Miracle-dz/UniFAR-FTUs huggingface dataset:
Cytokine response in asymptomatic and symptomatic Plasmodium falciparum infections in children in a rural area of south-eastern Gabon

Plasmodium falciparum is a parasite that causes asymptomatic or symptomatic malaria infections in humans depending on various factors. These infections are also a major cause of anemia in intertropical countries such as Gabon. Past studies have clearly demonstrated that inflammatory markers such as cytokines play a key role in the pathogenesis of malaria disease. However, the clinical manifestations of severe malaria vary according to the level of transmission and more information is needed to gain a better understanding of the factors involved. As such, the objective of this study was to investigate the circulating levels of nine cytokines in asymptomatic and symptomatic P. falciparum infections in Gabonese children and their roles in the pathogenesis of anemia. Blood samples were collected from 241 children aged 3 to 180 months in Lastourville, south-eastern Gabon. Diagnosis of P. falciparum infection was performed using Rapid Diagnosis Tests, microscopy and nested PCR. Levels in the plasma of the Th1 (IFN-γ, TNF-α, IL-6 and IL-12p70), Th17 (IL-17A and IL-22) and Th2 (IL-10, IL-4 and IL-13) cytokines were measured by ELISA. Data showed that IL-6, IFN-γ, IL-12p70, IL-10, and IL-13 levels were significantly higher in children with symptomatic P. falciparum infection compared to uninfected children. IL-10 levels were significantly higher in symptomatic children than in asymptomatic children, who had moderately increased levels compared to uninfected controls. Moreover, only IL-10 and IL-6 levels were significantly higher in children with severe malarial anemia compared to children with uncomplicated malaria who had significantly lower IL-10 levels than children with moderate malarial anemia. These data indicate that the progression of P. falciparum infection towards an advanced stage in children is accompanied by a significant increase in type Th1 and/or Th2 cytokines. These inflammatory mediators could serve as potential predictors of anemia for malaria patients.
Clinical outcomes of patients with rheumatoid arthritis who underwent percutaneous coronary intervention: A Korean nationwide cohort study

Objective Rheumatoid arthritis (RA) increases the risk of cardiovascular disease. This study aimed to investigate the short-and long-term prognosis of patients with and without RA who underwent percutaneous coronary intervention (PCI). Methods The Korean National Health Insurance Service claims database was used to extract data on 236,134 patients (34,493 with RA and 201,641 without RA) who underwent PCI between 2008 and 2019. The primary outcome was major adverse cardiovascular events (MACE), including all-cause mortality, myocardial infarction, stroke, transient ischemic attack, or coronary revascularization with short-term (30-day) and long-term outcomes. The secondary outcomes were the individual components of MACE. Results During a 10-year follow-up, patients with RA showed a shorter median survival time from MACE than their counterparts (with RA: 4.29 years vs. without RA: 6.10 years). RA was significantly associated with an increased risk of MACEs in long-term outcomes (hazard ratio (HR) 1.07, 95% confidence intervals (CI) 1.06–1.09, p<0.001), but not with short-term outcomes (HR 1.02, 95% CI 0.99–1.06, p = 0.222). RA was an independent predictor of an increased risk of all the MACE components. Conclusion In patients who underwent PCI, RA did not increase the risk of short-term cardiovascular outcomes but increased the risk of long-term adverse outcomes.
Single-Cell Analyses of the Oral Mucosa Reveal Immune Cell Signatures

Inflammatory bowel disease (IBD) is a common immune-related disease of the gastrointestinal tract that affects many people around the world. Extraintestinal manifestations of IBD have been frequently observed in recent years; one of these, periodontitis, has gained increasing attention. Periodontitis is a chronic inflammatory disease characterized by inflammation and destruction of periodontal tissues due to the disruption of host immune homeostasis. Clinical studies have revealed that periodontal inflammation is associated with IBD. However, the detailed heterogeneity of immune cells and their developmental relationships remain poorly understood at the single-cell level. In this study, we performed single-cell RNA (scRNA) sequencing to assess the transcriptome heterogeneity in periodontal tissues. We found the cellular composition and subclusters with specific gene expression profiles by uniform manifold approximation and projection. Pseudo-time analysis combined with gene enrichment analysis was performed to reveal cell states and key pathways. Ligand–receptor pairs revealed cell–cell communication among the immune cell types in periodontal tissues. Based on our analysis, we identified an essential role for Tcr+ macrophage, Prdx1+ neutrophil, and Mif+ T subpopulations with proinflammatory phenotype infiltration. Moreover, we examined the heterogeneity of monocytic cells and B cells. Collectively, the mapping of scRNA revealed the complex cellular landscape of oral mucosa immune cells and highlighted these immune cells as a previously unrecognized factor that may aggravate inflammation. Our analysis proves that periodontitis could exacerbate colitis and provides novel ideas for controlling and preventing IBD exacerbations.
A mid-infrared survey of the inner 2 ? 1.5 degrees of the Galaxy with Spitzer/IRAC

We present a survey of Spitzer Space Telescope/IRAC observations of the central 2 × 1.5 degrees (265 × 200 pc) of the Galaxy at 3-8 μm. These data represent the highest spatial resolution and sensitivity large-scale map made to date of the Galactic Center (GC) at mid-infrared wavelengths. The IRAC data provide a census of the optically obscured stellar sources as well as a detailed map of the highly filamentary structure in the interstellar medium. The diffuse emission is dominated by PAH emission from small grains in star-forming regions. Dark clouds displaying a large variety of sizes and morphologies are imaged, many of which remain opaque at IRAC wavelengths. Using a multiwavelength comparison, we determine which objects are likely to be in the foreground and which are located at the GC. We find no counterparts at IRAC wavelengths to the unique system of linear, nonthermal radio filaments present at the GC.
Ion kinetic processes and thermalization at quasi-perpendicular low Mach number shocks

Observations of some low Mach number collisionless shocks in space have revealed that their associated ion heating is greater than adiabatic, is very rapid, is primarily in the direction perpendicular to the magnetic field, and takes place in the bulk of the distribution. These features are very different from those encountered at crossings of supercritical shocks and not yet fully understood. In this paper the authors use a one-dimensional hybrid kinetic simulation to study the details of the evolution of the upcoming ions across a set of low Mach shocks. They finds that the bulk of the ion velocity distribution becomes elongated along the direction perpendicular to the shock front as the ions traverse the shock ramp. The subsequent gyration of the ions in the steady field downstream gives rise to a temperature signature with features that correspond closely to those enumerated above. This suggest that these features may originate primarily from the kinetic behavior of the ions at the shock rather than from the operation of some instability. They also investigate the dependence of their results on a number of physical and numerical parameters in the simulation.
Outcome of transcatheter atrial septal defect closure in a nationwide cohort

Abstract Background Transcatheter (TC) atrial septal defect (ASD) closure has been the mainstay of therapy for secundum-type ASDs for over 20 years. Aims This nationwide cohort evaluated the long-term outcome of transcatheter-closed ASDs. Methods The study enrolled every transcatheter ASD closure performed in Finland from 1999 to 2019. Five age, sex, and municipality-matched controls per ASD patient were gathered from the general population. The median follow-up period was 5.9 years (range 0–20.8). We used the hospital discharge register to gather all hospital visits and diagnoses. Closure complications and echocardiographic changes were collected from the electronic health records. Results Transcatheter ASD closure was performed in 1000 patients (68.5% females) during the study period. The median (range) age at the time of the procedure was 37.9 (1.8–87.5) years. ASD patients had an increased risk for new-onset atrial fibrillation (RR 2.45, 95% CI: 1.84–3.25), migraine (RR 3.61, 95% CI: 2.54–5.14), ischemic heart disease (RR 1.73, 95% CI: 1.23–2.45), ventricular fibrillation/tachycardia (RR 3.54 (95% CI: 1.48–8.43) and AV conduction disorder (RR 3.60, 95% CI: 1.94–6.70) compared to the control cohort. Stroke risk was not increased (RR 1.36, 95% CI: 0.91–2.03). Adverse events occurred in 6.3% (n = 63) of the patients, including four erosions and ten device embolizations. Conclusion After TC closure of ASD, patients had a higher risk of new-onset atrial fibrillation and migraine than controls without ASD. As novel findings, we found an increased risk for ischemic heart disease, AV conduction disorders, and ventricular fibrillation/tachycardia. Key messages Even though patients have an excellent overall prognosis after percutaneous ASD closure, the increased incidence of major comorbidities like atrial fibrillation and heart failure prompts more thorough lifelong follow-up. This study’s novel findings revealed the increased risk for ischemic heart disease, AV conduction disorders, or ventricular tachycardia/fibrillation during the follow-up. Major complications after the closure are rare; erosion is seen in 0.4% of the patients and embolization in 1.0% of the patients.
Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization

Parameterized quantum circuits (PQCs) have been widely used as a machine learning model to explore the potential of achieving quantum advantages for various tasks. However, the training of PQCs is notoriously challenging owing to the phenomenon of plateaus and/or the existence of (exponentially) many spurious local minima. In this work, we propose an efficient parameter initialization strategy with theoretical guarantees. We prove that if the initial domain of each parameter is reduced inversely proportional to the square root of circuit depth, then the magnitude of the cost gradient decays at most polynomially as a function of the depth. Our theoretical results are verified by numerical simulations of variational quantum eigensolver tasks. Moreover, we demonstrate that the reduced-domain initialization strategy can protect specific quantum neural networks from exponentially many spurious local minima. Our results highlight the significance of an appropriate parameter initialization strategy and can be used to enhance the trainability of PQCs in variational quantum algorithms.
The Terrestrial Density of Strongly-Coupled Relics

The simplest cosmologies motivate the consideration of dark matter subcomponents that interact significantly with normal matter. Moreover, such strongly-coupled relics may have evaded detection to date if upon encountering the Earth they rapidly thermalize down to terrestrial temperatures, $T_\oplus \sim 300 \ \text{K} \sim 25 \ \text{meV}$, well below the thresholds of most existing dark matter detectors. This shedding of kinetic energy implies a drastic enhancement to the local density, motivating the consideration of alternative detection techniques sensitive to a large density of slowly-moving dark matter particles. In this work, we provide a rigorous semi-analytic derivation of the terrestrial overdensities of strongly-coupled relics, with a particular focus on millicharged particles (MCPs). We go beyond previous studies by incorporating improved estimates of the MCP-atomic scattering cross section, new contributions to the terrestrial density of sub-GeV relics that are independent of Earth's gravitational field, and local modifications that can arise due to the cryogenic environments of precision sensors. We also generalize our analysis in order to estimate the terrestrial density of thermalized MCPs that are produced from the collisions of high-energy cosmic rays and become bound by Earth's electric field.
A modern look at the relationship between sharpness and generalization

Sharpness of minima is a promising quantity that can correlate with generalization in deep networks and, when optimized during training, can improve generalization. However, standard sharpness is not invariant under reparametrizations of neural networks, and, to fix this, reparametrization-invariant sharpness definitions have been proposed, most prominently adaptive sharpness (Kwon et al., 2021). But does it really capture generalization in modern practical settings? We comprehensively explore this question in a detailed study of various definitions of adaptive sharpness in settings ranging from training from scratch on ImageNet and CIFAR-10 to fine-tuning CLIP on ImageNet and BERT on MNLI. We focus mostly on transformers for which little is known in terms of sharpness despite their widespread usage. Overall, we observe that sharpness does not correlate well with generalization but rather with some training parameters like the learning rate that can be positively or negatively correlated with generalization depending on the setup. Interestingly, in multiple cases, we observe a consistent negative correlation of sharpness with out-of-distribution error implying that sharper minima can generalize better. Finally, we illustrate on a simple model that the right sharpness measure is highly data-dependent, and that we do not understand well this aspect for realistic data distributions. The code of our experiments is available at https://github.com/tml-epfl/sharpness-vs-generalization.
A Psycholinguistic Analysis of BERT’s Representations of Compounds

This work studies the semantic representations learned by BERT for compounds, that is, expressions such as sunlight or bodyguard. We build on recent studies that explore semantic information in Transformers at the word level and test whether BERT aligns with human semantic intuitions when dealing with expressions (e.g., sunlight) whose overall meaning depends—to a various extent—on the semantics of the constituent words (sun, light). We leverage a dataset that includes human judgments on two psycholinguistic measures of compound semantic analysis: lexeme meaning dominance (LMD; quantifying the weight of each constituent toward the compound meaning) and semantic transparency (ST; evaluating the extent to which the compound meaning is recoverable from the constituents’ semantics). We show that BERT-based measures moderately align with human intuitions, especially when using contextualized representations, and that LMD is overall more predictable than ST. Contrary to the results reported for ‘standard’ words, higher, more contextualized layers are the best at representing compound meaning. These findings shed new light on the abilities of BERT in dealing with fine-grained semantic phenomena. Moreover, they can provide insights into how speakers represent compounds.
Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks

The psychological plausibility of word embeddings has been studied through different tasks such as word similarity, semantic priming, and lexical entailment. Recent work on predicting category structure with word embeddings report low correlations with human ratings. (Heyman and Heyman, 2019) showed that static word embeddings fail at predicting typicality using cosine similarity between category and exemplar words, while (Misra et al., 2021)obtain equally modest results for various contextual language models (CLMs) using a Cloze task formulation over hand-crafted taxonomic sentences. In this work, we test a wider array of methods for probing CLMs for predicting typicality scores. Our experiments, using BERT (Devlin et al., 2018), show the importance of using the right type of CLM probes, as our best BERT-based typicality prediction methods improve on previous works. Second, our results highlight the importance of polysemy in this task, as our best results are obtained when contextualization is paired with a disambiguation mechanism as in (Chronis and Erk, 2020). Finally, additional experiments and analyses reveal that Information Content-based WordNet (Miller, 1995) similarities with disambiguation match the performance of the best BERT-based method, and in fact capture complementary information, and when combined with BERT allow for enhanced typicality predictions.
Self-supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes

Robust autonomous driving requires agents to accurately identify unexpected areas (anomalies) in urban scenes. To this end, some critical issues remain open: how to design advisable metric to measure anomalies, and how to properly generate training samples of anomaly data? Classical effort in anomaly detection usually resorts to pixel-wise uncertainty or sample synthesis, which ignores the contextual information and sometimes requires auxiliary data with fine-grained annotations. On the contrary, in this paper, we exploit the strong context-dependent nature of segmentation task and design an energy-guided self-supervised frameworks for anomaly segmentation, which optimizes an anomaly head by maximizing likelihood of self-generated anomaly pixels. For this purpose, we design two estimators to model anomaly likelihood, one is a task-agnostic binary estimator and the other depicts the likelihood as residual of task-oriented joint energy. Based on proposed estimators, we devise an adaptive self-supervised training framework, which exploits the contextual reliance and estimated likelihood to refine mask annotations in anomaly areas. We conduct extensive experiments on challenging Fishyscapes and Road Anomaly benchmarks, demonstrating that without any auxiliary data or synthetic models, our method can still achieves comparable performance to supervised competitors. Code is available at https://github.com/yuanpengtu/SLEEG.
Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration

We introduce and study Swap Agnostic Learning. The problem can be phrased as a game between a predictor and an adversary: first, the predictor selects a hypothesis $h$; then, the adversary plays in response, and for each level set of the predictor $\{x \in \mathcal{X} : h(x) = v\}$ selects a (different) loss-minimizing hypothesis $c_v \in \mathcal{C}$; the predictor wins if $h$ competes with the adaptive adversary's loss. Despite the strength of the adversary, we demonstrate the feasibility Swap Agnostic Learning for any convex loss. Somewhat surprisingly, the result follows through an investigation into the connections between Omniprediction and Multicalibration. Omniprediction is a new notion of optimality for predictors that strengthtens classical notions such as agnostic learning. It asks for loss minimization guarantees (relative to a hypothesis class) that apply not just for a specific loss function, but for any loss belonging to a rich family of losses. A recent line of work shows that omniprediction is implied by multicalibration and related multi-group fairness notions. This unexpected connection raises the question: is multi-group fairness necessary for omniprediction? Our work gives the first affirmative answer to this question. We establish an equivalence between swap variants of omniprediction and multicalibration and swap agnostic learning. Further, swap multicalibration is essentially equivalent to the standard notion of multicalibration, so existing learning algorithms can be used to achieve any of the three notions. Building on this characterization, we paint a complete picture of the relationship between different variants of multi-group fairness, omniprediction, and Outcome Indistinguishability. This inquiry reveals a unified notion of OI that captures all existing notions of omniprediction and multicalibration.
DiffFashion: Reference-Based Fashion Design With Structure-Aware Transfer by Diffusion Models

Image-based fashion design with AI techniques has attracted increasing attention in recent years. We focus on the reference-based fashion design task, where we aim to combine a reference appearance image and a clothing image to generate a new fashion clothing image. Although existing diffusion-based image translation methods have enabled flexible style transfer, it is often difficult to transfer the appearance of the image realistically during reverse diffusion. When the referenced appearance domain greatly differs from the source domain, it often leads to the collapse in the translation. To tackle this issue, we present a novel diffusion model-based unsupervised structure-aware transfer method, namely DiffFashion. Our method is free of model tuning and structure-preserving and has high flexibility in transferring from images with large domain gaps. Specifically, based on the optimal transport properties, we keep a shared latent across the clothing image and reference appearance image to bridge the gap between the two domains in the denoising process, and the latent of the reference image is gradually adapted to the clothing domain. Simultaneously, the structure is transferred from the source clothing to the output fashion image with mixed guidance, including pre-trained Vision Transformer (ViT) guidance and a foreground mask guidance, to further preserve the structure and appearance semantics from source and reference images. Our experimental results show that the proposed method outperforms state-of-the-art baseline models, generating more realistic images in the fashion design task.
Divisible convex sets with properly embedded cones

In this article we construct many examples of properly convex irreducible domains divided by Zariski dense relatively hyperbolic groups in every dimension at least 3. This answers a question of Benoist. Relative hyperbolicity and non-strict convexity are captured by a family of properly embedded cones (convex hulls of points and ellipsoids) in the domain. Our construction is most flexible in dimension 3 where we give a purely topological criterion for the existence of a large deformation space of geometrically controlled convex projective structures with totally geodesic boundary on a compact 3-manifold.