pwc-archive/papers-with-abstracts huggingface dataset:
Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy Perturbation
Authors: Kairi Furui, Masahito Ohue
Date: 2023-04-10 00:00:00
Tasks: Drug Discovery

In recent years, free energy perturbation (FEP) calculations have garnered increasing attention as tools to support drug discovery. The lead optimization mapper (Lomap) was proposed as an algorithm to calculate the relative free energy between ligands efficiently. However, Lomap requires checking whether each edge in the FEP graph is removable, which necessitates checking the constraints for all edges. Consequently, conventional Lomap requires significant computation time, at least several hours for cases involving hundreds of compounds, and is impractical for cases with more than tens of thousands of edges. In this study, we aimed to reduce the computational cost of Lomap to enable the construction of FEP graphs for hundreds of compounds. We can reduce the overall number of constraint checks required from an amount dependent on the number of edges to one dependent on the number of nodes by using the chunk check process to check the constraints for as many edges as possible simultaneously. Moreover, the output graph is equivalent to that obtained using conventional Lomap, enabling direct replacement of the original Lomap with our method. With our improvement, the execution was tens to hundreds of times faster than that of the original Lomap. https://github.com/ohuelab/FastLomap
Who are the gatekeepers of economics? Geographic diversity, gender composition, and interlocking editorship of journal boards
Authors: Alberto Baccini, Cristina Re
Date: 2023-04-09 00:00:00
Tasks: Diversity

This study investigates the role of editorial board members as gatekeepers in science, creating and utilizing a database of 1,516 active economics journals in 2019, which includes more than 44,000 scholars from over 6,000 institutions and 142 countries. The composition of these editorial boards is explored in terms of geographic affiliation, institutional affiliation, and gender. Results highlight that the academic publishing environment is primarily governed by men affiliated with elite universities in the United States. The study further explores social similarities among journals using a network analysis perspective based on interlocking editorship. Comparison of networks generated by all scholars, editorial leaders, and non-editorial leaders reveals significant structural similarities and associations among clusters of journals. These results indicate that links between pairs of journals tend to be redundant, and this can be interpreted in terms of social and intellectual homophily within each board, and between boards of journals belonging to the same cluster. Finally, the analysis of the most central journals and scholars in the networks suggests that journals probably adopt 'strategic decisions' in the selection of the editorial board members. The documented high concentration of editorial power poses a serious risk to innovative research in economics.
Optical Wireless Communications Using Intelligent Walls
Authors: Anil Yesilkaya, Hanaa Abumarshoud, Harald Haas
Date: 2023-04-08 00:00:00

This chapter is devoted to discussing the integration of intelligent reflecting surfaces (IRSs), or intelligent walls, in optical wireless communication (OWC) systems. IRS technology is a revolutionary concept that enables communication systems to harness the surrounding environment to control the propagation of light signals. Based on this, specific key performance indicators could be achieved by altering the electromagnetic response of the IRSs. In the following, we discuss the background theory and applications of IRSs and present a case study for an IRS-assisted indoor light-fidelity (LiFi) system. We then highlight some of the challenges related to this emerging concept and elaborate on future research directions.
On algorithmically boosting fixed-point computations
Authors: Ioannis Avramopoulos, Nikolaos Vasiloglou
Date: 2023-04-04 00:00:00

The main topic of this paper are algorithms for computing Nash equilibria. We cast our particular methods as instances of a general algorithmic abstraction, namely, a method we call {\em algorithmic boosting}, which is also relevant to other fixed-point computation problems. Algorithmic boosting is the principle of computing fixed points by taking (long-run) averages of iterated maps and it is a generalization of exponentiation. We first define our method in the setting of nonlinear maps. Secondly, we restrict attention to convergent linear maps (for computing dominant eigenvectors, for example, in the PageRank algorithm) and show that our algorithmic boosting method can set in motion {\em exponential speedups in the convergence rate}. Thirdly, we show that algorithmic boosting can convert a (weak) non-convergent iterator to a (strong) convergent one. We also consider a {\em variational approach} to algorithmic boosting providing tools to convert a non-convergent continuous flow to a convergent one. Then, by embedding the construction of averages in the design of the iterated map, we constructively prove the existence of Nash equilibria (and, therefore, Brouwer fixed points). We then discuss implementations of averaging and exponentiation, an important matter even for the scalar case. We finally discuss a relationship between dominant (PageRank) eigenvectors and Nash equilibria.
Electromagnetic Interference Cancellation for RIS-Assisted Communications
Authors: Aymen Khaleel, Ertugrul Basar
Date: 2023-04-10 00:00:00

Reconfigurable intelligent surface (RIS)-empowered communication is an emerging technology that has recently received growing attention as a potential candidate for next-generation wireless communications. Although RISs have shown the potential of manipulating the wireless channel through passive beamforming, it is shown that they can also bring undesired side effects, such as reflecting the electromagnetic interference (EMI) from the surrounding environment to the receiver side. In this study, we propose a novel EMI cancellation scheme to mitigate the impact of the EMI by exploiting its special time-domain structure and considering a clever passive beamforming method at the RIS. Compared to its benchmark, computer simulations show that the proposed scheme achieves superior performance in terms of the average signal-to-interference-plus-noise ratio (SINR) and outage probability (OP), especially when the EMI power is comparable to the power of the information signal impinging on the RIS surface.
Safe Explicable Planning
Authors: Akkamahadevi Hanni, Andrew Boateng, Yu Zhang
Date: 2023-04-04 00:00:00
Tasks: AI Agent, Decision Making

Human expectations arise from their understanding of others and the world. In the context of human-AI interaction, this understanding may not align with reality, leading to the AI agent failing to meet expectations and compromising team performance. Explicable planning, introduced as a method to bridge this gap, aims to reconcile human expectations with the agent's optimal behavior, facilitating interpretable decision-making. However, an unresolved critical issue is ensuring safety in explicable planning, as it could result in explicable behaviors that are unsafe. To address this, we propose Safe Explicable Planning (SEP), which extends the prior work to support the specification of a safety bound. The goal of SEP is to find behaviors that align with human expectations while adhering to the specified safety criterion. Our approach generalizes the consideration of multiple objectives stemming from multiple models rather than a single model, yielding a Pareto set of safe explicable policies. We present both an exact method, guaranteeing finding the Pareto set, and a more efficient greedy method that finds one of the policies in the Pareto set. Additionally, we offer approximate solutions based on state aggregation to improve scalability. We provide formal proofs that validate the desired theoretical properties of these methods. Evaluation through simulations and physical robot experiments confirms the effectiveness of our approach for safe explicable planning.
Motion Capture Benchmark of Real Industrial Tasks and Traditional Crafts for Human Movement Analysis
Authors: Brenda Elizabeth Olivas-Padilla, Alina Glushkova, Sotiris Manitsaris
Date: 2023-04-03 00:00:00

Human movement analysis is a key area of research in robotics, biomechanics, and data science. It encompasses tracking, posture estimation, and movement synthesis. While numerous methodologies have evolved over time, a systematic and quantitative evaluation of these approaches using verifiable ground truth data of three-dimensional human movement is still required to define the current state of the art. This paper presents seven datasets recorded using inertial-based motion capture. The datasets contain professional gestures carried out by industrial operators and skilled craftsmen performed in real conditions in-situ. The datasets were created with the intention of being used for research in human motion modeling, analysis, and generation. The protocols for data collection are described in detail, and a preliminary analysis of the collected data is provided as a benchmark. The Gesture Operational Model, a hybrid stochastic-biomechanical approach based on kinematic descriptors, is utilized to model the dynamics of the experts' movements and create mathematical representations of their motion trajectories for analysis and quantifying their body dexterity. The models allowed accurate the generation of human professional poses and an intuitive description of how body joints cooperate and change over time through the performance of the task.
Leveraging Neural Representations for Audio Manipulation
Authors: Scott H. Hawley, Christian J. Steinmetz
Date: 2023-04-10 00:00:00
Tasks: Disentanglement

We investigate applying audio manipulations using pretrained neural network-based autoencoders as an alternative to traditional signal processing methods, since the former may provide greater semantic or perceptual organization. To establish the potential of this approach, we first establish if representations from these models encode information about manipulations. We carry out experiments and produce visualizations using representations from two different pretrained autoencoders. Our findings indicate that, while some information about audio manipulations is encoded, this information is both limited and encoded in a non-trivial way. This is supported by our attempts to visualize these representations, which demonstrated that trajectories of representations for common manipulations are typically nonlinear and content dependent, even for linear signal manipulations. As a result, it is not yet clear how these pretrained autoencoders can be used to manipulate audio signals, however, our results indicate this may be due to the lack of disentanglement with respect to common audio manipulations.
Linearized Data Center Workload and Cooling Management
Authors: Somayye Rostami, Douglas G. Down, George Karakostas
Date: 2023-04-10 00:00:00
Tasks: Management

With the current high levels of energy consumption of data centers, reducing power consumption by even a small percentage is beneficial. We propose a framework for thermal-aware workload distribution in a data center to reduce cooling power consumption. The framework includes linearization of the general optimization problem and proposing a heuristic to approximate the solution for the resulting Integer Linear Programming (ILP) problems. We first define a general nonlinear power optimization problem including several cooling parameters, heat recirculation effects, and constraints on server temperatures. We propose to study a linearized version of the problem, which is easier to analyze. As an energy saving scenario and as a proof of concept for our approach, we also consider the possibility that the red-line temperature for idle servers is higher than that for busy servers. For the resulting ILP problem, we propose a heuristic for intelligent rounding of the fractional solution. Through numerical simulations, we compare our heuristics with two baseline algorithms. We also evaluate the performance of the solution of the linearized system on the original system. The results show that the proposed approach can reduce the cooling power consumption by more than 30 percent compared to the case of continuous utilizations and a single red-line temperature.
Shining light on the DVS pixel: A tutorial and discussion about biasing and optimization
Authors: Rui Graça, Brian Mcreynolds, Tobi Delbruck
Date: 2023-04-10 00:00:00

The operation of the DVS event camera is controlled by the user through adjusting different bias parameters. These biases affect the response of the camera by controlling - among other parameters - the bandwidth, sensitivity, and maximum firing rate of the pixels. Besides determining the response of the camera to input signals, biases significantly impact its noise performance. Bias optimization is a multivariate process depending on the task and the scene, to which the user's knowledge about pixel design and non-idealities can be of great importance. In this paper, we go step-by-step along the signal pathway of the DVS pixel, shining light on its low-level operation and non-idealities, comparing pixel level measurements with array level measurements, and discussing and how biasing and illumination affect the pixel's behavior. With the results and discussion presented, we aim to help DVS users achieve more hardware-aware camera utilization and modelling.
Arc-based Traffic Assignment: Equilibrium Characterization and Learning
Authors: Chih-Yuan Chiu, Chinmay Maheshwari, Pan-Yang Su, Shankar Sastry
Date: 2023-04-10 00:00:00
Tasks: ARC

Arc-based traffic assignment models (TAMs) are a popular framework for modeling traffic network congestion generated by self-interested travelers who sequentially select arcs based on their perceived latency on the network. However, existing arc-based TAMs either assign travelers to cyclic paths, or do not extend to networks with bi-directional arcs (or edges) between nodes. To overcome these difficulties, we propose a new modeling framework for stochastic arc-based TAMs. Given a traffic network with bidirectional arcs, we replicate its arcs and nodes to construct a directed acyclic graph (DAG), which we call the Condensed DAG (CoDAG) representation. Self-interested travelers sequentially select arcs on the CoDAG representation to reach their destination. We show that the associated equilibrium flow, which we call the Condensed DAG equilibrium, exists, is unique, and can be characterized as a strictly convex optimization problem. Moreover, we propose a discrete-time dynamical system that captures a natural adaptation rule employed by self-interested travelers to learn about the emergent congestion on the network. We show that the arc flows generated by this adaptation rule converges to a neighborhood of Condensed DAG equilibrium. To our knowledge, our work is the first to study learning and adaptation in an arc-based TAM. Finally, we present numerical results that corroborate our theoretical results.
Weight Try-Once-Discard Protocol-Based L_2 L_infinity State Estimation for Markovian Jumping Neural Networks with Partially Known Transition Probabilities
Authors: Cong Zou, Wei Chen
Date: 2023-04-10 00:00:00
Tasks: State Estimation

It was the L_2 L_infinity performance index that for the first time is initiated into the discussion on state estimation of delayed MJNNs with with partially known transition probabilities, which provides a more general promotion for the estimation error.The WTOD protocol is adopted to dispatch the sensor nodes so as to effectively alleviate the updating frequency of output signals. The hybrid effects of the time delays, Markov chain, and protocol parameters are apparently reflected in the co-designed estimator which can be solved by a combination of comprehensive matrix inequalities.
L0-norm constraint normalized subband adaptive filtering algorithm: Performance development and AEC application
Authors: Dongxu Liu, Haiquan Zhao, Yang Zhou
Date: 2023-04-10 00:00:00
Tasks: LEMMA

Limited by fixed step-size and sparsity penalty factor, the conventional sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms suffer from trade-off requirements of high filtering accurateness and quicker convergence behavior for sparse system identification. To deal with this problem, this paper proposes variable step-size L0-norm constraint NSAF algorithms (VSS-L0-NSAFs). We first analyze mean-square-deviation (MSD) statistics behavior of the L0-NSAF innovatively in according to novel weight recursion form and arrive at corresponding expressions for the cases that background noise variance is available and unavailable, where correlation degree of system input is indicated by scaling parameter r. Based on derivations, we develop an effective variable step-size scheme through minimizing the upper bounds of the MSD under some reasonable assumptions and lemma. Furthermore, an effective reset strategy is incorporated into presented algorithms to tackle with non-stationary situations. Finally, numerical simulations corroborate that the proposed algorithms achieve better performance in terms of estimation accurateness and tracking capability in comparison with existing related algorithms in sparse system identification and adaptive echo cancellation circumstances.
Inner approximations of stochastic programs for data-driven stochastic barrier function design
Authors: Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert, Alessandro Abate, Luca Laurenti
Date: 2023-04-10 00:00:00

This paper proposes a new framework to compute finite-horizon safety guarantees for discrete-time piece-wise affine systems with stochastic noise of unknown distributions. The approach is based on a novel approach to synthesise a stochastic barrier function (SBF) from noisy data and rely on the scenario optimization theory. In particular, we show that the stochastic program to synthesize a SBF can be relaxed into a chance-constrained optimisation problem on which scenario approach theory applies. We further show that the resulting program can be reduced to a linear programming problem, thus guaranteeing efficiency. In contrast to existing approaches, this method is data efficient as it only requires the number of data to be proportional to the logarithm in the negative inverse of the confidence level and is computationally efficient due to its reduction to linear programming. The efficacy of the method is empirically evaluated on various verification benchmarks. Experiments show a significant improvement with respect to state-of-the-art, obtaining tighter certificates with a confidence that is several orders of magnitude higher.
Control of bilinear systems using gain-scheduling: Stability and performance guarantees
Authors: Robin Strässer, Julian Berberich, Frank Allgöwer
Date: 2023-04-10 00:00:00
Tasks: Scheduling

In this paper, we present a state-feedback controller design method for bilinear systems. To this end, we write the bilinear system as a linear fractional representation by interpreting the state in the bilinearity as a structured uncertainty. Based on that, we derive convex conditions in terms of linear matrix inequalities for the controller design, which are efficiently solvable by semidefinite programming. Further, we prove asymptotic stability and quadratic performance of the resulting closed-loop system locally in a predefined region. The proposed design uses gain-scheduling techniques and results in a state feedback with rational dependence on the state, which can substantially reduce conservatism and improve performance in comparison to a simpler, linear state feedback. Moreover, the design method is easily adaptable to various scenarios due to its modular formulation in the robust control framework. Finally, we apply the developed approaches to numerical examples and illustrate the benefits of the approach.
Fast Charging of Lithium-Ion Batteries Using Deep Bayesian Optimization with Recurrent Neural Network
Authors: Benben Jiang, Yixing Wang, Zhenghua Ma, Qiugang Lu
Date: 2023-04-09 00:00:00
Tasks: Bayesian Optimization

Fast charging has attracted increasing attention from the battery community for electrical vehicles (EVs) to alleviate range anxiety and reduce charging time for EVs. However, inappropriate charging strategies would cause severe degradation of batteries or even hazardous accidents. To optimize fast-charging strategies under various constraints, particularly safety limits, we propose a novel deep Bayesian optimization (BO) approach that utilizes Bayesian recurrent neural network (BRNN) as the surrogate model, given its capability in handling sequential data. In addition, a combined acquisition function of expected improvement (EI) and upper confidence bound (UCB) is developed to better balance the exploitation and exploration. The effectiveness of the proposed approach is demonstrated on the PETLION, a porous electrode theory-based battery simulator. Our method is also compared with the state-of-the-art BO methods that use Gaussian process (GP) and non-recurrent network as surrogate models. The results verify the superior performance of the proposed fast charging approaches, which mainly results from that: (i) the BRNN-based surrogate model provides a more precise prediction of battery lifetime than that based on GP or non-recurrent network; and (ii) the combined acquisition function outperforms traditional EI or UCB criteria in exploring the optimal charging protocol that maintains the longest battery lifetime.
Exploring Operational Flexibility of Active Distribution Networks with Low Observability
Authors: Demetris Chrysostomou, Jose Luis Rueda Torres, Jochen Lorenz Cremer
Date: 2023-04-09 00:00:00

Power electronic interfaced devices progressively enable the increasing provision of flexible operational actions in distribution networks. The feasible flexibility these devices can effectively provide requires estimation and quantification so the network operators can plan operations close to real-time. Existing approaches estimating the distribution network flexibility require the full observability of the system, meaning topological and state knowledge. However, the assumption of full observability is unrealistic and represents a barrier to system operators' adaptation. This paper proposes a definition of the distribution network flexibility problem that considers the limited observability in real-time operation. A critical review and assessment of the most prominent approaches are done based on the proposed definition. This assessment showcases the limitations and benefits of existing approaches for estimating flexibility with low observability. A case study on the CIGRE MV distribution system highlights the drawbacks brought by low observability.
Adaptive Kalman Filtering with Exact Linearization and Decoupling Control on Three-Tank Process
Authors: Bambang L. Widjiantoro, Katherin Indriawati, Moh Kamalul Wafi
Date: 2023-04-09 00:00:00

Water treatment and liquid storage are the two plants implementing the hydraulic three-tank system. Maintaining certain levels is the critical scenario so that the systems run as desired. To deal with, the optimal linear control and the complex advanced non-linear problem have been proposed to track certain dynamic reference. This paper studies those two using the combination of linearization and decoupling control under some assumptions. The result shows that the designed methods have successfully traced the dynamic reference signals. Beyond that, the adaptive system noise Kalman filter (AKF) algorithm is used to examine the estimation performance of the true non-linear system and the performance yields a rewarding prediction of the true system.