pwc-archive/papers-with-abstracts huggingface dataset:
Roughness Signature Functions
Authors: Peter Christensen
Date: 2024-01-05 00:00:00

Inspired by the activity signature introduced by Todorov and Tauchen (2010), which was used to measure the activity of a semimartingale, this paper introduces the roughness signature function. The paper illustrates how it can be used to determine whether a discretely observed process is generated by a continuous process that is rougher than a Brownian motion, a pure-jump process, or a combination of the two. Further, if a continuous rough process is present, the function gives an estimate of the roughness index. This is done through an extensive simulation study, where we find that the roughness signature function works as expected on rough processes. We further derive some asymptotic properties of this new signature function. The function is applied empirically to three different volatility measures for the S&P500 index. The three measures are realized volatility, the VIX, and the option-extracted volatility estimator of Todorov (2019). The realized volatility and option-extracted volatility show signs of roughness, with the option-extracted volatility appearing smoother than the realized volatility, while the VIX appears to be driven by a continuous martingale with jumps.
Cuánto es demasiada inflación? Una clasificación de regímenes inflacionarios
Authors: Manuel de Mier, Fernando Delbianco
Date: 2023-11-10 00:00:00

The classifications of inflationary regimes proposed in the literature have mostly been based on arbitrary characterizations, subject to value judgments by researchers. The objective of this study is to propose a new methodological approach that reduces subjectivity and improves accuracy in the construction of such regimes. The method is built upon a combination of clustering techniques and classification trees, which allows for an historical periodization of Argentina's inflationary history for the period 1943-2022. Additionally, two procedures are introduced to smooth out the classification over time: a measure of temporal contiguity of observations and a rolling method based on the simple majority rule. The obtained regimes are compared against the existing literature on the inflation-relative price variability relationship, revealing a better performance of the proposed regimes.
Changing Simplistic Worldviews
Authors: Maxim Senkov, Toygar T. Kerman
Date: 2024-01-05 00:00:00

We study a Bayesian persuasion model with two-dimensional states of the world, in which the sender (she) and receiver (he) have heterogeneous prior beliefs and care about different dimensions. The receiver is a naive agent who has a simplistic worldview: he ignores the dependency between the two dimensions of the state. We provide a characterization for the sender's gain from persuasion both when the receiver is naive and when he is rational. We show that the receiver benefits from having a simplistic worldview if and only if it makes him perceive the states in which his interest is aligned with the sender as less likely.
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks
Authors: Kevin Everson, Yile Gu, Huck Yang, Prashanth Gurunath Shivakumar, Guan-Ting Lin, Jari Kolehmainen, Ivan Bulyko, Ankur Gandhe, Shalini Ghosh, Wael Hamza, Hung-Yi Lee, Ariya Rastrow, Andreas Stolcke
Date: 2024-01-05 00:00:00
Tasks: In-Context Learning, intent-classification, Intent Classification, Natural Language Understanding, Question Answering, speech-recognition, Speech Recognition, Spoken Language Understanding

In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text. In real-world scenarios, prior to input into an LLM, an automated speech recognition (ASR) system generates an output transcript hypothesis, where inherent errors can degrade subsequent SLU tasks. Here we introduce a method that utilizes the ASR system's lattice output instead of relying solely on the top hypothesis, aiming to encapsulate speech ambiguities and enhance SLU outcomes. Our in-context learning experiments, covering spoken question answering and intent classification, underline the LLM's resilience to noisy speech transcripts with the help of word confusion networks from lattices, bridging the SLU performance gap between using the top ASR hypothesis and an oracle upper bound. Additionally, we delve into the LLM's robustness to varying ASR performance conditions and scrutinize the aspects of in-context learning which prove the most influential.
Pheme: Efficient and Conversational Speech Generation
Authors: Paweł Budzianowski, Taras Sereda, Tomasz Cichy, Ivan Vulić
Date: 2024-01-05 00:00:00

In recent years, speech generation has seen remarkable progress, now achieving one-shot generation capability that is often virtually indistinguishable from real human voice. Integrating such advancements in speech generation with large language models might revolutionize a wide range of applications. However, certain applications, such as assistive conversational systems, require natural and conversational speech generation tools that also operate efficiently in real time. Current state-of-the-art models like VALL-E and SoundStorm, powered by hierarchical neural audio codecs, require large neural components and extensive training data to work well. In contrast, MQTTS aims to build more compact conversational TTS models while capitalizing on smaller-scale real-life conversational speech data. However, its autoregressive nature yields high inference latency and thus limits its real-time usage. In order to mitigate the current limitations of the state-of-the-art TTS models while capitalizing on their strengths, in this work we introduce the Pheme model series that 1) offers compact yet high-performing models, 2) allows for parallel speech generation of 3) natural conversational speech, and 4) it can be trained efficiently on smaller-scale conversational data, cutting data demands by more than 10x but still matching the quality of the autoregressive TTS models. We also show that through simple teacher-student distillation we can meet significant improvements in voice quality for single-speaker setups on top of pretrained Pheme checkpoints, relying solely on synthetic speech generated by much larger teacher models. Audio samples and pretrained models are available online.
MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multi-scale Dilated Convolution for Image Compressive Sensing (CS)
Authors: Youhao Yu, Richard M. Dansereau
Date: 2024-01-05 00:00:00
Tasks: Compressive Sensing, Diversity

Compressive sensing (CS) is a technique that enables the recovery of sparse signals using fewer measurements than traditional sampling methods. To address the computational challenges of CS reconstruction, our objective is to develop an interpretable and concise neural network model for reconstructing natural images using CS. We achieve this by mapping one step of the iterative shrinkage thresholding algorithm (ISTA) to a deep network block, representing one iteration of ISTA. To enhance learning ability and incorporate structural diversity, we integrate aggregated residual transformations (ResNeXt) and squeeze-and-excitation (SE) mechanisms into the ISTA block. This block serves as a deep equilibrium layer, connected to a semi-tensor product network (STP-Net) for convenient sampling and providing an initial reconstruction. The resulting model, called MsDC-DEQ-Net, exhibits competitive performance compared to state-of-the-art network-based methods. It significantly reduces storage requirements compared to deep unrolling methods, using only one iteration block instead of multiple iterations. Unlike deep unrolling models, MsDC-DEQ-Net can be iteratively used, gradually improving reconstruction accuracy while considering computation trade-offs. Additionally, the model benefits from multi-scale dilated convolutions, further enhancing performance.
Two-stage Progressive Residual Dense Attention Network for Image Denoising
Authors: Wencong Wu, An Ge, Guannan Lv, Yuelong Xia, Yungang Zhang, Wen Xiong
Date: 2024-01-05 00:00:00
Tasks: Denoising, Image Denoising

Deep convolutional neural networks (CNNs) for image denoising can effectively exploit rich hierarchical features and have achieved great success. However, many deep CNN-based denoising models equally utilize the hierarchical features of noisy images without paying attention to the more important and useful features, leading to relatively low performance. To address the issue, we design a new Two-stage Progressive Residual Dense Attention Network (TSP-RDANet) for image denoising, which divides the whole process of denoising into two sub-tasks to remove noise progressively. Two different attention mechanism-based denoising networks are designed for the two sequential sub-tasks: the residual dense attention module (RDAM) is designed for the first stage, and the hybrid dilated residual dense attention module (HDRDAM) is proposed for the second stage. The proposed attention modules are able to learn appropriate local features through dense connection between different convolutional layers, and the irrelevant features can also be suppressed. The two sub-networks are then connected by a long skip connection to retain the shallow feature to enhance the denoising performance. The experiments on seven benchmark datasets have verified that compared with many state-of-the-art methods, the proposed TSP-RDANet can obtain favorable results both on synthetic and real noisy image denoising. The code of our TSP-RDANet is available at https://github.com/WenCongWu/TSP-RDANet.
Subjective and Objective Analysis of Indian Social Media Video Quality
Authors: Sandeep Mishra, Mukul Jha, Alan C. Bovik
Date: 2024-01-05 00:00:00
Tasks: Mixture-of-Experts, Visual Question Answering (VQA)

We conducted a large-scale subjective study of the perceptual quality of User-Generated Mobile Video Content on a set of mobile-originated videos obtained from the Indian social media platform ShareChat. The content viewed by volunteer human subjects under controlled laboratory conditions has the benefit of culturally diversifying the existing corpus of User-Generated Content (UGC) video quality datasets. There is a great need for large and diverse UGC-VQA datasets, given the explosive global growth of the visual internet and social media platforms. This is particularly true in regard to videos obtained by smartphones, especially in rapidly emerging economies like India. ShareChat provides a safe and cultural community oriented space for users to generate and share content in their preferred Indian languages and dialects. Our subjective quality study, which is based on this data, offers a boost of cultural, visual, and language diversification to the video quality research community. We expect that this new data resource will also allow for the development of systems that can predict the perceived visual quality of Indian social media videos, to control scaling and compression protocols for streaming, provide better user recommendations, and guide content analysis and processing. We demonstrate the value of the new data resource by conducting a study of leading blind video quality models on it, including a new model, called MoEVA, which deploys a mixture of experts to predict video quality. Both the new LIVE-ShareChat dataset and sample source code for MoEVA are being made freely available to the research community at https://github.com/sandeep-sm/LIVE-SC
Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment Prediction
Authors: Manoj S H, Arya A Bosale
Date: 2024-01-05 00:00:00
Tasks: Data Augmentation, Diagnostic, Medical Image Analysis, Transfer Learning

This research paper addresses the critical challenge of diabetic retinopathy (DR), a severe complication of diabetes leading to potential blindness. The proposed methodology leverages transfer learning with convolutional neural networks (CNNs) for automatic DR detection using a single fundus photograph, demonstrating high effectiveness with a quadratic weighted kappa score of 0.92546 in the APTOS 2019 Blindness Detection Competition. The paper reviews existing literature on DR detection, spanning classical computer vision methods to deep learning approaches, particularly focusing on CNNs. It identifies gaps in the research, emphasizing the lack of exploration in integrating pretrained large language models with segmented image inputs for generating recommendations and understanding dynamic interactions within a web application context.Objectives include developing a comprehensive DR detection methodology, exploring model integration, evaluating performance through competition ranking, contributing significantly to DR detection methodologies, and identifying research gaps.The methodology involves data preprocessing, data augmentation, and the use of a U-Net neural network architecture for segmentation. The U-Net model efficiently segments retinal structures, including blood vessels, hard and soft exudates, haemorrhages, microaneurysms, and the optical disc. High evaluation scores in Jaccard, F1, recall, precision, and accuracy underscore the model's potential for enhancing diagnostic capabilities in retinal pathology assessment.The outcomes of this research hold promise for improving patient outcomes through timely diagnosis and intervention in the fight against diabetic retinopathy, marking a significant contribution to the field of medical image analysis.
PAHD: Perception-Action based Human Decision Making using Explainable Graph Neural Networks on SAR Images
Authors: Sasindu Wijeratne, Bingyi Zhang, Rajgopal Kannan, Viktor Prasanna, Carl Busart
Date: 2024-01-05 00:00:00
Tasks: Decision Making, Object

Synthetic Aperture Radar (SAR) images are commonly utilized in military applications for automatic target recognition (ATR). Machine learning (ML) methods, such as Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), are frequently used to identify ground-based objects, including battle tanks, personnel carriers, and missile launchers. Determining the vehicle class, such as the BRDM2 tank, BMP2 tank, BTR60 tank, and BTR70 tank, is crucial, as it can help determine whether the target object is an ally or an enemy. While the ML algorithm provides feedback on the recognized target, the final decision is left to the commanding officers. Therefore, providing detailed information alongside the identified target can significantly impact their actions. This detailed information includes the SAR image features that contributed to the classification, the classification confidence, and the probability of the identified object being classified as a different object type or class. We propose a GNN-based ATR framework that provides the final classified class and outputs the detailed information mentioned above. This is the first study to provide a detailed analysis of the classification class, making final decisions more straightforward. Moreover, our GNN framework achieves an overall accuracy of 99.2\% when evaluated on the MSTAR dataset, improving over previous state-of-the-art GNN methods.
Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging
Authors: Poojitha Thota, Jai Prakash Veerla, Partha Sai Guttikonda, Mohammad S. Nasr, Shirin Nilizadeh, Jacob M. Luber
Date: 2024-01-04 00:00:00
Tasks: Adversarial Attack, Domain Adaptation, Language Modeling, Language Modelling

In the context of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks. Leveraging the Kather Colon dataset with 7,180 H&E images across nine tissue types, our investigation employs Projected Gradient Descent (PGD) adversarial perturbation attacks to induce misclassifications intentionally. The outcomes reveal a 100% success rate in manipulating PLIP's predictions, underscoring its susceptibility to adversarial perturbations. The qualitative analysis of adversarial examples delves into the interpretability challenges, shedding light on nuanced changes in predictions induced by adversarial manipulations. These findings contribute crucial insights into the interpretability, domain adaptation, and trustworthiness of Vision Language Models in medical imaging. The study emphasizes the pressing need for robust defenses to ensure the reliability of AI models. The source codes for this experiment can be found at https://github.com/jaiprakash1824/VLM_Adv_Attack.
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy
Authors: Pegah Ahadian, Maryam Babaei, Kourosh Parand
Date: 2024-01-04 00:00:00
Tasks: Brain Tumor Segmentation, Tumor Segmentation

A brain tumor consists of cells showing abnormal brain growth. The area of the brain tumor significantly affects choosing the type of treatment and following the course of the disease during the treatment. At the same time, pictures of Brain MRIs are accompanied by noise. Eliminating existing noises can significantly impact the better segmentation and diagnosis of brain tumors. In this work, we have tried using the analysis of eigenvalues. We have used the MSVD algorithm, reducing the image noise and then using the deep neural network to segment the tumor in the images. The proposed method's accuracy was increased by 2.4% compared to using the original images. With Using the MSVD method, convergence speed has also increased, showing the proposed method's effectiveness
Novel End-to-End Production-Ready Machine Learning Flow for Nanolithography Modeling and Correction
Authors: Mohamed S. E. Habib, Hossam A. H. Fahmy, Mohamed F. Abu-ElYazeed
Date: 2024-01-04 00:00:00

Optical lithography is the main enabler to semiconductor manufacturing. It requires extensive processing to perform the Resolution Enhancement Techniques (RETs) required to transfer the design data to a working Integrated Circuits (ICs). The processing power and computational runtime for RETs tasks is ever increasing due to the continuous reduction of the feature size and the expansion of the chip area. State-of-the-art research sought Machine Learning (ML) technologies to reduce runtime and computational power, however they are still not used in production yet. In this study, we analyze the reasons holding back ML computational lithography from being production ready and present a novel highly scalable end-to-end flow that enables production ready ML-RET correction.
Digital Prototype Filter Alternatives for Processing Frequency-Stacked Mobile Subbands Deploying a Single ADC for Beamforming Satellites
Authors: Adem Coskun, Sevket Cetinsel, Izzet Kale, Robert Hughes, Piero Angeletti, Christoph Ernst
Date: 2024-01-05 00:00:00

This article presents a two-stage approach for the processing of frequency-stacked mobile subbands. The frequency stacking is performed in the analog domain to enable the use of a wideband analog-to-digital converter (ADC), instead of employing multiple narrowband ADCs, to support multiple antenna elements for digital satellite beamforming. This analog front end provides a common broadband digital interface to the on-board processor and can be configured to support multiple satellite missions, reducing the cost of commissioning a digital processor for individual satellite missions. This article proposes a framework on the specification of digital prototype filter for the analysis of frequency-stacked mobile subbands. The computational complexity of the analysis operation, with two digital filter alternatives, are evaluated. A series of results, taken from our European Space Agency sponsored project, are presented here to demonstrate the applicability of the proposed two stage approach, reporting on the savings in power consumption when an Nth-band all-pass-based recursive filter having an infinite impulse response is used as the digital prototype filter.
H2G2-Net: A Hierarchical Heterogeneous Graph Generative Network Framework for Discovery of Multi-Modal Physiological Responses
Authors: Haidong Gu, Nathan Gaw, Yinan Wang, Chancellor Johnstone, Christine Beauchene, Sophia Yuditskaya, Hrishikesh Rao, Chun-An Chou
Date: 2024-01-05 00:00:00

Discovering human cognitive and emotional states using multi-modal physiological signals draws attention across various research applications. Physiological responses of the human body are influenced by human cognition and commonly used to analyze cognitive states. From a network science perspective, the interactions of these heterogeneous physiological modalities in a graph structure may provide insightful information to support prediction of cognitive states. However, there is no clue to derive exact connectivity between heterogeneous modalities and there exists a hierarchical structure of sub-modalities. Existing graph neural networks are designed to learn on non-hierarchical homogeneous graphs with pre-defined graph structures; they failed to learn from hierarchical, multi-modal physiological data without a pre-defined graph structure. To this end, we propose a hierarchical heterogeneous graph generative network (H2G2-Net) that automatically learns a graph structure without domain knowledge, as well as a powerful representation on the hierarchical heterogeneous graph in an end-to-end fashion. We validate the proposed method on the CogPilot dataset that consists of multi-modal physiological signals. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art GNNs by 5%-20% in prediction accuracy.
Tackling Electrode Shift In Gesture Recognition with HD-EMG Electrode Subsets
Authors: Joao Pereira, Dimitrios Chalatsis, Balint Hodossy, Dario Farina
Date: 2024-01-05 00:00:00
Tasks: Gesture Recognition

sEMG pattern recognition algorithms have been explored extensively in decoding movement intent, yet are known to be vulnerable to changing recording conditions, exhibiting significant drops in performance across subjects, and even across sessions. Multi-channel surface EMG, also referred to as high-density sEMG (HD-sEMG) systems, have been used to improve performance with the information collected through the use of additional electrodes. However, a lack of robustness is ever present due to limited datasets and the difficulties in addressing sources of variability, such as electrode placement. In this study, we propose training on a collection of input channel subsets and augmenting our training distribution with data from different electrode locations, simultaneously targeting electrode shift and reducing input dimensionality. Our method increases robustness against electrode shift and results in significantly higher intersession performance across subjects and classification algorithms.
A Physics-Driven AI Approach for Microwave Imaging of Breast Tumors
Authors: Francesco Zardi, Luca Tosi, Marco Salucci, Andrea Massa
Date: 2024-01-05 00:00:00
Tasks: global-optimization

In this paper, an innovative microwave imaging (MI) approach for breast tumor diagnosis is proposed that employs a differential formulation of the inverse scattering problem (ISP) at hand to exploit arbitrary-fidelity priors on the inhomogeneous reference/healthy tissues. The quantitative imaging of the unknown tumor is then rephrased into a global optimization problem, which is efficiently solved with an ad-hoc physics-driven artificial intelligence (AI) strategy inspired by the concepts and guidelines of the System-by-Design (SbD) paradigm. The effectiveness, the robustness, the reliability, and the efficiency of the proposed method are assessed against both synthetic and experimental data.