Miracle-dz/UniFAR-FTUs huggingface dataset:
Segregated mobility patterns amplify neighborhood disparities in the spread of COVID-19

The global and uneven spread of COVID-19, mirrored at the local scale, reveals stark differences along racial and ethnic lines. We respond to the pressing need to understand these divergent outcomes via neighborhood level analysis of mobility and case count information. Using data from Chicago over 2020, we leverage a metapopulation Susceptible-Exposed-Infectious-Removed model to reconstruct and simulate the spread of SARS-CoV-2 at the ZIP Code level. We demonstrate that exposures are mostly contained within one’s own ZIP Code and demographic group. Building on this observation, we illustrate that we can understand epidemic progression using a composite metric combining the volume of mobility and the risk that each trip represents, while separately these factors fail to explain the observed heterogeneity in neighborhood level outcomes. Having established this result, we next uncover how group level differences in these factors give rise to disparities in case rates along racial and ethnic lines. Following this, we ask what-if questions to quantify how segregation impacts COVID-19 case rates via altering mobility patterns. We find that segregation in the mobility network has contributed to inequality in case rates across demographic groups.
XMM-Newton and INTEGRAL observations of new absorbed supergiant high-mass X-ray binaries

Context. During the first year in operation, INTEGRAL, the European Space Agency’s γ-ray observatory, has detected more than 28 new bright sources in the galactic plane which emit the bulk of their emission above 10 keV. Aims. To understand the nature of those sources we have obtained follow-up observations in the X-ray band with XMM-Newton. Methods. We derive accurate X-ray positions, propose infrared counterparts and study the source high energy long and short term variability and spectra. Results. 70% of the sources are strongly absorbed (NH ≥ 10 23 atom cm −2 ). More than half of these absorbed sources show pulsations with periods ranging from 139 to 1300 s, i.e., they are slow X-ray pulsars. The candidate infrared counterparts are not as strongly absorbed demonstrating that part of the absorbing matter is local to the sources. Conclusions. Many of these new sources are supergiant high-mass X-ray binaries (HMXB) in which the stellar wind of the companion star is accreted onto the compact object. The large local absorption in these new sources can be understood if the compact objects are buried deep in their stellar winds. These new objects represent half of the population of supergiant HMXB.
Towards an integrative philosophy of education: The contemplative case of economic education

Acquiring a basic knowledge of philosophy is usually a deprioritised choice for students in undergraduate programmes. Even a basic philosophy course is seldom mandatory in the hard sciences, although it may be an option in the social sciences. While many undergraduate teacher education programmes have stand-alone courses in the philosophy of education in general educational studies courses, philosophical inspiration is largely drawn from continental philosophy. The extent to which preservice teachers see the wider relevance and application of philosophy of education to the school subjects they teach is uncertain. Also, contemporary philosophy of education courses in the (South) African context may still be paying homage to Western, Eurocentric philosophical canons, despite calls by the student collective in South Africa for African contextual relevance. I present an account of a curriculum initiative in a teacher education course that attempts a disruption of traditional western canons that underpin economics and economic education. I argue that disciplines such as economics are fertile spaces for engaging teacher trainees in a philosophical exposé, with a view to contesting the universality of the philosophies of (economic) sciences to explain contemporary societal crises. I present insights into how the philosophy of education might be conceptualised as an across curriculum competence as opposed to an insular, stand-alone offering.
A Review of Latent Variable Modeling With R

Latent variable modeling refers to a class of models that includes factor analysis, structural equation modeling (SEM), growth curve modeling, item response theory (IRT), and latent class analysis (LCA)—all staple techniques in educational and psychological research. Increasingly these techniques are treated in a common ‘‘latent variable’’ framework. However, statistics textbooks that take this integrated approach, such as Skrondal and Rabe-Hesketh (2004), may be somewhat inaccessible for applied researchers and graduate students in these fields and do not contain examples to use as a starting point. A textbook that provides a bird’s-eye view of these techniques and shows how to apply them in R (R Core Team, 2014) is therefore a very welcome addition to the literature. Latent Variable Modeling With R (Finch & French, 2015) aims to be such a contribution. According to the publisher’s website, https://www.routledge. com/ products/9780415832458, the book is:
Improved Biomagnetic Signal-To-Noise Ratio and Source Localization Using Optically Pumped Magnetometers with Synthetic Gradiometers

Optically pumped magnetometers (OPMs) can capture brain activity but are susceptible to magnetic noise. The objective of this study was to evaluate a novel methodology used to reduce magnetic noise in OPM measurements. A portable magnetoencephalography (MEG) prototype was developed with OPMs. The OPMs were divided into primary sensors and reference sensors. For each primary sensor, a synthetic gradiometer (SG) was constructed by computing a secondary sensor that simulated noise with signals from the reference sensors. MEG data from a phantom with known source signals and six human participants were used to assess the efficacy of the SGs. Magnetic noise in the OPM data appeared predominantly in a low frequency range (<4 Hz) and varied among OPMs. The SGs significantly reduced magnetic noise (p < 0.01), enhanced the signal-to-noise ratio (SNR) (p < 0.001) and improved the accuracy of source localization (p < 0.02). The SGs precisely revealed movement-evoked magnetic fields in MEG data recorded from human participants. SGs provided an effective method to enhance SNR and improve the accuracy of source localization by suppressing noise. Software-simulated SGs may provide new opportunities regarding the use of OPM measurements in various clinical and research applications, especially those in which movement is relevant.
Urinary tract infections older adults at Mohammed VI University Hospital of Oujda: case series

Background: Due to its incidence, clinical polymorphism and severity, urinary tract infection is an important problem in elderly. The objectives of the authors’ work were to establish the bacteriological profile of urinary tract infection and/or colonization in the elderly and then to study drug resistance of bacterial strains isolated. Materials and methods: This is a 36 months retrospective study from 22 March 2016 to 11 May 2019. The study included urinary specimens of persons aged 65 years or over, hospitalized or consulting at the authors’ hospital. Urines were processed according to the recommendations of the medical microbiology reference system and European Committee on Antimicrobial Susceptibility Testing. Results: The authors collected 6552 requests for cytobacteriological examination of urine. Most of the specimens was collected in the middle stream (n=5503; 84%). Cultures were sterile in 49.77% of cases. Positive in 50.22% of cases. Among positive samples we had 53.41% polymorphic cultures, 32.75% urinary tract infection, and 13.82% urinary tract colonization. Gender distribution showed a sex ratio at 0.62. Gram-negative bacilli, with Escherichia coli as the main species, dominated the isolated bacteria. Resistance rates of E. coli strains that we isolated were 70% for amoxicillin, 36.31% for amoxicillin-clavulanate and 25% for ciprofloxacin. A high resistance rate was seen for third generation cephalosporins. Least resistance recorded to nitrofurantoin. Conclusion: ITU in the elderly is diverse and significantly different from that of younger patients, through its high contamination rate, difficulty in acquiring clinical information, high rate of asymptomatic bacteriuria, and high proportion of multidrug resistant bacteria.
Visual Prognostic Factors in Eyes with Subretinal Fluid Associated with Branch Retinal Vein Occlusion

We investigated whether foveal ellipsoid zone (EZ) status affects visual prognosis in eyes with subretinal fluid (SRF) associated with branch retinal vein occlusion (BRVO). We included 38 eyes retrospectively and classified those with or without a continuous EZ on the SRF of the central foveola on the vertical optical coherence tomography (OCT) image at the initial visit as intact (n = 26) and disruptive EZ (n = 12) groups, respectively. In addition, we classified the intact EZ eyes into clear (n = 15) and blurred (n = 11) EZ groups according to whether EZ on the SRF was observed distinctly or not. Multiple regression analyses showed that baseline EZ status significantly correlated (p = 0.0028) with the 12-months logarithm of the minimum angle of resolution (logMAR) best-corrected visual acuity (BCVA), indicating that baseline intact EZ significantly improves visual prognosis. The 12-months logMAR BCVA of the intact EZ group was significantly better (p < 0.001) than that of the disruptive EZ group, and did not differ significantly between the clear and blurred EZ groups. Thus, baseline foveal EZ status on vertical OCT images can be a novel biomarker for visual prognosis in eyes with SRF associated with BRVO.
Based on MOPSO Algorithm of Real-Time Traffic Signal Optimization Control for Intelligent Transportation Intersections

In order to improve the traffic efficiency of intersections, this paper establishes a multi-objective optimization model with the minimum real-time delay time and maximum capacity as the optimization objectives by applying multi-objective particle swarm optimization algorithm, and simulates the actual intersections. By optimizing the multi-objective model, a set of Pareto optimal solution sets can be obtained, and the optimal timing scheme can be determined by using multi-attribute decision-making algorithm and real-time update of signal timing configuration parameters to achieve dynamic optimization of signal timing. The simulation model of city road intersection is established by SUMO software. By using multi-objective particle swarm optimization algorithm to optimize the intersection simulation model, the traffic capacity of the simulated intersection has been greatly improved, with the average traffic capacity increasing by about 3.69% and the average vehicle delay time decreasing by about 21.35%.
Ideal fracton superfluids

We investigate the thermodynamics of equilibrium thermal states and their near-equilibrium dynamics in systems with fractonic symmetries in arbitrary curved space. By explicitly gauging the fracton algebra we obtain the geometry and gauge fields that field theories with conserved dipole moment couple to. We use the resultant fracton geometry to show that it is not possible to construct an equilibrium partition function for global thermal states unless part of the fractonic symmetries is spontaneously broken. This leads us to introduce two classes of fracton superfluids with conserved energy and momentum, namely pp-wave and ss-wave fracton superfluids. The latter phase is an Aristotelian superfluid at ideal order but with a velocity constraint and can be split into two separate regimes: The U(1) fracton superfluid and the pinned ss-wave superfluid regimes. For each of these classes and regimes we formulate a hydrodynamic expansion and study the resultant modes. We find distinctive features of each of these phases and regimes at ideal order in gradients, without introducing dissipative effects. In particular we note the appearance of a sound mode for ss-wave fracton superfluids. We show that previous work on fracton hydrodynamics falls into these classes. Finally, we study ultra-dense pp-wave fracton superfluids with a large kinetic mass in addition to studying the thermodynamics of ideal Aristotelian superfluids.
Generative models improve fairness of medical classifiers under distribution shifts

A ubiquitous challenge in machine learning is the problem of domain generalisation. This can exacerbate bias against groups or labels that are underrepresented in the datasets used for model development. Model bias can lead to unintended harms, especially in safety-critical applications like healthcare. Furthermore, the challenge is compounded by the difficulty of obtaining labelled data due to high cost or lack of readily available domain expertise. In our work, we show that learning realistic augmentations automatically from data is possible in a label-efficient manner using generative models. In particular, we leverage the higher abundance of unlabelled data to capture the underlying data distribution of different conditions and subgroups for an imaging modality. By conditioning generative models on appropriate labels, we can steer the distribution of synthetic examples according to specific requirements. We demonstrate that these learned augmentations can surpass heuristic ones by making models more robust and statistically fair in- and out-of-distribution. To evaluate the generality of our approach, we study 3 distinct medical imaging contexts of varying difficulty: (i) histopathology images from a publicly available generalisation benchmark, (ii) chest X-rays from publicly available clinical datasets, and (iii) dermatology images characterised by complex shifts and imaging conditions. Complementing real training samples with synthetic ones improves the robustness of models in all three medical tasks and increases fairness by improving the accuracy of diagnosis within underrepresented groups. This approach leads to stark improvements OOD across modalities: 7.7% prediction accuracy improvement in histopathology, 5.2% in chest radiology with 44.6% lower fairness gap and a striking 63.5% improvement in high-risk sensitivity for dermatology with a 7.5x reduction in fairness gap.
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models

Large language models (LLMs) have achieved remarkable progress in solving various natural language processing tasks due to emergent reasoning abilities. However, LLMs have inherent limitations as they are incapable of accessing up-to-date information (stored on the Web or in task-specific knowledge bases), using external tools, and performing precise mathematical and logical reasoning. In this paper, we present Chameleon, an AI system that mitigates these limitations by augmenting LLMs with plug-and-play modules for compositional reasoning. Chameleon synthesizes programs by composing various tools (e.g., LLMs, off-the-shelf vision models, web search engines, Python functions, and heuristic-based modules) for accomplishing complex reasoning tasks. At the heart of Chameleon is an LLM-based planner that assembles a sequence of tools to execute to generate the final response. We showcase the effectiveness of Chameleon on two multi-modal knowledge-intensive reasoning tasks: ScienceQA and TabMWP. Chameleon, powered by GPT-4, achieves an 86.54% overall accuracy on ScienceQA, improving the best published few-shot result by 11.37%. On TabMWP, GPT-4-powered Chameleon improves the accuracy by 17.0%, lifting the state of the art to 98.78%. Our analysis also shows that the GPT-4-powered planner exhibits more consistent and rational tool selection via inferring potential constraints from instructions, compared to a ChatGPT-powered planner. The project is available at https://chameleon-llm.github. io.
Network Pruning Spaces

Network pruning techniques, including weight pruning and filter pruning, reveal that most state-of-the-art neural networks can be accelerated without a significant performance drop. This work focuses on filter pruning which enables accelerated inference with any off-the-shelf deep learning library and hardware. We propose the concept of \emph{network pruning spaces} that parametrize populations of subnetwork architectures. Based on this concept, we explore the structure aspect of subnetworks that result in minimal loss of accuracy in different pruning regimes and arrive at a series of observations by comparing subnetwork distributions. We conjecture through empirical studies that there exists an optimal FLOPs-to-parameter-bucket ratio related to the design of original network in a pruning regime. Statistically, the structure of a winning subnetwork guarantees an approximately optimal ratio in this regime. Upon our conjectures, we further refine the initial pruning space to reduce the cost of searching a good subnetwork architecture. Our experimental results on ImageNet show that the subnetwork we found is superior to those from the state-of-the-art pruning methods under comparable FLOPs.
What Should Be Balanced in a "Balanced" Face Recognition Dataset?

The issue of demographic disparities in face recognition accuracy has attracted increasing attention in recent years. Various face image datasets have been proposed as 'fair' or 'balanced' to assess the accuracy of face recognition algorithms across demographics. These datasets typically balance the number of identities and images across demographics. It is important to note that the number of identities and images in an evaluation dataset are {\em not} driving factors for 1-to-1 face matching accuracy. Moreover, balancing the number of identities and images does not ensure balance in other factors known to impact accuracy, such as head pose, brightness, and image quality. We demonstrate these issues using several recently proposed datasets. To improve the ability to perform less biased evaluations, we propose a bias-aware toolkit that facilitates creation of cross-demographic evaluation datasets balanced on factors mentioned in this paper.
BDDC preconditioners for virtual element approximations of the three-dimensional Stokes equations

The Virtual Element Method (VEM) is a novel family of numerical methods for approximating partial differential equations on very general polygonal or polyhedral computational grids. This work aims to propose a Balancing Domain Decomposition by Constraints (BDDC) preconditioner that allows using the conjugate gradient method to compute the solution of the saddle-point linear systems arising from the VEM discretization of the three-dimensional Stokes equations. We prove the scalability and quasi-optimality of the algorithm and confirm the theoretical findings with parallel computations. Numerical results with adaptively generated coarse spaces confirm the method's robustness in the presence of large jumps in the viscosity and with high-order VEM discretizations.
BUFFALO/Flashlights: Constraints on the abundance of lensed supergiant stars in the Spock galaxy at redshift 1

In this work, we present a constraint on the abundance of supergiant (SG) stars at redshift $z 1$, based on recent observations of a strongly lensed arc at this redshift. First we derived a free-form model of MACS J0416.1-2403 using data from the Beyond Ultra-deep Frontier Fields and Legacy Observations (BUFFALO) program. The new lens model is based on 72 multiply lensed galaxies that produce 214 multiple images, making it the largest sample of spectroscopically confirmed lensed galaxies on this cluster. The larger coverage in BUFFALO allowed us to measure the shear up to the outskirts of the cluster, and extend the range of lensing constraints up to $ 1$\,Mpc from the central region, providing a mass estimate up to this radius. As an application, we make predictions for the number of high-redshift multiply lensed galaxies detected in future observations with the James Webb Space Telescope (JWST). Then we focus on a previously known lensed galaxy at $z=1.0054$, nicknamed Spock, which contains four previously reported transients. We interpret these transients as microcaustic crossings of SG stars and explain how we computed the probability of such events. Based on simplifications regarding the stellar evolution, we find that microlensing (by stars in the intracluster medium) of SG stars at $z=1.0054$ can fully explain these events. The inferred abundance of SG stars is consistent with either (1) a number density of stars with bolometric luminosities beyond the Humphreys-Davidson (HD) limit max for red stars), which is below $ $, or (2) the absence of stars beyond the HD limit but with a SG number density of $ $ for stars with luminosities between $10^5\ and $6 This is equivalent to one SG star per $10 pc$^2$. Finally, we make predictions for future observations with JWST's NIRcam. We find that in observations made with the F200W filter that reach 29 mag AB, if cool red SG stars exist at $z 1$ beyond the HD limit, they should be easily detected in this arc.
Probing Phonon dynamics and Electron-Phonon Coupling by High Harmonic Generation in Solids

Acting as a highly nonlinear response to the strong laser field, high harmonic generation (HHG) naturally contains the fingerprints of atomic and electronic properties of materials. Electronic properties of a solid such as band structure and topology can thus be probed, while the phonon dynamics during HHG are often neglected. Here we show that by exploiting the effects of phonon deformation on HHG, the intrinsic phonon information can be deciphered and direct probing of band- and mode-resolved electron-phonon couplings (EPC) of photoexcited materials is possible. Considering HHG spectroscopy can be vacuum free and unrestricted to electron occupation, this work suggests HHG is promising for all-optical characterization of EPC in solids, especially for gapped quantum states or materials under high pressure.
Convergence of stochastic gradient descent under a local Lajasiewicz condition for deep neural networks

We study the convergence of stochastic gradient descent (SGD) for non-convex objective functions. We establish the local convergence with positive probability under the local \L{}ojasiewicz condition introduced by Chatterjee in \cite{chatterjee2022convergence} and an additional local structural assumption of the loss function landscape. A key component of our proof is to ensure that the whole trajectories of SGD stay inside the local region with a positive probability. We also provide examples of neural networks with finite widths such that our assumptions hold.
Long-Term Fairness with Unknown Dynamics

While machine learning can myopically reinforce social inequalities, it may also be used to dynamically seek equitable outcomes. In this paper, we formalize long-term fairness in the context of online reinforcement learning. This formulation can accommodate dynamical control objectives, such as driving equity inherent in the state of a population, that cannot be incorporated into static formulations of fairness. We demonstrate that this framing allows an algorithm to adapt to unknown dynamics by sacrificing short-term incentives to drive a classifier-population system towards more desirable equilibria. For the proposed setting, we develop an algorithm that adapts recent work in online learning. We prove that this algorithm achieves simultaneous probabilistic bounds on cumulative loss and cumulative violations of fairness (as statistical regularities between demographic groups). We compare our proposed algorithm to the repeated retraining of myopic classifiers, as a baseline, and to a deep reinforcement learning algorithm that lacks safety guarantees. Our experiments model human populations according to evolutionary game theory and integrate real-world datasets.