Seawater carbonate chemistry, sample density and Strongylocentrotus purpuratus size, filtering and respiration rate during experiments, 2011

Anthropogenic CO2 emissions are acidifying the world's oceans. A growing body of evidence is showing that ocean acidification impacts growth and developmental rates of marine invertebrates. Here we test the impact of elevated seawater pCO2 (129 Pa, 1271 µatm) on early development, larval metabolic and feeding rates in a marine model organism, the sea urchin Strongylocentrotus purpuratus. Growth and development was assessed by measuring total body length, body rod length, postoral rod length and posterolateral rod length. Comparing these parameters between treatments suggests that larvae suffer from a developmental delay (by ca. 8%) rather than from the previously postulated reductions in size at comparable developmental stages. Further, we found maximum increases in respiration rates of + 100 % under elevated pCO2, while body length corrected feeding rates did not differ between larvae from both treatments. Calculating scope for growth illustrates that larvae raised under high pCO2 spent an average of 39 to 45% of the available energy for somatic growth, while control larvae could allocate between 78 and 80% of the available energy into growth processes. Our results highlight the importance of defining a standard frame of reference when comparing a given parameter between treatments, as observed differences can be easily due to comparison of different larval ages with their specific set of biological characters.

O e-mail e o marketing de permissão

No domínio do marketing, o e-mail é amplamente reconhecido na literatura como uma das mais eficazes ferramentas de comunicação ao serviço das organizações. Contudo, o seu manancial de potencialidades tem sido ensombrado por um crescente fenómeno de spam, capaz de influencia negativamente as suas taxas de eficácia. Face a este problema, a presente investigação foi dirigida em torno de uma das suas possíveis soluções - o marketing de permissão.

Collaborative Filtering with Data Classification : A Combined Approach to Hotel Recommendation Systems

Recommendation systems aim to help users make decisions more efficiently. The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the "cold start" problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.

Spam campaign detection, analysis, and formalization

Les courriels Spams (courriels indésirables ou pourriels) imposent des coûts annuels extrêmement lourds en termes de temps, d’espace de stockage et d’argent aux utilisateurs privés et aux entreprises. Afin de lutter efficacement contre le problème des spams, il ne suffit pas d’arrêter les messages de spam qui sont livrés à la boîte de réception de l’utilisateur. Il est obligatoire, soit d’essayer de trouver et de persécuter les spammeurs qui, généralement, se cachent derrière des réseaux complexes de dispositifs infectés, ou d’analyser le comportement des spammeurs afin de trouver des stratégies de défense appropriées. Cependant, une telle tâche est difficile en raison des techniques de camouflage, ce qui nécessite une analyse manuelle des spams corrélés pour trouver les spammeurs. Pour faciliter une telle analyse, qui doit être effectuée sur de grandes quantités des courriels non classés, nous proposons une méthodologie de regroupement catégorique, nommé CCTree, permettant de diviser un grand volume de spams en des campagnes, et ce, en se basant sur leur similarité structurale. Nous montrons l’efficacité et l’efficience de notre algorithme de clustering proposé par plusieurs expériences. Ensuite, une approche d’auto-apprentissage est proposée pour étiqueter les campagnes de spam en se basant sur le but des spammeur, par exemple, phishing. Les campagnes de spam marquées sont utilisées afin de former un classificateur, qui peut être appliqué dans la classification des nouveaux courriels de spam. En outre, les campagnes marquées, avec un ensemble de quatre autres critères de classement, sont ordonnées selon les priorités des enquêteurs. Finalement, une structure basée sur le semiring est proposée pour la représentation abstraite de CCTree. Le schéma abstrait de CCTree, nommé CCTree terme, est appliqué pour formaliser la parallélisation du CCTree. Grâce à un certain nombre d’analyses mathématiques et de résultats expérimentaux, nous montrons l’efficience et l’efficacité du cadre proposé.

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.

Helping you to make the right choice for your web filtering

We have designed this flowchart to help you choose the web filtering option that best suits your needs from three different options: Our free standard web filtering service, enhanced user based filtering or a solution from our framework agreement.

Las cadenas y el correo spam

Este artículo hace parte de la revista Papel de colgadura de la Facultad de Derecho y Ciencias Sociales de la Universidad Icesi de Cali, es una publicación de difusión y agitación cultural. La revista nace de la pasión por la música, los libros, las ilustraciones, el graffiti, los cómics, la web, la fiesta, el cine, la cafeína y de las tardes de tertulia con empanadas y cerveza, que circula en versión impresa dos veces al año, pero su versión digital se actualiza con mayor frecuencia.

Spatio-temporal filtering properties of a dendritic cable with active spines: a modeling study in the spike-diffuse-spike framework

The spike-diffuse-spike (SDS) model describes a passive dendritic tree with active dendritic spines. Spine-head dynamics is modeled with a simple integrate-and-fire process, whilst communication between spines is mediated by the cable equation. In this paper we develop a computational framework that allows the study of multiple spiking events in a network of such spines embedded on a simple one-dimensional cable. In the first instance this system is shown to support saltatory waves with the same qualitative features as those observed in a model with Hodgkin-Huxley kinetics in the spine-head. Moreover, there is excellent agreement with the analytically calculated speed for a solitary saltatory pulse. Upon driving the system with time varying external input we find that the distribution of spines can play a crucial role in determining spatio-temporal filtering properties. In particular, the SDS model in response to periodic pulse train shows a positive correlation between spine density and low-pass temporal filtering that is consistent with the experimental results of Rose and Fortune [1999, Mechanisms for generating temporal filters in the electrosensory system. The Journal of Experimental Biology 202, 1281-1289]. Further, we demonstrate the robustness of observed wave properties to natural sources of noise that arise both in the cable and the spine-head, and highlight the possibility of purely noise induced waves and coherent oscillations.

Variational ensemble Kalman filtering applied to data assimilation problems in computational fluid dynamics

One challenge on data assimilation (DA) methods is how the error covariance for the model state is computed. Ensemble methods have been proposed for producing error covariance estimates, as error is propagated in time using the non-linear model. Variational methods, on the other hand, use the concepts of control theory, whereby the state estimate is optimized from both the background and the measurements. Numerical optimization schemes are applied which solve the problem of memory storage and huge matrix inversion needed by classical Kalman filter methods. Variational Ensemble Kalman filter (VEnKF), as a method inspired the Variational Kalman Filter (VKF), enjoys the benefits from both ensemble methods and variational methods. It avoids filter inbreeding problems which emerge when the ensemble spread underestimates the true error covariance. In VEnKF this is tackled by resampling the ensemble every time measurements are available. One advantage of VEnKF over VKF is that it needs neither tangent linear code nor adjoint code. In this thesis, VEnKF has been applied to a two-dimensional shallow water model simulating a dam-break experiment. The model is a public code with water height measurements recorded in seven stations along the 21:2 m long 1:4 m wide flume’s mid-line. Because the data were too sparse to assimilate the 30 171 model state vector, we chose to interpolate the data both in time and in space. The results of the assimilation were compared with that of a pure simulation. We have found that the results revealed by the VEnKF were more realistic, without numerical artifacts present in the pure simulation. Creating a wrapper code for a model and DA scheme might be challenging, especially when the two were designed independently or are poorly documented. In this thesis we have presented a non-intrusive approach of coupling the model and a DA scheme. An external program is used to send and receive information between the model and DA procedure using files. The advantage of this method is that the model code changes needed are minimal, only a few lines which facilitate input and output. Apart from being simple to coupling, the approach can be employed even if the two were written in different programming languages, because the communication is not through code. The non-intrusive approach is made to accommodate parallel computing by just telling the control program to wait until all the processes have ended before the DA procedure is invoked. It is worth mentioning the overhead increase caused by the approach, as at every assimilation cycle both the model and the DA procedure have to be initialized. Nonetheless, the method can be an ideal approach for a benchmark platform in testing DA methods. The non-intrusive VEnKF has been applied to a multi-purpose hydrodynamic model COHERENS to assimilate Total Suspended Matter (TSM) in lake Säkylän Pyhäjärvi. The lake has an area of 154 km2 with an average depth of 5:4 m. Turbidity and chlorophyll-a concentrations from MERIS satellite images for 7 days between May 16 and July 6 2009 were available. The effect of the organic matter has been computationally eliminated to obtain TSM data. Because of computational demands from both COHERENS and VEnKF, we have chosen to use 1 km grid resolution. The results of the VEnKF have been compared with the measurements recorded at an automatic station located at the North-Western part of the lake. However, due to TSM data sparsity in both time and space, it could not be well matched. The use of multiple automatic stations with real time data is important to elude the time sparsity problem. With DA, this will help in better understanding the environmental hazard variables for instance. We have found that using a very high ensemble size does not necessarily improve the results, because there is a limit whereby additional ensemble members add very little to the performance. Successful implementation of the non-intrusive VEnKF and the ensemble size limit for performance leads to an emerging area of Reduced Order Modeling (ROM). To save computational resources, running full-blown model in ROM is avoided. When the ROM is applied with the non-intrusive DA approach, it might result in a cheaper algorithm that will relax computation challenges existing in the field of modelling and DA.