Miracle-dz/UniFAR-FTUs huggingface dataset:
FIU Digital Commons

systems theory posits that development cannot be segmented by influences acting in isolation, but should be studied through a scientific lens that highlights the complex interactions between these forces over time (Overton, 2013a). This poses a unique challenge for developmental psychologists studying complex processes like language development. In this paper, we advocate for the combining of highly sophisticated data collection technologies in an effort to move toward a more systemic approach to studying language development. We investigate the efficiency and appropriateness of combining eye-tracking technology and the LENA (Language Environment Analysis) system, an automated language analysis tool, in an effort to explore the relation between language processing in early development, and external dynamic influences like parent and educator language input in the home and school environments. Eye-tracking allows us to study language processing via eye movement analysis; these eye movements have been linked to both conscious and unconscious cognitive processing, and thus provide one means of evaluating cognitive processes underlying language development that does not require the use of subjective parent reports or checklists. The LENA system, on the other hand, provides automated language output that describes a child’s language-rich environment. In combination, these technologies provide critical information not only about a child’s language processing abilities but also about the complexity of the child’s language environment. Thus, when used in conjunction these technologies allow researchers to explore the nature of interacting systems involved in language development.
QUANTIFYING VARIABILITY OF BLACK CARBON TRANSPORT FROM CROPLAND BURNING IN RUSSIA TO THE ARCTIC DRIVEN BY ATMOSPHERIC BLOCKING EVENTS

Title of Dissertation: QUANTIFYING VARIABILITY OF BLACK CARBON TRANSPORT FROM CROPLAND BURNING IN RUSSIA TO THE ARCTIC DRIVEN BY ATMOSPHERIC BLOCKING EVENTS Joanne V. Hall, Doctor of Philosophy, 2017 Dissertation directed by: Tatiana V. Loboda, Associate Professor, Department of Geographical Sciences Short lived aerosols and pollutants transported from northern mid-latitudes have amplified the short term warming in the Arctic region. Specifically, black carbon is recognized as the second most important human emission in regards to climate forcing, behind carbon dioxide with a total climate forcing of +1.1Wm. Studies have suggested that cropland burning may be a large contributor to the black carbon emissions which are directly deposited on the snow in the Arctic region. However, accurate monitoring of cropland burning from existing active fire and burned area products is limited, thereby leading to an underestimation in black carbon emissions from cropland burning. This dissertation focuses on 1) assessing the potential for the deposition of hypothetical black carbon emissions from known cropland burning in Russia through low-level transport, and 2) identifying a possible atmospheric pattern that may enhance the transport of black carbon emissions to the Arctic. Specifically, atmospheric blocking events present a potential mechanism that could act to enhance the likelihood of transport or accelerate the transport of pollutants to the snow-covered Arctic from Russian cropland burning based on their persistent wind patterns. This research study confirmed the importance of Russian cropland burning as a potential source of black carbon deposition on the Arctic snow in the spring despite the low injection heights associated with cropland burning. Based on the successful transport pathways, this study identified the potential transport of black carbon from Russian cropland burning beyond 80°N which has important implications for permanent sea ice cover. Further, based on the persistent wind patterns of blocking events, this study identified that blocking events are able to accelerate potential transport and increase the success of transport of black carbon emissions to the snowcovered Arctic during spring when the impact on the snow/ice albedo is at its highest. The enhanced transport of black carbon has important implications for the efficacy of deposited black carbon. Therefore, understanding these relationships could lead to possible mitigation strategies for reducing the impact of deposition of black carbon from crop residue burning in the Arctic. QUANTIFYING VARIABILITY OF BLACK CARBON TRANSPORT FROM CROPLAND BURNING IN RUSSIA TO THE ARCTIC DRIVEN BY ATMOSPHERIC BLOCKING EVENTS
Transactions are back — but are they the same ?

Transactions are back in the spotlight! They are emerging in concurrent programming languages under the name of transactional memory (TM). Their new role? Concurrency control on new multi-core processors. From afar they look the same as good ol’ database transactions. But are they really? In this position paper, we reflect about the distinguishing features of these memory transactions with respect to their database cousins. Disclaimer: By its very nature, this position paper does not try to avoid subjectivity. ACM SIGACT News 48 March 2008 Vol. 39, No. 1
Prospectus for the Next LAPACK and ScaLAPACK Libraries: Basic ALgebra LIbraries for Sustainable Technology with Interdisciplinary Collaboration (BALLISTIC)∗

The convergence of several unprecedented changes, including formidable new system design constraints and revolutionary levels of heterogeneity, has made it clear that much of the essential software infrastructure of computational science and engineering is, or will soon be, obsolete. Math libraries have historically been in the vanguard of software that must be adapted first to such changes, both because these low-level workhorses are so critical to the accuracy and performance of so many different types of applications, and because they have proved to be outstanding vehicles for finding and implementing solutions to the problems that novel architectures pose. Under the Basic ALgebra LIbraries for Sustainable Technology with Interdisciplinary Collaboration (BALLISTIC) project, the principal designers of the Linear Algebra PACKage (LAPACK) and the Scalable Linear Algebra PACKage (ScaLAPACK), the combination of which is abbreviated Sca/LAPACK, aim to enhance and update these libraries for the ongoing revolution in processor architecture, system design, and application requirements by incorporating them into a layered package of software components—the BALLISTIC ecosystem—that provides users seamless access to state-of-the-art solver implementations through familiar and improved Sca/LAPACK interfaces. The set of innovations and improvements that will be made available through BALLISTIC is the result of a combination of inputs from a variety of sources: the authors’ own algorithmic and software research, which attacks the challenges of multi-core, hybrid, and extreme-scale system designs; extensive interactions with users, vendors, and the management of large high-performance computing (HPC) facilities to help anticipate the demands and opportunities of new architectures and programming languages; and, finally, the enthusiastic participation of the research community in developing and offering enhanced versions of existing dense linear algebra software components. Aiming to help applications run portably at all levels of the platform pyramid, including in cloud-based systems, BALLISTIC’s technical agenda includes: (1) adding new functionality requested by stakeholder communities; (2) incorporating vastly improved numerical methods and algorithms; (3) leveraging successful research results to transition Sca/LAPACK (interfaces) ∗Work supported in part by a grant from the NSF OAC: 2004235/2004541/2004763/2004850 (and collaborative proposals) – Collaborative Research: Frameworks: Basic ALgebra LIbraries for Sustainable Technology with Interdisciplinary Collaboration (BALLISTIC).
Creating a Finite-State Parser with Application Semantics

Parsli is a ﬁnite-state (FS) parser which can be tailored to the lexicon, syntax, and semantics of a particular application using a hand-editable declarative lexicon. The lexicon is deﬁned in terms of a lexicalized Tree Adjoining Grammar, which is subsequently mapped to a FS representation. This approach gives the application designer better and easier control over the natural language understanding component than using an off-the-shelf parser. We present results using Parsli on an application that creates 3D-images from typed input.
A Computational Model for Resolving Pronominal Anaphora in Turkish Using Hobbs ’ Naïve Algorithm

he this himself o bu kendi she that herself onu bunu kendisi it these itself onun bunun kendim his those themselves onlar bunlar kendin her others onları bunları kendimiz him onların bunların kendiniz its şu kendileri they şunu them şunun their şunlar şunları şunların Pronominal Anaphora in English Pronominal Anaphora in Turkish Abstract—In this paper we present a computational model for pronominal anaphora resolution in Turkish. The model is based on Hobbs’ Naїve Algorithm [4, 5, 6], which exploits only the surface syntax of sentences in a given text.
Parsing for agile modeling

Agile modeling refers to a set of methods that allow for a quick initial development of an importer and its further refinement. These requirements are not met simultaneously by the current parsing technology. Problems with parsing became a bottleneck in our research of agile modeling. In this thesis we introduce a novel approach to specify and build parsers. Our approach allows for expressive, tolerant and composable parsers without sacrificing performance. The approach is based on a context-sensitive extension of parsing expression grammars that allows a grammar engineer to specify complex language restrictions. To insure high parsing performance we automatically analyze a grammar definition and choose different parsing strategies for different parts of the grammar. We show that context-sensitive parsing expression grammars allow for highly composable, tolerant and variable-grained parsers that can be easily refined. Different parsing strategies significantly insure high-performance of parsers without sacrificing expressiveness of the underlying grammars.
A Hybrid Middleware for RFID-based Parking Management System Using Group Communication in Overlay Networks

A hybrid middleware for RFID-based parking management system is presented in this paper. The publish-subscribe approach is combined with group communication in overlay networks in the design of event monitors and application service connectors to comprise the core of a hybrid middleware. In this paper, group communication relevant to P2P networks is the focus of middleware technology development. A group of peer nodes provide an efficient abstraction to handle events from RFID readers and vehicle identity detectors to be processed by services and applications that run in the conventional IP networks. Programming abstractions for publish-substribe messaging (multicast and single) from the reference P2P systems are used to build new extensions. The hybrid middleware provides an efficient communication transport between the peer-based networks and existing application services and mobile communication infrastructure that require conventional IP-based networks. The simulation results show that the approach improved the performance of the P2P network. This paper provides an implementation model that can significantly lower the infrastructure cost for building an electronic parking management system.
A novel SoC architecture on FPGA for ultra fast face detection

Face detection is the cornerstone of a wide range of applications such as video surveillance, robotic vision and biometric authentication. One of the biggest challenges in face detection based applications is the speed at which faces can be accurately detected. In this paper, we present a novel SoC (System on Chip) architecture for ultra fast face detection in video or other image rich content. Our implementation is based on an efficient and robust algorithm that uses a cascade of Artificial Neural Network (ANN) classifiers on AdaBoost trained Haar features. The face detector architecture extracts the coarse grained parallelism by efficiently overlapping different computation phases while taking advantage of the finegrained parallelism at the module level. We provide details on the parallelism extraction achieved by our architecture and show experimental results that portray the efficiency of our face detection implementation. For the implementation and evaluation of our architecture we used the Xilinx FX130T Virtex5 FPGA device on the ML510 development board. Our performance evaluations indicate that a speedup of around 100X can be achieved over a SSE-optimized software implementation running on a 2.4GHz Core-2 Quad CPU. The detection speed reaches 625 frames per sec (fps).
V3D-LSFM:Video-based Human 3D Pose Estimation with Long-term and Short-term Pose Fusion Mechanism

Following the success of 2D pose estimation from a single image, a lot of work focus on 3D pose estimation on video by exploiting temporal information. In the scenarios of temporal 3D pose estimation, several recent works have achieved signiﬁcant advances via Temporal Convolution Network (TCN). However, the current TCN fashion suﬀers from lacking local coherence caused by excessive dependence on local frames and limited local dynamic range, failing to estimate poses correctly in real scenes, especially with high-speed motions. To tackle this problem, we design a Long-term Bank to select and collect candidate key poses, and further provide a LSFM ( L ong-term and S hort-term F usion M echanism) to integrate long-term pose information into short-term convolution window, such to enhance the temporal coherence of local neighbor frames. Experimental results and ablation studies demonstrate that proposed approaches signiﬁcantly promote the accuracy and robustness of the benchmark model.
Analyzing Social Media Data for Understanding Student's Problem

Social media allows the creation and interactions of user created content. Social medium places include Facebook, Twitter etc. Student's casual discussion on social media focused into their educational experience, mind-set, and worry about the learning procedure. Information from such uninstrumented environments can present valuable data to report student problem. Examining data from such a social meadiacan be challenging task. The problem of student's experiences reveal from social media sited need human analysis or Interaction. It pays attention on engineering student's Twitter posts to know problem and troubles in their educational practices. This paper proposes a workflow to put together both qualitative investigation and large-scale data mining scheme. First a sample is taken from student and then qualitative analysis conducted on that sample which is associated to engineering
Effect of foliar spray and soil application of micronutrients on major nutrient content and uptake of coriander in lateritic soils of Konkan region

Coriander (Coriandrum sativum L.) belongs to family Apiaceae is a native of Mediterranean region and is extensively grown in India. In order to study the effect of foliar spray and soil application of micronutrients on major nutrient content and uptake of coriander, the present investigation was undertaken. The highest nutrient content (1.64, 0.27, 1.69 and 0.26 % N, P, K and S respectively) of coriander was obtained with the application of ZnSO4 @ 20 kg ha-1 along with 100 per cent RDF (T6). However, the highest N uptake (27.57 kg ha-1) of coriander was obtained with the application of ZnSO4 @ 0.5 per cent foliar spray along with 100 per cent RDF (T5) and P, K and S uptake (4.43, 28.01 and 4.24 kg ha-1 respectively) of coriander was obtained with the application of ZnSO4 @ 20 kg ha-1 along with 100 per cent RDF (T6).
Rebuilding community resilience in a post-war context : developing insight and recommendations-a qualitative study in Northern Sri Lanka

This peer-reviewed article can be downloaded, printed and distributed freely for any purposes (see copyright notice below). Articles in International Journal of Mental Health Systems are listed in PubMed and archived at PubMed Central. For information about publishing your research in International Journal of Mental Health Systems or any BioMed Central journal, go to http://www.ijmhs. com/authors/instructions/ For information about other BioMed Central publications go to http://www.biomedcentral. com/ International Journal of Mental Health Systems
Judgment under uncertainty: References

The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important social, medical, and political situations as well. Individual chapters discuss the representativeness and availability heuristics, problems in judging covariation and control, overconfidence, multistage inference, social perception, medical diagnosis, risk perception, and methods for correcting and improving judgments under uncertainty. About half of the chapters are edited versions of classic articles; the remaining chapters are newly written for this book. Most review multiple studies or entire subareas of research and application rather than describing single experimental studies. This book will be useful to a wide range of students and researchers, as well as to decision makers seeking to gain insight into their judgments and to improve them.
Paleoecology of Pleistocene megafauna in southern Nevada, Usa: Isotopic evidence for browsing on halophytic plants

Paleoecology of Pleistocene megafauna in southern Nevada, USA: isotopic evidence for browsing on halophytic plants by Lael Vetter Dr. Stephen M. Rowland, Examination Committee Chair Professor of Geoscience University of Nevada, Las Vegas Stable isotopic techniques are emergent as a powerful reconstructive tool in Neogene paleoecology. The Las Vegas Valley in southern Nevada contains one of few diverse Late Pleistocene fossil assemblages in the Mojave Desert. This study investigates the diet of four megafaunal genera {Mammuthus, Equus, Bison, and Camelops) using 6 ^C signatures preserved in tooth enamel. Results from serial sampling are also presented as a subannual record of dietary variation and seasonality. During the Last Glacial Maximum, the three grazing genera {Mammuthus, Equus, and Bison) consumed C3 and C4 grasses in the naturally occurring proportion, which consisted primarily of C3 grasses. Camelops 6 '^C values indicate the highest dietary proportion of C4 plants; I interpret that these animals consumed browse material with a high proportion of the halophytic C4 shrub Atriplex, a substantial component of modern Mojave Desert vegetation. This study provides new insight into stable isotopic applications for reconstruction of arid paleoenvironments.
AIS Electronic Library (AISeL)

Understanding how to develop users(cid:146) skills and abilities and how to best support users in their everyday use of technology remains a key managerial problem. This study uses Bandura(cid:146)s social cognitive theory (1986) to focus on the everyday phenomenon of technical support as a mechanism of vicarious learning through which users learn about their technology. We argue that this conceptualization aids in understanding how and why users gain value from support. Focusing specifically on support provided through a technical help desk at a large, distributed financial services organization, this study randomly collected 100 audiotaped support calls and matched them with user evaluations of the support provided. By examining the behaviors that characterize the provision of support by IT professionals and linking these characteristics to users(cid:146) reactions including learning, this research will result in a more grounded model of support and its relationship to users(cid:146) development of self-efficacy, satisfaction, and knowledge about technology.
Connectionist Probability Estimation in HMM Speech Recognition

This report is concerned with integrating connectionist networks into a hidden Markov model (HMM) speech recognition system, This is achieved through a statistical understanding of connectionist networks as probability estimators, first elucidated by Hervé Bourlard. We review the basis of HMM speech recognition, and point out the possible benefits of incorporating connectionist networks. We discuss some issues necessary to the construction of a connectionist HMM recognition system, and describe the performance of such a system, including evaluations on the DARPA database, in collaboration with Mike Cohen and Horacio Franco of SRI International. In conclusion, we show that a connectionist component improves a state of the art HMM system.
Joint modeling of individual socio-professional trajectory and overall or cause-specific survival

Being in low socioeconomic position is associated with increased mortality risk from various causes of death. Previous studies have already shown the importance of considering different dimensions of socioeconomic trajectories across the life-course. Analyses of professional trajectories constitute a crucial step in order to better understand the association between socio-economic position and mortality. The main challenge in measuring this association is then to decompose the respectiveshare of these factors in explaining the survival level of individuals. The complexity lies in the bidirectional causality underlying the observed associations:Are mortality differentials due to differences in the initial health conditions that are jointly influencing employment status and mortality, or the professional trajectory influences directly health conditions and then mortality? Standard methods do not consider the interdependence of changes in occupational status and the bidirectional causal effect underlying the observed association and that leads to substantial bias in estimating the causal link between professional trajectory and mortality. Therefore, it is necessary to propose statistical methods that consider simultaneously repeated measurements (careers) and survivalvariables. This study was motivated by the Cosmop-DADS database, which is a sample of the French salaried population.The first aim of this dissertation was to consider the whole professional trajectories and an accurate occupational classification, instead of using limitednumber of stages during life course and a simple occupational classification that has been considered previously. For this purpose, we defined time-dependent variables to capture different life course dimensions, namely critical period, accumulation model and social mobility model, and we highlighted the association between professional trajectories and cause-specific mortality using the definedvariables in a Cox proportional hazards model.The second aim was to incorporate the employment episodes in a longitudinal sub-model within the joint model framework to reduce the bias resulting from the inclusion of internal time-dependent covariates in the Cox model. We proposed a joint model for longitudinal nominal outcomes and competing risks data in a likelihood-based approach. In addition, we proposed an approach mimicking meta-analysis to address the calculation problems in joint models and large datasets, by extracting independent stratified samples from the large dataset, applying the joint model on each sample and then combining the results. In the same objective, that is fitting joint model on large-scale data, we propose a procedure based on the appeal of the Poisson regression model. This approach consist of finding representativetrajectories by means of clustering methods and then applying the joint model on these representative trajectories.