Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Human Dynamics by Means of Cointegration Methodology

The cointegration approach, developed by Engle and Granger (1987), allows treating nonstationary (i.e., integrated) time series as a multivariate system if their linear combination is stationary. Various psychological processes are often integrated but interconnected. Thus, cointegration methodology offers flexible techniques for modeling human dynamics. This paper introduces cointegration methodology to psychological researchers. After a brief introduction to time-series analysis, we summarize cointegration techniques and describe the so-called cointegration vector (β) and adjustment coefficient (α), the main parameters of cointegrated systems. We show that β and α are flexible tools for modeling long-term equilibrium as well as short-term dynamic adjustment within the multivariate system. An empirical example demonstrates advantages of cointegration methods in a typical research situation. Possible domains of application within the scope of psychology are discussed.
Mechanisms for radiation dose-rate sensitivity of bipolar transistors

Mechanisms for enhanced low-dose-rate sensitivity are described. In these mechanisms, bimolecular reactions dominate the kinetics at high dose rates thereby causing a sub-linear dependence on total dose, and this leads to a dose-rate dependence. These bimolecular mechanisms include electron-hole recombination, hydrogen recapture at hydrogen source sites, and hydrogen dimerization to form hydrogen molecules. The essence of each of these mechanisms is the dominance of the bimolecular reactions over the radiolysis reaction at high dose rates. However, at low dose rates, the radiolysis reaction dominates leading to a maximum effect of the radiation.
Using Mendelian Randomization to model the causal effect of cancer on health economic outcomes and to simulate the cost-effectiveness of anti-cancer interventions

BACKGROUND Cancer is associated with significant economic impacts. Quantifying the scale of these impacts is challenged by confounding variables that jointly influence both cancer status and economic outcomes such as healthcare costs and quality of life. Moreover, the increasing costs attributed to cancer drug development complicate the cost-effective provision of cancer care. METHODS We address both challenges in this paper by using germline genetic variation in the risk of incident cancer as instrumental variables in Mendelian Randomization analyses of eight cancers. We developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate and thyroid cancers on healthcare costs and quality adjusted life years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used Mendelian Randomization to model a hypothetical population-wide preventative intervention based on a repurposed class of anti-diabetic drugs known as sodium-glucose co-transporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer. RESULTS Genetic liability to prostate cancer and to breast cancer had material causal impacts on healthcare costs and QALYs. Mendelian Randomization results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anti-cancer indication. IMPLICATIONS Our new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision analytic models of cancer interventions such as screening programmes or simulations of longer-term outcomes associated with therapies investigated in RCTs with short follow-ups. Our new method allows us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anti-cancer intervention to inform and complement other means of assessing long-term intervention cost-effectiveness. Keywords: Genetics, cancer, instrumental variables, healthcare costs, cost-effectiveness, quality of life, Mendelian Randomization
Genome-Wide Association Study of Blood Mercury in European Pregnant Women and Children

Background: Mercury (Hg) is a toxic heavy metal which humans are most commonly exposed to through food chain contamination, especially via fish consumption. Even low-level exposure can be harmful because of the poor clearance rate, particularly for methylmercury. It is likely that genetic variation modifies exposure through changes in the absorption, metabolism, and/or removal of mercury. Associations have been reported between Hg and variants at multiple genetic loci, but in many cases these results are not yet replicated. Methods: This study included two populations: pregnant women from the Avon Longitudinal Study of Parents and Children (ALSPAC, n=2,893) and children from the Human Early Life Exposome (HELIX, n=1,042). Genome-wide testing by cohort was performed by fitting linear regressions models on whole blood Hg levels and Haplotype Reference Consortium imputed single-nucleotide polymorphisms (SNPs). SNP heritability was estimated using linkage disequilibrium (LD)- score regression, and the biological functions of the top variants were investigated using resources which aggregate prior literature. Results: Hg SNP heritability was estimated to be 24.0% (95% CI: 16.9% to 46.4%) for pregnant women. The number of genetic variants independently associated with whole blood mercury levels above a suggestive p-value threshold (P < 1x10-5) was 16 for pregnant women and 21 for children. However, none were replicated in both populations, nor did any pass a stronger genome-wide significant threshold (P < 5x10-8). Several suggestive variants had possible biological links to Hg such as rs146099921 in metal transporter SLC39A14, and two variants (rs28618224, rs7154700) in potassium voltage-gated channels genes. Discussion: There was evidence for a considerable proportion of Hg variance being attributed to genome-wide variation in pregnant women. However, results between pregnant women and children were highly discordant which could reflect differences in metabolism and a gene-age interaction with Hg levels. There were a large number of SNPs suggestively associated with Hg levels, which likely include both true associations and false positives. These interim findings will be expanded following collaboration with additional study groups.
Month-to-month all-cause mortality forecasting: A method to rapidly detect changes in seasonal patterns

Background: Short-term forecasts of all-cause mortality are used retrospectively to estimate the baseline mortality and to obtain excess death after mortality shocks, such as heatwaves and pandemics, have occurred. In this study we propose a flexible method to forecast all-cause mortality in real-time and to rapidly identify short-term changes in all-cause mortality seasonal patterns within an epidemiological year. Methods: We use all-cause monthly death counts and ratios of death counts between adjacent months as inputs. The ratio between one month (earlier month) and the consecutive month (later month) is called later/earlier ratio. We forecast the deaths one-month-ahead based on their proportion to the previous month, defined by the average later/earlier ratio over the preceding years. We provide forecasting intervals by way of a bootstrapping procedure. Results: The method is applied to monthly mortality data for Denmark, France, Spain, and Sweden from 2012 through 2022. Over the epidemiological years before COVID-19, the method captures the variations in winter and summer mortality peaks. The results reflect the synchrony of COVID-19 waves and the corresponding mortality burdens in the four analyzed countries. The forecasts show a higher level of accuracy compared to traditional models for short-term forecasting, i.e., 5-year-average method and Serfling model. Conclusion: The method proposed is attractive for health researchers and governmental offices to aid public health responses, because it uses minimal input data, i.e., monthly all-cause mortality data, which are timely available and comparable across countries.
The Black-Scholes option pricing problem in mathematical finance : generalization and extensions for a large class of stochastic processes

The ability to price risks and devise optimal investment strategies in the presence of an uncertain "random" market is the cornerstone of modern finance theory. We first consider the simplest such problem of a so-called "European call option" initially solved by Black and Scholes using Ito stochastic calculus for markets modelled by a log-Brownien stochastic process. A simple and powerful formalism is presented which allows us to generalize the analysis to a large class of stochastic processes, such as ARCH, jump or Levy processes. We also address the case of correlated Gaussian processes, which is shown to be a good description of three different market indices (MATIF, CAC40, FTSE100). Our main result is the introduction of the concept of an optimal strategy in the sense of (functional) minimization of the risk with respect to the portfolio. If the risk may be made to vanish for particular continuous uncorrelated 'quasiGaussian' stochastic processes (including Black and Scholes model), this is no longer the case for more general stochastic processes. The value of the residual risk is obtained and suggests the concept of risk-corrected option prices. In the presence of very large deviations such as in Levy processes, new criteria for rational fixing of the option prices are discussed. We also apply our method to other types of options, `Asian', `American', and discuss new possibilities (`doubledecker'.). The inclusion of transaction costs leads to the appearance of a natural characteristic trading time scale.
Total and regional skeletal muscle mass references by sport participation and body size in youth

Skeletal muscle mass (SMM) is a relevant indicator of adolescent health influenced by sport participation, body size, and maturation. However, limited data are available from techniques accessible to field professionals. Purpose: To provide reference data of regional and total-body SMM and fat-free mass (FFM) derived from anthropometry among groups of age, sex, and sport participation in healthy Caucasian children and adolescents. Materials and methods: A cross-sectional dataset of 1,438 participants aged 8-18 years was analyzed for this study. Regional and total-body SMM and FFM were estimated from anthropometric variables. Sport participation was obtained by ad hoc questionnaires and maturity offset was calculated using predictive equations. General linear model of mixed factors was used to analyze the variance of FFM or SMM across groups and confounders. Ln-ln regression analysis was applied to describe the scaling relationship between SMM and height. Results: Positive interactions were found between sex, sport participation, and age for regional muscle variables (P<0.05). Adjusted total and regional SMM significantly increased along school-age periods in the active groups (~2.2 kg gain, P<0.05, boys and girls; arm, ~1.3 cm gain, P<0.01, boys), and allometric values were higher in boys than girls for regional muscle (P<0.01). Discussion: The novel contribution of this analysis includes regional indicators of SMM and the relevance of sport participation on SMM accrual in post-pubertal boys. Also, confirms that muscle growth rate varies across sex and phases of puberty, which arises a plausible sexual phenotype/dysmorphism of regional SMM influenced by sport participation.
A conduction cooled superconducting magnet using high-T/sub c/ oxide current leads

We demonstrated a conduction cooled (Nb,Ti)/sub 3/Sn superconducting magnet system without liquid helium. The magnet system is mainly composed of (Nb,Ti)/sub 3/Sn superconducting coil, high-T/sub c/ oxide current leads, a GM cryocooler and a cryostat. The (Nb,Ti)/sub 3/Sn superconducting coil cooled by the second stage of the cryocooler is operated at 11 K in vacuum. The cylindrical (Bi,Pb)/sub 2/Sr/sub 2/Ca/sub 2/Cu/sub 3/ oxide current leads were cooled below 77 K by the cryocooler. This system generates a magnetic field of 4.6 T in a 38 mm room temperature bore at the operating current of 465 A. Two hours holding at the operating current of 400 A were performed. >
Double-pulse machining as a technique for the enhancement of material removal rates in laser machining of metals

Several nanosecond 0.53-μm laser pulses separated by several tens of nanoseconds have been shown to significantly enhance (three to ten times) material removal rates while minimizing redeposition and heat-affected zones. Economic, high-quality, high-aspect ratio holes (>10:1) in metals are produced as a result. A phenomenological model whereby the second laser pulse interacts with the ejecta produced by the first laser pulse and in close proximity to the material surface is consistent with the observations. Incident laser wavelengths of 1.05 and 0.35 μm also benefit from this pulse format.
Development of the turbulent near wake of a tapered thick flat plate

The velocity field and turbulence structure in the near wake of a thick flat plate with a tapered trailing-edge geometry are examined using both hydrogen-bubble flow visualization and hot-film anemometry measurements. Tests were conducted for Re1 = 8.5 × 105 in the region 0 < x+ < 6400 behind the trailing edge. The probe and visualization results indicate a similarity between both (i) velocity and turbulence structure variations wih x+ in the near wake, and (ii) the corresponding changes in similar flow characteristics with y+ within a turbulent boundary layer. In particular, visualization data in the vicinity of the wake centreline reveal the existence of strong streamwise flow structures in the region close (x+ < 270) to the trailing edge. The streamwise orientation of the observed structures diminishes as x+ increases. From hot-film measurements, two separate regions along the wake centreline can be distinguished: (i) a linear growth region which extends over 0 < x+ < 100, wherein the centreline velocity varies linearly with x+; and (ii) a logarithmic growth region for x+ > 270, wherein the centreline velocity varies as log x+. The similarity in behaviour between these regions and the comparable wall region of a turbulent boundary layer suggests the existence of a common functionality. This similarity is demonstrated by a simple linear relationship of the form y+ = Kx+, which is shown to approximately collapse the velocity behaviour both across a turbulent boundary layer and along the wake centreline to a unified set of empirical relationships.
Investigating the efficacy of Ginkgo biloba on the cognitive function of patients undergoing treatment with electric shock: a double-blind clinical trial

Cognitive impairment is common in patients undergoing electroconvulsive therapy (ECT). Researchers are seeking pharmaceutical compounds with low side effects to decrease these outcomes. This study aimed to investigate the effectiveness of Ginkgo biloba therapy on the cognitive function of patients treated with electroshock. In a double-blinded clinical trial, 80 patients with psychiatric disorders who were candidates for ECT in 2019 were randomly assigned to two groups: the intervention group (receiving Ginkgo biloba drug) and the control group (receiving placebo). We used the Mini-Mental State Examination (MMSE) and Wechsler Memory Scale Recipe III (WMS-III) questionnaires to evaluate the efficacy of the drug on the cognitive function at time 0, after 4 sessions and 72 hours post-final session of ECT. The data were analyzed by SPSS version 22, with a significance level of 0.05. Patients' assessment after the intervention showed that the average MMSE scores in the intervention group (28.92±1.04) were statistically higher than in the control group (27.85±1.56). The average scores of the WMS-III in the intervention group and the control group were 97.45±8.04 and 92.00±4.45 after 4 sessions of ECT, and 100.26±8.33 and 92.40±3.92 after the intervention (p=0.001). According to the findings, Gingko biloba increased MMSE and WMS-III scores in older patients (patients over 40 had better scores in both questionnaires, the drug was more effective, and with no side effects).
Nephroprotective effects of Candesartan Cilexetil against Cyclosporine A-induced nephrotoxicity in a rat model

Cyclosporine A (CsA), a well-known immunosuppressive drug, has been prescribed after organ transplantation and in a variety of disorders with an immunological origin. Nephrotoxicity is one of the most frequently stated problems associated with CsA, and therefore the treatment with CsA remains a big challenge. This study sets out to assess the ameliorative influences of Candesartan Cilexetil (CC) on oxidative stress and the nephrotoxic effect of CsA in a rat model. Twenty-four Wister Albino rats, 7–8-week-old, weighing 150–250g, were randomly categorized into three groups (eight animals in each group). These groups were the (1) CsA-treated group, (2) vehicle-treated group, and (3) CC-treated group. Bodyweights were assessed at the start and end of experiments. Renal function test and levels of glutathione peroxidase 1 catalase -CAT (Gpx1), catalase (CAT), superoxide dismutase (SOD), interleukin -2 (IL-2), and malondialdehyde (MDA) were investigated in renal tissues. Histological changes in kidneys were also evaluated. Data showed that levels of urea and creatinine in serum and levels of IL-2 and MDA in renal tissues were elevated in the CsA-treated group, with severe histological changes compared with the control group. Furthermore, tissue levels of Gpx1, CAT, and SOD were significantly decreased in CsA-treated in comparison with the control group. Treatment with CC for the rats subjected to CSA resulted in a marked reduction in levels of serum urea and creatinine and tissue levels of IL-2 and MDA. Levels of Gpx1, CAT, and SOD in renal tissues were greater in the CC-treatment group compared with the CsA-treated group. CC treatment reduced the deterioration of renal morphology compared with CsA treatment. The findings of this study suggest that CC could prevent CSA-induced nephrotoxicity through its anti-inflammatory and antioxidant influences. Considerably more work needs to be done to determine the mechanistic insight behind the ameliorative effect of CC.
Neural Congealing: Aligning Images to a Joint Semantic Atlas

We present Neural Congealing – a zero-shot self-supervised framework for detecting and jointly aligning semantically-common content across a given set of images. Our approach harnesses the power of pre-trained DINO-ViT features to learn: (i) a joint semantic atlas – a 2D grid that captures the mode of DINO-ViT features in the input set, and (ii) dense mappings from the unified atlas to each of the input images. We derive a new robust self-supervised framework that optimizes the atlas representation and mappings per image set, requiring only a few real-world images as input without any additional input information (e.g., segmentation masks). Notably, we design our losses and training paradigm to account only for the shared content under severe variations in appearance, pose, background clutter or other distracting objects. We demonstrate results on a plethora of challenging image sets including sets of mixed domains (e.g., aligning images depicting sculpture and artwork of cats), sets depicting related yet different object categories (e.g., dogs and tigers), or domains for which large-scale training data is scarce (e.g., coffee mugs). We thoroughly evaluate our method and show that our test-time optimization approach performs favorably compared to a state-of-the-art method that requires extensive training on large-scale datasets. Project webpage: https://neural-congealing.github. io/
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection

Methods which utilize the outputs or feature representations of predictive models have emerged as promising approaches for out-of-distribution (ood) detection of image inputs. However, these methods struggle to detect ood inputs that share nuisance values (e.g. background) with in-distribution inputs. The detection of shared-nuisance out-of-distribution (sn-ood) inputs is particularly relevant in real-world applications, as anomalies and in-distribution inputs tend to be captured in the same settings during deployment. In this work, we provide a possible explanation for sn-ood detection failures and propose nuisance-aware ood detection to address them. Nuisance-aware ood detection substitutes a classifier trained via Empirical Risk Minimization (erm) and cross-entropy loss with one that 1. is trained under a distribution where the nuisance-label relationship is broken and 2. yields representations that are independent of the nuisance under this distribution, both marginally and conditioned on the label. We can train a classifier to achieve these objectives using Nuisance-Randomized Distillation (NURD), an algorithm developed for ood generalization under spurious correlations. Output- and feature-based nuisance-aware ood detection perform substantially better than their original counterparts, succeeding even when detection based on domain generalization algorithms fails to improve performance.
The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks

Energy efficient implementations and deployments of Spiking neural networks (SNNs) have been of great interest due to the possibility of developing artificial systems that can achieve the computational powers and energy efficiency of the biological brain. Efficient implementations of SNNs on modern digital hardware are also inspired by advances in machine learning and deep neural networks (DNNs). Two techniques widely employed in the efficient deployment of DNNs – the quantization and pruning of parameters, can both compress the model size, reduce memory footprints, and facilitate low-latency execution. The interaction between quantization and pruning and how they might impact model performance on SNN accelerators is currently unknown. We study various combinations of pruning and quantization in isolation, cumulatively, and simultaneously (jointly) to a state-of-the-art SNN targeting gesture recognition for dynamic vision sensor cameras (DVS). We show that this state-of-the-art model is amenable to aggressive parameter quantization, not suffering from any loss in accuracy down to ternary weights. However, pruning only maintains iso-accuracy up to 80% sparsity, which results in 45% more energy than the best quantization on our architectural model. Applying both pruning and quantization can result in an accuracy loss to offer a favourable trade-off on the energy-accuracy Pareto-frontier for the given hardware configuration.