Stay Awhile and Listen: User Interactions in a Crowdsourced Platform Offering Emotional Support

Internet and online-based social systems are rising as the dominant mode of communication in society. However, the public or semi-private environment under which most online communications operate under do not make them suitable channels for speaking with others about personal or emotional problems. This has led to the emergence of online platforms for emotional support offering free, anonymous, and confidential conversations with live listeners. Yet very little is known about the way these platforms are utilized, and if their features and design foster strong user engagement. This paper explores the utilization and the interaction features of hundreds of thousands of users on 7 Cups of Tea, a leading online platform offering online emotional support. It dissects the level of activity of hundreds of thousands of users, the patterns by which they engage in conversation with each other, and uses machine learning methods to find factors promoting engagement. The study may be the first to measure activities and interactions in a large-scale online social system that fosters peer-to-peer emotional support

Modeling and predicting temporal patterns of web content changes

AbstractThe technologies aimed at Web content discovery, retrieval and management face the compelling need of coping with its highly dynamic nature coupled with complex user interactions. This paper analyzes the temporal patterns of the content changes of three major news websites with the objective of modeling and predicting their dynamics. It has been observed that changes are characterized by a time dependent behavior with large fluctuations and significant differences across hours and days. To explain this behavior, we represent the change patterns as time series. The trend and seasonal components of the observed time series capture the weekly and daily periodicity, whereas the irregular components take into account the remaining fluctuations. Models based on trigonometric polynomials and ARMA components accurately reproduce the dynamics of the empirical change patterns and provide extrapolations into the future to be used for forecasting

An exploratory analysis of the novelty of a news Web site

Abstract The growing amount of information published on the Web, combine

Time series analysis of the dynamics of news websites

Abstract-The content of news websites changes frequently and rapidly and its relevance tends to decay with time. To be of any value to the users, tools, such as, search engines, have to cope with these evolving websites and detect in a timely manner their changes. In this paper we apply time series analysis to study the properties and the temporal patterns of the change rates of the content of three news websites. Our investigation shows that changes are characterized by large fluctuations with periodic patterns and time dependent behavior. The time series describing the change rate is decomposed into trend, seasonal and irregular components and models of each component are then identified. The trend and seasonal components describe the daily and weekly patterns of the change rates. Trigonometric polynomials best fit these deterministic components, whereas the class of ARMA models represents the irregular component. The resulting models can be used to describe the dynamics of the changes and predict future change rates

A role for the ELAV RNA-binding proteins in neural stem cells : stabilization of Msi1 mRNA

Post-transcriptional regulation exerted by neural-specific RNA-binding proteins plays a pivotal role in the development and maintenance of the nervous system. Neural ELAV proteins are key inducers of neuronal differentiation through the stabilization and/or translational enhancement of target transcripts bearing the AU-rich elements (AREs), whereas Musashi-1 maintains the stem cell proliferation state by acting as a translational repressor. Since the gene encoding Musashi-1 (Msi1) contains a conserved ARE in its 3' untranslated region, the authors focused on the possibility of a mechanistic relation between ELAV proteins and Musashi-1 in cell fate commitment. Colocalization of neural ELAV proteins with Musashi-1 clearly shows that ELAV proteins are expressed at early stages of neural commitment, whereas interaction studies demonstrate that neural ELAV proteins exert an ARE-dependent binding activity on the Msi1 mRNA. This binding activity has functional effects, since the ELAV protein family member HuD is able to stabilize the Msi1 ARE-contg. mRNA in a sequence-dependent way in a deadenylation/degrdn. assay. Furthermore activation of the neural ELAV proteins by phorbol esters in human SH-SY5Y cells is assocd. with an increase of Musashi-1 protein content in the cytoskeleton. The authors propose that ELAV RNA-binding proteins exert an important post-transcriptional control on Musashi-1 expression in the transition from proliferation to neural differentiation of stem/progenitor cells

A methodological framework for cloud resource provisioning and scheduling of data parallel applications under uncertainty

Data parallel applications are being extensively deployed in cloud environmentsbecause of the possibility of dynamically provisioning storage and computation re-sources. To identify cost-effective solutions that satisfy the desired service levels,resource provisioning and scheduling play a critical role. Nevertheless, the unpre-dictable behavior of cloud performance makes the estimation of the resources actu-ally needed quite complex. In this paper we propose a provisioning and schedulingframework that explicitly tackles uncertainties and performance variability of thecloud infrastructure and of the workload. This framework allows cloud users to es-timate in advance, i.e., prior to the actual execution of the applications, the resourcesettings that cope with uncertainty. We formulate an optimization problem wherethe characteristics not perfectly known or affected by uncertain phenomena arerepresented as random variables modeled by the corresponding probability distri-butions. Provisioning and scheduling decisions \u2013 while optimizing various metrics,such as monetary leasing costs of cloud resources and application execution time \u2013take fully account of uncertainties encountered in cloud environments. To test our framework, we consider data parallel applications characterized by a deadline con-straint and we investigate the impact of their characteristics and of the variabilityof the cloud infrastructure. The experiments show that the resource provisioningand scheduling plans identified by our approach nicely cope with uncertainties andensure that the application deadline is satisfied

The Resource Usage Aware Backfilling

Abstract. Job scheduling policies for HPC centers have been extensively stud-ied in the last few years, especially backfilling based policies. Almost all of these studies have been done using simulation tools. All the existent simulators use the runtime (either estimated or real) provided in the workload as a basis of their sim-ulations. In our previous work we analyzed the impact on system performance of considering the resource sharing (memory bandwidth) of running jobs including a new resource model in the Alvio simulator. Based on this studies we proposed the LessConsume and LessConsume Threshold resource selection policies. Both are oriented to reduce the saturation of the shared resources thus increasing the performance of the system. The results showed how both resource allocation poli-cies shown how the performance of the system can be improved by considering where the jobs are finally allocated. Using the LessConsume Threshold Resource Selection Policy, we propose a new backfilling strategy: the Resource Usage Aware Backfilling job scheduling policy. This is a backfilling based scheduling policy where the algorithms which decide which job has to be executed and how jobs have to be backfilled are based on a different Threshold configurations. This backfilling variant that considers how the shared resources are used by the scheduled jobs. Rather than backfilling the first job that can moved to the run queue based on the job arrival time or job size, it looks ahead to the next queued jobs, and tries to allocate jobs that would experience lower penalized runtime caused by the resource sharing saturation. In the paper we demostrate how the exchange of scheduling information between the local resource manager and the scheduler can improve substantially the per-formance of the system when the resource sharing is considered. We show how it can achieve a close response time performance that the shorest job first Back-filling with First Fit (oriented to improve the start time for the allocated jobs) providing a qualitative improvement in the number of killed jobs and in the per-centage of penalized runtime.

Il progetto CampusOne e la certificazione ECDL nelle universit\ue0 italiane

Questo articolo presenta i risultati del progetto CampusOne della Conferenza dei Rettori delle Universit\ue0 Italiane (CRUI), con particolare riferimento all\u2019accreditamento delle abilit\ue0 nell\u2019uso degli strumenti informatici di base e alla certificazione ECDL (European Computer Driving Licence). L\u2019articolo analizza il ruolo e la diffusione della certificazione ECDL nelle Universit\ue0 Italiane e discute i metodi di formazione e i criteri adottati per il suo riconoscimento come parte dei percorsi universitari

Induction of neurotrophin expression via human adult mesenchymal stem cells: implication for cell therapy in neurodegenerative diseases.

In animal models of neurological disorders for cerebral ischemia, Parkinson's disease, and spinal cord lesions, transplantation of mesenchymal stem cells (MSCs) has been reported to improve functional outcome. Three mechanisms have been suggested for the effects of the MSCs: transdifferentiation of the grafted cells with replacement of degenerating neural cells, cell fusion, and neuroprotection of the dying cells. Here we demonstrate that a restricted number of cells with differentiated astroglial features can be obtained from human adult MSCs (hMSCs) both in vitro using different induction protocols and in vivo after transplantation into the developing mouse brain. We then examined the in vitro differentiation capacity of the hMSCs in coculture with slices of neonatal brain cortex. In this condition the hMSCs did not show any neuronal transdifferentiation but expressed neurotrophin low-affinity (NGFRp75) and high-affinity (trkC) receptors and released nerve growth factor (NGF) and neurotrophin-3 (NT-3). The same neurotrophin's expression was demonstrated 45 days after the intracerebral transplantation of hMSCs into nude mice with surviving astroglial cells. These data further confirm the limited capability of adult hMSC to differentiate into neurons whereas they differentiated in astroglial cells. Moreover, the secretion of neurotrophic factors combined with activation of the specific receptors of transplanted hMSCs demonstrated an alternative mechanism for neuroprotection of degenerating neurons. hMSCs are further defined in their transplantation potential for treating neurological disorders