Acacia Plantation Development and the Configuration of Tree Farmers’ Agricultural Assets and Land Management—A Survey in Central Vietnam

Since 1990 acacia-based tree plantations have fast expanded in Vietnam, now supporting a multi-billion-dollar export-oriented wood industry which is transforming from woodchip production to value-added products. Within this dynamic context, tree farmer associations have started to produce sawlogs under FSC (Forest Stewardship Council) certification. In this paper, we retrace the development of plantation assets, investigating farmers’ current livelihoods and land management, specifically considering various aspects of sustainability. We interviewed 180 tree farmers in three districts (lowland–upland regions) of Thừa Thiên Huế Province, including sawlog producers with and without FSC and smallholder producers of woodchips. Acacia planting in ‘barren lands’ was initiated through state programs in the 1990s (low-/midlands) and 2010s (uplands). Farmers now producing FSC sawlogs were among the first to gain forestland tenure; they now own large plantations (on good terrain), are in tune with policies and maintain resources/capacities to adopt management in line with FSC standards. Yet, most farmers also retain plots for easy-to-manage and low-risk woodchip production. Soil/vegetation conservation depends on farmers’ status/capacities and environmental awareness; FSC membership added economic-political benefits. Findings are discussed within a regional historic context. Plantations contribute to economic development, but issues persist/emerged in terms of land equity and environmental governance, risks (e.g., plant pathogens), and spaces/impetus for farm-based innovation and adaptiveness.

Decontamination, pooling and dereplication of the 678 samples of the Marine Microbial Eukaryote Transcriptome Sequencing Project

Objectives Complex algae are photosynthetic organisms resulting from eukaryote-to-eukaryote endosymbiotic-like interactions. Yet the specific lineages and mechanisms are still under debate. That is why large scale phylogenomic studies are needed. Whereas available proteomes provide a limited diversity of complex algae, MMETSP (Marine Microbial Eukaryote Transcriptome Sequencing Project) transcriptomes represent a valuable resource for phylogenomic analyses, owing to their broad and rich taxonomic sampling, especially of photosynthetic species. Unfortunately, this sampling is unbalanced and sometimes highly redundant. Moreover, we observed contaminated sequences in some samples. In such a context, tree inference and readability are impaired. Consequently, the aim of the data processing reported here is to release a unique set of clean and non-redundant transcriptomes produced through an original protocol featuring decontamination, pooling and dereplication steps. Data description We submitted 678 MMETSP re-assembly samples to our parallel consolidation pipeline. Hence, we combined 423 samples into 110 consolidated transcriptomes, after the systematic removal of the most contaminated samples (186). This approach resulted in a total of 224 high-quality transcriptomes, easy to use and suitable to compute less contaminated, less redundant and more balanced phylogenies.

Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data

Using a new probabilistic approach we model the relationship between sequences of auditory stimuli generated by stochastic chains and the electroencephalographic (EEG) data acquired while 19 participants were exposed to those stimuli. The structure of the chains generating the stimuli are characterized by rooted and labeled trees whose leaves, henceforth called contexts, represent the sequences of past stimuli governing the choice of the next stimulus. A classical conjecture claims that the brain assigns probabilistic models to samples of stimuli. If this is true, then the context tree generating the sequence of stimuli should be encoded in the brain activity. Using an innovative statistical procedure we show that this context tree can effectively be extracted from the EEG data, thus giving support to the classical conjecture.

A Novel Framework for Identifying Customers’ Unmet Needs on Online Social Media Using Context Tree

Customer needs and user contexts play an important role in generating ideas for new products or new functions. This study proposes a novel framework for identifying customers’ unmet needs on online social media using the Context Tree through the Hierarchical Search of Concept Spaces (HSCS) algorithm. The Context Tree represents the hierarchical structure of nodes associated with related keywords and corresponding concept spaces. Unlike other methods, the Context Tree focuses on finding the unmet needs of customers from online social media. The proposed framework is applied to extract customer needs for home appliances. Identified customer needs are used to make user scenarios, which are used to develop new functions of home appliances.

Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SSAGD, is a novel variant of the Matrix-Factorization technique (MF), which is considered state-of-the-art in the recommender literature.We discuss the offline tests we carried out to select the best MF variant, and present the results of two A/B tests performed on live ecommerce websites after the deployment of the new algorithms. Comparing the new recommenders and Prediggo’s proprietary algorithm of Ontology Filtering, we show that the BVMM significantly outperforms the two others in terms of CTR and prediction speed, and leads to a strong increase in recommendation-mediated sales. Although MF exhibits reasonably good accuracy, the BVMM is still significantly more accurate and avoids the high memory requirements of MF. This scalability is essential for its application in online businesses.

Retrieving a Context Tree from EEG Data

It has been repeatedly conjectured that the brain retrieves statistical regularities from stimuli. Here, we present a new statistical approach allowing to address this conjecture. This approach is based on a new class of stochastic processes, namely, sequences of random objects driven by chains with memory of variable length.