pwc-archive/papers-with-abstracts huggingface dataset:
A Preliminary Framework for Intersectionality in ML Pipelines
Authors: Michelle Nashla Turcios, Alicia E. Boyd, Angela D. R. Smith, Brittany Johnson
Date: 2025-05-06 00:00:00

Machine learning (ML) has become a go-to solution for improving how we use, experience, and interact with technology (and the world around us). Unfortunately, studies have repeatedly shown that machine learning technologies may not provide adequate support for societal identities and experiences. Intersectionality is a sociological framework that provides a mechanism for explicitly considering complex social identities, focusing on social justice and power. While the framework of intersectionality can support the development of technologies that acknowledge and support all members of society, it has been adopted and adapted in ways that are not always true to its foundations, thereby weakening its potential for impact. To support the appropriate adoption and use of intersectionality for more equitable technological outcomes, we amplify the foundational intersectionality scholarship--Crenshaw, Combahee, and Collins (three C's), to create a socially relevant preliminary framework in developing machine-learning solutions. We use this framework to evaluate and report on the (mis)alignments of intersectionality application in machine learning literature.
On Unbiased Low-Rank Approximation with Minimum Distortion
Authors: Leighton Pate Barnes, Stephen Cameron, Benjamin Howard
Date: 2025-05-12 00:00:00

We describe an algorithm for sampling a low-rank random matrix $Q$ that best approximates a fixed target matrix $P\in\mathbb{C}^{n\times m}$ in the following sense: $Q$ is unbiased, i.e., $\mathbb{E}[Q] = P$; $\mathsf{rank}(Q)\leq r$; and $Q$ minimizes the expected Frobenius norm error $\mathbb{E}\|P-Q\|_F^2$. Our algorithm mirrors the solution to the efficient unbiased sparsification problem for vectors, except applied to the singular components of the matrix $P$. Optimality is proven by showing that our algorithm matches the error from an existing lower bound.
Unlocking Innate Computing Abilities in Electric Grids
Authors: Yubo Song, Subham Sahoo
Date: 2025-05-15 00:00:00

High energy consumption of artificial intelligence has gained momentum worldwide, which necessitates major investments on expanding efficient and carbon-neutral generation and data center infrastructure in electric power grids. Going beyond the conventional ideation, this article unleashes innate computational abilities in the power grid network circuits itself. By programming power electronic converters (PECs) to mimic biological neurons, we sustainably transform power grids into a neural network and enable it to optimize, compute and make data-driven decisions using distributed PECs. Instead of seen merely as an energy delivery platform, this article conceptualizes a novel application for electric grid to be used as a computing asset without affecting its operation. To illustrate its computational abilities, we solve a affine transformation task in a microgrid with five PECs. By encoding the digital data into the control of PECs, our preliminary results conclude that computing using electric grids does not disturb its operation. From a scientific perspective, this work fundamentally merges energy and computing optimization theories by harnessing inherent high-dimensional computational relationships in electric grids.
Generative diffusion model surrogates for mechanistic agent-based biological models
Authors: Tien Comlekoglu, J. Quetzalcoatl Toledo-Marín, Douglas W. DeSimone, Shayn M. Peirce, Geoffrey Fox, James A. Glazier
Date: 2025-05-01 00:00:00
Tasks: Denoising, Model Selection

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.
Study and improvement of search algorithms in two-players perfect information games
Authors: Quentin Cohen-Solal
Date: 2025-05-06 00:00:00
Tasks: All

Games, in their mathematical sense, are everywhere (game industries, economics, defense, education, chemistry, biology, ...).Search algorithms in games are artificial intelligence methods for playing such games. Unfortunately, there is no study on these algorithms that evaluates the generality of their performance. We propose to address this gap in the case of two-player zero-sum games with perfect information. Furthermore, we propose a new search algorithm and we show that, for a short search time, it outperforms all studied algorithms on all games in this large experiment and that, for a medium search time, it outperforms all studied algorithms on 17 of the 22 studied games.
NVSPolicy: Adaptive Novel-View Synthesis for Generalizable Language-Conditioned Policy Learning
Authors: Le Shi, Yifei Shi, Xin Xu, Tenglong Liu, Junhua Xi, Chengyuan Chen
Date: 2025-05-15 00:00:00
Tasks: Novel View Synthesis, Robot Manipulation, Zero-shot Generalization

Recent advances in deep generative models demonstrate unprecedented zero-shot generalization capabilities, offering great potential for robot manipulation in unstructured environments. Given a partial observation of a scene, deep generative models could generate the unseen regions and therefore provide more context, which enhances the capability of robots to generalize across unseen environments. However, due to the visual artifacts in generated images and inefficient integration of multi-modal features in policy learning, this direction remains an open challenge. We introduce NVSPolicy, a generalizable language-conditioned policy learning method that couples an adaptive novel-view synthesis module with a hierarchical policy network. Given an input image, NVSPolicy dynamically selects an informative viewpoint and synthesizes an adaptive novel-view image to enrich the visual context. To mitigate the impact of the imperfect synthesized images, we adopt a cycle-consistent VAE mechanism that disentangles the visual features into the semantic feature and the remaining feature. The two features are then fed into the hierarchical policy network respectively: the semantic feature informs the high-level meta-skill selection, and the remaining feature guides low-level action estimation. Moreover, we propose several practical mechanisms to make the proposed method efficient. Extensive experiments on CALVIN demonstrate the state-of-the-art performance of our method. Specifically, it achieves an average success rate of 90.4\% across all tasks, greatly outperforming the recent methods. Ablation studies confirm the significance of our adaptive novel-view synthesis paradigm. In addition, we evaluate NVSPolicy on a real-world robotic platform to demonstrate its practical applicability.
Threshold Strategy for Leaking Corner-Free Hamilton-Jacobi Reachability with Decomposed Computations
Authors: Chong He, Mugilan Mariappan, Keval Vora, Mo Chen
Date: 2025-05-15 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction

Hamilton-Jacobi (HJ) Reachability is widely used to compute value functions for states satisfying specific control objectives. However, it becomes intractable for high-dimensional problems due to the curse of dimensionality. Dimensionality reduction approaches are essential for mitigating this challenge, whereas they could introduce the ``leaking corner issue", leading to inaccuracies in the results. In this paper, we define the ``leaking corner issue" in terms of value functions, propose and prove a necessary condition for its occurrence. We then use these theoretical contributions to introduce a new local updating method that efficiently corrects inaccurate value functions while maintaining the computational efficiency of the dimensionality reduction approaches. We demonstrate the effectiveness of our method through numerical simulations. Although we validate our method with the self-contained subsystem decomposition (SCSD), our approach is applicable to other dimensionality reduction techniques that introduce the ``leaking corners".
Provably safe and human-like car-following behaviors: Part 2. A parsimonious multi-phase model with projected braking
Authors: Wen-Long Jin
Date: 2025-05-15 00:00:00
Tasks: Mathematical Proofs, Trajectory Planning

Ensuring safe and human-like trajectory planning for automated vehicles amidst real-world uncertainties remains a critical challenge. While existing car-following models often struggle to consistently provide rigorous safety proofs alongside human-like acceleration and deceleration patterns, we introduce a novel multi-phase projection-based car-following model. This model is designed to balance safety and performance by incorporating bounded acceleration and deceleration rates while emulating key human driving principles. Building upon a foundation of fundamental driving principles and a multi-phase dynamical systems analysis (detailed in Part 1 of this study \citep{jin2025WA20-02_Part1}), we first highlight the limitations of extending standard models like Newell's with simple bounded deceleration. Inspired by human drivers' anticipatory behavior, we mathematically define and analyze projected braking profiles for both leader and follower vehicles, establishing safety criteria and new phase definitions based on the projected braking lead-vehicle problem. The proposed parsimonious model combines an extended Newell's model for nominal driving with a new control law for scenarios requiring projected braking. Using speed-spacing phase plane analysis, we provide rigorous mathematical proofs of the model's adherence to defined safe and human-like driving principles, including collision-free operation, bounded deceleration, and acceptable safe stopping distance, under reasonable initial conditions. Numerical simulations validate the model's superior performance in achieving both safety and human-like braking profiles for the stationary lead-vehicle problem. Finally, we discuss the model's implications and future research directions.
Provably safe and human-like car-following behaviors: Part 1. Analysis of phases and dynamics in standard models
Authors: Wen-Long Jin
Date: 2025-05-15 00:00:00
Tasks: Trajectory Planning

Trajectory planning is essential for ensuring safe driving in the face of uncertainties related to communication, sensing, and dynamic factors such as weather, road conditions, policies, and other road users. Existing car-following models often lack rigorous safety proofs and the ability to replicate human-like driving behaviors consistently. This article applies multi-phase dynamical systems analysis to well-known car-following models to highlight the characteristics and limitations of existing approaches. We begin by formulating fundamental principles for safe and human-like car-following behaviors, which include zeroth-order principles for comfort and minimum jam spacings, first-order principles for speeds and time gaps, and second-order principles for comfort acceleration/deceleration bounds as well as braking profiles. From a set of these zeroth- and first-order principles, we derive Newell's simplified car-following model. Subsequently, we analyze phases within the speed-spacing plane for the stationary lead-vehicle problem in Newell's model and its extensions, which incorporate both bounded acceleration and deceleration. We then analyze the performance of the Intelligent Driver Model and the Gipps model. Through this analysis, we highlight the limitations of these models with respect to some of the aforementioned principles. Numerical simulations and empirical observations validate the theoretical insights. Finally, we discuss future research directions to further integrate safety, human-like behaviors, and vehicular automation in car-following models, which are addressed in Part 2 of this study \citep{jin2025WA20-02_Part2}, where we develop a novel multi-phase projection-based car-following model that addresses the limitations identified here.
Neural Associative Skill Memories for safer robotics and modelling human sensorimotor repertoires
Authors: Pranav Mahajan, Mufeng Tang, T. Ed Li, Ioannis Havoutis, Ben Seymour
Date: 2025-05-14 00:00:00
Tasks: Fault Detection

Modern robots face challenges shared by humans, where machines must learn multiple sensorimotor skills and express them adaptively. Equipping robots with a human-like memory of how it feels to do multiple stereotypical movements can make robots more aware of normal operational states and help develop self-preserving safer robots. Associative Skill Memories (ASMs) aim to address this by linking movement primitives to sensory feedback, but existing implementations rely on hard-coded libraries of individual skills. A key unresolved problem is how a single neural network can learn a repertoire of skills while enabling fault detection and context-aware execution. Here we introduce Neural Associative Skill Memories (ASMs), a framework that utilises self-supervised predictive coding for temporal prediction to unify skill learning and expression, using biologically plausible learning rules. Unlike traditional ASMs which require explicit skill selection, Neural ASMs implicitly recognize and express skills through contextual inference, enabling fault detection across learned behaviours without an explicit skill selection mechanism. Compared to recurrent neural networks trained via backpropagation through time, our model achieves comparable qualitative performance in skill memory expression while using local learning rules and predicts a biologically relevant speed-accuracy trade-off during skill memory expression. This work advances the field of neurorobotics by demonstrating how predictive coding principles can model adaptive robot control and human motor preparation. By unifying fault detection, reactive control, skill memorisation and expression into a single energy-based architecture, Neural ASMs contribute to safer robotics and provide a computational lens to study biological sensorimotor learning.
EnerVerse-AC: Envisioning Embodied Environments with Action Condition
Authors: Yuxin Jiang, Shengcong Chen, Siyuan Huang, Liliang Chen, Pengfei Zhou, Yue Liao, Xindong He, Chiming Liu, Hongsheng Li, Maoqing Yao, Guanghui Ren
Date: 2025-05-14 00:00:00
Tasks: Image Generation, Imitation Learning

Robotic imitation learning has advanced from solving static tasks to addressing dynamic interaction scenarios, but testing and evaluation remain costly and challenging due to the need for real-time interaction with dynamic environments. We propose EnerVerse-AC (EVAC), an action-conditional world model that generates future visual observations based on an agent's predicted actions, enabling realistic and controllable robotic inference. Building on prior architectures, EVAC introduces a multi-level action-conditioning mechanism and ray map encoding for dynamic multi-view image generation while expanding training data with diverse failure trajectories to improve generalization. As both a data engine and evaluator, EVAC augments human-collected trajectories into diverse datasets and generates realistic, action-conditioned video observations for policy testing, eliminating the need for physical robots or complex simulations. This approach significantly reduces costs while maintaining high fidelity in robotic manipulation evaluation. Extensive experiments validate the effectiveness of our method. Code, checkpoints, and datasets can be found at <https://annaj2178.github.io/EnerverseAC.github.io>.
Omni-R1: Do You Really Need Audio to Fine-Tune Your Audio LLM?
Authors: Andrew Rouditchenko, Saurabhchand Bhati, Edson Araujo, Samuel Thomas, Hilde Kuehne, Rogerio Feris, James Glass
Date: 2025-05-14 00:00:00
Tasks: Audio Question Answering, Question Answering

We propose Omni-R1 which fine-tunes a recent multi-modal LLM, Qwen2.5-Omni, on an audio question answering dataset with the reinforcement learning method GRPO. This leads to new State-of-the-Art performance on the recent MMAU and MMAR benchmarks. Omni-R1 achieves the highest accuracies on the sounds, music, speech, and overall average categories, both on the Test-mini and Test-full splits. To understand the performance improvement, we tested models both with and without audio and found that much of the performance improvement from GRPO could be attributed to better text-based reasoning. We also made a surprising discovery that fine-tuning without audio on a text-only dataset was effective at improving the audio-based performance.
Detecting Musical Deepfakes
Authors: Nick Sunday
Date: 2025-05-03 00:00:00
Tasks: Face Swapping

The proliferation of Text-to-Music (TTM) platforms has democratized music creation, enabling users to effortlessly generate high-quality compositions. However, this innovation also presents new challenges to musicians and the broader music industry. This study investigates the detection of AI-generated songs using the FakeMusicCaps dataset by classifying audio as either deepfake or human. To simulate real-world adversarial conditions, tempo stretching and pitch shifting were applied to the dataset. Mel spectrograms were generated from the modified audio, then used to train and evaluate a convolutional neural network. In addition to presenting technical results, this work explores the ethical and societal implications of TTM platforms, arguing that carefully designed detection systems are essential to both protecting artists and unlocking the positive potential of generative AI in music.
Can On Body Sensing Be Spatial Adaptive?
Authors: Shubham Rohal, Dong Yoon Lee, Phuc Nguyen, Shijia Pan
Date: 2025-05-15 00:00:00
Tasks: Position

Wearable sensors are typically affixed to specific locations on the human body, and their position remains static, only changing unintentionally due to motion artifacts. This static configuration introduces significant limitations. As a result, current systems miss the opportunity to capture dynamic physiological data from diverse body regions. This research investigates the potential of developing movable sensors that adaptively reposition themselves to sample different areas of interest on the body, addressing gaps in spatial coverage. We designed, developed, and fabricated a 3 x 3 matrix platform to support moving sensors from one location to another. We validated the feasibility through simulations on a matrix of up to 9 x 9 locations with up to 16 concurrent sensors and real-world prototype characterization.
Spatially Selective Active Noise Control for Open-fitting Hearables with Acausal Optimization
Authors: Tong Xiao, Simon Doclo
Date: 2025-05-15 00:00:00

Recent advances in active noise control have enabled the development of hearables with spatial selectivity, which actively suppress undesired noise while preserving desired sound from specific directions. In this work, we propose an improved approach to spatially selective active noise control that incorporates acausal relative impulse responses into the optimization process, resulting in significantly improved performance over the causal design. We evaluate the system through simulations using a pair of open-fitting hearables with spatially localized speech and noise sources in an anechoic environment. Performance is evaluated in terms of speech distortion, noise reduction, and signal-to-noise ratio improvement across different delays and degrees of acausality. Results show that the proposed acausal optimization consistently outperforms the causal approach across all metrics and scenarios, as acausal filters more effectively characterize the response of the desired source.
Angle diversity receiver as a key enabler for reliable ORIS-based Visible Light Communication
Authors: Borja Genoves Guzman, Maximo Morales-Cespedes, Ana Garcia Armada, Maite Brandt-Pearce
Date: 2025-05-15 00:00:00
Tasks: Diversity

Visible Light Communication (VLC) offers a promising solution to satisfy the increasing demand for wireless data. However, link blockages remain a significant challenge. This paper addresses this issue by investigating the combined use of angle diversity receivers (ADRs) and optical reconfigurable intelligent surfaces (ORISs) in multiuser VLC systems. We consider ORIS elements as small movable mirrors. We demonstrate the complementarity of ADR and ORIS in mitigating link blockages, as well as the advantages of using a larger number of ORIS elements due to the increased field-of-view (FoV) at the receiver enabled by the ADR. An optimization algorithm is proposed to maximize the minimum signal-to-noise power ratio (SNR) to deploy a fair communication network. Numerical results show that integrating ADR and ORIS significantly enhances VLC communication performance, achieving an SNR gain of up to 30 dB compared to a system without ORIS, and mitigating communication outages produced by link blockages or out-of-FoV received signals. We also prove that an ADR with a single tier of photodiodes is sufficient to complement ORIS-assisted VLC.
DB InfraGO's Automated Dispatching Assistant ADA-PMB
Authors: Stephan Zieger, Hannah Richta
Date: 2025-05-15 00:00:00

As railway infrastructure manager, DB InfraGO AG is faced with the challenge of offering fluid and punctual operation despite rising demand and increased construction activity. The high capacity utilisation, especially in the core network sections, causes delays to be propagated quickly and widely across the entire network. Up to now, conflicts between train runs can be identified automatically, but dispatching measures have been based on past human experience. An automated dispatching assistance system is currently being piloted to provide support for train dispatchers in their work. The aim is to offer them helpful dispatching recommendations, particularly in stressful situations with a high conflict density in the network section under consideration, in order to ensure the most efficient operation of the system. The recommendations are currently displayed separately alongside the central control system. In future, they will be integrated into the central control system, which will significantly simplify communication between the train dispatcher and signal setter. Further development steps for the integration process are also presented and discussed.