pwc-archive/papers-with-abstracts huggingface dataset:
GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval
Authors: Sibo Dong, Justin Goldstein, Grace Hui Yang
Date: 2022-07-04 00:00:00
Tasks: Information Retrieval, Retrieval, Text Retrieval

This paper is interested in investigating whether human gaze signals can be leveraged to improve state-of-the-art search engine performance and how to incorporate this new input signal marked by human attention into existing neural retrieval models. In this paper, we propose GazBy ({\bf Gaz}e-based {\bf B}ert model for document relevanc{\bf y}), a light-weight joint model that integrates human gaze fixation estimation into transformer models to predict document relevance, incorporating more nuanced information about cognitive processing into information retrieval (IR). We evaluate our model on the Text Retrieval Conference (TREC) Deep Learning (DL) 2019 and 2020 Tracks. Our experiments show encouraging results and illustrate the effective and ineffective entry points for using human gaze to help with transformer-based neural retrievers. With the rise of virtual reality (VR) and augmented reality (AR), human gaze data will become more available. We hope this work serves as a first step exploring using gaze signals in modern neural search engines.
NEAR - Newborns EEG Artifact Removal
Authors: Velu Prabhakar Kumaravel, Eugenio Parise, Elisabetta Farella, Marco Buiatti
Date: 2022-01-15 00:00:00
Proceeding: Developmental Cognitive Neuroscience special édition 2022 1
Tasks: EEG, EEG Artifact Removal, Electroencephalogram (EEG), ERP, Outlier Detection

Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’ limited attentional span and much noisier due to non-stereotyped artifacts mainly caused by uncontrollable movements. We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns. NEAR is based on two key steps: 1) A novel bad channel detection tool based on the Local Outlier Factor (LOF), a robust outlier detection algorithm; 2) A parameter calibration procedure for adapting to newborn EEG data the algorithm Artifacts Subspace Reconstruction (ASR), developed for artifact removal in mobile adult EEG. Tests on simulated data showed that NEAR outperforms existing methods in removing representative newborn non-stereotypical artifacts. NEAR was validated on two developmental populations (newborns and 9-month-old infants) recorded with two different experimental designs (frequency-tagging and ERP). Results show that NEAR artifact removal successfully reproduces established EEG responses from noisy datasets, with a higher statistical significance than the one obtained by existing artifact removal methods.
IA-GM: A Deep Bidirectional Learning Method for Graph Matching
Authors: Kaixuan Zhao, Shikui Tu, Lei Xu
Date: 2021-05-18 00:00:00
Proceeding: AAAI 2021 5
Tasks: Combinatorial Optimization, Graph Matching

Existing deep learning methods for graph matching(GM) problems usually considered affinity learningto assist combinatorial optimization in a feedforward pipeline, and parameter learning is executed by back-propagating the gradients of the matching loss. Such a pipeline pays little attention to the possible complementary benefit from the optimization layer to the learning component. In this paper, we overcome the above limitation under a deep bidirectional learning framework.Our method circulates the output of the GM optimization layer to fuse with the input for affinity learning. Such direct feedback enhances the input by a feature enrichment and fusion technique, which exploits andintegrates the global matching patterns from the deviation of the similarity permuted by the current matching estimate. As a result, the circulation enables the learning component to benefit from the optimization process, taking advantage of both global feature and the embedding result which is calculated by local propagationthrough node-neighbors. Moreover, circulation consistency induces an unsupervised loss that can be implemented individually or jointly to regularize the supervised loss. Experiments on challenging datasets demonstrate the effectiveness of our methods for both supervised learning and unsupervised learning.
GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network
Authors: Bo Jiang, Pengfei Sun, Ziyan Zhang, Jin Tang, Bin Luo
Date: 2021-10-17 00:00:00
Proceeding: MM 2021 10
Tasks: Graph Matching

Recently, deep graph matching (GM) methods have gained increasing attention. These methods integrate graph nodes¡¯s embedding, node/edges¡¯s affinity learning and final correspondence solver together in an end-to-end manner. For deep graph matching problem, one main issue is how to generate consensus node's embeddings for both source and target graphs that best serve graph matching tasks. In addition, it is also challenging to incorporate the discrete one-to-one matching constraints into the differentiable correspondence solver in deep matching network. To address these issues, we propose a novel Graph Adversarial Matching Network (GAMnet) for graph matching problem. GAMnet integrates graph adversarial embedding and graph matching simultaneously in a unified end-to-end network which aims to adaptively learn distribution consistent and domain invariant embeddings for GM tasks. Also, GAMnet exploits sparse GM optimization as correspondence solver which is differentiable and can also incorporate discrete one-to-one matching constraints approximately in natural in the final matching prediction. Experimental results on three public benchmarks demonstrate the effectiveness and benefits of the proposed GAMnet.
T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story Completion
Authors: Tianming Wang, Xiaojun Wan
Date: 2019-07-01 00:00:00
Proceeding: International Joint Conference on Artificial Intelligence 2019 7
Tasks: Decoder, Diversity, Story Completion

Story completion is a very challenging task of generating the missing plot for an incomplete story, which requires not only understanding but also inference of the given contextual clues. In this paper, we present a novel conditional variational autoencoder based on Transformer for missing plot generation. Our model uses shared attention layers for encoder and decoder, which make the most of the contextual clues, and a latent variable for learning the distribution of coherent story plots. Through drawing samples from the learned distribution, diverse reasonable plots can be generated. Both automatic and manual evaluations show that our model generates better story plots than state-of-the-art models in terms of readability, diversity and coherence.
Bayesian NVH metamodels to assess interior cabin noise using measurement databases
Authors: V. Prakash, O. Sauvage, J. Antoni, L. Gagliardini
Date: 2022-06-12 00:00:00
Tasks: Additive models

In recent years, a great emphasis has been put on engineering the acoustic signature of vehicles that represents the overall comfort level for passengers. Due to highly uncertain behavior of production cars, probabilistic metamodels or surrogates can be useful to estimate the NVH dispersion and assess different NVH risks. These metamodels follow physical behaviors and shall aid as a design space exploration tool during the early stage design process to support the NVH optimization. The measurement databases constitute different noise paths such as aerodynamic noise (wind-tunnel test), tire-pavement interaction noise (rolling noise), and noise due to electric motors (whining noise). This research work proposes a global NVH metamodeling technique for broadband noises such as aerodynamic and rolling noises exploiting the Bayesian framework that takes into account the prior (domain-expert) knowledge about complex physical mechanisms. Generalized additive models (GAMs) with polynomials and Gaussian basis functions are used to model the dependency of sound pressure level (SPL) on predictor variables. Moreover, parametric bootstrap algorithm based on data-generating mechanism using the point estimates is used to estimate the dispersion in unknown parameters. Probabilistic modelling is carried out using an open-source library PyMC3 that utilizes No-U-Turn sampler (NUTS) and the developed models are validated using Cross-Validation technique.
Optimal Multi-Dimensional Auctions: Conjectures and Simulations
Authors: Alexey Kushnir, James Michelson
Date: 2022-07-04 00:00:00

We explore the properties of optimal multi-dimensional auctions in a model where a single object of multiple qualities is sold to several buyers. Using simulations, we test some hypotheses conjectured by Belloni et al. [3] and Kushnir and Shourideh [7]. As part of this work, we provide the first open-source library for multi-dimensional auction simulations written in Python.
Detecting Abrupt Changes in Channel Covariance Matrix for MIMO Communication
Authors: Runnan Liu, Liang Liu, Dazhi He, Wenjun Zhang, Erik G. Larsson
Date: 2022-07-05 00:00:00
Tasks: Change Detection

The acquisition of the channel covariance matrix is of paramount importance to many strategies in multiple-input-multiple-output (MIMO) communications, such as the minimum mean-square error (MMSE) channel estimation. Therefore, plenty of efficient channel covariance matrix estimation schemes have been proposed in the literature. However, an abrupt change in the channel covariance matrix may happen occasionally in practice due to the change in the scattering environment and the user location. Our paper aims to adopt the classic change detection theory to detect the change in the channel covariance matrix as accurately and quickly as possible such that the new covariance matrix can be re-estimated in time. Specifically, this paper first considers the technique of on-line change detection (also known as quickest/sequential change detection), where we need to detect whether a change in the channel covariance matrix occurs at each channel coherence time interval. Next, because the complexity of detecting the change in a high-dimension covariance matrix at each coherence time interval is too high, we devise a low-complexity off-line strategy in massive MIMO systems, where change detection is merely performed at the last channel coherence time interval of a given time period. Numerical results show that our proposed on-line and off-line schemes can detect the channel covariance change with a small delay and a low false alarm rate. Therefore, our paper theoretically and numerically verifies the feasibility of detecting the channel covariance change accurately and quickly in practice.
ImageBox3: No-Server Tile Serving to Traverse Whole Slide Images on the Web
Authors: Praphulla MS Bhawsar, Erich Bremer, Máire A Duggan, Stephen Chanock, Montserrat Garcia-Closas, Joel Saltz, Jonas S Almeida
Date: 2022-07-04 00:00:00
Tasks: whole slide images

Whole slide imaging (WSI) has become the primary modality for digital pathology data. However, due to the size and high-resolution nature of these images, they are generally only accessed in smaller sections or tiles via specialized platforms, most of which require extensive setup and/or costly infrastructure. These platforms typically also need a copy of the images to be locally available to them, potentially causing issues with data governance and provenance. To address these concerns, we developed ImageBox3, an in-browser tiling mechanism to enable zero-footprint traversal of remote WSI data. All computation is performed client-side without compromising user governance, operating public and private images alike as long as the storage service supports HTTP range requests (standard in Cloud storage and most web servers). ImageBox3 thus removes significant hurdles to WSI operation and effective collaboration, allowing for the sort of democratized analytical tools needed to establish participative, FAIR digital pathology data commons. Availability: code - https://github.com/episphere/imagebox3; fig1 (live) - https://episphere.github.io/imagebox3/demo/scriptTag ; fig2 (live) - https://episphere.github.io/imagebox3/demo/serviceWorker ; fig 3 (live) - https://observablehq.com/@prafulb/imagebox3-in-observable .
Optimal Verification of Rumors in Networks
Authors: Luca Paolo Merlino, Nicole Tabasso
Date: 2022-07-05 00:00:00

We study the diffusion of a true and a false message when agents are biased and able to verify messages. As a recipient of a rumor who verifies it becomes informed of the truth, a higher rumor prevalence can increase the prevalence of the truth. We uncover conditions such that this happens and discuss policy implications. Specifically, a planner aiming to maximize the prevalence of the truth should allow rumors to circulate if: verification overcomes ignorance of messages, transmission of information is relatively low, and the planner's budget to induce verification is neither too low nor too high.
Hierarchical modeling for an industrial implementation of a Digital Twin for electrical drives
Authors: Karim Cherifi, Philipp Schulze, Volker Mehrmann, Leo Goßlau, Pascal Lünnemann
Date: 2022-07-05 00:00:00

Digital twins have become popular for their ability to monitor and optimize a process or a machine, ideally through its complete life cycle using simulations and sensor data. In this paper, we focus on the challenge of accurate and real-time simulations for digital twins in the context of electrical machines. To build such a digital twin involves not only computational models for the electromagnetic aspects, but also mechanical and thermal effects need to be taken into account. We address mathematical tools that can be employed to carry out the required simulations based on physical laws as well as surrogate or data-driven models. One of those tools is a model hierarchy of very fine to very coarse models as well as reduced order models for obtaining real-time simulations. The required software tools to carry out simulations in the digital twin are also discussed. The simulation models are implemented in a pipeline that allows for the automatic modeling of new machines and the automatic configuration of new digital twins. Finally, the overall implemented digital twin is tested and implemented in a physical demonstrator.
Centralized Networked Micro Water-Energy Nexus with Proportional Exchange Among Participants
Authors: Jesus Silva-Rodriguez, Xingpeng Li
Date: 2022-07-05 00:00:00

This paper proposes a Networked Micro Water-Energy Nexus (NetMicroWEN) capable of co-optimizing and simultaneously supplying water and energy to local consumers in nearby communities. The system manages different water and energy inputs of different communities in a local network to cooperatively meet their demands. This paper considers a centralized network topology that connects all members of the network under one control system. This paper also proposes a Proportional Exchange Algorithm (PEA) that allows members to benefit equally from exchanging both resources among other members of the NetMicroWEN and the main water and power distribution systems. The co-optimization model is a mixed-integer linear program, involving all necessary power and water related constraints for the network to achieve a feasible and practical solution. The economic benefits of the NetMicroWEN are illustrated by a comparison with separate Micro Water- Energy Nexus (MWEN) systems meeting their own demands individually. The case studies demonstrate that the proposed NetMicroWEN achieves substantially lower operating costs compared to the operation of separate MWEN systems.
Compositional Synthesis of Control Barrier Certificates for Networks of Stochastic Systems against $ω$-Regular Specifications
Authors: Mahathi Anand, Abolfazl Lavaei, Majid Zamani
Date: 2021-03-03 00:00:00

This paper is concerned with a compositional scheme for the construction of control barrier certificates for interconnected discrete-time stochastic systems. The main objective is to synthesize switching control policies against $\omega$-regular properties that can be described by accepting languages of deterministic Streett automata (DSA) along with providing probabilistic guarantees for the satisfaction of such specifications. The proposed framework leverages the interconnection topology and a notion of so-called \emph{control sub-barrier certificates} of subsystems, which are used to compositionally construct control barrier certificates of interconnected systems by imposing some dissipativity-type compositionality conditions. We propose a systematic approach to decompose high-level $\omega$-regular specifications into simpler tasks by utilizing the automata corresponding to the specifications. In addition, we formulate an alternating direction method of multipliers (ADMM) optimization problem in order to obtain suitable control sub-barrier certificates of subsystems while satisfying compositionality conditions. For systems with polynomial dynamics, we provide a sum-of-squares (SOS) optimization problem for the computation of control sub-barrier certificates and local control policies of subsystems. Finally, we demonstrate the effectiveness of our proposed approaches by applying them to a physical case study.
Distributed Adaptive Backstepping Control for Vehicular Platoons with Mismatched Disturbances Using Vector String Lyapunov Functions
Authors: Zihao Song, Shirantha Welikala, Panos J. Antsaklis, Hai Lin
Date: 2022-07-05 00:00:00

In this paper, we consider the problem of platooning control with mismatched disturbances using the distributed adaptive backstepping method. The main challenges are: (1) maintaining the compositionality and the distributed nature of the controller, and (2) ensuring the robustness of the controller with respect to general types of disturbances. To address these challenges, we first propose a novel notion that we named "Vector String Lyapunov Function", whose existence implies l2 weak string stability. This notion is based on the vector Lyapunov function-based stability analysis, which depends on the input-to-state-stability of a comparison system. Using this notion, we propose an adaptive backstepping controller for the platoon such that the compositionality and the distributed nature of the controller can be ensured while the internal stability and string stability of the closed-loop system are formally guaranteed. Finally, simulation examples are provided to illustrate the effectiveness of the proposed control algorithm. In particular, we provide simulation results comparing our proposed control algorithm with a recently proposed control algorithm from the literature, under two types of information flow topologies and disturbances.
Reward-modulated learning using spiking neural networks for vehicle lateral control
Authors: Javier Pérez, Manuel A. Vargas, Juan A. Cabrera, Juan J. Castillo, Barys Shyrokau
Date: 2022-07-05 00:00:00

This paper presents a vehicle lateral controller based on spiking neural networks capable of replicating the behavior of a model-based controller but with the additional ability to perform online adaptation. By making use of neural plasticity and thanks to reward modulation learning, neural connections are modified to adjust the response according to the committed error. Therefore, the error performs a similar role to dopamine in a biological system, modulating the learning process based on spiking time dependency. The connections are initially set to replicate behavior of the model-based controller. Online adaptation allows tuning connection parameters to improve controller performance. A path controller with a preview is used as a baseline controller to evaluate the performance of the proposed approach. Key performance indicators are obtained from simulation with a step response and a handling track with 20 curves of different radii.
Vector Field-based Collision Avoidance for Moving Obstacles with Time-Varying Elliptical Shape
Authors: Martin Braquet, Efstathios Bakolas
Date: 2022-07-04 00:00:00
Tasks: Collision Avoidance, Motion Planning, Navigate

This paper presents an algorithm for local motion planning in environments populated by moving elliptical obstacles whose velocity, shape and size are fully known but may change with time. We base the algorithm on a collision avoidance vector field (CAVF) that aims to steer an agent to a desired final state whose motion is described by a double integrator kinematic model. In addition to handling multiple obstacles, the method is applicable in bounded environments for more realistic applications (e.g., motion planning inside a building). We also incorporate a method to deal with agents whose control input is limited so that they safely navigate around the obstacles. To showcase our approach, extensive simulations results are presented in 2D and 3D scenarios.
Climate-Contingent Finance
Authors: John Nay
Date: 2022-07-05 00:00:00

Climate adaptation could yield significant benefits. However, the uncertainty of which future climate scenarios will occur decreases the feasibility of proactively adapting. Climate adaptation projects could be underwritten by benefits paid for in the climate scenarios that each adaptation project is designed to address because other entities would like to hedge the financial risk of those scenarios. Because the return on investment is a function of the level of climate change, it is optimal for the adapting entity to finance adaptation with repayment as a function of the climate. It is also optimal for entities with more financial downside under a more extreme climate to serve as an investing counterparty because they can obtain higher than market rates of return when they need it most. In this way, parties proactively adapting would reduce the risk they over-prepare, while their investors would reduce the risk they under-prepare. This is superior to typical insurance because, by investing in climate-contingent mechanisms, investors are not merely financially hedging but also outright preventing physical damage, and therefore creating economic value. This coordinates capital through time and place according to parties' risk reduction capabilities and financial profiles, while also providing a diversifying investment return. Climate-contingent finance can be generalized to any situation where entities share exposure to a risk where they lack direct control over whether it occurs (e.g., climate change, or a natural pandemic), and one type of entity can take proactive actions to benefit from addressing the effects of the risk if it occurs (e.g., through innovating on crops that would do well under extreme climate change or vaccination technology that could address particular viruses) with funding from another type of entity that seeks a targeted return to ameliorate the downside.