Miracle-dz/UniFAR-FTUs huggingface dataset:
Serum Vaccine Antibody Concentrations in Adolescents Exposed to Perfluorinated Compounds

Background: Postnatal exposure to perfluorinated alkylate substances (PFASs) is associated with lower serum concentrations of specific antibodies against certain childhood vaccines at 7 y. Objectives: We prospectively followed a Faroese birth cohort to determine these associations at 13 y. Methods: In 516 subjects (79% of eligible cohort members) who were 13 years old, serum concentrations of PFASs and of antibodies against diphtheria and tetanus were measured and were compared with data from the previous examination at 7 y. Multiple regression analyses and structural equation models were applied to determine the association between postnatal PFAS exposures and antibody concentrations. Results: Serum concentrations of PFASs and antibodies generally declined from 7 y to 13 y. However, 68 subjects had visited the emergency room and had likely received a vaccination booster, and a total of 202 children showed higher vaccine antibody concentrations at 13 y than at 7 y. Therefore, separate analyses were conducted after exclusion of these two subgroups. Diphtheria antibody concentrations decreased at elevated PFAS concentrations at 13 y and 7 y; the associations were statistically significant for perfluorodecanoate (PFDA) at 7 y and for perfluorooctanoate (PFOA) at 13 y, both suggesting a decrease by ∼25% for each doubling of exposure. Structural equation models showed that a doubling in PFAS exposure at 7 y was associated with losses in diphtheria antibody concentrations at 13 y of 10–30% for the five PFASs. Few associations were observed for anti-tetanus concentrations. Conclusions: These results are in accord with previous findings of PFAS immunotoxicity at current exposure levels. https://doi.org/10.1289/EHP275
Multi-queue CPU Process Prioritization using a Dynamic Quantum Time Algorithm Compared with Varying Time Quantum and Round-Robin Algorithms

In Round-Robin Scheduling, the quantum time is static and tasks are scheduled such that no process uses CPU time more than one slice time each cycle. If quantum time is too large, the response time of the processes will not be tolerated in an interactive environment. If quantum the time is too small, unnecessary frequent context switch may occur. Consequently, overheads result in fewer throughputs. In this study, we propose a priority multi queues algorithm with dynamic quantum time. The algorithm uses multi queues with different quantum times for the processes. The quantum times for the processes are depending on the priorities which in turn depending on the burst times of the processes. The proposed algorithm has been compared with varying time quantum algorithm which already exist to improve the original round robin algorithm. With proposed algorithm, the simple Round-Robin algorithm has been improved by about 35%. By controlling quantum time, we experience fewer context switches and shorter waiting and turnaround times, thereby obtaining higher throughput.
Copper Recovery by Bioleaching of Chalcopyrite: A Microcalorimetric Approach for the Fast Determination of Bioleaching Activity

Low grade copper ores containing chalcopyrite are increasingly used for copper recovery via biomining. Since metal sulfide oxidation is an exothememic process, bioleaching activity can be measured due to the heat output by microcalorimetry, which is a non-destructive and non-invasive method. The bioleaching activity of pure cultures of Sulfolobus metallicus, Metallosphaera hakonensis and a moderate thermophilic enrichment culture on high grade chalcopyrite was evaluated. Chalcopyrite leaching by microorganisms showed a higher copper recovery than sterile controls. Chemical chalcopyrite leaching by acid produced heat due to the exothermic reaction, the heat output was increased while metal sulfide oxidation by microorganisms.
A Review Paper on Torque Ripple Reduction in Brushless DC Motor Drives with Different Multilevel Inverter Topology

Cloud computing technologies are being used by many who need computing resources such as software, platform and infrastructure as per their business requirements in terms of provisioning and pay for the usage as per actual consumption of the services based on the SLA signed by the user and cloud service provider. Software running on a physical machine is being provided as services to the end users. For the reasons of cost economies access to software that uses a database is being provided to multiple users. The access to the software is provided either directly or through a virtual machine. The software being provided as service uses the same database for many of the users who have requisitioned for the same. As a result, there could be encroachments by the users into the data of others. There is a need to secure the data belonging to several users while all of them access the data using the same application. In this paper an efficient method is presented for securing the data processed by software which is offered as a service to multiple users either directly or through virtual machines.
Trellis coded modulation to improve dirty paper trellis watermarking

Lattice codes and quantization-based techniques have received considerable attention as a method of digital watermarking with side information. However, these algorithms are also recognized to be highly sensitive to common signal processing primitives such as valuemetric scaling, e.g. changes in volume of a song, or re-quantization, e.g. JPEG compression. Hence, it is reasonable to investigate alternative codes which may offer the potential for superior performance. In previous work, we designed a new class of codes, called dirty paper trellis codes (DPTC), which are by construction immune to valuemetric scaling. Although DPTC have shown themselves to be resistant to certain classes attacks, the trellises were generated randomly, thus leading to suboptimal codes. In this paper, Ungerboeck's work on trellis coded modulation (TCM) is exploited to significantly improve the performance of the DPTC. Experimental results show that the resulting trellises significantly outperform the original design.
GENERATIONAL ECONOMICS AND THE NATIONAL TRANSFER ACCOUNTS*

Abstract: This article provides a comprehensive picture of the National Transfer Accounts (NTA), a project that aims at measuring how people produce, consume, save, and share economic resources at every age. It stands today with a unique dataset that includes 47 countries from around the world, permitting a comparative understanding of economic flows within and between generations and over time.
Identification of Panel Data Models with Endogenous Censoring

This paper analyzes the identification question in censored panel data models, where the censoring can depend on both observable and unobservable variables in arbitrary ways. Under some general conditions, we derive the tightest sets on the parameter of interest. These sets (which can be singletons) represent the limit of what one can learn about the parameter of interest given the model and the data in that every parameter that belongs to these sets is observationally equivalent to the true parameter. We consider two separate sets of assumptions, motivated by the previous literature, each controlling for unobserved heterogeneity with an individual specific (fixed) effect. The first imposes a stationarity assumption on the unobserved disturbance terms, along the lines of Manski (1987), and Honore (1993). The second is a nonstationary model that imposes a conditional independence assumption. For both models, we provide sufficient conditions for these models to point identify the parameters. Since our identified sets are defined through parameters that obey first order dominance, we outline easily implementable approaches to build confidence regions based on recent advances in Linton et.al.(2010) on bootstrapping tests of stochastic dominance. We also extend our results to dynamic versions of the censored panel models in which we consider lagged observed, latent dependent variables and lagged censoring indicator variables as regressors.
Covariance Matrix Estimation in Massive MIMO

Interference during the uplink training phase significantly deteriorates the performance of a massive MIMO system. The impact of the interference can be reduced by exploiting the second-order statistics of the channel vectors, e.g., to obtain the minimum mean squared error estimates of the channel. In practice, the channel covariance matrices have to be estimated. The estimation of the covariance matrices is also impeded by the interference during the training phase. However, the coherence interval of the covariance matrices is larger than that of the channel vectors. This allows us to derive methods for accurate covariance matrix estimation by the appropriate assignment of pilot sequences to the users in consecutive channel coherence intervals. To keep the computational complexity in check, we exploit common structure of the covariance matrices.
How people describe themselves on Twitter

Twitter, being both a micro-blogging service and a social network, has become one of the primary means of communicating and disseminating information online. As such, significant amount of research has been devoted to analyzing the Twitter graph, the tweets, and the behavior of its users. In this work, we undertake a study of the user profile bios on Twitter. The goal of our study is two-fold: first, to understand what Twitter users choose to expose about themselves in their profile bio, and second, to investigate if it is possible to exploit the information in the user bio for tasks such as predicting connections between Twitter users.
Efficient human hand kinematics for manipulation tasks

This work is focused on obtaining efficient human hand models that are suitable for manipulation tasks. A 24 DoF kinematic model of the human hand is defined to realistic movements. This model is based on the human skeleton. Dynamic and static constraints have been included in order to improve the movement realism. Two simplified hand models with 9 and 6 DoF have been developed according to the constraints predefined. These simplified models involve some errors in reconstructing the hand posture. These errors are calculated with respect to the 24 DoF model and evaluated according to the hand gestures. Finally, some criteria are defined to select the hand description best suited to the features of the manipulation task.
Viscous behavior of ultrahigh molecular weight polyethylene solution

The viscous behavior of the decalin solution of ultrahigh molecular weight polyethylene (UHMEPE) was studied. The influence of the concentration of polymer as well as the temperature was investigated. The flow curve can be described by the power-law model. The dependence of the viscosity on the temperature can be described by the Arrhenius–Frenkel–Eyring equation. The dependence of viscosity on the concentration can also be described by a power-law correlation. The addition of aluminum stearate increased the activation energy of flow of the solution. The viscosity of UHMWPE solution was decreased at lower concentration and increased at higher concentration of UHMWPE. © 1997 John Wiley & Sons, Inc. J Appl Polym Sci 65:289–293, 1997
Towards Practical and Robust Labeled Pattern Matching in Trillion-Edge Graphs

Subgraph pattern matching is fundamental to graph analytics and has wide applications. Unfortunately, high computational complexity limits the robustness guarantees of existing algorithms: they do not scale for modern large graph datasets and/or they have limitations in terms of accuracy or in terms of the intricacy of the patterns supported. We present algorithms, theory, and empirical evidence that iteratively eliminating vertices that do not meet local constraints dramatically reduces the search space for pattern matching in real-world graphs, and demonstrate a scalable implementation of our algorithms. We additionally identify the characteristics of patterns for which every non-eliminated vertex participates in a match. These techniques are an essential step to enable scalable, practical solutions for robust pattern matching in large-scale labeled graphs.We demonstrate the advantages of the proposed approach through strong and weak scaling experiments on massive-scale real-world (up to 257 billion edges) and synthetic (up to 2.2 trillion edges) graphs and at scales (256 compute nodes with 6,144 processors) orders of magnitude larger than those used in the past for similar problems.
Doodle around the world: online scheduling behavior reflects cultural differences in time perception and group decision-making

Event scheduling is a group decision-making process in which social dynamics influence people's choices and the overall outcome. As a result, scheduling is not simply a matter of finding a mutually agreeable time, but a process that is shaped by social norms and values, which can highly vary between countries. To investigate the influence of national culture on people's scheduling behavior we analyzed more than 1.5 million Doodle date/time polls from 211 countries. We found strong correlations between characteristics of national culture and several behavioral phenomena, such as that poll participants from collectivist countries respond earlier, agree to fewer options but find more consensus than predominantly individualist societies. Our study provides empirical evidence of behavioral differences in group decision-making and time perception with implications for cross-cultural collaborative work.
A Method to Fabricate Liver Tissue Engineering Scaffold

In this paper, the authors describe a rapid prototyping method to produce vascularized tissue such as liver scaffold for tissue engineering applications. A scaffold with an interconnected channel was designed using a CAD environment. The data were transferred to a Polyjet 3D Printing machine (Eden 250, Object, Israel) to generate the models. Based on the 3D Printing model, a PDMS (polydimethyl-silicone) mould was created which can be used to cast the biodegradable material. The advantages and limitations of Rapid Prototyping (RP) techniques as well as the future direction of RP development in tissue engineering scaffold fabrication were reviewed.
Room temperature terahertz detectors based on semiconductor nanowire field effect transistors

Self-assembled nanowires represent a new interesting technology to be explored in order to increase the cut-off frequency of electronic THz detectors. They can be developed in field effect transistor (FET) and diode geometries exploiting non-linearities of either the transconductance or the current-voltage characteristic as detection mechanism. In this work we demonstrate that semiconductor nanowires can be used as building blocks for the realization of highsensitivity terahertz one-dimensional FET detectors. In order to take advantage of the low effective mass and high mobilities achievable in III-V compounds, we have used InAs nanowires, grown by vapor-phase epitaxy, and properly doped with selenium to control the charge density and to optimize source-drain and contact resistance. The detection mechanism exploits the non-linearity of the transconductance: the THz radiation field is fed at the gate-source electrodes with wide band antennas, and the rectified signal is then read at the drain output in the form of a DC voltage. Responsivity values as large as 1 V/W at 0.3 THz have been obtained, with noise equivalent powers (NEP) < 2 × 10-9 W/√Hz at room temperature. The large existing margins for technology improvements, the scalability to higher frequencies, and the possibility of realizing multi-pixel arrays, make these devices highly competitive as a future solution for THz detection.
Characterization of the Response of Embedded Thermocouples in Grinding

Temperature measurement in grinding has been a widely analyzed field in the study of the process. Temperatures in grinding are too difficult to measure due to the high gradients in the ground workpiece. A lot of different methods have been employed by many researches in the last years. In this paper the use of thermocouples is analyzed attending to the mathematical characterization of their response. It will be shown that correct modeling of the thermocouple’s response permits the avoidance of the problem of thermal inertia, making thus possible the use commercial thermocouples for temperature measurement in grinding.
Software Agents in Multiple Adaptive Double Auction Markets

The prevalence of on-line auctions has stimulated the interest of both the economists and computer scientists in an effort to understand and improve their rules. One famous form of these economic mechanisms is the double auction and, more specifically, the continuous double auction which is commonly used in today’s major stock exchanges worldwide. Software agents constitute a promising tool for the study and implementation of novel double auction mechanisms. TAC Market Design tournament makes an attempt to study the competition among such dynamically adjusted market institutions trying to attract potential traders while maximizing their profit. In this paper we describe the tournament and discuss about the importance of the global competitive equilibrium in its economy. Moreover,we provide a simple, yet effective estimation technique for the latter that our entrant, Mertacor, has utilized during the games of 2008.
A Case Study on the Different Algorithms used for Sentiment Analysis

This paper is a case study on the different algorithms and techniques used to build a robust sentiment analysis system. In this case study we take the problem of classifying movie reviews based on their sentiment. For this, machine learning algorithms based on naive Bayes and support vector machine were used. Naive Bayes with minimum cuts algorithm [1] to generate context sensitive summaries before classifying the document was also used. There are mainly four phases in this process. A tokenizer is used to normalize words and to split the text into sentences. A feature extraction model is used to remove unwanted words and to take into account negation. Then the Naive Bayes with minimum cuts algorithm is used to filter the subjective sentences. Finally, the standard SVM/Naive Bayes algorithm was used to arrive at a proper sentiment. General Terms Natural Language Processing, Sentiment Analysis, Algorithms.
Recruitment to the Nuclear Periphery Can Alter Expression of Genes in Human Cells

The spatial organisation of the genome in the nucleus has a role in the regulation of gene expression. In vertebrates, chromosomal regions with low gene-density are located close to the nuclear periphery. Correlations have also been made between the transcriptional state of some genes and their location near the nuclear periphery. However, a crucial issue is whether this level of nuclear organisation directly affects gene function, rather than merely reflecting it. To directly investigate whether proximity to the nuclear periphery can influence gene expression in mammalian cells, here we relocate specific human chromosomes to the nuclear periphery by tethering them to a protein of the inner nuclear membrane. We show that this can reversibly suppress the expression of some endogenous human genes located near the tethering sites, and even genes further away. However, the expression of many other genes is not detectably reduced and we show that location at the nuclear periphery is not incompatible with active transcription. The dampening of gene expression around the nuclear periphery is dependent on the activity of histone deacetylases. Our data show that the radial position within the nucleus can influence the expression of some, but not all, genes. This is compatible with the suggestion that re-localisation of genes relative to the peripheral zone of the nucleus could be used by metazoans to modulate the expression of selected genes during development and differentiation.
Comments on “Loss of intranetwork and internetwork resting state functional connections with Alzheimer’s disease progression”

Neuroimaging evidence of disconnection syndrome of Alzheimer’s disease (AD) is extremely fascinating. In the study by Brier et al.,[1] they examined resting-state functional-connectivity magnetic resonance imaging (rs-fcMRI) in 5 functionally defined brain networks: default mode network (DMN), executive control network (CON), salience network (SAL), dorsal attention network (DAN), and sensory-motor network (SMN). Within a large sample size of human participants of either sex (n = 510), they divided subjects into three subgroups according to different AD severities, i.e. unaffected (clinical dementia rating, CDR 0), very mild (CDR 0.5), and mild AD (CDR 1). The major findings of this study were as follows. First, they found a loss of intra-network correlations in the DMN and other networks at CDR 0.5. Second, they found increases of intra-network correlations within the SAL between CDR 0 and CDR 0.5; however, they found reduced intra-network correlations within all networks at CDR 1. Third, they found that the three network pairs, DMNDAN, DMN-SMN, and CON-SMN were preferentially affected at certain CDR stages. Finally, they found all inter-network correlations consistently reduced with advancing CDR stage.
Heron Inference for Bayesian Graphical Models

Bayesian graphical models have been shown to be a powerful tool for discovering uncertainty and causal structure from real-world data in many application fields. Current inference methods primarily follow different kinds of trade-offs between computational complexity and predictive accuracy. At one end of the spectrum, variational inference approaches perform well in computational efficiency, while at the other end, Gibbs sampling approaches are known to be relatively accurate for prediction in practice. In this paper, we extend an existing Gibbs sampling method, and propose a new deterministic Heron inference (Heron) for a family of Bayesian graphical models. In addition to the support for nontrivial distributability, one more benefit of Heron is that it is able to not only allow us to easily assess the convergence status but also largely improve the running efficiency. We evaluate Heron against the standard collapsed Gibbs sampler and state-of-the-art state augmentation method in inference for well-known graphical models. Experimental results using publicly available real-life data have demonstrated that Heron significantly outperforms the baseline methods for inferring Bayesian graphical models.
A Modular Approach to the Development of Interactive Augmented Reality Applications.

Augmented reality (AR) technologies are becoming increasingly popular as a result of the increase in the power of mobile computing devices. Emerging AR applications have the potential to have an enormous impact on industries such as education, healthcare, research, training and entertainment. There are currently a number of augmented reality toolkits and libraries available for the development of these applications; however, there is currently no standard tool for development. In this thesis we propose a modular approach to the organization and development of AR systems in order to enable the creation novel AR experiences. We also investigate the incorporation of the framework that resulted from our approach into game engines to enable the creation and visualization of immersive virtual reality experiences. We address issues in the development process of AR systems and provide a solution for reducing the time, cost and barrier of entry for development while simultaneously providing a framework in which researchers can test and apply advanced augmented reality technologies.