Miracle-dz/UniFAR-FTUs huggingface dataset:
How robust are the inferred density and metallicity of the circumgalactic medium?

Quantitative estimates of the basic properties of the circumgalactic medium (CGM), such as its density and metallicity, depend on the spectrum of incident UV background radiation. Models of UV background are known to have large variations, mainly because they are synthesized using poorly constrained parameters, which introduce uncertainty in the inferred properties of the CGM. Here, we quantify this uncertainty using a large set of new UV background models with physically motivated toy models of metal-enriched CGM. We find that, the inferred density and metallicity of low-density (10−5 cm−3) gas is uncertain by factors of 6.3 and 3.2, whereas high density (10−3 cm−3) gas by factors of 4 and 1.6, respectively. The variation in the shape of the UV background models is entirely responsible for such a variation in the metallicity while variation in the density arises from both normalization and shape of the UV background. Moreover, we find a harder (softer) UV background infers higher (lower) density and metallicity. We also study warm-hot gas at T = 105.5 K and find that metallicity is robustly estimated but the inferred density is uncertain by a factor of 3 to 5.4 for low to high-density gas. Such large uncertainties in density and metallicity may severely limit the studies of the CGM and demand better observational constraints on the input parameters used in synthesizing UV background.
DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks

Abstract The increasing quantity of multi-omic data, such as methylomic and transcriptomic profiles collected on the same specimen or even on the same cell, provides a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network’s complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics ‘layers. ’ In simulation studies, we show that DRAGON adapts to edge density and feature size differences between omics layers, improving model inference and edge recovery compared to state-of-the-art methods. We further demonstrate in an analysis of joint transcriptome - methylome data from TCGA breast cancer specimens that DRAGON can identify key molecular mechanisms such as gene regulation via promoter methylation. In particular, we identify Transcription Factor AP-2 Beta (TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer. DRAGON is available as open-source code in Python through the Network Zoo package (netZooPy v0.8; netzoo.github.io).
Rethinking Perturbations in Encoder-Decoders for Fast Training

We often use perturbations to regularize neural models. For neural encoder-decoders, previous studies applied the scheduled sampling (Bengio et al., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations but these methods require considerable computational time. Thus, this study addresses the question of whether these approaches are efficient enough for training time. We compare several perturbations in sequence-to-sequence problems with respect to computational time. Experimental results show that the simple techniques such as word dropout (Gal and Ghahramani, 2016) and random replacement of input tokens achieve comparable (or better) scores to the recently proposed perturbations, even though these simple methods are faster.
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modeling

Due to the sparsity of available features in web-scale predictive analytics, combinatorial features become a crucial means for deriving accurate predictions. As a well-established approach, a factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. With the prominent development of deep neural networks (DNNs), there is a recent and ongoing trend of enhancing the expressiveness of FM-based models with DNNs. However, though better results are obtained with DNN-based FM variants, such performance gain is paid off by an enormous amount (usually millions) of excessive model parameters on top of the plain FM. Consequently, the heavy parameterization impedes the real-life practicality of those deep models, especially efficient deployment on resource-constrained Internet of Things (IoT) and edge devices. In this article, we move beyond the traditional real space where most deep FM-based models are defined and seek solutions from quaternion representations within the hypercomplex space. Specifically, we propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM), which are two novel lightweight and memory-efficient quaternion-valued models for sparse predictive analytics. By introducing a brand new take on FM-based models with the notion of quaternion algebra, our models not only enable expressive inter-component feature interactions but also significantly reduce the parameter size due to lower degrees of freedom in the hypercomplex Hamilton product compared with real-valued matrix multiplication. Extensive experimental results on three large-scale datasets demonstrate that QFM achieves 4.36% performance improvement over the plain FM without introducing any extra parameters, while QNFM outperforms all baselines with up to two magnitudes’ parameter size reduction in comparison to state-of-the-art peer methods.
An algebraic estimator for large spectral density matrices

We propose a new estimator of high-dimensional spectral density matrices, called UNshrunk ALgebraic Spectral Estimator (UNALSE), under the assumption of an underlying low rank plus sparse structure, as typically assumed in dynamic factor models. The UNALSE is computed by minimizing a quadratic loss under a nuclear norm plus $l_1$ norm constraint to control the latent rank and the residual sparsity pattern. The loss function requires as input the classical smoothed periodogram estimator and two threshold parameters, the choice of which is thoroughly discussed. We prove consistency of UNALSE as both the dimension $p$ and the sample size $T$ diverge to infinity, as well as algebraic consistency, i.e., the recovery of latent rank and residual sparsity pattern with probability one. The finite sample properties of UNALSE are studied by means of an extended simulation exercise as well as an empirical analysis of US macroeconomic data.
Der flüssigkeitsseitige Stoffübergangskoeffizient in Blasenschichten

Auf der Grundlage von 135 Mesdaten 7 verschiedener Autorengruppen wird mit Hilfe der Parameter-Optimierung eine Korrelation fur die flussigkeitsseitige Stoffubergangszahl erstellt, die die verwendeten Mesergebnisse mit einer Standardabweichung von ± 5,3 · 10−3 cm/s beschreibt, entsprechend ± 20% eines mittleren Wertes der Stoffubergangszahl. Nach dieser Beziehung nimmt die Stoffubergangszahl mit der Flussigkeitskennzahl ϱ σ3/(gη4) und der Gasleerrohrgeschwindigkeit w zu und mit steigendem Schlankheitsgrad der Blasenschicht H0/D ab. Die den Mesdaten zugrundeliegenden Auswertungsmethoden werden erlautert und kritisch durchleuchtet. Nur verlasliche Ergebnisse werden zur Korrelationsfindung herangezogen. Weniger verlasliche Werte werden jedoch mit Ergebnissen verglichen, die die Korrelation liefert.
JDOI variance reduction method and the pricing of American-style options

This article revisits the Diffusion Operator Integral (DOI) variance reduction technique originally proposed in Heath and Platen (2002) and extends its theoretical concept to the pricing of American-style options under (time-homogeneous) Lévy stochastic differential equations. The resulting Jump Diffusion Operator Integral (JDOI) method can be combined with numerous Monte Carlo-based stopping-time algorithms, including the ubiquitous least-squares Monte Carlo (LSMC) algorithm of Longstaff and Schwartz (cf. Carriere (1996) and Longstaff and Schwartz (2001)). We exemplify the usefulness of our theoretical derivations under a concrete, though very general jump-diffusion stochastic volatility dynamics and test the resulting LSMC-based version of the JDOI method. The results provide evidence of a strong variance reduction when compared with a simple application of the LSMC algorithm and proves that applying our technique on top of Monte Carlo-based pricing schemes provides a powerful way to speed-up these methods.
Distill and Fine-tune: Effective Adaptation from a Black-box Source Model

To alleviate the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer knowledge in previous related labeled datasets (source) to a new unlabeled dataset (target). Despite impressive progress, prior meth-ods always need to access the raw source data and develop data-dependent alignment approaches to recognize the target samples in a transductive learning manner, which may raise privacy concerns from source individuals. Several recent studies resort to an alternative solution by exploiting the well-trained white-box model instead of the raw data from the source domain, however, it may leak the raw data through generative adversarial training. This paper studies a practical and interesting setting for UDA, where only a black-box source model (i.e., only network predictions are available) is provided during adaptation in the target domain. Besides, different neural networks are even allowed to be employed for different domains. For this new problem, we propose a novel two-step adaptation framework called Distill and Fine-tune (Dis-tune). Speciﬁcally, Dis-tune ﬁrst structurally distills the knowledge from the source model to a customized target model, then unsupervisedly ﬁne-tunes the distilled model to ﬁt the target domain. To verify the effectiveness, we consider two UDA scenarios ( i.e ., closed-set and partial-set), and discover that Dis-tune achieves highly competitive performance to state-of-the-art approaches.
GENERALISED SYLVESTER-KAC MATRICES GENERATED BY LINEAR DIFFERENTIAL EQUATIONS WITH POLYNOMIAL SOLUTIONS

A method of generating differential operators is used to solve the spectral problem for a generalisation of the Sylvester-Kac matrix. As a by-product, we find a linear differential operator with polynomial coefficients of the first order that has a finite sequence of polynomial eigenfunctions generalising the operator considered by M. Kac.
Self-Supervised Learning for Personalized Speech Enhancement

Speech enhancement systems can show improved performance by adapting the model towards a single test-time speaker. In this personalization context, the test-time user might only provide a small amount of noise-free speech data, likely insufﬁcient for traditional fully-supervised learning. One way to overcome the lack of personal data is to transfer the model parameters from a speaker-agnostic model to initialize the personalized model, and then to ﬁnetune the model us-ing the small amount of personal speech data. This baseline marginally adapts over the scarce clean speech data. Alternatively, we propose self-supervised methods that are designed speciﬁcally to learn personalized and discriminative features from abundant in-the-wild noisy, but still personal speech recordings. Our experiment shows that the proposed self-supervised learning methods initialize personalized speech enhancement models better than the baseline fully-supervised meth-ods, yielding superior speech enhancement performance. The proposed methods also result in a more robust feature set under the real-world conditions: compressed model sizes and fewness of the labeled data.
Use of pooled blood plasmas in the assessment of fouling resistance

The ability of a surface to resist fouling from blood plasma cannot be realistically estimated without measuring adsorption from real samples directly. Due to the variability of biological samples, pooled blood plasma is normally used. We show that even when using pooled plasma, a comparison of antifouling surfaces strongly depends on its source.
Jian-Gan-Xiao-Zhi Decoction Alleviates Inflammatory Response in Nonalcoholic Fatty Liver Disease Model Rats through Modulating Gut Microbiota

Background Jian-Gan-Xiao-Zhi decoction (JGXZ), composed of Salvia miltiorrhiza Bunge, Panax notoginseng, Curcuma zedoaria, and other 9 types of herbs, has demonstrated beneficial effects on nonalcoholic fatty liver disease (NAFLD). However, the mechanisms behind JGXZ's impact on NAFLD remain unknown. Methods In this study, a NAFLD rat model induced by a high-fat diet (HFD) received oral treatment of JGXZ (8 or 16 g crude herb/kg) for 12 weeks. The therapeutic effects of JGXZ on NAFLD model rats were investigated through blood lipid levels and pathological liver changes. 16S rRNA analysis was used to study the changes in gut microbiota after JGXZ treatment. The expressions of occludin and tight junction protein 1 (ZO-1) in the colon were investigated using immunostaining to study the effects of JGXZ on gut permeability. The anti-inflammatory effects of JGXZ were also studied through measuring the levels of IL-1β, IL-6, and TNF-α in the serum and liver. Results JGXZ treatment could decrease body weight and ameliorate dyslipidemia in NAFLD model rats. H&E and Oil Red O staining indicated that JGXZ reduced steatosis and infiltration of inflammatory cells in the liver. 16S rRNA analysis showed that JGXZ impacted the diversity of gut microbiota, decreasing the Firmicutes–to-Bacteroidetes ratio, and increasing the relative abundance of probiotics, such as Alloprevotella, Lactobacillus, and Turicibacter. Gut permeability evaluation found that the expressions of ZO-1 and occludin in the colon were increased after JGXZ treatment. Moreover, JGXZ treatment could decrease the levels of IL-1β, IL-6, and TNF-α in the serum and liver. Conclusions Our study illustrated that JGXZ could ameliorate NAFLD through modulating gut microbiota, decreasing gut permeability, and alleviating inflammatory response.
Association of Anti-tissue Transglutaminase Antibody Titers and Duodenal Biopsy Findings in Pediatric Patients of Celiac Disease

Aims & Objectives To ascertain the association of serum anti-tissue transglutaminase (anti-tTG) antibody titers with the severity of duodenal mucosal damage on histology andto predict a possible cut-off value of anti-tTG antibody titers for the diagnosis of Celiac disease. Marsh grading greater than two in conjunction with clinical assessment, which may help avert an invasive endoscopic procedure, especially in medically unfit children. Materials & Methods A retrospective study was designed wherein demographic and laboratory data of children aged less than 12 years with raised anti-tTG antibody titers with available histopathology of duodenal biopsies were extracted from the hospital medical records and reviewed. Results A total of 134 children were included in the study, which showed female preponderance. Histopathological changes, characteristic of Celiac disease, were observed in 116 cases; seven among the rest showed evidence of Giardiasis, and 13 could be considered potential Celiac. Of the 116 patients, 1.7% belonged to Marsh grade I, 5.2% grade II and 8.6%, 26.7%, and 57.7% to grade IIIA, IIIB, and IIIC, respectively. A significant association was found between anti-tTG antibody titers and Marsh grading. The cut-off value of anti-tTG antibody titer levels for diagnosing Celiac disease using receiver operating characteristics (ROC) curve in predicting Marsh greater than two at histology was observed to be 84.6 U/ml with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 91.7%, 68.4%, 94.2%, and 59%, respectively. Conclusion An anti-tTG antibody titer greater than 10 times the upper limit of normal (≥84 U/ml) is significantly associated with Marsh grade greater than two. Standard stool microscopy may be used as a simple tool in the workup of all children with raised anti-tTG antibody levels to rule out Giardiasis to avert unnecessary endoscopic evaluation for Celiac disease in such cases.
Pleural and Pericardial Infection Due to Cutibacterium acnes in a Splenectomized Patient: A Case Report of an Underreported Systemic Infection

Infectious pericarditis does not always present with all the classic findings. Some of the traditional signs of fever, pleuritic chest pain, and frictional rub may be missing. This presents a diagnostic challenge, thus clinical suspicion is important. The most common cause of infectious pericarditis is viral. However, bacterial pericarditis may occur with severe complications such as constrictive pericarditis, pericardial effusion, cardiac tamponade, left ventricular pseudoaneurysm, and aortic mycotic aneurysm. The purpose of this presentation is to increase awareness of Cutibacterium acnes (C. acnes) as a cause of bacterial pericarditis. This case report highlights C. acnes as a prevalent cause of both pleural and pericardial infections. The diagnosis can be challenging, considering that this bacterium is difficult to isolate, slow growing, and causes indolent illness. Prolonged incubation time may be required. In addition to the more traditional causes of bacterial pericarditis, namely Staphylococcus and Streptococcus species, C acnes appears to play an important role. It should not be considered a contaminant as it may require further investigation.
Safety and Efficacy of Transcatheter Occlusion of Perimembranous Ventricular Septal Defect with Aortic Valve Prolapse: A Six-Year Follow-Up Study

Background With the rapid development of transcatheter techniques and instruments, transcatheter occlusion for patients with perimembranous ventricular septal defect (pVSD) and aortic valve prolapse (AVP) was constantly being tried, while the efficacy and safety of pVSD with AVP remain controversial. Objective The aim of this study was to evaluate long-term efficacy and safety of transcatheter occlusion of pVSD with AVP. Methods We retrospectively analyzed 164 children with pVSD and AVP who underwent transcatheter occlusion between January 2013 and November 2014. AVP was divided into 3 degrees according to right coronary leaflet morphology at end-diastole during aortic root angiography. Patient demographic and clinical data were collected. Results There were 97 males and 67 females (median age, 40.0 (30.0–62.7) months; average weight, 16.94 ± 9.02 kg). Mild (n = 63), moderate (n = 89), and severe (n = 12) AVP success rates were 93.7%, 89.9%, and 58.3%, respectively. Immediately after procedure, there was no new-onset aortic regurgitation (AR) above trivial degree, residual shunt above mild degree, or complications requiring medication or operation, except for 1 patient who developed transient complete atrioventricular block. During follow-up, 1 mild AVP patient aggravated from mild to moderate AR and 1 moderate AVP patient aggravated from trivial to moderate AR. The new-onset AR in mild, moderate, and severe AVP was 2%, 1.8%, and 20%, respectively. AR disappeared in 17 patients. Residual shunt occurred in 9 patients after procedure, 4 of which disappeared during the follow-up period. No serious complications occurred in any patient during follow-up. Five-year cardiovascular event-free survival rates for mild, moderate, and severe AVP were 89.6%, 94.5%, and 80.0%, respectively. Conclusion Transcatheter occlusion of pVSD with mild and moderate AVP has a high success rate and few complications, which is safe and effective in long-term follow-up. Transcatheter occlusion of pVSD with severe AVP has low success rates and high AR incidence. Therefore, transcatheter occlusion of pVSD with AVP is recommended for mild to moderate, but not severe, AVP.
Formation and growth mechanism of porous, amorphous, and fine particles prepared by chemical vapor deposition. Titania from titanium tetraisopropoxide

The formation and growth mechanism of porous, amorphous, and fine particles were investigated. TiO2 particles were produced in a tubular flow reactor by a chemical vapor deposition technique using titanium tetraisopropoxide as a starting material at low temperatures (573-973 K) and atmospheric pressure. Prepared particles were of submicron size and had large surface area (as large as 270 × 103 m2/kg). According to the proposed mechanism, reactions begin on the reactor wall and then the primary particles form in the gas phase by chemical reactions. The primary particles collide, coalesce with each other and grow. However, significant experimental deviations from the Brownian collision and coalescence theory imply that other processes, such as the surface reactions on the particle, play an important role in the growth, in addition to coalescence. Intraparticle reactions decreased the surface area by filling the pores. On a etudie la formation et le mecanisme de croissance de particules fines, amorphes et poreuses. Des particules de TiO2 ont ete produites dans un reacteur tubulaire a ecoulement continu par une technique de depǒt de vapeur chimique en utilisant le tetra-isopropoxyde de titane comme materiau de depart, a basses temperatures (573-973 K) et a pression atmospherique. Les particules preparees ont une taille inferieure au micron et une grande surface specifique (jusqu'a 270 × 103 m2/kg). D'apres le mecanisme propose, les reactions commencent a la paroi du reacteur, puis les particules primaires se forment dans la phase gazeuse par reaction chimique. Les particules primaires se heurtent, s'agglomerent entre elles et croissent. Cependant, des ecarts experimentaux importants par rapport a la theorie de collision et d'agglomeration brownienne laissent supposer que d'autres phenomenes comme des reactions a la surface des particules jouent aussi un rǒle important dans le mecanisme de croissance. Des reactions a intraparticulaires diminuent la surface specifique en bouchant les pores.
Does HPV Subtype Predict Outcomes in Head and Neck Cancers?

Background Recently, reanalysis of The Cancer Genome Atlas study demonstrated that human papillomavirus (HPV) genotypes in head and neck cancers other than HPV-16 have inferior survival to HPV-16-positive tumors. We aimed to examine the association of HPV subtypes and survival in a large cohort of patient samples from our institution. Methods Fresh frozen primary site biopsy samples were collected either in clinic or at the time of surgery. Patient demographic, staging, and survival data were also collected. Tumors were tested for HPV subtypes by quantitative polymerase chain reaction (qPCR). Univariable and multivariable analyses were performed using Cox proportional hazards regression. Results 280 patient biopsy samples were collected between 2011 and 2017. Mean ± standard deviation (SD) age was 61.9 ± 11.1 years and most patients (78%) were male. The majority of cancers were of the oral cavity (60%) or oropharynx (25%) and 30% had HPV-positive disease. Median follow-up was 3.76 years and 96/280 patients (34%) developed recurrences. Patients with p16-positive versus negative disease had significantly improved 5-year overall survival (OS, 77.6% vs. 53.3%; p=0.009) and progression-free survival (PFS, 67.3% vs. 41.0%, p=0.006). Similarly improved 5-year OS and PFS were observed for patients with HPV-positive versus negative disease (65.0% vs. 55.0%, p=0.084; 53.3% vs. 43.2%, p=0.072, resp.). Patients with HPV-16 compared to other HPV diseases had worse 5-year OS and PFS (62.1% vs. 88.9%, p=0.273; 49.0% vs. 88.9%, p=0.081, resp.). Conclusions In contrast to the data derived from The Cancer Genome Atlas, patients with HPV-16 tumors trended towards decreased PFS and OS compared with tumors driven by other HPV genotypes. Further larger multi-institutional studies are necessary to understand the relationship between other HPV genotypes and survival in head and neck squamous cell carcinomas.