pwc-archive/papers-with-abstracts huggingface dataset:
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network
Authors: Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez, David González-Ortega
Date: 2024-02-04 00:00:00
Tasks: Brain Tumor Classification, Brain Tumor Segmentation, Tumor Segmentation

In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. One of the differences of our proposal with respect to previous works is that input images are processed in three spatial scales along different processing pathways. This mechanism is inspired in the inherent operation of the Human Visual System. The proposed neural model can analyze MRI images containing three types of tumors: meningioma, glioma, and pituitary tumor, over sagittal, coronal, and axial views and does not need preprocessing of input images to remove skull or vertebral column parts in advance. The performance of our method on a publicly available MRI image dataset of 3064 slices from 233 patients is compared with previously classical machine learning and deep learning published methods. In the comparison, our method remarkably obtained a tumor classification accuracy of 0.973, higher than the other approaches using the same database.
Accelerating Innovation in 6G Research: Real-Time Capable SDR System Architecture for Rapid Prototyping
Authors: Maximilian Engelhardt, Sebastian Giehl, Michael Schubert, Alexander Ihlow, Christian Schneider, Alexander Ebert, Markus Landmann, Giovanni Del Galdo, Carsten Andrich
Date: 2024-02-09 00:00:00
Tasks: Integrated sensing and communication, ISAC

The upcoming 3GPP global mobile communication standard 6G strives to push the technological limits of radio frequency (RF) communication even further than its predecessors: Sum data rates beyond 100 Gbit/s, RF bandwidths above 1 GHz per link, and sub-millisecond latency necessitate very high performance development tools. We propose a new SDR firmware and software architecture designed explicitly to meet these challenging requirements. It relies on Ethernet and commercial off-the-shelf network and server components to maximize flexibility and to reduce costs. We analyze state-of-the-art solutions (USRP X440 and other RFSoC-based systems), derive architectural design goals, explain resulting design decision in detail, and exemplify our architecture's implementation on the XCZU48DR RFSoC. Finally, we validate its performance via measurements and outline how the architecture surpasses the state-of-the-art with respect to sustained RF recording, while maintaining high Ethernet bandwidth efficiency. Building a 6G integrated sensing and communication (ISAC) example, we demonstrate its real-time and rapid application development capabilities.
A Statistical Model of Bursty Mixed Gaussian-impulsive Noise: Model and Parameter Estimation
Authors: Tianfu Qi, Jun Wang
Date: 2024-02-09 00:00:00
Tasks: model, parameter estimation

Non-Gaussian impulsive noise (IN) with memory exists in many practical applications. When it is mixed with white Gaussian noise (WGN), the resultant mixed noise will be bursty. The performance of communication systems will degrade significantly under bursty mixed noise if the bursty characteristic is ignored. A proper model for the bursty mixed noise and corresponding algorithms needs to be designed to obtain desirable performance but there is no such model reported to the best of our knowledge. The important problem is addressed in the two-part paper. In the first part, we propose a closed-form heavy-tailed multivariate probability density function (PDF) that to model the bursty mixed noise. This model is the weighted addition of gaussian distribution and student distribution. Then, we present the parameter estimation method based on the empirical characteristic function of the proposed model and analyze the performance of the parameter estimation. Numerical results show that our proposed bursty mixed noise model matches the measured bursty noise well. Meanwhile, the parameters of the proposed noise model can be accurately estimated in terms of mean square error (MSE).
Can 5G Coexist with Satellite Uplink in 28 GHz Band?
Authors: Bryce Jeffrey, Seungmo Kim
Date: 2024-02-09 00:00:00

5G standalone (SA) rollout is right around the corner. 28 GHz band is considered as one of the main spectrum bands for the 5G SA in many countries. However, the band has already been occupied by uplink of the fixed satellite service (FSS). Due to high equivalent isotropic radiated power (EIRP) adopted by the FSS, the interference that FSS may cause into 5G is garnering research interest. This research aims to establish an analytical framework that quantifies the FSS-to-5G interference.
Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) Framework in UAV Networks
Authors: Sana Hafeez, Lina Mohjazi, Muhammad Ali Imran, Yao Sun
Date: 2024-02-07 00:00:00
Tasks: Federated Learning, Graph Clustering

Privacy, scalability, and reliability are significant challenges in unmanned aerial vehicle (UAV) networks as distributed systems, especially when employing machine learning (ML) technologies with substantial data exchange. Recently, the application of federated learning (FL) to UAV networks has improved collaboration, privacy, resilience, and adaptability, making it a promising framework for UAV applications. However, implementing FL for UAV networks introduces drawbacks such as communication overhead, synchronization issues, scalability limitations, and resource constraints. To address these challenges, this paper presents the Blockchain-enabled Clustered and Scalable Federated Learning (BCS-FL) framework for UAV networks. This improves the decentralization, coordination, scalability, and efficiency of FL in large-scale UAV networks. The framework partitions UAV networks into separate clusters, coordinated by cluster head UAVs (CHs), to establish a connected graph. Clustering enables efficient coordination of updates to the ML model. Additionally, hybrid inter-cluster and intra-cluster model aggregation schemes generate the global model after each training round, improving collaboration and knowledge sharing among clusters. The numerical findings illustrate the achievement of convergence while also emphasizing the trade-offs between the effectiveness of training and communication efficiency.
Hybrid Neural Representations for Spherical Data
Authors: Hyomin Kim, Yunhui Jang, Jaeho Lee, Sungsoo Ahn
Date: 2024-02-05 00:00:00
Tasks: Super-Resolution

In this paper, we study hybrid neural representations for spherical data, a domain of increasing relevance in scientific research. In particular, our work focuses on weather and climate data as well as comic microwave background (CMB) data. Although previous studies have delved into coordinate-based neural representations for spherical signals, they often fail to capture the intricate details of highly nonlinear signals. To address this limitation, we introduce a novel approach named Hybrid Neural Representations for Spherical data (HNeR-S). Our main idea is to use spherical feature-grids to obtain positional features which are combined with a multilayer perception to predict the target signal. We consider feature-grids with equirectangular and hierarchical equal area isolatitude pixelization structures that align with weather data and CMB data, respectively. We extensively verify the effectiveness of our HNeR-S for regression, super-resolution, temporal interpolation, and compression tasks.
Phase-driven Domain Generalizable Learning for Nonstationary Time Series
Authors: Payal Mohapatra, Lixu Wang, Qi Zhu
Date: 2024-02-05 00:00:00
Tasks: Activity Recognition, Gesture Recognition, Human Activity Recognition, Time Series, Time Series Classification

Monitoring and recognizing patterns in continuous sensing data is crucial for many practical applications. These real-world time-series data are often nonstationary, characterized by varying statistical and spectral properties over time. This poses a significant challenge in developing learning models that can effectively generalize across different distributions. In this work, based on our observation that nonstationary statistics are intrinsically linked to the phase information, we propose a time-series learning framework, PhASER. It consists of three novel elements: 1) phase augmentation that diversifies non-stationarity while preserving discriminatory semantics, 2) separate feature encoding by viewing time-varying magnitude and phase as independent modalities, and 3) feature broadcasting by incorporating phase with a novel residual connection for inherent regularization to enhance distribution invariant learning. Upon extensive evaluation on 5 datasets from human activity recognition, sleep-stage classification, and gesture recognition against 10 state-of-the-art baseline methods, we demonstrate that PhASER consistently outperforms the best baselines by an average of 5% and up to 13% in some cases. Moreover, PhASER's principles can be applied broadly to boost the generalization ability of existing time series classification models.
A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures
Authors: Mario Martínez-Zarzuela, David González-Ortega, Míriam Antón-Rodríguez, Francisco Javier Díaz-Pernas, Henning Müller, Cristina Simón-Martínez
Date: 2024-02-04 00:00:00
Tasks: Activity Recognition

The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings. Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab settings, it is necessary to use a reduced number of devices for movement acquisition. Promising solutions in this context are IMU-based wearables and single camera systems. Additionally, the development of machine learning systems able to recognize and digest clinically relevant data in-the-wild is needed, and therefore determining the ideal input to those is crucial.
PULSAR Effect: Revealing Potential Synergies in Combined Radiation Therapy and Immunotherapy via Differential Equations
Authors: Samiha Rouf, Casey Moore, Debabrata Saha, Dan Nguyen, MaryLena Bleile, Robert Timmerman, Hao Peng, Steve Jiang
Date: 2024-02-08 00:00:00

PULSAR (personalized ultrafractionated stereotactic adaptive radiotherapy) is a form of radiotherapy method where a patient is given a large dose or pulse of radiation a couple of weeks apart rather than daily small doses. The tumor response is then monitored to determine when the subsequent pulse should be given. Pre-clinical trials have shown better tumor response in mice that received immunotherapy along with pulses spaced 10 days apart. However, this was not the case when the pulses were 1 day apart. Therefore, a synergistic effect between immunotherapy and PULSAR is observed when the pulses are spaced out by a certain number of days. In our study, we aimed to develop a mathematical model that can capture the synergistic effect by considering a time-dependent weight function that takes into account the spacing between pulses. By determining feasible parameters, and applying reasonable conditions, we utilize our model to simulate murine trials with varying sequencing of pulses. We successfully demonstrate that our model is simple to implement and can generate tumor volume data that is consistent with the pre-clinical trial data. Our model has the potential to aid in the development of clinical trials of PULSAR therapy.
The Almost Sure Evolution of Hierarchy Among Similar Competitors
Authors: Christopher Cebra, Alexander Strang
Date: 2024-02-08 00:00:00

While generic competitive systems exhibit mixtures of hierarchy and cycles, real-world systems are predominantly hierarchical. We demonstrate and extend a mechanism for hierarchy; systems with similar agents approach perfect hierarchy in expectation. A variety of evolutionary mechanisms plausibly select for nearly homogeneous populations, however, extant work does not explicitly link selection dynamics to hierarchy formation via population concentration. Moreover, previous work lacked numerical demonstration. This paper contributes in four ways. First, populations that converge to perfect hierarchy in expectation converge to hierarchy in probability. Second, we analyze hierarchy formation in populations subject to the continuous replicator dynamic with diffusive exploration, linking population dynamics to emergent structure. Third, we show how to predict the degree of cyclicity sustained by concentrated populations at internal equilibria. This theory can differentiate learning rules and random payout models. Finally, we provide direct numerical evidence by simulating finite populations of agents subject to a modified Moran process with Gaussian exploration. As examples, we consider three bimatrix games and an ensemble of games with random payouts. Through this analysis, we explicitly link the temporal dynamics of a population undergoing selection to the development of hierarchy.
A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints
Authors: Tran Anh Tuan, Nguyen Viet Dung, Tran Ngoc Thang
Date: 2024-02-04 00:00:00

Controllable Pareto front learning (CPFL) approximates the Pareto solution set and then locates a Pareto optimal solution with respect to a given reference vector. However, decision-maker objectives were limited to a constraint region in practice, so instead of training on the entire decision space, we only trained on the constraint region. Controllable Pareto front learning with Split Feasibility Constraints (SFC) is a way to find the best Pareto solutions to a split multi-objective optimization problem that meets certain constraints. In the previous study, CPFL used a Hypernetwork model comprising multi-layer perceptron (Hyper-MLP) blocks. With the substantial advancement of transformer architecture in deep learning, transformers can outperform other architectures in various tasks. Therefore, we have developed a hyper-transformer (Hyper-Trans) model for CPFL with SFC. We use the theory of universal approximation for the sequence-to-sequence function to show that the Hyper-Trans model makes MED errors smaller in computational experiments than the Hyper-MLP model.
Zeroth-Order primal-dual Alternating Projection Gradient Algorithms for Nonconvex Minimax Problems with Coupled linear Constraints
Authors: Huiling Zhang, Zi Xu, Yuhong Dai
Date: 2024-01-26 00:00:00

In this paper, we study zeroth-order algorithms for nonconvex minimax problems with coupled linear constraints under the deterministic and stochastic settings, which have attracted wide attention in machine learning, signal processing and many other fields in recent years, e.g., adversarial attacks in resource allocation problems and network flow problems etc. We propose two single-loop algorithms, namely the zeroth-order primal-dual alternating projected gradient (ZO-PDAPG) algorithm and the zeroth-order regularized momentum primal-dual projected gradient algorithm (ZO-RMPDPG), for solving deterministic and stochastic nonconvex-(strongly) concave minimax problems with coupled linear constraints. The iteration complexity of the two proposed algorithms to obtain an $\varepsilon$-stationary point are proved to be $\mathcal{O}(\varepsilon ^{-2})$ (resp. $\mathcal{O}(\varepsilon ^{-4})$) for solving nonconvex-strongly concave (resp. nonconvex-concave) minimax problems with coupled linear constraints under deterministic settings and $\tilde{\mathcal{O}}(\varepsilon ^{-3})$ (resp. $\tilde{\mathcal{O}}(\varepsilon ^{-6.5})$) under stochastic settings respectively. To the best of our knowledge, they are the first two zeroth-order algorithms with iterative complexity guarantees for solving nonconvex-(strongly) concave minimax problems with coupled linear constraints under the deterministic and stochastic settings.
Development of an updated, comprehensive food composition database for Australian-grown horticultural commodities
Authors: Eleanor Dunlop, Judy Cunningham, Paul Adorno, Shari Fatupaito, Stuart K Johnson, Lucinda J Black
Date: 2024-02-09 00:00:00

Australian agriculture supplies many horticultural commodities to domestic and international markets; however, food composition data for many commodities are outdated or unavailable. We produced an up-to-date, nationally representative dataset of up to 148 nutrients and related components in 92 Australian-grown fruit (fresh n=39, dried n=6), vegetables (n=43) and nuts (n=4) by replacing outdated data (pre-2000), confirming concentrations of important nutrients and retaining relevant existing data. Primary samples (n = 902) were purchased during peak growing season in Sydney, Melbourne and Perth between June 2021 and May 2022. While new data reflect current growing practices, varieties, climate and analytical methods, few notable differences were found between old and new data where methods of analysis are comparable. The new data will be incorporated into the Australian Food Composition Database, allowing free online access to stakeholders. The approach used could serve as a model for cost-effective updates of national food composition databases worldwide.
First explore, then settle: a theoretical analysis of evolvability as a driver of adaptation
Authors: Juan Jiménez-Sánchez, Carmen Ortega-Sabater, Philip K. Maini, Víctor M. Pérez-García, Tommaso Lorenzi
Date: 2024-02-09 00:00:00

Evolvability is defined as the ability of a population to generate heritable variation to facilitate its adaptation to new environments or selection pressures. In this article, we consider evolvability as a phenotypic trait subject to evolution and discuss its implications in the adaptation of populations of asexual individuals. We explore the evolutionary dynamics of an actively proliferating population of individuals, subject to changes in their proliferative potential and their evolvability, through mathematical simulations of a stochastic individual-based model and its deterministic continuum counterpart. We find robust adaptive trajectories that rely on individuals with high evolvability rapidly exploring the phenotypic landscape and reaching the proliferative potential with the highest fitness. The strength of selection on the proliferative potential, and the cost associated with evolvability, can alter these trajectories such that, if both are sufficiently constraining, highly evolvable populations can become extinct in our individual-based model simulations. We explore the impact of this interaction at various scales, discussing its effects in undisturbed environments and also in disrupted contexts, such as cancer.
Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk
Authors: Katerina Rigana, Ernst C. Wit, Samantha Cook
Date: 2024-02-08 00:00:00
Tasks: Causal Inference, Management

Accurately defining, measuring and mitigating risk is a cornerstone of financial risk management, especially in the presence of financial contagion. Traditional correlation-based risk assessment methods often struggle under volatile market conditions, particularly in the face of external shocks, highlighting the need for a more robust and invariant predictive approach. This paper introduces the Causal Network Contagion Value at Risk (Causal-NECO VaR), a novel methodology that significantly advances causal inference in financial risk analysis. Embracing a causal network framework, this method adeptly captures and analyses volatility and spillover effects, effectively setting it apart from conventional contagion-based VaR models. Causal-NECO VaR's key innovation lies in its ability to derive directional influences among assets from observational data, thereby offering robust risk predictions that remain invariant to market shocks and systemic changes. A comprehensive simulation study and the application to the Forex market show the robustness of the method. Causal-NECO VaR not only demonstrates predictive accuracy, but also maintains its reliability in unstable financial environments, offering clearer risk assessments even amidst unforeseen market disturbances. This research makes a significant contribution to the field of risk management and financial stability, presenting a causal approach to the computation of VaR. It emphasises the model's superior resilience and invariant predictive power, essential for navigating the complexities of today's ever-evolving financial markets.
Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study
Authors: Yufei Li, Simin Chen, Yanghong Guo, Wei Yang, Yue Dong, Cong Liu
Date: 2024-01-12 00:00:00

Large Language Models (LLMs) have been widely employed in programming language analysis to enhance human productivity. Yet, their reliability can be compromised by various code distribution shifts, leading to inconsistent outputs. While probabilistic methods are known to mitigate such impact through uncertainty calibration and estimation, their efficacy in the language domain remains underexplored compared to their application in image-based tasks. In this work, we first introduce a large-scale benchmark dataset, incorporating three realistic patterns of code distribution shifts at varying intensities. Then we thoroughly investigate state-of-the-art probabilistic methods applied to CodeLlama using these shifted code snippets. We observe that these methods generally improve the uncertainty awareness of CodeLlama, with increased calibration quality and higher uncertainty estimation~(UE) precision. However, our study further reveals varied performance dynamics across different criteria (e.g., calibration error vs misclassification detection) and trade-off between efficacy and efficiency, highlighting necessary methodological selection tailored to specific contexts.
DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks
Authors: Jianing He, Qi Zhang, Weiping Ding, Duoqian Miao, Jun Zhao, Liang Hu, Longbing Cao
Date: 2024-02-03 00:00:00

Early exiting has demonstrated its effectiveness in accelerating the inference of pre-trained language models like BERT by dynamically adjusting the number of layers executed. However, most existing early exiting methods only consider local information from an individual test sample to determine their exiting indicators, failing to leverage the global information offered by sample population. This leads to suboptimal estimation of prediction correctness, resulting in erroneous exiting decisions. To bridge the gap, we explore the necessity of effectively combining both local and global information to ensure reliable early exiting during inference. Purposefully, we leverage prototypical networks to learn class prototypes and devise a distance metric between samples and class prototypes. This enables us to utilize global information for estimating the correctness of early predictions. On this basis, we propose a novel Distance-Enhanced Early Exiting framework for BERT (DE$^3$-BERT). DE$^3$-BERT implements a hybrid exiting strategy that supplements classic entropy-based local information with distance-based global information to enhance the estimation of prediction correctness for more reliable early exiting decisions. Extensive experiments on the GLUE benchmark demonstrate that DE$^3$-BERT consistently outperforms state-of-the-art models under different speed-up ratios with minimal storage or computational overhead, yielding a better trade-off between model performance and inference efficiency. Additionally, an in-depth analysis further validates the generality and interpretability of our method.