Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of Crystal Structure Formation in Spin-Coated Colloidal Films

The spin-coating of colloidal suspensions is an inherently nonequilibrium process that gives rise to highly reproducible, but polycrystalline, films with different symmetries depending on experimental parameters. In this study, we explore the transient dynamics of evaporative colloid spin-coating for the first time, via a combination of high-speed imaging, atomic force microscopy, static photography, and scanning electron microscopy. As the wet colloidal film thins and dries, we observe several symmetry transitions, while at the same time remarkably, the thinning rate (in nondimensional time units) collapses to one universal curve for all rotation rates. We correlate static and dynamic measures of crossovers in ordering regimes, and obtain an estimate of the evaporation rate in the late stage of drying. We conclude that the thinning dynamics controls the local volume fraction and stress profiles, which in turn drives the structural transitions.
SIMILE: INTRODUCING SEQUENTIAL INFORMATION

Reinforcement learning (RL) is a metaheuristic aiming at teaching an agent to interact with an environment and maximizing the reward in a complex task. RL algorithms often encounter the difficulty in defining a reward function in a sparse solution space. Imitation learning (IL) deals with this issue by providing a few expert demonstrations, and then either mimicking the expert’s behavior (behavioral cloning, BC) or recovering the reward function by assuming the optimality of the expert (inverse reinforcement learning, IRL). Conventional IL approaches formulate the agent policy by mapping one single state to a distribution over actions, which did not consider sequential information. This strategy can be less accurate especially in IL, a weakly supervised learning environment, especially when the number of expert demonstrations is limited. This paper presents an effective approach named Sequential IMItation LEarning (SIMILE). The core idea is to introduce sequential information, so that an agent can refer to both the current state and past state-action pairs to make a decision. We formulate our approach into a recurrent model, and instantiate it using LSTM so as to fuse both long-term and short-term information. SIMILE is a generalized IL framework which is easily applied to BL and IRL, two major types of IL algorithms. Experiments are performed on several robot controlling tasks in OpenAI Gym. SIMILE not only achieves performance gain over the baseline approaches, but also enjoys the benefit of faster convergence and better stability of testing performance. These advantages verify a higher learning efficiency of SIMILE, and implies its potential applications in real-world scenarios, i.e., when the agent-environment interaction is more difficult and/or expensive.
ASSESSMENT OF MULTIFUNCTIONAL AGRICULTURE: APPLICATION OF SELECTED MULTI-CRITERIA METHODS IN CASE OF SLOVENIA

PazeK, K. , C. Rozman, D. majKoviC, j. TuRK, m. KljajiC, a. SKRaba, a. boReC, P. SimoniC and j. PRiSenK, 2014. assessment of multifunctional agriculture: application of selected multi-criteria methods in case of Slovenia. Bulg. J. Agric. Sci., 20: 1008-1017 The aim of this paper is to employ the use of multi-criteria analysis in quantification of the multifunctional agriculture (mFa). For this purpose, two methodological approaches are used and compared: analytical-Hierarchical Process (aHP) for group decision making and multi-attribute utility theory (mauT), under the assumption that mFa in Slovenia exist, while its level vary through regions/farms. The results showed that organic farms yielded with the highest final priority assessment with respect to the concept of multi-functionality.
Financial Market Effects on Aggregate Money Demand: A Bayesian Analysis

This paper evaluates the extent to which aggregate data yield robust inferences about the linkages between financial market effects and aggregate M1 and M2 holdings. In particular, the authors investigate whether stock market returns, stock market volume, and a new brokerage costs measure affect money demand. They employ recent advances in Bayesian econometric methods to control for model specification uncertainty, and provide a complete sensitivity analysis of their results to alternative prior distributions. The impact of nonstationarity on inferences is briefly explored with an error correction model. Copyright 1989 by Ohio State University Press.
Reorganization of graphite surfaces into carbon micro- and nanoparticles under high flux hydrogen plasma bombardment

Fine-grain graphite samples were exposed to high density low temperature (ne∼1020 m−3, Te∼1 eV) hydrogen plasmas in the Pilot-PSI linear plasma generator. Redeposition of eroded carbon is so strong that no external precursor gas injection is necessary for deposits to form on the exposed surface during the bombardment. In fact, up to 90% of carbon is redeposited, most noticeably in the region of the highest particle flux. The redeposits appear in the form of carbon microparticles of various sizes and structures. Discharge parameters influence the efficiency of the redeposition processes and the particle growth rate. Under favorable conditions, the growth rate reaches 0.15 μm/s. The authors used high resolution scanning electron microscopy and transmission electron microscopy to study the particle growth mode. The columnar structure of some of the large particles points toward surface growth, while observation of the spherical carbon nanoparticles indicates growth in the plasma phase. Multiple nanoparticles.
Identifying Negative Sentiment with Sentiment Based LDA and Support Vector Machine Classification

With the increasing development of online collaborative platforms, there emerge massive subjective texts. However, due to the massive negative news about eroticism, violence, extremity and corruption, as well as the influences of agitators and provocateurs, it is quite likely that Internet users can be turned from conscious individuals into unconscious groups, which contributes to the accumulation of public negative sentiment. In this work, we focus on the identification of sentiment and especially negative sentiment. Specifically, we introduce sentiment layer to the basic LDA topic model to map the texts into a lower dimensional space of topics and sentiment. Besides, we also consider the sentiment dictionary based sentiment feature word extraction method. By feeding the feature words into Support Vector Machine (SVM) classifier, we get the sentiment tendency of texts. Our experiments prove the efficiency of proposed method.
A model for evaluating the effect of fatigue crack repair by the infiltration method

Infiltration of foreign materials into a fatigue crack has previously been shown to be able to retard the crack and extend fatigue life. Most of the related studies were empirical and phenomenological in nature. To aid engineering decisions, it would be advantageous if the possible outcome of a repair can be evaluated beforehand. To this end, a crack closure model taking into account the additional closure effect of the infiltrant has been developed and verified against experimental results. With this model, the sensitivity of the repair effect to various parameters such as mechanical properties of the infiltrant, depth of penetration and infiltration load level can be assessed.
Optimizing Java Code for Mobile Computing: The Android Example

Java is one of the most popular programming languages and it is being used in a comprehensive manner across multiple systems due to its versatility. Being a versatile language, it allows more potential for code optimizations. As embedded and mobile computing devices are limited in terms of computational resources, it is important to optimize applications to be executed on these devices. There are several optimizations of Java code that can be manually applied by developers, but their automatic application is seen as an important task for supporting developers. In this work we consider the code optimizations regarding code motion of certain operations in loop conditions, the substitution of the use of the class String by the class StringBuffer and the initialization of objects. We developed a prototype compiler, based on the open source tool Spoon, which analyzes and transforms Java code. The compiler is able to analyze the code and to find possible code locations where the optimizations can be applied. The compiler also supports the use of directives by developers to instruct the application of those optimizations. The compiler was tested using a number of Java applications and the results show some performance gains.
Memory-Based Judgments About Multiple Categories: A Revision and Extension of Tajfel's Accentuation Theory

Accentuation theory states that the classification of stimuli produces encoding biases. Contrast effects enhance intercategory differences; assimilation effects enhance intracategory similarities. Do these biases affect the retrieval of stimuli distributed across many categories? The calendar superimposes arbitrary intermonth boundaries on day-to-day variations in temperature. In Experiment 1, Ss estimated the average temperatures of 48 days. Differences between estimates for 2 days belonging to neighboring months were greater (contrast) and differences between estimates for 2 days belonging to the same month were smaller than actual differences (assimilation). Experiment 2 showed that assimilation accounted for all categorization effects. When modified by assumptions from an exemplar model of category learning, accentuation theory accounts for the results. The relevance of these findings for social categorization is discussed. Merely placing objects into distinct categories affects judgments about those objects. Objects belonging to the same class are more likely to be confused with each other (Stangor, Lynch, Duan, & Glass, 1992; Taylor, Fiske, Etcoff, & Ruderman, 1978) and to be recalled as a cluster (MacNamara, Hardy, & Hirtle, 1989). Often, only the category label is recalled (Park & Rothbart, 1982). People over- or underestimate spatial distances between stimuli depending on whether a category boundary falls between them (Newcombe & Liben, 1982), and they underestimate the degree to which the stimulus distributions of different categories overlap (Krueger & Rothbart, 1990). Categorization effects are intriguing because the act of categorization does not necessarily add information to the stimulus itself, but places a stimulus in a context. Categorization determines which stimuli belong to the same class and which do not. In this study, we examine predictions derived from accentuation theory (Eiser & Stroebe, 1972;Tajfel, 1959,1969). According to this theory, people's perceptions minimize the differences between stimuli falling into the same category (assimilation effect) and maximize the differences between stimuli falling into different categories (contrast effect). The standard design involves the presentation of stimuli that are graded along a quantitative dimension and divided by a categorical boundary falling in the center of that dimension (Tajfel & Wilkes, 1963). That is, accentuation effects have been demonstrated as encoding biases in cases of dichotomous categorization. We first review theory and research on accentuation. Then,
Overview of toroidal momentum transport

Toroidal momentum transport mechanisms are reviewed and put in a broader perspective. The generation of a finite momentum flux is closely related to the breaking of symmetry (parity) along the field. The symmetry argument allows for the systematic identification of possible transport mechanisms. Those that appear to lowest order in the normalized Larmor radius (the diagonal part, Coriolis pinch, E × B shearing, particle flux, and up–down asymmetric equilibria) are reasonably well understood. At higher order, expected to be of importance in the plasma edge, the theory is still under development.
Assessing Performance of Bengali Speech Recognizers Under Real World Conditions using GMM-HMM and DNN based Methods.

Real world Automatic Speech Recognition (ASR) system development requires rigorous performance review under varying real world conditions. This paper reports our effort on ASR resource creation, transcription, system building and performance assessment for connected and continuous word applications in Bengali language (ranked seventh worldwide) using GMM-HMM and DNN framework on available open source toolkits. Baseline models are built from merging Bengali dataset hosted on government sites with application specific 100 hours indigenous audio collected under target deployment scenario. After feedback analysis of live systems by real users, novel Error Handling Techniques like Signal Analysis and Decision, Confidence based ASR output Polling and Runtime LM are implemented which results around 2%- 11% overall gain in WER with encouraging task success rates in final field trials. Results suggest that recent approaches along with application, environment, target user and runtime resource specific appropriate strategies will yield better acceptability of live ASR systems in India.
Discriminating between (in)valid External Instruments and (in)valid Exclusion Restrictions

Abstract In models estimated by (generalized) method of moments a test on coefficient restrictions can either be based on a Wald statistic or on the difference between evaluated criterion functions. From their correspondence it easily follows that the statistic used for testing instrument validity, the Sargan-Hansen overidentifying restrictions (OR) statistic, is equivalent to an exclusion restrictions test statistic for a nonunique group of regressor variables. We prove that asymptotically this is the case too for incremental OR tests. However, we also demonstrate that, despite this equivalence of test statistics, one can nevertheless distinguish between either the (in)validity of some additional instruments or the (un)tenability of particular exclusion restrictions. This, however, requires to be explicit about the adopted maintained hypothesis. It also highlights that recent warnings in the literature that overidentifying restrictions tests may mislead practitioners should not be directed towards the test, but to practitioners who do not realize that inference based on such tests is unavoidably conditional on the validity of particular just-identifying statistically untestable assumptions.
EVALUATION OF SOUND CLASSIFICATION USING MODIFIED CLASSIFIER AND SPEECH ENHANCEMENT USING ICA ALGORITHM FOR HEARING AID APPLICATION

Hearing aid users are exposed to diversified vocal scenarios. The necessity for sound classification algorithms becomes a vital factor to yield good listening experience. In this work, an approach is proposed to improve the speech quality in the hearing aids based on Independent Component Analysis (ICA) algorithm with modified speech signal classification methods. The proposed algorithm has better results on speech intelligibility than other existing algorithm and this result has been proved by the intelligibility experiments. The ICA algorithm and modified Bayesian with Adaptive Neural Fuzzy Interference System (ANFIS) is to effectiveness of the strategies of speech quality, thus this classification increases noise resistance of the new speech processing algorithm that proposed in this present work. This proposed work indicates that the new Modified classifier can be feasible in hearing aid applications.
A Parallel Boundary Element Formulation for TrackingMultiple Particle Trajectories in Stoke’s Flow forMicrofluidic Applications

A new formulation for tracking multiple particles in slow viscous flow for microfluidic applications is presented. The method employs the manipulation of the boundary element matrices so that finally a system of equations is obtained relating the rigid body velocities of the particle to the forces applied on the particle. The formulation is specially designed for particle trajectory tracking and involves successive matrix multiplications for which SMP (Symmetric multiprocessing) parallelisation is applied. It is observed that present formulation offers an efficient numerical model to be used for particle tracking and can easily be extended for multiphysics simulations in which several physics involved.
Staff experiences of the management of older adults with urinary incontinence

Background: Urinary incontinence is a complex public health problem for older adults both in Sweden and internationally. It is estimated that 50–80% of older adults in residential care facilities have problems with urinary incontinence. Several studies illuminate an attitude among residential care staff that incontinence is as a natural consequence of aging, which means that assessment and treatment are overlooked. There is also a lack of knowledge and compliance as to whether or not care staff follows guidelines appropriately. Therefore, the aim of the present study was to describe staff experiences of the management of older adults with urinary incontinence in residential care facility. Methods: This explorative study took an inductive approach using 17 individual interviews with residential care staff. Data were analyzed using qualitative content analysis. Results: Staff members’ experiences fell into three categories: staff members’ management, the impact of the residential organization, and the creation of well‐being factors for older adults with urinary incontinence. Members of staff had different views about what an investigation is and what measures are needed to manage patients effectively. To achieve a good individual patient care there is need of a greater knowledge about good nursing, communication and dialogue between the various working groups. Conclusions: Staffs’ management, the organization's impact, and creating wellbeing factors are central to older adults’ influence and to experience quality of life. Implementing evidence‐based practice requires a long‐term process‐focused approach to improve the structure of daily work and to encourage staffs’ learning and competence development.
A comparison of proton ranges in complex media using GATE/Geant4, MCNP6 and FLUKA

The Monte Carlo (MC) simulation method is a powerful tool for radiation physicists, and several general-purpose software packages are commonly applied in a myriad of different radiation physics fields today. In medical physics, charged particle detectors for proton Computed Tomography are under development, a modality introduced in order to increase the accuracy of proton radiation therapy. MC simulations are helpful during the development and optimization phase of such detector systems. In order to justify the usage of MC for such purposes, the simulation output must be validated against experimental or theoretical data, or even cross-checked between different MC software packages. In this study, we compare three general-purpose MC software packages (GATE/Geant4, MCNP6 and FLUKA) with respect to how they predict the spatial distribution of the stopping position of protons. They are compared to each other and to semi-empirical data, using the mean proton range, the longitudinal and lateral variation of individual proton ranges, and the fraction of primary protons lost to nuclear interactions. This comparison is performed in two homogeneous materials and in a detector geometry designed for proton Computed Tomography. The three MC software packages agree well, and sufficiently reproduce the semi-empirical data. Some discrepancies are observed, such as less lateral beam spreading in GATE/Geant4, and a small deficiency in the MCNP6 proton range in water: This is consistent with previously published data. Due to the general agreement, the choice of simulation framework may be made on personal preferences. It is important to note that the choice of physics packages, simulation parameter settings and material definitions are important aspects when performing MC simulations, both during the preparation, execution and interpretation of the simulation results.
Improved Edge Based Steganography Scheme for GrayScale Images in Spatial Domain

: The steganography algorithms are most popular in spatial domain. however in most existing algorithms the information used to embed in any region in spatial domain without considering any relationships of the image content . There for some structural asymmetry exists in those images which have smooth regions like sky, even when the size of information is small. There for this leads poor visual quality and low security based on some analysis and extensive experiments. The improved edge based steganography scheme embeds the secret information in the edge regions of the images without disturbing the smooth region. Generally the regions located at the shaper edges present more complicated statistical features and are highly dependent on image contents. So it is more difficult to observe changes at sharper edges than those in smooth regions. When embedding rate increases remaining edge regions can be released for information hiding by changing some parameters. So that it will achieve more embedding capacity also enhances the security as compared to existing approaches such as typical least significant bit based approaches and PVD based methods. The new scheme also improves the visual quality of stego images.
Coupled Ocean–Atmosphere Interactions between the Madden–Julian Oscillation and Synoptic-Scale Variability over the Warm Pool

Abstract A principal component analysis of the combined fields of sea surface temperature (SST) and surface zonal and meridional wind reveals that the dominant mode of intraseasonal (30 to 70 day) covariability during northern winter in the tropical Eastern Hemisphere is that of the Madden–Julian oscillation (MJO). Regression calculations show that the submonthly (30-day high-pass filtered) surface wind variability is significantly modulated during the MJO. Regions of increased (decreased) submonthly surface wind variability propagate eastward, approximately in phase with the intraseasonal surface westerly (easterly) anomalies of the MJO. Because of the dependence of the surface latent heat flux on the magnitude of the total wind speed, this systematic modulation of the submonthly surface wind variability produces a significant component in the intraseasonal latent heat flux anomalies, which partially cancels the latent heat flux anomalies due to the slowly varying intraseasonal wind anomalies, particular.
An Efficient Framework for Spam Mail Detection in Attachments using NLP

Now a day’s mail becomes a vital medium for communication as it offers a lot of advantage over SMS service. But spams also known as junks are immerged as hurdle for the email service. Spam attracting more attention as internet fields are widely affected due to such unwanted spams. Recently conducted study shows that 70% of today’s business mails are spams. Therefore many serious problems such as wasting storage space, growing volume of spams etc. are immerged. The main motto behind such spams is to damage financially to the company and individual. Hence number of approaches had been proposed to protect systems from such unwanted spams, and filtering is one of them. Proposed approach is working on the Gmail host id’s where it identifies the spam Emails by detecting signature of the Email with the support of the strong NLP protocols.