pwc-archive/papers-with-abstracts huggingface dataset:
CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis
Authors: Xiaodie Chen, Jiayu Li, Dicheng Chen, Yirong Zhou, Zhangren Tu, Meijin Lin, Taishan Kang, Jianzhong Lin, Tao Gong, Liuhong Zhu, Jianjun Zhou, Lin Ou-yang, Jiefeng Guo, Jiyang Dong, Di Guo, Xiaobo Qu
Date: 2023-06-19 00:00:00
Tasks: Cloud Computing

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.
High Order Dynamic Mode Decomposition for Mechanical Vibrations and Modal Analysis
Authors: Andreas Tuor, Nico Canzani, Tobias Rüggeberg, Stefan Gorenflo, Gerd Simons, Bruno Bättig, Daniel Iseli
Date: 2023-06-19 00:00:00

In many mechanical, electrical, and general physical systems evolving over time or space, spectral analysis methods as Fast Fourier Transform (FFT), Short Term Fourier Transform (STFT), Power Spectrum Density (PSD) plays a very important role. They allow an extraction of required information content from signals in another base by decomposing it in its spectral components for further processing.In theory this approach is very powerful, even in some 'simple' or 'not too complicated' practical cases it has proven its utility and efficiency. However, for real-world applications such as mechanical modal analysis of large dimension systems including damping, noise and unpredictable excitation those signals are often so complex that it can be almost impossible to obtain a high-resolution spectral decomposition with these methods due to the time-bandwidth limitation. In this paper we describe an alternative approach for spectral analysis based on the High Order Dynamical Mode Decomposition (HODMD) and Kernel Density Spectrum (KDS). We will show that this method allows overcoming some limitations of the FFT and may be a promising approach to for a much more precisely the spectral decomposition.
ApSense: Data-driven Algorithm in PPG-based Sleep Apnea Sensing
Authors: Tanut Choksatchawathi, Guntitat Sawadwuthikul, Punnawish Thuwajit, Thitikorn Keawlee, Thee Mateepithaktham, Siraphop Saisaard, Thapanun Sudhawiyangkul, Busarakum Chaitusaney, Wanumaidah Saengmolee, Theerawit Wilaiprasitporn
Date: 2023-06-19 00:00:00
Tasks: Photoplethysmography (PPG), Specificity

Detecting obstructive sleep apnea (OSA) is essential for diagnosing and managing sleep health. Traditionally, this involves clinical settings with hardly accessible processes. We propose that the automated detection of OSA events is achievable using features extracted from fingertip photoplethysmography (PPG) signals combined with modern deep learning (DL) techniques. Utilizing two benchmark data sets with extensive PPG recordings, we introduce ApSense, a DL model designed for the OSA event onset recognition from PPG features. ApSense presents a custom neural architecture and domain-specific feature extraction from PPG waveforms. We benchmark it against the state-of-the-art (SOTA) algorithms, including RRWaveNet, PPGNetSA, AIOSA, DRIVEN, and LeNet-5. In our evaluations, ApSense demonstrated improved sensitivity, specificity, and area under the receiver operating characteristic (AUROC) on the test data sets. Furthermore, an ablation study highlighted strategic customizations of ApSense, enhancing its performance and adaptability to different data sets. ApSense demonstrates high reliability, as its outstanding results were confirmed even in high-variance data sets. By detecting OSA events, ApSense enables the estimation of the predicted apnea-hypopnea index (pAHI), which can be used for prescreening individuals for sleep apnea in a low-cost setup. ApSense shows the potential for the PPG-based OSA detection and clinical applications for prescreening in the future.
Hidden Backdoor Attack against Deep Learning-Based Wireless Signal Modulation Classifiers
Authors: Yunsong Huang, Weicheng Liu, Hui-Ming Wang
Date: 2023-06-19 00:00:00
Tasks: Backdoor Attack, Classification

Recently, DL has been exploited in wireless communications such as modulation classification. However, due to the openness of wireless channel and unexplainability of DL, it is also vulnerable to adversarial attacks. In this correspondence, we investigate a so called hidden backdoor attack to modulation classification, where the adversary puts elaborately designed poisoned samples on the basis of IQ sequences into training dataset. These poisoned samples are hidden because it could not be found by traditional classification methods. And poisoned samples are same to samples with triggers which are patched samples in feature space. We show that the hidden backdoor attack can reduce the accuracy of modulation classification significantly with patched samples. At last, we propose activation cluster to detect abnormal samples in training dataset.
RIS-aided Positioning Experiments based on mmWave Indoor Channel Measurements
Authors: Moustafa Rahal, Benoit Denis, Taghrid Mazloum, Frederic Munoz, Raffaele D Errico
Date: 2023-06-18 00:00:00

Reconfigurable Intelligent Surfaces (RISs) are announced as a truly transformative technology, capable of smartly shaping wireless environments to optimize next-generation communication networks. Among their numerous foreseen applications, Reflective RISs (RRISs) have been shown theoretically beneficial not only to enable wireless localization through controlled multipath in situations where conventional systems would fail (e.g., with too few available base stations (BSs) and/or under radio blockages) but also to locally boost accuracy on demand (typically, in regions close to the surface). In this paper, leveraging a dedicated frequency-domain mmWave indoor channel sounding campaign, we present the first experimental evidences of such benefits, by emulating offline simple RIS-aided single-BS positioning scenarios including line-of-sight (LoS) and non-line-of-sight (NLoS), single-RIS and multi-RIS, and multiple user equipment (UE) locations, also by considering various combinations of estimated multipath parameters (e.g., delays, Angle of Departure (AoD) or gains) as inputs to basic Least Squares (LS) solvers. Despite their simplicity, these preliminary proof-of-concept validations show concretely how and when RIS-reflected paths could contribute to enhance localization performance.
minimizing estimation error variance using a weighted sum of samples from the soil moisture active passive (SMAP) satellite
Authors: Mohammad Koosha, Nicholas Mastronarde
Date: 2023-06-18 00:00:00

The National Aeronautics and Space Administration's (NASA) Soil Moisture Active Passive (SMAP) is the latest passive remote sensing satellite operating in the protected L-band spectrum from 1.400 to 1.427 GHz. SMAP provides global-scale soil moisture images with point-wise passive scanning of the earth's thermal radiations. SMAP takes multiple samples in frequency and time from each antenna footprint to increase the likelihood of capturing RFI-free samples. SMAP's current RFI detection and mitigation algorithm excludes samples detected to be RFI-contaminated and averages the remaining samples. But this approach can be less effective for harsh RFI environments, where RFI contamination is present in all or a large number of samples. In this paper, we investigate a bias-free weighted sum of samples estimator, where the weights can be computed based on the RFI's statistical properties.
RIS-Assisted Energy-Efficient LEO Satellite Communications with NOMA
Authors: Wali Ullah Khan, Eva Lagunas, Asad Mahmood, Symeon Chatzinotas, Björn Ottersten
Date: 2023-06-17 00:00:00

This paper proposes an energy-efficient RIS-assisted downlink NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals of the LEO satellite and the passive beamforming of RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the energy efficiency maximization problem is non-convex. In practice, obtaining the optimal solution for such problems is very challenging. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate satellite transmit power towards each ground terminal using the Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. We also compare our solution with a benchmark framework having a fixed phase shift design and a conventional NOMA framework without involving RIS. Numerical results show that the proposed optimization framework achieves 21.47\% and 54.9\% higher energy efficiency compared to the benchmark and conventional frameworks.
Energy Minimization for Active RIS-Aided UAV-Enabled SWIPT Systems
Authors: Zhangjie Peng, Ruijing Liu, Cunhua Pan, Zhenkun Zhang, Jiangzhou Wang
Date: 2023-06-17 00:00:00

In this paper, we consider an active reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle(UAV)-enabled simultaneous wireless information and power transfer(SWIPT) system with multiple ground users. Compared with the conventional passive RIS, the active RIS deploying the internally integrated amplifiers can offset part of the multiplicative fading. In this system, we deal with an optimization problem of minimizing the total energy cost of the UAV. Specifically, we alternately optimize the trajectories, the hovering time, and the reflection vectors at the active RIS by using the successive convex approximation (SCA) method. Simulation results show that the active RIS performs better in energy saving than the conventional passive RIS.
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
Authors: Garud Iyengar, Henry Lam, Tianyu Wang
Date: 2023-06-16 00:00:00
Tasks: Model Selection

In data-driven optimization, the sample performance of the obtained decision typically incurs an optimistic bias against the true performance, a phenomenon commonly known as the Optimizer's Curse and intimately related to overfitting in machine learning. Common techniques to correct this bias, such as cross-validation, require repeatedly solving additional optimization problems and are therefore computationally expensive. We develop a general bias correction approach, building on what we call Optimizer's Information Criterion (OIC), that directly approximates the first-order bias and does not require solving any additional optimization problems. Our OIC generalizes the celebrated Akaike Information Criterion to evaluate the objective performance in data-driven optimization, which crucially involves not only model fitting but also its interplay with the downstream optimization. As such it can be used for decision selection instead of only model selection. We apply our approach to a range of data-driven optimization formulations comprising empirical and parametric models, their regularized counterparts, and furthermore contextual optimization. Finally, we provide numerical validation on the superior performance of our approach under synthetic and real-world datasets.
Error-induced extinction in a multi-type critical birth-death process
Authors: Meritxell Brunet Guasch, P. L. Krapivsky, Tibor Antal
Date: 2023-06-20 00:00:00

Extreme mutation rates in microbes and cancer cells can result in error-induced extinction (EEX), where every descendant cell eventually acquires a lethal mutation. In this work, we investigate critical birth-death processes with $n$ distinct types as a birth-death model of EEX in a growing population. Each type-$i$ cell divides independently $(i)\to(i)+(i)$ or mutates $(i)\to(i+1)$ at the same rate. The total number of cells grows exponentially as a Yule process until a cell of type-$n$ appears, which cell type can only die at rate one. This makes the whole process critical and hence after the exponentially growing phase eventually all cells die with probability one. We present large-time asymptotic results for the general $n$-type critical birth-death process. We find that the mass function of the number of cells of type-$k$ has algebraic and stationary tail $(\text{size})^{-1-\chi_k}$, with $\chi_k=2^{1-k}$, for $k=2,\dots,n$, in sharp contrast to the exponential tail of the first type. The same exponents describe the tail of the asymptotic survival probability $(\text{time})^{-\chi_n}$. We present applications of the results for studying extinction due to intolerable mutation rates in biological populations.
Criteria for the absence of arbitrage in general diffusion markets
Authors: David Criens, Mikhail Urusov
Date: 2023-06-20 00:00:00

We establish deterministic necessary and sufficient conditions for the no-arbitrage notions NA ("no arbitrage"), NUPBR ("no unbounded profit with bounded risk") and NFLVR ("no free lunch with vanishing risk") in general diffusion market models with finite and infinite time horizons. These are single asset models whose (discounted) asset price process $Y$ is a regular continuous strong Markov process that is also a semimartingale. We further characterize the existence of an equivalent martingale measure in such models. All deterministic criteria are provided in terms of the scale function and the speed measure of $Y$.
Determination of the antibiotic susceptibility pattern of Gram positive bacteria causing UTI in Dhaka Bangladesh
Authors: Bushra Rahman Eipa, Md Riadul Islam, Raquiba Sultana, Seemi Tasnim Alam, Tanaj Mehjabin, Nohor Noon Haque Bushra, S M Moniruzzaman, Md Ifrat Hossain, Shamia Naz Rashna, Md Aftab Uddin
Date: 2023-06-18 00:00:00
Tasks: Diagnostic

Urinary Tract Infection (UTIs) is referred as one of the most common infection in medical sectors worldwide and antimicrobial resistance (AMR) is also a global threat to human that is related with many diseases. As antibiotics used for the treatment of infectious diseases, the rate of resistance is increasing day by day. Gram positive pathogens are commonly found in urine sample collected from different age groups of people, associated with UTI. The study was conducted in a diagnostic center in Dhaka, Bangladesh with total 1308 urine samples from November 2021 to April 2022. Gram positive pathogens were isolated and antimicrobial susceptibility tests were done. From total 121 samples of gram positive bacteria the highest prevalence rate of UTIs was found in age group of 21-30 year. Mostly Enterococcus spp. (33.05%) Staphylococcus aureus (27.27%), Streptococcus spp. (20.66%), Beta-hemolytic streptococci (19.00%) were found as causative agents of UTI compared to others. The majority of isolates have been detected as multi-drug resistant (MDR). The higher percentage of antibiotic resistance were found against Azithromycin (75%), and cefixime (64.46%). This research focused on the regular basis of surveillance for the Gram-positive bacteria antibiotic susceptibility to increase awareness about the use of proper antibiotic thus minimize the drug resistance.
grenedalf: population genetic statistics for the next generation of pool sequencing
Authors: Lucas Czech, Jeffrey P. Spence, Moisés Expósito-Alonso
Date: 2023-06-20 00:00:00
Tasks: Diversity

Pool sequencing is an efficient method for capturing genome-wide allele frequencies from multiple individuals, with broad applications such as studying adaptation in Evolve-and-Resequence experiments, monitoring of genetic diversity in wild populations, and genotype-to-phenotype mapping. Here, we present grenedalf, a command line tool written in C++ that implements common population genetic statistics such as $\theta$, Tajima's D, and FST for Pool sequencing. It is orders of magnitude faster than current tools, and is focused on providing usability and scalability, while also offering a plethora of input file formats and convenience options.
Endothelial Cell-specific Loss of Breast Cancer Susceptibility Gene 2 Exacerbates Atherosclerosis
Authors: David C. R. Michels, Sepideh Nikfarjam, Berk Rasheed, Margi Patel, Shuhan Bu, Mehroz Ehsan, Hien C. Nguyen, Aman Singh, Biao Feng, John McGuire, Robert Gros, Jefferson C. Frisbee, Krishna K. Singh
Date: 2023-06-19 00:00:00
Tasks: Protein Folding

The BReast CAncer type 2 susceptibility protein (BRCA2) responds to DNA damage by participating in homology-directed repair. BRCA2 deficiency culminates in defective DNA damage repair (DDR) that when prolonged leads to the accumulation of DNA damage causing cancer or apoptosis. Oxidative stress promotes DNA damage and apoptosis and is a common mechanism through which cardiovascular risk factors lead to endothelial dysfunction and atherosclerosis. Herein, we show that endothelial BRCA2 plays a protective role against atherosclerosis under hypercholesterolemic stress. We successfully generated and characterized endothelial cell (EC)-specific BRCA2 knockout (BRCA2endo) mice. To study the effect of EC-specific BRCA2-loss in atherosclerosis, we generated and characterized BRCA2endo mice on apolipoprotein E null background (ApoE-/-), fed them with high-fat diet (HFD) and evaluated atherosclerosis. Baseline phenotyping of BRCA2endo mice did not show any adverse effects in terms of DNA damage and apoptosis as well as cardiac and metabolic function. However, using HFD-fed apolipoprotein E knockout (ApoE-/-) background, we demonstrated that EC-specific loss of BRCA2 resulted in aortic plaque deposition and splenomegaly. Comparison of RNA sequencing data from aortas of EC-specific BRCA2-deficient ApoE-/- and BRCA2-intact ApoE-/- mice revealed a total of 530 significantly differentially expressed genes with Protein Folding Response and Lipid Metabolism as the most affected pathways. This study provides foundational knowledge regarding BRCA2 status and function in the cardiovascular system, and highlights the potential of BRCA2 as a novel therapeutic target in prevention and treatment of atherosclerosis. Our data indicate that BRCA2 mutation carriers may be at a previously unrecognized risk of atherosclerosis in addition to breast and ovarian cancer.
Data-driven Stochastic Model for Quantifying the Interplay Between Amyloid-beta and Calcium Levels in Alzheimer's Disease
Authors: Hina Shaheen, Roderick Melnik, Sundeep Singh
Date: 2023-06-17 00:00:00

The abnormal aggregation of extracellular amyloid-$\beta$ (A\beta) in senile plaques resulting in calcium (Ca^{+2}) dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD). Significant research efforts have been devoted in the past to better understand the underlying molecular mechanisms driving A\beta deposition and Ca^{+2} dysregulation. To better understand this interaction, we report a novel stochastic model where we analyze the positive feedback loop between A\beta and Ca^{+2} using ADNI data. A good therapeutic treatment plan for AD requires precise predictions. Stochastic models offer an appropriate framework for modelling AD since AD studies are observational in nature and involve regular patient visits. The etiology of AD may be described as a multi-state disease process using the approximate Bayesian computation method. So, utilizing ADNI data from $2$-year visits for AD patients, we employ this method to investigate the interplay between A\beta and Ca^{+2} levels at various disease development phases. Incorporating the ADNI data in our physics-based Bayesian model, we discovered that a sufficiently large disruption in either A\beta metabolism or intracellular Ca^{+2} homeostasis causes the relative growth rate in both Ca^{+2} and A\beta, which corresponds to the development of AD. The imbalance of Ca^{+2} ions causes A\beta disorders by directly or indirectly affecting a variety of cellular and subcellular processes, and the altered homeostasis may worsen the abnormalities of Ca^{+2} ion transportation and deposition. This suggests that altering the Ca^{+2} balance or the balance between A\beta and Ca^{+2} by chelating them may be able to reduce disorders associated with AD and open up new research possibilities for AD therapy.
Comments arising from WJ Thompson "Uncertainty in probabilistic genotyping of low template DNA A case study comparing STRmix and TrueAllele"
Authors: Tim Kalafut, James Curran, Mike Coble, John Buckleton
Date: 2023-06-10 00:00:00

Thompson reports a comparison of data from STRmix and TrueAllele. The data he has arises from different inputs to the two software. If the input data are made more similar the outputs become more similar. Thompson argues that the Analytical Threshold, AT, should be varied in casework. This produced different LRs but the analyst would be left deciding what to do with these options. This cannot be based on the LRs but should be based on whether any movement in the AT adds reliable or unreliable data. This is how most laboratories set their AT in the first place. Hence it is pointless, and potentially dangerous, to experimentally vary the AT in casework. The profile is low level and shows at most three peaks. Thompson argues that LR results assuming that the number of contributors (NoC) is 2 or 3 should be reported. Uncertainty in NoC should be treated as a nuisance variable and summed out.
The Impact of Rising Ocean Acidification Levels on Fish Migration
Authors: Asuna Gilfoyle, Willow Baird
Date: 2023-06-19 00:00:00

Ocean acidification, a direct consequence of increased carbon dioxide (CO2) emissions, has emerged as a critical area of concern within the scientific community. The world's oceans absorb approximately one-third of human-caused CO2 emissions, leading to chemical reactions that reduce seawater pH, carbonate ion concentration, and saturation states of biologically important calcium carbonate minerals. This process, known as ocean acidification, has far-reaching implications for marine ecosystems, particularly for marine organisms such as fish, whose migratory patterns are integral to the health and function of these ecosystems.