Term rewriting plays a crucial role in software verification and compiler optimization. With dozens of highly parameterizable techniques developed to prove various system properties, automatic term rewriting tools work in an extensive parameter space. This complexity exceeds human capacity for parameter selection, motivating an investigation into automated strategy invention. In this paper, we focus on confluence, an important property of term rewrite systems, and apply machine learning to develop the first learning-guided automatic confluence prover. Moreover, we randomly generate a large dataset to analyze confluence for term rewrite systems. Our results focus on improving the state-of-the-art automatic confluence prover CSI: When equipped with our invented strategies, it surpasses its human-designed strategies both on the augmented dataset and on the original human-created benchmark dataset Cops, proving/disproving the confluence of several term rewrite systems for which no automated proofs were known before.

Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Have you ever found a new favourite series on Netflix, picked up groceries curbside at Walmart, or paid for something using Square? That's the power of data in motion in action--giving organisations instant access to the massive amounts of data that is constantly flowing throughout their business. Our cloud-native offering is designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organisation. With Confluent, organisations can create a central nervous system to innovate and win in a digital-first world.

This paper presents a novel alternative to Greedy Non-Maxima Suppression (NMS) in the task of bounding box selection and suppression in object detection. It proposes Confluence, an algorithm which does not rely solely on individual confidence scores to select optimal bounding boxes, nor does it rely on Intersection Over Union (IoU) to remove false positives. Using Manhattan Distance, it selects the bounding box which is closest to every other bounding box within the cluster and removes highly confluent neighboring boxes. Thus, Confluence represents a paradigm shift in bounding box selection and suppression as it is based on fundamentally different theoretical principles to Greedy NMS and its variants. Confluence is experimentally validated on RetinaNet, YOLOv3 and Mask-RCNN, using both the MS COCO and PASCAL VOC 2007 datasets. Confluence outperforms Greedy NMS in both mAP and recall on both datasets, using the challenging 0.50:0.95 mAP evaluation metric. On each detector and dataset, mAP was improved by 0.3-0.7% while recall was improved by 1.4-2.5%. A theoretical comparison of Greedy NMS and the Confluence Algorithm is provided, and quantitative results are supported by extensive qualitative results analysis. Furthermore, sensitivity analysis experiments across mAP thresholds support the conclusion that Confluence is more robust than NMS.

Grimes has spent her career at Google, where she currently works on data-driven resource planning, cost analysis, and distributed cluster management software as part of the Technical Institute Group. Grimes holds a PhD in Statistics from Stanford University and an AB in Anthropology from Harvard University. Meta S. Brown is a consultant, speaker and writer who promotes the use of business analytics. A hands-on analyst who has tackled projects with up to $900 million at stake, she is a recognized expert in cutting-edge business analytics. Jennifer Chayes is a Distinguished Scientist and Managing Director at Microsoft Research.

The annual Workshop on the Validation and Verification of Knowledge-Based Systems is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). The 1994 workshop was significant in that there was a definitive move in the philosophical position of the workshop from a testing-and toolbased approach to KBS evaluation to that of a formal specification-based approach. This workshop included 12 full papers and 5 short papers and was attended by 35 researchers from government, industry, and academia. The workshop is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). It has influenced the evolution of the discipline from its origins in 1988; at this time, researchers were asking the questions, How can we evaluate the correctness of KBS? How is this process different from conventional system evolution?

Enterprise startups have always played a crucial role in IT portfolios. As much as CIOs would love to centralize technology purchasing decisions with a handful of strategic partners, incumbents can't always meet all of their needs, especially when it comes to emerging technologies. To become better acquainted with startups, many CIOs regularly travel to Silicon Valley to participate in "speed dating," in which venture capitalists invite them to meet the members of their portfolios. Some CIOs host hackathons or Shark Tank-like competitions for external developers to try coding their way into the company's graces. Still others learn of promising new companies from their peers.

Intelligent real time applications are a game changer in any industry. Deep Learning is one of the hottest buzzwords in this area. New technologies like GPUs combined with elastic cloud infrastructure enable the sophisticated usage of artificial neural networks to add business value in real world scenarios. Tech giants use it e.g. for image recognition and speech translation. This session discusses how any company can leverage deep learning in real time applications.

Known in behaviour as "habituation" and in perception as "adaptation", it has been recognised from time immemorial yet still lacks explanation. Only recently Sharpless and Jasper (1956, ref. 10) could say "Habituaticn... has yet to be explained by any known neurophysiological principles". A review of the subject need not be given here as it has been well reviewed by Humphrey (1933, ref.6), Harris (1943, ref. 5), and Thorpe (1956, ref. 11). On one important matter they are agreed: habituation of typical form occurs in almost every form of life; in particular it appears as readily in forms having no neural apparatus as in the forms having a well developed brain. Amoeba shows it as freely as does the cat. The phenomenon evidently does not depend on specifically neurophysiological details. Its origin must lie in some property of much wider occurrence. The possibility of "fatigue" as an explanation must be rejected.

Orthogonality is a discipline of programming that in a syntactic manner guarantees determinism of functional specifications. Essentially, orthogonality avoids, on the one side, the inherent ambiguity of non determinism, prohibiting the existence of different rules that specify the same function and that may apply simultaneously (non-ambiguity), and, on the other side, it eliminates the possibility of occurrence of repetitions of variables in the left-hand side of these rules (left linearity). In the theory of term rewriting systems (TRSs) determinism is captured by the well-known property of confluence, that basically states that whenever different computations or simplifications from a term are possible, the computed answers should coincide. Although the proofs are technically elaborated, confluence is well-known to be a consequence of orthogonality. Thus, orthogonality is an important mathematical discipline intrinsic to the specification of recursive functions that is naturally applied in functional programming and specification. Starting from a formalization of the theory of TRSs in the proof assistant PVS, this work describes how confluence of orthogonal TRSs has been formalized, based on axiomatizations of properties of rules, positions and substitutions involved in parallel steps of reduction, in this proof assistant. Proofs for some similar but restricted properties such as the property of confluence of non-ambiguous and (left and right) linear TRSs have been fully formalized.

The lex leader method for breaking symmetry in CSPs typically produces a large set of lexicographic ordering constraints. Several rules have been proposed to reduce such sets whilst preserving logical equivalence. These reduction rules are not generally confuent: they may reach more than one xpoint, depending on the order of application. These fixpoints vary in size. Smaller sets of lex constraints are desirable so ensuring reduction to a global minimum is essential. We characterise the systems of constraints for which the reduction rules are confluent in terms of a simple feature of the input, and define an algorithm to determine whether a set of lex constraints reduce confuently.