Miracle-dz/UniFAR-FTUs huggingface dataset:
The application of the connectionist method of semantic similarity for Kazakh language

The unsupervised algorithm for the calculation of semantic similarity are essential in many areas of modern natural language processing. One of the most promising methods for the calculation of semantic similarity is neural network algorithm "word2vec. This method has been tested for most European languages, namely English, German, French and Russian. This paper presents the results of numerical experiments for synthetic agglutinative Kazakh language, which has a number of features and differences from all the above languages.
Is the adiposity‐associated FTO gene variant related to all‐cause mortality independent of adiposity? Meta‐analysis of data from 169,551 Caucasian adults

Previously, a single nucleotide polymorphism (SNP), rs9939609, in the FTO gene showed a much stronger association with all‐cause mortality than expected from its association with body mass index (BMI), body fat mass index (FMI) and waist circumference (WC). This finding implies that the SNP has strong pleiotropic effects on adiposity and adiposity‐independent pathological pathways that leads to increased mortality. To investigate this further, we conducted a meta‐analysis of similar data from 34 longitudinal studies including 169,551 adult Caucasians among whom 27,100 died during follow‐up. Linear regression showed that the minor allele of the FTO SNP was associated with greater BMI (n = 169,551; 0.32 kg m−2; 95% CI 0.28–0.32, P < 1 × 10−32), WC (n = 152,631; 0.76 cm; 0.68–0.84, P < 1 × 10−32) and FMI (n = 48,192; 0.17 kg m−2; 0.13–0.22, P = 1.0 × 10−13). Cox proportional hazard regression analyses for mortality showed that the hazards ratio (HR) for the minor allele of the FTO SNPs was 1.02 (1.00–1.04, P = 0.097), but the apparent excess risk was eliminated after adjustment for BMI and WC (HR: 1.00; 0.98–1.03, P = 0.662) and for FMI (HR: 1.00; 0.96–1.04, P = 0.932). In conclusion, this study does not support that the FTO SNP is associated with all‐cause mortality independently of the adiposity phenotypes.
Airborne measurements of organosulfates over the continental U.S.

Organosulfates are important secondary organic aerosol (SOA) components and good tracers for aerosol heterogeneous reactions. However, the knowledge of their spatial distribution, formation conditions, and environmental impact is limited. In this study, we report two organosulfates, an isoprene‐derived isoprene epoxydiols (IEPOX) (2,3‐epoxy‐2‐methyl‐1,4‐butanediol) sulfate and a glycolic acid (GA) sulfate, measured using the NOAA Particle Analysis Laser Mass Spectrometer (PALMS) on board the NASA DC8 aircraft over the continental U.S. during the Deep Convective Clouds and Chemistry Experiment (DC3) and the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS). During these campaigns, IEPOX sulfate was estimated to account for 1.4% of submicron aerosol mass (or 2.2% of organic aerosol mass) on average near the ground in the southeast U.S., with lower concentrations in the western U.S. (0.2–0.4%) and at high altitudes (<0.2%). Compared to IEPOX sulfate, GA sulfate was more uniformly distributed, accounting for about 0.5% aerosol mass on average, and may be more abundant globally. A number of other organosulfates were detected; none were as abundant as these two. Ambient measurements confirmed that IEPOX sulfate is formed from isoprene oxidation and is a tracer for isoprene SOA formation. The organic precursors of GA sulfate may include glycolic acid and likely have both biogenic and anthropogenic sources. Higher aerosol acidity as measured by PALMS and relative humidity tend to promote IEPOX sulfate formation, and aerosol acidity largely drives in situ GA sulfate formation at high altitudes. This study suggests that the formation of aerosol organosulfates depends not only on the appropriate organic precursors but also on emissions of anthropogenic sulfur dioxide (SO2), which contributes to aerosol acidity.
Compartment-specific Synthesis of Phosphatidylethanolamine Is Required for Normal Heavy Metal Resistance

The enzyme Psd2 catalyzes endosomal synthesis of the phospholipid PE. While this pool of PE represents a minority of total cellular PE, function of Psd2 is required for normal activity of the vacuolar ABC transporter Ycf1. Psd2 controls vacuolar PE levels by acting at the level of the endosome.
Separation Logic and the Mashup Isolation Problem

This work was done as part of my PhD qualifier exam. My qualifier exam problem was to perform an in depth study of separation logic. In this paper, I survey two variants of separation and then show how they can applied to solve the isolation problem for mashups. I rigorously define the inter-component isolation property for basic a class of mashups, called variable separated mashups. I then derive two procedures for verifying isolation of variable separated mashups using separation logic and its variants. Finally, I provide a rigorous proof of correctness for both the procedures.
Temporal Pyramid Matching of Local Binary Subpatterns for Hand-Gesture Recognition

Human-computer Interaction systems based on hand-gesture recognition are nowadays of great interest to establish a natural communication between humans and machines. However, the visual recognition of gestures and other human poses remains a challenging problem. In this paper, the original volumetric spatiograms of local binary patterns descriptor has been extended to efficiently and robustly encode the spatial and temporal information of hand gestures. This enhancement mitigates the dimensionality problems of the previous approach, and considers more temporal information to achieve a higher recognition rate. Excellent results have been obtained, outperforming other existing approaches of the state of the art.
Relation based image retrival in online social network

Online social networks allow users to share their photos with friend, family and the community at large. Social networks are formed with a number of users connected through different relationships, and the strength of these relationships has an influence on the way users react on each other photos. In this paper we investigate how we can assist users to retrieve the most relevant images from their social network. We propose Relation-Based Image Retrieval (RBIR), where social relationships are of central importance. For each user we calculate their relationships with other members in the network, and a ranked list of the closest and most reputed friends is compiled by analyzing the mutual activates between two users and their overall individual reputation in the social network. Comments and likes made by highly ranked members hold more weight, and photos are ranked in accordance with the number and weight of likes and comments they receive. To test our approach, we developed a prototype based on the Facebook platform, allowing users to search for images among their Facebook friends. The results demonstrate that our techniques are useful for retrieving relevant images.
Research on Anti-Error Performance of Speed and Flux Estimator for Induction Motor Using Robust Reduced-Order EKF

A novel speed and flux estimator of induction motor based on robust reduced-order extended Kalman filter (RROEKF) is proposed in this paper, and the effect of gross error on estimation accuracy of reduced-order EKF (ROEKF) was analyzed. Whether RROEKF can give better estimation performance and robust ability than ROEKF for speed and flux estimation of induction machines is explored. The speed estimated error and the flux fluctuation of RROEKF by gross external disturbance and internal estimated error is compared with ROEKF. The simulation and experimental results show that RROEKF has the better estimation performance of anti-gross error than ROEKF and EKF.
Diagnostic Value of Coronary Artery Calcium Score for Cardiovascular Disease in African Americans: The Jackson Heart Study

Background The role of coronary artery calcium (CAC) as a screening tool for cardiovascular disease (CVD) risk in African Americans (AAs) is unclear. We compared the diagnostic accuracy for CVD prevalence using the CAC score and the Framingham Risk Score (FRS) in an adult population of AAs. Methods CAC was measured in 2944 participants AAs. Approximately 8% of this cohort had known CVD defined as prior myocardial infarction, stroke, percutaneous coronary intervention, coronary artery bypass grafting and peripheral artery disease. Logistic regression, receiver operating characteristic (ROC) and net reclassification index (NRI) analysis were used adjusting for age, gender, systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, smoking status, diabetes mellitus (DM), body mass index (BMI), blood pressure medication and statin use. Participants with prevalent clinical CVD and DM were classified as high FRS risk. Results The mean age of participants was 60 years, 65% were females, 26% had DM, 50% were obese and 30% were current or former smokers. Prevalent CVD was associated with older age, higher SBP, lower HDL and total cholesterol, and higher CAC. The prevalence of CAC was 83% in participants with prevalent CVD and 45% in those without CVD. CAC was independently associated with prevalent CVD in our multivariable model [OR (95% CI): 1.22 (1.12–1.32), p< 0.0001]. In ROC analysis, CAC improved the diagnostic accuracy (c statistic) of the FRS from 0.617 to 0.757 (p < 0.0001) for prevalent CVD. Addition of CAC to FRS resulted in net reclassification improvement of 4% for subjects with known CVD and 28.5% in those without CVD. Conclusion In AAs, CAC is independently associated with prevalent CVD and improves the diagnostic accuracy of FRS for prevalent CVD by 14%. Addition of CAC improves the NRI of those with prevalent CVD by 4% and the NRI of individuals without CVD by 28.5%. Determination of CAC may be useful in CVD risk stratification in AAs.
Valence-arousal ratings prediction with co-occurrence word-embedding

Dimensional approach has become a popular method in sentiment analysis because it represents emotions as continuous numerical values in multiple dimensions, such as valence-arousal (VA) space [1], therefore it can provide more fine-grained sentiment analysis compared to traditional categorical approach which represents affective states in discrete classes. However, affective lexicons and corpora with VA ratings are very rare and it makes dimensional approach hard to use in reality. This paper describes using KNN algorithm to predict VA ratings for new affective lexicons by leveraging word embedding and available affective corpora with VA ratings.
Discovering structure in social networks of 19th century fiction

Inspired by the increasing availability of large text corpora online, digital humanities scholars are adopting computational approaches to explore questions in the field of literature from new perspectives. In this paper, we examine detailed social networks of characters, extracted from several works of 19th century fiction by Jane Austen and Charles Dickens. This allows us to apply methodologies from social network analysis, such as community detection, to explore the structure of these networks. By evaluating the results in collaboration with literary scholars, we find that the structure of the character networks can reveal underlying structural aspects within a novel, particularly in relation to plot and characterisation.
The Frequency Offset Estimator Based on All-Phase FFT for OFDM Systems

Inter-Carriers Interference (IC1) caused by frequency offset can result in the performance loss of the OFDM system. Aiming at this problem, the method of All Phase FFT is proposed for reducing the IC1 and estimating frequency offset (CFO). Since the ICI energy and signal energy both contribute coherently to the estimator, the algorithm generates extremely accurate estimates even when the offset is far too great to demodulate the data values.
An incremental evolutionary method for optimizing dynamic image retrieval systems

This paper introduces a new incremental evolutionary optimization method based on evolutionary group algorithm (EGA). The EGA was presented as an approach to overcome time-consuming drawbacks related to general evolutionary algorithms in large scale content-based image indexing retrieval (CBIR) optimization tasks. Here, we consider another challengeable limitation of usual evolutionary learning and optimization systems: learning in the scale-varying and dynamic environments. Hence, we present a new strategy based on EGA that is enhanced with the ability of incremental learning. Evaluation results on scale-varying and simulated dynamic CBIR systems show that the proposed method can continuously obtain good performance in the presence of environmental or scale changes.
Accuracy Improvement for a Redundant Vehicle

This paper presents solutions to reduce the effects of wheel slippage and kinematic model uncertainty on low-level control and odometry of over-actuated wheeled mobile robots. Odometry aims at numerical integration of the robot direct kinematic model, whose errors are in part dues to model uncertainties. A parameter identiﬁcation can signiﬁcantly improve the odometry accuracy. Wheel slippage introduces large positioning errors. Therefore, robot control must be as smooth as possible, which is achieved by an approach to build a C2 continuous motion. The last contribution is speciﬁc to omnidirectional robots with offset steered wheels. The redundancy property is used to reduce wheel slippage.
Signals , States , Events , and Modes

For embedded control software – especially in form of networks of interacting components – correctness of inter-communication is a key issue. While architectural compatibility notions do not capture the necessary dynamic aspects, analysis based on full behavioral models does not yet scale for practical applications. Here, communication obligations offer a trade-off between completeness and usability. For describing embedded control software, these obligations essentially depend on signal-related issues like state/event communication as well as application-related issues like modes of operation. To motivate the use of communication obligations, we introduce the concepts of state and event signals as well as modes of communication; furthermore, their formalization and analysis using temporal model checking is sketched.
An asymptotic-numerical approach for examining global solutions to an ordinary differential equation

Abstract Purely numerical methods do not always provide an accurate way to find all the global solutions to nonlinear ordinary differential equations on infinite intervals. For example, finite-difference methods fail to capture the asymptotic behavior of solutions, which might be critical for ensuring global existence. We first show, by way of a detailed example, how asymptotic information alone provides significant insight into the structure of global solutions to a nonlinear ordinary differential equation. Then we propose a method for providing this missing asymptotic data to a numerical solver, and show how the combined approach provides more detailed results than either method alone.
Design of a Digitally Controlled Inductor-Less Voltage Multiplier for Non-Thermal Food Processing

Abstract—For non-thermal food processing systems utilizing an underwater shockwave, a digitally controlled voltage multiplier is proposed in this paper. The proposed voltage multiplier based on Cockcroft-Walton voltage multiplier (CWVM) has a bipolar structure. Unlike the conventional CWVM, the output voltage of the proposed multiplier is expressed by sum of the output voltage of positive and negative voltage multiplier blocks. Therefore, the number of stages of the proposed multiplier is about a half of that of the conventional CWVM. Furthermore, by utilizing high-low side drivers and a microcontroller, the diode switch of the proposed multiplier is driven by high-speed rectangular pulses. For these reasons, the proposed multiplier can achieve not only high voltage efficiency but also high speed operation. The validity of the circuit design is confirmed by theoretical analysis, simulation program with integrated circuit emphasis (SPICE) simulations, and experiments. The simulation results show that the settling time of the proposed voltage multiplier is less than 1/400 of that of the conventional CWVM. Furthermore, the experimental results show that the proposed voltage multiplier can improve voltage efficiency more than 21% from the conventional CWVM when the input voltage is 10V@60Hz and the output capacitor is 10μF.
Blog Mining Through Opinionated Words

Intent mining is a special kind of document analysis whose goal is to assess the attitude of the document author with respect to a given subject. Opinion mining is a kind of intent mining where the attitude is a positive or negative opinion. Most systems tackle the problem with a two step approach, an information retrieval followed by a postprocess or filter phase to identify opinionated blogs. We explored a single stage approach to opinion mining, retrieving opinionated documents ranked with a special ranking function which exploits an index enriched with opinion tags. A set of subjective words are used as tags for identifying opinionated sentences. Subjective words are marked as “opinionated” and are used in the retrieval phase to boost the rank of documents containing them. In indexing the collection, we recovered the relevant content from the blog permalink pages, exploiting HTML metadata about the generator and heuristics to remove irrelevant parts from the body. The index also contains information about the occurrence of opinionated words, extracted from an analysis of WordNet glosses. The experiments compared the precision of normal queries with respect to queries which included as constraint the proximity to an opinionated word. The results show a significant improvement in precision for both topic relevance and opinion relevance.
Numerical and Laboratory Study of a Horizontally Evolving Convective Boundary Layer. Part I: Transition Regimes and Development of the Mixed Layer

Results are presented from a large eddy simulation (LES) and wind tunnel study of the turbulence regime in a horizontally evolving sheared atmospheric convective boundary layer (CBL) capped by a temperature inversion. The wind tunnel part of the study has been conducted in the thermally stratified tunnel of the University of Karlsruhe. For the numerical part a modified LES procedure that was originally designed for simulation of the horizontally homogeneous atmospheric CBL has been employed. The study focuses on the transition between the neutrally buoyant boundary layer in the initial portion of the wind tunnel flow and a quasi-homogeneous convectively mixed layer developing downwind. The character of the transition between the two boundary layers and the associated changes in the turbulence structure are found to be strongly dependent on the magnitude and distribution of disturbances in the flow at the entrance of the wind tunnel test section. For all simulated inflow conditions, the transition is preceded by accumulation of potential energy in the premixed CBL. The eventual energy release in the transition zone leads to turbulence enhancement that has a form of turbulence outbreak for particular flow configurations. The numerically simulated CBL case with temperature fluctuations introduced in the lower portion of the incoming flow appears to be the closest to the basic CBL flow case studied in the wind tunnel. Second-order turbulence statistics derived from the LES are shown to be in good agreement with the wind tunnel measurements. Main features of transition, including the turbulence enhancement within the transition zone, are successfully reproduced by the LES.
Communication Over Finite-Field Matrix Channels

This paper is motivated by the problem of error control in network coding when errors are introduced in a random fashion (rather than chosen by an adversary). An additive-multiplicative matrix channel is considered as a model for random network coding. The model assumes that n packets of length m are transmitted over the network, and up to t erroneous packets are randomly chosen and injected into the network. Upper and lower bounds on capacity are obtained for any channel parameters, and asymptotic expressions are provided in the limit of large field or matrix size. A simple coding scheme is presented that achieves capacity in both limiting cases. The scheme has decoding complexity O(n 2 m) and a probability of error that decreases exponentially both in the packet length and in the field size in bits. Extensions of these results for coherent network coding are also presented.
A Defined, Feeder-Free, Serum-Free System to Generate In Vitro Hematopoietic Progenitors and Differentiated Blood Cells from hESCs and hiPSCs

Human ESC and iPSC are an attractive source of cells of high quantity and purity to be used to elucidate early human development processes, for drug discovery, and in clinical cell therapy applications. To efficiently differentiate pluripotent cells into a pure population of hematopoietic progenitors we have developed a new 2-dimentional, defined and highly efficient protocol that avoids the use of feeder cells, serum or embryoid body formation. Here we showed that a single matrix protein in combination with growth factors and a hypoxic environment is sufficient to generate from pluripotent cells hematopoietic progenitors capable of differentiating further in mature cell types of different lineages of the blood system. We tested the differentiation method using hESCs and 9 iPSC lines generated from different tissues. These data indicate the robustness of the protocol providing a valuable tool for the generation of clinical-grade hematopoietic cells from pluripotent cells.
Challenges for Data Mining in Distributed Sensor Networks

The way of collecting sensor data faces a revolution when the newly developing technology of distributed sensor networks becomes fully functional and widely available. Smart sensors acquire full interconnection capabilities with similar devices, so that run-time data aggregation, parallel computing, and distributed hypothesis formation become reality with off-the-shelf components and sensor boards. This revolution started around in 1996, and now hardware and network are converging on the first convincing solutions. Exploring and exploiting this paradigm are a renovated challenge for the pattern recognition and data mining community. This paper attempts a survey on state-of-the-art of wireless sensor technology, with an eye on data-related problems and technological limits. Although the possibilities seem promising, the limited computational resources of individual nodes hamper the elaboration of data with computationally-intensive algorithms. New software paradigms must be developed, both creating new techniques or adapting, for network computing old algorithms of earlier ages of computing
Size, structure and change implementation

Purpose – The purpose of this paper is to evaluate competing views of whether organization size creates conditions of rigidity or fluidity with respect to adaptation and change, this study empirically compares processes used to implement planned change and their associated outcomes in small and large organizations.Design/methodology/approach – Using Lewin's three‐phase model of change as an evaluative framework, questionnaire‐based data were obtained from change managers in small and large organizations. Analysis of variance and profile analysis were used to investigate size‐related differences in unfreezing, movement and refreezing phases of change, and in implementation success.Findings – Results indicated that small organizations employed significantly lower levels of refreezing activities and realized lower levels of implementation success relative to large organizations.Research limitations/implications – While cross sectional in nature, the sample's modest size limits the extent to which findings ca.