pwc-archive/papers-with-abstracts huggingface dataset:
Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Pooling
Authors: Wenjun Jiang, Tianlong Fan, Changhao Li, Chuanfu Zhang, Tao Zhang, Zong-fu Luo
Date: 2023-08-10 00:00:00

Connectivity robustness, a crucial aspect for understanding, optimizing, and repairing complex networks, has traditionally been evaluated through time-consuming and often impractical simulations. Fortunately, machine learning provides a new avenue for addressing this challenge. However, several key issues remain unresolved, including the performance in more general edge removal scenarios, capturing robustness through attack curves instead of directly training for robustness, scalability of predictive tasks, and transferability of predictive capabilities. In this paper, we address these challenges by designing a convolutional neural networks (CNN) model with spatial pyramid pooling networks (SPP-net), adapting existing evaluation metrics, redesigning the attack modes, introducing appropriate filtering rules, and incorporating the value of robustness as training data. The results demonstrate the thoroughness of the proposed CNN framework in addressing the challenges of high computational time across various network types, failure component types and failure scenarios. However, the performance of the proposed CNN model varies: for evaluation tasks that are consistent with the trained network type, the proposed CNN model consistently achieves accurate evaluations of both attack curves and robustness values across all removal scenarios. When the predicted network type differs from the trained network, the CNN model still demonstrates favorable performance in the scenario of random node failure, showcasing its scalability and performance transferability. Nevertheless, the performance falls short of expectations in other removal scenarios. This observed scenario-sensitivity in the evaluation of network features has been overlooked in previous studies and necessitates further attention and optimization. Lastly, we discuss important unresolved questions and further investigation.
Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion
Authors: Hashim A. Hashim, Abdelrahman E. E. Eltoukhy, Kyriakos G. Vamvoudakis, Mohammed I. Abouheaf
Date: 2023-08-15 00:00:00
Tasks: Position, Sensor Fusion

Navigation in Global Positioning Systems (GPS)-denied environments requires robust estimators reliant on fusion of inertial sensors able to estimate rigid-body's orientation, position, and linear velocity. Ultra-wideband (UWB) and Inertial Measurement Unit (IMU) represent low-cost measurement technology that can be utilized for successful Inertial Navigation. This paper presents a nonlinear deterministic navigation observer in a continuous form that directly employs UWB and IMU measurements. The estimator is developed on the extended Special Euclidean Group $\mathbb{SE}_{2}\left(3\right)$ and ensures exponential convergence of the closed loop error signals starting from almost any initial condition. The discrete version of the proposed observer is tested using a publicly available real-world dataset of a drone flight. Keywords: Ultra-wideband, Inertial measurement unit, Sensor Fusion, Positioning system, GPS-denied navigation.
Classifying Dementia in the Presence of Depression: A Cross-Corpus Study
Authors: Franziska Braun, Sebastian P. Bayerl, Paula A. Pérez-Toro, Florian Hönig, Hartmut Lehfeld, Thomas Hillemacher, Elmar Nöth, Tobias Bocklet, Korbinian Riedhammer
Date: 2023-08-16 00:00:00
Tasks: Binary Classification, Cross-corpus

Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected. Depression has shared symptoms with dementia, adding complexity to diagnoses. The research focus so far has been on binary classification of dementia (DEM) and healthy controls (HC) using speech from picture description tests from a single dataset. In this work, we apply established baseline systems to discriminate cognitive impairment in speech from the semantic Verbal Fluency Test and the Boston Naming Test using text, audio and emotion embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform cross-corpus and mixed-corpus experiments on two independently recorded German datasets to investigate generalization to larger populations and different recording conditions. In a detailed error analysis, we look at depression as a secondary diagnosis to understand what our classifiers actually learn.
Certifying Stability and Performance of Uncertain Differential-Algebraic Systems: A Dissipativity Framework
Authors: Emily Jensen, Neelay Junnarkar, Murat Arcak, Xiaofan Wu, Suat Gumussoy
Date: 2023-08-16 00:00:00

This paper presents a novel framework for characterizing dissipativity of uncertain systems whose dynamics evolve according to differential-algebraic equations. Sufficient conditions for dissipativity (specializing to, e.g., stability or $L_2$ gain bounds) are provided in the case that uncertainties are characterized by integral quadratic constraints. For polynomial or linear dynamics, these conditions can be efficiently verified through sum-of-squares or semidefinite programming. Performance analysis of the IEEE 39-bus power network with a set of potential line failures modeled as an uncertainty set provides an illustrative example that highlights the computational tractability of this approach; conservatism introduced in this example is shown to be quite minimal.
Equilibrium-Independent Control of Continuous-Time Nonlinear Systems via the LPV Framework -- Extended Version
Authors: Patrick J. W. Koelewijn, Siep Weiland, Roland Tóth
Date: 2023-08-16 00:00:00

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free" concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified in a simulation study and also experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach.
Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances
Authors: Lei Guo, Wenshuo Li, Yukai Zhu, Xiang Yu, Zidong Wang
Date: 2023-08-16 00:00:00
Tasks: State Estimation

State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the "single" disturbance can be either rejected or attenuated. Since the pioneering work in 2012, a novel state estimation methodology called {\it composite disturbance filtering} (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances.
A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning
Authors: Qinghui Nie, Jishun Ou, Haiyang Zhang, Jiawei Lu, Shen Li, Haotian Shi
Date: 2023-08-16 00:00:00
Tasks: Deep Reinforcement Learning

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers. However, bus operation efficiency can be impacted by bus bunching. This problem is notably exacerbated when the bus system operates along a signalized corridor with unpredictable travel demand. To mitigate this challenge, we introduce a multi-strategy fusion approach for the longitudinal control of connected and automated buses. The approach is driven by a physics-informed deep reinforcement learning (DRL) algorithm and takes into account a variety of traffic conditions along urban signalized corridors. Taking advantage of connected and autonomous vehicle (CAV) technology, the proposed approach can leverage real-time information regarding bus operating conditions and road traffic environment. By integrating the aforementioned information into the DRL-based bus control framework, our designed physics-informed DRL state fusion approach and reward function efficiently embed prior physics and leverage the merits of equilibrium and consensus concepts from control theory. This integration enables the framework to learn and adapt multiple control strategies to effectively manage complex traffic conditions and fluctuating passenger demands. Three control variables, i.e., dwell time at stops, speed between stations, and signal priority, are formulated to minimize travel duration and ensure bus stability with the aim of avoiding bus bunching. We present simulation results to validate the effectiveness of the proposed approach, underlining its superior performance when subjected to sensitivity analysis, specifically considering factors such as traffic volume, desired speed, and traffic signal conditions.
Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition
Authors: Huqiang Cheng, Xiaofeng Liao, Huaqing Li, You Zhao
Date: 2023-08-16 00:00:00
Tasks: Distributed Optimization, Privacy Preserving

Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, and resource allocation. Existing decentralized optimization algorithms require sharing explicit state information among the agents, which raises the risk of private information leakage. To ensure privacy security, combining information security mechanisms, such as differential privacy and homomorphic encryption, with traditional decentralized optimization algorithms is a commonly used means. However, this would either sacrifice optimization accuracy or incur heavy computational burden. To overcome these shortcomings, we develop a novel privacy-preserving decentralized optimization algorithm, called PPSD, that combines gradient tracking with a state decomposition mechanism. Specifically, each agent decomposes its state associated with the gradient into two substates. One substate is used for interaction with neighboring agents, and the other substate containing private information acts only on the first substate and thus is entirely agnostic to other agents. For the strongly convex and smooth objective functions, PPSD attains a $R$-linear convergence rate. Moreover, the algorithm can preserve the agents' private information from being leaked to honest-but-curious neighbors. Simulations further confirm the results.
Real-Time Numerical Differentiation of Sampled Data Using Adaptive Input and State Estimation
Authors: Shashank Verma, Sneha Sanjeevini, E. Dogan Sumer, Dennis S. Bernstein
Date: 2023-08-16 00:00:00
Tasks: Position, State Estimation

Real-time numerical differentiation plays a crucial role in many digital control algorithms, such as PID control, which requires numerical differentiation to implement derivative action. This paper addresses the problem of numerical differentiation for real-time implementation with minimal prior information about the signal and noise using adaptive input and state estimation. Adaptive input estimation with adaptive state estimation (AIE/ASE) is based on retrospective cost input estimation, while adaptive state estimation is based on an adaptive Kalman filter in which the input-estimation error covariance and the measurement-noise covariance are updated online. The accuracy of AIE/ASE is compared numerically to several conventional numerical differentiation methods. Finally, AIE/ASE is applied to simulated vehicle position data generated from CarSim.
Testing Partial Instrument Monotonicity
Authors: Hongyi Jiang, Zhenting Sun
Date: 2023-08-16 00:00:00

When multi-dimensional instruments are used to identify and estimate causal effects, the monotonicity condition may not hold due to heterogeneity in the population. Under a partial monotonicity condition, which only requires the monotonicity to hold for each instrument separately holding all the other instruments fixed, the 2SLS estimand can still be a positively weighted average of LATEs. In this paper, we provide a simple nonparametric test for partial instrument monotonicity. We demonstrate the good finite sample properties of the test through Monte Carlo simulations. We then apply the test to monetary incentives and distance from results centers as instruments for the knowledge of HIV status.
Estimating Effects of Long-Term Treatments
Authors: Shan Huang, Chen Wang, Yuan Yuan, Jinglong Zhao, Brocco, Zhang
Date: 2023-08-16 00:00:00

Estimating the effects of long-term treatments through A/B testing is challenging. Treatments, such as updates to product functionalities, user interface designs, and recommendation algorithms, are intended to persist within the system for a long duration of time after their initial launches. However, due to the constraints of conducting long-term experiments, practitioners often rely on short-term experimental results to make product launch decisions. It remains open how to accurately estimate the effects of long-term treatments using short-term experimental data. To address this question, we introduce a longitudinal surrogate framework that decomposes the long-term effects into functions based on user attributes, short-term metrics, and treatment assignments. We outline identification assumptions, estimation strategies, inferential techniques, and validation methods under this framework. Empirically, we demonstrate that our approach outperforms existing solutions by using data from two real-world experiments, each involving more than a million users on WeChat, one of the world's largest social networking platforms.
Emerging Frontiers: Exploring the Impact of Generative AI Platforms on University Quantitative Finance Examinations
Authors: Rama K. Malladi
Date: 2023-08-15 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

This study evaluated three Artificial Intelligence (AI) large language model (LLM) enabled platforms - ChatGPT, BARD, and Bing AI - to answer an undergraduate finance exam with 20 quantitative questions across various difficulty levels. ChatGPT scored 30 percent, outperforming Bing AI, which scored 20 percent, while Bard lagged behind with a score of 15 percent. These models faced common challenges, such as inaccurate computations and formula selection. While they are currently insufficient for helping students pass the finance exam, they serve as valuable tools for dedicated learners. Future advancements are expected to overcome these limitations, allowing for improved formula selection and accurate computations and potentially enabling students to score 90 percent or higher.
Does courier gender matter? Exploring mode choice behaviour for E-groceries crowd-shipping in developing economies
Authors: Oleksandr Rossolov, Anastasiia Botsman, Serhii Lyfenko, Yusak O. Susilo
Date: 2023-08-15 00:00:00

This paper examines the mode choice behaviour of people who may act as occasional couriers to provide crowd-shipping (CS) deliveries. Given its recent increase in popularity, online grocery services have become the main market for crowd-shipping deliveries' provider. The study included a behavioural survey, PTV Visum simulations and discrete choice behaviour modelling based on random utility maximization theory. Mode choice behaviour was examined by considering the gender heterogeneity of the occasional couriers in a multimodal urban transport network. The behavioural dataset was collected in the city of Kharkiv, Ukraine, at the beginning of 2021. The results indicated that women were willing to provide CS service with 8% less remuneration than men. Women were also more likely to make 10% longer detours by car and metro than men, while male couriers were willing to implement 25% longer detours when travelling by bike or walking. Considering the integration of CS detours into the couriers' routine trip chains, women couriers were more likely to attach the CS trip to the work-shopping trip chain whilst men would use the home-home evening time trip chain. The estimated marginal probability effect indicated a higher detour time sensitivity with respect to expected profit and the relative detour costs of the couriers.
A Majority Rule Philosophy for Instant Runoff Voting
Authors: Ross Hyman, Deb Otis, Seamus Allen, Greg Dennis
Date: 2023-08-16 00:00:00
Tasks: Philosophy

We present the core support criterion, a voting criterion satisfied by Instant Runoff Voting (IRV) that is analogous to the Condorcet criterion but reflective of a different majority rule philosophy. Condorcet methods can be thought of as conducting elections between each pair of candidates, counting all ballots to determine the winner of each pair-election. IRV can also be thought of as conducting elections between all pairs of candidates but for each pair-election only counting ballots from voters who do not prefer another major candidate (as determined self-consistently from the IRV social ranking) to the two candidates in contention. The appropriateness of including all ballots or a subset of ballots for a pair-election, depends on whether the society deems the entire or a selected ballot set in compliance with freedom of association (which implies freedom of non-association) for a given pair election. Arguments based on freedom of association rely on more information about an electorate than can be learned from ranked ballots alone. We present a freedom-of-association based argument to explain why IRV may be preferable to Condorcet in some circumstances, including the 2022 Alaska special congressional election, based on the political context of that election.
On the unimportance of commitment for monetary policy
Authors: Juan Paez-Farrell
Date: 2023-08-15 00:00:00

In a New Keynesian model where the trade-off between stabilising the aggregate inflation rate and the output gap arises from sectoral asymmetries, the gains from commitment are either zero or negligible. Thus, to the extent that economic fluctuations are caused by sectoral shocks, policies designed to overcome the stabilisation bias are aiming to correct an unimportant problem.
The ID R&D VoxCeleb Speaker Recognition Challenge 2023 System Description
Authors: Nikita Torgashov, Rostislav Makarov, Ivan Yakovlev, Pavel Malov, Andrei Balykin, Anton Okhotnikov
Date: 2023-08-16 00:00:00
Tasks: Self-Supervised Learning, Speaker Recognition

This report describes ID R&D team submissions for Track 2 (open) to the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). Our solution is based on the fusion of deep ResNets and self-supervised learning (SSL) based models trained on a mixture of a VoxCeleb2 dataset and a large version of a VoxTube dataset. The final submission to the Track 2 achieved the first place on the VoxSRC-23 public leaderboard with a minDCF(0.05) of 0.0762 and EER of 1.30%.
Deployment and Analysis of Instance Segmentation Algorithm for In-field Grade Estimation of Sweetpotatoes
Authors: Hoang M. Nguyen, Sydney Gyurek, Russell Mierop, Kenneth V. Pecota, Kylie LaGamba, Michael Boyette, G. Craig Yencho, Cranos M. Williams, Michael W. Kudenov
Date: 2023-08-16 00:00:00
Tasks: Instance Segmentation, object-detection, Object Detection, Semantic Segmentation

Shape estimation of sweetpotato (SP) storage roots is inherently challenging due to their varied size and shape characteristics. Even measuring "simple" metrics, such as length and width, requires significant time investments either directly in-field or afterward using automated graders. In this paper, we present the results of a model that can perform grading and provide yield estimates directly in the field quicker than manual measurements. Detectron2, a library consisting of deep-learning object detection algorithms, was used to implement Mask R-CNN, an instance segmentation model. This model was deployed for in-field grade estimation of SPs and evaluated against an optical sorter. Storage roots from various clones imaged with a cellphone during trials between 2019 and 2020, were used in the model's training and validation to fine-tune a model to detect SPs. Our results showed that the model could distinguish individual SPs in various environmental conditions including variations in lighting and soil characteristics. RMSE for length, width, and weight, from the model compared to a commercial optical sorter, were 0.66 cm, 1.22 cm, and 74.73 g, respectively, while the RMSE of root counts per plot was 5.27 roots, with r^2 = 0.8. This phenotyping strategy has the potential enable rapid yield estimates in the field without the need for sophisticated and costly optical sorters and may be more readily deployed in environments with limited access to these kinds of resources or facilities.
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
Authors: Vasileios Magoulianitis, Catherine A. Alexander, C. -C. Jay Kuo
Date: 2023-08-16 00:00:00
Tasks: Explainable Models

In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to high variance in appearance, sourcing either from the acquisition devices or the H\&E staining process. Nuclei segmentation is an important task, as it detects the nuclei cells over background tissue and gives rise to the topology, size, and count of nuclei which are determinant factors for cancer detection. Yet, it is a fairly time consuming task for pathologists, with reportedly high subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern Artificial Intelligence (AI) models enable the automation of nuclei segmentation. This can reduce the subjectivity in analysis and reading time. This paper provides an extensive review, beginning from earlier works use traditional image processing techniques and reaching up to modern approaches following the Deep Learning (DL) paradigm. Our review also focuses on the weak supervision aspect of the problem, motivated by the fact that annotated data is scarce. At the end, the advantages of different models and types of supervision are thoroughly discussed. Furthermore, we try to extrapolate and envision how future research lines will potentially be, so as to minimize the need for labeled data while maintaining high performance. Future methods should emphasize efficient and explainable models with a transparent underlying process so that physicians can trust their output.