Generating precise class-aware pseudo ground-truths, a.k.a, class activation maps (CAMs), is essential for Weakly-Supervised Semantic Segmentation. The original CAM method usually produces incomplete and inaccurate localization maps. To tackle with this issue, this paper proposes an Expansion and Shrinkage scheme based on the offset learning in the deformable convolution, to sequentially improve the recall and precision of the located object in the two respective stages. In the Expansion stage, an offset learning branch in a deformable convolution layer, referred to as expansion sampler'', seeks to sample increasingly less discriminative object regions, driven by an inverse supervision signal that maximizes image-level classification loss. The located more complete object region in the Expansion stage is then gradually narrowed down to the final object region during the Shrinkage stage.

Enforcing safety is a key aspect of many problems pertaining to sequential decision making under uncertainty, which require the decisions made at every step to be both informative of the optimal decision and also safe. For example, we value both efficacy and comfort in medical therapy, and efficiency and safety in robotic control. We consider this problem of optimizing an unknown utility function with absolute feedback or preference feedback subject to unknown safety constraints. We develop an efficient safe Bayesian optimization algorithm, StageOpt, that separates safe region expansion and utility function maximization into two distinct stages. Compared to existing approaches which interleave between expansion and optimization, we show that StageOpt is more efficient and naturally applicable to a broader class of problems. We provide theoretical guarantees for both the satisfaction of safety constraints as well as convergence to the optimal utility value. We evaluate StageOpt on both a variety of synthetic experiments, as well as in clinical practice. We demonstrate that StageOpt is more effective than existing safe optimization approaches, and is able to safely and effectively optimize spinal cord stimulation therapy in our clinical experiments.

According to CB Insights and PwC's Canada's latest MoneyTree report, 2017 is off to a slow start for VC funding in Canada. The report looks at VC activity in Q2 and provides perspective on the first half of this year overall (all dollar amounts in the MoneyTree Canada report are in USD, and have been converted to CAD throughout this article). Canadian VC-backed companies saw $504 million ($400 million USD) in total financing across 58 deals in Q2 2017, down 18 and 16 percent from Q1 2017, respectively. In Q1 2017, Canadian VCs invested $623 million ($460 million USD) in 64 deals. Activity has also slipped since hitting a high in Q4 2016, when deal activity for VC-backed companies hit 91 deals.

In this paper we propose a system to solve a language game, called Guillotine, which requires a player with a strong cultural and linguistic background knowledge. The player observes a set of five words, generally unrelated to each other, and in one minute she has to provide a sixth word, semantically connected to the others. Several knowledge sources, such as a dictionary  and a set of proverbs, have been modeled and integrated in order to realize a knowledge infusion process into the system. The main motivation for designing an artificial player for Guillotine is the challenge of providing the machine with the cultural and linguistic background knowledge which makes it similar to a human being, with the ability of interpreting natural language documents and reasoning on their content. Experiments carried out showed promising results, and both the knowledge source modeling and the reasoning mechanisms  (implementing  a spreading activation algorithm to find out the solution) seem to be appropriate. We are convinced that the approach has a great potential for other more practical applications besides solving a language game, such as semantic search.