Miracle-dz/UniFAR-FTUs huggingface dataset:
The estimation of b-value of the frequency-magnitude distribution and of its confidence intervals from binned magnitude data

The estimation of the slope (b-value) of the frequency magnitude distribution of earthquakes is based on a formula derived by Aki decades ago, assuming a continuous exponential distribution. However, as the magnitude is usually provided with a limited resolution, its distribution is not continuous but discrete. In the literature this problem was initially solved by an empirical correction (due to Utsu) to the minimum magnitude, and later by providing an exact formula such as that by Tinti and Mulargia, based on the geometric distribution theory. A recent paper by van der Elst showed that the b-value can be estimated also by considering the magnitude differences (which are proven to follow an exponential discrete Laplace distribution) and that in this case the estimator is more resilient to the incompleteness of the magnitude dataset. In this work we provide the complete theoretical formulation including i) the derivation of the means and standard deviations of the discrete exponential and Laplace distributions; ii) the estimators of the decay parameter of the discrete exponential and trimmed Laplace distributions; and iii) the corresponding formulas for the parameter b. We further deduce iv) the standard 1-sigma confidence limits for the estimated b. Moreover, we are able v) to quantify the error associated with the Utsu minimum-magnitude correction. We tested extensively such formulas on simulated synthetic datasets including complete catalogues as well as catalogues affected by a strong incompleteness degree such as aftershock sequences where the incompleteness is made to vary from one event to the next.
Modeling Transport in Graphene-Metal Contact and Verifying Transfer Length Method Characterization

Article History: Received 25 November 2022 Reviewed 10 January 2023 Revised 31 January 2023 Accepted 09 March 2023 Background and Objectives: One of the common methods for measuring the contact resistance of graphene sheets is the transfer length or transmission line method (TLM). Apart from the contact resistance, TLM gives the resistance of the channel sheet and the effective transfer length of the measured samples. Furthermore, the implementation of TLM is simple. To analyze this method, one can use circuit modeling (CM). Methods: An important parameter of TLM is the contact resistance between the metal electrode and the graphene channel. To compare this parameter with other measures, it is normalized by multiplying it by the channel width. In this research, for TLM analysis, all the components of the structure including electrodes, graphene channel, and metal-graphene contact are modeled in a circuit. Results: PSpice and MATLAB are integrated for TLM circuit modeling. The metal electrodes and the graphene channel are modeled based on the values of the resistances measured in the laboratory using the van der Pauw method and the resistances reported in the article in ohms per square. Moreover, the metalgraphene contact resistance is considered based on the values reported in the literature in ohms-micrometers. Conclusion: The modeling results show that, in addition to the effective transfer length, the effective transfer width can be defined on a contact, according to the dimensions of the structure. Therefore, the channel width is a vague characteristic of the TLM measurement, which plays a very important role in measuring contact resistance. Furthermore, the contact resistance and the resistance of the channel sheet are independent of each other and of the distance between the contacts. If defects in the graphene channel are randomly distributed along the channel between the contacts, they do not have a significant impact on the contact resistance, while they increase the resistance of the graphene sheet provided that they do not disrupt the channel. Indeed, for a 10% defect (or 90% coverage along the channel), the resistance of the sheet increases by 16%. In addition, by using this modeling, parameters such as the distribution of the contact current, the sources of errors, and their influence in determining the contact resistance and resistance of the channel sheet are investigated.
Enabling the Circular Economy: An Exploratory Study on Intellectual Property Challenges in Innovation Ecosystems

This paper explores circular economy related intellectual property (CEIP) challenges that actors in circular economy innovation ecosystems (CEIEs) face. Findings from a literature review are combined with empirical evidence from semi-structured interviews with different actors in CEIEs, including original equipment manufacturers as well as end-of-life solution providers of different company sizes and manufacturing industries. The main contributions of this paper to the literature are: (i) a preliminary framework to better understand IP challenges in CEIEs, (ii) a CEIP challenges framework which extends findings from the literature by categorising CEIP challenges mentioned in the conducted interviews and (iii) a visual mapping of CEIP challenges in the aerospace industry. © M. Wangrin, P. Vimalnath, F. Tietze. All rights reserved.
Application of Spline and Wavelet Smoothing Techniques For Functional Data

Advancement in statistical technologies has empowered scientists to collect more sophisticated data. This increasing sophistication demands a new conceptualization and consequently, new set of tools and techniques to extract additional information from the underlying patterns of the data. Functional Data Analysis is the study of examining the variability of a dataset when the observations are curves instead of discrete data samples, enabling statistical tools to further study the derivatives of the curve which may contain relavent information to the subject. The first step in Functional Data Analysis is to create smooth approximation functional curves from a set of discrete data points for which some approximation methods are required to describe the behavior of the data. Among the smoothing techniques to create the approximation function in Functional Data Analysis, Splines and Wavelets are of interest in this paper. Splines in nutshell employ a set of knots and fit peicewise polynomial functions between them to provide an estimation function for the data. Wavelets apply smoothing by decomposing the data into set of frequency sub-bands and filter out data samples which do not lie in the specified range. This will remove rapid changes from the data pattern, resulting in a smooth estimation.
PREVALENCE OF ASTHMA ACCORDING TO WILDFIRES IN CALIFORNIA: A CROSS-SECTIONAL SECONDARY DATA STUDY

This cross-sectional study aimed to test the associated risk factors between asthma and wildfires in all 58 counties of the state of California, USA using secondary data from the years 2013 to 2020. The dependent variable was respiratory disease measured by % of the population who have asthma during the sampled period. The main exposure was the number of wildfires measured as fire statistics at the county level according to The California Department of Forestry and Fire Protection. Risk factors associated with asthma were also tested: PM 2.5, Preventable Hospitalizations, Adult Obesity, Adult Smokers, Lung and Bronchus Cancer, Sex [Male/Female], Age [<5 years, <18 years, 65< years], and Ethnicity [Asian, White, Black, and Hispanic]. The average prevalence of asthma and number of wildfires was 15.8% (10.7%26.7%) and 5% (1%-33%) in the investigated counties. The Bivariate analysis showed a direct negative association: preventable hospitalization (p=0.006), average daily PM 2.5 (p=0.008), being Black (p=0.017), <5 years (p=0.020), <18 years (p=0.027), and 65< years(p=0.033). These variables remained associated with respiratory diseases after running them in the final regression model. Our results confirmed previous similar findings that support a possible relationship between respiratory disease and wildfires. Despite the limitations in a cross-sectional study, it emphasizes the demand for broader studies for finer clarification on the environmental impact of wildfires on related public health issues such as respiratory disease. Presented virtually at the Annual American Public Health Association (APHA) Conference, Denver, CO, October 22, 202
minBERT and Downstream Tasks

The focus of our paper is to propose a new set of techniques that can enhance the performance of BERT embeddings on three important and simultaneous tasks: sentiment analysis, paraphrase detection, and semantic textual similarity (STS). Our proposed paradigm is built on the prior work of Sun et al. [1], and it involves the implementation of several extensions, specifically layer-wise learning rate decay, cosine similarity fine-tuning for STS, contrastive learning, additional layers, additional finetuning datasets and modifying weights. Through these techniques, we aimed to address the limitations of existing methods and improve the accuracy and efficiency of BERT embeddings for the aforementioned tasks. We ran 9 experiments on models with individual extensions and combinations of extensions. Our best model outperformed the baseline with accuracies of 0.509 for sentiment analysis, 0.745 for paraphrase detection, and 0.506 for STS. The best model outperformed the baseline on STS by 42%. We learned that in multitask models, added complexity in the model improves accuracies of tasks with larger datasets but causes overfitting for tasks with smaller datasets. We also learned that weighted loss and additional datasets can help improve performance.
Performing ‘Husband and Wife’ Relationships among Chinese Gay Couples: Variations in Pitch Properties and Sibilants in Mandarin

Recent studies in sociophonetic variations and sexuality have been increasingly paying attention to the construction of queer identities in situated conversational contexts. Different from previous experimental investigations of a somehow uniform conceptualization of gay men’s speech, this study locates the performance of two distinct gay personae among Chinese gay couples in online vlogs through sociophonetic variables in Mandarin that have been shown to ideologically link with gender and sexuality. Results from the linear mixed-effects regression models showed that, as compared to gay men with a self-positioning of laogong (husband), gay men who performed a delicate and cute laopo (wife) persona exhibited higher mean pitch, wider pitch range, and more anterior production of the alveolo-palatal /ɕ/. These sociophonetic variations were also found between males and females in the previous literature, implying that gay couples have reappropriated the sex-based variations to create new socio-indexical meanings of ‘husband and wife’ relationships. Moreover, the somehow innovative /s/-fronting found among gay wives in Mandarin Chinese also provided new evidence on /s/ as a semiotic resource for performing gender and sexuality that has been found cross-linguistically. This working paper is available in University of Pennsylvania Working Papers in Linguistics: https://repository.upenn. edu/pwpl/vol29/iss1/15 Performing ‘Husband and Wife’ Relationships among Chinese Gay Couples: Variations in Pitch Properties and Sibilants in Mandarin
A Machine Learning Method for Prediction of Yogurt Quality and Consumers Preferencesusing Sensory Attributes and Image Processing Techniques

Prediction of quality and consumers’ preferences is essential task for food producers to improve their market share and reduce any gap in food safety standards. In this paper, we develop a machine learning method to predict yogurt preferences based on the sensory attributes and analysis of samples’ images using image processing texture and color feature extraction techniques. We compare three unsupervised ML feature selection techniques (Principal Component Analysis and Independent Component Analysis and t-distributed Stochastic Neighbour Embedding) with one supervised ML feature selection technique (Linear Discriminant Analysis) in terms of accuracy of classification. Results show the efficiency of the supervised ML feature selection technique over the traditional feature selection techniques.
Camera-based Recovery of Cardiovascular Signals from Unconstrained Face Videos using an Attention Network

This paper addresses the problem of recovering the shape morphology of blood volume pulse (BVP) information from a video of a person’s face. Video-based remote plethysmography methods have shown promising results in estimating vital signs such as heart rate and breathing rate. However, recovering the instantaneous pulse rate signals is still a challenge for the community. This is due to the fact that most of the previous methods concentrate on capturing the temporal average of the cardiovascular signals. In contrast, we present an approach in which BVP signals are extracted with a focus on the recovery of the signal shape morphology as a generalized form for the computation of physiological metrics. We also place emphasis on allowing natural movements by the subject. Furthermore, our system is capable of extracting individual BVP instances with sufficient signal detail to facilitate candidate re-identification. These improvements have resulted in part from the incorporation of a robust skin-detection module into the overall imaging-based photoplethysmography (iPPG) framework. We present extensive experimental results using the challenging UBFC-Phys dataset and the well-known COHFACE dataset. The source code is available at https://github.com/yogeshd21/CVPM-2023-iPPG-Paper.
Numerical methods to solve boundary value problems for ODEs

Shooting method. The shooting method is a method for solving a boundary value problem by reducing it an to initial value problem which is then solved multiple times until the boundary condition is met. To describe the method let us first consider the following two-point boundary value problem for a second-order nonlinear ODE with Dirichlet boundary conditions  d2y dt2 = f ( dy dt , y, t ) t ∈ [0, T ]
Resilient Baseband Processing in Virtualized RANs with Slingshot

In cellular networks, there is a growing adoption of virtualized radio access networks (vRANs), where operators are replacing the traditional specialized hardware for RAN processing with software running on commodity servers. Today's vRAN deployments lack resilience, since there is no support for vRAN failover or upgrades without long service interruptions. Enabling these features for vRANs is challenging because of their strict real-time latency requirements and black-box nature. Slingshot is a new system that transparently provides resilience for the vRAN's most performance-critical layer: the physical layer (PHY). We design new techniques for realtime workload migration with fast RAN protocol middle-boxes, and realtime RAN failure detection. A key insight in our design is to view the transient disruptions from resilience events to RAN computation state and I/O similarly to regular wireless signal impairments, and leverage the inherent resilience of cellular networks to these events. Experiments with a state-of-the-art 5G vRAN testbed show that Slingshot handles PHY failover with no disruption to video conferencing, and under 110 ms disruption to a TCP connection, and it also enables zero-downtime upgrades.
Integrated Measurement of Naval Sonar Operations and Precise Cetacean Locations: Integration of Fastloc GPS into a LIMPET Tag

Integrated Measurement of Naval Sonar Operations and Precise Cetacean Locations: Integration of Fastloc GPS into a LIMPET tag. Final Report for Task C, contract Number: N66604-14-C-2438 Reporting Period: September 6, 2016 December 31, 2017 Gregory S. Schorr, Brenda A. Rone, and Erin A. Falcone Marine Ecology & Telemetry Research 2420 Nellita Rd NW, Seabeck, WA 98380 Phone: (206) 931-4638 email: gschorr@marecotel.org / gschorr@cascadiaresearch.org
Wavelet Analysis of 3D Medical Imaging Data

Medical imaging is an important tool for effective medical diagnosis and treatment but is very reliant on signal processing. In recent years, volume resolution and extent of real world space has vastly improved in position emission tomography (PET) imaging with recent developments with whole body PET scanning. As a result, methods for compression, denoising, and more need to be re-evaluated for accuracy and efficiency on larger and increasingly complex datasets. Wavelet analysis is one such tool with the discrete wavelet transform enabling the data to be processed in the transform domain, rather than directly as raw data. In this thesis, we evaluate the use of wavelets in large 3D medical imaging data sets and discuss future uses and considerations for wavelets in medical imaging.
The Mexican dataset of a repetitive transcranial magnetic stimulation clinical trial on cocaine use disorder patients: SUDMEX TMS

Cocaine use disorder (CUD) is a worldwide problem with severe health and socio-economic consequences, which results in behavioral, cognitive, and neurobiological disturbances. Consensus on treatments are still under discussion, however, repetitive transcranial magnetic stimulation (rTMS) has been proposed as a promising treatment for medication-resistant disorders, including substance use disorders. Here, we describe the Mexican dataset of an rTMS clinical trial in patients with CUD (SUDMEX-TMS), a longitudinal dataset of 54 CUD participants (8 female) with five timepoints: baseline (T0), two weeks (T1), three months (T2), six months (T3) follow-up, and twelve months (T4) follow-up. Clinical rTMS treatment consisted of a double-blinded randomized clinical trial (n = 20 sham/24 active) for 2 weeks and open label afterwards, and includes demographic, clinical, and cognitive measures, as well as magnetic resonance imaging (MRI) acquisition in all timepoints: 1) structural (T1-weighted), 2) functional (resting state fMRI), and 3) multishell high-angular resolution diffusion-weighted (DWI-HARDI) sequences. The present dataset could be used to examine the impact of rTMS on CUD participants in clinical, cognitive, and multimodal MRI metrics in a longitudinal design.
Admission and discharge profiles of people with MS accessing in-patient rehabilitation in Canada

Background: Rehabilitation is the mainstay of management for people with disabilities of neurological origin to maximize function and reduce disability. Access to in-patient rehabilitation is usually reserved for people after crises or those who are discharged from acute care such as in stroke or trauma. Access to people with Multiple Sclerosis (MS) differs across countries and unknown for Canada. With the progression of MS, quality of life (QOL) becomes more closely coupled with independence and hence timely access to rehabilitation is important. The objectives of this paper are (i) to characterize the disability profiles of people with MS admitted to in-patient rehabilitation in Canada; and (ii) to estimate the extent to which disability profiles differ from admission to discharge. Methods: A longitudinal study of a rehabilitation admission-to-discharge cohort of 3500 people with MS was conducting using latent class analysis on the five Functional Independence Measure (FIM) subscales for Self-care, Transfers, Locomotion, Bladder/Bowel, and Cognition. The extent to which age, sex, and calendar time was associated with latent class membership, at both admission and discharge, was estimated using ordinal logistic regression, and proportional odds model was calculated for each age and sex. Results: A five-class model fit the data at admission and a four-class model fit the data at discharge determined using likelihood ratio G2, Akaike's Information Criterion, and Bayesian Information Criterion. Conclusion: At admission, the disability profiles showed a hierarchical progression across the FIM subscales. The least disabled profile was characterized by locomotion dependency only; the most disabled profile was characterized by dependencies in all subscales except cognition. At discharge, the least disabled class, representing 28% of discharges, was characterized by no dependencies; the most disabled class remained with dependencies (23%) in all areas. The study highlights the importance of reserving scarce rehabilitation services to those with more disability.
Preparedness in practice: An outbreak science approach to studying public health emergency response

Outbreak response, as a technical and specialized field of practice, is struggling to keep pace with the evolving dynamics of modern public health emergencies. Extensive scholarship across disciplines and epidemics has highlighted the importance of early action, the costs associated with delayed mobilization, the necessity of effective preparedness plans for complex crises, and the growing need for response to operate in spite of both uncertain information and social disruptions. Here, we present and analyze a new dataset of 235 different multisectoral activities that comprise outbreak preparedness and response. We explore the conditions under which these activities are applicable, including different phases of response, different operating circumstances, and different disease etiologies, and find that the core activities required for outbreak response largely apply across etiology and scale, but are more substantial during the early phases of response. To validate this framework with real-world examples, we then examine 279 reports from the WHO Disease Outbreak News (DON), a narrative record of outbreak history through time, and examine which of our activities are reported or implied in these narratives. We find that the core components of response are applicable across the vast majority of biological events, especially as they relate to basic epidemiology, infection prevention, and governance, and that many different kinds of real-world outbreaks require the same core set of responses. These findings point to a nearly-universal set of outbreak response activities that could be directly incorporated into national and international response plans, significantly reducing the risk and impact of infectious disease outbreaks.
Folate and Cobalamin Deficiencies during Pregnancy Disrupt the Glucocorticoid Response in Hypothalamus through N-Homocysteinilation of the Glucocorticoid Receptor

Vitamin B9 (folate)/B12 (cobalamin) deficiency is known to induce brain structural and/or functional retardations. In many countries, folate supplementation, targeting the most severe outcomes such as neural tube defects, is discontinued after the first trimester. However, adverse effects may occur after birth because of some mild misregulations. Various hormonal receptors were shown to be deregulated in brain tissue under these conditions. The glucocorticoid receptor (GR) is particularly sensitive to epigenetic regulation and post-translational modifications. In a mother–offspring rat model of vitamin B9/B12 deficiency, we investigated whether a prolonged folate supplementation could restore the GR signaling in the hypothalamus. Our data showed that a deficiency of folate and vitamin B12 during the in-utero and early postnatal periods was associated with reduced GR expression in the hypothalamus. We also described for the first time a novel post-translational modification of GR that impaired ligand binding and GR activation, leading to decrease expression of one of the GR targets in the hypothalamus, AgRP. Moreover, this brain-impaired GR signaling pathway was associated with behavioral perturbations during offspring growth. Importantly, perinatal and postnatal supplementation with folic acid helped restore GR mRNA levels and activity in hypothalamus cells and improved behavioral deficits.