pwc-archive/papers-with-abstracts huggingface dataset:
In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators
Authors: Dmytro Humeniuk, Houssem Ben Braiek, Thomas Reid, Foutse khomh
Date: 2024-10-25 00:00:00

Testing autonomous robotic manipulators is challenging due to the complex software interactions between vision and control components. A crucial element of modern robotic manipulators is the deep learning based object detection model. The creation and assessment of this model requires real world data, which can be hard to label and collect, especially when the hardware setup is not available. The current techniques primarily focus on using synthetic data to train deep neural networks (DDNs) and identifying failures through offline or online simulation-based testing. However, the process of exploiting the identified failures to uncover design flaws early on, and leveraging the optimized DNN within the simulation to accelerate the engineering of the DNN for real-world tasks remains unclear. To address these challenges, we propose the MARTENS (Manipulator Robot Testing and Enhancement in Simulation) framework, which integrates a photorealistic NVIDIA Isaac Sim simulator with evolutionary search to identify critical scenarios aiming at improving the deep learning vision model and uncovering system design flaws. Evaluation of two industrial case studies demonstrated that MARTENS effectively reveals robotic manipulator system failures, detecting 25 % to 50 % more failures with greater diversity compared to random test generation. The model trained and repaired using the MARTENS approach achieved mean average precision (mAP) scores of 0.91 and 0.82 on real-world images with no prior retraining. Further fine-tuning on real-world images for a few epochs (less than 10) increased the mAP to 0.95 and 0.89 for the first and second use cases, respectively. In contrast, a model trained solely on real-world data achieved mAPs of 0.8 and 0.75 for use case 1 and use case 2 after more than 25 epochs.
Learning ID-free Item Representation with Token Crossing for Multimodal Recommendation
Authors: Kangning Zhang, Jiarui Jin, Yingjie Qin, Ruilong Su, Jianghao Lin, Yong Yu, Weinan Zhang
Date: 2024-10-25 00:00:00
Tasks: Multimodal Recommendation, Quantization, Recommendation Systems

Current multimodal recommendation models have extensively explored the effective utilization of multimodal information; however, their reliance on ID embeddings remains a performance bottleneck. Even with the assistance of multimodal information, optimizing ID embeddings remains challenging for ID-based Multimodal Recommender when interaction data is sparse. Furthermore, the unique nature of item-specific ID embeddings hinders the information exchange among related items and the spatial requirement of ID embeddings increases with the scale of item. Based on these limitations, we propose an ID-free MultimOdal TOken Representation scheme named MOTOR that represents each item using learnable multimodal tokens and connects them through shared tokens. Specifically, we first employ product quantization to discretize each item's multimodal features (e.g., images, text) into discrete token IDs. We then interpret the token embeddings corresponding to these token IDs as implicit item features, introducing a new Token Cross Network to capture the implicit interaction patterns among these tokens. The resulting representations can replace the original ID embeddings and transform the original ID-based multimodal recommender into ID-free system, without introducing any additional loss design. MOTOR reduces the overall space requirements of these models, facilitating information interaction among related items, while also significantly enhancing the model's recommendation capability. Extensive experiments on nine mainstream models demonstrate the significant performance improvement achieved by MOTOR, highlighting its effectiveness in enhancing multimodal recommendation systems.
Optimising image capture for low-light widefield quantitative fluorescence microscopy
Authors: Zane Peterkovic, Avinash Upadhya, Christopher Perrella, Admir Bajraktarevic, Ramses Bautista Gonzalez, Megan Lim, Kylie R Dunning, Kishan Dholakia
Date: 2024-10-24 00:00:00
Tasks: Denoising

Low-light optical imaging refers to the use of cameras to capture images with minimal photon flux. This area has broad application to diverse fields, including optical microscopy for biological studies. In such studies, it is important to reduce the intensity of illumination to reduce adverse effects such as photobleaching and phototoxicity that may perturb the biological system under study. The challenge when minimising illumination is to maintain image quality that reflects the underlying biology and can be used for quantitative measurements. An example is the optical redox ratio which is computed from autofluorescence intensity to measure metabolism. In all such cases, it is critical for researchers to optimise selection and application of scientific cameras to their microscopes, but few resources discuss performance in the low-light regime. In this tutorial, we address the challenges in optical fluorescence imaging at low-light levels for quantitative microscopy, with an emphasis on live biological samples. We analyse the performance of specialised low-light scientific cameras such as the EMCCD, qCMOS, and sCMOS, while considering the differences in platform architecture and the contribution of various sources of noise. The tutorial covers a detailed discussion of user-controllable parameters, as well as the application of post-processing algorithms for denoising. We illustrate these concepts using autofluorescence images of live mammalian embryos captured with a two-photon light sheet fluorescence microscope.
Vestiges of impact-driven three-phase mixing in the chemistry and structure of Earth’s mantle
Authors: jun.korenaga and Simone Marchi
Date: 2023-08-22 00:00:00
Proceeding: received May 31, 2023; accepted August 22, 2023 2023 8

v\Highly siderophile elements (HSEs; namely Ru, Rh, Pd, Re, Os, Ir, Pt, and Au) in Earth’s mantle require the addition of metals after the formation of Earth’s core. Early, large collisions have the potential to deliver metals, but the details of their mixing with Earth’s mantle remain unresolved. As a large projectile disrupts and penetrates Earth’s mantle, a fraction of its metallic core may directly merge with Earth’s core. Ensuing gravitational instabilities remove the remaining projectile’s core stranded in Earth’s mantle, leaving the latter deprived of HSEs. Here, we propose a framework that can efficiently retain the metallic components during large impacts. The mechanism is based on the ubiquitous presence of a partially molten region in the mantle beneath an impact-generated magma ocean, and it involves rapid three-phase flow with solid silicate, molten silicate, and liquid metal as well as long-term mixing by mantle convection. In addition, large low-shear-velocity provinces in the lower mantle may originate from compositional heterogeneities resulting from the proposed three-phase flow during high-energy collisions.
Privacy-Computation trade-offs in Private Repetition and Metaselection
Authors: Kunal Talwar
Date: 2024-10-22 00:00:00

A Private Repetition algorithm takes as input a differentially private algorithm with constant success probability and boosts it to one that succeeds with high probability. These algorithms are closely related to private metaselection algorithms that compete with the best of many private algorithms, and private hyperparameter tuning algorithms that compete with the best hyperparameter settings for a private learning algorithm. Existing algorithms for these tasks pay either a large overhead in privacy cost, or a large overhead in computational cost. In this work, we show strong lower bounds for problems of this kind, showing in particular that for any algorithm that preserves the privacy cost up to a constant factor, the failure probability can only fall polynomially in the computational overhead. This is in stark contrast with the non-private setting, where the failure probability falls exponentially in the computational overhead. By carefully combining existing algorithms for metaselection, we prove computation-privacy tradeoffs that nearly match our lower bounds.
Loss Aversion and State-Dependent Linear Utility Functions for Monetary Returns
Authors: Somdeb Lahiri
Date: 2024-10-24 00:00:00

We present a theory of expected utility with state-dependent linear utility functions for monetary returns, that incorporates the possibility of loss-aversion. Our results relate to first order stochastic dominance, mean-preserving spread, increasing-concave linear utility profiles and risk aversion. As an application of the expected utility theory developed here, we analyze the contract that a monopolist would offer in an insurance market that allowed for partial coverage of loss.
Experimental implementation of an economic model predictive control for froth flotation
Authors: Paulina Quintanilla, Daniel Navia, Stephen Neethling, Pablo Brito-Parada
Date: 2024-10-25 00:00:00
Tasks: Model Predictive Control

We present the implementation of a novel economic model predictive control (E-MPC) strategy for froth flotation, the largest tonnage mineral separation process. A previously calibrated and validated dynamic model incorporating froth physics was used, which overcomes the limitations of previous simplified models reported in the literature. The E-MPC's optimal control problem was solved using full discretization with orthogonal collocation over finite elements, employing automatic differentiation via CasADi. This approach was applied in a 30-litre laboratory-scale flotation cell, significantly improving mineral recovery from 9% to 29% under feed flowrate disturbances while maintaining a minimum concentrate grade of 20%.
Sampling in Parametric and Nonparametric System Identification: Aliasing, Input Conditions, and Consistency
Authors: Rodrigo A. González, Max van Haren, Tom Oomen, Cristian R. Rojas
Date: 2024-10-25 00:00:00

The sampling rate of input and output signals is known to play a critical role in the identification and control of dynamical systems. For slow-sampled continuous-time systems that do not satisfy the Nyquist-Shannon sampling condition for perfect signal reconstructability, careful consideration is required when identifying parametric and nonparametric models. In this letter, a comprehensive statistical analysis of estimators under slow sampling is performed. Necessary and sufficient conditions are obtained for unbiased estimates of the frequency response function beyond the Nyquist frequency, and it is shown that consistency of parametric estimators can be achieved even if input frequencies overlap after aliasing. Monte Carlo simulations confirm the theoretical properties.
Flexibility Options: A Proposed Product for Managing Imbalance Risk
Authors: Elina Spyrou, Qiwei Zhang, Robin B. Hytowitz, Ben F. Hobbs, Siddharth Tyagi, Mengmeng Cai, Michael Blonsky
Date: 2024-10-25 00:00:00

The presence of variable renewable energy resources with uncertain outputs in day-ahead electricity markets results in additional balancing needs in real-time. Addressing those needs cost-effectively and reliably within a competitive market with unbundled products is challenging as both the demand for and the availability of flexibility depends on day-ahead energy schedules. Existing approaches for reserve procurement usually rely either on oversimplified demand curves that do not consider how system conditions that particular day affect the value of flexibility, or on bilateral trading of hedging instruments that are not co-optimized with day-ahead schedules. This article proposes a new product, `Flexibility Options', to address these two limitations. The demand for this product is endogenously determined in the day-ahead market and it is met cost-effectively by considering real-time supply curves for product providers, which are co-optimized with the energy supply. As we illustrate with numerical examples and mathematical analysis, the product addresses the hedging needs of participants with imbalances cost-effectively, provides a less intermittent revenue stream for participants with flexible outputs, promotes value-driven pricing of flexibility, and ensures that the system operator is revenue-neutral. This article provides a comprehensive design that can be further tested and applied in large-scale systems.
Quantum optimization for Nonlinear Model Predictive Control
Authors: Carlo Novara, Mattia Boggio, Deborah Volpe
Date: 2024-10-25 00:00:00
Tasks: model, Model Predictive Control

Nonlinear Model Predictive Control (NMPC) is a general and flexible control approach, used in many industrial contexts, and is based on the online solution of a nonlinear optimization problem. This operation requires in general a high computational cost, which may compromise the NMPC implementation in ``fast'' applications, especially if a large number variables is involved. To overcome this issue, we propose a quantum computing approach for the solution of the NMPC optimization problem. Assuming the availability of an efficient quantum computer, the approach has the potential to considerably decrease the computational time and/or enhance the solution quality compared to classical algorithms.
Collision Avoidance for Convex Primitives via Differentiable Optimization Based High-Order Control Barrier Functions
Authors: Shiqing Wei, Rooholla Khorrambakht, Prashanth Krishnamurthy, Vinicius Mariano Gonçalves, Farshad Khorrami
Date: 2024-10-24 00:00:00
Tasks: Collision Avoidance

Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method to integrate safety constraints into control synthesis through optimization techniques. However, challenges persist when dealing with convex primitives and tasks requiring torque control, as well as the occurrence of unintended equilibria. This work addresses these challenges by introducing a high-order CBF (HOCBF) framework for collision avoidance among convex primitives. We transform nonconvex safety constraints into linear constraints by differentiable optimization and prove the high-order continuous differentiability. Then, we employ HOCBFs to accommodate torque control, enabling tasks involving forces or high dynamics. Additionally, we analyze the issue of spurious equilibria in high-order cases and propose a circulation mechanism to prevent the undesired equilibria on the boundary of the safe set. Finally, we validate our framework with three experiments on the Franka Research 3 robotic manipulator, demonstrating successful collision avoidance and the efficacy of the circulation mechanism.
Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF
Authors: Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto
Date: 2024-10-24 00:00:00

Recent developments in applying machine learning to address Alternating Current Optimal Power Flow (AC OPF) problems have demonstrated significant potential in providing close to optimal solutions for generator dispatch in near real-time. While these learning to optimize methods have demonstrated remarkable performance on steady-state operations, practical applications often demand compliance with dynamic constraints when used for fast-timescale optimization. This paper addresses this gap and develops a real-time stability-constrained OPF model (DynOPF-Net) that simultaneously addresses both optimality and dynamical stability within learning-assisted grid operations. The model is a unique integration of learning to optimize that learns a mapping from load conditions to OPF solutions, capturing the OPF's physical and engineering constraints, with Neural Ordinary Differential Equations, capturing generator dynamics, enabling the inclusion of a subset of stability constraints. Numerical results on the WSCC 9-bus and IEEE 57-bus benchmark systems demonstrate that DynOPF-Net can produce highly accurate AC-OPF solutions while also ensuring system stability, contrasting the unstable results obtained by state-of-the-art LtO methods.
DamFormer: Generalizing Morphologies in Dam Break Simulations Using Transformer Model
Authors: Zhaoyang Mul, Aoming Liang, Mingming Ge, Dashuai Chen, Dixia Fan, Minyi Xu
Date: 2024-10-17 00:00:00

The interaction of waves with structural barriers such as dams breaking plays a critical role in flood defense and tsunami disasters. In this work, we explore the dynamic changes in wave surfaces impacting various structural shapes, e.g., circle, triangle, and square, by using deep learning techniques. We introduce the DamFormer, a novel transformer-based model designed to learn and simulate these complex interactions. The model was trained and tested on simulated data representing the three structural forms.
The replacement number dynamics in SIR-type epidemic models I: From SSISS to RND picture
Authors: Florian Nill
Date: 2024-10-25 00:00:00

In SIR-type epidemic models time derivative of prevalence $I$ can always be cast into the form $\dot{I}=(X-1)I$, where $X$ is the replacement number and recovery rate is normalized to one. Assuming $\dot{X}=f(X,I)$ for some smooth function $f$ defines a "replacement number dynamics" (RND). Choosing transmission coefficients $\beta_1>\beta_2$, any such system uniquely maps to an isomorphic "SSISS model", i.e. an abstract SIR-type 3-compartment model. Extending to negative values $\beta_i<0$ takes care of demographic dynamics with compartment dependent birth and death rates. Fixing $f$ and varying $\beta_i$ generates a family of isomorphic SSISS systems, the "SSISS fiber" $\cal{F}(f)$}. A symmetry group $G_\sigma$ acts freely and transitively on fibers $\cal{F}(f)$, so SSISS systems become a principal $G_\sigma$-fiber bundle over the space of RND systems. Choosing two specific 6-parameter polynomials $f$ at most quadratic in $(X,I)$ covers a large class of models in the literature, with in total up to 17 (largely redundant!) parameters, including also reactive social behavior models. Epidemiological admissibility conditions guarantee forward boundedness and absence of periodic solutions in all these models. Part II of this work will prove existence and stability properties of endemic equilibria, which in RND picture simply boils down to analyzing zeros of the parabolas $f|_{I=0}$ and $f|_{X=1}$. This will cover and extend well known results for a wide range of models by a unifying approach while also closing some open issues in the literature.
Most Swiss-system tournaments are unfair: Evidence from chess
Authors: László Csató
Date: 2024-10-25 00:00:00
Tasks: Fairness

Swiss-system is an increasingly popular tournament format as it provides an attractive trade-off between the number of matches and ranking accuracy. However, few empirical research consider the optimal design of the Swiss-system. We contribute to this issue by investigating the fairness of Swiss-system chess competitions with an odd number of rounds, where half of the players have an extra game with white pieces. They are proven to enjoy a significant advantage and to be overrepresented among both the highest-ranked and outperforming players. Therefore, Swiss-system tournaments should have an even number of rounds and use a pairing mechanism that guarantees a balanced colour assignment.
Inference on Multiple Winners with Applications to Microcredit and Economic Mobility
Authors: Andreas Petrou-Zeniou, Azeem M. Shaikh
Date: 2024-10-24 00:00:00

While policymakers and researchers are often concerned with conducting inference based on a data-dependent selection, a strictly larger class of inference problems arises when considering multiple data-dependent selections, such as when selecting on statistical significance or quantiles. Given this, we study the problem of conducting inference on multiple selections, which we dub the inference on multiple winners problem. In this setting, we encounter both selective and multiple testing problems, making existing approaches either not applicable or too conservative. Instead, we propose a novel, two-step approach to the inference on multiple winners problem, with the first step modeling the selection of winners, and the second step using this model to conduct inference only on the set of likely winners. Our two-step approach reduces over-coverage error by up to 96%. We apply our two-step approach to revisit the winner's curse in the creating moves to opportunity (CMTO) program, and to study external validity issues in the microcredit literature. In the CMTO application, we find that, after correcting for the inference on multiple winners problem, we fail to reject the possibility of null effects in the majority of census tracts selected by the CMTO program. In our microcredit application, we find that heterogeneity in treatment effect estimates remains largely unaffected even after our proposed inference corrections.
Heterogeneous Intertemporal Treatment Effects via Dynamic Panel Data Models
Authors: Philip Marx, Elie Tamer, Xun Tang
Date: 2024-10-24 00:00:00

We study the identification and estimation of heterogeneous, intertemporal treatment effects (TE) when potential outcomes depend on past treatments. First, applying a dynamic panel data model to observed outcomes, we show that instrument-based GMM estimators, such as Arellano and Bond (1991), converge to a non-convex (negatively weighted) aggregate of TE plus non-vanishing trends. We then provide restrictions on sequential exchangeability (SE) of treatment and TE heterogeneity that reduce the GMM estimand to a convex (positively weighted) aggregate of TE. Second, we introduce an adjusted inverse-propensity-weighted (IPW) estimator for a new notion of average treatment effect (ATE) over past observed treatments. Third, we show that when potential outcomes are generated by dynamic panel data models with homogeneous TE, such GMM estimators converge to causal parameters (even when SE is generically violated without conditioning on individual fixed effects). Finally, we motivate SE and compare it with parallel trends (PT) in various settings with observational data (when treatments are dynamic, rational choices under learning) or experimental data (when treatments are sequentially randomized).
The Impact of Industry Agglomeration on Land Use Efficiency: Insights from China's Yangtze River Delta
Authors: Hambur Wang
Date: 2024-10-25 00:00:00
Tasks: Management

This study investigates the impact of industrial agglomeration on land use intensification in the Yangtze River Delta (YRD) urban agglomeration. Utilizing spatial econometric models, we conduct an empirical analysis of the clustering phenomena in manufacturing and producer services. By employing the Location Quotient (LQ) and the Relative Diversification Index (RDI), we assess the degree of industrial specialization and diversification in the YRD. Additionally, Global Moran's I and Local Moran's I scatter plots are used to reveal the spatial distribution characteristics of land use intensification. Our findings indicate that industrial agglomeration has complex effects on land use intensification, showing positive, negative, and inverted U-shaped impacts. These synergistic effects exhibit significant regional variations across the YRD. The study provides both theoretical foundations and empirical support for the formulation of land management and industrial development policies. In conclusion, we propose policy recommendations aimed at optimizing industrial structures and enhancing land use efficiency to foster sustainable development in the YRD region.