pwc-archive/papers-with-abstracts huggingface dataset:
A hybrid IndRNNLSTM approach for real-time anomaly detection in software-defined networks
Authors: Sajjad Salem, Salman Asoudeh
Date: 2024-02-02 00:00:00
Tasks: Anomaly Detection, feature selection, Time Series

Anomaly detection in SDN using data flow prediction is a difficult task. This problem is included in the category of time series and regression problems. Machine learning approaches are challenging in this field due to the manual selection of features. On the other hand, deep learning approaches have important features due to the automatic selection of features. Meanwhile, RNN-based approaches have been used the most. The LSTM and GRU approaches learn dependent entities well; on the other hand, the IndRNN approach learns non-dependent entities in time series. The proposed approach tried to use a combination of IndRNN and LSTM approaches to learn dependent and non-dependent features. Feature selection approaches also provide a suitable view of features for the models; for this purpose, four feature selection models, Filter, Wrapper, Embedded, and Autoencoder were used. The proposed IndRNNLSTM algorithm, in combination with Embedded, was able to achieve MAE=1.22 and RMSE=9.92 on NSL-KDD data.
Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement Learning Using Unique Experiences
Authors: Nikhil Kumar Singh, Indranil Saha
Date: 2024-02-05 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Reinforcement Learning (RL)

Efficient utilization of the replay buffer plays a significant role in the off-policy actor-critic reinforcement learning (RL) algorithms used for model-free control policy synthesis for complex dynamical systems. We propose a method for achieving sample efficiency, which focuses on selecting unique samples and adding them to the replay buffer during the exploration with the goal of reducing the buffer size and maintaining the independent and identically distributed (IID) nature of the samples. Our method is based on selecting an important subset of the set of state variables from the experiences encountered during the initial phase of random exploration, partitioning the state space into a set of abstract states based on the selected important state variables, and finally selecting the experiences with unique state-reward combination by using a kernel density estimator. We formally prove that the off-policy actor-critic algorithm incorporating the proposed method for unique experience accumulation converges faster than the vanilla off-policy actor-critic algorithm. Furthermore, we evaluate our method by comparing it with two state-of-the-art actor-critic RL algorithms on several continuous control benchmarks available in the Gym environment. Experimental results demonstrate that our method achieves a significant reduction in the size of the replay buffer for all the benchmarks while achieving either faster convergent or better reward accumulation compared to the baseline algorithms.
A Transversal Study of Fundamental Frequency Contours in Parkinsonian Voices
Authors: Pablo Rodriguez-Perez, Ruben Fraile, Miguel Garcia-Escrig, Nicolas Saenz-Lechon, Juana M. Gutierrez-Arriola, Victor Osma-Ruiz
Date: 2024-02-09 00:00:00

A transversal study of the pitch variability of parkinsonian voices in read speech is presented. 30 patients suffering from Parkinson's disease (PD) and 32 healthy speakers were recorded while reading a text without voiceless phonemes. The fundamental frequency contours were calculated from the recordings, and the following measures were used for describing them: mean, minimum, maximum, and standard deviation of the estimated fundamental frequencies. Results based on these measures indicate that the influence of PD on some aspects of intonation can be masked by the effects of aging, especially for male voices. However, some parameters such as the relative fundamental frequency range exhibit lower correlations with age than with PD stage, as evaluated using the Hoehn and Yahr scale. These correlations between relative fundamental frequency range and PD stage reach moderate-to-high values in the case of women. Additionally, three parameters describing the form of the fundamental frequency modulation spectrum were investigated for correlation with age and PD stage. The study of this modulation spectrum provides some insight into the ability of the speakers to plan the intonation of full phrases. For both male and female populations, significant correlations were found between parameters obtained from the modulation spectrum of fundamental frequency and the PD stage. Nevertheless, the quantitative assessment of the performance of regression models built from these modulation parameters and fundamental frequency range suggests that such measures are likely to be of limited value in the early diagnosis of PD due to inter-speaker variability.
Data-driven Joint Detection and Localization of Acoustic Reflectors
Authors: H. Nazim Bicer, Cagdas Tuna, Andreas Walther, Emanuël A. P. Habets
Date: 2024-02-09 00:00:00

Room geometry inference algorithms rely on the localization of acoustic reflectors to identify boundary surfaces of an enclosure. Rooms with highly absorptive walls or walls at large distances from the measurement setup pose challenges for such algorithms. As it is not always possible to localize all walls, we present a data-driven method to jointly detect and localize acoustic reflectors that correspond to nearby and/or reflective walls. A multi-branch convolutional recurrent neural network is employed for this purpose. The network's input consists of a time-domain acoustic beamforming map, obtained via Radon transform from multi-channel room impulse responses. A modified loss function is proposed that forces the network to pay more attention to walls that can be estimated with a small error. Simulation results show that the proposed method can detect nearby and/or reflective walls and improve the localization performance for the detected walls.
Modular Redesign of Mechatronic Systems: Formulation of Module Specifications Guaranteeing System Dynamics Specifications
Authors: Lars A. L. Janssen, Rob H. B. Fey, Bart Besselink, Nathan van de Wouw
Date: 2024-02-09 00:00:00
Tasks: Management

Complex mechatronic systems are typically composed of interconnected modules, often developed by independent teams. This development process challenges the verification of system specifications before all modules are integrated. To address this challenge, a modular redesign framework is proposed in this paper. Herein, first, allowed changes in the dynamics (represented by frequency response functions (FRFs)) of the redesigned system are defined with respect to the original system model, which already satisfies system specifications. Second, these allowed changes in the overall system dynamics (or system redesign specifications) are automatically translated to dynamics (FRF) specifications on module level that, when satisfied, guarantee overall system dynamics (FRF) specifications. This modularity in specification management supports local analysis and verification of module design changes, enabling design teams to work in parallel without the need to iteratively rebuild the system model to check fulfilment of system FRF specifications. A modular redesign process results that shortens time-to-market and decreases redesign costs. The framework's effectiveness is demonstrated through three examples of increasing complexity, highlighting its potential to enable modular mechatronic system (re)design.
Provably Safe Finite-Time Guidance for Marine Vehicles
Authors: Bhawana Singh, Karim Ahmadi Dastgerdi, Nikolaos Athanasopoulos, Wasif Naeem, Benoit Lecallard
Date: 2024-02-09 00:00:00

We consider a new control strategy for marine navigation, equipped with finite-time convergence characteristics. We provide mathematical guarantees for waypoint reaching and obstacle avoidance for different encounter scenarios, by deriving conditions under which (i) convergence to waypoint and (ii) safe obstacle avoidance is achieved while (iii) satisfying input constraints. We propose a predefined-time heading control to enforce ship heading error convergence and waypoint reaching in finite time. Using this as a building block, we develop a provably safe algorithm for safe waypoint navigation by strategically and automatically introducing intermediate virtual waypoints. Using Imazu problems as benchmarks, we show that the proposed method is better than other existing strategies such as Velocity Obstacle Avoidance and biased Line-of-Sight methods, in terms of the safe distance between the ship and the obstacles, cross track error, control effort, waypoint reaching time and ship path length.
EJS, JIL Server, and LabVIEW: An Architecture for Rapid Development of Remote Labs
Authors: Jesús Chacón, Hector Vargas, Gonzalo Farias, Jose Sánchez, Sebastián Dormido
Date: 2024-02-09 00:00:00

Designing and developing web-enabled remote laboratories for pedagogical purposes is not an easy task. Often, developers (generally, educators who know the subjects they teach but lack of the technical and programming skills required to build Internet-based educational applications) end up discarding the idea of exploring these new teaching and learning experiences mainly due to the technical issues that must be mastered. To tackle this problem, authors present a novel technique that allows developers to create remote labs in a quick, didactical, and straightforward way. This framework is based on the use of two well-known software tools in the scope of engineering education, Easy Java Simulations and LabVIEW. The development exploits a new feature of Easy Java Simulations known as EJS-elements that enables Java developers to create and integrate their own authoring libraries (elements) into EJS, thus increasing its application possibilities. Particularly, the EJS element here presented allows to LabVIEW programs be controlled from EJS applications through a communication network. This paper presents the element creation details and how this can be used to create web-enabled experimentation environments for educational purposes. A step by step example of development of a remote lab for automatic control education is described.
Distributed Safe Navigation of Multi-Agent Systems using Control Barrier Function-Based Optimal Controllers
Authors: Pol Mestres, Carlos Nieto-Granda, Jorge Cortés
Date: 2024-02-09 00:00:00
Tasks: Collision Avoidance, valid

This paper proposes a distributed controller synthesis framework for safe navigation of multi-agent systems. We leverage control barrier functions to formulate collision avoidance with obstacles and teammates as constraints on the control input for a state-dependent network optimization problem that encodes team formation and the navigation task. Our algorithmic solution is valid for general nonlinear control dynamics and optimization problems. The resulting controller is distributed, satisfies the safety constraints at all times, and is asymptotically optimal. We illustrate its performance in a team of differential-drive robots in a variety of complex environments, both in simulation and in hardware.
Balancing Application Relevant and Sparsity Revealing Excitation in Input Design
Authors: Javad Parsa, Cristian R. Rojas, Håkan Hjalmarsson
Date: 2024-02-08 00:00:00

The maximum absolute correlation between regressors, which is called mutual coherence, plays an essential role in sparse estimation. A regressor matrix whose columns are highly correlated may result from optimal input design, since there is no constraint on the mutual coherence, making it difficult to handle sparse estimation. This paper aims to tackle this issue for fixed denominator models, which include Laguerre, Kautz, and generalized orthonormal basis function expansion models, for example. The paper proposes an optimal input design method where the achieved Fisher information matrix is fitted to the desired Fisher matrix, together with a coordinate transformation designed to make the regressors in the transformed coordinates have low mutual coherence. The method can be used together with any sparse estimation method and any desired Fisher matrix. A numerical study shows its potential for alleviating the problem of model order selection when used in conjunction with, for example, classical methods such as the Akaike Information Criterion.
Dynamic Electromagnetic Navigation
Authors: Jasan Zughaibi, Bradley J. Nelson, Michael Muehlebach
Date: 2024-02-08 00:00:00

Magnetic navigation offers wireless control over magnetic objects, which has important medical applications, such as targeted drug delivery and minimally invasive surgery. Magnetic navigation systems are categorized into systems using permanent magnets and systems based on electromagnets. Electromagnetic Navigation Systems (eMNSs) are believed to have a superior actuation bandwidth, facilitating trajectory tracking and disturbance rejection. This greatly expands the range of potential medical applications and includes even dynamic environments as encountered in cardiovascular interventions. To showcase the dynamic capabilities of eMNSs, we successfully stabilize a (non-magnetic) inverted pendulum on the tip of a magnetically driven arm. Our approach employs a model-based framework that leverages Lagrangian mechanics to capture the interaction between the mechanical dynamics and the magnetic field. Using system identification, we estimate unknown parameters, the actuation bandwidth, and characterize the system's nonlinearity. To explore the limits of electromagnetic navigation and evaluate its scalability, we characterize the electrical system dynamics and perform reference measurements on a clinical-scale eMNS, affirming that the proposed dynamic control methodologies effectively translate to larger coil configurations. A state-feedback controller stabilizes the inherently unstable pendulum, and an iterative learning control scheme enables accurate tracking of non-equilibrium trajectories. Furthermore, to understand structural limitations of our control strategy, we analyze the influence of magnetic field gradients on the motion of the system. To our knowledge, this is the first demonstration to stabilize a 3D inverted pendulum through electromagnetic navigation.
Antenna system for trilateral drone precise vertical landing
Authors: Víctor Araña-Pulido, Eugenio Jiménez-Yguácel, Francisco Cabrera-Almeida, Pedro Quintana-Morales
Date: 2024-02-08 00:00:00

This article presents a radio frequency system that can be used to perform precise vertical landings of drones. The system is based on the three-way phase shift detection of a signal transmitted from the landing point. The antenna system is designed by taking into account parameters such as landing tracking area, analog-to-digital converter (ADC) resolution, phase detector output range, antenna polarization, and the effect of antenna axial ratio. The fabricated prototype consists of a landing point antenna that transmits a signal at 2.46 GHz, as well as a drone triantenna system that includes a phase shift detection circuitry, ADC, and a simple control program that provides the correction instructions for landing. The prototype provides an averaged output data rate (ODR) suitable for landing maneuvers (>300 Hz). A simple system calibration procedure (detector output zeroing) is performed by aligning the antenna system. The measurements performed at different altitudes demonstrate both the correct operation of the proposed solution and its viability as an instrument for precision vertical landings.
Gaussian-process-regression-based method for the localization of exceptional points in complex resonance spectra
Authors: Patrick Egenlauf, Patric Rommel, Jörg Main
Date: 2024-02-07 00:00:00
Tasks: GPR

Resonances in open quantum systems depending on at least two controllable parameters can show the phenomenon of exceptional points (EPs), where not only the eigenvalues but also the eigenvectors of two or more resonances coalesce. Their exact localization in the parameter space is challenging, in particular in systems, where the computation of the quantum spectra and resonances is numerically very expensive. We introduce an efficient machine learning algorithm to find exceptional points based on Gaussian process regression (GPR). The GPR-model is trained with an initial set of eigenvalue pairs belonging to an EP and used for a first estimation of the EP position via a numerically cheap root search. The estimate is then improved iteratively by adding selected exact eigenvalue pairs as training points to the GPR-model. The GPR-based method is developed and tested on a simple low-dimensional matrix model and then applied to a challenging real physical system, viz., the localization of EPs in the resonance spectra of excitons in cuprous oxide in external electric and magnetic fields. The precise computation of EPs, by taking into account the complete valence band structure and central-cell corrections of the crystal, can be the basis for the experimental observation of EPs in this system.
Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective
Authors: Yihong Ma, Xiaobao Huang, Bozhao Nan, Nuno Moniz, Xiangliang Zhang, Olaf Wiest, Nitesh V. Chawla
Date: 2024-02-06 00:00:00
Tasks: Prediction

The yield of a chemical reaction quantifies the percentage of the target product formed in relation to the reactants consumed during the chemical reaction. Accurate yield prediction can guide chemists toward selecting high-yield reactions during synthesis planning, offering valuable insights before dedicating time and resources to wet lab experiments. While recent advancements in yield prediction have led to overall performance improvement across the entire yield range, an open challenge remains in enhancing predictions for high-yield reactions, which are of greater concern to chemists. In this paper, we argue that the performance gap in high-yield predictions results from the imbalanced distribution of real-world data skewed towards low-yield reactions, often due to unreacted starting materials and inherent ambiguities in the reaction processes. Despite this data imbalance, existing yield prediction methods continue to treat different yield ranges equally, assuming a balanced training distribution. Through extensive experiments on three real-world yield prediction datasets, we emphasize the urgent need to reframe reaction yield prediction as an imbalanced regression problem. Finally, we demonstrate that incorporating simple cost-sensitive re-weighting methods can significantly enhance the performance of yield prediction models on underrepresented high-yield regions.
They were robbed! Scoring by the middlemost to attenuate biased judging in boxing
Authors: Stuart Baumann, Carl Singleton
Date: 2024-02-09 00:00:00

Boxing has a long-standing problem with biased judging, impacting both professional and Olympic bouts. "Robberies", where boxers are widely seen as being denied rightful victories, threaten to drive fans and athletes away from the sport. To tackle this problem, we propose a minimalist adjustment in how boxing is scored: the winner would be decided by the majority of round-by-round victories according to the judges, rather than relying on the judges' overall bout scores. This approach, rooted in social choice theory and utilising majority rule and middlemost aggregation functions, creates a coordination problem for partisan judges and attenuates their influence. Our model analysis and simulations demonstrate the potential to significantly decrease the likelihood of a partisan judge swaying the result of a bout.
Monopoly agenda control with privately informed voters
Authors: Kirill S. Evdokimov
Date: 2024-02-09 00:00:00
Tasks: Diversity

An agenda-setter repeatedly proposes a spatial policy to voters until some proposal is accepted. Voters have distinct but correlated preferences and receive private signals about the common state. I investigate whether the agenda-setter retains the power to screen voters as players become perfectly patient and private signals become perfectly precise. I show that the extent of this power depends on the relative precision of private signals and the conflict of preferences among voters, confirming the crucial role of committee setting and single-peaked preferences. When the private signals have equal precision, the agenda-setter can achieve the full-information benchmark. When one voter receives an asymptotically more precise signal, the agenda-setter's power to screen depends on preference diversity. These results imply that the lack of commitment to a single proposal can benefit the agenda-setter. Surprisingly, an increase in the voting threshold can allow the agenda-setter to extract more surplus.
A Generalization of Arrow's Impossibility Theorem Through Combinatorial Topology
Authors: Isaac Lara, Sergio Rajsbaum, Armajac Raventós-Pujol
Date: 2024-02-08 00:00:00
Tasks: Distributed Computing

To the best of our knowledge, a complete characterization of the domains that escape the famous Arrow's impossibility theorem remains an open question. We believe that different ways of proving Arrovian theorems illuminate this problem. This paper presents a new combinatorial topology proof of Arrow's theorem. In PODC 2022, Rajsbaum and Ravent\'os-Pujol proved this theorem using a combinatorial topology approach. This approach uses simplicial complexes to represent the sets of profiles of preferences and that of single preferences. These complexes grow in dimension with the number of alternatives. This makes it difficult to think about the geometry of Arrow's theorem when there are (any) finite number of voters and alternatives. Rajsbaum and Ravent\'os-Pujol (2022) use their combinatorial topology approach only for the base case of two voters and three alternatives and then proceed by induction to prove the general version. The problem with this strategy is that it is unclear how to study domain restrictions in the general case by focusing on the base case and then using induction. Instead, the present article uses the two-dimensional structure of the high-dimensional simplicial complexes (formally, the $2$$\unicode{x2013}$skeleton), yielding a new combinatorial topology proof of this theorem. Moreover, we do not assume the unrestricted domain, but a domain restriction that we call the class of polarization and diversity over triples, which includes the unrestricted domain. By doing so, we obtain a new generalization of Arrow's theorem. This shows that the combinatorial topology approach can be used to study domain restrictions in high dimensions through the $2$$\unicode{x2013}$skeleton.
Energy- and Quality-Aware Video Request Policy for Wireless Adaptive Streaming Clients
Authors: César Díaz, Antonio Fernández, Fernando Sacristán, Narciso García
Date: 2024-02-08 00:00:00

We present a straightforward, non-intrusive adaptive bit rate streaming segment quality selection policy which aims at extending battery lifetime during playback while limiting the impact on the user's quality of experience, thus benefiting consumers of video streaming services. This policy relies on the relationship between the available channel bandwidth and the bit rate of the representations in the quality ladder. It results from the characterization of the energy consumed by smartphones when running adaptive streaming client applications for different network connections (Wifi, 4G, and 5G) and the modeling of the energy consumed as a function of said relationship. Results show that a significant amount of energy can be saved (10 to 30%) by slightly modifying the default policy at the expense of a controlled reduction of video quality.