Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal registration of FISH and nanoSIMS images using convolutional neural network models

Nanoscale secondary ion mass spectrometry (nanoSIMS) and fluorescence in situ hybridization (FISH) microscopy provide high-resolution, multimodal image representations of the identity and cell activity respectively of targeted microbial communities in microbiological research. Despite its importance to microbiologists, multimodal registration of FISH and nanoSIMS images is challenging given the morphological distortion and background noise in both images. In this study, we use convolutional neural networks (CNNs) for multiscale feature extraction, shape context for computation of the minimum transformation cost feature matching and the thin-plate spline (TPS) model for multimodal registration of the FISH and nanoSIMS images. Registration accuracy was quantitatively assessed against manually registered images, at both, the pixel and structural levels using standard metrics. Although all six tested CNN models performed well, ResNet18 was observed to outperform VGG16 , VGG19 , GoogLeNet and ShuffleNet and ResNet101 based on most metrics. This study demonstrates the utility of CNNs in the registration of multimodal images with significant background noise and morphology distortion. We also show aggregate shape, preserved by binarization, to be a robust feature for registering multimodal microbiology-related images.
The eROSITA Final Equatorial Depth Survey (eFEDS). X-ray emission around star-forming and quiescent galaxies at 0.05 < z < 0.3

We aim at characterizing the hot phase of the Circum-Galactic Medium in a large sample of galaxies. We stack X-ray events from the SRG/eROSITA eFEDS survey around central galaxies in the GAMA 9hr field to construct radially projected soft X-ray luminosity profiles as a function of their stellar mass and specific star formation rate. We consider samples of quiescent (star-forming) galaxies in the stellar mass range $2\times 10^{10}$ -- $10^{12}$ M$_\odot$ ($3\times 10^9$ -- $6\times10^{11}$ M$_\odot$). For quiescent galaxies, the X-ray profiles are clearly extended throughout the available mass range; however, the measured profile is likely biased high due to projection effects, as these galaxies tend to live in dense and hot environments. For the most massive star forming samples ($\geq10^{11}$ M$_\odot$), there is a hint of detection of extended emission. For star-forming galaxies with $<10^{11}$ M$_\odot$ the X-ray stacked profiles are compatible with unresolved sources and consistent with the expected emission from faint Active Galactic Nuclei and X-ray binaries. We measure for the first time the mean relation between average X-ray luminosity and stellar mass separately for quiescent and star-forming galaxies. High-mass ($\geq 10^{11}$ M$_\odot$) star-forming or quiescent galaxies follow the expected scaling of virialized hot haloes, while lower mass star-forming galaxies show a less prominent luminosity and a weaker dependence on stellar mass, consistent with empirical models of the weak AGN population. When comparing our results with state-of-the art numerical simulations, we find an overall consistency on large ($>80$ kpc) scales at masses $\geq 10^{11}$ M$_\odot$, but disagreement on the small scales, where brighter than observed compact cores are predicted. Simulations also do not predict the clear differentiation that we observe between quiescent and star-forming galaxies.
Constraining the Milky Way Halo Kinematics via Its Linear Response to the Large Magellanic Cloud

We model the response of spherical, nonrotating Milky Way (MW) dark matter and stellar halos to the Large Magellanic Cloud using the matrix method of linear response theory. Our computations reproduce the main features of the dark halo response from simulations. We show that these features can be well separated by a harmonic decomposition: the large-scale over/underdensity in the halo (associated with its reflex motion) corresponds to the ℓ = 1 terms, and the local overdensity to the ℓ ≥ 2 multipoles. Moreover, the dark halo response is largely dominated by the first-order forcing term, with little influence from self-gravity. This makes it difficult to constrain the underlying velocity distribution of the dark halo using the observed response of the stellar halo, but it allows us to investigate the response of stellar halo models with various velocity anisotropies: a tangential (respectively radial) halo produces a shallower (respectively stronger) response. We also show that only the local wake is responsible for these variations, the reflex motion being solely dependent on the MW potential. Therefore, we identify the structure (orientation and winding) of the in-plane quadrupolar (m = 2) response as a potentially good probe of the stellar halo anisotropy. Finally, our method allows us to tentatively relate the wake strength and shape to resonant effects: the strong radial response could be associated with the inner Lindblad resonance, and the weak tangential one with corotation.
When less is more: Simplifying inputs aids neural network understanding

How do neural network image classifiers respond to simpler and simpler inputs? And what do such responses reveal about the learning process? To answer these questions, we need a clear measure of input simplicity (or inversely, complexity), an optimization objective that correlates with simplification, and a framework to incorporate such objective into training and inference. Lastly we need a variety of testbeds to experiment and evaluate the impact of such simplification on learning. In this work, we measure simplicity with the encoding bit size given by a pretrained generative model, and minimize the bit size to simplify inputs in training and inference. We investigate the effect of such simplification in several scenarios: conventional training, dataset condensation and post-hoc explanations. In all settings, inputs are simplified along with the original classification task, and we investigate the trade-off between input simplicity and task performance. For images with injected distractors, such simplification naturally removes superfluous information. For dataset condensation, we find that inputs can be simplified with almost no accuracy degradation. When used in post-hoc explanation, our learning-based simplification approach offers a valuable new tool to explore the basis of network decisions.
Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier

Machine learning (ML) has shown great promise in optimizing various aspects of the physical layer processing in wireless communication systems. In this paper, we use ML to learn jointly the transmit waveform and the frequency-domain receiver. In particular, we consider a scenario where the transmitter power amplifier is operating in a nonlinear manner, and ML is used to optimize the waveform to minimize the out-of-band emissions. The system also learns a constellation shape that facilitates pilotless detection by the simultaneously learned receiver. The simulation results show that such an end-to-end optimized system can communicate data more accurately and with less out-of-band emissions than conventional systems, thereby demonstrating the potential of ML in optimizing the air interface. To the best of our knowledge, there are no prior works considering the power amplifier induced emissions in an end-to-end learned system. These findings pave the way towards an ML-native air interface, which could be one of the building blocks of 6G.
Dark matter scattering in astrophysical media: collective effects

It is well-known that stars have the potential to be excellent dark matter detectors. Infalling dark matter that scatters within stars could lead to a range of observational signatures, including stellar heating, black hole formation, and modified heat transport. To make robust predictions for such phenomena, it is necessary to calculate the scattering rate for dark matter inside the star. As we show in this paper, for small enough momentum transfers, this requires taking into account collective effects within the dense stellar medium. These effects have been neglected in many previous treatments; we demonstrate how to incorporate them systematically, and show that they can parametrically enhance or suppress dark matter scattering rates depending on how dark matter couples to the Standard Model. We show that, as a result, collective effects can significantly modify the potential discovery or exclusion reach for observations of compact objects such as white dwarfs and neutron stars. While the effects are more pronounced for dark matter coupling through a light mediator, we show that even for dark matter coupling via a heavy mediator, scattering rates can differ by orders of magnitude from their naive values for dark matter masses ≲ 100 MeV. We also illustrate how collective effects can be important for dark matter scattering in more dilute media, such as the Solar core. Our results demonstrate the need to systematically incorporate collective effects in a wide range of astroparticle contexts; to facilitate this, we provide expressions for in-medium self-energies for a variety of different media, which are applicable to many other processes of interest (such as particle production).
Domain-shift adaptation via linear transformations

A predictor, $f_A : X \to Y$, learned with data from a source domain (A) might not be accurate on a target domain (B) when their distributions are different. Domain adaptation aims to reduce the negative effects of this distribution mismatch. Here, we analyze the case where $P_A(Y\ |\ X) \neq P_B(Y\ |\ X)$, $P_A(X) \neq P_B(X)$ but $P_A(Y) = P_B(Y)$; where there are affine transformations of $X$ that makes all distributions equivalent. We propose an approach to project the source and target domains into a lower-dimensional, common space, by (1) projecting the domains into the eigenvectors of the empirical covariance matrices of each domain, then (2) finding an orthogonal matrix that minimizes the maximum mean discrepancy between the projections of both domains. For arbitrary affine transformations, there is an inherent unidentifiability problem when performing unsupervised domain adaptation that can be alleviated in the semi-supervised case. We show the effectiveness of our approach in simulated data and in binary digit classification tasks, obtaining improvements up to 48% accuracy when correcting for the domain shift in the data.
Synthesising Electronic Health Records: Cystic Fibrosis Patient Group

Class imbalance can often degrade predictive performance of supervised learning algorithms. Balanced classes can be obtained by oversampling exact copies, with noise, or interpolation between nearest neighbours (as in traditional SMOTE methods). Oversampling tabular data using augmentation, as is typical in computer vision tasks, can be achieved with deep generative models. Deep generative models are effective data synthesisers due to their ability to capture complex underlying distributions. Synthetic data in healthcare can enhance interoperability between healthcare providers by ensuring patient privacy. Equipped with large synthetic datasets which do well to represent small patient groups, machine learning in healthcare can address the current challenges of bias and generalisability. This paper evaluates synthetic data generators ability to synthesise patient electronic health records. We test the utility of synthetic data for patient outcome classification, observing increased predictive performance when augmenting imbalanced datasets with synthetic data.
A Tight Reverse Minkowski Inequality for the Epstein Zeta Function

We prove that if L ⊂ R n is a lattice such that det( L ′ ) ≥ 1 for all sublattices L ′ ⊆ L , then X y ∈L y 6 = 0 ( k y k 2 + q ) − s ≤ X z ∈ Z n z 6 = 0 ( k z k 2 + q ) − s for all s > n/ 2 and all 0 ≤ q ≤ (2 s − n ) / ( n + 2), with equality if and only if L is isomorphic to Z n .
A Very Metal-poor RR Lyrae Star with a Disk Orbit Found in the Solar Neighborhood

Metal-deficient stars are important tracers for understanding the early formation of the Galaxy. Recent large-scale surveys with both photometric and spectroscopic data have reported an increasing number of metal-deficient stars whose kinematic features are consistent with those of the disk stellar populations. We report the discovery of an RR Lyrae variable (hereafter RRL) that is located within the thick disk and has an orbit consistent with the thick-disk kinematics. Our target RRL (HD 331986) is located at around 1 kpc from the Sun and, with V ≃ 11.3, is among the ∼130 brightest RRLs known so far. However, this object has scarcely been studied because it is in the midplane of the Galaxy, at a Galactic latitude around –1°. Its near-infrared spectrum (0.91–1.32 μm) shows no absorption line except hydrogen lines of the Paschen series, suggesting [Fe/H] ≲ –2.5. It is the most metal-deficient RRL, at least among RRLs whose orbits are consistent with the disk kinematics, although we cannot determine to which of the disk and the halo it belongs. This unique RRL would provide us with essential clues for studying the early formation of stars in the inner Galaxy with further investigations, including high-resolution optical spectroscopy.
Multiscale Decomposition of Astronomical Maps: A Constrained Diffusion Method

We propose a new, efficient multiscale method to decompose a map (or signal in general) into component maps that contain structures of different sizes. In the widely used wave transform, artifacts containing negative values arise around regions with sharp transitions due to the application of band-limited filters. In our approach, the decomposition is achieved by solving a modified, nonlinear version of the diffusion equation. This is inspired by the anisotropic diffusion methods, which establish the link between image filtering and partial differential equations. In our case, the artifact issue is addressed where the positivity of the decomposed images is guaranteed. Our new method is particularly suitable for signals which contain localized, nonlinear features, as typical of astronomical observations. It can be used to study the multiscale structures of astronomical maps quantitatively and should be useful in observation-related tasks such as background removal. We thus propose a new measure called the “scale spectrum,” which describes how the image values distribute among different components in the scale space, to describe maps. The method allows for input arrays of an arbitrary number of dimensions, and a python3 implementation of the algorithms is included in the Appendix and available at https://github.com/gxli/constrained_diffusion_decomposition.
On the influence of several factors on pathway enrichment analysis

Abstract Pathway enrichment analysis has become a widely used knowledge-based approach for the interpretation of biomedical data. Its popularity has led to an explosion of both enrichment methods and pathway databases. While the elegance of pathway enrichment lies in its simplicity, multiple factors can impact the results of such an analysis, which may not be accounted for. Researchers may fail to give influential aspects their due, resorting instead to popular methods and gene set collections, or default settings. Despite ongoing efforts to establish set guidelines, meaningful results are still hampered by a lack of consensus or gold standards around how enrichment analysis should be conducted. Nonetheless, such concerns have prompted a series of benchmark studies specifically focused on evaluating the influence of various factors on pathway enrichment results. In this review, we organize and summarize the findings of these benchmarks to provide a comprehensive overview on the influence of these factors. Our work covers a broad spectrum of factors, spanning from methodological assumptions to those related to prior biological knowledge, such as pathway definitions and database choice. In doing so, we aim to shed light on how these aspects can lead to insignificant, uninteresting or even contradictory results. Finally, we conclude the review by proposing future benchmarks as well as solutions to overcome some of the challenges, which originate from the outlined factors.
A kernel‐expanded stochastic neural network

The deep neural network suffers from many fundamental issues in machine learning. For example, it often gets trapped into a local minimum in training, and its prediction uncertainty is hard to be assessed. To address these issues, we propose the so‐called kernel‐expanded stochastic neural network (K‐StoNet) model, which incorporates support vector regression as the first hidden layer and reformulates the neural network as a latent variable model. The former maps the input vector into an infinite dimensional feature space via a radial basis function kernel, ensuring the absence of local minima on its training loss surface. The latter breaks the high‐dimensional non‐convex neural network training problem into a series of low‐dimensional convex optimization problems, and enables its prediction uncertainty easily assessed. The K‐StoNet can be easily trained using the imputation‐regularized optimization algorithm. Compared to traditional deep neural networks, K‐StoNet possesses a theoretical guarantee to asymptotically converge to the global optimum and enables the prediction uncertainty easily assessed. The performances of the new model in training, prediction and uncertainty quantification are illustrated by simulated and real data examples.
Thermalization of a spin chain strongly coupled to its environment

We study the thermalization of individual spins of a short XYZ Heisenberg chain with strongly coupled thermal leads by checking the consistency of two-time correlations with the fluctuationdissipation theorem. To compute these correlations we develop and apply a general numerical method for chains of quantum systems, where each system may couple strongly to a structured environment. The method combines the process tensor formalism for general (possibly non-Markovian) open quantum systems with time evolving block decimation for 1D chains. It systematically reduces the numerical complexity originating from system-environment correlations before integrating them into the full many-body problem, making a wide range of applications numerically feasible. Our results show the complete thermalization of the chain when coupled to a single bath, and reveal distinct effective temperatures in low, mid, and high frequency regimes when placed between a hot and a cold bath.
Cross-token Modeling with Conditional Computation

Mixture-of-Experts (MoE), a conditional computation architecture, achieved promising performance by scaling local module (i.e. feed-forward network) of transformer. However, scaling the cross-token module (i.e. self-attention) is challenging due to the unstable training. This work proposes Sparse-MLP, an all-MLP model which applies sparsely-activated MLPs to cross-token modeling. Specifically, in each Sparse block of our all-MLP model, we apply two stages of MoE layers: one with MLP experts mixing information within channels along image patch dimension, the other with MLP experts mixing information within patches along the channel dimension. In addition, by proposing importance-score routing strategy for MoE and redesigning the image representation shape, we further improve our model's computational efficiency. Experimentally, we are more computation-efficient than Vision Transformers with comparable accuracy. Also, our models can outperform MLP-Mixer by 2.5\% on ImageNet Top-1 accuracy with fewer parameters and computational cost. On downstream tasks, i.e. Cifar10 and Cifar100, our models can still achieve better performance than baselines.
EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH

The measurement of the production of f0(980) in inelastic pp collisions at √ s = 5.02 TeV is presented. This is the first reported measurement of inclusive f0(980) yield at LHC energies. The production is measured at midrapidity, |y|< 0.5, in a wide transverse momentum range, 0 < pT < 16 GeV/c, by reconstructing the resonance in the f0(980)→ π+π− hadronic decay channel using the ALICE detector. The pT-differential yields are compared to those of pions, protons and φ mesons as well as to predictions from the HERWIG 7.2 QCD-inspired Monte Carlo event generator and calculations from a coalescence model that uses the AMPT model as an input. The ratio of the pT-integrated yield of f0(980) relative to pions is compared to measurements in e+e−and pp collisions at lower energies and predictions from statistical hadronisation models and HERWIG 7.2. A mild collision energy dependence of the f0(980) to pion production is observed in pp collisions from SPS to LHC energies. All considered models underpredict the pT-integrated f0(980)/(π +π−) ratio. The prediction from the canonical statistical hadronisation model assuming a zero total strangeness content of f0(980) is consistent with the data within 1. 9σ and is the closest to the data. The results provide an essential reference for future measurements of the particle yield and nuclear modification in p–Pb and Pb–Pb collisions, which have been proposed to be instrumental to probe the elusive nature and quark composition of the f0(980) scalar meson. ar X iv :2 20 6. 06 21 6v 1 [ nu cl -e x] 1 3 Ju n 20 22 f0(980) production in pp collisions ALICE Collaboration
Predictors of well‐being during the COVID‐19 pandemic: The importance of financial satisfaction and neuroticism

Abstract Current research suggests that the COVID‐19 pandemic adversely impacts well‐being. This study examined how subjective well‐being (SWB) and psychological well‐being (PWB) in Singapore were affected during the pandemic, and the factors that were important to SWB. A representative sample of 999 respondents completed an online survey during the pandemic in August 2020 (after a lockdown was lifted). Two hundred and thirty‐two respondents completed the follow‐up Wave 2 survey 4 months later. Analyses showed that SWB and PWB during the pandemic were significantly lower than the prepandemic period. However, by Wave 2, well‐being levels recovered to levels similar to those observed in the prepandemic period. Regression analyses showed that previously established key determinants of SWB were still important predictors during the pandemic. Specifically, neuroticism and financial satisfaction were strongly associated with all three major SWB components. PWB, particularly in terms of meeting one's psychological needs for autonomy and mastery, was also closely related to SWB. The importance of financial satisfaction suggests that one avenue to buffer the detrimental effects of the pandemic on well‐being would be to formulate economic policies that can alleviate the pandemic's negative financial impact.
Health Care Equity for Family Medicine Patients and Family Physician Equity

This is the first of two sequential equity and diversity-themed JABFM issues. Multiple articles address the social justice tenet of family medicine; ie, diversity and equity issues within the family medicine field and health care equity in the delivery of care to diverse patient populations. Within the field, we have a paper on how ABFM attempts to ensure equity in the board examination through differential item analyses review. Other articles report on the diversity of family physicians, the languages they use in their practices, family medicine department chairs, and deans of schools of medicine, as well as the diversity of the patients in family physician practices. Gender inequity is also important for family medicine. For care of diverse populations, there is a discussion of pseudofolliculitis barbae implications, race-specific blood pressure medications and control, location of family physicians and of social services by need, and a large study of laboratory testing by gender. Articles on various clinical topics are also included.