pwc-archive/papers-with-abstracts huggingface dataset:
Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness
Authors: Anaelia Ovalle, Arjun Subramonian, Vagrant Gautam, Gilbert Gee, Kai-Wei Chang
Date: 2023-03-16 00:00:00
Tasks: Fairness

Intersectionality is a critical framework that, through inquiry and praxis, allows us to examine how social inequalities persist through domains of structure and discipline. Given AI fairness' raison d'etre of "fairness", we argue that adopting intersectionality as an analytical framework is pivotal to effectively operationalizing fairness. Through a critical review of how intersectionality is discussed in 30 papers from the AI fairness literature, we deductively and inductively: 1) map how intersectionality tenets operate within the AI fairness paradigm and 2) uncover gaps between the conceptualization and operationalization of intersectionality. We find that researchers overwhelmingly reduce intersectionality to optimizing for fairness metrics over demographic subgroups. They also fail to discuss their social context and when mentioning power, they mostly situate it only within the AI pipeline. We: 3) outline and assess the implications of these gaps for critical inquiry and praxis, and 4) provide actionable recommendations for AI fairness researchers to engage with intersectionality in their work by grounding it in AI epistemology.
The AI Act proposal: a new right to technical interpretability?
Authors: Chiara Gallese
Date: 2023-03-14 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

The debate about the concept of the so called right to explanation in AI is the subject of a wealth of literature. It has focused, in the legal scholarship, on art. 22 GDPR and, in the technical scholarship, on techniques that help explain the output of a certain model (XAI). The purpose of this work is to investigate if the new provisions introduced by the proposal for a Regulation laying down harmonised rules on artificial intelligence (AI Act), in combination with Convention 108 plus and GDPR, are enough to indicate the existence of a right to technical explainability in the EU legal framework and, if not, whether the EU should include it in its current legislation. This is a preliminary work submitted to the online event organised by the Information Society Law Center and it will be later developed into a full paper.
Machine learning-based spin structure detection
Authors: Isaac Labrie-Boulay, Thomas Brian Winkler, Daniel Franzen, Alena Romanova, Hans Fangohr, Mathias Kläui
Date: 2023-03-24 00:00:00
Tasks: Position

One of the most important magnetic spin structure is the topologically stabilised skyrmion quasi-particle. Its interesting physical properties make them candidates for memory and efficient neuromorphic computation schemes. For the device operation, detection of the position, shape, and size of skyrmions is required and magnetic imaging is typically employed. A frequently used technique is magneto-optical Kerr microscopy where depending on the samples material composition, temperature, material growing procedures, etc., the measurements suffer from noise, low-contrast, intensity gradients, or other optical artifacts. Conventional image analysis packages require manual treatment, and a more automatic solution is required. We report a convolutional neural network specifically designed for segmentation problems to detect the position and shape of skyrmions in our measurements. The network is tuned using selected techniques to optimize predictions and in particular the number of detected classes is found to govern the performance. The results of this study shows that a well-trained network is a viable method of automating data pre-processing in magnetic microscopy. The approach is easily extendable to other spin structures and other magnetic imaging methods.
Urgency-aware Routing in Single Origin-destination Itineraries through Artificial Currencies
Authors: Leonardo Pedroso, W. P. M. H., Heemels, Mauro Salazar
Date: 2023-03-29 00:00:00
Tasks: ARC

Within mobility systems, the presence of self-interested users can lead to aggregate routing patterns that are far from the societal optimum which could be achieved by centrally controlling the users' choices. In this paper, we design a fair incentive mechanism to steer the selfish behavior of the users to align with the societally optimal aggregate routing. The proposed mechanism is based on an artificial currency that cannot be traded or bought, but only spent or received when traveling. Specifically, we consider a parallel-arc network with a single origin and destination node within a repeated game setting whereby each user chooses from one of the available arcs to reach their destination on a daily basis. In this framework, taking faster routes comes at a cost, whereas taking slower routes is incentivized by a reward. The users are thus playing against their future selves when choosing their present actions. To capture this complex behavior, we assume the users to be rational and to minimize an urgency-weighted combination of their immediate and future discomfort. To design the optimal pricing, we first derive a closed-form expression for the best individual response strategy. Second, we formulate the pricing design problem for each arc to achieve the societally optimal aggregate flows, and reformulate it so that it can be solved with gradient-free optimization methods. Our numerical simulations show that it is possible to achieve a near-optimal routing whilst significantly reducing the users' perceived discomfort when compared to a centralized optimal but urgency-unaware policy.
Signal processing on large networks with group symmetries
Authors: Kathryn Beck, Mahya Ghandehari, Jeannette Janssen, Nauzer Kalyaniwalla
Date: 2023-03-29 00:00:00
Tasks: Stochastic Block Model

Current methods of graph signal processing rely heavily on the specific structure of the underlying network: the shift operator and the graph Fourier transform are both derived directly from a specific graph. In many cases, the network is subject to error or natural changes over time. This motivated a new perspective on GSP, where the signal processing framework is developed for an entire class of graphs with similar structures. This approach can be formalized via the theory of graph limits, where graphs are considered as random samples from a distribution represented by a graphon. When the network under consideration has underlying symmetries, they may be modeled as samples from Cayley graphons. In Cayley graphons, vertices are sampled from a group, and the link probability between two vertices is determined by a function of the two corresponding group elements. Infinite groups such as the 1-dimensional torus can be used to model networks with an underlying spatial reality. Cayley graphons on finite groups give rise to a Stochastic Block Model, where the link probabilities between blocks form a (edge-weighted) Cayley graph. This manuscript summarizes some work on graph signal processing on large networks, in particular samples of Cayley graphons.
Improving the Diproche CNL through Autoformalization via Large Language Models
Authors: Merlin Carl
Date: 2023-03-12 00:00:00

The Diproche system is an automated proof checker for texts written in a controlled fragment of German, designed for didactical applications in classes introducing students to proofs for the first time. The first version of the system used a controlled natural language for which a Prolog formalization routine was written. In this paper, we explore the possibility of prompting large language models for autoformalization in the context of Diproche, with encouraging first results.
Prefix tuning for automated audio captioning
Authors: Minkyu Kim, Kim Sung-Bin, Tae-Hyun Oh
Date: 2023-03-30 00:00:00
Tasks: AudioCaps, Audio captioning, Language Modeling, Language Modelling, Retrieval-augmented Few-shot In-context Audio Captioning, Text Generation

Audio captioning aims to generate text descriptions from environmental sounds. One challenge of audio captioning is the difficulty of the generalization due to the lack of audio-text paired training data. In this work, we propose a simple yet effective method of dealing with small-scaled datasets by leveraging a pre-trained language model. We keep the language model frozen to maintain the expressivity for text generation, and we only learn to extract global and temporal features from the input audio. To bridge a modality gap between the audio features and the language model, we employ mapping networks that translate audio features to the continuous vectors the language model can understand, called prefixes. We evaluate our proposed method on the Clotho and AudioCaps dataset and show our method outperforms prior arts in diverse experimental settings.
PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers
Authors: Rahul Pandey, Roger Ren, Qi Luo, Jing Liu, Ariya Rastrow, Ankur Gandhe, Denis Filimonov, Grant Strimel, Andreas Stolcke, Ivan Bulyko
Date: 2023-03-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

End-to-End (E2E) automatic speech recognition (ASR) systems used in voice assistants often have difficulties recognizing infrequent words personalized to the user, such as names and places. Rare words often have non-trivial pronunciations, and in such cases, human knowledge in the form of a pronunciation lexicon can be useful. We propose a PROnunCiation-aware conTextual adaptER (PROCTER) that dynamically injects lexicon knowledge into an RNN-T model by adding a phonemic embedding along with a textual embedding. The experimental results show that the proposed PROCTER architecture outperforms the baseline RNN-T model by improving the word error rate (WER) by 44% and 57% when measured on personalized entities and personalized rare entities, respectively, while increasing the model size (number of trainable parameters) by only 1%. Furthermore, when evaluated in a zero-shot setting to recognize personalized device names, we observe 7% WER improvement with PROCTER, as compared to only 1% WER improvement with text-only contextual attention
Consensus controller with safety guarantee: an application to the kinematic bicycle model
Authors: Kaicheng Niu, Chaouki Abdallah, Mohammad Hayajneh
Date: 2023-03-30 00:00:00
Tasks: Autonomous Vehicles

This paper proposes a consensus controller for multi-agent systems that can guarantee the agents' safety. The controller, built with the idea of output prediction and the Newton-Raphson method, achieves consensus for a class of heterogeneous nonlinear systems. The Integral Control Barrier Function is applied in conjunction with the controller, such that the agents' states are confined within pre-defined safety sets. Due to the dynamically-defined control input, the resulting optimization problem from the barrier function is always a Quadratic Program, despite the nonlinearities that the system dynamics may have. We verify the proposed controller using a platoon of autonomous vehicles modeled by kinematic bicycles. A convergence analysis of the leader-follower consensus under the path graph topology is conducted. Simulation results show that the vehicles achieve consensus while keeping safe inter-agent distances, suggesting a potential in future applications.
Secure State Estimation with Asynchronous Measurements against Malicious Measurement-data and Time-stamp Manipulation
Authors: Zishuo Li, Anh Tung Nguyen, André Teixeira, Yilin Mo, Karl H. Johansson
Date: 2023-03-30 00:00:00
Tasks: Blocking, State Estimation

This paper proposes a secure state estimation scheme with non-periodic asynchronous measurements for linear continuous-time systems under false data attacks on the measurement transmit channel. After sampling the output of the system, a sensor transmits the measurement information in a triple composed of sensor index, time-stamp, and measurement value to the fusion center via vulnerable communication channels. The malicious attacker can corrupt a subset of the sensors through (i) manipulating the time-stamp and measurement value; (ii) blocking transmitted measurement triples; or (iii) injecting fake measurement triples. To deal with such attacks, we propose the design of local estimators based on observability space decomposition, where each local estimator updates the local state and sends it to the fusion center after sampling a measurement. Whenever there is a local update, the fusion center combines all the local states and generates a secure state estimate by adopting the median operator. We prove that local estimators of benign sensors are unbiased with stable covariance. Moreover, the fused central estimation error has bounded expectation and covariance against at most $p$ corrupted sensors as long as the system is $2p$-sparse observable. The efficacy of the proposed scheme is demonstrated through an application on a benchmark example of the IEEE 14-bus system.
Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligence
Authors: Sebatian Pütz, Johannes Kruse, Dirk Witthaut, Veit Hagenmeyer, Benjamin Schäfer
Date: 2023-03-30 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

A stable supply of electrical energy is essential for the functioning of our society. Therefore, the electrical power grid's operation and energy and balancing markets are subject to strict regulations. As the external technical, economic, or social influences on the power grid change, these regulations must also be constantly adapted. However, whether these regulatory changes lead to the intended results is not easy to assess. Could eXplainable Artificial Intelligence (XAI) models distinguish regulatory settings and support the understanding of the effects of these changes? In this article, we explore two examples of regulatory changes in the German energy markets for bulk electricity and for reserve power. We explore the splitting of the German-Austrian bidding zone and changes in the pricing schemes of the German balancing energy market. We find that boosted tree models and feedforward neural networks before and after a regulatory change differ in their respective parametrizations. Using Shapley additive explanations, we reveal model differences, e.g. in terms of feature importances, and identify key features of these distinct models. With this study, we demonstrate how XAI can be applied to investigate system changes in power systems.
Mastering Complex Modes: A New Method for Real-Time Modal Identification of Vibrating Systems
Authors: Satyam PAnda, Sanghamitra Das, Basuraj Bhowmik, Budhaditya Hazra
Date: 2023-03-30 00:00:00

A novel algorithm for real-time modal identification in linear vibrating systems with complex modes is introduced, utilizing a combination of first order eigen-perturbation and second order separation techniques. In practical settings, structures with complex modes are frequently encountered and their presence often poses a challenge in accurately estimating the source signal in real-time. The proposed methodology addresses this issue by incorporating the right angle phase shift of the response in the sensor output and updating the second order statistics of the complex response through first order eigen-perturbation. Empirical evidence of the efficacy of the technique is demonstrated through numerical case studies and validation using various numerically modeled systems, as well as a standard ASCE-SHM benchmark problem with complex modes, highlighting the capability of the proposed method to achieve precise real-time modal property identification and online source separation with a minimal number of initially required batch data.
Equivalent Conditions for the Synchronization of Identical Linear Systems over Arbitrary Interconnections
Authors: Nicola Zaupa, Giulia Giordano, Isabelle Queinnec, Sophie Tarbouriech, Luca Zaccarian
Date: 2023-03-30 00:00:00

We propose necessary and sufficient conditions for the synchronization of $N$ identical single-input-single-output (SISO) systems, connected through a directed graph {without imposing any assumption on the graph interconnection}. We consider both the continuous-time and the discrete-time case, and we provide conditions that {are equivalent to} the uniform global exponential stability, {with guaranteed convergence rate,} of the closed {and unbounded} attractor that corresponds to the synchronization set.
Computationally efficient predictive control based on ANN state-space models
Authors: Jan H. Hoekstra, Bence Cseppentő, Gerben I. Beintema, Maarten Schoukens, Zsolt Kollár, Roland Tóth
Date: 2023-03-30 00:00:00
Tasks: Model Predictive Control, State Space Models

Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme exploits little of the structural properties or available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN state-space models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing ANN identification methods, such as the SUBNET method that uses a state encoder, can provide efficient implementation of MPCs. The performance of the proposed approach is demonstrated via a simulation study on an unbalanced disc system.
Robust offset-free constrained Model Predictive Control with Long Short-Term Memory Networks -- Extended version
Authors: Irene Schimperna, Lalo Magni
Date: 2023-03-30 00:00:00
Tasks: Model Predictive Control

This paper develops a control scheme, based on the use of Long Short-Term Memory neural network models and Nonlinear Model Predictive Control, which guarantees recursive feasibility with slow time variant set-points and disturbances, input and output constraints and unmeasurable state. Moreover, if the set-point and the disturbance are asymptotically constant, offset-free tracking is guaranteed. Offset-free tracking is obtained by augmenting the model with a disturbance, to be estimated together with the states of the Long Short-Term Memory network model by a properly designed observer. Satisfaction of the output constraints in presence of observer estimation error, time variant set-points and disturbances is obtained using a constraint tightening approach.
Modularized Control Synthesis for Complex Signal Temporal Logic Specifications
Authors: Zengjie Zhang, Sofie Haesaert
Date: 2023-03-30 00:00:00

The control synthesis of a dynamic system subject to a signal temporal logic (STL) specification is commonly formulated as a mixed-integer linear/convex programming (MILP/MICP) problem. Solving such a problem is computationally expensive when the specification is long and complex. In this paper, we propose a framework to transform a long and complex specification into separate forms in time, to be more specific, the logical combination of a series of short and simple subformulas with non-overlapping timing intervals. In this way, one can easily modularize the synthesis of a long specification by solving its short subformulas, which improves the efficiency of the control problem. We first propose a syntactic timing separation form for a type of complex specifications based on a group of separation principles. Then, we further propose a complete specification split form with subformulas completely separated in time. Based on this, we develop a modularized synthesis algorithm that ensures the soundness of the solution to the original synthesis problem. The efficacy of the methods is validated with a robot monitoring case study in simulation. Our work is promising to promote the efficiency of control synthesis for systems with complicated specifications.
Stability bounds of droop-controlled inverters in power grid networks
Authors: Philipp C. Böttcher, Leonardo Rydin Gorjão, Dirk Witthaut
Date: 2023-03-29 00:00:00

The energy mix of future power systems will include high shares of wind power and solar PV. These generation facilities are generally connected via power-electronic inverters. While conventional generation responds dynamically to the state of the electric power system, inverters are power electronic hardware and need to be programmed to react to the state of the system. Choosing an appropriate control scheme and the corresponding parameters is necessary to guarantee that the system operates safely. A prominent control scheme for inverters is droop control, which mimics the response of conventional generation. In this work, we investigate the stability of coupled systems of droop-controlled inverters in arbitrary network topologies. Employing linear stability analysis, we derive effective local stability criteria that consider both the overall network topology as well as its interplay with the inverters' intrinsic parameters. First, we explore the stability of an inverter coupled to an infinite grid in an analytic fashion and uncover stability and instability regions. Secondly, we extend the analysis to a generic topology of inverters and provide mathematical criteria for stability and instability of the system. Last, we showcase the usefulness of the criteria by examining two model systems using numerical simulations. The developed criteria show which parameters might lead to an unstable operating state.
Thrust vector control and state estimation architecture for low-cost small-scale launchers
Authors: Pedro dos Santos, Paulo Oliveira
Date: 2023-03-29 00:00:00
Tasks: Scheduling, State Estimation

This paper proposes an integrated architecture for Thrust Vector Control (TVC) and state estimation for low-cost small-scale launchers, naturally unstable, and propelled by a solid motor. The architecture is based on a non-linear, six-degrees-of-freedom model for the generic thrust-vector-controlled launcher dynamics and kinematics, deduced and implemented in a realistic simulation environment. For estimation and control design purposes, a linearized version of the model is proposed. Single-nozzle TVC actuation is adopted, allowing for pitch and yaw control, with the control law being derived from the Linear Quadratic Regulator (LQR) with additional integral action (LQI). The control system is implemented through gain scheduling. Full state estimation is performed resorting to complementary kinematic filters, closely related to linear Kalman fitering theory. The architecture, composed by the navigation and control systems, is tested in simulation environment, demonstrating satisfactory attitude tracking performance and robustness to both external disturbances and model uncertainties.