Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking media systems : insights from a case study of paid news in India

This thesis examines whether and how an enhancement can be effected in the depth and accuracy with which media systems theory meets its proclaimed objective of understanding and describing the behaviour of media systems. It draws upon critiques of the applicability of media systems theory to non-Western media systems to ask whether the grounded study of media systems reveals additional variables or approaches to enhance its descriptive and explanatory power. It answers these questions by conducting an exploratory case study of the complex and relatively unmapped Indian media system, focusing on the system-wide phenomenon of paid news, of which the most egregious form is political advertising masquerading as news at election time. The case study draws upon a thematic analysis of rich empirical data from 47 in-depth semi-structured interviews conducted with top journalists, editors, owners, policy makers and heads of regulatory bodies, triangulated against a large variety of documents and statistical data. The study constructs a map of the Indian media system to establish its commercial, organisational and regulatory contours, and produces an analysis of the forces and relationships that define the systemic behaviour that is manifest as paid news. Its theoretical contribution includes the suggestion of candidate variables and approaches to supplement existing macro-level factors used by models and typologies of media systems theory. It also sets out seven theoretical propositions: 1) The wider applicability of media systems theory is hampered by the adoption of Western media as a pivotal reference point; 2) Normative beliefs and macro-level structures are inadequate descriptors of media systems; 3) Dimensions other than politics and economics can be critical to defining media systems; 4) Variables that account for forces and relationships within the media are important in characterising media systems; 5) Media systems are not passively shaped by political, economic and other structures; they have agency and can act independently; 6) Grounded case studies can yield variables and approaches that help characterise media systems; and 7) System-wide ethical or normative fault-lines such as paid news can reveal critical characteristics of a media system.
HDR video tone mapping based on gamma blending

In this paper we propose a new tone mapping method for the HDR video. Two types of gamma tone mapping are blended to preserve local contrasts in the entire range of luminance. Our method achieves a high quality tone mapping especially for HDR video that has a nonlinear response to scene radiance. We examine the validity of our method through simulation and comparison with conventional work
A framework of mobile context management for supporting context-aware environments in mobile ad hoc networks

In this paper, we propose a framework for supporting context-aware environments in mobile ad hoc networks (MANETs). A virtual overlay network and two novel approaches in this framework are addressed to significantly improve the efficiency of data delivery in MANETs. The surrounding context of mobile nodes is used to determine which scheme, push-based or pull-based approaches, is adopted. When a real-time event happened in a node, push-based approach is adopted to disseminate urgent messages to its neighboring nodes. On the contrary, pull-based approach is adopted by building a virtual backbone, namely segment-tree, for seeking for more contextual information. Finally, the simulation is conducted to illustrate the performance achievements that our methods outperform the existing publish/subscribe approach based on AODV routing protocol.
Synaptic E3 Ligase SCRAPPER in Contextual Fear Conditioning: Extensive Behavioral Phenotyping of Scrapper Heterozygote and Overexpressing Mutant Mice

SCRAPPER, an F-box protein coded by FBXL20, is a subunit of SCF type E3 ubiquitin ligase. SCRAPPER localizes synapses and directly binds to Rab3-interacting molecule 1 (RIM1), an essential factor for synaptic vesicle release, thus it regulates neural transmission via RIM1 degradation. A defect in SCRAPPER leads to neurotransmission abnormalities, which could subsequently result in neurodegenerative phenotypes. Because it is likely that the alteration of neural transmission in Scrapper mutant mice affect their systemic condition, we have analyzed the behavioral phenotypes of mice with decreased or increased the amount of SCRAPPER. We carried out a series of behavioral test batteries for Scrapper mutant mice. Scrapper transgenic mice overexpressing SCRAPPER in the hippocampus did not show any significant difference in every test argued in this manuscript by comparison with wild-type mice. On the other hand, heterozygotes of Scrapper knockout [SCR (+/−)] mice showed significant difference in the contextual but not cued fear conditioning test. In addition, SCR (+/−) mice altered in some tests reflecting anxiety, which implies the loss of functions of SCRAPPER in the hippocampus. The behavioral phenotypes of Scrapper mutant mice suggest that molecular degradation conferred by SCRAPPER play important roles in hippocampal-dependent fear memory formation.
“When Noah built the ark…”: Metaphor and biblical stories in Facebook preaching

This article investigates the use of biblical stories and text in the preaching of Joshua Feuerstein, a popular Facebook evangelist, and focuses on how biblical stories are used to position the viewer in comparison to biblical characters and texts. Taking a discourse dynamics approach ( Cameron & Maslen, 2010 ), a corpus of 8 short videos (17 minutes 34 seconds) and their comments (2,295) taken from the Facebook are analysed first, for the presence of metaphorical language and stories taken from the Bible. Second, they are analysed for the role of metaphor in the narrative positioning ( Bamberg, 1997 ) of the viewer, particularly as it relates to Gibbs’s notion of ‘allegorises’, or the ‘allegoric impulse’ ( Gibbs, 2011 ). The corresponding text comments from the videos are then also analysed for the presence of the same biblical metaphor, focusing on how commenters interact with the metaphor and Feuerstein’s positioning of them. Findings show that biblical metaphorical language is used to position viewers and their struggles in the context of larger storylines that compare everyday experiences to biblical texts. This comparison can happen both in explicit narrative positioning of viewers with explicit reference to the Bible, and implicit positioning, through the use of unmarked biblical language. Analysis of viewer comments shows that use of metaphorical language is successful in building a sense of camaraderie and shared belief among the viewer and Feuerstein, as well as viewers with one another.
On the performance of a dual round-robin switch

The dual round-robin matching (DRRM) switch has a scalable, low complexity architecture which allows for an aggregate bandwidth exceeding 1 Tb/s using current CMOS technology. In this paper we prove that the DRRM switch can achieve 100% throughput under i.i. d. and uniform traffic. The DRRM is the first practical matching scheme for which this property has been proved. The performance of the DRRM switch is then studied and compared with the iSLIP switch. The delay performance under uniform traffic and the hot-spot throughput of DRRM is better than that of iSLIP, while the throughput of iSLIP under some nonuniform traffic scenarios is slightly higher than that of DRRM. Since throughput drops below 100%, under nonuniform traffic, we also examine some variations of the DRRM matching scheme for nonuniform traffic.
Optimization of retraction in neurosurgery to avoid damage caused by deformation of brain tissues

In neurosurgery, effects of deformation should be considered to avoid damaging brain tissues. The goal of this study is to develop an automatic path planner considering the deformation of brain tissues. This paper shows a scheme which combines FEM (Finite Element Method) and an optimization method for optimization of retraction in order to approach a deep part of a brain. Also, evaluations of two optimization results are discussed. One optimization is for retraction of a simple shape model for comparing two solvers, Pattern Search and Genetic Algorithm. Pattern Search Algorithm obtained maximum view size for the simple model when the principal stress of the tissue is not more than the threshold 500 (Pa). The other optimization is for retraction of a brain fissure model. Based on the result of the simple shape model, Pattern Search Algorithm is used for this optimization. It successfully generated optimal position and posture of a spatula for opening the fissure model which has same mechanical property with the human brain. These results show the effectiveness of the proposed scheme.
Normalization of High Dimensional Genomics Data Where the Distribution of the Altered Variables Is Skewed

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.
Parental Psychopathology and Child-Rearing Practices in Young Alcoholic Families.

This study investigated the relationship between parental alcohol involvement, depression, and antisocial behavior, and selfreportel parenting practices in a population based sample of 79 intact alcoholic families with three to six year old boys. Higher family social prestige (SES) and parent intelligence were associated with reports of being more affectively positive toward the child, more child-centered, and more encouraging of autonomy, as well as teing more in agreement about parenting. For both mothers and fathers, self-reported depression was negatively associated with the level of positive affective parenting and the level of interparent agreement on child-rearing. lathers' history of depressioil was positively related to child-centered parenting. Mothers' involvement in antisocial behavior was associated with more negative affective parenting and more encouragement of competitive achievement. The results provide evidence that in young alcoholic families, parenting practices are related most heavily to nonalcohol specific factors; in particular, lower level of social status and greater parental depression are associated with more parenting disagreement and a more affectively negative parenting environment.
Functional significance of protein assemblies predicted by the crystal structure of the restriction endonuclease BsaWI

Type II restriction endonuclease BsaWI recognizes a degenerated sequence 5′-W/CCGGW-3′ (W stands for A or T, ‘/’ denotes the cleavage site). It belongs to a large family of restriction enzymes that contain a conserved CCGG tetranucleotide in their target sites. These enzymes are arranged as dimers or tetramers, and require binding of one, two or three DNA targets for their optimal catalytic activity. Here, we present a crystal structure and biochemical characterization of the restriction endonuclease BsaWI. BsaWI is arranged as an ‘open’ configuration dimer and binds a single DNA copy through a minor groove contacts. In the crystal primary BsaWI dimers form an indefinite linear chain via the C-terminal domain contacts implying possible higher order aggregates. We show that in solution BsaWI protein exists in a dimer-tetramer-oligomer equilibrium, but in the presence of specific DNA forms a tetramer bound to two target sites. Site-directed mutagenesis and kinetic experiments show that BsaWI is active as a tetramer and requires two target sites for optimal activity. We propose BsaWI mechanism that shares common features both with dimeric Ecl18kI/SgrAI and bona fide tetrameric NgoMIV/SfiI enzymes.
The convergence rate of linearly separable SMO

It is well known that the dual function value sequence generated by SMO has a linear convergence rate when the kernel matrix is positive definite and sublinear convergence is also known to hold for a general matrix. In this paper we will prove that, when applied to hard-margin, i.e., linearly separable SVM problems, a linear convergence rate holds for the SMO algorithm without any condition on the kernel matrix. Moreover, we will also show linear convergence for the multiplier sequence generated by SMO, the corresponding weight vectors and the KKT gap usually applied to control the number of SMO iterations. This gives a fairly complete picture of the convergence of the various sequences SMO generates. While linear SMO convergence for the general SVM L1 soft margin problem is still open, the approach followed here may lead to such a general result.
Differentiation of IKA in amphibian spinal neurons

We have examined the development of an inactivating outward current in embryonic amphibian neurons differentiating in culture. On the basis of ionic selectivity, voltage dependence of activation and inactivation and pharmacological sensitivity, it is similar to A currents described in other neurons. In these embryonic neurons the A current appears later than other voltage-dependent currents studied previously. Furthermore, its maturation extends to times later than those required for the differentiation of another potassium current, the voltage- dependent delayed rectifier. These changes in A current are consistent with parallel changes in the action potential and excitability of the developing neurons.
Direction-adaptive context modeling for sign coding in embedded wavelet image coder

The conventional embedded wavelet image coder exploiting the adjacent neighbors is limited to efficiently compress the sign coefficients, since the wavelet coefficients are highly correlated along the dominant image features, such as edges or contours. To solve the problem, in this paper, we propose the direction-adaptive sign context modeling by adaptively exploiting the neighbors suitable to the dominant image features. Experimental results show that the sign coding based on the proposed context modeling reduces the sign bits up to 5.5% compared to the conventional sign coding method.
Test theory for assessing IR test collections

How good is an IR test collection? A series of papers in recent years has addressed the question by empirically enumerating the consistency of performance comparisons using alternate subsets of the collection. In this paper we propose using Test Theory, which is based on analysis of variance and is specifically designed to assess test collections. Using the method, we not only can measure test reliability after the fact, but we can estimate the test collection's reliability before it is even built or used. We can also determine an optimal allocation of resources before the fact, e.g. whether to invest in more judges or queries. The method, which is in widespread use in the field of educational testing, complements data-driven approaches to assessing test collections. Whereas the data-driven method focuses on test results, test theory focuses on test designs. It offers unique practical results, as well as insights about the variety and implications of alternative test designs.
Research on In-Memory Computing Model and Data Analysis

The ever-increasing Big Data is acclaimed, and the key point of Big Data is data analysis. However focusing on the Big Data with dynamic and multiple-dimensional characteristic is difficult to obtain reliable and accurate analytical results by the traditional data analysis methods. Therefore this is an important opportunity and great challenge for the data analysis methods to be developed. This paper aims to make an important research and investigation of the multiple correlation analysis for dynamic Big Data. The paper is expected to reveal the multiple correlation analysis for dynamic Big Data. On one hand this paper research achievements would provide a scientific basis for multiple correlation analysis and revelation of the objective law in Big Data area. On the other hand it is also an important implication for sustainable development of Big Data.
A 5 GHz CMOS transceiver for IEEE 802.11a wireless LAN

A 5 GHz transceiver comprising the RF and analog circuits of an IEEE 802.11a-complaint WLAN using a 0.25 /spl mu/m CMOS technology occupies 22 mm/sup 2/. The IC has 22 dBm maximum transmitted power, 8 dB overall receive-chain noise figure, and -112 dBc/Hz synthesizer phase noise at 1 MHz offset.
Energy‐Efficient Protocol Design

Recent studies have shown that the total energy consumed by the Internet has followed an increasing trend over the years. A considerable part of this energy is wasted due to an inefficient utilization of edge devices, i.e., Personal Computers (PCs) and other user equipment in homes and offices. PCs are often left on even when they are not used (e.g., overnight or during weekends), while other edge devices, such as printers, IP phones, and displays, are typically kept always on, especially in public buildings. A number of solutions have been proposed for eliminating, or reducing, energy wastes due to edge devices. In this chapter we propose a general taxonomy for their classification. Then, according to the introduced taxonomy, we survey the main proposals appeared in the literature. Finally, we highlight some open issues.
Fast forward feature selection for the nonlinear classification of hyperspectral images

A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation. In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM can be updated when the estimation of the classification rate is computed, rather than re-estimate the full model. Secondly, using marginalization of the GMM, sub models can be directly obtained from the full model learned with all the spectral features. Experimental results for two real hyperspectral data sets show that the method performs very well in terms of classification accuracy and processing time. Furthermore, the extracted model contains very few spectral channels.
Sleep Debt Elicits Negative Emotional Reaction through Diminished Amygdala-Anterior Cingulate Functional Connectivity

Objectives Sleep debt reportedly increases emotional instability, such as anxiety and confusion, in addition to sleepiness and psychomotor impairment. However, the neural basis of emotional instability due to sleep debt has yet to be elucidated. This study investigated changes in emotional responses that are elicited by the simulation of short-term sleep loss and the brain regions responsible for these changes. Subjects and Methods Fourteen healthy adult men aged 24.1±3.3 years (range, 20–32 years) participated in a within-subject crossover study consisting of 5-day sessions of both sleep debt (4 h for time in bed) and sleep control (8 h for time in bed). On the last day of each session, participants underwent polysomnography and completed the State-Trait Anxiety Inventory and Profile of Mood States questionnaires. In addition, functional magnetic resonance imaging was conducted while performing an emotional face viewing task. Results Restricted sleep over the 5-day period increased the activity of the left amygdala in response to the facial expression of fear, whereas a happy facial expression did not change the activity. Restricted sleep also resulted in a significant decrease in the functional connectivity between the amygdala and the ventral anterior cingulate cortex (vACC) in proportion to the degree of sleep debt (as indicated by the percentage of slow wave sleep and δ wave power). This decrease was significantly correlated with activation of the left amygdala and deterioration of subjective mood state. Conclusion The results of this study suggest that continuous and accumulating sleep debt that can be experienced in everyday life can downregulate the functional suppression of the amygdala by the vACC and consequently enhance the response of the amygdala to negative emotional stimuli. Such functional alteration in emotional control may, in part, be attributed to the neural basis of emotional instability during sleep debt.
Clustering Learning Objects Collections Using Cluster Ensembles

Learning Object Repositories are increasingly being used in learning systems to provide high-quality, reusable educational materials. A relevant data mining problem associated with the automatic categorization of learning objects is the discovery of intrinsic classes based on the text ual contents of the meta-data records. In this paper, we present a cluster ensemble method, that is applicable for distributed clustering of learning objects, and for overcoming issues of large-sized collections and very high dimensionali ty of the vocabulary space. First, an initial significant reduc tion of the vocabulary space is proposed. Subsequently, a cluster ensemble method, consisting of three stages is proposed. Base clusterings for multiple random subsets of the data are generated, followed by the application of an adaptive voting algorithm for resolving the cluster label mapping problem. Finally, a consensus clustering is extracted by merging similar clusters that may be produced by fineresolution base clusterings, and maximum likelihood principle is applied on a resulting assignment probability matrix. The experimental analysis shows improvement in the consensus clustering quality over the base clusterings, an d a competitive performance compared to clustering the original collection, as measured using normalized mutual information. The analysis validates the applicability of the proposed ensemble method.