Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of two T-cell assays to evaluate T-cell responses to SARS-CoV-2 following vaccination in naïve and convalescent healthcare workers

Background: T cell responses to SARS-CoV-2 following infection and vaccination are less characterised than antibody responses, due to a more complex experimental pathway. Methods: We measured T cell responses in 108 healthcare workers (HCWs) in an observational cohort study, using the commercialised Oxford Immunotec T-SPOT Discovery SARS-CoV-2 assay (OI T-SPOT) and the PITCH ELISpot protocol established for academic research settings. Results: Both assays detected T cell responses to SARS-CoV-2 spike, membrane and nucleocapsid proteins. Responses were significantly lower when reported by OI T-SPOT than by PITCH ELISpot. Four weeks after two doses of either Pfizer/BioNTech BNT162b or ChAdOx1 nCoV-19 AZD1222 vaccine, the responder rate was 63% for OI T-SPOT Panels1+2 (peptides representing SARS-CoV-2 spike protein excluding regions present in seasonal coronaviruses), 69% for OI T-SPOT Panel 14 (peptides representing the entire SARS-CoV-2 spike), and 94% for the PITCH ELISpot assay. The two OI T-SPOT panels correlated strongly with each other showing that either readout quantifies spike-specific T cell responses, although the correlation between the OI T-SPOT panels and the PITCH ELISpot was moderate. Conclusion: The standardisation, relative scalability and longer interval between blood acquisition and processing are advantages of the commercial OI T-SPOT assay. However, the OI T-SPOT assay measures T cell responses at a significantly lower magnitude compared to the PITCH ELISpot assay, detecting T cell responses in a lower proportion of vaccinees. This has implications for the reporting of low-level T cell responses that may be observed in patient populations and for the assessment of T cell durability after vaccination.
The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

The effectiveness of artemisinin-based combination therapies (ACTs) to treat Plasmodium falciparum malaria is threatened by resistance. The complex interplay between sources of selective pressure - treatment properties, biological factors, transmission intensity, and access to treatment - obscures understanding how, when, and why resistance establishes and spreads across different locations. We developed a disease modelling approach with emulator-based global sensitivity analysis to systematically quantify which of these factors drive establishment and spread of drug resistance. Drug resistance was more likely to evolve in low transmission settings due to the lower levels of (i) immunity and (ii) within-host competition between genotypes. Spread of parasites resistant to artemisinin partner drugs depended on the period of low drug concentration (known as the selection window). Spread of partial artemisinin resistance was slowed with prolonged parasite exposure to artemisinin derivatives and accelerated when the parasite was also resistant to the partner drug. Thus, to slow the spread of partial artemisinin resistance, molecular surveillance should be supported to detect resistance to partner drugs and to change ACTs accordingly. Furthermore, implementing more sustainable artemisinin-based therapies will require extending parasite exposure to artemisinin derivatives, and mitigating the selection windows of partner drugs, which could be achieved by including an additional long-acting drug.
Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components

We present DeepDECS, a new method for the synthesis of correct-by-construction discrete-event controllers for autonomous systems that use deep neural network (DNN) classifiers for the perception step of their decision-making processes. Despite major advances in deep learning in recent years, providing safety guarantees for these systems remains very challenging. Our controller synthesis method addresses this challenge by integrating DNN verification with the synthesis of verified Markov models. The synthesised models correspond to discrete-event controllers guaranteed to satisfy the safety, dependability and performance requirements of the autonomous system, and to be Pareto optimal with respect to a set of optimisation objectives. We use the method in simulation to synthesise controllers for mobile-robot collision mitigation and for maintaining driver attentiveness in shared-control autonomous driving.
Painlevé-type asymptotics for the defocusing Hirota equation in transition region

We consider the long-time asymptotics for the defocusing Hirota equation with Schwartz Cauchy data in the transition region. On the basis of direct and inverse scattering transform of the Lax pair of Hirota equations, we first express the solution of the Cauchy problem in terms of the solution of a Riemann–Hilbert problem. Further, we apply nonlinear steepest descent analysis to obtain the long-time asymptotics of the solution in the critical transition region |x/t−(α2/3β)|t2/3≤M, where M is a positive constant. Our result shows that the long-time asymptotics of the Hirota equation can be expressed in terms of the solution of the Painlevé II equation.
Node-wise monotone barrier coupling law for central pattern generation

With the aim of providing new insights into the mechanisms behind the emergence of collective behaviors via nonlinear coupling, we study a node-wise monotone barrier coupling law, motivated by the synaptic coupling of neural central pattern generators. It is illustrated that this coupling imitates the desirable properties of neural central pattern generators. In particular, the coupling law 1) allows us to assign multiple central patterns on the circle and 2) allows for rapid switching between diﬀerent patterns via simple ‘kicks’. In the end, we achieve full control by partitioning the state space by utilizing a barrier eﬀect and assigning a unique steady-state behavior to each element of the resulting partition. We analyze the global behavior and study the viability of the design.
Inequalities and equalities on the joint and generalized spectral and essential spectral radius of the Hadamard geometric mean of bounded sets of positive kernel operators

We prove new inequalities and equalities for the generalized and the joint spectral radius (and their essential versions) of Hadamard (Schur) geometric means of bounded sets of positive kernel operators on Banach function spaces. In the case of non-negative matrices that define operators on Banach sequences, we obtain additional results. Our results extend the results of several authors that appeared relatively recently.
Stellar Models of Betelgeuse Constrained Using Observed Surface Conditions

We study stellar models for Betelgeuse using the HR diagram and surface abundances as observational constraints. Previous studies on Betelgeuse have not systematically investigated the surface abundances, but we believe they can be impacted by, and thus be used as an observational constraint for various parameters such as initial mass, rotation, and overshoot scheme. We investigate stellar models with varying initial mass as they evolve past the main sequence, and we examine the red supergiant (RSG) properties in detail. For each mass, we vary the initial rotation up to ∼300 km s−1, and test two different overshoot parameters. Overall, the acceptable initial mass range is 12–25 M ⊙, but for nonrotating models only, the range is decreased to 15–24 M ⊙. Also for rotating models, we find that v/v K = 0.3 is the upper limit for initial rotation, as more rapidly rotating models are unable to fit to Betelgeuse’s surface abundances as an RSG. In addition, we report two possibilities for the current stage of evolution, core helium burning or core carbon burning and beyond. We find that certain 17 M ⊙ models could fit both stages. Finally, we discuss the implications of our results in the context of merger scenarios which have been suggested as a mechanism to attain the observed surface velocity of Betelgeuse.
Redefining cyclic electron flow around photosystem I (cef1): The induction, pathway, and role of cef1 in c3 plants

Cyclic electron flow (CEFI) has been proposed to balance the chloroplast energy budget, but the pathway, mechanism and physiological role remains unclear. We isolated a new class of mutant in Arabidopsis thaliana, hcef for high CEF1, which shows constitutively elevated CEF1. The first of these, hcef1 was mapped to chloroplast fructose-1,6-bisphosphatase. Crossing hcef1 with pgr5, which is deficient in the antimycin A-sensitive pathway for plastoquinone reduction, resulted in a double mutant which maintained the high CEF1 phenotype, implying that the PGR5-dependent pathway is not involved. By contrast, crossing hcef1 with crr2-2, deficient in thylakoid NADPH dehydrogenase (NDH) complex, results in a double mutant which was highly light sensitive and lacked elevated CEF1, suggesting that NDH plays a direct role in catalyzing or regulating CEF1. Additionally, the NdhI component of the NDH complex was highly expressed in hcef1 whereas other photosynthetic complexes, as well as PGR5 decreased. We propose that 1) NDH is specifically up-regulated in hcef1, allowing for increased CEF1; 2) the hcef1 mutation imposes an elevated ATP demand that may trigger CEF1; and 3) alternative mechanisms for augmenting ATP cannot compensate for the loss of CEF1 through NDH.
Control of Separation in Turbine Boundary Layers

The opportunity for the reattachment and control of separated flows occurs in inlets, compressors, transition ducts and turbines. Passive and active control of separated flows has been demonstrated successfully by a number of techniques which employ the introduction of longitudinal or streamwise vortices. The role of these vortices is initially to reenergize the wall boundary layer flow by entraining and redistributing momentum from the primary flow to the wall layer and enhance early transition. A chain of non-linear interactions of these unsteady vortices with large scale unsteady separation vortices and the shed shear layer results in significant alteration of the circulation. The resulting increased circulation allows higher blade loadings, reduced part count, as well as increased performance at low Reynolds numbers. Flow control location has been investigated at chord locations ahead, at, and after separation. Passive dimples with single and multiple rows, varied dimple location and dimple shape have been investigated. Initial investigations of a single row of dimples and their wakes on a high pressure turbine vane ring have been performed at Reynolds numbers down to 13,500 in a full scale matched parameter rig. Properly placed dimples reattach separated flows at all Reynolds numbers investigated. Computations for the dimple geometries with VBI, MISES, and Fluent have been carried out to determine initial separation, compressible implications, reattachment locations, and predicted wake profiles or loss coefficients. Steady and pulsed vortex generator jets with duty cycles down to 1%, have both demonstrated reattachment and reduction of total losses in excess of 40% at Reynolds numbers down to 25,000, without incurring significant additional losses at higher Reynolds numbers. Pulsed vortex generator jets with a duty cycle of 1% have demonstrated blowing coefficients of <10 -4 .
Bayesian calibration of simulation models: A tutorial and an Australian smoking behaviour model

Simulation models of epidemiological, biological, ecological, and environmental processes are increasingly being calibrated using Bayesian statistics. The Bayesian approach provides simple rules to synthesise multiple data sources and to calculate uncertainty in model output due to uncertainty in the calibration data. As the number of tutorials and studies published grow, the solutions to common diﬃculties in Bayesian calibration across these ﬁelds have become more apparent, and a step-by-step process for successful calibration across all these ﬁelds is emerging. We provide a statement of the key steps in a Bayesian calibration, and we outline analyses and approaches to each step that have emerged from one or more of these applied sciences. Thus we present a synthesis of Bayesian calibration methodologies that cut across a number of scientiﬁc disciplines. To demonstrate these steps and to provide further detail on the computations involved in Bayesian calibration, we calibrated a compartmental model of tobacco smoking behaviour in Australia. We found that the proportion of a birth cohort estimated to take up smoking before they reach age 20 years in 2016 was at its lowest value since the early 20 th century, and that quit rates were at their highest. As a novel outcome, we quantiﬁed the rate that ex-smokers switched to reporting as a ‘never smoker’ when surveyed later in life; a phenomenon that, to our knowledge, has never been quantiﬁed using cross-sectional survey data.
Neutral Bremsstrahlung Emission in Xenon Unveiled

We present evidence of non-excimer-based secondary scintillation in gaseous xenon, obtained using both the NEXT-White TPC and a dedicated setup. Detailed comparison with first-principle calculations allows us to assign this scintillation mechanism to neutral bremsstrahlung (NBrS), a process that has been postulated to exist in xenon that has been largely overlooked. For photon emission below 1000 nm, the NBrS yield increases from about 10$^{-2}$ photon/e$^{-}$ cm$^{-1}$ bar$^{-1}$ at pressure-reduced electric field values of 50 V cm$^{-1}$ bar$^{-1}$ to above 3$\times$10$^{-1}$ photon/e$^{-}$ cm$^{-1}$ bar$^{-1}$ at 500 V cm$^{-1}$ bar$^{-1}$. Above 1.5 kV cm$^{-1}$ bar$^{-1}$, values that are typically employed for electroluminescence, it is estimated that NBrS is present with an intensity around 1 photon/e$^{-}$ cm$^{-1}$ bar$^{-1}$, which is about two orders of magnitude lower than conventional, excimer-based electroluminescence. Despite being fainter than its excimeric counterpart, our calculations reveal that NBrS causes luminous backgrounds that can interfere, in either gas or liquid phase, with the ability to distinguish and/or to precisely measure low primary-scintillation signals (S1). In particular, we show this to be the case in the"buffer"and"veto"regions, where keeping the electric field below the electroluminescence (EL) threshold will not suffice to extinguish secondary scintillation. The electric field in these regions should be chosen carefully to avoid intolerable levels of NBrS emission. Furthermore, we show that this new source of light emission opens up a viable path towards obtaining S2 signals for discrimination purposes in future single-phase liquid TPCs for neutrino and dark matter physics, with estimated yields up to 20-50 photons/e$^{-}$ cm$^{-1}$.
Fine-grained Rim Formation via Kinetic Dust Aggregation in Shock Waves Around Evaporating Icy Planetesimals

Fine-grained rims (FGRs) are frequently found around chondrules in primitive chondrites. The remarkable feature of FGRs is their submicron-sized and nonporous nature. The typical thickness of FGRs around chondrules is 10–100 μm. Recently, a novel idea was proposed for the origin of FGRs: high-speed collisions between chondrules and fine dust grains called the kinetic dust aggregation process. Experimental studies revealed that (sub)micron-sized ceramic particles can stick to a ceramic substrate in a vacuum when the impact velocity is approximately in the range of 0.1–1 km s−1. In this study, we examine the possibility of FGR formation via kinetic dust aggregation in chondrule-forming shock waves. When shock waves are created by undifferentiated icy planetesimals, fine dust grains would be released from the planetary surface due to the evaporation of icy planetesimals. We consider the dynamics of chondrules behind the shock front and calculate the growth of FGRs via kinetic dust aggregation based on simple one-dimensional calculations. We found that nonporous FGRs with a thickness of 10–100 μm would be formed in shock waves around evaporating icy planetesimals.
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models

Recent research has shown the existence of significant redundancy in large Transformer models. One can prune the redundant parameters without significantly sacrificing the generalization performance. However, we question whether the redundant parameters could have contributed more if they were properly trained. To answer this question, we propose a novel training strategy that encourages all parameters to be trained sufficiently. Specifically, we adaptively adjust the learning rate for each parameter according to its sensitivity, a robust gradient-based measure reflecting this parameter's contribution to the model performance. A parameter with low sensitivity is redundant, and we improve its fitting by increasing its learning rate. In contrast, a parameter with high sensitivity is well-trained, and we regularize it by decreasing its learning rate to prevent further overfitting. We conduct extensive experiments on natural language understanding, neural machine translation, and image classification to demonstrate the effectiveness of the proposed schedule. Analysis shows that the proposed schedule indeed reduces the redundancy and improves generalization performance.
Simple approach to current-induced bond weakening in ballistic conductors

We present a simple, first principles scheme for calculating mechanical properties of nonequilibrium bulk systems assuming an ideal ballistic distribution function for the electronic states described by the external voltage bias. This allows for fast calculations of estimates of the current-induced stresses inside bulk systems carrying a ballistic current. The stress is calculated using the Hellmann-Feynman theorem, and is in agreement with the derivative of the nonequilibrium free energy. We illustrate the theory and present results for one-dimensional (1D) metal chains. We find that the susceptibility of the yield stress to the applied voltage agrees with the ordering of break voltages among the metals found in experiments. In particular, gold is seen to be the most stable under strong current, while aluminum is the least stable.
Towards Training Reproducible Deep Learning Models

Reproducibility is an increasing concern in Artificial Intelligence (AI), particularly in the area of Deep Learning (DL). Being able to reproduce DL models is crucial for AI-based systems, as it is closely tied to various tasks like training, testing, debugging, and auditing. However, DL models are challenging to be reproduced due to issues like randomness in the software (e.g., DL algorithms) and non-determinism in the hardware (e.g., GPU). There are various practices to mitigate some of the aforementioned issues. However, many of them are either too intrusive or can only work for a specific usage context. In this paper, we propose a systematic approach to training reproducible DL models. Our approach includes three main parts: (1) a set of general criteria to thoroughly evaluate the reproducibility of DL models for two different domains, (2) a unified framework which leverages a record-and-replay technique to mitigate software-related randomness and a profile-and-patch technique to control hardware-related non-determinism, and (3) a reproducibility guideline which explains the rationales and the mitigation strategies on conducting a reproducible training process for DL models. Case study results show our approach can successfully reproduce six open source and one commercial DL models.
Free-breathing motion compensated 4D (3D+respiration) T2-weighted turbo spin-echo MRI for body imaging

Purpose: To develop and evaluate a free-breathing respiratory motion compensated 4D (3D+respiration) T2-weighted turbo spin echo sequence with application to radiology and MR-guided radiotherapy. Methods: k-space data are continuously acquired using a rewound Cartesian acquisition with spiral profile ordering (rCASPR) to provide matching contrast to the conventional linear phase encode ordering and to sort data into multiple respiratory phases. Low-resolution respiratory-correlated 4D images were reconstructed with compressed sensing and used to estimate non-rigid deformation vector fields, which were subsequently used for a motion compensated image reconstruction. rCASPR sampling was compared to linear and CASPR sampling in terms of point-spread-function (PSF) and image contrast with in silico, phantom and in vivo experiments. Reconstruction parameters for lowresolution 4D-MRI (spatial resolution and temporal regularization) were determined using a grid search. The proposed motion compensated rCASPR was evaluated in eight healthy volunteers and compared to free-breathing scans with linear sampling. Image quality was compared based on visual inspection and quantitatively by means of the gradient entropy. Results: rCASPR provided a superior PSF (similar in ky and narrower in kz) and showed no considerable differences in images contrast compared to linear sampling. The optimal 4D-MRI reconstruction parameters were spatial resolution=4.5 mm3 (3x reduction) and λt = 1 · 10−4. The groupwise average gradient entropy was 22.31± 0.07 for linear, 22.20± 0.09 for rCASPR, 22.14± 0.10 for soft-gated rCASPR and 22.02± 0.11 for motion compensated rCASPR. Conclusion: The proposed motion compensated rCASPR enables high quality free-breathing T2-TSE with minimal changes in image contrast and scan time. The proposed method therefore enables direct transfer of clinically used 3D TSE sequences to free-breathing.
FEAT: Face Editing with Attention

Employing the latent space of pretrained generators has recently been shown to be an effective means for GAN-based face manipulation. The success of this approach heavily relies on the innate disentanglement of the latent space axes of the generator. However, face manipulation often intends to affect local regions only, while common generators do not tend to have the necessary spatial disentanglement. In this paper, we build on the StyleGAN generator, and present a method that explicitly encourages face manipulation to focus on the intended regions by incorporating learned attention maps. During the generation of the edited image, the attention map serves as a mask that guides a blending between the original features and the modified ones. The guidance for the latent space edits is achieved by employing CLIP, which has recently been shown to be effective for text-driven edits. We perform extensive experiments and show that our method can perform disentangled and controllable face manipulations based on text descriptions by attending to the relevant regions only. Both qualitative and quantitative experimental results demonstrate the superiority of our method for facial region editing over alternative methods.
Spectrally adapted physics-informed neural networks for solving unbounded domain problems

Solving analytically intractable partial differential equations (PDEs) that involve at least one variable defined on an unbounded domain arises in numerous physical applications. Accurately solving unbounded domain PDEs requires efficient numerical methods that can resolve the dependence of the PDE on the unbounded variable over at least several orders of magnitude. We propose a solution to such problems by combining two classes of numerical methods: (i) adaptive spectral methods and (ii) physics-informed neural networks (PINNs). The numerical approach that we develop takes advantage of the ability of PINNs to easily implement high-order numerical schemes to efficiently solve PDEs and extrapolate numerical solutions at any point in space and time. We then show how recently introduced adaptive techniques for spectral methods can be integrated into PINN-based PDE solvers to obtain numerical solutions of unbounded domain problems that cannot be efficiently approximated by standard PINNs. Through a number of examples, we demonstrate the advantages of the proposed spectrally adapted PINNs in solving PDEs and estimating model parameters from noisy observations in unbounded domains.