decentralized PI-control and Anti-windup in Resource Sharing Networks
Authors: Felix Agner, Jonas Hansson, Pauline Kergus, Anders Rantzer, Sophie Tarbouriech, Luca Zaccarian
Date: 2023-11-01 00:00:00

We consider control of multiple stable first-order \review{agents} which have a control coupling described by an M-matrix. These agents are subject to incremental sector-bounded \review{input} nonlinearities. We show that such plants can be globally asymptotically stabilized to a unique equilibrium using fully decentralized proportional-integral controllers equipped with anti-windup and subject to local tuning rules. In addition, we show that when the nonlinearities correspond to the saturation function, the closed loop asymptotically minimizes a weighted 1-norm of the agents state mismatch. The control strategy is finally compared to other state-of-the-art controllers on a numerical district heating example.
Zeroth-Order Feedback-Based Optimization for Distributed Demand Response
Authors: Ruiyang Jin, Yujie Tang, Jie Song
Date: 2023-11-01 00:00:00
Tasks: Distributed Optimization

Distributed demand response is a typical distributed optimization problem that requires coordination among multiple agents to satisfy demand response requirements. However, existing distributed algorithms for this problem still face challenges such as unknown system models, nonconvexity, privacy issues, etc. To address these challenges, we propose and analyze two distributed algorithms, in which the agents do not share their information and instead perform local updates using zeroth-order feedback information to estimate the gradient of the global objective function. One algorithm applies to problems with general convex and compact feasible sets but has higher oracle complexity bounded by $O(d/\epsilon^2)$, while the other algorithm achieves lower complexity bound $O(d/\epsilon)$ but is only applicable to problems with box constraints. We conduct empirical experiments to validate their performance.