Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Liquid Hydrogen Leak Frequencies Using a Bayesian Update Process.

To quantify the risk of an accident in a liquid hydrogen system, it is necessary to determine how often a leak may occur. To do this, representative component leakage frequencies specific to liquid hydrogen can be determined as a function of the normalized leak size. Subsequently, the system characteristics (e.g., system pressure) can be used to calculate accident consequences. Operating data (such as leak frequencies) for liquid hydrogen systems are very limited; rather than selecting a single leak frequency value from a literature source, data from different sources can be combined using a Bayesian model. This approach provides leakage rates for different amounts of leakage, distributions for leakage rates to propagate through risk assessment models to establish risk result uncertainty, and a means for incorporating liquid hydrogen-specific leakage data with leakage frequencies from other fuels. Specifically, other cryogenic fluids like liquefied natural gas are used as a baseline for the Bayesian analysis. This Bayesian update process is used to develop leak frequency distributions for different system component types and leak sizes. These leak frequencies can be refined as liquid hydrogen data becomes available and may then inform safety code requirements based on the likelihood of liquid hydrogen release for different systems.
Developing a Virtual Campus Model in an Interactive Game-Engine Environment for Building Energy Benchmarking

AbstractEnergy benchmarking is a critical step of building-retrofit projects because it provides baseline energy information that could help building stakeholders understand energy performance and prioritize potential retrofit opportunities. The benefits of energy benchmarking are exponentially valuable for institutional building owners such as hospitals, universities, or public agencies. In light of the proliferation of building information modeling (BIM), building automation systems, and game engine technologies, campus-wide energy information can be linked through virtual three-dimensional (3D) models for interactive energy benchmarking. This paper discusses the development of a virtual campus model that integrates the theories and technologies with the process of developing workflows and implementation strategies that can be employed and adopted in campus-level building-retrofit practices. The result from a Proof of Concept project is summarized in order to demonstrate the effectiveness of the propose.
In vitro Storage of Peking Duck Semen in Different Diluents at + 5 °C

In this study, it was aimed to investigate the storage possibilities of Peking duck ( Anas platyrhynchos domesticus ) semen with different diluents at +5 ° C. Semen samples were collected from five male ducks, twice a week for five weeks, using the abdominal massage method. In the research, 50 semen samples were used. Semen from ducks were combined (mixed) after being evaluated individually. Mix volu me (ml), density (× 10 9 /mL), pH, motility (%) and vitality (%) values were determined as 1.78 ± 0.19, 1.65 ± 0.24, 6.95 ± 0.28, 70.80 ± 2.57, 76.60 ± 2.93 respectively. Glucose (G), Lactated Ringer's (L), Tris (T), Fructose (F), and Dextrose (D) were used as diluents. The reconstitute d mixed semen were equilibrated at 37 ° C for 30 minutes and 32 - 34 ° C for 30 minutes successively and then stored at +5 °C. Average motility (%) values of sperms stored at +5 °C with G, L, T, F and D diluents after 72 hours were found as 41.05 ± 1.79, 9.95 ± 6.50, 2.20 ± 1.13, 28.00 ± 4.15, 24.05±5.85 respectively. Vitality (%) values in the same diluents and under the same conditions were seen as 44.82 ± 1.73, 19.60 ± 9.08, 10.60 ± 4.50, 35.90 ± 4.83 and 33.90 ± 2.68 respectively. In this study, it was determined that the glucose diluent gives positive results compared to other diluents for the storage of peking duck semen at +5 ° C for 72 hours and it can be used for short term storage as a semen diluent in ducks. sulandırıcıları kullanılmıştır. Sulandırılan miks semenler sırasıyla 37 °C'de 30 dakika ve 32 - 34 °C'de 30 dakika ekilibrasyona tabi tutulduktan sonra, +5 °C'de saklanmıştır. G, L, T, F ve D sulandırıcıları ile +5 °C'de saklanan spermaların 72 saat sonra ortalama motilite (%) değerleri sırasıyla 41.05±1.79, 9.95±6.50, 2.20±1.13, 28.00±4.15, 24.05±5.85 olarak bulundu. Aynı sulandırıcılardave aynı şartlarda vitalite (%) değerleri ise; sırasıyla 44.82±1.73, 19.60±9.08, 10.60±4.50, 35.90±4.83 ve 33.90±2.68 olarak tespit edilmiştir. Bu çalışmada pekin ördeği semeninin +5 °C'de 72 saat süreli saklanmasında glikoz sulandırıcısının diğer sulandırıcılara göre iyi sonuç verdiği ve ördeklerde semen sulandırıcısı olarak kısa süreli saklamalarda kullanılabilir olduğu tespit edilmiştir.
Genetic Diversity and Population Structure in East Asian Populations of Plantagoasiatica

Plantagoasiatica(Plantaginaceae) is a wind-pollinated plant that grows mainly on fields in East Asia. Starch gel electrophoresis was used to investigate the allozyme diversity and population structure of 18 populations of this species. Although the plantain populations were isolated and patchily distributed, they maintained a high level of genetic diversity; the average percentage of polymorphic loci was 57.1%, the mean number of alleles per locus was 2.07, and the average heterozygosity for 18 populations was 0.201. The combination of a predominant wind-pollinated, mix-mating reproduction, large population sizes, high gene flow between subpopulations, and a propensity for high fecundity may explain the high level of genetic diversity within populations. A direct gradient in overall genetic diversity is associated with latitude. Genetic diversity of P.asiaticais markedly decreased from 35 o 3'N to high latitude and decreased from 35 o 3'N to low latitude, whereas there does not show a longitudinal gradient in genetic diversity.
Suppressive Activity of Regulatory T Cells Transcription Factor Regulate the Three Novel Acetylation Sites in the Foxp3

The Foxp3 transcription factor is the master regulator of regulatory T cell (Treg) differentiation and function. Its activity is regulated by reversible acetylation. Using mass spectrometry of immunoprecipitated proteins, we identify three novel acetylation sites in murine Foxp3 (K31, K262, and K267) and the corresponding sites in human FOXP3 proteins. Newly raised modiﬁcation-speciﬁc Abs against acetylated K31 and K267 conﬁrm acetylation of these residues in murine Tregs. Mutant Foxp3 proteins carrying ar-ginine substitutions at the three acetylation sites (3KR) accumulate in T cells to higher levels than wild-type Foxp3 and exert better suppressive activity in coculture experiments. Acetylation and stability of wild-type, but not mutant, Foxp3 is enhanced when cells are treated with Ex-527, an inhibitor of the NAD + -dependent deacetylase SIRT1. Treatment with Ex-527 promotes Foxp3 expression during induced Treg differentiation, enhances Foxp3 levels in natural Tregs, and prevents loss of Foxp3 expression in adoptively transferred Tregs in mice. Our data identify SIRT1 as a negative regulator of Treg function via deacetylation of three novel target sites in Foxp3. SIRT1 inhibitors strengthen the suppressive activity of Tregs and may be useful in enhancing Treg-based therapeutic approaches to autoimmune diseases or graft rejections. The Journal of Immunology , 2012, 188: 2712–2721.
Bayesian Modelling of Growth Retardation among Children Under-Five Years Old in Ethiopia

Malnutrition among children under age five is the major public health problem in the developing world particularly in Ethiopia. The aim of this study was then to determine statistically the determinants of children malnutrition, using 2011 DHS data. The overall prevalence of stunting among children in Ethiopia was 43.3%. Bayesian Semi-parametric regression model was used to flexibly model the effects of selected socioeconomic, demographic, health and environmental covariates. Inference was made using Bayesian approach with Markov chain Monte Carlo (MCMC) techniques. It was found that the covariates sex of child, preceding birth interval, birth order of child, place of residence, region, mother’s education level, husband educational level, toilet facility, number of household members, household economic status, diarrhea and fever were the most important determinants of children nutritional status in Ethiopia. The effect of child Age, mother’s age at child birth and mothers body mass index were also explored nonparametrically as determinants of children nutritional status. It is suggested that for reducing childhood malnutrition, due emphasis should be given in improving the knowledge and practice of parents on appropriate young child feeding practice and frequent growth monitoring together with appropriate and timely interventions.
Efficacy of Diode Laser Ablation and Low Level Laser Therapy on Healing and Bacterial Load Reduction at Intraoral Biopsy Site

Statement of the Problem: Healing complications after the conventional surgical biopsy procedure along with bacterial colonization indicates scope for sophisticated techniques. Amalgamation of photo-disinfection along with healing properties of diode laser with practiced biopsy technique can help in dealing with post biopsy complications. Purpose: The present study will analyze the possibility of conjugation of conventional surgical biopsy technique with diode laser regarding its superior properties for achieving better healing and analgesia along with sterilization of the biopsy site. Materials and Method: A randomized control trial was done where punch biopsy procedure was performed for homogenous leukoplakia. Patients were randomly divided into laser group (Test group) and control group. Test group received laser ablation and low level laser therapy (LLLT) on surgical site along with warm saline rinses whereas control group was prescribed with systemic analgesic and antibiotics. Pain on visual analogue scale (VAS), erythema along with the size of defect was evaluated on day 0, 2 and 4. Swabs were collected from the biopsy site and culture was done for evaluation of bacterial load. Results: Highly statistical significant values indicating laser induced analgesia were obtained after analysis for 2nd and 4th day (p= 0.00). Erythema and biopsy defect size evaluation showed significant results for 2nd day (p value 0.023 and 0.004 respectively), which showed absence of erythema and enhancement of healing in test group compared to controls. Statistical significant results were obtained for estimation of bacterial colonization with p value as 0.00, 0.00 for 2nd and 4th day claiming laser supported bacterial disinfection. There was a significant percentage increase on 2nd (p= 0.013) and decrease on 4th post-operative day (p= 0.022). Conclusion: The results encourage the conjugation of conventional incisional punch biopsy with low level lasers to avoid systemic intervention for post biopsy complications.
SECURE STORAGE AT CLOUD WITH DUPLICATION CHECKING

: Cloud computing plays a major role in the business domain today as computing resources are delivered as a utility on demand to customers over the Internet. Cloud storage is one of the services provided in cloud computing which has been increasing in popularity. The main advantage of using cloud storage from the customers’ point of view is that customers can reduce their expenditure in purchasing and maintaining storage infrastructure while only paying for the amount of storage requested, which can be scaled-up and down upon demand. With the growing data size of cloud computing, a reduction in data volumes could help providers reducing the costs of running large storage system and saving energy consumption. So data deduplication techniques have been brought to improve storage efficiency in cloud storages. With the dynamic nature of data in cloud storage, data usage in cloud changes overtime, some data chunks may be read frequently in period of time
Pipeline risk assessment using artificial intelligence: A case from the colombian oil network

Currently, in order to make decisions regarding the safety of pipelines, the risk values and risk targets are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve a large number of variables and deal with high amounts of uncertainty. Therefore, there is a strong need for a powerful tool to cope with that uncertainty, and one of the best tools dealing with uncertainty is the implementation of artificial intelligence methods using fuzzy logic.
THE RELATIONSHIP BETWEEN RESOURCE ALLOCATION FLOW AND FIRM PERFORMANCE

This article is protected by copyright. All rights reserved. This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please article Research summary: We examine the correlations between financial resource allocation flow between business segments and two measures of overall firm financial performance using a sample of Compustat firms. Our analysis finds a consistent inverted U-shape (or alternatively, V-shape) relationship between those measures and firm profitability. Interestingly, nearly all the data lie on the upward sloping part of the curve. Taken together, these results support the notion that flows of financial capital are most often positively correlated with financial performance.
ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System

This paper introduces our proposed system for the MIA Shared Task on Cross-lingual Openretrieval Question Answering (COQA). In this challenging scenario, given an input question the system has to gather evidence documents from a multilingual pool and generate from them an answer in the language of the question. We devised several approaches combining different model variants for three main components: Data Augmentation, Passage Retrieval, and Answer Generation. For passage retrieval, we evaluated the monolingual BM25 ranker against the ensemble of re-rankers based on multilingual pretrained language models (PLMs) and also variants of the shared task baseline, re-training it from scratch using a recently introduced contrastive loss that maintains a strong gradient signal throughout training by means of mixed negative samples. For answer generation, we focused on languageand domain-specialization by means of continued language model (LM) pretraining of existing multilingual encoders. Additionally, for both passage retrieval and answer generation, we augmented the training data provided by the task organizers with automatically generated question-answer pairs created from Wikipedia passages to mitigate the issue of data scarcity, particularly for the low-resource languages for which no training data were provided. Our results show that language- and domain-specialization as well as data augmentation help, especially for low-resource languages.
FedControl: When Control Theory Meets Federated Learning

To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters. We depart from this approach by differentiating client contributions according to the performance of local learning and its evo-lution. The technique is inspired from control theory and its classiﬁcation performance is evalu-ated extensively in IID framework and compared with FedAvg.
Bias in the representative volume element method: periodize the ensemble instead of its realizations

We study the Representative Volume Element (RVE) method, which is a method to approximately infer the effective behavior ahom of a stationary random medium. The latter is described by a coefficient field a(x) generated from a given ensemble 〈·〉 and the corresponding linear elliptic operator −∇ · a∇. In line with the theory of homogenization, the method proceeds by computing d = 3 correctors (d denoting the space dimension). To be numerically tractable, this computation has to be done on a finite domain: the so-called “representative” volume element, i. e. a large box with, say, periodic boundary conditions. The main message of this article is: Periodize the ensemble instead of its realizations. By this we mean that it is better to sample from a suitably periodized ensemble than to periodically extend the restriction of a realization a(x) from the whole-space ensemble 〈·〉. We make this point by investigating the bias (or systematic error), i. e. the difference between ahom and the expected value of the RVE method, in terms of its scaling w. r. t. the lateral size L of the box. In case of periodizing a(x), we heuristically argue that this error is generically O(L−1). In case of a suitable periodization of 〈·〉, we rigorously show that it is O(L−d). In fact, we give a characterization of the leading-order error term for both strategies, and argue that even in the isotropic case it is generically non-degenerate. We carry out the rigorous analysis in the convenient setting of ensembles 〈·〉 of Gaussian type, which allow for a straightforward periodization, passing via the (integrable) covariance function. This setting has also the advantage of making the Price theorem and the Malliavin calculus available for optimal stochastic estimates of correctors. We actually need control of second-order correctors to capture the leading-order error term. This is due to inversion symmetry when applying the two-scale expansion to the Green function. As a bonus, we present a stream-lined strategy to estimate the error in a higher-order two-scale expansion of the Green function.
Differentially Private Covariance Revisited

In this paper, we present two new algorithms for covariance estimation under concentrated differential privacy (zCDP). The first algorithm achieves a Frobenius error of $\tilde{O}(d^{1/4}\sqrt{\mathrm{tr}}/\sqrt{n} + \sqrt{d}/n)$, where $\mathrm{tr}$ is the trace of the covariance matrix. By taking $\mathrm{tr}=1$, this also implies a worst-case error bound of $\tilde{O}(d^{1/4}/\sqrt{n})$, which improves the standard Gaussian mechanism's $\tilde{O}(d/n)$ for the regime $d>\widetilde{\Omega}(n^{2/3})$. Our second algorithm offers a tail-sensitive bound that could be much better on skewed data. The corresponding algorithms are also simple and efficient. Experimental results show that they offer significant improvements over prior work.
Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits: A Distributional Learning Perspective

Reward-biased maximum likelihood estimation (RBMLE) is a classic principle in the adaptive control literature for tackling explore-exploit trade-offs. This paper studies the neural contextual bandit problem from a distributional perspective and proposes NeuralRBMLE, which leverages the likelihood of surrogate parametric distributions to learn the unknown reward distributions and thereafter adapts the RBMLE principle to achieve efficient exploration by properly adding a reward-bias term. NeuralRBMLE leverages the representation power of neural networks and directly encodes exploratory behavior in the parameter space, without constructing confidence intervals of the estimated rewards. We propose two variants of NeuralRBMLE algorithms: The first variant directly obtains the RBMLE estimator by gradient ascent, and the second variant simplifies RBMLE to a simple index policy through an approximation. We show that both algorithms achieve order-optimality. Through extensive experiments, we demonstrate that the NeuralRBMLE algorithms achieve comparable or better empirical regrets than the state-of-the-art methods on real-world datasets with non-linear reward functions.
Mitigating Out-of-Distribution Data Density Overestimation in Energy-Based Models

Deep energy-based models (EBMs), which use deep neural networks (DNNs) as energy functions, are receiving increasing attention due to their ability to learn complex distributions. To train deep EBMs, the maximum likelihood estimation (MLE) with short-run Langevin Monte Carlo (LMC) is often used. While the MLE with short-run LMC is computationally efficient compared to an MLE with full Markov Chain Monte Carlo (MCMC), it often assigns high density to out-of-distribution (OOD) data. To address this issue, here we systematically investigate why the MLE with short-run LMC can converge to EBMs with wrong density estimates, and reveal that the heuristic modifications to LMC introduced by previous works were the main problem. We then propose a Uniform Support Partitioning (USP) scheme that optimizes a set of points to evenly partition the support of the EBM and then uses the resulting points to approximate the EBM-MLE loss gradient. We empirically demonstrate that USP avoids the pitfalls of short-run LMC, leading to significantly improved OOD data detection performance on Fashion-MNIST.
Double-Cover-Based Analysis of the Bethe Permanent of Non-negative Matrices

The permanent of a non-negative matrix appears naturally in many information processing scenarios. Because of the intractability of the permanent beyond small matrices, various approximation techniques have been developed in the past. In this paper, we study the Bethe approximation of the permanent and add to the body of literature showing that this approximation is very well behaved in many respects. Our main technical tool are topological double covers of the normal factor graph whose partition function equals the permanent of interest, along with a transformation of these double covers.
Comparative characterization study of LYSO:Ce crystals for timing applications

Cerium-doped Lutetium-Yttrium Oxyorthosilicate (LYSO:Ce) is one of the most widely used Cerium-doped Lutetium based scintillation crystals. Initially developed for medical detectors it rapidly became attractive for High Energy Particle Physics (HEP) applications, especially in the frame of high luminosity particle colliders. In this paper, a comprehensive and systematic study of LYSO:Ce ([Lu(1-x)Y x ]2SiO5:Ce) crystals is presented. It involves for the first time a large number of crystal samples (180) of the same size from a dozen of producers. The study consists of a comparative characterization of LYSO:Ce crystal products available on the market by mechanical, optical and scintillation measurements and aims specifically, to investigate key parameters of timing applications for HEP.
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport

The problem of optimization on Stiefel manifold, i.e., minimizing functions of (not necessarily square) matrices that satisfy orthogonality constraints, has been extensively studied. Yet, a new approach is proposed based on, for the first time, an interplay between thoughtfully designed continuous and discrete dynamics. It leads to a gradient-based optimizer with intrinsically added momentum. This method exactly preserves the manifold structure but does not require additional operation to keep momentum in the changing (co)tangent space, and thus has low computational cost and pleasant accuracy. Its generalization to adaptive learning rates is also demonstrated. Notable performances are observed in practical tasks. For instance, we found that placing orthogonal constraints on attention heads of trained-from-scratch Vision Transformer [Dosovitskiy et al. 2022] could markedly improve its performance, when our optimizer is used, and it is better that each head is made orthogonal within itself but not necessarily to other heads. This optimizer also makes the useful notion of Projection Robust Wasserstein Distance [Paty&Cuturi 2019; Lin et al. 2020] for high-dim. optimal transport even more effective.