pwc-archive/papers-with-abstracts huggingface dataset:
NNgTL: Neural Network Guided Optimal Temporal Logic Task Planning for Mobile Robots
Authors: Ruijia Liu, ShaoYuan Li, Xiang Yin
Date: 2023-09-25 00:00:00
Tasks: Navigate, Task Planning

In this work, we investigate task planning for mobile robots under linear temporal logic (LTL) specifications. This problem is particularly challenging when robots navigate in continuous workspaces due to the high computational complexity involved. Sampling-based methods have emerged as a promising avenue for addressing this challenge by incrementally constructing random trees, thereby sidestepping the need to explicitly explore the entire state-space. However, the performance of this sampling-based approach hinges crucially on the chosen sampling strategy, and a well-informed heuristic can notably enhance sample efficiency. In this work, we propose a novel neural-network guided (NN-guided) sampling strategy tailored for LTL planning. Specifically, we employ a multi-modal neural network capable of extracting features concurrently from both the workspace and the B\"{u}chi automaton. This neural network generates predictions that serve as guidance for random tree construction, directing the sampling process toward more optimal directions. Through numerical experiments, we compare our approach with existing methods and demonstrate its superior efficiency, requiring less than 15% of the time of the existing methods to find a feasible solution.
Resilient State Estimation for Nonlinear Discrete-Time Systems via Input and State Interval Observer Synthesis
Authors: Mohammad Khajenejad, Zeyuan Jin, Thach Ngoc Dinh, Sze Zheng Yong
Date: 2023-09-25 00:00:00
Tasks: State Estimation

This paper addresses the problem of resilient state estimation and attack reconstruction for bounded-error nonlinear discrete-time systems with nonlinear observations/ constraints, where both sensors and actuators can be compromised by false data injection attack signals/unknown inputs. By leveraging mixed-monotone decomposition of nonlinear functions, as well as affine parallel outer-approximation of the observation functions, along with introducing auxiliary states to cancel out the effect of the attacks/unknown inputs, our proposed observer recursively computes interval estimates that by construction, contain the true states and unknown inputs of the system. Moreover, we provide several semi-definite programs to synthesize observer gains to ensure input-to-state stability of the proposed observer and optimality of the design in the sense of minimum $\mathcal{H}_{\infty}$ gain.
Guaranteed Privacy-Preserving $\mathcal{H}_{\infty}$-Optimal Interval Observer Design for Bounded-Error LTI Systems
Authors: Mohammad Khajenejad, Sonia Martinez
Date: 2023-09-25 00:00:00
Tasks: Privacy Preserving

This paper furthers current research into the notion of guaranteed privacy, which provides a deterministic characterization of the privacy of output signals of a dynamical system or mechanism. Unlike stochastic differential privacy, guaranteed privacy offers strict bounds on the proximity between the ranges of two sets of estimated data. Our approach relies on synthesizing an interval observer for a perturbed linear time-invariant (LTI) bounded-error system. The design procedure incorporates a bounded noise perturbation factor computation and observer gains synthesis. Consequently, the observer simultaneously provides guaranteed private and stable interval-valued estimates for a desired variable. We demonstrate the optimality of our design by minimizing the $\mathcal{H}_{\infty}$ norm of the observer error system. Furthermore, we assess the accuracy of our proposed mechanism by quantifying the loss incurred when considering guaranteed privacy specifications. Finally, we illustrate the outperformance of the proposed approach to differential privacy through simulations.
Efficient Recursive Data-enabled Predictive Control (Extended Version)
Authors: Jicheng Shi, Yingzhao Lian, Colin N. Jones
Date: 2023-09-24 00:00:00
Tasks: Form, Model Predictive Control

In the field of model predictive control, Data-enabled Predictive Control (DeePC) offers direct predictive control, bypassing traditional modeling. However, challenges emerge with increased computational demand due to recursive data updates. This paper introduces a novel recursive updating algorithm for DeePC. It emphasizes the use of Singular Value Decomposition (SVD) for efficient low-dimensional transformations of DeePC in its general form, as well as a fast SVD update scheme. Importantly, our proposed algorithm is highly flexible due to its reliance on the general form of DeePC, which is demonstrated to encompass various data-driven methods that utilize Pseudoinverse and Hankel matrices. This is exemplified through a comparison to Subspace Predictive Control, where the algorithm achieves asymptotically consistent prediction for stochastic linear time-invariant systems. Our proposed methodologies' efficacy is validated through simulation studies.
Robust Adaptive MPC Using Uncertainty Compensation
Authors: Ran Tao, Pan Zhao, Ilya Kolmanovsky, Naira Hovakimyan
Date: 2023-09-24 00:00:00
Tasks: Model Predictive Control

This paper presents an uncertainty compensation-based robust adaptive model predictive control (MPC) framework for linear systems with both matched and unmatched nonlinear uncertainties subject to both state and input constraints. In particular, the proposed control framework leverages an L1 adaptive controller (L1AC) to compensate for the matched uncertainties and to provide guaranteed uniform bounds on the error between the states and control inputs of the actual system and those of a nominal i.e., uncertainty-free, system. The performance bounds provided by the L1AC are then used to tighten the state and control constraints of the actual system, and a model predictive controller is designed for the nominal system with the tightened constraints. The proposed control framework, which we denote as uncertainty compensation-based MPC (UC-MPC), guarantees constraint satisfaction and achieves improved performance compared with existing methods. Simulation results on a flight control example demonstrate the benefits of the proposed framework.
An Optimal Control Framework for Influencing Human Driving Behavior in Mixed-Autonomy Traffic
Authors: Anirudh Chari, Rui Chen, Jaskaran Grover, Changliu Liu
Date: 2023-09-23 00:00:00
Tasks: Autonomous Vehicles

As autonomous vehicles (AVs) become increasingly prevalent, their interaction with human drivers presents a critical challenge. Current AVs lack social awareness, causing behavior that is often awkward or unsafe. To combat this, social AVs, which are proactive rather than reactive in their behavior, have been explored in recent years. With knowledge of robot-human interaction dynamics, a social AV can influence a human driver to exhibit desired behaviors by strategically altering its own behaviors. In this paper, we present a novel framework for achieving human influence. The foundation of our framework lies in an innovative use of control barrier functions to formulate the desired objectives of influence as constraints in an optimal control problem. The computed controls gradually push the system state toward satisfaction of the objectives, e.g. slowing the human down to some desired speed. We demonstrate the proposed framework's feasibility in a variety of scenarios related to car-following and lane changes, including multi-robot and multi-human configurations. In two case studies, we validate the framework's effectiveness when applied to the problems of traffic flow optimization and aggressive behavior mitigation. Given these results, the main contribution of our framework is its versatility in a wide spectrum of influence objectives and mixed-autonomy configurations.
Output-Sampled Model Predictive Path Integral Control (o-MPPI) for Increased Efficiency
Authors: Leon, Yan, Santosh Devasia
Date: 2023-09-22 00:00:00
Tasks: Autonomous Driving

The success of the model predictive path integral control (MPPI) approach depends on the appropriate selection of the input distribution used for sampling. However, it can be challenging to select inputs that satisfy output constraints in dynamic environments. The main contribution of this paper is to propose an output-sampling-based MPPI (o-MPPI), which improves the ability of samples to satisfy output constraints and thereby increases MPPI efficiency. Comparative simulations and experiments of dynamic autonomous driving of bots around a track are provided to show that the proposed o-MPPI is more efficient and requires substantially (20-times) less number of rollouts and (4-times) smaller prediction horizon when compared with the standard MPPI for similar success rates. The supporting video for the paper can be found at https://youtu.be/snhlZj3l5CE.
Statistically Adaptive Filtering for Low Signal Correction in X-ray Computed Tomography
Authors: Obaidullah Rahman, Ken D. Sauer, Charles A. Bouman, Roman Melnyk, Brian Nett
Date: 2023-09-23 00:00:00

Low x-ray dose is desirable in x-ray computed tomographic (CT) imaging due to health concerns. But low dose comes with a cost of low signal artifacts such as streaks and low frequency bias in the reconstruction. As a result, low signal correction is needed to help reduce artifacts while retaining relevant anatomical structures. Low signal can be encountered in cases where sufficient number of photons do not reach the detector to have confidence in the recorded data. % NOTE: SNR is ratio of powers, not std. dev. X-ray photons, assumed to have Poisson distribution, have signal to noise ratio proportional to the dose, with poorer SNR in low signal areas. Electronic noise added by the data acquisition system further reduces the signal quality. In this paper we will demonstrate a technique to combat low signal artifacts through adaptive filtration. It entails statistics-based filtering on the uncorrected data, correcting the lower signal areas more aggressively than the high signal ones. We look at local averages to decide how aggressive the filtering should be, and local standard deviation to decide how much detail preservation to apply. Implementation consists of a pre-correction step i.e. local linear minimum mean-squared error correction, followed by a variance stabilizing transform, and finally adaptive bilateral filtering. The coefficients of the bilateral filter are computed using local statistics. Results show that improvements were made in terms of low frequency bias, streaks, local average and standard deviation, modulation transfer function and noise power spectrum.
Unified Inference for Dynamic Quantile Predictive Regression
Authors: Christis Katsouris
Date: 2023-09-25 00:00:00
Tasks: regression

This paper develops unified asymptotic distribution theory for dynamic quantile predictive regressions which is useful when examining quantile predictability in stock returns under possible presence of nonstationarity.
Nonparametric estimation of conditional densities by generalized random forests
Authors: Federico Zincenko
Date: 2023-09-23 00:00:00
Tasks: valid

Considering a continuous random variable Y together with a continuous random vector X, I propose a nonparametric estimator f^(.|x) for the conditional density of Y given X=x. This estimator takes the form of an exponential series whose coefficients Tx = (Tx1,...,TxJ) are the solution of a system of nonlinear equations that depends on an estimator of the conditional expectation E[p(Y)|X=x], where p is a J-dimensional vector of basis functions. The distinguishing feature of the proposed estimator is that E[p(Y)|X=x] is estimated by generalized random forest (Athey, Tibshirani, and Wager, Annals of Statistics, 2019), targeting the heterogeneity of Tx across x. I show that f^(.|x) is uniformly consistent and asymptotically normal, allowing J to grow to infinity. I also provide a standard error formula to construct asymptotically valid confidence intervals. Results from Monte Carlo experiments are provided.
Nonparametric mixed logit model with market-level parameters estimated from market share data
Authors: Xiyuan Ren, Joseph Y. J. Chow, Prateek Bansal
Date: 2023-09-22 00:00:00
Tasks: Computational Efficiency

We propose a nonparametric mixed logit model that is estimated using market-level choice share data. The model treats each market as an agent and represents taste heterogeneity through market-specific parameters by solving a multiagent inverse utility maximization problem, addressing the limitations of existing market-level choice models with parametric estimation. A simulation study is conducted to evaluate the performance of our model in terms of estimation time, estimation accuracy, and out-of-sample predictive accuracy. In a real data application, we estimate the travel mode choice of 53.55 million trips made by 19.53 million residents in New York State. These trips are aggregated based on population segments and census block group-level origin-destination (OD) pairs, resulting in 120,740 markets. We benchmark our model against multinomial logit (MNL), nested logit (NL), inverse product differentiation logit (IPDL), and the BLP models. The results show that the proposed model improves the out-of-sample accuracy from 65.30% to 81.78%, with a computation time less than one-tenth of that taken to estimate the BLP model. The price elasticities and diversion ratios retrieved from our model and benchmark models exhibit similar substitution patterns. Moreover, the market-level parameters estimated by our model provide additional insights and facilitate their seamless integration into supply-side optimization models for transportation design. By measuring the compensating variation for the driving mode, we found that a $9 congestion toll would impact roughly 60 % of the total travelers. As an application of supply-demand integration, we showed that a 50% discount of transit fare could bring a maximum ridership increase of 9402 trips per day under a budget of $50,000 per day.
Leveraging Uncertainties to Infer Preferences: Robust Analysis of School Choice
Authors: Yeon-Koo Che, Dong Woo Hahm, YingHua He
Date: 2023-09-25 00:00:00
Tasks: counterfactual

Inferring applicant preferences is fundamental in many analyses of school-choice data. Application mistakes make this task challenging. We propose a novel approach to deal with the mistakes in a deferred-acceptance matching environment. The key insight is that the uncertainties faced by applicants, e.g., due to tie-breaking lotteries, render some mistakes costly, allowing us to reliably infer relevant preferences. Our approach extracts all information on preferences robustly to payoff-insignificant mistakes. We apply it to school-choice data from Staten Island, NYC. Counterfactual analysis suggests that we underestimate the effects of proposed desegregation reforms when applicants' mistakes are not accounted for in preference inference and estimation.
Value-transforming financial, carbon and biodiversity footprint accounting
Authors: S. El Geneidy, S. Baumeister, M. Peura, J. S. Kotiaho
Date: 2023-09-25 00:00:00

Transformative changes in our production and consumption habits are needed to enable the sustainability transition towards carbon neutrality, no net loss of biodiversity, and planetary well-being. Organizations are the way we humans have organized our everyday life, and much of our negative environmental impacts, also called carbon and biodiversity footprints, are caused by organizations. Here we show how the financial accounts of any organization can be exploited to develop an integrated carbon and biodiversity footprint account. As a metric we utilize spatially explicit potential global loss of species which, we argue, can be understood as the biodiversity equivalent, the utility of which for biodiversity is similar to what carbon dioxide equivalent is for climate. We provide a global Biodiversity Footprint Database that organizations, experts and researchers can use to assess consumption-based biodiversity footprints. We also argue that the current integration of financial and environmental accounting is superficial, and provide a framework for a more robust financial value-transforming accounting model. To test the methodologies, we utilized a Finnish university as a living lab. Assigning an offsetting cost to the footprints significantly altered the financial value of the organization. We believe such value-transforming accounting is needed in order to draw the attention of senior executives and investors to the negative environmental impacts of their organizations.
The Accuracy of Job Seekers' Wage Expectations
Authors: Marco Caliendo, Robert Mahlstedt, Aiko Schmeißer, Sophie Wagner
Date: 2023-09-25 00:00:00
Tasks: Decision Making

We study the accuracy of job seekers' wage expectations by comparing subjective beliefs to objective benchmarks using linked administrative and survey data. Our findings show that especially job seekers with low objective earnings potential and those predicted to face a penalty compared to their pre-unemployment wage display overly optimistic wage expectations. Moreover, wage optimism is amplified by increased job search incentives and job seekers with overoptimistic wage expectations tend to overestimate their reemployment chances. We discuss the labor market implications of wage optimism, as well as the role of information frictions and motivated beliefs as sources of overoptimism.
Discounting and Impatience
Authors: Salvatore Greco, Diego Rago
Date: 2023-09-25 00:00:00
Tasks: Decision Making

Understanding how people actually trade off time for money is perhaps the major question in the field of time discounting. There is indeed a vast body of work devoted to explore the underlying mechanisms of the individual decision making process in an intertemporal context. This paper presents a family of new discount functions whereof we derive a formal axiomatization. Applying the framework proposed by Bleichrodt, Rohde and Wakker, we further extend their formulation of CADI and CRDI functions, making discounting a function not only of time delay but, simultaneously, also of time distortion. Our main purpose is, in practice, to provide a tractable setting within which individual intertemporal preferences can be outlined. Furthermore, we apply our models to study the relation between individual time preferences and personality traits. For the CADI-CADI, results show that the habit of smoking is heavily related with both impatience and time perception. Within the Big-Five framework, conscientiousness, agreeableness and openness are positively related with patience (low r, initial discount rate).
Profit shifting under the arm's length principle
Authors: Alex A. T. Rathke
Date: 2023-09-23 00:00:00

This study analyses the tax-induced profit shifting behaviour of firms and the impact of governments' anti-shifting rules. We derive a model of a firm that combines internal sales and internal debt in a full profit shifting strategy, and which is required to apply the arm's length principle and a general thin capitalisation rule. We find several cases where the firm may shift profits to low-tax countries while satisfying the usual arm's length conditions in all countries. Internal sales and internal debt may be regarded either as complementary or as substitute shifting channels, depending on how the implicit concealment costs vary after changes in all transactions. We show that the cross-effect between the shifting channels facilitates profit shifting by means of accepted transfer prices and interest rates.
Waiting for Dr. Godot: how much and who responds to predicted health care wait times?
Authors: Stephenson Strobel
Date: 2023-09-22 00:00:00

Asymmetric information in healthcare implies that patients could have difficulty trading off non-health and health related information. I document effects on patient demand when predicted wait time is disclosed to patients in an emergency department (ED) system. I use a regression discontinuity where EDs with similar predicted wait times display different online wait times to patients. I use impulse response functions estimated by local projections to demonstrate effects of the higher wait time. I find that an additional thirty minutes of wait time results in 15% fewer waiting patients at urgent cares and 2% fewer waiting patients at EDs within 3 hours of display. I find that the type of patient that stops using emergency care is triaged as having lower acuity and would have used an urgent care. However, I find that at very high wait times there are declines in all acuity patients including sick patients.
Autopet Challenge 2023: nnUNet-based whole-body 3D PET-CT Tumour Segmentation
Authors: Anissa Alloula, Daniel R McGowan, Bartłomiej W. Papież
Date: 2023-09-24 00:00:00
Tasks: Computed Tomography (CT), Lesion Segmentation, Segmentation

Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) combined with Computed Tomography (CT) scans are critical in oncology to the identification of solid tumours and the monitoring of their progression. However, precise and consistent lesion segmentation remains challenging, as manual segmentation is time-consuming and subject to intra- and inter-observer variability. Despite their promise, automated segmentation methods often struggle with false positive segmentation of regions of healthy metabolic activity, particularly when presented with such a complex range of tumours across the whole body. In this paper, we explore the application of the nnUNet to tumour segmentation of whole-body PET-CT scans and conduct different experiments on optimal training and post-processing strategies. Our best model obtains a Dice score of 69\% and a false negative and false positive volume of 6.27 and 5.78 mL respectively, on our internal test set. This model is submitted as part of the autoPET 2023 challenge. Our code is available at: https://github.com/anissa218/autopet\_nnunet
MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography
Authors: Sekeun Kim, Pengfei Jin, Cheng Chen, Kyungsang Kim, Zhiliang Lyu, Hui Ren, Sunghwan Kim, Zhengliang Liu, Aoxiao Zhong, Tianming Liu, Xiang Li, Quanzheng Li
Date: 2023-09-24 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, parameter-efficient fine-tuning, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation, Zero-shot Generalization, Zero Shot Segmentation

Despite achieving impressive results in general-purpose semantic segmentation with strong generalization on natural images, the Segment Anything Model (SAM) has shown less precision and stability in medical image segmentation. In particular, the original SAM architecture is designed for 2D natural images and is therefore not support to handle three-dimensional information, which is particularly important for medical imaging modalities that are often volumetric or video data. In this paper, we introduce MediViSTA, a parameter-efficient fine-tuning method designed to adapt the vision foundation model for medical video, with a specific focus on echocardiographic segmentation. To achieve spatial adaptation, we propose a frequency feature fusion technique that injects spatial frequency information from a CNN branch. For temporal adaptation, we integrate temporal adapters within the transformer blocks of the image encoder. Using a fine-tuning strategy, only a small subset of pre-trained parameters is updated, allowing efficient adaptation to echocardiographic data. The effectiveness of our method has been comprehensively evaluated on three datasets, comprising two public datasets and one multi-center in-house dataset. Our method consistently outperforms various state-of-the-art approaches without using any prompts. Furthermore, our model exhibits strong generalization capabilities on unseen datasets, surpassing the second-best approach by 2.15\% in Dice and 0.09 in temporal consistency. The results demonstrate the potential of MediViSTA to significantly advance echocardiographical video segmentation, offering improved accuracy and robustness in cardiac assessment applications.