Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy efficient hybrid MCDS algorithm for MANETS

In this paper a new Hybrid Minimum Connected Dominating Set [MCDS] algorithm is proposed. This algorithm achieves energy efficiency by minimizing the Broadcast Storm Problem [1]. The minimum connected dominating set (MCDS) is widely used as a virtual spine for mobile ad-hoc networks. Here the MCDS is a mixture of Centralized and Distributed approaches based on Unit Disk Graph [2]. The node's mobility and energy are also considered in the maintenance of MCDS. The time complexity of this algorithm is O (n) and the message complexity is O (n). The performance evaluation of this algorithm yields better results in both dense and sparse networks. Size of the MCDS is also optimal compared to other MCDS algorithms [3] [4] [5].
Heating groups and clusters of galaxies: The role of AGN jets

X-ray observations of groups and clusters of galaxies show that the Intra-Cluster Medium (ICM) in their cores is hotter than expected from cosmological numerical simulations of cluster formation which include star formation, radiative cooling and SN feedback. We investigate the effect of the injection of supersonic AGN jets into the ICM using axisymmetric hydrodynamical numerical simulations. A simple model for the ICM, describing the radial properties of gas and the gravitational potential in cosmological N-Body+SPH simulations of one cluster and three groups of galaxies at redshift z = 0, is obtained and used to set the environment in which the jets are injected. We varied the kinetic power of the jet and the emission-weighted X-ray temperature of the ICM. The jets transfer their energy to the ICM mainly by the effects of their terminal shocks. A high fraction of the injected energy can be deposited through irreversible processes in the cluster gas, up to 75% in our simulations. We show how one single, powerful jet can reconcile the predicted X-Ray properties of small groups, e.g. the LX − TX relation, with observations. We argue that the interaction between AGN jets and galaxy groups and cluster atmospheres is a viable feedback mechanism.
People tracking across two distant self-calibrated cameras

People tracking is of fundamental importance in multi-camera surveillance systems. In recent years, many approaches for multi-camera tracking have been discussed. Most methods use either various image features or the geometric relation between the cameras or both as a cue. It is a desire to know the geometry for distant cameras, because geometry is not influenced by, for example, drastic changes in object appearance or in scene illumination. However, the determination of the camera geometry is cumbersome. The paper tries to solve this problem and contributes in two different ways. On the one hand, an approach is presented that calibrates two distant cameras automatically. We continue previous work and focus especially on the calibration of the extrinsic parameters. Point correspondences are used for this task which are acquired by detecting points on top of people's heads. On the other hand, qualitative experimental results with the PETS 2006 benchmark data show that the self-calibration is accurate enough for a solely geometric tracking of people across distant cameras. Reliable features for a matching are hardly available in such cases.
COGNITIVE NEUROSCIENCE AND NEUROPSYCHOLOGY: Timbre-specific enhancement of auditory cortical representations in musicians

Neural imaging studies have shown that the brains of skilled musicians respond differently to musical stimuli than do the brains of non-musicians, particularly for musicians who commenced practice at an early age. Whether brain attributes related to musical skill are attributable to musical practice or are hereditary traits that in ̄uence the decision to train musically is a subject of controversy, owing to its pedagogic implications. Here we report that auditory cortical representations measured neuromagnetically for tones of different timbre (violin and trumpet) are enhanced compared to sine tones in violinists and trumpeters, preferentially for timbres of the instrument of training. Timbre speci®city is predicted by a principle of use-dependent plasticity and imposes new requirements on nativistic accounts of brain attributes associated with musical skill. NeuroReport 12:169±174 & 2001 Lippincott Williams & Wilkins.
Understanding What Instrumental Variables Estimate : Estimating Marginal and Average Returns to Education

This paper develops and applies new methods for estimating marginal and average returns to economic activities when returns vary in the population and people sort into these activities with at least partial knowledge of their returns. Different valid instruments identify different parameters which do not, in general, answer well-posed economic questions. We start with a well-posed economic question and develop methods for answering it. We extend the standard instrumental variables literature to estimate marginal returns and to construct policy relevant parameters. Applying our methods to an analysis of the economic returns to college education, we find that marginal entrants earn substantially less than average college students, that comparative advantage is a central feature of modern labor markets and that ability bias is an empirically important phenomenon.
Addressing the “Union Problem” during the Great Recession: State Approaches to Reforming Collective Bargaining

Conventional narratives suggest that during the Great Recession, Republican-controlled state governments seized a political opportunity to de-unionize labor strongholds by enacting sweeping right-to-work laws. However, I contend that two distinct reform approaches were pursued during the recession. One type aimed to restrict the ability of unions to organize and maintain membership (the “right-to-work” approach), while the other sought to constrain collective bargaining without hampering union organizing. My analysis of 2,545 labor relations bills introduced across the U.S. States from 2007 to 2014 confirms the existence of two broad models of reform, each with differing implications for organized labor and partisan politics.
Time Series Data Mining Methods : A Review

Today, real world time series data sets can take a size up to a trillion observations and even more. Data miners’ task is it to detect new information that is hidden in this massive amount of data. While well known techniques for data mining in cross sections have been developed, time series data mining methods are not as sophisticated and established yet. Large time series bring along problems like very high dimensionality and up to today, researchers haven’t agreed on best practices in this regard. This review gives an overview of the challenges of large time series and the proposed problem solving approaches from time series data mining community. We illustrate the most important techniques with Google trends data. Moreover, we review current research directions and point out open research questions. Heutzutage sind die Möglichkeiten der Datensammlung und -Speicherung unvorstellbar weitreichend und somit können Zeitreihendatensätze mittlerweile bis zu einer Billion Beobachtungen enthalten. Die Aufgabe von Data Mining ist es, versteckte Informationen aus dieser Datenschwemme herauszufiltern. Während es für Querschnittsdaten viele verschiedene und sehr gut entwickelte Techniken gibt, hinken die Zeitreihen Data Mining Methoden weit hinterher. Die Forschungspraxis hat sich in diesem Bereich noch nicht auf standardisierte Vorgehensweisen geeinigt. Dieser Literaturüberblick stellt zunächst die typischen Probleme, die Zeitreihen mit sich bringen, dar und systematisiert daraufhin die von der Forschungsgemeinde vorgeschlagenen Lösungsansätze hierfür. Die wichtigsten Ansätze werden anhand von Google Trends Daten illustriert. Darüber hinaus werfen wir einen Blick auf aktuelle Forschungsströme und zeigen noch offene Forschungsfragen auf.
Smart-mat: recognizing and counting gym exercises with low-cost resistive pressure sensing matrix

There is a large class of routine physical exercises that are performed on the ground, often on dedicated "mats" (e.g. push-ups, crunches, bridge). Such exercises involve coordinated motions of different body parts and are difficult to recognize with a single body worn motion sensors (like a step counter). Instead a network of sensors on different body parts would be needed, which is not always practicable. As an alternative we describe a cheap, simple textile pressure sensor matrix, that can be unobtrusively integrated into exercise mats to recognize and count such exercises. We evaluate the system on a set of 10 standard exercises. In an experiment with 7 subjects, each repeating each exercise 20 times, we achieve a user independent recognition rate of 82.5% and a user independent counting accuracy of 89.9%. The paper describes the sensor system, the recognition methods and the experimental results.
Shannon Capacity for a Simple Communication Channel Model in Dense MANETs

Capacity computation for mobile ad hoc networks (MANETs) for a simple communication channel model, which considers Euclidean distance and path loss, is described in this paper and compared with Monte-Carlo simulations up to one million nodes. We show that, depending on the path loss parameter (a), the average signal to noise and interference ratio (SNIR) and consequently the Shannon capacity either remain constant, or decay, when the number of interferers scales to infinity in the network, for nodes communicating with their close neighbors. Accordingly, for this model, we observe that communication is feasible in dense ad hoc networks as long as alpha > 2 and a node attempts to transmit to other nodes located close so that interference can be overcome.
Towards automatic clustering of similar pages in web applications

In this paper, we propose an automatic approach to group web pages that are similar at the content level. The approach uses the Levenshtein string edit distance and Latent Semantic Indexing to compute page dissimilarity and then groups them using iteratively a Graph-Theoretic clustering algorithm. To automate the clustering process a prototype has been implemented and used to assess the proposed approach on three web sites.
Mutual control of intracellular localisation of the patterning proteins AtMYC1, GL1 and TRY/CPC in Arabidopsis

Trichome and root hair patterning is governed by a gene regulatory network involving TTG1 and several homologous MYB and bHLH proteins. The bHLH proteins GL3 and EGL3 are core components that serve as a regulatory platform for the activation of downstream genes. In this study we show that a homologue of GL3 and EGL3, AtMYC1, can regulate the intracellular localisation of GL1 and TRY. AtMYC1 protein is predominantly localised in the cytoplasm and can relocate GL1 from the nucleus into the cytoplasm. Conversely, AtMYC1 can be recruited into the nucleus by TRY and CPC, concomitant with a strong accumulation of TRY and CPC in the nucleus. When AtMYC1 is targeted to the nucleus or cytoplasm by nuclear localisation or export signals (NLS or NES), respectively, the intracellular localisation of GL1 and TRY also changes accordingly. The biological significance of this intracellular localisation is suggested by the finding that the efficiency of rescue of trichome number is significantly altered in NES and NLS fusions as compared with wild-type AtMYC1. Genetic analysis of mutants and overexpression lines supports the hypothesis that AtMYC1 represses the activity of TRY and CPC.
Multiclass Classifier Building with Amazon Data to Classify Customer Reviews into Product Categories

As one of the world’s largest online retailers, Amazon.com has a sales volume of tens of billions dollars and as a result huge amounts of customer reviews accumulate. These reviews are great learning samples which could help promote commercial prediction and advertisement. Our project aims to build a multiclass classifier based on the customer reviews which are labeled by product categories in Amazon. Different feature selections and models are tested when building this classifier. As we will show below, the random forest classifier performs the best.
Optimizing Fsync Performance with Dynamic Queue Depth Adaptation

Existing flash storage devices such as universal flash storage and solid state disk support command queuing to improve storage I/O bandwidth. Command queuing allows multiple read/write requests to be pending in a device queue. Because multi-channel and multi-way architecture of flash storage devices can handle multiple requests simultaneously, command queuing is an indispensable technique for utilizing parallel architecture. However, command queuing can be harmful to the latency of fsync system call, which is critical to application responsiveness. We propose a dynamic queue depth adaptation technique, which reduces the queue depth if user application is expected to send fsync calls. Experiments show that the proposed technique reduces the fsync latency by 79% on average compared to the original scheme.
The Efficacy and Safety of Preoperative Lumbar Drain Placement in Anterior Skull Base Surgery

Abstract This study assesses the efficacy of preoperative lumbar drain (LD) placement prior to elective open cranial and endoscopic anterior skull base (ASB) surgery in reducing postoperative cerebrospinal fluid (CSF) leak. A retrospective review of 93 patients who underwent LD placement at our institution between 2006 and 2011 was performed. Of these patients, 43 underwent elective LD placement prior to ASB surgery; 2 patients had evidence of CSF rhinorrhea prior to surgery, and 41 had no evidence of a preoperative CSF leak. Of those 41 patients, 2 developed CSF rhinorrhea (2/41= 4.9%) as a result of surgery—all in our endoscopic patient population (N = 21; 2/21= 9.5%). No postoperative CSF leaks were noted in our open ASB surgery cohort (N = 20). Other complications were rare, but we encountered two instances of delayed malignant cerebral edema in the open ASB cohort that are discussed in detail. Overall, preoperative LD placement was found to be an effective means of preventing postoperative CSF leaks after ASB approaches, but potential and significant intracranial complications may occur in select patients that merit careful consideration prior to LD placement.
Document Classification with ACM Subject Hierarchy

Text categorization or text classification (TC) has recently received increased research attention from information retrieval and machine learning communities, this focus is driven mostly by the ever growing demand for effective and efficient content-based, document management. In the context of digital library or Web portal application, the problem of text categorization is normally that of classification scheme with a topic hierarchy containing all the pre-defined categories. This paper describes our approach to building the hierarchical text classifier for the experimental CINDI Digital Library . The classification system constructed features a top-to-down, coarse-to-fine categorization procedure. We evaluate our system's performance by experiment on a self-generated corpus of the Computer Science papers archived in ACM DL.
Preferential rearrangement of V kappa 4 gene segments in pre-B cell lines

Examination of the in vitro V kappa gene rearrangements of murine adult bone marrow-derived pre-B cell lines reveals that 21 of 25 (84%) cell lines have rearranged a member of the V kappa 4 family. In contrast, analysis of two V kappa cDNA libraries prepared from LPS-stimulated adult spleen cells indicates that only 17% of the Ig kappa cDNAs contain sequences belonging to the V kappa 4 gene family. Half of the pre-B cell lines examined also share an 8-kbp BamHI reciprocal product (rp). However, these rp do not involve the same V kappa gene, indicating that conserved BamHI sites exist 3' of some V kappa genes. This rp is also readily detected in DNA from normal adult spleen cells, suggesting that the in vitro rearrangements examined in this study are representative of kappa rearrangements that occur in vivo. We suggest that, unlike the diverse V kappa repertoire expressed by mature B cells, the germline V kappa segments involved in initial rearrangements of the Ig kappa locus are highly restricted, and that an initial V kappa 4 rearrangement is probably followed by other, more random recombination events.
Driving intention identification and maneuvering behavior prediction of drivers on cornering

An integrated model, which combines Artificial Neural Network (ANN) and Hidden Markov chain Model (HMM), is proposed to identify the driving intention and predict the maneuvering behavior of the driver, which accordingly realizes intelligent driving assistance and improves active safety of the vehicle. Driving simulator tests on straight line and double-lane change are employed to validate the availability of this model. The test results indicate that this model serves accurate identification on driving intention as well as precise prediction on maneuvering behavior of the driver at certain vehicle speed.
From Public Mental Health to Community Oral Health: The Impact of Dental Anxiety and Fear on Dental Status

Dental fear is a widely experienced problem. Through a “vicious cycle dynamic,” fear of dental treatment, lower use of dental services, and oral health diseases reinforce each other. Research on the antecedents of dental anxiety could help to break this cycle, providing useful knowledge to design effective community programs aimed at preventing dental fear and its oral health-related consequences. In this regard, frameworks that analyze the interplay between cognitive and psychosocial determinants of fear, such as the Cognitive Vulnerability Model, are promising. The onset of dental fear often occurs in childhood, so focusing on the child population could greatly contribute to understanding dental fear mechanisms and prevent this problem extending into adulthood. Not only can public mental health contribute to population health, but also community dentistry programs can help to prevent dental fear. Regular dental visits seem to act in a prophylactic way, with dental professionals playing an important role in the regulation of the patients’ anxiety-related responses. Both public mental health and community dentistry could therefore benefit from a multidisciplinary approach to dental fear and oral health.
Efficacious control of cytomegalovirus infection after long-term depletion of CD8+ T lymphocytes

Although the relative contribution of different immune effector functions to clearing tissues of cytomegalovirus is controversial, the contribution of CD8+ T lymphocytes has generally been accepted as essential. In this report, we show that under certain conditions the CD8+ T-lymphocyte subset can be dispensable for clearance of cytomegalovirus. Mice depleted of the CD8+ T-lymphocyte subset eliminated infectious virus with a clearance kinetics similar to that of normal mice. Adoptive transfer studies revealed that the limitation of virus spread required the cooperation between the CD4+ subset and other cells. Comparison between protective functions generated in fully immunocompetent and in CD8- mice demonstrated that elimination of the CD8+ subset before infection altered the quality of the antiviral immune response. The compensatory protective activity gained by CD4+ cells in CD8- mice was absent in normal mice recovering from virus infection.
Cerebellar Depolarization-Induced Suppression of Inhibition Is Mediated by Endogenous Cannabinoids

Depolarization of cerebellar Purkinje neurons transiently suppresses IPSCs through a process known as depolarization-induced suppression of inhibition (DSI). This IPSC suppression occurs presynaptically and results from an unknown retrograde signal released from Purkinje cells. We recorded IPSCs from voltage-clamped Purkinje cells in cerebellar brain slices to identify the retrograde signal for cerebellar DSI. We find that DSI persists in the presence of the broad-spectrum metabotropic glutamate receptor antagonist LY341495 and the GABA(B) receptor antagonist CGP55845, suggesting that the retrograde signal is not acting through these receptors. However, an antagonist of the cannabinoid CB1 receptor AM251 completely blocked cerebellar DSI. Additionally, the cannabinoid receptor agonist WIN55,212-2 suppressed IPSCs and occluded any additional IPSC reduction by DSI. These results indicate that cannabinoids released from Purkinje cells after depolarization activate CB1 receptors on inhibitory neurons and suppress IPSCs for tens of seconds. Cerebellar DSI thus shares a common retrograde messenger with DSI in the hippocampus and depolarization-induced suppression of excitation in the cerebellum, suggesting that retrograde synaptic suppression by endogenous cannabinoids represents a widespread signaling mechanism.
Quark confinement and the bosonic string Martin Lüscher

Using a new type of simulation algorithm for the standard SU(3) lattice gauge theory that yields results with unprecedented precision, we confirm the presence of a γ/r correction to the static quark potential at large distances r, with a coefficient γ as predicted by the bosonic string theory. In both three and four dimensions, the transition from perturbative to string behaviour is evident from the data and takes place at surprisingly small distances.
PRIVACY PRESERVING BIG DATA MINING: ASSOCIATION RULE HIDING

Data repositories contain sensitive information which must be protected from unauthorized access. Existing data mining techniques can be considered as a privacy threat to sensitive data. Association rule mining is one of the utmost data mining techniques which tries to cover relationships between seemingly unrelated data in a data base. Association rule hiding is a research area in privacy preserving data mining (PPDM) which addresses a solution for hiding sensitive rules within the data problem. Many researches have be done in this area, but most of them focus on reducing undesired side effect of deleting sensitive association rules in static databases. However, in the age of big data, we confront with dynamic data bases with new data entrance at any time. So, most of existing techniques would not be practical and must be updated in order to be appropriate for these huge volume data bases. In this paper, data anonymization technique is used for association rule hiding, while parallelization and scalability features are also embedded in the proposed model, in order to speed up big data mining process. In this way, instead of removing some instances of an existing important association rule, generalization is used to anonymize items in appropriate level. So, if necessary, we can update important association rules based on the new data entrances. We have conducted some experiments using three datasets in order to evaluate performance of the proposed model in comparison with Max-Min2 and HSCRIL. Experimental results show that the information loss of the proposed model is less than existing researches in this area and this model can be executed in a parallel manner for less execution time