pwc-archive/papers-with-abstracts huggingface dataset:
StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching
Authors: Jixun Yao, Yuguang Yang, Yu Pan, Ziqian Ning, Jiaohao Ye, Hongbin Zhou, Lei Xie
Date: 2024-12-06 00:00:00
Tasks: Voice Conversion

Zero-shot voice conversion (VC) aims to transfer the timbre from the source speaker to an arbitrary unseen speaker while preserving the original linguistic content. Despite recent advancements in zero-shot VC using language model-based or diffusion-based approaches, several challenges remain: 1) current approaches primarily focus on adapting timbre from unseen speakers and are unable to transfer style and timbre to different unseen speakers independently; 2) these approaches often suffer from slower inference speeds due to the autoregressive modeling methods or the need for numerous sampling steps; 3) the quality and similarity of the converted samples are still not fully satisfactory. To address these challenges, we propose a style controllable zero-shot VC approach named StableVC, which aims to transfer timbre and style from source speech to different unseen target speakers. Specifically, we decompose speech into linguistic content, timbre, and style, and then employ a conditional flow matching module to reconstruct the high-quality mel-spectrogram based on these decomposed features. To effectively capture timbre and style in a zero-shot manner, we introduce a novel dual attention mechanism with an adaptive gate, rather than using conventional feature concatenation. With this non-autoregressive design, StableVC can efficiently capture the intricate timbre and style from different unseen speakers and generate high-quality speech significantly faster than real-time. Experiments demonstrate that our proposed StableVC outperforms state-of-the-art baseline systems in zero-shot VC and achieves flexible control over timbre and style from different unseen speakers. Moreover, StableVC offers approximately 25x and 1.65x faster sampling compared to autoregressive and diffusion-based baselines.
Recursive Projection-Free Identification with Binary-Valued Observations
Authors: Tianning Han, Ying Wang, Yanlong Zhao
Date: 2024-12-06 00:00:00

This paper is concerned with parameter identification problem for finite impulse response (FIR) systems with binary-valued observations under low computational complexity. Most of the existing algorithms under binary-valued observations rely on projection operators, which leads to a high computational complexity of much higher than O(n^2). In response, this paper introduces a recursive projection-free identification algorithm that incorporates a specialized cut-off coefficient to fully utilize prior information, thereby eliminating the need for projection operators. The algorithm is proved to be mean square and almost surely convergent. Furthermore, to better leverage prior information, an adaptive accelerated coefficient is introduced, resulting in a mean square convergence rate of O(1/k) , which matches the convergence rate with accurate observations. Inspired by the structure of the Cramer-Rao lower bound, the algorithm can be extended to an information-matrix projection-free algorithm by designing adaptive weight coefficients. This extension is proved to be asymptotically efficient for first-order FIR systems, with simulations indicating similar results for high order FIR systems. Finally, numerical examples are provided to demonstrate the main results.
Tuning Trains Speed in Railway Scheduling
Authors: Étienne André
Date: 2024-12-06 00:00:00
Tasks: Scheduling

Railway scheduling consists in ensuring that a set of trains evolve in a shared rail network without collisions, while meeting schedule constraints. This problem is notoriously difficult, even more in the case of uncertain or even unknown train speeds. We propose here a modeling and verification approach for railway scheduling in the presence of uncertain speeds, encoded here as uncertain segment durations. We formalize the system and propose a formal translation to parametric timed automata. As a proof of concept, we apply our approach to benchmarks, for which we synthesize using IMITATOR suitable valuations for the segment durations.
A CAV-based perimeter-free regional traffic control strategy utilizing existing parking infrastructure
Authors: Hao liu, Vikash V. Gayah
Date: 2024-12-05 00:00:00
Tasks: Traffic Signal Control

This paper proposes a novel perimeter-free regional traffic management strategy for traffic networks under a connected and autonomous vehicle (CAV) environment. The proposed strategy requires CAVs, especially those with long remaining travel distances, to temporarily wait at nearby parking facilities when the network is congested. After a designated holding time, these CAVs are allowed to re-enter the network. Doing so helps reduce congestion and improve overall operational efficiency. Unlike traditional perimeter control approaches that restrict inflows to congested regions, the proposed holding strategy leverages existing parking infrastructure to temporarily hold vehicles in a way that partially avoids local queue accumulation issues. The proposed method can be easily integrated with existing signal control methods and retains the maximum stability property of the original traffic signal control methods. Simulation results show that the proposed strategy not only reduces travel time for vehicles that are not held, but can also reduce travel times for some of the held vehicles as well, which serves as another key merit of the proposed approach. Compared to the two benchmark perimeter control algorithms, the proposed strategy is more robust against demand patterns and generates stronger improvements in the operational efficiency. Importantly, since the proposed strategy requires existing parking infrastructure, its performance has been demonstrated under various configurations of parking locations and capacities. Particularly, it is demonstrated that the utilization of the parking facility consistently improves overall traffic efficiency, regardless of the facility's size. Lastly, the proposed strategy is shown to be beneficial in a partial CAV environment where only a subset of vehicles are available for holding.
Physics-informed Gaussian Processes as Linear Model Predictive Controller
Authors: Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann
Date: 2024-12-02 00:00:00
Tasks: Bayesian Inference, Gaussian Processes, Model Predictive Control

We introduce a novel algorithm for controlling linear time invariant systems in a tracking problem. The controller is based on a Gaussian Process (GP) whose realizations satisfy a system of linear ordinary differential equations with constant coefficients. Control inputs for tracking are determined by conditioning the prior GP on the setpoints, i.e. control as inference. The resulting Model Predictive Control scheme incorporates pointwise soft constraints by introducing virtual setpoints to the posterior Gaussian process. We show theoretically that our controller satisfies asymptotical stability for the optimal control problem by leveraging general results from Bayesian inference and demonstrate this result in a numerical example.
Go-or-Grow Models in Biology: a Monster on a Leash
Authors: R. Thiessen, M. Conte, T. L. Stepien, T. Hillen
Date: 2024-12-06 00:00:00

Go-or-grow approaches represent a specific class of mathematical models used to describe populations where individuals either migrate or reproduce, but not both simultaneously. These models have a wide range of applications in biology and medicine, chiefly among those the modeling of brain cancer spread. The analysis of go-or-grow models has inspired new mathematics, and it is the purpose of this review to highlight interesting and challenging mathematical properties of reaction--diffusion models of the go-or-grow type. We provide a detailed review of biological and medical applications before focusing on key results concerning solution existence and uniqueness, pattern formation, critical domain size problems, and traveling waves. We present new general results related to the critical domain size and traveling wave problems, and we connect these findings to the existing literature. Moreover, we demonstrate the high level of instability inherent in go-or-grow models. We argue that there is currently no accurate numerical solver for these models, and emphasize that special care must be taken when dealing with the "monster on a leash".
Advancing Marine Heatwave Forecasts: An Integrated Deep Learning Approach
Authors: Ding Ning, Varvara Vetrova, Yun Sing Koh, Karin R. Bryan
Date: 2024-11-19 00:00:00
Tasks: Deep Learning

Marine heatwaves (MHWs), an extreme climate phenomenon, pose significant challenges to marine ecosystems and industries, with their frequency and intensity increasing due to climate change. This study introduces an integrated deep learning approach to forecast short-to-long-term MHWs on a global scale. The approach combines graph representation for modeling spatial properties in climate data, imbalanced regression to handle skewed data distributions, and temporal diffusion to enhance forecast accuracy across various lead times. To the best of our knowledge, this is the first study that synthesizes three spatiotemporal anomaly methodologies to predict MHWs. Additionally, we introduce a method for constructing graphs that avoids isolated nodes and provide a new publicly available sea surface temperature anomaly graph dataset. We examine the trade-offs in the selection of loss functions and evaluation metrics for MHWs. We analyze spatial patterns in global MHW predictability by focusing on historical hotspots, and our approach demonstrates better performance compared to traditional numerical models in regions such as the middle south Pacific, equatorial Atlantic near Africa, south Atlantic, and high-latitude Indian Ocean. We highlight the potential of temporal diffusion to replace the conventional sliding window approach for long-term forecasts, achieving improved prediction up to six months in advance. These insights not only establish benchmarks for machine learning applications in MHW forecasting but also enhance understanding of general climate forecasting methodologies.
Kinetic-Diffusion-Rotation Algorithm for Dose Estimation in Electron Beam Therapy
Authors: Klaas Willems, Vince Maes, Zhirui Tang, Giovanni Samaey
Date: 2024-12-06 00:00:00

Monte Carlo methods are state-of-the-art when it comes to dosimetric computations in radiotherapy. However, the execution time of these methods suffers in high-collisional regimes. We address this problem by introducing a kinetic-diffusion particle tracing scheme. This algorithm, first proposed in the context of neutral transport in fusion energy, relies on explicit simulation of the kinetic motion in low-collisional regimes and dynamically switches to motion based on a random walk in high-collisional regimes. The random walk motion maintains the first two moments (mean and variance) of the kinetic motion. We derive an analytic formula for the mean kinetic motion and discuss the addition of a multiple scattering distribution to the algorithm. In contrast to neutral transport, the radiation transfer setting does not readily admit to an analytical expression for the variance of the kinetic motion, and we therefore resort to the use of a lookup table. We test the algorithm for dosimetric computations in radiation therapy on a 2D CT scan of a lung patient. Using a simple particle model, our Python implementation of the algorithm is nearly 33 times faster than an equivalent kinetic simulation at the cost of a small modeling error.
Cross-feeding Creates Tipping Points in Microbiome Diversity
Authors: Tom Clegg, Thilo Gross
Date: 2024-12-06 00:00:00
Tasks: Diversity

A key unresolved question in microbial ecology is how the extraordinary diversity of microbiomes emerges from the behaviour of individual populations. This process is driven by the cross-feeding networks that structure these communities, but are hard to untangle due to their inherent complexity. We address this problem using the tools of network science to develop a model of microbial community structure. We discover tipping points at which diversity abruptly declines due to the catastrophic collapse of cross-feeding networks. Our results are a rare example of an ecological tipping point in diversity and provide insight into the fundamental processes shaping microbiota and their robustness. We illustrate this by showing how the unculturability of microbial diversity emerges as an inherent property of their microbial cross-feeding networks.
Linear Regressions with Combined Data
Authors: Xavier D'Haultfoeuille, Christophe Gaillac, Arnaud Maurel
Date: 2024-12-06 00:00:00

We study best linear predictions in a context where the outcome of interest and some of the covariates are observed in two different datasets that cannot be matched. Traditional approaches obtain point identification by relying, often implicitly, on exclusion restrictions. We show that without such restrictions, coefficients of interest can still be partially identified and we derive a constructive characterization of the sharp identified set. We then build on this characterization to develop computationally simple and asymptotically normal estimators of the corresponding bounds. We show that these estimators exhibit good finite sample performances.
AI and the law
Authors: Henry A. Thompson
Date: 2024-12-06 00:00:00

I argue that generative AI will have an uneven effect on the evolution of the law. To do so, I consider generative AI as a labor-augmenting technology that reduces the cost of both writing more complete contracts and litigating in court. The contracting effect reduces the demand for court services by making contracts more complete. The litigation effect, by contrast, increases the demand for court services by a) making contracts less complete and b) reducing litigants' incentive to settle, all else equal. Where contracts are common, as in property and contract law, the change in the quantity of litigation is uncertain due to offsetting contracting and litigation effects. However, in areas where contracts are rare, as in tort law, the amount of litigation is likely to rise. Following Rubin (1977) and Priest (1977) generative AI will accelerate the evolution of tort law toward efficiency.
Achieving Semantic Consistency: Contextualized Word Representations for Political Text Analysis
Authors: Ruiyu Zhang, Lin Nie, Ce Zhao, Qingyang Chen
Date: 2024-12-03 00:00:00
Tasks: Articles

Accurately interpreting words is vital in political science text analysis; some tasks require assuming semantic stability, while others aim to trace semantic shifts. Traditional static embeddings, like Word2Vec effectively capture long-term semantic changes but often lack stability in short-term contexts due to embedding fluctuations caused by unbalanced training data. BERT, which features transformer-based architecture and contextual embeddings, offers greater semantic consistency, making it suitable for analyses in which stability is crucial. This study compares Word2Vec and BERT using 20 years of People's Daily articles to evaluate their performance in semantic representations across different timeframes. The results indicate that BERT outperforms Word2Vec in maintaining semantic stability and still recognizes subtle semantic variations. These findings support BERT's use in text analysis tasks that require stability, where semantic changes are not assumed, offering a more reliable foundation than static alternatives.
Utilizing WaveFunctionCollapse Algorithm for Procedural Generation of Terrains using Remotely Sensed Elevation Data
Authors: Seyedparsa Dajkhosh
Date: 2024-12-06 00:00:00

Procedural terrain generation plays a vital role in creating virtual landscapes for games, simulations, and various applications. The WaveFunctionCollapse (WFC) algorithm has proven effective in generating content by learning patterns from example data. In this research, we adapt WFC to generate terrain height maps using Shuttle Radar Topography Mission (SRTM) data. Instead of directly using raw height values, we use slopes to better capture structural features and preserve terrain patterns. Statistical comparisons, including histogram analysis, as well as evaluations of the mean, median, and standard deviation of input and output data, demonstrate that the algorithm effectively retains the input's structural characteristics while generating new terrain. the results show that WFC, with slope-based input and height-level transformations, can generate realistic terrain patterns for applications in game development and beyond.
2.5D Super-Resolution Approaches for X-ray Computed Tomography-based Inspection of Additively Manufactured Parts
Authors: Haley Duba-Sullivan, Obaidullah Rahman, Singanallur Venkatakrishnan, Amirkoushyar Ziabari
Date: 2024-12-05 00:00:00
Tasks: Computational Efficiency, Defect Detection, Super-Resolution

X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite its widespread use, obtaining high-resolution CT scans can be extremely time consuming. This issue can be mitigated by performing scans at lower resolutions; however, reducing the resolution compromises spatial detail, limiting the accuracy of defect detection. Super-resolution algorithms offer a promising solution for overcoming resolution limitations in XCT reconstructions of AM parts, enabling more accurate detection of defects. While 2D super-resolution methods have demonstrated state-of-the-art performance on natural images, they tend to under-perform when directly applied to XCT slices. On the other hand, 3D super-resolution methods are computationally expensive, making them infeasible for large-scale applications. To address these challenges, we propose a 2.5D super-resolution approach tailored for XCT of AM parts. Our method enhances the resolution of individual slices by leveraging multi-slice information from neighboring 2D slices without the significant computational overhead of full 3D methods. Specifically, we use neighboring low-resolution slices to super-resolve the center slice, exploiting inter-slice spatial context while maintaining computational efficiency. This approach bridges the gap between 2D and 3D methods, offering a practical solution for high-throughput defect detection in AM parts.
Video Quality Assessment: A Comprehensive Survey
Authors: Qi Zheng, Yibo Fan, Leilei Huang, Tianyu Zhu, Jiaming Liu, Zhijian Hao, Shuo Xing, Chia-Ju Chen, Xiongkuo Min, Alan C. Bovik, Zhengzhong Tu
Date: 2024-12-04 00:00:00
Tasks: Benchmarking, Survey, Video Quality Assessment, Visual Question Answering (VQA)

Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality. Traditional VQA models based on natural image and/or video statistics, which are inspired both by models of projected images of the real world and by dual models of the human visual system, deliver only limited prediction performances on real-world user-generated content (UGC), as exemplified in recent large-scale VQA databases containing large numbers of diverse video contents crawled from the web. Fortunately, recent advances in deep neural networks and Large Multimodality Models (LMMs) have enabled significant progress in solving this problem, yielding better results than prior handcrafted models. Numerous deep learning-based VQA models have been developed, with progress in this direction driven by the creation of content-diverse, large-scale human-labeled databases that supply ground truth psychometric video quality data. Here, we present a comprehensive survey of recent progress in the development of VQA algorithms and the benchmarking studies and databases that make them possible. We also analyze open research directions on study design and VQA algorithm architectures. Github link: https://github.com/taco-group/Video-Quality-Assessment-A-Comprehensive-Survey.
Asynchronous Random Access in Massive MIMO Systems Facilitated by the Delay-Angle Domain
Authors: Ao Chen, Wei Chen, Bo Ai, Petar Popovski
Date: 2024-12-06 00:00:00
Tasks: Action Detection, Activity Detection

The problem of uplink transmissions in massive connectivity is commonly dealt with using schemes for grant-free random access. When a large number of devices transmit almost synchronously, the receiver may not be able to resolve the collision. This could be addressed by assigning dedicated pilots to each user, leading to a contention-free random access (CFRA), which suffers from low scalability and efficiency. This paper explores contention-based random access (CBRA) schemes for asynchronous access in massive multiple-input multiple-output (MIMO) systems. The symmetry across the accessing users with the same pilots is broken by leveraging the delay information inherent to asynchronous systems and the angle information from massive MIMO to enhance activity detection (AD) and channel estimation (CE). The problem is formulated as a sparse recovery in the delay-angle domain. The challenge is that the recovery signal exhibits both row-sparse and cluster-sparse structure, with unknown cluster sizes and locations. We address this by a cluster-extended sparse Bayesian learning (CE-SBL) algorithm that introduces a new weighted prior to capture the signal structure and extends the expectation maximization (EM) algorithm for hyperparameter estimation. Simulation results demonstrate the superiority of the proposed method in joint AD and CE.
Space-Time-Modulated Wideband Radiation-Type Programmable Metasurface for Low Sidelobe Beamforming
Authors: Xudong Bai, Longpan Wang, Yuhua Chen, Xilong Lu, Fuli Zhang, Jingfeng Chen, Wen Chen, He-Xiu Xu
Date: 2024-12-06 00:00:00

Programmable metasurfaces promise a great potential to construct low-cost phased array systems due to the capability of elaborate modulation over electromagnetic (EM) waves. However, they are in either reflective or transmissive mode, and usually possess a relatively high profile as a result of the external feed source. Besides, it is difficult to conduct multibit phase shift in metasurfaces, when comparing with conventional phased arrays. Here, we propose a strategy of space-time modulated wideband radiation-type programmable metasurface for low side-lobe beamforming. The wideband programmable metasurface avoids the space-feed external source required by its traditional counterpart, thus achieving a significant reduction of profile through integration of a highefficiency microwave-fed excitation network and metasurface. Furthermore, through introducing space-time-modulated strategy, the high-accuracy amplitude-phase weight algorithm can also be synchronously carried out on the first harmonic component for low side-lobe beam-scanning. Most importantly, adaptive beamforming and generation of interference null can further be created after analyzing the harmonic component characteristics of received signals.