Miracle-dz/UniFAR-FTUs huggingface dataset:
MAVEN Observations of Solar Wind‐Driven Magnetosonic Waves Heating the Martian Dayside Ionosphere

We present Mars Atmosphere and Volatile EvolutioN observations of large‐amplitude magnetosonic waves propagating through the magnetosheath into the Martian ionosphere near the subsolar point on the dayside of the planet. The observed waves grow in amplitude as predicted for a wave propagating into a denser, charged medium, with wave amplitudes reaching 25 nT, equivalent to ∼40% of the background field strength. These waves drive significant density and temperature variations (∼20% to 100% in amplitude) in the suprathermal electrons and light ion species (H+) that correlate with compressional fronts of the magnetosonic waves. Density and temperature variations are also observed for the ionospheric electrons, and heavy ion species (O+ and O 2+ ); however, these variations are not in phase with the magnetic field variations. Whistler waves are observed at compressional wave fronts and are thought to be produced by unstable, anistropic suprathermal electrons. The magnetosonic waves drive significant ion and electron heating down to just above the exobase region. Ion heating rates are estimated to be between 0.03 and 0.2 eVs−1 per ion, and heavier ions could thus gain escape energy if located in this heating region for ∼10–70 s. The measured ionospheric density profile indicates severe ionospheric erosion above the exobase region, and this is likely caused by substantial ion outflow that is driven by the observed heating. The effectiveness of these magnetosonic waves to energize the plasma close to the exobase could have important implications for the long‐term climate evolution for unmagnetized bodies that are exposed to the solar wind.
Furthering Scientific Inquiry for Weight Loss Maintenance: Assessing the Psychological Processes Impacted by a Low intensity Technology-Assisted Intervention (NULevel Trial)

Abstract Background NULevel was a randomized control trial to evaluate a technology-assisted weight loss maintenance (WLM) program in the UK. The program included: (a) a face-to-face goal-setting session; (b) an internet platform, a pedometer, and wirelessly connected scales to monitor and report diet, physical activity, and weight, and; (c) regular automated feedback delivered by mobile phone, tailored to participants’ progress. Components were designed to target psychological processes linked to weight-related behavior. Though intervention participants showed increased physical activity, there was no difference in WLM between the intervention and control groups after 12 months (Sniehotta FF, Evans EH, Sainsbury K, et al. Behavioural intervention for weight loss maintenance versus standard weight advice in adults with obesity: A randomized controlled trial in the UK (NULevel Trial). PLoS Med. 2019; 16(5):e1002793. doi:10.1371/journal.pmed. 1002793). It is unclear whether the program failed to alter targeted psychological processes, or whether changes in these processes failed to influence WLM. Purpose We evaluate whether the program influenced 16 prespecified psychological processes (e.g., self-efficacy and automaticity toward diet and physical activity), and whether these processes (at 6 months) were associated with successful WLM (at 12 months). Methods 288 adults who had previously lost weight were randomized to the intervention or control groups. The control group received wireless scales and standard advice via newsletters. Assessments occurred in person at 0, 6, and 12 months. Results The intervention significantly altered 10 of the 16 psychological processes, compared with the control group. However, few processes were associated with WLM, leading to no significant indirect effects of the intervention via the processes on WLM. Conclusions Changes in targeted processes were insufficient to support WLM. Future efforts may more closely examine the sequence of effects between processes, behavior, and WLM.
Adaptively Learning the Crowd Kernel

We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form "is object a more similar to b or to c? " and is chosen to be maximally informative given the preceding responses. The output is an embedding of the objects into Euclidean space (like MDS); we refer to this as the "crowd kernel. " SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as "is striped" among neckties and "vowel vs. consonant" among letters.
Human Metapneumovirus in Children, Singapore

Four hundred specimens were collected from pediatric patients hospitalized in Singapore; 21 of these specimens tested positive for human metapneumovirus (HMPV), with the A2 genotype predominating. A 5% infection rate was estimated, suggesting that HMPV is a significant cause of morbidity among the pediatric population of Singapore.
Effect of End Groups on the Cloud Point Temperature of Aqueous Solutions of Thermoresponsive Polymers: An Inside View by Flory–Huggins Theory

In an effort to gain insight into the origin of the effects of end groups on the cloud point temperature (Tcp) as a function of the polymer molar mass of thermoresponsive polymers with lower critical solution behavior in dilute aqueous solutions, we use the Flory–Huggins (FH) theory amended for end groups. The theory was applied to available experimental data sets of poly(N-isopropylacrylamide) (PNIPAM), poly(4-vinylbenzyl methoxytris(oxyethylene) ether) (PTEGSt), and poly(α-hydro-ω-(4-vinylbenzyl)tetrakis(oxyethylene) ether) (PHTrEGSt). The theory relates the variations in TcpM,ϕcp for different end groups to the effective FH χ parameter of the end groups and explains the qualitative notion that the influence of the end groups is related to the hydrophobicity/hydrophilicity of the end groups relative to that of the so called intrinsic TcpM,ϕcp response of a polymer without end groups. The limits to the applicability of the FH theory are established, and a set of possible theoretical improvements is considered. The ultimate scrutiny of the simple FH theory and suggested improved theories must await the measurement of truly thermodynamic cloud points; the available cloud points are merely estimations of the thermodynamic cloud point, for which the deviation to the true cloud point cannot be established with sufficient accuracy.
A Study of the Genetics and Physical Properties of Dentine Defects

Dentinogenesis Imperfecta (DI) and Dentine Dysplasia (DD) are rare inherited condition affecting the dentine. Previous studies have reported the histological presentation of teeth with dentine defects but the differences between clinical appearances and physical properties, and how do they link to the underlying gene defects, is limited. In addition, the differences between primary and permanent teeth are poorly understood. Aim and Objective: The aim of this study was to investigate if there is an association between the underlying gene defect (genotype) and the physical properties and appearance (phenotype) of teeth with dentine defects. The objectives were to assess the colour, radiographic feature and dentine hardness of the teeth with dentine defects, to discover any other unidentified genes that might involve in causing inherited dentine diseases, and to relate them to the phenotype recorded clinically. Materials and Method: The discolouration and radiographic features of teeth from 16 patients (6 DI type I, 5DI type II, 4 OI and 1 DD type I respectively) were recorded. 20 control primary teeth and five DI teeth (four DI type I, one DI type II) were sectioned transversely using a diamond wheel saw. The dentine hardness measurement was done using Wallace Hardness Machine and recorded as Vickers Hardness Number (VHN). Dentine microstructure was examined using scanning electron microscope (SEM). Saliva samples were obtained from 14 patients for genomic analysis. Results: All DI type II and DI type I patients had marked discolouration of their teeth. DD and OI patients had normal teeth colour. The mean value of dentine hardness of control teeth was VHN=57.11±62. The VHN for DI teeth were significantly low compared to control teeth. The mean value for DI teeth was 30.61±8.24 VHN. Histologically, both DI type I and DI type II teeth showed reduced number of dentine tubules. DI type II had irregular dentine surface, while DI type I dentine showed a tear-like appearance on an empty dentine surface. Genetic analysis did not show any known mutations on COL1A1 and no novel genetic mutations has been observed. Conclusion: Collagen plays a role in maintenance of healthy dentine. Therefore, defects on COL1A1 and COL1A2 lead to abnormality of collagen thus the occurrence of OI and DI type I. Mutation in DSPP caused disruption in dentine mineralisation which also present as dentine defects in DI type II. Both DSPP and COL1A1/COL1A2 caused dentine abnormalities which present as teeth discolouration, abnormal appearance and reduced dentine hardness.
Backend Model Consolidation & Incremental Training Matching Frequent NPs + Dictionary Crowd Annotators A batch of raw sentences Recommendations Crowd Annotations Unlabeled Instances Instance Sampling via Active Learning Real-time Deployment API Complicated Downstream Systems Alpaca Tag

We introduce an open-source web-based data annotation framework (AlpacaTag) for sequence tagging tasks such as named-entity recognition (NER). The distinctive advantages of AlpacaTag are three-fold. 1) Active intelligent recommendation: dynamically suggesting annotations and sampling the most informative unlabeled instances with a back-end active learned model; 2) Automatic crowd consolidation: enhancing real-time interannotator agreement by merging inconsistent labels from multiple annotators; 3) Real-time model deployment: users can deploy their models in downstream systems while new annotations are being made. AlpacaTag is a comprehensive solution for sequence labeling tasks, ranging from rapid tagging with recommendations powered by active learning and auto-consolidation of crowd annotations to real-time model deployment.
Wrinkling of Dielectric Elastomer Membranes

Wrinkling of thin membranes due to different in-plane loading and boundary conditions has drawn attention of researchers in structural engineering since the development of thin webs for early aircraft structures. More recently, prestressed lightweight membrane structures have been proposed for future space missions, for example solar sails, the next generation space telescope sunshield and space-based radar systems. These structures are often partially wrinkled during operation. The formation of wrinkles alters the load paths and the structural stiffness of the membranes. More importantly its occurrence degrades the surface accuracy of these structures, which is a key design parameter. This dissertation focuses on wrinkling of thin rectangular membranes subjected to uniaxial tension and investigates the onset and profiles of wrinkles using both experimental and numerical approaches. An optical method, which integrates fringe projection method with four-frame phase-shifting technique, pre-conditioned conjugate gradient phase unwrapping algorithm and series-expansion carrier removal technique was developed in order to measure the full-field out-of-plane displacement of membranes, and an optical system was constructed including a uniaxial tension testbed, a LCD projector and a CCD camera. A series of uniaxial tensile tests were carried out on silicone rubber membranes of varying dimensions and aspect ratios in order to investigate the effect of geometric factors such as membrane dimension and aspect ratio on wrinkling onset; and a series of measurements were performed on each membrane at several desired strain levels to understand the evolution of the wrinkles, in particular wrinkle amplitude and wavelength. A numerical study was carried out using the commercial finite element software ABAQUS to further understand the important characteristics of wrinkling of thin membranes observed in the physical model. Geometrically nonlinear finite element models of membrane structures were constructed with thin-shell elements. A series of simulations were carried out for different membrane dimensions. The critical buckling load and buckling modes was predicted for each dimension using a pre-buckling eigenvalue analysis. The desirable buckling mode was selected and introduced into the structure as a geometric imperfection. The formation and growth of wrinkles were simulated in the post-buckling analysis. Finally, an idea of suppressing wrinkle instabilities of dielectric elastomer membranes using through-thickness electric field was proposed and verified in both experiment and numerical simulations.
ARE BEHAVIORAL BIASES INFLUENCED BY DEMOGRAPHIC CHARACTERISTICS & PERSONALITY TRAITS? EVIDENCE FROM PAKISTAN

The study investigates the influence of demographics (residential area, age, gender, marital status, education background) and personality traits (extraversion, openness, conscientiousness, neuroticism, and agreeableness) on the financial behavioral biases (overconfidence, herding/mass behavior and disposition effect) and risk taking behavior in Pakistan. The study will be beneficial for the financial advisors and individual investors. As it will help financial advisor to know potential behavioral biases in each type of investors while making investment decision and therefore they can advise investors properly to mitigate such biases. Personality dimensions are categorized under Big Five Personality model. Questionnaire survey method is used to collect the data from a Sample size of 225 respondents that includes bankers, finance students as well as investors. Structure equation modeling (SEM) analysis is used to analyze the impact of personality traits and demographics on the investment biases through Amos 20. The results show that big five personality traits have a significant relationship with overconfidence, herding/mass behavior and risk taking except disposition effect.
Testing digital pen technology in a hybrid mode of interpreting

In 2009, during a technology seminar on how new technologies could influence language education, I was introduced to new technology, the Livescribe digital pen, whose advanced features allow its users to record and capture simultaneously what they write in relation to what is said in the room. The device was originally invented to assist secretaries in their taking of minutes during meetings and in retrieving their notes, and has since been trialled and used for research projects in various fields such as education, engineering, health and allied health, journalism or science (e.g. Boyle 2012; Dawson & Plummer 2010; Greeve & McGee-Lennon 2010).
Stress Change and Fault Interaction from a Two Century‐Long Earthquake Sequence in the Central Tell Atlas, Algeria

We study the role and distribution of stress transfer that may trigger destructive earthquakes in the Central Tell Atlas (Algeria). A sequence of historical events reaching Ms 7.3 and related stress tensor with thrust faulting mechanism illustrates the Coulomb Failure Function (CFF) modelling [1]. We explore here the physical pattern for a stress transfer along the Tell thrust-and-fold belt taking into account an eastward trending earthquake migration from 1891 to 2003. The Computation integrated the seismici ty rate in the CFF computation, which is in good agreement with the migration seismicity. The stress transfer progression and increase of 0.1 to 0.8 bar are obtained on fault planes at 7-km-depth with a friction coefficient μ’ 0.4 showing stress loading lobes on targeted coseism ic fault zone and location of stress shadow across other thrust-andfold regions.
Preface Pattern Recognition in Medicine Association Rule Mining

The importance of Information Technology in healthcare cannot be overestimated in today's world. With the rapid development of Information Technology (IT) there is an ever-growing emphasis on the use of IT in the research and practice of healthcare. The research in health information technology has been motivated by several developments, including electronic medical records, emphasis on personalized medicine and research on genomic data, and development of computational techniques for finding patterns , to name a few. Along with these new privacy concerns for electronic medical records and their access to third parties has become an important issue. In this article we discuss some of the recent research on health information technology within the context of the following areas: pattern recognition in medicine, privacy and security issues in healthcare, and clinical decision support. Pattern recognition algorithms are powerful techniques that can identify hidden patterns in data that can be beneficial for medical diagnosis, treatment management, epidemiological studies, finding longitudinal patterns, information retrieval in medical literature, and integration of different types of data. There is vast potential for applying pattern recognition techniques including algorithms from data mining, machine learning, statistical analysis, and matrix-based methods such as singular value decomposition and spectral analysis in healthcare data. In this section we first provide an overview of some of the common methods that have been developed in the recent literature and then describe a few articles in more details. In our discussion we recognize the overlap between machine learning and data mining methods but differentiate the two fields by the fact that data mining has a more explicit focus on dealing with large databases. Statistical methods are hypothetico-deductive where the main focus is to test and either validate or refute, through rigorous mathematical treatment, a given hypothesis (Hand 1998). Data mining is useful in exploratory data analysis where patterns are found using inductive methods (see Han et al 2006, Tan et al 2006, Dunham 2000 for more details). Traditionally, data mining methods have been categorized into association rule mining, classification, clustering, and outlier or anomaly detection. Association rules have their roots in market basket analysis where the goal is to find frequently co-occurring items in retail store transactions. Since different transactions (market baskets) typically belong to different customers it is assumed that the transactions
On Adaptive Security of Delayed-Input Sigma Protocols and Fiat-

We study adaptive security of delayed-input Sigma protocols and non-interactive zero-knowledge (NIZK) proof systems in the common reference string (CRS) model. Our contributions are threefold: – We exhibit a generic compiler taking any delayed-input Sigma protocol and returning a delayed-input Sigma protocol satisfying adaptiveinput special honest-verifier zero knowledge (SHVZK). In case the initial Sigma protocol also satisfies adaptive-input special soundness, our compiler preserves this property. – We revisit the recent paradigm by Canetti et al. (STOC 2019) for obtaining NIZK proof systems in the CRS model via the Fiat-Shamir transform applied to so-called trapdoor Sigma protocols, in the context of adaptive security. In particular, assuming correlation-intractable hash functions for all sparse relations, we prove that Fiat-Shamir NIZKs satisfy either: (i) Adaptive soundness (and non-adaptive zero knowledge), so long as the challenge is obtained by hashing both the prover’s first round and the instance being proven; (ii) Adaptive zero knowledge (and non-adaptive soundness), so long as the challenge is obtained by hashing only the prover’s first round, and further assuming that the initial trapdoor Sigma protocol satisfies adaptive-input SHVZK. – We exhibit a generic compiler taking any Sigma protocol and returning a trapdoor Sigma protocol. Unfortunately, this transform does not preserve the delayed-input property of the initial Sigma protocol (if any). To complement this result, we also give yet another compiler taking any delayed-input trapdoor Sigma protocol and returning a delayed-input trapdoor Sigma protocol with adaptive-input SHVZK. An attractive feature of our first two compilers is that they allow obtaining efficient delayed-input Sigma protocols with adaptive security, and efficient Fiat-Shamir NIZKs with adaptive soundness (and non-adaptive zero knowledge) in the CRS model. Prior to our work, the latter was only possible using generic NP reductions.
AIS Electronic Library (AISeL)

The study aims to examine factors that are influencing knowledge management success in one of the leading company in oil and gas industry in Saudi Arabia. It equally evaluates and validates a new proposed framework Information System Technology Acceptance Model (ISTAM) by combining two previously validated information system questionnaires. Data were collected from 401 employees of an oil and gas company in Saudi Arabia by adopting simple random sampling technique. Confirmatory factor analysis was used to measure and validate the hypothesized model while Structural Equation Modeling (SEM) and AMOS were used to test the highlighted hypothesis. The major findings which can be deduced from the overall results of the study is that, IS theories’ underline’s construct and components fitted well in overcoming knowledge management success challenges. This result practically states that, IS theories as embedded in the KM model can suitably bring about knowledge management success in corporate organization.
Probabilistic Packet Scheduling: Achieving Proportional Share Bandwidth Allocation

This paper describes and evaluates a probabilistic packet scheduling algorithm for achieving proportional bandwidth allocation among TCP ﬂows. With our approach, either end hosts or edge routers tag each packet with a ticket that represents the share of network bandwidth this ﬂow should receive. Routers then probabilistically decide when to forward/drop a packet based on the value of this ticket and the current congestion level. Our approach accommodates network topologies that span multiple domains by allowing a packet to trade in the tickets it was granted in the source domain for an equitable number of tickets in the target domain. The bandwidth allocation can be controlled by either a sender-based or a receiver-based version of the algorithm.
IMPACT OF FIRE HISTORY ON CARBON STORAGE AND VEGETATION STRUCTURE IN THE SAVANNA ECOSYSTEM OF MOLE NATIONAL PARK IN THE NORTHERN REGION OF GHANA

i DECLARATION . iii ACKNOWLEDGEMENT . vii TABLE OF CONTENT . ix TABLE OF FIGURES AND TABLES . x INTRODUCTION . 1 Research Question . 7 Hypotheses: . 7 METHODOLOGY . 8 Study Area . 8 Site selection and study design . 10 Data Collection . 11 Woody and Herbaceous vegetation . 11 Deadwood and litter sampling . 12 Soil sampling . 12 Aboveground carbon stock estimation . 12 Soil carbon stock estimation . 13 Data Analysis . 13 RESULTS . 14 Effect of fire history on vegetation structure. 14 Effect of fire history on total ecosystem carbon stocks. 15 Soil organic carbon differs at different depths beneath the surface of the soil and decreases with depth across all treatments. 16 Effect of Early and Late dry season fires on ecosystem carbon stocks in the study areas. 17 DISCUSSION . 19 Woody and Herbaceous vegetation biomass in burnt and unburnt sites. 19 Ecosystem carbon storage in burnt and unburnt sites. 20 Soil organic carbon at different depths beneath surface of soil across all fire histories . 21