pwc-archive/papers-with-abstracts huggingface dataset:
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
Authors: Kangkang Lu, Yanhua Yu, Hao Fei, Xuan Li, Zixuan Yang, Zirui Guo, Meiyu Liang, Mengran Yin, Tat-Seng Chua
Date: 2024-01-28 00:00:00
Tasks: Node Classification

In recent years, spectral graph neural networks, characterized by polynomial filters, have garnered increasing attention and have achieved remarkable performance in tasks such as node classification. These models typically assume that eigenvalues for the normalized Laplacian matrix are distinct from each other, thus expecting a polynomial filter to have a high fitting ability. However, this paper empirically observes that normalized Laplacian matrices frequently possess repeated eigenvalues. Moreover, we theoretically establish that the number of distinguishable eigenvalues plays a pivotal role in determining the expressive power of spectral graph neural networks. In light of this observation, we propose an eigenvalue correction strategy that can free polynomial filters from the constraints of repeated eigenvalue inputs. Concretely, the proposed eigenvalue correction strategy enhances the uniform distribution of eigenvalues, thus mitigating repeated eigenvalues, and improving the fitting capacity and expressive power of polynomial filters. Extensive experimental results on both synthetic and real-world datasets demonstrate the superiority of our method. The code is available at: https://github.com/Lukangkang123/EC-GNN
MunTTS: A Text-to-Speech System for Mundari
Authors: Varun Gumma, Rishav Hada, Aditya Yadavalli, Pamir Gogoi, Ishani Mondal, Vivek Seshadri, Kalika Bali
Date: 2024-01-28 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family. Our work addresses the gap in linguistic technology for underrepresented languages by collecting and processing data to build a speech synthesis system. We begin our study by gathering a substantial dataset of Mundari text and speech and train end-to-end speech models. We also delve into the methods used for training our models, ensuring they are efficient and effective despite the data constraints. We evaluate our system with native speakers and objective metrics, demonstrating its potential as a tool for preserving and promoting the Mundari language in the digital age.
The McCormick martingale optimal transport
Authors: Erhan Bayraktar, Bingyan Han, Dominykas Norgilas
Date: 2024-01-28 00:00:00

Martingale optimal transport (MOT) often yields broad price bounds for options, constraining their practical applicability. In this study, we extend MOT by incorporating causality constraints among assets, inspired by the nonanticipativity condition of stochastic processes. This, however, introduces a computationally challenging bilinear program. To tackle this issue, we propose McCormick relaxations to ease the bicausal formulation and refer to it as McCormick MOT. The primal attainment and strong duality of McCormick MOT are established under standard assumptions. Empirically, we apply McCormick MOT to basket and digital options. With natural bounds on probability masses, the average price reduction for basket options is approximately 1.08% to 3.90%. When tighter probability bounds are available, the reduction increases to 12.26%, compared to the classic MOT, which also incorporates tighter bounds. For most dates considered, there are basket options with suitable payoffs, where the price reduction exceeds 10.00%. For digital options, McCormick MOT results in an average price reduction of over 20.00%, with the best case exceeding 99.00%.
Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance
Authors: Adrian Theuma, Ehsan Shareghi
Date: 2024-01-27 00:00:00
Tasks: Hallucination, Language Modeling, Language Modelling, Question Answering

Large language models (LLMs) have exhibited an array of reasoning capabilities but face challenges like error propagation and hallucination, particularly in specialised areas like finance, where data is heterogeneous, and precision is paramount. We explore the potential of language model augmentation with external tools to mitigate these limitations and offload certain reasoning steps to external tools that are more suited for the task, instead of solely depending on the LLM's inherent abilities. More concretely, using financial domain question-answering datasets, we apply supervised fine-tuning on a LLaMA-2 13B Chat model to act both as a 'task router' and 'task solver'. The 'task router' dynamically directs a question to either be answered internally by the LLM or externally via the right tool from the tool set. Our tool-equipped SFT model, Raven, demonstrates an improvement of 35.2% and 5.06% over the base model and SFT-only baselines, respectively, and is highly competitive with strong GPT-3.5 results. To the best of our knowledge, our work is the first that investigates tool augmentation of language models for the finance domain.
Could AI change the scientific publishing market once and for all?
Authors: Wadim Strielkowski
Date: 2024-01-26 00:00:00
Tasks: All

Artificial-intelligence tools in research like ChatGPT are playing an increasingly transformative role in revolutionizing scientific publishing and re-shaping its economic background. They can help academics to tackle such issues as limited space in academic journals, accessibility of knowledge, delayed dissemination, or the exponential growth of academic output. Moreover, AI tools could potentially change scientific communication and academic publishing market as we know them. They can help to promote Open Access (OA) in the form of preprints, dethrone the entrenched journals and publishers, as well as introduce novel approaches to the assessment of research output. It is also imperative that they should do just that, once and for all.
ISP pricing and Platform pricing interaction under net neutrality
Authors: Luis Guijarro, Vicent Pla, Jose Ramon Vidal
Date: 2024-01-26 00:00:00

We analyze the effects of enforcing vs. exempting access ISP from net neutrality regulations when platforms are present and operate two-sided pricing in their business models. This study is conducted in a scenario where users and Content Providers (CPs) have access to the internet by means of their serving ISPs and to a platform that intermediates and matches users and CPs, among other service offerings. Our hypothesis is that platform two-sided pricing interacts in a relevant manner with the access ISP, which may be allowed (an hypothetical non-neutrality scenario) or not (the current neutrality regulation status) to apply two-sided pricing on its service business model. We preliminarily conclude that the platforms are extracting surplus from the CPs under the current net neutrality regime for the ISP, and that the platforms would not be able to do so under the counter-factual situation where the ISPs could apply two-sided prices.
Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room
Authors: Alan Pawlak, Hyunkook Lee, Aki Mäkivirta, Thomas Lund
Date: 2024-01-26 00:00:00

Parametric sound field synthesis methods, such as the Spatial Decomposition Method (SDM) and Higher-Order Spatial Impulse Response Rendering (HO-SIRR), are widely used for the analysis and auralization of sound fields. This paper studies the performances of various sound field synthesis methods in the context of the auralization of a critical listening room. The influence on the perceived spatial and timbral fidelity of the following factors is considered: the rendering framework, direction of arrival (DOA) estimation method, microphone array structure, and use of a dedicated center reference microphone with SDM. Listening tests compare the synthesized sound fields to a reference binaural rendering condition. Several acoustic parameters are measured to gain insights into objective differences between methods. A high-quality pressure microphone improves the SDM framework's timbral fidelity. Additionally, SDM and HO-SIRR show similarities in spatial fidelity. Performance variation between SDM configurations is influenced by the DOA estimation method and microphone array construction. The binaural SDM (BSDM) presentations display temporal artifacts impacting sound quality.
Chaos-Based Bitwise Dynamical Pseudorandom Number Generator on FPGA
Authors: Miguel Garcia-Bosque, Adrián Pérez-Resa, Carlos Sánchez-Azqueta, Concepción Aldea, Santiago Celma
Date: 2024-01-26 00:00:00

In this paper, a new pseudorandom number generator (PRNG) based on the logistic map has been proposed. To prevent the system to fall into short period orbits as well as increasing the randomness of the generated sequences, the proposed algorithm dynamically changes the parameters of the chaotic system. This PRNG has been implemented in a Virtex 7 field-programmable gate array (FPGA) with a 32-bit fixed point precision, using a total of 510 lookup tables (LUTs) and 120 registers. The sequences generated by the proposed algorithm have been subjected to the National Institute of Standards and Technology (NIST) randomness tests, passing all of them. By comparing the randomness with the sequences generated by a raw 32-bit logistic map, it is shown that, by using only an additional 16% of LUTs, the proposed PRNG obtains a much better performance in terms of randomness, increasing the NIST passing rate from 0.252 to 0.989. Finally, the proposed bitwise dynamical PRNG is compared with other chaos-based realizations previously proposed, showing great improvement in terms of resources and randomness.
Modulating function based algebraic observer coupled with stable output predictor for LTV and sampled-data systems
Authors: Matti Noack, Ibrahima N'Doye, Johann Reger, Taous-Meriem Laleg-Kirati
Date: 2024-01-26 00:00:00
Tasks: State Estimation

This paper proposes an algebraic observer-based modulating function approach for linear time-variant systems and a class of nonlinear systems with discrete measurements. The underlying idea lies in constructing an observability transformation that infers some properties of the modulating function approach for designing such algebraic observers. First, we investigate the algebraic observer design for linear time-variant systems under an observable canonical form for continuous-time measurements. Then, we provide the convergence of the observation error in an L2-gain stability sense. Next, we develop an exponentially stable sampled-data observer which relies on the design of the algebraic observer and an output predictor to achieve state estimation from available measurements and under small inter-sampling periods. Using a trajectory-based approach, we prove the convergence of the observation error within a convergence rate that can be adjusted through the fixed time-horizon length of the modulating function and the upper bound of the sampling period. Furthermore, robustness of the sampled-data algebraic observer, which yields input-to-state stability, is inherited by the modulating kernel and the closed-loop output predictor design. Finally, we discuss the implementation procedure of the MF-based observer realization, demonstrate the applicability of the algebraic observer, and illustrate its performance through two examples given by linear time-invariant and linear time-variant systems with nonlinear input-output injection terms.
Renewable energy exporting consumption-oriented transfer limit switching control: A unsupervised learning-based method
Authors: Gao Qiu, Haojin Peng, Youbo Liu, Tingjian Liu, Junyong Liu
Date: 2024-01-26 00:00:00

A method for generating unsupervised conditional mapping rules for multi-inter-corridor transfer limits and their integration into unit commitment through banding-switching is proposed in this paper. The method starts by using Ant colony clustering(ACC) to identify different operating modes with renewable energy penetration. For each sub-pattern, coupling inter-corridors are determined using correlation coefficients. An algorithm for constructing coupled inter-corridors' limits boundaries, employing grid partitioning, is proposed to establish conditional mappings from sub-patterns to multi-inter-corridor limits. Additionally, a banding matching model is proposed, incorporating distance criteria and the Big-M method. It also includes a limit-switching method based on Lagrange multipliers. Case studies on the IEEE 39-node system illustrate the effectiveness of this method in increasing consumption of renewable energy and reducing operational costs while adhering to stability verification requirements.
A Simulation Preorder for Koopman-like Lifted Control Systems
Authors: Antoine Aspeel, Necmiye Ozay
Date: 2024-01-26 00:00:00
Tasks: Relation

This paper introduces a simulation preorder among lifted systems, a generalization of finite-dimensional Koopman approximations (also known as approximate immersions) to systems with inputs. It is proved that this simulation relation implies the containment of both the open- and closed-loop behaviors. Optimization-based sufficient conditions are derived to verify the simulation relation in two special cases: i) a nonlinear (unlifted) system and an affine lifted system and, ii) two affine lifted systems. Numerical examples demonstrate the approach.
A Stochastic Hybrid Approach to Decentralized Networked Control: Stochastic Network Delays and Poisson Pulsing Attacks
Authors: Dandan Zhang, Xin Jin, Hongye Su
Date: 2024-01-26 00:00:00

By designing the decentralized time-regularized (Zeno-free) event-triggered strategies for the state-feedback control law, this paper considers the stochastic stabilization of a class of networked control systems, where two sources of randomness exist in multiple decentralized networks that operate asynchronously and independently: the communication channels are constrained by the stochastic network delays and also by Poisson pulsing denial-of-service (Pp-DoS) attacks. The time delay in the network denotes the length from a transmission instant to the corresponding update instant, and is supposed to be a continuous random variable subject to certain continuous probability distribution; while the attacks' cardinal number is a discrete random variable supposed to be subject to Poisson distribution, so the inter-attack time, i.e., the time between two consecutive attack instants, is subject to exponential distribution. The considered system is modeled as a stochastic hybrid formalism, where the randomness enters through the jump map into the reset value (the inter-attack time directly related) of each triggered strategy. By only sampling/transmitting state measurements when needed and simultaneously by taking the specific medium access protocols into account, the designed event-triggered strategies are synthesized in a state-based and decentralized form, which are robust (tolerable well) to stochastic network delays, under different tradeoff-conditions between the minimum inter-event times, maximum allowable delays (i.e., potentially tolerable delays) and the frequencies of attacks. Using stochastic hybrid tools to combine attack-active parts with attack-over parts, the designed triggered strategies, if designed well according to the actual system needs, can tolerate (be resilient to) the Pp-DoS attacks and stochastic network delays without jeopardizing the stability and Zeno-freeness.
A comprehensive review of electricity storage applications in island systems
Authors: Georgios N. Psarros, Pantelis A. Dratsas, Stavros A. Papathanassiou
Date: 2024-01-26 00:00:00

Electricity storage is crucial for power systems to achieve higher levels of renewable energy penetration. This is especially significant for non-interconnected island (NII) systems, which are electrically isolated and vulnerable to the fluctuations of intermittent renewable generation. This paper comprehensively reviews existing literature on electricity storage in island systems, documenting relevant storage applications worldwide and emphasizing the role of storage in transitioning NII towards a fossil-fuel-independent electricity sector. On this topic, the literature review indicates that the implementation of storage is a prerequisite for attaining renewable penetration rates of over 50% due to the amplified requirements for system flexibility and renewable energy arbitrage. The analysis also identifies potential storage services and classifies applicable storage architectures for islands. Amongst the available storage designs, two have emerged as particularly important for further investigation; standalone, centrally managed storage stations and storage combined with renewables to form a hybrid plant that operates indivisibly in the market. For each design, the operating principles, remuneration schemes, investment feasibility, and applications discussed in the literature are presented in-depth, while possible implementation barriers are acknowledged. The literature on hybrid power plants mainly focuses on wind-powered pumped-hydro stations. However, recently, PV-powered battery-based hybrid plants have gained momentum due to the decreasing cost of Li-ion technology. On the other hand, standalone storage establishments rely heavily on battery technology and are mainly used to provide flexibility to the island grid. Nevertheless, these investments often suffer from insufficient remunerating frameworks, making it challenging for storage projects to be financially secure.
A Unified KKL-based Interval Observer for Nonlinear Discrete-time Systems
Authors: Thach Ngoc Dinh, Gia Quoc Bao Tran
Date: 2024-01-25 00:00:00

This work proposes an interval observer design for nonlinear discrete-time systems based on the Kazantzis-Kravaris/Luenberger (KKL) paradigm. Our design extends to generic nonlinear systems without any assumption on the structure of its dynamics and output maps. Relying on a transformation putting the system into a target form where an interval observer can be directly designed, we then propose a method to reconstruct the bounds in the original coordinates using the bounds in the target coordinates, thanks to the Lipschitz injectivity of this transformation achieved under Lipschitz distinguishability when the target dynamics have a high enough dimension and are pushed sufficiently fast. An academic example serves to illustrate our methods.
Connection of converters to a low and medium power DC network using an inductor circuit
Authors: Francisco Perez-Hidalgo, Juan-R Heredia-Larrubia, Antonio Ruiz-Gonzalez, Mario Meco-Gutierrez, Francisco Vargas-Merino
Date: 2024-01-25 00:00:00

This letter describes an alternative for the connection of power converters to a direct current network without the installation of a capacitor in the DC-Link. The circuit allows the connection of converters through a coil and avoids short-circuit currents with different instantaneous values of voltage output. A description of the calculation and the choice of components together with a real implemented example in a DC network within Smart City project (Endes Utility) is presented.
Nonlocal Models in Biology and Life Sciences: Sources, Developments, and Applications
Authors: Swadesh Pal, Roderick Melnik
Date: 2024-01-26 00:00:00

Nonlocality is important in realistic mathematical models of physical and biological systems when local models fail to capture the essential dynamics and interactions that occur over a range of distances. This review illustrates different nonlocal mathematical models applied to biology and life sciences. The major focus has been given to sources, developments, and applications of such models. Among other things, a systematic discussion has been provided for the conditions of pattern formations in biological systems of population dynamics. Special attention has also been given to nonlocal interactions on networks, network coupling and integration, including brain dynamics models that provide an important tool to understand neurodegenerative diseases better. In addition, we have discussed nonlocal modelling approaches for cancer stem cells and tumor cells that are widely applied in the cell migration processes, growth, and avascular tumors in any organ. Furthermore, the discussed nonlocal continuum models can go sufficiently smaller scales, including nanotechnology, where classical local models often fail to capture the complexities of nanoscale interactions, applied to build biosensors to sense biomaterial and its concentration. Piezoelectric and other smart materials are among them, and these devices are becoming increasingly important in the digital and physical world that is intrinsically interconnected with biological systems. Additionally, we have reviewed a nonlocal theory of peridynamics, which deals with continuous and discrete media and applies to model the relationship between fracture and healing in cortical bone, tissue growth and shrinkage, and other areas increasingly important in biomedical and bioengineering applications. Finally, we provided a comprehensive summary of emerging trends and highlighted future directions in this rapidly expanding field.
Object-Driven One-Shot Fine-tuning of Text-to-Image Diffusion with Prototypical Embedding
Authors: Jianxiang Lu, Cong Xie, Hui Guo
Date: 2024-01-28 00:00:00
Tasks: Image Generation, Object, Text to Image Generation, Text-to-Image Generation

As large-scale text-to-image generation models have made remarkable progress in the field of text-to-image generation, many fine-tuning methods have been proposed. However, these models often struggle with novel objects, especially with one-shot scenarios. Our proposed method aims to address the challenges of generalizability and fidelity in an object-driven way, using only a single input image and the object-specific regions of interest. To improve generalizability and mitigate overfitting, in our paradigm, a prototypical embedding is initialized based on the object's appearance and its class, before fine-tuning the diffusion model. And during fine-tuning, we propose a class-characterizing regularization to preserve prior knowledge of object classes. To further improve fidelity, we introduce object-specific loss, which can also use to implant multiple objects. Overall, our proposed object-driven method for implanting new objects can integrate seamlessly with existing concepts as well as with high fidelity and generalization. Our method outperforms several existing works. The code will be released.
High-dimensional forecasting with known knowns and known unknowns
Authors: M. Hashem Pesaran, Ron P. Smith
Date: 2024-01-26 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Known Unknowns, Variable Selection

Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1-2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.