Miracle-dz/UniFAR-FTUs huggingface dataset:
X-RAY EMISSION FROM THE SUPER-EARTH HOST GJ 1214

Stellar activity can produce large amounts of high-energy radiation, which is absorbed by the planetary atmosphere leading to irradiation-driven mass loss. We present the detection and an investigation of high-energy emission in a transiting super-Earth host system, GJ 1214, based on XMM-Newton observations. We derive an X-ray luminosity of LX = 7.4 × 1025 erg s−1 and a corresponding activity level of log (LX/Lbol) ∼ −5.3. Further, we determine a coronal temperature of about ∼3.5 MK, which is typical for coronal emission of moderately active low-mass stars. We estimate that GJ 1214 b evaporates at a rate of 1.3× 1010 g s−1 and has lost a total of ≈2–5.6 M⊕.
Linear kinetic theory and particle transport in stochastic mixtures. Progress report, June 15, 1993--March 21, 1994

The primary goal in this research is to develop a comprehensive theory of linear transport/kinetic theory in a stochastic mixture of solids and immiscible fluids. The statistics considered correspond to N-state discrete random variables for the interaction coefficients and sources, with N denoting the number of components of the mixture. The mixing statistics studied are Markovian as well as more general statistics, such as renewal processes. A further goal of this work is to demonstrate the applicability of the formalism to real world engineering problems. This three year program was initiated June 15, 1993 and has been underway nine months. Many significant results have been obtained, both in the formalism development and in representative applications. These results are summarized by listing the archival publications resulting from this grant, including the abstracts taken directly from the papers.
Evaluation of Pregnant and Lactating Women's Prescriptions for Drug-drug Interactions (DDI) And Contraindications

Introduction: In the pregnancy cycle and lactation period women may develop complications. Thus, special attention to pharmacotherapy is necessary, particularly to drug interactions (DDIs) and to the effect of the drugs on the foetus and the mother. Objective: The aim of this study was to determine the profile of DDIs, and the risk of drugs used during pregnancy and breastfeeding among patients admitted in different hospitals. Methodology: We conducted an observational and prospective study, including pregnant and breastfeeding women admitted to the different hospitals and clinics of KPK, PUNJAB and SINDH. Online databases were used to identify and classify the DDIs, and the potential risk of the drugs used during pregnancy and breastfeeding. Results: On the basis of subgroups data was evaluated for pregnant and lactating women. 128 (62.0%) of the women involved in their research were pregnant women and 78 (38.0%) of them were lactating women. In pregnant women number of interactions found were 91 (42.52%) while in lactating women number of interactions found were 123 (57.48%). Total contraindicated drugs prescribed in the prescriptions of pregnant and lactating women were 4 which was 0.48% of the total drugs prescribed from which 3 (75%) were found in prescriptions of pregnant women and 1 (15%) was found in prescription of a lactating women. Conclusion: Our study demonstrated a significant number of drug- drug interactions in both pregnant and lactating mothers due to complexity in prescribed pharmacotherapy. Most of the interactions found in our prescriptions had minor to major severity level. A little percent of contraindicated drugs was also seen which could be easily substituted with safer medicine.
Evaluation of eleven commercially available PCR kits for the detection of Monkeypox virus DNA

Prior to the international spread of Monkeypox in May 2022, PCR kits for the detection of Orthopoxviruses, and specifically for Monkeypox virus, were rarely available. Here we describe the evaluation of eleven recently-developed commercially available PCR kits for the detection of Monkeypox virus DNA.
Effects of Return-to-Office, Public Schools Reopening, and Vaccination Mandates on COVID-19 Cases Among Municipal Employee Residents of New York City

A return-to-office mandate did not result in a relative increase in COVID-19 cases among New York City municipal employees. Reopening public schools resulted in a relative increase in cases among Department of Education employees. A vaccination mandate resulted in a relative decrease in cases and hospitalizations among municipal employees. Objective On September 13, 2021, teleworking ended for New York City municipal employees, and Department of Education employees returned to reopened schools. On October 29, COVID-19 vaccination was mandated. We assessed these mandates' short-term effects on disease transmission. Methods Using difference-in-difference analyses, we calculated COVID-19 incidence rate ratios (IRRs) among residents 18 to 64 years old by employment status before and after policy implementation. Results IRRs after (September 23–October 28) versus before (July 5–September 12) the return-to-office mandate were similar between office-based City employees and non-City employees. Among Department of Education employees, the IRR after schools reopened was elevated by 28.4% (95% confidence interval, 17.3%–40.3%). Among City employees, the IRR after (October 29–November 30) versus before (September 23–October 28) the vaccination mandate was lowered by 20.1% (95% confidence interval, 13.7%–26.0%). Conclusions Workforce mandates influenced disease transmission, among other societal effects.
The true Blazhko behaviour of DM Cyg

We present preliminary results of our work on DM Cyg, an RRab star with steadily increasing pulsation period. The Blazhko modulation of the light curve of DM Cyg has not been undoubtedly confirmed yet. A reanalysis of the original data (Sódor & Jurcsik 2005) could not confirm the 26 d periodicity found by Lysova & Firmanyuk (1980) in the timings of maximum brightness data of visual observations. Neither the scarce photoelectric observations (Fitch 1966, Sturch 1966, Hipparcos 1997) nor the CCD data of the NSVS (Woźniak 2004) survey suggested a notable light curve modulation. In order to get a definite answer whether the light curve of DM Cyg is stable or it shows any kind of modulation it was observed in the course of the Konkoly Blazhko Survey in the 2007 and 2008 seasons. Using the automated 60 cm telescope of the Konkoly Observatory, Svábhegy, Budapest, equipped with a Wright 750 x 1100 CCD camera and BVIC filters we obtained more than 3000 data points on about 80 nights in each band. Archive photoelectric and photographic observations obtained with the 60 cm telescope and a 16 cm astrograph of the Konkoly Observatory in 1978 and between 1934 and 1958 were also analyzed. The photoelectric and photographic photometry provided 75 B,V and 1031 pg data points from 4 and 40 nights, respectively. The CCD observations revealed that the light curve of DM Cyg is in fact modulated, but with very small amplitude. The maximum brightness variation hardly exceeds 0.05 mag in the V band, while no definite phase modulation of the light curve and/or maximum timings is evident. The amplitudes of the modulation frequencies that form equidistant triplets around the pulsation frequency and its harmonics are below 15 mmag. There is some indication of light curve modulation in the Konkoly photographic data as well. Our data confirm that DM Cyg shows Blazhko modulation but with significantly different period and character (amplitude/phase modulation) than it was found by Lysova & Firmanyuk (2000). A detailed analysis of our observations of DM Cyg with its true Blazhko period will be submitted to MNRAS in early 2009.
Electroencephalographic Findings, Antiepileptic Drugs and Risk Factors of 433 Individuals Referred to a Tertiary Care Hospital in Ethiopia

Background Little is known about the characteristics of electroencephalogram (EEG) findings in epileptic patients in Ethiopia. The objective of this study was to characterize the EEG patterns, indications, antiepileptic drugs (AEDs), and epilepsy risk factors. Methods A retrospective observational review of EEG test records of 433 patients referred to our electrophysiology unit between July 01, 2020 and December 31, 2021. Results The age distribution in the study participants was right skewed unipolar age distribution for both sexes and the mean age of 33.8 (SD=15.7) years. Male accounted for 51.7%. Generalized tonic clonic seizure was the most common seizure type. The commonest indication for EEG was abnormal body movement with loss of consciousness (35.2%). Abnormal EEG findings were observed in 55.2%; more than half of them were Interictal epileptiform discharges, followed by focal/or generalized slowing. Phenobarbitone was the commonest AEDs. A quarter (20.1%) of the patients were getting a combination of two AEDs and 5.2% were on 3 different AEDs. Individuals taking the older AEDs and those on 2 or more AEDs tended to have abnormal EEG findings. A cerebrovascular disorder (27.4%) is the prevalent risk factor identified followed by brain tumor, HIV infection, and traumatic head injury respectively. Conclusions High burden of abnormal EEG findings among epileptic patients referred to our unit. The proportion of abnormal EEG patterns was higher in patients taking older generation AEDs and in those on 2 or more AEDs. Stroke, brain tumor, HIV infection and traumatic head injury were the commonest identified epilepsy risk factors.
Unifying the Convergences in Multilingual Neural Machine Translation

Although all-in-one-model multilingual neural machine translation (MNMT) has achieved remarkable progress, the convergence inconsistency in the joint training is ignored, i.e., different language pairs reaching convergence in different epochs. This leads to the trained MNMT model over-fitting low-resource language translations while under-fitting high-resource ones. In this paper, we propose a novel training strategy named LSSD (LanguageSpecific Self-Distillation), which can alleviate the convergence inconsistency and help MNMT models achieve the best performance on each language pair simultaneously. Specifically, LSSD picks up language-specific best checkpoints for each language pair to teach the current model on the fly. Furthermore, we systematically explore three sample-level manipulations of knowledge transferring. Experimental results on three datasets show that LSSD obtains consistent improvements towards all language pairs and achieves the state-of-the-art.
NEUTRAL HYDROGEN OBSERVATIONS OF THE IRREGULAR GALAXY IC-10

Les nouvelles observations radiointerferometriques de la galaxie IC 10 confirment que la structure en forme de disque centrale de HI est en rotation opposee par rapport a l'enveloppe d'hydrogene exterieure. Plusieurs caracteristiques a petite echelle possedent des gradients de vitesse orientes de facon aleatoire par rapport au disque central et representent probablement les composantes les plus internes de l'enveloppe HI. L'analyse morphologique et cinematique suggerent que quelques-unes de ces structures a basses densites de colonne interagissent avec le disque de IC 10
A Study on the Error Analysis of the Numerical Solution using Inverse Method

ABSTRACT An inverse method is introduced to construct the problem for the error analysis of the numerical solution of initial value problem. These problems constructed through this method have a known exact solution, even though analytical solutions are generally not obtainable. The process leading to the exact solution makes use of an initi ally available approximate numerical solution. A smooth interpolation of the approximate s olution is forced to exactly satisfy the differential equation by analytically deriving a sm all forcing function to absorb all of the errors in the interpolated approximate solution. Usi ng this special case exact solution, it is possible to investigate the relationship betwee n global errors of a candidate numerical solution process and the associated tuning parameters for a given problem. Under the assumption that the original differential equation is well- posed with respect to the small perturbations, we thereby obtain valuable information about the optimal choice of the tuning parameters and the achievable accuracy of the numerical solution.key Words : Inverse Method(역해석 기법), Numerical Solution(수치해), Error Analysis(오차 분석), Chebyshev Polynomials (Chebyshev 다항식)
Uncertainty in Extreme Multi-label Classification

Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small label spaces and ignored eXtreme Multi-label Classification (XMC), which is an essential task in the era of big data for web-scale machine learning applications. Moreover, enormous label spaces could also lead to noisy retrieval results and intractable computational challenges for uncertainty quantification. In this paper, we aim to investigate general uncertainty quantification approaches for tree-based XMC models with a probabilistic ensemble-based framework. In particular, we analyze label-level and instance-level uncertainty in XMC, and propose a general approximation framework based on beam search to efficiently estimate the uncertainty with a theoretical guarantee under long-tail XMC predictions. Empirical studies on six large-scale real-world datasets show that our framework not only outperforms single models in predictive performance, but also can serve as strong uncertainty-based baselines for label misclassification and out-of-distribution detection, with significant speedup. Besides, our framework can further yield better state-of-the-art results based on deep XMC models with uncertainty quantification.
Controlling Travel Path of Original Cobra

In this paper we propose a kernel based COBRA which is a direct approximation of the original COBRA. We propose a novel tuning procedure for original COBRA parameters based on this kernel approximation. We show that our proposed algo- rithm provides much better accuracy than other COBRAs and faster than usual Gridsearch COBRA. We use two datasets to illustrate our proposed methodology over existing COBRAs.
Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning

Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the context of various applications, e.g., next-word prediction and eHealth. FL involves various clients participating in the training process by uploading their local models to an FL server in each global iteration. The server aggregates these models to update a global model. The traditional FL process may encounter bottlenecks, known as the straggler problem, where slower clients delay the overall training time. This paper introduces the Latency-awarE Semi-synchronous client Selection and mOdel aggregation for federated learNing (LESSON) method. LESSON allows clients to participate at different frequencies: faster clients contribute more frequently, therefore mitigating the straggler problem and expediting convergence. Moreover, LESSON provides a tunable trade-off between model accuracy and convergence rate by setting varying deadlines. Simulation results show that LESSON outperforms two baseline methods, namely FedAvg and FedCS, in terms of convergence speed and maintains higher model accuracy compared to FedCS.
Heteroscedasticity-aware sample trimming for causal inference

A popular method for variance reduction in observational causal inference is propensity-based trimming, the practice of removing units with extreme propensities from the sample. This practice has theoretical grounding when the data are homoscedastic and the propensity model is parametric (Yang and Ding, 2018; Crump et al., 2009), but in modern settings where heteroscedastic data are analyzed with non-parametric models, existing theory fails to support current practice. In this work, we address this challenge by developing new methods and theory for sample trimming. Our contributions are three-fold: ﬁrst, we describe novel procedures for selecting which units to trim. Our procedures diﬀer from previous work in that we trim not only units with small propensities, but also units with extreme conditional variances. Second, we give new theoretical guarantees for inference after trimming. In particular, we show how to perform inference on the trimmed subpopulation without requiring that our regressions converge at parametric rates. Instead, we make only fourth-root rate assumptions like those in the double machine learning literature. This result applies to conventional propensity-based trimming as well and thus may be of independent interest. Finally, we propose a bootstrap-based method for constructing simultaneously valid conﬁdence intervals for multiple trimmed sub-populations, which are valuable for navigating the trade-oﬀ between sample size and variance reduction inherent in trimming. We validate our methods in simulation, on the 2007-2008 National Health and Nutrition Examination Survey, and on a semi-synthetic Medicare dataset and ﬁnd promising results in all settings.
Estimating Network Reliability with Accelerated Convergence Rates on Error Bounds.

Abstract : The purpose of this paper is to describe related estimation techniques that improve the convergence rate to 1/K when a specified nonrandom finite sequence of sample points is used and to (log K)/K when certain nonrandom infinite sequences of sample points are used. In addition, algorithms are given for computing estimates of g(s,t) with 1/K and (log K)/K convergence rates with computation time complexities per replication having an upper bound O(N) + O(max(N,/V/)) as K approaches limit of infinity. Although the infinite sequences have a slower convergence rate than the finite sequence has, these offer the convenience of allowing one to add replications as desired and retaining the rate. By contrast, the finite sequence does not allow this addition, once K is initially fixed.
Dilepton production in the photodisintegration of the deuteron

We study the lepton pair production in the photodisintegration of the deuteron process. The complete seven-fold differential cross section is calculated via the Bethe-Heitler mechanism with final state interactions taken into account. The deuteron bound state is described by a relativistic covariant deuteron-nucleon vertex. With numerical results, we find that the differential cross section has strong dependence on the lepton azimuthal angle in the small polar angle region and sharp peaks appear in the dependence on the invariant mass of the produced lepton pair or the two nucleons in the final state. We demonstrate that such nearly singular feature originates from the collinearity between the produced lepton or antilepton and the incident photon, and it is physically regularized by the lepton mass in our calculation. The final state interaction between the knocked-out nucleon and the recoil nucleon redistributes the differential cross section over the missing momentum, with a significant enhancement at large missing momentum and a suppression in the intermediate region. With a further decomposition of the final state interaction contribution, it is found that the on-shell term dominates the near quasi-elastic region while the off-shell term dominates the other end. In addition, we examine the contribution from the interference between the proton amplitude and the neutron amplitude, which as expected is found negligible even if the proton-neutron rescattering is included. The result in this work can serve as an input for the analysis and background estimation of multiple exclusive measurements at Jefferson Lab and future electron-ion colliders.
Multi-Modal Recommendation System with Auxiliary Information

Context-aware recommendation systems improve upon classical recommender systems by including, in the modelling, a user's behaviour. Research into context-aware recommendation systems has previously only considered the sequential ordering of items as contextual information. However, there is a wealth of unexploited additional multi-modal information available in auxiliary knowledge related to items. This study extends the existing research by evaluating a multi-modal recommendation system that exploits the inclusion of comprehensive auxiliary knowledge related to an item. The empirical results explore extracting vector representations (embeddings) from unstructured and structured data using data2vec. The fused embeddings are then used to train several state-of-the-art transformer architectures for sequential user-item representations. The analysis of the experimental results shows a statistically significant improvement in prediction accuracy, which confirms the effectiveness of including auxiliary information in a context-aware recommendation system. We report a 4% and 11% increase in the NDCG score for long and short user sequence datasets, respectively.
Variational And Analytical Methods In Nonlinear Acoustics

The review is devoted to description of variational and analytical methods in nonlinear wave theory. A part of this methods are known, others are quite novel and haven't yet been presented in the literature. The principal direction of the review consists in analyzing localized wave processes. The methods under consideration are aimed mainly to obtain explicit formulae for the most important characteristics of the nonlinear waves in concrete physical problems.
Convergence of binomial tree method and explicit difference scheme for American put options with time dependent coefficients

Convergence of binomial tree method and explicit difference schemes for the variational inequality model of American put options with time dependent coefficients is studied. When volatility is time dependent, it is not reasonable to assume that the dynamics of the underlying asset's price forms a binomial tree if a partition of time interval with equal parts is used. A time interval partition method that allows binomial tree dynamics of the underlying asset's price is provided. Conditions under which the prices of American put option by BTM and explicit difference scheme have the monotonic property on time variable are found. Convergence of BTM and explicit difference schemes for the variational inequality model of American put options to viscosity solution is proved.