pwc-archive/papers-with-abstracts huggingface dataset:
Dynamics of niche construction in adaptable populations evolving in diverse environments
Authors: Eleni Nisioti, Clément Moulin-Frier
Date: 2023-05-16 00:00:00
Tasks: Diversity

In both natural and artificial studies, evolution is often seen as synonymous to natural selection. Individuals evolve under pressures set by environments that are either reset or do not carry over significant changes from previous generations. Thus, niche construction (NC), the reciprocal process to natural selection where individuals incur inheritable changes to their environment, is ignored. Arguably due to this lack of study, the dynamics of NC are today little understood, especially in real-world settings. In this work, we study NC in simulation environments that consist of multiple, diverse niches and populations that evolve their plasticity, evolvability and niche-constructing behaviors. Our empirical analysis reveals many interesting dynamics, with populations experiencing mass extinctions, arms races and oscillations. To understand these behaviors, we analyze the interaction between NC and adaptability and the effect of NC on the population's genomic diversity and dispersal, observing that NC diversifies niches. Our study suggests that complexifying the simulation environments studying NC, by considering multiple and diverse niches, is necessary for understanding its dynamics and can lend testable hypotheses to future studies of both natural and artificial systems.
CDDO-HS:Child Drawing Development Optimization Harmony Search Algorithm
Authors: Azad A. Ameen, Tarik A. Rashid, Shavan Askar
Date: 2023-05-12 00:00:00

Child drawing development optimization (CDDO) is a recent example of a metaheuristic algorithm. The motive for inventing this method is children's learning behavior and cognitive development, with the golden ratio employed to optimize their artwork's aesthetic value. Unfortunately, CDDO suffers from low performance in the exploration phase, and the local best solution stagnates. Harmony search (HS) is a highly competitive algorithm relative to other prevalent metaheuristic algorithms, as its exploration phase performance on unimodal benchmark functions is outstanding. Thus, to avoid these issues, we present CDDOHS, a hybridization of both standards of CDDO and HS. The hybridized model proposed consists of two phases. Initially, the pattern size (PS) is relocated to the algorithm's core and the initial pattern size is set to 80 % of the total population size. Second, the standard harmony search (HS) is added to the pattern size (PS) for the exploration phase to enhance and update the solution after each iteration. Experiments are evaluated using two distinct standard benchmark functions, known as classical test functions, including 23 common functions and 10 CEC-C06 2019 functions. Additionally, the suggested CDDOHS is compared to CDDO, HS, and six other widely used algorithms. Using the Wilcoxon ranksum test, the results indicate that CDDOHS beats alternative algorithms.
Errors-in-variables Fréchet Regression with Low-rank Covariate Approximation
Authors: Kyunghee Han, Dogyoon Song
Date: 2023-05-16 00:00:00
Tasks: regression

Fr\'echet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables. However, its practical applicability has been hindered by its reliance on ideal scenarios with abundant and noiseless covariate data. In this paper, we present a novel estimation method that tackles these limitations by leveraging the low-rank structure inherent in the covariate matrix. Our proposed framework combines the concepts of global Fr\'echet regression and principal component regression, aiming to improve the efficiency and accuracy of the regression estimator. By incorporating the low-rank structure, our method enables more effective modeling and estimation, particularly in high-dimensional and errors-in-variables regression settings. We provide a theoretical analysis of the proposed estimator's large-sample properties, including a comprehensive rate analysis of bias, variance, and additional variations due to measurement errors. Furthermore, our numerical experiments provide empirical evidence that supports the theoretical findings, demonstrating the superior performance of our approach. Overall, this work introduces a promising framework for regression analysis of non-Euclidean variables, effectively addressing the challenges associated with limited and noisy covariate data, with potential applications in diverse fields.
A Causal Inference Framework for Leveraging External Controls in Hybrid Trials
Authors: Michael Valancius, Herb Pang, Jiawen Zhu, Stephen R Cole, Michele Jonsson Funk, Michael R Kosorok
Date: 2023-05-15 00:00:00
Tasks: Causal Inference

We consider the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE). Through the development of a formal causal inference framework, we outline sufficient causal assumptions about the exchangeability between the internal and external controls to identify the ATE and establish the connection to a novel graphical criteria. We propose estimators, review efficiency bounds, develop an approach for efficient doubly-robust estimation even when unknown nuisance models are estimated with flexible machine learning methods, and demonstrate finite-sample performance through a simulation study. To illustrate the ideas and methods, we apply the framework to a trial investigating the effect of risdisplam on motor function in patients with spinal muscular atrophy for which there exists an external set of control patients from a previous trial.
Robust and lightweight audio fingerprint for Automatic Content Recognition
Authors: Anoubhav Agarwaal, Prabhat Kanaujia, Sartaki Sinha Roy, Susmita Ghose
Date: 2023-05-16 00:00:00
Tasks: Audio Fingerprint, GPU, Retrieval

This research paper presents a novel audio fingerprinting system for Automatic Content Recognition (ACR). By using signal processing techniques and statistical transformations, our proposed method generates compact fingerprints of audio segments that are robust to noise degradations present in real-world audio. The system is designed to be highly scalable, with the ability to identify thousands of hours of content using fingerprints generated from millions of TVs. The fingerprint's high temporal correlation and utilization of existing GPU-compatible Approximate Nearest Neighbour (ANN) search algorithms make this possible. Furthermore, the fingerprint generation can run on low-power devices with limited compute, making it accessible to a wide range of applications. Experimental results show improvements in our proposed system compared to a min-hash based audio fingerprint on all evaluated metrics, including accuracy on proprietary ACR datasets, retrieval speed, memory usage, and robustness to various noises. For similar retrieval accuracy, our system is 30x faster and uses 6x fewer fingerprints than the min-hash method.
Fairness and Diversity in Information Access Systems
Authors: Lorenzo Porcaro, Carlos Castillo, Emilia Gómez, João Vinagre
Date: 2023-05-16 00:00:00
Tasks: Diversity, Fairness, Recommendation Systems

Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission (EC), the fifth requirement ("Diversity, non-discrimination and fairness") declares: "In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system's life cycle. [...] This requirement is closely linked with the principle of fairness". In this paper, we try to shed light on how closely these two distinct concepts, diversity and fairness, may be treated by focusing on information access systems and ranking literature. These concepts should not be used interchangeably because they do represent two different values, but what we argue is that they also cannot be considered totally unrelated or divergent. Having diversity does not imply fairness, but fostering diversity can effectively lead to fair outcomes, an intuition behind several methods proposed to mitigate the disparate impact of information access systems, i.e. recommender systems and search engines.
AMULET: Adaptive Matrix-Multiplication-Like Tasks
Authors: Junyoung Kim, Kenneth Ross, Eric Sedlar, Lukas Stadler
Date: 2023-05-12 00:00:00
Tasks: Compiler Optimization

Many useful tasks in data science and machine learning applications can be written as simple variations of matrix multiplication. However, users have difficulty performing such tasks as existing matrix/vector libraries support only a limited class of computations hand-tuned for each unique hardware platform. Users can alternatively write the task as a simple nested loop but current compilers are not sophisticated enough to generate fast code for the task written in this way. To address these issues, we extend an open-source compiler to recognize and optimize these matrix multiplication-like tasks. Our framework, called Amulet, uses both database-style and compiler optimization techniques to generate fast code tailored to its execution environment. We show through experiments that Amulet achieves speedups on a variety of matrix multiplication-like tasks compared to existing compilers. For large matrices Amulet typically performs within 15% of hand-tuned matrix multiplication libraries, while handling a much broader class of computations.
STLCCP: Efficient Convex Optimization-based Framework for Signal Temporal Logic Specifications
Authors: Yoshinari Takayama, Kazumune Hashimoto, Toshiyuki Ohtsuka
Date: 2023-05-16 00:00:00
Tasks: Motion Planning

Signal temporal logic (STL) is a powerful formalism for specifying various temporal properties in dynamical systems. However, existing methods, such as mixed-integer programming and nonlinear programming, often struggle to efficiently solve control problems with complex, long-horizon STL specifications. This study introduces \textit{STLCCP}, a novel convex optimization-based framework that leverages key structural properties of STL: monotonicity of the robustness function, its hierarchical tree structure, and correspondence between convexity/concavity in optimizations and conjunctiveness/disjunctiveness in specifications. The framework begins with a structure-aware decomposition of STL formulas, transforming the problem into an equivalent difference of convex (DC) programs. This is then solved sequentially as a convex quadratic program using an improved version of the convex-concave procedure (CCP). To further enhance efficiency, we develop a smooth approximation of the robustness function using a function termed the \textit{mellowmin} function, specifically tailored to the proposed framework. Numerical experiments on motion planning benchmarks demonstrate that \textit{STLCCP} can efficiently handle complex scenarios over long horizons, outperforming existing methods.
Robust Auction Design with Support Information
Authors: Jerry Anunrojwong, Santiago R. Balseiro, Omar Besbes
Date: 2023-05-15 00:00:00
Tasks: Single Particle Analysis

A seller wants to sell an item to $n$ buyers. Buyer valuations are drawn i.i.d. from a distribution unknown to the seller; the seller only knows that the support is included in $[a, b]$. To be robust, the seller chooses a DSIC mechanism that optimizes the worst-case performance relative to the ideal expected revenue the seller could have collected with knowledge of buyers' valuations. Our analysis unifies the regret and the ratio objectives. For these objectives, we derive an optimal mechanism and the corresponding performance in quasi-closed form, as a function of the support information $[a, b]$ and the number of buyers $n$. Our analysis reveals three regimes of support information and a new class of robust mechanisms. i.) When $a/b$ is below a threshold, the optimal mechanism is a second-price auction (SPA) with random reserve, a focal class in earlier literature. ii.) When $a/b$ is above another threshold, SPAs are strictly suboptimal, and an optimal mechanism belongs to a class of mechanisms we introduce, which we call pooling auctions (POOL); whenever the highest value is above a threshold, the mechanism still allocates to the highest bidder, but otherwise the mechanism allocates to a uniformly random buyer, i.e., pools low types. iii.) When $a/b$ is between two thresholds, a randomization between SPA and POOL is optimal. We also characterize optimal mechanisms within nested central subclasses of mechanisms: standard mechanisms that only allocate to the highest bidder, SPA with random reserve, and SPA with no reserve. We show strict separations in terms of performance across classes, implying that deviating from standard mechanisms is necessary for robustness.
Unjustified Sample Sizes and Generalizations in Explainable AI Research: Principles for More Inclusive User Studies
Authors: Uwe Peters, Mary Carman
Date: 2023-05-08 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

Many ethical frameworks require artificial intelligence (AI) systems to be explainable. Explainable AI (XAI) models are frequently tested for their adequacy in user studies. Since different people may have different explanatory needs, it is important that participant samples in user studies are large enough to represent the target population to enable generalizations. However, it is unclear to what extent XAI researchers reflect on and justify their sample sizes or avoid broad generalizations across people. We analyzed XAI user studies (n = 220) published between 2012 and 2022. Most studies did not offer rationales for their sample sizes. Moreover, most papers generalized their conclusions beyond their target population, and there was no evidence that broader conclusions in quantitative studies were correlated with larger samples. These methodological problems can impede evaluations of whether XAI systems implement the explainability called for in ethical frameworks. We outline principles for more inclusive XAI user studies.
Power Grid Transient Analysis via Open-Source Circuit Simulator: A Case Study of HVDC
Authors: Yongli Zhu, Xiang Zhang, Renchang Dai
Date: 2023-05-16 00:00:00

This paper proposes an electronic circuit simulator-based method to accelerate the power system transient simulation, where the modeling of a generic HVDC (High Voltage Direct Current) system is focused. The electronic circuit simulation equations and the backward differentiation formula for numerical solving are described. Then, the circuit modeling process for power system components such as slack bus, constant power load, and HVDC are respectively illustrated. Finally, a case study is conducted on a four-bus power system to demonstrate the effectiveness of the proposed modeling and simulation method.
Hardware Acceleration for Open Radio Access Networks: A Contemporary Overview
Authors: Lopamudra Kundu, Xingqin Lin, Elena Agostini, Vikrama Ditya
Date: 2023-05-16 00:00:00

Radio access networks (RAN) are going through a paradigm shift towards interoperable, intelligent, software-defined, and cloud-native open RAN solutions. A key challenge towards the adoption and deployment of open RAN at scale is performance. Hence, it is critical to leverage the power of hardware acceleration to offload compute-heavy RAN workloads to specialized hardware devices to enable accelerated compute for open RAN deployments. In this article, we provide a state-of-the-art overview of hardware acceleration for open RAN in the fifth generation (5G) wireless networks. We also present a practical implementation of inline hardware acceleration for open RAN layer 1 processing and identify several areas for future exploration towards the sixth generation (6G) wireless networks.
Phase Retrieval via Model-Free Power Flow Jacobian Recovery
Authors: Samuel Talkington, Santiago Grijalva
Date: 2023-05-16 00:00:00
Tasks: Retrieval

Phase retrieval is a prevalent problem in digital signal processing and experimental physics that consists of estimating a complex signal from magnitude measurements. This paper expands the classical phase retrieval framework to electric power systems with unknown network models and limited access to observations of voltage magnitudes, active power injections, and reactive power injections. The proposed method recovers the phase angles and the power-phase angle submatrices of the AC power flow Jacobian matrix. This is made possible by deriving topology and parameter-free expressions for the structural symmetries of the power flow Jacobian that do not depend on the phase angles. These physical laws provide structural constraints for the proposed phase retrieval method. The paper then presents sufficient conditions for guaranteed recovery of the voltage phase angles, which also depend solely on voltage magnitudes, active power injections, and reactive power injections. The method offers two significant benefits: both estimating the voltage phase angles and recovering the power flow Jacobian matrix, a basis for approximating the power flow equations. Simulations on widely studied open-source test networks validate the findings.
A Dynamically Similar Lab-Scale District Heating Network via Dimensional Analysis
Authors: Audrey Blizard, Stephanie Stockar
Date: 2023-05-16 00:00:00

Strict user demands and large variability in external disturbances, along with limited richness in the data collected on the daily operating conditions of district heating networks makes the design and testing of novel energy-reducing control algorithms for district heating networks challenging. This paper presents the development of a dynamically similar lab-scale district heating network that can be used as a test bench for such control algorithms. This test bench is developed using the Buckingham pi theorem to the match the lab-scale components to the full-scale. By retaining the relative thermodynamics and fluid dynamics of a full-scale network in the lab-scale system, the experimental setup allows for repeatability of the experiments being performed and flexibility in the testing conditions. Moreover, the down-scaling of the experiment is leveraged to accelerate testing, allowing for the recreation of operating periods of weeks and months in hours and days. A PID controller is implemented on the lab-scale test bench to validate its response against literature data. Results show 63% efficiency during heating operations compared to 70% efficiency for a similar full-scale system, with comparable pressure losses across the system.
Control of distributed-parameter systems using normal forms: An introduction
Authors: Nicole Gehring, Abdurrahman Irscheid, Joachim Deutscher, Frank Woittennek, Joachim Rudolph
Date: 2023-05-16 00:00:00
Tasks: Form

This paper gives an overview of the control of distributed-parameter systems using normal forms. Considering linear controllable PDE-ODE systems of hyperbolic type, two methods derive tracking controllers by mapping the system into a form that is advantageous for the control design, analogous to the finite-dimensional case. A flatness-based controller makes use of the hyperbolic controller canonical form that follows from a parametrization of the system's solutions. A backstepping design exploits the strict-feedback form of the system to recursively stabilize and transform the subsystems.
Cooperative Aerial Transportation of Nonuniform Load through Quadrotors by Elastic and Flexible Cables
Authors: Ali Akbar Rezaei Lori, Mohammad Danesh, Iman Izadi
Date: 2023-05-16 00:00:00

In this paper, first the full dynamics of aerial transportation of a rigid body with arbitrary number of quadrotors is derived. Then a control strategy is proposed to convey the nonuniform rigid body appropriately to the desired trajectory. In the dynamical model of this transportation system, not only the load is considered as a nonuniform and non-homogeneous rigid body but also mass, flexibility, and tension of the cables are considered. Each cable is modeled as successive masses, springs, and dampers where each mass, spring, and damper has 4 degrees of freedom (DOF). The Euler-Lagrange equations are used to derive the motion equation. The control strategy includes three loops of attitude control, formation control, and navigation control. The sliding mode control is designed based on multi-agent systems for the formation control where the controller is proven to be asymptotically stable. The navigation control loop, based on the load states, guarantees that the load reaches the desired location. Finally, numerical examples and simulations are presented to verify the appropriate operation of the proposed system for transporting both homogeneous and non-homogeneous bodies by spreading quadrotors according to mass distribution of the body.
Topological Clusters in Multi-Agent Networks: Analysis and Algorithm
Authors: Jeong-Min Ma, Hyung-Gon Lee, Kevin L. Moore, Hyo-Sung Ahn, Kwang-Kyo Oh
Date: 2023-05-16 00:00:00
Tasks: Clustering

We study clustering properties of networks of single integrator nodes over a directed graph, in which the nodes converge to steady-state values. These values define clustering groups of nodes, which depend on interaction topology, edge weights, and initial values. Focusing on the interaction topology of the network, we introduce the notion of topological clusters, which are sets of nodes that converge to an identical value due to the topological characteristics of the network, independent of the value of the edge weights. We then investigate properties of topological clusters and present a necessary and sufficient condition for a set of nodes to form a topological cluster. We also provide an algorithm for finding topological clusters. Examples show the validity of the analysis and algorithm.
Constructing Feedback Linearizable Discretizations for Continuous-Time Systems using Retraction Maps
Authors: Ashutosh Jindal, Ravi Banavar, David Martin Diego
Date: 2023-05-16 00:00:00

Control laws for continuous-time dynamical systems are most often implemented via digital controllers using a sample-and-hold technique. Numerical discretization of the continuous system is an integral part of subsequent analysis. Feedback linearizability of such sampled systems is dependent upon the choice of discretization map or technique. In this article, for feedback linearizable continuous-time systems, we utilize the idea of retraction maps to construct discretizations that are feedback linearizable as well. We also propose a method to functionally compose discretizations to obtain higher-order integrators that are feedback linearizable.