Miracle-dz/UniFAR-FTUs huggingface dataset:
PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection

Recent generative models show impressive performance in generating photographic images. Humans can hardly distinguish such incredibly realistic-looking AI-generated images from real ones. AI-generated images may lead to ubiquitous disinformation dissemination. Therefore, it is of utmost urgency to develop a detector to identify AI generated images. Most existing detectors suffer from sharp performance drops over unseen generative models. In this paper, we propose a novel AI-generated image detector capable of identifying fake images created by a wide range of generative models. We observe that the texture patches of images tend to reveal more traces left by generative models compared to the global semantic information of the images. A novel Smash&Reconstruction preprocessing is proposed to erase the global semantic information and enhance texture patches. Furthermore, pixels in rich texture regions exhibit more significant fluctuations than those in poor texture regions. Synthesizing realistic rich texture regions proves to be more challenging for existing generative models. Based on this principle, we leverage the inter-pixel correlation contrast between rich and poor texture regions within an image to further boost the detection performance. In addition, we build a comprehensive AI-generated image detection benchmark, which includes 17 kinds of prevalent generative models, to evaluate the effectiveness of existing baselines and our approach. Our benchmark provides a leaderboard for follow-up studies. Extensive experimental results show that our approach outperforms state-of-the-art baselines by a significant margin. Our project: https://fdmas.github. io/AIGCDetect
Schur multiplier and Schur covers of relative Rota-Baxter groups

Relative Rota-Baxter groups are generalizations of Rota-Baxter groups and share a close connection with skew left braces. These structures are well-known for offering bijective non-degenerate set-theoretical solutions to the Yang-Baxter equation. This paper builds upon the recently introduced extension theory and low-dimensional cohomology of relative Rota-Baxter groups. We prove an analogue of the Hochschild-Serre exact sequence for central extensions of relative Rota-Baxter groups. We introduce the Schur multiplier $M_{RRB}(\mathcal{A})$ of a relative Rota-Baxter group $\mathcal{A} =(A,B,\beta,T)$, and prove that the exponent of $M_{RRB}(\mathcal{A})$ divides $|A||B|$ when $\mathcal{A}$ is finite. We define weak isoclinism of relative Rota-Baxter groups, introduce their Schur covers, and prove that any two Schur covers of a finite bijective relative Rota-Baxter group are weakly isoclinic. The results align with recent results of Letourmy and Vendramin for skew left braces.
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity

We study causal representation learning, the task of recovering high-level latent variables and their causal relationships in the form of a causal graph from low-level observed data (such as text and images), assuming access to observations generated from multiple environments. Prior results on the identifiability of causal representations typically assume access to single-node interventions which is rather unrealistic in practice, since the latent variables are unknown in the first place. In this work, we provide the first identifiability results based on data that stem from general environments. We show that for linear causal models, while the causal graph can be fully recovered, the latent variables are only identified up to the surrounded-node ambiguity (SNA) \citep{varici2023score}. We provide a counterpart of our guarantee, showing that SNA is basically unavoidable in our setting. We also propose an algorithm, \texttt{LiNGCReL} which provably recovers the ground-truth model up to SNA, and we demonstrate its effectiveness via numerical experiments. Finally, we consider general non-parametric causal models and show that the same identification barrier holds when assuming access to groups of soft single-node interventions.
Quantum Inception Score

Motivated by the great success of classical generative models in machine learning, enthusiastic exploration of their quantum version has recently started. To depart on this journey, it is important to develop a relevant metric to evaluate the quality of quantum generative models; in the classical case, one such example is the inception score. In this paper, we propose the quantum inception score, which relates the quality to the Holevo information of the quantum channel that classifies a given dataset. We prove that, under this proposed measure, the quantum generative models provide better quality than their classical counterparts because of the presence of quantum coherence, characterized by the resource theory of asymmetry, and entanglement. Furthermore, we harness the quantum fluctuation theorem to characterize the physical limitation of the quality of quantum generative models. Finally, we apply the quantum inception score to assess the quality of the one-dimensional spin chain model as a quantum generative model, with the quantum convolutional neural network as a quantum classifier, for the phase classification problem in the quantum many-body physics.
The Quantum Rabi model: Towards Braak's conjecture

We establish a density one version of Braak's conjecture on the fine structure of the spectrum of the quantum Rabi model, as well as a recent conjecture of Braak, Nguyen, Reyes-Bustos and Wakayama on the nearest neighbor spacings of the spectrum. The proof uses a three-term asymptotics for large eigenvalues due to Boutet de Monvel and Zielinski, and a number theoretic argument from uniform distribution theory.
Exploring the Relationship Between COVID-19 Induced Economic Downturn and Women's Nutritional Health Disparities (preprint)

This study explores how the COVID-19 pandemic's economic impact has exacerbated nutritional health disparities among women. It sought to understand the effects of economic challenges on women's dietary choices and access to nutritious food across different socioeconomic groups. Using a mixed-methods approach, the research combined quantitative data from health and economic records with qualitative insights from interviews with diverse women. The study analyzed trends in nutritional health and economic factors before and after the pandemic and gathered personal accounts regarding nutrition and economic difficulties during this period. Findings showed a clear link between the economic downturn and deteriorating nutritional health, particularly in low-income and marginalized groups. These women reported decreased access to healthy foods and an increased dependence on less nutritious options due to budget constraints, leading to a decline in dietary quality. This trend was less evident in higher-income groups, highlighting stark disparities. The pandemic intensified pre-existing nutritional inequalities, with the most vulnerable groups facing greater adverse effects. However, community support and public health measures provided some relief. In summary, the pandemic's economic repercussions have indirectly impaired women's nutritional health, especially among the socioeconomically disadvantaged. This highlights the necessity for tailored nutritional interventions and economic policies focused on safeguarding women's health.
Plant MDL proteins synergize with the cytokine MIF at CXCR2 and CXCR4 receptors in human cells

Mammalian macrophage migration inhibitory factor (MIF) and its paralog, D-dopachrome tautomerase, are multifunctional inflammatory cytokines. Plants have orthologous MIF and D-dopachrome tautomerase–like (MDL) proteins that mimic some of the effects of MIF on immune cells in vitro. We explored the structural and functional similarities between the three Arabidopsis thaliana MDLs and MIF. X-ray crystallography of the MDLs revealed high structural similarity between MDL and MIF homotrimers and suggested a potential explanation for the lack of tautomerase activity in the MDLs. MDL1 and MDL2 interacted with each other and with MIF in vitro, in yeast, and in plant leaves and formed hetero-oligomeric complexes with MIF in vitro. The MDLs stimulated signaling through the MIF receptors CXCR2 or CXCR4 and enhanced the responses to MIF in a yeast reporter system, in human neutrophils, and in human lung epithelial cells. Pharmacological inhibitors that disrupted MIF activity or prevented the formation of MIF-MDL hetero-oligomers blocked the observed synergism. These findings demonstrate that MDLs can enhance cellular responses to MIF, which may have functional implications in tissues exposed to MDLs from the diet or environment. Description Plant MDL proteins can modulate the response of human cells to the proinflammatory cytokine MIF. Editor’s summary Plant MDL proteins are homologous to the proinflammatory cytokine MIF and mimic some of the effects of MIF in human cells. Spiller et al. demonstrated synergism between MIF and plant MDL proteins. The three Arabidopsis thaliana MDLs formed trimers with high structural similarity to those formed by human MIF. In vitro, yeast, and plant assays demonstrated that MDL1 and MDL2 oligomerized with each other and with MIF and enhanced MIF-mediated activation of the receptors CXCR2 and CXCR4. MDL1 augmented MIF-induced chemotaxis and inflammatory gene expression in human neutrophils and lung epithelial cells, respectively, indicating that these plant proteins have the potential to modulate cellular responses to MIF. —Annalisa M. VanHook
Robust and time-resolved estimation of cardiac sympathetic and parasympathetic indices

The time-resolved analysis of heart rate (HR) and heart rate variability (HRV) is crucial for the evaluation of the dynamic changes of autonomic activity under different clinical and behavioral conditions. Standard HRV analysis is performed in the frequency domain because the sympathetic activations tend to increase low-frequency HRV oscillations, while the parasympathetic ones increase high-frequency HRV oscillations. However, a strict separation of HRV in frequency bands may cause biased estimations, especially in the low frequency range. To overcome this limitation, we propose a robust estimator that combines HR and HRV dynamics, based on the correlation of the Poincaré plot descriptors of interbeat intervals from the electrocardiogram. To validate our method, we used electrocardiograms gathered from different open databases where standardized paradigms were applied to elicit changes in autonomic activity. Our proposal outperforms the standard spectral approach for the estimation of low- and high-frequency fluctuations in HRV, which are mostly triggered by sympathetic and parasympathetic activity, respectively. Our method constitutes a valuable, robust, time-resolved, and cost-effective tool for a better understanding of autonomic activity through HR and HRV in healthy state and potentially for pathological conditions.
Protein-Ligand Binding Site Prediction and De Novo Ligand Generation from Cryo-EM Maps

Identification of protein-ligand binding sites is one of the most challenging tasks in drug discovery and design. Recent advances in machine learning community, particularly deep learning, inspired considerable research into deep learning-based methods for protein ligand binding site prediction (PLBP) and have achieved promising results. However one limitation of these methods and models is that they are developed and evaluated using conventional databases consisting of relatively small protein structures determined from X-ray crystallography and NMR with high resolution. When current PLBP methods are directly applied to large protein complexes, they either fail or produce very poor results. Given the increasing popularity of protein structures determined from Cryo-EM maps, which usually capture large protein complexes at relatively low resolution, there is a strong need to apply PLBP methods to Cryo-EM maps. For this purpose, we created a novel database (EMD-Ligand Dataset) consisting of Cryo-EM maps and information about their ligand-binding partners. Our study of several state-of-the-art PLBP methods show that even though they perform reasonably well on conventional databases, they do poorly on the novel EMD-Ligand Dataset, which calls for more research and work to be done by the community. As current methods for determining protein structures experimentally from Cryo-EM maps are time-consuming and expensive, we integrated PLBP methods with DeepTracer, an automatic and fast, de novo Cryo-EM protein structure modeling method, and established an end-to-end system capable of predicting ligand binding sites directly from Cryo-EM maps. Additionally, we leveraged an existing ligand generator to generate drug-like ligands from our predicted ligand-binding pockets and demonstrated its effectiveness.
Dormant Mycobacterium tuberculosis reactivates ex vivo in blood from HIV infected individuals

Reports of tuberculosis in Human Immunodeficiency Virus infected patients despite high CD4+ T-cell counts suggest different mechanisms of reactivation. In vitro developed dormant Mycobacterium tuberculosis H37Rv were cultured in HIV blood and normal blood. Colony forming units analysis was performed and expression of mycobacterial Resuscitation Promomoting Factor genes was analyzed by qRT-PCR. The in vitro developed dormant M. tb resuscitated in HIV blood as evident from increase in CFUs and up-regulation in the expression of mycobacterial rpf genes. Reactivation of dormant tubercle bacilli following HIV infection in spite of high CD4+ T-cell counts could be due to some viral or host factors which needs further studies.
Monocyte subpopulations exhibit distinct TNF-dependent aging signatures

Monocytes are a key cell type contributing to age-associated inflammation or inflammaging. Since monocytes have the potential to enter circulation and differentiate into macrophages in tissues, they are capable of having a systemic effect on health. Here, we characterize the effect of aging on the transcriptional regulation of monocyte subpopulations in the bone marrow. We find that aging classical (Ly6c high) and non-classical (Ly6c low) monocytes exhibit distinct transcriptional profiles. These were associated with changes to the epigenomic landscape driven by the activity of specific TFs that often had opposing effects in monocyte subpopulations. Next, we determined that the aging signature was diminished in TNF-KO mice indicating that monocytes are altered in a TNF-dependent manner. Finally, we found that a subset of the aging signature was triggered by TNF over-expression. Together, our results implicate key factors driving age-associated changes to the transcriptional regulation of monocyte subpopulations. This study provides a better understanding of the impact of aging on monocytes and identifies targets for future investigation aiming to improve health in aging.
Truly the best of both worlds: merging lineage-specific and universal baiting kits to maximize phylogenomic inference

PREMISE The development of RNA baiting kits for reduced representation approaches of genomic sequencing is popularized, with universal and clade-specific kits for flowering plants available. Here, we provided an updated version of the Annonaceae bait kit targeting 799 low copy genes, known as Annonaceae799. METHODS This new version of the kit combines the original 469 genes from the previous version of the Annonaceae kit with 334 genes from the universal Angiosperms353 kit. We also compared the results obtained using the Original Angiosperms353 kit with our custom approach. Parsimony informative sites (pis) were evaluated for all genes and combined matrices. RESULTS The new version of the kit has extremely high rates of gene recovery. On average, 796 genes were recovered per sample, and 777.5 genes recovered with at least 50% of their size. Off-target reads were also obtained. Evaluating size, the proportion of on- and off-target regions, and the number of pis, the genes from the Angiosperms353 usually outperform the genes from the original Annonaceae bait kit. DISCUSSION The results obtained show that the new sequences from the Angiosperms353 aggregate variable and putative relevant bases for future studies on species-level phylogenomics, and within species studies. The merging of kits also creates a link between projects and makes available new genes for phylogenetic and populational studies.
N-Glycans on the extracellular domain of the Notch1 receptor control Jagged-1 induced Notch signalling and myogenic differentiation of S100β resident vascular stem cells

Notch signalling, critical for development and postnatal homeostasis of the vascular system, is highly regulated by several mechanisms including glycosylation. While the importance of O-linked glycosylation is widely accepted, the structure and function of N-glycans has yet to be defined. Here, we take advantage of lectin binding assays in combination with pharmacological, molecular, and site-directed mutagenetic approaches to study N-glycosylation of the Notch1 receptor. We find that several key oligosaccharides containing bisecting or core fucosylated structures decorate the receptor, control expression and receptor trafficking, and dictate Jagged-1 activation of Notch target genes and myogenic differentiation of multipotent S100β vascular stem cells. N-glycans at asparagine (N) 1241 and 1587 protect the receptor from accelerated degradation, while the oligosaccharide at N888 directly affects signal transduction. Conversely, N-linked glycans at N959, N1179, N1489 do not impact canonical signalling but inhibit differentiation. Our work highlights a novel functional role for N-glycans in controlling Notch1 signalling and differentiation of vascular stem cells. Teaser A sweet development in Notch regulation of vascular smooth muscle cell differentiation
Cycle annuel du peuplement macrobenthique du port d'Alger

L'impact de la pollution sur le peuplement macrobenthique a ete etudie durant un cycle saisonnier a travers l'analyse de 34 stations reparties dans les differents bassins du port d'Alger (Mustapha, Agha et Vieux Port). Pour chacune de ces stations la composition faunistique et la signification ecologique de chaque espece ou groupe d'especes ont permis de mettre en evidence la specificite de chaque bassin et de determiner sept zones de pollution qui ont ete delimitees dans les differents bassins. L'evolution saisonniere du peuplement des differentes stations est tres importante dans le bassin de Mustapha, moins notable dans le bassin de l'Agha et faible dans le bassin du Vieux Port. Le peuplement des fonds de darses est toujours plus degrade que celui des bassins d'evolution. Le peuplement est soumis a un tres fort gradient de degradation depuis la passe nord du Bassin du Vieux Port jusqu'au fond des darses du bassin de Mustapha.
Suppressing DNMT3a Alleviates the Intrinsic Epigenetic Barrier for Optic Nerve Regeneration and Restores Vision in Adult Mice

The limited regenerative potential of the optic nerve in adult mammals presents a major challenge for restoring vision after optic nerve trauma or disease. The mechanisms of this regenerative failure are not fully understood1,2. Here, through small-molecule and genetic screening for epigenetic modulators3, we identify DNA methyltransferase 3a (DNMT3a) as a potent inhibitor of axon regeneration in mouse and human retinal explants. Selective suppression of DNMT3a in retinal ganglion cells (RGCs) by gene targeting or delivery of shRNA leads to robust, full-length regeneration of RGC axons through the optic nerve and restoration of vision in adult mice after nerve crush injury. Genome-wide bisulfite and transcriptome profiling in combination with single nucleus RNA-sequencing of RGCs revealed selective DNA demethylation and reactivation of genetic programs supporting neuronal survival and axonal growth/regeneration by DNMT3a deficiency. This was accompanied by the suppression of gene networks associated with apoptosis and inflammation. Our results identify DNMT3a as the central orchestrator of an RGC-intrinsic mechanism that limits optic nerve regeneration. Suppressing DNMT3a expression in RGCs unlocks the epigenetic switch for optic nerve regeneration and presents a promising therapeutic avenue for effectively reversing vision loss resulted from optic nerve trauma or diseases.
Expression-aware video inpainting for HMD removal in XR applications

Head-mounted displays (HMDs) serve as indispensable devices for observing extended reality (XR) environments and virtual content. However, HMDs present an obstacle to external recording techniques as they block the upper face of the user. This limitation significantly affects social XR applications, specifically teleconferencing, where facial features and eye gaze information play a vital role in creating an immersive user experience. In this study, we propose a new network for expression-aware video inpainting for HMD removal (EVI-HRnet) based on generative adversarial networks (GANs). Our model effectively fills in missing information with regard to facial landmarks and a single occlusion-free reference image of the user. The framework and its components ensure the preservation of the user’s identity across frames using the reference frame. To further improve the level of realism of the inpainted output, we introduce a novel facial expression recognition (FER) loss function for emotion preservation. Our results demonstrate the remarkable capability of the proposed framework to remove HMDs from facial videos while maintaining the subject’s facial expression and identity. Moreover, the outputs exhibit temporal consistency along the inpainted frames. This lightweight framework presents a practical approach for HMD occlusion removal, with the potential to enhance various collaborative XR applications without the need for additional hardware.
Contingent Rewards in the Elementary Classroom: The Teacher’s Contingent Rewards in the Elementary Classroom: The Teacher’s Perspective Perspective

With growing force, extrinsic motivators, such as stickers, certificates, gold stars, and monetary compensation, permeate the educational environment (Kohn, 1993). While innocuous on the surface, such incentive-laden practices represent a level of teacher control that has profound consequences for student motivation (Reeve, 2006). Although considerable field experiments have shown the effects of contingent rewards on subsequent intrinsic motivation for engaging in proscribed activities (Deci & Ryan, 1985), such studies do not shed light on the motivational realities of the classroom environment, complete with student discipline, standardized curricula, and accountability measures. One hundred five (105) elementary teachers of grades one to five within a single school district in the Southern United States responded to Likert-type items and open-ended questions, allowing them to articulate and justify their use of systems of rewards and sanctions in th e classroom . Results indicated that the overwhelming majority of teachers (95%) had systems of rewards and consequences/sanctions, which they deemed effective and pedagogically appropriate. Teachers revealed highly developed token economies spanning both the students’ behavioral and academic outcomes. While this study is descriptive and exploratory in nature, it attempts to provide context for further research in an area of pressing concern that needs to be reclaimed.