pwc-archive/papers-with-abstracts huggingface dataset:
Advancements in Radiomics and Artificial Intelligence for Thyroid Cancer Diagnosis
Authors: Milad Yousefi, Shadi Farabi Maleki, Ali Jafarizadeh, Mahya Ahmadpour Youshanlui, Aida Jafari, Siamak Pedrammehr, Roohallah Alizadehsani, Ryszard Tadeusiewicz, Pawel Plawiak
Date: 2024-04-09 00:00:00
Tasks: Articles, Diagnostic

Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2023. A combination of keywords led to the discovery of an English academic publication on thyroid cancer and related subjects. 267 papers were returned from the original search after 109 duplicates were removed. Relevant studies were selected according to predetermined criteria after 124 articles were eliminated based on an examination of their abstract and title. After the comprehensive analysis, an additional six studies were excluded. Among the 28 included studies, radiomics analysis, which incorporates ultrasound (US) images, demonstrated its effectiveness in diagnosing thyroid cancer. Various results were noted, some of the studies presenting new strategies that outperformed the status quo. The literature has emphasized various challenges faced by AI models, including interpretability issues, dataset constraints, and operator dependence. The synthesized findings of the 28 included studies mentioned the need for standardization efforts and prospective multicenter studies to address these concerns. Furthermore, approaches to overcome these obstacles were identified, such as advances in explainable AI technology and personalized medicine techniques. The review focuses on how AI and radiomics could transform the diagnosis and treatment of thyroid cancer. Despite challenges, future research on multidisciplinary cooperation, clinical applicability validation, and algorithm improvement holds the potential to improve patient outcomes and diagnostic precision in the treatment of thyroid cancer.
Coexistence of Pull and Push Communication in Wireless Access for IoT Devices
Authors: Sara Cavallero, Fabio Saggese, Junya Shiraishi, Shashi Raj Pandey, Chiara Buratti, Petar Popovski
Date: 2024-04-11 00:00:00

We consider a setup with Internet of Things (IoT), where a base station (BS) collects data from nodes that use two different communication modes. The first is pull-based, where the BS retrieves the data from specific nodes through queries. In addition, the nodes that apply pull-based communication contain a wake-up receiver: upon a query, the BS sends wake-up signal (WuS) to activate the corresponding devices equipped with wake-up receiver (WuDs). The second one is push-based communication, in which the nodes decide when to send to the BS. Consider a time-slotted model, where the time slots in each frame are shared for both pull-based and push-based communications. Therein, this coexistence scenario gives rise to a new type of problem with fundamental trade-offs in sharing communication resources: the objective to serve a maximum number of queries, within a specified deadline, limits the transmission opportunities for push sensors, and vice versa. This work develops a mathematical model that characterizes these trade-offs, validates them through simulations, and optimizes the frame design to meet the objectives of both the pull- and push-based communications.
Novel Active Sensing and Inference for mmWave Beam Alignment Using Single RF Chain Systems
Authors: Rohan R. Pote, Bhaskar D. Rao
Date: 2024-04-11 00:00:00

We propose a novel sensing approach for the beam alignment problem in millimeter wave systems using a single Radio Frequency (RF) chain. Conventionally, beam alignment using a single phased array involves comparing beamformer output power across different spatial regions. This incurs large training overhead due to the need to perform the beam scan operation. The proposed Synthesis of Virtual Array Manifold (SVAM) sensing methodology is inspired from synthetic aperture radar systems and realizes a virtual array geometry over temporal measurements. We demonstrate the benefits of SVAM using Cram\'er-Rao bound (CRB) analysis over schemes that repeat beam pattern to boost signal-to-noise (SNR) ratio. We also showcase versatile applicability of the proposed SVAM sensing by incorporating it within existing beam alignment procedures that assume perfect knowledge of the small-scale fading coefficient. We further consider the practical scenario wherein we estimate the fading coefficient and propose a novel beam alignment procedure based on efficient computation of an approximate posterior density on dominant path angle. We provide numerical experiments to study the impact of parameters involved in the procedure. The performance of the proposed sensing and beam alignment algorithm is empirically observed to approach the fading coefficient-perfectly known performance, even at low SNR.
Revisiting Data Recovery Loops in 6G Networks
Authors: Uyoata E. Uyoata, Abolfazl Amiri, Enric Juan, Guillermo Pocovi, Pilar Andres-Maldonado, Klaus I. Pedersen, Troels Kolding
Date: 2024-04-11 00:00:00

Mechanisms for data recovery and packet reliability are essential components of the upcoming 6th generation (6G) communication system. In this paper, we evaluate the interaction between a fast hybrid automatic repeat request (HARQ) scheme, present in the physical and medium access control layers, and a higher layer automatic repeat request (ARQ) scheme which may be present in the radio link control layer. Through extensive system-level simulations, we show that despite its higher complexity, a fast HARQ scheme yields > 66 % downlink average user throughput gains over simpler solutions without energy combining gains and orders of magnitude larger gains for users in challenging radio conditions. We present results for the design trade-off between HARQ and higher-layer data recovery mechanisms in the presence of realistic control and data channel errors, network delays, and transport protocols. We derive that, with a suitable design of 6G control and data channels reaching residual errors at the medium access control layer of 5 E-5 or better, a higher layer data recovery mechanism can be disabled. We then derive design targets for 6G control channel design, as well as promising enhancements to 6G higher layer data recovery to extend support for latency-intolerant services.
Integrated Sensing and Communication Under DISCO Physical-Layer Jamming Attacks
Authors: Huan Huang, Hongliang Zhang, Weidong Mei, Jun Li, Yi Cai, A. Lee Swindlehurst, Zhu Han
Date: 2024-04-11 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) systems traditionally presuppose that sensing and communication (S&C) channels remain approximately constant during their coherence time. However, a "DISCO" reconfigurable intelligent surface (DRIS), i.e., an illegitimate RIS with random, time-varying reflection properties that acts like a "disco ball," introduces a paradigm shift that enables active channel aging more rapidly during the channel coherence time. In this letter, we investigate the impact of DISCO jamming attacks launched by a DRISbased fully-passive jammer (FPJ) on an ISAC system. Specifically, an ISAC problem formulation and a corresponding waveform optimization are presented in which the ISAC waveform design considers the trade-off between the S&C performance and is formulated as a Pareto optimization problem. Moreover, a theoretical analysis is conducted to quantify the impact of DISCO jamming attacks. Numerical results are presented to evaluate the S&C performance under DISCO jamming attacks and to validate the derived theoretical analysis.
Cramer-Rao Bounds for Near-Field Sensing: A Generic Modular Architecture
Authors: Chunwei Meng, Dingyou Ma, Xu Chen, Zhiyong Feng, Yuanwei Liu
Date: 2024-04-11 00:00:00

A generic modular array architecture is proposed, featuring uniform/non-uniform subarray layouts that allows for flexible deployment. The bistatic near-field sensing system is considered, where the target is located in the near-field of the whole modular array and the far-field of each subarray. Then, the closed-form expressions of Cramer-Rao bounds (CRBs) for range and angle estimations are derived based on the hybrid spherical and planar wave model (HSPM). Simulation results validate the accuracy of the derived closed-form CRBs and demonstrate that: i) The HSPM with varying angles of arrival (AoAs) between subarrays can reduce the CRB for range estimation compared to the traditional HSPM with shared AoA; and ii) The proposed generic modular architecture with subarrays positioned closer to the edges can significantly reduce the CRBs compared to the traditional modular architecture with uniform subarray layout, when the array aperture is fixed.
Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
Authors: Xi Chen, Julien Cumin, Fano Ramparany, Dominique Vaufreydaz
Date: 2024-04-10 00:00:00
Tasks: Activity Recognition, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home. The first model, Seq2Res, uses a sequence generation approach to separate sensor events from different residents. The second model, BiGRU+Q2L, uses a Query2Label multi-label classifier to predict multiple activities simultaneously. Performances of these models are compared to a state-of-the-art model in different experimental scenarios, using a state-of-the-art dataset of two residents in a home instrumented with ambient sensors. These results lead to a discussion on the advantages and drawbacks of resident separation and multi-label classification for multi-person activity recognition.
Interval-valued fuzzy soft $β$-covering approximation spaces
Authors: Shizhan Lu
Date: 2024-04-03 00:00:00

The concept of interval-valued fuzzy soft $\beta$-covering approximation spaces (IFS$\beta$CASs) is introduced to combine the theories of soft sets, rough sets and interval-valued fuzzy sets, and some fundamental propositions concerning interval-valued fuzzy soft $\beta$-neighborhoods and soft $\beta$-neighborhoods of IFS$\beta$CASs are explored. And then four kinds of interval-valued fuzzy soft $\beta$-coverings based fuzzy rough sets are researched. Finally, the relationships of four kinds of interval-valued fuzzy soft $\beta$-coverings based fuzzy rough sets are investigated.
A New Route for the Determination of Protein Structure and Function
Authors: S. H. Mejias, A. L. Cortajarena, R. Mincigrucci, C. Svetina, C. Masciovecchio
Date: 2024-04-11 00:00:00

Understanding complex biological macromolecules, especially proteins, is vital for grasping their diverse chemical functions with direct impact in biology and pharmacology. While techniques like X-ray crystallography and cryo-electron microscopy have been valuable, they face limitations such as radiation damage and difficulties in crystallizing certain proteins. X-ray free-electron lasers (XFELs) offer promising solutions with their ultrafast, high-intensity pulses, potentially enabling structural determination before radiation damage occurs. However, challenges like low signal-to-noise ratio persist, particularly for single protein molecules. To address this, we propose a new method involving engineered protein scaffolds to create ordered arrays of proteins with controlled orientations, aiming at enhancing the signal at the detector. This innovative strategy has the potential to address signal limitations and protein crystallization issues, opening avenues for determining protein structures under physiological conditions. Moreover, it holds promise for studying conformational changes resulting from photo-induced changes, protein-drug and/or protein-protein interactions. Indeed, the prediction of protein-protein interactions, fundamental to numerous biochemical and cellular processes, and the time-dependent conformational changes they undergo, continue to pose a considerable challenge in biology and biochemistry.
The morphology of cell spheroids in simple shear flow
Authors: Rosalia Ferraro, Jasmin Di Franco, Sergio Caserta, Stefano Guido
Date: 2024-04-11 00:00:00

Cell spheroids are a widely used model to investigate cell-cell and cell-matrix interactions in a 3D microenvironment in vitro. Most research on cell spheroids has been focused on their response to various stimuli under static conditions. Recently, the effect of flow on cell spheroids has been investigated in the context of tumor invasion in interstitial space. In particular, microfluidic perfusion of cell spheroids embedded in a collagen matrix has been shown to modulate cell-cell adhesion and to represent a possible mechanism promoting tumor invasion by interstitial flow. However, studies on the effects of well-defined flow fields on cell spheroids are lacking in the literature. Here, we apply simple shear flow to cell spheroids in a parallel plate apparatus while observing their morphology by optical microscopy. By using image analysis techniques, we show that cell spheroids rotate under flow as rigid particles. As time goes on, cells from the outer layer detach from the sheared cell spheroids and are carried away by the flow. Hence, the size of cell spheroids declines with time at a rate increasing with the external shear stress, which can be used to estimate cell-cell adhesion. The technique proposed in this work allows one to correlate flow-induced effects with microscopy imaging of cell spheroids in a well-established shear flow field, thus providing a method to obtain quantitative results which are relevant in the general field of mechanobiology.
Assessing Engraftment Following Fecal Microbiota Transplant
Authors: Chloe Herman, Bridget M. Barker, Thais F. Bartelli, Vidhi Chandra, Rosa Krajmalnik-Brown, Mary Jewell, Le Li, Chen Liao, Florencia McAllister, Khemlal Nirmalkar, Joao B. Xavier, J. Gregory Caporaso
Date: 2024-04-10 00:00:00

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn't respond because the engrafted microbiome didn't produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn't engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment. 2) Donated Microbiome Indicator Features tracks donated microbiome features as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified. 3) Temporal Stability examines how resistant post-FMT recipient's microbiomes are to reverting back to their baseline microbiome. Investigated together, these criteria provide a clear assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria, providing illustrative examples of their application. We also introduce key terminology and recommendations on how FMT studies can be analyzed for rigorous engraftment extent assessment.
Enhancing Valuation of Variable Annuities in Lévy Models with Stochastic Interest Rate
Authors: Ludovic Goudenège, Andrea Molent, Xiao Wei, Antonino Zanette
Date: 2024-04-11 00:00:00
Tasks: Decision Making

This paper extends the valuation and optimal surrender framework for variable annuities with guaranteed minimum benefits in a L\'evy equity market environment by incorporating a stochastic interest rate described by the Hull-White model. This approach frames a more dynamic and realistic financial setting compared to previous literature. We exploit a robust valuation mechanism employing a hybrid numerical method that merges tree methods for interest rate modeling with finite difference techniques for the underlying asset price. This method is particularly effective for addressing the complexities of variable annuities, where periodic fees and mortality risks are significant factors. Our findings reveal the influence of stochastic interest rates on the strategic decision-making process concerning the surrender of these financial instruments. Through comprehensive numerical experiments, and by comparing our results with those obtained through the Longstaff-Schwartz Monte Carlo method, we illustrate how our refined model can guide insurers in designing contracts that equitably balance the interests of both parties. This is particularly relevant in discouraging premature surrenders while adapting to the realistic fluctuations of financial markets. Lastly, a comparative statics analysis with varying interest rate parameters underscores the impact of interest rates on the cost of the optimal surrender strategy, emphasizing the importance of accurately modeling stochastic interest rates.
Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets
Authors: Anoop Kumar, Suresh Dodda, Navin Kamuni, Rajeev Kumar Arora
Date: 2024-03-31 00:00:00
Tasks: Causal Inference

This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines Machine Learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double Machine Learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by -11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors
Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling
Authors: Youngbin Lee, Yejin Kim, Javier Sanz-Cruzado, Richard McCreadie, YongJae lee
Date: 2024-03-27 00:00:00
Tasks: Contrastive Learning, Recommendation Systems

Recommender systems can be helpful for individuals to make well-informed decisions in complex financial markets. While many studies have focused on predicting stock prices, even advanced models fall short of accurately forecasting them. Additionally, previous studies indicate that individual investors often disregard established investment theories, favoring their personal preferences instead. This presents a challenge for stock recommendation systems, which must not only provide strong investment performance but also respect these individual preferences. To create effective stock recommender systems, three critical elements must be incorporated: 1) individual preferences, 2) portfolio diversification, and 3) the temporal dynamics of the first two. In response, we propose a new model, Portfolio Temporal Graph Network Recommender PfoTGNRec, which can handle time-varying collaborative signals and incorporates diversification-enhancing sampling. On real-world individual trading data, our approach demonstrates superior performance compared to state-of-the-art baselines, including cutting-edge dynamic embedding models and existing stock recommendation models. Indeed, we show that PfoTGNRec is an effective solution that can balance customer preferences with the need to suggest portfolios with high Return-on-Investment. The source code and data are available at https://github.com/youngandbin/PfoTGNRec.
An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting
Authors: Chufeng Li, Jianyong Chen
Date: 2024-03-25 00:00:00
Tasks: Position, Time Series, Time Series Forecasting

As a branch of time series forecasting, stock movement forecasting is one of the challenging problems for investors and researchers. Since Transformer was introduced to analyze financial data, many researchers have dedicated themselves to forecasting stock movement using Transformer or attention mechanisms. However, existing research mostly focuses on individual stock information but ignores stock market information and high noise in stock data. In this paper, we propose a novel method using the attention mechanism in which both stock market information and individual stock information are considered. Meanwhile, we propose a novel EMD-based algorithm for reducing short-term noise in stock data. Two randomly selected exchange-traded funds (ETFs) spanning over ten years from US stock markets are used to demonstrate the superior performance of the proposed attention-based method. The experimental analysis demonstrates that the proposed attention-based method significantly outperforms other state-of-the-art baselines. Code is available at https://github.com/DurandalLee/ACEFormer.
Uncertainty-guided annotation enhances segmentation with the human-in-the-loop
Authors: Nadieh Khalili, Joey Spronck, Francesco Ciompi, Jeroen van der Laak, Geert Litjens
Date: 2024-02-16 00:00:00
Tasks: Continual Learning

Deep learning algorithms, often critiqued for their 'black box' nature, traditionally fall short in providing the necessary transparency for trusted clinical use. This challenge is particularly evident when such models are deployed in local hospitals, encountering out-of-domain distributions due to varying imaging techniques and patient-specific pathologies. Yet, this limitation offers a unique avenue for continual learning. The Uncertainty-Guided Annotation (UGA) framework introduces a human-in-the-loop approach, enabling AI to convey its uncertainties to clinicians, effectively acting as an automated quality control mechanism. UGA eases this interaction by quantifying uncertainty at the pixel level, thereby revealing the model's limitations and opening the door for clinician-guided corrections. We evaluated UGA on the Camelyon dataset for lymph node metastasis segmentation which revealed that UGA improved the Dice coefficient (DC), from 0.66 to 0.76 by adding 5 patches, and further to 0.84 with 10 patches. To foster broader application and community contribution, we have made our code accessible at
A real-time Artificial Intelligence system for learning Sign Language
Authors: Elisa Cabana
Date: 2024-02-19 00:00:00

A primary challenge for the deaf and hearing-impaired community stems from the communication gap with the hearing society, which can greatly impact their daily lives and result in social exclusion. To foster inclusivity in society, our endeavor focuses on developing a cost-effective, resource-efficient, and open technology based on Artificial Intelligence, designed to assist people in learning and using Sign Language for communication. The analysis presented in this research paper intends to enrich the recent academic scientific literature on Sign Language solutions based on Artificial Intelligence, with a particular focus on American Sign Language (ASL). This research has yielded promising preliminary results and serves as a basis for further development.