Miracle-dz/UniFAR-FTUs huggingface dataset:
Trapping of HII regions in Population III star formation

Radiative feedback from massive Population III (Pop. III) stars in the form of ionising and photodissociating photons is widely believed to play a central role in shutting off accretion onto these stars. Understanding whether and how this occurs is vital for predicting the final masses reached by these stars and the form of the Pop. III stellar initial mass function. To help us better understand the impact of UV radiation from massive Pop. III stars on the gas surrounding them, we carry out high resolution simulations of the formation and early evolution of these stars, using the AREPOmoving-mesh code coupled with the innovative radiative transfer module SPRAI. Contrary to most previous results, we find that the ionising radiation from these stars is trapped in the dense accretion disk surrounding them. Consequently, the inclusion of radiative feedback has no significant impact on either the number or the total mass of protostars formed during the 20 kyr period that we simulate. We show that the reason that we obtain qualitatively different results from previous studies of Pop. III stellar feedback lies in how the radiation is injected into the simulation. HII region trapping only occurs if the photons are injected on scales smaller than the local scale height of the accretion disk, a criterion not fulfilled in previous 3D simulations of this process. Finally, we speculate as to whether outflows driven by the magnetic field or by Lyman-α radiation pressure may be able to clear enough gas away from the star to allow the HII region to escape from the disk.
Performance of dense wireless networks in 5G and beyond using stochastic geometry

Device density in cellular networks is expected to increase considerably in the near future. Accordingly, the access point (AP) will be equipped with massive multiple-input multiple-output (mMIMO) antennas, using collimated millimeter-wave (mmW) and sub-THz communications, and increasing the bandwidth to accommodate the growing data rate demands. In this scenario, interference plays a critical role and, if not characterized and mitigated properly, might limit the performances of the network. In this context, this paper derives the statistical properties of the aggregated interference power for a cellular network equipping a mMIMO cylindrical array. The proposed statistical model considers the link blockage and other network parameters such as antenna configuration and device density. The findings show that the characteristic function (CF) of the aggregated interference power can be regarded as a weighted mixture of two alpha-stable distributions. Furthermore, by analyzing the service probability, it is found that there is an optimal configuration of the array depending on the AP height and device density. The proposed statistical model can be part of the design of dense networks providing valuable insights for optimal network deployment and resource management and scheduling.
Almost disjoint paths and separating by forbidden pairs

By Menger's theorem the maximum number of arc-disjoint paths from a vertex s to a vertex t in a directed graph equals the minumum number of arcs needed to disconnect s and t, i.e., the minimum size of an s-t-cut. The max-flow problem in a network with unit capacities is equivalent to the arc-disjoint paths problem. Moreover the max-flow and min-cut problems form a strongly dual pair. We relax the disjointedness requirement on the paths, allowing them to be almost disjoint, meaning they may share up to one arc. The resulting almost disjoint paths problem (ADP) asks for k s-t-paths such that any two of them are almost disjoint. The separating by forbidden pairs problem (SFP) is the corresponding dual problem and calls for a set of k arc pairs such that every s-t-path contains both arcs of at least one such pair. In this paper, we explore these two problems, showing that they have an unbounded duality gap in general and analyzing their complexity. We prove that ADP is NP-complete when k is part of the input and that SFP is Sigma_2P-complete, even for acyclic graphs. Furthermore, we efficiently solve ADP when k<=2 is fixed and present a polynomial time algorithm based on dynamic programming for ADP when k is constant and the considered graphs are acyclic.
PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-Step Point Moving Paths

Point cloud completion concerns to predict missing part for incomplete 3D shapes. A common strategy is to generate complete shape according to incomplete input. However, unordered nature of point clouds will degrade generation of high-quality 3D shapes, as detailed topology and structure of unordered points are hard to be captured during the generative process using an extracted latent code. We address this problem by formulating completion as point cloud deformation process. Specifically, we design a novel neural network, named PMP-Net++, to mimic behavior of an earth mover. It moves each point of incomplete input to obtain a complete point cloud, where total distance of point moving paths (PMPs) should be the shortest. Therefore, PMP-Net++ predicts unique PMP for each point according to constraint of point moving distances. The network learns a strict and unique correspondence on point-level, and thus improves quality of predicted complete shape. Moreover, since moving points heavily relies on per-point features learned by network, we further introduce a transformer-enhanced representation learning network, which significantly improves completion performance of PMP-Net++. We conduct comprehensive experiments in shape completion, and further explore application on point cloud up-sampling, which demonstrate non-trivial improvement of PMP-Net++ over state-of-the-art point cloud completion/up-sampling methods.
Items from Psychometric Tests as Training Data for Personality Profiling Models of Twitter Users

Machine-learned models for author profiling in social media often rely on data acquired via self-reporting-based psychometric tests (questionnaires) filled out by social media users. This is an expensive but accurate data collection strategy. Another, less costly alternative, which leads to potentially more noisy and biased data, is to rely on labels inferred from publicly available information in the profiles of the users, for instance self-reported diagnoses or test results. In this paper, we explore a third strategy, namely to directly use a corpus of items from validated psychometric tests as training data. Items from psychometric tests often consist of sentences from an I-perspective (e.g., ‘I make friends easily.’). Such corpora of test items constitute ‘small data’, but their availability for many concepts is a rich resource. We investigate this approach for personality profiling, and evaluate BERT classifiers fine-tuned on such psychometric test items for the big five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and analyze various augmentation strategies regarding their potential to address the challenges coming with such a small corpus. Our evaluation on a publicly available Twitter corpus shows a comparable performance to in-domain training for 4/5 personality traits with T5-based data augmentation.
Score-driven generalized fitness model for sparse and weighted temporal networks

While the vast majority of the literature on models for temporal networks focuses on binary graphs, often one can associate a weight to each link. In such cases the data are better described by a weighted, or valued, network. An important well known fact is that real world weighted networks are typically sparse. We propose a novel time varying parameter model for sparse and weighted temporal networks as a combination of the ﬁtness model, appropriately extended, and the score driven framework. We consider a zero augmented generalized linear model to handle the weights and an observation driven approach to describe time varying parameters. The result is a ﬂexible approach where the probability of a link to exist is independent from its expected weight. This represents a crucial diﬀerence with alternative speciﬁcations proposed in the recent literature, with relevant implications for the ﬂexibility of the model. Our approach also accommodates for the dependence of the network dynamics on external variables. We present a link forecasting analysis to data describing the overnight exposures in the Euro interbank market and investigate whether the inﬂuence of EONIA rates on the interbank network dynamics has changed over time.
MILP-based optimal day-ahead scheduling for system-centric CEMS supporting different types of homes and energy trading

Optimal day-ahead scheduling for a system-centric community energy management system (CEMS) is proposed to provide economic benefits and user comfort of energy management at the community level. Our proposed community includes different types of homes and allows prosumers to trade energy locally using mid-market rate pricing. A mathematical model of the community is constructed and the optimization problem of this model is transformed into an MILP problem that can be solved in a short time. By solving this MILP problem, the optimization of the overall energy cost of the community and satisfaction of the thermal comfort at every home are achieved. For comparison, we also establish two different scenarios for the same community: a prosumer-centric CEMS and no CEMS. The simulation results demonstrate that the overall energy cost of the community with the system-centric CEMS is the smallest among the three scenarios and is only half that of the community with the prosumer-centric CEMS. Moreover, by using linear transformation, the computational time of the optimization problem of the proposed system-centric CEMS is only 118.2 s for a 500-home community, which is a short time for day-ahead scheduling of a community.
GANmapper: geographical data translation

Abstract We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset to another with high fidelity and morphological accuracy. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, the experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data or inpainting geospatial data in regions with incomplete data.
Consequences of silvicultural treatments on stand dynamics at Paracou

The different components of stand dynamics - growth, mortality and recruitment - were affected by silvicultural treatments of growing intensity applied in the Paracou plots, resulting in contrasted behaviours in the stands. We first present and discuss the evolution over time of the plots in terms of their total number of trees and basal area. We then examine the evolution of the diameter structures, the dynamic components and the basal area balance for the global set of species and for the marketable species, emphasising the differences between treatments. Estimates of the time required to return to a hypothetical initial state are made according to different methods. One of the conclusions is that a felling cycle of 50 years in stands identical to those logged-over at Paracou, could allow recovery of 80-90% of the initial number of valuable trees above the diameter cutting limit, but with a shift in the floristic composition. We discuss some practical consequences for forest managers of the results found at Paracou and comparisons are made with other known experimental designs such as CELOS in Suriname and Tapajos in Brazil. (Resume d'auteur)
Electronic Cigarette Use Promotes a Unique Periodontal Microbiome

Electronic cigarette (e-cig) use is gaining in popularity and is often perceived as a healthier alternative to conventional smoking. Yet there is little evidence of the effects of long-term use of e-cigs on oral health. ABSTRACT Electronic cigarettes (e-cigs) have become prevalent as an alternative to conventional cigarette smoking, particularly in youth. E-cig aerosols contain unique chemicals which alter the oral microbiome and promote dysbiosis in ways we are just beginning to investigate. We conducted a 6-month longitudinal study involving 84 subjects who were either e-cig users, conventional smokers, or nonsmokers. Periodontal condition, cytokine levels, and subgingival microbial community composition were assessed, with periodontal, clinical, and cytokine measures reflecting cohort habit and positively correlating with pathogenic taxa (e.g., Treponema, Saccharibacteria, and Porphyromonas). α-Diversity increased similarly across cohorts longitudinally, yet each cohort maintained a unique microbiome. The e-cig microbiome shared many characteristics with the microbiome of conventional smokers and some with nonsmokers, yet it maintained a unique subgingival microbial community enriched in Fusobacterium and Bacteroidales (G-2). Our data suggest that e-cig use promotes a unique periodontal microbiome, existing as a stable heterogeneous state between those of conventional smokers and nonsmokers and presenting unique oral health challenges. IMPORTANCE Electronic cigarette (e-cig) use is gaining in popularity and is often perceived as a healthier alternative to conventional smoking. Yet there is little evidence of the effects of long-term use of e-cigs on oral health. Conventional cigarette smoking is a prominent risk factor for the development of periodontitis, an oral disease affecting nearly half of adults over 30 years of age in the United States. Periodontitis is initiated through a disturbance in the microbial biofilm communities inhabiting the unique space between teeth and gingival tissues. This disturbance instigates host inflammatory and immune responses and, if left untreated, leads to tooth and bone loss and systemic diseases. We found that the e-cig user’s periodontal microbiome is unique, eliciting unique host responses. Yet some similarities to the microbiomes of both conventional smokers and nonsmokers exist, with strikingly more in common with that of cigarette smokers, suggesting that there is a unique periodontal risk associated with e-cig use.
Effects of bilateral sequential theta-burst stimulation on 5-HT1A receptors on dorsolateral prefrontal cortex in treatment resistant depression

Theta-burst stimulation (TBS) represents a brain stimulation technique effective for treatment-resistant depression (TRD) as underlined by meta-analyses. While the methodology undergoes constant refinement, bilateral stimulation of the dorsolateral prefrontal cortex (DLPFC) appears promising to restore left DLPFC hypoactivity and right hyperactivity found in depression. The post-synaptic inhibitory serotonin-1A (5-HT1A) receptor, also occurring in the DLPFC, might be involved in this mechanism of action. To test this hypothesis, we performed PET-imaging using the tracer [carbonyl-11C]WAY-100635 including arterial blood sampling before and after a three-week treatment with TBS in 11 TRD patients compared to sham stimulation (n=8 and n=3, respectively). Treatment groups were randomly assigned, and TBS protocol consisted in excitatory intermittent TBS to the left and inhibitory continuous TBS to the right DLPFC. A linear mixed model including group, hemisphere time and Hamilton Rating Scale for Depression (HAMD) score revealed a 3-way interaction effect of group time and HAMD on 5-HT1A receptor specific binding VS. While post-hoc comparisons showed no significant changes of 5-HT1A VS in either group, higher 5-HT1A VS after treatment correlated with greater difference in HAMD (r=-0.62), indicative of potential effects of TBS on the 5-HT1A receptor. Due to the small sample size, all results, however, must be regarded with caution.
The epigenetic dimension of protein structure

Abstract Accurate prediction of protein structure is one of the most challenging goals of biology. The most recent achievement is AlphaFold, a machine learning method that has claimed to have solved the structure of almost all human proteins. This technological breakthrough has been compared to the sequencing of the human genome. However, this triumphal statement should be treated with caution, as we identified serious flaws in some AlphaFold models. Disordered regions are often represented by large loops that clash with the overall protein geometry, leading to unrealistic structures, especially for membrane proteins. In fact, AlphaFold comes up against the notion that protein folding is not solely determined by genomic information. We suggest that all parameters controlling the structure of a protein without being strictly encoded in its amino acid sequence should be coined “epigenetic dimension of protein structure. ” Such parameters include for instance protein solvation by membrane lipids, or the structuration of disordered proteins upon ligand binding, but exclude sequence-encoded sites of post-translational modifications such as glycosylation. In our view, this paradigm is necessary to reconcile two opposite properties of living systems: beyond rigorous biological coding, evolution has given way to a certain level of uncertainty and anarchy.
NADPH-derived ROS generation drives fibrosis and endothelial-to-mesenchymal transition in systemic sclerosis: Potential cross talk with circulating miRNAs

Abstract Systemic sclerosis (SSc) is an immune disorder characterized by diffuse fibrosis and vascular abnormalities of the affected organs. Although the etiopathology of this disease is largely unknown, endothelial damage and oxidative stress appear implicated in its initiation and maintenance. Here, we show for the first time that circulating factors present in SSc sera increased reactive oxygen species (ROS) production, collagen synthesis, and proliferation of human pulmonary microvascular endothelial cells (HPMECs). The observed phenomena were also associated with endothelial to mesenchymal transition (EndMT) as indicated by decreased von Willebrand factor (vWF) expression and increased alpha-smooth muscle actin, respectively, an endothelial and mesenchymal marker. SSc-induced fibroproliferative effects were prevented by HPMECs exposition to the NADPH oxidase inhibitor diphenyleneiodonium, demonstrating ROS’s causative role and suggesting their cellular origin. Sera from SSc patients showed significant changes in the expression of a set of fibrosis/EndMT-associated microRNAs (miRNA), including miR-21, miR-92a, miR-24, miR-27b, miR-125b, miR-29c, and miR-181b, which resulted significantly upregulated as compared to healthy donors sera. However, miR29b resulted downregulated in SSc sera, whereas no significant differences were found in the expression of miR-29a in the two experimental groups of samples. Taking together our data indicate NADPH oxidase-induced EndMT as a potential mechanism of SSc-associated fibrosis, suggesting fibrosis-associated miRNAs as potentially responsible for initiating and sustaining the vascular alterations observed in this pathological condition.
Deep Monochromatic Metal Artifact Reduction for Computed Tomography

Computed tomography (CT) is a three-dimensional medical imaging modality that uses X-ray beams to generate cross-sectional images of the human anatomy. Although CT is widely used in medical diagnosis, the presence of metal implants often severely impair the diagnostic value of CT images. The presence of metal implants causes errors in the image that are called “metal artifacts”. Existing metal artifact reduction (MAR) algorithms are either ineffective or require a large training dataset which is difficult to attain due to the inaccessibility of clinical data. Thus, this study proposes a novel end-to-end convolutional neural network with autoencoder embeddings for MAR that overcomes the shortcomings of existing methods. Unlike existing methods that simulate training data using artificially synthesized metal implant shapes, our research proposes a new data synthesis technique that uses randomly generated polygons to automate the data simulation process. Experimental results prove that this method drastically improves the efficiency of the data generation process. Our proposed network also significantly outperforms state-of-the-art MAR techniques, achieving an MSE < 7 × 10− 6, an SSIM index > 0.994, and a PSNR > 58 dB on a simulated training dataset of 130 samples.
Alternative c-MYC mRNA Transcripts as an Additional Tool for c-Myc2 and c-MycS Production in BL60 Tumors

While studying c-Myc protein expression in several Burkitt lymphoma cell lines and in lymph nodes from a mouse model bearing a translocated c-MYC gene from the human BL line IARC-BL60, we surprisingly discovered a complex electrophoretic profile. Indeed, the BL60 cell line carrying the t(8;22) c-MYC translocation exhibits a simple pattern, with a single c-Myc2 isoform. Analysis of the c-MYC transcripts expressed by tumor lymph nodes in the mouse λc-MYC (Avy/a) showed for the first time five transcripts associated with t(8;22) c-MYC translocation. The five transcripts were correlated with the production of c-Myc2 and c-MycS, and loss of c-Myc1. The contribution of these transcripts to the oncogenic activation of the t(8;22) c-MYC is discussed.