Miracle-dz/UniFAR-FTUs huggingface dataset:
GLOBAL BEAUTY INDUSTRY TRENDS IN THE 21 st CENTURY

This paper provides an overview of the global beauty care products industry at the beginning of the XXI century, tracking product categories, main geographic regions and mass/premium cosmetics segments. The study presents annual trend analysis that highlights market size, industry dynamics, consumers attitudes and new ways of engaging with beauty shoppers. Worldwide market for cosmetics and toiletries products was severely affected by economic downturn, but with the global economy recovering, this market is projected to witness a significant growth. Rising consumer income and changing lifestyles drive the global beauty market. This growth is driven mostly by richer, ageing baby-boomers and increased discretionary income in the West, and by the growing middle classes in developing countries (China, Brazil) which are turning into huge, emerging Markets. These emerging markets, still in its embryonic stage, offer great potential for value growth thanks to rising disposable incomes and growing consumer demand for better products. The cosmetic and toiletries producers face strong challenges and obstacles in their expansion in these emerging markets, which hold strong growth potential in near future. One of the major reasons behind the considerable growth of global beauty industry is the off-shoring of production units to cost effective Asian economies. Emergence of new distribution channels like online retailing is driving the growth of this market. Increasing consumer preferences towards the use of natural cosmetic products is forcing the cosmetic and toiletries manufacturers to change strategies according to consumer preferences.
User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models

With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy concerns arise as people start to widely adopt these applications. Users will need to maintain policies to determine under which circumstances to share their location. Specifying these policies however, is a cumbersome task, suggesting that machine learning might be helpful. In this paper, we present a user-controllable method for learning location sharing policies. We use a classifier based on multivariate Gaussian mixtures that is suitably modified so as to restrict the evolution of the underlying policy to favor incremental and therefore human-understandable changes as new data arrives. We evaluate the model on real location-sharing policies collected from a live location-sharing social network, and we show that our method can learn policies in a user-controllable setting that are just as accurate as policies that do not evolve incrementally. Additionally, we highlight the strength of the generative modeling approach we take, by showing how our model easily extends to the semi-supervised setting.
Simulation 1 : Festinger and Carlsmith ( 1959 ) Counter-attitudinal Advocacy

This paper presents a recurrent neural network model of long term attitude change resulting from the reduction of cognitive dissonance. The model uses Contrastive Hebbian Learning (CHL) to capture changes in weight strength among cognitions resulting from dissonance reduction. Several authors have presented recurrent network models of dissonance reduction that capture the constraint satisfaction nature of dissonance. But as these models did not learn they could only model short-term attitude change represented by changes in activatio. In response, Van Overwalle and Jordens (2002) presented a feedforward model, with delta rule learning, in an attempt to capture long-term attitude change caused by dissonance reduction. However, the feedforward nature of their model created two problems. First, it could not capture the parallel constraint satisfaction mechanisms that underlie dissonance reduction. Second, and perhaps more important, the network was not able to “reason” backwards from its inconsistent behavior to its new attitude, but instead had to be explicitly taught its new attitude. The present model overcomes the weaknesses of the previous approaches to modeling dissonance reduction. Because it has learning it can represent long-term attitude change by weight change and because it is a recurrent model it can propagate changes from inconsistent behavior to the attitudes linked to that behavior.
Behavioral modeling of microelectromechanical filters

The current system on chip (SOC) designs containing microelectromechanical systems (MEMS) require the combination of the analog and digital signal processing circuitry besides the non-electrical components and to verify the whole system performance, the MEMS devices should be integrated in a mixed-domain simulator, for this reason appropriate system level modeling and simulation techniques are required. In this paper we present the application of lumped element equivalent networks and reduced order modeling (ROM) in behavioral modeling of band-pass filters based on MEMS resonators.
The ForwardDiffSig Scheme for Multicast Authentication

This paper describes ForwardDiffSig, an efficient scheme for multicast authentication with forward security. This scheme provides source authentication, data integrity, and non-repudiation since it is based on the use of asymmetric cryptography. At the same time, it offers also protection against key exposure as it exploits OptiSum, our optimized implementation of the ISum forward-secure signature scheme. A tradeoff exists in the used keys: Short keys provide speed at the signer, whereas long keys are preferable for long-term non-repudiation. Performance has been evaluated with a custom packet simulator and shows that, by grouping the packets, ForwardDiffSig is efficient in terms of speed even for long keys at the price of a significant signature overhead. Therefore, ForwardDiffSig is fast, exhibits low delay, and provides non-repudiation and protection against key exposure, but has a nonnegligible impact in applications with strict energy or bandwidth constraints.
β1-Integrin Signaling is Essential for Lens Fiber Survival

Integrins have been proposed to play a major role in lens morphogenesis. To determine the role of β1-integrin and its down-stream signaling partner, integrin linked kinase (ILK), in lens morphogenesis, eyes of WT mice and mice with a nestin-linked conditional knockout of β1-integrin or ILK were analyzed for defects in lens development. Mice, lacking the genes encoding the β1-integrin subunit (Itgb1) or ILK (Ilk), showed a perinatal degeneration of the lens. Early signs of lens degeneration included vacuolization, random distribution of lens cell nuclei, disrupted fiber morphology and attenuation and separation of the lens capsule. The phenotype became progressively more severe during the first postnatal week eventually leading to the complete loss of the lens. A more severe phenotype was observed in ILK mutants at similar stages. Eyes from embryonic day 13 β1-integrin-mutant embryos showed no obvious signs of lens degeneration, indicating that mutant lens develops normally until peri-recombination. Our findings suggest that β1-integrins and ILK cooperate to control lens cell survival and link lens fibers to the surrounding extracellular matrix. The assembly and integrity of the lens capsule also appears to be reliant on integrin signaling within lens fibers. Extrapolation of these results indicates a novel role of integrins in lens cell-cell adhesions as well as a potential role in the pathogenesis of congenital cataracts.
Microreactor Research and Development at Louisiana Tech University: Fabrication of Silicon Microchannel Reactors for Catalyst Studies on Conversion of Cyclohexene as a Prototype and Syngas to Alkanes

Design and fabrication of microreactors and catalyst development of two reactions are described. A prototype reaction - gas phase conversion of cyclohexene to cyclohexane and benzene - has been studied extensively. The conversion is significantly improved (>90%) using silica sol-gel impregnated catalyst. An industrially relevant reaction on syngas conversion (CO+H 2 ) to alkanes using mixed metal catalysts encapsulated in alumina or silica sol-gel has also been invetigated. A high conversion of CO (∼73 %) in the presence of a catalyst promoter with high selectivity to propane (∼80%) has been achieved. Characterization of the catalysts, design of experiments on syngas conversion, and development of a parallel array microreactor system for fast and effective screening of catalysts are discussed in this chapter.
Making Sense of the Past—Perspectives on Resilience Among Holocaust Survivors

The concept of “survivor guilt” has come into increasingly widespread use over the past four decades. However, there has been almost no research on the resolution of moral dilemmas following exposure to severe trauma. The present study employed qualitative analyses of five testimonies of holocaust survivors to explore moral dilemmas and their resolution. In all five testimonies, survivors discussed a discrepancy between their behaviors prior to the war and after the war in contrast to their behaviors during the war. Remaining human in the face of dehumanizing experiences was described as a major struggle in all five testimonies. Ways of resolving this discrepancy varied, with some survivors willingly acknowledging its presence and others denying it. In all cases reviewed, however, the discrepancy continued to exist even five decades after the Holocaust.
An Efficient Single and Double-Adjacent Error Correcting Parallel Decoder for the (24,12) Extended Golay Code

Memories that operate in harsh environments, like for example space, suffer a significant number of errors. The error correction codes (ECCs) are routinely used to ensure that those errors do not cause data corruption. However, ECCs introduce overheads both in terms of memory bits and decoding time that limit speed. In particular, this is an issue for applications that require strong error correction capabilities. A number of recent works have proposed advanced ECCs, such as orthogonal Latin squares or difference set codes that can be decoded with relatively low delay. The price paid for the low decoding time is that in most cases, the codes are not optimal in terms of memory overhead and require more parity check bits. On the other hand, codes like the (24,12) Golay code that minimize the number of parity check bits have a more complex decoding. A compromise solution has been recently explored for Bose-Chaudhuri-Hocquenghem codes. The idea is to implement a fast parallel decoder to correct the most common error patterns (single and double adjacent) and use a slower serial decoder for the rest of the patterns. In this brief, it is shown that the same scheme can be efficiently implemented for the (24,12) Golay code. In this case, the properties of the Golay code can be exploited to implement a parallel decoder that corrects single- and double-adjacent errors that is faster and simpler than a single-error correction decoder. The evaluation results using a 65-nm library show significant reductions in area, power, and delay compared with the traditional decoder that can correct single and double-adjacent errors. In addition, the proposed decoder is also able to correct some triple-adjacent errors, thus covering the most common error patterns.
The Role of Metaphor in the Structuring of Emotion Concepts

Abstract Conceptual metaphor theory (CMT) is one of the most prolific frameworks in the study of emotion concepts. Following the seminal work of Lakoff and Johnson (1980) and subsequent work by Kövecses (1986, 1990) and Kövecses and Lakoff (1987), an impressive number of studies in cognitive linguistics and psycholinguistics have sought to document and confirm the claim that conceptual metaphor (CM) structures affective concepts. I attempt a brief overview of CMT claims about and CMT-inspired research on emotion concepts. I continue by presenting a study based on data collected in six languages, to assess the role of CM in the structuring of emotion concepts. I introduce the procedure, the corpus, and the analyses that have been carried out, including a detailed discussion of the considerations that informed the coding decisions applied to the corpus in a tentative quantitative analysis. Finally, I highlight a series of difficulties and controversies raised by CMT-driven analysis of emotion concepts that could be employed in hypothesis-driven experiments to test conceptual processing claims made within CMT.
Prolyl endopeptidase is involved in the degradation of neural cell adhesion molecules in vitro

ABSTRACT Membrane-associated glycoprotein neural cell adhesion molecule (NCAM) and its polysialylated form (PSA-NCAM) play an important role in brain plasticity by regulating cell–cell interactions. Here, we demonstrate that the cytosolic serine protease prolyl endopeptidase (PREP) is able to regulate NCAM and PSA-NCAM. Using a SH-SY5Y neuroblastoma cell line with stable overexpression of PREP, we found a remarkable loss of PSA-NCAM, reduced levels of NCAM180 and NCAM140 protein species, and a significant increase in the NCAM immunoreactive band migrating at an apparent molecular weight of 120 kDa in PREP-overexpressing cells. Moreover, increased levels of NCAM fragments were found in the concentrated medium derived from PREP-overexpressing cells. PREP overexpression selectively induced an activation of matrix metalloproteinase-9 (MMP-9), which could be involved in the observed degradation of NCAM, as MMP-9 neutralization reduced the levels of NCAM fragments in cell culture medium. We propose that increased PREP levels promote epidermal growth factor receptor (EGFR) signaling, which in turn activates MMP-9. In conclusion, our findings provide evidence for newly-discovered roles for PREP in mechanisms regulating cellular plasticity through NCAM and PSA-NCAM. Summary: NCAM and its polysialylated form (PSA-NCAM) are important regulators of brain plasticity. We demonstrate that prolyl endopeptidase (PREP) is involved in the regulation of NCAM and PSA-NCAM.
A contribution to the method of automatic identification of human emotions by using semantic structures

The study of human emotions has been significantly important in recent years, mainly due to its incidence in human behavior. Additionally, having semantic tools that infer emotions from multisensory sources is a crucial aspect, especially because the feelings or actions of a person might be identified through these semantic tools. In the present research, a methodology that uses semantic structures is proposed in order to identify complex emotions on the basis of simple emotions. For this purpose, the SHEO ontology was used. This ontology is designed to conceptualize simple emotions, combine them, and work with axioms and rules that infer complex emotions. SHEO takes simple emotions as instances. These emotions can be identified using computer algorithms. This is demonstrated in the testing phase in which the authors of this research designed the software called DetectionEmotion, which is used to identify simple emotions in video and text. The result of the authors' proposal proved the easiness to infer complex emotions by using SHEO. SHEO is not a final solution in this research, but rather a contribution to the semantic management of emotions.
Finding abnormal events in home sensor network environment using correlation graph

Anomaly detection in sensor network seems a challenge when encountering the limitation of the energy requirement and dynamics environments. It is to rapidly analyze and identify the abnormal events among the extreme volume data. Using correlation graph representation to correlate the events generated by sensor networks is capable to find the intentional dependency behavior's insight for detecting home sensor network abnormal events. In this study, we proposed an anomaly detection mechanism based on correlation graphs of sensor networks for rapidly identifying abnormal home events. The proposed mechanism which makes the following contributions: (a) it is automatically identify the abnormal event under home sensor network environment (b) it eliminates irrelevant events for saving the computation power (c) it is easily to apply on different machine learning classifiers for enhancement. The evaluation from Intel Berkeley Research lab sensor network data set. The proposed mechanism performs well in sensor events elimination and abnormal event detection.
Waltz: an exploratory visualization tool for volume data using multiform abstract displays

Although, visualization is now widely used, misinterpretations still occur. There are three primary solutions intended to aid a user interpret data correctly. These are: displaying the data in different forms (Multiform visualization); simplifying (or abstracting) the structure of the viewed information; and linking objects and views together (allowing corresponding objects to be jointly manipulated and interrogated). These well-known visualization techniques, provide an emphasis towards the visualization display. We believe however that current visualization systems do not effectively utilise the display, for example, often placing it at the end of a long visualization process. Our visualization system, based on an adapted visualization model, allows a display method to be used throughout the visualization process, in which the user operates a 'Display (correlate) and Refine' visualization cycle. This display integration provides a useful exploration environment, where objects and views may be directly manipulated; a set of 'portions of interest' can be selected to generate a specialized dataset. This may subsequently be further displayed, manipulated and filtered.
Text detection in color scene images based on unsupervised clustering of multi-channel wavelet features

Texts in natural scenes provide us with much useful information. In order to use such information automatically, it is necessary to make computers detect text regions in the images. Gllavata et. al. proposed a method based on unsupervised classification of high frequency wavelet coefficients for text detection in video frames [Gllavata et. al. (2004)]. Although the method is very accurate, it does not work so well with some color images, since it lacks the ability of discriminating color difference. This paper proposes an enhanced version of the method. We develop a new unsupervised clustering technique for the classification of multi-channel wavelet features to deal with color images. Experimental results show that the new method yields better results for color scene images.
Distribution of immunity to pertussis in the population of England and Wales

SUMMARY This paper presents three different methods of estimating the number and age distribution of individuals susceptible to pertussis in England and Wales. The first approach is an extrapolation from data in the prevaccine era, the second is based upon theoretical consideration of the transmission dynamics of pertussis, and the third is a detailed cohort analysis of available notification and vaccination data. Each of these analyses suggests that the total number of people susceptible to pertussis infection in England and Wales has remained at 3–4 million for the past 40 years, despite the increase and changes in numbers vaccinated. The effect of vaccination has been to reduce the incidence of infection and disease, but not to reduce the number of susceptibles. These findings, which are consistent with ‘mass action theory’, could be tested by an appropriately designed seroepidemiological survey.
Memory and the theory of signal detection.

A recurrent problem in the study of recognition memory has been that of combining hits and false alarms (or correct and incorrect responses) into a single index of performance. That the proportion of correct responses in a yes/no or multiple-choice recognition test may in some way be contaminated by factors other than the state of the memory system has never been seriously disputed, but only recently has the question received detailed and systematic attention. Traditionally the problem has been viewed in terms of "correcting for chance success"; in current terminology, and stated more generally, it is a problem of providing an adequate theory of the decision system which maps a given state of the memory system into an overt response. The reasons for this increased attention to decision processes in memory are not difficult to trace. In the first place, the development of quantitative theories of recognition memory has necessitated a precise and explicit account of how the memory and decision systems interact to produce a given response. If a theory of memory is to be tested against data from a recognition-me mory experiment, it is necessary to obtain performance measures that are independent of those parameters
of very short term frequency management strategies for integration of high penetrations of non-synchronous utility-scale PV in electricity markets

The increasing penetration of large-scale variable renewable energy (VRE) power plants creates new frequency stability challenges for grid operation. Balancing in electricity systems has traditionally involved matching dispatchable, highly reliable, generation to variable and uncertain loads. The increasing penetration of variable and somewhat unpredictable renewable energy increases the balancing challenge. In particular, for Photovoltaic (PV) plants, changes in very short term weather conditions such as cloud movement can result in an abrupt change in power output. In traditional electricity systems, balancing over very short timeframes has been assisted by the inertia in synchronous generators which are directly coupled to the grid, store considerable energy through their mass and high speed of rotation, and therefore inherently and instantaneously resist frequency deviations. However, some key VRE generation technologies have non-synchronous grid connections through power electronics. As such, they do not provide inherent inertia to resist immediate frequency deviations as supply demand balance changes over time. However, there are opportunities for these renewables to provide very rapid frequency response actions under some circumstances. At present, limits on the total percentage of non-synchronous generation capacity have been introduced or are being considered in a number of electricity industries with high variable renewable generation to ensure sufficient system inertia. In restructured, market oriented, industries, there are opportunities to establish suitable ancillary service arrangements that provide commercial incentives for market participants to provide inertia. Adding to the complexities yet opportunities for market based frequency management, very fast response power injection or removal from renewables and other suitable technologies, sometimes termed synthetic inertia or fast frequency response (FFR), can reduce, or perhaps even replace, the need for inertia. Suitable market arrangements might see highly innovative solutions towards short-term frequency management. This paper presents a review of the challenges that utility PV may raise for short-term frequency management, and opportunities that it may offer towards solving these challenges. It considers research to date investigating the temporal and locational characteristics of utility PV generation over very short time frames, their implications for fast frequency management, and areas requiring further investigation. It then considers work exploring opportunities for utility PV plants to provide very fast frequency response, as well as other possible technology options. Finally, it considers existing and possible future regulatory and market approaches to managing short term frequency management with high penetrations of variable renewable generation.