Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Wireless Sensor Network Routing by High Quality Link Set Cooperative Routing Algorithm

In general Wireless Sensor Networks (WSN’s), improving the network reliability by using cooperative communication to select the relay node with low overhead is a challenging task. Also the design of routing scheme based on link quality is a critical problem. Due to the resource constraints, connections between the nodes are changeable one. It leads to the network to create a various routes which are necessary. By using the established path, it delivers the data to the base station in a reliable manner. This paper concentrates on a cross layer approach by using various cooperative metric such as Packet Delivery Rate (PDR), Packet Reception Rate (PRR), Required Number of Packet transmission (RNP), Average number of packet sent/resent and Expected Transmission Time (ETX). These parameters are taken as a fuzzy parameter that exploits the cooperative diversity at the routing layer. We propose a Fuzzy based High Quality Link Set Routing (FHQLSR) which selects the high quality relay node and evaluates the quality of link in each node by using RNP and ETX parameters. This FHQLSR scheme is used to find the effective relay node that forward packets to the base station via high quality link and also find the optimal path using Improved Dijikstra’s Algorithm (IDA) in the multi hop networks. By considering routing and link quality parameters, the FHQLSR scheme improves the overall reliability, scalability and stability of the network.
On-Line Handwritten Text Line Detection Using Dynamic Programming

In this paper we propose a novel approach to th tion of on-line handwritten text lines based on dynamic programming. We try to find the paths with the minimum cost between two consecutive text lines. Most steps of the proposed algorithm are based on off-line information. Hence the method can also be applied to off-line documents after a few minor changes. In our experiments we show that this dynamic programming based approach is better than a common on-line segmentation procedure.
Diffusion and Emergence in Social Networks

Diffusion is the process by which new products and practices are invented and successfully introduced into society. Numerous studies on the diffusion of individual innovations have been conducted, many exhibiting common features such as the famous S-shaped diffusion curve. One basic question posed by innovation diffusion is why there is often a long lag time between an innovation’s first appearance and the time when a substantial number of people have adopted it. An extensive amount of theoretical and empirical literature has been devoted to this phenomenon and the mechanisms behind it. New ideas, products, and innovations often take time to diffuse, a fact that is often attributed to the heterogeneity of human populations. In this chapter, we provide an overview of research examining how the structure of social networks impacts diffusion process. The diffusion process enhances innovations via feedback of information about the innovation’s utility—which can be used to make future improvements—to many different users. This aspect of the diffusion process is similar to the micro-macro loop, which is an essential part of emergence. The aim of this research is to understand how the structure of social networks determines the dynamics of various types of emergent properties occurring within those networks. For emergence at the social level, patterns of social interactions are critical.
Optimal coding for the deletion channel with small deletion probability

The deletion channel is the simplest point-to-point communication channel that models lack of synchronization. Input bits are deleted independently with probability d, and when they are not deleted, they are not affected by the channel. Despite significant effort, little is known about the capacity of this channel, and even less about optimal coding schemes. In this paper we develop a new systematic approach to this problem, by demonstrating that capacity can be computed in a series expansion for small deletion probability. We compute three leading terms of this expansion, and find an input distribution that achieves capacity up to this order. This constitutes the first optimal coding result for the deletion channel. The key idea employed is the following: We understand perfectly the deletion channel with deletion probability d=0. It has capacity 1 and the optimal input distribution is i.i. d. Bernoulli(1/2). It is natural to expect that the channel with small deletion probabilities has a capacity that varies smoothly with d, and that the optimal input distribution is obtained by smoothly perturbing the i.i. d. Bernoulli(1/2) process. Our results show that this is indeed the case. We think that this general strategy can be useful in a number of capacity calculations.
Proxemics-Based Visual Concepts to Attract and Engage Public Display Users: Adaptive Content Motion and Adaptive User Shadow

Large interactive displays presenting useful content responding to users' actions are becoming ubiquitous in public places such as museums and campuses. Being an integral part of the surrounding environment, a major design challenge is to first attract passersby's attention and entice them to interact. One promising approach is to incorporate proxemics parameters to guide how the content are visually presented. In this paper we describe the design of a field experiment using user-display distances in determining how visual content was displayed, and its deployment at a university campus with frequent foot traffic. We also offer some selection criteria for reference when conducting a field experiment with public large interactive displays.
The Power of Linear Estimators

For a broad class of practically relevant distribution properties, which includes entropy and support size, nearly all of the proposed estimators have an especially simple form. Given a set of independent samples from a discrete distribution, these estimators tally the vector of summary statistics -- the number of domain elements seen once, twice, etc. in the sample -- and output the dot product between these summary statistics, and a fixed vector of coefficients. We term such estimators \emph{linear}. This historical proclivity towards linear estimators is slightly perplexing, since, despite many efforts over nearly 60 years, all proposed such estimators have significantly sub optimal convergence, compared to the bounds shown in [VV11]. Our main result, in some sense vindicating this insistence on linear estimators, is that for any property in this broad class, there exists a near-optimal linear estimator. Additionally, we give a practical and polynomial-time algorithm for constructing such estimators for any given parameters. While this result does not yield explicit bounds on the sample complexities of these estimation tasks, we leverage the insights provided by this result to give explicit constructions of near-optimal linear estimators for three properties: entropy, $L_1$ distance to uniformity, and for pairs of distributions, $L_1$ distance. Our entropy estimator, when given $O(\frac{n}{\eps \log n})$ independent samples from a distribution of support at most $n,$ will estimate the entropy of the distribution to within additive accuracy $\epsilon$, with probability of failure $o(1/poly(n)). $ From the recent lower bounds given in [VV11], this estimator is optimal, to constant factor, both in its dependence on $n$, and its dependence on $\epsilon. $ In particular, the inverse-linear convergence rate of this estimator resolves the main open question of [VV11], which left open the possibility that the error decreased only with the square root of the number of samples. Our distance to uniformity estimator, when given $O(\frac{m}{\eps^2\log m})$ independent samples from any distribution, returns an $\eps$-accurate estimate of the $L_1$ distance to the uniform distribution of support $m$. This is constant-factor optimal, for constant $\epsilon$. Finally, our framework extends naturally to properties of pairs of distributions, including estimating the $L_1$ distance and KL-divergence between pairs of distributions. We give an explicit linear estimator for estimating $L_1$ distance to additive accuracy $\epsilon$ using $O(\frac{n}{\eps^2\log n})$ samples from each distribution, which is constant-factor optimal, for constant $\epsilon$. This is the first sub linear-sample estimator for this fundamental property.
The economic vote at the party level: Electoral behaviour during the Great Recession

The Great Recession is a non-trivial test bed for the theory of economic voting, especially if its predictions are decomposed at the party level, as done in this article by analysing the electoral performances of parties competing in 89 national elections held in the 28 member states of the EU between 2003 and 2015. We acknowledge counterintuitively that prime ministers’ parties are able to exploit the relatively good state of the economy, while sharing the blame with their allies in times of crisis, counting on the lack of clarity in the attribution of responsibilities and deploying their heresthetic capacities. We further recognize that new parties, more than opposition ones, proportionally profited from the recession. Tough times magnify the alternation between left- and right-wing victories, without necessarily favouring the most radical parties, whereas the EU’s supposed responsibility in prolonging the crisis fuelled the success of Eurosceptic parties.
ThinkLets: achieving predictable, repeatable patterns of group interaction with group support systems (GSS)

Over the past decade, Group Support Systems (GSS) has shown that, under certain circumstances, teams using GSS can be far more productive than teams who do not use GSS. However, research results are not unequivocal; they have been ambiguous, and sometimes conflicting which makes it difficult for GSS research to inform GSS practice. One cause of the conflict and ambiguity in GSS research results may be the result of focusing on a less-than-useful level of abstraction: GSS itself. This paper argues that in GSS research, the thinkLet may be a more useful unit of comparison than the GSS. A thinkLet encapsulates three components of a GSS stimulus: The tool, its configuration, and the script. Field experience shows that thinkLets may be used to create repeatable, predictable patterns of thinking among people making an effort toward a goal. To date we have documented about 60 thinkLets that map to seven basic patterns of thinking: Diverge, Converge, Organize, Elaborate, Abstract, Evaluate, and Build Consensus. Each thinkLet creates some unique variation on its basic pattern. By focusing research on thinkLets, rather than GSS, field and laboratory research may be more controllable, more replicable, and better able to inform GSS development and use.
Identification of novel bovine RPE and retinal genes by subtractive hybridization.

PURPOSE Understanding of the specialized function of the retinal pigment epithelium (RPE) can be aided by the identification and characterization of genes that are preferentially expressed in the RPE. With this aim, we undertook a systematic effort to identify and begin characterization of such genes. METHODS A subtracted bovine RPE cDNA library was generated through subtractive hybridization using a single-stranded circular bovine RPE cDNA library as target and biotinylated mRNA from bovine heart and liver as alternate drivers. Approximately one thousand of the resulting subtracted cDNA clones were partially sequenced and analyzed, and a non-redundant set of one hundred of these cDNAs were examined for tissue expression pattern using a mini-Northern blot procedure and for identity by sequence analysis. RESULTS The subtraction method successfully allowed the enrichment of cDNAs that are preferentially expressed in the RPE. Out of the analyzed clones, expression of forty-five clones was verifiable by Northern blotting. Of these, a significant proportion of cDNAs were preferentially expressed in the RPE. We observed that the expression of some subtracted cDNAs was restricted to the retina and no expression was detected in the RPE. These retinal clones were obtained in addition to RPE clones presumably because the initial RPE RNA population was contaminated with a small proportion of retinal RNA. Two thirds of the identified RPE and retinal cDNAs are likely to represent novel genes because they do not have homology to known genes in the databases. CONCLUSIONS Genes that are specifically or predominantly expressed in the RPE/retina are likely to be important for retinal function. We have identified novel cDNAs from bovine RPE and retina by subtractive hybridization. These cDNAs can be used as starting material for the identification of corresponding human genes expressed in the RPE and retina. The human genes thus identified are likely to contain good candidate genes for retinal disease.
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LERMA & UMR 8112 du CNRS, Observatoire de Paris, 61 Av. de l'Observatoire, 75014 Paris, France Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg, Germany Université Joseph Fourier Grenoble 1 / CNRS, Laboratoire d'Astrophysique de Grenoble (LAOG) UMR 5571, BP 53, 38041 Grenoble Cedex 09, France Department of Astronomy and Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721, USA
Signal recovery from partial fractional Fourier domain information and its applications

The problem of recovering signals from partial fractional Fourier transform information arises in wave propagation problems where the measured information is partial, spread over several observation planes, or not of sufficient spatial resolution or accuracy. This problem can be solved with the method of projections onto convex sets, with the convergence of the iterative algorithm being assured. Several prototypical application scenarios and simulation examples are presented.
Fast segmentation of 3D data using an octree

The algorithm developed uses an octree pyramid in which noise is reduced at the expense of the spatial resolution. At a certain level an unsupervised clustering without spatial connectivity constraints is applied. After the classification, isolated voxels and insignificant regions are removed by assigning them to their neighbours. The spatial resolution is then increased by the downprojection of the regions, level by level. At each level the uncertainty of the boundary voxels is minimised by a dynamic selection and classification of these, using an adaptive 3D filtering. The algorithm is tested using different data sets, including NMR data.
Feasibility of a web-based suicide awareness programme for Asian American college students

Objective The Truth about Suicide video has been widely used but has never been empirically tested regarding its cultural appropriateness for Asian Americans. The purpose of the study was to determine the feasibility of using the video in a web-based suicide awareness programme for Asian American and non-Hispanic white college students. Methods A cross-sectional, comparative, web-based study was conducted with 227 Asian Americans and 204 non-Hispanic whites at a university in the Midwest region of the USA. Study participants completed a questionnaire measuring their cultural orientation and attitudes towards suicide, watched the 27 min video, completed a debriefing session and evaluated the video's overall suitability. Results Asian Americans rated the suicide awareness video significantly lower for cultural relevance than did non-Hispanic whites (F=5.479, p=0.02). Collectivist cultural orientation was a significant predictor for cultural relevance, credibility and appeal; however, evaluation of the video's cultural relevance was negatively affected by Asian ethnicity. Conclusions Cultural orientation and race/ethnicity should be strongly considered when web-based suicide awareness programmes are developed for college students.
Androgens downregulate miR-21 expression in breast cancer cells underlining the protective role of androgen receptor

Although the protective role of androgen receptor (AR) in breast cancer (BC) is well established, the mechanisms involved remains largely unexplored. MicroRNAs play fundamental roles in many biological processes, including tumor cell development and metastasis. Herein, we report that androgens reduce BC cells proliferation acting as a negative modulator of the onco-miRNA-21. The synthetic androgen miboleron (Mib) decreases BC cell proliferation induced by miR-21 over-expression and AR knockdown evidenced the requirement of AR in the down-regulation of miR-21 expression. These effects seem to be a general mechanism occurring in BC tissues. Chromatin immune-precipitation (ChIP) analysis disclosed the binding of AR to a specific ARE sequence in miR-21 proximal promoter and recognizes the recruitment of HDAC3 as component for AR-mediated transcriptional repression. Such event is associated to a significantly reduced PolII binding in Mib treated extracts confirming that activated AR is a transcriptional repressor of miR-21 expression, providing further insight into the protective role of androgens in breast cancer cells. Collectively, our data and the widespread AR expression in primary and metastatic breast tumours, suggest a careful examination of the therapeutic potential of androgens also in potentiating the effectiveness of anti-oestrogen adjuvant therapies.
Pipeline for the creation of surface-based averaged brain atlases

Digital atlases of the brain serve as a spatial reference frame which can be used to relate data from different image modalities and experiments. In this paper we describe a standardized pipeline for the creation of extendable surface-based anatomical insect brain atlases from 3D image data of a population of individuals. The pipeline consists of the major steps imaging and preprocessing, segmentation, averaging, surface reconstruction, and surface simplification. At first, 3D image data sets from confocal microscopy are resized, stitched, and initially displayed using standard image processing and visualization tools. Then brain structures, such as neuropils and neurons, are labeled by means of manual segmentation and line extraction algorithms. The averaging step comprises affine and elastic registration and a mean shape selection strategy. Finally non-manifold surfaces of the labeled and aligned structures are reconstructed using a generalized surface reconstruction algorithm. These surfaces are simplified and adapted to further needs by decimation and retriangulation. The chosen methods of each step are adequate for a variety of data. We propose an iterative application of the pipeline in order to build the atlas in a hierarchical fashion, integrating successively more levels of detail. The approach is applied in several different neurobiological research fields.
A double-blinded randomised controlled trial exploring the effect of anodal transcranial direct current stimulation and uni-lateral robot therapy for the impaired upper limb in sub-acute and chronic stroke.

BACKGROUND Neurorehabilitation technologies such as robot therapy (RT) and transcranial Direct Current Stimulation (tDCS) can promote upper limb (UL) motor recovery after stroke. OBJECTIVE To explore the effect of anodal tDCS with uni-lateral and three-dimensional RT for the impaired UL in people with sub-acute and chronic stroke. METHODS A pilot randomised controlled trial was conducted. Stroke participants had 18 one-hour sessions of RT (ArmeoSpring) over eight weeks during which they received 20 minutes of either real tDCS or sham tDCS during each session. The primary outcome measure was the Fugl-Meyer assessment (FMA) for UL impairments and secondary were: UL function, activities and stroke impact collected at baseline, post-intervention and three-month follow-up. RESULTS 22 participants (12 sub-acute and 10 chronic) completed the trial. No significant difference was found in FMA between the real and sham tDCS groups at post-intervention and follow-up (p = 0.123). A significant 'time' x 'stage of stroke' was found for FMA (p = 0.016). A higher percentage improvement was noted in UL function, activities and stroke impact in people with sub-acute compared to chronic stroke. CONCLUSIONS Adding tDCS did not result in an additional effect on UL impairment in stroke. RT may be of more benefit in the sub-acute than chronic phase.
A Systematic State–Space Approach to Large-Signal Transistor Modeling

A state-space approach to large-signal (LS) modeling of high-speed transistors is presented and used as a general framework for various model descriptions of the dispersive features frequently observed for HEMTs at low frequency. Ensuring unrestricted LS-small-signal (SS) model compatibility, the approach allows to construct LS models from multibias SS S-parameter measurements. A general transformation between state-space models is derived, which are equivalent in the SS limit, but nonequivalent under LS stimuli. This transformation has the potential to compensate deviations observed by comparing model predictions with LS measurements and to find an optimum state linear LS model without any change of the SS behavior
H.263 to H.264 Transconding using Data Mining

In this paper, we propose the use of data mining algorithms to create a macroblock partition mode decision algorithm for inter-frame prediction, to be used as part of a high-efficient H.263 to H.264 transcoder. We use machine learning tools to exploit the correlation and derive decision trees to classify the incoming H.263 MC residual into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. Experimental results show that the proposed approach reduces the inter-prediction complexity by as much as 60% while maintaining the coding efficiency.
A memory efficient reachability data structure through bit vector compression

When answering many reachability queries on a large graph, the principal challenge is to represent the transitive closure of the graph compactly, while still allowing fast membership tests on that transitive closure. Recent attempts to address this problem are complex data structures and algorithms such as Path-Tree and 3-HOP. We propose a simple alternative based on a novel form of bit-vector compression. Our starting point is the observation that when computing the transitive closure, reachable vertices tend to cluster together. We adapt the well-known scheme of word-aligned hybrid compression (WAH) to work more efficiently by introducing word partitions. We prove that the resulting scheme leads to a more compact data structure than its closest competitor, namely interval lists. In extensive and detailed experiments, this is confirmed in practice. We also demonstrate that the new technique can handle much larger graphs than alternative algorithms.
Jena Research Papers in Business and Economics Sequencing Mixed-Model Assembly Lines to Minimize Part Inventory Cost

A mixed-model assembly line enables the joint production of di erent models of a common base product in intermixed model sequence (lot size one). Previous approaches for the short-term planning task of model sequencing either aim at minimizing work overload (mixed-model sequencing and car sequencing) or leveling part usages (level scheduling). However, at many manufacturers parts are consolidated by a third party logistics provider, who stocks Just-in-Time delivered parts in a consignment warehouse adjacent to the line. The manufacturer issues a complete cargo carrier (e.g. a euro-pallet) whenever his own intermediate storage of parts is depleted. Thus, the manufacturer aims at a model sequence which minimizes his own inventory costs. This paper formalizes this novel model sequencing problem and describes di erent heuristic and exact procedures. Furthermore, the solutions yielded by these approaches are compared to the traditional level scheduling.