Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations

People break up, miscarry, and lose loved ones. Their online streaming and shopping recommendations, however, do not necessarily update, and may serve as unhappy reminders of their loss. When users want to renege on their past actions, they expect the recommender platforms to erase selective data at the model level. Ideally, given any specified user history, the recommender can unwind or "forget", as if the record was not part of training. To that end, this paper focuses on simple but widely deployed bi-linear models for recommendations based on matrix completion. Without incurring the cost of re-training, and without degrading the model unnecessarily, we develop Unlearn-ALS by making a few key modifications to the fine-tuning procedure under Alternating Least Squares optimisation, thus applicable to any bi-linear models regardless of the training procedure. We show that Unlearn-ALS is consistent with retraining without \emph{any} model degradation and exhibits rapid convergence, making it suitable for a large class of existing recommenders.Comment: 8 pages, 8 figure

A novel tightly regulated gene expression system for the human intestinal symbiont Bacteroides thetaiotaomicron

There is considerable interest in studying the function of Bacteroides species resident in the human gastrointestinal (GI)-tract and the contribution they make to host health. Reverse genetics and protein expression techniques, such as those developed for well-characterized Escherichia coli cannot be applied to Bacteroides species as they and other members of the Bacteriodetes phylum have unique promoter structures. The availability of useful Bacteroides-specific genetic tools is therefore limited. Here we describe the development of an effective mannan-controlled gene expression system for Bacteroides thetaiotaomicron containing the mannan-inducible promoter–region of an α-1,2-mannosidase gene (BT_3784), a ribosomal binding site designed to modulate expression, a multiple cloning site to facilitate the cloning of genes of interest, and a transcriptional terminator. Using the Lactobacillus pepI as a reporter gene, mannan induction resulted in an increase of reporter activity in a time- and concentration-dependent manner with a wide range of activity. The endogenous BtcepA cephalosporinase gene was used to demonstrate the suitability of this novel expression system, enabling the isolation of a His-tagged version of BtCepA. We have also shown with experiments performed in mice that the system can be induced in vivo in the presence of an exogenous source of mannan. By enabling the controlled expression of endogenous and exogenous genes in B. thetaiotaomicron this novel inducer-dependent expression system will aid in defining the physiological role of individual genes and the functional analyses of their products

Synthetic mixed-signal computation in living cells

Living cells implement complex computations on the continuous environmental signals that they encounter. These computations involve both analogue- and digital-like processing of signals to give rise to complex developmental programs, context-dependent behaviours and homeostatic activities. In contrast to natural biological systems, synthetic biological systems have largely focused on either digital or analogue computation separately. Here we integrate analogue and digital computation to implement complex hybrid synthetic genetic programs in living cells. We present a framework for building comparator gene circuits to digitize analogue inputs based on different thresholds. We then demonstrate that comparators can be predictably composed together to build band-pass filters, ternary logic systems and multi-level analogue-to-digital converters. In addition, we interface these analogue-to-digital circuits with other digital gene circuits to enable concentration-dependent logic. We expect that this hybrid computational paradigm will enable new industrial, diagnostic and therapeutic applications with engineered cells.Fundacao para a Ciencia e a Tecnologia (Fellowship SFRH/BD/51576/2011)National Science Foundation (U.S.) (1350625)National Science Foundation (U.S.) (1124247)United States. Office of Naval Research (N000141310424)National Institutes of Health (U.S.) (New Innovator Award 1DP2OD008435)National Centers for Systems Biology (U.S.) (1P50GM098792