Miracle-dz/UniFAR-FTUs huggingface dataset:
Who pays for e-Junk?

Background: Viewed through the micro focus of an interpretive lens, medical anthropology remains mystified because interpretivist explanations seriously downplay the given context in which individual health seeking-behaviours occur. This paper draws upon both the interpretivist and political economy perspectives to reflect on the ethno medical practices within the KoreanAustralian community in Sydney. Methods: We draw on research data collected between 1995 and 1997 for an earlier study of the use of biomedical and traditional medicine by Korean-Australians in Sydney. A total of 120 interviews were conducted with a range of participants, including biomedical doctors, traditional health professionals, Korean community leaders and Korean migrants representing a range of socio-economic backgrounds and migration patterns. Results and Discussion: First, the paper highlights the extent to which the social location of migrants in a host society alters or restructures their initial cultural practices they bring with them. Second, taking hanbang medicine in the Korean-Australian community as an illustrative case, the paper explores the transformation of the dominant biomedicine in Australia as a result of the influx of ethnomedicine in the era of global capitalism and global movement. Conclusion: In seeking to explain the popularity and supply of alternative health care, it is important to go beyond the culture of each kind of health care itself and to take into consideration the changes occurring at societal, national and global levels as well as consequential individual response to the changes. New social conditions influence the choice of health care methods, including herbal/alternative medicine, health foods and what are often called New Age therapies. Background There is no simple answer to the question, what happens when oriental medicine is transplanted and practised in an Australian context? The question touches on a number of issues relating to the application of traditional nonWestern medical practices in western societies [1,2]. Ethnomedicine in its given oriental cultural and historical contexts does not cease to exert itself upon transplantation into its new social context, but continues to influence health care choices and practices. The life experiences of Published: 03 January 2007 Journal of Ethnobiology and Ethnomedicine 2007, 3:1 doi:10.1186/1746-4269-3-1 Received: 21 July 2006 Accepted: 03 January 2007 This article is available from: http://www.ethnobiomed. com/content/3/1/1 © 2007 Han and Ballis; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Resolution-Based Content Discovery in Network of Caches: Is the Control Traffic an Issue?

As networking attempts to cleanly separate the control plane and forwarding plane abstractions, it also defines a clear interface between these two layers. An underlying network state is represented as a view to act upon in the control plane. We are interested in studying some fundamental properties of this interface, both in a general framework, and in the specific case of content routing. We try to evaluate the traffic between the two planes based on allowing a minimum level of acceptable distortion in the network state representation in the control plane. We apply our framework to content distribution, and see how we can compute the overhead of maintaining the location of content in the control plane. This is of importance to evaluate resolution-based content discovery in content-oriented network architectures: we identify scenarios where the cost of updating the control plane for content routing overwhelms the benefit of fetching the nearest copy. We also show how to minimize the cost of this overhead when associating costs to peering traffic and to internal traffic for network of caches.
Online Video Stream Abstraction and Stylization

This paper gives an automatic method for online video stream abstraction, producing a temporally coherent output video stream, in a style with large regions of constant color and highlighted bold edges. Our system includes two novel components. Firstly, to provide coherent and simplified output, we segment frames, and use optical flow to propagate segmentation information from frame to frame; an error control strategy is used to help ensure that the propagated information is reliable. Secondly, to achieve coherent and attractive coloring of the output, we use a color scheme replacement algorithm specifically designed for an online video stream. We demonstrate real-time performance for CIF videos, allowing our approach to be used for live communication and other related applications.
Sharpening "Primes is in P" for a large family of numbers

We present algorithms that are deterministic primality tests for a large family of integers, namely, integers n ≡ 1 (mod 4) for which an integer a is given such that the Jacobi symbol (a/n) = -1, and integers n ≡ -1 (mod 4) for which an integer a is given such that (a/n) = (1-a / n) = -1. The algorithms we present run in 2 -min(k,[2 log log n]) O((log n) 6 ) time, where k = v 2 (n - 1) is the exact power of 2 dividing n - 1 when n ≡ 1 (mod 4) and k = v 2 (n + 1) if n ≡ -1 (mod 4). The complexity of our algorithms improves up to O((log n) 4 ) when k ≥ [2 log log n]. We also give tests for a more general family of numbers and study their complexity.
Moderating Impact of Environmental Turbulence on Relationship between Business Innovation and Business Performance

The importance of Pakistan’s Tourism sector has increased manifold due to economic activities under China Pakistan Economic Corridor (CPEC) initiative. However, it has been in crisis phase due to the influence of the war on terror and other administrative issues. This paper is an effort to analyze the impact of environmental turbulence on the relationship between Customer Relationship Management effectiveness (CRMe’), business innovation and business performance in organizations comprising tourism sector. Responses from 382 respondents were collected through a questionnaire. The analysis is done using Structural Equation Modeling (SEM) through Amos software. It is observed that all the moderating effects on hypothesized relations are statistically significant. In other words, technological turbulence and competitive intensity weaken (strengthen) the relation of CRMe’ with business innovation and business performance. While, market turbulence strengthens (weakens) the said relation. The outcomes of this study will help stakeholders in understanding the market forces and their impact on innovation and business performance in a better way to prepare themselves for the varied challenges of CPEC.
Taking Stock of Two Relational Aspects of Organizational Life: Tracing the History and Shaping the Future of Socialization and Mentoring Research

As part of the centennial celebration for the Journal of Applied Psychology, this article reviews the literature on organizational socialization and mentoring. Our review includes a comparison of organizational socialization and mentoring as processes for employee adjustment and development, the historical context that fueled the emergence of these two areas of study, and a chronological mapping of key foundations, trends, themes that emerged across time, and major milestones. Along the way, a special emphasis is placed on research published in the Journal of Applied Psychology and high impact work is highlighted. We conclude with a discussion of five areas for future research. Specifically, we outline ideas for bridging the socialization and mentoring literatures, better understanding and capturing dynamic processes across time, the development of multilevel theories and models, addressing causality, and considering the implications for organizational socialization and mentoring research based on how technology is changing the way we work.
IDEIXIS --- IMAGE-BASED DEIXIS FOR RECOGNIZING

In this thesis, we describe an approach to recognizing location from camera-equipped mobile devices using image-based web search. This is an image-based deixis capable of pointing at a distant location away from the user's current location. We demonstrate our approach on an application allowing users to browse web pages matching the image of a nearby location. Common image search metrics can match images captured with a camera-equipped mobile device to images found on the World Wide Web. The users can recognize the location if those pages contain information about this location (e.g. name, facts, stories . etc). Since the amount of information displayable on the device is limited, automatic keyword extraction methods can be applied to help efficiently identify relevant pieces of location information. Searching the entire web can be computationally overwhelming, so we devise a hybrid image-andkeyword searching technique. First, image-search is performed over images and links to their source web pages in a database that indexes only a small fraction of the web. Then, relevant keywords on these web pages are automatically identified and submitted to an existing text-based search engine (e.g. Google) that indexes a much larger portion of the web. Finally, the resulting image set is filtered to retain images close to the original query in terms of visual similarity. It is thus possible to efficiently search hundreds of millions of images that are not only textually related but also visually relevant. Thesis Supervisor: Trevor Darrell Title: Professor of Computer Science
Exploration of the pore structure of a peptide‐gated Na+ channel

The FMRF‐amide‐activated sodium channel (FaNaC), a member of the ENaC/Degenerin family, is a homotetramer, each subunit containing two transmembrane segments. We changed independently every residue of the first transmembrane segment (TM1) into a cysteine and tested each position's accessibility to the cysteine covalent reagents MTSET and MTSES. Eleven mutants were accessible to the cationic MTSET, showing that TM1 faces the ion translocation pathway. This was confirmed by the accessibility of cysteines present in the acid‐sensing ion channels and other mutations introduced in FaNaC TM1. Modification of accessibilities for positions 69, 71 and 72 in the open state shows that the gating mechanism consists of the opening of a constriction close to the intracellular side. The anionic MTSES did not penetrate into the channel, indicating the presence of a charge selectivity filter in the outer vestibule. Furthermore, amiloride inhibition resulted in the channel occlusion in the middle of the pore. Summarizing, the ionic pore of FaNaC includes a large aqueous cavity, with a charge selectivity filter in the outer vestibule and the gate close to the interior.
Lawrence Berkeley National Laboratory Lawrence Berkeley National Laboratory Title The

The indoor atmosphere is an ecological unit that impacts on public health. To investigate the composition of organisms in this space, we applied culture-independent approaches to microbes harvested from the air of two densely populated urban buildings, from which we analyzed 80 megabases genomic DNA sequence and 6000 16S rDNA clones. The air microbiota is primarily bacteria, including potential opportunistic pathogens commonly isolated from human-inhabited environments such as hospitals, but none of the data contain matches to virulent pathogens or bioterror agents. Comparison of air samples with each other and nearby environments suggested that the indoor air microbes are not random transients from surrounding outdoor environments, but rather originate from indoor niches. Sequence annotation by gene function revealed specific adaptive capabilities enriched in the air environment, including genes potentially involved in resistance to desiccation and oxidative damage. This baseline index of air microbiota will be valuable for improving designs of surveillance for natural or man-made release of virulent pathogens.
A Hybrid Approach to Extract and Classify Relation from Biomedical Text

Unstructured biomedical text is a key source of knowledge. Information extraction in biomedical is a complex task due to the high volume of data. Manual efforts produce the best results; however, it is a near impossible task for such a large amount of data. Thus, there is a need of tools and techniques in biomedical text to extract the information automatically. Biomedical text contains relationships between entities of importance for practitioner and researcher. Relation extraction is an important area in biomedical which has gained much importance. The main work is done on rule based and machine learning techniques in biomedical relation extraction. Recently the focus has changed to hybrid algorithms which have shown better results. This research proposed a hybrid rule based approach to classify relations between biomedical entities. This approach uses Noun-Verb based rules for the identification of noun and verb phrases. It then uses support vector machine, a machine learning technique to classify these relations. Our approach has been validated on standard biomedical text corpus obtained from MEDLINE 2001, an accuracy of 94.91%.
Fast exact maximum likelihood estimation for mixture of language models

A common language modeling approach assumes the data D is generated from a mixture of several language models. EM algorithm is usually used to find the maximum likelihood estimation of one unknown mixture component, given the mixture weights and the other language models. In this paper, we provide an efficient algorithm of O(k) complexity to find the exact solution, where k is the number of words occurred at least once in D. Another merit is that the probabilities of many words are exactly zeros, which means that the mixture language model also serves as a feature selection technique.
Designing and Modeling of Covert Channels in Operating Systems

Covert channels are widely considered as a major risk of information leakage in various operating systems, such as desktop, cloud, and mobile systems. The existing works of modeling covert channels have mainly focused on using finite state machines (FSMs) and their transforms to describe the process of covert channel transmission. However, a FSM is rather an abstract model, where information about the shared resource, synchronization, and encoding/decoding cannot be presented in the model, making it difficult for researchers to realize and analyze the covert channels. In this paper, we use the high-level Petri Nets (HLPN) to model the structural and behavioral properties of covert channels. We use the HLPN to model the classic covert channel protocol. Moreover, the results from the analysis of the HLPN model are used to highlight the major shortcomings and interferences in the protocol. Furthermore, we propose two new covert channel models, namely: (a) two channel transmission protocol (TCTP) model and (b) self-adaptive protocol (SAP) model. The TCTP model circumvents the mutual inferences in encoding and synchronization operations; whereas the SAP model uses sleeping time and redundancy check to ensure correct transmission in an environment with strong noise. To demonstrate the correctness and usability of our proposed models in heterogeneous environments, we implement the TCTP and SAP in three different systems: (a) Linux, (b) Xen, and (c) Fiasco.OC. Our implementation also indicates the practicability of the models in heterogeneous, scalable and flexible environments.
Probabilistic Latent Semantic Analysis Applied to Whole Bacterial Genomes Identifies Common Genomic Features

Summary The spread of drug resistance amongst clinically-important bacteria is a serious, and growing, problem [1]. However, the analysis of entire genomes requires considerable computational effort, usually including the assembly of the genome and subsequent identification of genes known to be important in pathology. An alternative approach is to use computational algorithms to identify genomic differences between pathogenic and non-pathogenic bacteria, even without knowing the biological meaning of those differences. To overcome this problem, a range of techniques for dimensionality reduction have been developed. One such approach is known as latent-variable models [2]. In latent-variable models dimensionality reduction is achieved by representing a high-dimensional data by a few hidden or latent variables, which are not directly observed but inferred from the observed variables present in the model. Probabilistic Latent Semantic Indexing (PLSA) is an extention of LSA [3]. PLSA is based on a mixture decomposition derived from a latent class model. The main objective of the algorithm, as in LSA, is to represent high-dimensional co-occurrence information in a lower-dimensional way in order to discover the hidden semantic structure of the data using a probabilistic framework. In this work we applied the PLSA approach to analyse the common genomic features in methicillin resistant Staphylococcus aureus, using tokens derived from amino acid sequences rather than DNA. We characterised genome-scale amino acid sequences in terms of their components, and then investigated the relationships between genomes and tokens and the phenotypes they generated. As a control we used the non-pathogenic model Gram-positive bacterium Bacillus subtilis.
Mixing in Some Knowledge: Enriched Context Patterns for Bayesian Word Sense Induction

Bayesian topic models have recently been shown to perform well in word sense induction (WSI) tasks. Such models have almost exclusively used bag-of-words features, and failed to attain improvement by including other feature types. In this paper, we investigate the impact of integrating syntactic and knowledge-based features and show that both parametric and non-parametric models consistently beneﬁt from additional feature types. We perform evaluation on the Se-mEval2010 WSI verb data and show statistically signiﬁcant improvement in accuracy (p < 0.001) both over the bag-of-words baselines and over the best system that competed in the SemEval2010 WSI task.
Functional Graphical Models for Manufacturing Process Modeling

Graphical models are widely used to model the statistical relationships among variables in a system. Existing graphical models can be used to model the relationships among scalar variables, but cannot be directly applied to model a system with functional variables. In this paper, a novel functional graphical model is proposed to model complex systems where functional variables are measured. To cope with the small sample size problem, we further develop a special sparsity penalization approach to robustly learn the graphical model from limited sample size, and develop a difference from the mean penalization for functional variables. Simulation studies and a case study in a plasma spray manufacturing process are used to demonstrate the effectiveness of the proposed method.Note to Practitioners—Emerging sensing and information technologies have provided unprecedented functional data collection capacity from systems over time. It is important and challenging to model the relationships among these variables. Among many different modeling alternatives, graphical models are powerful tools to characterize the underlying relationships among variables in systems. However, traditional graphical models cannot directly model a system with a mixture of functional and scalar variables. The proposed model aims to address this challenge by proposing a functional graphical model. Based on simulation studies and a case study for a plasma spray manufacturing process, it is shown that the proposed method performs well under various conditions.
Minimum Wheel-Rotation Paths for Differential-Drive Mobile Robots

The shortest paths for a mobile robot are a fundamental property of the mechanism, and may also be used as a family of primitives for motion planning in the presence of obstacles. This paper characterizes shortest paths for differential-drive mobile robots, with the goal of classifying solutions in the spirit of Dubins curves and Reeds—Shepp curves for car-like robots. To obtain a well-defined notion of shortest, the total amount of wheel-rotation is optimized. Using the Pontryagin Maximum Principle and other tools, we derive the set of optimal paths, and we give a representation of the extremals in the form of finite automata. It turns out that minimum time for the Reeds—Shepp car is equal to minimum wheel-rotation for the differential-drive, and minimum time curves for the convexified Reeds—Shepp car are exactly the same as minimum wheel-rotation paths for the differential-drive. It is currently unknown whether there is a simpler proof for this fact.
Computing replacement paths in surface embedded graphs

Let <i>s</i> and <i>t</i> be vertices in a directed graph <i>G</i> with non-negative edge weights. The replacement paths problem asks us to compute, for each edge <i>e</i> in <i>G</i>, the length of the shortest path from <i>s</i> to <i>t</i> that does not traverse <i>e</i>. We describe an algorithm that solves the replacement paths problem for directed graphs embedded on a surface of any genus <i>g</i> in <i>O</i>(<i>gn</i> log <i>n</i>) time, generalizing a recent <i>O</i>(<i>n</i> log <i>n</i>)-time algorithm of Wulff-Nilsen for planar graphs [SODA 2010].
The computational linguistics summarization pilot task

The Computational Linguistics (CL) Summa-rization Pilot Task was created to encourage a community effort to address the research problem of summarizing research articles as “faceted summaries” in the domain of computational linguistics. In this pilot stage, a hand-annotated set of citing papers was provided for ten reference papers to help in automating the citation span and discourse facet identiﬁcation problems. This paper details the corpus construction efforts by the organizers and the participating teams, who also participated in the task-based evaluation. The annotated development corpus used for this pilot task is publicly available at:
SNAP II and SNAPPE II as Predictors of Neonatal Mortality in a Pediatric Intensive Care Unit: Does Postnatal Age Play a Role?

Introduction. In developing countries, a lack of decentralization of perinatal care leads to many high-risk births occurring in facilities that do not have NICU, leading to admission to a PICU. Objective. To assess SNAP II and SNAPPE II as predictors of neonatal death in the PICU. Methodology. A prospective study of newborns divided into 3 groups according to postnatal age: Group 1 (G1), of 0 to 6 days; Group 2 (G2) of 7 to 14 days; and Group 3 (G3), of 15 to 28 days. Variables analyzed were SNAP II, SNAPPE II, perinatal data, and known risk factors for death. The Hosmer-Lemeshow test and the receiver operating characteristics (ROC) curve were used with SPSS 17.0 for statistical analysis. An Alpha error <5% was considered significant. Results. We analyzed 290 newborns, including 192 from G1, 41 from G2, and 57 from G3. Mortality was similar in all 3 groups. Median SNAP II was higher in newborns that died in all 3 groups (P < 0.05). The area under the ROC curve for SNAP II for G1 was 0.78 (CI 95% 0.70–0.86), for G2 0.66 (CI 95% 0.37–0.94), and for G3 0.74 (CI 95% 0.53–0.93). The area under the ROC curve for SNAPPE II for G1 was 0.76 (CI 95% 0.67–0.85), for G2 0.60 (CI 95% 0.30–0.90), and for G3 0.74 (CI 95% 0.52–0.95). Conclusions. SNAP II and SNAPPE II showed moderate discrimination in predicting mortality. The results are not strong enough to establish the correlation between the score and the risk of mortality.