CAP-Theorem (Brewer’s theorem) 
In theoretical laptop science, the CAP theorem, also called Brewer’s theorem, states that it’s unattainable for a distributed laptop system to concurrently present all three of the next ensures:<BR/>
· Consistency (all nodes see the identical information on the similar time)<BR/>
· Availability (a assure that each request receives a response about whether or not it was profitable or failed)<BR/>
· Partition tolerance (the system continues to function regardless of arbitrary message loss or failure of a part of the system) …
Subgraphs
Subgraphs is a visible IDE for creating computational graphs, notably designed for deep neural networks. Subgraphs is constructed with tensorflow.js, node, and react, and serves on Google Cloud. An occasion of subgraphs is offered at https://…/. …
Trend Feature Symbolic Aggregate Approximation (TFSAX)
Symbolic Combination approximation (SAX) is a classical symbolic method in lots of time sequence information mining functions. Nonetheless, SAX solely displays the phase imply worth characteristic and misses vital info in a phase, particularly the development of the worth change within the phase. Such a miss could trigger a improper classification in some circumstances, because the SAX illustration can’t distinguish completely different time sequence with comparable common values however completely different traits. On this paper, we current Development Function Symbolic Combination approximation (TFSAX) to unravel this downside. First, we make the most of Piecewise Combination Approximation (PAA) method to cut back dimensionality and discretize the imply worth of every phase by SAX. Second, extract development characteristic in every phase through the use of development distance issue and development form issue. Then, design multi-resolution symbolic mapping guidelines to discretize development info into symbols. We additionally suggest a modified distance measure by integrating the SAX distance with a weighted development distance. We present that our distance measure has a tighter decrease certain to the Euclidean distance than that of the unique SAX. The experimental outcomes on various time sequence information units reveal that our proposed illustration considerably outperforms the unique SAX illustration and an improved SAX illustration for classification. …
Linked Causal Variational Autoencoder (LCVA)
Modeling spillover results from observational information is a crucial downside in economics, enterprise, and different fields of analysis. % It helps us infer the causality between two seemingly unrelated set of occasions. For instance, if shopper spending in the USA declines, it has spillover results on economies that depend upon the U.S. as their largest export market. On this paper, we goal to deduce the causation that ends in spillover results between pairs of entities (or models), we name this impact as textit{paired spillover}. To attain this, we leverage the current developments in variational inference and deep studying strategies to suggest a generative mannequin referred to as Linked Causal Variational Autoencoder (LCVA). Much like variational autoencoders (VAE), LCVA incorporates an encoder neural community to be taught the latent attributes and a decoder community to reconstruct the inputs. Nonetheless, not like VAE, LCVA treats the textit{latent attributes as confounders which might be assumed to have an effect on each the remedy and the result of models}. Particularly, given a pair of models $u$ and $bar{u}$, their particular person remedy and outcomes, the encoder community of LCVA samples the confounders by conditioning on the noticed covariates of $u$, the remedies of each $u$ and $bar{u}$ and the result of $u$. As soon as inferred, the latent attributes (or confounders) of $u$ captures the spillover impact of $bar{u}$ on $u$. Utilizing a community of customers from job coaching dataset (LaLonde (1986)) and co-purchase dataset from Amazon e-commerce area, we present that LCVA is considerably extra strong than current strategies in capturing spillover results. …
