Miracle-dz/UniFAR-FTUs huggingface dataset:
A detector block-pairwise dead time correction method for improved quantitation with a dedicated BrainPET scanner

‘Objective. Dead time correction (DTC) is an important factor in ensuring accurate quantification in PET measurements. This is currently often achieved using a global DTC method, i.e., an average DTC factor is computed. For PET scanners designed to image dedicated organs, e.g., those used in brain imaging or positron emission mammography (PEM), a substantial amount of the administered radioactivity is located outside of the PET field-of-view (FOV). This activity contributes to the dead time (DT) of the scintillation detectors. Moreover, the count rates of the individual scintillation detectors are potentially very inhomogeneous due to the specific irradiation of each detector, especially for combined MR/PET systems, where radiation shields cannot be applied. Approach: We have developed a block-pairwise DTC method for our Siemens 3T MR BrainPET insert by extending a previously published method that uses the delayed random coincidence count rate to estimate the DT in the individual scans and planes (i.e., scintillation pixel rings). The method was validated in decay experiments using phantoms with a homogenous activity concentration and with and without out-of-FOV activity. Based on a three-compartment phantom, we compared the accuracy and noise properties of the block-pairwise DTC and the global DTC method. Main results. The currently used global DTC led to a substantial positive bias in regions with high activity; the block-pairwise DTC resulted in substantially less bias. The noise level for the block-pairwise DTC was comparable to the global DTC and image reconstructions without any DTC. Finally, we tested the block-pairwise DTC with a data set obtained from volunteer measurements using the mGluR5 (metabotropic glutamate receptor subtype 5) antagonist [11C]ABP688. When the relative differences in activity concentrations obtained with global DTC and block-pairwise DTC for the ACC and the cerebellum GM were compared, the ratios differed by a factor of up to 1.4 at the beginning—when the first injection is administered as a bolus with high radioactivity. Significance. In this work, global DTC was shown to have the potential to introduce quantification bias, while better quantitation accuracy was achieved with the presented block-pairwise DTC method. The method can be implemented in all systems that use the delayed window technique and is particulary expected to improve the quantiation accuracy of dedicated brain PET scanners due to their geometry. ’
The clinical pharmacology of tafenoquine in the radical cure of Plasmodium vivax malaria: An individual patient data meta-analysis

Background: Tafenoquine is a newly licensed antimalarial drug for the radical cure of Plasmodium vivax malaria. The mechanism of action and the optimal dosing - a balance between haemolytic risk and radical cure efficacy - are uncertain. Methods: We pooled individual data from 1,102 patients with acute vivax malaria and 72 healthy volunteers (all with >70% of the population median whole blood glucose-6-phosphate dehydrogenase [G6PD] activity) who were studied in the pre-registration trials of tafenoquine. Plasma tafenoquine concentrations were analysed under a population pharmacokinetic model. A series of Bayesian logistic and Emax dose-response models were fitted to the primary outcome of any P. vivax recurrence within 4 months. Acute vomiting and haemolysis were secondary outcomes. The key exposure variables were: the mg/kg tafenoquine dose; pharmacokinetic summaries of parent compound exposure (AUC[0,{infty}], Cmax) and metabolism (terminal elimination half-life); and day 7 methaemoglobin concentration (%) as a measure of oxidative activity. Results Tafenoquine dose (mg/kg) was a major determinant of recurrence (odds ratio [OR]: 0.70 per mg/kg increase, 95% credible interval [CI]: 0.65-0.75). After adjustment for dose, the tafenoquine terminal elimination half-life (OR: 1.15 per day increase, 95% CI: 1.06-1.25), and the day 7 methaemoglobin concentration (OR: 0.81 per absolute percentage point increase, 95% CI: 0.65 to 0.99), but not the parent compound exposure, were also associated with recurrence. Under the Emax model we estimate that the currently recommended 300mg dose in a 60kg adult (5mg/kg) results in approximately 70% of the maximal obtainable hypnozoiticidal effect. Increasing the dose to 7.5mg/kg (i.e. 450mg) would result in 90% reduction in the risk of P. vivax recurrence. Tafenoquine was well tolerated. No patients had severe haemolysis. Acute vomiting was not dose-related. In patients with normal G6PD enzyme concentrations, tafenoquine dose was associated with minor post-treatment haemoglobin reductions on days 2 or 3 (0.02 g/dL per mg/kg increase [95% CI: 0.04 to 0.00]). Conclusions and interpretation The currently recommended 300mg adult dose for radical cure of vivax malaria has suboptimal efficacy. Increasing to 450mg is predicted to increase radical cure rates substantially. The production of oxidative metabolites is central to tafenoquine's hypnozoiticidal efficacy. Clinical trials of higher tafenoquine doses are now needed to characterise efficacy, safety and tolerability.
Humoral and Cellular Immunogenicity and Safety of 3 Doses of CoronaVac and BNT162b2 in Young Children and Adolescents with Kidney Diseases

Background: Patients with kidney diseases are at risk of severe complications from COVID-19, yet little is known about the effectiveness of COVID-19 vaccines in children and adolescents with kidney diseases. Methods: We investigated the immunogenicity and safety of an accelerated, 3-dose primary series of COVID-19 vaccines among 64 pediatric chronic kidney disease patients (mean age 12.2; 32 male) with or without immunosuppression, dialysis, or kidney transplant. CoronaVac was given to those aged <5 years, 0.1ml BNT162b2 to those aged 5-11 years, and 0.3ml BNT162b2 to those aged 11-18 years. Results: Antibody responses including S-RBD IgG (90.9-100% seropositive) and surrogate virus neutralization (geometric mean sVNT% level, 78.6-94.0%) were significantly elicited by 3 doses of any vaccine. T cell responses were also elicited. Weaker neutralization responses were observed among kidney transplant recipients and non-dialysis children receiving rituximab for glomerular diseases. Neutralization was reduced against Omicron BA.1 compared to wild-type (post-dose 3 sVNT% level; 84% vs 27.2%; p<0.0001). However, T cell response against Omicron BA.1 was preserved, which likely confer protection against severe COVID-19. Hybrid immunity was observed after vaccination in infected patients, as evidenced by higher Omicron BA.1 neutralization response among infected patients receiving 2 doses than those uninfected. Generally mild or moderate adverse reactions following vaccines were reported. Conclusions: Our findings support that an accelerated 3-dose primary series with CoronaVac and BNT162b2 is safe and immunogenic in young children and adolescents with kidney diseases.
Dynamic Copula-Based Nonparametric Estimation of Rank-Tracking Probabilities With Longitudinal Data

: The rank-tracking probability (RTP) is a useful statistical index for measuring the “tracking ability” of longitudinal disease risk factors in biomedical studies. A ﬂexible nonparametric method for estimating the RTP is the two-step un-structured kernel smoothing estimator, which can be applied when there are time-invariant and categorical covariates. We propose a dynamic copula-based smoothing method for estimating the RTP, and show that it is both theoretically and practically superior to the unstructured smoothing method. We derive the asymptotic mean squared errors of the copula-based kernel smoothing estimators, and use a simulation study to show that the proposed method has smaller empirical mean squared errors than those of the unstructured smoothing method. We apply the proposed estimation method to a longitudinal epidemiological study and show that it leads to clinically meaningful ﬁndings in biomedical applications.
Studentization and deriving accurate p-values

The original Studentization was the conversion of a sample mean departure into the familiar t-statistic, plus the derivation of the corresponding Student distribution function; the observed value of the distribution function is the observed p-value, as presented in an elemental form. We examine this process in a broadly general context: a null statistical model is available together with observed data; a statistic t(y) has been proposed as a plausible measure of the location of the data relative to what is expected under the null; a modified statistic, say ~t(y), is developed that is ancillary; the corresponding distribution function is determined, exactly or approximately; and the observed value of the distribution function is the p-value or percentage position of the data with respect to the model. Such p-values have had extensive coverage in the recent Bayesian literature, with many variations and some preference for two versions labelled pppost and pcpred. The bootstrap method also directly addresses this Studentization process. We use recent likelihood theory that gives a third order factorization of a regular statistical model into a marginal density for a full dimensional ancillary and a conditional density for the maximum likelihood variable. The full dimensional ancillary is shown to lead to an explicit determination of the Studentized version ~t(y) together with a highly accurate approximation to its distribution function; the observed value of the distribution function is the p-value and is available numerically by direct calculation or by Markov chain Monte Carlo or by other simulations. In this paper, for any given initial or trial test statistic proposed as a location indicator for a data point, we develop: an ancillary based p-value designated panc; a special version of the Bayesian pcpred; and a bootstrap based p-value designated pbs. We then show under moderate regularity that these are equivalent to the third order and have uniqueness as a determination of the statistical location of the data point, as of course derived from the initial location measure. We also show that these p-values have a uniform distribution to third order, as based on calculations in the moderate-deviations region. For implementation the Bayesian and likelihood procedures would perhaps require the same numerical computations, while the bootstrap would require a magnitude more in computation and would perhaps not be accessible. Examples are given to indicate the ease and exibility of the approach
Modern Theory of Critical Phenomena

An important contributor to our current understanding of critical phenomena, Ma introduces the beginner--especially the graduate student with no previous knowledge of the subject-to fundamental theoretical concepts such as mean field theory, the scaling hypothesis, and the renormalization group. He then goes on to apply the renormalization group to selected problems, with emphasis on the underlying physics and the basic assumptions involved.
Generalizations of theorems of Chernoff and Savage on the asymptotic normality of test statistics

The purpose of the present paper is to generalize the results obtained by Chernoff and Savage [5] on the asymptotic normality of a large class of twosample nonparametric test statistics. The assumptions made in [5] involve a certain function J which is assumed to possess two derivatives satisfying boundedness restrictions. However, certain test statistics, for instance those proposed by Ansari and Bradley [1] and Siegel and Tukey [15], do not satisfy the regularity conditions imposed by Chernoff and Savage. In particular, the first derivative of the appropriate function J fails to exist at certain points, so that the arguments of Chernoff and Savage are no longer directly applicable. It will be shown here that the basic asymptotic normality result of [5] remains valid without any assumptions whatsoever or the existence of second derivatives. The assumption of existence of the first derivative is replaced by an assumption of absolute continuity. It should be noted that even this assumption is somewhat too stringent if one is willing to impose restrictions on the couple (F, G). However, the discussion of such possibilities remains beyond the purview of the present paper. Section 2 of the paper gives a number of definitions which will be used throughout. Section 3 summarizes some properties of the set of functions J which will be used later. The main results are a lemma (lemma 2) on uniform square integrability and a continuity theorem (lemma 3) for the variances of the normal approximations to the distributions of the Chernoff-Savage statistics. Section 4 gives an account of convergence properties of empirical cumulative distributions and of their inverse functions. The tails of the Chernoff-Savage statistics are bounded in section 5, and the main asymptotic normality theorem appears in section 6. Finally, natural extensions to the c-sample situation are provided in section 7.
Free Actions of Extraspecial $p$-Groups on $S^n \times S^n$

Let $p$ be an odd regular prime, and let $G_p$ denote the extraspecial $p$--group of order $p^{3}$ and exponent $p$. We show that $G_p$ acts freely and smoothly on $S^{2p-1} \times S^{2p-1}$. For $p=3$ we explicitly construct a free smooth action of a Lie group $\widetilde{G}_3$ containing $G_3$ on $S^{5} \times S^{5}$. In addition, we show that any finite odd order subgroup of the exceptional Lie group $\Gtwo $ admits a free smooth action on $S^{11}\times S^{11}$.
Real-time Physics Based Simulation for 3D Computer Graphics

Restoration of realistic animation is a critical part in the area of computer graphics. The goal of this sort of simulation is to imitate the behavior of the transformation in real life to the greatest extent. Physics-based simulation provides a solid background and proficient theories that can be applied in the simulation. In this dissertation, I will present real-time simulations which are physics-based in the area of terrain deformation and ship oscillations. When ground vehicles navigate on soft terrains such as sand, snow and mud, they often leave distinctive tracks. The realistic simulation of such vehicle-terrain interaction is important for ground based visual simulations and many video games. However, the existing research in terrain deformation has not addressed this issue effectively. In this dissertation, I present a new terrain deformation algorithm for simulating vehicle-terrain interaction in real time. The algorithm is based on the classic terramechanics theories, and calculates terrain deformation according to the vehicle load, velocity, tire size, and soil concentration. As a result, this algorithm can simulate different vehicle tracks on different types of terrains with different vehicle properties. I demonstrate my algorithm by vehicle tracks on soft terrain. In the field of ship oscillation simulation, I propose a new method for simulating ship motions in waves. Although there have been plenty of previous work on physics based fluidsolid simulation, most of these methods are not suitable for real-time applications. In particular, few methods are designed specifically for simulating ship motion in waves. My method is based on physics theories of ship motion, but with necessary simplifications to ensure real-time performance. My results show that this method is well suited to simulate sophisticated ship motions in real time applications. INDEX WORDS: Terrain deformation, Ship oscillation, Virtual reality REAL-TIME PHYSICS BASED SIMULATION FOR 3D COMPUTER GRAPHICS AT GEORGIA STATE UNIVERSITY
Programación VHDL de algoritmos de codificación para dispositivos de hardware reconfigurable

Esta investigacion trata acerca de la implementacion sobre hardware reconfigurable de modulos de operacion en algebra de campos finitos de Galois, GF (2m), aplicados al area de codificacion. Se ha seleccionado esta area de las matematicas por ser la base de varios algoritmos en el area de correccion de errores y procesamiento digital de senales para criptografia,los cuales, por razones de desempeno y seguridad, es preferible implementarlos a nivel de hardware sobre dispositivos reconfigurables . El desarrollo metodologico comprende la definicion de los componentes y establecimiento del modelo, usando para ello la sintaxis en lenguaje desciptor de hardware (VHDL)y es capturado el diseno sobre el dispositivo de arreglos de compuertas programables (FPGA); finalmente se llevo acabo la validacion de las salidas del diseno utilizando ModelSim 5.7 a traves de simulaciones
Beat Transformer: Demixed Beat and Downbeat Tracking with Dilated Self-Attention

We propose Beat Transformer, a novel Transformer encoder architecture for joint beat and downbeat tracking. Different from previous models that track beats solely based on the spectrogram of an audio mixture, our model deals with demixed spectrograms with multiple instrument channels. This is inspired by the fact that humans perceive metrical structures from richer musical contexts, such as chord progression and instrumentation. To this end, we develop a Transformer model with both time-wise attention and instrument-wise attention to capture deep-buried metrical cues. Moreover, our model adopts a novel dilated self-attention mechanism, which achieves powerful hierarchical modelling with only linear complexity. Experiments demonstrate a significant improvement in demixed beat tracking over the non-demixed version. Also, Beat Transformer achieves up to 4% point improvement in downbeat tracking accuracy over the TCN architectures. We further discover an interpretable attention pattern that mirrors our understanding of hierarchical metrical structures.
Feedback Loops in Opinion Dynamics of Agent-Based Models with Multiplicative Noise

We introduce an agent-based model for co-evolving opinions and social dynamics, under the influence of multiplicative noise. In this model, every agent is characterized by a position in a social space and a continuous opinion state variable. Agents’ movements are governed by the positions and opinions of other agents and similarly, the opinion dynamics are influenced by agents’ spatial proximity and their opinion similarity. Using numerical simulations and formal analyses, we study this feedback loop between opinion dynamics and the mobility of agents in a social space. We investigate the behaviour of this ABM in different regimes and explore the influence of various factors on the appearance of emerging phenomena such as group formation and opinion consensus. We study the empirical distribution, and, in the limit of infinite number of agents, we derive a corresponding reduced model given by a partial differential equation (PDE). Finally, using numerical examples, we show that a resulting PDE model is a good approximation of the original ABM.
Towards self-attention based navigation in the real world

Vision-based navigation requires processing complex information to make task-orientated decisions. Applications include autonomous robots, self-driving cars, and assistive vision for humans. One of the key elements in the process is the extraction and selection of relevant features in pixel space upon which to base action choices, for which Machine Learning techniques are well suited. However, Deep Rein-forcement Learning agents trained in simulation often exhibit unsatisfactory results when deployed in the real-world due to perceptual diﬀerences known as the reality gap . An approach that is yet to be explored to bridge this gap is self-attention. In this paper we (1) perform a systematic exploration of the hyperpa-rameter space for self-attention based navigation of 3D environments and qualitatively ap-praise behaviour observed from diﬀerent hyper-parameter sets, including their ability to generalise; (2) present strategies to improve the agents’ generalisation abilities and navigation behaviour; and (3) show how models trained in simulation are capable of processing real world images meaningfully in real time. To our knowledge, this is the ﬁrst demonstration of a self-attention based agent successfully trained in navigating a 3D action space, using less than 4000 parameters.
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks

Solutions to differential equations are of significant scientific and engineering relevance. Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving differential equations, but they lack a theoretical justification for the use of any particular loss function. This work presents Differential Equation GAN (DEQGAN), a novel method for solving differential equations using generative adversarial networks to"learn the loss function"for optimizing the neural network. Presenting results on a suite of twelve ordinary and partial differential equations, including the nonlinear Burgers', Allen-Cahn, Hamilton, and modified Einstein's gravity equations, we show that DEQGAN can obtain multiple orders of magnitude lower mean squared errors than PINNs that use $L_2$, $L_1$, and Huber loss functions. We also show that DEQGAN achieves solution accuracies that are competitive with popular numerical methods. Finally, we present two methods to improve the robustness of DEQGAN to different hyperparameter settings.
Gluing of Lorentzian length spaces and the causal ladder

We investigate the compatibility of Lorentzian amalgamation with various properties of Lorentzian pre-length spaces. In particular, we give conditions under which gluing of Lorentzian length spaces yields again a Lorentzian length space and we give criteria which preserve many steps of the causal ladder. We conclude with some thoughts on the causal properties which seem not so easily transferable.
AB
 /Push-Pull method for distributed optimization in time-varying directed networks

In this paper, we study the distributed optimization problem for a system of agents embedded in time-varying directed communication networks. Each agent has its own cost function and agents cooperate to determine the global decision that minimizes the summation of all individual cost functions. We consider the so-called push-pull gradient-based algorithm (termed as AB/Push-Pull) which employs both row- and column-stochastic weights simultaneously to track the optimal decision and the gradient of the global cost while ensuring consensus and optimality. We show that the algorithm converges linearly to the optimal solution over a time-varying directed network for a constant stepsize when the agent's cost function is smooth and strongly convex. The linear convergence of the method has been shown in Saadatniaki et al. (2020), where the multi-step consensus contraction parameters for row- and column-stochastic mixing matrices are not directly related to the underlying graph structure, and the explicit range for the stepsize value is not provided. With respect to Saadatniaki et al. (2020), the novelty of this work is twofold: (1) we establish the one-step consensus contraction for both row- and column-stochastic mixing matrices with the contraction parameters given explicitly in terms of the graph diameter and other graph properties; and (2) we provide explicit upper bounds for the stepsize value in terms of the properties of the cost functions, the mixing matrices, and the graph connectivity structure.