Miracle-dz/UniFAR-FTUs huggingface dataset:
It's Not What You Draw, It's What You Don't Draw

The R graphics engine has new support for drawing complex paths via the functions polypath() and grid.path(). This article explains what is meant by a complex path and demonstrates the usefulness of complex paths in drawing non-trivial shapes, logos, customised data symbols, and maps. One of the design goals of the R graphics system (R Development Core Team, 2011) is to allow fine control over the small details of plots. One way that the R graphics system does this is by providing access to low-level generic graphics facilities, such as the ability to draw basic shapes and the ability to control apparently esoteric, but still useful, features of those shapes, such as the line end style used for drawing lines. In R version 2.12. 0, another low-level graphics facility was added to R: the ability to draw complex paths (not just polygons). This article describes this new facility and presents some examples that show how complex paths might be useful. Drawing paths with holes The concept of a path is similar to the concept of a polygon: a path is defined by a series of (x,y) locations that describe the boundary of the path. For example, the following code defines a set of (x,y) locations that describe a simple triangle. > x y <c(.1, .8, .1) A triangle can be drawn from these locations using either the polypath() function from the graphics package or, as shown below and in Figure 1, using the grid.path() function from the grid package.
Stability of the Atlantic Meridional Overturning Circulation: A Review and Synthesis

The notion that the Atlantic Meridional Overturning Circulation (AMOC) can have more than one stable equilibrium emerged in the 1980s as a powerful hypothesis to explain rapid climate variability during the Pleistocene. Ever since, the idea that a temporary perturbation of the AMOC—or a permanent change in its forcing—could trigger an irreversible collapse has remained a reason for concern. Here we review literature on the equilibrium stability of the AMOC and present a synthesis that puts our understanding of past and future AMOC behavior in a unifying framework. This framework is based on concepts from Dynamical Systems Theory, which has proven to be an important tool in interpreting a wide range of model behavior. We conclude that it cannot be ruled out that the AMOC in our current climate is in, or close to, a regime of multiple equilibria. But there is considerable uncertainty in the location of stability thresholds with respect to our current climate state, so we have no credible indications of where our present-day AMOC is located with respect to thresholds. We conclude by identifying gaps in our knowledge and proposing possible ways forward to address these gaps.
Usage of Ontologies for Multilingual Information Retrieval in the Semantic Web

The wide spread adoption of world wide web (www) and the passage to Semantic Web (SW), the increasing amount of information produced every day in all languages and the need to gain access to such a multicultural information have generated interest in the development of techniques for enabling multilingual Information Retrieval (IR). Multilingual IR (MLIR) is defined as the task of searching either a specific database or a set of databases, or the www for relevant information by using criteria in a chosen language (preferable in mother tongue) and retrieve all documents which match all the search criteria, regardless of the language of the document or the indexed language and present them in a unified list [1][2]. In this context, a variety of techniques have been introduced and are still under development aiming to cope with and overcome a series of issues regarding linguistic and semantic barriers mainly generated from the need to efficiently and effective retrieve and gain access to such a multilingual and at the same time multicultural information.
Associations between Executive Function and Type of Sedentary Behavior in Older Adults with and without Alzheimer’s Disease

Objectives: Sedentary behavior may be both a cause and a consequence of cognitive decline in older adults. While previous literature provides plausible explanations for the relationship between sedentary behavior and cognition, research examining the directionality of this relationship is inconclusive. Little is known about how modality of sedentary behavior (e.g., screen time and non-screen time) is related to cognition. Individuals with dementia, a population known to have executive function impairment, have been found to sit more than their age and sex based counterparts. Therefore, the primary purpose of the present study was to investigate the potential bi-directional relationship between types of sedentary behavior and executive function in older adults with and without Alzheimer’s disease. Methods: One hundred community-dwelling older adults were recruited from an existing data registry at the University of Kansas Alzheimer’s Disease Center. In the present study, sedentary behavior data were collected over a one-week period using both objective (body worn inclinometers) and subjective methods (self-report diaries). Based on combining objective with subjective data, sedentary behavior modality was categorized into seven distinct categories which included screen based behaviors (television watching, computer use, multiple types of screen use, and other types of screen use), non-screen based behaviors, a combination of screen and non-screen behaviors, and unknown sedentary behaviors. Executive function was measured by creating a factor score comprised of four tests of executive function administered in a battery of neuropsychological tests. Linear stepwise regressions were used to assess directionality of the relationship between sedentary behavior and cognitive performance with age, sex, race, education level, body mass index, and dementia status used as covariates.
Problems and Technology Evolution in Handwritten Numeral Recognition of Marathi

With the increasing Computerization in every sector, there is growing demand for processing of handwritten documents by computer. Researchers are doing efforts to give efficient solution to this problem. Various Schemes have been used by researchers for Handwritten Numeral Recognition. Many of these systems have been developed for English, Arabic scripts. Indian languages especially Marathi needs attention. As India is Multi-lingual country the problem of handwriting recognition becomes a complex task. Some of benchmark work including development of database, efficient recognition systems have been done by researchers including U. Bhattacharya, et.al, N. Das, et. Al. In this study; the research done in field of Marathi Numeral recognition has been reviewed.
The lead lag relationship between oil prices and exchange rate in an oil importing country: evidence fromThailand using ARDL

The purpose of this study is to explore the Granger-causal relationship between oil prices, exchange rates and inflation rates using Thailand as a case study. Discerning this relationship will help us understand the mechanics of the Thai economy and the factors contributing to its development. Standard time-series and ARDL methods were used to investigate this relation. The reason for choosing to apply both methods is that ARDL is a more robust technique than the standard time series technique. Also, ARDL helped us overcome a problem regarding the variables used in the study as we had a mix of I(0) and I(1) variables which is unacceptable under the standard time-series technique when cointegration of the variables is examined. Our empirical findings tend to indicate that there is a long run relationship existing between these variables and that the exchange rate appears to be the variable leading oil prices and inflation at least in the context of Thailand during the period under review. The results are reasonable and have strong policy implications.
Hardware support for real-time network security and packet classification using field programmable gate arrays

HARDWARE SUPPORT FOR REAL-TIME NETWORK SECURITY AND PACKET CLASSIFICATION USING FIELD PROGRAMMABLE GATE ARRAYS by Nitesh Bhicu Guinde Deep packet inspection and packet classification are the most computationally expensive operations in a Network Intrusion Detection (NID) system. Deep packet inspection involves content matching where the payload of the incoming packets is matched against a set of signatures in the database. Packet classification involves inspection of the packet header fields and is basically a multi-dimensional matching problem. Any matching in software is very slow in comparison to current network speeds. Also, both of these problems need a solution which is scalable and can work at high speeds. Due to the high complexity of these matching problems, only Field-Programmable Gate Array (FPGA) or Application-Specific Integrated Circuit (ASIC) platforms can facilitate efficient designs. Two novel FPGA-based NID solutions were developed and implemented that not only carry out pattern matching at high speed but also allow changes to the set of stored patterns without resource/hardware reconfiguration; to their advantage, the solutions can easily be adopted by software or ASIC approaches as well. In both solutions, the proposed NID system can run while pattern updates occur. The designs can operate at 2.4 Gbps line rates, and have a memory consumption of around 17 bits per character and a logic cell usage of around 0.05 logic cells per character, which are the smallest compared to any other existing FPGA-based solution. In addition to these solutions for pattern matching, a novel packet classification algorithm was developed and implemented on a FPGA. The method involves a two-field matching process at a time that then combines the constituent results to identify longer matches involving more header fields. The design can achieve a throughput larger than 9.72 Gbps and has an on-chip memory consumption of around 256Kbytes when dealing with more than 10,000 rules (without using external RAM). This memory consumption is the lowest among all the previously proposed FPGA-based designs for packet classification. HARDWARE SUPPORT FOR REAL-TIME NETWORK SECURITY AND PACKET CLASSIFICATION USING FIELD PROGRAMMABLE GATE ARRAYS by Nitesh Bhicu Guinde A Dissertation Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Computer Engineering Department of Electrical and Computer Engineering
Magnetic Properties of Semiconducting Spinel CdCr2S4 Nanostructured Films Grown by Low-Pressure Metal–Organic Chemical Vapor Deposition

Magnetic semiconductors are being extensively investigated because of their promise to provide new functionalities and device concepts for spintronic applications. Nevertheless, it remains a challenge to obtain high-quality thin films of these materials for device usage, particularly of chalcogenide-based magnetic semiconductors. The chalcogenide spinel CdCr2S4 is a well-established ferromagnetic semiconductor. In bulk form, it exhibits unique properties such as colossal magnetocapacitance and magnetoresistance effects and is a promising candidate for spintronic applications. Because of its semiconducting properties and band gap in the visible wavelength range, CdCr2S4 offers exciting possibilities for tailoring its optical and magnetic properties. However, applications that utilize its unique combination of optical, electrical, and magnetic properties are limited because of difficulties in growing high-quality films. Herein, we demonstrate a simple low-pressure metal organic chemical vapor deposition (MO.
Analysis of the electrical, magnetic and structural properties of A3B6 type layered semiconductor crystals intercalated with metals with reference to their military applications

Magnetic sensors are key elements in many security and military systems. Magnetic sensors are often used for military applications such as navigation, position tracking and antitheft systems. Nowadays sensitive magnetic sensors are used in many technical systems, including modern anti-tank missiles to identify the center of the target area and a minimal armor region. The applications of magnetoresistive structures based on semiconductor crystals of InSe for high precision measurement of the magnetic field are outlined in this article. Possibilities of using magnetic field sensors based on InSe structures for revealing the armour military vehicles are discussed. The impact of metal impurities on the layered structure of the semiconductor material as referred to the strong covalent bond within the layers as well as the weak van-der-Waals bond in the interlayer space is studied. Nyquist diagrams for InSe crystal with the impurities of nickel at different temperatures ranging from liquid nitrogen to room temperature are analyzed. Temperature is shown to be a significant factor for affecting the Nyquist diagrams. The curvature of the Nyquist diagrams for intercalated InSe varies with the temperature as opposed to the pure InSe. Topological images of crystal surface obtained by using atomic force microscopy confirmed the layer structure of nickel-intercalated InSe. Structures with alternating layers of semiconductor and metal provide the fundamental control of magnetic properties. These structures have a sharp anisotropy of magnetic susceptibility. The investigated semiconductor crystals with impurities of 3d-elements can extend the functionality of modern magnetic sensors designed to detect heavy armor.
Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications

Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications Rishabh Iyer Chair of the Supervisory Committee: Professor Jeff Bilmes Department of Electrical Engineering In this dissertation, we explore a class of unifying and scalable algorithms for a number of submodular optimization problems, and connect them to several machine learning applications. These optimization problems include, 1. Constrained and Unconstrained Submodular Minimization, 2. Constrained and Unconstrained Submodular Maximization, 3. Difference of Submodular Optimization 4. Submodular Optimization subject to Submodular Constraints The main focus of this thesis, is to study these problems theoretically, in the light of the machine learning problems where they naturally occur. We provide scalable, practical and unifying algorithms for all the above optimization problems, which retain good theoretical guarantees, and investigate the underlying hardness of these optimization problems. We also study natural subclasses of submodular functions, along with theoretical constructs, which help connect theory to practice by providing tighter worst case guarantees. While the focus of this thesis is mainly theoretical, we also empirically demonstrate the applicability of these techniques on several synthetic and real world problems.
Smoke regions extraction based on two steps segmentation and motion detection in early fire

Aiming at the early problems of video-based smoke detection in fire video, this paper proposes a method to extract smoke suspected regions by combining two steps segmentation and motion characteristics. Early smoldering smoke can be seen as gray or gray-white regions. In the first stage, regions of interests (ROIs) with smoke are obtained by using two step segmentation methods. Then, suspected smoke regions are detected by combining the two step segmentation and motion detection. Finally, morphological processing is used for smoke regions extracting. The Otsu algorithm is used as segmentation method and the ViBe algorithm is used to detect the motion of smoke. The proposed method was tested on 6 test videos with smoke. The experimental results show the effectiveness of our proposed method over visual observation.
Impact of Mobile Assisted Language Learning on Learner Autonomy in EFL Reading Context

Traditional classrooms confine English as a Foreign Language (EFL) reading to the textbook and the classroom setting, something that demotivates active reading. With the advent of mobile technology, however, such boundaries can be broken to include external reading materials where students could read and share anytime and anywhere. This paper investigates the role of mobile technology in enhancing Learner Autonomy (LA) in the EFL reading context among students in the Preparatory Year (PY) of Najran University in Saudi Arabia. A reading class of 30 students utilised mobile applications (WhatsApp and internet search engines such as Google) to access external reading materials and interact with their peers and teachers outside the classroom. Qualitative data collection underwent a number of procedures. The baseline data was constructed from the students’ portfolios, which reported the participants’ traditional reading practices and use of mobiles. Then, the participants were encouraged to use internet search engines and WhatsApp group to share their readings. Finally, five participants were interviewed. The data analysis revealed that the participants’ LA is improved through the use of selected mobile applications in terms of taking responsibility for and making decisions about reading materials and the time and place of reading. The study recommends further investigation into the role of mobile applications for generating learners’ own tasks and writing skills.
News text generation with adversarial deep learning

In this work we carry out a thorough analysis of applying a specific field within machine learning called generative adversarial networks, to the art of natural language generation; more specifically we generate news text articles in an automated fashion. To do this, we experimented with a few different architectures and representations of text, evaluated the results and used the information retrieved from the results, to create a model that should give the best result. For evaluation, we used perplexity and human evaluation. We also looked at the token distribution to see which model captures the texts most successfully. We show that it is possible to use generative adversarial networks to generate sequences of tokens that resemble natural language, but this does not yet reach the quality of human-written text. Further hyperparameter tuning and using a narrower-subjected corpus could improve the output.
Point cloud modeling using algebraic template

Point cloud reconstruction is a fundamental and important research topic with many applications in the fields of geomatics and computer graphics. In this paper, a novel approach for reconstructing point clouds by a hierarchical template model is presented. The template model is composed of three types of primitive geometric shape in a hierarchical manner. Compared to previous approaches which are based on an iterative fitting process, the primitive shapes are represented in algebraic form and fit to a point cloud by solving a least-square linear system. This non-iterative process makes the proposed approach feasible and robust for modeling huge amounts of point data. Furthermore, some geometric constraints are integrated into the least-square fitting system to retain the geometric relations between the primitive shapes in the template model, which can improve modeling quality. The experiment results for various point clouds show that the proposed approach is capable of handling point clouds with both noise and sharp features.
A generalized framework for legacy application modernization to the cloud

The ever-growing demand of cloud computing services has presented a major impact on the technologies being used for the IT service provisioning. This change has been benefited in the form of increased business agility, scalability, flexibility and reduced operational costs of the computing systems. The pay-as-you-use model of computing as utility model has been facing obstacles in myriad of traditional on premise computing systems which are offering needful services with legacy computing environment. These systems have been built on older programming environments with sequential programming methods but the valuable business logic and data being handled cannot be discarded. This legacy problem is one of the main challenges for cloud computing to deal with. When re-architecting and reengineering these legacy systems, parallel computing approaches also need to be assessed for resource optimization in cloud environments. The proposed approach presents a generalized framework for modernization of the legacy applications to the cloud. Beginning from the source application assessment to the target infrastructure decision making process, essential quality characteristics are also considered to handle this multi-dimensional issue. The decision support module is followed by the elaboration of available modernization approaches and their associated risks with implementation phases. Additionally, this work explores the usage of parallel programming methodologies to deal with existing sequential approaches and large datasets are introduced with Hadoop architecture. The configuration and deployment of applications with parallelism presents a powerful manner of these cloud computing technologies.
Reduplicated words and templates in Brazilian and European Portuguese

Within the Dynamic Systems Theory (DST) in the study of child language, variability, flexibility and asynchrony tend to occur in the developmental process (Thelen & Smith, 1994). Following this perspective, templates are explained within an emergentist account of phonological development (Kelso, 1995; Keren-Portnoy, Vihman, DePaolis, Bidgood, & McGillion, 2011; Larsen-Freeman & Cameron, 2008; Thelen & Smith, 1994). The dynamic perspective was introduced into mainstream psychology by the pioneers Ester Thelen and Linda Smith (1994) and, more recently, in linguistics studies. This new linguistic perspective stresses the continuity between the development of phonological structure and the development of all other structures in nature (Szreder, 2012). Furthermore, according to this view, there is instability in development as there are adjustments in the system due to the self-organization principle, which can be understood as a spontaneous pattern formation (Kelso, 1995).
Account-Making Following Relationship Dissolution: Exploring Sex as a Moderator in Public and Private Breakup Accounts

ABSTRACT This study examines sex differences in emotion word use in retrospective private and public relationship dissolution accounts through two studies. Study 1 (n = 423) found men’s use of negative emotion words in private breakup accounts correlated with their use of negative emotion words in their public accounts; however, women’s emotion words in their private breakup accounts did not correlate with their use of emotion words in their public accounts. Study 2 (n = 284) replicated Study 1 with a broader sample and revealed the same pattern of negative emotion word use. These studies suggest that after a breakup women and men may process emotions differently in their public and private breakup accounts depending on their audience. The findings offer insights into how relationship dissolution model processes, evolutionary mating strategies, and self-presentation may influence linguistic variations accounting for sex differences.