Miracle-dz/UniFAR-FTUs huggingface dataset:
Markov Processes: Estimation and Sampling in Undersampled Regime

This work concentrates on estimation and sampling aspects in slow mixing Markov Processes. When a process has slow mixing property, a large number of observations is needed before exploring the state space of the process properly. Empirical properties of finite sized samples from Markov processes need not reflect stationary properties. When empirical counts of samples eventually reflect the stationary properties, we say that the process has mixed. The contributions of this work revolve around interpretation of samples before mixing has occurred. In first part of the work, we deal with estimation of parameters of the process, while in the second part, we will show how the evolution of the samples obtained from the process can be exploited to identify subsets of state space which communicate well together. This insight will be translated into algorithmic rules for detecting communities in graphs. Estimation : We observe a length-n sample generated by an unknown, stationary ergodic Markov process (model) over a finite alphabet A. Given any string w of symbols from A we want estimates of the conditional probability distribution of symbols following w (model parameters), as well as the stationary probability of w. Two distinct problems that complicate estimation in this setting are (i) long memory, and (ii) slow mixing which could happen even with only one bit of memory. Any consistent estimator can only converge pointwise over the class of all ergodic Markov models. Namely, given any estimator and any sample size n, the underlying model could be such that the estimator performs poorly on a sample of size n with high probability. But can we look at a length-n sample and identify if an estimate is likely to be accurate ? Since the memory is unknown a-priori, a natural approach is to estimate a potentially coarser model with memory kn = O(log n). As n grows, estimates get refined and this approach is consistent with the above scaling of kn also known to be essentially optimal. But while effective asymptotically, the situation is quite different when we want the best
Identi ﬁ cation of a 16-MTGs Prognostic Signature in Diffuse Large B-Cell Lymphoma

Background . Diffuse large B-cell lymphoma (DLBCL) is one of the largest lymphoma subcategories. Usually, 50 % – 70 % of DLBCL patients can be cured by the standard treatment. But, at least one third have bad prognosis. Based on this situation, the research on DLBCL therapy strategy is still indispensable. Methods . A prognostic signature was built according to the public data and bioinformatics methods, the stability and reliability was assessed and validated. GSEA was performed to explore the difference in different groups. Consensus clustering and immune in ﬁ ltration analysis were conducted comprehensively. Results . In this work, a signature based on multiple metabolism-associated genes (MTGs) was established, containing 16 MTGs, to predict the prognosis of DLBCL patients. The accuracy and effectiveness of this signature have been veri ﬁ ed by three external validation sets. According to the risk formula, DLBCL patients were divided into high-and low-risk groups, and the survival rate of the low-risk group was signi ﬁ cantly higher than that of the high-risk group. Furthermore, gene set enrichment analysis (GSEA) demonstrated that beta-alanine metabolism and regulation of actin cytoskeleton signal pathways were enriched in the low-risk group. The actual survival and nomogram-predicted survival matched well both in the training cohort and veri ﬁ cation cohorts. Conclusion . In general, our prognostic signature can provide reliable and valuable information for medical workers in predicting the prognosis of DLBCL. A preprint was made available by the research square in the following link: “ https://www.researchsquare. com/article/rs-1468741/v2. ”
Acceleration of the discrete dipole approximation for multiple incident fields and refractive indices

We have implemented the block bi-conjugate-gradient method and its two variants, using the initial deflation and orthogonalization of the residual matrix. We tested them for solving multiple linear systems in the discrete dipole approximation (DDA). The tests showed that the second variant of the algorithm outperforms other methods and is helpful for orientation-averaging problem, leading to 4-fold acceleration. Next, we have proposed to use the conjugate gradient method with many shifts, and adapted it for the DDA matrix. The first tests showed that it is useful when many values of the particle refractive index need to be considered for the same scattering problem.
ASSESSMENT OF ENGINE OPERABILITY AND OVERALL PERFORMANCE FOR PARALLEL HYBRID ELECTRIC PROPULSION SYSTEMS FOR A SINGLE-AISLE AIRCRAFT

This paper aims to assess the gas turbine operability and overall hybrid electric propulsion system performance for a parallel configuration applied to a 150 passenger single-aisle aircraft. Two arrangements are considered: one where the low pressure shaft is boosted and one where the high pressure shaft is boosted. For identifying limits in the hybridization strategy steady state and transient operation are considered and the hybridization effect on compressor operability is determined. Having established the electric power on-take limits with respect to gas turbine operation the systems performance at aircraft level is quantified for the relevant cases. Different power management strategies are applied for the two arrangements and for different power degrees of hybridization. The results indicate that despite the fact that pollutant emission and fuel consumption may improved for hybrid propulsion, this comes at the cost of reduced payload and operability margins. Boosting the low pressure shaft may give the highest engine performance benefits but with a significant weight penalty, while the low pressure compressor system operability is negatively affected. On the other hand boosting the high pressure shaft provides lower engine performance benefits but with smaller weight penalty and with less operability concerns.
training effect on mental health and biological indicators in post-COVID-19 patients

Introduction. One of the aspects of health most affected in patients infected by COVID-19 was mental health. Leading organizations worldwide have recommended physical activity as one of the main actions. Objective. To analyze the effect of a short-term aerobic training program on biological and mental health and quality of life indicators in post-Covid-19 patients. Methods. Symp-tomatology of depression, anxiety, stress, quality of life, vital signs, physical fitness and body composition were measured in 10 post COVID-19 patients. An aerobic physical exercise program was applied at 40-60% intensity, five days a week, 50 minutes per session for four weeks. Results. A 30% decrease in depression symptomatology, 58% in stress and 44% in anxiety was found during the intervention period (p<0.001). Regarding the perception of quality of life, the psychological area was the one that presented a positive effect during the intervention (p=0.035). An increase in leg strength (p=0.018) and a decrease in body fat (p=0.005) were found during the intervention. Conclusion. A short-term physical exercise program shows benefits in mental health indicators in post-COVID-19 patients.
Cushing's Syndrome : hormonal secretion patterns, treatment and outcome.

To evaluate the pathophysiology of altered cortisol secretion in patients with primary adrenal hypercortisolism cortisol secretion was investigated in 1 2 patients, seven with an unilateral adenoma and 5 with ACTH -independent macronodular adrenal hyperplasia (AIM AH ) compared with age-and gender-matched controls and with patients with pituitary-dependent hypercortisolism. Pulsatile secretion was increased two-fold (P= 0 .0 4 ), attrib utab le to increased event frequency (P= 0 .0 0 2 ). All patients showed a signi(cid:147) cant diurnal rhythm with a delay phase shift of 3 h (P= 0 .0 1 ). Approx imate entropy ratio, a feedb ack -sensitive measure, was increased compared with controls (P= 0 .0 0 0 0 3 ), b ut similar to that of pituitary-dependent hypercortisolism (P= 0 .7 7 ), denoting loss of autoregulation. Cortisol b urst-mass tended to b e smaller in patients with AIM AH than in unilateral adenoma (P= 0 .0 6 ). In conclusion, increased cortisol secretion in patients with primary adrenal Cushing(cid:144)s syndrome is caused b y ampli(cid:147) ed pulsatile secretion via event frequency modulation. W e speculate that partial preservation of secretory regularity and diurnal rhythmicity point to incomplete autonomy of these tumors.
EGFR in Enamel Matrix Derivative-induced Gingival Fibroblast Mitogenesis

We previously reported that EMD (Enamel Matrix Derivative) induces proliferation of human gingival fibroblasts via activation of Extracellular Regulated Kinase (ERK), and this study assessed the possible mediatory role of EGFR (Epidermal Growth Factor Receptor) in this effect. Treatment of gingival fibroblasts with EMD resulted in tyrosine phosphorylation of the EGFR, as assessed by immunoblotting and ELISA, while EMD-induced ERK activation and thymidine incorporation were markedly inhibited (~ 40–50%) by a specific EGFR tyrosine kinase inhibitor. Using appropriate inhibitors, we established that EMD-induced EGFR activation is largely due to shedding of HB-EGF (Heparin-binding EGF) from the cell membrane via a metalloproteinase-mediated process. Finally, the addition of PP1, a Src family inhibitor, abrogated both EGFR phosphorylation and ERK activation. Taken together, these results indicate that, at least in human gingival fibroblasts, EMD-induced ERK activation and proliferation are partially due to a Src-dependent, metalloproteinase-mediated transactivation of EGFR.
Modes collectifs et hydrodynamique dans la croûte interne des étoiles à neutrons

Les etoiles a neutrons ont ete largement etudiees depuis que Baade and Zwicky ont postule leur existence en 1934. Ces etudes ont ete et sont realisees a l'interface de differents domaines de la physique tels que : l'astrophysique en rayons X, l'observation des pulsars, la relativite generale et plus dernierement les ondes gravitationnelles, la physique du solide, ainsi que la physique nucleaire. Dans cette these nous nous concentrerons sur la description des etoiles a neutrons dans le cadre de la physique nucleaire et precisement de la croute interne de l'etoile. Ces etoiles sont caracterisees par une masse importante de l'ordre d'une a deux masses solaires dans un rayon de 10 km. Quant a leur structure interne elle peut etre decrite en trois strates : la croute externe, la croute interne et le cœur. La croute externe correspond a un reseau cristallin de noyaux atomiques et un gaz d'electrons relativistes. Vient ensuite la croute interne, definie lorsque les noyaux de la croute externe sont si riches en neutrons qu'ils les liberent dans le milieu pour former un gaz. Ici, nous ne parlons plus de noyaux mais d'agregats car tous les nucleons qui les composent ne sont plus systematiquement lies. Cette structure complexe et sa composition est a l'origine de nombreuses proprietes caracteristiques des etoiles a neutrons.C'est ainsi que nous construirons notre etude en trois parties. Tout d'abord nous commencerons par traiter le gaz de neutrons entourant les agregats. Le gaz de neutrons que nous considerons uniforme ici est superfluide et devrait donc presenter un mode de Goldstone. Cette description sera effectuee a l'aide de la QRPA. Puis nous en viendrons a la description des agregats. Dans ces conditions on s'attend a observer des cristaux de spheres, des cylindres et des plaques de matiere nucleaire, que nous decrirons grâce a l'approximation ETF. Puis nous terminerons par la description de l'interaction entre les agregats et le gaz au niveau dynamique, et ce dans le cadre de la theorie hydrodynamique. Ces resultats seront appliques a l'astrophysique et en particuliers aux glitches.
A lack of genetic diversity and minimal adaptive evolutionary divergence in introduced Mysis shrimp after 50 years

Abstract The successes of introduced populations in novel habitats often provide powerful examples of evolution and adaptation. In the 1950s, opossum shrimp (Mysis diluviana) individuals from Clearwater Lake in Minnesota, USA were transported and introduced to Twin Lakes in Colorado, USA by fisheries managers to supplement food sources for trout. Mysis were subsequently introduced from Twin Lakes into numerous lakes throughout Colorado. Because managers kept detailed records of the timing of the introductions, we had the opportunity to test for evolutionary divergence within a known time interval. Here, we used reduced representation genomic data to investigate patterns of genetic diversity, test for genetic divergence between populations, and for evidence of adaptive evolution within the introduced populations in Colorado. We found very low levels of genetic diversity across all populations, with evidence for some genetic divergence between the Minnesota source population and the introduced populations in Colorado. There was little differentiation among the Colorado populations, consistent with the known provenance of a single founding population, with the exception of the population from Gross Reservoir, Colorado. Demographic modeling suggests that at least one undocumented introduction from an unknown source population hybridized with the population in Gross Reservoir. Despite the overall low genetic diversity we observed, F ST outlier and environmental association analyses identified multiple loci exhibiting signatures of selection and adaptive variation related to elevation and lake depth. The success of introduced species is thought to be limited by genetic variation, but our results imply that populations with limited genetic variation can become established in a wide range of novel environments. From an applied perspective, the observed patterns of divergence between populations suggest that genetic analysis can be a useful forensic tool to determine likely sources of invasive species.
The effect of cortisol on equine monocyte–derived dendritic cell phenotype and cytokine production

Abstract Background Increased risk of infection in neonates, including foals, is associated with the naivety of the immune system and the immaturity of the endocrine system. Foal dendritic cells (DCs) are phenotypically and functionally less mature than adult horse DCs. Exposure of foal DCs to foal plasma alters DC phenotypic maturation, cytokine secretion and bacterial endocytosis. Specific plasma factors that impact equine DC activity are unknown. Endocrine dysfunction, resulting in altered cortisol availability, is common in foals. Cortisol impacts DC maturation and function in other species, but the effect of cortisol on equine DCs is not described. We hypothesized that exposure to cortisol would impact both the phenotype and function of equine monocyte–derived DCs (MoDCs), perpetuating an immature phenotype and altered cytokine profile. Methods MoDCs were generated from foals (n = 8) on 1, 7 and 30 days of age and adult horses (n = 9). MoDCs were exposed to killed bacteria in the presence or absence of cortisol. The expression of surface markers (MHC class‐II, CD86 and CD14) was measured by flow cytometry. Supernatant cytokine concentrations (IL‐4, IL‐17, IFN‐γ and IL‐10) were quantified using a validated bead‐based immunoassay. Results Cortisol exposure reduced the percentage of equine MoDCs expressing MHC class‐II and CD86 (p ≤ 0.03). Adult horse MoDCs exposed to bacteria and cortisol produced less IL‐4, IL‐17, IFN‐γ and IL‐10 than cells not treated with cortisol, and foal MoDCs secreted less IL‐10 and IL‐4 (p ≤ 0.009). Conclusions In sum, cortisol exposure perpetuates an immature MoDC phenotype and alters cytokine responses, which might impact the foal's susceptibility to infection.
Chemical Rules for Stacked Kagome and Honeycomb Topological Semimetals.

We study the chemical rules for predicting and understanding topological states in stacked kagome and honeycomb lattices in both analytical and numerical ways. Starting with a minimal five-band tight-binding model, we sort out all the topological states into five groups, which are determined by the interlayer and intralayer hopping parameters. Combined with the model, we design an algorithm to obtain a series of experimentally synthesized topological semimetals with kagome and honeycomb layers, i.e., IAMX family (IA = Alkali metal element, M = Rare earth metal element, X = Carbon group element), in the inorganic crystal structure database. A follow-up high-throughput calculation shows that IAMX family materials are all nodal-line semimetals and they will be Weyl semimetals after taking spin-orbit coupling into consideration. To have further insights into the topology of the IAMX family, a detailed chemical rule analysis is carried out on the high-throughput calculations, including the lattice constants of the structure, intralayer and interlayer couplings, bond strengths, electronegativity, and so on, which are consistent with our tight-binding model. our study provides a way to discover and modulate topological properties in stacked kagome and honeycomb crystals and offers candidates for studying topology-related properties like topological superconductors and axion insulators.
Women’s attitudes, prevalence, related factors, and perceived barriers of birth companionship in Saudi Arabia

Background: The World Health Organization recommends companionship as a critical element of respectful maternity care. However, there is paucity of literature regarding women’s attitudes, related factors, and barriers of implementation in Saudi Arabia. Objective: This study aimed to assess women’s attitudes toward birth companionship during labor and delivery, related factors, and barriers of implementing labor companionship in Saudi Arabia. Design/Methods: This study was carried out in Al Ahsa, Saudi Arabia, using a cross-sectional design. The data collection took place over a period of approximately 6 months, specifically from 16 August 2021 to 20 February 2022. A total of 418 women who had given birth at three governmental hospitals and three private hospitals in Al Ahsa participated in the survey. The study included women between the ages of 18 and 49 years who had delivered during the study period. Results: The study revealed that while around 82.8% of mothers expressed a desire for companionship during labor, only 43.2% actually had a labor companion present during delivery. The primary obstacle to labor companionship reported by 21.5% of respondents was hospital policies. Other barriers identified included husbands’ fear of being present during labor (4.7%) and limited availability of time for companionship (3.8%). Conclusion: The study concludes that birth companionship is highly desired by women in the study area. However, various physical, emotional, and institutional barriers, such as hospital policies and regulations, impede its implementation. Addressing these barriers and promoting supportive policies are essential to facilitate the provision of birth companionship and improve the childbirth experience for women in Al Ahsa.
TIFu: Tri-directional Implicit Function for High-Fidelity 3D Character Reconstruction

Recent advances in implicit function-based approaches have shown promising results in 3D human reconstruction from a single RGB image. However, these methods are not sufficient to extend to more general cases, often generating dragged or disconnected body parts, particularly for animated characters. We argue that these limitations stem from the use of the existing point-level 3D shape representation, which lacks holistic 3D context understanding. Voxel-based reconstruction methods are more suitable for capturing the entire 3D space at once, however, these methods are not practical for high-resolution reconstructions due to their excessive memory usage. To address these challenges, we introduce Tri-directional Implicit Function (TIFu), which is a vector-level representation that increases global 3D consistencies while significantly reducing memory usage compared to voxel representations. We also introduce a new algorithm in 3D reconstruction at an arbitrary resolution by aggregating vectors along three orthogonal axes, resolving inherent problems with regressing fixed dimension of vectors. Our approach achieves state-of-the-art performances in both our self-curated character dataset and the benchmark 3D human dataset. We provide both quantitative and qualitative analyses to support our findings.
Assembling a multi-platform ensemble social bot detector with applications to US 2020 elections

Bots have been in the spotlight for many social media studies, for they have been observed to be participating in the manipulation of information and opinions on social media. These studies analyzed the activity and influence of bots in a variety of contexts: elections, protests, health communication and so forth. Prior to this analyzes is the identification of bot accounts to segregate the class of social media users. In this work, we propose an ensemble method for bot detection, designing a multi-platform bot detection architecture to handle several problems along the bot detection pipeline: incomplete data input, minimal feature engineering, optimized classifiers for each data field, and also eliminate the need for a threshold value for classification determination. With these design decisions, we generalize our bot detection framework across Twitter, Reddit and Instagram. We also perform feature importance analysis, observing that the entropy of names and number of interactions (retweets/shares) are important factors in bot determination. Finally, we apply our multi-platform bot detector to the US 2020 presidential elections to identify and analyze bot activity across multiple social media platforms, showcasing the difference in online discourse of bots from different platforms.
Challenging Low Homophily in Social Recommendation

Social relations are leveraged to tackle the sparsity issue of user-item interaction data in recommendation under the assumption of social homophily. However, social recommendation paradigms predominantly focus on homophily based on user preferences. While social information can enhance recommendations, its alignment with user preferences is not guaranteed, thereby posing the risk of introducing informational redundancy. We empirically discover that social graphs in real recommendation data exhibit low preference-aware homophily, which limits the effect of social recommendation models. To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models. We adopt Graph Rewiring technique to capture and add highly homophilic social relations, and cut low homophilic (or heterophilic) relations. To better refine the user representations from reliable social relations, we integrate a contrastive learning method into the training of SHaRe, aiming to calibrate the user representations for enhancing the result of Graph Rewiring. Experiments on real-world datasets show that the proposed framework not only exhibits enhanced performances across varying homophily ratios but also improves the performance of existing state-of-the-art (SOTA) social recommendation models.