Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical document queries

A query against a database behind a site like Napster may search, e.g., for all users who have downloaded more jazz titles than pop music titles. In order to express such queries, we extend classical monadic second-order logic by Presburger predicates which pose numerical restrictions on the children (content) of an element node and provide a precise automata-theoretic characterization. While the existential fragment of the resulting logic is decidable, it turns out that satisfiability of the full logic is undecidable. Decidable satisfiability and a querying algorithm even with linear data complexity can be obtained if numerical constraints are only applied to those contents of elements where ordering is irrelevant. Finally, it is sketched how these techniques can be extended also to answer questions like, e.g., whether the total price of the jazz music downloaded so far exceeds a user's budget.
19.11 Radar Networking: Considerations for Data transfer Protocols and Network Characteristics

QoS requirements for real-time time-series data transfer over Internet are significantly different from those of traditional applications like voice and video streaming. We evaluate and compare the efficacy of three transport protocols, TCP, UDP and TRABOL for radar data streaming. The impact of network dynamics on the quality of radar data is investigated as well. TRABOL, an application level transport protocol combined with a time-series sample selection scheme is proposed to meet the QoS requirements. Performance results obtained using radar emulation test-bed shows that proposed strategy meets the application QoS requirements without overly impacting the performance of other cross-traffic streams on the network.
Age-dependent effects of milrinone and levosimendan on ventricular function and haemodynamics in newborn and mature pigs

Abstract Inodilators are used in the treatment of low cardiac output, mainly after cardiac surgery. At present, there is little knowledge of the effect of inodilators in the newborn heart. Immediately after birth and in the neonatal period, the metabolism and physiology of the heart undergo major changes. We hypothesised that effects of the inodilators milrinone and levosimendan on myocardial contractility and haemodynamics under normal physiological conditions were age dependent. Animal studies were conducted on 48 pigs using a closed-chest biventricular conductance catheter method. Pigs in two age groups, that is, 5–6 days and 5–6 weeks, were assigned to milrinone, levosimendan, or a control group. We observed that both milrinone – 19.2% with a p value of 0.05 – and levosimendan – 25.7% with a p value of 0.03 compared with the control group increased cardiac output, as well as myocardial contractility with a maximum pressure development over time: milrinone 28.2%, p = 0.01 and levosimendan 19.4%, p = 0.05. Milrinone improved diastolic performance (p < 0.05) in the left ventricle in the 5–6-week-old animals. In the newborn animals, neither of the inodilators increased ventricular contractility or cardiac output; however, we observed a significant decrease in the mean arterial pressure: milrinone 34.6%, p < 0.01 and levosimendan 30.1%, p = 0.02. Both inodilators demonstrated age-dependent haemodynamic effects, and it is noteworthy that neither milrinone nor levosimendan was able to increase cardiac output in the newborn heart.
QM/MM MD and Free Energy Simulations of G9a-Like Protein (GLP) and Its Mutants: Understanding the Factors that Determine the Product Specificity

Certain lysine residues on histone tails could be methylated by protein lysine methyltransferases (PKMTs) using S-adenosyl-L-methionine (AdoMet) as the methyl donor. Since the methylation states of the target lysines play a fundamental role in the regulation of chromatin structure and gene expression, it is important to study the property of PKMTs that allows a specific number of methyl groups (one, two or three) to be added (termed as product specificity). It has been shown that the product specificity of PKMTs may be controlled in part by the existence of specific residues at the active site. One of the best examples is a Phe/Tyr switch found in many PKMTs. Here quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) and free energy simulations are performed on wild type G9a-like protein (GLP) and its F1209Y and Y1124F mutants for understanding the energetic origin of the product specificity and the reasons for the change of product specificity as a result of single-residue mutations at the Phe/Tyr switch as well as other positions. The free energy barriers of the methyl transfer processes calculated from our simulations are consistent with experimental data, supporting the suggestion that the relative free energy barriers may determine, at least in part, the product specificity of PKMTs. The changes of the free energy barriers as a result of the mutations are also discussed based on the structural information obtained from the simulations. The results suggest that the space and active-site interactions around the ε-amino group of the target lysine available for methyl addition appear to among the key structural factors in controlling the product specificity and activity of PKMTs.
A Collaborative Framework for Distributed Privacy-Preserving Support Vector Machine Learning

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns. This creates a substantial barrier for researchers to effectively learn from the distributed data using machine learning tools like SVMs. To overcome this difficulty and promote efficient information exchange without sharing sensitive raw data, we developed a Distributed Privacy Preserving Support Vector Machine (DPP-SVM). The DPP-SVM enables privacy-preserving collaborative learning, in which a trusted server integrates "privacy-insensitive" intermediary results. The globally learned model is guaranteed to be exactly the same as learned from combined data. We also provide a free web-service (http://privacy.ucsd.edu:8080/ppsvm/) for multiple participants to collaborate and complete the SVM-learning task in an efficient and privacy-preserving manner.
Paper Title: an Inertial Measurement Framework for Gesture Recognition and Applications Inertial Measurement Framework for Gesture Recognition and Applications

We describe an inertial gesture recognition framework composed of three parts. The first is a compact, six-axis inertial measurement unit to fully capture three-dimensional motion. The second, a gesture recognition algorithm, analyzes the data and categorizes it on an axis-by-axis basis as simple motions (straight line, twist, etc.) with magnitude and duration. The third allows an application designer to combine recognized gestures both concurrently and consecutively to create specific composite gestures which are tied to output routines. This framework was implemented on a Palm III to demonstrate its lightweight nature and to evoke devices which possess both a sense of their own motion and the ability to respond to it. Subfields: 1) Motion analysis 2) Applications Abstract. We describe an inertial gesture recognition framework composed of three parts. The first is a compact, six-axis inertial measurement unit to fully capture three-dimensional motion. The second, a gesture recognition algorithm, analyzes the data and categorizes it on an axis-by-axis basis as simple motions (straight line, twist, etc.) with magnitude and duration. The third allows an application designer to combine recognized gestures both concurrently and consecutively to create specific composite gestures which are tied to output routines. This framework was implemented on a Palm III to demonstrate its lightweight nature and to evoke devices which possess both a sense of their own motion and the ability to respond to it.
Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression

Multiple-output regression models require estimating multiple parameters, one for each output. Structural regularization is usually employed to improve parameter estimation in such models. In this paper, we present a multiple-output regression model that leverages the covariance structure of the latent model parameters as well as the conditional covariance structure of the observed outputs. This is in contrast with existing methods that usually take into account only one of these structures. More importantly, unlike some of the other existing methods, none of these structures need be known a priori in our model, and are learned from the data. Several previously proposed structural regularization based multiple-output regression models turn out to be special cases of our model. Moreover, in addition to being a rich model for multiple-output regression, our model can also be used in estimating the graphical model structure of a set of variables (multivariate outputs) conditioned on another set of variables (inputs). Experimental results on both synthetic and real datasets demonstrate the effectiveness of our method.
Production and Characterization of Cosmetic Nanoemulsions Containing Opuntia ficus-indica (L.) Mill Extract as Moisturizing Agent

This study aimed to produce and characterize an oil in water (O/W) nanoemulsion containing Opuntia ficus-indica (L.) Mill hydroglycolic extract, as well as evaluate its preliminary and accelerated thermal stability and moisturizing efficacy. The formulations containing 0.5% of xanthan gum (FX) and 0.5% of xanthan gum and 1% of Opuntia ficus-indica MILL extract (FXE) were white, homogeneus and fluid in aspect. Both formulations were stable during preliminary and accelerated stability tests. FX and FXE presented a pH compatible to skin pH (4.5–6.0); droplet size varying from 92.2 to 233.6 nm; a polydispersion index (PDI) around 0.200 and a zeta potential from −26.71 to −47.01 mV. FXE was able to increase the water content of the stratum corneum for 5 h after application on the forearm. The O/W nanoemulsions containing 1% of Opuntia ficus-indica (L.) Mill extract presented suitable stability for at least for 60 days. Besides, this formulation was able to increase the water content of stratum corneum, showing its moisturizing efficacy.
Can Human Development be Measured with Satellite Imagery?

In many developing country environments, it is difficult or impossible to obtain recent, reliable estimates of human development. Nationally representative household surveys, which are the standard instrument for determining development policy and priorities, are typically too expensive to collect with any regularity. Recently, however, researchers have shown the potential for remote sensing technologies to provide a possible solution to this data constraint. In particular, recent work indicates that satellite imagery can be processed with deep neural networks to accurately estimate the sub-regional distribution of wealth in sub-Saharan Africa. In this paper, we explore the extent to which the same approach--- of using convolutional neural networks to process satellite imagery--- can be used to measure a broader set of human development indicators, in a broader range of geographic contexts. Our analysis produces three main results: First, we successfully replicate prior work showing that satellite images can accurately infer a wealth-based index of poverty in sub-Saharan Africa. Second, we show that this approach can generalize to predicting poverty in other countries and continents, but that the performance is sensitive to the hyperparameters used to tune the learning algorithm. Finally, we find that this approach does not trivially generalize to predicting other measures of development such as educational attainment, access to drinking water, and a variety of health-related indicators. We discuss in detail whether these findings represent a fundamental limitation of this approach, or could be fixed through more concerted adaptations of the machine learning environment.
Nanomachining with a focused neon beam: A preliminary investigation for semiconductor circuit editing and failure analysis

As the semiconductor device scaling trend continues, advancement in both focused ion beam source development and application innovations are needed to retain failure analysis and nanomachining application capabilities. In this work, a neon gas field ionization source was studied for its nanomachining properties. The authors have analyzed neon’s nanomachining precision at 10 and 20 keV on blank Cu and SiO2 thin films. Subsurface material amorphization from neon and its correlation with beam current distribution are characterized by TEM. In addition, some preliminary nanomachining work was performed on a 32 nm test chip and successfully demonstrated end-pointing on various device layers.
SCADAMAR: scalable and data-efficient internet MapReduce

Recent developments of popular programming models, namely MapReduce, have raised the interest of running MapReduce applications over the large scale Internet. However, current data distribution techniques used in Internet wide computing platforms to distribute the high volumes of information, which are needed to run MapReduce jobs, are naive, and therefore need to be re-thought. Thus, we present a computing platform called SCADAMAR that runs MapReduce jobs over the Internet and provides two new main contributions: i) improves data distribution by using the BitTorrent protocol to distribute all data, and ii) improves intermediate data availability by replicating tasks or data through nodes in order to avoid losing intermediate data and consequently preventing big delays on the MapReduce overall execution time. Along with the design of our solution, we present an extensive set of performance results which confirm the usefulness of the above mentioned contributions, improved data distribution and availability, thus making our platform a feasible approach to run MapReduce jobs.
Social contagion theory: examining dynamic social networks and human behavior

Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a ‘three degrees of influence’ property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations. Copyright © 2012 John Wiley & Sons, Ltd.
Thermal Performance Evaluation of Wax Type Solar Distillation System

The alternative energy sources are new option in front of world to overcome energy crisis and pollution related issues. The solar energy, wind energy and bio mass are three major sources and out of these three energy sources solar energy is the easiest source to extract useful energy because the wind energy can be useful particularly in coastal area where there is high wind velocity and energy extraction bio mass needs either chemical conversion or thermo chemical conversion process. The objective of present work is to developed solar distillation system consists of wax as phase change material to improve the thermal performance.
Cold Vapor Atomic Absorption Determination of Hg in Crude Oil and Gasoline Samples after Solid Phase Extraction Using Modified Disks

A facile and highly efficient method have been developed for the preconcentration of the mercury content in crude oil and gasoline samples after digestion with microwave-assisted digestion. Octadecyl silica membrane disk has been modified by the recently synthesized triazene ligand, (E)-1-(2-ethoxyphenyl)-3-(4-nitrophenyl) triaz-1-ene (ENT), then the modified membrane was used for the preconcentration of mercury(II) ions and Cold Vapor Atomic Absorption Spectrometry (CV-AAS) have been used to determine the Hg (II) ion. Solution studies of ENT with a series of metal ions have been done in advance, and the results showed a strong affinity of ENT to the mercury ion. For solid phase extraction, pH of sample 6.0, flow rates 3.0 mL / min, enrichment factor 240, capacity of modified disk 690 µg Hg per 8.0 mg of ligand, eluent solvent 5.0 mL, 1.5M HClO 4, and the amount of the ligand 8.0 - 10.0 mg, have been optimized. A linear calibration curve has been obtained in the range of 0.80 - 65 µg / L with R 2 = 0.9991 and the Limit Of Detection (LOD) based on three times the standard deviation of the blank was 0.25 µg / L. The Relative Standard Deviation (RSD) for the determination of 50 mL aqueous solution containing 0.5 µg / L Hg (II) found to be 1.0 % while a RSD value of 1.9% have been obtained for the determination of 0.1 µg / L Hg (II) (n=3). The characteristic concentration is 1.2 µg / L in the original samples. The newly developed method was successfully applied to the determination of mercury ion in real crude oil samples, which are very important in environment and industries process.
Latent Topic Analysis for Predicting Group Purchasing Behavior on the Social Web

Group-deal websites, where customers purchase products or services in groups, are an interesting phenomenon on the Web. Each purchase is kicked off by a group initiator, and other customers can join in. Customers form communities with people with similar interests and preferences (as in a social network), and this drives bulk purchasing (similar to online stores, but in larger quantities per order, thus customers get a better deal). In this work, we aim to better understand what factors influence customers' purchasing behavior for such social group-deal websites. We propose two probabilistic graphical models, i.e., a product-centric inference model (PCIM) and a group-initiator-centric inference model (GICIM), based on Latent Dirichlet Allocation (LDA). Instead of merely using customers' own purchase history to predict purchasing decisions, these two models include other social factors. Using a lift curve analysis, we show that by including social factors in the inference models, PCIM achieves 35% of the target customers within 5% of the total number of customers while GICIM is able to reach 85% of the target customers. Both PCIM and GICIM outperform random guessing and models that do not take social factors into account.
Geometrically uniform frames

We introduce a new class of finite-dimensional frames with strong symmetry properties, called geometrically uniform (GU) frames, that are defined over a finite Abelian group of unitary matrices and are generated by a single generating vector. The notion of GU frames is then extended to compound GU (CGU) frames which are generated by a finite Abelian group of unitary matrices using multiple generating vectors. The dual frame vectors and canonical tight frame vectors associated with GU frames are shown to be GU and, therefore, also generated by a single generating vector, which can be computed very efficiently using a Fourier transform (FT) defined over the generating group of the frame. Similarly, the dual frame vectors and canonical tight frame vectors associated with CGU frames are shown to be CGU. The impact of removing single or multiple elements from a GU frame is considered. A systematic method for constructing optimal GU frames from a given set of frame vectors that are not GU is also developed. Finally, the Euclidean distance properties of GU frames are discussed and conditions are derived on the Abelian group of unitary matrices to yield GU frames with strictly positive distance spectrum irrespective of the generating vector.
Compatibility Evaluations of Sealing Materials with Aged Biofuels

The objective of this research was to determine the resistance of frequently used sealing materials such as EPDM, FKM, FVMQ, CR, CSM, IIR, NBR, PA, PUR and VMQ in aged biodiesel and heating oil B10 with 10 % biodiesel. The mass, tensile strength, breaking elongation and Shore hardnesses of the test specimens were determined before and after exposure in non-aged, one-year, two-year, three-year and four-year aged biodiesel and B10 at 20 °C, 40 °C and 70 °C. A threshold for the reduction in tensile properties and Shore hardness is not set in the international standards. Therefore, a threshold of 15 % was set for the evaluation of the compatibility. The decrease in tensile properties and Shore hardness increased with the age and the temperature of the biodiesel and the heating oil. The age of the biodiesel was not relevant for the sealing materials CR, CSM, EPDM, IIR and NBR, which were generally not resistant to biodiesel and B10. FKM and PA showed high compatibility in non-aged and aged biodiesel. FVMQ and PA could be evaluated as resistant in non-aged and aged B10 at 20°C and 40°C, whereas FKM was resistant up to 70°C despite of the age of B10.
Resistance spot welding of steel and aluminium sheet using insert metal sheet

AbstractThe present paper reports the resistance spot welding of steel and aluminium sheets using aluminium clad steel sheets as insert metals. Intermetallic compound layers were formed in the weld zones in direct spot welding of steel sheets to aluminium sheets. Thus, the strength of these joints was lower than that of aluminium to aluminium joints. Intermetallic compound layers were also formed at the steel/aluminium interfaces of the insert metal in welding of steel to aluminium using an insert metal sheet. However, the strength of these joints was of the same order as that of the aluminium joints. The fracture mode of these joints varied with the welding current. The suitable welding current for steel to aluminium joints varied between the values suitable for steel to steel and aluminium to aluminium joints. The fatigue strength of joints using insert metals was somewhat lower than that of the aluminium joints.