Brief Announcement: The Fault-Tolerant Cluster-Sending Problem

The development of fault-tolerant distributed systems that can tolerate Byzantine behavior has traditionally been focused on consensus protocols, which support fully-replicated designs. For the development of more sophisticated high-performance Byzantine distributed systems, more specialized fault-tolerant communication primitives are necessary, however. In this brief announcement, we identify the cluster-sending problem - the problem of sending a message from one Byzantine cluster to another Byzantine cluster in a reliable manner - as such an essential communication primitive. We not only formalize this fundamental problem, but also establish lower bounds on the complexity of this problem under crash failures and Byzantine failures. Furthermore, we develop practical cluster-sending protocols that meet these lower bounds and, hence, have optimal complexity. As such, our work provides a strong foundation for the further exploration of novel designs that address challenges encountered in fault-tolerant distributed systems

Interactive (statistical) visualisation and exploration of a billion objects with Vaex

With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new methods of handling and visualizing these data volumes are needed. In visualization, one problem is that the number of datapoints can become so large, that a scatter plot becomes cluttered. Another problem is that with over a billion objects, only a few cpu cycles are available per object if one wants to process them within a second, making traditional methods by rendering glyphs not viable. Instead, we show that by calculating statistics on a regular (N-dimensional) grid, visualizations of a billion objects can be done within a second on a modern desktop computer. This is achieved using memory mapping of hdf5 files together with a simple binning algorithm, which are part of a Python library called vaex. This enables efficient exploration or large datasets interactively, making science exploration of large catalogues feasible. Vaex is a Python library, which also integrates well in the Jupyter/Numpy/Astropy/matplotlib stack. Build on top of this is the vaex application, which allows for interactive exploration and visualization. The motivation for developing vaex is the catalogue of the Gaia satellite, however, vaex can also be used on SPH or N-body simulations, any other (future) catalogues such as SDSS, Pan-STARRS, LSST, WISE, 2MASS, etc. or other tabular data. The homepage for vaex is http://vaex.astro.rug.nl.Comment: 6 pages, 4 figures, conference proceeding for the IAU symposium 325 on Astroinformatics (accepted), webpage http://vaex.astro.rug.n

Layout-driven allocation for high level synthesis

We propose a hypergraph model and a new algorithm for hardware allocation. The use of a hypergraph model facilitates the identification of sharable resources and the calculation of interconnect costs. Using the hyper graph model, the algorithm performs interconnect optimization by taking into account interdependent relationships between three allocation subtasks: register, operation, and interconnect allocations simultaneously. Previous algorithms considered these three tasks serially. Another novel contribution of our algorithm is the exploration of design space by trading off storage units and interconnects. We also demonstrate that traditional cost functions using the number of registers and the number of mux-inputs can not guarantee the minimal area. To rectify the problem, we introduce a new layout area cost function and compare it to the traditional cost functions. Our experiments show that our algorithm is superior to previously published algorithms under traditional cost functions

Finding High-Dimensional D-OptimalDesigns for Logistic Models via Differential Evolution

D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem

Understanding the Complexity Gains of Single-Task RL with a Curriculum

Reinforcement learning (RL) problems can be challenging without well-shaped rewards. Prior work on provably efficient RL methods generally proposes to address this issue with dedicated exploration strategies. However, another way to tackle this challenge is to reformulate it as a multi-task RL problem, where the task space contains not only the challenging task of interest but also easier tasks that implicitly function as a curriculum. Such a reformulation opens up the possibility of running existing multi-task RL methods as a more efficient alternative to solving a single challenging task from scratch. In this work, we provide a theoretical framework that reformulates a single-task RL problem as a multi-task RL problem defined by a curriculum. Under mild regularity conditions on the curriculum, we show that sequentially solving each task in the multi-task RL problem is more computationally efficient than solving the original single-task problem, without any explicit exploration bonuses or other exploration strategies. We also show that our theoretical insights can be translated into an effective practical learning algorithm that can accelerate curriculum learning on simulated robotic tasks

MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position

Multi-agent exploration of a bounded 3D environment with unknown initial positions of agents is a challenging problem. It requires quickly exploring the environments as well as robustly merging the sub-maps built by the agents. We take the view that the existing approaches are either aggressive or conservative: Aggressive strategies merge two sub-maps built by different agents together when overlap is detected, which can lead to incorrect merging due to the false-positive detection of the overlap and is thus not robust. Conservative strategies direct one agent to revisit an excessive amount of the historical trajectory of another agent for verification before merging, which can lower the exploration efficiency due to the repeated exploration of the same space. To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an \emph{adaptive} manner based on the quality indicator of the sub-map merging process. Additionally, our approach extends the recent single-agent hierarchical exploration strategy to multiple agents in a \emph{cooperative} manner by planning for agents with merged sub-maps together to further improve exploration efficiency. Our experiments show that our approach is up to 50\% more efficient than the baselines on average while merging sub-maps robustly.Comment: 8 pages, 8 figures, Submitted to IEEE RA