Miracle-dz/UniFAR-FTUs huggingface dataset:
Suppression of Zeroth-Order Diffraction in Phase-Only Spatial Light Modulator via Destructive Interference with a Correction Beam

We suppress the unwanted zeroth order diffraction (ZOD) contributed by the dead areas of a spatial light modulator with a correction beam that is independently created from the desired target. We use the GerchbergSaxton algorithm to generate the phase of the correction beam profile that would match correctly with that of the ZOD. The correction beam intensity is regulated using a coefficient to match also with that of the ZOD. Numerical simulation reveals a ZOD suppression that is as high as -99% but only -32% has been achieved so
Thinking about people: Contributions of a typological alternative to associationistic and dimensional models of person perception.

The associationistic view of person perception states that people are perceived in terms of trait covariations. The dimensional view maintains that others are perceived by means of a limited number of dimensions. In contrast, the typological view is that others are perceived in terms of person types, and traits within a given person type have a unique interactive relation. In Experiment l, associationistic, dimensional, and typological representations of implicit personality theories were empirically derived. The derived stimuli were used in Experiment 2, which examined the effects of person type membership on impression priming and perceived trait belongingness, controlling for associationistic and dimensional factors. As expected, results validated the unique contribution of a typological approach to person perception. Implications of the present findings for the implicit personality theory, stereotype, and person memory literatures are discussed. People constitute important categories of objects. It is of no surprise, then, that thinking about people is such a pervasive phenomenon. We routinely form impressions of other people's personalities, make predictions about their behavior, wonder about their intentions, and evaluate their performance outcomes. In this article, we are concerned with the issue of how we think about, perceive, or cognitively represent other people.
Tectonic geomorphology at small catchment sizes – extensions of the stream-power approach and the χ method

Abstract. Quantitative tectonic geomorphology hinges on the analysis of longitudinal river profiles. The model behind almost all approaches in this field originates from an empirical relationship between channel slope and catchment size, often substantiated in the form of the stream-power model for fluvial incision. Significant methodological progress was recently achieved by introducing the χ transform. It defines a nonlinear length coordinate in such a way that the inherent curvature of river profiles due to the increase of catchment sizes in the downstream direction is removed from the analysis. However, the limitation to large catchment sizes inherited from the stream-power approach for fluvial incision persists. As a consequence, only a small fraction of all nodes of a digital elevation model (DEM) can be used for the analysis. In this study we present and discuss some empirically derived extensions of the stream power law towards small catchment sizes in order to overcome this limitation. Beyond this, we introduce a simple method for estimating the adjustable parameters in the original χ method as well as in our extended approaches. As a main result, an approach originally suggested for debris flow channels seems to be the best approximation if both large and small catchment sizes are included in the same analysis.
The Influence of Guangdong Province Basic Education on Residents' Consumption Structure —Based on Grey Correlation Analysis Method

In order to know the influence of basic education on residents’ consumption structure, this paper uses grey correlation method to analyze the relationship between based education spending and consumer spending in Guangdong. The author discovered the based education spending is not only the important index of basic education services equalization degree, but also associated with the urban and rural residents’ consumption structure. Balancing urban and rural basic education spending can improve the structure of urban and rural residents’ consumption.
Integrative Application of Biostimulants and Nutrients Improves Vegetative Growth of ‘ Kinnow ’ Mandarin

Vegetative growth is a very vital factor to determine the health of tree. However, vegetative growth is little focused area by researchers. The present study evaluated the effect of foliar applied moringa leaf extract (MLE), ascorbic acid, cytokinins (BAP), potassium (K) and zinc (Zn) on vegetative growth [plant height (cm), plant width (cm), trunk size (cm), leaf area (cm), leaf length (cm), leaf width (cm), flush length (cm), and leaf age (number of days)] of ‘Kinnow’ mandarin trees. Four experiments were conducted during year 2012-2015. In experiment-I, 3% MLE was sprayed at different growth stages i.e., before flowering, at full bloom stage, before flowering + fruit set stage and at premature stage. While in experiment-II, 3% MLE was applied before flowering + fruit set stage and at premature stage, 300 mg L ascorbic acid and 800 mg L BAP at fruit set stage. In experiment-III, 3% MLE before flowering + fruit set stage and pre-mature stage, Zn (0.6%) and K (0.25%) at fruit set stage) and integrative MLE (3%) + K (0.6%) + Zn (0.25%) at fruit set stage were sprayed. Treatments selected for experiment-IV were the best treatments from experiment I, II and III, respectively. The results from experiment-I showed that maximum trunk diameter and leaf age was observed in trees applied with 3% MLE at before flowering + fruit set stage. In experiment-II, highest increase in tree height and trunk diameter was observed in trees treated with BAP, whereas increase in tree width and leaf age was found in trees treated with MLE at before flowering + fruit set stage. In experiment-III and IV highest tree height, tree width, leaf length, leaf area, flush length and leaf age were found in trees sprayed with MLE +K +Zn at fruit set stage, respectively. Conclusively, it can be suggested that foliar application of integrative 3% MLE + 0.25% K + 0.6% Zn at fruit set stage can improve the vegetative growth of ‘Kinnow’ mandarin trees. © 2018 Friends Science Publishers
Integration of prior knowledge into dense image matching for video surveillance

Three-dimensional information from dense image matching is a valuable input for a broad range of vision applications. While reliable approaches exist for dedicated stereo setups they do not easily generalize to more challenging camera configurations. In the context of video surveillance the typically large spatial extent of the region of interest and repetitive structures in the scene render the application of dense image matching a challenging task. In this paper we present an approach that derives strong prior knowledge from a planar approximation of the scene. This information is integrated into a graph-cut based image matching framework that treats the assignment of optimal disparity values as a labelling task. Introducing the planar prior heavily reduces ambiguities together with the search space and increases computational efficiency. The results provide a proof of concept of the proposed approach. It allows the reconstruction of dense point clouds in more general surveillance camera setups with wider stereo baselines.
USBeSafe: Applying One Class SVM for Effective USB Event Anomaly Detection

Abstract : Increased use of transient devices such as wireless keyboards, webcams, and flash storage in the last ten years has drastically increased the surface area on which attackers can target vulnerable systems. USB devices, a subclass of transient devices (TDs), have become a common transport mechanism for malware making its way into a target machine or network. The rogue-TD attack class, demonstrated by BadUSB, relies on updating the device firmware to perform malicious actions and can be undetectable at the end-user level if written effectively, as the attack hides in plain sight. In this thesis, we present USBeSafe as a first-of-its-kind machine learning-based anomaly detection framework for detecting a specific subclass of rogue-TD attack in which a covert keyboard interface is defined on a seemingly benign device. We apply machine learning techniques, specifically one-class support vector machines, to create an offline USB event anomaly detection system that serves as the basis for a live detection system. The USBeSafe system provides an extensible framework for efficient USB traffic feature extraction, model selection and training, and classification.
GIS Based Landslide Hazard Zonation Mapping (LHZM) for Rattota DS Division, Matale District, Sri Lanka

Landslides have become the most significant disaster and frequent phenomenon in Central Highland of Sri Lanka in recent decades. And also, the recent landslides have brought intense impacts to the communities living in landslide prone areas of Rattota, in Matale District. Thus, preparation of Landslide Hazard Zonation Maps (LHZ) has become an essential tool in the landslide risk reduction especially in this area. This study has been mainly focused on producing LHZ map as the beneficial to community as well as the relevant institutions. The LHZ map was prepared for the selected 8 GN Divisions from Rattota DS Division, based on the influence of different causative factors. These causative factors, including geology, landforms, land use, slope gradient, past landslide locations, and drainage density were considered for the generation of LHZ map. The data for the generation of LHZ map were obtained from several sources and then were integrated using GIS to the demonstration of map. GIS based bivariate statistical approach (Weighted Overlay Model) was adopted in this study. The map delineated the unstable hazard-prone areas in the study location. Based on the results from the hazard map, different levels were identified as follows; very high hazard, high hazard, moderate hazard, low hazard, and very low hazard. Most part of the study area was belonged to moderate to high hazard area. Very high hazard areas, and low hazard areas were found at lower level. High hazard areas of landslides widely existed in Bambarakiriella, Madakumbura, and Bodhikotuwa GN Divisions. Subsequently, Horagolla, Mousagolla and Welangahawatta Divisions were also resulted as high hazard with the lowest proportion. Accordingly, these results clearly emphasize that hazard level varied from division to division and some zones in the study area potentially dangerous for further development of habitation. There must be immediate implementation of proper mitigation works in the high hazard zones like Bodhikotuwa Division. Accordingly, effective utilization of this map would considerably reduce the damage potential and also facilitate the planning process in the study area.
Bayesian Variable Selection for Globally Sparse Probabilistic PCA

Sparse versions of principal component analysis (PCA) have imposed themselves as simple, yet powerful ways of selecting relevant features of high-dimensional data in an unsupervised manner. However, when several sparse principal components are computed, the interpretation of the selected variables is difficult since each axis has its own sparsity pattern and has to be interpreted separately. To overcome this drawback, we propose a Bayesian procedure called globally sparse probabilistic PCA (GSPPCA) that allows to obtain several sparse components with the same sparsity pattern. This allows the practitioner to identify the original variables which are relevant to describe the data. To this end, using Roweis' probabilistic interpretation of PCA and a Gaussian prior on the loading matrix, we provide the first exact computation of the marginal likelihood of a Bayesian PCA model. To avoid the drawbacks of discrete model selection, a simple relaxation of this framework is presented. It allows to find a path of models using a variational expectation-maximization algorithm. The exact marginal likelihood is then maximized over this path. This approach is illustrated on real and synthetic data sets. In particular, using unlabeled microarray data, GSPPCA infers much more relevant gene subsets than traditional sparse PCA algorithms.
Forecasting U . S . Business Fixed Investment Spending

In this paper, we use a number of recently developed econometric procedures to analyze the simulated out-of-sample forecasting ability of a host of variables with respect to U.S. business fixed investment spending growth over the 1980s and 1990s. The variables we consider include four “traditional” variables from the empirical literature, as well as five “new” variables recently suggested by Lettau and Ludvigson (2002) that display predictive ability with respect to excess stock returns. Overall, our empirical results indicate that traditional variables contain information useful for forecasting U.S. business fixed investment spending growth over the 1980s, while both traditional and new variables are useful for forecasting U.S. business fixed investment spending growth over the 1990s. JEL classification codes: C22, C53, E22, E27
Pulmonary surfactant and lung transplantation

Pulmonary surfactant lowers the surface tension at the air-water interface inside the alveolus. This is achieved by adsorption of surfactant phospholipids at the air-water interface, a process controlled by surfactant-associated proteins, such as SP-A. In this way, surfactant prevents collapse of the alveolus at end expiration, prevents formation of alveolar edema and increases the compliance of the lung. In chapter 1a an overview is given how the normal function of surfactant can be affected by lung transplantation. Zie: Summary
Top-down RSFQ logic design based on a binary decision diagram

We have proposed a top-down design methodology for RSFQ logic circuits using a binary decision diagram (BDD). The BDD is a way to represent a logical function by a directed graph, which consists of binary switches having one input and two outputs. The important features of the BDD RSFQ logic circuits are a small number of primitives, dual rail and non-clocked logic style, and a small gate count. We have constructed a cell library for the BDD RSFQ logic design, which is composed of five square basic cells. Any logic function can be constructed by simply connecting the library cells. CAD tools for the logic level simulation, the circuit simulation and a layout view extraction have been developed to carry out the top-down RSFQ logic design on the Cadence CAD environment. A design flow of the RSFQ full adder is demonstrated to show the potential of the top-down design methodology for the design of large-scale RSFQ integrated circuits.
Performance Analysis of LDA, AdaBoost and Ensemble Bag Classifiers for Automatically Recognizing Nine Common Facial Expressions

Facial expression recognition holds a prominent role in today’s digital world with more Human –Computer Interaction happening in day to day life. Successful identification of facial expressions needs extraction of descriptive attributes from the active facial patches and accurate classification. This paper is presented with the comparison of three multi class classifiers namely Linear Discriminant Analysis, AdaBoost and Ensemble bag. Nine common facial emotions such as happiness, sad, anger, fear, disgust, surprised, confused, neutral and sleepy are recognized and classified. The feature descriptors are formed by combining Local Binary Patterns and Grey Level Co-occurrence Matrix. Feature descriptor formed from LBP operator supports handling the illumination invariance in the image and GLCM being capable of deriving second order textual information proves to be a good feature descriptor. Twenty-one active facial patches are extracted from the facial land marks eyebrows, iris, nose, sides of nose and lip corners. Feature vectors are generated for these twenty one facial patches, which considerably reduced the dimension feature vectors for classification. Each classifier is then trained using training set which consists of feature vector and corresponding expression of the image used for training. After training testing was done and the accuracy of recognition are analyzed. The experiments were done on facial expression data bases JAFFE and YALE. The proposed method obtained an accuracy of 98.03% for recognizing nine expressions.
THE PROTECTIVE EFFICACY OF VITAMINS ( C AND E ) , SELENIUM AND SILYMARIN SUPPLEMENTS AGAINST ALCOHOL TOXICITY

Correspondence: M.G. Shalan, mohamedshalan@yahoo.com Received May 2006 Accepted November 2006. ABSTRACT: This study aimed at investigating the efficacy of vitamins (C and E), selenium and silymarin (an antioxidant complex from Silybum marianum) supplementation in reducing toxic effects of ethanol on liver weight and some blood parameters. Sixty male rabbits, individually housed in steel cages, were randomly divided into three groups. The first was a control group, the second received balanced diet and daily 20% (v/ v) ethyl alcohol in their drinking water, the third received the same diet and 20% (v/v) ethanol in their drinking water and treated with vitamin C (1 mg/100 g body weight, BW), vitamin E (1 mg/100 g BW), selenium (0.01 mg/ 100 g BW) and silymarin (1 mg/100 g body weight) by gastric tube daily. Five animals per group were slaughtered every two weeks and liver and blood samples were taken after 2, 4, 6 and 8 weeks of treatment. Ethanol decreased body weight of rabbits and induced hepatomegally and apoptotic DNA fragmentation in hepatocytes. Chronic alcohol consumption induced significant increases in serum glucose, triglycerides and cholesterol levels whereas serum total protein content decreased. Significant increases in serum ALT, AST, ALP and LDH activities were observed in ethanol-treated rabbits. The treatment of alcohol-abused animals with vitamins (C and E), selenium and silymarin enhanced significant improvement in the biochemical, physiological and molecular aspects indicating their protective effects against alcohol toxicity.
Strategies of Coping with Deactivation of NH3-SCR Catalysts Due to Biomass Firing

Firing of biomass can lead to rapid deactivation of the vanadia-based NH3-SCR catalyst, which reduces NOx to harmless N2. The deactivation is mostly due to the high potassium content in biomasses, which results in submicron aerosols containing mostly KCl and K2SO4. The main mode of deactivation is neutralization of the catalyst’s acid sites. Four ways of dealing with high potassium contents were identified: (1) potassium removal by adsorption, (2) tail-end placement of the SCR unit, (3) coating SCR monoliths with a protective layer, and (4) intrinsically potassium tolerant catalysts. Addition of alumino silicates, often in the form of coal fly ash, is an industrially proven method of removing K aerosols from flue gases. Tail-end placement of the SCR unit was also reported to result in acceptable catalyst stability; however, flue-gas reheating after the flue gas desulfurization is, at present, unavoidable due to the lack of sulfur and water tolerant low temperature catalysts. Coating the shaped catalysts with thin layers of, e.g., MgO or sepiolite reduces the K uptake by hindering the diffusion of K+ into the catalyst pore system. Intrinsically potassium tolerant catalysts typically contain a high number of acid sites. This can be achieved by, e.g., using zeolites as support, replacing WO3 with heteropoly acids, and by preparing highly loaded, high surface area, very active V2O5/TiO2 catalyst using a special sol-gel method.
Puberty-Blocking Hormonal Therapy for Adolescents with Gender Identity Disorder: A Descriptive Clinical Study

The use of puberty-delaying or blocking hormonal treatment of adolescents with gender identity disorder (GID) has become increasingly common. In the present study, we examined demographic, behavior problem, and psychosexual measures to see if any of them correlated with the clinical decision to recommend, or not recommend, puberty-blocking hormonal therapy in a consecutive series of 109 adolescents (55 females, 54 males) with GID evaluated between 2000 and 2009. Of the 109 adolescents, 66 (60.6%) were recommended for puberty-blocking hormonal therapy and 43 (39.4%) were not. A combination of five (of 15) demographic, behavior problem, and psychosexual measures were identified in a logistic regression analysis to significantly predict this clinical recommendation. The quantitative data were complemented by clinical case descriptions and some follow-up information. We discuss our data in relation to the Dutch model of early biomedical treatment for youth with GID and consider areas that require further clinical and empirical examination.
What Controls ENSO‐Amplitude Diversity in Climate Models?

Climate models depict large diversity in the strength of the El Niño/Southern Oscillation (ENSO) (ENSO amplitude). Here we investigate ENSO‐amplitude diversity in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by means of the linear recharge oscillator model, which reduces ENSO dynamics to a two‐dimensional problem in terms of eastern equatorial Pacific sea surface temperature anomalies (T) and equatorial Pacific upper ocean heat content anomalies (h). We find that a large contribution to ENSO‐amplitude diversity originates from stochastic forcing. Further, significant interactions exist between the stochastic forcing and the growth rates of T and h with competing effects on ENSO amplitude. The joint consideration of stochastic forcing and growth rates explains more than 80% of the ENSO‐amplitude variance within CMIP5. Our results can readily explain the lack of correlation between the Bjerknes Stability index, a measure of the growth rate of T, and ENSO amplitude in a multimodel ensemble.
FAT-Forensics : A Python Toolbox for Fairness , Accountability and Transparency Forensics

Reproducibility in artificial intelligence and machine learning research is a well 1 known issue. One approach to battle it is open source software that allows other 2 researchers to compare and contrast their novel approach against other implemen3 tations without the burden of re-implementing them. In this paper we discuss a 4 fragmented landscape of open source tools in the Fairness, Accountability and 5 Transparency (FAT) research field and propose an open-sourced Python framework, 6 called FAT-Forensics, that aims at providing a common research platform in this 7 area. 8
Common Neural Basis of Motor Sequence Learning and Word Recognition and Its Relation With Individual Differences in Reading Skill

ABSTRACT To investigate the neural basis of a common statistical learning mechanism involved in motor sequence learning and decoding, we recorded brain activation from participants during a serial reaction time (SRT) task and a word reading task using functional magnetic resonance imaging. In the SRT task, a manual response was made depending on the location of a visual cue, and the order of the locations was either fixed or random. In the word reading task, visual words were passively presented. In the inferior frontal gyrus pars triangularis (IFGpTr) and the insula, differences in activation between the ordered and random condition in the SRT task and activation to printed words in the word reading task were correlated with the participants’ decoding ability. We speculate that extraction of statistically predictable patterns in the IFGpTr and insula contributes to both motor sequence learning and orthographic learning, and therefore predicts individual differences in decoding skill.