Miracle-dz/UniFAR-FTUs huggingface dataset:
Parsing parallel evolution: ecological divergence and differential gene expression in the adaptive radiations of thick‐lipped Midas cichlid fishes from Nicaragua

The study of parallel evolution facilitates the discovery of common rules of diversification. Here, we examine the repeated evolution of thick lips in Midas cichlid fishes (the Amphilophus citrinellus species complex)—from two Great Lakes and two crater lakes in Nicaragua—to assess whether similar changes in ecology, phenotypic trophic traits and gene expression accompany parallel trait evolution. Using next‐generation sequencing technology, we characterize transcriptome‐wide differential gene expression in the lips of wild‐caught sympatric thick‐ and thin‐lipped cichlids from all four instances of repeated thick‐lip evolution. Six genes (apolipoprotein D, myelin‐associated glycoprotein precursor, four‐and‐a‐half LIM domain protein 2, calpain‐9, GTPase IMAP family member 8‐like and one hypothetical protein) are significantly underexpressed in the thick‐lipped morph across all four lakes. However, other aspects of lips' gene expression in sympatric morphs differ in a lake‐specific pattern, including the magnitude of differentially expressed genes (97‐510). Generally, fewer genes are differentially expressed among morphs in the younger crater lakes than in those from the older Great Lakes. Body shape, lower pharyngeal jaw size and shape, and stable isotopes (δ13C and δ15N) differ between all sympatric morphs, with the greatest differentiation in the Great Lake Nicaragua. Some ecological traits evolve in parallel (those related to foraging ecology; e.g. lip size, body and head shape) but others, somewhat surprisingly, do not (those related to diet and food processing; e.g. jaw size and shape, stable isotopes). Taken together, this case of parallelism among thick‐ and thin‐lipped cichlids shows a mosaic pattern of parallel and nonparallel evolution.
Universal Data Compression Based on the Burrows-Wheeler Transformation: Theory and Practice

A very interesting recent development in data compression is the Burrows-Wheeler Transformation. The idea is to permute the input sequence in such a way that characters with a similar context are grouped together. We provide a thorough analysis of the Burrows-Wheeler Transformation from an information theoretic point of view. Based on this analysis, the main part of the paper systematically considers techniques to efficiently implement a practical data compression program based on the transformation. We show that our program achieves a better compression rate than other programs that have similar requirements in space and time.
A PRAGMATIC APPROACH TO CHARACTERISING INSECT COMMUNITIES IN NEW ZEALAND: MALAISE TRAPPED BEETLES

Insect communities from a range of successional vegetation stages on the central North Island volcanic plateau were characterised and compared using Malaise trapped beetle samples. Results were derived from sampling series conducted in a total of ten sites over three separate summers. Divisive classification successfully grouped samples according to four main habitat types despite temporal and spatial separation of samples within these groups. A four-week period in early summer was found to be optimum for sample discrimination according to the main vegetation types. Component species and the functional (trophic) structure of samples showed a relationship with generic habitat types as well as reflecting attributes of the individual sites sampled. The most useful information was derived from the identity of the biological entities rather than simply from numerical relationships. Use of this pragmatic methodology has potential to improve our ecosystem understanding and the sustainable management of the dominant area of terrestrial biodiversity provided sufficient attention is given to standardisation, particularly of taxonomy. __________________________________________________________________________________________________________________________________
How Industry Is Approaching the Search for New Diagnostic Markers and Biomarkers*

In the diagnostic and the pharmaceutical industry there is a constant need for new diagnostic markers and biomarkers with improved sensitivity and specificity. During the last 5 years, only a few novel diagnostic markers have been introduced into the market. Proteomics technologies are now offering unique chances to identify new candidate markers. Before a marker can be introduced into the market, three successive developmental phases have to be completed: the discovery phase, in which a variety of proteomics technologies are applied to identify marker candidates; the prototype developmental phase, in which immunological assays are established and validated in defined sample collectives; and finally the product development phase, with assay formats suitable for automated platforms. The hurdles that a potential candidate marker has to pass in each developmental phase before reaching the market are considerable. The costs are increasing from phase to phase, and in industry a number of questions concerning the medical need and the potential return on investment have to be answered before a proteomics discovery project is started. In this review, we will cover aspects of all three developmental phases including the repertoire of discovery tools for protein separation as well as giving an outline of modern principles of mass spectrometry for the identification of proteins.
Measuring the Impacts of Teachers: Comment†

Chetty, Friedman, and Rockoff (2014a, b) study value-added (VA) measures of teacher effectiveness. CFR (2014a) exploits teacher switching as a quasi-experiment, concluding that student sorting creates negligible bias in VA scores. CFR (2014b) finds VA scores are useful proxies for teachers' effects on students' long-run outcomes. I successfully reproduce each in North Carolina data. But I find that the quasi-experiment is invalid, as teacher switching is correlated with changes in student preparedness. Adjusting for this, I find moderate bias in VA scores, perhaps 10-35 percent as large, in variance terms, as teachers' causal effects. Long-run results are sensitive to controls and cannot support strong conclusions.
The Role of Metamotivational Monitoring in Motivation Regulation

This article builds on existing models of motivation regulation in order to examine how students identify and address motivational deficits (e.g., not enough motivation or not the right type of motivation). Integrating perspectives from the achievement motivation, metacognition, and emotion regulation literatures, we propose that metamotivational processes play an essential role in students' monitoring of their motivational states. By emphasizing the ways in which students monitor not only the quantity but also the quality of their motivation, our model extends existing perspectives. We identify different components of motivation that are likely to be the target of monitoring (e.g., self-efficacy, intrinsic value), specify the metamotivational feelings (e.g., hopelessness, boredom) that signal problems with each component, and discuss how strategies are selected to address these problems. Our framework generates new questions about how students monitor (and control) their task-specific motivation.
Does the Political System Determine Media Visibility of Politicians? A Comparative Analysis of Political Functions in the News in Sixteen Countries

News coverage of politicians is very unequally distributed: a few powerful politicians receive the bulk of media attention, while the large majority hardly gets into the news. However, case studies show that news outlets in some countries give more attention to ordinary politicians compared to other democracies. This study examines and explains the variation in media visibility of politicians with different institutional functions across Western democracies. We employ a large-scale content analysis of television news, newspapers and online news in sixteen countries to analyze whether a political system logic determines the distribution of political functions appearing in the news. This logic suggests that journalists follow the political hierarchy of the country when covering politicians. We also check for an additional media logic that would push journalists to focus on a limited number of high-standing politicians. The results confirm that both logics matter, but that mainly the structural characteristics of the political system have an impact on the distribution of news coverage of politicians. In countries where political power is more equally distributed across politicians, a broader range of (elite) politicians makes it into the news. Our results suggest that the media logic is nested in the broader political context and in some cases even strengthens the logic of the political system.
Flipping Engineering Courses: A School Wide Initiative.

In the 2013–2014 school year, we implemented the “flipped classroom” as part of an initiative to drive active learning, student engagement and enhanced learning in our school. The flipped courses consisted of freshman through senior engineering classes in introductory programming, statics/mechanics, mechanical design, bio-thermodynamics, facilities layout/material handling, and chemical engineering dynamics and modeling. In the flipped classroom, students watch video lectures beforehand to obtain the foundational knowledge and then demonstrate skills during class. Our study set out to address the following research questions: 1) Does the flipped classroom promote student engagement during class, and does it positively impact the classroom environment? , 2) Is the flipped classroom associated with increased student achievement and learning of content? , and 3) What strengths, benefits, and drawbacks do students perceive with the flipped classroom? To address these, we used a mixed methods approach, including environment and evaluation surveys, instructor interviews, exam and homework results, video access data, and structured classroom observation. Based on our use of the College and University Classroom Environment Inventory ( CUCEI), we found evidence that flipped instruction can positively impact the classroom
Temporal changes in the structure of a plant-frugivore network are influenced by bird migration and fruit availability

Background. Ecological communities are dynamic collections whose composition and structure change over time, making up complex interspecific interaction networks. Mutualistic plant–animal networks can be approached through complex network analysis; these networks are characterized by a nested structure consisting of a core of generalist species, which endows the network with stability and robustness against disturbance. Those mutualistic network structures can vary as a consequence of seasonal fluctuations and food availability, as well as the arrival of new species into the system that might disorder the mutualistic network structure (e.g., a decrease in nested pattern). However, there is no assessment on how the arrival of migratory species into seasonal tropical systems can modify such patterns. Emergent and fine structural temporal patterns are adressed here for the first time for plant-frugivorous bird networks in a highly seasonal tropical environment. Methods. In a plant-frugivorous bird community, we analyzed the temporal turnover of bird species comprising the network core and periphery of ten temporal interaction networks resulting from different bird migration periods. Additionally, we evaluated how fruit abundance and richness, as well as the arrival of migratory birds into the system, explained the temporal changes in network parameters such as network size, connectance, nestedness, specialization, interaction strength asymmetry and niche overlap. The analysis included data from 10 quantitative plant-frugivorous bird networks registered from November 2013 to November 2014. Results. We registered a total of 319 interactions between 42 plant species and 44 frugivorous bird species; only ten bird species were part of the network core. We witnessed a noteworthy turnover of the species comprising the network periphery during migration periods, as opposed to the network core, which did not show significant temporal changes in species composition. Our results revealed that migration and fruit richness explain the temporal variations in network size, connectance, nestedness and interaction strength asymmetry. On the other hand, fruit abundance only explained connectance and nestedness. Discussion. By means of a fine-resolution temporal analysis, we evidenced for the first time how temporal changes in the interaction network structure respond to the arrival of migratory species into the system and to fruit availability. Additionally, few migratory bird species are important links for structuring networks, while most of them were peripheral species. We showed the relevance of studying bird–plant interactions at fine temporal scales, considering changing scenarios of species composition with a quantitative network approach.
Cardinality Restricted Boltzmann Machines

The Restricted Boltzmann Machine (RBM) is a popular density model that is also good for extracting features. A main source of tractability in RBM models is that, given an input, the posterior distribution over hidden variables is factorizable and can be easily computed and sampled from. Sparsity and competition in the hidden representation is beneficial, and while an RBM with competition among its hidden units would acquire some of the attractive properties of sparse coding, such constraints are typically not added, as the resulting posterior over the hidden units seemingly becomes intractable. In this paper we show that a dynamic programming algorithm can be used to implement exact sparsity in the RBM's hidden units. We also show how to pass derivatives through the resulting posterior marginals, which makes it possible to fine-tune a pre-trained neural network with sparse hidden layers.
An Analytical Study of Various Ad-hoc Network Routing Protocols Under Certain Parameters using Qualnet-7 . 1

The Ad hoc network is the different kind of mobile wireless network where a set of mobile nodes form an impermanent network without using any backbone such as base station or infrastructure. However, numbers of routing protocols are there for the ad-hoc network environment, but it is not an easy task to decide which one routing protocol is efficiently best for particular situations. This paper attempts to identify the best routing protocols based on some Quality of Service (QoS) metrics. Here, the research article is subjected to the three different on-demand routing protocols (DYMO, AODV, and DSR) and two different Zone Routing Protocols (IARP, and IERP) with IEEE 802.11 MAC layer protocol in Random_Way_Point Mobility Model and 50 numbers of fixed nodes. These five different routing protocols have been analysed and their performances are compared in this paper. The comparative performance evaluation has been done with the help of performance measuring metrics such as jitter, average end-to-end delay, throughput and packet delivery ratio (pdr) by using MAC and physical layer model. The simulation has been performed for the number of times with different values of pause time such as 0, 20, 40, 60, 80, and 100 seconds for all taken routing protocols. Experimental results have been carried out by using simulation tool QualNet (version7.1). Further, the excel sheet has been used for preparing the graphs from the collected data for different metrics. KeywordsAd-hoc network, IERP, IARP, AODV, DSR, DYMO, QualNet-7.1, PDR, MANET. African Journal of Computing & ICT Reference Format: S. K. Gupta, M. Yadav & R. K. Saket (2015): An Analytical Study of Various Ad-hoc Network Routing Protocols Under Certain Parameters using Qualnet-7.1. Afr J. of Comp & ICTs. Vol 8, No. 3. Pp 51.58.
Charles A. Dice Center for Research in Financial Economics Do Entrenched Managers Pay Their Workers More? Do Entrenched Managers Pay Their Workers More? *

Analyzing a large panel that matches public firms with worker-level data, we find that managerial entrenchment affects workers' pay. CEOs with more control pay their workers more, but financial incentives through ownership of cash flow rights mitigate such behavior. These findings do not seem to be driven by productivity differences, and are not affected by a series of robustness tests. Moreover, we find that entrenched CEOs pay more to (i) workers associated with aggressive unions; (ii) workers closer to the CEO in the corporate hierarchy, such as CFOs, division vice-presidents and other top-executives; and (iii) workers geographically closer to the corporate headquarters. This evidence is consistent with entrenched CEOs paying higher wages to enjoy non-pecuniary private benefits such as lower effort wage bargaining and improved social relations with certain workers. More generally, our results show that managerial ownership and corporate governance can play an important role for labor market outcomes. The comments by an anonymous referee, the Associate Editor, Angie Low and René Stulz were particularly helpful. We are grateful to Statistics Sweden, MM Partners, and Findata for providing firm-, subsidiary-, and worker-level data for this project, and to Henrik Lindberg at the RATIO Institute for sharing with us his data on labor market conflicts.
Self-Similarity in Social Network Dynamics

Analyzing and modeling social network dynamics are key to accurately predicting resource needs and system behavior in online social networks. The presence of statistical scaling properties, that is, self-similarity, is critical for determining how to model network dynamics. In this work, we study the role that self-similarity scaling plays in a social network edge creation (that is, links created between users) process, through analysis of two detailed, time-stamped traces, a 199 million edge trace over 2 years in the Renren social network, and 876K interactions in a 4-year trace of Facebook. Using wavelet-based analysis, we find that the edge creation process in both networks is consistent with self-similarity scaling, once we account for periodic user activity that makes edge creation process non-stationary. Using these findings, we build a complete model of social network dynamics that combines temporal and spatial components. Specifically, the temporal behavior of our model reflects self-similar scaling properties, and accounts for certain deterministic non-stationary features. The spatial side accounts for observed long-term graph properties, such as graph distance shrinkage and local declustering. We validate our model against network dynamics in Renren and Facebook datasets, and show that it succeeds in producing desired properties in both temporal patterns and graph structural features.
Shocked gas layers surrounding the WR nebula NGC 2359

NGC 2359 is a Wolf-Rayet (W-R) nebula partially bound by a rather dense and warm molecular cloud. We present the results derived from CO and 13 CO fully sampled maps of the molecular material with angular resolutions up to 12 00 . W e have detected three dierent velocity components, and determined their spatial distribution and physical properties. The kinematics, morphology, mass and density are clearly stratified with respect to the W-R star. These features allow us to learn about the recent evolutionary history of HD 56925, because the multiple layers could be associated to several energetic events which have acted upon the surrounding circumstellar medium. Hence, a careful study of the dierent shockfronts contain clues in determining the present and past interaction of this evolved massive star with its surroundings. From the analysis of the mass-loss history in massive stars like HD 56925, we suggest that the multiple layers of shocked molecular gas are likely to be produced during the earlier LBV phase and/or the actual W-R stage of HD 56925.
Implementation of image fusion algorithm using MATLAB (LAPLACIAN PYRAMID)

This paper represents a approach to implement image fusion algorithm ie LAPLACIAN PYRAMID. In this technique implements a pattern selective approach to image fusion. The basic idea is to perform a pyramid decomposition on each source image and finally reconstruct the fused image by performing an inverse pyramid transform. It offers benefits like resolution, S/N ratio and pixel size. The aim of image fusion, apart from reducing the amount of data, is to create new images that are more suitable for the purposes of human/machine perception, and for further image-processing tasks such as segmentation, object detection or target recognition in applications such as remote sensing and medical imaging Based on this technique finally it reconstructs the fused image from the fused pyramid.
U-duality Covariant Membranes

We outline a formulation of membrane dynamics in D=8 which is fully covariant under the U-duality group SL(2,Z) x SL(3,Z), and encodes all interactions to fields in the eight-dimensional supergravity, which is constructed through Kaluza--Klein reduction on T^3. Among the membrane degrees of freedom is an SL(2,R) doublet of world-volume 2-form potentials, whose quantised electric fluxes determine the membrane charges, and are conjectured to provide an interpretation of the variables occurring in minimal representations of U-duality groups. We solve the relevant equations for the action for a restricted class of supergravity backgrounds. Some comments are made on supersymmetry and lower dimensions.
Microscopic Muscle Image Enhancement

We propose a robust image enhancement algorithm dedicated for muscle fiber specimen images captured by optical microscopes. Blur or out of focus problems are prevalent in muscle images during the image acquisition stage. Traditional image deconvolution methods do not work since they assume the blur kernels are known and also produce ring artifacts. We provide a compact framework which involves a novel spatially non-uniform blind deblurring approach specialized to muscle images which automatically detects and alleviates degraded regions. Ring artifacts problems are addressed and a kernel propagation strategy is proposed to speedup the algorithm and deals with the high non-uniformity of the blur kernels on muscle images. Experiments show that the proposed framework performs well on muscle images taken with modern advanced optical microscopes. Our framework is free of laborious parameter settings and is computationally efficient.
MimeoDroid: Large Scale Dynamic App Analysis on Cloned Devices via Machine Learning Classifiers

The exponential adoption of Android applications (apps) among the users has attracted malware authors to evade the default emulator based dynamic analysis systems. The evolving Android malware behaves benign once it identifies Goldfish emulator, often used for app development and malware analysis. Once a malware identifies the Goldfish virtual device, it behaves benign or prevents malicious code execution. The exponential increase of such stealth malware necessitates a detection approach which coerces the malicious apps to reveal the hidden behavior. To detect malicious apps and characterize their association we propose MimeoDroid (enriched replica of real Android device), a modified virtual clone to coerce the malware to believe being executed on an actual device. We automate relevant feature extraction and classification of Processor, memory usage, Binder IPC transfers, network interaction, battery charging status and manifest permission(s) to detect malicious behavior using Tree based machine learning classifiers. MimeoDroid is a lightweight machine learning based malware analysis and characterization to detect malicious apps that would evade the existing analyzers.