pwc-archive/papers-with-abstracts huggingface dataset:
Impact of 3D Antenna Radiation Pattern in UAV Air-to-Ground Path Loss Modeling and RSRP-based Localization in Rural Area
Authors: Sung Joon Maeng, Hyeokjun Kwon, Ozgur Ozdemir, İsmail Güvenç
Date: 2023-07-24 00:00:00

Ensuring reliable and seamless wireless connectivity for unmanned aerial vehicles (UAVs) has emerged as a critical requirement for a wide range of applications. The increasing deployment of UAVs has increased the significance of cellular-connected UAVs (C-UAVs) in enabling beyond-visual line of sight (BVLOS) communications. To ensure the successful operation of C-UAVs within existing terrestrial networks, it is vital to understand the distinctive characteristics associated with air-to-ground signal propagation. In this paper, we investigate the impact of 3D antenna patterns on a UAV air-to-ground path loss model, utilizing datasets obtained from a measurement campaign. We conducted UAV experiments in a rural area at various fixed heights, while also characterizing the 3D antenna radiation pattern by using an anechoic chamber facility. By analyzing reference signal received power (RSRP) using path loss models that account for antenna patterns, we observed that our measurement results, obtained at different UAV heights, aligned well with the two-ray path loss model when incorporating the measured antenna pattern. We propose an RSRP-based localization algorithm at a UAV that takes into account antenna patterns in both offline and online scenarios. Through our experimentation dataset, we show that incorporating measured antenna patterns significantly enhances the source localization accuracy.
Implementation Considerations for ACAS and Simulation Results
Authors: Jón Winkel, Ignacio Fernandez-Hernandez, Cillian O'Driscoll
Date: 2023-07-23 00:00:00

The Assisted Commercial Authentication Service (ACAS) is a semi-assisted signal authentication concept currently being defined for Galileo, based on the E6-C encrypted signal. Leveraging the assumption that the true E6-C encrypted signal always arrives before any inauthentic signal, we define user concepts for signal detection, including vestigial signal search. We define three mitigation levels, each level defending against an increasing set of threats, incorporating the described concepts and additional checks. The concepts are analyzed and implemented in a simulation environment, and tested in both nominal conditions and under advanced spoofing attacks. The results suggest that even advanced attacks can be detected and mitigated by ACAS receivers.
Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT
Authors: Rui Meng, Fangzhou Zhu, Xiqi Cheng, Xiaodong Xu, Bizhu Wang, Chen Dong, Bingxuan Xu, Xiaofeng Tao, Ping Zhang
Date: 2023-07-23 00:00:00
Tasks: Active Learning, Gaussian Processes

The future 6G-enabled IoT will facilitate seamless global connectivity among ubiquitous wireless devices, but this advancement also introduces heightened security risks such as spoofing attacks. Physical-Layer Authentication (PLA) has emerged as a promising, inherently secure, and energy-efficient technique for authenticating IoT terminals. Nonetheless, the direct application of state-of-the-art PLA schemes to 6G-enabled IoT encounters two major hurdles: inaccurate channel fingerprints and the inefficient utilization of prior fingerprint information. To tackle these challenges, we leverage Reconfigurable Intelligent Surfaces (RISs) to enhance fingerprint accuracy. Additionally, we integrate active learning and Gaussian Processes (GPs) to propose an Efficient Gaussian Process Classification (EGPC)-based PLA scheme, aiming for reliable and lightweight authentication. Following Bayes' theorem, we model configurable fingerprints using GPs and employ the expectation propagation method to identify unknown fingerprints. Given the difficulty of obtaining sufficient labeled fingerprint samples to train PLA models, we propose three fingerprint selection algorithms. These algorithms select unlabeled fingerprints and query their identities using upper-layer authentication mechanisms. Among these methods, the optimal algorithm reduces the number of training fingerprints needed through importance sampling and eliminates the requirement for PLA model retraining through joint distribution calculation. Simulations results reveal that, in comparison with non-RIS-based approaches, the RIS-aided PLA framework decreases the authentication error rate by 98.69%. In addition, our designed fingerprint selection algorithms achieve a reduction in the authentication error rate of up to 86.93% compared to baseline active learning schemes.
A Simple and Efficient RSS-AOA Based Localization with Heterogeneous Anchor Nodes
Authors: Weizhong Ding, Shengming Chang, Shudi Bao
Date: 2023-07-22 00:00:00

Accurate and reliable localization is crucial for various wireless communication applications. Numerous studies have proposed accurate localization methods using hybrid received signal strength (RSS) and angle of arrival (AOA) measurements. However, these studies typically assume identical measurement noise distributions for different anchor nodes, which may not accurately reflect real-world scenarios with varying noise distributions. In this paper, we propose a simple and efficient localization method based on hybrid RSS-AOA measurements that accounts for the varying measurement noises of different nodes. We derive a closed-form estimator for the target location based on the linear weighted least squares (LWLS) algorithm, with each LWLS equation weight being the inverse of its residual variance. Due to the unknown variances of LWLS equation residuals, we employ a two-stage LWLS method for estimation. The proposed method is computationally efficient, adaptable to different types of wireless communication systems and environments, and provides more accurate and reliable localization results compared to existing RSS-AOA localization techniques. Additionally, we derive the Cramer-Rao Lower Bound (CRLB) for the RSS-AOA signal sequences used in the proposed method. Simulation results demonstrate the superiority of the proposed method.
Accurate RSS-Based Localization Using an Opposition-Based Learning Simulated Annealing Algorithm
Authors: Weizhong Ding, Shengming Chang, Shudi Bao, Meng Chen, Jie Sun
Date: 2023-07-22 00:00:00
Tasks: Diversity

Wireless sensor networks require accurate target localization, often achieved through received signal strength (RSS) localization estimation based on maximum likelihood (ML). However, ML-based algorithms can suffer from issues such as low diversity, slow convergence, and local optima, which can significantly affect localization performance. In this paper, we propose a novel localization algorithm that combines opposition-based learning (OBL) and simulated annealing algorithm (SAA) to address these challenges. The algorithm begins by generating an initial solution randomly, which serves as the starting point for the SAA. Subsequently, OBL is employed to generate an opposing initial solution, effectively providing an alternative initial solution. The SAA is then executed independently on both the original and opposing initial solutions, optimizing each towards a potential optimal solution. The final solution is selected as the more effective of the two outcomes from the SAA, thereby reducing the likelihood of the algorithm becoming trapped in local optima. Simulation results indicate that the proposed algorithm consistently outperforms existing algorithms in terms of localization accuracy, demonstrating the effectiveness of our approach.
Pro-PRIME: A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins
Authors: Fan Jiang, Mingchen Li, Jiajun Dong, Yuanxi Yu, Xinyu Sun, Banghao Wu, Jin Huang, Liqi Kang, Yufeng Pei, Liang Zhang, Shaojie Wang, Wenxue Xu, Jingyao Xin, Wanli Ouyang, Guisheng Fan, Lirong Zheng, Yang Tan, Zhiqiang Hu, Yi Xiong, Yan Feng, Guangyu Yang, Qian Liu, Jie Song, Jia Liu, Liang Hong, Pan Tan
Date: 2023-07-24 00:00:00
Tasks: Language Modeling, Language Modelling

Designing protein mutants of both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants of improved stability and activity without any prior experimental mutagenesis data of the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive power compared to current state-of-the-art models on the public mutagenesis dataset over 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the top 30-45 single-site mutations' impact on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize non-natural nucleic acid or resilience to extreme alkaline conditions. Remarkably, over 30% of the AI-recommended mutants exhibited superior performance compared to their pre-mutation counterparts across all proteins and desired properties. Moreover, we have developed an efficient, and successful method based on PRIME to rapidly obtain multi-site mutants with enhanced activity and stability. Hence, PRIME demonstrates the general applicability in protein engineering.
An individual-based model to explore the impact of psychological stress on immune infiltration into tumour spheroids
Authors: Emma Leschiera, Gheed Al-Hity, Melanie S. Flint, Chandrasekhar Venkataraman, Tommaso Lorenzi, Luis Almeida, Chloe Audebert
Date: 2023-07-24 00:00:00

In recent in vitro experiments on co-culture between breast tumour spheroids and activated immune cells, it was observed that the introduction of the stress hormone cortisol resulted in a decreased immune cell infiltration into the spheroids. Moreover, the presence of cortisol deregulated the normal levels of the pro- and anti-inflammatory cytokines IFN-{\gamma} and IL-10. We present an individual-based model to explore the interaction dynamics between tumour and immune cells under psychological stress conditions. With our model, we explore the processes underlying the emergence of different levels of immune infiltration, with particular focus on the biological mechanisms regulated by IFN-{\gamma} and IL-10. The set-up of numerical simulations is defined to mimic the scenarios considered in the experimental study. Similarly to the experimental quantitative analysis, we compute a score that quantifies the level of immune cell infiltration into the tumour. The results of numerical simulations indicate that the motility of immune cells, their capability to infiltrate through tumour cells, their growth rate and the interplay between these cell parameters can affect the level of immune cell infiltration in different ways. Ultimately, numerical simulations of this model support a deeper understanding of the impact of biological stress-induced mechanisms on immune infiltration.
Disease from opposing forces in regulatory control
Authors: Steven A. Frank
Date: 2023-07-23 00:00:00

Danger requires a strong rapid response. Speedy triggers are prone to false signals. False alarms can be costly, requiring strong negative regulators to oppose the initial triggers. Strongly opposed forces can easily be perturbed, leading to imbalance and disease. For example, immunity and fear response balance strong rapid triggers against widespread slow negative regulators. Diseases of immunity and behavior arise from imbalance. A different opposition of forces occurs in mammalian growth, which balances strong paternally expressed accelerators against maternally expressed suppressors. Diseases of overgrowth or undergrowth arise from imbalance. Other examples of opposing forces and disease include control of dopamine expression and male versus female favored traits.
Post-COVID-19 Effects on Female Fertility: An In-Depth Scientific Investigation
Authors: Maitham G. Yousif, Lamiaa Al-Maliki, Jinan J. Al-Baghdadi, Nasser Ghaly Yousif
Date: 2023-07-24 00:00:00

This study aimed to comprehensively investigate the post-COVID-19 effects on female fertility in patients with a history of severe COVID-19 infection. Data were collected from 340 patients who had previously experienced severe COVID-19 symptoms and sought medical assistance at private clinics and fertility centers in various provinces of Iraq. A comparative control group of 280 patients, who had not contracted COVID-19 or had mild cases, was included. The study assessed ovarian reserve, hormonal imbalances, and endometrial health in the post-recovery phase. The findings revealed a significant decrease in ovarian reserve, hormonal disturbances, and endometrial abnormalities among patients with a history of severe COVID-19 infection compared to the control group. This in-depth investigation sheds light on the potential long-term impacts of severe COVID-19 on female fertility. The results emphasize the need for further research and targeted interventions to support women affected by post-COVID-19 fertility issues. Understanding these effects is crucial for providing appropriate medical care and support to women on their reproductive journey after recovering from severe COVID-19.
Low-Cost Lung Cancer Detection Using Machine Learning on Breath Samples
Authors: Jayanth Mokkapati
Date: 2023-07-22 00:00:00
Tasks: Diagnostic, Specificity

In recent years, electronic nose devices have become a popular approach for identifying respiratory disorders including lung cancer. Traditional e-nose systems have had very consistent principles and patterns of sensor responses. After coming to the realization that detecting cancer at early stages can save 99 percent of lives, it has become imperative to design a machine that can easily detect for lung cancer(the most common type of cancer) in a cost-effective and accurate way. Designing an Al Nose was a perfect way to counteract the problem. A tiny e-nose system with 14 gas sensors of four types was created and fifty breath samples were analyzed. Five feature extraction techniques and two classifiers were used to test the system's efficiency in recognizing and discriminating lung cancer from other respiratory disorders and healthy controls. Finally, the impact of different sensor types on the capacity of e-nose systems to identify objects was investigated. The sensitivity, specificity, and accuracy of distinguishing lung cancer patients from healthy controls are 91.58 percent, 91.72 percent, and 91.59 percent, respectively, when utilizing the DA fuzzy 5-NIN classification approach. The findings imply that type-specific sensors might improve the diagnostic accuracy of e-nose devices greatly. These findings indicated that the e-nose system described in this work might be used in lung cancer screening with good results. Furthermore, while creating e-nose systems, it is critical to consider type-different sensors. This machine covers all aspects to most effectively develop a machine that can detect lung cancer that is user-friendly and low cost.
PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking
Authors: Michael Brocidiacono, Konstantin I. Popov, David Ryan Koes, Alexander Tropsha
Date: 2023-07-22 00:00:00
Tasks: Benchmarking, Molecular Docking

Molecular docking aims to predict the 3D pose of a small molecule in a protein binding site. Traditional docking methods predict ligand poses by minimizing a physics-inspired scoring function. Recently, a diffusion model has been proposed that iteratively refines a ligand pose. We combine these two approaches by training a pose scoring function in a diffusion-inspired manner. In our method, PLANTAIN, a neural network is used to develop a very fast pose scoring function. We parameterize a simple scoring function on the fly and use L-BFGS minimization to optimize an initially random ligand pose. Using rigorous benchmarking practices, we demonstrate that our method achieves state-of-the-art performance while running ten times faster than the next-best method. We release PLANTAIN publicly and hope that it improves the utility of virtual screening workflows.
From characteristic functions to multivariate distribution functions and European option prices by the damped COS method
Authors: Gero Junike, Hauke Stier
Date: 2023-07-24 00:00:00

We provide a unified framework to obtain numerically certain quantities, such as the distribution function, absolute moments and prices of financial options, from the characteristic function of some (unknown) probability density function using the Fourier-cosine expansion (COS) method. The classical COS method is numerically very efficient in one-dimension, but it cannot deal very well with certain integrands in general dimensions. Therefore, we introduce the damped COS method, which can handle a large class of integrands very efficiently. We prove the convergence of the (damped) COS method and study its order of convergence. The method converges exponentially if the characteristic function decays exponentially. To apply the (damped) COS method, one has to specify two parameters: a truncation range for the multivariate density and the number of terms to approximate the truncated density by a cosine series. We provide an explicit formula for the truncation range and an implicit formula for the number of terms. Numerical experiments up to five dimensions confirm the theoretical results.
Discrete time optimal investment under model uncertainty
Authors: Laurence Carassus, Massinissa Ferhoune
Date: 2023-07-21 00:00:00
Tasks: model

We study a robust utility maximization problem in a general discrete-time frictionless market under quasi-sure no-arbitrage. The investor is assumed to have a random and concave utility function defined on the whole real-line. She also faces model ambiguity on her beliefs about the market, which is modeled through a set of priors. We prove the existence of an optimal investment strategy using only primal methods. For that we assume classical assumptions on the market and on the random utility function as asymptotic elasticity constraints. Most of our other assumptions are stated on a prior-by-prior basis and correspond to generally accepted assumptions in the literature on markets without ambiguity. We also propose a general setting including utility functions with benchmark for which our assumptions are easily checked.
Unraveling the Trade-off between Sustainability and Returns: A Multivariate Utility Analysis
Authors: Marcos Escobar-Anel, Yiyao Jiao
Date: 2023-07-22 00:00:00

This paper proposes an expected multivariate utility analysis for ESG investors in which green stocks, brown stocks, and a market index are modeled in a one-factor, CAPM-type structure. This setting allows investors to accommodate their preferences for green investments according to proper risk aversion levels. We find closed-form solutions for optimal allocations, wealth and value functions. As by-products, we first demonstrate that investors do not need to reduce their pecuniary satisfaction in order to increase green investments. Secondly, we propose a parameterization to capture investors' preferences for green assets over brown or market assets, independent of performance. The paper uses the RepRisk Rating of U.S. stocks from 2010 to 2020 to select companies that are representative of various ESG ratings. Our empirical analysis reveals drastic increases in wealth allocation toward high-rated ESG stocks for ESG-sensitive investors; this holds even as the overall level of pecuniary satisfaction is kept unchanged.
Prediction of Handball Matches with Statistically Enhanced Learning via Estimated Team Strengths
Authors: Florian Felice, Christophe Ley
Date: 2023-07-20 00:00:00

We propose a Statistically Enhanced Learning (aka. SEL) model to predict handball games. Our Machine Learning model augmented with SEL features outperforms state-of-the-art models with an accuracy beyond 80%. In this work, we show how we construct the data set to train Machine Learning models on past female club matches. We then compare different models and evaluate them to assess their performance capabilities. Finally, explainability methods allow us to change the scope of our tool from a purely predictive solution to a highly insightful analytical tool. This can become a valuable asset for handball teams' coaches providing valuable statistical and predictive insights to prepare future competitions.
External Reasoning: Towards Multi-Large-Language-Models Interchangeable Assistance with Human Feedback
Authors: Akide Liu
Date: 2023-07-05 00:00:00
Tasks: Hippocampus

Memory is identified as a crucial human faculty that allows for the retention of visual and linguistic information within the hippocampus and neurons in the brain, which can subsequently be retrieved to address real-world challenges that arise through a lifetime of learning. The resolution of complex AI tasks through the application of acquired knowledge represents a stride toward the realization of artificial general intelligence. However, despite the prevalence of Large Language Models (LLMs) like GPT-3.5 and GPT-4 \cite{brown2020language, leiter2023chatgpt, zaitsu2023distinguishing, OpenAI2023GPT4TR} , which have displayed remarkable capabilities in language comprehension, generation, interaction, and reasoning, they are inhibited by constraints on context length that preclude the processing of extensive, continually evolving knowledge bases. This paper proposes that LLMs could be augmented through the selective integration of knowledge from external repositories, and in doing so, introduces a novel methodology for External Reasoning, exemplified by ChatPDF. Central to this approach is the establishment of a tiered policy for \textbf{External Reasoning based on Multiple LLM Interchange Assistance} in \cref{fig:overall}, where the level of support rendered is modulated across entry, intermediate, and advanced tiers based on the complexity of the query, with adjustments made in response to human feedback. A comprehensive evaluation of this methodology is conducted using multiple LLMs and the results indicate state-of-the-art performance in \cref{comparison} , surpassing existing solutions including ChatPDF.com. Moreover, the paper emphasizes that this approach is more efficient compared to the direct processing of full text by LLMs. The source code is publicly available at: \url{https://github.com/AkideLiu/ANLP}.
Improving Video Colorization by Test-Time Tuning
Authors: Yaping Zhao, Haitian Zheng, Jiebo Luo, Edmund Y. Lam
Date: 2023-06-25 00:00:00
Tasks: Colorization

With the advancements in deep learning, video colorization by propagating color information from a colorized reference frame to a monochrome video sequence has been well explored. However, the existing approaches often suffer from overfitting the training dataset and sequentially lead to suboptimal performance on colorizing testing samples. To address this issue, we propose an effective method, which aims to enhance video colorization through test-time tuning. By exploiting the reference to construct additional training samples during testing, our approach achieves a performance boost of 1~3 dB in PSNR on average compared to the baseline. Code is available at: https://github.com/IndigoPurple/T3