pwc-archive/papers-with-abstracts huggingface dataset:
Optical Music Recognition in Manuscripts from the Ricordi Archive
Authors: Federico Simonetta, Rishav Mondal, Luca Andrea Ludovico, Stavros Ntalampiras
Date: 2024-08-14 00:00:00

The Ricordi archive, a prestigious collection of significant musical manuscripts from renowned opera composers such as Donizetti, Verdi and Puccini, has been digitized. This process has allowed us to automatically extract samples that represent various musical elements depicted on the manuscripts, including notes, staves, clefs, erasures, and composer's annotations, among others. To distinguish between digitization noise and actual music elements, a subset of these images was meticulously grouped and labeled by multiple individuals into several classes. After assessing the consistency of the annotations, we trained multiple neural network-based classifiers to differentiate between the identified music elements. The primary objective of this study was to evaluate the reliability of these classifiers, with the ultimate goal of using them for the automatic categorization of the remaining unannotated data set. The dataset, complemented by manual annotations, models, and source code used in these experiments are publicly accessible for replication purposes.
Rank-Guaranteed Auctions
Authors: wei he, Jiangtao Li, Weijie Zhong
Date: 2024-08-21 00:00:00

We propose a combinatorial ascending auction that is "approximately" optimal, requiring minimal rationality to achieve this level of optimality, and is robust to strategic and distributional uncertainties. Specifically, the auction is rank-guaranteed, meaning that for any menu M and any valuation profile, the ex-post revenue is guaranteed to be at least as high as the highest revenue achievable from feasible allocations, taking the (|M|+ 1)th-highest valuation for each bundle as the price. Our analysis highlights a crucial aspect of combinatorial auction design, namely, the design of menus. We provide simple and approximately optimal menus in various settings.
Evaluating S-Band Interference: Impact of Satellite Systems on Terrestrial Networks
Authors: Lingrui Zhang, Zheng Li, Sheng Yang
Date: 2024-08-21 00:00:00

The co-existence of terrestrial and non-terrestrial networks (NTNs) is essential for achieving global coverage in sixth-generation cellular networks. Due to increasing spectrum demand, there is discussion in the world level to share some frequencies used in terrestrial Networks (TNs) with NTNs, resulting in co-channel interference and performance degradation. This paper analyzes the interference caused by satellite networks on TN in the S-band. We examined the transmission mechanisms of satellite signals and conducted simulations to evaluate interference intensity across varying slant ranges. Our findings indicate that the angle between the user equipment direction and the sub-satellite point direction from the beam center significantly impacts the interference level.
Near-Field Signal Processing: Unleashing the Power of Proximity
Authors: Ahmet M. Elbir, Özlem Tuğfe Demir, Kumar Vijay Mishra, Symeon Chatzinotas, Martin Haardt
Date: 2024-08-21 00:00:00

After nearly a century of specialized applications in optics, remote sensing, and acoustics, the near-field (NF) electromagnetic propagation zone is experiencing a resurgence in research interest. This renewed attention is fueled by the emergence of promising applications in various fields such as wireless communications, holography, medical imaging, and quantum-inspired systems. Signal processing within NF sensing and wireless communications environments entails addressing issues related to extended scatterers, range-dependent beampatterns, spherical wavefronts, mutual coupling effects, and the presence of both reactive and radiative fields. Recent investigations have focused on these aspects in the context of extremely large arrays and wide bandwidths, giving rise to novel challenges in channel estimation, beamforming, beam training, sensing, and localization. While NF optics has a longstanding history, advancements in NF phase retrieval techniques and their applications have lately garnered significant research attention. Similarly, utilizing NF localization with acoustic arrays represents a contemporary extension of established principles in NF acoustic array signal processing. This article aims to provide an overview of state-of-the-art signal processing techniques within the NF domain, offering a comprehensive perspective on recent advances in diverse applications.
Privacy Preservation in Delay-Based Localization Systems: Artificial Noise or Artificial Multipath?
Authors: Yuchen Zhang, Hui Chen, Henk Wymeersch
Date: 2024-08-21 00:00:00

Localization plays an increasingly pivotal role in 5G/6G systems, enabling various applications. This paper focuses on the privacy concerns associated with delay-based localization, where unauthorized base stations attempt to infer the location of the end user. We propose a method to disrupt localization at unauthorized nodes by injecting artificial components into the pilot signal, exploiting model mismatches inherent in these nodes. Specifically, we investigate the effectiveness of two techniques, namely artificial multipath (AM) and artificial noise (AN), in mitigating location leakage. By leveraging the misspecified Cram\'er-Rao bound framework, we evaluate the impact of these techniques on unauthorized localization performance. Our results demonstrate that pilot manipulation significantly degrades the accuracy of unauthorized localization while minimally affecting legitimate localization. Moreover, we find that the superiority of AM over AN varies depending on the specific scenario.
Relational Graph Convolutional Networks Do Not Learn Sound Rules
Authors: Matthew Morris, David J. Tena Cucala, Bernardo Cuenca Grau, Ian Horrocks
Date: 2024-08-14 00:00:00
Proceeding: KR 2024 11
Tasks: Knowledge Graphs

Graph neural networks (GNNs) are frequently used to predict missing facts in knowledge graphs (KGs). Motivated by the lack of explainability for the outputs of these models, recent work has aimed to explain their predictions using Datalog, a widely used logic-based formalism. However, such work has been restricted to certain subclasses of GNNs. In this paper, we consider one of the most popular GNN architectures for KGs, R-GCN, and we provide two methods to extract rules that explain its predictions and are sound, in the sense that each fact derived by the rules is also predicted by the GNN, for any input dataset. Furthermore, we provide a method that can verify that certain classes of Datalog rules are not sound for the R-GCN. In our experiments, we train R-GCNs on KG completion benchmarks, and we are able to verify that no Datalog rule is sound for these models, even though the models often obtain high to near-perfect accuracy. This raises some concerns about the ability of R-GCN models to generalise and about the explainability of their predictions. We further provide two variations to the training paradigm of R-GCN that encourage it to learn sound rules and find a trade-off between model accuracy and the number of learned sound rules.
A case study on different one-factor Cheyette models for short maturity caplet calibration
Authors: Arun Kumar Polala, Bernhard Hientzsch
Date: 2024-08-21 00:00:00

In [1], we calibrated a one-factor Cheyette SLV model with a local volatility that is linear in the benchmark forward rate and an uncorrelated CIR stochastic variance to 3M caplets of various maturities. While caplet smiles for many maturities could be reasonably well calibrated across the range of strikes, for instance the 1Y maturity could not be calibrated well across that entire range of strikes. Here, we study whether models with alternative local volatility terms and/or alternative stochastic volatility or variance models can calibrate the 1Y caplet smile better across the strike range better than the model studied in [1]. This is made possible and feasible by the generic simulation, pricing, and calibration frameworks introduced in [1] and some new frameworks presented in this paper. We find that some model settings calibrate well to the 1Y smile across the strike range under study. In particular, a model setting with a local volatility that is piece-wise linear in the benchmark forward rate together with an uncorrelated CIR stochastic variance and one with a local volatility that is linear in the benchmark rate together with a correlated lognormal stochastic volatility with quadratic drift (QDLNSV) as in [2] calibrate well. We discuss why the later might be a preferable model. [1] Arun Kumar Polala and Bernhard Hientzsch. Parametric differential machine learning for pricing and calibration. arXiv preprint arXiv:2302.06682 , 2023. [2] Artur Sepp and Parviz Rakhmonov. A Robust Stochastic Volatility Model for Interest Rate Dynamics. Risk Magazine, 2023
Star-shaped Tilted Hexarotor Maneuverability: Analysis of the Role of the Tilt Cant Angles
Authors: Marco Perin, Massimiliano Bertoni, Nicolas Viezzer, Giulia Michieletto, Angelo Cenedese
Date: 2024-08-22 00:00:00

Star-shaped Tilted Hexarotors are rapidly emerging for applications highly demanding in terms of robustness and maneuverability. To ensure improvement in such features, a careful selection of the tilt angles is mandatory. In this work, we present a rigorous analysis of how the force subspace varies with the tilt cant angles, namely the tilt angles along the vehicle arms, taking into account gravity compensation and torque decoupling to abide by the hovering condition. Novel metrics are introduced to assess the performance of existing tilted platforms, as well as to provide some guidelines for the selection of the tilt cant angle in the design phase.
Diffusion Model for Planning: A Systematic Literature Review
Authors: Toshihide Ubukata, Jialong Li, Kenji Tei
Date: 2024-08-16 00:00:00
Tasks: Autonomous Driving, Denoising, model, Systematic Literature Review

Diffusion models, which leverage stochastic processes to capture complex data distributions effectively, have shown their performance as generative models, achieving notable success in image-related tasks through iterative denoising processes. Recently, diffusion models have been further applied and show their strong abilities in planning tasks, leading to a significant growth in related publications since 2023. To help researchers better understand the field and promote the development of the field, we conduct a systematic literature review of recent advancements in the application of diffusion models for planning. Specifically, this paper categorizes and discusses the current literature from the following perspectives: (i) relevant datasets and benchmarks used for evaluating diffusion modelbased planning; (ii) fundamental studies that address aspects such as sampling efficiency; (iii) skill-centric and condition-guided planning for enhancing adaptability; (iv) safety and uncertainty managing mechanism for enhancing safety and robustness; and (v) domain-specific application such as autonomous driving. Finally, given the above literature review, we further discuss the challenges and future directions in this field.
LCM-SVC: Latent Diffusion Model Based Singing Voice Conversion with Inference Acceleration via Latent Consistency Distillation
Authors: Shihao Chen, Yu Gu, Jianwei Cui, Jie Zhang, Rilin Chen, LiRong Dai
Date: 2024-08-22 00:00:00
Tasks: Voice Conversion

Any-to-any singing voice conversion (SVC) aims to transfer a target singer's timbre to other songs using a short voice sample. However many diffusion model based any-to-any SVC methods, which have achieved impressive results, usually suffered from low efficiency caused by a mass of inference steps. In this paper, we propose LCM-SVC, a latent consistency distillation (LCD) based latent diffusion model (LDM) to accelerate inference speed. We achieved one-step or few-step inference while maintaining the high performance by distilling a pre-trained LDM based SVC model, which had the advantages of timbre decoupling and sound quality. Experimental results show that our proposed method can significantly reduce the inference time and largely preserve the sound quality and timbre similarity comparing with other state-of-the-art SVC models. Audio samples are available at https://sounddemos.github.io/lcm-svc.
Prosody of speech production in latent post-stroke aphasia
Authors: Cong Zhang, Tong Li, Gayle DeDe, Christos Salis
Date: 2024-08-21 00:00:00
Tasks: Diagnostic

This study explores prosodic production in latent aphasia, a mild form of aphasia associated with left-hemisphere brain damage (e.g. stroke). Unlike prior research on moderate to severe aphasia, we investigated latent aphasia, which can seem to have very similar speech production with neurotypical speech. We analysed the f0, intensity and duration of utterance-initial and utterance-final words of ten speakers with latent aphasia and ten matching controls. Regression models were fitted to improve our understanding of this understudied type of very mild aphasia. The results highlighted varying degrees of differences in all three prosodic measures between groups. We also investigated the diagnostic classification of latent aphasia versus neurotypical control using random forest, aiming to build a fast and reliable tool to assist with the identification of latent aphasia. The random forest analysis also reinforced the significance of prosodic features in distinguishing latent aphasia.
ASASVIcomtech: The Vicomtech-UGR Speech Deepfake Detection and SASV Systems for the ASVspoof5 Challenge
Authors: Juan M. Martín-Doñas, Eros Roselló, Angel M. Gomez, Aitor Álvarez, Iván López-Espejo, Antonio M. Peinado
Date: 2024-08-19 00:00:00
Tasks: DeepFake Detection, Face Swapping, Speaker Verification

This paper presents the work carried out by the ASASVIcomtech team, made up of researchers from Vicomtech and University of Granada, for the ASVspoof5 Challenge. The team has participated in both Track 1 (speech deepfake detection) and Track 2 (spoofing-aware speaker verification). This work started with an analysis of the challenge available data, which was regarded as an essential step to avoid later potential biases of the trained models, and whose main conclusions are presented here. With respect to the proposed approaches, a closed-condition system employing a deep complex convolutional recurrent architecture was developed for Track 1, although, unfortunately, no noteworthy results were achieved. On the other hand, different possibilities of open-condition systems, based on leveraging self-supervised models, augmented training data from previous challenges, and novel vocoders, were explored for both tracks, finally achieving very competitive results with an ensemble system.
Generating Streamlining Constraints with Large Language Models
Authors: Florentina Voboril, Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider
Date: 2024-08-16 00:00:00
Tasks: Memorization

Streamlining constraints (or streamliners, for short) narrow the search space, enhancing the speed and feasibility of solving complex constraint satisfaction problems. Traditionally, streamliners were crafted manually or generated through systematically combined atomic constraints with high-effort offline testing. Our approach utilizes the creativity of Large Language Models (LLMs) to propose effective streamliners for problems specified in the MiniZinc constraint programming language and integrates feedback to the LLM with quick empirical tests for validation. Evaluated across seven diverse constraint satisfaction problems, our method achieves substantial runtime reductions. We compare the results to obfuscated and disguised variants of the problem to see whether the results depend on LLM memorization. We also analyze whether longer off-line runs improve the quality of streamliners and whether the LLM can propose good combinations of streamliners.
Balancing Innovation and Ethics in AI-Driven Software Development
Authors: Mohammad Baqar
Date: 2024-08-10 00:00:00
Tasks: Ethics

This paper critically examines the ethical implications of integrating AI tools like GitHub Copilot and ChatGPT into the software development process. It explores issues such as code ownership, bias, accountability, privacy, and the potential impact on the job market. While these AI tools offer significant benefits in terms of productivity and efficiency, they also introduce complex ethical challenges. The paper argues that addressing these challenges is essential to ensuring that AI's integration into software development is both responsible and beneficial to society
Dynamic Code Orchestration: Harnessing the Power of Large Language Models for Adaptive Script Execution
Authors: Justin Del Vecchio, Andrew Perreault, Eliana Furmanek
Date: 2024-08-07 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Computer programming initially required humans to directly translate their goals into machine code. These goals could have easily been expressed as a written (or human) language directive. Computers, however, had no capacity to satisfactorily interpret written language. Large language model's provide exactly this capability; automatic generation of computer programs or even assembly code from written language directives. This research examines dynamic code execution of written language directives within the context of a running application. It implements a text editor whose business logic is purely backed by large language model prompts. That is, the program's execution uses prompts and written language directives to dynamically generate application logic at the point in time it is needed. The research clearly shows how written language directives, backed by a large language model, offer radically new programming and operating system paradigms. For example, empowerment of users to directly implement requirements via written language directives, thus supplanting the need for a team ofprogrammers, a release schedule and the like. Or, new security mechanisms where static executables, always a target for reverse engineering or fuzzing, no longer exist. They are replaced by ephemeral executables that may continually change, be completely removed, and are easily updated.
Controllability and Observability of Temporal Hypergraphs
Authors: Anqi Dong, Xin Mao, Can Chen
Date: 2024-08-22 00:00:00

Numerous complex systems, such as those arisen in ecological networks, genomic contact networks, and social networks, exhibit higher-order and time-varying characteristics, which can be effectively modeled using temporal hypergraphs. However, analyzing and controlling temporal hypergraphs poses significant challenges due to their inherent time-varying and nonlinear nature, while most existing methods predominantly target static hypergraphs. In this article, we generalize the notions of controllability and observability to temporal hypergraphs by leveraging tensor and nonlinear systems theory. Specifically, we establish tensor-based rank conditions to determine the weak controllability and observability of temporal hypergraphs. The proposed framework is further demonstrated with synthetic and real-world examples.
Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid
Authors: Samrat Acharya, Malini Ghosal, Travis Thurber, Ying Zhang, Casey D. Burleyson, Nathalie Voisin
Date: 2024-08-22 00:00:00

The electrification of transportation is critical to mitigate Greenhouse Gas (GHG) emissions. The United States (U.S.) government's Inflation Reduction Act (IRA) of 2022 introduces policies to promote the electrification of transportation. In addition to electrifying transportation, clean energy technologies such as Carbon Capture and Storage (CCS) may play a major role in achieving a net-zero energy system. Utilizing scenarios simulated by the U.S. version of the Global Change Analysis Model (GCAM-USA), we analyze the individual and compound contributions of the IRA and CCS to reach a clean U.S. grid by 2035 and net-zero GHG emissions by 2050. We analyze the contributions based on three metrics: i) transportation electrification rate, ii) transportation fuel mix, and iii) spatio-temporal charging loads. Our findings indicate that the IRA significantly accelerates transportation electrification in the near-term (until 2035). In contrast, CCS technologies, by enabling the continued use of internal combustion vehicles while still advancing torward net-zero, potentially suppresses the rate of transportation electrification in the long-term. This study underscores how policy and technology innovation can interact and sensitivity studies with different combination are essential to characterize the potential contributions of each to the transportation electrification.
A Note on an Upper-Bound for the Sum of a Class K and an Extended Class K Function
Authors: Adrian Wiltz, Dimos V. Dimarogonas
Date: 2024-08-22 00:00:00

In this short note, we derive an upper-bound for the sum of two comparison functions, namely for the sum of a class K and an extended class K function. To the best of our knowledge, the relations derived in this note have not been previously derived in the literature.
Data-driven MPC with terminal conditions in the Koopman framework
Authors: Karl Worthmann, Robin Strässer, Manuel Schaller, Julian Berberich, Frank Allgöwer
Date: 2024-08-22 00:00:00
Tasks: Model Predictive Control

We investigate nonlinear model predictive control (MPC) with terminal conditions in the Koopman framework using extended dynamic mode decomposition (EDMD) to generate a data-based surrogate model for prediction and optimization. We rigorously show recursive feasibility and prove practical asymptotic stability w.r.t. the approximation accuracy. To this end, finite-data error bounds are employed. The construction of the terminal conditions is based on recently derived proportional error bounds to ensure the required Lyapunov decrease. Finally, we illustrate the effectiveness of the proposed data-driven predictive controller including the design procedure to construct the terminal region and controller.
The Hybrid Hospital: Balancing On-Site and Remote Hospitalization
Authors: Noa Zychlinski, Gal Mendelson, Andrew Daw
Date: 2024-08-22 00:00:00

Hybrid hospitals offer on-site and remote hospitalization through telemedicine. These new healthcare models require novel operational policies to balance costs, efficiency, and patient well-being. Our study addresses two first-order questions: (i) how to direct patient admission and call-in based on individual characteristics and proximity and (ii) how to determine the optimal allocation of medical resources between these two hospitalization options and among different patient types. We develop a model that uses Brownian Motion to capture the patient's health evolution during remote/on-site hospitalization and during travel. Under cost-minimizing call-in policies, we find that remote hospitalization can be cost-effective for moderately distant patients, as the optimal call-in threshold is non-monotonic in the patient's travel time. Subject to scarce resources, the optimal solution structure becomes equivalent to a simultaneous, identically sized increase of remote and on-site costs under abundant resources. When limited resources must be divided among multiple patient types, the optimal thresholds shift in non-obvious ways as resource availability changes. Finally, we develop a practical and efficient policy that allows for swapping an on-site patient with a remote patient when the latter is called-in and sufficient resources are not available to treat both on-site. Contrary to the widely held view that telemedicine can mitigate rural and non-rural healthcare disparities, our research suggests that on-site care may actually be more cost-effective than remote hospitalization for patients in distant locations, due to (potentially overlooked) risks during patient travel. This finding may be of particular concern in light of the growing number of ``hospital deserts'' amid recent rural hospital closures, as these communities may in fact not be well-served through at-home care.