Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Modeling of Hospitalization Occurrence Timing Using a Joint Model Based on Longitudinal Clinical Features

In order to provide efficient health services, it is necessary to predict individual health conditions. In this study, we constructed a method that predicts the probability of hospitalization and its timing by applying the Joint model, which models both past data transitions and future hospitalization occurrence timing. Experimental result shows that the discrimination accuracy of the proposed method is 0.81. From the result, it is suggested that an effective health service planning is possible using the proposed method. Clinical Relevance— This achieves to predict not only the aggravation probability of lifestyle-related diseases, but also the time of aggravation.
Highly efficient single-mode fiber for broadband dispersion compensation

The advent of erbium-doped optical fiber amplifiers which operate at 1.55 µm has triggered considerable interest in schemes that will permit the upgrade of installed systems operating at 1.31 µm to operate in the 1.55 µm window. Since installed systems use standard single mode fibers with a dispersion-zero at 1.31 µm, any upgrade scheme must incorporate a method of compensating for the positive dispersion of the standard (non-dispersion-shifted) fiber at 1.55 µm if high transmission rates are to be achieved. An equalization technique that uses two-mode fibers was suggested by Poole et al.1 While high compensation ratios (defined as the ratio of the length of the communications fiber to the length of the compensating fiber) of 30:1 have been achieved, the higher-order mode technique requires intermodal convertors and polarization rotators, thus adding to the system cost and complexity. Dugan et al. 2 and Izadpanah et al.3 demonstrated the use of single-mode fiber compensators (using the fundamental LP01 mode). However, the single-mode fiber-based compensating schemes could not compensate for the second-order dispersion and absolute values of the dispersion were typically less than 100 ps/nm-km. In this paper, we present a multiclad dispersion compensating fiber (DCF) operated in the single-mode regime that has a dispersion of -212 ps/nm-km and is capable of compensating dispersion over a broad wavelength range.
PlasmaNet : a framework to study and solve elliptic differential equations using neural networks in plasma ﬂuid simulations

Elliptic partial differential equations (PDEs) are common in many areas of physics, from the Poisson equation in plasmas and incompressible flows to the Helmholtz equation in electromagnetism. Their numerical solution requires to solve a linear system and many libraries have been developed for this task [1]. Solving a linear system efficiently requires preconditioning the system which is a difficult task. It can become a bottleneck for performance when the number of nodes increases.
Breakthrough R&D Stakeholders: The Challenges of Legitimacy in Highly Uncertain Projects

We studied the management of internal R&D stakeholders and their involvement dynamics in breakthrough R&D projects. Building on a longitudinal research partnership with a global car manufacturer since 2005, this research highlights the important dynamics of involvement among internal R&D stakeholders in the engineering development organization. Some stakeholders—who served as experts, innovation design strategists, or internal collaboration strategists—succeeded in involving the individuals needed for the project's progress, sometimes generating an over-commitment. The success of the rationale of these stakeholders on engineering resource involvement depended on the perceived legitimacy of their owners.
Robust outlier detection by de-biasing VAE likelihoods

Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoods are unreliable and can be easily biased by simple transformations to input data. Here, we examine outlier detection with variational autoencoders (VAEs), among the simplest of DGMs. We propose novel analytical and algorithmic approaches to ameliorate key biases with VAE likelihoods. Our bias corrections are sample-specific, computationally inexpensive, and readily computed for various decoder visible distributions. Next, we show that a well-known image pre-processing technique – contrast stretching – extends the effectiveness of bias correction to further improve outlier detection. Our approach achieves state-of-the-art accuracies with nine grayscale and natural image datasets, and demonstrates significant advantages – both with speed and performance – over four recent, competing approaches. In summary, lightweight remedies suffice to achieve robust outlier detection with VAEs.11Code is available at https://github.com/google-research/google-research/tree/master/vae_ood.
Panel data analysis of the factors for determining inbound tourism in Japan∗

The demand for inbound tourism in Japan has been increasing since 2013. In 2015, the number of inbound tourists reached approximately 20 million. The Japanese government is making various efforts to increase the number of foreign visitors and has set a target of approximately 40 million inbound tourists to be reached by 2020. The aim of this paper is to demonstrate empirically the demand for inbound tourism in Japan by using the gravity model with panel data. According to demand theory, the income factor, as well as the relative prices in origin countries, influence the demand for inbound tourism. Focusing on a visa-permission tourism policy, the paper clarifies that not only income and relative price effects but also travel policy will have a significant effect on increasing inbound tourism in Japan.
Dynamic ATC

Available transfer capability (ATC) quantifies the viable increase in real power transfer from one point to another in a power system. ATC calculation has predominantly focused on steady-state viability. Point-to-point transfer can be increased until equilibrium point quantities, given by a power flow, reach security limits. Generally the equilibria are evaluated for a number of contingencies. Security limits typically include voltage thresholds, and limits associated with feeder thermal capacity and generator reactive power output. In many power systems, however, point-to-point transfer is not restricted by steady-state limits, but by undesirable dynamic behaviour following large disturbances. The post-disturbance operating point certainly must be viable; but it is also important to ensure that the system can safely make the transition from the pre- to the post-disturbance operating point. Here, the authors describe dynamic ATC, which is concerned with calculating the maximum increase in point-to-point transfer such that the transient response remains stable and viable.
EXPLORING NDVI AND NDBI RELATIONSHIP USING LANDSAT 8 OLI/TIRS IN KHANGARH TALUKA, GHOTKI

Built-up is one of the most significant type of land use-cover linked with urbanization. Computing, classifying, and mapping the built-up areas by Landsat image, is on priority demand for municipal and policymakers to investigate urban extension. Thus, increasing population and conversion of agricultural land into urban is a major topic understanding the bond between both types of land use. In the context of this, this study investigates the relationship between Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) in Khangarh taluka. Therefore, satellite images of Landsat 8 OLI/TIRS 2014, 2016, 2018, and 2021 were downloaded freely from the USGS-GloVIS Earth Explorer website. The images were processed in the ArcGIS 10.3 environment. NDVI was calculated using the Near-Infrared NIR (band 5), Red (band 4) and for NDBI, Middle Infrared Reflectance MIR (band 6) and NIR (band 5) was used following the equation of both indices. The calculated values were then exported in SPSS software for correlation determination and scatter plot development. The results from the case showed that there was a linear and negative correlation between vegetation index and built-up index in all years over the study area. Furthermore, in 2014 the coefficient of correlation explicated R2=0.96; in 2016 R2=0.23, in 2018 R2=0.34, and in 2021 R2=0.22 which indicated that NDBI could be used to illustrate the evaluation of urban construction land. The all-over study recommends that built-up index NDBI not only can be used as a significant indicator for built-up or urban areas estimation but also deliver a consistent source for urban development and planning.
An Overview on Floating Drug Delivery Systems (FDDS); Conventional and New Approaches for Preparation and In Vitro –In Vivo Evaluation

* ORCID: 0000-0002-5324-8267, Nanotechnology Research Center, Department of Pharmaceutics, School of Pharmacy, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. ** ORCID: 0000-0002-1399-7335, Nanotechnology Research Center, Department of Pharmaceutics, School of Pharmacy, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. *** ORCID: 0000-0002-2558-9777, Department of Pharmaceutics, School of Pharmacy, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. **** ORCID: 0000-0003-1358-6963, Department of Pharmaceutics, School of Pharmacy, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Performance Ongole Grade and Simmental Ongole Crossbred Cow at Village Breeding Center and Non Village Breeding Center at Special Region Yogyakarta, Indonesia

The research aims to determine cow performance of Ongole Grade (OG) and Simmental Ongole Crossbred cow (SOC) in terms of feed consumption, body condition score (BCS) and post partum estrus (PPE) at village breeding center (VBC) vs non village breeding center (NVBC). This research was conducted on April until December 2012 in 6 groups VBC (Sumber Ayu, Andhini Rahayu, Ngudi Makmur I, Andhini Mulyo, Trimanunggal Lembusuro, Trihandini Rejo) vs 2 groups NVBC (Sedyo Makmur, Andhini). The material used were OG (22 heads), SOC cow (16 heads) at VBC and OG (8 heads), SOC cow (10 heads) at NVBC. All the animals had been observed since the last pregnant period until PPE. Cow weight ranged from 260 kg to 380 kg with BCS score 2 to 3 on a scale of 5. Data of feed intake, BCS and PPE were collected and analyzed by randomized complete block design (RCBD) with location and cow factor by block. The feed intake of OG and SOC cow in term of dry matter (DM), crude protein (CP) and total digestible nutrients (TDN) was not different (P > 0,05). There were not significant different at BCS of OG and SOC cow at VBC vs NVBC from calving until PPE, the range BCS on a scale 2 to 2.8. Similarly, no significant identified between PPE of OG and SOC cow at VBC and at VBC in 105.42±30.75 days, 104±24,46 days vs 103.85±17.70 days, 110.57±14.60 days). It’s concluded that cow performance of OG and SOC cow were not significant different at VBC vs NVBC. Key Words: Performance, OG, SOC, VBC, NVBC
Federated Functional Variational Inference

Traditional federated learning involves optimizing point estimates for the parameters of the server model, via a maximum likelihood objective. Models trained with such objectives show competitive predictive accuracy, however they are poorly calibrated and provide no reliable uncertainty estimates. These, however, are particularly important in safety critical applications of federated learning, such as self-driving cars and healthcare. In this work, we propose FSVI , a method to train Bayesian neural networks in the federated setting. Bayesian neural networks provide a distribution over the model parameters, which allows to obtain uncertainty estimates. Instead of employing prior distributions and doing inference over the model parameters, FSVI builds upon recent advances in functional variational inference and posits prior distributions directly in the function space of the network. We discuss two different approaches to federated FSVI , based on FedAvg and model distillation respectively, and show its beneﬁts compared to traditional weight-space inference methods.
Reproducible, incremental representation learning with Rosetta VAE

Variational autoencoders are among the most popular methods for distilling low-dimensional structure from high-dimensional data, making them increasingly valuable as tools for data exploration and scientific discovery. However, unlike typical machine learning problems in which a single model is trained once on a single large dataset, scientific workflows privilege learned features that are reproducible, portable across labs, and capable of incrementally adding new data. Ideally, methods used by different research groups should produce comparable results, even without sharing fully trained models or entire data sets. Here, we address this challenge by introducing the Rosetta VAE (R-VAE), a method of distilling previously learned representations and retraining new models to reproduce and build on prior results. The R-VAE uses post hoc clustering over the latent space of a fully-trained model to identify a small number of Rosetta Points (input, latent pairs) to serve as anchors for training future models. An adjustable hyperparameter, $\rho$, balances fidelity to the previously learned latent space against accommodation of new data. We demonstrate that the R-VAE reconstructs data as well as the VAE and $\beta$-VAE, outperforms both methods in recovery of a target latent space in a sequential training setting, and dramatically increases consistency of the learned representation across training runs.
MODELING THE RELATION OF FINANCIAL INTEGRATION- ECONOMIC GROWTH WITH GMM AND QR METHODS2

Although international financial integration is an essential topic in economics and finance disciplines, researchers do not have a clear consensus on the relationship between financial integration and economic growth. The main reason for this situation is that financial integration can be heterogeneous and may differ according to the countries’ income levels. On the other hand, most studies on the subject have accepted that financial integration has a homogeneous effect on growth by using a sample of countries with a heterogeneous structure. In this study, unlike traditional methods, the generalized method of moments and quantile regression analysis allows a comparison according to the countries’ income levels, used together. Fifty-two countries were taken as a basis for the 2000-2019 period. The effect of financial integration and control variables on economic growth was tried to be measured. As a result of the study, direct foreign capital investments, portfolio investments, and current account balance have positive and significant effects on economic growth, and the effects of direct foreign capital and portfolio investments, current account balance, and inflation on economic growth differ between countries with high and low-income levels.
An Upper Limit on the Amplitude of Stochastic Gravitational-Wave Background of Cosmological Origin

A stochastic background of gravitational waves is expected to arise from a superposition of a large number of unresolved gravitational-wave sources of astrophysical and cosmological origin. It is expected to carry unique signatures from the earliest epochs in the evolution of the universe, inaccessible to the standard astrophysical observations [1]. Direct measurements of the amplitude of this background therefore are of fundamental importance for understanding the evolution of the universe when it was younger than one minute. Here we report direct limits on the amplitude of the stochastic gravitational-wave background using the data from a two-year science run of the Laser Interferometer Gravitational-wave Observatory (LIGO) [2]. Our result constrains the energy density of the stochastic gravitational-wave background normalized by the critical energy density of the universe, in the frequency band around 100 Hz, to be less than 6.9 × 10 at 95% confidence. The data rule out models of early universe evolution with relatively large equation-of-state parameter [3], as well as cosmic (super)string models with relatively small string tension [4] that are favoured in some string theory models [5]. This search for the stochastic background improves upon the indirect limits from the Big Bang Nucleosynthesis [1, 6] and cosmic microwave background [7] at 100 Hz. According to the general theory of relativity, gravitational waves (GWs) are produced by accelerating mass distributions with a quadrupole (or higher) moment. Moreover, in the early phases of the evolution of the universe, they can be produced by the mechanism of amplification of vacuum fluctuations. Once produced, GWs travel through space-time at the speed of light, and are essentially unaffected by the matter they encounter. As a result, GWs emitted shortly after the Big Bang (and observed today) would carry unaltered information about the physical processes that generated them. These waves are expected to be generated by a large number of unresolved sources, forming a stochastic gravitational-wave background (SGWB) that is usually described in terms of the GW spectrum:
A decision support system for ERP projects in make-to-order manufacturing SMEs

Almost 40 to 57% of Enterprise Resource Planning (ERP) projects fail to realise any benefit, run over budget or time. Only a few published works explicitly focus on ERP Critical Success Factors (CSFs) in Make-to-Order (MTO) manufacturing small and medium size enterprises (SMEs). A dynamic Decision Support System (DSS) is developed for selecting and managing CSFs including production strategy and their interrelationships during and after completion of ERP projects in MTO SMEs. The research work carried out was based on a 30 month Knowledge Transfer Partnership (KTP) ERP implementation project. Based on the research objectives and the characteristics of the challenges facing the case company, action research method was assessed to be the most appropriate. Two Discrete Event (DE) simulation based DSS were developed. The first DSS studies the interrelationships of over thirty CSFs as an ERP system is being implemented. Users can determine the attributes of the CSF from real-time data and visualise the interrelationships of CSFs during phases of the ERP project. After the ERP system was implemented, a three stage DSS was developed to manage production strategy to realise benefits of ERP system. A prototype production planning and scheduling system (PPSS) using Microsoft Excel formed an ERP linkage for manufacturing lead-time analysis, Customer Relationship Management (CRM) activities and planning. The final stage involved managing the job release decisions based on Work Load Control (WLC) logic in purely manual assembly lines requiring high skill levels. This research contributes to limited research data available on managing ERP related CSFs in Make-to-Order (MTO) manufacturing firms. Also, a unified approach has ensured that a number of strategies that are not currently synchronised can be implemented successfully. The proposed methodology will enable small and medium size enterprises (SMEs) realise ERP benefits by focussing on CSFs during and after ERP implementation.
Comparing the Effects of Individual and Group Schema Therapy on Depression and Anxiety in Adolescents

Author Address 1. Student Ph.D. in General Psychology, Psychology Department, Karaj Branch, Islamic Azad University, Karaj, Iran; 2. Assistant Professor, Department of Psychology, Karaj Branch, Islamic Azad University, Karaj, Iran; 3. Professor, Department of Clinical Psychology, Faculty of Psychology and Educational Sciences, Allameh Tabataba'i University, Tehran, Iran. *Corresponding author’s email: msf_3@yahoo.com
Evaluating the English-Turkish parallel treebank for machine translation

: This study extends our initial efforts in building an English-Turkish parallel treebank corpus for statistical machine translation tasks. We manually generated parallel trees for about 17K sentences selected from the Penn Treebank corpus. English sentences vary in length: 15 to 50 tokens including punctuation. We constrained the translation of trees by (i) reordering of leaf nodes based on suﬀixation rules in Turkish, and (ii) gloss replacement. We aim to mimic human annotator’s behavior in real translation task. In order to fill the morphological and syntactic gap between languages, we do morphological annotation and disambiguation. We also apply our heuristics by creating Nokia English-Turkish Treebank (NTB) to address technical document translation tasks. NTB also includes 8.3K sentences in varying lengths. We validate the corpus both extrinsically and intrinsically, and report our evaluation results regarding perplexity analysis and translation task results. Results prove that our heuristics yield promising results in terms of perplexity and are suitable for translation tasks in terms of BLEU scores.
Using Deep Learning for estimation of river surface elevation from photogrammetric Digital Surface Models

Development of the new methods of surface water observation is crucial in the perspective of increasingly frequent extreme hydrological events related to global warming and increasing demand for water. Orthophotos and digital surface models (DSMs) obtained using UAV photogrammetry can be used to determine the Water Surface Elevation (WSE) of a river. However, this task is difﬁcult due to disturbances of the water surface on DSMs caused by limitations of photogrammetric algorithms. In this study, machine learning was used to extract a WSE value from disturbed photogrammetric data. A brand new dataset has been prepared speciﬁcally for this purpose by hydrology and photogrammetry experts. The new method is an important step toward automating water surface level measurements with high spatial and temporal resolution. Such data can be used to validate and calibrate of hydrological, hydraulic and hydrodynamic models making hydrological forecasts more accurate, in particular predicting extreme and dangerous events such as ﬂoods or droughts. For our knowledge this is the ﬁrst approach in which dataset was created for this purpose and deep learning models were used for this task. Additionally, neuroevolution algorithm was employed to explore different architectures to ﬁnd optimal models. The obtained results have better accuracy compared to manual methods of determining WSE from photogrammetric DSMs.