Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of socioeconomic status and ethnicity on children's excess body weight

Purpose – The purpose of this paper is to describe and briefly discuss the prevalence of childhood obesity in selected countries of the WHO European Region in relation to socioeconomic status (SES) and ethnic origin. Emphasis was given on the most recent research papers available.Design/methodology/approach – A search was performed on relevant papers on “Prevalence of Obesity and Overweight in relation to socio‐economic status and ethnicity” using MEDLINE and the ISI Web of Science. The search was limited to the age range 0‐20 years to the countries of the WHO European Region.Findings – A growing number of studies suggest that children in lower‐income families in developed countries are particularly vulnerable to becoming obese. Plausible explanations for the differences in obesity prevalence in migrant children and children with lower SES include poor diet, limited opportunity for physical activity and differences in the perception of ideal body weight together with possible food security issues. It is i.
PRICING AND INSOLVENCY RISK EVALUATING OF EMBEDDED OPTIONS IN UNIVERSAL INSURANCE CONSIDERING MORTALITY RATE

Focusing on universal life insurance, this paper calculates level premium and constructs the basic model of assets and liabilities considering mortality rate. The model contains interest rate guarantees, surrender options, and mortality rate to fully reflect the reality of prevailing insurance policies. Based on the pricing of universal life insurance, the paper also establishes the insolvency model. Finally, Monte Carlo simulations are conducted to provide numerical results and sensitivity analysis. In order to show the advantages of our model against those without consideration of mortality, we also conducted a mortality scenario test by introducing a mortality modifying factor. Results show that the two embedded derivatives, interest rate guarantee and cancellable option, account for a large proportion in universal insurance’s value. This finding may suggest the necessity to consider the interest rate guarantee and cancellable option while assessing the value of the universal insurance, in order to correctly evaluate the solvency of the insurance companies. Results also show that the mortality effect is significant on both embedded option and insolvency risk, which indicates a great necessity of considering mortality in valuation of universal insurance.
The Mechanism of Changes in Hydraulic Properties of Populus euphratica in Response to Drought Stress

Stable hydraulic conductivity in forest trees maintains the survival of trees which contribute to productivity in forest ecosystems. Drought conditions break down this relationship, but the mechanisms are poorly known. To increase the understanding of the mechanism of hydraulic characteristics during drought, we determined hydraulic parameters in Populus euphratica Oliv. (P. euphratica) in a time-series of drought using a high-pressure flow meter. We found that P. euphratica could enhance hydraulic transport in severe drought stress under a threshold of soil water content. Drought-induced loss of hydraulic conductance could seriously impair water transport capacity. The soil water content of about 4.5% in the rhizosphere could lead to canopy mortality yet maintain live roots. Hydraulic conductance could be changed under drought stress as a consequence of changes in the anatomical structure and physiology. Furthermore, there was also a trade-off between hydraulic efficiency and safety. The consideration of hydraulic efficiency was first within the range of hydraulic safety limit. Once the hydraulic safety limit was reached, safety would be taken as the first consideration and hydraulic efficiency would be reduced. Research on the mechanism of hydraulic properties in riparian plants in arid areas provides a scientific basis for riparian forest restoration.
Identifying events in mobility data

Today we are used to being interconnected via our smartphones and having our phone location tracked by different apps. ICT technology enables real-time monitoring and processing the user location data from GPS coordinates of a phone. Based on observing the user mobility, Artificial Intelligence methods can be used to improve transportation, proactively provide mobility recommendations and acquire knowledge using the user context. This paper describes the application of machine learning algorithms on user mobility data to identify and understand potentially interesting events. The data for this research was collected from a sample of users consenting to be monitored through our in-house developed smart phone app. A pilot study that includes 227 users that were tracked over a period of 7 years yields fairly positive evaluation results in terms of predictive accuracy of identified events but succeeds in identifying exclusively “well-known” events related to users going to or coming from the office and/or lunch. This shows that machine learning methods can be a suitable choice for identifying events in mobility data but there is still room for improvement.
Toxicity and bioaccumulation of lead , cadmium and zinc in Chroococcus minutus and Chlorococcum aegyptiacum

Studies on toxicity and bioaccumulation of lead, cadmium and zinc in the blue-green alga Chroococcus minutus and the green alga Chlorococcum aegyptiacum were conducted by short-term bioassays using toxicity symptoms, scientific growth rate, chlorophyll a, removal % and bioaccumulation factor parameters. The toxicity symptoms of the studied metals include increasing in crystalline inclusions, aggregation of thylakoid membranes at the sides of the cell and partial disorganization of the cell wall in the case of Chroococcus minutus and a major change in shape of cell wall, damage of chloroplasts, reduction in number of chloroplasts, complete disorganization of the cell components and formation of granules and disintegrated cell wall and cell death in the case of Chlorococcum aegyptiacum. There were significant decreases in the specific growth rate and chlorophyll a contents when the metal concentrations were increased. The removal % and bioaccumulation factor (BCF) of Zn were higher than that of Cd and Pb suggesting that the accumulation potential of the studied two microalgae for Zn was higher than that for Cd and Pb.
Question Answering with Self-Attention and Residuals

Current end-to-end machine question answering models often use recurrent neural networks (RNNs) to encode sequential information within texts. However, due to their sequential nature, RNNs, especially when layered together, can be difficult to train and scale up. We propose a new question answering architecture that combines RNNs with self-attention and residual connections to speed up learning as well as improve the model performance. On the SQuAD2.0 dataset, our model achieves 60.90 F1 score after 1.5M training steps, which is better than our baseline BiDAF model that achieves a 59.92 F1 score after 3.9M training steps.
Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) can be applied to monitor the structural health of infrastructure such as railways, bridges, and highways. However, for the successful interpretation of the observed deformation within a structure, or between structures, it is imperative to associate a radar scatterer unambiguously with an actual physical object. Unfortunately, the limited positioning accuracy of the radar scatterers hampers this attribution, which limits the applicability of MT-InSAR. In this study, we propose an approach for health monitoring of railway system combining MT-InSAR and LiDAR (laser scanning) data. An amplitude-augmented interferometric processing approach is applied to extract continuously coherent scatterers (CCS) and temporary coherent scatterers (TCS), and estimate the parameters of interest. Based on the 3D confidence ellipsoid and a decorrelation transformation, all radar scatterers are linked to points in the point cloud and their coordinates are corrected as well. Additionally, several quality metrics defined using both the covariance matrix and the radar geometry are introduced to evaluate the results. Experimental results show that most radar scatterers match well with laser points and that LiDAR data are valuable as auxiliary data to classify the radar scatterers.
Effects of herbal extracts and antimicrobials on apparent digestibility , performance , organs morphometry and intestinal histology of weanling pigs

The purpose of this trial was to evaluate the effects of antimicrobial and herbal extracts on digestibility, performance, morphology and histology of weanling pigs. One hundred and twenty weaned pigs were used to compare five treatments: control basal diet; antimicrobial basal diet plus Zn bacitracina, olaquindox, and colistin (50 ppm of each); herbal extract A, B and C basal diet plus 700 ppm, 1,400 ppm and 2,100 ppm of herbal extract, respectively, with 12 replications (pens of two pigs each) per treatment in a 35-d randomized complete block design experiment. The herbal extract consisted of mixture of equal amounts of essential oils of thyme, clove, oregano, eugenol and carvacrol. In digestibility assay, 40 pigs (four replications per treatment) were fed a diet with chromium oxide (marker method). On 35th day of experimental period, one animal of each experimental unit was slaughtered and samples were collected for analysis. The herbal extracts increased dietary DM apparent digestibility compared to control and antimicrobial treatments. The antimicrobial agents improved performance of weanling pigs during all phases of the experimental period compared to the other treatments. Pigs fed high dietary level of herbal extract showed better results for pig performance and other variables. The antimicrobial treatment improved the relative weights of intestinal tract and empty small intestine and improved ileum villus height and ileum villus height:crypt depth ratio of animals. However, more studies are necessary to evaluate which type of essential oil should be used and the best combination and inclusion level of herbal extract in the diet.
How ill is your IT Portfolio? - Measuring Criticality in IT Portfolios Using Epidemiology

IT project portfolios, consisting of IT projects, also interact with the entire IT landscape. In case of a failure of only one element, existing dependencies can lead to a cascade failure, which can cause high losses. Despite the present effects of systemic risk, research into IT portfolio management lacks suitable methods to quantitatively assess systemic risk. We follow the design science research paradigm to develop and evaluate our ‘on track’ or ‘in difficulty’ (TD) method by applying the SI model, representing a recognized network diffusion model in epidemiology, in an IT portfolio context. We evaluate our method using a real-world dataset. We introduce a criticality measure for diffusion models in IT portfolios and compare the TD method’s results and the alpha centrality to human judgment as a benchmark. From our evaluation, we conclude that the TD method outperforms alpha centrality and is a suitable risk measure in IT portfolio management.
Techniques of Artificial Intelligence for Space Applications-A Survey

The possibilities to observe and interact with any given spacecraft are naturally limited compared to groundbased systems due to a number of factors. These include but are not limited to the availability and bandwidth of their connection to ground, the availability of staff, communication latencies and power budgets. While a minimum level of autonomy is required for every spacecraft, past experiments and missions have shown that introducing more sophisticated autonomy mechanisms can drastically increase the efficiency for many missions in terms of reliability, science output and required operational effort. Artificial intelligence poses an increasingly popular approach for implementing on-board autonomy. The number of techniques and variants of artificial intelligence available in the literature is, however, just as diversified as their potential field of application. To provide an overview of the current state of the art of artificial intelligence and its application for space systems, this paper provides an extensive survey on existing techniques and algorithms as well as existing and potential applications on board spacecraft and on ground. The survey focuses on autonomous planning and scheduling of operations, self-awareness, anomaly detection and Fault Detection Isolation and Recovery (FDIR), on-board data analysis as well as onboard operations and processing of earth-observation data.
Nonlinear and Non-Ideal Sampling : Theory and Methods

We study a sampling setup where a continuous-time signal is mapped by a memoryless, invertible and nonlinear transformation, and then sampled in a non-ideal manner. Such scenarios appear, for example, in acquisition systems where a sensor introduces static nonlinearity, before the signal is sampled by a practical analog-to-digital converter. We develop the theory and a concrete algorithm to perfectly recover a signal within a subspace, from its nonlinear and non-ideal samples. Three alternative formulations of the algorithm are developed that provide different insights into the structure of the solution: A series of oblique projections, approximated projections onto convex sets, and quasi-Newton iterations. Using classical analysis techniques for descent-based methods, and recent results on frame perturbation theory, we prove convergence of our algorithm to the true input signal. We demonstrate our method by simulations, and explain the applicability of our theory to WienerHammerstein analog-to-digital hybrid systems.
Feature-aware Multitask feature learning for Predicting Cognitive Outcomes in Alzheimer ’ s disease

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease. Multi-task based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the intercorrelation among different brain regions. However, the multitask based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multi-task learning approach via a joint sparsity-inducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features with feature-aware sparsity-inducing regularization. It allows the simultaneous selection of a common set of biomarkers at all tasks and preserving the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising. Keywords—Alzheimer’s disease, Machine learning, Regression model, Multi-task learning, Magnetic resonance imaging, Biomarkers discovery
FIELD CONTROL CHALLENGES FOR DIFFERENT LINAC TYPES

Linacs for free-electron lasers typically require cavity field stabilities of 0.01% and 0.01 degree, while the requirements for high-intensity proton linacs are on the order of 0.1–1% and 0.1–1 degrees. From these numbers it is easy to believe that the field control problem for proton linacs is many times easier than for free-electron lasers linacs. In this contribution we explain why this is not necessarily the case, and discuss the factors that make field control challenging. We also discuss the drivers for field stability, and how high-level decisions on the linac design affect the difficulty of the field control problem.
Moving Around [Women in Engineering]

Writing something about my personal life is always difficult. What would be interesting about it for people to read? After giving it some thought, I have decided to explain an aspect that has been fundamental for my professional success, and it is relevant for both women and men: the impact of moving. Changing a work location and going outside one’s comfort zone helps to enhance one’s academic and personal ambitions. This is the reason why I decided to explain my experiences with moving, what drove me to make key decisions, and the positive consequences. I have moved quite a bit during my career and have always been interested in other cultures, visiting new places, and meeting different people. It all started with my master’s studies, when I went to Friedrich-Alexander-Universität Erlangen-Nürnberg in Germany to study during my final year. This short stay became longer when I decided to do my diplomarbeit at the Fraunhofer Institute in Erlangen, Germany (Figure 1). It was a very nice place; a relaxed work environment with exceptional colleagues. My project was on software development for a GPS information-based car system. I enjoyed it very much, but I also realized that developing software was not my ideal job. I wanted a career with more physical and hardware content. Although I was offered a position at the Fraunhofer Institute, I decided to go back to Spain and find a different opportunity. Luckily, it came faster than I had hoped: I was offered a oneyear position at TNO, to work in the Digital Object Identifier 10.1109/MAP.2019. 2935636 Date of publication: 4 October 2019 EDITOR’S NOTE
Integrating and Accelerating Content Classification

Peer-to-peer (P2P) software runs over dynamic ports in order to disguise their existence. Conventional port-redirecting proxy architecture cannot manage P2P traffic effectively. A gateway-based P2P administration (P2PADM) architecture had been proposed for managing P2P traffic by inspecting content at the application layer. P2PADM executes connection classification in the kernel space and connection management in the user space. Context switch and data passing from the kernel space to the user space is necessary. We propose a new architecture called kP2PADM for improving the performance of P2PADM by moving the P2PADM package from the user space to the kernel space. The main challenge in this work is how to move the code to the kernel space compatibly with Linux kernel without panicking the kernel. The external benchmarking reveals that the throughput of kP2PADM is 88.85 Mbps higher than that of P2PADM. This work also addresses two weaknesses of P2PADM: (1) reconnection issue and (2) redundant delay due to packet loss. Two solutions are proposed for the two weaknesses: (1) connection cache and (2) fast pass.
Transcytosis - An effective targeting strategy that is complementary to “EPR effect” for pancreatic cancer nano drug delivery

Numerous nano drug delivery systems have been developed for preclinical cancer research in the past 15 years with the hope for a fundamental change in oncology. The robust nanotherapeutic research has yielded early-stage clinical products as exemplified by the FDA-approved nano formulations (Abraxane® for paclitaxel and Onyvide® for irinotecan) for the treatment of solid tumors, including pancreatic ductal adenocarcinoma (PDAC). It is generally believed that enhanced permeability and retention (EPR) plays a key role in nanocarriers' accumulation in preclinical tumor models and is a clinically relevant phenomenon in certain cancer types. However, use of EPR effect as an across-the-board explanation for nanoparticle tumor access is likely over-simplified, particularly in the stroma rich solid tumors such as PDAC. Recently, ample evidences including our own data showed that it is possible to use transcytosis as a major mechanism for PDAC drug delivery. In this mini-review, we summarize the key studies that discuss how transcytosis can be employed to enhance EPR effect in PDAC, and potentially, other cancer malignancies. We also mentioned other vasculature engineering approaches that work beyond the classic EPR effect.
Control Policies of Batch Processors in Semiconductor Wafer Fabrication

Wafer fabrication (wafer fab) is a manufacturing procedure composed of many different processes to build electrical circuits on wafers layer by layer. Among the processes, there exist batch processes (BPs) where multiple wafer lots are processed on a BP machine simultaneously as a batch. The batch processing leads to a non-smooth product flow due to frequent batching (before BP processing step) and splitting (after BP processing step). In addition, the processing time at the BP station is very large compared to the processing time at discrete processing stations. These characteristics make the BP stations have a critical effect on the performance of the system-wide wafer fab. In most real-world wafer fabs, the production facilities are controlled in realtime due to such uncertainties as machine failure and quality problems. This paper examines control strategies for batch processors in semiconductor manufacturing. We come up with some BP control issues that are important but mostly ignored in existing studies. A few preliminary experimental results are presented to support our findings.
Framing and moderating conflicts among securities analysts

In this study, we investigated conflicts of interest wherein proprietary departments of securities companies had engaged in opposite-direction trading, in the midst of optimistic securities analyst forecasts. The study also explored the market forces moderating these conflicts of interest. Using a sample of listed Taiwanese companies, the findings indicated that the trading actions of proprietary departments affiliated with securities companies tended to contradict those issued by securities analysts’ optimistic earnings forecasts. In addition, the shareholding rates of Investment Trust Companies and Securities Dealers affected the biases of securities analysts’ optimistic earnings forecasts; although only foreign investment shareholdings were linked to the opposite-direction trading. Finally, following a series of mergers in Taiwan financial market between 2001 and 2006, a number of sample securities firms joined bank holding companies (BHCs). The function of the BHCs was able to moderate optimistic earnings forecasts, caused by the biases of non-affiliated securities analysts. However, the BHCs could not alleviate the conflicts of interest among all securities analysts sampled in this study. Key words: Securities analysts, proprietary department, optimistic earnings forecasts, opposite-direction trading, conflicts of interest.
Vortex RISC-V GPGPU System : Extending the ISA , Synthesizing the Microarchitecture , and Modeling the Soware Stack

The open-source RISC-V instruction set architecture (ISA) has enabled computer architects to propose several innovative processors for a wide range of applications. One of the domains of processor design that can take advantage from RISC-V, but has not seen enough attention, is general-purpose GPU (GPGPU) design. To support the development of open source GPGPU system, we present Vortex, our solution for building single instruction, multiple thread (SIMT) execution using RISC-V. In addition to a synthesizable microarchitecture model, we propose a GPU ISA extension to RISC-V and a software model, in the form of a runtime kernel, which makes Vortex practical and easy to integrate. As a result, Vortex does not require any modi cations to the current RISC-V compilers. ACM Reference Format: Fares Elsabbagh, Bahar Asgari, Hyesoon Kim, and Sudhakar Yalamanchili. 2019. Vortex RISC-V GPGPU System: Extending the ISA, Synthesizing the Microarchitecture, and Modeling the Software Stack. In Proceedings of Third Workshop on Computer Architecture Research with RISC-V (CARRV 2019). , 6 pages.