pwc-archive/papers-with-abstracts huggingface dataset:
Brain Tumor Radiogenomic Classification
Authors: Amr Mohamed, Mahmoud Rabea, Aya Sameh, Ehab Kamal
Date: 2024-01-11 00:00:00
Tasks: Binary Classification, Classification, valid

The RSNA-MICCAI brain tumor radiogenomic classification challenge aimed to predict MGMT biomarker status in glioblastoma through binary classification on Multi parameter mpMRI scans: T1w, T1wCE, T2w and FLAIR. The dataset is splitted into three main cohorts: training set, validation set which were used during training, and the testing were only used during final evaluation. Images were either in a DICOM format or in Png format. different architectures were used to investigate the problem including the 3D version of Vision Transformer (ViT3D), ResNet50, Xception and EfficientNet-B3. AUC was used as the main evaluation metric and the results showed an advantage for both the ViT3D and the Xception models achieving 0.6015 and 0.61745 respectively on the testing set. compared to other results, our results proved to be valid given the complexity of the task. further improvements can be made through exploring different strategies, different architectures and more diverse datasets.
Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach
Authors: Fatih Ozyurt, Jafar Majidpour, Tarik A. Rashid, Canan Koc
Date: 2024-01-05 00:00:00
Tasks: feature selection, Transfer Learning

Handwritten signature verification poses a formidable challenge in biometrics and document authenticity. The objective is to ascertain the authenticity of a provided handwritten signature, distinguishing between genuine and forged ones. This issue has many applications in sectors such as finance, legal documentation, and security. Currently, the field of computer vision and machine learning has made significant progress in the domain of handwritten signature verification. The outcomes, however, may be enhanced depending on the acquired findings, the structure of the datasets, and the used models. Four stages make up our suggested strategy. First, we collected a large dataset of 12600 images from 420 distinct individuals, and each individual has 30 signatures of a certain kind (All authors signatures are genuine). In the subsequent stage, the best features from each image were extracted using a deep learning model named MobileNetV2. During the feature selection step, three selectors neighborhood component analysis (NCA), Chi2, and mutual info (MI) were used to pull out 200, 300, 400, and 500 features, giving a total of 12 feature vectors. Finally, 12 results have been obtained by applying machine learning techniques such as SVM with kernels (rbf, poly, and linear), KNN, DT, Linear Discriminant Analysis, and Naive Bayes. Without employing feature selection techniques, our suggested offline signature verification achieved a classification accuracy of 91.3%, whereas using the NCA feature selection approach with just 300 features it achieved a classification accuracy of 97.7%. High classification accuracy was achieved using the designed and suggested model, which also has the benefit of being a self-organized framework. Consequently, using the optimum minimally chosen features, the proposed method could identify the best model performance and result validation prediction vectors.
Preoperative Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation Patients through Spectral Organization Analysis of the Surface Fibrillatory Waves
Authors: P. Escribano, J. Rodenas, M. Garcia, M. A. Arias, V. M. Hidalgo, S. Calero, J. J. Rieta, R. Alcaraz
Date: 2024-01-18 00:00:00

Catheter ablation (CA) is a commonly used treatment for persistent atrial fibrillation (AF). Since its medium/long-term success rate remains limited, preoperative prediction of its outcome is gaining clinical interest to optimally select candidates for the procedure. Among predictors based on the surface electrocardiogram, the dominant frequency (DF) and harmonic exponential decay (g) of the fibrillatory waves ( f -waves) have reported promising but clinically insufficient results. Hence, the main goal of this work was to conduct a broader analysis of the f -wave harmonic spectral structure to improve CA outcome prediction through several entropy-based measures computed on different frequency bands. On a database of 151 persistent AF patients under radio-frequency CA and a follow-up of 9 months, the newly introduced parameters discriminated between patients who relapsed to AF and those who maintained SR at about 70%, which was statistically superior to the DF and approximately similar to g. They also provided complementary information to g through different combinations in multivariate models based on lineal discriminant analysis and report classification performance improvement of about 5%. These results suggest that the presence of larger harmonics and a proportionally smaller DF peak is associated with a decreased probability of AF recurrence after CA.
Interference Cancellation for UWA Random Access Data Packet Transmission
Authors: Yuriy Zakharov, Lu Shen, Benjamin Henson, Nils Morozs, Paul D. Mitchell
Date: 2024-01-18 00:00:00

In underwater acoustic (UWA) random access communication networks with multiple users and data packet transmissions, the packet collisions are the main cause of the network performance degradation. The aim of this paper is to investigate interference cancellation (IC) techniques capable of resolving such collisions in a low-complexity modem with single-carrier modulation and single transducer. More specifically, in this modem, the IC is used at multiple stages of the receiver. Firstly, the IC is performed for cancelling the multipath interference to improve the equalization performance in comparison with the linear equalization and Rake combining. Secondly, the IC removes the interference from collided data packets within extracted signal segments after identifying the collisions. Finally, the IC is applied to the received baseband signal to improve the data packet detection. The modem performance is investigated in a lake experiment with intensive multipath channels. The experimental results demonstrate high detection performance of the proposed modem design and show that the proposed IC techniques can significantly improve the throughput of random access UWA networks.
Full-Duplex UWA communication with a two-element transducer
Authors: Lu Shen, Benjamin Henson, Long Shi, Yuriy Zakharov
Date: 2024-01-18 00:00:00

In this work we present a full-duplex (FD) underwater acoustic (UWA) communication system simultaneously transmitting and receiving acoustic signals in the same frequency bandwidth. To simplify the FD hardware, the system exploits a recently designed two-element transducer capable of simultaneously transmitting and receiving signals. The key challenge of implementing an FD system is to cancel the strong self-interference (SI) from the near-end transmitter. By using advanced adaptive filtering algorithms providing high accuracy channel estimates, a high level of SI cancellation can be achieved when the far-end signal is absent. However, the SI channel estimation performance is limited in FD scenarios since the far-end signal acts as an interference. In this paper, we propose an FD UWA communication system which alternates between the SI cancellation and far-end data demodulation. An adaptive Rake combiner with multipath interference cancellation is implemented to improve the demodulation performance in time-varying multipath channels. The performance of the FD UWA system is evaluated in lake experiments. The proposed adaptive Rake combiner with multipath interference cancellation significantly outperforms the conventional Rake combiner in all the experiments. With the new Rake combiner, the detection performance of the proposed FD UWA system is comparable with that of the half-duplex system.
Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
Authors: Mohammad Farsi, Christian Häger, Magnus Karlsson, Erik Agrell
Date: 2024-01-18 00:00:00

We consider the problem of recovering spatially resolved polarization information from receiver Jones matrices. We introduce a physics-based learning approach, improving noise resilience compared to previous inverse scattering methods, while highlighting challenges related to model overparameterization.
Distributed Task-Oriented Communication Networks with Multimodal Semantic Relay and Edge Intelligence
Authors: Jie Guo, Hao Chen, Bin Song, Yuhao Chi, Chau Yuen, Fei Richard Yu, Geoffrey Ye Li, Dusit Niyato
Date: 2024-01-18 00:00:00

In this article, we present a novel framework, named distributed task-oriented communication networks (DTCN), based on recent advances in multimodal semantic transmission and edge intelligence. In DTCN, the multimodal knowledge of semantic relays and the adaptive adjustment capability of edge intelligence can be integrated to improve task performance. Specifically, we propose the key techniques in the framework, such as semantic alignment and complement, a semantic relay scheme for deep joint source-channel relay coding, and collaborative device-server optimization and inference. Furthermore, a multimodal classification task is used as an example to demonstrate the benefits of the proposed DTCN over existing methods. Numerical results validate that DTCN can significantly improve the accuracy of classification tasks, even in harsh communication scenarios (e.g., low signal-to-noise regime), thanks to multimodal semantic relay and edge intelligence.
Optimizing Visible Light Communication Efficiency Through Reinforcement Learning-Based NOMA-CSK Integration
Authors: Serkan Vela, Gokce Hacioglu
Date: 2024-01-18 00:00:00

In this paper, we explore the use of Non-Orthogonal Multiple Access (NOMA) and Color Shift Keying (CSK) for Visible Light Communication (VLC) systems. VLC is a wireless communication technology that uses visible light as the carrier signal to transmit information. It has several advantages over traditional radio frequency communication, including higher bandwidth, lower interference, and greater security. We first provide an introduction to NOMA and CSK and explain how they can be applied to VLC systems. NOMA is a technique that allows multiple users to share the same frequency channel by allocating different power levels to each user. This enables more users to connect to a single VLC transmitter simultaneously, thereby improving system capacity and spectral efficiency. CSK, on the other hand, is a modulation technique that uses different colors of light to represent digital information. By changing the color of the transmitted signal, information can be encoded and decoded at the receiver. Next, we discuss how NOMA and CSK can be combined in VLC systems by using different power levels to represent different users. This allows for more efficient use of the frequency spectrum, as multiple users can share the same channel at the same time. Additionally, we examine the potential benefits of using NOMA and CSK together in VLC systems to increase data rate. Finally, we discuss how reinforcement learning, a machine learning technique used to train agents to make decisions based on environmental feedback, can be used to optimize NOMA-CSK-VLC networks by allowing agents to learn and adapt to changing network conditions. Overall, our paper provides insights into the benefits of combining NOMA and CSK for VLC systems, highlighting the potential for improving communication efficiency and performance.
Statistical Analysis and Optimization of a Fifth-Percentile User Rate Constrained Design for FFR/SFR-Aided OFDMA-Based Cellular Networks
Authors: Jan García Morales, Guillem Femenias, Felip Riera Palou
Date: 2024-01-17 00:00:00
Tasks: Fairness, Scheduling

Interference mitigation strategies are deemed to play a key role in the context of the next generation (B4G/5G) of multicellular networks based on orthogonal frequency division multiple access. Fractional and soft frequency reuse (FFR, SFR) constitute two powerful mechanisms for intercell interference coordination that have been already adopted by emerging cellular deployments as an efficient way to improve the throughput performance perceived by cell-edge users. This paper presents a novel optimal fifth-percentile user rate constrained design for FFR/SFR-based networks that, by appropriately dimensioning the center and edge regions of the cell, rightly splitting the available bandwidth among these two areas while assigning the corresponding transmit power, allows a tradeoff between cell throughput performance and fairness to be established. To this end, both the cumulative distribution function of the user throughput and the average spectral efficiency of the system are derived assuming the use of the ubiquitous proportional fair scheduling policy. The mathematical framework is then used to obtain numerical results showing that the novel proposed design clearly outperforms previous schemes in terms of throughput fairness control due to a more rational compromise between average cell throughput and cell-edge ICIC.
Modulating Retroreflector-based Satellite-to-Ground Optical Communications: Sensing and Positioning
Authors: Mohammad Taghi Dabiri, Mazen Hasna, Saud Althunibat, Khalid Qaraqe
Date: 2024-01-17 00:00:00

This paper focuses on the optimal design of a modulated retroreflector (MRR) laser link to establish a high-speed downlink for cube satellites (CubeSats), taking into account the weight and power limitations commonly encountered by these tiny satellites. To this end, first, a comprehensive channel modeling is conducted considering key real channel parameters including mechanical gimbal error, fast steering mirror angle error, laser beamwidth, MRR area, atmospheric turbulence, and channel coherence time. Accordingly, a closed-form expression for the distribution of the received signal is derived and utilized to propose a maximum likelihood based method to sense and estimate the initial position of the satellite. Subsequently, the distribution of the distance estimation error during the sensing phase is formulated as a function of the laser beamwidth and the gimbal error, which enables us to fine-tune the optimal laser beamwidth to minimize sensing time. Moreover, using the sensing and initial satellite distance estimation, two positioning algorithms are proposed. To compare the performance of the proposed positioning method, we obtain the lower bound of the positioning error as a benchmark. Finally, by providing comprehensive simulations, we evaluate the effect of different parameters on the performance of the considered MRR-based system in both the sensing and positioning phases.
LoS Coverage Analysis for UAV-based THz Communication Networks: Towards 3D Visualization of Wireless Networks
Authors: Mohammad Taghi Dabiri, MazenHasna, Saud Althunibat, Khalid Qaraqe
Date: 2024-01-17 00:00:00

Terahertz (THz) links require a line-of-sight (LoS) connection, which is hard to be obtained in most scenarios. For THz communications, analyses based on LoS probability are not accurate, and a new real LoS model should be considered to determine the LoS status of the link in a real 3D environment. Considering unmanned aerial vehicle (UAV)-based THz networks, LoS coverage is analyzed in this work, where nodes are accurately determined to be in LoS or not. Specifically, by modeling an environment with 3D blocks, our target is to locate a set of UAVs equipped with directional THz links to provide LoS connectivity for the distributed users among the 3D obstacles. To this end, we first characterize and model the environment with 3D blocks. Then, we propose a user-friendly algorithm based on 3D spatial vectors, which determines the LoS status of nodes in the target area. In addition, using 3D modeling, several meta-heuristic algorithms are proposed for UAVs' positioning under 3D blocks in order to maximize the LoS coverage percentage. In the second part of the paper, for a UAV-based THz communication network, a geometrical analysis-based algorithm is proposed, which jointly clusters the distributed nodes and locates the set of UAVs to maximize average network capacity while ensuring the LoS state of distributed nodes among 3D obstacles. Moreover, we also propose a sub-optimal hybrid k-means-geometrical-based algorithm with a low computational complexity that can be used for networks where the topology continuously changes, and thus, users' clustering and UAVs' positioning need to be regularly updated. Finally, by providing various 3D simulations, we evaluate the effect of various system parameters such as the number and heights of UAVs, as well as the density and height of 3D obstacles on the LoS coverage.
Molecular causality in the advent of foundation models
Authors: Sebastian Lobentanzer, Pablo Rodriguez-Mier, Stefan Bauer, Julio Saez-Rodriguez
Date: 2024-01-17 00:00:00

Correlation is not causation. As simple as this widely agreed-upon statement may seem, scientifically defining causality and using it to drive our modern biomedical research is immensely challenging. In this perspective, we attempt to synergise the partly disparate fields of systems biology, causal reasoning, and machine learning, to inform future approaches in the field of systems biology and molecular networks.
Artificial Intelligence-based algorithms in medical image scan seg-mentation and intelligent visual-content generation -- a concise overview
Authors: Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
Date: 2024-01-18 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes also with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended Reality-based medical solutions are discussed
MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation
Authors: Nianzu Yang, Kaipeng Zeng, Haotian Lu, Yexin Wu, Zexin Yuan, Danni Chen, Shengdian Jiang, Jiaxiang Wu, Yimin Wang, Junchi Yan
Date: 2024-01-17 00:00:00

Neuronal morphology is essential for studying brain functioning and understanding neurodegenerative disorders. As acquiring real-world morphology data is expensive, computational approaches for morphology generation have been studied. Traditional methods heavily rely on expert-set rules and parameter tuning, making it difficult to generalize across different types of morphologies. Recently, MorphVAE was introduced as the sole learning-based method, but its generated morphologies lack plausibility, i.e., they do not appear realistic enough and most of the generated samples are topologically invalid. To fill this gap, this paper proposes MorphGrower, which mimicks the neuron natural growth mechanism for generation. Specifically, MorphGrower generates morphologies layer by layer, with each subsequent layer conditioned on the previously generated structure. During each layer generation, MorphGrower utilizes a pair of sibling branches as the basic generation block and generates branch pairs synchronously. This approach ensures topological validity and allows for fine-grained generation, thereby enhancing the realism of the final generated morphologies. Results on four real-world datasets demonstrate that MorphGrower outperforms MorphVAE by a notable margin. Importantly, the electrophysiological response simulation demonstrates the plausibility of our generated samples from a neuroscience perspective. Our code is available at https://github.com/Thinklab-SJTU/MorphGrower.
The Interplay Between Logical Phenomena and the Cognitive System of the Mind
Authors: Kazem Haghnejad Azar
Date: 2024-01-08 00:00:00

In this article, we employ mathematical concepts as a tool to examine the phenomenon of consciousness experience and logical phenomena. Through our investigation, we aim to demonstrate that our experiences, while not confined to limitations, cannot be neatly encapsulated within a singular collection. Our conscious experience emerges as a result of the developmental and augmentative trajectory of our cognitive system. As our cognitive abilities undergo refinement and advancement, our capacity for logical thinking likewise evolves, thereby manifesting a heightened level of conscious experience. The primary objective of this article is to embark upon a profound exploration of the concept of logical experience, delving into the intricate process by which these experiences are derived from our mind.
Role of Upwelling on Larval Dispersal and Productivity of Gooseneck Barnacle Populations in the Cantabrian Sea: Management Implications
Authors: Antonella Rivera, Nicolas Weidberg, Antonio F. Pardiñas, Ricardo Gonzalez-Gil, Lucıa Garcıa- Florez, Jose Luis Acuña
Date: 2024-01-17 00:00:00
Tasks: Management

The effect of coastal upwelling on the recruitment and connectivity of coastal marine populations has rarely been characterized to a level of detail to be included into sound fishery management strategies. The gooseneck barnacle (Pollicipes pollicipes) fishery at the Cantabrian Coast (Northern Spain) is located at the fringes of the NW Spanish Upwelling system. This fishery is being co-managed through a fine-scale, interspersed set of protected rocks where each rock receives a distinct level of protection. Such interspersion is potentially beneficial, but the extent to which such spacing is consistent with mean larval dispersal distances is as yet unknown. We have simulated the spread of gooseneck barnacle larvae in the Central Cantabrian Coast using a high-resolution time-series of current profiles measured at a nearshore location. During a year of high upwelling activity (2009), theoretical recruitment success was 94% with peak recruitment predicted 56 km west of the emission point. However, for a year of low upwelling activity (2011) theoretical recruitment success dropped to 15.4% and peak recruitment was expected 13 km east of the emission point. This is consistent with a positive correlation between catch rates and the Integrated Upwelling Index, using a 4-year lag to allow recruits to reach commercial size. Furthermore, a net long-term westward larval transport was estimated by means of mitochondrial cytochrome c oxidase subunit I (COI) sequences for five populations in the Cantabrian Sea. Our results call into question the role of long distance dispersal, driven by the mesoscale processes in the area, in gooseneck barnacle populations and point to the prevalent role of small-scale, asymmetric connectivity more consistent with the typical scale of the co-management process in this fishery.