pwc-archive/papers-with-abstracts huggingface dataset:
Generalized Nonconvex Approach for Low-Tubal-Rank Tensor Recovery
Authors: Hailin Wang, Feng Zhang, Jianjun Wang, TingWen Huang, Jianwen Huang, and Xinling Liu
Date: 2022-08-04 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2022 8
Tasks: Image Inpainting, Low-Rank Matrix Completion

The tensor-tensor product-induced tensor nuclear norm (t-TNN) (Lu et al., 2020) minimization for low-tubal-rank tensor recovery attracts broad attention recently. However, minimizing the t-TNN faces some drawbacks. For example, the obtained solution could be suboptimal to the original problem due to its loose approximation. In this article, we extract a unified nonconvex surrogate of the tensor tubal rank as a tighter regularizer, which involves many popular nonconvex penalty functions. An iterative reweighted t-TNN algorithm is proposed to solve the resulting generalized nonconvex tubal rank minimization for tensor recovery. It converges to a critical point globally with rigorous proofs based on the Kurdyka-Łojasiwicz property. Furthermore, we provide the theoretical guarantees for exact and robust recovery by developing the tensor null space property. Extensive experiments demonstrate that our approach markedly enhances recovery performance compared with several state-of-the-art convex and nonconvex methods.
A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation
Authors: José-Manuel Peña, Fernando Suárez, Omar Larré, Domingo Ramírez, Arturo Cifuentes
Date: 2023-02-05 00:00:00
Tasks: Portfolio Optimization, Synthetic Data Generation

We propose a new approach to portfolio optimization that utilizes a unique combination of synthetic data generation and a CVaR-constraint. We formulate the portfolio optimization problem as an asset allocation problem in which each asset class is accessed through a passive (index) fund. The asset-class weights are determined by solving an optimization problem which includes a CVaR-constraint. The optimization is carried out by means of a Modified CTGAN algorithm which incorporates features (contextual information) and is used to generate synthetic return scenarios, which, in turn, are fed into the optimization engine. For contextual information we rely on several points along the U.S. Treasury yield curve. The merits of this approach are demonstrated with an example based on ten asset classes (covering stocks, bonds, and commodities) over a fourteen-and-half year period (January 2008-June 2022). We also show that the synthetic generation process is able to capture well the key characteristics of the original data, and the optimization scheme results in portfolios that exhibit satisfactory out-of-sample performance. We also show that this approach outperforms the conventional equal-weights (1/N) asset allocation strategy and other optimization formulations based on historical data only.
A Valid Self-Report is Never Late, Nor is it Early: On Considering the "Right" Temporal Distance for Assessing Emotional Experience
Authors: Bernd Dudzik, Joost Broekens
Date: 2023-01-27 00:00:00
Tasks: valid

Developing computational models for automatic affect prediction requires valid self-reports about individuals' emotional interpretations of stimuli. In this article, we highlight the important influence of the temporal distance between a stimulus event and the moment when its experience is reported on the provided information's validity. This influence stems from the time-dependent and time-demanding nature of the involved cognitive processes. As such, reports can be collected too late: forgetting is a widely acknowledged challenge for accurate descriptions of past experience. For this reason, methods striving for assessment as early as possible have become increasingly popular. However, here we argue that collection may also occur too early: descriptions about very recent stimuli might be collected before emotional processing has fully converged. Based on these notions, we champion the existence of a temporal distance for each type of stimulus that maximizes the validity of self-reports -- a "right" time. Consequently, we recommend future research to (1) consciously consider the potential influence of temporal distance on affective self-reports when planning data collection, (2) document the temporal distance of affective self-reports wherever possible as part of corpora for computational modelling, and finally (3) and explore the effect of temporal distance on self-reports across different types of stimuli.
Investigating the use of ChatGPT for the scheduling of construction projects
Authors: Samuel A. Prieto, Eyob T. Mengiste, Borja García de Soto
Date: 2023-01-27 00:00:00
Tasks: Scheduling

Large language models such as ChatGPT have the potential to revolutionize the construction industry by automating repetitive and time-consuming tasks. This paper presents a study in which ChatGPT was used to generate a construction schedule for a simple construction project. The output from ChatGPT was evaluated by a pool of participants that provided feedback regarding their overall interaction experience and the quality of the output. The results show that ChatGPT can generate a coherent schedule that follows a logical approach to fulfill the requirements of the scope indicated. The participants had an overall positive interaction experience and indicated the great potential of such a tool to automate many preliminary and time-consuming tasks. However, the technology still has limitations, and further development is needed before it can be widely adopted in the industry. Overall, this study highlights the potential of using large language models in the construction industry and the need for further research.
Optimizing Energy-Harvesting Hybrid VLC/RF Networks with Random Receiver Orientation
Authors: Amir Hossein Fahim Raouf, Chethan Kumar Anjinappa, Ismail Guvenc
Date: 2023-02-06 00:00:00

This paper investigates an indoor hybrid visible light communication (VLC) and radio frequency (RF) scenario with two-hop downlink transmission. A light emitting diode (LED) transmits both data and energy via VLC to an energy-harvesting relay node, which then uses the harvested energy to retransmit the decoded information to an RF user in the second phase. The design parameters include the direct current (DC) bias and the time allocation for VLC transmission. We formulate an optimization problem to maximize the data rate under decode-and-forward relaying with fixed receiver orientation. The non-convex problem is decomposed into two sub-problems, solved iteratively by fixing one parameter while optimizing the other. Additionally, we analyze the impact of random receiver orientation on the data rate, deriving closed-form expressions for both VLC and RF rates. An exhaustive search approach is employed to solve the optimization, demonstrating that joint optimization of DC bias and time allocation significantly enhances the data rate compared to optimizing DC bias alone.
BSA-OMP: Beam-Split-Aware Orthogonal Matching Pursuit for THz Channel Estimation
Authors: Ahmet M. Elbir, Symeon Chatzinotas
Date: 2023-02-05 00:00:00

Terahertz (THz)-band has been envisioned for the sixth generation wireless networks thanks to its ultra-wide bandwidth and very narrow beamwidth. Nevertheless, THz-band transmission faces several unique challenges, one of which is beam-split which occurs due to the usage of subcarrier-independent analog beamformers and causes the generated beams at different subcarriers split, and point to different directions. Unlike the prior works dealing with beam-split by employing additional complex hardware components, e.g., time-delayer networks, a beam-split-aware orthogonal matching pursuit (BSA-OMP) approach is introduced to efficiently estimate the THz channel and beamformer design without any additional hardware. Specifically, we design a BSA dictionary comprised of beam-split-corrected steering vectors which inherently include the effect of beam-split so that the proposed BSA-OMP solution automatically yields the beam-split-corrected physical channel directions. Numerical results demonstrate the superior performance of BSA-OMP approach against the existing state-of-the-art techniques.
Simulation algorithms for Markovian and non-Markovian epidemics
Authors: Guohao Dou
Date: 2023-02-06 00:00:00
Tasks: Epidemiology

Researchers have employed stochastic simulations to determine the validity of their theoretical findings and to study analytically intractable spreading dynamics. In both cases, the correctness and efficiency of the simulation algorithm are of paramount importance. We prove in this article that the Next Reaction Method and the non-Markovian Gillespie algorithm, two algorithms for simulating non-Markovian epidemics, are statistically equivalent. We also study the performance and applicability under various circumstances through complexity analyses and numerical experiments. In our numerical simulations, we apply the Next Reaction Method and the Gillespie algorithm to epidemic simulations on time-varying networks and epidemic simulations with cooperative infections. Both tasks have only been done using the Gillespie algorithm, while we show that the Next Reaction Method is a good alternative. We believe this article may also serve as a guide for choosing simulation algorithms that are both correct and efficient for researchers from epidemiology and beyond.
cross-modal fusion techniques for utterance-level emotion recognition from text and speech
Authors: Jiachen Luo, Huy Phan, Joshua Reiss
Date: 2023-02-05 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition

Multimodal emotion recognition (MER) is a fundamental complex research problem due to the uncertainty of human emotional expression and the heterogeneity gap between different modalities. Audio and text modalities are particularly important for a human participant in understanding emotions. Although many successful attempts have been designed multimodal representations for MER, there still exist multiple challenges to be addressed: 1) bridging the heterogeneity gap between multimodal features and model inter- and intra-modal interactions of multiple modalities; 2) effectively and efficiently modelling the contextual dynamics in the conversation sequence. In this paper, we propose Cross-Modal RoBERTa (CM-RoBERTa) model for emotion detection from spoken audio and corresponding transcripts. As the core unit of the CM-RoBERTa, parallel self- and cross- attention is designed to dynamically capture inter- and intra-modal interactions of audio and text. Specially, the mid-level fusion and residual module are employed to model long-term contextual dependencies and learn modality-specific patterns. We evaluate the approach on the MELD dataset and the experimental results show the proposed approach achieves the state-of-art performance on the dataset.
Sparse Resource Allocation for Spreading Processes on Temporal-Switching Networks
Authors: Vera L. J. Somers, Ian R. Manchester
Date: 2023-02-04 00:00:00
Tasks: Misinformation

Spreading processes, e.g. epidemics, wildfires and rumors, are often modeled on static networks. However, their underlying network structures, e.g. changing contacts in social networks, different weather forecasts for wildfires, are due to ever-changing circumstances inherently time-varying in nature. In this paper, we therefore, propose an optimization framework for sparse resource allocation for control of spreading processes over temporal networks with known connectivity patterns. We use convex optimization, in particular exponential cone programming, and dynamic programming techniques to bound and minimize the risk of an undetected outbreak by allocating budgeted resources each time step. We demonstrate with misinformation, epidemic and wildfire examples how the method can provide targeted allocation of resources.
Run Time Assurance for Autonomous Spacecraft Inspection
Authors: Kyle Dunlap, David van Wijk, Kerianne L. Hobbs
Date: 2023-02-06 00:00:00

As autonomous systems become more prevalent in the real world, it is critical to ensure they operate safely. One approach is the use of Run Time Assurance (RTA), which is a real-time safety assurance technique that monitors a primary controller and intervenes to assure safety when necessary. As these autonomous systems become more complex, RTA is useful because it can be designed completely independent of the primary controller. This paper develops several translational motion safety constraints for a multi-agent autonomous spacecraft inspection problem, where all of these constraints can be enforced with RTA. A comparison is made between centralized and decentralized control, where simulations of the inspection problem then demonstrate that RTA can assure safety of all constraints. Monte Carlo analysis is then used to show that no scenarios were found where the centralized RTA cannot assure safety. While some scenarios were found where decentralized RTA cannot assure safety, solutions are discussed to mitigate these failures.
Harmonic Power-Flow Study of Hybrid AC/DC Grids with Converter-Interfaced Distributed Energy Resources
Authors: Johanna Kristin Maria Becker, Andreas Martin Kettner, Yihui Zuo, Mario Paolone
Date: 2023-02-06 00:00:00

As the share of Converter-Interfaced Distributed Energy Resources (CIDERs) in power distribution systems increases, hybrid AC/DC distribution systems are drawing more interest. Indeed, CIDERs usually rely on DC power and hybrid AC/DC grids with few Network-Interfacing Converters (NICs) are a promising solution to decrease installation costs and conversion losses compared to a pure AC grid. However, interactions between the AC and DC subsystem of a hybrid AC/DC grid can lead to undesirable amplification and propagation of harmonics. The authors of this paper have recently proposed a Harmonic Power-Flow (HPF) method that accurately represents the AC power flows including the coupling between different harmonics. The HPF framework is formulated through the mismatch equations of the nodal equations between the grid and the resource models and solved by a Newton Raphson algorithm. This paper updates the HPF method to model hybrid AC/DC grids interconnected through NICs. To this end, the model of the NICs and the resulting coupling between the AC and DC subsystems is included in the mismatch equations and the Jacobian matrix of the algorithm. The updated HPF method is applied to a typical hybrid AC/DC grid, and its accuracy is validated through detailed time-domain simulations with Simulink.
What may future electricity markets look like?
Authors: Pierre Pinson
Date: 2023-02-06 00:00:00

Should the organization, design and functioning of electricity markets be taken for granted? Definitely not. While decades of evolution of electricity markets in countries that committed early to restructure their electric power sector made us believe that we may have found the right and future-proof model, the substantially and rapidly evolving context of our power and energy systems is challenging this idea in many ways. Actually, that situation brings both challenges and opportunities. Challenges include accommodation of renewable energy generation, decentralization and support to investment, while opportunities are mainly that advances in technical and social sciences provide us with many more options in terms of future market design. We here take a holistic point of view, by trying to understand where we are coming from with electricity markets and where we may be going. Future electricity markets should be made fit for purpose by considering them as a way to organize and operate a socio-techno-economic system.
Reach-avoid Verification using Lyapunov Densities
Authors: Bai Xue
Date: 2023-02-06 00:00:00

Reach-avoid analysis combines the construction of safety and specific progress guarantees, and is able to formalize many important engineering problems. In this paper we study the reach-avoid verification problem of systems modelled by ordinary differential equations using Lyapunov densities. Firstly, the weak reach-avoid verification is considered. Given an initial set, a safe set and a target set, the weak reach-avoid verification is to verify whether the reach-avoid property (i.e., the system will enter the target set eventually while staying inside the safe set before the first target hitting time) holds for almost all states in the initial set. We propose two novel sufficient conditions using Lyapunov densities for the weak reach-avoid verification. These two sufficient conditions are shown to be weaker than existing ones, providing more possibilities of verifying weak reach-avoid properties successfully. Then, we generalize these conditions to the verification of reach-avoid properties for all states in the initial set. Finally, an example demonstrates theoretical developments of proposed conditions.
A Stability Analysis for the Reaction Torque Observer-based Sensorless Force Control Systems
Authors: Emre Sariyildiz
Date: 2023-02-06 00:00:00
Tasks: Friction

This paper proposes a new stability analysis for the Reaction Torque Observer (RTOb) based robust force control systems in the discrete-time domain. The robust force controller is implemented by employing a Disturbance Observer (DOb) to suppress disturbances, such as friction and hysteresis, in an inner-loop and another disturbance observer, viz RTOb, to estimate contact forces without using a force sensor. Since the RTOb-based robust force controllers are always implemented using computers and/or microcontrollers, this paper proposes a stability analysis in the discrete-time domain. It is shown that the bandwidth of the DOb is limited not only by the noise of velocity measurement but also by the waterbed effect. It is also shown that the stability of the robust force controller may significantly deteriorate when the design parameters of the RTOb are not properly tuned. For example, the robust force controller may have a non-minimum phase zero(s) as the design parameter of the identified inertia (torque coefficient) of the RTOb is increased (decreased). This may lead to poor stability and performance in force control applications. The proposed stability analysis conducted in the discrete-time domain is verified by simulations and experiments.
An Online Model-Following Projection Mechanism Using Reinforcement Learning
Authors: Mohammed I. Abouheaf, Hashim A. Hashim, Mohammad A. Mayyas, Kyriakos G. Vamvoudakis
Date: 2023-02-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose a model-free adaptive learning solution for a model-following control problem. This approach employs policy iteration, to find an optimal adaptive control solution. It utilizes a moving finite-horizon of model-following error measurements. In addition, the control strategy is designed by using a projection mechanism that employs Lagrange dynamics. It allows for real-time tuning of derived actor-critic structures to find the optimal model-following strategy and sustain optimized adaptation performance. Finally, the efficacy of the proposed framework is emphasized through a comparison with sliding mode and high-order model-free adaptive control approaches. Keywords: Model Reference Adaptive Systems, Reinforcement Learning, adaptive critics, control system, stochastic, nonlinear system
Model-free Quantum Gate Design and Calibration using Deep Reinforcement Learning
Authors: Omar Shindi, Qi Yu, Parth Girdhar, Daoyi Dong
Date: 2023-02-05 00:00:00
Tasks: Deep Reinforcement Learning, Model free quantum gate design, quantum gate calibration, quantum gate design, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

High-fidelity quantum gate design is important for various quantum technologies, such as quantum computation and quantum communication. Numerous control policies for quantum gate design have been proposed given a dynamical model of the quantum system of interest. However, a quantum system is often highly sensitive to noise, and obtaining its accurate modeling can be difficult for many practical applications. Thus, the control policy based on a quantum system model may be unpractical for quantum gate design. Also, quantum measurements collapse quantum states, which makes it challenging to obtain information through measurements during the control process. In this paper, we propose a novel training framework using deep reinforcement learning for model-free quantum control. The proposed framework relies only on the measurement at the end of the control process and offers the ability to find the optimal control policy without access to quantum systems during the learning process. The effectiveness of the proposed technique is numerically demonstrated for model-free quantum gate design and quantum gate calibration using off-policy reinforcement learning algorithms.
Corrected: On Confident Policy Evaluation for Factored Markov Decision Processes with Node Dropouts
Authors: Carmel Fiscko, Soummya Kar, Bruno Sinopoli
Date: 2023-02-05 00:00:00

In this work we investigate an importance sampling approach for evaluating policies for a structurally time-varying factored Markov decision process (MDP), i.e. the policy's value is estimated with a high-probability confidence interval. In particular, we begin with a multi-agent MDP controlled by a known policy but with unknown transition dynamics. One agent is then removed from the system - i.e. the system experiences node dropout - forming a new MDP of the remaining agents, with a new state space, action space, and new transition dynamics. We assume that the effect of removing an agent corresponds to the marginalization of its factor in the transition dynamics. The reward function may likewise be marginalized, or it may be entirely redefined for the new system. Robust policy importance sampling is then used to evaluate candidate policies for the new system, and estimated values are presented with probabilistic confidence bounds. This computation is completed with no observations of the new system, meaning that a safe policy may be found before dropout occurs. The utility of this approach is demonstrated in simulation and compared to Monte Carlo simulation of the new system.
Regret-Guaranteed Safe Switching with Minimum Cost: LQR Setting with Unknown Dynamics
Authors: Jafar Abbaszadeh Chekan, Cedric Langbort
Date: 2023-02-05 00:00:00

Externally Forced Switched (EFS) systems represent a subset of switched systems where switches occur deliberately to meet an external requirement. However, fast switching can lead to instability, even when all closed-loop modes are stable. In this study, our focus is on an EFS scenario with \textit{unknown system dynamics}, where the next mode to switch to is revealed by an external entity in real-time as the switch occurs. The challenge is to track the revealed sequence while (1) minimizing accumulated cost in a regretful sense and (2) ensuring that the norm of the system's state does not grow excessively-a property we refer to as 'the safety of switching.' Achieving the latter involves requiring the closed-loop system to remain in each revealed mode for some minimum dwell time, which must be learned online. We propose an algorithm based on the principles of Optimism in the Face of Uncertainty. This algorithm jointly establishes confidence sets for unknown parameters, devises a feedback policy, and estimates a minimum dwell time for each revealed mode from data. By precisely estimating dwell-time error, our strategy yields an expected regret of $\mathcal{O}(|M| \sqrt{ns})$, where $ns$ and $|M|$ denote the total switches and mode count, respectively. We benchmark this approach against scenarios with known parameters.