Miracle-dz/UniFAR-FTUs huggingface dataset:
Bubble formation with a high repetition rate pulsed Tm laser

Thulium lasers are commonly used in medical applications for tissue ablation due to the strong absorption of water in the midinfrared spectrum. Using high-speed photography, we present an experimental study of bubble formation at the tip of a fiber optic that delivers short pulses of Tm laser at high repetition rates. While a hemispherical bubble forms at the fiber tip at all laser settings, if the power is sufficiently high, a secondary bubble forms during the collapse of the first bubble. The secondary bubble expands at the tip of the initial bubble, moves away from it, and collapses with a jet pointing away from the fiber. A mathematical model is provided to estimate when secondary bubble formation can occur by calculating the maximum bubble radius as a function of the laser power. Finally, the relative translational motion of the bubbles is explained by considering the interaction between two bubbles that oscillate out-of-phase.
Towards a formalisation of Action Identification Hierarchies

Our goal is to develop a formal knowledge representation (KR) language oriented to capturing in a natural way the subjective view people have of their behaviour. We draw together Action Identification Theory (AIT) from social psychology and research on cognitive agent programming languages to derive a set of meta-level requirements that the KR language should satisfy. We show that the existing approaches must be extended to suit our purposes, and we propose a general solution: a novel KR language able to express Action Identification Hierarchies (AIHs). Then, we use AIT to give a preliminary discussion of a set of rationality constraints that we can use to tell pathological AIHs from ‘good’ ones.
The 2019 Inaugural Fearless Steps Challenge: A Giant Leap for Naturalistic Audio

The 2019 FEARLESS STEPS (FS-1) Challenge is an initial step to motivate a streamlined and collaborative effort from the speech and language community towards addressing massive naturalistic audio, the ﬁrst of its kind. The Fearless Steps Corpus is a collection of 19,000 hours of multi-channel recordings of spontaneous speech from over 450 speakers under multiple noise conditions. A majority of the Apollo Missions original analog data is unlabeled and has thus far motivated the development of both unsupervised and semi-supervised strategies. This edition of the challenge encourages the development of core speech and language technology systems for data with limited ground-truth/low resource availability and is intended to serve as the “First Step” towards extracting high-level information from such massive unlabeled corpora. In conjunction with the Challenge, 11,000 hours of synchronized 30-channel Apollo-11 audio data has also been released to the public by CRSS-UTDallas. We describe in this paper the Fearless Steps Corpus, Challenge Tasks, their associated baseline systems, and results. In conclusion, we also provide insights gained by the CRSS-UTDallas team during the inaugural Fearless Steps Challenge.
Cross-Pose LFW : A Database for Studying Cross-Pose Face Recognition in Unconstrained Environments

Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification. Recently, due to big data driven machine learning methods, the performance on the database approaches nearly 100%. However, we argue that this accuracy may be too optimistic. Besides illuminations, occlusions and expressions which have been considered as intra-class variations in LFW positive pairs, cross-pose faces of the same individual is another challenge in face recognition. Therefore, we construct a Cross-Pose LFW (CPLFW) database to add pose influence in face recognition. Also, considering that in the psychology literature, the different-identity pairs should show people of the same gender and race, negative pairs in CPLFW are selected using people with the same gender and race. This assures that performance on the database indicates true ability to distinguish individuals using their identities instead of depending on differences in gender and race. We evaluate some deep learning methods on the new database. Compared to the accuracy on LFW, the accuracy drops about 8%-20% on CPLFW.
On Robustness of Unsupervised Domain Adaptation for Speaker Recognition

Current speaker recognition systems, that are learned by us-ing wide training datasets and include sophisticated modelings, turn out to be very speciﬁc, providing sometimes disappointing results in real-life applications. Any shift between training and test data, in terms of device, language, duration, noise or other tends to degrade accuracy of speaker detection. This study investigates unsupervised domain adaptation,when only a scarce and unlabeled “in-domain” development dataset is available. Details and relevance of different approaches are described and commented, leading to a new robust method that we call feature-Distribution Adaptor. Efﬁciency of the proposed technique is experimentally validated on the recent NIST 2016 and 2018 Speaker Recognition Evaluation datasets.
Change Impact Analysis and Optimization in Ontology-based Content Management Systems

Ontologies are used to semantically enrich content in conte nt management systems. Ontologies cover a wide range of disc iplines enabling machines to understand meanings and reason in diff erent contexts. We use ontologies for semantic annotation t o facilitate understandability of the content by humans and machines. We call OCMS and ontologies evolve due to changes in the concept ualization, representation or specification of the domain kn owledge. These changes are often substantial and frequent i n relatively complex systems such as DBpedia 1. Implementing the changes and adapting the OCMS accordingl y requires a considerable effort. This is due to complex impacts of the changes on the onto l gies, the content and dependent applications. We approac h the problem of evolution by proposing a framework which clearly represents the dependencies of the components of an OCMS. We propose a layered OCMS framework which contains an ontology layer, content layer and annotation layer. Further, we pres ent a novel approach for analysing impacts of atomic and composit e change operations. The approach uses impact cancellation , impact balancing and impact transformation as a mechanism to analy se impacts of composite change operations. We propose a mode l which estimates the cost of evolving an OCMS using four crite ria. The model ranks available evolution strategies and ide nt fi s the best strategy. The approach allows the ontology engineer to x-ante evaluate the impacts and select an optimal strategy during the ontology evolution.
GENETIC DIVERSITY OF Trichoderma sp . OBTAINED FROM TOMATO RHIZOSPHERE USING RAPD

Ten isolates of Trichoderma sp. viz., T. viride (five isolate), Trichoderma sp. (two isolates) and T. harzianum (three isolates) obtained from the rhizosphere soil of tomato fields of south Gujarat region were studied using RAPD. The genetic diversity among ten isolates of Trichoderma sp. were analyzed with five random RAPD primers. The percentage of polymorphism ranged from 60.00% to 83.33%. Analysis of dendrogram revealed that similarity coefficient ranged from 0.51 to 0.89. RAPD profiles showed genetic diversity among the isolates with the formation of two clusters. First cluster involved five isolates of T. viride and two isolates of Trichoderma sp. while second cluster involves three isolates of T. harzianum. This result indicated the identification patterns of Trichoderma isolates.
14 FROM FIELD TO TABLE : PERSPECTIVES AND POTENTIAL FOR FRUIT DOMESTICATION

Domestication, the evolution of species in response to human selection, is the foundation upon which agriculture is built. Most contemporary crops are the products of evolutionary processes that began thousands of years ago, and that continue today as scientists harness emerging technologies to develop new crop varieties for a rapidly changing world. Current understanding of evolution under domestication is based primarily on annual plants, often self-compatible species that are propagated from seed each year. However, attention is refocusing on the development
Documentation of the Eife-Algorithm Implementation for the Network Simulator ( NS )

This document describes the implementation of the TCP-Eifel algorithm[2] for ns[1]. It shows how Eifel is integrated into the existing ns object hierarchy and which files need to be modified. Further it explains how to set-up a simulation using the Eifel-Algorithm and presents some simulation results to visualize the operation of Eifel. Finally it shortly explains how to integrate the NS-Eifel distribution into an installed ns version. This document is neither an introduction to ns nor to Eifel. A good place to start with the former is the ns homepage[1]. For the latter see [2].
The Confirmatory Factor Analysis of Critical Thinking Disposition of Education Students

The purpose of this study were to 1) study critical thinking disposition of education students 2) to study the confirmatory factors analysis of students. The populations in this study were 5,589 students, whom were first year students on academic year 2016 of 11 Rajabhat Universities in Northeastern region of Thailand. The sample of study was 1,594 students (73.1% female, 26.9% male) by multi-stage random sampling. Empirical data were collected by using interview expert form and critical thinking disposition questionnaire, which is a 1-5 rating scale questionnaire of 51 question items with reliability (α) of . 954. For analyzing data, various statistics were used such as frequency, percentage, average, standard deviation, factor analysis and content analysis. The results shown that 1) there are 7 factors of critical thinking disposition in Thais context: open-minded, systematic, truthSeeking, self-confidence, maturity, empathy and persisting, 2) for analyzing data with second order confirmatory factor analysis (CFA), found that the critical thinking disposition The Confirmatory Factor Analysis of Critical Thinking Disposition of Education Students
Energy Consumption and Economic growth in Iran : Cointegration and Causality Analysis

This study seeks to determine the direction of causality between energy consumption and economic growth (EG), using annual data from 1967 to 2008. In our empirical analysis, we implement a bounds-testing approach to co-integration and an augmented form of the Granger causality test to identify the direction of the relationship between these variables both in the short and long run. Stationarity tests reveal that both series are non-stationary, or I (1). Moreover, we found a cointegration relationship between energy consumption and GDP. The short-run dynamics of the variables show that the flow of causality runs from energy use to GDP, and there is a long-run bi-directional causal relationship (or feedback effect) between the two series. In addition, we find that energy consumption keeps on growing as long as the economy grows in Iran. Key-Words: Energy consumption, Economic growth, Error Correction, Iran.
Latent Topic Attention for Domain Classification

Attention-based bidirectional long short-term network (BiLSTM) models have recently shown promising results in text classification tasks. However, when the amount of training data is restricted, or the distribution of the test data is quite different from the training data, some potential informative words maybe hard to be captured in training. In this work, we propose a new method to learn attention mechanism for domain classification. Unlike the past attention mechanisms only guided by domain tags of training data, we explore using the latent topics in the data set to learn topic attention, and employ it for BiLSTM. Experiments on the SMP-ECDT benchmark corpus show that the proposed latent topic attention mechanism outperforms the state-of-the-art soft and hard attention mechanisms in domain classification. Moreover, experiment result shows that the proposed method can be trained with additional unlabeled data and further improve the domain classification performance.
ISOLATION OF AN OIL PALM CONSTITUTIVE PROMOTER DERIVED FROM UBIQUITIN EXTENSION PROTEIN ( uep 2 ) GENE

Constitutive promoters are essential component in genetic engineering. The promoters are usually derived from genes that are constitutively expressed or function as housekeeping genes. In this study, the ubiquitin extension protein (uep2) gene was identified as a constitutively expressed gene in oil palm. The 5’ region of the oil palm uep2 gene was isolated and the promoter truncation analysis was performed. The full length of uep2 (uep2a) and its truncation derivatives (uep2b and uep2c) were linked to β-glucuronidase (gus) reporter gene. The strength and the activity of the uep2 promoter were transiently evaluated in various oil palm tissues and tobacco leaves. Histochemical GUS analysis showed that the activity of truncated uep2b promoter was higher than the full length promoter and uep2c derivative in most tissues tested. Result also showed that uep2b was capable of directing the expression of GUS in all tissues with the high activity detected in oil palm embryoid, green leaves, and tobacco leaves. The full length of uep2 (uep2a) promoter also exhibited the ability to direct the expression of GUS in all tissues with the high activity detected in the plantlet stem. By contrast, although the uep2c showed high activity in young leaves and embryoid, a significant decrease of GUS expression was seen in green tissues. These results indicated that the region between position -1096 and -3237 is essential for constitutive transcriptional regulation as deletion of the region resulted in a significant decrease in the promoter activity. Though maize ubi1 and CaMV35S promoters showed a relatively higher activity than uep2, the uep2 has the ability to induce the constitutive GUS expression in all tissues tested. These results showed that 5’ flanking region of uep2 gene is active in oil palm and tobacco, suggesting that the promoter could be used as an alternative promoter for driving constitutive expression of transgenes in oil palm and other plants.
DomainScouter: Understanding the Risks of Deceptive IDNs

Cyber attackers create domain names that are visually similar to those of legitimate/popular brands by abusing valid internationalized domain names (IDNs). In this work, we systematize such domain names, which we call deceptive IDNs, and understand the risks associated with them. In particular, we propose a new system called DomainScouter to detect various deceptive IDNs and calculate a deceptive IDN score, a new metric indicating the number of users that are likely to be misled by a deceptive IDN. We perform a comprehensive measurement study on the identified deceptive IDNs using over 4.4 million registered IDNs under 570 top level domains (TLDs). The measurement results demonstrate that there are many previously unexplored deceptive IDNs targeting nonEnglish brands or combining other domain squatting methods. Furthermore, we conduct online surveys to examine and highlight vulnerabilities in user perceptions when encountering such IDNs. Finally, we discuss the practical countermeasures that stakeholders can take against deceptive IDNs.
Probabilistic Graphical Models 10-708 , Spring 2018 4 : Causal Graphic Models

How do we use causal graphs and causal representations? How do we discover causal information from data? How do we do machine learning from the causal perspective? What’s the difference between conditional independence and causality? We know that formally X and Y are associated iff ∃ x1 6= x2 such that P (Y |X = x1) 6= P (Y |X = x2). This does necessary mean a causal relationship; causation is stronger than correlation/dependence.
Analysis of the financial impact of regulation for operators in the private healthcare sector

Greice Maria Mansini dos Santos Holds a Bachelor’s degree in nutrition from the Faculty of Public Health of the Universidade de São Paulo, master’s degree in Applied Human Nutrition from the Faculty of Pharmaceutical Sciences, Public Health and Economics, Administration and Accounting of the University of São Paulo, MBA in Economics and Assessment of Health Technology from the Economic Research Institute and the University of São Paulo. greice@usp.br
Specialization and Coevolution of Agricultural Markets

This publication was made possible through support provided by the U.S. Agency for International Development, under Cooperative Agreement No. DHR-0015-A-00-0031-00 to the Center on Institutional Reform and the Informal Sector (IRIS) and administered by the Office of Economic and Institutional Reform, Center for Economic Growth, Bureau for Global Programs, Field Support and Research. The views and analyses in the paper do not necessarily reflect the official position of the IRIS Center or the U.S.A.I.D.
Head-Related Transfer Functions and Head Tracking

Head-related transfer functions (HRTF) can be utilized in binaural synthesis to reproduce spatial sound scenes with headphones. The spatial sound scene may not be correctly localized or externalized because the sound scene moves simultaneously with the listener’s head movements. Head tracking system can be utilized to hold the sound scene static by compensating the head movements in the binaural synthesis. Results indicate that head tracking reduces the localization error and the rate of reversals. It improves the localization when using generic HRTFs for binaural synthesis. The externalization of the sound scene is improved when head tracking is used and the listener moves their head.
RED-NN: Rotation-Equivariant Deep Neural Network for Classification and Prediction of Rotation

In this work, we propose a new Convolutional Neural Network (CNN) for classification of rotated objects. This architecture is built around an ordered ensemble of oriented edge detectors to create a roto-translational space that transforms the input rotation into translation. This space allows the subsequent predictor to learn the internal spatial and angular relations of the objects regardless of their orientation. No data augmentation is needed and the model remains significantly smaller. It presents a self-organization capability and learns to predict the class and the rotation angle without requiring an angle-labeled dataset. We present the results of training with both upright and randomly rotated datasets. The accuracy outperforms the current state of the art on upright oriented training.
Emotion Recognition from Natural Phone Conversations in Individuals with and without Recent Suicidal Ideation

Suicide is a serious public health concern in the U.S., taking the lives of over 47,000 people in 2017. Early detection of suicidal ideation is key to prevention. One promising approach to symptom monitoring is suicidal speech prediction, as speech can be passively collected and may indicate changes in risk. However, directly identifying suicidal speech is difficult, as characteristics of speech can vary rapidly compared with suicidal thoughts. Suicidal ideation is also associated with emotion dysregulation. Therefore, in this work, we focus on the detection of emotion from speech and its relation to suicide. We introduce the Ecological Measurement of Affect, Speech, and Suicide (EMASS) dataset, which contains phone call recordings of individuals recently discharged from the hospital following admission for suicidal ideation or behavior, along with controls. Participants selfreport their emotion periodically throughout the study. However, the dataset is relatively small and has uncertain labels. Because of this, we find that most features traditionally used for emotion classification fail. We demonstrate how outside emotion datasets can be used to generate more relevant features, making this analysis possible. Finally, we use emotion predictions to differentiate healthy controls from those with suicidal ideation, providing evidence for suicidal speech detection using emotion.
Loya Antimicrobial activity of Red Sea corals

Scleractinian corals and alcyonacean soft corals are the two most dominant groups of benthic marine organisms inhabiting the coral reefs of the Gulf of Eilat, northern Red Sea. Antimicrobial assays performed with extracts of six dominant Red Sea stony corals and six dominant soft corals against marine bacteria isolated from the seawater surrounding the corals revealed considerable variability in antimicrobial activity. The results demonstrated that, while the majority (83%) of Red Sea alcyonacean soft corals exhibited appreciable antimicrobial activity against marine bacteria isolated from the seawater surrounding the corals, the stony corals had little or no antimicrobial activity. From the active soft coral species examined, Xenia macrospiculata exhibited the highest and most potent antimicrobial activity. Bioassay-directed fractionation indicated that the antimicrobial activity was due to the presence of a range of compounds of different polarities. One of these antibiotic compounds was isolated and identified as desoxyhavannahine, with a minimum inhibitory concentration (MIC) of 48 lg ml 1 against a marine bacterium. The results of the current study suggest that soft and hard corals have developed different means to combat potential microbial infections. Alcyonacean soft corals use chemical defense through the production of antibiotic compounds to combat microbial attack, whereas stony corals seem to rely on other means.
Copelatus sibelaemontis sp . nov . ( Coleoptera : Dytiscidae ) from the Moluccas with generic assignment based on morphology and DNA sequence data

We describe Copelatus sibelaemontis sp. nov. from Bacan and Morotai Islands, Moluccas (Maluku), Indonesia. This is the only known Indonesian Copelatus Erichson, 1832 with smooth elytra. It has been assigned to Copelatus based on morphology and DNA sequence data.
Three‐Dimensional Graphene‐Based Structures: Production Methods, Properties, and Applications

Graphene, a two-dimensional (2D) sp2-hybridized carbon sheet, shows excellent chemical, mechanical, and physical properties owing to its unique structure, which makes it a great potential in the energy storage devices, sensors, composite materials, and biotechnology. The utilization of graphene sheets into the macroscopic structures is one of the important issues since 2D graphene sheets tend to restack together in bulk materials due to strong π–π interactions and van der Waals forces. The aggregation of graphene sheets and their crumbling lead to a significant decrease in electrical conductivity, surface area, and mechanical strength which negatively affects the utilization of graphene in the practical applications. Recently, three-dimensional (3D) graphene materials have been attracting much attention since they not only preserve the intrinsic properties of 2D graphene sheets by inhibiting the agglomeration behavior of 2D graphene sheets but also provide advanced functions with improved performance in various applications. The content of this chapter covers (i) a brief summary of production techniques of 2D graphene and its drawbacks, (ii) main strategies for the development of 3D graphene structures, (iii) production methods, and (iv) possible applications of 3D graphene architectures in composites and energystorage devices.