pwc-archive/papers-with-abstracts huggingface dataset:
Plausible Extractive Rationalization through Semi-Supervised Entailment Signal
Authors: Wei Jie Yeo, Ranjan Satapathy, Erik Cambria
Date: 2024-02-13 00:00:00
Tasks: Natural Language Inference, Question Answering

The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative. These models, also known as Explain-Then-Predict models, employ an explainer model to extract rationales and subsequently condition the predictor with the extracted information. Their primary objective is to provide precise and faithful explanations, represented by the extracted rationales. In this paper, we take a semi-supervised approach to optimize for the plausibility of extracted rationales. We adopt a pre-trained natural language inference (NLI) model and further fine-tune it on a small set of supervised rationales ($10\%$). The NLI predictor is leveraged as a source of supervisory signals to the explainer via entailment alignment. We show that, by enforcing the alignment agreement between the explanation and answer in a question-answering task, the performance can be improved without access to ground truth labels. We evaluate our approach on the ERASER dataset and show that our approach achieves comparable results with supervised extractive models and outperforms unsupervised approaches by $> 100\%$.
A Unified MPC Strategy for a Tilt-rotor VTOL UAV Towards Seamless Mode Transitioning
Authors: Qizhao Chen, Ziqi Hu, Junyi Geng, Dongwei Bai, Mohammad Mousaei, Sebastian Scherer
Date: 2024-02-12 00:00:00

Capabilities of long-range flight and vertical take-off and landing (VTOL) are essential for Urban Air Mobility (UAM). Tiltrotor VTOLs have the advantage of balancing control simplicity and system complexity due to their redundant control authority. Prior work on controlling these aircraft either requires separate controllers and switching modes for different vehicle configurations or performs the control allocation on separate actuator sets, which cannot fully use the potential of the redundancy of tiltrotor. This paper introduces a unified MPC-based control strategy for a customized tiltrotor VTOL Unmanned Aerial Vehicle (UAV), which does not require mode-switching and can perform the control allocation in a consistent way. The incorporation of four independently controllable rotors in VTOL design offers an extra level of redundancy, allowing the VTOL to accommodate actuator failures. The result shows that our approach outperforms PID controllers while maintaining unified control. It allows the VTOL to perform smooth acceleration/deceleration, and precise coordinated turns. In addition, the independently controlled tilts enable the vehicle to handle actuator failures, ensuring that the aircraft remains operational even in the event of a servo or motor malfunction.
The Essential Role of Causality in Foundation World Models for Embodied AI
Authors: Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Marc Rigter, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang
Date: 2024-02-06 00:00:00
Tasks: Misconceptions

Recent advances in foundation models, especially in large multi-modal models and conversational agents, have ignited interest in the potential of generally capable embodied agents. Such agents will require the ability to perform new tasks in many different real-world environments. However, current foundation models fail to accurately model physical interactions and are therefore insufficient for Embodied AI. The study of causality lends itself to the construction of veridical world models, which are crucial for accurately predicting the outcomes of possible interactions. This paper focuses on the prospects of building foundation world models for the upcoming generation of embodied agents and presents a novel viewpoint on the significance of causality within these. We posit that integrating causal considerations is vital to facilitating meaningful physical interactions with the world. Finally, we demystify misconceptions about causality in this context and present our outlook for future research.
Interactive singing melody extraction based on active adaptation
Authors: Kavya Ranjan Saxena, Vipul Arora
Date: 2024-02-12 00:00:00
Tasks: Information Retrieval, Melody Extraction, Meta-Learning, Music Information Retrieval

Extraction of predominant pitch from polyphonic audio is one of the fundamental tasks in the field of music information retrieval and computational musicology. To accomplish this task using machine learning, a large amount of labeled audio data is required to train the model. However, a classical model pre-trained on data from one domain (source), e.g., songs of a particular singer or genre, may not perform comparatively well in extracting melody from other domains (target). The performance of such models can be boosted by adapting the model using very little annotated data from the target domain. In this work, we propose an efficient interactive melody adaptation method. Our method selects the regions in the target audio that require human annotation using a confidence criterion based on normalized true class probability. The annotations are used by the model to adapt itself to the target domain using meta-learning. Our method also provides a novel meta-learning approach that handles class imbalance, i.e., a few representative samples from a few classes are available for adaptation in the target domain. Experimental results show that the proposed method outperforms other adaptive melody extraction baselines. The proposed method is model-agnostic and hence can be applied to other non-adaptive melody extraction models to boost their performance. Also, we released a Hindustani Alankaar and Raga (HAR) dataset containing 523 audio files of about 6.86 hours of duration intended for singing melody extraction tasks.
Analysis of Self-Supervised Speech Models on Children's Speech and Infant Vocalizations
Authors: Jialu Li, Mark Hasegawa-Johnson, Nancy L. McElwain
Date: 2024-02-10 00:00:00
Tasks: Phoneme Recognition, Self-Supervised Learning

To understand why self-supervised learning (SSL) models have empirically achieved strong performances on several speech-processing downstream tasks, numerous studies have focused on analyzing the encoded information of the SSL layer representations in adult speech. Limited work has investigated how pre-training and fine-tuning affect SSL models encoding children's speech and vocalizations. In this study, we aim to bridge this gap by probing SSL models on two relevant downstream tasks: (1) phoneme recognition (PR) on the speech of adults, older children (8-10 years old), and younger children (1-4 years old), and (2) vocalization classification (VC) distinguishing cry, fuss, and babble for infants under 14 months old. For younger children's PR, the superiority of fine-tuned SSL models is largely due to their ability to learn features that represent older children's speech and then adapt those features to the speech of younger children. For infant VC, SSL models pre-trained on large-scale home recordings learn to leverage phonetic representations at middle layers, and thereby enhance the performance of this task.
Sound Source Separation Using Latent Variational Block-Wise Disentanglement
Authors: Karim Helwani, Masahito Togami, Paris Smaragdis, Michael M. Goodwin
Date: 2024-02-08 00:00:00
Tasks: Disentanglement

While neural network approaches have made significant strides in resolving classical signal processing problems, it is often the case that hybrid approaches that draw insight from both signal processing and neural networks produce more complete solutions. In this paper, we present a hybrid classical digital signal processing/deep neural network (DSP/DNN) approach to source separation (SS) highlighting the theoretical link between variational autoencoder and classical approaches to SS. We propose a system that transforms the single channel under-determined SS task to an equivalent multichannel over-determined SS problem in a properly designed latent space. The separation task in the latent space is treated as finding a variational block-wise disentangled representation of the mixture. We show empirically, that the design choices and the variational formulation of the task at hand motivated by the classical signal processing theoretical results lead to robustness to unseen out-of-distribution data and reduction of the overfitting risk. To address the resulting permutation issue we explicitly incorporate a novel differentiable permutation loss function and augment the model with a memory mechanism to keep track of the statistics of the individual sources.
Distributed Anomaly Detection in Modern Power Systems: A Penalty-based Mitigation Approach
Authors: Erfan Mehdipour Abadi, Masoud H. Nazari
Date: 2024-02-12 00:00:00
Tasks: Anomaly Detection, Decision Making

The evolving landscape of electric power networks, influenced by the integration of distributed energy resources require the development of novel power system monitoring and control architectures. This paper develops algorithm to monitor and detect anomalies of different parts of a power system that cannot be measured directly, by applying neighboring measurements and a dynamic probing technique in a distributed fashion. Additionally, the proposed method accurately assesses the severity of the anomaly. A decision-making algorithm is introduced to effectively penalize anomalous agents, ensuring vigilant oversight of the entire power system's functioning. Simulation results show the efficacy of algorithms in distributed anomaly detection and mitigation.
Novel Low-Complexity Model Development for Li-ion Cells Using Online Impedance Measurement
Authors: Abhijit Kulkarni, Ahsan Nadeem, Roberta Di Fonso, Yusheng Zheng, Remus Teodorescu
Date: 2024-02-12 00:00:00
Tasks: Management

Modeling of Li-ion cells is used in battery management systems (BMS) to determine key states such as state-of-charge (SoC), state-of-health (SoH), etc. Accurate models are also useful in developing a cell-level digital-twin that can be used for protection and diagnostics in the BMS. In this paper, a low-complexity model development is proposed based on the equivalent circuit model (ECM) of the Li-ion cells. The proposed approach uses online impedance measurement at discrete frequencies to derive the ECM that matches closely with the results from the electro-impedance spectroscopy (EIS). The proposed method is suitable to be implemented in a microcontroller with low-computational power, typically used in BMS. Practical design guidelines are proposed to ensure fast and accurate model development. Using the proposed method to enhance the functions of a typical automotive BMS is described. Experimental validation is performed using large prismatic cells and small-capacity cylindrical cells. Root-mean-square error (RMSE) of less than 3\% is observed for a wide variation of operating conditions.
Joint User and Beam Selection in Millimeter Wave Networks
Authors: Santosh Kumar Singh, Satyabrata Sahu, Ayushi Thawait, Prasanna Chaporkar, Gaurav S. Kasbekar
Date: 2024-02-12 00:00:00

We study the problem of selecting a user equipment (UE) and a beam for each access point (AP) for concurrent transmissions in a millimeter wave (mmWave) network, such that the sum of weighted rates of UEs is maximized. We prove that this problem is NP-complete. We propose two algorithms -- Markov Chain Monte Carlo (MCMC) based and local interaction game (LIG) based UE and beam selection -- and prove that both of them asymptotically achieve the optimal solution. Also, we propose two fast greedy algorithms -- NGUB1 and NGUB2 -- for UE and beam selection. Through extensive simulations, we show that our proposed greedy algorithms outperform the most relevant algorithms proposed in prior work and perform close to the asymptotically optimal algorithms.
Distribution Locational Marginal Emission for Carbon Alleviation in Distribution Networks: Formulation, Calculation, and Implication
Authors: Linwei Sang, Yinliang Xu, Hongbin Sun, Qiuwei Wu, Wenchuan Wu
Date: 2024-02-12 00:00:00
Tasks: Scheduling

Regulating the proper carbon-aware intervention policy is one of the keys to emission alleviation in the distribution network, whose basis lies in effectively attributing the emission responsibility using emission factors. This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the marginal change of both active and reactive load demand for incentivizing carbon alleviation. It first formulates the day-head distribution network scheduling model based on the second-order cone program (SOCP). The emission propagation and responsibility are analyzed from demand to supply to system emission. Considering the complex and implicit mapping of the SOCP-based scheduling model, the implicit theorem is leveraged to exploit the optimal condition of SOCP. The corresponding SOCP-based implicit derivation approach is proposed to calculate the DLMEs effectively in a model-based way. Comprehensive numerical studies are conducted to verify the superiority of the proposed method by comparing its calculation efficacy to the conventional marginal estimation approach, assessing its effectiveness in carbon alleviation with comparison to the average emission factors, and evaluating its carbon alleviation ability of reactive DLME.
A Review of the GIC Blocker Placement Problem
Authors: Arthur K. Barnes, Adam Mate, Russell Bent
Date: 2024-02-11 00:00:00
Tasks: Blocking

Space weather poses a tremendous threat to power systems: geomagnetic disturbances could result in widespread disruptions and long-duration blackouts, including severe damage to system components. To mitigate their impacts, a handful of strategies exist, with the most promising being the deployment of transformer neutral blocking devices. The high cost of these devices, however, precludes their installation at all substations; this motivates the development of effective solutions for the cost-effective placement of such devices. While the current state-of-the-art in blocker placement methods is insufficient to be applied to real-sized power grids, ongoing research continues to increase the size of networks for which the placement problem remains tractable. Along these lines, the contributions of this paper are two fold: first, a comprehensive overview of the current state-of-the-art in blocker placement methods is provided; and second, a complete optimization formulation - implemented and benchmarked in an open-source software - for the blocker placement problem is presented.
Field demonstration of predictive heating control for an all-electric house in a cold climate
Authors: Elias N. Pergantis, Priyadarshan, Nadah Al Theeb, Parveen Dhillon, Jonathan P. Ore, Davide Ziviani, Eckhard A. Groll, Kevin J. Kircher
Date: 2024-02-10 00:00:00
Tasks: All

Efficient electric heat pumps that replace fossil-fueled heating systems could significantly reduce greenhouse gas emissions. However, electric heat pumps can sharply increase electricity demand, causing high utility bills and stressing the power grid. Residential neighborhoods could see particularly high electricity demand during cold weather, when heat demand rises and heat pump efficiencies fall. This paper presents the development and field demonstration of a predictive control system for an air-to-air heat pump with backup electric resistance heat. The control system adjusts indoor temperature set-points based on weather forecasts, occupancy conditions, and data-driven models of the building and heating equipment. Field tests from January to March of 2023 in an occupied, all-electric, 208 m^2 detached single-family house in Indiana, USA, included outdoor temperatures as low as -15 C. On average over these tests, the control system reduced daily heating energy use by 19% (95% confidence interval: 13--24%), energy used for backup heat by 38%, and the frequency of using the highest stage (19 kW) of backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. The control system could reduce the house's total annual heating costs by about $300 (95% confidence interval: 23--34%). These real-world results could strengthen the case for deploying predictive home heating control, bringing the technology one step closer to reducing emissions, utility bills, and power grid impacts at scale.
Structures vibration control via tuned mass dampers using a co-evolution coral reefs optimization algorithm
Authors: S Salcedo-Sanz, C Camacho-Gómez, A Magdaleno, E Pereira, A Lorenzana
Date: 2024-02-10 00:00:00

In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.
High-Performance Distributed Control for Large-Scale Linear Systems: A Partitioned Distributed Observer Approach
Authors: Haotian Xu, Shuai Liu, Ling Shi
Date: 2024-02-10 00:00:00
Tasks: LEMMA

In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance. However, the traditional distributed observer requires each local observer to reconstruct the state information of the whole system, which is unrealistic for large-scale scenarios. To fill this gap, this paper develops a greedy-idea-based large-scale system partition algorithm, which can significantly reduce the dimension of local observers. Then, the partitioned distributed observer for large-scale systems is proposed to overcome the problem that the system dynamics are difficult to estimate due to the coupling between partitions. Furthermore, the two-layer Lyapunov analysis method is adopted and the dynamic transformation lemma of compact errors is proven, which solves the problem of analyzing stability of the error dynamic of the partitioned distributed observer. Finally, it is proved that the distributed control law based on the partitioned distributed observer can also arbitrarily approximate the control performance of the centralized control law, and the dimension of the local observer is greatly reduced compared with the traditional method. The simulation results show that when the similarity between the physical network and the communication network is about 80%, the local observer dimension is greatly reduced by 90% and the relative error between the performance of the distributed control law and that of the centralized control law is less than 1%.
Implementation of Kalman Filter Approach for Active Noise Control by Using MATLAB: Dynamic Noise Cancellation
Authors: Guo Yu
Date: 2024-02-10 00:00:00
Tasks: Management

This article offers an elaborate description of a Kalman filter code employed in the active control system. Conventional active noise management methods usually employ an adaptive filter, such as the filtered reference least mean square (FxLMS) algorithm, to adjust to changes in the primary noise and acoustic environment. Nevertheless, the slow convergence characteristics of the FxLMS algorithm typically impact the effectiveness of reducing dynamic noise. Hence, this study suggests employing the Kalman filter in the active noise control (ANC) system to enhance the efficacy of noise reduction for dynamic noise. The ANC application effectively utilizes the Kalman filter with a novel dynamic ANC model. The numerical simulation revealed that the proposed Kalman filter exhibits superior convergence performance compared to the FxLMS algorithm for handling dynamic noise. The code is available on \href{https://github.com/ShiDongyuan/Kalman_Filter_for_ANC.git}{GitHub} and \href{https://www.mathworks.com/matlabcentral/fileexchange/159311-kalman-filter-for-active-noise-control}{MathWorks}.
Reduced-order Modeling of Modular, Position-dependent Systems with Translating Interfaces
Authors: Robert A. Egelmeers, Lars A. L. Janssen, Rob H. B. Fey, Jasper Gerritsen, Nathan van de Wouw
Date: 2024-02-09 00:00:00
Tasks: Position

Many complex mechatronic systems consist of multiple interconnected dynamical subsystems, which are designed, developed, analyzed, and manufactured by multiple independent teams. To support such a design approach, a modular model framework is needed to reduce computational complexity and, at the same time, enable multiple teams to develop and analyze the subsystems in parallel. In such a modular framework, the subsystem models are typically interconnected by means of a static interconnection structure. However, many complex dynamical systems exhibit position-dependent behavior (e.g., induced by translating interfaces) which cannot be not captured by such static interconnection models. In this paper, a modular model framework is proposed, which allows to construct an interconnected system model, which captures the position-dependent behavior of systems with translating interfaces, such as linear guide rails, through a position-dependent interconnection structure. Additionally, this framework allows to apply model reduction on subsystem level, enabling a more effective reduction approach, tailored to the specific requirements of each subsystem. Furthermore, we show the effectiveness of this framework on an industrial wire bonder. Here, we show that including a position-dependent model of the interconnection structure 1) enables to accurately model the dynamics of a system over the operating range of the system and, 2) modular model reduction methods can be used to obtain a computationally efficient interconnected system model with guaranteed accuracy specifications.
Advecting Scaffolds: Controlling The Remodelling Of Actomyosin With Anillin
Authors: Denni Currin-Ross, Sami C. Al-Izzi, Ivar Noordstra, Alpha S. Yap, Richard G. Morris
Date: 2024-02-12 00:00:00

We propose and analyse an active hydrodynamic theory that characterises the effects of the scaffold protein anillin. Anillin is found at major sites of cortical activity, such as adherens junctions and the cytokinetic furrow, where the canonical regulator of actomyosin remodelling is the small GTPase, RhoA. RhoA acts via intermediary 'effectors' to increase both the rates of activation of myosin motors and the polymerisation of actin filaments. Anillin has been shown to scaffold this action of RhoA - improving critical rates in the signalling pathway without altering the essential biochemistry - but its contribution to the wider spatio-temporal organisation of the cortical cytoskeleton remains poorly understood. Here, we combine analytics and numerics to show how anillin can non-trivially regulate the cytoskeleton at hydrodynamic scales. At short times, anillin can amplify or dampen existing contractile instabilities, as well as alter the parameter ranges over which they occur. At long times, it can change both the size and speed of steady-state travelling pulses. The primary mechanism that underpins these behaviours is established to be the advection of anillin by myosin II motors, with the specifics relying on the values of two coupling parameters. These codify anillin's effect on local signalling kinetics and can be traced back to its interaction with the acidic phospholipid phosphatidylinositol 4,5-bisphosphate (PIP2), thereby establishing a putative connection between actomyosin remodelling and membrane composition.