Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Optimization of 3D Non-Cartesian Sampling Trajectory (SNOPY)

Purpose: Optimizing 3D k-space sampling trajectories for eﬃcient MRI is important yet challenging. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patterns via data-driven optimization. Methods: We built a diﬀerentiable MRI system model to enable gradient-based methods for sampling trajectory optimization. By combining training losses, the algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method can either optimize the gradient waveform (spline-based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method optimizes sampling trajectories synergistically with either model-based or learning-based reconstruction methods. We proposed several strategies to alleviate the severe non-convexity and huge computation demand posed by the high-dimensional optimization. The corresponding code is orga-nized as an open-source, easy-to-use toolbox. Results: We applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the reconstruction PSNR of a 3D kooshball trajectory was increased by 4 dB with SNOPY optimization. In the prospective studies, by optimizing the rotation angles of a stack-of-stars (SOS) trajectory, SNOPY improved the PSNR by 1.4 dB compared to the best empirical method. Optimizing the gradient waveform of a rotational EPI trajectory improved subjects’ rating of the PNS eﬀect from ‘strong’ to ‘mild. ’ Conclusion: SNOPY provides an eﬃcient data-driven and optimization-based method to tailor non-Cartesian sampling trajectories.
MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translation

We present a novel method for exemplar-based image translation, called matching interleaved diffusion models (MIDMs). Most existing methods for this task were formulated as GAN-based matching-then-generation framework. However, in this framework, matching errors induced by the difficulty of semantic matching across cross-domain, e.g., sketch and photo, can be easily propagated to the generation step, which in turn leads to the degenerated results. Motivated by the recent success of diffusion models, overcoming the shortcomings of GANs, we incorporate the diffusion models to overcome these limitations. Specifically, we formulate a diffusion-based matching-and-generation framework that interleaves cross-domain matching and diffusion steps in the latent space by iteratively feeding the intermediate warp into the noising process and denoising it to generate a translated image. In addition, to improve the reliability of diffusion process, we design confidence-aware process using cycle-consistency to consider only confident regions during translation. Experimental results show that our MIDMs generate more plausible images than state-of-the-art methods.
Marginal stability and the metamorphosis of Bogomol’nyi-Prasad-Sommerfield states

We discuss the restructuring of the BPS spectrum which occurs on certain submanifolds of the moduli or parameter space\char22{}the curves of the marginal stability (CMS)\char22{}using quasiclassical methods. We argue that in general a ``composite'' BPS soliton swells in coordinate space as one approaches the CMS and that, as a bound state of two ``primary'' solitons, its dynamics in this region is determined by nonrelativistic supersymmetric quantum mechanics. Near the CMS the bound state has a wave function which is highly spread out. Precisely on the CMS the bound state level reaches the continuum, the composite state delocalizes in coordinate space, and restructuring of the spectrum can occur. We present a detailed analysis of this behavior in a two-dimensional $\mathcal{N}=2$ Wess-Zumino model with two chiral fields, and then discuss how it arises in the context of ``composite'' dyons near weak coupling CMS curves in $\mathcal{N}=2$ supersymmetric gauge theories. We also consider cases where some states become massless on the CMS.
On the Wiener index and Laplacian coefficients of graphs with given diameter or radius

Let $G$ be a simple undirected $n$-vertex graph with the characteristic polynomial of its Laplacian matrix $L(G)$, $\det (\lambda I - L (G))=\sum_{k = 0}^n (-1)^k c_k \lambda^{n - k}$. It is well known that for trees the Laplacian coefficient $c_{n-2}$ is equal to the Wiener index of $G$. Using a result of Zhou and Gutman on the relation between the Laplacian coefficients and the matching numbers in subdivided bipartite graphs, we characterize first the trees with given diameter and then the connected graphs with given radius which simultaneously minimize all Laplacian coefficients. This approach generalizes recent results of Liu and Pan [MATCH Commun. Math. Comput. Chem. 60 (2008), 85--94] and Wang and Guo [MATCH Commun. Math. Comput. Chem. 60 (2008), 609--622] who characterized $n$-vertex trees with fixed diameter $d$ which minimize the Wiener index. In conclusion, we illustrate on examples with Wiener and modified hyper-Wiener index that the opposite problem of simultaneously maximizing all Laplacian coefficients has no solution.
Memory-Augmented Graph Neural Networks: A Neuroscience Perspective

—Graph neural networks (GNNs) have been extensively used for many domains where data are represented as graphs, including social networks, recommender systems, biology, chemistry, etc. Recently, the expressive power of GNNs has drawn much interest. It has been shown that, despite the promising empirical results achieved by GNNs for many applications, there are some limitations in GNNs that hinder their performance for some tasks. For example, since GNNs update node features mainly based on local information, they have limited expressive power in capturing long-range dependencies among nodes in graphs. To address some of the limitations of GNNs, several recent works started to explore augmenting GNNs with memory for improving their expressive power in the relevant tasks. In this paper, we provide a comprehensive review of the existing literature of memory-augmented GNNs. We review these works through the lens of psychology and neuroscience, which has established multiple memory systems and mechanisms in biological brains. We propose a taxonomy of the memory GNN works, as well as a set of criteria for comparing the memory mechanisms. We also provide critical discussions on the limitations of these works. Finally, we discuss the challenges and future directions for this area. Impact Statement —Memory-augmentation of graph neural networks is an emerging research ﬁeld in the deep graph learning community. These augmentations can enhance GNNs’ capabilities for structured representation learning and relational reasoning tasks, such as human-object interaction prediction, question answering, and algorithm reasoning, etc. This paper provides a systematic review of the existing works in this ﬁeld from the perspective of neuroscience and psychology. As the ﬁrst review of memory GNNs, we identify the open challenges in this ﬁeld and shed light on the promising directions for future work. By providing a neuroscience perspective, our work will also help facilitate novel brain-inspired memory model designs to advance graph neural networks for various domains.
Fully Data‐Driven Time‐Delay Interferometry with Time‐Varying Delays

Raw space-based gravitational-wave data like LISA's phase measurements are dominated by laser frequency noise. The standard technique to make this data usable for science is time-delay interferometry (TDI), which cancels laser noise terms by forming suitable combinations of delayed measurements. We recently introduced the basic concepts of an alternative approach which, unlike TDI, does not rely on independent knowledge of temporal correlations in the dominant noise. Instead, our automated Principal Component Interferometry (aPCI) processing only assumes that one can produce some linear combinations of the temporally nearby regularly spaced phase measurements, which cancel the laser noise. Then we let the data reveal those combinations. Our previous work relies on the simplifying additional assumption that the filters which lead to the laser-noise-free data streams are time-independent. In LISA, however, these filters will vary as the constellation armlengths evolve. Here, we discuss a generalization of the basic aPCI concept compatible with data dominated by a still unmodeled but slowly varying noise covariance. Despite its independence on any model, aPCI successfully mitigates laser frequency noise below the other noises' level, and its sensitivity to gravitational waves is the same as the state-of-the-art second-generation TDI, up to a 2\% error.
Theory Techniques for Precision Physics -- Snowmass 2021 TF06 Topical Group Report

The wealth of experimental data collected at laboratory experiments suggests that there is some scale separation between the Standard Model (SM) and phenomena beyond the SM (BSM). New phenomena can manifest itself as small corrections to SM predictions, or as signals in processes where the SM predictions vanish or are exceedingly small. This makes precise calculations of the SM expectations essential, in order to maximize the sensitivity of current and forthcoming experiments to BSM physics. This topical group report highlights some past and forthcoming theory developments critical for maximizing the sensitivity of the experimental program to understanding Nature at the shortest distances.
Altered) processing of facial expressions

Many research fields concerned with the processing of information contained in human faces would benefit from face stimulus sets in which specific facial characteristics are systematically varied while other im­ portant picture characteristics are kept constant. Specifically, a face database in which displayed expressions, gaze direction, and head ori­ entation are parametrically varied in a complete factorial design would be highly useful in many research domains. Furthermore, these stim­ uli should be standardized in several important, technical aspects. The present article presents the freely available Radboud Faces Database of­ fering such a stimulus set, containing both Caucasian adult and children images. This face database is described both procedurally and in terms of content, and a validation study concerning its most important character­ istics is presented. In the validation study, all frontal images were rated with respect to the shown facial expression, intensity of expression, clar­ ity of expression, genuineness of expression, attractiveness, and valence. The results show very high recognition of the intended facial expressions.
The local behaviour of holomorphic curves in almost complex 4-manifolds

In this paper we prove various results about the positivity of intersections of holomorphic curves in almost complex 4-manifolds which were stated by Gromov. We also show that the virtual genus of any closed holomorphic curve in an almost complex 4-manifold is nonnegative. These technical results form the basis of the classification of rational and ruled symplectic 4-manifolds given in [5].
Forks over knives: Predictive inconsistency in criminal justice algorithmic risk assessment tools

Big data and algorithmic risk prediction tools promise to improve criminal justice systems by reducing human biases and inconsistencies in decision‐making. Yet different, equally justifiable choices when developing, testing and deploying these socio‐technical tools can lead to disparate predicted risk scores for the same individual. Synthesising diverse perspectives from machine learning, statistics, sociology, criminology, law, philosophy and economics, we conceptualise this phenomenon as predictive inconsistency. We describe sources of predictive inconsistency at different stages of algorithmic risk assessment tool development and deployment and consider how future technological developments may amplify predictive inconsistency. We argue, however, that in a diverse and pluralistic society we should not expect to completely eliminate predictive inconsistency. Instead, to bolster the legal, political and scientific legitimacy of algorithmic risk prediction tools, we propose identifying and documenting relevant and reasonable ‘forking paths’ to enable quantifiable, reproducible multiverse and specification curve analyses of predictive inconsistency at the individual level.
A study of geometry content knowledge of elementary preservice teachers: The case of quadrilaterals

The purpose of this research is to examine preservice elementary school teachers’ geometry learning as investigated by both qualitative and quantitative methods. For the qualitative investigation, narrative analysis and thematic analysis methods were used. The findings of narrative analysis indicated two main kinds of stories: as a learner and as a beginning teacher. The thematic analysis findings yield to three themes: history of learning geometry, perceptions about geometry, effective geometry instructional practices. The findings informed the quantitative investigation on geometry content knowledge for the case of quadrilaterals. During the second phase of the study, 102 participants who enrolled in the methods course completed pre and post test of teachers’ geometry content knowledge. Treatment group participants (n=54) received series of activities (geometry activities and student work analysis) focusing on quadrilaterals, and control group participants (n=48) received traditional instruction. Repeated measures ANOVA results showed a significant change in treatment group participants’ geometry content knowledge. The mixed ANOVA results indicated a significant main effect of knowledge but no significant interaction between geometry content knowledge and grouping. Even though treatment group participants’ geometry content knowledge growth was significant, the difference between treatment group and control group participants’ growth in geometry content knowledge was not significant. This study informs mathematics teacher education in three important areas; limited knowledge of preservice teachers’ geometry content knowledge, integrating mathematics content and the context of teaching into methods course, and use of student work with preservice teachers.
Strange magnetars admixed with fermionic dark matter

We discuss strange stars admixed with fermionic dark matter in the presence of a strong magnetic field using the two-fluid Tolman-Oppenheimer-Volkov equations. We describe strange quark matter using the MIT bag model and consider magnetic fields in the range ∼ 1017 - 1018 G. For the fermionic dark matter, we consider the cases of free particles and strongly self-interacting particles, with dark fermion masses m = 5, 100, 500 GeV. We discuss the effects of dark matter and a strong magnetic field on the masses and radii of the stars, as well as on its tidal deformability. Even though strong magnetic fields contribute to decreasing the total mass of the star, they attenuate the rate of decrease in the maximum mass brought about by increasing the dark matter fraction in the admixed system. The most intensely affected observable, however, is the tidal deformability, with variations on the range of 70%-90% for reasonable values of the magnetic field or dark matter central energy density.
Uniform Strain Elements for Three-Node Triangular and Four-Node Tetrahedral Meshes

A family of uniform strain elements is presented for three-node triangular and four-node tetrahedral meshes. The elements use the linear interpolation functions of the original mesh, but each element is associated with a single node. As a result, a favorable constraint ratio for the volumetric response is obtained for problems in solid mechanics. The uniform strain elements do not require the introduction of additional degrees of freedom and their performance is shown to be significantly better than that of three-node triangular or four-node tetrahedral elements. In addition, nodes inside the boundary of the mesh are observed to exhibit superconvergent behavior for a set of example problems.
Ridge Estimation of the Inverse of a Covariance Matrix.

Abstract : In many situations the standard estimators for the inverse of a covariance matrix from a normal population provide unsatisfactory results. Proposed as an alternative is a random matrix which undergoes an adjustment similar to that undergone by the normal equations matrix in ridge regression. This ridge adjustment is justified by appealing to the spectral decomposition of the inverse of the sample covariance matrix, the bias in the estimators of the characteristic roots of this matrix, and a Monte Carlo simulation. The ridge adjusted estimator is shown to be superior in many cases, especially, for small sample sizes.
A comprehensive analysis of free‐space and guided‐wave techniques for extracting the permeability and permittivity of materials using reflection‐only measurements

The electromagnetic characterization of layered materials is often performed using reflection‐only measurements, and through the years several different techniques have been developed to accomplish this both in the laboratory and in situ. It is shown in this paper that the majority of these methods are variations on a single approach, and that closed‐form expressions are available to obtain the permittivity and permeability from the measurements. The approach is applicable to free‐space (plane wave) measurements, waveguide (non‐TEM) measurements, and transmission line (TEM) measurements, and is based on varying the underlying structure of the material stack to obtain independent measurements. Because the formulation is based on specifying the impedances of adjacent regions, the same formula for permittivity and permeability is valid regardless of the complexity of the stack containing the material under test. Examples of several techniques are given using free‐space, waveguide, and transmission‐line measurements of both dielectric materials and magnetic absorbing materials.
An in situ x-ray photoelectron spectroscopy study of AlOx spin tunnel barrier formation

Thin aluminum oxide barrier layers have been studied by in situ x-ray photoelectron spectroscopy to determine their oxidation degree dependence on oxidation time and method (thermal or plasma oxidation). A 1.5 nm thick Al layer is found to completely oxidize by exposure to an oxygen plasma for a time in the interval 30–60 s, i.e., using the conventional plasma oxidation method. For times less than 30 s, however, we observed not only a metallic-Al peak but the formed oxide was substoichiometric. The composition of the formed oxide increased towards Al2O3 as the oxidation continued. It was also found possible to oxidize up to 1 nm of Al, at room temperature, upon deposition on Co previously exposed to 9.3 Pa (70 mTorr) oxygen for 10 s. Annealing junctions with the idealized structure Co/Al2O3/Co at up to 275 °C was found to increase their magnetoresistance (up to 35%) and resistance (up to a decade), if the Al was deposited on an oxidized Co bottom electrode.
Modified Profile Likelihood for Panel Data Models

We show how modified profile likelihood methods, developed in the statistical literature, may be effectively applied to estimate the structural parameters of econometric models for panel data, with a remarkable reduction of bias with respect to the ordinary likelihood methods. The implementation of these methods is illustrated in detail for certain static and dynamic models which are commonly used in economic applications. We consider, in particular, the truncated linear regression model, the first order autoregressive model, the (static and dynamic) logit model, and the (static and dynamic) probit model. Differently from static models, dynamic models include the lagged response variable among the regressors. For each of these models, we report the results of simulation studies showing the good behaviour of the proposed estimation methods, even with respect to an ideal, although infeasible, procedure. The methods are made available through an R package.
Bounded Rationality and Theory Absorption

In plausible theories of bounded rationality actors are not stimulus-response machines but human beings. As such they are guided by theories that predict the course of the world and prescribe how they should try to intervene in that course. Since boundedly rational human beings cannot only observe but can also modify their theories, in particular if they are not satis? ed with the results, a self-application of concepts of boundedly rational behaviour to theory choice and an inquiry of theory absorption seems natural. The paper explores by means of speci? c examples some issues that are raised by combining the concept of satisficing behaviour with that of theory absorption.
Assessing the effects of alternative plant-based meats v. animal meats on biomarkers of inflammation: a secondary analysis of the SWAP-MEAT randomized crossover trial

Abstract Alternative plant-based meats have grown in popularity with consumers recently and researchers are examining the potential health effects, or risks, from consuming these products. Because there have been no studies to date that have specifically assessed the health effects of plant-based meats on biomarkers of inflammation, the purpose of this work was to conduct a secondary analysis of the Study With Appetizing Plantfood – Meat Eating Alternatives Trial (SWAP-MEAT). SWAP-MEAT was a randomised crossover trial that involved generally healthy adults eating 2 or more servings of plant-based meats per day for 8 weeks (i.e. Plant phase) followed by 2 or more servings of animal meats per day for 8 weeks (i.e. Animal phase). Results of linear mixed-effects models indicated only 4 out of 92 biomarkers reached statistical significance. The results were contrary to our hypothesis, since we expected relative improvements in biomarkers of inflammation from the plant-based meats.